This gazette is also available free online at www.gpwonline.co.za STAATSKOERANT, 2 MEI 2024 No. 50607 3 Government notices • GoewermentskennisGewinGs DEPARTMENT OF FORESTRY, FISHERIES AND THE ENVIRONMENT NO. 4772 2 May 2024 GOVERNMENT NOTICE DEPARTMENT OF FORESTRY, FISHERIES AND THE ENVIRONMENT NO. 2024 DRAFT 9 th NATIONAL GREENHOUSE GAS INVENTORY REPORT FOR THE REPUBLIC OF SOUTH AFRICA FOR PUBLIC COMMENT I, Barbara Dallas Creecy, Minister Forestry, Fisheries and the Environment, hereby publish the draft 9 th National Greenhouse Gas Inventory Report (the draft 9 th National GHG Inventory Report) for public comment. BACKGROUND TO THE DRAFT 9 TH GHG INVENTORY REPORT • In 1997 the Republic of South Africa ratified the United Nations Framework Convention on Climate Change, 1992 (the Convention). The main objective of the Convention is to stabilize Greenhouse Gas (GHG) concentrations in the atmosphere at a level that would prevent dangerous human-induced interference with the climate system. • The ability of South Africa to achieve the objective of the Convention is highly dependent on the accurate knowledge of emissions trends and on collective ability to alter these trends. One of the mechanisms used to understand these emission trends is to prepare and update a National GHG Inventory Report. Articles 4.1(a) and 12.1(a) of the Convention obliges each State Party to prepare and report national GHG emissions and removals to the Conference of the Parties (COP). The key mechanism for reporting these GHG emissions is through the National Communications (NCs) every four years and the Biennial Update Reports (BURs) every two years. The GHG Inventory Report therefore forms an integral part of these two key reporting mechanisms to the Convention and the broader international community. • In addition to international obligations and reporting requirements under the Convention, South Africa through its White Paper on National Climate Change Response Policy published in 2011 (the White Paper), has committed itself to prepare and update a GHG Emissions Inventory Report annually. • According to Decision 17/CP.8,3, related to Annexure 1 of the Convention, South Africa is required as appropriate and to the extent possible, to provide in its national inventory on gas-by-gas basis and its units’ mass, estimates of anthropogenic emissions of carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) by sources and removals by sinks. Non-Annex I Parties are encouraged, as appropriate to provide information on anthropogenic emissions by sources of hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphur hexafluoride (SF 6 ). • Paragraph 6.7 of the White Paper requires the Department of Forestry, Fisheries and the Environment (the Department) to compile accurate, complete and updated GHG emissions inventory to ensure a This gazette is also available free online at www.gpwonline.co.za 4 No. 50607 GOVERNMENT GAZETTE, 2 MAy 2024 good foundation for effective mitigation responses. The first National GHG inventory in South Africa was prepared in 1998, using 1990 data; and was updated to include 1994 data and published in 2004. • In 2009, the country published the third GHG inventory which was based on the base year 2000. As recently as the year 2014, South Africa published its fourth GHG inventory covering the period 2000 – 2010. The Department subsequently compiled and published the fifth GHG Inventory Report which covered the period 2000 – 2012. The sixth GHG Inventory Report which covered the period 2000 – 2015 and the seventh GHG Inventory Report which covered a period 2000 – 2017 were also published. Recently the Department has published the eighth GHG Inventory Report covering the data from the period 2000 - 2020. SUMMARY CONTENT OF THE DRAFT 9 TH GHG INVENTORY REPORT • The draft 9 th GHG Inventory Report covers the period 2000 – 2022. It provides preliminary estimates of current emissions levels and trends in the country. It documents South Africa’s submission of its National Greenhouse Gas Inventory for the period 2000 – 2022 and will form part of the first biennial transparency report (BTR1) to the Convention. It presents preliminary emissions estimates to be currently at emissions 478 887.5 GgCO 2 eq (excl. LULUCF) and 435 827.7GgCO 2 eq (incl. LULUCF). • The draft 9 th GHG Inventory Report indicates that South Africa’s GHG emissions excl. LULUCF were 489 748 Gg CO2e in 2000 and these decreased by 2.2% by 2022. Emissions (excl. LULUCF) in 2022 were estimated at 478 888 Gg CO2e. The overall emissions (incl. LULUCF) were 435 828 Gg CO2e in 2022. South Africa’s GHG emissions (incl. LULUCF) were 462 205 Gg CO2e in 2000 and these decreased by 5.5% by 2022. In 2022 the Energy sector was the largest contributor to the overall emissions (excl. LULUCF) in 2022, comprising 78% of total emissions. In 2022 the IPPU sector contributed 6.4% (excl. LULUCF). In 2022 the Agriculture sector contributed 11% (excl. LULUCF). Overall, the LULUCF sector increased its sink by 56.3% since 2000 and by 17.9% since 2020. The waste sector contributed 4% overall. An accurate, complete and updated greenhouse gas emissions inventory is a good foundation for effective mitigation responses. • The draft 9 th GHG Inventory Report follows the standard National Inventory Report format in line with the Convention’s Reporting Guidelines (the Guidelines). Chapter 1 of the Guidelines provides an introductory chapter which contains background information for South Africa, the country’s inventory preparation and reporting process, key categories, a description of the methodologies, activity data and emission factors, and a description of the Quality Assurance/Quality Control (QA/QC) process. • A summary of the aggregated GHG trends by gas and emission source is provided in Chapter 2 of the Guidelines. Chapters 3 to 7 of the Guidelines deal with detailed explanations of the emissions in the Energy; Industrial Processes and Product Use (IPPU); Agriculture; Land Use, Land-Use Change and Forestry (LULUCF); and Waste sectors respectively. They include an overall trend assessment, methodology, data sources, recalculations, uncertainty and time-series consistency, QA/QC procedures and planned improvements and recommendations. • The draft 9 th GHG Inventory Report is compiled in accordance with the guidelines provided by the Convention and follows the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines and IPCC Good Practice Guidance (GPG). The draft 9 th GHG Inventory Report also provides a detailed explanation of the methods (Tier 1 and Tier 2 approaches), parameters such as activity data, and emission factors used to develop the inventory. In addition, it assesses the uncertainty and describes the quality assurance and quality control (QA/QC) activities. Quality assurance for this GHG inventory was undertaken by independent reviewers. This gazette is also available free online at www.gpwonline.co.za STAATSKOERANT, 2 MEI 2024 No. 50607 5 Members of the public are invited to submit written comments, within 30 days from the date of the publication of this Notice in the Gazette or in the newspaper, whichever date comes last, to any of the following addresses: By post to: Director-General: Department of Forestry, Fisheries and the Environment Attention: Mr Jongikhaya Witi Department of Forestry, Fisheries and the Environment Private Bag X447 Pretoria 0001 By email to: GHGreporting@dffe.gov.za Hand delivered at: Environment House, 473 Steve Biko Road, Arcadia, Pretoria, 0083. Any inquiries in connection with the notice can be directed to Mr Jongikhaya Witi at Tel: 012 399 9151 or by email at jwiti@dffe.gov.za This Government Notice and the draft 9 th National Greenhouse Gas Inventory Report are available at the following link on the Departmental website https://www.dffe.gov.za/legislation/gazetted_notices Comments received after the closing date may be disregarded. BARBARA DALLAS CREECY MINISTER OF FORESTRY, FISHERIES AND THE ENVIRONMENT --- Document Separator --- NATIONAL GHG INVENTORY REPORT SOUTH AFRICA 2000 - 2022 February 2024 i GHG Inventory for South Africa: 2000 - 2022 Acknowledgements The DFFE wishes to acknowledge the many individuals, Institutes and organisations that contributed in the compilation of this National Inventory Report (NIR) for 2022. The information used in the compilation of the NIR was sourced from multiple organizations We greatly appreciate all the contributions from organizations and individuals who were involved in the process of completing this NIR. We would also like to thank all reviewers of the various sector sections as well as the reviewers of the completed NIR. ii GHG Inventory for South Africa: 2000 - 2022 Report compilation details Internal report reviewers: Jongikhaya Witi External report reviewers: Luanne Stevens Compilers and technical contributors Executive Summary Luanne Stevens (Gondwana Environmental Solutions), Rumbidzai Damita Mhunduru (DFFE), Adele Mhangani (DFFE), Sewela Malaka (DFFE), Tshamaano Khalushi (DFFE), Sindisiwe Mashele (DFFE) Chapter 1: Introduction Luanne Stevens (Gondwana Environmental Solutions), Rumbidzai Damita Mhunduru (DFFE), Adele Mhangani (DFFE), Sewela Malaka (DFFE), Tshamaano Khalushi (DFFE), Sindisiwe Mashele (DFFE) Chapter 2: Greenhouse Gas Emission Trends Luanne Stevens (Gondwana Environmental Solutions), Rumbidzai Damita Mhunduru (DFFE), Adele Mhangani (DFFE), Sewela Malaka (DFFE), Tshamaano Khalushi (DFFE), Sindisiwe Mashele (DFFE) Chapter 3: Energy (CRF Sector 1) Rumbidzai Damita Mhunduru (DFFE) Chapter 4: Industrial Processes and Product Use (CRF Sector 2) Hendrik Louw (DFFE) Chapter 5: Agriculture (CRF Sector 3) Sewela Malaka (DFFE) iii GHG Inventory for South Africa: 2000 - 2022 Chapter 6: Land use, Forestry and Land Use Change (LULUCF) (CRT sector 4) Luanne Stevens (Gondwana Environmental Solutions), Chapter 7: Waste (CRF Sector 5) Adele Mhangani (DFFE), Tshamaano Khalushi (DFFE) Other Contributors Lungile Moyo (DFFE) – Spatial Data Analysis Farrn Sherman - IPPU Data Collection iv GHG Inventory for South Africa: 2000 - 2022 List of abbreviations AFOLU Agriculture, Forestry and Other Land Use AGB Above-ground biomass ARC Agricultural Research Council Bbl/d Barrels per day BCEF Biomass conversion and expansion factor BEF Biomass expansion factor BNF Biological nitrogen fixing BOD Biological oxygen demand C Carbon C 2 F 4 Tetrafluoroethylene C 2 F 6 Carbon hexafluoroethane CF 4 Carbon tetrafluoromethane CFC Chlorofluorocarbons CH 4 Methane CO Carbon monoxide CO 2 Carbon dioxide CO 2 e Carbon dioxide equivalent CRF Common reporting format DAFF Department of Agriculture Affairs, Forestry and Fisheries DEA Department of Environmental Affairs DFID Department for International Development DM Dry matter DMD Dry matter digestibility DMR Department of Mineral Resources DoE Department of Energy DOM Dead organic matter DTI Department of Trade and Industry DWA Department of Water Affairs DWAF Department of Water Affairs and Forestry EF Emission factor F-gases Flourinated gases: e.g., HFC, PFC, SF 6 and NF 3 FOD First order decay FOLU Forestry and Other Land Use FRA Forest resource assessment FSA Forestry South Africa GDP Gross domestic product GEI Gross energy intake v GHG Inventory for South Africa: 2000 - 2022 GFRSA Global Forest Resource Assessment for South Africa Gg Gigagram GHG Greenhouse gas GHGI Greenhouse Gas Inventory GIS Geographical Information Systems GPG Good Practice Guidance GWH Gigawatt hour GWP Global warming potential HFC Hydrofluorocarbons HWP Harvested wood products IEF Implied emission factor INC Initial National Communication IPCC Intergovernmental Panel on Climate Change IPPU Industrial Processes and Product Use ISO International Organization for Standardization ISWC Institute of Soil, Water and Climate KCA Key category analysis LC Land cover LPG Liquefied petroleum gas LTO Landing/take off LULUCF Land Use, Land Use Change and Forestry MCF Methane conversion factor MEF Manure emission factor MW Megawatt MWH Megawatt hours MWTP Municipal wastewater treatment plant NAEIS National Atmospheric Emissions Inventory System N 2 O Nitrous oxide NCCC National Climate Change Committee NCV Net calorific value NE Not estimated NERSA National Energy Regulator of South Africa NGHGIS National Greenhouse Gas Inventory System NIR National Inventory Report NIU National Inventory Unit NMVOC Non-methane volatile organic compound NO Not occurring NOx Oxides of nitrogen NTCSA National Terrestrial Carbon Sinks Assessment NWBIR National Waste Baseline Information Report vi GHG Inventory for South Africa: 2000 - 2022 PFC Perfluorocarbons PPM Parts per million PRP Pastures, rangelands and paddocks QA/QC Quality assurance/quality control RSA Republic of South Africa SAAQIS South African Air Quality Information System SAGERS South African GHG Emissions Reporting System SAISA South African Iron and Steel Institute SAMI South African Minerals Industry SAPIA South African Petroleum Industry Association SAR Second Assessment Report SASQF South African Statistical Quality Assurance Framework SADC Southern African Development Community SF 6 Sulphur hexafluoride SNE Single National Entity SOC Soil organic carbon TAM Typical animal mass TAR Third Assessment Report (IPCC) TJ Terajoule TM Tier method TMR Total mixed ratio TOW Total organics in wastewater UN United Nations UNEP United Nations Environmental Programme UNFCCC United Nations Framework Convention on Climate Change WRI World Resources Institute WWTP Wastewater treatment plant-derived VS Volatile solids vii GHG Inventory for South Africa: 2000 - 2022 Executive summary E.S.1. Background E.S.1.1. Background information on greenhouse gas inventories This report documents South Africa’s submission of its national greenhouse gas (GHG) inventory for the year 2022. It also reports on the greenhouse gas trends for the period 2000 to 2022. It is compiled in accordance with the guidelines provided by the United Nations Framework Convention on Climate Change (UNFCCC) and follows the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National GHG Inventories (IPCC, 2006), IPCC Good Practice Guidance (GPG) (IPCC, 2000; IPCC, 2003; IPCC, 2014) and the 2019 Refinement to the 2006 IPCC Guidelines (IPCC, 2019). This report provides an explanation of the methods (Tier 1, 2 and 3 approaches), activity data and emission factors used to develop the inventory. In addition, it assesses the uncertainty and describes the quality assurance and quality control (QA/QC) activities. In August 1997, the Republic of South Africa joined the majority of countries in the international community in ratifying the UNFCCC. The first national GHG inventory for South Africa was prepared in 1998, using 1990 data (Van der Merwe & Scholes, 1998). It was updated to include 1994 data and published in 2004. It was developed using the 1996 IPCC Guidelines for National Greenhouse Gas Inventories. For the 2000 national inventory (DEAT, 2009), a decision was made to use the recently published 2006 IPCC Guidelines (IPCC, 2006) to enhance accuracy and transparency, and to familiarise researchers with the latest inventory preparation guidelines. Following these guidelines, in 2014 the GHG inventory for the years 2000 to 2010 were compiled (DEA, 2014). An update was completed for 2011 and 2012 in 2017 (DEA, 2017), for 2013 to 2015 in 2019 (DEA, 2019), for 2017 in 2021 (DFFE, 2021), and for 2018, 2019 and 2020 in 2023 (DFFE, 2023). E.S.1.2. Institutional arrangements for inventory preparation The Department of Forestry, Fisheries and the Environment (DFFE) is responsible for the co-ordination and management of all climate change-related information, including mitigation, adaption, monitoring and evaluation, and GHG inventories. Although the DFFE takes a lead role in the compilation, implementation and reporting of the national GHG inventories, other relevant agencies and ministries play supportive roles in terms of data viii GHG Inventory for South Africa: 2000 - 2022 provision across relevant sectors. Figure ES 1 gives an overview of the institutional arrangements for the compilation of the 2000 – 2022 GHG emissions inventory. Figure ES 1: Institutional arrangements for the compilation of the 2000 – 2022 inventory for South Africa. The Minister of DFFE promulgated the National Greenhouse Gas Emission Reporting Regulations, 2016 under Notice No. 275 in the Gazette No. 40762 of 03 April 2017 (DEA, 2017a). The purpose of the NGERs is to enable the DFFE to collate and publish GHG emissions data and information in an effective and efficient manner. The NGERs were promulgated in fulfilment of the implementation of the regulatory framework to support the collection of the requisite activity and GHG emissions data necessary for the compilation of the National GHG emissions Inventory to improve the ix GHG Inventory for South Africa: 2000 - 2022 quality, sustainability, accuracy, completeness and consistency of the National GHG Inventories. The National Greenhouse Gas Emissions Regulation came into effect on 03 April 2017. In accordance with regulation 7(1) of the regulation the initial reporting cycle commenced on 31 March of 2018 requiring data providers to register and submit activity and GHG emissions data to the competent authority (DFFE). As required in the 2011 White Paper (DEA, 2011), the DFFE has developed the South African Greenhouse Gas Emissions Reporting System (SAGERS) which is the GHG module of National Atmospheric Emissions Inventory System (NAEIS). The SAGERS module helps to facilitate the process of enabling Industry to meet its GHG reporting requirements in a web-based secure environment and facilitates the data collection process for energy related activities and Industrial Processes and Product Use (IPPU). E.S.1.3. Organisation of report This report follows a standard NIR format in line with the UNFCCC Reporting Guidelines or requirements under the enhanced transparency framework. Chapter 1 is the introductory chapter which contains background information for South Africa, the country’s inventory preparation and reporting process, key categories, a description of the methodologies, activity data, emission factors, QA/QC process and uncertainty. A summary of the aggregated GHG trends by gas and emission source is provided in Chapter 2. Chapters 3 to 7 deal with detailed explanations of the emissions in the Energy; IPPU; Agriculture; Land Use, Land Use Change and Forestry (LULUCF) and Waste sectors, respectively. They include an overall trend assessment, methodology, data sources, recalculations, uncertainty and time-series consistency, QA/QC and planned improvements and recommendations. It should be noted that in the previous inventory Agriculture and LULUCF were grouped together under the Agriculture, Forestry and Other Land Use (AFOLU) sector as outlined in the 2006 IPCC Guidelines. However, in the Common Reporting Tables (CRT) under the Enhanced Transparency Framework (ETF) these two sectors are separated. South Africa is transitioning to the ETF CRT reporting format and therefore in this report Agriculture and LULUCF are treated as separate sectors with the categories and sub-categories following those outlined in the CRT formats. E.S.2. Summary of national emission and removal trends GWP In this inventory the emissions for each of the major GHGs are presented as carbon dioxide equivalents (CO 2 e) using the 100- year global warming potentials (GWPs) from x GHG Inventory for South Africa: 2000 - 2022 the 2014 IPCC Fifth Assessment Report (AR5) (IPCC, 2014b) in accordance with the Modalities, Procedures, and Guidelines (MPGs) for transparency framework for action and support referred to in Article 13 of the Paris Agreement. It should be noted that in the previous inventory for the years 2000 -2020 the 100 – year GWPs from the 1995 IPPC Second Assessment Report (AR2) (IPCC, 1996) were applied in line with the UNFCCC requirements for Non-Annex I Parties. Transitioning from the AR2 GWPs to AR5 GWPs led to a 2.9% and 3.7% increase in overall emissions in 2022 excluding and including LULUCF, respectively (Table ES 1). Table ES 1: Impacts of transitioning from AR2 to AR5 GWPs on the overall emissions between 2000 and 2022. Emissions with AR2 GWPs Emissions with AR5 GWPs Impact of change in GWP (excl. LULUCF) (incl. LULUCF) (excl. LULUCF) (incl. LULUCF) (excl. LULUCF) (incl. LULUCF) Gg CO 2 e % 2000 476 861.9 446 961.4 489 747.5 462 205.4 2.7 3.4 2001 476 005.2 456 651.0 488 964.7 472 002.4 2.7 3.4 2002 484 725.3 468 360.2 497 651.3 483 692.9 2.7 3.3 2003 500 068.0 465 695.7 513 190.2 481 053.7 2.6 3.3 2004 511 790.2 485 929.7 524 902.7 501 254.4 2.6 3.2 2005 509 889.7 504 190.6 523 122.9 519 760.2 2.6 3.1 2006 500 850.5 489 020.8 514 091.2 504 500.4 2.6 3.2 2007 522 052.1 502 714.5 535 672.5 518 559.4 2.6 3.2 2008 518 440.6 512 877.7 532 320.7 529 003.6 2.7 3.1 2009 533 591.0 507 147.8 547 270.5 522 920.8 2.6 3.1 2010 513 118.4 497 009.7 526 971.4 512 987.3 2.7 3.2 2011 513 303.8 488 737.4 527 161.3 504 638.0 2.7 3.3 2012 521 851.5 496 005.2 536 003.0 512 196.6 2.7 3.3 2013 508 978.0 471 945.6 522 220.3 487 134.0 2.6 3.2 2014 509 474.4 481 452.6 522 899.6 496 835.8 2.6 3.2 2015 501 905.1 495 884.8 515 516.2 511 428.6 2.7 3.1 2016 496 339.9 473 786.2 509 870.0 489 254.4 2.7 3.3 2017 488 362.0 481 787.6 501 987.5 497 553.7 2.8 3.3 2018 489 041.8 487 530.8 502 130.2 502 893.9 2.7 3.2 2019 488 548.6 461 472.2 501 485.2 476 686.4 2.6 3.3 2020 459 358.9 420 560.5 472 437.8 435 918.6 2.8 3.7 2021 475 175.9 449 326.2 488 322.3 464 957.7 2.8 3.5 2022 465 609.9 420 146.1 478 887.5 435 827.7 2.9 3.7 xi GHG Inventory for South Africa: 2000 - 2022 E.S.3. Overview of source and sink category emission estimates and trends E.S.3.1. Gas trends Carbon dioxide Carbon dioxide (CO 2 ) is the largest contributor to South Africa’s emissions. CO 2 emissions contributed 81.9 % (excl. LULUCF) to South Africa’s emissions in 2022 (Figure ES 2). Majority of CO 2 emissions are from the Energy sector, contributing an average of 92.7% (excl. LULUCF) to the total CO 2 emissions between 2000 and 2022 followed by the IPPU sector contribution (excl. LULUCF) an average of 6.8% between 2000 and 2022, while the Agriculture sector (excl. LULUCF) contributed an average of 0.6%. Methane Methane (CH 4 ) emissions have increased from 12.9% to 13.2% (excl. LULUCF) between 2000 and 2022 (Figure ES 2). The Agriculture (specifically Enteric Fermentation) and Waste (specifically Solid Waste Disposal) sectors are the major contributors to the total CH 4 emissions in 2022. Nitrous oxide Nitrous oxide (N 2 O) emissions increased from 3.56% to 3.63% (excl. LULUCF) between 2000 and 2022 . The main contributor to N 2 O emissions is the Agriculture sector followed by Energy sector, contributing 74.4% and 14.4% (excl. LULUCF) respectively. xii GHG Inventory for South Africa: 2000 - 2022 Figure ES 2: Gas contribution to South Africa’s emissions (excl. LULUCF) between 2000 and 2022. F-gases Fluorinated gases (F-gases) emissions were only estimated for the IPPU sector in South Africa. Emissions for F-gases have increased from 0.2% to 1.3% (excl. LULUCF) between 2000 and 2022. Emissions increase from 2011 due to the addition of hydrofluorocarbons (HFCs) emissions from Air conditioning, foam blowing agents, fire protection and aerosols. There is no data prior to 2005 so this time-series is not consistent. The replacement of ozone depleting substances did contribute to this increase. Perfluorocarbons (PFCs) are produced during the production of aluminium. xiii GHG Inventory for South Africa: 2000 - 2022 E.S.3.2. GHG precursors Carbon monoxide (CO), nitrogen oxides (NOx) and non-methane volatile organic compounds (NMVOCs) were estimated from biomass burning only. CO emissions varied between 414 Gg CO to 1 335 Gg CO between 2000 and 2022. The NOx emissions were between 31 Gg NOx and 60 Gg NOx, while NMVOCs were between 5 Gg NMVOCs and 90 Gg NMVOCs over the period 2000 to 2022. There is annual variability because the emissions include wildfires as well as controlled fires. E.S.3.3. Sectoral trends Energy 2022 Energy emissions in 2022 accounted for 78 % (excl. LULUCF) and 86 % (incl. LULUCF) of total emissions for South Africa (Figure ES 3). Majority of the Energy sector emissions were from Energy Industries (60 %) and Transport (14 %), while the least emissions were from Solid Fuels (0.6 %) and Oil and Natural Gas activities (0.03 %). 2000 – 2022 Since 2000, Energy sector emissions decreased by 2.5 % (Table ES 2). Emissions from the Other Sectors category decreased the most (by 62 %) during this period mainly because of the Residential sector. This is likely due to increased electrification over the years, as well as an increase in the use of renewable energy. Emissions from domestic aviation and petroleum refining also decreased significantly, 70 % and 73 % respectively since 2000. Although Energy sector emissions slightly decreased from 2000-2022 emissions from electricity production increased by 11.5 % due to an 8.7 % increase in power generation during that period. 1 2020-2022 Energy emissions declined by 0.2% since 2020 (Table ES 2). In 2021 emissions increased by 3.5 % but then dropped again in 2022 to pandemic levels. This decrease is due to a 6.5% decrease in emissions from the Energy Industries sub-sector, which on average accounts for 67 % of the Energy sector emissions. 1 Electricity production data from Eskom’s Annual Integrated reports 2000, 2011 – 2022. Integrated results - Eskom xiv GHG Inventory for South Africa: 2000 - 2022 Figure ES 3: Sector contribution to total emissions (excluding LULUCF) in South Africa between 2000 and 2022. Table ES 2: Change in sector emissions since 2000 and since the last inventory period (2000 - 2020). Emissions (Gg CO 2 e) Change 2000 to 2022 Change 2020 to 2022 2000 2020 2022 Gg CO2e % Gg CO2e % Energy 383 820.6 374 748.2 374 072.4 -9 748.2 -2.5 -675.7 -0.2 IPPU 32 781.3 24 857.6 30 598.0 -2 183.3 -6.7 5 740.4 23.1 Agriculture 58 782.9 53 830.0 53 518.7 -5 264.2 -9.0 -311.3 -0.6 xv GHG Inventory for South Africa: 2000 - 2022 LULUCF -27 542.1 -36 519.2 -43 059.8 -15 517.7 56.3 -6 540.6 17.9 Waste 14 362.7 19 002.1 20 698.4 6 335.7 44.1 1 696.3 8.9 # Calculated with AR5 GWP Industrial processes and product use (IPPU) 2022 IPPU emissions for 2022, accounted 6.4% of South Africa’s emissions (excl. LULUCF) (Figure ES 3). The largest source category is the Metal industry category, which contributes 51.2% to the total IPPU sector emissions. The Mineral industry and the Product used as substitutes for ozone depleting substances subsectors contribute 19.8% and 19.4%, respectively, to the IPPU sector emissions. 2000 - 2022 Since 2000, IPPU emissions have decreased by 6.7% (Table ES 2). The decline can be attributed to the decline in metals production (-39.0%), specifically Iron and Steel, and Aluminium. This can be ascribed to a decrease in global demand. The chemicals industry also declined (-31.4%). Cement Production and Non-Energy Product Use from Fuels and Solvents increased emissions from 2000 by 2 081 Gg CO 2 e. The local demand for cement increased dramatically from 2000. 2020-2022 The IPPU sector emissions increased by 23.1% since 2020. This was mainly due to the restoration of production in the minerals (26.8%) and metals (26.83%) industry post covid. The year 2020 was used by some industries for maintenance during the covid period. A contribution was also made by the increased replacement of ozone depleting substances by fluorinated gases (12.5%). The chemicals industry had lower emissions (- 22.5%) due to lower production. Agriculture 2022 Agriculture emissions in 2022 accounted for 12% (excl. LULUCF) and 7% (incl. LULUCF) of total emissions for South Africa (Figure ES 3). Majority of the Agriculture sector emissions were from Enteric fermentation (68 %), Agricultural soils (19 %), and Manure xvi GHG Inventory for South Africa: 2000 - 2022 management (8 %), while the least emissions were from Field burning of agricultural residues (0.1 %). 2000 - 2022 Since 2000, Agriculture sector emissions decreased by 8.9 % (Table ES 2). Emissions from Enteric fermentation decreased the most (by 14 %) during this period mainly because of the reduced non-dairy cattle total emissions. This is likely due to decreased livestock population numbers since 2000. Emissions from Manure management increased by 8% and this was due to the increase in N 2 O emission from manure management. The rise in N 2 O emissions from handling manure occurred primarily due to the practice of leaving most manure in dry lot/ kraals across various livestock categories. As a result, the storage of dry-based manure predominantly contributes to increased N 2 O production. 2020-2022 Since 2020 the Agriculture sector emissions have declined by 0.6% and this is due mainly to declining livestock population. LULUCF 2022 LULUCF sector was a sink of 43 060 Gg CO 2 e in 2022. Forest lands were the largest contributors to this sink, while all other land sectors were estimated to be an overall source of emissions. The dominant Forest land sinks were thickets and woodlands. Harvested wood products (HWP) were a very small sink in 2022 (181 Gg CO 2 e). 2000 - 2022 The LULUCF sector increased its sink by 56.3% between 2000 and 2022 (Table ES 2). Forest lands were the largest contributor to this increased sink. Forest lands remaining forest lands showed a general increase from 2009. Between 2014 and 2022 the was an increase in the conversion of grasslands to woodlands which contributed to the increasing sink during this time. Grasslands led to increased emissions between 2000 and 2022 due to land conversions between low shrublands and grasslands. 2020-2022 The LULUCF sink grew by a further 17.9% between 2020 and 2022 (Table ES 2), There was first a decline in 2021 due to increased losses from fires, but this then recovered in xvii GHG Inventory for South Africa: 2000 - 2022 2022 were an increase in the conversion of Grassland to Forest land produced an increased Forest land sink. Waste 2022 In South Africa, Waste sector emissions for 2022 accounted 4.3% (excl. LULUCF) and 4.8% (incl. LULUCF) of the total emissions (Figure ES 3). Majority of Waste sector emissions are from Wastewater treatment and discharge which is contributing 44.7% to the total Waste sector emissions. This is, followed by Solid waste disposal which contributes 41.5%. Biological treatment of solid waste and Incineration and Open Burning of Waste contributed 12.2 % and 1.6 % of the total Waste emissions respectively. 2000 – 2022 Waste sector emissions have increased by 44.1% since 2000 (Table ES 2). Solid waste disposal emissions have increased by 47.6% since 2000. Incineration and open burning of waste emissions increased by 53.1% since 2000, while emissions from Wastewater treatment and discharge increased slightly throughout the time series. This is largely driven by increases of 35.6% in Domestic wastewater treatment and discharge emissions, whilst there was a 9.4% decline in Industrial wastewater treatment and discharge emissions. E.S.4. Improvements and recalculations Improvements introduced in the current inventory Energy The Energy sector emission estimates were improved through the use of country-specific emission factors, based on a study completed in 2022, for commonly used liquid and gas fuels; incorporation of data from SAGERS for categories 1A2a Iron & Steel and 1A2b Non- ferrous Metals; use of updated activity data from the 2020 DMRE energy balance; inclusion of emissions from charcoal and coke production under 1B1c; inclusion of emissions from natural gas activities under and the inclusion of other emissions from Energy Production. IPPU xviii GHG Inventory for South Africa: 2000 - 2022 Tier 2 and Tier 3 estimates were introduced into the SAGERS data from 2022 onwards in the IPPU sector. Calculations with increased accuracy were achieved for Other Chemicals and Product Use, specifically ceramics, soda ash and dolomite usage. Verification of some selected production plants identified areas previously not included and increased accuracy. Agriculture Agriculture and LULUCF were split into separate chapters. All the other land category for non-CO 2 biomass burning emissions were removed from agriculture sector and it was incorporated into the LULUCF sector. Non-CO 2 biomass burning emissions in agriculture sector only included the cropland biomass burning of agricultural residues. LULUCF Several improvements were made in the LULUCF sector. The largest changes were to the land areas and land change data. Land change maps for 1990 to 2014, 2014 to 2018 and 2018 to 2020 were included in this inventory. Land change data was also improved by making corrections for changes in land classifications; removing potential seasonal variation for the conversions between Forest lands and Grasslands, and Grasslands and bare ground; and making corrections for the conversions from previously converted land. These improvements in the land change data had the biggest impact on the recalculations. Other improvements included updates to BCEF data for plantations, carbon stock data for all land categories, emission factors for wetlands and the incorporation on non-CO 2 biomass burning emissions into the LULUCF sector. Waste A change in the source of the population data to a more country-specific source led to changes in emission estimates in this sector. There were also further updates made to the COD values for various industries and this impacted the industrial wastewater treatment emissions. Recalculations The improvements led to recalculations of the emission estimates across the time-series. The 2020 inventory estimates were recalculated using the AR5 GWP to remove the impacts of the changing GWP. The overall improvements led to a reduction in emissions (excl. LULUCF) of 0.4% to 5.3% across the time-series. Including LULUCF in the total produced estimates that were between 0.2% and 4.9% lower than the 2020 estimates. The energy sector improvements are the largest contributors to the reduction. xix GHG Inventory for South Africa: 2000 - 2022 E.S.5. Key category analysis A key category is one that is prioritised within the national inventory system because its estimate has a significant influence on a country's total inventory of GHG’s in terms of the absolute level of emissions and removals, the trend in emissions and removals, or uncertainty in emissions or removals. This includes both source and sink categories. A Tier 1 level and trend assessment were conducted, following Approach 1 (IPCC, 2006), on both the emissions including and excluding LULUCF to determine the key categories for South Africa. For the 2000-2022 inventory the level of disaggregation for each sector was updated. However, as with the last two inventories the key categories were then ranked according to their combined contribution to the level and trend assessments. In the previous inventory there were 58 key categories, while in this inventory there are 53. Table ES 3 shows the top 30 key categories. Forest land converted to grassland, Direct N 2 O emissions from managed soils and Ferroalloy production are new to the top 10 key categories. In the previous inventory these categories were in 26 th (as Land converted to grassland), 16 th and 15 th place, respectively. Table ES 3: Key categories for South Africa for 2022 (including LULUCF) and their ranking. Rank IPCC code IPCC Category GHG # Criteria 1 4.A.2.b Grassland converted to forest land – all pools CO 2 L,T 2 1.A.3.b Road Transportation – Liquid Fuels CO 2 L,T 3 4.A.1.a Forest land remaining forest land – biomass CO 2 L,T 4 1.A.1 Energy Industries – Solid Fuels CO 2 L,T 5 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O L 6 4.C.1.a Grassland remaining Grassland – biomass CO 2 L,T 7 4.F.2.c Grassland converted to other land – all pools CO 2 L,T 8 1.A.5 Other – Solid Fuels CO 2 L,T 9 2.C.2 Ferroalloys Production C L 10 1.B.3 Other emissions from energy production CO 2 L,T 11 1.A.4 Other Sectors – Liquid Fuels CO 2 L,T 12 3.A.1.a.ii Non-dairy Cattle CH 4 L,T 13 4.G Harvested Wood Products CO 2 T 14 5.D Wastewater Treatment and Discharge – Industrial CH 4 L 15 1.A.3.a Domestic Aviation CO 2 T xx GHG Inventory for South Africa: 2000 - 2022 Rank IPCC code IPCC Category GHG # Criteria 16 1.A.1 Energy Industries – Gaseous Fuels CO 2 T 17 1.A.5 Other – Liquid Fuels CO 2 T 18 1.A.4 Other Sectors – Solid Fuels CO 2 L,T 19 2.C.1 Iron and Steel Production CO 2 L,T 20 3.A.1.a.i Dairy Cattle CH 4 L 21 4.A.1.b Forest land remaining forest land – dead organic matter CO 2 T 22 5.A Solid Waste Disposal CH 4 L,T 23 1.B.3 Other emissions from energy production CH 4 L 24 2.B.2 Nitric Acid Production N 2 O T 25 2.C.3 Aluminium Production F-gases T 26 1.A.2 Manufacturing Industries and Construction – Gaseous Fuels CH 4 L,T 27 4.C.2.a Forest land converted to Grassland – all pools CO 2 L,T 28 1.B.2 Oil and Natural Gas CO 2 T 29 4(V) Biomass Burning CH 4 L 30 4.B.2.b Grassland converted to Cropland – all pools CO 2 L # C=Confidential E.S.6. Indicator trends The carbon intensity of the population (i.e. total net emissions per capita) has decreased by 33 % since 2000 (Figure ES 4) with 2020 experiencing the highest annual decrease of 10 %. Similarly, both the carbon intensity of economy and the carbon intensity of the energy supply have decreased, by 42 % and 35 % respectively, since 2000. These carbon intensity indicators show that overall South Africa’s carbon intensity is decreasing overtime. xxi GHG Inventory for South Africa: 2000 - 2022 Figure ES 4: Trend in carbon intensity indicators for South Africa between 2000 - 2022 E.S.7. Other information General uncertainty evaluation Uncertainty analysis is regarded by the IPPC Guidelines as an essential element of a complete and transparent inventory. The uncertainty information helps prioritizing efforts to improve the accuracy of future inventories and guide future decisions on methodological choice. Hence the reporting of uncertainties requires a complete understanding of the processes of compiling the inventory, so that potential sources of inaccuracy can be qualified and possibly quantified. South Africa still makes use of numerous IPCC default uncertainties, but as data becomes available on country-specific uncertainties these values are improved. This inventory included some updated uncertainty data for various categories within all sectors, and a more detailed uncertainty analysis was included for the LULUCF sector A trend uncertainty between the base year and 2022, as well as a combined uncertainty of activity data and emission factor uncertainty was determined using an Approach 1. The total uncertainty for the inventory was determined to be between 12.1% and 12.5% including LULUCF, with an uncertainty of 8.9% introduced into the trends. Excluding LULUCF xxii GHG Inventory for South Africa: 2000 - 2022 reduced the overall uncertainty to 5.7%-6.4% with the trend uncertainty dropping to 4.4%. Quality control and quality assurance In accordance with IPCC requirements, the national GHG inventory preparation process must include quality control and quality assurance (QC/QA) procedures. The objective of quality checking is to improve the transparency, consistency, comparability, completeness, and accuracy of the national GHG inventory. QC procedures, performed by the compilers, were carried out at various stages throughout the inventory compilation process. Quality checks were completed at four different levels, namely (a) inventory data (activity data, EF data, uncertainty, and recalculations), (b) database (data transcriptions and aggregations), (c) metadata (documentation of data, experts and supporting data), and (d) inventory report.. Completeness of the national inventory The South African GHG emission inventory for the period 2000 – 2022 is not complete, mainly due to the lack of sufficient data. Table ES 4 identifies some of the sources in the 2006 IPCC Guidelines which were not included in this inventory and the reason for their omission. Some emissions are included under other categories of the inventory due to insufficient granularity in the activity data. Lastly, there are a few activities which do not occur in South Africa, and these are also highlighted in the table. Further detail on completeness is provided in the various sector tables (see Appendix C). It is also noted that some precursor gases (sulphur dioxide and ammonia) and SF 6 have not yet been included in the inventory. Table ES 4: Activities in the 2022 inventory which are not estimated (NE), included elsewhere (IE) or not occurring (NO). NE, IE or NO IPCC Category Activity Comments NE 1B1b CO 2 , CH 4 and N 2 O from spontaneous combustion of coal seams Research to be initiated in future, depended on availability of funding to initiate a study. 1B1ai2 CH 4 emissions from abandoned mines Research to be initiated in future, depended on availability of funding to initiate a study. 1C1 CO 2 transport Insufficient data to include 1C2 Injection and storage Insufficient data to include xxiii GHG Inventory for South Africa: 2000 - 2022 2B7 CH 4 and N 2 O emissions from Soda Ash Production CO 2 included in this inventory. CH 4 and N 2 O is not anticipated to be included in future inventories. 2C1 N 2 O emissions from iron and steel production Insufficient data to include 2C2 N 2 O emissions from ferroalloy production Insufficient data to include 2D2 CH 4 and N 2 O emissions from paraffin wax use Insufficient data to include 2E Electronics industry Insufficient data to include 2F5 PFCs and HFCs from solvents Insufficient data to include 2G1 PFCs from electrical equipment Insufficient data to include 2G2 PFCs from other product uses Insufficient data to include 2G3 N 2 O from product uses Insufficient data to include 3D1f Cultivation of organic soils Organis soils are assumed to be insignificant, but incorporating detail on organic soils is included in the improvement plan All sectors NOx, CO, NMVOC emissions These have only been included for biomass burning due to a lack of data in other sectors All sectors SO 2 emissions Insufficient data. IE 1A1aii CO 2 , CH 4 and N 2 O emissions from Combined Heat and Power (CHP) combustion systems Not separated out but is included within 1A1ai 1A3eii CO 2 , CH 4 and N 2 O emissions from off-road vehicles and other machinery Included under Road transportation. 1A5b Non-specified mobile Included under Non-specified Mobile (1A3b) 3E Prescribed burning of savannas Emissions are included under LULUCF Forest land (4A) 4B Non-CO 2 emissions from cropland burning These are included under Field burning of agricultural residues (3F). NO 2B3 CO 2 , CH 4 and N 2 O emissions from Adipic acid production 2B4 CO 2 , CH 4 and N 2 O Caprolactam, Glyoxal and Glyoxylic acid production 3C7 Rice cultivation xxiv GHG Inventory for South Africa: 2000 - 2022 Planned improvements Table ES 5: Improvements that are currently in progress and improvements planned or proposed Sector Improvement Barriers and constraints Tasks in progress Cross- cutting Improve uncertainty data for all sectors by incorporating more country specific uncertainty values Lack of uncertainty data constrains this activity. As data becomes available it will be incorporated. In this inventory a more detailed analysis of uncertainty for LULUCF sector was completed. Improve QA/QC process by addressing all issues in external review Challenges in addressing external review comments have been limited by resources and process management. The DFFE inventory team has increased in size which should assist in addressing this issue. There are still many issues not resolved but the inventory team is working through them. It is an ongoing process. Energy Improve the improvement plan by incorporating all review activities not addressed in current inventory Partly resolved. Challenges around inclusion of further improvements into the improvement plan are limited resources and process management. The DFFE inventory team has increased in size, but it is still taking time to completely address all the issues. The review outputs are included in this report as a reminder of what still needs to be completed. Improve explanation of large changes in trends Partly resolved. Additional explanations have been provided, but there are still areas where this can be improved further. Ongoing process. Agriculture Incorporate all background data and equations for the Tier 2 calculations of enteric fermentation Partly completed. All the background equations have been included, but average data is still being used for some of the factors (instead of annual data) due to a lack of a sustainable data source. This will be further investigated in the 2021 inventory. LULUCF Complete a full uncertainty analysis for the Land sector, including area bias corrections A more detailed uncertainty analysis was included for biomass, DOM and SOC data in the LULUCF sector. Mapping uncertainties were improved, however these will be improved further during the land change improvement plan. xxv GHG Inventory for South Africa: 2000 - 2022 Improvement of land change data through detailed assessment of maps and tracking of land parcels This 2022 inventory incorporated a more detailed assessment of the land change data and identified the most important land change categories that need attention. The assessment also identified improvements that need to be done moving forward and the priority of these improvements. Removing changes due to seasonality is top priority and this will be improved as more land change maps are obtained. The 2022 SANLC map will already assist with making improvements. Tracking of land parcels over time is the second most important issue and training on Collect Earth to assist in this process is underway. Include deadwood in the DOM pool for all land categories Deadwood has been included for the forest land category. The other land categories with woody components are settlements and perennial crops, but data is very limited for these land categories. Deadwood in these categories is assumed to be very small, but an explanation of this will be included in the next inventory. Move to a higher tier level for DOM in forest lands This was considered in the 2022 inventory and more detailed disturbance matrix data was included to determine the amount of biomass entering the DOM pool, however there were still one or two pieces of data misisng which requires further investigation. This will be completed and included in the next inventory. Waste Data collection on quantities of waste disposed of into managed and unmanaged landfills Project is underway so data will be included in 2024 inventory. Tasks outstanding Cross cutting Investigate inconsistencies in lime activity data (for lime production in IPPU and lime application emission in Agriculture), explore alternative data sources or improve consistency. Not resolved. Various methods were compared but give varying results. Alternative data sources have not yet been found, but it may be possible to collect further data through the SAGERS system in future. Incorporate NOx, CO, NMVOC, and SOx emissions Not resolved. Energy Further disaggregation of 1A2 Current inventory breaks down 1A2 into 1A2a, 1A2b and 1A2- ab. Further work is require to further disaggregate this sector and have emissions calculated per sub-sector. More activity data for estimating emissions associated with non- energy fuel use Research to be initiated in future. xxvi GHG Inventory for South Africa: 2000 - 2022 Inclusion of methodology documentation/summary/approval process for tier 3 methods To be collated and included in future. CO 2 , CH 4 and N 2 O from spontaneous combustion of coal seams Research to be initiated in future. CH 4 emissions from abandoned mines Research to be initiated in future. Investigate pipeline transport Proposed Investigate ground activities at airports and harbours Proposed Update of the VKT study, including segregation of on-road/off-road Proposed Comparison of next VKT approach with fuel statistics Proposed Segregation of military energy use. Proposed Incorporate emissions from biogas This would require a study and so should be recommended as a project under the GHGIP. CO 2 transport and storage Proposed CO 2 , CH 4 and N 2 O emissions from combined heat and power (CHP) combustion systems Proposed Development of T3 methods for CTL-GTC and GTL Resources and funding are required to complete this study so it will be incorporated into the GHGIP. IPPU Update the fluorinated gasses with higher accuracy figures from SARS Access to all data and ensuring that the data is accurate. Include emissions from electronics industry A study needs to be undertaken to understand emissions from this source so it should be highlighted as a project for the GHGIP. Incorporate emissions SF 6 emissions Lack of data is still a challenge. Agriculture Improve manure management data, including biogas digesters as a management system Proposed project as there is a high variability in this dataset. xxvii GHG Inventory for South Africa: 2000 - 2022 LULUCF Incorporate organic soils study to include emissions from organic soils Not resolved. Due to the other more pressing issues relating to land this was not a priority and will be incorporated once the land mapping system is running. Complete an assessment of crop types and areas and investigate discrepancies between crop statistics and NLC data This was partially investigated in this inventory; however a proper assessment will be included in the land use change improvement plan that will run over the next few years. Perform a more detailed assessment of HWP to include a wider range of products Proposed project that could be considered under the GHGIP. For future evaluation. Report activity data and parameters (e.g. half-life) used for HWP emission estimation for the whole time-series This will be included in the 2024 inventory. Report on the frequency of the HWP activity data collection This will be included in the 2024 inventory. Assess if the emissions/removals in the overseas territories are significant Currently these emissions are assumed insignificant as the area of these territories is extremely small, but an assessment of possible emissions still needs to be explored. Collect data on other disturbances besides fire in forest lands This would require a study so will be recommended as a project under the GHGIP. Develop a spatial map of fuel wood consumption based on litertarure to further improve fuel wood consumption This would require a study so will be recommended as a project under the GHGIP. Assess the significance of peatlands Asessing the areas of peatlands would be the first step and this part could be done as part of the land use improvement plan. Waste Improve MCF and rate constants This would require a study so will be recommended as a project under the GHGIP. Include economic data for different population groups Will be included in the 2024 inventory Include information on population distribution in rural and urban areas as a function of income Insufficient data. Include HWP in solid waste Insufficient data. Obtain data on waste streams and the bucket system Insufficient data. xxviii GHG Inventory for South Africa: 2000 - 2022 CH 4 , N 2 O emissions from biological treatment of waste Insufficient data. CO 2 , CH 4 and N 2 O from waste incineration Insufficient data. E.S.8. Conclusions The 2000 to 2022 GHG emissions results revealed that emissions have decreased since 2000 from the Energy and IPPU and Agriculture sector. Even though the South African economy recovered after the COVID-19 pandemic, estimated emissions from the IPPU sector are lower than the emissions in 2000. The decline in emissions in the IPPU sector can be attributed to the decline in metals production (-39.0%), specifically Iron and Steel, and Aluminium. This can be ascribed to a decrease in global demand. The chemicals industry also declined (-31.4%). The energy sector continued to be the main contributor of GHG emissions as was found in previous inventories. The estimated emissions for the Energy sector are lower than the emissions in 2000, this decline in emissions is attributed to significant reductions in other energy sub sectors, particularly in the residential sector, driven by increased electrification and renewable energy use. Notably, domestic aviation and petroleum refining emissions decreased by 70% and 73%, respectively. Despite an overall decrease in Energy sector emissions since 2000, emissions from electricity production increased by 11.5% due to a rise in power generation. Improvements in the current inventory were implemented across different categories for the energy sector. These included the introduction of country-specific emission factors for liquid and gas fuels based on a 2022 study, incorporation of data from SAGERS for Iron & Steel and Non-ferrous Metals categories, and the utilisation of updated activity data from the 2020 DMRE energy balance. Charcoal and coke production emissions were included under category 1B1c and emissions from natural gas activities and other emissions from Energy Production were also included. For the IPPU sector Tier 2 and Tier 3 estimates from SAGERS data were introduced from 2020 onwards. Agriculture and LULUCF were separated into distinct chapters. Non-CO 2 biomass burning emissions, excluding cropland biomass burning of agricultural residues, were transferred from the agriculture sector to the LULUCF sector. Significant improvements were made in the LULUCF sector, particularly in land areas and land change data. Land change maps for different periods were included, corrections were made for changes in land classifications and potential seasonal variations. For the Waste sector the source of xxix GHG Inventory for South Africa: 2000 - 2022 population data was changed to a more country-specific one. Further updates were made to COD values for various industries, impacting industrial wastewater treatment emissions. xxx GHG Inventory for South Africa: 2000 - 2022 Contents E.S.1. Background ............................................................................................................. vii E.S.2. Summary of national emission and removal trends .................................. ix E.S.3. Overview of source and sink category emission estimates and trends xi E.S.4. Improvements and recalculations ................................................................ xvii E.S.5. Key category analysis ......................................................................................... xix E.S.6. Indicator trends ..................................................................................................... xx E.S.7. Other information ................................................................................................ xxi E.S.8. Conclusions ....................................................................................................... xxviii Chapter 1: Introduction ............................................................................................................ 48 1.1.1 Overall structure ................................................................................................................ 48 1.1.2 Structure of sectoral chapters ....................................................................................... 48 1.2.1 Background information on climate change ........................................................... 49 1.2.2 Background information on GHG inventories ........................................................ 49 1.2.3 Global warming potentials ............................................................................................. 50 1.3.1 National Entity .................................................................................................................... 52 1.3.2 Legal arrangements .......................................................................................................... 54 1.4.1 Inventory management ................................................................................................... 55 1.4.2 Procedural arrangements ............................................................................................... 56 1.4.3 Data collection, storage and archiving ....................................................................... 57 1.5.1 Data sources ........................................................................................................................... 1 1.5.2 Methods ................................................................................................................................... 8 1.6.1 Guiding principles................................................................................................................ 8 1.6.2 QA/QC plan and procedures ............................................................................................ 9 1.6.3 General QC procedures ...................................................................................................... 9 1.6.4 Workshops ........................................................................................................................... 13 xxxi GHG Inventory for South Africa: 2000 - 2022 1.6.5 Review process ................................................................................................................... 13 1.6.6 Verification activities........................................................................................................ 14 1.8.1 Procedures for uncertainty determination .............................................................. 17 1.8.2 Uncertainty assessment results ................................................................................... 17 Chapter 2: Trends in GHG Emissions ................................................................................... 31 2.1.1 National trends .................................................................................................................. 31 2.1.2 Indicator trends .................................................................................................................. 34 2.2.1 Carbon dioxide (CO 2 ) ........................................................................................................ 39 2.2.2 Methane (CH 4 ) ..................................................................................................................... 39 2.2.3 Nitrous oxide (N 2 O) .......................................................................................................... 40 2.2.4 F gases .................................................................................................................................... 41 2.3.1 Energy .................................................................................................................................... 42 2.3.2 IPPU ......................................................................................................................................... 44 2.3.3 Agriculture ............................................................................................................................ 45 2.3.4 LULUCF .................................................................................................................................. 46 2.3.5 Waste ...................................................................................................................................... 47 Chapter 3: Energy (CRT sector 1) .......................................................................................... 49 3.1.1 Shares and trends in emissions .................................................................................... 50 3.1.2 Overview of methodology and completeness ......................................................... 69 3.1.3 Improvements and recalculations............................................................................... 71 3.1.4 Key categories in the energy sector ............................................................................ 72 xxxii GHG Inventory for South Africa: 2000 - 2022 3.1.5 QA/QC process and verification ................................................................................... 73 3.1.6 Planned improvements ................................................................................................... 74 3.2.1 Comparison between sectoral and reference approach ..................................... 75 3.2.2 Feedstock and non-energy use of fuels ..................................................................... 76 3.2.3 Common Data used for Fuel Combustion Activities ............................................. 76 3.2.4 Energy industries (1.A.1) ................................................................................................ 78 3.2.5 Manufacturing industries and construction (1.A.2) ............................................. 83 3.2.6 Transport (1.A.3) ............................................................................................................... 87 3.2.7 Other sectors (1.A.4) ......................................................................................................... 90 3.2.8 Non-specified sectors (1.A.5) ........................................................................................ 94 3.3.1 Solid fuels (1.B.1) ............................................................................................................... 96 3.3.2 Oil and natural gas (1.B.2) ........................................................................................... 100 3.3.3 Other emissions from energy production (1.B.3) .............................................. 102 Chapter 4: Industrial Processes and Product Use (IPPU) (CRT sector 2) ............ 105 4.1.1 Shares and trends in emissions ................................................................................. 106 4.1.2 Overview of methodology and completeness ...................................................... 115 4.1.3 Improvements and recalculations............................................................................ 119 4.1.4 Key categories in the IPPU sector ............................................................................. 119 4.1.5 Planned improvements and recommendations .................................................. 119 4.2.1 Cement production (2.A.1) .......................................................................................... 120 4.2.2 Lime production (2.A.2) ............................................................................................... 124 4.2.3 Glass production (2.A.3) ............................................................................................... 127 4.2.4 Other process uses of carbonates (2.A.4) .............................................................. 128 4.2.5 Other mineral products processes ........................................................................... 130 4.3.1 Ammonia production (2.B.1)...................................................................................... 131 4.3.2 Nitric acid production (2.B.2) .................................................................................... 133 xxxiii GHG Inventory for South Africa: 2000 - 2022 4.3.3 Adipic acid production (2.B.3) ................................................................................... 134 4.3.4 Caprolactuam, glyoxal and glyoxylic acid production (2.B.4) ....................... 134 4.3.5 Carbide production (2.B.5) ......................................................................................... 134 4.3.6 Titanium dioxide production (2.B.6) ...................................................................... 136 4.3.7 Soda ash production (2.B.7) ....................................................................................... 137 4.3.8 Carbon black production (2.B.8.f) ............................................................................ 139 4.3.9 Hydrogen Production (2.B.8.g) .................................................................................. 140 4.3.10 Other Chemical Processes (2.B.10) ..................................................................... 142 4.4.1 Iron and steel production (2.C.1) ............................................................................. 143 4.4.2 Ferroalloys production (.C.2) ..................................................................................... 146 4.4.3 Aluminium production (2.C.3) ................................................................................... 149 4.4.4 Magnesium production (2.C.4) .................................................................................. 150 4.4.5 Lead production (2.C.5) ................................................................................................ 151 4.4.6 Zinc production (2.C.6) ................................................................................................. 153 4.4.7 Other (2.C.7)...................................................................................................................... 154 4.5.1 Lubricant use (2.D.1) ..................................................................................................... 155 4.5.2 Paraffin wax use (2.D.2) ............................................................................................... 157 4.5.3 Solvent use (2.D.3) ......................................................................................................... 158 4.7.1 Refrigeration and air conditioning (2.F.1) ............................................................ 159 4.7.2 Foam blowing agents (2.F.2) ...................................................................................... 162 4.7.3 Fire protection (2.F.3) ................................................................................................... 164 4.7.4 Aerosols (2.F.4) ................................................................................................................ 166 Chapter 5: Agriculture (CRT sector 3) .............................................................................. 171 5.1.1 Shares and trends in emissions ................................................................................. 172 5.1.2 Overview of methodology and completeness ...................................................... 180 xxxiv GHG Inventory for South Africa: 2000 - 2022 5.1.3 Improvements and recalculations............................................................................ 184 5.1.4 Key categories in the Agriculture sector ................................................................ 185 5.1.5 Planned improvements and recommendations .................................................. 186 5.2.1 Livestock population and characterisation .......................................................... 186 5.2.2 Manure management .................................................................................................... 193 5.3.1 Category description ..................................................................................................... 198 5.3.2 Methodological issues ................................................................................................... 198 5.3.3 Uncertainty ........................................................................................................................ 206 5.3.4 Time-series consistency ............................................................................................... 207 5.3.5 Category specific QA/QC and verification ............................................................. 207 5.3.6 Category-specific recalculations ............................................................................... 207 5.3.7 Category-specific planned improvements ............................................................ 208 5.4.1 CH 4 emissions from manure management (3.B) ................................................ 208 5.4.2 Direct and indirect N 2 O emissions from manure management (3.B) ........ 211 5.6.1 Direct N 2 O emissions from managed soils (3.D.1) ............................................. 214 5.6.2 Indirect nitrous oxide emissions from managed soils (3.D.2) ...................... 221 5.7.1 Category description ..................................................................................................... 223 5.7.2 Methodological issues ................................................................................................... 224 5.7.3 Uncertainty ........................................................................................................................ 225 5.7.4 Time-series consistency ............................................................................................... 226 5.7.5 Category specific QA/QC and verification ............................................................. 226 5.7.6 Category-specific recalculations ............................................................................... 226 5.7.7 Category-specific planned improvements ............................................................ 226 5.8.1 Category description ..................................................................................................... 226 5.8.2 Methodological issues ................................................................................................... 227 xxxv GHG Inventory for South Africa: 2000 - 2022 5.8.3 Uncertainty ........................................................................................................................ 227 5.8.4 Time-series consistency ............................................................................................... 227 5.8.5 Category specific QA/QC and verification ............................................................. 228 5.8.6 Category-specific recalculations ............................................................................... 228 5.8.7 Category-specific planned improvements ............................................................ 228 5.9.1 Category description ..................................................................................................... 228 5.9.2 Methodological issues ................................................................................................... 228 5.9.3 Uncertainty ........................................................................................................................ 229 5.9.4 Time-series consistency ............................................................................................... 229 5.9.5 Category specific QA/QC and verification ............................................................. 229 5.9.6 Category-specific recalculations ............................................................................... 229 5.9.7 Category-specific planned improvements ............................................................ 229 Chapter 6: Land use, Forestry and Land Use Change (LULUCF) (CRT sector 4) . 232 6.1.1 Shares and trends in emissions ................................................................................. 233 6.1.2 Impacts of natural disturbance ................................................................................. 238 6.1.3 Overview of methodology and completeness ...................................................... 238 6.1.4 Improvements and recalculations............................................................................ 253 6.1.5 Key categories in the LULUCF sector ...................................................................... 255 6.1.6 QA/QC process and verification ................................................................................ 256 6.1.7 Planned improvements ................................................................................................ 256 6.2.1 Forest land ......................................................................................................................... 259 6.2.2 Cropland ............................................................................................................................. 261 6.2.3 Grassland ............................................................................................................................ 262 6.2.4 Wetlands ............................................................................................................................ 263 6.2.5 Settlements ........................................................................................................................ 263 6.2.6 Other land .......................................................................................................................... 265 6.3.1 Database and data processing ................................................................................... 266 xxxvi GHG Inventory for South Africa: 2000 - 2022 6.3.2 Uncertainty ........................................................................................................................ 269 6.3.3 Comparison between Landsat and Sentinel maps ............................................. 270 6.3.4 Land use change mapping ........................................................................................... 271 6.4.1 Category description ..................................................................................................... 284 6.4.2 Methodological issues ................................................................................................... 285 6.4.3 Uncertainty ........................................................................................................................ 294 6.4.4 Time-series consistency ............................................................................................... 295 6.4.5 Category specific QA/QC and verification ............................................................. 295 6.4.6 Category-specific recalculations ............................................................................... 297 6.4.7 Category-specific planned improvements ............................................................ 298 6.5.1 Category description ..................................................................................................... 298 6.5.2 Methodological issues ................................................................................................... 299 6.5.3 Uncertainty ........................................................................................................................ 303 6.5.4 Time-series consistency ............................................................................................... 304 6.5.5 Category specific QA/QC and verification ............................................................. 304 6.5.6 Category-specific recalculations ............................................................................... 305 6.5.7 Category-specific planned improvements ............................................................ 305 6.6.1 Category description ..................................................................................................... 306 6.6.2 Methodological issues ................................................................................................... 306 6.6.3 Uncertainty ........................................................................................................................ 311 6.6.4 Time-series consistency ............................................................................................... 312 6.6.5 Category specific QA/QC and verification ............................................................. 312 6.6.6 Category-specific recalculations ............................................................................... 312 6.6.7 Category-specific planned improvements ............................................................ 313 6.7.1 Category description ..................................................................................................... 313 6.7.2 Methodological issues ................................................................................................... 313 6.7.3 Uncertainty ........................................................................................................................ 315 6.7.4 Time-series consistency ............................................................................................... 316 xxxvii GHG Inventory for South Africa: 2000 - 2022 6.7.5 Category specific QA/QC and verification ............................................................. 316 6.7.6 Category-specific recalculations ............................................................................... 316 6.7.7 Category-specific planned improvements ............................................................ 317 6.8.1 Category description ..................................................................................................... 317 6.8.2 Methodological issues ................................................................................................... 317 6.8.3 Uncertainty ........................................................................................................................ 320 6.8.4 Time-series consistency ............................................................................................... 321 6.8.5 Category specific QA/QC and verification ............................................................. 321 6.8.6 Category-specific recalculations ............................................................................... 321 6.8.7 Category-specific planned improvements ............................................................ 322 6.9.1 Category description ..................................................................................................... 322 6.9.2 Methodological issues ................................................................................................... 322 6.9.3 Uncertainty ........................................................................................................................ 323 6.9.4 Time-series consistency ............................................................................................... 323 6.9.5 Category specific QA/QC and verification ............................................................. 323 6.9.6 Category specific recalculations ................................................................................ 323 6.9.7 Category specific planned improvements ............................................................. 323 6.10.1 Category description ................................................................................................. 324 6.10.2 Methodological issues ............................................................................................... 324 6.10.3 Uncertainty ................................................................................................................... 326 6.10.4 Time-series consistency........................................................................................... 326 6.10.5 Category specific QA/QC and verification ......................................................... 326 6.10.6 Category specific recalculations ........................................................................... 327 6.10.7 Category specific planned improvements ........................................................ 328 Chapter 7: Waste (CRT sector 5) ......................................................................................... 333 7.1.1 Overview of shares and trends in emissions........................................................ 333 7.1.2 Overview of methodology and completeness ...................................................... 334 xxxviii GHG Inventory for South Africa: 2000 - 2022 7.1.3 Improvements and recalculations............................................................................ 335 7.1.4 Key categories in the Waste sector .......................................................................... 335 7.1.5 QA/QC process and verification ................................................................................ 336 7.1.6 Planned improvements. ............................................................................................... 337 7.2.1 Category description ..................................................................................................... 337 7.2.2 Methodological issues ................................................................................................... 338 7.2.3 Uncertainty ........................................................................................................................ 339 7.2.4 Time series consistency ............................................................................................... 340 7.2.5 Category specific QA/QC and verification ............................................................. 340 7.2.6 Category specific recalculations ................................................................................ 340 7.2.7 Category specific planned improvements ............................................................. 341 7.3.1 Category description ..................................................................................................... 341 7.3.2 Methodological issues ................................................................................................... 341 7.3.3 Uncertainty ........................................................................................................................ 343 7.3.4 Time-series consistency ............................................................................................... 343 7.3.5 Category specific QA/QC and verification ............................................................. 343 7.3.6 Category specific recalculations ................................................................................ 344 7.3.7 Category specific planned improvements ............................................................. 344 7.4.1 Category description ..................................................................................................... 344 7.4.2 Methodological issues ................................................................................................... 345 7.4.3 Uncertainty ........................................................................................................................ 346 7.4.4 Time series consistency ............................................................................................... 346 7.4.5 Category specific QA/QC and verification ............................................................. 346 7.4.6 Category specific recalculations ................................................................................ 346 7.4.7 Category specific planned improvements ............................................................. 347 7.5.1 Domestic wastewater treatments and discharge (5.D.1) ................................ 347 7.5.2 Industrial wastewater treatment and discharge (5.D.2) ................................. 350 xxxix GHG Inventory for South Africa: 2000 - 2022 Appendix A: Key category analys.................................................................................355 Appendix B: Uncertainty analysis................................................................................397 Appendix C: Summary emission tables for 2022.......................................................407 Appendix D: .Reference and sectoral fuel consumption...........................................427 xl GHG Inventory for South Africa: 2000 - 2022 Tables Table 1.1: Global warming potential (GWP) of greenhouse gases used in this report and taken from AR5 (IPCC, 2014b). ..................................................................................................... 50 Table 1.2: Trends in national GHG emissions (excluding and including LULUCF) between 2000 and 2022 based on the three different GWPs ........................................... 51 Table 1.3: Principal data sources for the Energy sector inventory. ........................................... 2 Table 1.4: Principal data sources for the IPPU sector inventory. ................................................ 3 Table 1.5: Principal data sources for the Agriculture sector inventory. ................................... 5 Table 1.6: Principal data sources for the LULUCF sector inventory. ......................................... 6 Table 1.7: Principal data sources for the Waste sector inventory. .............................................. 7 Table 1.8: Guiding principles applied during the preparation of the 2022 inventory. ....... 8 Table 1.9: Quality control checks carried out on South Africa’s 2022 GHG inventory. .... 10 Table 1.10: Key categories for South Africa for 2022 (including LULUCF) and their ranking. ................................................................................................................................................... 15 Table 1.11: Activities in the 2022 inventory which are not estimated (NE), included elsewhere (IE) or not occurring (NO). ....................................................................................... 18 Table 1.12: List of planned improvements for South Africa’s GHG inventory. .................... 22 Table 2.1: Changes in South Africa’s emissions excluding and including LULUCF between 2000, 2020 and 2022. ....................................................................................................................... 32 Table 2.2: Trends and annual change in emissions (excluding and including LULUCF), 2000 – 2022. ......................................................................................................................................... 33 Table 2.3: Trend in CO 2 , CH 4 , N 2 O and F-gases between 2000 and 2022. .............................. 38 Table 2.4: Trend in emissions and removals by sector for 2000 to 2022. ............................. 45 Table 2.5: Trends in indirect GHG emissions between 2000 and 2022. ................................. 47 Table 3.1: Summary of emissions from the Energy sector in 2022 .......................................... 50 Table 3.2: Summary of the change in emissions from the Energy sector between 2000 and 2022. ............................................................................................................................................... 53 Table 3.3: Emission trends for the main electricity producer, 2000 - 2022 ......................... 55 Table 3.4: Trend in emissions from other electricity producers, 2000 – 2022. .................. 58 Table 3.5: Trend in emissions from other sectors, 2000 – 2022. .............................................. 65 Table 3.6: Trend in fugitive emissions, 2000 – 2022. .................................................................... 67 Table 3.7: Summary of methods and emission factors for the energy sector and an assessment of the completeness of the energy sector emissions. ................................... 70 Table 3.8: Key categories identified in the Energy sector. ........................................................... 73 Table 3.9: QA/QC measures for the Energy sector inventory .................................................... 74 Table 3.10: Net calorific values for solid, liquid and gaseous fuels .......................................... 77 xli GHG Inventory for South Africa: 2000 - 2022 Table 3.11: IPCC default and country-specific emission factors for fuel combustion ....... 77 Table 3.12: Methodological details - Energy Industries ................................................................. 79 Table 3.13: Activity data used for 1A1 Energy Industries ............................................................. 80 Table 3.14: Trend in fuel consumption for the various categories in the energy industry sector, 2000 – 2022. .......................................................................................................................... 80 Table 3.15: Uncertainty for the Energy Industries category. ....................................................... 81 Table 3.16: Disaggregation of Manufacturing industries and construction category for reporting ................................................................................................................................................ 83 Table 3.17: Methodological details for the Manufacturing industries and construction category. ................................................................................................................................................. 84 Table 3.18: Fuel consumption (TJ) in the manufacturing industries and construction category, 2000 – 2022. ..................................................................................................................... 85 Table 3.19: Uncertainty for the Manufacturing industries and construction category. ..... 86 Table 3.20: Methodological details for the Transport category. ................................................ 87 Table 3.21: Activity data used for 1A3 Transport ............................................................................ 88 Table 3.22: Uncertainty for South Africa’s transport emission estimates. ............................ 89 Table 3.23: Methodological details for the Other sectors category. .......................................... 91 Table 3.24: Activity data used for 1A4 Other Sectors ..................................................................... 91 Table 3.25: Uncertainty for South Africa’s Other sectors emission estimates. ..................... 93 Table 3.26: Uncertainty for South Africa’s Non-specified sectors emission estimates....... 95 Table 3.27: Activity data used for 1B1 Solid Fuels ........................................................................... 97 Table 3.28: Amount of coal and charcoal produced, 2000 – 2022. , .......................................... 97 Table 3.29: Country-specific emission factors for coal mining and handling. ..................... 98 Table 3.30: Tier 1 emission factors for charcoal and coke production ................................... 99 Table 3.31: Uncertainty for 1B1 Solid fuels sector .......................................................................... 99 Table 3.32: Uncertainty for 1B2 Oil and natural gas sector. .................................................... 101 Table 3.33: Uncertainty for 1B3 Other emissions from energy production sector. ........... 102 Table 4.1: Summary of the estimated emissions from the IPPU sector in 2022 for South Africa. ................................................................................................................................................... 107 Table 4.2: Summary of the change in emissions from the IPPU sector between 2000 and 2022. ..................................................................................................................................................... 108 Table 4.3: Summary of methods and emission factors for the IPPU sector and an assessment of the completeness of the IPPU sector emissions. .................................... 116 Table 4.4: Key categories identified in the IPPU sector. ............................................................ 119 Table 4.5: Planned improvements and recommendations ....................................................... 119 Table 4.6: Production data for the mineral industries, 2000 – 2022. ................................... 121 Table 4.7: Emission factors applied for carbon black production emission estimates. 139 Table 4.8: Production data for the iron and steel industry, 2000 – 2022. .......................... 144 Table 4.9: Production data for the ferroalloy industry, 2000 – 2022. .................................. 147 xlii GHG Inventory for South Africa: 2000 - 2022 Table 4.10: Production data for the lead and zinc industries, 2000 – 2022. ..................... 151 Table 4.11: Lubricant and paraffin wax consumption, 2000 – 2022. ................................... 155 Table 4.12: Methodology and data sources used for each RAC sub-application. ............. 160 Table 4.13: Emission factors and defaults applied in the foam blowing agents emission estimates. ............................................................................................................................................ 163 Table 5.1: Main IPCC categories included in the Agriculture sector emission estimates. ................................................................................................................................................................ 171 Table 5.2: Enteric fermentation emission trends between 2000 and 2022........................ 174 Table 5.3: Change in Enteric fermentation emissions (2000 – 2022) and relative contribution of the various livestock categories to the total emissions. ................... 175 Table 5.4: Trends and changes in manure management emissions (2000 to 2022)...... 175 Table 5.5: Changes in emissions related to agricultural soils between 2000 and 2022. ................................................................................................................................................................ 179 Table 5.6: Summary of methods and emission factors for the Agriculture sector and an assessment of the completeness of the Agriculture sector emissions. ....................... 181 Table 5.7: Key categories in the Agriculture sector in 2022. ................................................... 185 Table 5.8: Livestock population data sources. ............................................................................... 187 Table 5.9: Livestock characterization and herd composition data sources and assumptions. ...................................................................................................................................... 189 Table 5.10: Manure management systems for the various livestock types and their data sources. ................................................................................................................................................ 195 Table 5.11: Verification of livestock manure management systems. ................................... 196 Table 5.12: Methane conversion factors and their sources. ..................................................... 199 Table 5.13: Enteric fermentation emission factors and their sources per livestock category. .............................................................................................................................................. 200 Table 5.14: Equations for the calculation of emissions factors for cattle using the Tier 2 approach. ............................................................................................................................................ 202 Table 5.15: Equations for the calculation of emissions factors for sheep and goats using the Tier 2 approach. ....................................................................................................................... 204 Table 5.16: Enteric fermentation emission factor uncertainties. ........................................... 206 Table 5.17: Uncertainty data for livestock manure CH 4 emission factors. ......................... 210 Table 5.18: Nitrogen excretion rate for different livestock categories................................. 213 Table 5.19: Emission trends for the various sub-categories of direct N 2 O emissions from managed soils. ................................................................................................................................... 215 Table 6.1: Summary of emissions from the LULUCF sector in 2022. .................................... 234 Table 6.2: Summary of the change in emissions from the LULUCF sector over the time- series 2000-2022 and since the last inventory in 2020. .................................................. 237 xliii GHG Inventory for South Africa: 2000 - 2022 Table 6.3: Summary of methods and emission factors for the LULUCF sector and an assessment of the completeness of the sector. .................................................................... 242 Table 6.4: Soil types and their description (Source: DEA, 2015) ........................................... 247 Table 6.5: Soil carbon reference data per soil type as obtained from ISRIC database. .. 249 Table 6.6: Soil carbon stock change factors for the various land types. .............................. 249 Table 6.7: Comparison of total CO 2 (Gg CO 2 per year) from soils between the Tier 1 and Tier 2 approaches ............................................................................................................................ 250 Table 6.8: Key categories in the LULUCF sector in 2022. .......................................................... 255 Table 6.9: Relationship between the IPCC categories, land change categories and the SANLC datasets in the 2022 inventory.................................................................................... 257 Table 6.10: Definitions of the land classes, as given in the SANLC 2018, that are included in the Forest land category (Source: DEA, 2019). ............................................................... 260 Table 6.11: Definitions of the land classes, as given in the SANLC 2018, that are included in the Cropland category (Source: DEA, 2019). ................................................................... 261 Table 6.12: Definitions of the land classes, as given in the SANLC 2018, that are included in the Grassland category (Source: DEA, 2019). .................................................................. 262 Table 6.13: Definitions of the land classes, as given in the SANLC 2018, that are included in the Wetland category (Source: DEA, 2019). ..................................................................... 263 Table 6.14: Definitions of the land classes, as given in the SANLC 2018, that are included in the Settlement category (Source: DEA, 2019). ................................................................ 264 Table 6.15: Definitions of the land classes, as given in the SANLC 2018, that are included in the Other land category (Source: DEA, 2019). ................................................................ 265 Table 6.16: Comparison of mapping accuracies between SANLC 2014 and SANLC 2018. ................................................................................................................................................................ 271 Table 6.17: Corrections made to 2014-2018 change matrix. .................................................. 273 Table 6.18: Land change (ha) matrix for South Africa between 2018 and 2020 prior to corrections. ........................................................................................................................................ 280 Table 6.19: Land change (ha) matrix for South Africa between 2018 and 2020 after corrections. ........................................................................................................................................ 281 Table 6.20: Biomass growth or accumulation rates for the forest land categories......... 286 Table 6.21: Root:shoot ratios for the forest land categories. ................................................... 286 Table 6.22: Factors applied for plantation data in 2022 with IPCC default values for comparison. ....................................................................................................................................... 287 Table 6.23: Above ground biomass for the various forest land categories. ....................... 290 Table 6.24: Deadwood and litter biomass for forest lands. ...................................................... 293 Table 6.25: Biomass and growth factors for the various cropland categories. ................. 300 Table 6.26: Litter data applied in the inventory for the various cropland categories. .. 302 Table 6.27: Stock change factors for the specific crops in South Africa. ............................. 302 Table 6.28: Above ground biomass factors for grassland categories. .................................. 309 Table 6.29: Litter data applied in the inventory for the grassland categories. ................. 310 xliv GHG Inventory for South Africa: 2000 - 2022 Table 6.30: Stock change factors for grasslands in South Africa. ........................................... 310 Table 6.31: Fuel density and combustion fractions for the various grassland classes. . 311 Table 6.32: Biomass growth and accumulation rates for the various settlement categories. ........................................................................................................................................... 319 Table 6.33: Litter data applied in the inventory for each of the settlement land classes. ................................................................................................................................................................ 319 Table 6.34: Fuel density and combustion fractions for the various settlement classes.320 Table 7.1: Summary of the change in emissions from South Africa’s Waste sector between 2000 and 2022. .............................................................................................................. 334 Table 7.2: Summary of methods and emission factors for the Waste sector and an assessment of the completeness of the Waste sector emissions. ................................. 334 Table 7.3: Key categories in the Waste sector. .............................................................................. 336 Table 7.4: General QA/QC checks completed for the Waste sector. ...................................... 336 Table 7.5: IPCC default factors utilized in the FOD Model to determine emissions from solid waste disposal. ....................................................................................................................... 339 Table 7.6: Uncertainties associated with emissions from South Africa’s solid waste disposal. ............................................................................................................................................... 340 Table 7.7: Activity data for biological treatment systems. ........................................................ 341 Table 7.8: Emission Factors for Biological Treatment of Solid Waste.................................. 343 Table 7.9: Emission factors for estimating emissions from open burning of waste. ...... 345 Table 7.10: Emission factors for different wastewater treatment and discharge systems ................................................................................................................................................................ 348 Table 7.11: Distribution and utilization of different treatment and discharge systems348 Table 7.12: MCF values and emission factors ................................................................................ 352 Table 7.13: Average COD values used to estimate CH 4 emissions from industrial wastewater......................................................................................................................................... 352 xlv GHG Inventory for South Africa: 2000 - 2022 Figures Figure 1.1: Organogram showing where the GHG Inventory compilation occurs within DFFE. ....................................................................................................................................................... 53 Figure 1.2: The inventory compilation process in South Africa. ............................................... 55 Figure 1.3: Overview of the phases of the GHG inventory compilation and improvement process undertaken for South Africa’s 2020 GHG inventory. ........................................... 57 Figure 1.4: Data collection process for the 2022 Energy inventory. .......................................... 2 Figure 1.5: The quality assurance review process for the 2000 – 2022 inventory. ........... 14 Figure 1.6: Impacts of recalculations on the overall emission estimates for South Africa. ................................................................................................................................................................... 21 Figure 2.1: National GHG emissions (excluding LULUCF) for South Africa, 2022 .............. 31 Figure 2.2: National GHG emissions (excluding LULUCF) for South Africa, 2000 – 2022 ................................................................................................................................................................... 33 Figure 2.3: Trends in overall carbon intensity of the population and the economy of South Africa between 2000 and 2022. ....................................................................................... 35 Figure 2.4: Trends in energy intensity indicators for South Africa between 2000 and 2022. ........................................................................................................................................................ 36 Figure 2.5: Percentage contributions from each of the gases to South Africa’s emissions (excl. LULUCF (top) and incl. LULUCF (bottom)) between 2000 and 2022. ............... 37 Figure 2.6: Trend and sectoral contribution to CO 2 emissions (excl. LULUCF), 2000 – 2022. ........................................................................................................................................................ 39 Figure 2.7: Trend and sectoral contribution to the CH 4 emissions, 2000 – 2022. .............. 40 Figure 2.8: Trend and sectoral contribution to N 2 O emissions in South Africa, 2000 – 2022. ........................................................................................................................................................ 41 Figure 2.9: Trend in F-gas emissions in South Africa, 2000 – 2022. ........................................ 42 Figure 2.10: Percentage contributions from each of the sectors to South Africa’s emissions (excluding LULUCF) between 2000 and 2022. .................................................. 43 Figure 2.11: Emissions and sequestration trends per sector across the time-series, 2000-2022. ............................................................................................................................................ 44 Figure 3.1: Energy sector contribution by gas (excl. LULUCF) for 2022. ............................... 49 Figure 3.2: Total primary energy supply in South Africa, 2020 ................................................. 50 Figure 3.3: Energy emissions by sub-sector, 2000 – 2022 .......................................................... 51 Figure 3.4: Trend in annual change in the total energy emissions in South Africa, 2000 – 2022. ........................................................................................................................................................ 52 Figure 3.5: Trends and subcategory contributions to fuel combustion activity emissions in South Africa, 2000 – 2022. ......................................................................................................... 54 xlvi GHG Inventory for South Africa: 2000 - 2022 Figure 3.6: Contributions to Energy Industries emissions, 2022 .............................................. 55 Figure 3.7: Historical energy availability factors and electricity generation information ................................................................................................................................................................... 57 Figure 3.8: Annual electricity from renewable energy, 2019 - 2022 ....................................... 57 Figure 3.9: Historical emissions from Petroleum Refining (1.A.1.b) and Manufacture of solid fuels and other energy industries (1.A.1c) ........................................................................ 59 Figure 3.10: 1A2 Disaggregated emissions 2000 - 2022 .............................................................. 60 Figure 3.11: Manufacturing industries and construction – fuel consumption contributions ........................................................................................................................................ 61 Figure 3.12: Transport sector emission contributions, 2022 ..................................................... 62 Figure 3.13: Annual volume changes for fuel oil, jet fuel and diesel sold since 2018 ....... 63 Figure 3.14: Historical share of diesel and motor gasoline consumption for road transportation ...................................................................................................................................... 64 Figure 3.15: Historical fuel prices for motor gasoline and diesel ............................................. 64 Figure 3.16: Fuel consumption trends for the Commercial/Institutional sector................ 66 Figure 3.17: Trends in household energy supply for cooking .................................................... 67 Figure 3.18: Annual change in Coal Production, 2010 - 2022 .................................................... 69 Figure 3.19: Recalculations for the Energy sector between 2000 and 2022. ....................... 72 Figure 3.20: Comparisons between the reference and sectoral approach of determining the CO 2 emissions for the energy sector for South Africa. .................................................. 75 Figure 3.21: Fuel consumption for the transport sector (1A3), 2000 – 2022. ..................... 88 Figure 3.22: Fuel consumption for the 1A4b residential category, 2000-2022 ................... 92 Figure 3.23: Fuel consumption for the 1A4c agriculture/forestry/fishing/fish farms category, 2000-2022 ......................................................................................................................... 92 Figure 3.24: Fuel consumption for 1A5 Non-specified sectors, 2000-2022 ........................... 95 Figure 4.1: Trend in South Africa’s IPPU sector emissions, 2000 – 2022. .......................... 110 Figure 4.2: Category contribution and trend for the mineral subsector, 2000 – 2022. 111 Figure 4.3: Trend and category contribution to emissions from the metal industries, 2000 – 2022. ...................................................................................................................................... 113 Figure 4.4: Trend and category contribution in the emissions from non-energy products from fuels and solvents. 2000 – 2022...................................................................................... 114 Figure 4.5: Trend and category contribution to the product uses as substitutes for ODS emissions, 2000 – 2022. ................................................................................................................ 115 Figure 5.1: Enteric fermentation emission trends, 2000 – 2022. ............................................ 173 Figure 5.2: Livestock population trends, 2000 – 2022 (Data source: DALRRD, 2022). 174 Figure 5.3: Trend in manure management CH 4 emissions from livestock, 2000 – 2022. ................................................................................................................................................................ 176 xlvii GHG Inventory for South Africa: 2000 - 2022 Figure 5.4: Trend in manure management N 2 O emissions from livestock, 2000 – 2022. ................................................................................................................................................................ 177 Figure 5.5: Trends in emissions related to agricultural soils, 2000 – 2022. ...................... 179 Figure 5.6: Change in Agriculture emission estimates due to recalculations since 2022 submission. ........................................................................................................................................ 185 Figure 6.1: Time series for GHG emissions and removals by land type in the LULUCF sector in South Africa, 2000 - 2022. ......................................................................................... 235 Figure 6.2: Time-series of GHG emission and removals by carbon pool for the LULUCF sector in South Africa, 2000-2022. ........................................................................................... 236 Figure 6.3: Time-series of GHG emission and removals by land type for the LULUCF sector when fire impacts are excluded. .................................................................................. 238 Figure 6.4: South African soils classified into the IPCC classes (DEA, 2015). .................... 247 Figure 6.5: South Africa’s long term climate map classified into the IPCC climate classes (Source: DEA, 2015). ...................................................................................................................... 253 Figure 6.6: Change in LULUCF emission estimates due to recalculations since 2020 submission. ........................................................................................................................................ 255 Figure 6.7: Carbon change for plantations estimated by the Gain-loss and Stock- difference methods. ........................................................................................................................ 297 Figure 6.8: Comparison of HWP emissions estimated with the different IPCC methods. ................................................................................................................................................................ 327 Figure 7.1: Trend in emissions from Waste sector, 2000 – 2022........................................... 333 Figure 7.2: Change in Waste emission estimates due to recalculations since 2020 submission. ........................................................................................................................................ 335 48 GHG Inventory for South Africa: 2000 - 2022 Chapter 1: Introduction Structure of the report 1.1.1 Overall structure This National Inventory Report (NIR) follows the format prescribed by the UNFCCC in the updated UNFCCC reporting guidelines on annual inventories (UNFCCC, 2013) following incorporation of the provisions of decision 14/CP.11 (FCCC/SBSTA/2006/9): • Chapter 1 is the introductory chapter which contains background information on climate change and GHG inventories, South Africa’s inventory institutional arrangements, preparation and reporting process, key categories and uncertainty analysis, a description of the methodologies, activity data, emission factors, and the overall QA/QC process. • Chapter 2 provides an overview of the trends in aggregated GHG emissions and indicators, as well as an analysis and interpretation of the trends in emissions by gas (CO 2 , CH 4 , N 2 O, F gases, indirect precursors) and by category (Energy, IPPU, Agriculture, LULUCF, Waste). • Chapters 3 to 7 deal with detailed explanations of the emissions in the Energy, IPPU, Agriculture, LULUCF and Waste sectors, respectively. They include an overall trend assessment, methodology, data sources, recalculations, uncertainty, time-series consistency, QA/QC process, verification and planned improvements and recommendations. • References are provided at the end of each chapter. • The Appendices are found at the end of the report and contain the detailed key category (Appendix A) and uncertainty analysis results (Appendix B), all summary tables in the IPCC prescribed format (Appendix C), and the energy sector reference approach data (Appendix D). 1.1.2 Structure of sectoral chapters In this submission the Agriculture and LULUCF sectors have been split into two chapters to bring the report in alignment with the required Common Reporting Tables (CRT) that have to be submitted as part of the Enhanced Transparency Framework (ETF) reporting. The layout and structure of the detailed sectoral chapters (chapters 3 to 7) is as follows: • Sector overview is at the front of each chapter, and this provides a detailed analysis of the emission trends and the drivers in the sector and the various sub- categories. In addition, this section provides an overview of the methodologies, 49 GHG Inventory for South Africa: 2000 - 2022 completeness, improvements, recalculations, key categories, and planned improvements for the sector. This section in each chapter, therefore, highlights the main key points and take-home messages for each sector. • Category and sub-category sections are provided below this, identified by the category name and number, and these provide the details on the methodologies, activity data, emission factors, uncertainties, time-series consistency, category specific recalculations, category specific quality assurance and quality control (QA/QC) procedures and category specific planned improvements. This is the overall structure followed throughout the chapters, although there are some variations in the chapters due to the different data in each sector, for example: • In the Energy sector the Fuel combustion section has an overall, common methodology upfront with additional sections on the sectoral and reference approach, international bunkers, feedstocks, and CO 2 storage. • The Agriculture sector has a section upfront in the Livestock category explaining the livestock population and manure management as this information is used in several sub-category sections. • The LULUCF sector also has additional information at the beginning to provide detail on the land classifications and approaches for determining land areas and land use change that is used throughout the section. National circumstances 1.2.1 Background information on climate change The United Nations Framework Convention on Climate Change (UNFCCC was signed by South Africa in 1993 and ratified in 1997. All countries that ratify the Convention (the Parties) are required to address climate change, including monitoring trends in anthropogenic greenhouse gas emissions. One of the principal commitments made by the ratifying Parties under the Convention was to develop, publish and regularly update national emission inventories of greenhouse gases. Parties are also obligated to protect and enhance carbon sinks and reservoirs, for example forests, and implement measures that assist in national and/or regional climate change adaptation and mitigation. 1.2.2 Background information on GHG inventories In August 1997, the Republic of South Africa joined the majority of countries in the international community in ratifying the UNFCCC. The first national GHG inventory for South Africa was prepared in 1998, using 1990 data (Van der Merwe & Scholes, 1998). It 50 GHG Inventory for South Africa: 2000 - 2022 was updated to include 1994 data and published in 2004. It was developed using the 1996 IPCC Guidelines for National Greenhouse Gas Inventories. For the 2000 national inventory (DEAT, 2009), a decision was made to use the recently published 2006 IPCC Guidelines (IPCC, 2006) to enhance accuracy and transparency, and to familiarise researchers with the latest inventory preparation guidelines. Following these guidelines, in 2014 the GHG inventory for the years 2000 to 2010 were compiled (DEA, 2014). An update was completed for 2011 and 2012 in 2017 (DEA, 2017), for 2013 to 2015 in 2019 (DEA, 2019), for 2017 in 2021 (DFFE, 2021), and for 2018, 2019 and 2020 in 2023 (DFFE, 2023). 1.2.3 Global warming potentials As greenhouse gases (GHG) vary in their radiative activity, and in their atmospheric residence time, converting emissions into carbon dioxide equivalents (CO 2 e) allows the integrated effect of emissions of the various gases to be compared. To comply with international reporting obligations under the UNFCCC, emissions are presented as CO 2 e using the 100- year global warming potentials (GWPs) from the 2014 IPCC Fifth Assessment Report (AR5) in accordance with the Modalities, Procedures, and Guidelines (MPGs) for transparency framework for action and support referred to in Article 13 of the Paris Agreement (Table 1.1). Table 1.1: Global warming potential (GWP) of greenhouse gases used in this report and taken from AR5 (IPCC, 2014b). Greenhouse gas Chemical formula AR5 GWP Carbon dioxide CO 2 1 Methane CH 4 28 Nitrous oxide N 2 O 265 Hydrofluorocarbons (HFCs) HFC-23 CHF 3 12 400 HFC-32 CH 2 F 2 677 HFC-125 CHF 2 CF 3 3170 HFC-134a CH 2 FCF 3 1300 HFC-143a CF 3 CH 3 4 800 HFC-227ea C 3 HF 7 3350 HFC-365mfc C 4 H 5 F 5 804 HFC-152a CH 3 CHF 2 138 Perfluorocarbons (PFCs) PFC-14 CF 4 6 630 PF-116 C 2 F 6 11 100 51 GHG Inventory for South Africa: 2000 - 2022 Inventories prior to the 2000 – 2020 inventory were prepared using the IPCC Second Assessment Report (AR2) (IPCC, 1996) GWPs. For purposes of comparison with past inventories, and due to the use of the IPCC Third Assessment Report (AR3) (IPCC, 2001) GWPs in other national regulations, the national emissions trends are presented using GWPs from AR2, AR3 and AR5 in Table 1.2. All text references, tables and graphs throughout the report refer to the AR5 GWP estimates, unless it is otherwise stated. Table 1.2: Trends in national GHG emissions (excluding and including LULUCF) between 2000 and 2022 based on the three different GWPs National emissions (Gg CO 2 e) AR2 GWP AR3 GWP AR5 GWP (excl. LULUCF) (incl. LULUCF) (excl. LULUCF) (incl. LULUCF) (excl. LULUCF) (incl. LULUCF) 2000 476 861.9 446 961.4 480 384.7 451 149.8 489 747.5 462 205.4 2001 476 005.2 456 651.0 479 546.4 460 866.0 488 964.7 472 002.4 2002 484 725.3 468 360.2 488 264.0 472 575.8 497 651.3 483 692.9 2003 500 068.0 465 695.7 503 666.1 469 925.2 513 190.2 481 053.7 2004 511 790.2 485 929.7 515 384.5 490 148.5 524 902.7 501 254.4 2005 509 889.7 504 190.6 513 517.9 508 475.4 523 122.9 519 760.2 2006 500 850.5 489 020.8 504 508.7 493 309.3 514 091.2 504 500.4 2007 522 052.1 502 714.5 525 791.8 507 080.9 535 672.5 518 559.4 2008 518 440.6 512 877.7 522 289.4 517 358.0 532 320.7 529 003.6 2009 533 591.0 507 147.8 537 416.2 511 562.8 547 270.5 522 920.8 2010 513 118.4 497 009.7 517 007.1 501 495.5 526 971.4 512 987.3 2011 513 303.8 488 737.4 517 020.8 493 029.0 527 161.3 504 638.0 2012 521 851.5 496 005.2 525 694.8 500 422.0 536 003.0 512 196.6 2013 508 978.0 471 945.6 512 741.3 476 257.3 522 220.3 487 134.0 2014 509 474.4 481 452.6 513 287.3 485 816.5 522 899.6 496 835.8 2015 501 905.1 495 884.8 505 793.3 500 318.6 515 516.2 511 428.6 2016 496 339.9 473 786.2 500 222.5 478 217.8 509 870.0 489 254.4 2017 488 362.0 481 787.6 492 288.7 486 318.6 501 987.5 497 553.7 2018 489 041.8 487 530.8 492 825.3 491 956.2 502 130.2 502 893.9 2019 488 548.6 461 472.2 492 302.3 465 869.3 501 485.2 476 686.4 2020 459 358.9 420 560.5 463 172.7 425 018.2 472 437.8 435 918.6 2021 475 175.9 449 326.2 479 020.9 453 870.1 488 322.3 464 957.7 2022 465 609.9 420 146.1 469 510.1 424 724.2 478 887.5 435 827.7 Institutional arrangements 52 GHG Inventory for South Africa: 2000 - 2022 1.3.1 National Entity In South Africa the Department of Forestry, Fisheries and the Environment (DFFE) is the central co-ordinating and policy-making authority with respect to environmental conservation. The DFFE is mandated by the Air Quality Act (Act 39 of 2004) (RSA, 2004) to formulate, co-ordinate and monitor national environmental information, policies, programmes and legislation. The work of the DFFE is underpinned by the Constitution of the Republic of South Africa (RSA) and all other relevant legislation and policies applicable to government to address environmental management, including climate change. In its capacity as a lead climate institution, the DFFE is responsible for co-ordination and management of all climate change-related information, such as mitigation, adaption, monitoring and evaluation programmes, including the compilation and update of National GHG Inventories. The branch responsible for the management and co-ordination of GHG inventories at the DFFE is the Climate Change and Air Quality branch (Figure 1.1), whose purpose is to monitor and ensure compliance on air and atmospheric quality, as well as support, monitor and report international, national, provincial and local responses to climate change. 53 GHG Inventory for South Africa: 2000 - 2022 Figure 1.1: Organogram showing where the GHG Inventory compilation occurs within DFFE. DFFE is currently responsible for managing all aspects of the National GHG Inventory development. The Director of the Climate Change Monitoring and Evaluation (M&E): GHG Inventory and Systems directorate is the National Inventory Co-ordinator (NIC) and the tasks of the coordinator include: • Managing and supporting the National GHG Inventory staff, schedule, and budget to develop the inventory in a timely and efficient manner: o Prepare work plans o Establish internal processes o Ensure funding is in place o Appoint consultants where necessary o Oversee consultants and internal DFFE technical staff handling the report compilation • Identifying, assigning, and overseeing national inventory sector leads. DIRECTORATE CHIEF DIRECTORATE BRANCHDEPARTMENT DFFE Climate Change and Air Quality Climate Change Mitigation & Specilist Monitoring Services Climate Change M&SMS: GHG Inventory and Systems Climate Change Mitigation Climate Change Adaptation International Climate Change Relations and Negotiations 54 GHG Inventory for South Africa: 2000 - 2022 • Assigning cross-cutting roles and responsibilities, including those for QA/QC, archiving, key category analysis (KCA), uncertainty analysis, and compilation of the inventory section of the National Communications (NC) and/or Biennial Update Report (BUR). Managing the QA (external review and public comment) process: o Appoint external reviewers o Liaise between the reviewers and the NIR authors o Obtain approval from the minister of the DFFE for the NIR to go for public comment o Manage the incoming public comments and liaise with NIR authors and experts to address any issues • Maintaining and implementing a national GHG inventory improvement plan: o Manage the GHG Improvement programme (including sourcing of funds and appointing service providers for required projects) • Obtaining official approval (from Cabinet) of the GHG inventory and the NIR and submit reports (NIR, BUR, NC) to the UNFCCC. • Fostering and establishing links with related national projects, and other regional, international programmes as appropriate. 1.3.2 Legal arrangements Data is sourced from many institutes, associations, companies and ministerial branches. There are no formal agreements between the various government departments for the collection of data for the GHG Inventory. To aid in the collection of data from the energy sector and industries (including plantation industries and certain agricultural industries) the government published the National Greenhouse Gas Emission Reporting Regulations (NGERs) (DEA, 2017a), under Section 53(a), (o) and (p) read with section 12 of the National Environmental Management: Air Quality Act, 2004 (Act No. 39 of 2004), in the Government Gazette of the 3rd April 2017. The purpose of the GHG reporting regulation is to introduce a single national reporting system for the transparent reporting of GHG emissions, which will be used (a) to update and maintain a National Greenhouse Gas Inventory; (b) for the Republic of South Africa to meet its reporting obligations under the UNFCCC and instrument treaties to which it is bound; and (c) to inform the formulation and implementation of legislation and policy. The NGERs were promulgated in fulfilment of the implementation of the regulatory framework to support the collection of the requisite activity and GHG emissions data necessary for the compilation of the National GHG emissions Inventory to improve the quality, sustainability, accuracy, completeness and consistency of the National GHG Inventories. In accordance with regulation 7(1) of the NGERS the initial reporting cycle commenced on 31 March of 2018 requiring data providers to register and submit activity and GHG emissions data to the competent authority, namely DFFE. 55 GHG Inventory for South Africa: 2000 - 2022 As required in the 2011 White Paper (DEA, 2011), the DFFE has subsequently developed the South African Greenhouse Gas Emissions Reporting System (SAGERS) which is the GHG module of the National Atmospheric Emissions Inventory System (NAEIS). The SAGERS module helps to facilitate the process of enabling Industry to meet its GHG reporting requirements in a web-based secure environment and facilitates the data collection process for energy related activities and IPPU. The inventory compilation process (Figure 1.2) is co-ordinated through a central web- based inventory management system (National GHG Information Management System (NGHGIS)). Figure 1.2: The inventory compilation process in South Africa. Overview of inventory preparation and management 1.4.1 Inventory management South Africa uses a hybrid (centralised/distributed) approach to manage the inventory programme. Management and coordination of the inventory programme, as well as compilation, publication and submission of the inventory are carried out by the Single National Entity (being the DFFE) in a centralised manner. Currently DFFE is responsible for collecting data, compiling and QC of the Energy, IPPU, Agriculture and Waste sector inventories, while the LULUCF sector is compiled (in collaboration with DFFE) by GHG Regulation Government departments Industry Other stakeholders NAEIS MOUs Reporting: NC, BUR, Policies DEA: Energy IPPU Waste GES: AFOLU Sector compilers DEA Inventory manager Inventory approval DEA (SNE) Contract NGHGIS Public website 56 GHG Inventory for South Africa: 2000 - 2022 external consultants (Gondwana Environmental Solutions (GES)) who are appointed via a formal project-based contract with the UNDP as part of the Capacity Building Initiative for Transparency (CBIT) project of South Africa. DFFE assists with the QC of the LULUCF sector. DFFE were also responsible for combining and compiling the overall inventory and compiling the draft National Inventory Report. 1.4.2 Procedural arrangements 1.4.2.1 Inventory planning A planning meeting was held in January 2022 to engage with the whole team and to plan the timelines for the inventory preparation process, from data collection to finalisation of the NIR. Since there were several new team members various training sessions (on inventory preparation, calculation files, QC procedures, time-series consistency, splicing techniques, and uncertainty analysis) were included in the inventory compilation plan. The planning phase also involved the preparation of files and templates for the inventory compilation. 1.4.2.2 Inventory preparation After planning there are five main steps in the preparation of a National GHG Inventory: • Collect • Compile • Write • Improve and • Finalize The stages and activities undertaken in the inventory update and improvement process are shown in Figure 1.3. 57 GHG Inventory for South Africa: 2000 - 2022 Plan Collect CompileWrite Improve Finalise Inventory planning meeting; Decide on improvements to be incorporated; Decide on roles and responsibilities; Set a timeline for inventory update. Data input into updated calculation files; Documentation of methods; Annotate calculation files with method, data source and improvement details; Calculate emission estimates; QC data and estimates and log responses in calculation files; Conduct uncertainty and key category analysis; Upload calculation files to NGHGIS. Prepare draft GHG Inventory report; QC draft report. Complete a public commenting process; Complete an independent review; Address all comments from QA process; Upload all QA documents onto the NGHGIS. Finalise inventory report; Complete improvement plan; Upload report onto NGHGIS; Log improvements onto the NGHGIS; Prepare all archives. Obtain document approval from government. Submit to UN. Asses data requirements; Source data from various stakeholders; Log all data source details and contacts onto NGHGIS; Screen data and select appropriate data sets; Assess uncertainty; Data quality control. Figure 1.3: Overview of the phases of the GHG inventory compilation and improvement process undertaken for South Africa’s 2020 GHG inventory. The collection phase is dedicated to data collection and preliminary processing, such as data cleansing, data checks and preliminary formatting for further use. The compilation phase involves the preparation and QC of initial estimates, as well as the uncertainty and key category analysis. This phase may also include analysis of potential recalculations involved in the inventory. The writing phase is where the draft inventory report is prepared, including all crosscutting components (KCA, trends by gas and sector, etc.) and QC of the draft is completed. At the end of this component the draft document is subjected to a QA, or review process. The review is done by independent consultants and/or public commenting process. For the 2022 inventory only the public commenting process was utilised. Comments from the review process are used to improve the Report, after which it is finalized. During the finalization phase the archives are prepared and final Report approvals are obtained before being submitted to UNFCCC. 1.4.3 Data collection, storage and archiving 1.4.3.1 Data collection Data collection and documentation take place under the responsibility of the relevant experts. Three different processes are used to collect data for South Africa’s inventory. The first is by obtaining data from government departments, institutes, companies, and organisations through an informal process, i.e. where data is obtained without any formal 58 GHG Inventory for South Africa: 2000 - 2022 data collection agreements. The second is through the evaluation of publicly available data, official statistics, association statistics, studies, periodicals and third-party research projects. Most of the inventory data is collected using these two approaches, but for industry South Africa has started to move towards a more formalised data collection system. DFFE has setup the National Atmospheric Emissions Inventory System (NAEIS), which is an online reporting platform for air quality and GHG emissions from companies to manage the mandatory reporting of GHG emissions. Emissions information including activity data from the NAEIS serves as input data during the national inventory compilation process. DFFE has modified the NAEIS to meet the requirements of the NGERs (DEA, 2017a). This component of the portal, the SAGERS, serves as a tool for the implementation of the online registration and reporting by industry in fulfilment of mandatory NGERs (https://ghgreporting- public.environment.gov.za/GHGLanding/SAGERSHome.html). The key benefit of the portal is that it will enhance the data collection process for the inventory, therefore improving the quality of the national GHG inventories consistent with the requisite principles of completeness, consistency, accuracy, comparability, and transparency. 1.4.3.2 Data preparation and emission calculation The process of data preparation and emissions calculation comprises the following steps: • Data entry • Data preparation (model formation, disaggregation, aggregation) • Calculation of emissions • Preparation of report sections (texts) and • Approval by the relevant experts. Report texts are prepared along with the time series for activity data, emission factors, uncertainties, and emissions. As a result, the term "data" is understood in a broad sense. In addition to number data, time series, etc., it also includes contextual information such as the sources for time series, and descriptions of calculation methods, and it also refers to preparation of report sections for the NIR and documentation of recalculations. After all checks have been carried out, and the relevant parties have been consulted where necessary, the emissions are calculated in excel by each sector lead based on the following principle: activity data * emission factor = emission As much of the data as possible is included in the calculation files, but where larger data sets are referred to these are stored in the NGHGIS. 59 GHG Inventory for South Africa: 2000 - 2022 Following complete preparation of data, report sections and QC/QA checklists by the responsible experts, these materials are transmitted to the Single National Entity where it is reviewed by category-specific specialists at the Single National Entity. The results of this review are then provided to the sector lead experts to revise and finalise their contribution. 1.4.3.3 Report preparation Report preparation includes the following steps: • Aggregation of emissions data for the national trend tables and reporting formats, preparation of data tables for the NIR • Compilation of submitted report texts to form a report draft (NIR), and editing of the complete NIR • Internal review of the draft (national trend tables and NIR) by DFFE • Public commenting process • External review by 3rd party • Finalization of NIR report • Approval by DFFE Minister • Submission to UNFCCC • Archiving 1.4.3.4 Data storage and archiving The NGHGIS for South Africa assists in managing and storing the inventory related documents and processes. The NGHGIS, amongst other things, keeps records of the following: • Stakeholder list with full contact details and responsibilities • List of input datasets which are linked to the stakeholder list • QA/QC plan • QA/QC checks • QA/QC logs which will provide details of all QA/QC activities • All method statements • IPCC categories and their links to the relevant method statements together with details of the type of method (Tier 1, 2 or 3) and emission factors (default or country-specific) applied • Calculation and supporting files • Key references • Key categories; and • All inventory reports. 1 GHG Inventory for South Africa: 2000 - 2022 The procedures for data storage and archiving are described in detail in the QA/QC plan that has been developed and is discussed in section 1.6 below. The NGHGIS is used to archive inventory data. Brief description of data sources and methods 1.5.1 Data sources 1.5.1.1 Energy sector The main sources of data for the Energy sector are the energy balance data compiled by the Department of Mineral Resources and Energy (DMRE), data supplied by the main electricity provider, Eskom, and petroleum companies, i.e., PetroSA and Sasol. Annual reports from the South African Petroleum Industry Association (SAPIA) and Transnet are also considered. There are currently no formal processes in place for requesting or obtaining data from DMRE. Data from major companies are gathered via SAGERS, through the GHG Reporting Programme. The data collection process for the Energy sector is shown in Figure 1.4 and data sources are provided in Table 1.3. 2 GHG Inventory for South Africa: 2000 - 2022 Figure 1.4: Data collection process for the 2022 Energy inventory. Table 1.3: Principal data sources for the Energy sector inventory. Sub-category Activity data Activity data sources Electricity generation Fuel consumption for public electricity generation SAGERS Fuel consumption for auto electricity producers SAGERS NCVs SAGERS Petroleum refining Fuel consumption Refineries Manufacture of solid fuels and other energy industries No activity data, only emission data - based on Mass Balance and measurement SAGERS, Food and Agriculture Organisation (FAO) of UN Manufacturing industries and construction Other kerosene, bitumen and natural gas consumption Energy balance (DMRE) Gas/Diesel consumption Energy balance (DMRE) Residual fuel oil consumption Energy digest LPG consumption SAMI report (DMRE) Transport Vehicle kilometres travelled for road transport Fuel consumption study Domestic aviation gasoline consumption Fuel consumption study Domestic aviation jet kerosene consumption Fuel consumption study Road transport fuel consumption Fuel consumption study Road transportation other kerosene consumption Energy balance (DMRE) 3 GHG Inventory for South Africa: 2000 - 2022 Railway fuel oil consumption Energy balance (DMRE) Railway gas/diesel oil consumption Energy balance (DMRE) Water-borne navigation fuel consumption Energy balance (DMRE) / Fuel consumption study International aviation Jet Kerosene consumption Energy balance (DMRE) Commercial/institutional Other kerosene, gas/diesel oil, gas works gas and natural gas consumption Energy balance (DMRE) Sub-bituminous coal consumption Energy balance (DMRE) Residual fuel oil consumption Energy balance (DMRE) Residential Coal consumption Energy balance (DMRE) LPG consumption Energy balance (DMRE) Sub-bituminous coal consumption Energy balance (DMRE) Other fuel consumption Energy balance (DMRE) Agriculture/forestry/fishing /fish farms Other kerosene consumption Energy balance (DMRE) Gas/diesel oil consumption Energy balance (DMRE) Other fuel consumption Energy balance (DMRE) Stationary non-specified Fuel consumption Energy balance (DMRE) Fugitive emissions Solid fuels SAGERS Oil and Natural Gas SAGERS Other fugitive emissions SAGERS / Fuel consumption study 1.5.1.2 IPPU sector Data for the IPPU sector is obtained mostly through the SAGERS system. The HFC and PFC data was supplied by the DFFE waste branch and supplemented with the 2016 5- year periodic survey conducted by DFFE There is no formal data collection process in place for this. Table 1.4: Principal data sources for the IPPU sector inventory. Sub-category Activity data Data source Cement production Cement produced SAGERS Clinker fraction SAGERS Lime production Mass of lime produced SAGERS Glass production Glass production SAGERS Other product uses of carbonates (OPUC) Emissions from OPUC SAGERS Ammonia production Emissions from ammonia production SAGERS Nitric acid production Emissions from nitric acid production SAGERS Carbide production Raw material (petroleum coke) consumption SAGERS 4 GHG Inventory for South Africa: 2000 - 2022 Titanium dioxide production Emissions from titanium dioxide production SAGERS Soda Ash production Emissions from soda ash production SAGERS Carbon black production Amount of carbon black produced SAGERS Hydrogen production Emissions from hydrogen production SAGERS Other chemical processes Emissions from other chemical processes SAGERS Iron and steel production Production data SAGERS Ferroalloys production Production data SAGERS Aluminium production Production data SAGERS Lead production Production data SAGERS Zinc Production Production data SAGERS Lubricant use Lubricant consumption Extrapolated Paraffin wax use Paraffin wax consumption Extrapolated Refrigeration and air conditioning Existing, new and retired refrigerators HFC Survey, Stats SA Annual data on stationary air conditioning units HFC Survey, DEA 2016; BSRIA Existing, new and retired refrigeration trucks HFC study; South African Refrigerated Distribution Association (SARDA) Existing. New and retired vehicles eNaTIS; National Association of Automobile Manufacturers of South Africa (NAAMSA) Foam blowing agents Total HFC used in foam manufacturing in a year HFC Survey (DEA, 2016) Fire protection Bank of agent in fire protection equipment in a year HFC Survey (DEA, 2016) 1.5.1.3 Agriculture sector Data is obtained from various sources as indicated in Table 1.5. There are no formal data collection processes in place for all the agriculture data. Data is obtained from available government reports, statistical databases, and the literature. Cropland data is supplied by the Department of Agriculture, Land Reform and Rural development (DALRRD). Fertiliser and liming data are sourced from South African Revenue Service (SARS), DMRE and Fertilizer Association of South Africa (FertASA). Small amounts of crop statistics data are obtained from Statistics SA. The main sources of livestock data for agriculture are the DALRRD and the Agricultural Research Council (ARC). Data from the ARC is completed on a project-by-project basis and is therefore often once-off data. The SAGERS has recently introduced a small 5 GHG Inventory for South Africa: 2000 - 2022 component of data for agriculture, such as data from poultry farms. Reporting has only just started (in 2022), but the SAGERS will be a mechanism to collect some data for the agriculture sector in future. Table 1.5: Principal data sources for the Agriculture sector inventory. Sub- category Activity data Data source Data collection mechanism Livestock DALRRD DFFE is in the process of developing an MOU with DALRRD FAO Statistics available on FAO Stats website (unofficial) South African Poultry Association (SAPA) Information obtained through direct contact. No formal mechanism is in place. ARC, Tshwane University of Technology (TUT) and University of Pretoria Data is available through scientific publications. 3C Aggregat ed & non- CO 2 emissions from land South African Mineral Industry Report compiled by DMRE No formal mechanism is in place, but data is currently publicly available. MODIS burnt area data No formal process for obtaining this data. FAO Statistics available on FAO Stats website ARC DFFE is in the process of developing an MOU with DALRRD. Statistics SA Agricultural census data are available from Statistics SA. No formal mechanism is in place. Fertilizer Association of SA Annual nitrogen application data for crops SARS Provides annual import data for urea ISRIC SOC ref data 1.5.1.4 LULUCF sector Data for the LULUCF sector sub-categories is obtained from various sources as indicated in Table 1.6. The DFFE employs consultants to process the satellite imagery to generate land cover datasets which are used to determine land cover change for the LULUCF sector. This is usually done on a project-by- project basis. To improve the consistency and frequency of the land cover data, DFFE has developed a Computer Automated Land Cover (CALC) model which can generate land cover maps based on Sentinel 2 data and these maps can be developed from 2016 onwards, however the first map developed is for 2018. 6 GHG Inventory for South Africa: 2000 - 2022 The aim is to generate a map every two years. All South African National Land Cover (SANLC) data (including the CALC data) can be obtained from https://egis.environment.gov.za/gis_data_downloads. Other spatial land data, such as the carbon density maps, are obtained from https://catalogue.saeon.ac.za/. These products are also developed on a project-by- project basis. There are no formal data collection processes in place for all the other land, such as carbon stock data and fuel wood removals. Data is obtained from available government reports, agricultural association reports, statistical databases and scientific literature. Plantation data is supplied by Forestry SA, and the cropland data is supplied by DALRRD. Burnt area data is obtained from the MODIS burnt area product which is processed by Gondwana Environmental Solutions. Plantation data is also being reported to DFFE through the SAGERS system (https://ghgreporting-public.environment.gov.za/GHGLanding/SAGERSHome.html) and so this data could be utilised in future inventories. Table 1.6: Principal data sources for the LULUCF sector inventory. Sub-category Activity data Data source Data collection mechanism All land types (Forest land, Cropland, Grassland, wetland, Settlements, Other land) Land cover and land use change maps DFFE Data and land maps are developed (usually by GTI) or funded through DFFE and are freely available on the EGIS website. Maps to be produced every 2 years. Soil maps ISRIC Available online (https://www.isric.org/) Carbon stock data Scientific literature Carbon stock data for the various vegetation classes is obtained for literature which is available online. Where data not available information is obtained from the carbon density maps. Carbon density maps SAEON Carbon density maps were produced through a project name the Terrestrial Carbon Sinks Assessment. This data is available online (https://catalogue.saeon.ac.za /). Burnt area data MODIS Available online Forest land Timber statistics DALRRD Statistical data is released annually and is freely available. DFFE is in the process of developing an MOU with DALRRD 7 GHG Inventory for South Africa: 2000 - 2022 Plantation data Forestry South Africa Data obtained through direct request, no formal mechanism in place. Data is also freely available on their website. Household number and wood fuel use Statistics SA Available online Household fuel consumption data Scientific literature Available online Charcoal production FAOStat Available online Harvest wood products Production data Forestry South Africa Data obtained through direct request, no formal mechanism in place. Data is also freely available on their website. Import/export data FAOStat Available online 1.5.1.5 Waste sector Waste data is collected from various data reports, statistics and global data sets (Table 1.7). As with the Agriculture and LULUCF sectors there is no formal data collection processes. The main data providers for the Waste sector are Statistics SA, DFFE, Department of Water and Sanitation (DWS) and UN. In time it may be possible to collect some of the Waste data through the SAGERS system. Table 1.7: Principal data sources for the Waste sector inventory. Sub-category Activity data Data source Solid waste disposal Population data Statistics SA (2015); UN (2012) Waste composition IPCC 2006, and 2019 IPCC Refinement Waste generation rate for each component State of Waste Report (2018) GDP World bank Biological treatment of solid waste Mass of organic waste by biological treatment type SAWIC, DFFE (wastewater treatment and biological treatment of solid waste study) Open burning of waste Population data Statistics SA (2015); UN (2012) Fraction of population burning waste Assumption based on population without access to waste collection services. Wastewater treatment Population data Statistics SA (2015); UN (2012) Split of population by income group Statistics SA (2015) 8 GHG Inventory for South Africa: 2000 - 2022 and discharge BOD generation rates per treatment type IPCC 2006, DWS Per capita nitrogen generation rate IPCC 2006 1.5.2 Methods The methods used for the individual categories are outlined in the sector overview sections in each of the sector chapters. In addition, detailed descriptions are provided in the relevant category chapters. A distinction is made between calculations made with country-specific ("CS") methods and calculations made, in the various categories, with IPCC calculation methods of varying degrees of detail ("Tiers"). Similarly for the emission factors. The manner in which a calculation is assigned to the various IPCC methods depends on the pertinent category's share (expressed as equivalent emissions) of total emissions and this determined via the key category analysis (see section 1.7). Quality assurance, quality control and verification plans and procedures 1.6.1 Guiding principles This report is guided by the inventory quality principles as defined in 2006 IPCC Guidelines (IPCC, 2006) (Table 1.8Error! Reference source not found.). Table 1.8: Guiding principles applied during the preparation of the 2022 inventory. Principle Application of principle Transparency Sufficient and clear documentation is provided such that individuals or groups other than the inventory compilers can understand how the inventory was compiled and can assure themselves it meets the good practice requirements for national greenhouse gas emissions inventories. Completeness Estimates are reported for all relevant categories of sources and sinks, and gases. The entire geographic area of South Africa is included. Where elements are missing their absence is clearly documented together with a justification for exclusion. 9 GHG Inventory for South Africa: 2000 - 2022 Consistency Estimates for different inventory years, gases and categories are made in such a way that differences in the results between years and categories reflect real differences in emissions. Inventory annual trends, as far as possible, are calculated using the same method and data sources in all years and aim to reflect the real annual fluctuations in emissions or removals and not be subject to changes resulting from methodological differences. Comparability The national greenhouse gas inventory is reported in a way that allows it to be compared with national greenhouse gas inventories for other countries. This comparability is reflected in the appropriate choice of key categories and in the use of the reporting guidance and tables and use of the classification and definition of categories of emissions and removals. Accuracy The national greenhouse gas inventory contains neither over- nor under- estimates so far as can be judged. All endeavours were made to remove bias from the inventory estimates. 1.6.2 QA/QC plan and procedures As part of the NGHGIS, South Africa developed a formal quality assurance/quality control plan (see Appendix 1.A of 2015 NIR (DEA, 2019)). This provides a list of QC procedures that are to be undertaken during the preparation of the inventory. In this inventory the relatively new team was provided with QA/QC training and each team member was assigned to a sector. Each quality controller went through the sector calculation files and provided comments. A programme, QA Analyst, assisted with the process of tracking the comments by keeping a log in the front of each file. 1.6.3 General QC procedures The QC procedures are performed by the experts during inventory calculation and compilation. QC measures are aimed at the attainment of the quality objectives. The QC procedures comply with the IPCC good practice guidance and the 2006 IPCC Guidelines. General inventory QC checks include routine checks of the integrity, correctness and completeness of data, identification of errors and deficiencies and documentation and archiving of inventory data and quality control actions. In addition to general QC checks, category-specific QC checks including technical reviews of the source categories, activity data, emission factors and methods are applied on a case-by-case basis focusing on key categories and on categories where significant methodological and data revisions have taken place. The general quality checks are used routinely throughout the inventory compilation process. Although general QC procedures are designed to be implemented for all categories and on a routine basis, it is not always necessary or possible to check all 10 GHG Inventory for South Africa: 2000 - 2022 aspects of inventory input data, parameters, and calculations every year. Checks are then performed on selected sets of data and processes. A representative sample of data and calculations from every category may be subjected to general QC procedures each year. The general QC checks carried out on South Africa’s 2022 inventory are provided in Table 1.9. Table 1.9: Quality control checks carried out on South Africa’s 2022 GHG inventory. ID Type of check Description Level QC001 Activity data source Is the appropriate data source being used for activity data? Calculation file QC002 Correct units Check that the correct units are being used Calculation file QC003 Unit carry through Are all units correctly carried through calculations to the summary table? This includes activity data and emission factors. Calculation file QC004 Method validity Are the methods used valid and appropriate? Calculation file QC005 Uncertainties Carry out uncertainties analysis Supporting file QC006 Double counting – Categories Check to ensure no double counting is present at category level Calculation file QC007 Notation keys Review the use of notation keys and the associated assumption to ensure they are correct. Calculation file QC008 Trend check Carry out checks on the trend to identify possible errors. Document any stand out data points. Calculation file QC009 Emission factor applicability Where default emission factors are used, are they correct? Is source information provided? Calculation file QC010 Emission factor applicability Where country specific emission factors are used, are they correct? Is source information provided? Calculation file QC011 Recalculations Check values against previous submission. Explain any changes in data due to recalculations. Calculation file QC012 Sub-category completeness Is the reporting of each sub-category complete? If not this should be highlighted. Calculation file QC013 Time series consistency Are activity data and emission factor time series consistent? Calculation file QC014 Colour coding Has colour coding been used in a consistent and accurate manner? Are there any significant data gaps of weaknesses? Calculation file QC015 Cross check data Where possible cross check data against alternative data sources. This includes activity data and EF. If CS EF are used they must be compared to IPCC values as well as any other available data sets. Supporting file QC016 Spot checks Complete random spot checks on a data set. Calculation file 11 GHG Inventory for South Africa: 2000 - 2022 ID Type of check Description Level QC017 Transcription checks Complete checks to ensure data has been transcribed from models to spreadsheet correctly. Calculation file QC018 Transcription to document Complete checks to ensure data has been transcribed from spreadsheets to documents correctly. Sector report QC019 Data source referencing All source data submitted must be referenced Calculation file QC020 Data traceability Can data be traced back to its original source? Calculation file QC021 Links to source data Where possible, links to the source data must be provided Calculation file QC022 Raw primary data All raw primary data must be present in the workbook Calculation file QC023 QA review Data must be reviewed and checked by a second person Calculation file QC024 Verification Where possible have calculated emissions been checked against other data sets? Sector report QC025 Archiving Are all supporting files and references supplied? Archive manager QC026 Data calculations Can a representative sample of the emission calculations be reproduced? Calculation file QC027 Unit conversions Have the correct conversion factors been used? Calculation file QC028 Common factor consistency Is there consistency in common factor use between sub-categories (such as GWP, Carbon content, Calorific values)? Calculation file QC029 Data aggregation Has the data been correctly aggregated within a sector? Calculation file QC030 Trend documentation Have significant trend changes been adequately explained? Sector report QC031 Consistency between sectors Identify parameters that are common across sectors and check for consistency. Draft NIR QC032 Data aggregation Has the data been correctly aggregated across the sectors? Draft NIR QC033 Documentation - CRF tables Check CRF tables are included. Draft NIR QC034 Documentation - KCA Check that key category analyses have been included. Draft NIR QC035 Documentation - Uncertainty Check uncertainty analysis have been included. Draft NIR QC036 Documentation - Overall trends Check overall trends are described both by sector and gas species. Draft NIR QC037 Documentation - NIR sections complete Check all relevant sections are included in the NIR. Draft NIR QC038 Documentation - Improvement plan Check that the improvement plan has been included. Draft NIR 12 GHG Inventory for South Africa: 2000 - 2022 ID Type of check Description Level QC039 Documentation - Completeness Check for completeness Draft NIR QC040 Documentation - Tables and figures Check numbers in tables match spreadsheet; check for consistent table formatting; check the table and figure numbers are correct. Draft NIR QC041 Documentation - References Check consistency of references. Draft NIR QC042 Documentation - General format Check general NIR format - acronyms, spelling, all notes removed; size, style and indenting of bullets are consistent. Draft NIR QC043 Documentation - Updated Check that each section is updated with current year information. Draft NIR QC044 Double counting - Sectors Check there is no double counting between the sectors. Draft NIR QC045 National coverage Check that activity data is representative of the national territory. Calculation file QC046 Review comments implemented Check that review comments have been implemented. Calculation file QC047 Methodology documentation Are the methods described in sufficient detail? Sector report QC048 Recalculation documentation Are changes due to recalculations explained? Sector report QC049 Trend documentation Are any significant changes in the trend explained? Sector report QC050 Documentation - QA/QC Check the QA/QC procedure is adequately described. Draft NIR QC051 Complete uncertainty check Check that the uncertainty analysis is complete. Draft NIR QC052 Consistency in methodology Check that there is consistency in the methodology across the time series Calculation file QC053 Data gaps Is there sufficient documentation of data gaps? Sector report QC054 Steering committee review Has the draft NIR been approved by the steering committee? Was there public consultation? Draft NIR QC055 Check calorific values Have the correct net calorific values been used? Are they consistent between sectors? Are they documented? Calculation file QC056 Check carbon content Have the correct carbon content values been used? Are they consistent between sectors? Are they documented? Calculation file QC057 Supplied emission check If emissions are supplied by industry have they been calculated using international standards? Have the methods been adequately described? Sector report QC058 Livestock population checks Have the livestock population data been checked against the FAO database? Calculation file 13 GHG Inventory for South Africa: 2000 - 2022 ID Type of check Description Level QC059 Land area consistency Do the land areas for the land classes add up to the total land area for South Africa? Calculation file QC060 Biomass data checks Have the biomass factors been compared to IPCC default values or the EFDB? Calculation file QC061 Fertilizer data checks Has the fertilizer consumption data been compared to the FAO database? Calculation file QC062 Wastewater flow checks Do the wastewater flows to the various treatments add up to 100? Calculation file QC063 Reference approach Has the reference approach been completed for the Energy sector? Have the values been compared to the sector approach? Has sufficient explanation of differences been given? Calculation file QC064 Coal production checks Has the industry-specific coal production been checked against the coal production statistics from Department of Mineral Resources? Calculation file 1.6.4 Workshops Several workshops and training sessions between the team and the wider group at the DFFE were held during the preparation of this inventory. An initial planning meeting started the process off, followed by several team meetings to share data and queries. This has been useful even with the previous inventories. Everyone had a chance to understand other sectors and it also improved the cross linkages. This should continue in future inventories. 1.6.5 Review process General public review (Figure 1.5). The expert and public reviews each present opportunity to uncover technical issues related to the application of methodologies, selection of activity data, or the development and choice of emission factors. The expert and public reviews of the draft document offer a broader range of researchers and practitioners in government, industry and academia, as well as the general public, the opportunity to contribute to the final document. The comments received during these processes are reviewed and, as appropriate, incorporated into the NIR or reflected in the inventory estimates. 14 GHG Inventory for South Africa: 2000 - 2022 Figure 1.5: The quality assurance review process for the 2000 – 2022 inventory. 1.6.6 Verification activities Emission and activity data are verified by comparing them with other available data compiled independently of the GHG inventory system where available. These include national and international statistics, measurement and research projects and programmes initiated to support the inventory system, or for other purposes, but producing information relevant to the inventory preparation. The specific verification activities are described in detail in the relevant category chapters. Key categories A key category is one that is prioritised within the national inventory system because its estimate has a significant influence (either as a source or a sink) on a country’s total inventory of GHG’s in terms of the absolute level, the trend, or the uncertainty in emissions and removals (IPCC, 2006). There are two approaches which can be used to determine the key categories: namely, the level approach and the trend approach. The former is used if only one year of data is available, while the latter can be used if there are 15 GHG Inventory for South Africa: 2000 - 2022 two comparable years. The level assessment determines the contribution from the categories to the total national inventory. The trend assessment identifies categories that may not be large enough to be identified by the level assessment, but whose trend is significantly different from the trend of the overall inventory and should therefore receive particular attention. The trend can be an increase or a decrease in emissions. This inventory provides emissions for more than one year; therefore, both the level and trend assessments for key category analysis were performed. The key categories have been assessed using the Approach 1 level (L1) and Approach 1 trend (T1) methodologies from the 2006 IPCC Guidelines (IPCC, 2006). Key categories based on uncertainty have not yet been included due to a lack of country specific data on uncertainties. The level and trend key category analysis identify key categories of emissions and removals as those that sum to 95 % of the gross (excluding LULUCF) or net (including LULUCF) emissions and those that are within the top 95 % of the categories that contribute to the change between 2000 and 2022, or the trend of emissions. This includes both source and sink categories. The level assessment was conducted on the base year (2000) and the current year (2022), while the trend assessment utilised the base year 2000 and 2022. For the 2000-2022 inventory the level of disaggregation for each sector was updated. However, as with the last two inventories the key categories were then ranked according to their combined contribution to the level and trend assessments. In the previous inventory there were 58 key categories, while in this inventory there are 53. Table ES 3 shows the top 30 key categories. Table 1.10 shows the 53 key categories identified for the 2000-2022 inventory. Table 1.10: Key categories for South Africa for 2022 (including LULUCF) and their ranking. Rank IPCC code IPCC Category GHG # Criteria 1 4.A.2.b Grassland converted to forest land – all pools CO 2 L,T 2 1.A.3.b Road Transportation – Liquid Fuels CO 2 L,T 3 4.A.1.a Forest land remaining forest land – biomass CO 2 L,T 4 1.A.1 Energy Industries – Solid Fuels CO 2 L,T 5 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O L 6 4.C.1.a Grassland remaining Grassland – biomass CO 2 L,T 7 4.F.2.c Grassland converted to other land – all pools CO 2 L,T 8 1.A.5 Other – Solid Fuels CO 2 L,T 9 2.C.2 Ferroalloys Production C L 10 1.B.3 Other emissions from energy production CO 2 L,T 11 1.A.4 Other Sectors – Liquid Fuels CO 2 L,T 12 3.A.1.a.ii Non-dairy Cattle CH 4 L,T 13 4.G Harvested Wood Products CO 2 T 14 5.D Wastewater Treatment and Discharge – Industrial CH 4 L 16 GHG Inventory for South Africa: 2000 - 2022 Rank IPCC code IPCC Category GHG # Criteria 15 1.A.3.a Domestic Aviation CO 2 T 16 1.A.1 Energy Industries – Gaseous Fuels CO 2 T 17 1.A.5 Other – Liquid Fuels CO 2 T 18 1.A.4 Other Sectors – Solid Fuels CO 2 L,T 19 2.C.1 Iron and Steel Production CO 2 L,T 20 3.A.1.a.i Dairy Cattle CH 4 L 21 4.A.1.b Forest land remaining forest land – dead organic matter CO 2 T 22 5.A Solid Waste Disposal CH 4 L,T 23 1.B.3 Other emissions from energy production CH 4 L 24 2.B.2 Nitric Acid Production N 2 O T 25 2.C.3 Aluminium Production F-gases T 26 1.A.2 Manufacturing Industries and Construction – Gaseous Fuels CH 4 L,T 27 4.C.2.a Forest land converted to Grassland – all pools CO 2 L,T 28 1.B.2 Oil and Natural Gas CO 2 T 29 4(V) Biomass Burning CH 4 L 30 4.B.2.b Grassland converted to Cropland – all pools CO 2 L 31 3.G Liming CO 2 L 32 4.C.2.c Wetland converted to Grassland – all pools CO 2 T 33 2.D.2 Paraffin was use CO 2 T 34 1.B.1.a Coal mining & handling CH 4 L 35 3.A.4 Goats CH 4 L 36 1.A.4 Other Sectors – Gaseous Fuels CO 2 T 37 3.A.2 Sheep CH 4 L,T 38 4.A.2.a Cropland converted to forest land – all pools CO 2 L,T 39 4.A.2.e Other land converted to forest land – all pools CO 2 L 40 4.B.2.a Forest land converted to Cropland – all pools CO 2 L 41 4.C.1.b Grassland remaining Grassland – dead organic matter CO 2 T 42 1.A.2 Manufacturing Industries and Construction – Solid Fuels CO 2 L,T 43 2.F.1.a Refrigeration and Air conditioning – stationary CO 2 L,T 44 4.D.1 Wetlands Remaining Wetlands CH 4 L,T 45 1.A.1 Energy Industries – Liquid Fuels CO 2 L,T 46 4.D.2 Land Converted to Wetlands CH 4 L,T 47 4.C.2.e Other land converted to Grassland – all pools CO 2 L,T 48 2.F.1.b Refrigeration and Air conditioning F-gases L,T 49 2.A.1 Cement Production C L,T 50 5.D Wastewater Treatment and Discharge – domestic CH 4 L,T 51 1.A.2 Manufacturing Industries and Construction – Liquid Fuels CO 2 L,T 52 4.C.2.b Cropland converted to Grassland – all pools CO 2 L,T 53 5.B Biological Treatment of Solid Waste CH 4 L,T # C=Confidential Forest land converted to grassland, Direct N 2 O emissions from managed soils and Ferroalloy production are new to the top 10 key categories. In the previous inventory these categories were in 26 th (as Land converted to grassland), 16 th and 15 th place, respectively. 17 GHG Inventory for South Africa: 2000 - 2022 Appendix A contains the full key category analysis results for the level assessments (2000 and 2022) and trend assessment, including and excluding LULUCF. General uncertainty evaluation 1.8.1 Procedures for uncertainty determination Uncertainty is inherent within any kind of estimation and arises from the limitations of the measuring instruments, sampling processes and model complexities and assumptions. Managing these uncertainties, and reducing them over time, is recognised by IPCC 2006 as an important element of inventory preparation and development. Chapter 3 of the 2006 IPCC Guidelines (IPCC, 2006) describes the methodology for estimating and reporting uncertainties associated with annual estimates of emissions and removals. There are two methods for determining uncertainty: • Tier 1 methodology which combines the uncertainties in activity rates and emission factors for each source category and GHG in a simple way; and • Tier 2 methodology which is generally the same as Tier 1; however, it is taken a step further by considering the distribution function for each uncertainty, and then carries out an aggregation using the Monte Carlo simulation. South Africa still lacks data in terms of country specific uncertainty for all sectors. Based on this the uncertainty in this inventory was determined using the simple propagation of error (Approach 1) method. A trend uncertainty between the base year (2000) and 2022 was determined, as well as a combined uncertainty of activity data and emission factor uncertainty. As more uncertainty data becomes available it will be incorporated but there is a general need to build capacity and develop projects to assess the uncertainty in each sector. 1.8.2 Uncertainty assessment results Emission estimate uncertainties typically are low for CO2 from energy consumption as well as from some industrial process emissions. Uncertainty surrounding estimates of emissions are higher for LULUCF and synthetic gases. Uncertainty ranges for the various sectors (Appendix B) are largely consistent with typical uncertainty ranges expected for each sector (IPCC, 2014). 18 GHG Inventory for South Africa: 2000 - 2022 The total uncertainty for the inventory including LULUCF was determined to be between 12.1% and 12.5%, with a trend uncertainty of 8.9% 2 . This is an increase on the uncertainty in the last inventory (8.7%), however there were a few new categories in this inventory and a more detailed uncertainty assessment for Forest lands have been included. This led to an increase in uncertainty on the activity data (mostly due to the increased variability in the land change maps), but a reduction in the emission factor uncertainty. Excluding LULUCF reduces the overall uncertainty to 5.7%-6.4%, which is a reduction of the uncertainty from the last inventory. The Energy sector uncertainty was reduced slightly (by 5% on the upper limit) compared to the previous inventory. The IPPU trend uncertainty increased by 32% compared to the last inventory. The IPPU sector did introduce some new categories in 2021 and 2022 and update some of the uncertainty estimates to be based on country specific data rather than defaults so these changes could contribute to the uncertainty change. The Agriculture sector uncertainty reduced for both the overall uncertainty and trend uncertainty. The uncertainty data did not really change but the emission estimates did, and there was also reallocation of some categories between Agriculture and LULUCF to align with the ETF CRT reporting. LULUCF uncertainty increased by about 30% compared to the previous inventory uncertainty analysis. Part of this change was due to the introduction of new land change maps, however additional changes were made due to a more in depth analysis of the uncertainty data rather then it being an actual change in uncertainty. The uncertainty data is one aspect of the inventory, which is being improved, so it is likely there will be further changes in the next inventory. Waste sector uncertainty was reduced, but this s likely due to the reduction in the emissions in this sector due to improvements. General assessment of completeness Table 1.11 provides information on the completeness of the inventory. It shows which activities are not estimated (NE), included elsewhere (IE) or that is not occurring (NO) within the South African jurisdiction. Table 1.11: Activities in the 2022 inventory which are not estimated (NE), included elsewhere (IE) or not occurring (NO). NE, IE or NO IPCC Category Activity Comments 2 The full uncertainty assessment is provided in Appendix B. 19 GHG Inventory for South Africa: 2000 - 2022 1B1b CO 2 , CH 4 and N 2 O from spontaneous combustion of coal seams Researched to be considered in future 1B1ai2 CH 4 emissions from abandoned mines Research to be considered in future 1C1 CO 2 transport Insufficient data to include 1C2 Injection and storage Insufficient data to include 2B7 CH 4 and N 2 O emissions from Soda Ash Production CO 2 included in this inventory. CH 4 and N 2 O will not be included in future 2C1 N 2 O emissions from iron and steel production Insufficient data to include 2C2 N 2 O emissions from ferroalloy production Insufficient data to include 2D2 CH 4 and N 2 O emissions from paraffin wax use Insufficient data to include 2E Electronics industry Insufficient data to include 2F5 PFCs and HFCs from solvents Insufficient data to include 2G1 PFCs from electrical equipment Insufficient data to include 2G2 PFCs from other product uses Insufficient data to include 2G3 N 2 O from product uses Insufficient data to include 3B CO 2 from organic soils Due to priority this will only be included in the next inventory. 3C4 N 2 O from organic soils Insufficient data to include 4B CH 4 , N 2 O emissions from biological treatment of waste Insufficient data to include 4C1 CO 2 , CH 4 and N 2 O from waste incineration Insufficient data to include 2 SF6 emissions in the IPPU sector Insufficient data. Some information is included where possible All sectors NOx, CO, NMVOC emissions These have only been included for biomass burning due to a lack of data in other sectors All sectors SO 2 emissions Insufficient data IE 1A1aii CO 2 , CH 4 and N 2 O emissions from Combined Heat and Power (CHP) combustion systems Not separated out but is included within 1A1ai 1A3eii CO 2 , CH 4 and N 2 O emissions from off-road vehicles and other machinery Included under Road transportation. 1A5b Mobile unspecified Included under 1A3b 3B Precursor emissions from controlled burning Emissions from controlled burning are not separated from biomass burning and so are included under Biomass burning (3C1) 20 GHG Inventory for South Africa: 2000 - 2022 3B SOC changes for converted land This data is included under the land remaining land category as Formulation A of the SOC equation was applied. 3C1 CO 2 emissions from biomass burning These are not included under biomass burning, but rather under disturbance losses in the Land sector (3B). NO 2B3 CO 2 , CH 4 and N 2 O emissions from Adipic acid production 2B4 CO 2 , CH 4 and N 2 O Caprolactam, Glyoxal and Glyoxylic acid production 3C7 Rice cultivation Recalculations Each sector made improvements to their emission estimates and therefore recalculations were completed for the full time-series. The current inventory applied the AR5 GWPs so to see the actual impacts of the improvements alone the previous inventory data was converted to CO 2 equivalents by using the AR5 GWP. The data shows that the current inventory estimates (excl. LULUCF) are between 0.43% and 5.3% lower than the 2020 inventory estimates, while the estimates including LULUCF are between 0.23% and 4.94% lower than the 2020 estimates except for 2017 when emission were slightly higher (Figure 1.6). The Energy sector improvements contribute the most to the reduction in the estimates in this inventory, with an average reduction of 14 294 Gg CO 2 e since 2007 compared to the previous inventory. The LULUCF sector showed an average reduction of 16 929 Gg CO 2 e between 2003 and 2014, after which it increases emissions by an average of 14 899 Gg CO 2 e until 2018. This then changes to a reduction of 9 832 Gg CO 2 e in 2020. The Agriculture sector shows an increase in emissions by an average of 5 234 Gg CO 2 e across the time-series, while Waste emissions are reduced by an average of 7 082 Gg CO 2 e over the same period. 21 GHG Inventory for South Africa: 2000 - 2022 Figure 1.6: Impacts of recalculations on the overall emission estimates for South Africa. Improvement plan Table 1.12 shows planned improvements and the timelines for the completion of these improvements. 22 GHG Inventory for South Africa: 2000 - 2022 Table 1.12: List of planned improvements for South Africa’s GHG inventory. Sector Improvement Priority Reason Status Completion timeframe Barriers and constraints Completed Tasks Energy CO 2 and CH 4 fugitive emissions from oil and natural gas operations Medium Completeness Completed 2022 inventory Included in this inventory (2000-2022). Fugitive emissions from coke production to be reported separately from 2C process emissions Low Transparency Completed 2022 inventory Included in this inventory (2000-2022). Develop EFs, carbon content of fuels and NCVs of liquid fuels High Key category; Accuracy Completed 2022 inventory Included in this inventory (2000-2022). Agriculture Incorporate all background equations for the Tier 2 calculations of enteric fermentation High Key category; Accuracy; Transparency Completed 2022 inventory Included in this inventory (2000-2022). LULUCF Estimate the HWP contribution based on other approaches. Medium Key Category; Accuracy Completed 2022 inventory Included in this inventory (2000-2022). Incorporate updated National Terrestrial Carbon Sinks Assessment (NTCSA) data to improve estimates, particularly for soils High Key category; Accuracy Resolved The NTCSA data is not being updated regularly and is not incorporating the latest land cover maps. This data is therefore not being incorporated into the inventory due to issues of sustainability. The general carbon stock data per land type is being used for verification purposes. Include CO2 estimates for wetlands Low Accuracy; Completeness Completed 2022 inventory Included in this inventory (2000-2022). Included 2018 and 2020 SANLC maps High Key category; Accuracy; Completeness Completed 2022 inventory Included in this inventory (2000-2022), however there are numerous improvements that need to be made to the LULUCF data so a detailed LULUCF improvement plan has been developed and these ongoing improvements are listed below. 23 GHG Inventory for South Africa: 2000 - 2022 Justify reason for use of Tier 1 methods for key categories and pools and include moving to higher tiers in the improvement plan. Medium Transparency Completed 2022 inventory Included in this inventory (2000-2022). Highlight the need for a National Forest Inventory High Key category; Accuracy Completed 2022 inventory Included in this inventory (2000-2022). The need for a national forest inventory was highlighted in the report, and is listed in the improvement plan below as an outstanding task. Include growth of biomass of the last 5 years in the maturity cycle in the cropland remaining cropland Low Accuracy Completed 2022 inventory Included in this inventory (2000-2022). Identify significant sub-categories and carbon pools Medium Key category; Accuracy Completed 2022 inventory Included the pools for each of the land classes and also included these in the KCA. Further explain the appropriateness of the country specific EF for CH4 for wetlands Medium Accuracy Completed 2022 inventory Included in this inventory (2000-2022). The country specific emission factor was assessed and it was found that it was a very site and condition specific factors, therefore the value was reverted back to the default emissions factor. Another country specific factor was found to be similar to the default factor in support of this change. Tasks in progress Cross- cutting Improve uncertainty data for all sectors by incorporating more country specific uncertainty values Medium Accuracy In progress Incorporated as data becomes available Lack of uncertainty data constrains this activity. As data becomes available it will be incorporated. In this inventory a more detailed analysis of uncertainty for LULUCF sector was completed. Improve QA/QC process by addressing all issues in external review High Transparency In progress Future inventories Challenges in addressing external review comments have been limited by resources and process management. The DFFE inventory team has increased in size which should assist in addressing this issue. There are still many issues not resolved but the inventory team is working through them. It is an ongoing process. 24 GHG Inventory for South Africa: 2000 - 2022 Energy Improve the improvement plan by incorporating all review activities not addressed in current inventory High Transparency In progress Ongoing Partly resolved. Challenges around inclusion of further improvements into the improvement plan are limited resources and process management. The DFFE inventory team has increased in size, but it is still taking time to completely address all the issues. The review outputs are included in this report as a reminder of what still needs to be completed. Improve explanation of large changes in trends High Transparency In progress Ongoing Partly resolved. Additional explanations have been provided, but there are still areas where this can be improved further. Ongoing process. Agriculture Incorporate all background data for the Tier 2 calculations of enteric fermentation High Key category; Accuracy; Transparency In progress 2024 inventory All the background equations have been included, but average data is still being used for the majority of the factors (instead of annual data) due to a lack of a sustainable data source. Data sources are being investigated and data will be included once it becomes available. There is a need for an alternate data source for Lime data Medium Key category; Accuracy In progress 2024 inventory Past inventory reviews have mentioned upgrading this information and investigating the alternate method of calculating potential lime use. LULUCF Complete a full uncertainty analysis for the Land sector, including area bias corrections High Key category; Accuracy In progress 2024 inventory A more detailed uncertainty analysis was included for biomass, DOM and SOC data in the LULUCF sector. Mapping uncertainties were improved, however these will be improved further during the land change improvement plan. 25 GHG Inventory for South Africa: 2000 - 2022 Improvement of land change data through detailed assessment of maps and tracking of land parcels High Key category; Accuracy; Consistency In progress The land use improvement plan will be completed over the next 3 years so data will be incorporated as it becomes available (2024 and 2026 inventory). This 2022 inventory incorporated a more detailed assessment of the land change data and identified the most important land change categories that need attention. The assessment also identified improvements that need to be done moving forward and the priority of these improvements. Removing changes due to seasonality is top priority and this will be improved as more land change maps are obtained. The 2022 SANLC map will already assist with making improvements. Tracking of land parcels over time is the second most important issue and training on Collect Earth to assist in this process is underway. Include deadwood in the DOM pool for all land categories Low Completeness In progress 2024 Deadwood has been included for the forest land category. The other land categories with woody components are settlements and perennial crops, but data is very limited for these land categories. Deadwood in these categories is assumed to be very small, but an explanation of this will be included in the next inventory. Move to a higher tier level for DOM in forest lands Medium Key category; Accuracy In progress 2024 This was considered in the 2022 inventory and more detailed disturbance matrix data was included to determine the amount of biomass entering the DOM pool, however there were still one or two pieces of data misisng which requires further investigation. This will be completed and included in the next inventory. Waste Data collection on quantities of waste disposed of into managed and unmanaged landfills Key category; Accuracy In progress 2024 Project is underway so data will be included in 2024 inventory. Tasks outstanding 26 GHG Inventory for South Africa: 2000 - 2022 Cross cutting Investigate inconsistencies in lime activity data (for lime production in IPPU and lime application emission in Agriculture), explore alternative data sources or improve consistency. Low Consistency Planned 5 th BUR Not resolved. Various methods were compared but give varying results. Alternative data sources have not yet been found, but it may be possible to collect further data through the SAGERS system in future. Incorporate NOx, CO, NMVOC, and SOx emissions High Completeness Proposed 5 th BUR Not resolved. Energy Further disaggregation of 1A2 Medium Accuracy Planned Future inventories Current inventory breaks down 1A2 into 1A2a, 1A2b and 1A2-ab. Further work is require to further disaggregate this sector and have emissions calculated per sub-sector. More activity data for estimating emissions associated with non- energy fuel use Low Accuracy Planned Future inventories Research to be initiated in future. Inclusion of methodology documentation/summary/approval process for tier 3 methods Low Transparency Planned Future inventories To be collated and included in future. CO 2 , CH 4 and N 2 O from spontaneous combustion of coal seams Low Completeness Planned Future inventories Research to be initiated in future. CH 4 emissions from abandoned mines Low Completeness Planned Future inventories Research to be initiated in future. Investigate pipeline transport Low Completeness Proposed Future inventories Proposed but nothing planned. Investigate ground activities at airports and harbours Low Accuracy Proposed Future inventories Proposed but nothing planned. Update of the VKT study, including segregation of on-road/off-road Medium Accuracy Proposed Future inventories Proposed but nothing planned. Comparison of next VKT approach with fuel statistics Low Accuracy Proposed Future inventories Proposed but nothing planned. Segregation of military energy use. Low Accuracy Proposed Future inventories Proposed but nothing planned. 27 GHG Inventory for South Africa: 2000 - 2022 Incorporate emissions from biogas Low Completeness Proposed Future inventories This would require a study and so should be recommended as a project under the GHGIP. CO 2 transport and storage Low Completeness Proposed Future inventories Proposed but nothing planned. CO 2 , CH 4 and N 2 O emissions from combined heat and power (CHP) combustion systems Medium Completeness Proposed Future inventories Proposed but nothing planned. Development of T3 methods for CTL- GTC and GTL Low Accuracy Proposed Future inventories Resources and funding are required to complete this study so it will be incorporated into the GHGIP. IPPU Include emissions from electronics industry Medium Completeness Planned A study needs to be undertaken to understand emissions from this source so it should be highlighted as a project for the GHGIP. Incorporate emissions SF 6 emissions Medium Completeness In progress Lack of data is still a challenge. Agriculture Improve manure management data, including biogas digesters as a management system Medium Accuracy Proposed 2024 Proposed project as there is a high variability in this dataset. LULUCF Incorporate organic soils study to include emissions from organic soils Low Completeness Planned Future inventories Not resolved. Due to the other more pressing issues relating to land this was not a priority and will be incorporated once the land mapping system is running. Undertake a National Forest Inventory and include SOC in the inventory High Key category; Accuracy Proposed Future inventories This is an activity which would need to be completed by the Department of Forestry, therefore the date for completion is not known. Complete an assessment of crop types and areas and investigate discrepancies between crop statistics and NLC data Medium Consistency; Comparability Planned 2024 inventory This was partially investigated in this inventory; however a proper assessment will be included in the land use change improvement plan that will run over the next few years. Perform a more detailed assessment of HWP to include a wider range of products Medium Key category; Accuracy Proposed Proposed project that could be considered under the GHGIP. For future evaluation. 28 GHG Inventory for South Africa: 2000 - 2022 Report activity data and parameters (e.g. half-life) used for HWP emission estimation for the whole time-series Medium Transparency Planned 2024 inventory This will be included in the 2024 inventory. Report on the frequency of the HWP activity data collection Low Transparency Planned 2024 inventory This will be included in the 2024 inventory. Assess if the emissions/removals in the overseas territories are significant Low Completeness Planned 2024 inventory Currently these emissions are assumed insignificant as the area of these territories is extremely small, but an assessment of possible emissions still needs to be explored. Collect data on other disturbances besides fire in forest lands Low Key category; Accuracy Proposed Future inventory This would require a study so will be recommended as a project under the GHGIP. Develop a spatial map of fuel wood consumption based on literature to further improve fuel wood consumption High Key category; Accuracy Proposed Future inventory This would require a study so will be recommended as a project under the GHGIP. Assess the significance of peatlands Low Completeness Proposed 2026 inventory Assessing the areas of peatlands would be the first step and this part could be done as part of the land use improvement plan. Waste Improve MCF and rate constants Medium Key category; Accuracy Proposed This would require a study so will be recommended as a project under the GHGIP. Include economic data for different population groups Medium Key category; Accuracy In progress 2024 inventory Will be included in the 2024 inventory Include information on population distribution in rural and urban areas as a function of income Medium Key category; Accuracy In progress 2024 inventory Insufficient data. Include HWP in solid waste Medium Key category; Completeness Proposed Future inventory Once HWP data is improved (see above) it will be incorporated into solid waste. Obtain data on waste streams and the bucket system Medium Accuracy In progress 2024 inventory Insufficient data. CH 4 , N 2 O emissions from biological treatment of waste Medium Completeness In progress 2024 inventory Insufficient data. 29 GHG Inventory for South Africa: 2000 - 2022 CO 2 , CH 4 and N 2 O from waste incineration High Completeness In progress 2024 inventory Insufficient data. 30 GHG Inventory for South Africa: 2000 - 2022 Approval and publishing process The BUR and National Inventory Reports (NIRs) are endorsed by the Project Steering Committee (PSC), being chaired by the DFFE and comprising of various state departments before they are submitted to the Minister of the DFFE for approval. Once the reports are approved by Minister, they are submitted to the UNFCCC by the Chief Directorate for Climate Change International Relations and Reporting and undergo an international review process. References IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). ed. Japan: IGES. 31 GHG Inventory for South Africa: 2000 - 2022 Chapter 2: Trends in GHG Emissions Description and interpretation of trends for aggregated GHG emissions This chapter provides a description and interpretation of emission trends by sector and describes trends for the aggregated national emission totals. 2.1.1 National trends 2.1.1.1 Overall emissions (excluding LULUCF) Overall emissions (excl. LULUCF) include those from Energy, IPPU, Agriculture and Waste. It does not include the sources and removals from land use change and Harvested wood products, which together form the LULUCF sector. 2022 In 2022 the Energy sector was the largest contributor to South Africa’s gross emissions (excl. LULUCF) in 2022, comprising 78% of total emissions (Figure 2). This was followed by the agriculture sector (11%), IPPU sector (6%) and the Waste sector (4 %). Figure 2.1: National GHG emissions (excluding LULUCF) for South Africa, 2022 32 GHG Inventory for South Africa: 2000 - 2022 2000 - 2022 South Africa’s GHG emissions excl. LULUCF were 489 748 Gg CO 2 e in 2000 and these decreased by 2.2% by 2022 (Table 2.1). Emissions (excl. LULUCF) in 2022 were estimated at 478 888 Gg CO 2 e. The decrease in emissions compared to 2020 is attributed to the marginal decrease in emissions across all the sectors (Energy, IPPU, Agriculture and Waste). There was an overall decrease in Energy emissions of 2.5%, this is attributed to the decrease in the Residential sector, Aviation as well Petroleum refining, there was also decrease in the IPPU sector in 2022. Despite the overall economic recovery after the COVID-19 pandemic, the IPPU sector's estimated emissions in 2022 were 6.7% lower than those in 2000. This decline is primarily attributed to a significant reduction in metals production (specifically Iron and Steel, and Aluminium) by 39.0%, driven by a decrease in global demand. The chemicals industry also experienced a notable decline of 31.4%. The Table 2.2 provides annual emissions data for South Africa from 2000 to 2022, both excluding and including LULUCF, measured in gigagrams of carbon dioxide equivalent (Gg CO 2 e). Notable points include a peak in emissions in 2004 at 524 903 Gg CO 2 e, a dip in 2010 with a 3.71% decrease, and a significant decline in 2020 by -5.79% (Figure 2). The emissions increased in 2021 by 3.36% due to post covid economic recovery. The data reflects South Africa's ongoing journey to balance economic development with environmental sustainability. 2020 - 2022 Emissions (excl. LULUCF) in 2022 were estimated at 478 888 Gg CO 2 e, this represents an increase of 1,4% compared to 2020 and was influenced by increased activities post the covid pandemic in the manufacturing sector. Table 2.1: Changes in South Africa’s emissions excluding and including LULUCF between 2000, 2020 and 2022. Emissions (Gg CO 2 e) Change between 2000 and 2022 Change between 2020 and 2022 2000 2020 2022 Gg CO 2 e % Gg CO 2 e % Emissions (excl. LULUCF) 489 747.5 472 437.8 478 887.5 -10,860.03 -2.2% 6,449.7 1.4% Emissions (incl. LULUCF) 462 205.4 435 918.6 435 827.7 -26,377.69 -5.7% -90.93 0.00% 33 GHG Inventory for South Africa: 2000 - 2022 Figure 2.2: National GHG emissions (excluding LULUCF) for South Africa, 2000 – 2022 Table 2.2: Trends and annual change in emissions (excluding and including LULUCF), 2000 – 2022. Emissions (excl. LULUCF Emissions (incl. LULUCF) Gg CO 2 e Annual change (%) Gg CO 2 e Annual change (%) 2000 489 747.5 462 205.4 2001 488 964.7 -0.16 472 002.4 2.12 2002 497 651.3 1.78 483 692.9 2.48 2003 513 190.2 3.12 481 053.7 -0.55 2004 524 902.7 2.28 501 254.4 4.20 2005 523 122.9 -0.34 519 760.2 3.69 2006 514 091.2 -1.73 504 500.4 -2.94 2007 535 672.5 4.20 518 559.4 2.79 2008 532 320.7 -0.63 529 003.6 2.01 2009 547 270.5 2.81 522 920.8 -1.15 2010 526 971.4 -3.71 512 987.3 -1.90 34 GHG Inventory for South Africa: 2000 - 2022 2011 527 161.3 0.04 504 638.0 -1.63 2012 536 003.0 1.68 512 196.6 1.50 2013 522 220.3 -2.57 487 134.0 -4.89 2014 522 899.6 0.13 496 835.8 1.99 2015 515 516.2 -1.41 511 428.6 2.94 2016 509 870.0 -1.10 489 254.4 -4.34 2017 501 987.5 -1.55 497 553.7 1.70 2018 502 130.2 0.03 502 893.9 1.07 2019 501 485.2 -0.13 476 686.4 -5.21 2020 472 437.8 -5.79 435 918.6 -8.55 2021 488 322.3 3.36 464 957.7 6.66 2022 478 887.5 -1.93 435 827.7 -6.27 2.1.1.2 Net emissions (including LULUCF) Overall emissions (incl. LULUCF) include those from Energy, IPPU, Agriculture, Waste and the sources and removals from the LULUCF sector. 2022 The overall emissions (incl. LULUCF) were 435 828 Gg CO 2 e in 2022. The LULUCF sector was a sink in 2022 with Forest lands being the largest contributor to the sink. 2000 – 2022 South Africa’s GHG emissions (incl. LULUCF) were 462 205 Gg CO 2 e in 2000 and these decreased by 5.5% by 2022 (Table 2.1). Emissions (incl. LULUCF) in 2022 were estimated at 435 828 Gg CO 2 e. The emissions incl. LULUCF followed a similar trend to the emissions excl. LULUCF. 2020 – 2022 There was a negligible change in overall emissions (incl. LULUCF) since the last inventory submission (see Table 2.1). 2.1.2 Indicator trends 35 GHG Inventory for South Africa: 2000 - 2022 South Africa’s carbon and energy intensity trends are determined from GHG emissions, GDP data (StatsSA 2023) 3 , total primary energy supply data (DRME 2023) and population data (Stats SA 2022). 2.1.2.1 Total emission indicators The carbon intensity of the population (i.e. total net emissions per capita) has decreased by 33% since 2000 (Figure 2.3). In 2020 the carbon intensity of the population decreased by 10%, which is the highest decrease since 2000. The carbon intensity continues to decrease beyond 2020 due to an increase in population without a proportional increase in energy emissions. The carbon intensity of economy has decreased by 42% since 2000. In 2020 the carbon intensity of the economy decreased by 3%, however, the largest decrease was 8% in 2006. Figure 2.3: Trends in overall carbon intensity of the population and the economy of South Africa between 2000 and 2022. 2.1.2.2 Energy emission indicators 3 In previous inventories the GDP at constant 2010 prices was used. However, in this inventory the GDP at constant 2015 prices was used in line with the changes made by StatsSA in their publication. 36 GHG Inventory for South Africa: 2000 - 2022 The energy carbon intensity of the population (energy emissions per capita, Figure 2.4) follows the same trend as the carbon intensity of the population and has decreased by 31% since 2000. This is expected since the energy sector contributes the most to the total emissions each year. The carbon intensity of energy supply has decreased by 35% since 2000. However, the energy intensity of the population increased by 6% while the energy intensity of the economy decreased by 8% since 2000. Figure 2.4: Trends in energy intensity indicators for South Africa between 2000 and 2022. Description and interpretation of emission trends by greenhouse gas Figure 2.5 represents the percentage contribution of each gas to South Africa’s total emissions between 2000 and 2022, while Table 2.3 shows the emission amounts per gas. 37 GHG Inventory for South Africa: 2000 - 2022 Figure 2.5: Percentage contributions from each of the gases to South Africa’s emissions (excl. LULUCF (top) and incl. LULUCF (bottom)) between 2000 and 2022. 38 GHG Inventory for South Africa: 2000 - 2022 Table 2.3: Trend in CO 2 , CH 4 , N 2 O and F-gases between 2000 and 2022. Emissions CO 2 (excl. LULUCF) CO 2 (incl. LULUCF) CH 4 (excl. LULUCF) CH 4 (incl. LULUCF) N 2 O (excl. LULUCF) N 2 O (incl. LULUCF) F-gases Gg CO 2 e Gg CO 2 e Gg CH 4 Gg CO 2 e Gg CH 4 Gg CO 2 e Gg N 2 O Gg CO 2 e Gg N 2 O Gg CO 2 e 2000 408 046.9 367 847.3 63 227.2 2 258.1 73 964.7 2 641.6 17 448.4 65.8 19 368.4 73.1 1 025.0 2001 406 610.1 376 331.1 63 654.4 2 273.4 74 738.5 2 669.2 17 649.5 66.6 19 882.2 75.0 1 050.6 2002 414 913.0 387 175.3 63 788.9 2 278.2 75 095.4 2 682.0 18 014.5 68.0 20 487.2 77.3 935.0 2003 431 054.0 387 095.3 64 013.6 2 286.2 74 121.2 2 647.2 17 188.7 64.9 18 903.2 71.3 933.9 2004 442 206.3 406 758.9 64 220.6 2 293.6 74 262.8 2 652.2 17 549.0 66.2 19 306.0 72.9 926.8 2005 438 097.3 421 153.9 65 117.4 2 325.6 76 176.0 2 720.6 18 157.6 68.5 20 679.7 78.0 1 750.5 2006 428 594.6 406 347.5 64 943.6 2 319.4 75 396.0 2 692.7 18 436.6 69.6 20 640.5 77.9 2 116.5 2007 448 922.5 419 464.3 66 544.5 2 376.6 76 837.0 2 744.2 18 111.6 68.3 20 164.2 76.1 2 093.9 2008 445 413.9 429 142.6 67 445.4 2 408.8 78 034.7 2 787.0 17 839.7 67.3 20 204.6 76.2 1 621.7 2009 461 484.1 425 532.5 66 808.1 2 386.0 76 487.7 2 731.7 17 866.6 67.4 19 788.8 74.7 1 111.7 2010 440 316.3 413 986.9 67 000.3 2 392.9 77 054.9 2 752.0 17 394.5 65.6 19 685.1 74.3 2 260.3 2011 438 186.5 403 922.4 66 593.4 2 378.3 76 209.1 2 721.8 17 513.1 66.1 19 638.2 74.1 4 868.3 2012 444 870.7 409 289.9 68 235.6 2 437.0 77 857.6 2 780.6 18 205.8 68.7 20 358.2 76.8 4 690.9 2013 436 580.2 390 699.9 64 672.7 2 309.7 73 674.3 2 631.2 17 986.4 67.9 19 778.8 74.6 2 981.0 2014 435 945.3 398 763.0 65 358.6 2 334.2 74 520.3 2 661.4 18 141.4 68.5 20 098.3 75.8 3 454.3 2015 428 318.4 413 934.3 65 597.2 2 342.8 74 365.9 2 655.9 17 683.2 66.7 19 211.1 72.5 3 917.4 2016 423 288.9 393 063.3 65 028.8 2 322.5 73 532.7 2 626.2 17 433.4 65.8 18 539.5 70.0 4 119.0 2017 414 660.4 398 814.6 65 370.1 2 334.6 75 084.9 2 681.6 17 546.5 66.2 19 243.6 72.6 4 410.6 2018 415 337.5 403 912.3 63 662.0 2 273.6 74 010.8 2 643.2 18 380.6 69.4 20 220.8 76.3 4 750.0 2019 415 177.7 378 412.3 62 888.1 2 246.0 73 153.9 2 612.6 18 322.7 69.1 20 023.5 75.6 5 096.8 2020 386 604.5 338 137.1 62 808.0 2 243.1 73 070.3 2 609.7 17 537.7 66.2 19 223.7 72.5 5 487.6 2021 402 172.8 364 510.8 62 876.8 2 245.6 74 579.5 2 663.6 17 437.0 65.8 20 031.7 75.6 5 835.7 2022 392 138.4 335 992.1 63 240.6 2 258.6 74 260.4 2 652.2 17 372.1 65.6 19 438.8 73.4 136.4 39 GHG Inventory for South Africa: 2000 - 2022 2.2.1 Carbon dioxide (CO 2 ) CO 2 is the largest contributor to South Africa’s emissions; followed by CH 4 and then N 2 O (Figure 2.5). CO 2 emissions contributed 81.9% (excl. LULUCF) to South Africa’s emissions in 2022. Majority of CO 2 emissions are from the Energy sector, contributing an average of 92.7% (excl. LULUCF)to the total CO 2 emissions between 2000 and 2022. The IPPU sector’s contribution (excl. LULUCF) is an average of 6.8% of the total CO 2 emissions between 2000 and 2020, while the Agriculture sector contributed an average of 0.6%. The trend and contribution of each sector to CO 2 emissions can be seen in Figure 2.6. Figure 2.6: Trend and sectoral contribution to CO 2 emissions (excl. LULUCF), 2000 – 2022. 2.2.2 Methane (CH 4 ) The trend and contribution of all the sectors to the total CH 4 emissions in South Africa are shown in Figure 2.7. CH 4 emissions (excl. LULUCF) only increased by 0.02% between 2000 and 2022. The Enteric Fermentation from Agriculture and Solid Waste Disposal from Waste were the major contributors to the total CH 4 emissions in 2022. 40 GHG Inventory for South Africa: 2000 - 2022 Figure 2.7: Trend and sectoral contribution to the CH 4 emissions, 2000 – 2022. 2.2.3 Nitrous oxide (N 2 O) The contribution of all the sectors to the N 2 O emissions is shown in Figure 2.8. The main contributor to N 2 O emissions is the Agriculture sector followed by Energy sector, contributing 74.4% and 14.4% (excl. LULUCF) respectively. N 2 O emissions for 2022 from IPPU sector have decreased by 74.0% since 2000. 41 GHG Inventory for South Africa: 2000 - 2022 Figure 2.8: Trend and sectoral contribution to N 2 O emissions in South Africa, 2000 – 2022. 2.2.4 F gases Estimates of HFC and PFC emissions were only estimated for the IPPU sector in South Africa. F-gas emission estimates varied annually (Figure 2.9) and contributed 1.3% to overall emissions (excl. LULUCF) in 2022. Emissions increase from 2011 due to the addition of HFC emissions from air conditioning, foam blowing agents, fire protection and aerosols. There is no data prior to 2005 so this time-series is not consistent. The elevated F-gas emissions are therefore not necessarily due to an increase in emissions but rather due to the incorporation of new categories. PFCs are produced during the production of aluminium. The Aluminium production data was updated for the years 2014 onwards and the updated data was an order of magnitude lower than the previous years. This is causing the decline in the PFC emissions. There is a sharp decline in emissions from the Metal industry between 2007 and 2009 and this is attributed to reduced production caused by electricity supply challenges and decreased demand following the economic crisis that occurred during 2008/2009. Increases in 2011 and 2012 were due to increased emissions from aluminium plants due to inefficient operations. The industry was used to assist with the rotational electricity load shedding in the country at the time and which necessitated switching on and off at short notice 42 GHG Inventory for South Africa: 2000 - 2022 leading to large emissions of Tetrafluoroethylene (C 2 F 4 ) and Carbon tetrafluoromethane (CF 4 ). Figure 2.9: Trend in F-gas emissions in South Africa, 2000 – 2022. Description and interpretation of emission trends by category 2.3.1 Energy The Energy sector is the largest contributor to South Africa’ emissions (excl. LULUCF), contributing 78% in 2022 and on average since 2000 (Figure 2.10). Since 2000, the Energy sector emissions have decreased by 2.5% with the biggest decrease (6.4%) being in 2020 due to COVID19 lockdown restrictions (Figure 2.11). In 2021 emissions increased by 3.5% and then dropped by 3.4% in 2022 to pandemic levels (2020). This decrease is due to a 6.5% decrease in emissions from the Energy Industries sub-sector, which on average accounts for 67% of the Energy sector emissions. 43 GHG Inventory for South Africa: 2000 - 2022 Figure 2.10: Percentage contributions from each of the sectors to South Africa’s emissions (excluding LULUCF) between 2000 and 2022. 44 GHG Inventory for South Africa: 2000 - 2022 Figure 2.11: Emissions and sequestration trends per sector across the time-series, 2000- 2022. 2.3.2 IPPU In 2022 the IPPU sector produced 30 598 Gg CO 2 e which is 6.4% of South Africa’s emission (excl. LULUCF) (Figure 2.10). The IPPU sector produces CO 2 emissions (78.4%), fluorinated gases (20.0%) and smaller amounts of CH 4 (0.4%) and N 2 O (1.2%). Carbon dioxide and any other emissions from combustion of fuels in these industries are reported under the energy sector. The largest source category is the Metal industry category. which contributes 51.2% to the total IPPU sector emissions. The Mineral industry and the Product used as substitutes for ozone depleting substances subsectors contribute 19.8% and 19.4%, respectively, to the IPPU sector emissions. Even though the South African economy recovered after the COVID-19 pandemic, estimated emissions from the IPPU sector are 2 183 Gg CO 2 e (-6.7%) lower than the emissions in 2000 (Table 2.4). The decline can be attributed to the decline in metals production (-39.0%), specifically Iron and Steel, and Aluminium. This can be ascribed to a decrease in global demand. The chemicals industry also declined (-31.4%). Cement 45 GHG Inventory for South Africa: 2000 - 2022 Production and Non-Energy Product Use from Fuels and Solvents increased emissions from 2000 by 2 081 Gg CO 2 e. The local demand for cement increased dramatically from 2000. 2.3.3 Agriculture The Agriculture sector contributed between 10% and 12% to the total emissions (excl. LULUCF) between 2000 and 2022 (Figure 2.10). The overall emissions have declined by 8.9% since 2000 (Table 2.4). The main driver of change in the Agriculture sector is the livestock population. Livestock have input into the Enteric fermentation, Manure management, as well as Direct and Indirect N 2 O emissions from managed soils. Enteric fermentation emissions show a declining trend due to a decline in livestock population. Dairy cattle, pigs and poultry are the largest contributors to Manure management emissions, and with increasing poultry numbers these emissions increase over the 22- year period. The Agriculture sector produced 53 519 Gg CO 2 e (excl. LULUCF) in 2022 (Table 2.4). Livestock contributed 76% to the Agriculture emissions in 2022, and the largest contributor to this category is CH 4 from Enteric fermentation (68%), while Manure management contributed (8%) to the total livestock emissions. Agricultural soils contributed 19 % to the total Agriculture emissions, while the least emissions were from Liming (3%), Urea application (1.1%) and Field burning of agricultural residues (0.1%). Table 2.4: Trend in emissions and removals by sector for 2000 to 2022. Energy IPPU Agriculture LULUCF Waste Emissions (Gg CO 2 e) 2000 383 820.6 32 781.3 58 782.9 -27 542.1 14 362.7 2001 382 286.1 33 056.9 58 837.9 -16 962.3 14 783.7 2002 388 662.6 35 034.9 58 339.7 -13 958.4 15 614.1 2003 405 095.8 34 425.3 57 628.0 -32 136.5 16 041.1 2004 416 547.4 34 495.2 57 447.7 -23 648.3 16 412.4 2005 410 739.3 37 594.4 57 899.2 -3 362.8 16 890.0 2006 400 662.4 38 760.4 57 205.7 -9 590.8 17 462.7 2007 421 917.7 37 163.9 58 397.1 -17 113.1 18 193.7 2008 418 700.4 35 433.0 59 222.5 -3 317.1 18 964.8 2009 436 680.2 33 563.3 58 789.6 -24 349.8 18 237.4 2010 413 693.1 35 956.0 58 411.5 -13 984.1 18 910.7 2011 410 126.9 39 675.3 58 419.9 -22 523.3 18 939.2 2012 417 707.8 38 809.7 60 231.6 -23 806.4 19 254.0 2013 408 544.5 38 279.1 58 894.0 -35 086.3 16 502.7 2014 407 035.4 39 234.0 59 265.0 -26 063.7 17 365.2 46 GHG Inventory for South Africa: 2000 - 2022 2015 397 417.7 41 596.6 58 210.2 -4 087.6 18 291.8 2016 393 788.7 40 337.2 57 002.1 -20 615.6 18 742.0 2017 393 232.8 32 508.5 56 230.6 -4 433.8 20 015.7 2018 397 219.1 30 256.4 55 529.1 763.7 19 125.6 2019 400 292.8 27 289.3 54 590.5 -24 798.8 19 312.6 2020 374 748.2 24 857.6 53 830.0 -36 519.2 19 002.1 2021 387 309.9 27 547.4 53 523.2 -23 364.6 19 941.8 2022 374 072.4 30 598.0 53 518.7 -43 059.8 20 698.4 Change between 2000 and 2022 (Gg CO 2 e) -9 748.2 -2 183.3 -5 264.2 -15 517.7 6 335.7 Percentage change (%) -2.5 -6.7 -9.0 56.3 44.1 2.3.4 LULUCF The LULUCF sink was estimated at 43 060 Gg CO 2 e in 2022. Forest lands were estimated to have a sink of 87 559 Gg CO 2 e, with 62% of this being due to land being converted to Forest lands. The conversion of Grasslands to Forest land (mainly woodlands) accounted for 86.3% of the total land conversion sink. All other land categories were sources of emissions with Other lands contributing the most (42.5%) to this source, followed by Grasslands (26.6%). All the emissions for Other lands was from land being converted to bare ground and the largest conversion was low shrublands to bare ground. The LULUCF sector showed an increase of 56.3% in its sink since 2000 (Table 2.4). The sink increased by 17.9% since 2020. The sink was reduced between 2015 and 2018 (Figure 2.11) and these changes were brought about by the change in land conversions introduced by the 2014-2018 change maps. During this period there was an increase in the emissions from Grasslands (due mainly to conversion between low shrubland and grasslands) and Croplands. The increased Cropland emissions were attributed to an increase in conversion of Forest land to Cropland and conversions between perennial and annual crops. These increased emissions led to a reduction in the sink during this period. Grasslands were estimated to be a small sink between 2000 and 2014, but the trends show that from 2015 onwards the become a source of emissions. This is due to the conversions between low shrublands and grasslands, however this is one of the categories that can be impacted by seasonal variation. The land change improvement plan will investigate these changes further over the next few years in the aim of better understanding the changes and reducing the uncertainty. The trends also show an increase in the Forest land sink. There is an increase in conversion of Grassland to Forest land, mainly grasslands to woodlands, which also 47 GHG Inventory for South Africa: 2000 - 2022 supports the findings of a reduced Grassland sink. Thickets are an important sink in the Forest land category and the sink is seen to be fairly stable, however thickets are the land category that showed the most change in the land change maps due to reclassification, so the improvement plan will interrogate this further. 2.3.5 Waste In South Africa the total Waste sector emissions for 2022 were 20 698 Gg CO 2 e (Table 2.4). Most of these emissions are from Wastewater treatment and discharge contributing 9247 Gg CO 2 e (44.1%) of the total Waste sector emissions. Solid waste disposal contributed a further 8 596 Gg CO 2 e (41.5%) of waste emissions while biological treatment of solid waste contributed 2 530 Gg CO 2 e (12.2%). Emissions from Incineration and Open Burning of Waste were estimated to be 325 Gg CO 2 e (1.57%). Solid waste disposal emissions have increased 47.6% since 2000. Incineration and open burning of waste emissions increased by 53.1% since 2000, while emissions from Wastewater treatment and discharge increased slightly across the time series. This overall increase in emissions is largely driven by increases of 35.6% in Domestic wastewater treatment and discharge emissions, whilst there was a 9.4% decline in Industrial wastewater treatment and discharge emissions. Description and interpretation of emission trends for indirect GHG The trend in emissions of CO, NOx and NMVOCs is shown in Error! Reference source n ot found.. These emissions were estimated for biomass burning only. There is annual variability because the emissions include wildfires as well as controlled fires. Table 2.5: Trends in indirect GHG emissions between 2000 and 2022. NOx CO NMVOC (Gg) 2000 98.3 1 994.2 187.2 2001 121.3 2 446.9 228.7 2002 136.8 2 775.0 259.8 2003 82.5 1 755.8 171.0 2004 87.9 1 833.6 176.0 2005 141.4 2 908.0 275.7 48 GHG Inventory for South Africa: 2000 - 2022 2006 119.9 2 473.1 235.1 2007 103.5 2 371.6 242.2 2008 127.9 2 799.2 277.2 2009 100.8 2 119.8 204.5 2010 127.9 2 613.1 246.2 2011 118.1 2 350.0 217.4 2012 118.0 2 450.2 233.3 2013 93.0 1 981.2 193.5 2014 105.5 2 251.8 219.8 2015 76.3 1 555.6 147.9 2016 44.8 945.2 91.9 2017 83.6 1 710.8 163.0 2018 90.3 1 915.6 187.4 2019 80.9 1 741.1 172.7 2020 80.3 1 666.3 161.6 2021 140.7 2 897.5 279.3 2022 105.1 2 184.7 211.3 References Energy.gov.za. (n.d.). Statutes & Practices | Department: Energy | REPUBLIC OF SOUTH AFRICA. [online] Available at: https://www.energy.gov.za/files/energyStats_frame.html. Gross Domestic Product (GDP). (2023). [online] South Africa: Statistics South Africa. Available at: https://www.statssa.gov.za/?page_id=1854&PPN=P0441&SCH=73562. Mid-year population estimates. (2022). [online] South Africa: Statistics South Africa. Available at: https://www.statssa.gov.za/?page_id=1859. 49 GHG Inventory for South Africa: 2000 - 2022 Chapter 3: Energy (CRT sector 1) Sector overview South Africa’s Energy sector inventory includes: • Exploration and exploitation of primary energy sources; • Conversion of primary energy source into more useable energy forms in refineries and power plants; • Transmission and distribution of fuels; and • Final use of fuels in stationary and mobile applications. The Energy sector is South Africa’s largest emitting sector. This sector is the largest source of CO 2 emissions (Figure 3.1) and the second largest source of N 2 O emissions mainly as a result of Fuel Combustion activities. Figure 3.1: Energy sector contribution by gas (excl. LULUCF) for 2022. The primary energy supply for South Africa is dominated by coal and crude oil as shown in Figure 3.2. The share of coal has increased by 5% since 2018 while the share of gas has decreased by 2% (DMRE 2023). 50 GHG Inventory for South Africa: 2000 - 2022 Figure 3.2: Total primary energy supply in South Africa, 2020 4 3.1.1 Shares and trends in emissions The total emissions from the Energy sector for 2022 are shown in Table 3.1. Emissions from Fuel Combustion activities accounted for 92% of the Energy sector emissions, with Energy Industries accounting for 65 % of emissions from Fuel Combustion activities. Table 3.1: Summary of emissions from the Energy sector in 2022 Greenhouse gas source and sink categories CO 2 CH 4 N 2 O Total Gg CO 2 e Gg Gg CO 2 e Gg Gg CO 2 e Gg CO 2 e 1. ENERGY 365 688 210 5 881 9 2 492 374 061 1A Fuel combustion activities 341 776 36 1 011 9 2 487 345 274 1B Fugitive emissions from fuels 23 912 174 4 870 0 5 28 787 1C CO 2 transport and storage NE NE NE NE NE NE The time series of energy emissions by sector is shown in Figure 3.3. Fuel Combustion activities have varied over the years, however, they have remained the largest 4 Values from the 2020 Energy Balance, DMRE 2023 Statutes & Practices | Department: Energy | REPUBLIC OF SOUTH AFRICA 51 GHG Inventory for South Africa: 2000 - 2022 contributor to the Energy sector emissions. Fugitive Emissions have remained fairly constant since 2000. Figure 3.3: Energy emissions by sub-sector, 2000 – 2022 The changes in Energy sector emissions since 2000, at an aggregated level, are shown in Figure 3.4. The impact of stringent lockdown restrictions in 2020, due to the COVID-19 pandemic, is seen in the drop in emissions. Thereafter emissions increased in 2021 as less stringent measures were put in place in the first half of the year and all restrictions were then revoked for the remainder of the year. 52 GHG Inventory for South Africa: 2000 - 2022 Figure 3.4: Trend in annual change in the total energy emissions in South Africa, 2000 – 2022. Table 3.2 shows the disaggregated changes in emissions from 2000 and from 2022. The highest percentage increase in emissions since 2000 was experienced by the Transport and the Manufacturing industries and construction sub-sectors. This is due to a growing population and to growth in the manufacturing industry resulting in increased energy demand. Similarly, since 2020 the Manufacturing industries and construction sub-sector experienced the highest increase in emissions. This was because of increased demand in production following a temporary drop in 2020 caused by stringent lockdown restrictions. The highest percentage decrease in emissions since 2000 was experienced by the Other Sectors sub-sector mainly due to a significant decrease in emissions from households due to increased electrification across the country since 2000 as well as an increase in residential solar installations. 53 GHG Inventory for South Africa: 2000 - 2022 Table 3.2: Summary of the change in emissions from the Energy sector between 2000 and 2022. Greenhouse gas source and sink categories Emissions (Gg CO 2 e) Difference (Gg CO 2 e) Change 2000 2020 2022 2000-2022 2020-2022 2000-2022 2020-2022 1. ENERGY 383 821 374 748 374 072 -9 748 -676 -3% 0% 1A Fuel combustion activities 349 932 342 640 345 275 -4 657 2 634 -1% 1% 1A1 Energy industries 220 522 237 956 225 656 5 134 -12 300 2% -5% 1A1a Electricity and heat production 185 947 206 059 196 977 11 031 -9 081 6% -4% 1A1b Petroleum refining 4 012 3 289 1 080 -2 932 -2 209 -73% -67% 1A1c Manufacture of solid fuels 30 563 28 609 27 599 -2 965 -1 010 -10% -4% 1A2 Manufacturing industries and construction 21 132 19 958 26 647 5 515 6 688 26% 34% 1A2a Iron & Steel 1 604 1 170 1 630 26 460 2% 39% 1A2b Non-ferrous Metals 475 691 789 313 97 66% 14% 1A2-ab 1A2 Remainder 19 052 18 097 24 228 5 176 6 131 27% 34% 1A3 Transport 41 624 47 665 52 744 11 121 5 079 27% 11% 1A3a Domestic aviation 2 333 704 689 -1 644 -15 -70% -2% 1A3b Road transportation 38 245 45 942 51 159 12 914 5 218 34% 11% 1A3c Railways 613 520 395 -217 -124 -35% -24% 1A3d Water-borne navigation (domestic) 432 500 500 68 0 16% 0% 1A3e Other transportation NE NE NE NA NA 1A4 Other sectors 50 570 17 194 19 029 -31 541 1 835 -62% 11% 1A4a Commercial/Institutional 20 604 10 985 12 077 -8 528 1 092 -41% 10% 1A4b Residential 27 550 3 165 3 858 -23 692 693 -86% 22% 1A4c Agriculture/Forestry/Fishing/Fish farms 2 416 3 044 3 095 679 50 28% 2% 1A5 Non-specified 16 084 19 867 21 198 5 115 1 332 32% 7% 1B Fugitive emissions from fuels 33 889 32 108 28 798 -5 091 -3 310 -15% -10% 1B1 Solid fuels 2 028 3 292 2 293 265 -999 13% -30% 1B2 Oil and natural gas 765 434 94 -671 -340 -88% -78% 1B3 Other emissions from energy production 28 147 25 645 23 477 -4 670 -2 168 -17% -8% 1C Carbon dioxide transport and storage NE NE NE NA NA NA NA Note: Columns may not add up exactly due to rounding off. 54 3.1.1.1 Fuel combustion activities The trends and subcategory contributions to emissions from Fuel Combustion activities since 2000 are shown in Figure 3.5. Energy Industries continues to be the largest source of emissions and this due to the high carbon intensity of South Africa’s electricity production sector. The second largest source of emissions from Fuel Combustion activities is the Transport sector, which is largely dominated by emissions from road transportation. All the Fuel Combustion activity subcategories experienced a drop in emissions in 2020 due to stringent COVID19 lockdown restrictions. Figure 3.5: Trends and subcategory contributions to fuel combustion activity emissions in South Africa, 2000 – 2022. Energy industries Emissions from Electricity and Heat Production contribute the most to the Energy Industries sub-sector with Petroleum Refining contributing the least (Figure 3.6). 55 Figure 3.6: Contributions to Energy Industries emissions, 2022 Main electricity producer (1.A.1.a) Table 3.3 shows historical emissions from electricity produced by the main electricity producer in South Africa. Emissive electricity in South Africa is mainly produced from coal, diesel and fuel oil. Table 3.3: Emission trends for the main electricity producer, 2000 - 2022 CO 2 CH 4 N 2 O Total Gg CO 2 Gg CH 4 Gg N 2 O Gg CO 2 e 2000 173 858 1.8 2.7 174 627 2001 175 475 1.8 2.7 176 251 2002 181 307 1.9 2.8 182 109 2003 194 985 2.0 3.0 195 847 2004 204 690 2.1 3.2 205 595 2005 206 211 2.1 3.2 207 122 2006 207 465 2.2 3.2 208 382 2007 228 111 2.4 3.6 229 120 2008 218 518 2.3 3.4 219 484 56 CO 2 CH 4 N 2 O Total Gg CO 2 Gg CH 4 Gg N 2 O Gg CO 2 e 2009 224 545 2.4 3.5 225 537 2010 231 371 2.4 3.6 232 393 2011 233 159 2.5 3.6 234 188 2012 243 475 2.6 3.8 244 548 2013 237 434 2.6 3.7 238 479 2014 232 085 2.5 3.6 233 106 2015 223 979 2.5 3.4 224 963 2016 217 055 2.3 3.4 218 014 2017 214 602 2.3 3.3 215 550 2018 213 292 2.3 3.3 214 233 2019 210 819 2.3 3.3 211 747 2020 202 929 2.2 3.1 203 823 2021 207 292 2.3 3.2 208 204 2022 193 930 2.1 3.0 194 783 Emissions decreased in 2022 compared to the 2021 in line with the decreasing energy availability factor and electricity production, as seen Figure 3.7. Although electricity generation from fossil fuels has been decreasing Figure 3.8 shows a steady increase in electricity generated from renewable energy. 57 Figure 3.7: Historical energy availability factors and electricity generation information 5 Figure 3.8: Annual electricity from renewable energy, 2019 - 2022 6 5 Data from Eskom’s Annual Integrated reports 2011 – 2022. Integrated results - Eskom 6 Data from CSIR Statistics of utility-scale power generation in South Africa reports, 2020 - 2023 58 Other electricity producers (1.A.1.a) Emissions from other electricity producers have varied since 2000, as shown in Table 3.4. However, since 2019 these emissions have been decreasing and this is likely due to the increasing infiltration of renewable energy in this sector. Table 3.4: Trend in emissions from other electricity producers, 2000 – 2022. CO 2 CH 4 N 2 O Total Gg CO 2 Gg CH 4 Gg N 2 O Gg CO 2 e 2000 11 271 0.12 0.17 11 320 2001 4 598 0.05 0.07 4 619 2002 4 983 0.05 0.08 5 005 2003 7 967 0.08 0.12 8 003 2004 6 248 0.07 0.10 6 276 2005 2 722 0.03 0.04 2 734 2006 3 849 0.04 0.06 3 866 2007 4 216 0.04 0.07 4 235 2008 3 502 0.04 0.05 3 518 2009 5 977 0.06 0.09 6 003 2010 1 977 0.02 0.03 1 985 2011 801 0.01 0.01 805 2012 1 076 0.01 0.02 1 080 2013 1 068 0.01 0.02 1 073 2014 1 096 0.01 0.02 1 100 2015 1 080 0.01 0.02 1 084 2016 186 0.00 0.00 187 2017 661 0.01 0.01 664 2018 661 0.01 0.01 664 2019 3 857 0.10 0.04 3 871 2020 2 227 0.04 0.03 2 236 2021 2 906 0.06 0.04 2 918 2022 2 186 0.05 0.03 2 195 Petroleum refining (1.A.1.b) Figure 3.9 shows the historical emissions from Petroleum Refining and the Manufacture of solid fuels and other energy industries. 59 Emissions from Petroleum Refining have been decreasing since 2000 mainly due to a decrease in the amount of fuel oil and petroleum coke used. The amount of refinery gas used changed marginally between 2000 and 2019. Thereafter, a significant drop in refinery gas was reported. Manufacture of solid fuels and other energy industries (1.A.1.c) Emissions from the Manufacture of solid fuels and other energy industries have not changed by more 6% annually since 2000 (see Figure 3.9). Figure 3.9: Historical emissions from Petroleum Refining (1.A.1.b) and Manufacture of solid fuels and other energy industries (1.A.1c) Manufacturing industries and construction Total emissions from fuel combustion activities in the Manufacturing industries and construction sectors are shown in Figure 3.10. Emissions from this sector include disaggregated estimates from the Iron and Steel and Non-ferrous Metals and aggregated emissions for the remainder of 1A2. Emissions from the remainder of 1A2 emissions are reported at an aggregated level because of insufficient data to disaggregate these emissions per manufacturing industry. 60 Emissions from Iron & Steel and Non-Ferrous Metals make up approximately 9% of the total 1A2 emissions. Figure 3.10: 1A2 Disaggregated emissions 2000 - 2022 Figure 3.11 shows the trends in fuel use in the Manufacturing industries and construction sector. Coal usage dominants the energy demand in industry in South Africa. 61 Figure 3.11: Manufacturing industries and construction – fuel consumption contributions There was a significant increase (34%, see Table 3.2) in emissions between 2020 and 2022 and this is aligned with the significant increase (23%) in emissions from the IPPU sector during this period. However, the increase in emissions from 1A2 is more than that of the IPPU sector because in addition to the energy demand required for IPPU sectors, 1A2 also captures the energy demand in other industry sectors, such as the food industry, that are not reported under IPPU. Transport Historical emissions from different modes of transport are shown in Table 3.2. Road transport has always contributed the most to Transport sector emissions. Figure 3.12 shows the sub-sector contributions for 2022. 62 Figure 3.12: Transport sector emission contributions, 2022 Civil Aviation, Railway Transport & Waterborne navigation (1.A.3.a, 1.A.3.c & 1.A.3.d) Emissions from civil aviation, railway transport and waterborne navigation cumulatively contribute 3 % of the Transport sector emissions in 2022. In South Africa these three modes of transportation typically use jet fuel, diesel and fuel oil. Figure 3.13 shows the annual changes of volumes sold for these fuels since 2018. In 2020, jet fuel experienced the largest decrease sales which in turn resulted in a large drop in emissions from Civil aviation. 63 Figure 3.13: Annual volume changes for fuel oil, jet fuel and diesel sold since 2018 7 Road Transport (1.A.3.b) The share of diesel consumption for Road transportation has increased significantly since 2000, from 34% to 55%. This trend has resulted in a decrease in motor gasoline consumption from 66 % to 45 % (see Figure 3.14). Figure 3.15 shows the historical price of both motor gasoline and diesel since 2018. Although the price for both fuels has been increasing, on average the diesel price has been 9% lower than the price of motor gasoline. 7 Data from SAPIA 2021 Annual Report, https://www.sapia.org.za/wp-content/uploads/2023/01/SAPIA_AR- 2021_Final_LR.pdf 64 Figure 3.14: Historical share of diesel and motor gasoline consumption for road transportation Figure 3.15: Historical fuel prices for motor gasoline and diesel 8 8 Figure 3.15 shows the simple average price for the different variations of both motor gasoline and diesel found across the country. https://www.sapia.org.za/old-fuel-prices/ 65 Other sectors Table 3.5 shows the disaggregated and total emissions from Other sectors. The Commercial/institutional and Residential sectors have both significantly contributed to emissions Other sectors. Table 3.5: Trend in emissions from other sectors, 2000 – 2022. Commercial/ Institutional Residential Agriculture/ Forestry/ Fishing/ Fish farms Total Gg CO 2 e 2000 20 604 27 550 2 416 50 570 2001 20 339 24 999 2 428 47 766 2002 20 163 22 658 2 456 45 277 2003 21 056 20 540 2 520 44 116 2004 22 537 18 415 2 574 43 526 2005 21 851 18 443 2 573 42 867 2006 9 054 16 312 2 597 27 964 2007 9 808 18 156 2 650 30 614 2008 11 301 19 778 2 691 33 769 2009 12 609 20 868 2 759 36 237 2010 7 978 8 365 2 945 19 287 2011 5 813 9 720 3 144 18 677 2012 3 666 8 777 3 074 15 517 2013 1 753 2 590 3 012 7 354 2014 1 783 2 433 2 999 7 215 2015 1 560 2 274 3 013 6 847 2016 10 487 2 164 3 060 15 711 2017 6 805 2 820 3 102 12 727 2018 13 362 2 178 3 143 18 682 2019 14 615 2 970 3 132 20 718 2020 10 985 3 165 3 044 17 194 2021 11 667 3 799 3 058 18 524 2022 12 077 3 858 3 095 19 029 Emissions from the Commercial/Institutional sector have significantly fluctuated and have overall decreased by 41 % since 2000. This decrease in emissions is aligned with the fuel consumption trends for the sector (see Figure 3.16). Although overall emissions 66 have decreased over the years, there has been an increase in diesel, other kerosene and motor gasoline consumption likely in response to increased loadshedding. Figure 3.16: Fuel consumption trends for the Commercial/Institutional sector Emissions from the Residential sector have significantly decreased steadily by 86% since 2000 and this is aligned with the energy supply trends in households seen in Figure 3.17. The number of households using electricity and other electricity (i.e. generators) has increased since 2002 and because of this the number of households using fuels for cooking have also decreased since 2002, with the exception of the use of gas which has been increasing since 2012. 67 Figure 3.17: Trends in household energy supply for cooking 9 Non-specified The Non-specified category is used to report on other Fuel Combustion activities that do not fall under the other four sectors (Energy industries, Manufacturing and construction, Transport & Other). Emissions allocated to the category are shown in Table 3.2. In 2022, this category made up 6% of the emissions from Fuel Combustion activities. 3.1.1.2 Fugitive emissions from fuels The Fugitive emissions from fuels sector in 2022 contributed 7.7% to total emissions from the Energy sector. The majority of these emissions are from the Other emissions from energy production. Table 3.6 shows total historical disaggregated emissions. Table 3.6: Trend in fugitive emissions, 2000 – 2022. Solid Fuels Oil and Natural Gas Other Emissions Total 9 Figure 3.17 shows the cooking energy supply trends of households since 2002. This data excludes the “Other” proportion (which contributes on average about 1% each year) from the StatsSA data. GHS 2020 P0318 (statssa.gov.za) 68 from Energy Production Gg CO 2 e 2000 2 028 765 31 096 33 889 2001 2 077 766 31 325 34 167 2002 2 193 972 31 325 34 489 2003 2 647 1 483 30 190 34 320 2004 2 467 1 403 32 027 35 896 2005 2 633 1 180 28 217 32 030 2006 2 658 1 152 28 057 31 867 2007 2 701 1 150 28 523 32 374 2008 2 667 1 157 27 129 30 954 2009 3 063 1 266 27 426 31 755 2010 2 917 982 27 206 31 105 2011 2 685 805 26 817 30 307 2012 2 655 659 27 821 31 135 2013 2 794 686 28 227 31 707 2014 2 765 783 27 783 31 331 2015 2 609 728 27 343 30 680 2016 2 526 744 27 548 30 818 2017 2 662 625 28 446 31 733 2018 2 697 755 27 548 31 000 2019 2 408 666 28 362 31 436 2020 3 292 434 28 381 32 108 2021 3 208 154 27 973 31 335 2022 2 293 94 26 410 28 798 Solid fuels Emissions from Solid Fuels have not varied significantly since 2000 and this is due to slight annual changes in coal production as shown in Figure 3.18. 69 Figure 3.18: Annual change in Coal Production, 2010 - 2022 10 Oil and natural gas Emissions from the Oil and natural gas industry have contributed the least (< 5%) to the Fugitive emissions from fuels sector since 2000. Additionally, emissions from the Oil and natural gas industry have been decreasing steadily. Two refineries have shut down since 2020 and although the remaining refineries still operational they have not been processing any crude oil in the recent years. Other emissions from energy production Other emissions from energy production have, on average, accounted for 89% of Fugitive emissions from fuels sector since 2000. 3.1.2 Overview of methodology and completeness Emissions for the Energy sector were estimated with a sectoral approach. Table 3.7 provides a summary of the methods and emission factors applied to each subsector. 10 Annual percentage change based on annual indexed coal production data from StatsSA. https://www.statssa.gov.za/publications/P2041/P2041May2023.pdf 70 Table 3.7: Summary of methods and emission factors for the energy sector and an assessment of the completeness of the energy sector emissions. GHG Source and sink category CO 2 CH 4 N 2 O NOx CO NM VOC SO 2 Method applied Emission factor Method applied Emission factor Method applied Emission factor 1A Fuel combustion activitie 1A1 Energy industries a. Main activity electricity and heat production T2 CS T1 DF T1 DF NE NE NE NE b. Petroleum refining T1, T2 DF, CS T1 DF T1 DF NE NE NE NE c. Manufacture of solid fuels and other energy industries T3 CS T3 CS T3 CS NE NE NE NE 1A2 Manufacturing industries and construction T1, T2 DF, CS T1 DF T1 DF NE NE NE NE 1A3 Transport a. Civil aviation T1 CS T1 DF T1 DF NE NE NE NE b. Road transportation T2 CS T1 DF T1 DF NE NE NE NE c. Railways T2 CS T1 DF T1 DF NE NE NE NE d. Water-borne navigation T2 CS T1 DF T1 DF NE NE NE NE e. Other transportation NO NO NO NO NO NO NO 1A4 Other sectors a. Commercial/ Institutional T1, T2 DF, CS T1 DF T1 DF NE NE NE NE b. Residential T1, T2 DF, CS T1 DF T1 DF NE NE NE NE c. Agriculture/ Forestry/ Fishing/ Fish farms T1, T2 DF, CS T1 DF T1 DF NE NE NE NE 1A5 Non-specified a. Stationary T1, T2 DF, CS T1 DF T1 DF NE NE NE NE b. Mobile IE IE IE IE IE IE IE 1B Fugitive emissions from fuels 1B1 Solid fuels 71 GHG Source and sink category CO 2 CH 4 N 2 O NOx CO NM VOC SO 2 Method applied Emission factor Method applied Emission factor Method applied Emission factor a. Coal mining and handling T2 CS T2 CS b. Uncontrolled combustion and burning coal dumps NE NE NE NE NE NE NE c. Solid fuel transformation T1 DF T1 DF T1 DF NE T1 NE NE 1B2 Oil and natural gas a. Oil T3 T3 T3 NE NE NE b. Natural gas T3 T3 T3 NE NE NE 1B3 Other emissions from energy production T3 T3 NO NE NE NE NE 1C Carbon dioxide transport and storage 1C1 Transport of CO 2 a. Pipelines NE b. Ships NE c. Other NE 1C2 Injection and storage a. Injection NE b. Storage NE 1C3 Other NE 3.1.3 Improvements and recalculations Recalculations were performed for the Energy sector because of the following reasons: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities. 72 • Estimates from 1A2 were recalculated based on data from SAGERS and disaggregated to include emissions from 1A2a Iron & Steel, 1A2b Non-ferrous Metals and 1A2-ab the remainder of 1A2. • Inclusion of emissions from charcoal and coke production under 1B1c for the entire time-series. • Recalculation of 2019 and 2020 emissions estimates, where applicable 11 , based on the updated activity data from the energy balance. The improvements mentioned above resulted in the current inventory being 2.3% lower than the previous inventory estimates. Figure 3.19: Recalculations for the Energy sector between 2000 and 2022. 3.1.4 Key categories in the energy sector The key categories for the Energy sector as determined by the level (L) and trend (T) assessment are shown in Table 3.8. 11 This only affects categories 1A4 and 1A5 73 The following categories are new to the top 30 key categories indicated in Table ES 3: • 1A3a – Domestic aviation - CO 2 • 1A1 – Energy industries – gaseous fuels CO 2 • 1B3 – Other emissions from energy production – CH 4 • 1A5 Other – liquid fuels – CO 2 These additions to the key category list are mainly due to the observed trend of the respective emissions, i.e. a significant decrease, since 2000. Table 3.8: Key categories identified in the Energy sector. IPCC code IPCC Category GHG # Criteria 1.A.3.b Road Transportation – Liquid Fuels CO 2 L,T 1.A.1 Energy Industries – Solid Fuels CO 2 L,T 1.A.5 Other – Solid Fuels CO 2 L,T 1.B.3 Other emissions from energy production CO 2 L,T 1.A.4 Other Sectors – Liquid Fuels CO 2 L,T 1.A.3.a Domestic Aviation CO 2 T 1.A.1 Energy Industries – Gaseous Fuels CO 2 T 1.A.5 Other – Liquid Fuels CO 2 T 1.A.4 Other Sectors – Solid Fuels CO 2 L,T 1.B.3 Other emissions from energy production CH 4 L 1.A.2 Manufacturing Industries and Construction – Gaseous Fuels CH 4 L,T 1.B.2 Oil and Natural Gas CO 2 T 1.B.1.a Coal mining & handling CH 4 L 1.A.4 Other Sectors – Gaseous Fuels CO 2 T 1.A.2 Manufacturing Industries and Construction – Solid Fuels CO 2 L,T 1.A.1 Energy Industries – Liquid Fuels CO 2 L,T 1.A.2 Manufacturing Industries and Construction – Liquid Fuels CO 2 L,T 3.1.5 QA/QC process and verification Generic QA/QC measures were conducted for the Energy sector calculation file, as indicated in Table 3.9. 74 Table 3.9: QA/QC measures for the Energy sector inventory QA/QC principle Check Accuracy Activity data source Accuracy Correct units Accuracy Unit carry through Accuracy Method validity Accuracy Calculations check Accuracy Uncertainties Accuracy Double counting Accuracy Correct GWP Accuracy Notation keys Accuracy Calorific values Accuracy Oxidation factors Accuracy Carbon content Accuracy, Completeness, Consistency, Transparency Trend check Accuracy, Transparency Emission factor applicability Accuracy, Transparency, Consistency, Completeness Recalculations Completeness, Comparability Sub-category completeness Consistency Time series consistency Transparency Documentation Transparency, Completeness, Consistency, Accuracy Colour coding Accuracy, Comparability Cross check data Accuracy Spot checks Transparency, Consistency Data source referencing Transparency Links to source data Transparency Raw primary data Accuracy QA review 3.1.6 Planned improvements Improvements planned for the Energy sector are as follows: • Moving to country-specific CO 2 factors for key solid fuels. A study was initiated in 2023 to determine the country-specific carbon contents, NCVs and emission factors for commonly used solid fuels. • Improving activity data for fuel wood consumption in different sub-sectors. • Including emissions from abandoned mines and spontaneous combustion. • Disaggregating the uncertainty assessment to align with the disaggregation used for key category analysis. 75 Fuel combustion (1.A) 3.2.1 Comparison between sectoral and reference approach The emissions reported for Fuel combustion activities are estimated using the Sectoral Approach. The Reference Approach was also used estimate emissions and the comparison of the two approaches is shown in Figure 3.20. The comparison is done until 2020 in accordance with the latest energy balance data from the DMRE. The Reference Approach resulted in higher estimates than the Sectoral Approach, as was the case with previous inventories. On average the Sectoral Approach has resulted in estimates that are 19% lower since 2000. Figure 3.20: Comparisons between the reference and sectoral approach of determining the CO 2 emissions for the energy sector for South Africa. Appendix D shows the comparison between the Reference and Sectoral Approach for solid, fuel and gaseous fuels consumption between 2000 and 2020. The Sectoral Approach, on average, shows consumption that is 30% lower for solid fuels, 6% higher 76 for liquid fuels and 20% lower for gaseous fuels. The difference in consumption and estimates for both approaches is due to: • Missing information on stock changes that may occur at the final consumer level. The relevance of consumer stocks depends on the method used for the Sectoral Approach. • Unrecorded consumption of gas or other fuels may lead to an underestimation of the Sectoral Approach. • The treatment of transfers and reclassifications of energy products may cause a difference in the Sectoral Approach estimation since different net calorific values and emission factors may be used depending on how the fuel is classified. • Activity data on liquid fuels in the Sectoral Approach particularly for energy industries is sourced directly from the companies involved and has been reconciled with other publicly available datasets. • Inconsistencies on the sources of activity data within the time series and in some cases the application of extrapolation. • The misallocation of the quantities of fuels used for conversion into derived products (other than power or heat) or quantities combusted in the energy sector. 3.2.2 Feedstock and non-energy use of fuels There are cases where fuels are used as raw materials in production processes. For example, in iron and steel production, coal is used as a feedstock in the manufacture of steel. The 2006 IPCC Guidelines emphasize the significance of separating energy and process emissions to prevent double counting the industrial and energy sectors. Therefore, to avoid double counting, coal used for metallurgical purposes has been accounted for under the IPPU sector. Information on feed stocks and non-energy use of fuels has been sourced from the national energy balance tables. The sources considered include coal used in iron and steel production, the use of fuels as solvents, lubricants and waxes, and the use of bitumen in road construction. 3.2.3 Common Data used for Fuel Combustion Activities This section outlines the net calorific values, densities (Table 3.10) and emission factors (Table 3.11) used to estimate emissions from fuels used in under the various Fuel Combustion subcategories. 77 Table 3.10: Net calorific values for solid, liquid and gaseous fuels 12 Fuel Net calorific value Unit Density (kg/l) Solid fuels Coal: Eskom Average 20.1 MJ/kg Coal: General purpose 24.3 MJ/kg Coal: Coking 30.1 MJ/kg Coke 27.9 MJ/kg Biomass (wood dry typical) 17 MJ/kg Wood charcoal 31 MJ/kg Liquid fuels Paraffin 37.5 MJ/l 0.765 Diesel 35.5 MJ/l 0.826 Heavy Fuel Oil 43 MJ/kg 0.994 Fuel Oil 180 42 MJ/kg 0.99 Petrol 32.5 MJ/l 0.741 Avgas (100LL) 33.9 MJ/l 0.714 Jet Fuel (Jet-A1) 37.5 MJ/l 0.79 Gaseous fuels LPG 46.29 MJ/Nm 3 0.555 Sasol gas (MRG) 33.6 MJ/Nm 3 Natural gas 38.1 MJ/Nm 3 Blast furnace gas 3.1 MJ/Nm 3 Refinery gas 20 MJ/Nm 3 Coke oven gas 17.3 MJ/Nm 3 Table 3.11: IPCC default and country-specific emission factors for fuel combustion 13 Fuel CO 2 CH 4 N 2 O DF (Tier 1) CS (Tier 2) DF (Tier 1) DF (Tier 1) Liquid Motor gasoline 69 300 72 430 3 0.6 Aviation gasoline 70 000 65 752 3 0.6 Jet gasoline 70 000 65 752 3 0.6 Jet kerosene 71 500 73 463 3 0.6 Other kerosene 71 900 64 640 3 0.6 Gas/Diesel oil 74 100 74 638 3 0.6 Residual fuel oil 77 400 73 090 3 0.6 Liquified petroleum gases 63 100 64 852 1 0.1 Bitumen 80 700 NA 3 0.6 Petroleum coke 97 500 NA 3 0.6 Solid Anthracite 98 300 NA 1 1.5 Coking coal 94 600 NA 1 1.5 Other bituminous coal 94 600 NA 1 1.5 Sub-bituminous coal 96 100 96 250 1 1.5 Coke oven coke and lignite coke 107 000 NA 1 1.5 Gas coke 107 000 NA 1 0.1 12 Values are from the South African Petroleum Industry Association (SAPIA) and from the study Development of country- specific CO 2 emissions factors for key liquid and gaseous fuels in South Africa https://energyjournal.africa/article/view/13592 13 Country-specific emission factors for liquid and gaseous fuels are from the study Development of country-specific CO 2 emissions factors for key liquid and gaseous fuels in South Africa https://energyjournal.africa/article/view/13592 78 Fuel CO 2 CH 4 N 2 O DF (Tier 1) CS (Tier 2) DF (Tier 1) DF (Tier 1) Gas works gas 44 400 NA 1 0.1 Coke oven gas 44 400 NA 1 0.1 Blast furnace gas 260 000 NA 1 0.1 Oxygen steel furnace gas 182 000 NA 1 0.1 Wood/wood waste 112 000 NA 30 4 Other primary solid biomass 100 000 NA 30 4 Charcoal 112 000 NA 30 4 Gases Natural gas 56 100 55 709 1 0.1 South Africa’s country-specific CO 2 emission factors were higher than the IPCC default emission factors for motor gasoline (5%), jet kerosene (3%), diesel (1%), LPG (3%) and sub-bituminous coal (0.2%). On the other hand, the country-specific CO 2 emission factors were lower than the default emission factors for aviation gasoline (6%), jet gasoline (6%), other kerosene (10%) and residual fuel oil (6%). The country-specific CO 2 emission factors in Table 3.11 were developed from studies that included collecting fuel samples from across the country and during different seasons, where applicable. 14 Hence, even though these emission factors may differ from the IPCC default emissions factors, they more accurately represent the fuels used in the country. 3.2.4 Energy industries (1.A.1) 3.2.4.1 Category description The Energy Industries sub-category includes fuel combustion activities from the following industries: • 1A1a Electricity and heat production – In South Africa’s inventory this is further disaggregated into main electricity producer and other electricity producers and hence includes all emissive electricity production plants that feed into the national grid. Emissions from auto-producers are accounted for under the respective main industry for which the fuel combustion occurs. • 1A1b Petroleum refining – this includes all fuel combustion emissions from crude oil refining. Emissions from this activity have been decreasing due to limited oil reserves and the closure of major refineries in the recent years. 14 LPG is the only exception due to limitations on analysis. More details on this can be found in the journal article for the study. https://energyjournal.africa/article/view/13592 79 • 1A1c Manufacture of solid fuels and other energy industries – this includes fuel combustion emissions associated with the production of secondary and tertiary fuels. 3.2.4.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The activities shown in Table 3.12 were indicated as key categories in the 2000-2020 inventory. Emissions from 1A1a Electricity and Heat Production (solid) and 1A1c Manufacture of solid fuels and other energy industries (liquid) were estimated using higher tier methods for the 2000-2020 inventory. Hence, no change in methodology was required for these sub-categories for the 2000-2022 inventory. However, to ensure that good practise was followed, changes in methodology were required for emissions from 1A1a Electricity and Heat Production (liquid) and 1A1b Petroleum Refining (gas). These changes are indicated in Table 3.12. The only deviation is the estimation of emissions from refinery gas under 1A1b Petroleum Refining, which still uses Tier 1 and the IPCC default emission factor. This deviation is due to a lack of country specific emission factors. Table 3.12: Methodological details - Energy Industries 2000-2020 Key category 2000-2020 inventory 2000-2022 inventory Notes Method Emission Factor Method Emission Factor 1A1a Electricity and heat production (solid, CO 2 ) T2 CS T2 CS NA 1A1a Electricity and heat production (liquid, CO 2 ) T1 DF T2 CS Emissions from all liquid fuels estimated using CS parameters. 1A1b Petroleum refining (gas, CO 2 ) T1 DF T1, T2 DF, CS Natural gas: T2, CS MRG: T2, CS Refinery gas: T1, DF 1A1c Manufacture of solid fuels and other energy industries (liquid, CO 2 ) T3 NA T3 NA NA Activity data 80 Table 3.13 shows the type and sources of activity data used to estimate emissions from 1A1 Energy industries. Majority of the activity data for the Energy industries sub-category is now retrieved from the SAGERS with the assistance of the NGERs. Table 3.14 shows the trend in fuel consumption for 1A1a and 1A1b. Table 3.13: Activity data used for 1A1 Energy Industries Sub-category Activity Data Source 1A1a Electricity & heat production Fuel consumption for both main and other electricity producers Electricity producers SAGERS NCVs & densities SAGERS Kornelius et al 2023 1A1b Petroluem refining Fuel consumption Refineries SAGERS NCVs & densities Kornelius et al 2023 1A1c Manufacture of solid fuels & other energy industries NA – Emissions sourced from Producers SAGERS Table 3.14: Trend in fuel consumption for the various categories in the energy industry sector, 2000 – 2022. Public electricity producer Other electricity producer Petroleum refining Fuel consumption (TJ) 2000 1 806 317 117 430 59 638 2001 1 823 119 47 910 57 599 2002 1 883 709 51 923 50 680 2003 2 025 822 83 014 57 487 2004 2 126 649 65 100 53 292 2005 2 142 682 28 363 51 610 2006 2 155 477 40 100 55 121 2007 2 369 988 43 928 56 073 2008 2 271 791 36 489 57 870 2009 2 335 101 62 276 56 523 2010 2 406 936 20 594 52 520 2011 2 426 965 8 346 50 235 2012 2 537 365 11 206 51 049 2013 2 477 632 11 129 51 890 2014 2 423 731 11 414 51 504 2015 2 343 934 11 250 51 118 81 Public electricity producer Other electricity producer Petroleum refining Fuel consumption (TJ) 2016 2 259 087 1 943 50 731 2017 2 233 426 6 887 50 345 2018 2 221 443 6 887 50 147 2019 2 199 965 46 215 49 952 2020 2 116 465 25 074 52 844 2021 2 165 560 32 941 35 140 2022 2 028 038 25 307 17 065 Emission factors The emission factors used are shown in Table 3.11. Where a country-specific emission factor is available, it was used to estimate emissions from that fuel. 3.2.4.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from Energy Industries is shown in Table 3.15. Table 3.15: Uncertainty for the Energy Industries category. Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1A1a Electricity generation – liquid 5 IPCC 2006 7 IPCC 2006 1A1a Electricity generation – solid 5 IPCC 2006 7 IPCC 2006 1A1b Petroleum refining – liquid 5 IPCC 2006 7 IPCC 2006 1A1b Petroleum refining – gaseous 5 IPCC 2006 7 IPCC 2006 1A1c Manufacture of solid fuels – liquid 5 IPCC 2006 7 IPCC 2006 CH 4 1A1a Electricity generation – liquid 5 IPCC 2006 75 IPCC 2006 1A1a Electricity generation – solid 5 IPCC 2006 75 IPCC 2006 1A1b Petroleum refining – liquid 5 IPCC 2006 75 IPCC 2006 1A1b Petroleum refining – gaseous 5 IPCC 2006 75 IPCC 2006 1A1c Manufacture of solid fuels – liquid 5 IPCC 2006 75 IPCC 2006 N 2 O 1A1a Electricity generation – liquid 5 IPCC 2006 75 IPCC 2006 1A1a Electricity generation – solid 5 IPCC 2006 75 IPCC 2006 1A1b Petroleum refining – liquid 5 IPCC 2006 75 IPCC 2006 1A1b Petroleum refining – gaseous 5 IPCC 2006 75 IPCC 2006 1A1c Manufacture of solid fuels – liquid 5 IPCC 2006 75 IPCC 2006 82 3.2.4.4 Time-series consistency 1A1a Electricity and heat production The time-series for 1A1a Electricity and heat production is incomplete with regards to fuel consumption data for diesel from other electricity producers prior to 2019. In the last two years diesel has not contributed more than 0.6% to the total fuel consumption for other electricity producers. Additionally, the time-series is not consistent because from 2019 onwards data from the SAGERS is used as it is regarded to be more accurate. 1A1b Petroleum refining The time-series for 1A1b Petroleum refining is incomplete with regards to fuel consumption data for diesel, natural gas, MRG and LPG prior to 2019. In the last two years these fuels have not contributed more than 8% to the total fuel consumption for the sub-category. Additionally, the time-series is not consistent because from 2019 onwards data from the SAGERS is used as it is regarded to be more accurate. 1A1c Manufacture of solid fuels and other energy industries The time-series for 1A1c Manufacture of solid fuels and other energy industries is complete and consistent from 2000-2022. 3.2.4.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.2.4.6 Category specific improvements and recalculations Recalculations were performed for the Energy industries sector because of the following improvement: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities. 3.2.4.7 Category specific planned improvements 83 Improvements planned specifically for the Energy Industries sector are as follows: • Completing the time-series for: o diesel consumption from other electricity producers prior to 2019 for 1A1a Electricity and Heat Production. o diesel, natural gas, MRG and LPG consumption prior to 2019 for 1A1b Petroleum refining. 3.2.5 Manufacturing industries and construction (1.A.2) 3.2.5.1 Category description The Manufacturing industries and construction sub-category includes fuel combustion activities from the industries shown in Table 3.16. In previous inventories emissions from this sub-category were reported at an aggregated level due to lack of information to disaggregate the fuel use. Table 3.16 also shows the new disaggregation used for reporting the 2000 – 2022 inventory. Table 3.16: Disaggregation of Manufacturing industries and construction category for reporting Reporting group Activities included 1A2a 1A2a Iron & Steel 1A2b 1A2b Non-ferrous Metals 1A2-ab 1A2c Chemicals 1A2d Pulp, Paper & Print 1A2e Food Processing, Beverages & Tobacco 1A2f Non-metallic Minerals 1A2g Transport Equipment 1A2h Machinery 1A2i Mining & Quarrying 1A2j Wood & Wood Products 1A2k Construction 1A2l Textile & Leather 1A2m Brick Manufacturing 1A2n Manufacture of ceramic products 84 3.2.5.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The activities shown in Table 3.17 were indicated as key categories in the 2000-2020 inventory. To ensure that good practise was followed, changes in methodology were required for emissions from all fuel types used in the Manufacturing industries and construction sector. However, the move to higher reporting tier was only possible for the liquid and gaseous fuels indicated in Table 3.11 with country-specific emission factors. Hence, there was a deviation with emissions estimated from other bituminous coal, biomass and coal tar due to lack of country-specific emission factors. Table 3.17: Methodological details for the Manufacturing industries and construction category. 2000-2020 Key category 2000-2020 inventory 2000-2022 inventory Notes Method Emission Factor Method Emission Factor 1A2 Manufacturing industries & construction (solid, CO 2 ) T1 DF T1 DF NA 1A2 Manufacturing industries & construction (liquid, CO 2 ) T1 DF T1, T2 DF, CS Coal Tar: T1, DF All others: T2, CS 1A2 Manufacturing industries & construction (gas, CO 2 ) T1 DF T2 CS NA Activity data Fuel consumption data for the Manufacturing industries and construction sector is taken from SAGERS from 2020 to 2022 and calculated from 2000-2019 based on SAGERS data and the Fuel Consumption Study. Table 3.18 shows the fuel consumption trend for this sector. 85 Table 3.18: Fuel consumption (TJ) in the manufacturing industries and construction category, 2000 – 2022. 15 Diesel LPG NG Coal OK Petrol HFO MRG Bio Total (TJ) 2000 14 742 1 399 26 710 186 895 432 284 687 8 903 52 513 292 565 2001 15 897 1 377 27 397 190 082 340 310 714 9 132 52 631 297 879 2002 17 572 1 367 29 020 197 860 269 346 752 9 673 53 940 310 797 2003 18 173 1 234 28 118 193 902 227 356 729 9 373 51 561 303 674 2004 19 914 1 147 29 720 201 606 301 378 741 9 907 52 401 316 112 2005 21 522 1 011 32 792 204 047 524 384 747 10 931 53 482 325 440 2006 23 107 827 38 731 206 229 903 390 795 12 910 56 529 340 421 2007 22 618 617 44 093 211 501 1 217 377 869 14 698 58 169 354 159 2008 19 948 435 41 040 221 143 1 192 328 935 13 680 54 440 353 142 2009 17 084 288 33 500 196 613 1 004 249 877 11 167 43 401 304 183 2010 20 740 299 39 014 194 689 1 194 267 982 13 005 47 957 318 147 2011 24 319 336 45 460 186 577 1 326 288 1 051 15 153 52 853 327 362 2012 27 028 363 50 089 180 869 1 362 300 1 036 16 696 56 997 334 740 2013 28 701 376 52 315 178 419 1 417 309 979 17 438 59 023 338 980 2014 29 242 379 52 841 178 613 1 454 306 913 17 614 58 325 339 687 2015 28 617 373 52 494 181 544 1 457 293 867 17 498 56 290 339 432 2016 27 476 366 52 045 186 114 1 453 277 934 17 348 55 934 341 948 2017 25 438 354 51 412 186 102 1 425 258 1 098 17 137 56 829 340 054 2018 22 822 348 51 999 188 114 1 418 244 1 199 17 333 59 290 342 768 2019 22 568 344 51 420 186 020 1 403 241 1 185 17 140 58 630 338 952 2020 19 641 2 106 53 916 147 643 1 242 218 1 580 18 106 51 154 295 731 2021 23 691 2 059 64 986 174 905 1 471 259 1 827 21 217 60 802 351 509 2022 26 500 2 310 66 140 194 761 1 649 290 2 379 31 539 68 484 395 918 Emission factors The emission factors used are shown in Table 3.11. Where a country-specific emission factor is available, it was used to estimate emissions from that fuel. 3.2.5.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from Energy Industries is shown in Table 3.19. 15 LPG – Liquified petroleum gases, NG – natural gas, Coal – other bituminous coal, OK – other kerosene, HFO – residual fuel oil, MRG – Sasol methane rich gas, Bio - biomass 86 Table 3.19: Uncertainty for the Manufacturing industries and construction category. Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1A2 Manufacturing industries and construction – liquid 10 IPCC 2006 7 IPCC 2006 1A2 Manufacturing industries and construction – solid 10 IPCC 2006 7 IPCC 2006 1A2 Manufacturing industries and construction – gaseous 10 IPCC 2006 7 IPCC 2006 CH 4 1A2 Manufacturing industries and construction – liquid 10 IPCC 2006 75 IPCC 2006 1A2 Manufacturing industries and construction – solid 10 IPCC 2006 75 IPCC 2006 1A2 Manufacturing industries and construction – gaseous 10 IPCC 2006 75 IPCC 2006 N 2 O 1A2 Manufacturing industries and construction – liquid 10 IPCC 2006 75 IPCC 2006 1A2 Manufacturing industries and construction – solid 10 IPCC 2006 75 IPCC 2006 1A2 Manufacturing industries and construction – gaseous 10 IPCC 2006 75 IPCC 2006 3.2.5.4 Time-series consistency The time-series for 1A2 Manufacturing industries and construction is complete. 3.2.5.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.2.5.6 Category-specific recalculations Recalculations were performed for the Manufacturing industries and construction sector because of the following improvements: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities. • 1A2a and 1A2b was calculated separately and hence this led to a recalculation of the entire time-series to accommodate this improvement. 3.2.5.7 Category-specific planned improvements Improvements planned specifically for the Manufacturing industries and construction sector are as follows: • Disaggregating the remainder of the 1A2. 87 3.2.6 Transport (1.A.3) 3.2.6.1 Category description The Transport sub-category includes fuel combustion activities from the following types of transportation: • 1A3a Civil aviation – domestic aviation with international aviation indicated as a memo item. • 1A3b Road transportation – this includes the use of agricultural vehicles on paved roads. • 1A3c Railways – this includes both freight and passenger travel. • 1A3d Waterborne navigation – this includes navigation bunkers as memo line items and includes fishing vessels. 3.2.6.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The activities shown in Table 3.20 were indicated as key categories in the 2000-2020 inventory. To ensure that good practise was followed, changes in methodology were required for emissions from all fuels used in the Transport sector. The country-specific CO 2 emission factors indicated Table 3.11 were used to move to Tier 2 report for the Transport sector. Table 3.20: Methodological details for the Transport category. 2000-2020 Key category 2000-2020 inventory 2000-2022 inventory Method Emission Factor Method Emission Factor 1A3b Road transport (liquid, CO 2 ) T1 DF T2 CS 1A3d Waterborne navigation (liquid, CO 2 ) T1 DF T2 CS Activity data Table 3.21 shows the type and sources of activity data used to estimate emissions from 1A3 Transport. 88 Table 3.21: Activity data used for 1A3 Transport Sub-category Activity Data Source 1A3a Civil aviation Fuel consumption Fuel consumption study 1A3b Road Transport VKT for Fuel consumption estimation Fuel consumption study 1A3c Railways Fuel consumption SAPIA & SAGERS 1A3d Water-borne navigation Fuel consumption Fuel consumption study Figure 3.21 shows the fuel consumption contribution per sub-category to the total Transport sector fuel consumption from 2000-2022. The majority of the fuel consumption is as a result of road transportation. Figure 3.21: Fuel consumption for the transport sector (1A3), 2000 – 2022. 16 Emission factors 16 AvGas – aviation gasoline, JK – jet kerosene, MG – motor gasoline, OK – other kerosene, RFO – residual fuel oil, NG – natural gas, CA – civil aviation, RT – road transportation, RW- railways , WBN – waterborne navigation. Fuel consumption for international aviation and international bunkers are not included. 89 The emission factors used are shown in Table 3.11. Where a country-specific emission factor is available, it was used to estimate emissions from that fuel. 3.2.6.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from the Transport sector is shown in Table 3.22. Table 3.22: Uncertainty for South Africa’s transport emission estimates. Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1A3a Civil aviation – liquid 5 IPCC 2006 1.5 IPCC 2006 1A3b Road liquid 5 IPCC 2006 2 IPCC 2006 1A3c Railways liquid 5 IPCC 2006 2 IPCC 2006 1A3d Waterborne navigation – liquid 5 IPCC 2006 3 IPCC 2006 CH 4 1A3a Civil aviation – liquid 5 IPCC 2006 50 IPCC 2006 1A3b Road liquid 5 IPCC 2006 9 IPCC 2006 1A3c Railways – liquid 5 IPCC 2006 9 IPCC 2006 1A3d Waterborne navigation – liquid 5 IPCC 2006 50 IPCC 2006 N 2 O 1A3a Civil aviation – liquid 5 IPCC 2006 50 IPCC 2006 1A3b Road liquid 5 IPCC 2006 72 IPCC 2006 1A3c Railways - liquid 5 IPCC 2006 72 IPCC 2006 1A3d Waterborne navigation – liquid 5 IPCC 2006 50 IPCC 2006 3.2.6.4 Time-series consistency The time-series for all four sub-categories are complete and consistent. 3.2.6.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.2.6.6 Category-specific improvements and recalculations Recalculations were performed for the Transport sector because of the following improvement: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. 90 Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities. 3.2.6.7 Category-specific planned improvements Improvements planned specifically for the Transport sector is as follows: • Updating the VKT and fuel consumption studies. 3.2.7 Other sectors (1.A.4) 3.2.7.1 Category description The Other sectors sub-category includes fuel combustion activities from the following industries: • 1A4a Commercial/Institutional – includes all fuel combustion in commercial and institutional buildings. • 1A4b Residential – includes all fuel combustion from households. • 1A4c Agriculture/Forestry/Fishing/Fish Farms – this includes fuel combustion from agriculture, forestry, fishing and fish farms. 3.2.7.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The activities shown in Table 3.23 were indicated as key categories in the 2000-2020 inventory. To ensure that good practise was followed, changes in methodology were required for the sub-sector key categories. Changes were implemented for the 2000- 2022 inventory for liquid fuels used in the 1A4b residential and 1A4c agriculture/forestry/fishing/fish farms sectors. The deviation is with the estimation of emissions from solid fuels used in the 1A4a commercial/institutional and 1A4b residential sectors. This deviation is due to a lack of country specific emission factors for solid fuels. 91 Table 3.23: Methodological details for the Other sectors category. 2000-2020 Key category 2000-2020 inventory 2000-2022 inventory Method Emission Factor Method Emission Factor 1A4a Commercial/Institutional (solid, CO 2 ) T1 DF T1 DF 1A4b Residential (solid, CO 2 ) T1 DF T1 DF 1A4b Residential (liquid, CO 2 ) T1 DF T2 CS 1A4c Agriculture/Forestry/Fishing/Fish Farms (liquid, CO 2 ) T1 DF T2 CS Activity data Table 3.24 shows the type and sources of activity data used to estimate emissions from 1A4 Other Sectors. Table 3.24: Activity data used for 1A4 Other Sectors Sub-category Activity Data Source 1A4a Commercial/Institutional Fuel consumption Energy balance 1A4b Residential VKT for Fuel consumption estimation Energy balance & Fuel consumption study 1Ac Agriculture/Forestry/Fishing/Fish farms Fuel consumption Fuel consumption study Figure 3.22 and Figure 3.23 show the fuel consumption trends for the 1A4 Other sectors sub-categories, since 2000. 92 Figure 3.22: Fuel consumption for the 1A4b residential category, 2000-2022 Figure 3.23: Fuel consumption for the 1A4c agriculture/forestry/fishing/fish farms category, 2000-2022 93 Emission factors The emission factors used are shown in Table 3.11. Where a country-specific emission factor is available, it was used to estimate emissions from that fuel. 3.2.7.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from Other sectors is shown in Table 3.25. Table 3.25: Uncertainty for South Africa’s Other sectors emission estimates. Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1A4 Other sectors – liquid 10 IPCC 2006 7 IPCC 2006 1A4 Other sectors – solid 10 IPCC 2006 7 IPCC 2006 1A4 Other sectors – gaseous 10 IPCC 2006 7 IPCC 2006 CH 4 1A4 Other sectors – liquid 10 IPCC 2006 75 IPCC 2006 1A4 Other sectors – solid 10 IPCC 2006 75 IPCC 2006 1A4 Other sectors – gaseous 10 IPCC 2006 75 IPCC 2006 N 2 O 1A4 Other sectors – liquid 10 IPCC 2006 75 IPCC 2006 1A4 Other sectors – solid 10 IPCC 2006 75 IPCC 2006 1A4 Other sectors – gaseous 10 IPCC 2006 75 IPCC 2006 3.2.7.4 Time-series consistency The time-series for 1A4 Other sectors is complete. 3.2.7.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.2.7.6 Category-specific recalculations Recalculations were performed for Other sectors because of the following improvement: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities. 94 3.2.7.7 Category-specific planned improvements There are no category specific improvements planned for Other sectors, other than the use of country-specific emission factors for solid fuels. 3.2.8 Non-specified sectors (1.A.5) 3.2.8.1 Category description The Non-specified sectors sub-category includes fuel combustion activities from the following industries: • 1A5a Stationary – includes all fuel combustion not allocated in the other Fuel Combustion activities. • 1A5b Mobile– no emissions are estimated for this sub-category. 3.2.8.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The only key category identified for the Non-specified sectors in the 2000-2020 inventory was 1A5a stationary (solid, CO 2 ). In the 2000-2020 inventory emissions from this key category were estimated using Tier 1 and default emission factors. This is the same in this inventory and hence this deviates from good practice. This deviation is due to a lack of country specific emission factors for solid fuels. Activity data Fuel consumption data for the Non-specified sectors is taken from the energy balance. Figure 3.24 shows the fuel consumption trends for 1A5 Non-specified sectors since 2000. 95 Figure 3.24: Fuel consumption for 1A5 Non-specified sectors, 2000-2022 Emission factors The emission factors used are shown in Table 3.11. Where a country-specific emission factor is available, it was used to estimate emissions from that fuel. 3.2.8.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from Non-specified sectors is shown in Table 3.26. Table 3.26: Uncertainty for South Africa’s Non-specified sectors emission estimates. Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1A5 Other sectors – liquid 5 IPCC 2006 7 IPCC 2006 1A5 Other sectors – solid 5 IPCC 2006 7 IPCC 2006 1A5 Other sectors – gaseous 5 IPCC 2006 7 IPCC 2006 CH 4 1A5 Other sectors – liquid 5 IPCC 2006 75 IPCC 2006 1A5 Other sectors – solid 5 IPCC 2006 75 IPCC 2006 96 Gas Sectors Activity data uncertainty Emission factor uncertainty % Source % Source 1A5 Other sectors – gaseous 5 IPCC 2006 75 IPCC 2006 N 2 O 1A5 Other sectors – liquid 5 IPCC 2006 75 IPCC 2006 1A5 Other sectors – solid 5 IPCC 2006 75 IPCC 2006 1A5 Other sectors – gaseous 5 IPCC 2006 75 IPCC 2006 3.2.8.4 Time-series consistency The time-series is complete this category. 3.2.8.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.2.8.6 Category-specific recalculations Recalculations were performed for Non-specified sectors because of the following improvement: • The use of country-specific emission factors for commonly used liquid and gas fuels. The emission factors result from a study that was completed in 2022. Emissions from the entire time-series were recalculated for the applicable Fuel combustion activities 3.2.8.7 Category-specific planned improvements There are no category specific improvements planned for Non-specified sectors, other than the use of country-specific emission factors for solid fuels. Fugitive emissions from fuels (1.B) 3.3.1 Solid fuels (1.B.1) 3.3.1.1 Category description The Solid Fuels sub-category includes fugitive emissions from the following activities: 97 • 1B1a Coal mining and handling – this includes fugitive emissions from the extraction, processing, storage and transport of coal from surface and underground mines. • 1B1c Solid fuel transformation – this includes all fugitive emissions associated with the manufacture of charcoal and coke. This sub-category does not include emissions from abandoned mines or uncontrolled combustion and burning coal dumps. 3.3.1.2 Methodological issues Table 3.7 indicates the type of methods and emission factors used for all categories under the Energy sector, including at sub-category level. The only key category identified for the 1B1 Solid fuels sector in the 2000-2020 inventory was 1B1a Coal mining and handling (CH 4 ). In the 2000-2020 inventory emissions from this key category were estimated using Tier 2 and country-specific emission factors. Hence, no change in the methodology was required for the 2000-2022 to ensure good practise was implemented. Activity data Table 3.27 shows the type and sources of activity data used to estimate emissions from 1B1 Solid fuels. Table 3.27: Activity data used for 1B1 Solid Fuels Sub-category Activity Data Source 1B1a Undergound and surface mines Amount of coal produced from both underground and surface mines SAMI Minerals Council SA 1B1c Solid fuel transformation - charcoal Amount of charcoal produced FAO 1B1c Solid fuel transformation - coke Amount of coke produced Amount of iron produced locally SAGERS Minerals Council SA Table 3.28 shows the production figures for coal (from surface and underground mines) and charcoal since 2000. Table 3.28: Amount of coal and charcoal produced, 2000 – 2022. 17,18 17 Coal production data calculated based on information published in the SAMI Annual reports and Minerals Council SA Facts & Figures reports. 18 Charcoal data from the FAO 98 Surface Underground Charcoal Coke Amount produced (tonnes) 2000 174 918 853 106 308 704 41 000 2 451 940 2001 179 330 802 108 990 110 41 000 2 547 566 2002 189 951 547 115 444 975 41 000 2 646 801 2003 191 768 929 116 549 508 200 600 2 891 467 2004 193 506 539 117 605 558 127 300 2 975 656 2005 193 348 574 117 509 553 188 900 2 874 759 2006 195 639 060 118 901 619 188 900 2 881 078 2007 199 587 383 121 301 252 188 900 2 970 030 2008 196 569 036 119 466 821 188 900 2 694 892 2009 175 784 200 141 515 800 188 900 2 003 359 2010 184 150 000 133 350 000 188 900 2 528 025 2011 195 727 800 120 472 200 188 900 2 356 492 2012 211 200 000 118 800 000 188 900 2 009 041 2013 204 681 600 126 518 400 188 900 2 225 078 2014 213 399 640 124 900 360 188 900 2 291 173 2015 207 716 400 116 283 600 188 900 1 798 356 2016 207 415 000 111 685 000 188 900 1 474 697 2017 212 733 600 116 066 400 209 783 1 718 018 2018 227 288 282 105 101 718 295 500 1 681 607 2019 234 234 200 99 765 800 223 195 1 813 063 2020 217 240 013 103 472 569 231 779 1 433 006 2021 200 219 454 95 365 587 220 334 1 597 100 2022 197 015 943 93 839 738 220 334 1 504 969 Emission factors 1B1a coal mining and handling Country-specific emission factors are available for CO 2 and CH 4 for surface and underground mining in South Africa (Table 3.29). Table 3.29: Country-specific emission factors for coal mining and handling. Mining method Activity GHG Emission factor (m 3 tonne -1 ) South African EF IPCC default Underground mining Coal mining CH 4 0.77 18 Post-mining (handling and transport) 0.18 2.5 Coal mining 0 1.2 99 Mining method Activity GHG Emission factor (m 3 tonne -1 ) South African EF IPCC default Surface mining Post-mining (storage and transport) 0 0.1 Underground mining Coal mining CO 2 0.077 NA Post-mining (handling and transport) 0.018 NA Surface mining Coal mining 0 NA Post-mining (storage and transport) 0 NA 1B1c solid fuel transformation Emissions from charcoal and coke production were estimated using Tier 1 and the IPCC default emission factors shown in Table 3.30, with the exception of CO 2 emissions from coke production that are based on measurements. Table 3.30: Tier 1 emission factors for charcoal and coke production Activity GHG Emission factors (kg/tonne) Source Charcoal production CO 2 1 570 IPCC 2019 Refinement CH 4 40.3 IPCC 2019 Refinement N 2 O 0.08 IPCC 2019 Refinement CO 220 IPCC 2019 Refinement Coke production CH 4 0.049 IPCC 2019 Refinement 3.3.1.3 Uncertainty The uncertainties for activity data and the emission factors used to estimate emission from Solid Fuels is shown in Table 3.31. Table 3.31: Uncertainty for 1B1 Solid fuels sector Gas Sub-category Activity data uncertainty Emission factor uncertainty % Source % Source CO 2 1B1ai1 Mining 10 IPCC 2006 63 IPCC 2006 1B1ai2 Post-mining seam gas emissions 10 IPCC 2006 50 IPCC 2006 1B1ci Charcoal production 10 IPCC 2006 60 IPCC 2006 CH 4 1B1ai1 Mining 10 IPCC 2006 63 IPCC 2006 1B1ai2 Post-mining seam gas emissions 10 IPCC 2006 50 IPCC 2006 1B1ci Charcoal production 10 IPCC 2006 121 IPCC 2006 100 Gas Sub-category Activity data uncertainty Emission factor uncertainty % Source % Source 1B1cii Coke production 10 IPCC 2006 90 IPCC 2006 N 2 O 1B1ci Charcoal production 10 IPCC 2006 163 IPCC 2006 CO 1B1ci Charcoal production 10 IPCC 2006 53 IPCC 2006 3.3.1.4 Time-series consistency The time-series is complete for coal, charcoal and coke production. 3.3.1.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.3.1.6 Category-specific recalculations No category specific recalculations were conducted for coal mining. The improvement for this sector was the inclusion of emissions from charcoal and coke production. 3.3.1.7 Category-specific planned improvements Improvements planned for the 1B1 Solid fuels sector are as follows: • Including emissions from abandoned mines and spontaneous combustion. 3.3.2 Oil and natural gas (1.B.2) 3.3.2.1 Category description The Oil and natural gas sub-category includes fugitive emissions from the following activities: • 1B2a Oil – this includes fugitive emissions from venting, flaring and all other sources associated with the exploration, production, transmission, upgrading and refining of crude oil and the distribution of crude oil products. • 1B2b Natural gas – this includes fugitive emissions from venting, flaring and all other sources associated with the exploration, production, processing, transmission, storage, upgrading and the distribution of natural gas. 101 3.3.2.2 Methodological issues No 1B2 activities were indicated as key categories in the 2000-2020 inventory. Additionally, emissions from this category are based on direct measurements. Activity data Emissions from this category are based on direct measurements from the relevant refineries. Hence, no activity data or emission factors were used to estimate emissions. Emission factors Emissions from this category are based on direct measurements. Hence, no activity data or emission factors were used to estimate emissions. 3.3.2.3 Uncertainty The uncertainty of the emission estimates (Table 3.32) is based on the assumption of a normal distribution of the emission estimates. Table 3.32: Uncertainty for 1B2 Oil and natural gas sector. Gas Sub-category Emissions uncertainty % Source CO 2 1B2 Oil & natural gas 65 SAGERS CH 4 0.005 SAGERS N 2 O 0.02 SAGERS 3.3.2.4 Time-series consistency The time-series is complete for 1B2a Oil, however the time-series is not consistent as emissions estimates are sourced from SAGERS from 2019 onwards. For 1B2b Natural gas the time-series is not complete as emissions only from 2019 onwards from SAGERS are included in the inventory. 3.3.2.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.3.2.6 Category-specific recalculations 102 No category specific recalculations were conducted for the 1B2 Oil and natural gas sector. The improvement for this sector was the inclusion of emissions from natural gas activities from 2019. 3.3.2.7 Category-specific planned improvements An improvements planned for the Oil and natural gas sector is ensuring time-series completeness for fugitive emissions from natural gas 3.3.3 Other emissions from energy production (1.B.3) 3.3.3.1 Category description The Other emissions from energy production sub-category includes fugitive emissions from the following activities: • 1B3 Coal mining and handling – this includes other fugitive emissions from energy production not included in 1B2 Oil and natural gas. 3.3.3.2 Methodological issues Carbon dioxide from Other emissions from energy production was indicated as a key category in the 2000-2020 inventory. Emissions from this sector were already based on direct measurements, hence, no methodological changes were required for the 2000- 2022 inventory to ensure alignment with good practise. Activity data Emissions from this category are based on direct measurements from the relevant producers. Hence, no activity data or emission factors were used to estimate emissions. Emission factors Emissions from this category are based on direct measurements from the relevant producers. Hence, no activity data or emission factors were used to estimate emissions. 3.3.3.3 Uncertainty The uncertainty of the emission estimates (Table 3.33) is based on the assumption of a normal distribution of the emission estimates. Table 3.33: Uncertainty for 1B3 Other emissions from energy production sector. 103 Gas Sub-category Emissions uncertainty % Source CO 2 1B3 Other emissions from energy production 0.001 SAGERS CH 4 0.001 SAGERS 3.3.3.4 Time-series consistency The times-series of emissions from 1B3 Other emissions from energy production is complete and consistent. 3.3.3.5 Category specific QA/QC and verification The QA/QC measures indicated in Table 3.9 were used for this category. 3.3.3.6 Category-specific recalculations No category specific recalculations were conducted for the 1B3 Other emissions from energy production sector. 3.3.3.7 Category-specific planned improvements There are no category-specific improvements planned for the 1B3 Other emissions from energy production sector. References 2021 Annual Report. (2023). [online] South Africa: SAPIA. Available at: https://www.sapia.org.za/wp-content/uploads/2023/01/SAPIA_AR- 2021_Final_LR.pdf. Calitz, J. and Wright, J. (2020). Statistics of utility-scale solar PV, wind and CSP in South Africa in 2019. South Africa: CSIR Energy Centre. Calitz, J. and Wright, J. (2021). Statistics of utility-scale power generation in South Africa in 2020. South Africa: CSIR Energy Centre. Data.un.org. (n.d.). UNdata | A World of Information. [online] Available at: https://data.un.org/Data.aspx?d=FAO&f=itemCode%3A1630%3BcountryCode% 3A226 Wood charcoal Production data 2000-2019. Energy.gov.za. (n.d.). Statutes & Practices | Department: Energy | REPUBLIC OF SOUTH AFRICA. [online] Available at: https://www.energy.gov.za/files/energyStats_frame.html. 104 Eskom.co.za. (n.d.). Integrated results - Eskom. [online] Available at: https://www.eskom.co.za/investors/integrated-results/ 2011 - 2022 Annual Integrated Reports. Facts and Figures Reports. (2012). [online] South Africa: Minerals Council South Africa. Available at: https://www.mineralscouncil.org.za/industry- news/publications/facts-and-figures Facts and Figures 2010, 2011 and 2021. General Household Survey 2022. (2023). [online] South Africa: Statistics South Africa. Available at: https://www.statssa.gov.za/publications/P0318/P03182022.pdf. Kornelius, G., Forbes, P., Fischer, T. and Govender, M. (2022). Determination of country- specific greenhouse gas emission factors for South African liquid and gaseous fuels. Journal of Energy in Southern Africa, 33(3), pp.1–11. doi:https://doi.org/10.17159/2413-3051/2022/v33i3a13592. Mining: Production and Sales (Preliminary). (2023). [online] South Africa: Statistics South Africa. Available at: https://www.statssa.gov.za/publications/P2041/P2041May2023.pdf. Pierce, W. and Ferreira, B. (2022). Statistics of utility-scale power generation in South Africa in 2021. South Africa: CSIR Energy Centre. Pierce, W. and Le Roux, M. (2023). Statistics of utility-scale power generation in South Africa 2022. CSIR Energy Centre. SAMI - South Africa’s Mineral Industry Annual Reports. (2018). [online] SAMI. Available at: https://www.dmr.gov.za/resources SAMI Reports 2009-2010, 2011-2012, 2012-2013, 2013-2014, 2014-2015, 2015-2016, 2016-2017. Sapia.org.za/. (n.d.). Old Fuel Prices - SAPIA. [online] Available at: https://www.sapia.org.za/old-fuel-prices/. 105 Chapter 4: Industrial Processes and Product Use (IPPU) (CRT sector 2) Sector overview The IPPU sector includes non-energy related emissions from industrial processing plants. The main emission sources are released from industrial processes that chemically or physically transform raw materials and thereby release GHGs, (e.g., ammonia products manufactured from fossil fuels), GHG emissions released during these processes are CO 2 , CH 4 , N 2 O, HFCs, PFCs and SF 6 . Emissions from the following industrial processes are included in South Africa’s IPPU sector: • Cement Production • Hydrogen Production • Lime Production • Other Chemicals • Glass Production • Production of steel from iron and scrap steel • Other Product Uses of Carbonates • Ferroalloys Production • Ammonia Production • Aluminium Production • Nitric Acid Production • Lead Production • Carbide Production • Zinc Production • Titanium Dioxide Production • Vanadium Production • Soda Ash Production • Lubricant Use • Petrochemical and carbon black production • Paraffin Wax Use • Product Uses as Substitutes for Ozone Depleting Substances HFCs and PFCs are used in many products and in refrigeration and air conditioning equipment. PFCs are also emitted because of anode effects in aluminium smelting. Therefore, the IPPU sector includes estimates of PFCs from aluminium production, and HFCs from refrigeration and air conditioning. SF 6 is also included in IPPU due to the use of electrical equipment. The estimation of GHG emissions from non-energy sources is often difficult because they are widespread and diverse. The difficulties in the allocation of GHG emissions between 106 fuel combustion and industrial processes arise when by-product fuels or waste gases are transferred from the manufacturing site and combusted elsewhere in different activities. The performance of the economy is the key driver for trends in the IPPU sector. The South African economy is directly related to the global economy, mainly through exports and imports. South Africa officially entered an economic recession in May 2009, which was the first in 17 years. Until the global economic recession affected South Africa in late 2008, economic growth had been stable and consistent. As a result of the recession, GHG emissions during that period decreased across almost all categories in the IPPU sector. Since then, GDP annual growth has slowed compared to growth before the recession. The Covid 19 pandemic caused economic growth to decline during 2020, especially during the second half of the year when lockdown measures where stricter. During 2021 and 2022 economic growth recovered to pre-covid figures. During 2022 South Africa moved away from Tier 1 reporting towards Tier 2 and Tier 3 calculations. In certain sectors, this made quite a difference. The Metal Industry was affected most, but the accuracy has improved. The 2023 Verification programme also enhanced accuracy of Tier 3 reporting. Due to these changes, comparison with historical figures is compromised in the short term. The largest source of emissions in the IPPU sector in South Africa is the production of ferroalloys, iron and steel followed by cement production. 4.1.1 Shares and trends in emissions In 2022 the IPPU sector produced 30 598 Gg CO 2 e which is 6.4% of South Africa’s emission (excl. LULUCF). The IPPU sector produces CO 2 emissions (78.4%), fluorinated gases (20.0%) and smaller amounts of CH 4 (0.4%) and N 2 O (1.2%) (Table 4.1). Carbon dioxide and any other emissions from combustion of fuels in these industries are reported under the Energy sector. The largest source category is the Metal industry category. which contributes 51.2% to the total IPPU sector emissions. The Mineral industry and the Product used as substitutes for ozone depleting substances subsectors contribute 19.8% and 19.4%, respectively, to the IPPU sector emissions (Table 4.1). Iron and steel production and Ferroalloys production are the biggest CO 2 contributors to the Metal industry subsector, producing 6 307 Gg CO 2 (40.3%) and 8 081 Gg CO 2 (51.6%) respectively to the total metal industry Greenhouse Gas emissions. 107 Ammonia production produce 114 Gg CO 2 e of CH 4 , while chemical industries are estimated to produce 364 Gg CO 2 e of N 2 O. A summary table of all emissions from the IPPU sector by gas is provided in Appendix C. Table 4.1: Summary of the estimated emissions from the IPPU sector in 2022 for South Africa. GHG source categories CO 2 CH 4 N 2 O HFCs PFCs SF 6 Total Gg CO 2 e 2.IPPU 23 976 121 364 5 945 126 66 30 598 2.A Mineral industry 6 055 NA NA NA NA NA 6 055 2.B Chemical industry 1 268 121 364 NA NA NA 1 753 2.C Metal industry 15 529 NE NA NA 126 NA 15 655 2.D Non-energy products from fuels and solvents 1 125 NA NA NA NA NA 1 125 2.E Electronic industry NE NE NE NE NE NA NE 2.F Product uses as substitute ODS NA NA NA 5 945 NE NA 5 945 2.G Other product manufacture and use NE NE NE NE NE 66 66 2.H Other NE NE NE NE NE 66 NE Numbers may not sum exactly due to rounding off. Even though the South African economy recovered after the COVID-19 pandemic, estimated emissions from the IPPU sector are 2 183 Gg CO 2 e (-6.7%) lower than the emissions in 2000 (Table 4.2). The decline can be attributed to the decline in metals production (-39.0%), specifically Iron and Steel production and Aluminium production. This can be ascribed to a decrease in global demand. The decline in the chemicals industry also made a huge difference (-31.4%). Cement production and Non-energy product use from fuels and solvents increased emissions from 2000 by 2 081 Gg CO 2 e. The local demand for cement increased dramatically from 2000. Figure 4.1 shows that IPPU emissions increased by 18.0% between 2000 and 2006, after which there was a 13.6% decline to 2009. This decrease was mainly due to the global economic recession and the electricity crisis that occurred in South Africa during this period. From 2010 emissions increased due to an increase in the metal industry and products used as substitutes for ozone depleting substances subsectors. The economy was also beginning to recover from the global recession. 108 Emissions decreased from 2016 as demand for South African chemical and metals dropped. COVID-19 also had a major local and international impact between 2020 and 2021. Table 4.2: Summary of the change in emissions from the IPPU sector between 2000 and 2022. GHG source categories Emissions (Gg CO 2 e) Difference (Gg CO 2 e) Change (%) 2000 2020 2022 2000-2022 2020-2022 2000-2022 2020-2022 2.IPPU 32 781 24 858 30 598 -2 183 5 740 -6,7 23,1 2.A Mineral industry 4 371 4 774 6 055 1 684 1 281 38,5 26,8 2A1 Cement Production 3 871 3 796 5 023 1 152 1 227 29,8 32,3 2A2 Lime Production 426 715 694 268 -21 62,8 -3,0 2A3 Glass Production 74 154 191 117 37 157,5 23,9 2A4 Other Process Uses of Carbonates NE 109 147 38 34,7 2.B Chemical industry 2 557 2 247 1 753 -804 -494 -31,4 -22,0 2B1 Ammonia Production C C C 2B2 Nitric Acid Production C C C 2B5 Carbide Production C C C 2B6 Titanium Production C C C 2B7 Soda Ash Production NE C C 2B8f Petrochemical and Black Carbon Production C C C 2B8g Hydrogen Production NE C C 2B10 Other NE C C 2.C Metal industry 25 658 12 391 15 655 -10 003 3 263 -39,0 26,3 2C1 Iron and Steel Production 15 334 3 854 6 307 -9 027 2 453 -58,9 63,6 2C2 Ferroalloy Production 8 084 7 233 8 081 -3 848 0,0 11,7 2C3 Aluminium Production 2 116 1 261 1 259 -857 -2 -40,5 -0,1 109 2C5 Lead Production 15 7 7 -8 1 -52,4 9,0 2C6 Zinc Production 108 37 0 -108 -37 -100,0 -100,0 2.D Non- energy products from fuels and solvents 196 84 1 125 929 1 041 474,1 1 245,5 2D1 Lubricant Use 189 82 516 327 434 173,6 527,5 2D2 Paraffin Wax Use 7 1 609 602 608 8 091,2 43 042,8 2.E Electronic industry NE NE NE 2.F Product uses as substitute ODS NE 5 284 5 945 5 945 661 12,5 2F1 Refrigeration and Air Conditioning NE 5 187 5 837 650 12,5 2F2 Foam Blowing Agents NE 2 2 0 0,0 2F3 Fire Protection NE 76 88 11 15,0 2F4 Aerosols NE 18 18 0 0,0 2.G Other product manufacture and use NE 78 66 66 -12 -15,5 2.H Other NE NE NE 110 Figure 4.1: Trend in South Africa’s IPPU sector emissions, 2000 – 2022. 4.1.1.1 Mineral industry In 2022 the mineral industries produced 6 055 Gg CO 2 , which is 19.8% of the IPPU sector emissions. Cement production accounted for 83.0% of emissions from the Mineral industry. All the emissions in this category were CO 2 emissions. The emissions were 38.5% (1 684 Gg CO 2 ) higher than the 4 371 Gg CO 2 in 2000. There was a 49.9% increase in the Mineral industry emissions between 2000 and 2009, after which emissions declined by 17.3% in 2012 (Figure 4.2). The increase between 2000 and 2009 was due to increased emissions from cement and lime production because of economic growth during this period. In 2009 the South African economy went into recession and the GDP decreased by 1.8% in that year. Cement demand in the residential market and construction industry in 2009/2010 decreased due to higher interest rates, increased inflation, and the introduction of the National Credit Act (DMR, 2010). Other process uses of carbonates was included in the category from the 2018 inventory. Emissions declined between 2019 and 2020 mainly due to the COVID-19 pandemic and the stringent lockdown regulations within South Africa during 2020. The market did recover after this. Cement production is the largest contributor to the emissions from this category (Figure 4.2). 111 Figure 4.2: Category contribution and trend for the mineral subsector, 2000 – 2022. Cement production Cement production was estimated to produce 16.4% of the IPPU sector emissions. Emissions were 29.8% above the 2000 level but fluctuate with economic conditions. Lime production Lime production was estimated to produce 2.3% of the IPPU sector emissions. Emissions were 29.8% above the 2000 level. The fluctuations in Lime production were directly linked to developments and investments in the steel and metallurgical industries. It should however be noted that there is an inconsistency in the time series with the data prior to 2008. Only pyrometallurgical quicklime and hydrated lime (only included lime for water purification) were included. The production data prior to 2008 is therefore much lower than the data for the later years. This means that the change from 2000 to 2020 is not only an increase due to emissions but also due to a change in the activity data. Emissions for 2018 and 2019 were extrapolated for quicklime due to a lack of activity data received. The 2019 inventory included the addition of dolomitic lime to the calculation, as previously this type of lime was not reported. 112 Glass production Glass production was estimated to produce 0.6% of the IPPU sector emissions. Emissions were 62.8% above the 2000 level. Emissions increased steady until 2018 from when it stabilised. Other Process Uses of Carbonates (OPUC) Emissions from the Other Process Uses of Carbonates (OPUC) were only reported from the 2018 inventory due to a lack in activity data and emissions. OPUC emissions were estimated to produce 0.5% of the IPPU sector emissions. Emissions do fluctuate with normal industrial adjustment to economic circumstances. 4.1.1.2 Chemical industry The Chemical industries were estimated to produce 1 753 Gg CO 2 e in 2022, which is 5.7% of the IPPU sector emissions. The largest contributions are from Titanium dioxide production, Nitric acid production and Ammonia production. Emissions from the Chemical industries decreased by 804 Gg CO 2 e (31.4%) since 2000 (2 774 Gg CO 2 e). Emissions from this subsector fluctuated considerably over the 20-year period (Figure 4.1) due to the nature of the business. Between 2000 and 2006 emissions fluctuated between 2 169 Gg CO 2 e and 2 974 Gg CO 2 e (Figure 4.1) then there was a decline of 55.4% between 2006 and 2008, largely due to N 2 O emission reductions in Nitric acid production. Hydrogen production was included since 2018, while Silicon carbide and Soda ash production since 2019 and Other chemicals since 2020. 4.1.1.3 Metal industry The Metal industry was estimated to produce 15 655 Gg CO 2 e in 2022, which is 51.2% of the IPPU sector emissions. The largest contribution to the Metal industries comes from Ferroalloy production (8 081 Gg CO 2 e or 51.6%), followed by Iron and steel production (6 307 Gg CO 2 e or 40.3%). Emissions from the Metal industry decreased by 10 003 Gg CO 2 e (39.0%) below the 2000 emissions of 25 615 Gg CO 2 e. Figure 4.3 shows that emissions from the Metal industries increased slowly (11.2%) between 2000 and 2006, mainly due to Ferrometals production. A sharp decline in production and related emissions from 2016 can be attributed to increased input cost, energy supply challenges and low-cost international alternatives in the Iron and steel and Ferrometals sectors. Zinc production was minimal for 2022. Various entities used the COVID-19 period to stop production in favour of major maintenance after which production increased again. 113 Aluminium production emissions more than doubled between 2010 and 2011 due to increased PFC emissions (Figure 4.3). In 2000 almost half (47.4%) of the Aluminium production emissions were PFC emissions. This rose to 65.0% in 2011 and 2012 due to the closure of the Soderberg and Side-Worked Pre-Bake processes in 2009. The aluminium plants released large amounts of C 2 F 4 and CF 4 during 2011 and 2012 due to inefficient operations (switching on and off at short notice) as they were used to control the South African electricity grid. PFCs emissions afterwards stabilised to a point where it is currently (2022) contributing 10.0%. Figure 4.3: Trend and category contribution to emissions from the metal industries, 2000 – 2022. 4.1.1.4 Non-energy products from fuels and solvents The Non-energy products from fuels and solvent use were estimated to produce 1 125 Gg CO 2 e in 2022, which is 3.7% of the IPPU sector emissions. Emissions from the Non-energy products from fuels and solvent use category were 474% higher than the 2000 level of 196 Gg CO 2 e. Emissions increased steadily from 2000 to 2015 to 2 495 Gg CO 2 e as usage of lubricant wax increased. A sharp increase in paraffin wax usage was seen in 2015 which contributed largely to the steep increase in emissions (Figure 4.4). Emissions dropped from 2015 to 2018 to 639 Gg CO 2 e as consumption of 114 both lubricants and paraffin wax decreased. 2020 saw a steep decrease in the use of lubricants and paraffin wax, which is most probably ascribed to COVID-19. No information for 2021 and 2022 is available and extrapolation was done. Figure 4.4: Trend and category contribution in the emissions from non-energy products from fuels and solvents. 2000 – 2022. 4.1.1.5 Products used as substitutes ODS The Products used as substitutes for ODSs category was estimated to produce 5 945 Gg CO 2 e in 2022, which is 19.4% of the IPPU sector emissions. The largest contribution comes from Refrigeration and air conditioning (5 837 Gg CO 2 e or 98.2%). Emissions were only estimated from 2005 when emissions were estimated at 842 Gg CO 2 e in 2005. In 2010 emissions more than doubled (Figure 4.5) due to an increase in the Refrigeration and stationary air conditioning emissions. In 2011 emissions from Mobile air conditioning, Foam blowing agents, Fire protection and Aerosols were added, therefore the emissions for this subcategory increased to 2 853 Gg CO 2 e in 2013. Recalculations were not done for the years prior to 2011 due to a lack of data. Refrigeration and air conditioning as well as Fire protection has been extrapolated since 2016. Emissions from foam blowing agents and aerosols have not changed since 2015. 115 Figure 4.5: Trend and category contribution to the product uses as substitutes for ODS emissions, 2000 – 2022. 4.1.2 Overview of methodology and completeness Table 4.1 provides a summary of the methods and emission factors applied to each subsector of IPPU. 116 Table 4.3: Summary of methods and emission factors for the IPPU sector and an assessment of the completeness of the IPPU sector emissions. GHG Source and sink category CO 2 CH 4 N 2 O HFCs PFCs SF6 NOx CO NMVOC SO 2 Method applied Emission factor Method applied Emission factor Method applied Emission factor Method applied Emission factor Method applied Emission factor Method Applied Emission Factor A Mineral industry 1 Cement production T2 CS NE NE NE NE NE 2 Lime production T3 CS NE NE NE NE NE 3 Glass production T3 CS NE NE NE NE NE 4 Other process uses of carbonates T3 CS NE NE NE NE NE B Chemical industry 1 Ammonia production T3 CS T3 CS NE NE NE NE NE 2 Nitric acid production NE NE T3 CS NE NE NE NE 3 Adipic acid production NO NO NO NO NO NO NO 4 Caprolactam, glyoxal and glyoxylic acid production NO NO NO NO NO NO NO 5 Carbide production T3 CS T1 DF NE NE NE NE NE 6 Titanium dioxide production T3 CS NE NE NE NE NE NE 7 Soda Ash production T3 CS NE NE NE NE NE NE 8a Methanol NO NO NO NO NO NO NO 8b Ethylene NO NO NO NO NO NO NO 8c Ethylene Dichloride and Vinyl Chloride Monomer NO NO NO NO NO NO NO 8d Ethylene Oxide NO NO NO NO NO NO NO 117 8e Acrylontrile NO NO NO NO NO NO NO 8f Petrochemical and carbon black production T1 DF T1 DF NE NE NE NE NE 8g Hydrogen Production T3 CS NE NE NE NE NE NE 9 Fluorochemical production NO NO NO NO NO NO NO 11 Other T2 CS T2 CS NE NE NE NE NE NE NE NE C Metal industry 1 Iron and steel production T3 CS NE NE NE NE NE NE 2 Ferroalloy production T3 CS T3 CS NE NE NE NE NE 3 Aluminium production T3 CS NE T3 CS NE NE NE NE 4 Magnesium production NO NO NO NO NO NO NO NO 5 Lead production T1 DF NE NE NE NE 6 Zinc production T1 DF NE NE NE NE D Non-energy products from fuels and solvents 1 Lubricant use T1 DF NE NE NE NE 2 Paraffin wax use T1 DF NE NE NE NE NE NE 3 Solvent use NE NE NE NE E Electronics industry 1 Integrated circuit or semiconductor NE NE NE NE NE NE NE NE NE 2 TFT flat panel display NE NE NE NE NE NE NE 3 Photovoltaics NE NE NE NE NE NE NE 4 Heat transfer fluid NE NE NE NE F Product uses as substitute ODS 1 Refrigeration and air conditioning NE T2a, T2b DF NE NE NE NE NE 2 Foam blowing agents NE T1 DF NE NE NE NE NE 3 Fire protection NE T1 DF NE NE NE NE NE 4 Aerosols T1a, T2a DF NE NE NE NE NE 118 5 Solvents NE NE NE NE NE NE G Other product manufacture and use 1 Electrical equipment NE NE NE NE NE NE 2 SF6 and PFCs from other product uses NE NE NE NE NE NE 3 N 2 O from product uses NE NE NE NE NE H Other 1 Pulp and paper industry NE NE NE NE NE NE 2 Food and beverage industry NE NE NE NE NE NE 119 4.1.3 Improvements and recalculations Through the introduction of the NGER in 2017 (DEA, 2017a), amendments to these regulation in 2020 (DEFF, 2020) as well as the introduction of the SAGERS, the GHG reporting tool, there have been various additions to the inventory as well as recalculations up to 2020. During 2020 a major change was the introduction of Tier 2 and Tier 3 calculation methods as far as possible. This did lead to improved accuracy of the inventory. An external verification process was launched for key emission contributors which increased the level of accuracy. The accuracy improvement has not been quantified. No categories were added from 2020. 4.1.4 Key categories in the IPPU sector The key categories identified in the IPPU sector by the level (L) and trend (T) analysis are shown in Table 4.4. Table 4.4: Key categories identified in the IPPU sector. IPCC Code Category GHG Criteria 2A1 Cement Production CO 2 L 2B Chemical industry C L 2C1 Iron and Steel Production CO 2 L, T 2C2 Ferroalloys Production CO 2 L, T 2C2 Ferroalloys Production CH 4 L 2C3 Aluminium Production CO 2 L 2D1 Lubricant Use CO 2 T 2D2 Paraffin Wax Use CO 2 T 2F1 Refrigeration and Air Conditioning HFCs L, T 4.1.5 Planned improvements and recommendations Planned improvements and recommendations are given in Table 4.5. These will be prioritised as required and are dependent on resources. Table 4.5: Planned improvements and recommendations 120 IPCC Code Improvement/Recommendation General • Have sector specific engagements discussing the expectations regarding moving to the higher Tier methods. • Address time series consistency issues as data becomes available for specific categories that have been newly included in the inventory. • Ensure that facility intensity data is available (even though not publicly). • Expand reporting to have sector and technology specific intensities. 2A1 • Investigate historical data for the imports and exports of clinker 2A2 • Undertake a completeness assessment to determine if non-marketed lime is reported 2A3 • Disaggregate the cullet ratio by facility. 2B6 • Investigate the availability of the historical data. 2C5 • Investigate the air quality database for those data providers that trigger reporting under Lead Battery processing 2C6 • Investigate if secondary zinc production occurs in South Africa • Investigate the air quality database regarding pyrometallurgical process involving the use of an imperial smelting furnace is used for combined zinc and lead production. 2D1 • South Africa to undertake a desktop study regarding two-stroke engines and the use of blended lubricant. 2D • Investigate the availability of more accurate data 2F • Investigate the availability of more accurate data Mineral industry (2.A) Mineral production emissions are process related GHG emissions resulting from the use of carbonate raw materials. The Mineral production category is divided into five subcategories: Cement production, Lime production, Glass production, Process uses of carbonates, and Other mineral products processes. For this inventory report, emissions are reported for four subcategories: Cement production (2A1), Lime production (2A2). Glass production (2A3) and Other process uses of carbonates (2A4). Other mineral products processes (2A5) are not included due to a lack of data. 4.2.1 Cement production (2.A.1) 4.2.1.1 Category description The South African cement industry’s plants vary widely in age, ranging from five to over 70 years (DMR, 2009). The most common materials used for cement production are limestone, shells, and chalk or marl combined with shale, clay, slate or blast-furnace slag, silica sand, iron ore and gypsum. For certain cement plants, low-grade limestone appears to be the only raw material feedstock for clinker production (DMR, 2009). Portland cement, which has a clinker content of >95%, is described by the class CEM I, CEM II 121 cements can be grouped depending on their clinker content into categories A (80 – 94%) and B (65 – 79%). Portland cement contains other pozzolanic components such as blast- furnace slag, micro silica, fly ash and ground limestone. CEM III cements have a lower clinker content and are also split into subgroups: A (35 – 64% clinker) and B (20 – 34% clinker). South Africa's cement production plants produce Portland cement and blended cement products, such as CEM I, and more recently CEM II and CEM III. Cement produced in South Africa is sold locally and to other countries in the Southern Africa region, such as Namibia, Botswana, Lesotho, and Swaziland. The main GHG emission in Cement production is CO 2 emitted through the production of clinker, an intermediate stage in the cement production process. Non-carbonate materials may also be used in cement production, which reduce the amount of CO 2 emitted. However, the amounts of non-carbonate materials used are generally very small and not reported in cement production processes in South Africa. An example of non- carbonate materials would be impurities in primary limestone raw materials. It is estimated that 50% of the cement produced goes to the residential building market (DMR. 2009); therefore, any changes in the interest rates that affect the residential market will affect cement sales. COVID-19 effected the local market in the sense that lower strength cement was more in demand verses high strength cement during the 2020 period. 4.2.1.2 Methodological issues From 2022, most cement producers used a Tier 2 methodology. Previously a Tier 1 approach was used to determine emissions from clinker produced in the Cement production category as per the 2006 IPCC Guidelines. From 2008 to 2015 imports of clinker were included in the calculations as the information was available for these years. Activity data Data on cement production in South Africa was obtained from the SAMI Annual Reports produced by the DMRE for 2000 to 2017. Clinker production for the years 2018 and 2019 were provided by the cement industries (Table 4.) via the GHG Reporting Programme. Clinker fraction for the years 2000 to 2012 were obtained from cement industries but was not available for the period between 2013 and 2017, therefore it was assumed to remain unchanged between 2012 and 2017. The clinker fraction for the years 2018 and 2019 were not provided by all cement industries, however the clinker produced was provided (Table 4.). The clinker fraction was available from the 2020 reporting period through the GHG reporting programme. Table 4.6: Production data for the mineral industries, 2000 – 2022. 122 Cement production Clinker production Quick lime production Hydrated lime production Dolomitic lime production Glass production Ceramics Other uses of soda ash Other Production (tonne) 2000 9 794 000 532 100 46 270 561 754 2001 9 700 000 522 910 45 470 624 156 2002 11 218 000 572 369 49 771 667 110 2003 11 893 000 586 969 51 041 702 008 2004 11 565 000 608 056 52 874 726 644 2005 13 519 000 685 860 59 640 775 839 2006 14 225 000 755 302 65 678 808 328 2007 14 647 000 660 772 57 458 858 382 2008 14 252 000 1 436 000 142 000 978 488 2009 14 860 000 1 264 000 104 000 993 784 2010 13 458 000 1 179 000 113 000 1 009 043 2011 12 373 000 1 422 000 118 000 1 019 755 2012 12 358 000 1 113 000 97 000 1 095 264 2013 13 053 000 1 091 000 100 000 1 095 264 2014 13 099 000 1 111 579 148 760 1 095 264 2015 14 456 000 1 026 591 92 623 1 095 264 2016 15 182 000 1 035 000 93 000 1 146 296 2017 14 622 000 1 112 000 96 000 1 162 436 2018 9 429 754 1 055 730 689 841.2 12 135 278 551 2019 9 174 824 1055315 61 810 752 256.2 115 048 2020 7 299 346 818211 146 158 24 729 1 418 5061 146 936 58 757 114 871 2021 7 081 758 782 545 156 361 27 202 1 718 883 392 129 69 140 149 781 2022 9 545 453 707 912 204 874 36 175 1 798 427 255 071 75 765 187 763 Emission factors For the calculation of GHG emissions in Cement production, CO 2 emission factors were sourced from the 2006 IPCC Guidelines. It was assumed that the Calcium Oxide (CaO) composition (one tonne of clinker) contains 0.65 tonnes of CaO from Calcium Carbonate (CaCO 3 ). This carbonate is 56.03% of CaO and 43.97% of CO 2 by weight (IPCC, 2006, p. 2.11). The emission factor for CO 2 , provided by IPCC 2006 Guidelines, is 0.52 tonnes of CO 2 per tonne clinker. The country-specific clinker fraction for the period 2000 to 2021 ranged between 69% - 76%. From 2022 a Tier 2 calculation was done by production plants. The implied emission factor of 0.526 is very close that of the IPCC. 4.2.1.3 Uncertainties and time-series consistency 123 Activity data uncertainty The largest uncertainty in this sub-category is the import/export data. According to IPCC 2006 the uncertainties are: 1% for chemical analysis of clinker to determine CaO; 10% for country production data; 30% for the Cement Kiln Dust (CKD) correction factor default assumption; and 10% on the trade data. From 2022, these uncertainties will be much less due to the Tier 2 and Tier 3 reporting protocols but will only be updated once enough data is available to determine these. Emission factor uncertainty Since this submission moved back to a Tier 1 method uncertainty has increased. According to the 2006 IPCC Guidelines, uncertainty with a Tier 1 approach could be as much as 35% in terms of estimation of % of calcination of CKD (IPCC, 2006, Table 2.3). From 2022 Tier 2 and Tier 3 methods are used. Uncertainty should be much less, but is not quantified yet. Time-series consistency The time-series for this sub-category is not consistent. A Tier 1 method has been used from 2000 to 2021 to estimate emissions from this category. Tier 2 and Tier 3 methodologies have been used for 2022. Import data was available and used for estimations from 2008 to 2015. Emissions were estimated throughout the time-series by using clinker production, which was calculated by multiplying cement production, obtained from the SAMI Report, by clinker fraction from 2000 to 2017. Company specific clinker production was provided from 2018 onwards. 4.2.1.4 Category specific QA/QC and verification External verification of 2022 emissions were requested for most of the cement producers. For verification of cement production data, the facility-level clinker production submitted by cement industries via the SAGERS for the inventory was compared with estimated clinker production from previous inventories. The clinker production however for 2020 showed a decrease compared to previous years. This decrease could be due to the COVID- 19 pandemic which saw stringent lockdown measures being implemented in South Africa during 2020. In previous years (2000 -2017) comparisons against the SAMI reports produced by DMR was also conducted although the information with the report is not clinker production but rather the total amount of lime and dolomite sold to the cement industry however using a clinker fraction of 69% - 76% indicates that clinker production follows the same trend as that from the facility level data from cement industries. It is important to note 124 that the numbers in the SAMI Report may produce slightly overestimated values if not all lime is converted to cement in that year. In addition, the estimates of clinker production from the DMR data do not include clinker exports due to a lack of data. It is not clear if the industry level clinker data within the report takes imports and exports into account. These differences lead to increased uncertainty and the reasons for the discrepancies need to be further investigated. Direct reporting and verification by data providers have limited these uncertainties. 4.2.1.5 Category specific recalculations Recalculations were not performed for this category. 4.2.1.6 Category specific planned improvements It is evident that there are discrepancies between the cement production data from industry and the cement production data published by the DMR (2000 – 2017), however further data collected by SAGERS could assist in reducing this uncertainty and aid in more consistent reporting in future. 4.2.2 Lime production (2.A.2) 4.2.2.1 Category description Lime is the most widely used chemical alkali in the world. Calcium oxide (CaO or quicklime or slaked lime) is sourced from CaCO 3 which occurs naturally as limestone (CaCO 3 ) or dolomite (CaMg (CO 3 ) 2 ). CaO is formed by heating limestone at high temperatures to decompose the carbonates (IPCC, 2006 pg 2.19) and produce CaO. This calcination reaction produces CO 2 emissions. Lime kilns are typically rotary-type kilns, which are long, cylindrical, slightly inclined and lined with refractory material. At some facilities, the lime may be subsequently reacted (slaked) with water to produce hydrated lime. In South Africa the market for lime is divided into pyrometallurgical and chemical components. Hydrated lime is divided into three sectors: chemical, water purification and other sectors (DMR, 2019). Lime has wide applications, e.g., it is used as a neutralizing and coagulating agent in chemical, hydrometallurgical and water treatment processes and a fluxing agent in pyrometallurgical processes. Quicklime sales for both pyrometallurgical uses and chemical uses both increased in 2017 (DMR, 2019). Demand for hydrated lime for water purification purposes decreased from 2016 to 2017. while demand for sales for chemical applications increased during the same period (DMR, 2019). 125 4.2.2.2 Methodological issues A Tier 1 and Tier 2 approach was used to estimate emissions from this category as per the 2006 IPCC Guidelines. The production of lime involves various steps, which include the quarrying of raw materials, crushing and sizing, calcining the raw materials to produce lime, and (if required) hydrating the lime to calcium hydroxide. The Tier 2 approach was used for the calculation of GHG emissions from Lime production (Equation 2.6, IPCC 2006 Guidelines). This report estimated the total Lime production based on the quantity of quicklime, hydrated lime and dolomitic lime produced. For 2022 a Tier 2 methodology is used for the calculation of emissions. Activity data The DMRE publishes data on lime products that is divided into quicklime which includes pyrometallurgical and chemical components; and hydrated lime that includes water purification, chemical and other uses (DMR, 2019). In the previous submissions only pyrometallurgical quicklime and water purification hydrated lime was incorporated from the SAMI Reports (DMR, 2019) were used (Table 4.). It was assumed that all quicklime is high calcium lime. From the 2020 inventory included activity data provided by the lime industries reported via the South African GHG Reporting Programme. The types of lime reported were quicklime, hydrated lime and dolomitic lime. Quicklime activity data for 2018 and 2019 were extrapolated due to lack of data received for these years. Activity data for hydrated lime during 2018 and 2019 was not provided by industry through the Reporting Programme and the updated SAMI report was not available for use. Emission factors Quicklime is indicated to be high-calcium lime. The 2006 IPCC default emission factor for high-calcium lime (0.75 tonnes CO 2 per tonne lime) was applied. The 2006 IPCC default emission factor of 0.77 tonnes CO 2 per tonne lime was applied to dolomitic lime. The 2006 IPCC default emission factor of 0.59 tonnes CO 2 per tonne lime was applied to hydrated lime. From 2022, a Tier 3 methodology was used with implied emission factors of 0.78 and 0.58 tonnes CO 2 per ton lime for quicklime and hydrated lime respectively. 4.2.2.3 Uncertainties and time-series consistency Activity data uncertainty According to the 2006 IPCC Guidelines, uncertainty of the Lime Kiln Dust (LKD) correction factor for hydrated lime adds to the uncertainty of the activity data due to not 126 knowing the amount of LKD produced and what percentage is calcined, as with the CKD correction factor used for estimating emissions from cement production. Therefore, it can be assumed that the uncertainty for LKD is equal to the uncertainty of CKD which is 30%. For 2022 a Tier 2 methodology is used, but even though uncertainty would be much less, it has not been calculated. Emission factor uncertainty According to the IPCC 2006 Guidelines, the uncertainty on Lime production emissions is: 6% for assuming an average CaO in lime; 2% for high-calcium EF; 5% for correction for hydrated lime. Time-series consistency A Tier 1 and Tier 2 method was used to estimate emissions from 2000 to 2021. The time series was updated to include the 2006 IPCC default emission factor of 0.59 for hydrated lime in 2017. Dolomitic lime was added to the time series in 2019. From 2022 a Tier 3 methodology is used. 4.2.2.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. The only available data for Lime production was sourced from the SAMI report for the 2000 to 2017 inventories, however, from the 2020 inventory, data was sourced from the lime production industry: therefore, there was no comparison of data across different plants previously. The numbers in the DMR report are the total amount of lime (quicklime and hydrated lime) sold locally so may produce slightly overestimated values if not all lime is produced in South Africa or during that year. Reporting is currently not consistent between plants, and verification will be undertaken as consistency is achieved in the future. The SAGERS which became active in 2020, will allow for consistency to be achieve going forward. 4.2.2.5 Category specific recalculations Recalculations were not undertaken for this category. 4.2.2.6 Category specific planned improvements The inclusion of Tier 3 emission estimates and external verification of emissions data is planned for the future. 127 4.2.3 Glass production (2.A.3) 4.2.3.1 Category description There are many types of glass and compositions used commercially, however the glass industry is divided into four categories: containers, flat (window) glass, fibre glass and speciality glass. When other materials (including metal) solidify, they become crystalline, whereas glass (a super cool liquid) is non-crystalline. The raw materials used in glass production are sand, limestone, soda ash, dolomite, feldspar and saltcake. The major glass raw materials which emit CO 2 during the melting process are limestone (CaCO 3 ), dolomite CaMg (CO 3 ) 2 and soda ash (Na 2 CO 3 ). Glass makers do not produce glass only from raw materials, they also use a certain amount of recycled scrap glass (cullet). The chemical composition of most glass is silica (72%), iron oxide (0.075%), alumina (0.75%), magnesium oxide (2.5%), sodium oxide (14.5%), potassium oxide (0.5%), sulphur trioxide (0.25%) and calcium oxide (7.5%) (PFG glass, 2010). 4.2.3.2 Methodological issues A Tier 3 approach was used to determine estimates of the GHG emissions from Glass production by the glass production industries for the 2022 inventory. Data providers used a mass balance approach to estimate emissions providing inputs and outputs of their process and carbon content factors specific to their materials. External verification is done on the 2022 data. Activity data Production data was provided by the glass production industries (PG Group, Consol Glass and Isanti Glass), via the SAGERS. Emission factors All glass production industries determined their own emissions using a Tier 3 approach and provided these emission estimates to DFFE. In most cases the activity data and emission factors used are not supplied due to confidentiality issues. External verification is however done on the 2022 data. 4.2.3.3 Uncertainties and time-series consistency Activity data uncertainty 128 As per the 2006 IPCC Guidelines. uncertainty related to activity data using a Tier 3 approach is between 1 – 3% (IPCC, 2006, Chapter 2.4.2.2). Carbonates lost as dust is negligible under the Tier 3 method. Emission factor uncertainty As per the 2006 IPCC Guidelines, uncertainty related to emission factors developed using a Tier 3 approach is between 1 – 3% (IPCC, 2006, p. 2.31). There is some uncertainty when assuming that all carbonates are calcined, but this is minimal. Time-series consistency A Tier 1 method was used to estimate emissions from 2000 to 2017, using the cullet ratio and the default emission factor of 0.2. From 2018 onwards after the promulgation of the NGERS, glass production industries began using the Tier 2 and Tier 3 methods to estimate emissions, using a mass balance approach. 4.2.3.4 Category specific QA/QC and verification External verification of emissions were done for the 2022 data from individual companies. 4.2.3.5 Category specific recalculations No recalculations were performed for this category. 4.2.3.6 Category specific planned improvements There is an improvement project planned to investigate the inclusion separation of cullet ratio reported per facility. 4.2.4 Other process uses of carbonates (2.A.4) 4.2.4.1 Category description Limestone (CaCO 3 ), dolomite (CaMg (CO 3 ) 2 ) and other carbonates (e.g., MgCO 3 and FeCO 3 ) are basic raw materials having commercial applications in a number of industries. The calcination of carbonates at high temperatures yields CO 2 . In addition to those industries already discussed individually above, carbonates are also consumed in metallurgy, agriculture, construction and environmental pollution control (IPCC, 2006). The use of lime in the following specific source categories are reported within the mineral 129 category, such as ceramics, other soda ash usage and non-metallurgical magnesia production. Ceramics include the production of bricks and roof tiles, vitrified clay pipes, refractory products, expanded clay products, wall and floor tiles, table and ornamental ware (household ceramics), sanitary ware, technical ceramics, and inorganic bonded abrasives. Emissions from the ceramic production industry are process related and are emitted when calcination of clay occurs and when additives are added to the process (IPCC, 2006). Within South Africa it has been shown that carbonates are very low in raw materials. Soda ash is used in a variety of applications, including, glass production, soaps and detergents, flue gas desulphurisation, chemicals, pulp and paper and other common consumer products. Soda ash production and consumption results in the release of CO 2 emissions (IPCC, 2006). Magnesite (MgCO 3 ) is one of the key inputs into the production of magnesia, and ultimately fused magnesia. There are three major categories of magnesia products: calcined magnesia, dead burned magnesia (periclase) and fused magnesia. Magnesia is produced by calcining MgCO 3 which results in the release of CO 2 (IPCC, 2006). Emissions may result from several other source categories that are not included above. 4.2.4.2 Methodological issues A Tier 1 and 3 approach was adopted to estimate emissions from the Ceramics and Soda ash usage subcategories. The Tier 3 approach included data providers undertaking analysis of their raw materials used within the process to determine the carbon content and then applying this value to their production data. A Tier 1 approach was used to estimate emissions from other uses of carbonates. Other uses of carbonates included the use of dolomite and calcite within separate processes. Activity data Activity data were obtained from industries operating under the relevant sector. Activity data was reported via the GHG Reporting Programme. This is a new category added to the inventory from 2018 and all activity data has been reported via the SAGERS. Emission factors The 2006 IPCC default emission factors for calcite (0.43971 tonnes CO 2 per tonne carbonate), magnesite (0.52197 tonnes CO 2 per tonne carbonate) and for soda ash usage (0.41492 tonnes CO 2 per tonne carbonate) was applied where a Tier 1 approach was used. The 2006 IPCC default emission factor for other usage of carbonates (0.47732 130 tonnes CO 2 per tonne lime) was applied to dolomite usage. Most industrial operators have however switched to a Tier 3 methodology. 4.2.4.3 Uncertainties and time-series consistency Activity data uncertainty According to the 2006 IPCC Guidelines. uncertainty arising from a Tier 3 approach is between 1-3%, and where a Tier 1 approach has been used uncertainty rises to between 15-85%. As both approaches were used to estimate emissions in 2020 activity data for subcategories Ceramics and Soda Ash Usage uncertainty will fall between the range of 1 - 85%. Emission factor uncertainty As per the 2006 IPCC Guidelines, emission factors used correctly to activity data that has been collected correctly uncertainty is negligible. However, should there be an error in the assumption of carbonates an uncertainty of between 1-5% should apply. Time-series consistency The Other Process Uses of Carbonates is a new category introduced to the inventory in 2018. This has resulted in an inconsistent time series as historical data is unavailable currently. The time series consistency will be updated as industry continues to report in future via the SAGERS Portal. More data providers are also switching to Tier 3 methodologies over time. 4.2.4.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.2.4.5 Category specific recalculations Recalculations were not undertaken for this category. 4.2.4.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.2.5 Other mineral products processes 131 No activities were included under this sub-category. Currently there are no processes registered in South Africa. Chemical industry (2.B) This category estimates GHG emissions from the production of both organic and inorganic chemicals in South Africa. The chemical industry in South Africa is mainly developed through the gasification of coal because the country has no significant oil reserves. GHG emissions from the following chemical production processes were reported: Ammonia production, Nitric acid production, Carbide production, Soda ash production, Titanium dioxide production, Carbon black, Hydrogen production and Other chemical processes. The chemical industry in South Africa contributes approximately 3.0% to the GDP and 23% of its manufacturing. The chemical products in South Africa can be divided into four categories: base chemicals, intermediate chemicals, chemical end-products, and speciality end-products. Chemical products include ammonia, waxes, solvents, plastics, paints, explosives and fertilizers. The chemical industries subsector contains confidential information, so, following the IPCC Guidelines for reporting confidential information, no disaggregated source-category level emission data are reported; only the emissions at the sector scale are discussed. Emission estimates are, however, based on bottom-up activity data and methodologies. 4.3.1 Ammonia production (2.B.1) 4.3.1.1 Category description Ammonia production is the most important nitrogenous material produced and is a major industrial chemical. According to the 2006 IPCC Guidelines (p.3.11), ammonia gas can be used directly as a fertilizer, in heat treating, paper pulping, nitric acid and nitrates manufacture, nitric acid ester and nitro compound manufacture, in explosives of various types and as a refrigerant. 4.3.1.2 Methodological issues Emission estimates from Ammonia production were obtained through the Tier 3 approach. Emissions were calculated based on actual process balance analysis. Total emission estimates were obtained from the ammonia production plants. 2022 data was verified externally. 132 Activity data Consumption data is not provided within this report due to confidentiality. It is however reported via the SAGERS system. Emission factors The emission factors are not provided within this report due to confidentiality reasons. 4.3.1.3 Uncertainties and time-series consistency Activity data uncertainty According to the 2006 IPCC Guidelines (p.3.16), the plant-level activity data required for the Tier 3 approach are the total fuel requirement classified by fuel type; CO 2 recovered for downstream use or other applications; and Ammonia production. Uncertainty of ±2% was applied to activity data as advised in the 2006 IPCC Guidelines (p.3.17) should activity data be obtained from producers. The uncertainty was assumed to be the same for both CO 2 and CH 4 emissions. Emission factor uncertainty The 2006 IPCC Guidelines default uncertainty for Tier 1 is 6% (IPCC, 2006, Table 3.1) therefore it is expected that for a Tier 3 approach the uncertainty would be less, therefore an assumption of ±2% is made as per information provided in the 2006 IPCC Guidelines (p. 3.17) for activity data obtained from plants. The uncertainty was assumed to be the same for both CO 2 and CH 4 emission estimates as guidance was not provided per GHG. Time-series consistency A Tier 3 method was used throughout the time-series, with emission data being provided by industry. 4.3.1.4 Category specific QA/QC and verification The 2022 data was verified externally. 4.3.1.5 Category specific recalculations No recalculations were performed for this category. 4.3.1.6 Category specific planned improvements 133 There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.2 Nitric acid production (2.B.2) 4.3.2.1 Category description Nitric acid is a raw material used mainly in the production of nitrogenous-based fertilizer. According to the 2006 IPCC Guidelines (p.3.19), during the production of nitric acid, nitrous oxide is generated as an unintended by-product of high-temperature catalytic oxidation of ammonia. 4.3.2.2 Methodological issues The emissions from Nitric acid production were calculated based on continuous monitoring (Tier 3 approach) and approved country specific emission factors. Activity data Consumption data is not provided within this report due to confidentiality. It is however reported via SAGERS. Emission factors The emission factors are not provided as the information is confidential. 4.3.2.3 Uncertainties and time-series consistency Activity data uncertainty According to the 2006 IPCC Guidelines (p. 3.24) the plant-level activity data required for the Tier 3 approach includes production data disaggregated by technology and abatement system type. The IPCC guidelines suggest that where uncertainty values are not available from other sources, as is the case for this inventory, this default value of ±2 percent should be applied to the activity data (IPCC, 2006, p.3.25). Emission factor uncertainty According the 2006 IPCC Guidelines (p. 3.24), default emission factors have very high uncertainties for two reasons: a) N 2 O may be generated in the gauze reactor section of nitric acid production as an unintended reaction by-product; and b) the exhaust gas may or may not be treated for NO x control and the NO x abatement system may or may not 134 reduce the N 2 O concentration of the treated gas. Since a Tier 3 approach was applied in this inventory it was assumed that the uncertainty value was ±2 as per Tier 3 approach (IPCC, 2006, p.3.24). Time-series consistency A Tier 3 method was used throughout the time-series, with emission data being provided by industry. 4.3.2.4 Category specific QA/QC and verification The 2022 data were verified externally. 4.3.2.5 Category specific recalculations No recalculations were performed on this category. 4.3.2.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.3 Adipic acid production (2.B.3) There is no Adipic acid production occurring in South Africa. 4.3.4 Caprolactuam, glyoxal and glyoxylic acid production (2.B.4) There is no Caprolactuam, glyoxal and glyoxylic acid production occurring in South Africa. 4.3.5 Carbide production (2.B.5) 4.3.5.1 Category description Carbide production can result in GHG emissions such as CO 2 and CH 4 . According to the 2006 IPCC Guidelines (p.3.39), calcium carbide is manufactured by heating calcium carbonate (limestone) and subsequently reducing CaO with carbon (e.g., petroleum coke) while silicon carbide is produced from silica sand or quartz and petroleum coke. 4.3.5.2 Methodological issues 135 Emissions from Carbide production were calculated based on a Tier 1 approach as per the 2006 IPCC Guidelines. From 2022 emissions are reported based on Tier 3 methodologies. Activity data Calcium carbide and silicon carbide consumption values were sourced from the carbide production plants but are not shown due to confidentiality issues. Calcium carbide is not estimated for 2019 and the production plant is no longer operational. Silicon carbide emission estimates have been included since 2019 through the GHG Reporting Programme. Emission factors An IPCC 2006 default emission factor was applied historically. Since 2022 Tier 3 methodologies are used. 4.3.5.3 Uncertainties and time-series consistency Activity data uncertainty According to the IPCC 2006 Guidelines (p. 3.45), the uncertainty of the activity data that accompanies the method used here is approximately ±5%. The uncertainty was assumed to be the same for both CO 2 and CH 4 emission estimates as guidance was not provided per GHG. Emission factor uncertainty The default emission factors are generally uncertain because industrial-scale carbide production processes differ from the stoichiometry of theoretical chemical reactions (IPCC, 2006. p. 3.45). Emission factor uncertainty for Tier 1 is ±10%. The uncertainty was assumed to be the same for both CO 2 and CH 4 emission estimates as guidance was not provided per GHG. From 2022 Tier 3 methodologies are used with indicated uncertainty of ±2%. Time-series uncertainty The emissions from Carbide production were sourced from the specific carbide production plants therefore there was no comparison of data across different plants. A Tier 1 method was used across the time series, with the addition of silicon carbide in 2019. From 2022 a Tier 3 method is used. 4.3.5.4 Category specific QA/QC and verification 136 No category specific QC checks were carried out. 4.3.5.5 Category specific recalculations No recalculations were performed on this category. 4.3.5.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.6 Titanium dioxide production (2.B.6) 4.3.6.1 Category description Titanium dioxide (TiO 2 ) is a white pigment used mainly in paint manufacture, paper, plastics, rubber, ceramics, fabrics, floor coverings, printing ink, among others. According 2006 IPCC Guidelines (p. 3.47), there are three processes in TiO 2 production that result in GHG emissions, namely, a) titanium slag production in electric furnaces; b) synthetic rutile production using the Becher Process and c) rutile TiO 2 production through the chloride route. 4.3.6.2 Methodological issues A Tier 3 approach was used for calculating GHG emissions from titanium dioxide production. Data providers used a mass balance approach to estimate emissions. Activity data The Titanium dioxide production emissions data were sourced from the titanium dioxide production plants and activity data was not supplied in this report due to confidentiality issues. Emission factors The emission factors are not provided in this report as the information is confidential. Tier 3 calculations are done for this sector. 4.3.6.3 Uncertainties and time-series consistency Activity data uncertainty 137 According to the IPCC 2006 Guidelines (p. 3.50), the uncertainty of the activity data when using a Tier 2 approach is ±2% for activity data collected at plant level. It is therefore assumed that the uncertainty is ±2 as data was provided at a plant level. Emission factor uncertainty The 2006 IPCC Guidelines state that uncertainty for titanium slag is unavailable due to confidentiality and lack of production plants. However, an assumption of ±2 was made as per the uncertainty of the activity data (IPCC, 2006, p.3.50) Time-series consistency The total GHG emissions were sourced from the specific titanium dioxide production plants therefore, no comparison of data across different plants was made. A Tier 3 approach was used across the time series. 4.3.6.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.3.6.5 Category specific recalculations No recalculations were performed on this category. 4.3.6.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.7 Soda ash production (2.B.7) 4.3.7.1 Category description Soda ash (sodium carbonate, Na 2 CO 3 ) is a white crystalline solid that is used as a raw material in many industries including glass manufacture, soap and detergents, pulp and paper production and water treatment. CO 2 is emitted during production with the quantity emitted dependent on the industrial process used to manufacture soda ash (IPCC, 2006). Emissions of CO 2 from the production of soda ash vary substantially with the manufacturing process. Four different processes may be used commercially to produce soda ash. Three of these processes, monohydrate, sodium sesquicarbonate (trona) and direct carbonation, are referred to as natural processes. The fourth, the Solvay process, is 138 classified as a synthetic process. Calcium carbonate (limestone) is used as a source of CO 2 in the Solvay process. 4.3.7.2 Methodological issues A Tier 3 approach was used for calculating GHG emissions from soda production. Data providers used a mass balance approach to estimate emissions from 2020 onwards. Activity data The Soda ash production emissions data were sourced from the production plants and activity data was not supplied in this report due to confidentiality issues. Emission factors The emission factors are not provided in this report as the information is confidential. It is however reported via SAGERS. 4.3.7.3 Uncertainties and time-series consistency Activity data uncertainty According to the IPCC 2006 Guidelines (p. 3.54), the uncertainty of the activity data when uncertainty is unavailable is ±5% for activity data collected at plant level. Emission factor uncertainty The 2006 IPCC Guidelines state that the uncertainty of the default emission factor is negligible as an assumption of 100% purity of input and output materials is made (IPCC, 2006, p.3.54). Time-series consistency The time series is not consistent as soda ash production is a new category added in 2019. Historical data is not available. The time series will be built on in future as more data becomes available. 4.3.7.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.3.7.5 Category specific recalculations No recalculations were performed on this category. 139 4.3.7.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.8 Carbon black production (2.B.8.f) 4.3.8.1 Category description Carbon black is produced from petroleum-based or coal-based feed stocks using the furnace black process (IPCC, 2006). Primary fossil fuels in carbon black production include natural gas, petroleum and coal. The use of these fossil fuels may involve the combustion of hydrocarbon content for heat rising and the production of secondary fuels (IPCC, 2006, p.3.56). GHG emissions from the combustion of fuels obtained from feed stocks should be allocated to the source category in the IPPU sector, however, where the fuels are not used within the source category but are transferred out of the process for combustion elsewhere, these emissions should be reported in the appropriate energy sector source category (IPCC, 2006, p. 3.56). Commonly, the largest percentage of carbon black is used in the tyre and rubber industry, and the rest is used as pigment in applications such as ink and carbon dry-cell batteries. 4.3.8.2 Methodological issues Tier 1 was the main approach used in estimating emissions from Carbon black production, using production data and relevant emission factors. Activity data Carbon black activity data was sourced directly from industry but is not shown due to confidentiality issues. Emission factors For the calculation of emissions from Carbon black production, the IPCC 2006 default CO 2 and CH 4 emission factors were applied (Table 4.7). Carbon black is mainly produced through the furnace black process; however, a small portion of carbon black production is known to be produced through the acetylene black process. Table 4.7: Emission factors applied for carbon black production emission estimates. 140 Sub-category CO 2 EF CH 4 EF Source (tonnes CO 2 /tonne product) (kg CH 4 /tonne product) Carbon black production (Furnace Black Process) 2.62 0.06 IPCC 2006 Carbon black production (Acetylene Black Process) 0.12 IPCC 2006 4.3.8.3 Uncertainties and time-series consistency Activity data uncertainty According to the IPCC 2006 Guidelines, the uncertainty of the activity data that accompanies the method used here is in the range of ±15% for CO 2 and ±85% for CH 4 as per the 2006 IPCC Guidelines, Table 3.27. Emission factor uncertainty According to the IPCC 2006 Guidelines, the uncertainty of the emission factors that accompanies the method used here is in the range of ±15% for CO 2 and ±85% for CH 4 as per the 2006 IPCC Guidelines, Table 3.27. Time-series consistency A Tier 1 approach was used throughout the time series with activity data being collected from industry. Carbon black produced via the acetylene black process was reported for the years 2016 to 2018 when the company shut down. 4.3.8.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.3.8.5 Category specific recalculations No recalculations were performed on this category. 4.3.8.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.9 Hydrogen Production (2.B.8.g) 141 4.3.9.1 Category description Hydrogen (H 2 ) is a gas with flammable properties like natural gas and gasoline. Currently hydrogen is used as raw material in refineries and in the production of ammonia, methanol and various chemicals (IPCC, 2019). Hydrogen can also be used as an energy carrier in the transport sector, as energy storage and buffer systems in renewable electricity production, as a main constituent in coal gas used for heating and cooking, as well as in semiconductor industry processing and welding (IPCC, 2019). Hydrogen can be produced through various processes, the most common being steam reforming (95%), which uses fossil fuels or renewable fuels as a feedstock. The use of fossil fuels results in GHGs being emitted (Zohuri, 2018). 4.3.9.2 Methodological issues A Tier 3 approach was used for calculating GHG emissions from hydrogen production. Data Providers used a mass balance approach to estimate emissions from 2020 onwards. Activity data The hydrogen production emissions data were sourced from the production plants and activity data was not supplied in this report due to confidentiality issues. Emission factors The emission factors are not provided in this report as the information is confidential. 4.3.9.3 Uncertainties and time-series consistency Activity data uncertainty According to the IPCC 2019 Refinements (p. 3.50), the uncertainty of the activity data when plant level data is available is ±2%. Emission factor uncertainty Uncertainty is not provided in the IPCC 2019 Refinements for emission factors; therefore, it was assumed to be the same as with activity data, ±2%. Time-series consistency The time series is not consistent as hydrogen production is a new category added in 2018. Historical data is not available. The time series will be built on in future as more data becomes available. 142 4.3.9.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.3.9.5 Category specific recalculations No recalculations were performed on this category. 4.3.9.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.3.10 Other Chemical Processes (2.B.10) 4.3.10.1 Category description This category includes emissions from chemicals manufacturing industries that are not covered elsewhere in the 2006 IPCC Guidelines. Emissions of CO 2 and CH 4 are reported within this category. Process emission from the Phthalic Anhydride process and partial oxidation of Butane and Ortho-Xylene are included in this category. 4.3.10.2 Methodological issues A Tier 3 approach was used for calculating GHG emissions from this subcategory. Data Providers used stack monitoring reports to estimate emissions from 2020 onwards. Activity data The emissions data were sourced from the production plants and activity data was not supplied in this report due to confidentiality issues. Emission factors The emission factors are not provided in this report as the information is confidential. Tier 3 calculations are used for reporting in this sectors. 4.3.10.3 Uncertainties and time-series consistency Activity data uncertainty 143 The 2006 IPCC Guidelines have not provided guidance on the uncertainty from activity data therefore an assumption of ±5% was made based on the default uncertainty from Carbide production for both CO 2 and CH 4 . Emission factor uncertainty The 2006 IPCC Guidelines have not provided guidance on the uncertainty from emission factors therefore an assumption of ±5% was made based on the default uncertainty from Carbide production for both CO 2 and CH 4 . Time-series consistency The time series is not consistent as this is a new category added in 2020. Historical data is not available. The time series will be built on in future as more data becomes available. 4.3.10.4 Category specific QA/QC and verification No category specific QC checks were completed for this sub-category. 4.3.10.5 Category specific recalculations No recalculations were performed on this category. 4.3.10.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. Metal industry (2.C) This subcategory relates to emissions resulting from the production of metals. Processes covered for this inventory report include the production of iron and steel, ferroalloys, aluminium, lead, and zinc. Estimates were made for emissions of CO 2 from the manufacture of all the metals and emissions of CH 4 were estimated from Iron and steel production and Ferroalloy production, and perfluorocarbons (CF 4 and C 2 F 6 ) from aluminium production. Due to the moving towards Tier 3 calculations and estimation methodologies, CH 4 emissions are not estimated for all entities and uncertainties might be higher than stated. 4.4.1 Iron and steel production (2.C.1) 144 4.4.1.1 Category description Iron and steel production results in the emission of CO 2 , CH 4 and N 2 O. According to the 2006 IPCC Guidelines (p. 4.9), the iron and steel industry broadly consists of primary facilities that produce both iron and steel; secondary steel-making facilities; iron production facilities; and offsite production of metallurgical coke. According to the World Steel Association (2010), South Africa is the 21 st -largest crude steel producer in the world. The range of primary steel products and semi-finished products manufactured in South Africa includes billets; blooms; slabs; forgings; light-, medium- and heavy sections and bars; reinforcing bar; railway track material; wire rod; seamless tubes; plates; hot- and cold-rolled coils and sheets; electrolytic galvanised coils and sheets; tinplate; and pre-painted coils and sheets. The range of primary stainless-steel products and semi- finished products manufactured in South Africa include slabs, plates, and hot- and cold- rolled coils and sheets. COVID-19 had a negative effect on iron and steel production, but the market recovered to a large extend afterwards. The negative market effect was however used to shut down certain plants or areas for major maintenance. 4.4.1.2 Methodological issues Historically a Tier 1 approach was applied to calculation emissions from Iron and steel production. A Tier 1 and 3 approach was applied for the different process types in 2020 and Tier 3 was used from 2022. Default IPCC emission factors were used for the calculation of GHG emissions from basic oxygen furnace, electric arc furnace, pig iron production, direct reduced iron production and sinter production. Industry also used a Tier 3 approach to estimate emissions from electric arc furnaces using raw material input and output. Energy-related emissions from Iron and steel production have been accounted for under the Energy Sector. Activity data Activity data was provided by the Iron and steel production plants (Table 4.8) via the SAGERS Portal, through the GHG Reporting Programme. Table 4.8: Production data for the iron and steel industry, 2000 – 2022. Basic oxygen furnace Electric arc Pig iron Direct reduced iron Sinter Other Production (tonne) 2000 4 674 511 4 549 828 4 674 511 1 552 553 705 872 2001 4 849 655 4 716 954 4 849 655 1 220 890 706 225 145 2002 5 051 936 4 888 870 5 051 936 1 340 976 706 578 2003 5 083 168 5 353 456 4 474 699 1 542 008 706 931 2004 4 949 693 5 508 488 4 224 487 1 632 767 733 761 2005 5 255 831 5 089 818 4 441 904 1 781 108 735 378 2006 5 173 676 5 413 204 4 435 551 1 753 585 739 818 2007 4 521 461 5 473 908 3 642 520 1 735 914 705 428 2008 4 504 275 4 581 523 3 746 786 1 177 925 460 746 2009 3 953 709 4 359 556 3 184 566 1 339 720 429 916 2010 4 366 727 4 235 993 3 695 327 1 120 452 584 452 2011 3 991 686 3 554 803 4 603 558 1 414 164 570 129 2012 3 904 276 3 904 276 4 599 015 1 493 420 677 891 2013 4 271 948 3 292 870 4 927 550 1 295 000 590 356 2014 3 622 909 2 789 291 4 401 734 1 611 530 585 728 2015 3 907 513 2 490 587 4 463 759 1 124 971 581 399 2016 3 498 862 3 039 702 4 650 922 1 806 067 577 332 2017 4 242 431 1 153 047 101 115 660 605 3 631 445 107 153 2018 4 967 074 1 443 030 837 515 4 060 025 107 153 2019 4 242 431 1 555 741 10 115 684 134 3 631 445 2020 2 085 522 1 785 642 48 670 236 497 2 173 112 2021 2 898 267 1 789 950 220 417 297 316 2 712 917 2022 3 801 800 149 784 Emission factors Historically IPCC default emission factors were applied for the calculation of emissions from Iron and steel production. From 2022 all reporting was done using a Tier 3 approach based on mass balance. 4.4.1.3 Uncertainties and time-series consistency Activity data uncertainty As historically both a Tier 1 and Tier 3 approach was used to estimate emissions from this category it was assumed that the uncertainty ranged from ±5% to ±10% as per the 2006 IPCC Guidelines (Table 4.4). From 2022 a Tier 3 approach is used, and uncertainty is estimated as ±5%. Emission factor uncertainty The IPCC 2006 Guidelines indicate that applying Tier 1 to default emission factors for iron and steel production may have an uncertainty of ± 25% (IPCC, 2006, Table 4.4). As both a Tier 1 and Tier 3 approach was used historically to estimate emissions an uncertainty 146 range of ±5% and ±25% was used for the EF. From 2022 a Tier 3 approach is used, and uncertainty is estimated as ±5%. Time-series consistency Data was not consistent throughout the time series as the data was provided by different sources for the 2019 inventory. From 2020 the inventory included activity data directly from industry via the SAGERS through the GHG Reporting Programme. Prior to 2017 sinter production was not reported, however, since the promulgation of the NGER there has been disaggregation between direct reduced iron and sinter processes. Due to the effects of COVID-19, steel production values also differ with regards to production methodology for the past few years. 4.4.1.4 Category specific QA/QC and verification External verification of 2022 data was done. 4.4.1.5 Category specific recalculations No recalculations were performed on the emissions from this subcategory. 4.4.1.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. An improvement to consider in the future is the estimation of CH 4 emissions. 4.4.2 Ferroalloys production (.C.2) 4.4.2.1 Category description Ferroalloy refers to concentrated alloys of iron and one or more metals such as silicon, manganese, chromium, molybdenum, vanadium and tungsten. Ferroalloy plants manufacture concentrated compounds that are delivered to steel production plants to be incorporated in alloy steels. Ferroalloy production involves a metallurgical reduction process that results in significant CO 2 emissions (IPCC, 2006, p. 4.32). South Africa is the world’s largest producer of chromium and vanadium ores, and the leading supplier of these alloys (DMR, 2015). South Africa is also the largest producer of iron and manganese ores and an important supplier of ferromanganese, ferrosilicon and silicon metal (DMR, 2013). COVID-19 had a negative effect on ferroalloys production, but the market recovered to a large extend afterwards. The negative market effect was however used to shut down certain plants or areas for major maintenance. 147 4.4.2.2 Methodological issues Historically a Tier 1 and 3 approach was applied across the different ferroalloy production plants for different types of ferroalloys. From 2022 only a Tier 3 approach is applied. Activity data Ferroalloy production data for 2000 to 2012 were obtained from ferroalloy production plants. Activity data from 2013 to 2017 were obtained from the SAMI Annual Reports (DMR. 2019), however, from 2018 activity data was again supplied by the ferroalloy production plants due to the implementation of the GHG Reporting Regulations. Activity data is provided in Table 4.9. For ferromanganese production the 7% C values were taken to be the high and medium carbon ferromanganese and the 1% C values were the other manganese alloys (DMR, 2013, 2015). For 2014 and 2017 the split between 7% and 1% was not provided (only a total manganese value) therefore the split from 2013 was applied. A drop in silicon metal production was observed in 2017 due to the closure of most furnaces because of low demand and high electricity tariffs (DMR, 2019). Most plants provided a split between ferromanganese 1% and 7%, however, where the split was not made, the split from 2013 was applied. This inconsistency was overcome form 2018 as the SAGERS requires the split to be made. Table 4.9: Production data for the ferroalloy industry, 2000 – 2022. Ferro- chromium Ferro- manganese (7% C) Ferro- manganese (1% C) Ferro- silicon (65% Si) Ferro- silicon (75% Si) Silicon metal Ferro- vanadium Silico- manganese Other Production (tonne) 2000 2 574 000 596 873 310 400 108 500 40 600 2001 2 141 000 523 844 259 176 107 600 39 400 2002 2 351 000 618 954 315 802 141 700 42 500 2003 2 813 000 607 362 313 152 135 300 48 500 2004 3 032 000 611 914 373 928 140 600 50 500 2005 2 802 000 570 574 275 324 127 000 53 500 2006 3 030 000 656 235 277 703 148 900 53 300 2007 3 561 000 698 654 327 794 139 600 50 300 2008 3 269 000 502 631 259 014 134 500 51 800 2009 2 346 000 274 923 117 683 110 400 38 600 2010 3 607 000 473 000 317 000 127 700 46 400 2011 3 422 000 714 000 350 000 126 200 58 800 2012 3 063 000 706 000 177 000 83 100 53 000 148 2013 3 219 000 681 000 163 000 78 400 34 000 2014 3 719 000 814 263 194 737 87 700 47 200 2015 3 685 000 492 000 123 000 91 800 46 300 2016 3 524 000 296 000 74 000 73 200 26 600 2017 3 268 000 354 400 88 600 48 200 4 700 2018 3 578 355 144 436 36 109 101 815 46 062 3 049 2019 1 854 178 106 397 100 858 96 614 31 285 38 627 2020 2 971 325 1 178 851 55 395 47 925 5 414 108 060 66 370 2021 3 772 366 897 259 56 579 63 105 150 553 231 789 2022 3 828 284 821 314 76 986 43 766 1 139 1 137 133 889 133 889 Emission factors Historically Tier 1 emission factors where used. Over the past 3 years it moved more towards a Tier 3 approach to the point that 2022 is purely Tier 3. Due to certain assumptions for the Tier 3 approach, there is still higher uncertainty with CH 4 emissions and Tier 1 is used in some instances. 4.4.2.3 Uncertainties and time-series consistency Activity data uncertainty An uncertainty of ±5% on activity data was assumed as per the 2006 IPCC Guidelines (Table 4.9) (IPCC, 2006, Table 4.9). The uncertainty was assumed to be the same for both CO2 and CH 4 . Emission factor uncertainty IPCC 2006 Guidelines indicates that for Tier 1, the default emission factors may have an uncertainty of ± 25% (IPCC, 2006, Table 4.9). For this inventory, as both a Tier 1 and Tier 3 approach was used for CH 4 the uncertainty range was assumed to be between ±5% and ±25% for and CH 4 . CO 2 uncertainty is assumed to be ±5%. Time-series consistency The time series is not consistent due to a change in data sources in 2018 and 2019. From 2020 industry reported via the SAGERS through the GHG Reporting Programme brought better consistency. 4.4.2.4 Category specific QA/QC and verification The 2022 data was verified externally by the respective companies. 149 4.4.2.5 Category specific recalculations No recalculations were performed for the category. 4.4.2.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.4.3 Aluminium production (2.C.3) 4.4.3.1 Category description Aluminium production takes place via the Hall-Heroult electrolytic process. In this process, electrolytic reduction cells differ in the form and configuration of the carbon anode and alumina feed system. The most significant process emissions are (IPCC, 2006, p. 4.43): • CO 2 emissions from the consumption of carbon anodes in the reaction to convert aluminium oxide to aluminium metal; • PFC emissions of CF 4 and C 2 F 6 during anode effects. Also emitted are smaller amounts of process emissions, CO, Sulphur dioxide (SO 2), and NMVOCs. 4.4.3.2 Methodological issues A Tier 1 approach was used for CO 2 emission estimation, while a Tier 3 methodology was applied to the PFCs between 2000 and 2012. In the Tier 3 approach the amount of CF 4 and C 2 F 6 produced were tracked and used to determine emissions in this category. The Tier 3 method was then extrapolated for the 2013-15 period (using activity data and an implied emission factor). It is considered that the extrapolation of a Tier 3 method might overestimate or underestimate the emissions. Therefore, in the 2000-2017 inventory this was corrected so that actual plant-performance data is used to quantify emissions for the 2013-2017 period. A Tier 3 method was used to estimate emissions for both CO 2 and PFCs from 2018. Activity data The source of activity data for Aluminium production were the aluminium production plants. Data is not shown in this report due to confidentiality. Emission factors The emission factors are not provided in this report as the information is confidential. 150 4.4.3.3 Uncertainties and time-series consistency Activity data uncertainty As per the 2006 IPCC Guidelines, uncertainty is minimal regarding aluminium production data. A default uncertainty of ±1% is used (IPCC, 2006, Page 4.57). Emission factor uncertainty The uncertainty on the Tier 3 CO 2 emission factors for aluminium production is ±5% (IPCC, 2006, Page 4.56) for estimating CO 2 emissions. Even though a Tier 3 approach was used for aluminium production PFC emissions, no data was collected on uncertainty. The Tier 3 default uncertainty for CF 4 and C 2 F 6 are indicated to be ±15% (IPCC, 2006, page 4.56). Time-series uncertainty Emissions were estimated using a Tier 1 approach for Prebake and Soderberg processes from 2000 to 2013 and 2000 to 2008, respectively. A Tier 3 approach was used to estimate emissions from Centre-Worked Prebake (CWPB), Side Worked Prebake (SWPB), Vertical Stud Søderberg (VSS) and Horizontal Stud Søderberg (HSS) processes from 2000 to 2008. The Soderberg, SWPB, VSS and HSS processes were stopped in 2008. A Tier 3 approach was used to estimate emissions for CWPB from 2012 onwards. 4.4.3.4 Category specific QA/QC and verification The 2022 data has been externally verified. 4.4.3.5 Category specific recalculations No recalculations were performed for this category. 4.4.3.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.4.4 Magnesium production (2.C.4) There is no Magnesium production occurring in South Africa. 151 4.4.5 Lead production (2.C.5) 4.4.5.1 Category description According to the 2006 IPCC Guidelines, there are two primary processes to produce lead bullion from lead concentrates: • Sintering/smelting, which consists of sequential sintering and smelting steps and constitutes approximately 7% of the primary production; and • Direct smelting, which eliminates the sintering step and constitutes 22% of primary lead production. Secondary lead processing, according to the 2006 IPCC Guidelines, involves the recycling of lead acid batteries. Recycling occurs through the following processes: • crushed using a hammer mill and entered the smelting process with or without desulphurization; or • smelted whole 4.4.5.2 Methodological issues Emissions from Lead production were estimated using a Tier 1 approach. Activity data Lead production data for 2000 to 2017 were obtained from the SAMI Annual Reports (DMR, 2019) and are provided in Table 4.10, however from 2018 activity data was supplied by the lead production plants through the GHG Reporting Programme, via the SAGERS. Table 4.10: Production data for the lead and zinc industries, 2000 – 2022. Lead Zinc Production (tonne) 2000 75 300 63 000 2001 51 800 61 000 2002 49 400 64 000 2003 39 900 41 000 2004 37 500 32 000 2005 42 200 32 000 2006 48 300 34 000 2007 41 900 31 000 152 2008 46 400 29 000 2009 49 100 28 000 2010 50 600 36 000 2011 54 460 37 000 2012 52 489 37 000 2013 41 848 30 145 2014 29 348 26 141 2015 34 573 29 040 2016 39 344 26 695 2017 48 150 30 778 2018 32 383 21 090 2019 37 519 20 918 2020 32 860 21 268 2021 35 948 22 874 2023 35 819 3 Emission factors IPCC 2006 default emission factor of 0.2 t CO 2 per tonne of lead produced was applied (IPCC, 2006, Table 4.21). 4.4.5.3 Uncertainties and time-series consistency Activity data uncertainty For lead production emissions using the Tier 1 method there is a ±10% uncertainty on the activity data (IPCC, 2006, Table 4.23). Emission factor uncertainty Uncertainty for default lead production emission factors has an uncertainty of ±50% (IPCC, 2006, Table 4.23). Time-series consistency The time series is not consistent as the source of activity data changed in 2018 from the SAMI Report (DMR, 2019) to industry reporting via the SAGERS Portal. 4.4.5.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 153 4.4.5.5 Category specific recalculations A recalculation was undertaken for the 2000 – 2019 where initially emissions were estimated using the emissions factor 0.52 tonnes CO 2 /tonne product. In 2020 DFFE was made aware that the production was not primary but secondary and the emission factor was adjusted to 0.2 tonnes CO 2 /tonne product. 4.4.5.6 Category specific planned improvements There is currently no planned improvement projects. 4.4.6 Zinc production (2.C.6) 4.4.6.1 Category description According to the 2006 IPCC Guidelines, there are three primary processes to produce zinc: • Electro-thermic distillation: this is a metallurgical process that combines roasted concentrate and secondary zinc products into sinter that is combusted to remove zinc. Halides, cadmium, and other impurities. The reduction results in the release of non-energy CO 2 emissions. • The pyrometallurgical process: this involves the utilization of an Imperial Smelting Furnace. Which allows for the simultaneous treatment of zinc and zinc concentrates. The process results in the simultaneous production of lead and zinc and the release of non-energy CO 2 emissions. • Electrolytic: this is a hydrometallurgical technique. During which zinc sulphide is calcinated. Resulting in the production of zinc oxide. The process does not result in non-energy CO 2 emissions. 4.4.6.2 Methodological issues Emissions from Zinc production were estimated using a Tier 1 approach. Activity data In the previous submission the Zinc production data was supplied by industry, however this was not available for the period 2018 to 2021 and an extrapolation was applied. The 2022 data was sourced from industry. Emission factors 154 The IPCC, 2006 default emission factor of 1.72 t CO 2 per tonne zinc produced was applied. It was assumed that for Zinc production it was 60% imperial smelting and 40% Waelz Kiln (IPCC, 2006). 4.4.6.3 Uncertainties and time-series consistency Activity data uncertainty For Zinc production emissions there is a ±10% uncertainty on the activity data (IPCC, 2006, Table 4.25). Emission factor uncertainty Uncertainty for default Zinc production emission factors has an uncertainty of ±50% (IPCC, 2006, Page 4.25). Time-series consistency The time series is inconsistent as activity data has not been available consistently since 2017 and an extrapolation was undertaken to obtain data values where necessary. 4.4.6.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.4.6.5 Category specific recalculations No recalculations were performed for this category. 4.4.6.6 Category specific planned improvements There is currently no planned improvement projects. 4.4.7 Other (2.C.7) No activities were included under this sub-category. Non-Energy Products from Fuels and Solvent Use (2.D) 155 Non-energy use of fuels and solvents includes lubricants. Paraffin wax and solvents. Lubricants are divided into two types, namely, motor and industrial oils, and greases that differ in physical characteristics. Paraffin wax is used in products such as petroleum jelly, paraffin waxes and other waxes (saturated hydrocarbons). Paraffin waxes are used in applications such as candles, corrugated boxes, paper coating, board sizing, food production, wax polishes, surfactants (as used in detergents) and many others (IPCC, 2006, p.5.11). The use of solvents can result in evaporative emissions of various NMVOCs. Which can be oxidized and released into the atmosphere. According to the 2006 IPCC Guidelines (p. 5.16). white spirit is used as an extraction solvent. Cleaning solvent. Degreasing solvent and as a solvent in aerosols, paints, wood preservatives, varnishes and asphalt products. Lubricants are used in industrial and transport applications. Emissions from solvents are not estimated due to a lack of data. 4.5.1 Lubricant use (2.D.1) 4.5.1.1 Category description According to the 2006 IPCC Guidelines Lubricants are produced either at refineries through separation from crude oil or at petrochemical facilities. 4.5.1.2 Methodological issues A Tier 1 method was applied to this subcategory. Activity data The source of activity data for solvents was the energy balance tables published annually by the DoE (Table 4.11). Activity data was interpolated for 2007 to 2010 using 2006 and 2011 data as the data was unreliable for this period. An extrapolation was applied to the 2020 activity data, as the updated energy balance tables are currently unavailable. Table 4.11: Lubricant and paraffin wax consumption, 2000 – 2022. Lubricant Paraffin wax Consumption (tonne) 2000 12 851 507 2001 15 093 314 2002 16 561 506 2003 16 430 521 2004 16 295 490 156 2005 31 549 350 2006 34 391 324 2007 34 913 141 2008 35 435 182 2009 35 957 231 2010 36 478 27.9 2011 37 000 52.6 2012 57 160 13 939 2013 35 574 207.2 2014 48 652 366.2 2015 48 531 121 608 2016 25 490 6 756 2017 28 940 7 240 2018 41 671 1 872 2019 38 180 96 263 2020 5 603 96 2021 36 769 37 944 2022 35 161 41 529 Emission factors The IPCC, 2006 default carbon content (20 tC/TJ) and oxidised fraction factor (0.2) applied to this subsector. 4.5.1.3 Uncertainties and time-series consistency Activity data uncertainty According to the IPCC guidelines much of the uncertainty in emission estimates is related to the difficulty in determining the quantity of non-energy products used in individual countries. For this a default of 5% may be used in countries with well-developed energy statistics and 10 to 20 % in other countries. Based on expert judgement of the accuracy of energy statistics (IPCC, 2006, Page 5.10). Therefore, an uncertainty range of ±10% to ±20% was used. Emission factor uncertainty The default oxidised during use (ODU) factors available in the IPCC guidelines are uncertain, as they are based on limited knowledge of typical lubricant oxidation rates. Expert judgment suggests using a default uncertainty of 50% (IPCC, 2006, Page 5.10). Time-series consistency 157 The time series is inconsistent as activity data has not been available for 2020 to 2022 and an extrapolation was undertaken to obtain data values. 4.5.1.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.5.1.5 Category specific recalculations No recalculations were performed for this category. 4.5.1.6 Category specific planned improvements The is a planned improvement project to determine the use of blended lubricant in two- stroke engines. 4.5.2 Paraffin wax use (2.D.2) 4.5.2.1 Category description According to the 2006 IPCC Guidelines this category includes petroleum jelly, paraffin waxes and other waxes, including ozokerite (mixtures of saturated hydrocarbons, solid at ambient temperature). Paraffin waxes are separated from crude oil during the production of light lubricating oils. 4.5.2.2 Methodological issues A Tier 1 method was applied to this subcategory. Activity data The source of activity data for solvents was the energy balance tables published annually by the DMRE (Table 4.11). An extrapolation was applied to the activity data from 2020, as the updated energy balance tables are currently unavailable. Emission factors The IPCC, 2006 default carbon content (20 tC/TJ) and oxidised fraction factor (0.2) applied to this subsector. 4.5.2.3 Uncertainties and time-series consistency Activity data uncertainty 158 According to the IPCC guidelines much of the uncertainty in emission estimates is related to the difficulty in determining the quantity of non-energy products used in individual countries. For this a default of 5% may be used in countries with well-developed energy statistics and 10 to 20 % in other countries, based on expert judgement of the accuracy of energy statistics (IPCC, 2006, Page 5.13). Emission factor uncertainty The default ODU factors available in the IPCC guidelines are very uncertain, as they are based on limited knowledge of typical lubricant oxidation rates. Expert judgment suggests using a default uncertainty of 50%. The carbon content coefficients are based on two studies of the carbon content and heating value of lubricants from which an uncertainty range of about ±3 % was estimated (IPCC, 2006, Page 5.13). Time-series consistency The time series is inconsistent as activity data has not been available from 2020 and an extrapolation was undertaken to obtain data values. 4.5.2.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.5.2.5 Category specific recalculations No recalculations were performed for this category. 4.5.2.6 Category specific planned improvements There are no subcategory specific planned improvements, but any updated information from SAGERS will be included. 4.5.3 Solvent use (2.D.3) No activities were included under this sub-category, due to the lack of data. Electronics industry (2.E) 159 Emissions from the Electronics industry in South Africa are not estimated due to a lack of data. DFFE will undertake a survey to estimate greenhouse gas emissions for this category and report progress in its future GHG inventory submissions. Product Uses as Substitutes for Ozone Depleting Substances (ODS) (2.F) The Montreal Protocol on Substances that Deplete the Ozone Layer (a protocol to the Vienna Convention for the Protection of the Ozone Layer) is an international treaty designed to protect the ozone layer by phasing out the production of numerous substances believed to be responsible for ozone depletion. HFCs and, to a limited extent, PFCs are ODS being phased out under this protocol. According to the 2006 IPCC Guidelines, current application areas of HFCs and PFCs include refrigeration and air conditioning; fire suppression and explosion protection; aerosols; solvent cleaning; foam blowing; and other applications (equipment sterilisation, tobacco expansion applications, and as solvents in the manufacture of adhesives, coatings, and inks). Emissions were only estimated from 2005 onwards due to a lack of data prior to that. The 2012 inventory only estimated emissions from refrigeration, but due to recent studies, this inventory includes emissions from air conditioning, foam blowing agents, fire protection and aerosols. Emissions from solvents are not estimated due to a lack of data. 4.7.1 Refrigeration and air conditioning (2.F.1) 4.7.1.1 Category description According to the 2006 IPCC Guidelines, refrigeration, and air-conditioning (RAC) systems may be classified in up to six sub-application categories: • Domestic refrigeration • Commercial refrigeration including different types of equipment, from vending machines to centralised refrigeration systems in supermarkets • Industrial processes including chillers, cold storage, and industrial heat pumps used in the food, petrochemical and other industries • Transport refrigeration including equipment and systems used in refrigerated trucks, containers, reefers, and wagons 160 • Stationary air conditioning including air-to-air systems, heat pumps, and chillers for building and residential applications • Mobile air-conditioning systems used in passenger cars, truck cabins, buses, and trains 4.7.1.2 Methodological issues The IPCC guidelines (IPCC, 2006) propose either an emissions factor approach at the sub- application level (Tier 2a) or a mass balance approach at the sub-application level (Tier 2b) to calculate emissions from RAC applications. In the HFC Emissions Database the emissions factor approach (Tier 2a) is primarily applied, with the mass balance approach applied for uncertainty purposes/checking. There was insufficient data to follow this approach for Commercial Refrigeration and Industrial Processes. Thus, a hybrid approach is applied for these sub-applications, which were combined into one application. Table 4.12 summarises the approach used for each sub-application in the RAC sector. Table 4.12: Methodology and data sources used for each RAC sub-application. Sub-application Method Motivation 161 Domestic Refrigeration Tier 2a (2b) Estimated the yearly data on existing, new and retired domestic refrigerators in South Africa based on data from Stats SA. Emission factors based on IPCC (2006) and other international studies. Estimated yearly sales of R134a for servicing and/or new equipment into domestic refrigeration from survey for cross checking. Commercial Refrigeration and Industrial Processes Tier 2b Estimated early sales of refrigerants into commercial refrigeration. Assumed share of refrigerant taken up into charging of new equipment. Emission factors based on IPCC (2006) and other international studies. Stationary Air Conditioning Tier 2a Yearly data on stationary air conditioning units (BSRIA) Emission factors based on IPCC (2006) and other international studies. Estimated yearly sales of refrigerants into stationary air conditioning for servicing and/or new equipment from survey for cross checking. Transport Refrigeration Tier 2a (2b) Yearly data on existing. new and retired refrigerated trucks based on previous studies (GIZ, 2014) and expert knowledge (SARDA). Emission factors based on IPCC (2006) and other international studies. Estimated yearly sales of R134a and R404a into transport refrigeration for servicing and/or new equipment from survey for cross checking. Mobile Air Conditioning Tier 2a (2b) Yearly data on existing. new and retired vehicles from eNaTIS and NAAMSA. Emission factors based on IPCC (2006) and other international studies. Estimated yearly sales of R-134a into mobile air conditioning for servicing and/or new equipment from survey for cross checking. Activity data Stakeholders in the refrigeration and air conditioning sector in South Africa were identified by means of desktop research and the membership lists of the various industry associates in the refrigeration and air conditioning sector, such as the South African Institute of Refrigeration and Air Conditioning (SAIRAC), the South African Refrigeration & Air Conditioning Contractors' Association (SARACCA) and SARDA. Other sources included the members of the DFFE’s Chemical Management Hydrochlorofluorocarbons (HCFC) working group, and importers and exporters listed in the International Trade Centre (ITC) website (Market Analysis and Research). Other literature and statistical data sources provided the activity data for other sub- applications, e.g., eNaTIS for vehicle data for mobile air conditioning and transport refrigeration and Stats SA for data on the number of households with refrigerators. Activity data from 2016 onwards was extrapolated due to inconsistent data availability. Emission factors 162 It was assumed that the equipment lifespan was 15 years and the emission factor from the installed base was 15% (IPCC, 2006). The percentage of HFC destroyed at the end- of-life was assumed to be 25% (IPCC, 2006). 4.7.1.3 Uncertainties and time-series consistency Activity data uncertainty An uncertainty of ±25% was assumed for activity data (IPCC, 2006). Emission factor uncertainty An uncertainty of ±25% was assumed for emission factors (IPCC, 2006). Time-series consistency Time series is not consistent over the full 20-year period as emission data is only available from 2005, with an enhanced data set (including mobile air conditioning) from 2011. Activity data from 2016 onwards was extrapolated due to inconsistent data availability. 4.7.1.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.7.1.5 Category specific recalculations No recalculations were performed for this category. 4.7.1.6 Category specific planned improvements It is planned that the HFC survey will be updated and will focus mostly on the refrigeration and air conditioning sector to improve emissions estimates form this category. 4.7.2 Foam blowing agents (2.F.2) 4.7.2.1 Category description HFCs are being used as replacements for Chlorofluorocarbons (CFCs) and HCFCs in foams and particularly in insulation applications according to the 2006 IPCC Guidelines. Compounds that are being used include HFC-245fa, HFC-365mfc, HFC-227ea, HFC-134a, and HFC-152a. The division of foams into open-cell or closed-cell relates to the way in which blowing agent is lost from the products. 163 For open-cell foam. emissions of HFCs used as blowing agents are likely to occur during the manufacturing process and shortly thereafter. In closed-cell foam, only a minority of emissions occur during the manufacturing phase. Emissions therefore extend into the in- use phase, with often the majority of emissions not occurring until end-of-life (de- commissioning losses). 4.7.2.2 Methodological issues HFC emissions from foam blowing applications are calculated in the HFC Emissions Database following the approach in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Chapter 7: Emissions of Fluorinated Substitutes for Ozone Depleting Substances), as given in Equation 3 (IPCC, 2006). This formula calculates the emissions based on the amount of HFC lost during manufacture and the first year of foam use, the annual amount lost from HFC-containing foams in use (banks), and the amount lost at the end of the foams’ life when products are decommissioned, less the amount of HFC recovered or destroyed from decommissioned foam products. Activity data Where data is difficult to obtain in the country the IPCC guidelines suggest obtaining historic regional usage to account for HFC banks and emissions factors from the UNEP Foams Technical Options Committee (FTOC). The latest United Nations Environmental Programme (UNEP) FTOC report suggests that in 2008 only 0.15% of the foam bank within developing nations contained HFCs and that sub-Saharan Africa had not utilised any HFC for foam manufacture at this time (UNEP, 2010). This suggests that the HFC- containing foam bank in South Africa is limited and the foam bank in the HFC Emissions are therefore estimated by simply extrapolating the annual net consumption data for 2010-2020 back to the date HFC blowing agent was introduced into South Africa (2005). Activity data was assumed to be stable from 2016. Emission factors It was assumed that the equipment lifespan was 15 years and the emission factor from the installed base was 15% (IPCC, 2006). The percentage of HFC destroyed at the end- of-life was assumed to be 25% (IPCC, 2006). Other factors applied are shown in Table 4.13. Table 4.13: Emission factors and defaults applied in the foam blowing agents emission estimates. Sub-category Value Units Source Product life 34 Years 164 First year loss 14 % (UNEP, 2005; IPCC. 2006) Annual loss 0.66 % Landfilling loss 16 % Landfill annual loss 0.75 % 4.7.2.3 Uncertainties and time-series consistency Activity data uncertainty An uncertainty of ±25% was assumed for activity data (IPCC, 2006). Emission factor uncertainty An uncertainty of ±25% was assumed for emission factors (IPCC, 2006). Time-series consistency Time series is not consistent over the full 20-year period as emission data for this sub- category is only available from 2011. Due to a lack of consistency in data availability, activity data was assumed to be the same from 2016. 4.7.2.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.7.2.5 Category specific recalculations No recalculations were performed for this category. 4.7.2.6 Category specific planned improvements No improvements are planned currently. 4.7.3 Fire protection (2.F.3) 4.7.3.1 Category description According to the 2006 IPCC Guidelines there are two general types of fire protection (fire suppression) equipment that use HFCs and/or PFCs as partial replacements for halons: portable (streaming) equipment, and fixed (flooding) equipment. While actual emissions from the fire protection sub-sector are expected to be quite small, the use is normally non-emissive in provision of stand-by fire protection and is growing. 165 4.7.3.2 Methodological issues Emissions from fire protection applications are expected to be small because their use is non-emissive, that is, they are used in the provision of stand-by fire protection equipment. However, this does result in an accumulating bank of gas that has the potential to be released in the future when equipment is decommissioned (IPCC, 2006). The emissions from the fire protection sector are calculated in accordance with the approach suggested by the IPCC guidelines, Equation 12 and Equation 13. Activity data Emissions from fire protection equipment are estimated using local sales data from eight importers/distributors of fire protection equipment and gases. This yielded very similar results to those calculated from net consumption (imports minus exports) of ten companies importing fire suppression agents. Activity data from 2016 onwards was extrapolated due to inconsistent data availability. Emission factors Emissions from Fire Protection were calculated in accordance with the IPCC guidelines and an emission factor was calculated based on the fraction of agent in equipment emitted each year (excluding emissions from retired equipment or otherwise removed from service), dimensionless. However, none of the contractors or wholesalers of the agents interviewed could provide an estimation of the fraction of agent emitted each year r the emissions of agent during recovery, recycling or disposal at the time of removal from service. However, experience gained with the emissions patterns of halon substances has yielded valuable lessons in terms of emissions factors for fire suppression agents. A proposed emissions factor of 4% of in-use quantities is assumed. as proposed by the IPCC (IPCC, 2006). 4.7.3.3 Uncertainties and time-series consistency Activity data uncertainty An uncertainty of ±25% was assumed for activity data (IPCC, 2006). Emission factor uncertainty An uncertainty of ±25% was assumed for emission factors (IPCC, 2006). Time-series consistency 166 Time series is not consistent over the full 20-year period as emission data for this sub- category is only available from 2011. Activity data from 2016 onwards was extrapolated due to inconsistent data availability. 4.7.3.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.7.3.5 Category specific recalculations No recalculations were undertaken for this category as they were not previously estimated. 4.7.3.6 Category specific planned improvements No further improvements are planned for this sub-category. 4.7.4 Aerosols (2.F.4) 4.7.4.1 Category description Most aerosol packages contain hydrocarbon (HC) as propellants but. in a small fraction of the total. HFCs and PFCs may be used as propellants or solvents. Emissions from aerosols usually occur shortly after production. on average six months after sale. During the use of aerosols, 100 percent of the chemical is emitted. The 5 main sub-applications are as follows: • Metered dose inhalers (MDIs) • Personal care products (e.g., hair care. deodorant. shaving cream) • Household products (e.g., air-fresheners. oven and fabric cleaners) • Industrial products (e.g., special cleaning sprays such as those for operating electrical contact. lubricants. pipe-freezers) • Other general products (e.g., silly string. tyre inflators. klaxons) 4.7.4.2 Methodological issues An emission factor approach on a sub-application level (Tier 2a) was applied to calculate emissions from aerosols. However, data from gas suppliers could not be disaggregated into sub-applications, resulting in a Tier 1a approach being applied in addition to the Tier 2a approach. Activity data 167 Data on the number of aerosol products sold locally at the sub-application level (e.g. number of individual metered dose inhalers, hair care products, and tyre inflators. etc.), as well as the average charge of propellant per container, is required. In the HFC emissions database aerosols are grouped into the following sub-applications: • MDIs • Personal care products • Household products • Industrial products • Other general products Data on aerosol imports and exports had to be obtained directly from the companies/distributors, as trade data could not be used because official import statistics for aerosol products do not differentiate HFC-containing aerosols from other alternatives. Furthermore, import/export figures are typically reported in million units with no indication of the mass of the product or the type or loading of propellant, rendering them unusable for HFC emissions estimation. Due to a lack of consistency in data availability, activity data was assumed to be the same from 2016. Emission factors The simplified default approach in Equation 2 assumes that all emissions associated with aerosols and metered dose inhalers occur during the use phase, that there are zero losses on the initial charge of the product during manufacture, zero leakages during the life of the product and zero emissions from the disposal of the product. A product life span of two years translates to a default EF of 50% of the initial charge per year (IPCC, 2006). 4.7.4.3 Uncertainties and time-series consistency Activity data uncertainty An uncertainty of ±25% was assumed for activity data (IPCC, 2006). Emission factor uncertainty An uncertainty of ±25% was assumed for emission factors (IPCC, 2006). Time-series consistency Time series is not consistent over the full 20-year period as emission data for this sub- category is only available from 2011. Due to a lack of consistency in data availability, activity data was assumed to be the same from 2016. 168 4.7.4.4 Category specific QA/QC and verification No specific QC checks were completed for this sub-category. 4.7.4.5 Category specific recalculations No recalculations were performed for this category as they were not previously estimated. 4.7.4.6 Category specific planned improvements No improvements are currently planned. Other product manufacture and use (2.G) Emissions from this category were not estimated for South Africa due to a lack of data. Other (2.H) Emissions from this category were not estimated for South Africa due to a lack of data from the Pulp and Paper Industry and the Food and Beverages Industry. 169 Chapter 4: References DEA, 2016, National Greenhouse Gas Emission Reporting Regulations, https://cer.org.za/wp-content/uploads/2016/08/GHG-emission-reporting- regs.pdf DEA, 2016, Survey on HFC and PFC consumption. application and production in South Africa. DEFF, 2020, Amendments to the National Greenhouse Gas Emission Reporting Regulations, https://www.gov.za/documents/national-environmental- management-air-quality-act-regulations-national-greenhouse-gas-3 DFFE, 2022, SAGERS Portal, https://ghgreporting- public.environment.gov.za/GHGLanding/SAGERSHome.html [Accessed September 2022 DMR, 2001a, Department of Minerals and Resources Annual Report 2001/2002, Pretoria DMR, 2001b, South Africa’s mineral industry (SAMI) 2001/2002, Pretoria DMR, 2004, South Africa’s mineral industry (SAMI) 2004/2005, Pretoria DMR, 2005, South Africa’s mineral industry (SAMI) 2005/2006, Pretoria DMR, 2006, South Africa’s mineral industry (SAMI) 2006/2007, Pretoria DMR, 2006, South African Ferroalloy Production Trends 1995- 2004, Directorate: Mineral Economics, Report 52/2006, Pretoria DMR, 2008, South Africa’s mineral industry (SAMI) 2007/2008, Pretoria DMR, 2009, South Africa’s mineral industry (SAMI) 2009/2010, Pretoria DMR, 2010c, Lime Industry SA 2010, Directorate: Mineral Economics, Report 85, Pretoria DMR, 2010d, Operating and Developing Coal Mines in the Republic of South Africa 2010, Directorate: Mineral Economics, Directory D2/2010, Pretoria DMR, 2010e, Producers of Industrial Mineral Commodities in South Africa, Directorate: Mineral Economics, Directory D11/2010, Pretoria DMR, 2010f, South Africa’s mineral industry (SAMI) 2009/2010, Pretoria DMR, 2010g, Statistical Tables, 1988 -2009, Directorate: Mineral Economics, Bulletin B1/2010, Pretoria DMR, 2010h, Dolomites and Limestone in South Africa: Supply and Demand, Directorate: Mineral Economics, Report R49/2005, Pretoria 170 DMR, 2011, Operating Mines and Quarries and Mineral Processing Plants in the Republic of South Africa, 2011, Directorate: Mineral Economics, Directory D1/2011, Pretoria DMR, 2016, South Africa’s mineral industry (SAMI) 2014/2015, Pretoria DMR, 2019, South Africa’s mineral industry (SAMI) 2017/2018, Pretoria IPCC, 2006, 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme. Eggleston H S. Buenida L. Miwa K. Ngara T. and Tanabe K. eds; Institute for Global Environmental Strategies (IGES). Hayama. Kanagawa, Japan IPCC, 2019, 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 3 Industrial Processes and Product Use, https://www.ipcc- nggip.iges.or.jp/public/2019rf/vol3.html [Accessed: September 2022] PFG Building Glass, 2022, All you need to know about glass, https://pfg.co.za/all-about- glass/ [ Accessed September 2022]. SAISI, 2009, Local Sales of Iron and Crude Steel Production in South Africa, South Africa Iron and Steel Institute, http://www.saisi.co.za/localsales.php, [Accessed September 2012]. StatsSA., 2022, Economic recovery from Covid-19: Not all countries are equal, https://www.statssa.gov.za/?p=15690 [Accessed September 2022] United Nations Environment Programme, 2010, 2010 Rigid and Flexible Foams Report, https://ozone.unep.org/sites/default/files/2019-05/FTOC-2010-Assessment- Report.pdf [Accessed September 2022] United Nations, 1989, Montreal Protocol on Substances that Deplete the Ozone Layer (with annex), Concluded at Montreal on 16 September 1987, https://treaties.un.org/doc/publication/unts/volume%201522/volume-1522-i- 26369-english.pdf [Accessed September 2022] World Steel Association, 2010, Key Facts about World Steel Industry, http://www.worldsteel.org/media-centre/key-facts.html [Accessed May 2012] Zohuri, 2018, Hydrogen Energy Technology. Renewable Source of Energy, https://www.researchgate.net/publication/321303044_Hydrogen_Energy_Tech nology_Renewable_Source_of_Energy?enrichId=rgreq- bc7add7b764529b522a3033f1b0aae38- XXX&enrichSource=Y292ZXJQYWdlOzMyMTMwMzA0NDtBUzo2MDA2Mzc5ODk 0NjIwMTdAMTUyMDIxNDY2NDk1NA%3D%3D&el=1_x_3&_esc=publicationCov erPdf [Accessed September 2022] 171 Chapter 5: Agriculture (CRT sector 3) Sector overview This chapter includes GHG emissions and removals from Agriculture sector. Based on the IPCC 2006 Guidelines, the main categories included in the emission estimates for the agriculture sector are shown in Table 5.1. Table 5.1: Main IPCC categories included in the Agriculture sector emission estimates. IPCC Category Category name Included 3A Enteric fermentation √ 3B Manure management √ 3C Rice cultivation NO 3D1 Direct N 2 O emissions from managed soils √ 3D2 Indirect N 2 O from managed soils √ 3E Prescribed burning of savannahs IE 3F Field burning of agricultural residues √ 3G Liming √ 3H Urea application √ 3I Other carbon containing fertilisers NE Livestock included are dairy cattle, other cattle, sheep, goats, horses, mules and asses, swine and poultry. Emissions from ruminants in privately owned game parks were excluded due to comments made during the UNFCCC review. Rice cultivation is not included. Food and Agriculture Organization (FAO) statistics indicate that there is a small area of rice cultivation in South Africa and therefore in the UNFCCC review it was indicated that this should be investigated and included if necessary. Discussions with various experts at the ARC suggests that there have been some small experimental plots for rice cultivation, but the precise area was not known but it is thought to be less than 50 ha. For this reason, rice cultivation is considered insignificant. Emissions from fuel combustion in this sector are not included here as these falls under Transport (category 1A4c Agriculture/forestry/fisheries) in the Energy sector subsector (see Section 3.2.7). 172 5.1.1 Overview of shares and trends in emissions In 2022 the Agriculture sector produced 53 519 Gg CO 2 e which is 11% of South Africa’s total emissions. The largest source category in 2022 is Enteric fermentation category which contributed 36 352 Gg CO 2 e, 68% to the total agricultural sector emissions as shown in Figure 5.1 below. Overall decreasing trend, the total emissions were 9% lower in 2022 compared to 2000 levels, this is due to decrease in livestock population numbers. Figure 5.1: Trend in emissions from Agriculture sector, 2000 – 2022 5.1.1.1 Enteric fermentation Enteric fermentation emissions have been fairly constant between 2000 and 2015, with a slight decline in 2006 and a slight increase in 2012. After 2015 emissions declined to 2020 (Table 5.2; Figure 5 ). This trend follows the same pattern as cattle population, the largest contributors to emissions from the livestock population data (Figure 5). The main reasons for the declining livestock numbers in recent years are the consecutive droughts that occurred in 2015 and 2016 (BFAP, 2018) and livestock owners struggling to rebuild their herds to pre-2014 levels. 173 The other cattle population has declined by 12.5% since 2014, leading to a decline in other cattle emissions. In comparison to other cattle, the total number of dairy cattle (less than 10% of the cattle population) declined slightly between 2000 and 2007 but returned to similar levels by 2017. There was a slight drop in 2019, with numbers recovering in 2020 and this is reflected in the Enteric fermentation emissions. Poultry numbers have also increased, mainly due to chicken being a cheaper meat and in higher demand. Poultry do not use enteric fermentation to break down food, therefore do not contribute to the Enteric fermentation emissions. In 2022 the Enteric fermentation category contributed 36 352 Gg CO 2 e (Table 5.2). Other cattle and sheep were the largest contributors to the Enteric fermentation category). Emissions from horses showed a slight increase between 2000 and 2022, while emissions from all other livestock declined during this time. Figure 5.2: Enteric fermentation emission trends, 2000 – 2022. 0 5 000 10 000 15 000 20 000 25 000 30 000 35 000 20002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022 Emissions (Gg CO 2 e) Dairy CattleOther CattleSheepGoatsHorsesMules and AssesSwine 174 Table 5.2: Enteric fermentation emission trends between 2000 and 2022. 2000 2005 2010 2015 2016 2017 2018 2019 2020 2021 2022 Gg CO 2 e Dairy cattle 3 884.1 3 217.1 3 923.3 3 805.6 3 962.6 4 237.2 3 884.1 3 452.5 3 727.2 3 648.7 3 639.7 Other cattle 27 776.4 28 385.9 27 823.1 27 740.6 26 948.0 25 776.9 25 633.8 25 436.0 24 814.0 24 559.9 24 499.2 Buffalo NO NO NO NO NO NO NO NO NO NO NO Sheep 8 121.1 7 579.3 7 281.5 7 075.9 6 891.6 6 726.3 6 702.6 6 608.8 6 394.7 6 374.9 6 377.5 Goats 1 990.8 1 950.5 1 921.3 1 801.0 1 731.4 1 680.2 1 658.5 1 612.6 1 586.6 1 580.3 1 578.6 Camels NO NO NO NO NO NO NO NO NO NO NO Horses 136.1 136.1 151.2 159.2 161.0 162.8 164.4 165.2 164.0 165.4 168.0 Mules & asses 45.9 45.9 46.6 47.4 46.7 45.9 45.8 46.2 46.2 46.1 45.8 Swine 53.0 53.1 51.3 49.0 48.7 47.7 46.8 44.7 43.7 43.2 43.2 Other NO NO NO NO NO NO NO NO NO NO NO Total 42 007.4 41 368.0 41 198.3 40 678.7 39 789.8 38 677.0 38 136.0 37 366.1 36 776.3 36 418.6 36 352.1 Note: Numbers may not add exactly due to rounding off. Figure 5.3: Livestock population trends, 2000 – 2022 (Data source: DALRRD, 2022). 175 Table 5.4 illustrates the fluctuations in enteric fermentation emissions from 2000 to 2022, along with the proportional contributions of different livestock categories to the overall emissions. The total enteric fermentation emissions showed an increase from 9.2% to 10% over the period from 2000 to 2022. Among the livestock categories, non- dairy cattle made the most substantial contribution, accounting for 67% of the total enteric fermentation emissions, followed by sheep, which contributed 18% to the overall enteric fermentation emissions. Table 5.3: Change in Enteric fermentation emissions (2000 – 2022) and relative contribution of the various livestock categories to the total emissions. Emissions Change Share of enteric fermentation (%) (Gg CO 2 e) (2000-2022) 2000 2022 Diff % 2000 2022 Dairy cattle 3 884.1 3 639.7 -244.3 -6.3 9.2 10.0 Non-dairy cattle 27 776.4 24 499.2 -3 277.2 -11.8 66.1 67.4 Buffalo NO NO NO NO NO NO Sheep 8 121.1 6 377.5 -1 743.5 -21.5 19.3 17.5 Goats 1 990.8 1 578.6 -412.2 -20.7 4.7 4.3 Camels NO NO NO NO NO NO Horses 136.1 168.0 31.9 23.4 0.3 0.5 Mules and asses 45.9 45.8 -0.1 -0.3 0.1 0.1 Swine 53.0 43.2 -9.8 -18.5 0.1 0.1 Other NO NO Total 42 007.4 36 352.1 -5 655.3 -13.5 100 100 Note: Numbers may not add exactly due to rounding off. 5.1.1.2 Manure management Emissions from Manure management increased by 8.0% between 2000 and 2022 (Table 5.3). CH 4 emissions declined, while N 2 O emissions increased. N 2 O contributed 56% of the total emissions from manure management, while methane emissions contributed 44% in the year 2022. From 2000 to 2022, CH 4 emissions showed a 5% increase, whereas N 2 O showed a 5% decrease over the same period. Table 5.3: Trends and changes in manure management emissions (2000 to 2022). Emissions Change Share of manure management (Gg CO 2 e) (2000 – 2022) 176 2000 2022 Diff % 2000 2022 % % Methane 1 812.4 1 741.9 -70.4 -3.9 49.4 44.0 Nitrous oxide 1 853.8 2 217.4 363.6 19.6 50.6 56.0 Total manure management 3 666.2 3 959.4 293.2 8.0 100 100 Most of South Africa’s livestock (cattle, sheep, goats, horses, mules and asses) are kept on pasture, range and paddock, therefore the Manure management category emissions were relatively small in 2022. Methane from Manure management declined in 2019 and started to increase again in 2020 and started decreasing in 2021 and 2022 (Figure 5), while the N2O emissions have been increasing from 2000 to 2015, after which the emissions started to level off (Figure 5). The N2O emissions have remained constant since the 2017 inventory and started decreasing in 2018 to 2022 inventory. This is because, even though, poultry numbers are increasing, the decline in cattle numbers has been counteracting this increase. Managed manure from Non-dairy cattle and Dairy cattle contributed the most to the CH4 emissions (39% and 32% respectively); while the largest contributors to the N2O emissions were Non- dairy cattle cattle (42%) and Poultry (39%). Figure 5.4: Trend in manure management CH 4 emissions from livestock, 2000 – 2022. 177 Figure 5.5: Trend in manure management N 2 O emissions from livestock, 2000 – 2022. Table 5.7 illustrates the indirect N 2 O emissions resulting from manure management across different livestock types. This encompasses N 2 O emissions arising from both volatilization and leaching/runoff. In 2022, indirect N 2 O emissions from manure management amounted to 354.9 Gg CO 2 e in total. There has been a slight shift since the year 2000. Table 5.7: Indirect N 2 O emissions from manure management (3B5) 2000 2005 2010 2015 2016 2017 2018 2019 2020 2021 2022 Gg CO 2 e Emissions due to volatilization N2O from dairy cattle manure 53.7 44.5 54.3 52.7 54.8 58.6 53.7 47.8 51.6 50.5 50.4 N2O from other cattle manure 129.8 140.3 136.2 152.4 153.7 152.4 151.9 151.7 148.2 149.3 149.1 N2O from sheep manure 24.3 24.8 26.2 26.9 25.9 24.8 23.3 21.7 23.9 22.6 21.9 0 500 1 000 1 500 2 000 2 500 3 000 20002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022 Manure N 2 O emissions (Gg CO 2 e) Dairy CattleOther CattleSheepGoatsHorsesMules and AssesSwinePoultry 178 2000 2005 2010 2015 2016 2017 2018 2019 2020 2021 2022 Gg CO 2 e N2O from goat manure 0.8 0.8 0.8 0.7 0.7 0.7 0.7 0.6 0.6 0.6 0.6 N2O from horse manure NO NO NO NO NO NO NO NO NO NO NO N2O from mule and ass manure NO NO NO NO NO NO NO NO NO NO NO N2O from swine manure 24.4 24.4 23.6 22.5 22.4 21.9 21.5 20.6 20.1 19.9 19.9 N2O from poultry manure 34.5 39.0 45.9 50.9 48.4 47.5 50.8 53.6 52.2 52.5 53.5 Emissions due to leaching/runoff N2O from dairy cattle manure 2.9 2.4 2.9 2.8 3.0 3.2 2.9 2.6 2.8 2.7 2.7 N2O from other cattle manure 10.8 11.7 11.4 12.6 12.7 12.5 12.5 12.4 12.1 12.2 12.2 N2O from sheep manure 2.1 2.1 2.2 2.3 2.2 2.1 2.0 1.9 2.1 1.9 1.9 N2O from goat manure 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 N2O from horse manure NO NO NO NO NO NO NO NO NO NO NO N2O from mule and ass manure NO NO NO NO NO NO NO NO NO NO NO N2O from swine manure 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.3 0.3 N2O from poultry manure 8.2 9.2 10.8 12.0 11.4 11.2 12.0 12.6 12.3 12.4 12.6 Total 316.3 325.7 342.6 366.5 365.2 364.8 361.5 355.7 355.7 354.8 354.9 5.1.1.3 Agricultural soils Emissions from Agricultural soils are summarised in Table 5.4. Direct N 2 O from managed soils and liming contribute the most toward this category. The contribution from field burning of agricultural residue, direct and indirect N 2 O from managed soils has declined since 2000, while all others increased. Emissions in this category have remained constant over the period of 2000 to 2020, except for a decline in 2003-2004 and 2015-2016 (Figure 5.6). Reduced emissions due to field burning of agricultural residue appears to be the main contributor to these decrease. In the year 2022, direct N 2 O from managed soils contributed the most toward this category (73%), while Liming is the second largest contributor (14%). 179 Table 5.4: Changes in emissions related to agricultural soils between 2000 and 2022. Category Emissions (Gg CO 2 e) Change (2000 – 2022) 2000 2022 Diff % 3D1 Direct N 2 O from managed soils 9 574.6 9 382.9 -191.7 -2.0 3D2 Indirect N 2 O from managed soils 1 056.5 995.2 -61.3 -5.8 3F Field burning of agricultural residue 69.1 58.5 -10.5 -15.2 3G Liming 1 820.0 1 860.7 40.7 2.2 3H Urea application 297.3 584.7 287.3 96.6 Note: Numbers may not sum exactly due to rounding off. Figure 5.6: Trends in emissions related to agricultural soils, 2000 – 2022. 180 5.1.2 Overview of methodology and completeness The IPCC 2006 methodology (IPCC, 2006) is applied in this sector, with a few updated methodologies being taken from the IPCC 2019 Refinement of the 2006 Guidelines (IPCC, 2019). Default constants and emission factors are also sourced from these two guideline documents, with details being provided in the methodology sections within each category section. Table 5.5Table 5. 9 shows the methods and types of EF used in the Agriculture inventory. 181 Table 5.5: Summary of methods and emission factors for the Agriculture sector and an assessment of the completeness of the Agriculture sector emissions. GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor 3A LIVESTOCK 3.A Enteric fermentation 3.A.1- Cattle 3.A.1.a - Dairy cows NA NA T2 CS NA NA NA NA NA NA 3.A.1.b - Other cattle NA NA T2 CS NA NA NA NA NA NA 3.A.2 - Sheep NA NA T2 CS NA NA NA NA NA NA 3.A.3 - Swine NA NA T2 CS NA NA NA NA NA NA 3.A.4 - Other 3.A.4.a - Buffalo NA NA NO NO NA NA NA NA NA NA 3.A.4.b - Camels NA NA NO NO NA NA NA NA NA NA 3.A.4.d - Goats NA NA T2 CS NA NA NA NA NA NA 3.A.4.e - Horses NA NA T1 DF NA NA NA NA NA NA 3.A.4.f – Mules and Asses NA NA T1 DF NA NA NA NA NA NA 3.B Manure management 3.B.1- Cattle 3.B.1.a - Dairy cows NA NA T2 CS T2 DF NE NA NA NE 3.B.1.b - Other cattle NA NA T2 CS T2 DF NE NA NA NE 3.B.2 - Sheep NA NA T2 CS NO NO NE NA NA NE 3.B.3 - Swine NA NA T2 CS T2 DF NE NA NA NE 3.B.4 - Other 3.B.4.a - Buffalo NA NA NO NO NO NO NE NA NA NE 3.B.4.b - Camels NA NA NO NO NO NO NE NA NA NE 3.B.4.d - Goats NA NA T2 CS NO NO NE NA NA NE 3.B.4.e - Horses NA NA T2 CS NO NO NE NA NA NE 3.B.4.f – Mules and Asses NA NA T2 CS NO NO NE NA NA NE 3.B.4.g - Poultry NA NA T2 CS T2 DF NE NA NA NE 3.B.5-Indirect N 2 O emissions 182 GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor 3.C – Rice cultivation NO NO NO 3.D –Agricultural soils 3.D.1 – Direct N 2 O Emissions from managed soils Synthetic fertilizers NA NA T1 DF NA NA Animal waste added to soils NA NA T1 DF NA NA Other organic fertilizers NA NA T1 DF NA NA Urine and dung deposited by grazing livestock NA NA T1 DF NA NA Crop residues NA NA T1 DF NA NA 3.D.2 – Indirect N 2 O Emissions from managed soils Atmospheric deposition NA NA T1 DF Nitrogen leaching and runoff NA NA T1 DF 3.E – Prescribed burning of savannas Prescribed burning of savannas NE NE NE 3.F – Field burning of Agricultural residues Field burning of Agricultural residues T2 DF, CS T2 DF, CS T2 DF, CS 3.G - Liming Liming T1 DF NA NA NA NA NA NA 3. H - Urea application Urea application T1 DF NA NA NA NA NA NA 3.I – Other carbon containing fertilisers 183 GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor Other carbon containing fertilisers NE NE NE 3.J - Other Other NE NE NE NA = Not applicable; NO = Not occurring; NE = Not estimated; IE = Included elsewhere; T1 = Tier 1; T2 = Tier 2; DF = IPCC default factor; CS = Country specific factor. 184 5.1.3 Improvements and recalculations Agriculture and LULUCF were split into separate chapters. All the other land category (Forest land, Grassland, Wetland and Settlement) non-CO 2 biomass burning emissions were removed from agriculture sector and incorporated into the LULUCF sector. The agricultural burning emissions, which in this inventory were assumed to be emissions from pre-harvest burning of sugarcane, were incorporated into category 3F (Field burning of agricultural residues). The Agriculture sector is under continual improvement which leads to recalculations. Figure 5.7 shows the changes in Agriculture emission estimates due to recalculations since 2022 submission. The recalculations for the Agriculture sector led to an increase of 20% increase in the estimates over the times series. The recalculations resulted in a 20% increase in the 2022 estimates for agriculture, primarily attributed to significant improvements. In addition, the weighting is applied to the emission factors and not to the population data as was done in the previous inventory. Emissions were therefore recalculated for the entire time series and led to a 59.1% increase (79.8% for manure CH 4 emissions) in emissions compared to the previous inventory. The calculation of lime application emissions per crop type was based on crop area data and application rates. This involved utilizing crop area estimates in combined with application rates sourced from Tongwane et al. (2016). However, this approach resulted in an estimate exceeding 3 million tons of lime in 2008, in contrast to the 1.5 million tons reported by the Fertiliser Association of South Africa. 185 Figure 5.7: Change in Agriculture emission estimates due to recalculations since 2022 submission. 5.1.4 Key categories in the Agriculture sector The key categories for the Agriculture sector as determined by the level (L) and trend (T) assessment are shown in Table 5.6. Table 5.6: Key categories in the Agriculture sector in 2022. CRT code Category GHG Criteria 3.A.1.a.ii Non-dairy Cattle - CH 4 CH 4 L, T 3.A.1.a.i Dairy Cattle - CH 4 CH 4 L 3.A.2 Sheep - CH4 CH 4 L, T 3.A.4 Goats - CH4 CH 4 T 3D1 Direct N 2 O Emissions From Managed Soils - N 2 O N 2 O L 3.G Liming - CO2 CO 2 L # L = Level Assessment; T = Trend Assessment 186 5.1.5 Planned improvements and recommendations In terms of livestock there are six recommendations for improving estimates in the future: a) Improve livestock population data: There have been several studies on the emission factors and now the population data is the most uncertain component. Setting up a Livestock Estimates Committee could assist with this, although this has been mentioned before and not much progress has been made in terms of the committee. Further engagement is required between DFFE and the Department of Agriculture. It could also be an activity to discuss with the Agricultural Research Council which has a livestock division. b) National data set on manure management systems: This data seems to be highly variable depending on where the information comes from. In addition, data on the amount of manure being diverted to biogas needs to be included as this is a mitigation option and has been highlighted in previous inventory reviews. It is recommended to find a mechanism to track manure management practices or systems used in South Africa, as this could allow for incorporation of dynamics driven by changes in management regimes, and thus improve the accuracy of manure related emissions. c) A detailed study on the herd composition of the various livestock and the number of days each livestock sub-category is alive in a year would contribute to a reduction in uncertainty. d) Collect and include in NIR background information of the livestock population original data sources (surveys, questionnaires etc.) e) Use appropriate MCFs depending on the average temperature for each year of the time series. Stratify the estimates depending on the average temperature in different regions in South Africa. f) Investigate if there are studies available about the burning of manure in South Africa. g) There is a need for alternate data source for lime data Livestock and manure characterisation 5.2.1 Livestock population and characterisation 187 5.2.1.1 Population data sources and estimations The data sources for the livestock population estimates are shown in Table 5.7. The main data source is the Abstracts of Agricultural Statistics (DALRRD, 2022a) which is a sustainable data source released every year. The livestock population data is collected 4 times in a year (every 3 months). For the year 2022, the data was initially collected in February, the second datasets were collected in May, the 3rd in August and the 4th in November. In addition, the total livestock numbers for cattle, sheep, goats and pigs is obtained from data provided by the DALRRD (2022b). The same data for number of animals of the various animal groups is used in all the different calculations of emissions. Table 5.7: Livestock population data sources. Livestock category Data source Cattle Total cattle DALRRD (2022b) Commercial dairy cows (>2yrs) DALRRD (2022a) Commercial dairy heifers (1-2 yrs) DALRRD (2022a) Feedlot cattle Feedlot SA (2022) Commercial other cattle total DALRRD (2022a) Subsistence cattle Calculated (see text) Sheep Total sheep DALRRD (2022b) Commercial sheep total DALRRD (2022a) Feedlot sheep Calculated from DALRRD (2022a) slaughter data (see text) Subsistence sheep Calculated (see text) Goats Total goats DALRRD (2022b) Commercial goats total DALRRD (2022a) Subsistence goats Calculated (see text) Horses, mules and asses FAOSTAT (2022) Swine Total swine DALRRD (2022b) Commercial swine Calculated (see text) Subsistence swine Calculated (see text) Poultry Commercial broilers Extrapolated based on the data from Leading edge Poultry Software CC (2022) Commercial broiler parents Extrapolated based on the data from Leadingedge Poultry Software CC (2022) Commercial layers Extrapolated based on the data from Leadingedge Poultry Software CC (2022) Commercial pullets Extrapolated based on the data from Leadingedge Poultry Software CC (2022) Subsistence broilers Calculated (see text) Subsistence layers Calculated (see text) 188 Other cattle calves and subsistence cattle The total number of calves is obtained from the Abstracts of Agricultural Statistics (DALRRD, 2022a), but the feedlot cattle are all assumed to be calves, so these are subtracted from the total calves to obtain the estimate of commercial calves in the other cattle category. The total number of cattle is provided by DALRRD (2022b). The number of dairy heifers and cows is subtracted from the total number of cattle to obtain the total number of other cattle. The Abstracts of Agricultural Statistics (2022a) provides the total commercial other cattle; therefore, this is subtracted from the total other cattle to obtain an estimate of the subsistence cattle population. Subsistence and feedlot sheep There is limited data on feedlot sheep, so several assumptions were applied. The number of sheep slaughtered is obtained from the Abstracts of Agricultural Statistics (DALRRD, 2022a). It was assumed that 70% of the slaughtered animals come from feedlots. Feedlot sheep are estimated to be in the feedlots on average for 35 days (pers. comm. Mokhele Moeletsi, 2021) and are weaned or sold at about 120 days which means the feedlot sheep are alive for 155 days. This data was applied to equation 10.1 of the IPCC 2006 Guidelines to determine the annual average population. The feedlot total is assumed to be included in the total commercial sheep numbers provided by Abstracts of Agricultural Statistics (DALRRD, 2022a). The subsistence sheep population is estimated by subtracting the total commercial sheep (DALLRD, 2022a) from the total sheep (DALRRD, 2022b). Subsistence goats The subsistence goat population is estimated by subtracting the total commercial goats (DALLRD, 2022a) from the total goats (DALRRD, 2022b). Commercial and subsistence swine The total swine population provided by DALRRD (2022b) is almost the same as that provided in the Abstracts of Agricultural Statistics (DALRRD, 2022a), suggesting that there are no subsistence swine. Du Toit et al (2013c) noted that there was a large discrepancy between DALRRD (2022a) data and data from industry with industry suggesting a much lower population. Du Toit et al. (2013c) indicated that 26% of the population was subsistence, so based on this it is assumed that the total swine population 189 is as given in the DALRRD (2022b) data, but that this data is split into commercial and subsistence using the 0.26 ratio provided by Du Toit et al. (2013c). Poultry The number of broilers and layers was extrapolated based on the data from Leadingedge Poultry Software CC (2022), who provides data to the South African Poultry Association (SAPA). The data is modelled data. In this inventory the total broiler parents and pullets are also included. Data for parents and pullets are only available for 2018 onwards for these categories. A ratio between the parents and the broilers, as well as the pullets and the layers, is determined and this ratio is extrapolated (linear) backwards to 1990 to complete the time-series for the parent population. Leading Poultry Software CC (2021) reports that in 2015, subsistence populations accounted for 4-5% of commercial poultry populations. The percentage (4%) is assumed to be constant between 2000 and 20015. In 2022, the projected number of subsistence poultry is 4-5% of commercial poultry, therefore, 5% was applied in 2022 and linear extrapolation was used to estimate the percentage of poultry subsistence poultry in other years. 5.2.1.2 Population characterisation and herd composition The previous inventory included more detailed characterization of livestock. This is based on a study conducted in 2010 (Du Toit et al., 2013a, b, c) which provided country- specific emission factors for all livestock types. The population data for the detailed livestock characterisation in the previous inventory utilised the same national data as provided in this inventory but applied the population and herd composition data provided in Du Toit et al. (2013a, b, c) to split the population into the more detailed categories. Subsequent to this there have been further studies on livestock emissions which made use of fewer herd composition categories but added more detailed breed classes. To accommodate new data and improve consistency, the list of categories has been revised (Table 5.8) and some of the herd classes were aggregated. In addition, instead of incorporating the detailed category information into the population break down, the detail is incorporated into the emission factors. Thus, the national statistics population data categories are kept, and the detailed breed and herd composition data is used to calculate a weighted average emission factor (based on the composition and assumptions provided in Table 5.8) for each class. This way the national population data categories can be kept constant for reporting purposes, and any detailed data gathered in the future can be incorporated into the emission factors. Table 5.8: Livestock characterization and herd composition data sources and assumptions. 190 Main category Production category Subcategory Herd composition data and assumptions Dairy cattle • High production (Holstein) • Medium production (Jersey) • Mature female cows • Heifers (12-18 months) • Heifers (6-12 months) • Heifers (3-6 months) Herd composition based on DALRRD (2021a) data; 57% Holstein and 43% Jersey; even distribution of heifer categories Other cattle • Commercial (Afrikaner; Angus; Beefmaster; Bonsmara; Boran; Brahams; Brangus; Braunvich; Charolais; Drakensburger; Hereford; Hugenoot; Limousin; Bradford; Santa Gertrudis; Simbra; Simmentaler; Susses; Wagyu; Tuli) • Mature cows • Bulls • Young bulls • Heifers (12-18 months) • Heifers (6-12 months) • Heifers (3-6 months) • Ox • Young ox • Calves Commercial herd composition based on ARC Report (2021) data; even distribution of heifer categories; herd composition assumed to be the same for all breeds; even distribution of breeds • Subsistence • Mature cows • Bulls • Heifers (12-18 months) • Heifers (6-12 months) • Heifers (3-6 months) • Ox • Young ox • Calves Subsistence herd composition based on ARC Report (2021) data; even distribution of heifer categories • Feedlot All calves Sheep • Commercial wool (Merino) • Commercial meat (Dorper) • Commercial dual- purpose (Mutton Merino, Dohne merino) • Mature ewe • Replacement ewe • Mature ram • Young ram • Female lamb • Male lamb Commercial sheep herd composition based on ARC Report (2021) data; even distribution of mature and replacement ewes; even distribution of mature and young rams; even distribution of male and female lambs; species distribution taken from ARC Report (2021a) • Subsistence • Mature ewe • Replacement ewe • Mature ram • Young ram • Female lamb • Male lamb Subsistence sheep herd composition based on ARC Report (2021) data; even distribution of mature and replacement ewes; even distribution of mature and young rams; 191 even distribution of male and female lambs; all species assumed to be dual purpose • Feedlot All weaned sheep Goats • Commercial (Angora; Boer) • Commercial dairy goat (Saanen; Toggenburg; British Alpine) • Mature doe • Replacement doe • Mature buck • Young buck • Female kid • Male kid Commercial goat herd composition based on ARC Report (2021) data; even distribution of mature and replacement does; even distribution of mature and replacement buck; even distribution of male and female kids; distribution of commercial and dairy goats taken from ARC Report (2021a); even distribution of dairy goats and also commercial goats • Subsistence • Mature doe • Replacement doe • Mature buck • Young buck • Female kid • Male kid Subsistence goat herd composition based on ARC Report (2021) data; even distribution of mature and replacement does; even distribution of mature and replacement buck; even distribution of male and female kids) Horses, mules, asses • All breeds Swine • Commercial • Boars • Cull boars • Replacement boars • Cull sows • Replacement sows • Dry gestating sows • Lactating sows • Pre-wean-piglets Commercial swine herd composition based on Du Toit et al. (2013c) data • Subsistence • Boars • Cull boars • Replacement boars • Cull sows • Replacement sows • Dry gestating sows • Lactating sows • Pre-wean-piglets Commercial swine herd composition based on Du Toit et al. (2013c) data 192 Poultry • Commercial • Subsistence • Broilers • Broiler parents • Layers • Pullets Composition determined from population data 5.2.1.3 Population data verification Livestock data were compared with FAO data and other studies. For all livestock except poultry, the national statistics data (DALRRD, 2022a) used in this inventory correspond to those in the FAO database. There were small differences in the early years (1990 – 2002), but the data were then adjusted accordingly. Regarding poultry data, the data applied in this inventory are on average 33% lower than the FAO data. However, it should be noted that the majority of FAO poultry data are estimates and do not come from official data sources. In 2005 and 2006 official data is indicated to be reported and, in these years, the current data is 9% and 5% lower, respectively. Official data is also indicated to be used between 2013 and 2015, and in these years the data is 12% to 19% lower than the reported data in the FAO database. In another study by Moeletsi and Tongwane (2015) the same national statistics data was applied for all livestock except poultry. Moeletsi and Tongwane (2015) reported broiler data that is 4% higher and layer data that is 24% lower for the year 2004. A study by Du Toit et al. (2013a, b, c) made use of population data from the livestock industry associations for the year 2010. This study provides a total dairy population that is 7% higher than the DALRRD (2022a) data. The commercial and subsistence other cattle numbers provided by Du Toit et al. (2013a) are 22% and 4% higher than the DALRRD (2022a) data, respectively. For sheep Du Toit et al. (2013b) utilized national statistics from Statistics SA and these numbers are within 0.5% of the data from DALRRD (2022a). Goat population data from Du Toit et al. (2013b) were obtained from the industry associations and are 34% higher for commercial goats, while subsistence numbers are only 1% higher. Swine population data from industry (Du Toit et al., 2013c) are 60% lower than that provided in DALRRD (2022a). Broiler and layer commercial population data is 19% and 15% lower than the Du Toit et al. (2013c) data for 2010. All of this data shows that there is still a gap between the national statistics and data from livestock associations. As has been mentioned in previous inventories, it would be beneficial to set up a Livestock Estimates Committee which brings together government and livestock association representatives on an annual basis to discuss livestock population data to obtain consensus. The committee could also be used for discussions on ways to improve livestock population estimates and the reporting thereof. 193 5.2.1.4 Population data uncertainty Uncertainty data is provided in Moeletsi et al. (2015a), although it is not clear how the uncertainty was derived. In this report dairy population data is indicated to have an uncertainty of ±10%. Considering the comparisons above this uncertainty value appears reasonable. A ±10% uncertainty is also assigned to commercial beef cattle categories, except calves which has an uncertainty of ±5%. The comparisons with Du Toit et al. (2013a) suggest that the uncertainty could be around ±20%. The comparison is only for one year, therefore an uncertainty of ±20% is applied to all commercial beef cattle categories except calves (which remains at ±5%). Moeletsi et al. (2015a) indicate a ±10% uncertainty on commercial sheep and goat population data. The comparisons with Du Toit et al. (2013b) suggest that this uncertainty is reasonable for sheep but may be too low for goats. The goat uncertainty is therefore increased to ±20%. The ±10-20% uncertainty provided in Moeletsi et al. (2015a) for swine appears to be low with Du Toit et al. (2013c) reporting values 60% lower. The uncertainty for swine population is therefore increased to ±50%. Moeletsi et al. (2015a) does not provide uncertainty data for poultry but considering the comparisons with FAO and Du Toit et al. (2013c) an uncertainty of ±20% is assigned to poultry population data. 5.2.2 Manure management 5.2.2.1 Data sources The manure management data for cattle, sheep and goats was sourced from the ARC Report (2021) and there were various sources for this data as indicated in Table 5.9. Data for horses, swine and poultry is sourced from Moeletsi and Tongwane (2015) and Du Toit et al. (2013c). 5.2.2.2 Data verification Manure management data is compared to data from various studies and Table 5.10 shows that there is high variation in the results for cattle and poultry. The data variability is much less for sheep, goats, horses, mules/asses and swine. The percentage allocation differs significantly from the IPCC 2019 Refinement Sub-Saharan Africa (SSA) default value, but this is not unexpected as the default is for the whole SSA region while the numbers from the other studies are more specific to SA which has some high productivity systems. There is some agreement on the types of manure management systems utilised, except that SA does not mention pit storage systems. In addition, no manure is allocated to burning or digesters since there is no data and it is not significant. There are some cattle feedlots and piggeries which make use of digesters, but the data is limited, and it is thought to be minimal. This should, however, be monitored in future, particularly because of the energy production benefits of the digesters. 194 5.2.2.3 Manure management data uncertainty Moeletsi et al. (2015) reported a ±20% uncertainty on mixed diet dairy cattle manure management, and a ±25% and ±10% uncertainty on pasture and Total Mixed Ratio (TMR) manure management systems, respectively. Considering the variation in the reported data (Table 5.10) the uncertainty seems to be much higher, therefore an uncertainty of ±50% is assigned to dairy cattle manure management. For non-dairy cattle an uncertainty of ±15% is provided, and considering the variation shown in this is not unreasonable, although it might be slightly on the low side. The uncertainty is therefore adjusted to ±20%. The ±5% uncertainty assigned by Moeletsi et al. (2015) to goats and horses, and the ±2% assigned to horses, mules/asses and sheep appears to be reasonable, but it is unclear why the uncertainty for sheep is lower than goats when the manure management data is the same in all studies for these two livestock categories. Based on this, and the data in Table 5.10, the uncertainty for goats is adjusted to ±2%. Poultry and swine manure management data has an uncertainty of ±15% (Moeletsi et al., 2015). The variation in the data reported in Table 5.10 is low for swine but is high for poultry, therefore the uncertainty is adjusted to ±10% and ±25% for swine and poultry, respectively. 195 Table 5.9: Manure management systems for the various livestock types and their data sources. Livestock Lagoon Liquid/ slurry Dry lot/ Kraals Solid storage Daily spread Compost Manure with litter Manure without litter Pasture, range, paddock (PRP) Dairy cattle Mature female cows 5 1 5 1 20 1 5 5 60 3 Heifers (6 - 24 months) 2 98 4 Other cattle Commercial 3 97 4 Subsistence 35 65 4 Feedlot 5 85 2 10 Sheep Commercial 4 2 98 Subsistence 35 6 65 4 Feedlot 6 2 98 Goats Commercial 2 2 98 4 Subsistence 4 35 65 Dairy goats 4 10 90 Horses, mules, asses 2 100 Swine Commercial 2,5 71 11 13 3 2 Subsistence 5 25 10 35 30 Poultry Commercial 2 80 5 15 Subsistence 2 5 70 5 10 10 1 DEA (2014); 2 Moeletsi and Tongwane (2015); 3 Malaka (2017) ; 4,6 Expert opinion, 5 Du Toit et al. (2013c); 196 Table 5.10: Verification of livestock manure management systems. Livestock DAFF (2010) Du Toit et al. (2013a, b, c) Moeletsi et al. (2015) Moeletsi and Tongwane (2015) ARC Report (2021) IPCC 2019 Refinement (SSA default) Dairy cattle Cows and heifers: 45% lagoon, 10% liquid slurry, 15% dry lot, 10% compost, 20% PRP TMR: 10% lagoon, 0.5% liquid slurry, 1% daily spread, 88.5% PRP; Pasture: 3% lagoon, 7% daily spread, 90% PRP TMR: 95% lagoon, 5% manure with bedding; Pasture: 11% lagoon, 8% dry lot, 10% solid storage, 3% daily spread, 60% PRP Cows: 20% lagoon, 5% liquid slurry, 25% dry lot, 45% PRP, 5% manure with bedding; Heifers: 5% dry lot, 2% compost, 93% PRP Cows: 5% lagoon, 5% liquid slurry, 20% dry lot, 5% solid storage, 5% daily spread, 60% PRP; Heifers: 2% dry lot, 98% PRP 20% solid storage, 29% dry lot, 45% PRP, 6% burned for fuel Other cattle Commercial: 25% dry lot, 5% compost, 70% PRP; Feedlot: 1.5% lagoon, 1.5% liquid slurry, 20% dry lot, 2% daily spread, 10% compost, 65% PRP Subsistence: 10% dry lot, 90% PRP Commercial: 100% PRP; Feedlot: 80% dry lot, 20% solid storage; Subsistence: 100% PRP Commercial: 2% solid storage, 1% daily spread, 95% PRP, 2% manure with bedding; Feedlot: 20% solid storage, 80% manure with bedding; Subsistence: 10% dry lot, 30% PRP, 60% manure with bedding Commercial: 5% dry lot, 5% compost, 90% PRP Feedlot: 5% lagoon, 5% liquid slurry, 75% dry lot, 5% daily spread, 10% compost; Subsistence: 10% dry lot, 80% PRP, 10% manure with bedding Commercial: 3% dry lot, 97% PRP; Feedlot: 5% lagoon, 85% dry lot, 10% solid storage; Subsistence: 35% dry lot, 65% PRP 15% solid storage, 30% dry lot, 50% PRP, 5% burned for fuel Sheep Commercial: 2% dry lot, 98% PRP; Subsistence: 5% dry lot, 95% PRP Commercial and subsistence: 100% PRP Commercial and subsistence: 100% PRP Commercial: 2% dry lot, 98% PRP; Subsistence: 5% dry lot, 95% PRP Commercial: 2% dry lot, 98% PRP; Subsistence: 35% dry lot, 65% PRP; Feedlot: 2% lagoon, 98% dry lot 17% solid storage, 3% dry lot, 80% PRP 197 Livestock DAFF (2010) Du Toit et al. (2013a, b, c) Moeletsi et al. (2015) Moeletsi and Tongwane (2015) ARC Report (2021) IPCC 2019 Refinement (SSA default) Goats Commercial: 2% dry lot, 98% PRP; Subsistence: 5% dry lot, 95% PRP Commercial and subsistence: 100% PRP Commercial and subsistence: 100% PRP Commercial: 2% dry lot, 98% PRP; Subsistence: 5% dry lot, 95%PRP Commercial: 2% dry lot, 98% PRP; Subsistence: 35% dry lot, 65% PRP 17% solid storage, 3% dry lot, 80% PRP Horses 100% PRP 40% dry lot, 60% PRP 100% PRP 100% PRP Mules/asses 100% PRP 100% PRP 100% PRP 100% PRP Swine Commercial: 50% lagoon, 20% liquid slurry, 20% dry lot, 5% daily spread, 5% compost Commercial: 92% lagoon, 1.5% liquid slurry, 5% dry lot, 1.5% daily spread; Subsistence: 50% dry lot, 50% daily spread Commercial: 50% lagoon, 20% liquid slurry, 20% dry lot, 10% solid storage Commercial: 50% lagoon, 20% liquid slurry, 20% dry lot, 5% daily spread, 5% compost High productivity: 7% liquid slurry, 6% solid storage, 86% dry lot, 1% pit<1; Low productivity: 5% lagoon, 30% liquid slurry, 15% solid storage, 15% dry lot, 15% pit<1, 5% pit>1, 5% daily spread, 5% digester, 5% PRP Poultry Layers and broilers: 80% dry lot, 20% compost Layers: 100% poultry manure without litter; Broilers: 100% poultry manure with litter Layers and broilers: 10% dry lot, 5% daily spread, 2% compost, 5% poultry manure without litter, 78% poultry manure with litter Layers: 5% liquid slurry, 70% dry lot, 5% daily spread, 10% compost, 10% poultry manure without litter; Broilers: 80% dry lot, 5% compost, 15% poultry manure with litter Layers: 90% pit>1 month, 10% poultry manure with litter; Broilers: 100% poultry manure with litter 198 Enteric Fermentation (3.A) 5.3.1 Category description Methane is produced in herbivores as a by-product of enteric fermentation, a digestive process by which plant material consumed by an animal is broken down by bacteria in the gut under anaerobic conditions. A portion of the plant material is fermented in the rumen to simple fatty acids, CO 2 and CH 4 . The fatty acids are absorbed into the bloodstream, and the gases vented by eructation and exhalation by the animal. Unfermented feed and microbial cells pass to the intestines. According to IPCC the method for estimating CH 4 emissions from enteric fermentation requires three basic steps: • Divide livestock population into animal subgroups based on sex, age, and production level. • Estimate the emission factors for each subgroup in terms of kilograms of CH4 per animal per year. • Multiply the subgroup emission factors by the subgroup populations to estimate subgroup emissions and sum across the subgroups to estimate total emission. Enteric fermentation emissions decreased by 20.7% in the period 2000-2020 and decreased by 4.6% between 2017-2020. Enteric fermentation contributed 36 352 Gg CO 2 e in 2022, which is 67.9% of the agriculture sector emissions. South Africa identified, through Tier 1 level and trend assessments, Enteric fermentation as a key source category. In accordance with IPCC good practice requirements Tier 2 methods are therefore used, to estimate Enteric fermentation emissions from the major livestock sub-categories. 5.3.2 Methodological issues A Tier 1 methodology is used to calculate CH 4 emissions from enteric fermentation in horses, mules and asses by multiplying the population data by IPCC default emissions factors (IPCC Equation 10.19, IPCC 2019 Refinement). For all other livestock a Tier 2 methodology was applied by following the basic IPCC equations: CH 4 emissions = EF * Population and 199 EF = (GE * (Ym/100) * 365 days)/ 55.65 MJ (kg CH 4 ) -1 Where: EF = emission factor (kg CH 4 head -1 yr -1 ) GE = gross energy intake (MJ head -1 day -1 ) Ym = methane conversion factor (percentage gross energy in feed converted to methane) (Table 5.11) These equations assume that the emission factors are for an entire year. Since the population data is census data it is assumed that the population data is representative of a typical population on any one day of the year. Table 5.11: Methane conversion factors and their sources. Livestock category Sub-category Breed Methane conversion factor (Ym) Dairy cattle Mature female cows Holstein 6 1 Jersey 6.3 1 Heifers (6 - 24 months) 7 1 Other cattle Commercial All breeds 7 1 Subsistence 6.5 2 Feedlot 3 1 Sheep Commercial All breeds 6.7 1 Subsistence 6.7 1 Feedlot 6.7 1 Goats Commercial All breeds 5.5 1 Subsistence 5.5 1 Swine Commercial 0.7 3 Subsistence 0.7 3 1 IPCC 2019 Refinement, 2 IPCC 2006 Guidelines, 3 Du Toit et al. (2013c). 5.3.2.1 Activity data The activity data for enteric fermentation is livestock population and this is described in detail in section 5.2.1. 5.3.2.2 Emission factors 200 Tier 1 IPCC default emission factors of 18 kg CH 4 head -1 yr -1 and 10 kg CH 4 head -1 yr -1 are applied to horses, and mules/asses, respectively (IPCC Table 10.10, IPCC 2019 Refinement). Emission factors for all other livestock are determined using the IPCC Tier 2 methodology. The emission factors for the detailed livestock categories are used to determine weighted averages (Table 5.12) for the livestock categories with population data. The weighting is based on the herd and breed composition assumptions provided in Table 5.13. Table 5.12: Enteric fermentation emission factors and their sources per livestock category. Livestock category Sub-category Enteric EF (kg CH 4 head -1 yr -1 ) Reference Dairy cattle Mature cows 141.07 Calculated (see text) Heifers 70.91 Calculated (see text) Other cattle Commercial bulls 113.00 Du Toit et al. (2013a) Commercial young bulls 51.6 Assumed the same as young ox Commercial cows 118.41 Calculated (see text) Commercial heifers 65.23 Calculated (see text) Commercial ox 89.40 Du Toit et al. (2013a) Commercial young ox 51.60 Du Toit et al. (2013a) Commercial calves 51.60 Du Toit et al. (2013a) Feedlot 41.61 Calculated (see text) Subsistence cattle 61.47 Calculated (see text) Sheep Commercial wool 9.95 Calculated (see text) Commercial dual-purpose 10.89 Calculated (see text) Commercial meat 13.80 Calculated (see text) Feedlot 7.22 Calculated (see text) Subsistence sheep 5.61 Calculated (see text) Goats Commercial mohair 6.64 Calculated (see text) Commercial meat 14.38 Calculated (see text) Commercial dairy 19.99 Calculated (see text) Subsistence goats 9.33 Calculated (see text) Swine Commercial swine 1.09 Calculated (see text) Subsistence swine 1.33 Calculated (see text) Horses 18 IPCC 2019 Refinement, Table 10.10 Mules/asses 10 IPCC 2019 Refinement, Table 10.10 Cattle (3.A.1) Emission factors for commercial and subsistence bulls, young bulls, ox, young ox and calves are taken directly from Du Toit et al. (2013a) where the methods are described in 201 detail. For the other, more dominant livestock categories, all the background data is included in the calculation files to enable the direct calculation of the emission factors using the IPCC Tier 2 equations (Table 5.13). Sheep (3.A.2) and goats (3.A.4.d) Emission factors for sheep and goats are calculated using a Tier 2 methodology (Table 5.14). The net energy for growth and lactation for goats is determined in the same way as for sheep as the IPCC 2019 Refinement indicates that this is the updated approach for goats.Swine (3.A.3) Gross energy intake data for swine is taken from Du Toit et al. (2013c) and applied in the IPCC Equation 10.21 to determine the emission factors. 202 Table 5.13: Equations for the calculation of emissions factors for cattle using the Tier 2 approach. IPCC Equation IPCC 2006 equations Variables/constants/assumptions 10.3 NE m =Cf 푖 × ( 푤푒푖푔ℎ푡 ) 0.75 NE m = net energy for maintenance (MJ day -1 ) Cf i = maintenance coefficient (MJ day -1 kg -1 ) [IPCC 2019 Refinement, Table 10.4: Lactating dairy cows = 0.386, Non-lactating cattle = 0.322, Bulls = 0.37] Weight = live weight of animal (kg) 10.4 NE a =C 푎 ×NEm NE a = net energy for activity (MJ day -1 ) C a = coefficient corresponding to animal’s feeding situation [Dairy cattle assumed to feed in pastures 1 , other cattle assume to graze on large grazing areas 1 ; IPCC 2019 Refinement, Table 10.5: Dairy cattle = 0.17, Other cattle = 0.36] 10.6 NE g =22.02 ×( 퐵푊 퐶×푀푊 ) 0.75 × ( 푊퐺 ) 1.097 NE g = net energy for growth (MJ day -1 ) BW = average live body weight of animals in the population (kg) C = growth coefficient [IPCC 2019 Refinement, Equation 10.6: Females = 0.8, Castrates = 1.0, Bulls = 1.2] MW = mature live body weight of an adult animal in moderate condition (kg) WG = average daily weight gain of animals in the population (kg day -1 ) 10.8 NE l =푀푖푙푘×(1.47+0.40×퐹푎푡) NE l = net energy for lactation (MJ day -1 ) Milk = amount of milk produced (kg milk day -1 ) Fat = milk fat content (% by weight) 10.11 NE work =0.10×NE m ×퐻표푢푟푠 NE work = net energy for work (MJ day -1 ) Hours = number of hours of work daily 10.13 NE p = 퐶 푝푟푒푔 × NE m NE m = net energy for pregnancy (MJ day -1 ) C preg = pregnancy coefficient [IPCC 2019 Refinement, Table 10.7: Cattle = 0.1] 203 10.14 푅퐸푀=[1.123− ( 4.092×10 −3 ×퐷퐸% ) + [ 1.126×10 −5 × ( 퐷퐸% ) 2 ] −( 25.4 퐷퐸% )] REM = ratio of net energy available in a diet for maintenance to digestible energy consumed DE% = digestible energy as a percentage of gross energy [ARC (2021): Mature dairy cows = 70%, Dairy heifers = 60%, Other commercial cattle in grazing land = 60%, Cattle feedlot = 75%, Other subsistence cattle in grazing land = 50%] 10.15 푅퐸퐺=[1.164− ( 5.160×10 −3 ×퐷퐸% ) + [ 1.308×10 −5 × ( 퐷퐸% ) 2 ] −( 37.4 퐷퐸% )] REG = ratio of net energy available for growth in a diet to digestible energy consumed DE% = digestible energy as a percentage of gross energy [see above] 10.16 퐺퐸= [ ( NE m +NE a +NE l +NE work +NE p 푅퐸푀 )+( NE g 푅퐸퐺 ) 퐷퐸% 100 ] GE = gross energy intake (MJ head -1 day -1 ) DE% = digestible energy as a percentage of gross energy [see above] 10.21 퐸퐹=[ 퐺퐸×( 푌 푚 100 )×365 55.65 ] EF = emissions factor (kg CH 4 head -1 yr -1 ) Y m = methane conversion factor (percent of gross energy in feed converted to methane) [IPCC 2019 Refinement, Table 10.12: Holstein high milk producing dairy cows = 6%, Jersey medium milk producing dairy cows = 6.3%, Dairy heifers = 7%, Non-dairy commercial cattle = 7%, Non-dairy subsistence cattle = 6.5, Feedlot cattle = 3%] 55.65 (MJ kg -1 CH 4 ) is the energy content of CH 4 constant 1 ARC (2021) 204 Table 5.14: Equations for the calculation of emissions factors for sheep and goats using the Tier 2 approach. IPCC Equation IPCC 2006 equation Variables/constants/assumptions 10.3 NE m =Cf 푖 × ( 푤푒푖푔ℎ푡 ) 0.75 NE m = net energy for maintenance (MJ day -1 ) Cf i = maintenance coefficient (MJ day -1 kg -1 ) [IPCC 2019 Refinement, Table 10.4: Mature and replacement ewes = 0.217, Mature ram = 0.250, Young ram and male lamb = 0.271, Female lamb = 0.236] Weight = live weight of animal (kg) 10.5 NE a =C 푎 ×(푤푒푖푔ℎ푡) NE a = net energy for activity (MJ day -1 ) C a = coefficient corresponding to animal’s feeding situation (MJ day -1 ) [Sheep grazing assumed to occur on flat pastures 1 ; IPCC 2019 Refinement, Table 10.5: all sheep = 0.0107] Weight = live weight of animal (kg) 10.7 NE g = 푊퐺 푙푎푚푏/푘푖푑 ×(푎 +0.5푏(퐵푊 푖 +퐵푊 푓 )) 365 NE g = net energy for growth (MJ day -1 ) WG lamb/kid = weight gain (BW f – BW i ) (kg yr -1 ) BW i = the live bodyweight at weaning (kg) BW f = the live bodyweight at 1-year old or at slaughter (kg) a = constant (MJ kg -1 ) [IPCC 2019 Refinement, Table 10.6: Female sheep = 2..1; Male sheep = 2.5 as it is assumed all male sheep are intact 1 ] b = constant (MJ kg -1 ) [IPCC 2019 Refinement, Table 10.6: Female sheep = 0.45; Male sheep = 0.35 as it is assumed all male sheep are intact 1 ] 10.10 NE l =[ 5 × 푊퐺 푤푒푎푛 365 ]×퐸푉 푚푖푙푘 NE l = net energy for lactation (MJ day -1 ) WG wean = the weight gain of the lamb/kid between birth and weaning (kg) EVmilk = the energy required to produce 1 kg of milk (MJ kg -1 ) [IPCC 2019 Refinement, Equation 10.10: sheep = 4.6, goats = 3] 205 10.12 NE wool = 퐸푉 푤표표푙 × 푃푟표푑푢푐푡푖표푛 푤표표푙 365 NEwool = net energy required to produce wool (MJ day -1 ) EV wool = the energy value of each kg of wool produced (weighed after drying but before scouring) (MJ kg -1 ) [IPCC 2019 Refinement, Equation 10.12: sheep = 24, for goats this energy value is not considered] Production wool = annual wool production per sheep (kg yr -1 ) 10.13 NE p = 퐶 푝푟푒푔 × NE m NE m = net energy for pregnancy (MJ day -1 ) C preg = pregnancy coefficient [The number of lambs/kids born in a year divided by the number of ewes that are pregnant in a year yields a value less than 1 1 , therefore single birth coefficient is applied 2 ; IPCC 2019 Refinement, Table 10.7: single births for sheep and goats = 0.077] 10.14 푅퐸푀=[1.123− ( 4.092×10 −3 ×퐷퐸% ) + [ 1.126×10 −5 × ( 퐷퐸% ) 2 ] −( 25.4 퐷퐸% )] REM = ratio of net energy available in a diet for maintenance to digestible energy consumed DE% = digestible energy as a percentage of gross energy [ARC (2021): Sheep in grazing land = 60%, Sheep in feedlots = 75%, Subsistence sheep = 50%, Goats in grazing land = 60%, Dairy goats = 70%, Subsistence goats = 50%] 10.15 푅퐸퐺=[1.164− ( 5.160×10 −3 ×퐷퐸% ) + [ 1.308×10 −5 × ( 퐷퐸% ) 2 ] −( 37.4 퐷퐸% )] REG = ratio of net energy available for growth in a diet to digestible energy consumed DE% = digestible energy as a percentage of gross energy [see above] 10.16 퐺퐸= [ ( NE m +NE a +NE l +NE work +NE p 푅퐸푀 )+( NE g 푅퐸퐺 ) 퐷퐸% 100 ] GE = gross energy intake (MJ head -1 day -1 ) DE% = digestible energy as a percentage of gross energy [see above] 10.21 퐸퐹=[ 퐺퐸×( 푌 푚 100 )×365 55.65 ] EF = emissions factor (kg CH 4 head -1 yr -1 ) Y m = methane conversion factor (percent of gross energy in feed converted to methane) [IPCC 2019 Refinement, Table 10.13: Sheep = 6.7%, Goats = 5.5%] 55.65 (MJ kg -1 CH 4 ) is the energy content of CH 4 constant 1 ARC (2021); 2 IPCC 2019 Refinement 206 5.3.3 Uncertainty 5.3.3.1 Activity data uncertainty Activity data time-series is complete from 1990 to 2022, however only data from 2000 is presented due to incomplete time-series in other sectors. Activity data uncertainty is discussed in section 5.2.1.4. 5.3.3.2 Emission factor uncertainty The uncertainties on the emission factors are provided by ARC (2021) for mature cows, heifers, feedlot cattle, all sheep and all goat sub-categories (Table 5.15). No uncertainty data is provided by Du Toit et al. (2013c) for bulls, oxen, young oxen, calves, or swine emission factors. For the bulls, young bulls, oxen, young oxen, and calf sub-categories the uncertainty was assumed to be an average of those for mature cows and heifers in each sub-category. For swine IPCC 2006 indicates that a Tier 2 approach is likely to have an uncertainty of around ± 20%. Tier 1 default factors for horses, and mules/asses have an uncertainty of between ± 30% - 50%, hence an average of ± 40% is applied. Table 5.15: Enteric fermentation emission factor uncertainties. Animal categories Animal subcategories Emission Factor uncertainty Reference 3.A.1.a - Dairy cattle Mature cows ± 23 ARC (2021) Heifer (12 – 18 months) ± 21 ARC (2021) Heifer (6 – 12 months) ± 17 ARC (2021) Heifer (3 – 6 months) ± 13 ARC (2021) 3.A.1.b - Commercial beef cattle Bulls ± 10 Average of uncertainty estimates for cows and heifers Young bulls ± 10 Mature cows ± 7 ARC (2021) Heifer (12 – 18 months) ± 9 ARC (2021) Heifer (6 – 12 months) ± 9 ARC (2021) Heifer (3 – 6 months) ± 13 ARC (2021) Oxen ± 10 Average of uncertainty estimates for cows and heifers Young oxen ± 10 Calves ± 10 Feedlot cattle ± 15 ARC (2021) 3.A.1.b - Subsistence cattle Bulls ± 26 Average of uncertainty estimates for cows and heifers Young bulls ± 26 Mature cows ± 24 ARC (2021) Heifer (12 – 18 months) ± 28 ARC (2021) Heifer (6 – 12 months) ± 27 ARC (2021) Heifer (3 – 6 months) ± 27 ARC (2021) 207 Oxen ± 26 Average of uncertainty estimates for cows and heifers Young oxen ± 26 Calves ± 26 3.B.2 - Sheep Mature ewes ± 30 ARC (2021) Replacement ewes ± 22 ARC (2021) Female lambs ± 36 ARC (2021) Male lambs ± 27 ARC (2021) Young rams ± 33 ARC (2021) Rams ± 24 ARC (2021) Feedlot sheep ± 29 ARC (2021) 3.A.4.d - Goats Mature does ± 35 ARC (2021) Replacement does ± 31 ARC (2021) Female kids ± 34 ARC (2021) Male kids ± 34 ARC (2021) Young buck ± 39 ARC (2021) Buck ± 40 ARC (2021) 3.A.4.e - Horses ± 40 IPCC 2006 3.A.4.f - Mules & asses ± 40 IPCC 2006 3.A.3 - Swine ± 20 IPCC 2006 5.3.4 Time-series consistency The time-series for enteric fermentation is consistent. 5.3.5 Category specific QA/QC and verification Activity data verification is provided in section 5.2.1.3. For the emission factor data, a literature search was conducted, and the results are shown in the calculation files. Data was also compared to the IPCC default data. The Dairy cattle Implied Emission Factor (IEF) is higher than the Africa default and is slightly higher than the default values for Oceania and Western Europe. The weight and milk production of SA dairy cattle are closer to those in Oceania and Western Europe than those in Africa, hence the closer alignment of the emission factors with these regions. This is the same for Non-dairy cattle. The sheep, goat and swine IEFs are generally consistent with the IPCC defaults. 5.3.6 Category-specific recalculations The weighting is applied to the emission factors and not to the population data as was done in the previous inventory and the recalculations were done. 208 5.3.7 Category-specific planned improvements In this inventory all the background calculations to complete the Tier 2 calculations has been included, however average activity data (i.e., livestock weights, milk production, fat content) are applied in many cases. No specific improvements plans are in place; however, it is recommended that in future inventories annual activity data be collected and incorporated. This means that the emission factor will vary annually and may better reflect any emission changes due to implemented changes (or mitigation actions) in livestock population management, feeding situations and pasture management. South Africa would then be able to track the impacts of possible mitigation actions through the inventory. Manure Management (3.B) 5.4.1 CH 4 emissions from manure management (3.B) 5.4.1.1 Category description Livestock manure is composed principally of organic material. When the manure decomposes in the absence of oxygen, methanogenic bacteria produce CH 4 . The amount of CH 4 emissions is related to the amount of manure produced and the amount that decomposes anaerobically. According to IPCC the method for estimating CH 4 emissions from manure management requires the following steps: • Divide livestock population into animal subgroups based on sex, age, and production level. • For CH 4 emissions: i. Estimate the emission factors for each subgroup in terms of kilograms of CH 4 per animal per year. ii. Multiply the subgroup emission factors by the subgroup populations to estimate subgroup emissions and sum across the subgroups to estimate total emission. Manure management contributes 4 284.52 Gg CO 2 e in 2022, which is 8.1% of the Agriculture sector emissions. Methane emissions contribute 40.7% to these emissions. 209 5.4.1.2 Methodological issues Activity data Activity for manure CH 4 emissions is livestock population data which is described in section 5.2.1. Emission factors Annual volatile solid (VS) excretion from horses and mules/asses is determined with a Tier 1 methodology (IPCC 2006, Equation 10.22) by using a default VS excretion rate of 7.2 kg VS (1000 kg animal mass) -1 day -1 (IPCC 2019 Refinement, Table 10.13A). The annual VS excretion for all other livestock is determined with the IPCC 2006 Equation 10.24. IPCC 2006 suggests a value of 0.04 for the fraction of urinary energy (UE) for all livestock except swine where a value of 0.02 is applied (IPCC 2006, Equation 10.24). The fraction of ash content of feed (ASH) is set at 0.08 for all livestock (IPCC 2006, Equation 10.24), except for swine where a value of 0.17 is applied (Du Toit et al., 2013c). 5.4.1.3 Uncertainty Activity data uncertainty The population data uncertainties are discussed in section 5.2.1.4. Uncertainty on manure management systems is provided in section 5.2.2.3. The IPCC 2006 default uncertainty for N excretion is ± 50%, but this is for a Tier 1 method. It is therefore assumed that the Tier 2 method reduces uncertainty to ± 30%. Emission factor uncertainty The data on uncertainty for CH 4 emission factors from livestock manure is presented in Table 5.17. Uncertainty on the CH 4 manure emission factors is provided by ARC (2021) for mature cows, heifers, feedlot cattle, all sheep and all goat sub-categories. No uncertainty data is provided by Du Toit et al. (2013c) for bulls, oxen, young oxen, calves, or swine emission factors. For the bulls, young bulls, oxen, young oxen, and calf sub- categories the uncertainty was assumed to be an average of those for mature cows and heifers in each sub-category. For swine IPCC 2006 indicates that a Tier 2 approach is likely to have an uncertainty of around ± 20%. The same applies to poultry. Tier 1 default factors for horses, and mules/asses have an uncertainty of between ± 30% - 50%, that an average of ± 40% is applied. 210 Table 5.16: Uncertainty data for livestock manure CH 4 emission factors. Animal categories Animal subcategories Emission Factor uncertainty Reference 3.B.1.a - Dairy cattle Mature female ±24 ARC (2021) Heifer (12 – 18 months) ±21 ARC (2021) Heifer (6 – 12 months) ±18 ARC (2021) Heifer (3 – 6 months) ±13 ARC (2021) 3.B.1.b - Commercial beef cattle Bulls ± 10 Average of uncertainty estimates for cows and heifers Young bulls ± 10 Mature female ±7 ARC (2021) Heifer (12 – 18 months) ±11 ARC (2021) Heifer (6 – 12 months) ±9 ARC (2021) Heifer (3 – 6 months) ±9 ARC (2021) Oxen ± 10 Average of uncertainty estimates for cows and heifers Young oxen ± 10 Calves ± 9 Feedlot cattle ± 6 ARC (2021) 3.B.1.b - Subsistence cattle Bulls ± 17 Average of uncertainty estimates for cows and heifers Young bulls ± 17 Mature female ±16 ARC (2021) Heifer (12 – 18 months) ±19 ARC (2021) Heifer (6 – 12 months) ±18 ARC (2021) Heifer (3 – 6 months) ±19 ARC (2021) Oxen ± 17 Average of uncertainty estimates for cows and heifers Young oxen ± 17 Calves ± 17 3.B.2 - Sheep Mature ewes ±15 ARC (2021) Replacement ewes ±8 ARC (2021) Female lambs ±15 ARC (2021) Male lambs ±16 ARC (2021) Young rams ±11 ARC (2021) Rams ±9 ARC (2021) Feedlot sheep ±15 ARC (2021) 3.B.4.d - Goats Mature does ±46 ARC (2021) Replacement does ±42 ARC (2021) Female kids ±46 ARC (2021) Male kids ±46 ARC (2021) Young buck ±50 ARC (2021) Buck ±50 ARC (2021) 3.B.4.e - Horses ± 30 IPCC 2006 Mules & asses ± 30 IPCC 2006 211 3.B.3 - Swine ± 20 IPCC 2006 3.B.4.g - Poultry ± 20 IPCC 2006 5.4.1.4 Time-series consistency The time-series is consistent throughout the period 2000 – 2022. 5.4.1.5 Category specific QA/QC and verification Activity data verification is provided in section 5.2.1.3. For the emission factor data, a literature search was conducted, and the results are shown in the calculation files. Data was also compared to the IPCC default data. The dairy cattle emission factor is higher than the default for Africa. The differences are due to the different manure management systems in these regions which impacts the MCF. The situation is similar for the emission factor for swine. The Other cattle emission factor is much lower than that in other countries and is even lower than the default value for Africa. Sheep and goat emission factors are lower than IPCC default values. 5.4.1.6 Category-specific recalculations This inventory incorporates new data for the calculation of the Tier 2 emission factors and includes changes and updates to the herd composition and characterisation. In addition, the weighting is applied to the emission factors and not to the population data as was done in the previous inventory. Emissions were therefore recalculated for the entire time series and led to a 59.1% increase (79.8% for manure CH 4 emissions) in emissions compared to the previous inventory. 5.4.1.7 Category-specific planned improvements There are no planned improvements for this category in the next year. 5.4.2 Direct and indirect N 2 O emissions from manure management (3.B) 5.4.2.1 Category description The Manure management category also includes N 2 O emissions related to manure handling before it is added to agricultural soil. The amount of N 2 O emissions depends on the system of waste management and the duration of storage. This category therefore includes emissions of both CH 4 and N 2 O. 212 Ammonia (NH 3 ) and NO x volatilise from manure storage; however, these emissions are not estimated due to a lack of data. According to IPCC the method for estimating direct N 2 O emissions from manure management requires the following steps: • Divide livestock population into animal subgroups based on sex, age, and production level. • For N 2 O emissions: i. Determine average annual nitrogen excretion rate per head for each livestock subcategory. ii. Determine the fraction of total annual nitrogen excretion for each livestock category that is managed in each manure management system. iii. Determine N 2 O emission factors for each manure management system. iv. Multiply the emission factor by the total nitrogen managed in the system and sum over all manure management systems. Manure management contributes 4 284.52 Gg CO 2 e in 2022, which is 8.1% of the Agriculture sector emissions. N 2 O emissions contributed 59.3% of the total Manure management emissions in 2022. 5.4.2.2 Methodological issues Direct manure N 2 O emissions are calculated following the IPCC equation 10.25 (IPCC, 2006, volume 4, chapter 10). Activity data Direct N 2 O emissions require information on livestock population (section 5.2.1), manure management (section 5.2.2) and annual average nitrogen (N) excretion per head of livestock (N ex(T) ). Annual N excretion for horses and mules/asses is calculated with a Tier 1 methodology, based on the IPCC default N excretion rate of 0.46 kg N (1000 kg animal mass) -1 day -1 , and typical animal mass. ARC (2021) provides a typical animal mass of 595 kg for horses and 250 kg for mules and asses. A Tier 2 methodology (following IPCC 2006 Equation 10.31 and 10.32) is utilised to calculate annual N excretion rates for cattle, sheep, and goats (Table 5.17). The updated IPCC 2019 Refinement Tier 2 methodology (Equation 10.32A) is applied to swine and poultry to determine N intake rates. The dry matter intake (DMI) data for swine and poultry is taken from Du Toit et al. (2013c). In all cases the N retention values are sourced from IPCC 2006 Table 10.2. 213 Table 5.17: Nitrogen excretion rate for different livestock categories Livestock category Subcategories Nitrogen excretion rate (kg N/animal/year) 3.A.1.a - Dairy cattle Mature female cows 113.71 Heifer 12 - 18 months 27.06 Heifer 6 - 12 months 21.87 Calves 0 - 6 months 12.09 3.A.1.b - Commercial beef cattle Mature female cows 44.60 Heifers 12 - 18 months 25.74 Heifers 6 - 12 months 19.86 Calves 3 - 6 months 13.34 Feedlot cattle 59.00 3.A.1.b - Subsistence cattle Mature female cows 46.05 Heifers 12 - 18 months 26.09 Heifers 6 - 12 months 24.00 Calves 0 - 6 months 16.46 3.A.2 - Commercial sheep Mature ewes 4.00 Replacement ewes 2.93 Female lambs 3.28 Male lambs 4.06 Replacement rams 3.82 Mature rams 5.11 Feedlot sheep 4.33 3.A.2 - Subsistence sheep Mature ewes 4.47 Replacement ewes 3.27 Female lambs 2.42 Male lambs 2.63 Replacement rams 2.84 Mature rams 5.77 3.A.4.d - Goats Mature does 6.33 Replacement does 2.99 Female kids 2.51 Male kids 3.31 Replacement bucks 3.51 Bucks 5.59 Dairy goats - Emission factors 214 Emission factors for all livestock are obtained from IPCC 2019 Refinement (Table 10.21). 5.4.2.3 Uncertainty Activity data uncertainty The population data uncertainties are discussed in section 5.2.1.4. Uncertainty on manure management systems is provided in section 5.2.2.3. The IPCC 2006 default uncertainty for N excretion is ± 50%, but this is for a Tier 1 method. It is therefore assumed that the Tier 2 method reduces uncertainty to ± 30%. Emission factor uncertainty Uncertainty on the IPCC default N 2 O manure emission factors is -50% to +100%. 5.4.2.4 Time-series consistency The time-series is consistent throughout the period 2000 – 2022. 5.4.2.5 Category specific QA/QC and verification Category specific QA/QC and verification is provided in section 5.2.1.3. 5.4.2.6 Category-specific recalculations There are no specific recalculations for this category. 5.4.2.7 Category-specific planned improvements There are no planned improvements for this category in the next year. Rice cultivation (3.C) Rice cultivation is not included. The explanation is provided in section 5.1. Agricultural soils (3.D) 5.6.1 Direct N 2 O emissions from managed soils (3.D.1) 215 5.6.1.1 Category description Agricultural soils contribute to GHGs in three ways: • CO 2 through the loss of soil organic matter. This is a result of land-use change, and is, therefore, dealt with in the land sector, not in this section. CH 4 from anaerobic soils. Anaerobic cultivation, such as rice paddies, is not practised in South Africa, and therefore CH 4 emissions from agricultural soils are not included in this inventory; and • N 2 O from fertilizer use and intensive cultivation. This is a significant fraction of non-carbon emissions from agriculture and is the focus of this section of the inventory. The IPCC (2006) identifies several pathways of nitrogen inputs to agricultural soils that can result in direct N 2 O emissions: • Nitrogen inputs: o Synthetic nitrogen fertilizers. o Organic fertilizers (including animal manure, compost, and sewage sludge); and o Crop residue (including nitrogen fixing crops). • Soil organic matter lost from mineral soils through land-use change. • Organic soil that is drained or managed for agricultural purposes; and • Animal manure deposited on pastures, rangelands, and paddocks. In this inventory emissions from sewage sludge are included under waste (IE) and organic soils are not included (NE) due to their insignificance. Direct N 2 O emissions from managed soils amounted to 9 382.9 Gg CO 2 e in 2022. The emission contribution of the various types of N inputs is shown in Table 5.18 . The total Direct N 2 O emissions increased by 28.9% between 2000 and 2020, showing a 3.3% increase since 2017. Direct N 2 O emissions from managed soils is a key category based on the level and trend assessments. Table 5.18: Emission trends for the various sub-categories of direct N 2 O emissions from managed soils. Inorganic N fertilisers Organic N fertilisers Urine and dung from grazing animals Crop residues 2000 Emissions (Gg CO 2 e) 1 732,1 643,4 6 179,9 426,9 % Agriculture 2,9 1,1 10,5 0,7 216 2017 Emissions (Gg CO 2 e) 1 844,4 770,6 5 618,3 894,4 % Agriculture 3,3 1,4 10,0 1,6 2022 Emissions (Gg CO 2 e) 1 713,6 775,6 5 295,9 977,7 % Agriculture 3,2 1,4 9,8 1,8 Change 2000 – 2020 (%) -1,1 20,5 -14,3 129,0 2017 – 2020 (%) -7,1 0,6 -5,7 9,3 5.6.1.2 Methodological issues A Tier 1 approach is used to calculate Direct N 2 O emission from managed soils following the IPCC 2006 Equation 11.1 (IPCC, 2006, Volume 4, Chapter 11). Inorganic fertiliser N inputs (F N ) (3.D.1.a) IPCC Tier 1 methodologies and default emission factors (IPCC 2006) are used for estimating direct N 2 O emissions from managed soils. The amount of inorganic N applied to soils is multiplied with the IPCC default emission factor (IPCC 2006). Organic fertiliser N inputs (F ON ) (3.D.1.b) The amount of N (kg N yr -1 ) from organic N additions applied to soil is calculated using IPCC 2006 Equation 11.3 (IPCC, 2006; Volume 4, chapter 11). Animal manure applied to soils A Tier 1 approach was used to calculate N from animal manure applied to soils (IPCC 2006, Equation 11.4, vol 4, chapt 11). The amount of animal manure applied is equal to the amount of managed manure N available for soil application minus that used for feed and construction. The amount of managed manure N available for soil application is calculated from IPCC 2006 Equation 10.34 (IPCC, 2006, volume 4, chapter 10). Sewage sludge applied to soils N 2 O emissions from sludge are included in the Waste sector and are therefore excluded here to avoid double counting. Compost applied to soils 217 The amount of compost N applied on managed soils each year is estimated from the synthetic fertilizer consumption data. The synthetic fertilizer input changed each year, while the rest of the factors were assumed to remain unchanged over the 20-year period. It is estimated that a total of 5% of all farmers use compost (DAFF, 2010). Compost is seldom, if ever, used as the only nutrient source for crops or vegetables. It is used as a supplement for synthetic fertilizers, and it is estimated that farmers would supply about 33% of nutrient needs through compost. All of this is considered when estimating N inputs from compost (details provided in DAFF (2010) and Otter (2011)). Urine and dung N deposited by grazing animals (F PRP ) (3.D.1.c) Manure N deposited in pastures, rangelands and paddocks include all the open areas where animal excretions are not removed or managed. It also includes emissions from daily spread. This manure remains on the land, where it is returned to the soil, and contributes to GHG emissions. In South Africa, the majority of animals spend most of their lives on pastures and rangelands. The annual amount of urine and dung N deposited on pastures, ranges, or paddocks by grazing animals (F PRP ; kg N yr -1 ) is calculated using Equation 11.5 in the IPCC 2006 Guidelines (Chapter 11, Volume 4). Crop residues N inputs (F CR ) (3.D.1.d) The amount of N in crop residues is estimated using the updated IPCC Tier 1 approach for the IPCC 2019 Refinement (Equation 11.6) but with some national factors. Some country specific factors are given for fraction of dry matter, fraction of residue burnt, fraction of residue removed, and ratio of above-ground and below ground residues to harvested yield (Tongwane et al., 2016). IPCC default factors are applied for combustion factors. IPCC default values for N content of above-ground residue, N content of below ground residue, and ratio of below-ground biomass to above-ground biomass (IPCC 2019 Refinement, Table 11.1A) are applied. Mineralisation/immobilisation associated with loss/gain of soil organic matter (F SOM ) (3.D.1.e) The mineralised N resulting from loss of soil organic carbon stocks in mineral soils through land-use change (F SOM ) was estimated following the equation 11.8 from the IPCC 2006 guidelines. IPCC recommended defaults for C:N ratio (IPCC 2006, Equation 11.8) are used in the calculation. Activity data Inorganic fertiliser N inputs For nitrogen emissions the Fertilizer Association of SA reports total N consumption (http://www.fertasa.co.za/fertilizer-information/historic-sales-data/ and 218 http://www.fertasa.co.za/wp-content/uploads/2021/03/Fertilizer-Usage-2020-and- 2021-RSA.pdf ). This value is the total nitrogen consumed in all fertilizer types and it accounts for the different N content of urea, ammonia, etc. It should be noted that the N consumption data between 2000 and 2009 was based on actual data, but thereafter the numbers are estimates. Organic fertiliser N inputs Animal manure inputs IPCC 2006 Equation 10.34 (IPCC, 2006; volume 4, chapter 10) requires the following data: • Livestock population data (section 5.2.1) • N excretion data (section 5.2.4.2) • Manure management system usage data (Table 5. 30) • Amount of managed manure nitrogen that is lost in each manure management system (Frac LossMS ). IPCC 2006 default values were used here (Table 10.23, Chapter 10, Volume 4, IPCC 2006). • Amount of nitrogen from bedding. There were no data available for this, so the values provided by IPCC (IPCC, 2006; pg. 10.66) are utilized and • The fraction of managed manure used for feed, fuel, or construction. Again, there were insufficient data and thus F AM was not adjusted for these fractions (IPCC 2006 Guidelines, p. 11.13). Compost inputs The activity data to calculate compost is inorganic N fertiliser data which is described above. Urine and dung deposited by grazing animals Activity data for this sub-category are livestock population (section 5.2.1) and manure management (section 5.2.4) data. Crop residue N inputs Planted area and production data is obtained from Abstracts of Agricultural Statistics (DALRRD, 2022) and FAO (FAOStat). Production data is used to calculate yield and this is usually dry mass (as indicated in FAOSTAT) so these values are not multiplied by the dry matter content to get dry yield. 219 Mineralised N due to land use change The average annual loss of soil carbon from the various land types is the activity data for this subcategory and this data comes from the land conversions data discussed in the Land sector (section 5.4). Emission factors Inorganic and organic N fertiliser, crop residues and F SOM The IPCC default emission factor of 0.005 kg N2O-N/kg N applied (IPCC 2019 Refinement, Table 11.1, dry climate). Urine and dung deposited by grazing animals The IPCC default emission factor of 0.002 kg N 2 O-N (kg N) -1 for cattle, poultry and pigs and 0.003 kg N 2 O-N (kg N) -1 for sheep and other animals is applied (IPCC 2019 Refinement, Table 11.1, dry climate). 5.6.1.3 Uncertainty Activity data uncertainty Inorganic N fertiliser Expert opinion (Corne Louw, corne@grainsa.co.za) suggests that the N consumption would likely be within 15% of the number. Organic N fertiliser For animal manure inputs the uncertainty is estimated at ±41% based on the uncertainty on livestock population, manure management and N excretion data. No uncertainty data was provided for compost, so a ±25% uncertainty is assumed. This is based on the 15% uncertainty on N consumption, with additional uncertainty on the percentage nitrogen, percentage of compost in total fertiliser application and amount of animal manure in compost. Urine and dung deposited by grazing animals 220 The uncertainty estimate for this activity data is 53.6% based on the uncertainty on livestock population data, manure management data and the 50% uncertainty on the FracLossMS factor (IPCC 2006, Table 10.23). Crop residues The uncertainty on crop residue input data is high (average of 91.2%) which is estimated from a ±10% uncertainty on crop production data, a ±75% uncertainty on N content, a ±50 uncertainty on above and below ground ratios (IPCC 2019 Refinement, Table 11.1A) and a ±10% uncertainty on residue management data. FSOM Uncertainty is estimated at ±33% due to a ±13% uncertainty on land area data, ±30% uncertainty on SOC data and ±5% uncertainty on the C:N ratio. Emission factor uncertainty Inorganic and organic N fertiliser, crop residues and F SOM Uncertainty on the default emission factor applied to these sub-categories is -100% and +200% (IPCC 2019 Refinement, Table 11.1). Urine and dung deposited by grazing animals Uncertainty on the default emission factor for cattle, poultry and pigs is -100% to +200% and for the sheep and other animals it is -100% to +230% (IPCC 2019 Refinement, Table 11.1). 5.6.1.4 Time-series consistency The time-series for direct N 2 O emissions is complete and consistent. 5.6.1.5 Category specific QA/QC and verification Synthetic N fertiliser consumption data was compared to the FAO data (FAOSTAT) and there was no difference in the data between 2000 and 2014, but after that data varied by up to 23%. It was noted in the FAO data that the last few years were indicated to be unofficial data so this could account for the discrepancy. 5.6.1.6 Category-specific recalculations 221 Recalculations were performed for the entire time-series and the updated data is on average 71.8% lower than provided in the previous inventory. The difference seen is because of changes in both the activity (livestock herd composition and manure management) and emission factor data. Much of the decline in emissions is due to the updated emission factors in this category. The emission factors provided in the IPCC 2019 Refinement are half of what was used in the previous inventory. This is because the 2019 Refinement disaggregates the data into wet and dry climates and the dry climate data, (which is applied in this inventory) are much lower. 5.6.1.7 Category-specific planned improvements There are no category specific improvements planned. 5.6.2 Indirect nitrous oxide emissions from managed soils (3.D.2) 5.6.2.1 Category description Indirect emissions of N 2 O-N from managed soils can take place in two ways: i) volatilization of N as NH 3 and oxides of N, and the deposition of these gases onto water surfaces, and ii) through runoff and leaching from land where N was applied (IPCC, 2006). Indirect emissions due to atmospheric deposition/volatilisation occur from inorganic and organic N application and urine and dung N inputs, while indirect runoff/leaching emissions can also occur from crop residue application and N losses due to changes in land management practices and land use (see Figure 11.1 of the 2006 IPCC guidelines). Indirect N 2 O from managed soils contributed 995.2 Gg CO 2 e to the total emissions in 2022. 5.6.2.2 Methodological issues Due to limited data a Tier 1 approach was used to calculate the indirect N 2 O emissions in this category. Indirect N 2 O from atmospheric deposition of volatilized N The annual amount of N 2 O-N produced from atmospheric deposition of N volatilized from managed soils (N 2 O (ATD)-N ) was calculated with IPCC 2006 Equation 11.9. Indirect N 2 O from leaching/runoff The annual amount of N 2 O-N produced from leaching and runoff of N additions to managed soils (N 2 O (L)-N ) is determined by IPCC 2006 Equation 11.10. IPCC 2019 222 Refinement (Chapter 11, Vol. 4, Table 11.3) indicates that the term Frac LEACH-(H) only applies to wet climates, while for dry climates Frac LEACH-(H) is taken as zero. South Africa has a dry climate, therefore zero was applied to urine and dung deposits. The fraction of all N added to/mineralised in cultivated lands that is lost through leaching and runoff (Frac LEACH-(H) ) was determined by using a weighted average as leaching was assumed to occur on irrigated lands. WRI (2018) indicated that 10% of cultivated land is irrigated, therefore a Frac LEACH-(H) value of 0.02 kg N (kg N additions)-1 was applied to cultivated lands. Activity data The calculation of F SN , F ON , and F PRP are described above. Emission factors Indirect N 2 O from atmospheric deposition of volatilized N The emission factor (EF 4 ), and the volatilization fractions (Frac GASF and Frac GASM ) were all taken from the IPCC 2019 Refinement default table (Table 11.3, Chapter 11, Volume 4). Indirect N 2 O from leaching/runoff The emission factor (EF 5 ) was taken from the IPCC 2019 Refinement (Table 11.3, Chapter 11, Volume 4). 5.6.2.3 Uncertainty Activity data uncertainty Uncertainty on activity data for F SN , F ON , and F PRP are provided in section 5.3.5.3. Frac GASF has an uncertainty of -82% and +200%, while Frac GASM has an uncertainty of -100% + 48% (IPCC 2019 Refinement, Table 11.3). Emission factor uncertainty The uncertainty on EF 4 is -100% and +120%, and for EF 5 it is -100% and +82% (IPCC 2019 Refinement, Table 11.3). 5.6.2.4 Time-series consistency The time-series is consistent throughout the inventory time period. 223 5.6.2.5 Category specific QA/QC and verification There were no category specific quality control checks. 5.6.2.6 Category-specific recalculations Recalculations across the entire time-series produced lower emissions, with an average reduction of 68% for volatilisation and 15% for leaching losses. The reduction in emissions is due mostly to the updated emission factors from the IPCC 2019 Refinement, but also due to changes in livestock herd composition and manure management. 5.6.2.7 Category-specific planned improvements There are no planned improvements for this category. Field burning of agricultural residues (3.F) 5.7.1 Category description Agricultural burning is the intentional use of fire for residue management in agricultural fields. Agricultural burning helps farmers remove crop residues left in the field after harvesting grains. This section reports emissions of non-CO 2 gases (CH 4 , CO, N 2 O and NOx) from cropland while other categories are discussed in the LULUCF section. In this inventory this category includes biomass burning of sugarcane residues only since the burning of other crop residues is not significant in South Africa. Sugarcane is the only crop which utilises pre-harvest burning to expose the cane stalks removing the outer leaves, driving away insects, snakes and other wild animals making easy not only manual, but also mechanical harvesting additionally, after harvesting, sugarcane residues are burned releasing to the atmosphere GHGs. In addition to CO 2 , the burning of biomass results in the release of other GHGs or precursors of GHGs that originate from incomplete combustion of the fuel. The key GHGs are CO 2 , CH 4 , and N 2 O; however, NOx, NH 3 , NMVOC and CO are also produced, and these are precursors for the formation of GHG in the atmosphere (IPCC, 2006). The CO 2 emissions from burning are not included in this category as they are accounted under the disturbance losses in the Land (3B) category. All non-CO 2 emissions, on the other hand, are included in this category. Field burning of agricultural residues produced 58.54 Gg CO 2 in 2022 which is 0.1% of the agriculture sector emissions. 224 5.7.2 Methodological issues The Tier 1 methodology was applied, with the emissions from biomass burning being calculated using the following equation (Equation 2.27 from IPCC 2006 Guidelines): L fire = A * M B * Cf * G ef * 10 -3 Where: L fire = mass of GHG emissions from the fire (t GHG) A = area burnt (ha) M B = mass of fuel available for combustion (t dm ha - 1 ) Cf = combustion factor (dimensionless) G ef = emission factor (g kg -1 dm burnt) 5.7.2.1 Activity data Burnt area data The MODIS Collection 6 Burnt Area data was utilised in this inventory, which is an upgrade from the Collection 5 used in the last inventory. Annual burnt-area maps were produced from the MODIS monthly burnt-area product for the years 2001, 2005, 2010, and 2014-2018. Data was incomplete for 2000 so this data set was excluded, and there was insufficient time to process all the years in between. An IPCC splicing technique using the previous inventory data as proxy data was applied to update the data for the years in between. The MODIS Collection 6 Burned Area Product Geotiff version (http://modis- fire.umd.edu/pages/BurnedArea.php?target=GeoTIFF) is used in this inventory. This is a level 3 gridded 500 m product, and the quality of the information is described in Giglio et al. (2018). Data processing involved the following steps: a) Burnt area: i) Each year’s dataset contained 12 files (1 per month). Each month was reclassed to remove NoData (classes -2, -1, and 0) and each day value was changed to 1 to obtain total area burnt per month. ii) Every month of data was reprojected into the UTM 35S projection to remain consistent with the 2014 and 2018 land-cover dataset project. iii) Each month’s dataset was clipped (extract by mask) to the South African boundary (South African Boundary shapefile (2018) obtained from the Municipal Demarcation Board (https://dataportal- mdb- sa.opendata.arcgis.com/search?tags=2018) with a 1km buffer. This 225 allowed for improved data retention. iv) Each month’s dataset was combined using the “mosaic to new raster” to create a dataset per year. v) Each year’s dataset was resampled to change the cell size to the same as the land cover dataset: • For all years the 2014 land cover dataset was used with a cell size of 30 x 30m. vi) Each year’s dataset was clipped (extract by mask) to the actual South African boundary. b) Land cover/land use: i) The 2014 (GTI, 2015) and 2018 land cover datasets (GTI, 2019a) (72 and 73 classes, respectively) were reclassed into 20 categories (see section 5.4.2 for more details). The 20 categories are the same as the latest land change categories for 1990-2018 and 2014-2018 using the same process as conducted by the land change study (GTI, 2019a; GTI, 2019b). c) Combining burnt area and land cover datasets: i) The land cover type where each year’s burnt area was associated with was conducted using the raster calculator feature. This was done for each year. The output dataset for each year was collated in Microsoft Excel and the total area burnt was calculated in hectares. Due to time limitations not all the post years could be reanalysed using the Collection 6 data. The years 2001, 2005, 2010 and 2014 onwards were analysed and for the in between years the previous Collection 5 data was used as a proxy and the IPCC splicing technique was applied to adjust the years in between. There was not much difference in the two data sets, however, it would be ideal to maintain consistency throughout the time-series. Mass of fuel available for combustion (MB) and the combustion factor (Cf) Fuel density of 7 t/ha was used for cropland and it was sourced from DAFF (2010), and the combustion factor of 1 was used and sourced from DAFF (2010). The IPCC value of fuel combustion (t/ha) of 4-10 was used for cropland (IPCC, 2006). 5.7.2.2 Emission factors IPCC 2006 default emission factors (IPCC, 2006, vol 4, chapter 2, Table 2.5, page 2.47) are applied. 5.7.3 Uncertainty 5.7.3.1 Activity data uncertainty 226 Boschetti et al. (2019) indicates that MODIS collection 6 burnt area products have an uncertainty of less than 6%. The 2013/14 land cover map showed an average user and producer accuracy of 83.74% and 88.34% respectively. Based on the accuracy assessment in the 2014 SANLC report, cropland was assigned ± 8% uncertainties. Based on uncertainties in IPCC (IPCC, 2006; Table 2.4) and van Leeuwen et al. (2014) fuel consumption uncertainty was determined to be 20% for croplands. 5.7.3.2 Emission factor uncertainty IPCC default uncertainties for emission factors are provided in the guidelines (IPCC, 2006; Table 2.5). 5.7.4 Time-series consistency Time series is consistent as same data sources are used throughout. 5.7.5 Category specific QA/QC and verification Burnt area data was compared to those from the Meraka Institute (Meraka Institute, 2019) who are also using the MODIS collection 6 data. The datasets compare very well (within 10% of each other) and show the same annual trend. 5.7.6 Category-specific recalculations There are no specific recalculations for this category. 5.7.7 Category-specific planned improvements There are no specific planned improvements for this category. Liming (3.G) 5.8.1 Category description Liming is used to reduce soil acidity and improve plant growth in managed systems. Adding carbonates to soils in the form of lime (limestone or dolomite) leads to CO 2 emissions as the carbonate limes dissolve and release bicarbonate. Lime is commonly 227 applied to agricultural lands where nitrogenous fertilisers are continuously used and where precipitation exceeds evapotranspiration. Liming produced 1 860.71 Gg CO 2 in 2022 which is 3% of the agriculture sector emissions. These emissions have more than doubled since 2000, however, there was a 23% reduction in emissions since 2017. 5.8.2 Methodological issues A Tier 1 approach of the IPCC 2006 Guidelines was used to calculate annual CO 2 emissions from lime application (Equation 11.12, IPCC 2006). Tier 2 approach is not available for this category. 5.8.2.1 Activity data Emissions from lime application was estimated per crop type using crop area data and application rates. The calculation was derived from crop areas and application rates, employing estimates from Tongwane et al. (2016). However, this resulted in a figure exceeding 3 million tons of lime in 2008, contrasting with the 1.5 million tons reported by the Fertiliser Association of South Africa. 5.8.2.2 Emission factors The IPCC default emission factors of 0.12 t C (t limestone)-1 and 0.13 t C (t dolomite)-1 were used to calculate the CO 2 emissions from Liming. 5.8.3 Uncertainty 5.8.3.1 Activity data uncertainty Uncertainty is determined from the difference between the SAMI (DMR, 2018) report data and the Fertilizer Association data. For limestone it is -90% to 25% and for dolomite it was determined to be -75% to 15%. 5.8.3.2 Emission factor uncertainty The dolomite and limestone default emission factors have an uncertainty of -50% (IPCC 2006 Guidelines, p. 11.27). 5.8.4 Time-series consistency A splicing technique was used to combine data sets to ensure a consistent time-series. 228 5.8.5 Category specific QA/QC and verification Past inventory reviews have mentioned upgrading this information and investigating the alternate method of calculating potential lime use. This was done by using the crop area estimates combined with application rates from Tongwane et al. (2016) but this yielded a value of over 3 million tons of lime in 2008 compared to the 1.5 million tons provided by the Fertiliser Association of SA. This data was therefore incorporated and there is a need for an alternate data source should be sought in future. 5.8.6 Category-specific recalculations The methodology was changed, the calculation of lime application emissions per crop type was based on crop area data and application rates. This involved utilizing crop area estimates in combined with application rates sourced from Tongwane et al. (2016). 5.8.7 Category-specific planned improvements There is a need for alternate data source for lime data. Urea application (3.H) 5.9.1 Category description Adding urea to soils during fertilization leads to a loss of CO 2 that was fixed in the industrial production process. Urea application is estimated to produce 584.6 Gg CO 2 in 2022 (1.1% of the agriculture sector emissions) and this is almost double what was emitted in 2000. Emissions declined by 14.0% since 2017. 5.9.2 Methodological issues A Tier 1 approach of the IPCC 2006 Guidelines is used to calculate CO 2 emissions from urea fertilization (Equation 11.13, IPCC 2006). 5.9.2.1 Activity data Import and export data for urea was obtained from South African Revenue Service (SARS) (downloaded from http://www.sagis.org.za/sars.html on 13/07/2022). 229 5.9.2.2 Emission factors The IPCC default emission factor of 0.2 t C (t urea)-1 is applied in the equation to calculate the CO 2 emissions. 5.9.3 Uncertainty 5.9.3.1 Activity data uncertainty In terms of urea application, it is assumed that all urea imported is applied to agricultural soils and this approach may lead to an over- or under-estimate if the total imported urea is not applied in that particular year. However, over the long-term this bias should be negligible (IPCC, 2006). Urea data is based on import data, which is well controlled, so a nominal 5% uncertainty was assumed. There is also some uncertainty with regards to the use and distribution of this urea. Again, there are no uncertainty estimates provided for this so an additional 5% was assumed. 5.9.3.2 Emission factor uncertainty As for the liming emission factors, the urea emission factor also has an uncertainty of - 50% (IPCC 2006 Guidelines, p. 11.32). 5.9.4 Time-series consistency The time-series is consistent. 5.9.5 Category specific QA/QC and verification Urea data is checked against the FAOStat dataset and found to be very similar. 5.9.6 Category-specific recalculations No recalculations are performed for this category. 5.9.7 Category-specific planned improvements No improvements are planned for this category in the next year. References 230 DAFF, 2010. The South African Agricultural GHG Inventory for 2004. Department of Agriculture, Forestry and Fisheries, ISBN: 978-1-86871-321-9, DAFF, Pretoria, RSA. DMR, 2018. South Africa’s Mineral Industry 2017/2018. Department of Mineral Resources, Pretoria, RSA. https://www.dmr.gov.za/LinkClick.aspx?fileticket=PClz- cRGkyg%3D&portalid=0 [Accessed October, 2018]. Du Toit, C.J.L., Meissner, H.H. and van Niekerk, W.A., 2013a. Direct methane and nitrous oxide emissions of South African dairy and beef cattle. South African Journal of Animal Science, 43 (3): 320 – 339. Du Toit, C.J.L., Meissner, H.H. and van Niekerk, W.A., 2013d. Direct greenhouse gas emissions of the game industry in South Africa. South African Journal of Animal Science, 43 (3): 376 – 393. Du Toit, C.J.L., van Niekerk, W.A. and Meissner, H.H., 2013b. Direct greenhouse gas emissions of the South African small stock sectors. South African Journal of Animal Science, 43 (3): 340 – 361. Du Toit, C.J.L., van Niekerk, W.A. and Meissner, H.H., 2013c. Direct methane and nitrous oxide emissions of monogastric livestock in South Africa. South African Journal of Animal Science, 43 (3): 362 - 375. IPCC, 2019. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osaka, A., Pyrozhenco, Y., Shermanau, P. and Federici, S. ed. Switzerland: IPCC. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, Eggleston H S, Buenida L, Miwa K, Ngara T, and Tanabe K, eds; Institute for Global Environmental Strategies (IGES). Hayama, Kanagawa, Japan. Meraka Institute, 2019. Advanced fire information system, South Africa burnt area statistics. Pretoria, South Africa. Moeletsi, M.E., and Tongwane, M.I., 2015. Methane and Nitrous Oxide Emissions from Manure Management in South Africa. Animals, 5, 193–205. Moeletsi, M.E.; Tongwane, M.I.; Mdlambuzi, T.; Grootboom, L.; Mliswa, V.K.; Nape, K.M.; Mazibuko, S., 2015. Improvement of the Greenhouse Gas Emissions Inventory for the Agricultural Sector; DFID: Pretoria, Southern Africa. 231 Van Leeuwen, TT. et al. 2014. Biomass burning fuel consumption rates: a field measurement database. Biogeoscinces Discussions, 11, pp 8115-8180. (https://bg.copernicus.org/articles/11/7305/2014/bg-11-7305-2014.pdf) 232 Chapter 6: Land use, Forestry and Land Use Change (LULUCF) (CRT sector 4) Sector overview In the previous inventory the LULUCF sector was referred to as the FOLU sector (Forestry and Other Land Use) and it formed part of the AFOLU (Agriculture, Forestry and Other Land Use) sector. In this inventory the Agriculture and LULUCF sectors have been separated to bring the inventory into alignments with the Common Reporting Table (CRT) reporting requirements under the Enhanced Transparency Framework (ETF). In the LULUCF sector South Africa reports on the emissions (positive) and removals (negative) of CO 2 from the following carbon pools: • Above-ground biomass: o Includes all living biomass above the soil including stem, stump, branches, bark, seeds, and foliage. o Included for all land categories. • Below-ground biomass: o Includes all biomass of live roots. o Included for all land categories. • Litter: o Included all non-living biomass, lying dead, in various states of decomposition above the mineral or organic soil. o Included for all land categories. • Dead wood: o Includes all non-living woody biomass not contained in the litter, either standing, lying on the ground, or in the soil. o Only included for the Forest land category. • Mineral soils: o Includes organic carbon in mineral soils to a depth of 30cm. o Included for all land categories. • Harvested wood products (4.G.). Organic soils were assumed to be negligible (Moeletsi et al., 2015) with further details being provided in section 6.1.3.4. The carbon pools are reported for the following land-use categories: 233 • Forest land (4.A.) • Cropland (4.B.) • Grassland (4.C.) • Wetlands (4.D.) • Settlements (4.E.) • Other land (4.F.) As well as the relevant land-use changes between these categories. A distinction is made between areas which, during the reporting period: • undergo no land-use changes, and thus remain, in unchanged form, in the land-use category they are in (“land remaining” categories 4.A.1 – 4.F.1) • undergo land-use changes: o From the time of conversion onward, these areas are reported in the category to which they were converted. Within those land-use categories, the converted areas are then reported in conversion categories (“land conversion” categories 4.A.2 – 4.F.2) for a total of 20 years. After 20 years in a conversion category, the areas are reported under the relevant remaining categories. Wetlands also include the emissions of CH 4 and N 2 O and non-CO 2 gases emitted from biomass burning are included for Forest land, Grassland, Wetlands, and Settlements. In Croplands the burning is due to pre-harvest burning of sugarcane and this is included under Agriculture in category 3.F. Other lands are assumed not to burn due to the absence of biomass. Emissions from humus mineralisation in mineral soils as a result of land use change and/or land management are reported in the Agriculture sector under category 3.D.1. The reporting of non-CO 2 emissions from biomass burning was incorporated into the Agriculture component of the AFOLU sector in the previous inventory (under the Aggregated and non-CO 2 emissions category (3.C)), however due to the adjusted reporting requirements these non-CO 2 emissions are now included within each land use category under the LULUCF sector. 6.1.1 Shares and trends in emissions The LULUCF sector was a sink in 2022 (Table 6.1) with Forest lands being the largest contributor to the sink. All other land categories were a source of emissions in 2022, with Other lands being the largest. 234 Table 6.1: Summary of emissions from the LULUCF sector in 2022. Greenhouse gas source and sink categories CO 2 CH 4 N 2 O Total Gg CO 2 Gg Gg CO 2 e Gg Gg CO 2 eq Gg CO 2 e 4. LULUCF -56 146.3 11 019.8 2 066.7 -43 059.8 4.A. Forest land -90 082.4 57.7 1 614.3 3.4 909.3 -87 558.8 4.B. Cropland 3 508.9 IE IE IE IE 3 508.9 4.C. Grassland 10 576.8 25.2 705.87 2.3 610.65 11 893.4 4.D. Wetlands 748.0 310.6 8 697.0 2.1 544.4 9 989.3 4.E. Settlements 258.5 0.1 2.7 0.01 2.4 263.6 4.F. Other land 19 025.4 NA NA NA NA 19 025.4 4.G. Harvested wood products -181.4 NA NA NA NA -181.4 Figure 6.1 provides an overview of the LULUCF emission trends for South Africa over the time-series 2000-2022. The time-series reflects the trends in land-use changes. The land- use changes have been determined based on South African National Land Cover (SANLC) data sets for the reference years 1990, 2014, 2018 and 2020. Between the reference years, the land-use changes have been linearly interpolated, hence the larger changes after each refence year and the constant average land-use changes between reference periods. A map is being developed for 2022 but was not finalised before the preparation of this inventory and so the data was not included in this inventory. Data for the period 2020-2022 was extrapolated based on the change area provided between 2018 and 2020. Forest lands were the largest contributor to the sink across the time-series. The dominant Forest land sinks were thickets and woodlands. The increasing Forest land sink between 2014 and 2022 is due to an increasing woodland sink because of an increase in the conversion of grassland to woodland. The increasing woodland sink was reduced slightly between 2017 and 2022 by the decreasing thicket sink caused by a reduction in the thicket area. The overall variation in the Forest land category follows a similar pattern to the burnt area data. For example, the years 2005 and 2021 were high burn years which meant an increase in emissions and an increase in disturbance losses in Forest lands therefore a reduced sink in 2005 and 2021. 235 Figure 6.1: Time series for GHG emissions and removals by land type in the LULUCF sector in South Africa, 2000 - 2022. Croplands showed an increase in emissions between 2015 and 2018 due to the conversion of perennial crops to annual crops and the conversion of woodlands to subsistence crops. These emissions were reduced between 2019 and 2022 as these conversions were reduced and there was some conversion of annual crops to perennial crops. The change in Grasslands is what was causing the reduced sink between 2017 and 2022. Between 2015 and 2018 there is an increase in the conversion of low shrublands to grasslands, while between 2019 and 2022 there is an increase in the conversion of woodland to grassland leading to an increased source. Converted lands were the largest contributors to all land categories except Wetlands (Table 6.2) where non-CO 2 emissions from wetlands play a role. Overall, the LULUCF sector increased its sink by 56.3% since 2000 and by 17.9% since 2020. Grasslands changed from a small sink in 2000 to a large source in 2020 and 2022. Changes were large between 2000 and 2020 (Table 6.2) with part of the reason being that the data for these years came from two different land change maps, however change is small between 2020 236 and 2022 as would be expected from a shorter change period and where the comparison is between two maps produced in exactly the same way. Biomass is the dominant carbon pool (Error! Reference source not found.). Soils r emain a small sink throughout the time-series, while The DOM pool increases its sink strength between 2019 and 2022. Figure 6.2: Time-series of GHG emission and removals by carbon pool for the LULUCF sector in South Africa, 2000-2022. 237 Table 6.2: Summary of the change in emissions from the LULUCF sector over the time-series 2000-2022 and since the last inventory in 2020. GHG source and sink categories Emissions (Gg CO 2 eq) Difference (Gg CO 2 eq) Change (%) 2000 2020 2022 2000-2022 2020-2022 2000-2022 2020-2022 4. LULUCF -27 840.2 -36 519.2 -43 059.8 -15 875.4 -6 540.6 56.3 17.9 4.A. Forest land -45 816.6 -81 062.2 -87 558.8 -41 742.2 -6 496.6 91.1 8.0 4.A.1. Forest land remaining forest land -16 662.6 -34 344.8 -32 696.7 -16 034.1 1 648.1 96.2 -4.8 4.A.2. Land converted to forest land -29 154.0 -46 717.4 -54 862.1 -25 708.1 -8 144.7 88.2 17.4 4.B. Cropland 4 365.1 3 353.3 3 508.9 -856.3 155.6 -19.6 4.6 4.B.1. Cropland remaining cropland 234.6 151.6 79.5 -155.1 -72.1 -66.1 -47.6 4.B.2. Land converted to cropland 4 130.5 3 201.7 3 429.4 -701.2 227.7 -17.0 7.1 4.C. Grassland -2 205.6 12 026.9 11 893.4 14 098.9 -133.5 -639.2 -1.1 4.C.1. Grassland remaining grassland -611.5 13 538.6 13 728.0 14 339.5 189.3 -2 344.9 1.4 4.C.2. Land converted to grassland -1 594.0 -1 511.7 -1 834.6 -240.6 -322.9 15.1 21.4 4.D. Wetlands 9 848.8 9 774.4 9 989.3 140.5 214.9 1.4 2.2 4.D.1. Wetlands remaining wetlands 7 476.6 4 889.4 5 088.5 -2 388.1 199.1 -31.9 4.1 4.D.2. Land converted to wetlands 2 372.2 4 885.0 4 900.8 2 528.6 15.7 106.6 0.3 4.E. Settlements 559.8 302.9 263.6 -296.2 -39.3 -52.9 -13.0 4.E.1. Settlements remaining settlements -261.2 -421.6 -442.6 -181.4 -21.0 69.4 5.0 4.E.2. Land converted to settlements 820.9 724.5 706.1 -114.9 -18.3 -14.0 -2.5 4.F. Other land 7 812.5 19 025.7 19 025.4 11 212.8 -0.3 143.5 0.0 4.F.1. Other land remaining other land 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.F.2. Land converted to other land 7 812.5 19 025.7 19 025.4 11 212.8 -0.3 143.5 0.0 4.G. Harvested wood products -2 106.2 59.9 -181.4 1 924.8 -241.4 -91.4 -402.8 Note: Columns may not add up exactly due to rounding off 238 6.1.2 Impacts of natural disturbance The inventory includes both wildfires (natural disturbance) and controlled fires as there was insufficient data to separate the two. Fires were included in the inventory through disturbance losses (which removes biomass and litter and leads to emissions) and through the non-CO 2 emissions from fires (which also contribute towards emissions). Fires are an important component of South Africa’s natural ecosystems and cannot always be controlled. Removing all fire disturbance impacts leads to an enhanced sink with reduced annual variability (Figure 6.3). Not all fires are natural as a small percentage will be controlled fires. Collecting data to enable the fire data to be split into controlled and wildfires is important for understanding how or where fire emissions can be reduced and for establishing a baseline for natural disturbance contributions. Figure 6.3: Time-series of GHG emission and removals by land type for the LULUCF sector when fire impacts are excluded. 6.1.3 Overview of methodology and completeness 239 The following basic elements and process was required to prepare the LULUCF inventory: 1. Land cover/use maps: SANLC maps, containing 73 land classes, were obtained for 1990, 2014, 2018 and 2020. The relevant land uses, and the specific areas assigned to them, were explicitly determined for these years, while the areas applying to the periods between those years were obtained by linearly interpolating the calculated areas. 2. Land change matrix: The 73 classes from the SANLC maps were aggregated into 20 classes for land change determination. The classes were Indigenous forests, Thicket/Dense bush, Woodlands/Open bush, Forest plantations, Vineyards, Orchards, Annual pivot crops, Annual non-pivot crops, Subsistence crops, Grasslands, Low shrublands, Eroded land, Wetlands, Waterbodies, Residential areas, Commercial areas, Industrial areas, Small-holdings, Mines, and Bare ground. The annual land change areas were determined from the change matrix for 1990-2014, 2014-2018 and 2018-2020 for the above-mentioned land categories to assist in the calculation of “land remaining” and “land converted” categories. 3. 20-year default transition period: The land-use-matrix calculation is referenced to the year 1970, to make it possible to determine land-use-change areas for years prior to the actual reporting period. Identified conversion areas were assigned to the relevant land-use-change category, in the year in which the land-use change took place, and they remained in that category for a maximum of 20 years. At the end of the 20-year period, the areas were assigned to the remaining category within the final use category. The “land remaining” was calculated from the land area converted to that land category and the area coveted to another land category. The “land converted” was calculated from the annual converted area accumulated over the period in which it remains in the “land converted” up to the default 20-year conversion period. Consequently, as of the second reporting year, the areas in the remaining categories are smaller, and those in the conversion categories larger, than the corresponding areas in an annual land-use matrix. Since land parcels were not tracked the amount of change allocated to “land remaining” and “converted land” was determined through a process of overlaying the ”land remaining” areas for 1990-2014, 2014-2018 and 2018-202 to determine by what fraction the “land remaining” area was reduced. This provided the area that was converted to other lands and the rest of the conversions were assumed to occur on already converted land (i.e. removed from the “converted land” category). 4. Annual increase in carbon stocks due to biomass growth: Estimated the annual increase in biomass carbon stocks for each land class by applying the 2006 IPCC Guidelines equations 2.9 and 2.10. 5. Annual decrease in biomass carbon stocks due to losses: Losses due to harvest, fuelwood collection, disturbances and conversion losses were estimated using 240 2006 IPCC Guidelines equations 2.11 to 2.14. The disturbance losses were only for fires except for Forest plantations where other disturbance losses (such as insects, wind) were also included. 6. Annual change in carbon stocks in biomass in land remaining categories: Outputs from points 4 and 5 above were incorporated into the IPCC Gain-loss equation (2006 IPCC Guideline equation 2.7) to determine the annual change in biomass carbon stocks. 7. Biomass change due to land conversions: Estimated annual change in biomass carbon stocks due to land changes by apply the 2006 IPCC Guidelines equation 2.15 and 2.16 (Tier 2). 8. Annual change in carbon stocks in deadwood and litter (DOM): For “land remaining” the DOM was assumed not to change (Tier 1 assumption) so a value of zero was applied. The “converted land” categories the annual change in stocks in DOM were estimated using 2006 IPCC Guideline equation 2.23. 9. Soil organic carbon reference values: Soils were classified into the 6 IPCC soil classes and this map was intersected with the ISRIC soils dataset to obtain a SOCRef value for each soil type. 10. Land conversions by soil type: The land conversion maps were clipped by soil type and a land change matrix for each conversion period for each soil type were developed. 11. Soil carbon stock change factors: The IPCC stock change factors were applied for Forest land, Grassland, Wetlands and Settlements, while detailed data on cropland management provided estimates of the stock change factors for croplands. 12. Annual change in carbon stocks in mineral soils: This was estimated by applying the 2006 IPCC Guideline equation 2.25 (Approach B). 13. Calculation of CO 2 emissions on the basis of the carbon-stocks values for the NIR, via multiplication of carbon-stock changes by the factor -44/12. 14. Non-CO 2 emission estimates: CH 4 and N 2 O emissions from each land class was determined from burnt area data (MODIS Collection 6), fuel loads, combustion factors and emission factors using 2006 IPCC equation 2.27. Equation 5.1. of the IPCC Wetland Supplement (IPCC, 2014; Chapter 5) was used to estimate non-CO 2 emission estimates from Wetlands. 15. Calculation of N 2 O emissions from nitrogen values, via multiplication of the nitrogen-stock changes by the factor -44/28; the N 2 O values are converted into CO 2 equivalents using the factor 265 (GWP 100 pursuant to IPCC AR5 (IPCC, 2014b)). 16. The CH 4 emissions are converted into CO 2 equivalents using the factor 28 (GWP 100 pursuant to IPCC AR5). 17. The data from the 20 classes were aggregated (as discussed in section 6.3) into the 6 IPCC categories for reporting purposes. 241 A summary of the tier level of the methods and types of emission factors incorporated into the inventory are provided in Table 6.3. 242 Table 6.3: Summary of methods and emission factors for the LULUCF sector and an assessment of the completeness of the sector. GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor 4A Forest land 4.A.1. Forest land remaining forest land Biomass: T2 Biomass: CS T1 DF T1 DF T1/DF T1/DF T1/DF NE Litter: T2 Litter: CS Soil: T2 Soil: CS 4.A.2. Land converted to forest land Biomass: T2 Biomass: CS IE DF IE DF T1/DF T1/DF T1/DF NE Litter: T2 Litter: CS Soil: T2 Soil: CS 4B Cropland 4.B.1. Cropland remaining cropland Biomass: T1 (annuals), T2 (perennials) Biomass: DF/CS IE IE IE IE IE NE Litter: T1/T2 Litter: DF/CS Soil: T2 Soil: CS 4.B.2. Land converted to cropland Biomass: T2 Biomass: CS IE IE IE IE IE NE Litter: T2 Litter: CS Soil: T2 Soil: CS 4C Grassland 4.C.1. Grassland remaining grassland Biomass: T1 (grasslands), T2 (low shrubland) Biomass: DF/CS T1 DF T1 DF T1/DF T1/DF T1/DF NE Litter: T1/T2 Litter: DF/CS Soil: T2 Soil: CS 243 GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor 4.C.2. Land converted to grassland Biomass: T2 Biomass: CS T1 DF T1 DF T1/DF T1/DF T1/DF NE Litter: T2 Litter: CS Soil: T2 Soil: CS 4D Wetland 4.D.1. Wetland remaining wetland Biomass: T1/T2 Litter: T1/T2 Soils: T2 Biomass: DF/CS Litter: DF/CS Soil: CS T1 DF/CS T1 DF/CS T1/DF T1/DF T1/DF NE 4.D.2. Land converted to wetland Biomass: T2 Litter: T2 Soil: T2 Biomass: CS Litter: CS Soil: CS T1 DF T1 DF T1/DF T1/DF T1/DF NE 4E Settlements 4.E.1. Settlements remaining settlements Biomass: T2 Biomass: CS T1 DF T1 DF T1/DF T1/DF T1/DF NE Litter: T2 Litter: CS Soil: T2 Soil: CS 4.E.2. Land converted to settlements Biomass: T2 Biomass: CS T1 DF T1 DF T1/DF T1/DF T1/DF NE DOM: T2 Litter: CS Soil: T2 Soil: CS 4F Other land 4.F.1. Other land remaining other land Biomass: NA NA NA NA NA NA NA Litter: NA Soil: T2 Soil: CS 4.F.2. Land converted to other land Biomass: T2 Biomass: CS NA NA NA NA NA NA 244 GHG Source and sink category CO 2 CH 4 N 2 O NO x CO NMVOC NH 3 Method applied Emission factor Method applied Emission factor Method applied Emission factor Litter: T2 Litter: CS Soil: T2 Soil: CS 4G Harvested wood products Harvested wood products T2 DF NA NA NA NA NA NA NA = Not applicable; NO = Not occurring; NE = Not estimated; IE = Included elsewhere; T1 = Tier 1; T2 = Tier 2; DF = IPCC default factor; CS = Country specific factor. 245 6.1.3.1 Carbon emissions from biomass South Africa uses a combination of Tier 1, and Tier 2 methods for estimating emissions for the LULUCF sector. There are two methods for estimating carbon stock changes in biomass, namely the Gain-loss method and the Stock-difference method. The Stock- difference method requires carbon stock inventories for a given land area, at two points in time. This method is applicable in countries that have national inventory systems for forests and other land-use categories where the stocks of different biomass pools are measured at periodic intervals (IPCC, 2006). South Africa does not have a forest inventory and so the Gain-loss method was applied. For the “land remaining” category in Forest land, Cropland, Grasslands and Settlements the annual increases in biomass carbon stocks were estimated using equation 2.9 of the IPCC 2006 Guidelines, where the mean annual biomass growth was estimated using equation 2.10 (Tier 1 approach) but with country specific data. For plantations the Tier 2 approach of this equation was applied. For pure grasslands the assumption of equivalence was applied (Tier 1) but incorporated in the Grassland category is the low shrubland class which has a small shrub element which was accounted for. For Wetlands and Other lands the Tier 1 assumption of equivalence was applied. The above-ground biomass and biomass gains for all woody or perennial land categories were sources from the scientific literature. Literature was also relied on for root to shoot ratios so that below-ground biomass could be determined. Data on MAI for estimating growth in forest plantations were obtained from collected forestry data. The annual decrease in carbon stocks due to disturbance losses were estimated from IPCC equations 2.11 to 2.14 (IPCC, 2006). Fire disturbance losses (from both Wildfires and controlled fires) were incorporated for Forest land, Grassland, Wetlands, and Settlements. In Croplands the burning losses were only for pre-harvest burning of sugarcane, and Other lands were assumed not to burn due to the lack of biomass on these lands. The burnt area estimation is discussed under section 6.1.3.6. Harvest losses were included for forest plantations and perennial crops, while the woodland/open bush category included fuelwood collection losses. Forest plantations were the only category where other losses (such as losses due to insects and wind) were included due to a lack of data in other land categories. The 2006 IPCC Guideline equations 2.15 and 20.16 were utilised for estimating emissions from biomass changes on converted land. A Tier 2 approach was applied as country specific biomass stocks were applied and natural land classes were not assumed to be cleared but rather transition slowly from initial to final biomass levels. Land was only assumed to be cleared for forest plantations, croplands and settlements, so carbon stocks immediately after conversion were set to zero. For land transitions to land categories with woody components the carbon growth values were used and carbon was gained 246 over the 20 default transition period. Woody losses were assumed to be immediate an accounted for in the year of conversion. Growth in converted lands was estimated using the growth rates for the various land categories. 6.1.3.2 Carbon emissions from litter and deadwood The dead organic matter pool includes litter and deadwood for Forest lands and only litter for other land categories. It is assumed that there are no DOM changes in “land remaining” categories (Tier 1), and it is assumed that in “converted land” categories all litter pool carbon losses occur entirely in the year of transition (Tier 1). The 2006 IPCC Guidelines equation 2.23 was used to estimate changes in litter in converted land. Litter and deadwood (DOM) estimates for the various land categories were determined from literature or from intersecting the SANLC data with the National Terrestrial Carbon Sinks Atlas (NTCSA) carbon density maps (DEFF, 2020) and are provided in the relevant land category sections below. 6.1.3.3 Carbon emissions from mineral soils Soil categorisation South Africa’s detailed soils map was reclassified into the 7 soil classes provided by IPCC 2006 Guidelines (DEA, 2015) (Figure 6.4). The map was derived from the 1:250 000-scale Land-type Survey of South Africa. This survey mapped over 7 000 unique land types, each of which has a specific combination of soil, terrain form and macroclimate. Within each land-type mapping unit, a number of different soil forms, as well as other land classes, such as rock, stream beds and pans, are recorded, and their percentage within the land type is used to allocate the land type to a specific broad soil pattern. Table 6.4 indicates the soil categories and what criteria was used for their classification. Volcanic mineral soils are not found in South Africa so this category was excluded. Organic soils were found to be insignificant, so these were also excluded. 247 Figure 6.4: South African soils classified into the IPCC classes (DEA, 2015). Table 6.4: Soil types and their description (Source: DEA, 2015) Description Criteria Sandy mineral soils Sandy mineral soils comprise all soils where the texture class is sandy (irrespective of taxonomy). These areas generally have either sandy parent materials or have been subject to aeolian (wind-blown) deposition (such as the Kalahari sands of the Northern Cape). Land types where soils with an average topsoil clay content less than 8% comprise more than 40%. Wetland mineral soils This map unit comprises all land types where soils with wetland characteristics are dominant. Most land types will have wetland soils in the lower parts of the landscape, but only a few land types have these soils as dominant, mainly in the north- east of KwaZulu-Natal. Land types where Katspruit and Fernwood (series 30-42) soil forms, along with streambeds and pans, comprise more than 40%. Organic soils This map unit comprises all land types dominated by ‘peat’ soils. These soils typically occur in cool, often upland areas, Land types where champagne soil forms comprise more than 40%. 248 so their distribution is limited to small zones in KwaZulu-Natal. Spodic mineral soils This map unit comprises all land types where podzols (where leaching of iron/aluminium and organic matter has occurred) predominate. These areas are restricted to small zones in the south and south-west of the Western Cape. Land types where Houwhoek and Lamotte soil forms comprise more than 30%. Rocky areas All I b and I c land types (rock outcrops more than 60%). Low activity clay mineral soils This map unit comprises all land types dominated by highly weathered, apedal (structureless) soils dominated by low activity (1:1) clay minerals such as kaolinite. Only soils where the base status is defined as part of the soil classification could be used, so it is very probable that the extent of such soils is larger than that shown on the map. These soils are found mainly in the warmer, higher rainfall areas, such as KwaZulu-Natal and Mpumalanga. Land types where Kranskop, Magwa, Inanda, Nomanci, Avalon (series 10–17), Glencoe (series 10–17), Pinedene (series 10–17), Griffin (10–13), Clovelly (series 10–18), Bainsvlei (series 10–17), Hutton (series 10–18) and Shortlands (all series) comprise more than 40%, and where average topsoil clay percentage is more than 8%. High activity clay mineral soils This map unit comprises all land types dominated by lightly to moderately weathered soils, dominated by 2:1 silicate clay minerals, including vertisols, mollisols, calcareous soils, shallow soils and various others. This group covers most of South Africa. Land types not falling into one of the categories given above. SOC reference values The ISRIC (2017) soil organic carbon stock at 30 cm depth (https://data.isric.org/geonetwork/srv/eng/catalog.search;jsessionid=D59E70CBD401 A199C806783D11B4B656#/metadata/ea80098c-bb18-44d8-84dc-a8a1fbadc061), which is based on many data points in South Africa, was used as the SOC reference data. A soil carbon value for each soil type was determined by extracting the ISRIC SOC to fit the South African boundary (South African Boundary shapefile (2018)) obtained from the Municipal Demarcation Board (https://dataportal-mdb- sa.opendata.arcgis.com/search?tags=2018)). This was then re-projected to match the same co-ordinates as the land cover dataset (UTM 35 S). The SOC data set was re-sampled to change the cell size from 250 x 250 m to 100 x 100m to match the soil map. The soil data was sub-divided to create a stand-alone file for each soil type and the SOC data was extracted for each soil type to determine a SOC reference value for each soil type (weighted average SOC per soil type using the ‘Value’ (t/ha) and counts or number of cells 249 that match the value). The resulting SOC reference values are shown in Table 6.5. Comparisons were made with the IPCC data and this showed that the country specific values for wetlands mineral soils and spodic soils were lower than the IPCC default values, but for low activity clay the value was much higher. The values for sandy soil and high activity clay matched well with the IPCC data. These differences will need to be investigated further in the next inventory. Table 6.5: Soil carbon reference data per soil type as obtained from ISRIC database. SOCRef value (t C/ha) IPCC 2006 default 19 IPCC 2019 Refinement Sandy mineral soils 20.85 19 10 ±5% Wetland soils 40.53 88 74 ±17% Spodic soils 71.65 115 20 143 ±30% Rocky soils 47.03 NA NA Low activity clay 65.54 24 19 ±16% High activity clay 35.21 38 24 ±5% Soil carbon stock change factors Country specific stock change factors were applied where possible. For Forest lands and Wetlands a stock change factor of 1 (IPCC default) was applied, while country specific factors (described in the relevant land category sections) were incorporated for Croplands, Grasslands and Settlements. Table 6.6: Soil carbon stock change factors for the various land types. Land class F LU F MG F I Reference Indigenous forest, thickets, woodlands, plantations, wetlands, grasslands, low shrublands 1 1 1 IPCC 2006 Degraded lands 1 0.85 (moderately degraded) 1 IPCC 2006 Orchards, vineyards 0.72 1 (full till) 1 IPCC 2006 19 Defaults are for warm, temperate climates 20 This is the default value for cold, temperate climates as there is no value for warm, temperate environments. 250 Annual crops Combined sock change factor varies annually (between 0.68 and 0.74) based on crop types, crop areas, management and inputs Moeletsi et al. (2015) Subsistence crops 0.58 NTCSA (DFFE 2020) Settlements and mines 1 1 1 IPCC 2006 SOC emission estimation The soil map in Figure 6.4 was intersected with the land change maps to obtain a land change matrix per soil type. This information, along with the SOC reference values and stock change data, were incorporated into the 2006 IPCC Guideline equation 2.25 (Formulation B) to determine the annual SOC change for each land category. Verification Soils estimates were verified by comparing the outputs to the Tier 1 approach (Formulation A of 2006 IPCC Guideline equation 2.25) for the data between 1990 and 2020. The total estimated CO 2 sequestration per year using the Tier 2 approach was 2 101 Gg CO 2 while applying the Tier 1 approach gave a value of 2 186 Gg CO 2 which are very similar (Table 6.7). Table 6.7: Comparison of total CO 2 (Gg CO 2 per year) from soils between the Tier 1 and Tier 2 approaches Soil type Tier 1 (Formulation A) Tier 2 (Formulation B) Sandy soils 27.1 24.1 Wetland soils 10.8 10.9 Spodic soils 0.2 0.2 Rocky soils -31.2 -27.0 Low activity clay soils 63.0 61.8 High activity clay soils -2 252.8 -2 167.9 6.1.3.4 Organic soils As mentioned above, organic soils were assumed to be negligible, however a more recent study by Schulze and Schutte (2018) indicates there is a small area of organic soils. This study indicated that high-C soils are very small in area and only 885 out of 27 491 terrain units contain either humic or organic soils in terms of the binomial classification system. 251 In addition, a blue carbon study (DEFF, 2021a) indicates that wetlands cover approximately 19 000ha of which most are assumed to contain high organic soils. This will be included in the improvement plan but the priority, due to its small area and impact to overall emissions, is low and will therefore only be incorporated at a later stage (see details in improvement plan). 6.1.3.5 Nitrous oxide and methane emissions from wetlands CH 4 emissions from wetlands were calculated following equation 5.1. of the IPCC Wetland Supplement (IPCC, 2014; Chapter 5) which is the wetland area multiplied by the emission factor. N 2 O was calculated in the same way. 6.1.3.6 Biomass burning The CO 2 emissions from biomass burning are included as biomass disturbance losses (as discussed in section 6.1.3.1). South Africa reports emissions of non-CO 2 gases (CH 4 , CO, N 2 O, and NO x ) from Forest land, Grasslands, Wetlands and Settlements. Other lands are assumed not to burn as there is no biomass. For Croplands only the pre-harvest burning of sugarcane is accounted for and this is not included here bur rather under agriculture as burning of crop residues. There is insufficient data to separate controlled fires from wildfires, so all emissions are for total fires. The IPCC Guidelines suggest that emissions from savanna burning should be included under the Grassland category; however, since, in this inventory woodlands and open bush have been classified as forest land, their emissions were dealt with under Forest land. IPCC indicates that net CO 2 emissions should be reported when CO 2 emissions and removals from the biomass pool are not equivalent in the inventory year. For grasslands and annual croplands, the annual CO 2 removals (through growth) and emissions (whether by decay or fire) are in balance. CO 2 emissions are therefore assumed to be zero for these categories. All non-CO 2 emissions, on the other hand, are included here. Burnt area data The MODIS Collection 5 Burnt Area data is available for the years 2001 to 2017 as this is the data that was used in inventories prior to the 2020 inventory. In 2020 the MODIS Collection 6 Burnt Area data was utilised, which is an upgrade from the Collection 5. Due to time limitations not all the post years could be reanalysed using the Collection 6 data. The years 2001, 2005, 2010 and 2014 onwards were analysed and for the in between years the previous Collection 5 data was used as a proxy and the IPCC splicing technique was applied to adjust the years in between. There was very little difference in the two 252 data sets for the years since 2010 (<1%), but there was a 15% difference for the 2001 dataset. Data was incomplete for 2000 so this data set was extrapolated. The MODIS Collection 6 Burned Area Product Geotiff version (http://modis- fire.umd.edu/pages/BurnedArea.php?target=GeoTIFF) is used in this inventory. This is a level 3 gridded 500 m product and the quality of the information is described in Giglio et al. (2018). Monthly data was combined to obtain annual burnt area maps for each year. Each year’s dataset was clipped (extract by mask) to the actual South African boundary The burnt area maps were intersected with the SANLC data to obtain burnt area per land class. Land cover maps are not available for every year, so the 2014 map was intersected with the burnt areas for 2000 to 2014, the 2018 intersected with the years 2015-2019 and 2020 with the rest of the years. To estimate the burnt area per land conversion category, the fraction of the land class that was burnt was assumed to occur evenly across all conversions, so the fraction was applied to each land conversion area. Emission estimation calculation The Tier 2 methodology was applied, with the emissions from biomass burning being calculated using the 2006 IPCC Guideline equation 2.27. This equation combines the burnt area with the mass of fuel available (see specific land category sections for details) and the 2006 IPCC default emission factors (IPCC, 2006, vol 4, chapter 2, Table 2.5, page 2.47). Indirect emissions of CO, NOx and VOCs were also estimated and reported as indirect emissions in Gg of gas. The CO and NOx emissions factors were the default factors provided by IPCC (IPCC, 2006, vol 4, chapter 2, Table 2.5, page 2.47), while the VOC emission factors were sourced from Andreae (2019). Verification Burnt area data was compared to those from the Meraka Institute (Meraka Institute, 2019) who are also using the MODIS collection 6 data. The datasets compare very well (within 10% of each other) and show the same annual trend. 6.1.3.7 Climate Long term climate maps were developed for South Africa (DEA, 2015) which categorize the climate into the classes provided by 2006 IPCC Guidelines. Although 4 climate types (Figure 6.5) are present in South Africa, the dominant climate is the warm, temperate dry climate. Incorporating all 4 climate types into the inventory adds a significant amount of additional calculations and therefore resources and time. The size of the emission impact of including the other three small climate zones was thought to be small relative to the amount of resources needed to incorporate all climates. Based on this it was decided that 253 there were other more important aspects of the LULUCF inventory that needed attention before this. Therefore, for the purpose of this inventory the dominant climate type (warm, temperate, dry) was assumed for the whole area. Including the addition climate zones is on the improvement list but it has a low priority. Figure 6.5: South Africa’s long term climate map classified into the IPCC climate classes (Source: DEA, 2015). 6.1.4 Improvements and recalculations The main inventory improvements that were carried out and which led to recalculations were: • Inclusion of the three separate land change maps: o 1990-2014 o 2014-2018 o 2018-2020 • Updated corrections and assumptions for the 2014-2018 change to correct for the change in land classification due to improved resolution of the maps • Removal of “unlikely” conversions from all land change matrix 254 • Removal of potential seasonal variation for the conversion between Forest lands and Grasslands • Inclusion of corrections for conversions from converted lands • Inclusion of country specific BCEF factors for plantations • Inclusion of disturbance matrix for plantations • Updated the household fuelwood consumption factor • Updated carbon stock data for the various land categories • Inclusion of an assumption that the only cropland area that burned was sugarcane and that 90% of the sugarcane was burnt during pre-harvest and these emissions move to category 3.F • Inclusion of the growth of biomass of the last 5 years of the maturity cycle in the cropland remaining cropland category • Implementation of a corrected CH 4 emission factor for wetlands • Incorporation of non-CO 2 emissions from biomass burning into LULUCF to align with ETF reporting requirements • Change from SAR to AR5 GWPs The specific details of these improvements are discussed in the relevant category or sub- category sections within this report. The recalculations showed the same trend between 1990 and 2014 as the previous data except that the sink was enhanced in this inventory (Figure 6.6). After 2014, with the introduction of the new land change maps, the LULUCF sector sink was reduced compared to the previous inventory. The sink then returned to previous levels in 2014 by 2019. In the previous inventory the 2020 estimated sink was -27 321 Gg CO 2 , and in this inventory the LULUCF sink was estimated at -36 891 Gg CO 2 in 2020. 255 Figure 6.6: Change in LULUCF emission estimates due to recalculations since 2020 submission. 6.1.5 Key categories in the LULUCF sector The key categories in the LULUCF sector and their ranking are shown in Table 6.8. Table 6.8: Key categories in the LULUCF sector in 2022. Rank IPCC code IPCC Category GHG # Criteria 2 4.C.2.a Forest land converted to Grassland – all pools CO 2 L,T 4 4.A.2.b Grassland converted to forest land – all pools CO 2 L,T 11 4.F.2.c Grassland converted to other land – all pools CO 2 L,T 12 4.C.1.a Grassland remaining Grassland – biomass CO 2 L,T 17 4.G Harvested Wood Products CO 2 T 21 4.B.2.b Grassland converted to Cropland – all pools CO 2 T 24 4.A.1.a Forest land remaining forest land – biomass CO 2 L,T 29 4.C.2.b Cropland converted to Grassland – all pools CO 2 L 30 4.E.2.a Forest land converted to settlements – all pools CO 2 T # L = Level Assessment; T = Trend Assessment 256 6.1.6 QA/QC process and verification The QA/QC processes applied are detailed in each of the category sections below. 6.1.7 Planned improvements The major uncertainty in the LULUCF sector is related to the land conversion areas. Currently there are various corrections that are being applied to distinguish seasonal change from actual change and to estimate which fraction of land conversion are occurring on “land remaining”. An additional land change map for the period 2020-2022 will be available in the next year and along with this will be the basic 8 class maps for each year between 2018 and 2022. These additional data sets could assist in reducing uncertainty on these land conversion corrections and areas. A detailed plan is being setup to improve the land change data in a systematic manner and to incorporate all corrections spatially to insure more consistency in the overlays. The aim is to obtain a better understanding of actual change. This will involve more detailed analysis of the land change data will take time to complete, so the improvement plan for this component will run over the next 3 years. The initial phases of the improvement plan will be focussed on Forest land and Grasslands as these are the key categories, after which the other categories will be addressed. As part of this improvement plan the difference between the SANLC cropland area and the reported agricultural area in the Agricultural Abstracts will be investigated. As part of this improvement plan the difference between the SANLC cropland area and the reported agricultural area in the Agricultural Abstracts will be investigated. Incorporated into the plan will be the use of Collect Earth to verify land use change and assist in tracking these changes. These planned land change mapping improvements will also lead to an improved soil overlay, thereby allowing for an improvement in the SOC data. The overall improvement would mean a reducing in the uncertainty on the SOC data. The other proposed improvement is a more detailed analysis of the HWP. This was proposed in the last inventory, however the incorporation of the additional land change maps and the more detailed analysis of the land change took preference because this data influenced the key categories. The HWP analysis will need to be done as a project and funds will need to be sought to complete this improvement. The incorporation of organic soils still remains on the improvement list, but the priority is lower than that of the improvements to the land change analysis. Land category classifications and definitions 257 The land-cover legend and associated information content within the SANLC 2018 (DEA, 2019) and 2020 (DFFE, 2021) dataset was based primarily on the new gazetted land- cover classification standard (SANS 19144-2) 21 , but with modifications to ensure, as far as possible, comparability and compatibility with the legend and information content associated with the previous 1990 and 2013-14 South African National Land-Cover (SANLC) datasets (DEA, 2015a). The SANLC maps have 73 land classes (definitions are included in each of the relevant sections below) which were aggregated firstly into 20 classes for land change assessment, and then further aggregated into the 6 IPCC classes for reporting. The relationship between the IPCC classes, the change classes and the various SANLC datasets are shown in Table 6.9. The land cover maps for South Africa cover the national territory, however it does not include overseas territories at this point. South Africa possesses two subantarctic islands, namely Marion Island (46° 54 ́ S, 37° 45 ́ E; 29 300 ha) and Prince Edward Island (46° 38 ́ S, 37° 57 ́ E; 4 500 ha) and together they are known as The Prince Edward Islands (Nel et al., 2020; Smith and Mucina, 2006). These overseas territories are not included in the inventory currently due to their small size and difficulties with accessibility. Marion Island has been occupied permanently by South African research and logistic personnel since February 1948, while there is no occupation of Prince Edward Island. The vegetation on these islands is indicated to be subantarctic tundra, polar desert and marine microalga vegetation (Smith and Mucina, 2006). With the cold climate it is not expected that these islands will produce any significant emissions relative to the rest of South Africa. Table 6.9: Relationship between the IPCC categories, land change categories and the SANLC datasets in the 2022 inventory. SANLC 1990 and 2013/14 Class Change class IPCC category SANLC 2018 and 2020 Class 4 Indigenous forest 1 Indigenous forest Forest land 1 Contiguous forest 5 Thicket/ dense bush 2 Thicket/dense bush Forest land 2 24 Contiguous low forest & thicket Mangrove wetlands 6 Woodland/open bush 3 Natural woodland Forest land 3 4 42 43 Dense forest and woodland Open woodland Fallow lands (trees) Fallow lands (bushes) 32 33 34 Plantations and woodlots 4 Planted forest Forest land 5 6 7 Contiguous and dense plantation forest Open and sparse plantation forest Temporary unplanted plantation 21 South African Land Cover Classes and Definitions (SDI Act No. 54 of 2003). 258 8 9 Shrublands fynbos Low shrubland 5 Shrubland Grassland 8 9 10 11 Low shrubland (other, fynbos, succulent karoo, nama karoo) Fallow lands (low shrub) 7 Grassland 6 Grassland Grassland 12 13 44 Sparsely wooded grassland Natural grassland Fallow land (grass) 1 2 37 38 Water seasonal/permanent Mine water seasonal/permanent 7 Waterbodies Wetland 14 15 16 17 18 19 20 21 Rivers Estuaries and lagoons Ocean and coastal Lakes Pans Artificial dams Artificial sewage ponds Artificial flooded mine pits 3 Wetlands 8 Wetlands Wetlands 22 23 73 Herbaceous wetlands Fallow land (wetlands) 41 Bare non vegetated 9 Barren land Other land 25 26 28 29 30 31 45 Rock surfaces Dry pans Sand dunes Coastal sand dunes Bare riverbed Other bare Fallow land bare 40 Erosion (donga) 10 Eroded land Grassland 27 Eroded land 16 17 18 22 Cultivated orchards Cultivated pineapples 11 Permanent orchards Cropland 32 35 Permanent orchards Permanent pineapples 19 20 21 Cultivated vines 12 Permanent vines Cropland 33 Permanent vines 13 14 15 26 27 Commercial pivot crops Cane pivots Cane pivot - fallow 13 Annual pivot irrigated Cropland 34 38 Sugarcane pivot irrigated Annual crops pivot irrigated 10 11 12 28 29 30 31 Commercial non- pivot crops Cane crop Cane fallow Cane emerging crop Cane emerging fallow 14 Annual non-pivot crops Cropland 36 37 39 40 Sugarcane non-pivot Sugarcane emerging non-pivot Non-pivot irrigated Rainfed dryland 23 24 25 Cultivated subsistence 15 Cultivated subsistence Cropland 41 Subsistence annual crops 44- 52, Various types of urban classes (informal, residential, 16 Built-up residential Settlements 47–56, 61-64 Various types of residential (formal, 259 57- 72 sport, township, village, built-up) informal, village, recreational, urban) 53 54 55 56 42 Various urban small holdings 17 Built-up smallholdings Settlements 57 58 59 60 Various smallholdings 42 Urban commercial 18 Built-up commercial Settlements 65 Commercial 43 Urban industrial 19 Built-up industrial Settlements 66 Industrial 35 36 39 Mine bare Mine semi-bare Mine buildings 20 Mines Settlements 68 69 70 71 72 Mines Land-fills 6.2.1 Forest land The National Forest Act (Act 84 of 1998) (NFA) that: • “forest” includes a natural forest, a woodland and a plantation (Section 1(2)(x) of NFA); • “natural forest” means a group of trees whose crowns are largely contiguous, or which have been declared by the Minister to be a natural forest (Section 1(2)(xx) of NFA); • “plantation” means a group of trees cultivated for exploitation of the wood, bark, leaves or essential oils (Section 1(2)(xxii) of NFA); and • “woodland” means a group of indigenous trees which are not a natural forest, but whose crowns cover more than five percent of the area bounded by the trees forming the perimeter of the group (Section 1(2)(xxxix) of NFA). The definition of Forests in South Africa’s National Forest Act relates to international definitions and corresponds with the FAO definition of forests except that the FAO regards 10% as the lower boundary for woodland canopy cover. South Africa’s NFA definition is lower (5%) and thus also includes degraded woodland into that definition so that other provisions of the statute would still remain applicable even to degraded woodlands. Taking guidance from the Marrakesh Accord (2011) and adjusting the specific thresholds so as to be consistent with the NFA, the forest definition is given as “a minimum area of land of 0.05 hectares with tree crown cover of more than 10 per cent with trees with the potential to reach a minimum height of 2.5 metres at maturity in situ. A forest may consist either of closed forest formations where trees of various storeys and undergrowth cover 260 a high portion of the ground open forest. Young natural stands and all plantations which have yet to reach a crown density of 10 per cent or tree height of 2.5 metres are included under forest, as are areas normally forming part of the forest area which are temporarily unstocked as a result of human intervention such as harvesting or natural causes, but which are expected to revert to forest.” The Forest land category therefore includes indigenous forests, plantation/woodlots, thicket/dense bush and woodland/open bush as defined in Table 6.10. Note that Mangroves are included under Forest land but they are currently aggregated with thickets for the change analysis. Table 6.10: Definitions of the land classes, as given in the SANLC 2018, that are included in the Forest land category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 1 Indigenous forest Contiguous Forest (combined very high, high, medium) Natural tall woody vegetation communities, with 75% or more canopy cover, and canopy heights exceeding 6 metres. Typically representative of tall, indigenous forests. 2 Thicket/Dense bush Contiguous Low Forest & Thicket Natural tall woody vegetation communities, with 75% or more canopy cover, and canopy heights ranging between 2.5 - 6 metres. Typically representative of low, indigenous forests and dense thicket communities. 3 Woodland Dense Forest & Woodland Natural tall woody vegetation communities, with canopy cover ranging between 35 - 75%, and canopy heights exceeding 2.5 metres. Typically represented by dense bush, dense woodland and thicket communities. 4 Woodland Open Woodland Natural tall woody vegetation communities, with canopy cover ranging between 10 - 35%, and canopy heights exceeding 2.5 metres. Typically represented by open bush and woodland communities. 5 Plantation Contiguous & Dense Planted Forest Dense to contiguous cover, planted tree forests, consisting primarily of exotic timber species, with canopy cover exceeding 35%, and canopy heights exceeding 2.5 metres. Typically represented by mature commercial plantation tree stands. This class also includes smaller woodlots and windbreaks 6 Plantation Open & Sparse Planted Forest Open to sparse cover, planted tree forests, consisting primarily of exotic timber species, with canopy cover ranging between 5 - 35%, and canopy heights exceeding 2.5 metres. Typically represented by young or recently planted commercial plantation tree stands. This class also include smaller woodlots and windbreaks 7 Plantation Temporary Unplanted Forest Temporarily unplanted stands within commercial forest plantations that have recently been harvested, and/or re- planted but the tree saplings are undetectable on the imagery. 24 Thicket/Dense bush Mangrove Wetlands Naturally occurring mangrove community wetlands. 42 Woodland Fallow Land & Old Fields (Trees) Long-term, non-active, previously cultivated lands that are now overgrown with tree-dominated woody vegetation. Typically the cultivated land unit boundary is no longer image detectable. 43 Woodland Fallow Land & Old Fields (Bush) Long-term, non-active, previously cultivated lands that are now overgrown with bush dominated woody vegetation. 261 6.2.2 Cropland Croplands include annual commercial croplands (pivot and non-pivot), permanent perennial orchards, permanent perennial vines, and semi-commercial or subsistence croplands. The SANLC classes that are included under croplands, along with their definitions, are provided in Table 6.11. Table 6.11: Definitions of the land classes, as given in the SANLC 2018, that are included in the Cropland category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 32 Perennial orchards Cultivated Commercial Permanent Orchards Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial orchards consisting of tree and/or bush based plants. 33 Perennial vines Cultivated Commercial Permanent Vines Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial viticulture. 34 Annual pivot irrigated Cultivated Commercial Sugarcane Pivot Irrigated Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial sugarcane. Grown in pivot irrigated fields. 35 Perennial orchards Commercial Permanent (Pineapples) Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with pineapples. 36 Annual non- pivot crops Cultivated Commercial Sugarcane Non-Pivot (all other) Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial sugarcane. Grown in rainfed or non-pivot irrigation fields. 37 Annual non- pivot crops Cultivated Emerging Farmer Sugarcane Non- Pivot (all other) Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with small-scale / emerging farmer sugarcane. 38 Annual pivot irrigated Cultivated Commercial Annuals Pivot Irrigated Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial annual crops. Pivot irrigation. 39 Annual non- pivot crops Cultivated Commercial Annuals Non-Pivot Irrigated Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial annual crops. Non-pivot irrigation. 40 Annual non- pivot crops Cultivated Commercial Annuals Non-Pivot / Non-Irrigated Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with commercial annual crops. 41 Cultivated subsistence crops Subsistence Annual Crops Active or recently active cultivated lands used for the production of agricultural crops, in this case specifically associated with small-scale commercial or subsistence-level annual crops. 262 6.2.3 Grassland Grassland includes grasslands, low shrublands and degraded land. It also includes range and pasture lands that were not considered cropland. The category covers all grasslands from wild lands to recreational areas as well as agricultural and silvi-pastural systems, consistent with national definitions. Definitions of land areas included in Grasslands are given in Table 6.12. Table 6.12: Definitions of the land classes, as given in the SANLC 2018, that are included in the Grassland category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 8 Low shrubland Low Shrubland (other regions) Natural, low woody shrubland communities, where the total plant canopy cover is typically both dominant over any adjacent bare ground exposure, and the canopy height ranges between 0.2 – 2 metres. Typically representative of low, indigenous karoo-type vegetation communities, which have been identified using image- based spectral models, but which fall spatially outside the SANBI defined boundaries for Fynbos, Succulent and Nama-Karoo vegetation communities. 9 Low shrubland Low Shrubland (Fynbos) This is the same as class 8, Low Shrubland, but now represents low, indigenous karoo-type vegetation communities, which have been identified using image-based spectral models, but which fall spatially inside the SANBI defined boundaries for Fynbos vegetation communities. 10 Low shrubland Low Shrubland (Succulent Karoo) This is the same as class 8, Low Shrubland, but now represents low, indigenous karoo-type vegetation communities, which have been identified using image-based spectral models, but which fall spatially inside the SANBI defined boundaries for Succulent Karoo vegetation communities. 11 Low shrubland Low Shrubland (Nama Karoo) This is the same as class 8, Low Shrubland, but now represents low, indigenous karoo-type vegetation communities, which have been identified using image-based spectral models, but which fall spatially inside the SANBI defined boundaries for Nama Karoo vegetation communities. 12 Grassland Sparsely Wooded Grassland Natural woody vegetation, with a woody canopy cover ranging between only 5 - 10%, and canopy heights exceeding 2.5 metres, in a grass-dominated environment. Typically represented by very sparse woodland or lightly wooded grassland communities. 13 Grassland Natural Grassland Natural and/or semi-natural indigenous grasslands, typically devoid of any significant tree or bush cover, and where the grassland component is typically dominant over any adjacent bare ground exposure. 27 Eroded land Eroded Lands Permanent or semi-permanent, non-vegetated erosion surfaces, typically represented by gullies, dongas, and/or sheet erosion areas. 44 Grassland Fallow Land & Old Fields (Grass) Long-term, non-active, previously cultivated lands that are now overgrown with grass dominated woody vegetation. 263 6.2.4 Wetlands Wetlands include all wetlands and waterbodies as defined in the SANLC 2018 (Table 6.13). Mangroves are classified in the Forest land category so do not fall under Wetlands. Table 6.13: Definitions of the land classes, as given in the SANLC 2018, that are included in the Wetland category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 14 Waterbodies Natural Rivers Naturally occurring waterbodies associated with perennial and non-perennial rivers and associated tributaries. 15 Waterbodies Natural Estuaries & Lagoons Naturally occurring coastal region water bodies that are located at river mouths or are replenished by coastal tidal flows. 16 Waterbodies Natural Ocean Naturally occurring saltwater coastal and ocean waterbodies. 17 Waterbodies Natural Lakes Naturally occurring, large inland waterbodies containing freshwater. 18 Waterbodies Natural Pans (flooded) Naturally occurring inland waterbodies within pans, where the water extent is both spatially and temporally sufficient to be image-detectable. 19 Waterbodies Artificial Dams Man-constructed artificial inland waterbodies, ranging from small farm dams to large reservoirs, and if image-detectable, large irrigation canals. 20 Waterbodies Artificial Sewage Ponds Man-constructed artificial inland waterbodies, specifically associated with water and effluent treatment activities. 21 Waterbodies Artificial Flooded Mine Pits Man-generated artificial inland waterbodies, specifically associated with flooded mine pits, tailings ponds, or other surface-based mining activities. 22 Wetlands Herbaceous Wetlands (currently mapped) Natural or semi-natural wetlands covered in permanent or seasonal herbaceous vegetation. The class represents primarily riparian wetland areas, but can also include emergent aquatic vegetation in pans. 23 Wetlands Herbaceous Wetlands (previous mapped extent) Natural or semi-natural wetlands covered in permanent or seasonal herbaceous vegetation. The class represents primarily riparian wetland areas, but can also include emergent aquatic vegetation in pans. 73 Wetlands Fallow Land & Old Fields (wetlands) Long-term, non-active, previously cultivated lands that are currently classified as wetland vegetation. 6.2.5 Settlements The Settlement category covers transportation infrastructure and human settlements. This includes formal built-up areas in which people reside on a permanent or near- permanent basis identifiable by the high density of residential and associated 264 infrastructure, as well as towns and villages, smallholdings, commercial, residential and industrial areas. Mines are also included. Definitions as given in the SANLC 2018 are provided in Table 6.14. Table 6.14: Definitions of the land classes, as given in the SANLC 2018, that are included in the Settlement category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 47 Built-up residential Residential Formal (Tree) Built-up areas primarily containing formally planned and constructed residential structures and associated utilities. The dominant vegetation (in gardens etc) is tree-based. 48 Built-up residential Residential Formal (Bush) Built-up areas primarily containing formally planned and constructed residential structures and associated utilities. The dominant vegetation (in gardens etc) is bush-based. 49 Built-up residential Residential Formal (low veg / grass) Built-up areas primarily containing formally planned and constructed residential structures and associated utilities. The dominant vegetation (in gardens etc) is grass and/or low shrub based. 50 Built-up residential Residential Formal (Bare) Built-up areas primarily containing formally planned and constructed residential structures and associated utilities. The surface is predominantly non-vegetated. 51 Built-up residential Residential Informal (Tree) Built-up areas primarily containing informal, often unplanned residential structures and associated utilities. The dominant vegetation (in surrounding areas etc) is tree-based. 52 Built-up residential Residential Informal (Bush) Built-up areas primarily containing informal, often unplanned residential structures and associated utilities. The dominant vegetation (in surrounding areas etc) is bush-based. 53 Built-up residential Residential Informal (low veg / grass) Built-up areas primarily containing informal, often unplanned residential structures and associated utilities. The dominant vegetation (in surrounding areas etc) is grass and/or low shrub- based. 54 Built-up residential Residential Informal (Bare) Built-up areas primarily containing informal, often unplanned residential structures and associated utilities. The surface is predominantly non-vegetated. 55 Built-up residential Village Scattered Built-up areas primarily associated with scattered rural settlements and associated utilities. Scattered villages are defined as those represented by contiguous / adjacent village- classified cells which collectively do not form the majority cover in a surrounding 1 ha window. 56 Built-up residential Village Dense Built-up areas primarily associated with scattered rural settlements and associated utilities. Dense villages are defined as those represented by contiguous / adjacent village-classified cells which collectively do form the majority cover in a surrounding 1 ha window. 57 Built-up smallholdings Smallholdings (Tree) Agricultural holdings typically located in peri-urban environments, where the dominant vegetation is tree-based. 58 Built-up smallholdings Smallholdings (Bush) Agricultural holdings typically located in peri-urban environments, where the dominant vegetation is bush-based. 59 Built-up smallholdings Smallholdings (low veg / grass) Agricultural holdings typically located in peri-urban environments, where the dominant vegetation is low shrub or grass based. 60 Built-up smallholdings Smallholdings (Bare) Agricultural holdings typically located in peri-urban environments, where the dominant cover is non-vegetation is bush-based. 265 61 Built-up residential Urban Recreational Fields (Tree) Non-built-up, vegetated urban areas primarily associated with formally planned and established parks, sports fields, and golf courses. 62 Built-up residential Urban Recreational Fields (Bush) Non-built-up, vegetated urban areas primarily associated with formally planned and established parks, sports fields, and golf courses. 63 Built-up residential Urban Recreational Fields (Grass) Non-built-up, vegetated urban areas primarily associated with formally planned and established parks, sports fields, and golf courses. 64 Built-up residential Urban Recreational Fields (Bare) Non-built-up, open urban areas primarily associated with formally planned and established parks, sports fields, and golf courses. 65 Built-up commercial Commercial Built-up areas primarily containing formally planned and constructed commercial structures and associated utilities. Includes shops, offices, schools, hospitals, and administration structures. 66 Built-up industrial Industrial Built-up areas primarily containing formally planned and constructed industrial structures and associated utilities. Includes both light and heavy industry, power generation, airports, rail terminals and ports. 67 Built-up commercial Roads & Rail (Major Linear) Built-up features represented by primary road and rail networks that are image-detectable (i.e. networks are non-contiguous), as well as smaller airfields and airstrips. 68 Mines Mines: Surface Infrastructure Built-up structures associated with the administration and/or industrial processing and extraction of mined resources. 69 Mines Mines: Extraction Sites: Open Cast & Quarries combined Non-vegetated, active and/or non-active extraction pits associated with surface-based mining activities, including open- cast mines, quarries, and road-side borrow pits etc. 70 Mines Mines: Extraction Sites: Salt Mines Non-vegetated, active or non-active extraction pits associated with evaporative salt-mining activities, typically associated with coastal or inland saline pan localities. 71 Mines Mines: Waste (Tailings) & Resource Dumps Non-vegetated, active or non-active mine generated material dumps or stockpiles, associated with both mine waste material (i.e. tailings dams) or mine generated resource stockpiles (i.e. coal stockpiles). 72 Mines Land-fills Primarily non-vegetated, active or non-active land-fill sites used for the large scale disposal of urban waste. 6.2.6 Other land The definitions of the land classes included in Other lands are provided in Table 6.15. Table 6.15: Definitions of the land classes, as given in the SANLC 2018, that are included in the Other land category (Source: DEA, 2019). SANLC class number Aggregated change class Class name Class definition 25 Barren land Natural Rock Surfaces Naturally occurring areas of non-vegetated, exposed rock and consolidated substrate. 26 Barren land Dry Pans Naturally occurring areas of non-vegetated, consolidated substrate, associated with permanent or long-term dry pans. 266 28 Barren land Sand Dunes (terrestrial) Non-vegetated, naturally occurring inland (non-coastal) sand dunes, which are typically associated with arid, desert-like environments. 29 Barren land Coastal Dunes & Beach Sand Non-vegetated, naturally occurring coastal sands, typically associated with both coastal dunes and beach environments. 30 Barren land Bare Riverbed Material Natural or semi-natural, non-vegetated, consolidated or unnaturally occurring coastal sands, typically associated with both coastal dunes and beach environments. 31 Barren land Other Bare Other natural, semi-natural or man-created non-vegetated areas. 45 Barren land Fallow Land & Old Fields (Bare) Long-term, non-active, previously cultivated lands that are now predominately non-vegetated bare ground surfaces. Approaches for determining land areas and land-use change data South Africa covers an area of 123 564 kha and has a warm, temperate and dry climate. Land-cover 22 maps were developed for 1990 (DEA, 2016), 2013-14 (DEA, 2015a), 2018 (DEA, 2019) and more recently, 2020 (DFFE, 2021). The details of the processing and development of each of the maps is provided in the relevant reports and are summarised below. A map for 2022 is currently being developed but had not been completed by the time this inventory was compiled and therefore is not included and data was extrapolated for 2021 and 2022 assuming the same rate of land change as seen between 2018 and 2020. 6.3.1 Database and data processing 6.3.1.1 SANLC 1990 The 1990 72 x class South African National Land-cover dataset was generated primarily from digital, multi-seasonal Landsat 5 multispectral imagery, acquired mainly between 1990 and 1991. In excess of 600 Landsat images were used to generate the land-cover information, based on an average of 8 different seasonal image acquisition dates, within each of the 76 x image frames required to cover South Africa. The land-cover dataset, which covered the whole of South Africa, was presented in a map-corrected, raster format, based on 30x30m cells equivalent to the image resolution of the source Landsat 8 multi-spectral imagery. Each data cell contains a single code representing the dominant land-cover class (by area) within that 30x30m unit, as determined from analysis of the multi-date imagery acquired over that image frame. The original land-cover dataset was processed in UTM (north) / WGS84 map projection format based on the standard Landsat map projection format provided by the USGS2. The final product is available in 22 The term ‘land cover’ is used loosely here as the classes are a combination of land cover and land use. 267 UTM35(north) and (south), WGS84 map projections and Geographic Coordinates, WGS84. The generation of the 1990 national land-cover dataset is based on the same mapping and modelling techniques developed for and used in the operational production of the 2013-14 South African national land-cover dataset and is described in the next section. 6.3.1.2 SANLC 2013-14 The primary imagery source used in the generation of the 2013-14 SA National Land- cover dataset (referred to as SANLC 2014 in this report) was 30m resolution Landsat 8 imagery, acquired between April 2013 and March 2014. Unfortunately, due to localised, prolonged cloud cover problems in some regions during this April 2013 - March 2014 period, there were gaps in the data. In these cases, Landsat 5 imagery was used as a substitute, but only if it was from a suitable seasonal period to compliment the Landsat 8 data, and was ± 100% cloud free. All Landsat 8 and 5 imagery was standardised to 16-bit, Top-of-Atmosphere (ToA) reflectance values prior to land-cover modelling using a generic modelling approach. The original USGS generated UTM (north), WGS84 map projection format was retained without change or modification throughout all image-frame based modelling procedures. As far as possible only cloud free or image dates with limited cloud cover were used in the land-cover modelling (i.e. maximum ± 20% terrestrial cloud cover in any one date). Any cloud affected regions were corrected by merging cloud affected ToA corrected imagery with cloud free ToA corrected data from preferably the preceding or following overpass date; so that as far as possible, the final cloud-free merged imagery composite only represented a maximum difference of ± 16 days. Cloud masks were created using either using conventional pixel classification procedures (within analyst defined sub- image areas), or spectral based modelling using generic thermal and blue light reflectance thresholds; depending on cloud characteristics. This was deemed acceptable in terms of minimising any changes in local vegetation cover growth changes. Approximately 35% of the 616 x images used in the landcover modelling were cloud-masked composites. No external atmospheric correction was applied to the image data. Derived spectral indices, generated from the ToA corrected imagery were used as the only inputs into the land-cover models. No original spectral (i.e. ToA reflectance) data was used as an input, although collectively, 5 out of the 8 available non-thermal spectral bands were used in the various spectral indices. The same two step process as outlined below for the SANLC 2018 map was then followed to generate the land-cover dataset. 6.3.1.3 SANLC 2018 268 The 20m resolution, raster format SANLC 2018 dataset was generated from automated mapping models (as opposed to conventional image classification procedures), using multi-seasonal 20m resolution Sentinel 2 satellite imagery. The imagery used represents the full temporal range of available imagery acquired by Sentinel 2 during the period 01 January 2018 to 31 December 2018. The automated land-cover mapping models and associated procedures utilise both cloud- based image archives and cloud-based geo-data computing capabilities; although the final compilation and merging of the different land-cover and land-use information components (i.e. water, mining extent, forest plantations etc.), were completed in a conventional desk-top environment, using automated modelling capabilities within commercial mapping software. The generation of the automated land-cover data in the new, Sentinel 2 based procedure involved two separate, but sequential processing steps: • Step 1: Fully automated image modelling procedures were used to generate what is referred to as the “spectrally-defined” base land-cover characteristics. These ‘base’ land-cover characteristics were the primary ‘building blocks’ which were used to describe the entire landscape in terms of primary cover characteristics such as woody vegetation, grass, bare or water dominated surfaces. A standardised set of spectral indices were identified from which the required foundation cover classes, namely (1) tree dominated, (2) bush dominated, (3) grass dominated, (4) water and (5) bare ground could be modelled, using pre- determined, generic spectral threshold values. The generic threshold values associated with each indices and cover type were tested over several landscapes and seasons before being confirmed and accepted as such. The spectral indices included both existing algorithms such as the Normalised Difference Vegetation Index (NDVI) and Normalised Difference Water Index (NDWI), as well as algorithms developed in-house specifically for the GTI land-cover modelling requirements. No attempt was made at this stage to define additional detail such as whether the tree cover is a natural forest, or a managed forest plantation. • Step 2: Ancillary spatial datasets, referred to as ‘geographical masks’ (such as “Mines & Quarries”, “Built-up”), were used to convert the base land-cover classes into more detailed sub-classes. The geographical masks define specific, pre- determined areas-of-interest within the South African landscape, within which the primary, spectrally-defined base classes were de-constructed into more specific land-cover and/or land-use sub-classes. In each case, a specific set of modification rules were used to either amalgamate, sub-divide or re-allocate the primary, base- level class(es) to the required sub-class detail. The primary objective and reason behind the use of these geographical masks is to facilitate the delineation of sub- classes that cannot be achieved using spectral data alone, since many unrelated 269 sub-classes can often share similar spectral characteristics, e.g. river water versus water-in-pans, or non-vegetated mine dumps and some natural rock exposures, or coastal dunes and sand-roads etc. The ‘geographical masks’ are described in detail in the SANLC 2018 report (DEA, 2019). The automated land-cover mapping models and associated procedures used in the production of the SANLC 2018 dataset utilise both cloud-based image archives and cloud- based geo-data computing capabilities; although the final compilation and merging of the different land-cover and land-use information components (i.e. water, mining extent, forest plantations etc), has been completed in a conventional desk-top environment, using automated modelling capabilities within proprietary, i.e. commercial mapping software. 6.3.1.4 SANLC 2020 For the SANLC 2020 the entire workflow discusses for SANLC 2018 was migrated to cloud-based technologies and became a fully automated process termed “CALC” for Computer Automated Land-Cover”. All future maps will be generated in this way. 6.3.2 Uncertainty 6.3.2.1 SANLC 1990 and 2014 No accuracy assessment was undertaken on the historical 1990 South African National Land-Cover dataset, because no suitable historical reference data was available in the same format as that used for the verification of the 2013-14 dataset. However, since exactly the same mapping and modelling procedures and image formats have been used in the generation of the 1990 land-cover data, the map accuracies determined for the 2013-14 dataset can be used as a reliable indication of the likely mapping accuracies achieved for the 1990 dataset. The overall map accuracy for the 2013-14 South African National Land-Cover, modelled from multi-seasonal Landsat 8 imagery is 82.53%, with a mean land-cover / land-use class accuracy of 88.36 %. This was determined from 6415 sample points representing. The associated Kappa Index value of 80.87 indicates that these results are very unlikely to be the result of chance occurrence. 6.3.2.2 SANLC 2018 The overall map accuracy for the SANLC 2018 land-cover dataset, calculated on the 47 x class accuracy (detailed) legend is 90.14%, with a mean class accuracy of 89.63%, and 90% confidence limits of 89.65 – 90.62 %. The kappa index for this is 89.91. 270 6.3.2.3 SANLC 2020 The overall map accuracy for the SANLC 2020 land-cover dataset, calculated on the 47 x class accuracy (detailed) legend is 85.47%, with a mean class accuracy of 84.66%, and 90% confidence limits of 84.91 – 96.03 %. The kappa index for this is 85.13. 6.3.3 Comparison between Landsat and Sentinel maps One of the key intentions behind the production of the new Sentinel land cover datasets was to expand the time-line of available South African national land-cover datasets in support of long-term environmental monitoring and change detection. To that end, the SANLC 2018 and 2020 datasets have been generated to provide as close as possible a repeat of the SANLC 1990 and SANLC 2014 data contents, format and landscape representations. However, improvements in landscape interpretation and mapping resulting from the improved spatial, spectral and temporal characteristics of Sentinel 2 imagery have resulted in changes between SANLC 2018 and the previous SANLC 1990 and SANLC 2014 datasets, which are not necessarily associated with true landscape changes, but rather improved interpretation and thus mapping quality. There has been no attempt to perpetuate both known and unknown errors from the SANLC 1990 and SANLC 2014 datasets into the SANLC 2018 and 2020 datasets if more accurate mapping could be achieved from the Sentinel 2 image interpretations, whilst retaining as far as possible the same class-based information content. Generally, it can be assumed that comparisons between the current SANLC 2018 and both historical SANLC’s will be more accurate in terms of documenting true land-use related changes, compared to land-cover related changes, as land-use features are typically associated with easily (image) detectable boundaries and/or unique (image) spectral characteristics. Mapping, and by association, change detection in natural landscapes, such as woodland, grassland and shrublands etc. are typically more challenging; since these landscape features typically do not have ‘hard’ boundaries, but rather gradients, within which a class boundary must be defined. This challenge is often compounded as a result of inter-annual seasonal differences, and associated differences in interpretable spectral/spatial image characteristics. The end-result is that, if a user compares the current and historical SANLC datasets, many landscape differences will be correct, it is also likely that some of these differences, especially in natural vegetation landscape can be attributed to mapping errors in one or both land-cover input datasets. Although these are most likely to be associated with the historical Landsat generated SANLC’s as a result of Landsat’s poorer spatial, spectral and temporal characteristics, compared to Sentinel 2 imagery. The enhanced spectral, spatial and temporal characteristics associated with Sentinel 2 imagery, as well as enhancements to the content and integration of the geographical masks used in the latest SANLC 2018 271 and 2020 landcover modelling, are considered to have resulted in a more accurate SANLC 2018 landscape interpretation. This assumption is supported by the significantly higher woody class mapping accuracies reported for SANLC 2018 compared to those reported in the SANLC 2014 dataset (see Table 6.16). Table 6.16: Comparison of mapping accuracies between SANLC 2014 and SANLC 2018. SANLC 2014 Class Mapping Accuracies User/Producer Accuracy (2014) User/Producer Accuracy (2018) SANLC 2018 Class Mapping Accuracies Indigenous forest 72.60/94.64 98.90/84.11 Contiguous (indigenous) forest 80.60/92.31 Contiguous low forest & thicket Dense bush/Thicket 53.74/83.64 76.44/93.59 Dense forest & woodland Woodland/Open bush 60.84/54.13 78.79/92.86 Open woodland Grassland 84.56/69.82 85.90/87.58 Grassland Shrubland (non-fynbos) 70.59/61.82 66.67/86.25 Shrubland (non-fynbos) 6.3.4 Land use change mapping 6.3.4.1 Data processing Land-use changes were mapped using an Approach 2 method as described in 2006 IPCC Guidelines. The 1990-2014, 2014-2018 and 2018-2020 change maps were utilized in this inventory and data was extrapolated to 2022 assuming the same rate of change between 2018-2020. The key data processing steps used in Computer Automated Land Cover (CALC) system to generate any two-date SANLC change assessment are: • Convert all Geographic coordinate format SANLC datasets to the new, simplified land-cover change legend format and content. • Re-project the legend modified Geographic Coordinate format 1990, 2013/14 and CALC generated SANLC datasets to a standardised Albers Equal Area map projection, including a single-step spatial resampling from 20 x 20 m to 30 x 30 m resolution for all CALC generated SANLC datasets, using nearest-neighbour class code allocation. • Ensure pixel-to-pixel registration between all 30 m resolution Albers Equal Area map projection outcomes, using the CALC generated 2018 SANLC as the generic reference dataset against which the both the pre-CALC 1990 and 2013/14 datasets and all other CALC generated SANLC datasets are registered. This approach has been taken as the ortho-precision of the source Sentinel 2 imagery 272 from which all SANLC datasets are generated is considered superior to the Landsat imagery used in the compilation of the pre-CALC 1990 and 2013/14 SANLC datasets. • Ensure pixel-level equivalent land-cover geographic coverage between all SANLC datasets, so that the change assessment results do not include a null class values as a result of differences in total mapped extent resulting from different boundary delineations and associated buffer zones. • Generate a national 30 x 30 m resolution land-cover class-pair based change- reporting matrix for each two-year land-cover change assessment. 6.3.4.2 Land change maps and annual change data Adjustments to 2014-2018 change map A 2018 South African National Land Cover Change Assessments report (DEA, 2019a) was completed as part of the 2018 land cover mapping project to verify the natural land class changes between 1990/2014/2018. This assessment showed that most of the land changes between the natural land classes were not real changes. It indicated that there may be some loss of indigenous forest to grassland, loss of thicket to woodland (although hard to detect due to extent of area), and thicket to shrubland. This information was incorporated into the 2014-2018 land change data by making some assumptions (Table 6.17). The conversion areas where no conversion is considered to occur were reverted back to the 2018 land remaining land class as the 2018 areas were assumed to be more accurate. In addition to the changes based on the 1990/2014/2018 change assessment report, further corrections were made based on expert input in terms of unlikely changes. These are also documented in Table 6.17. 273 Table 6.17: Corrections made to 2014-2018 change matrix. Conversion category Correction Assumption Basis for correction Indigenous forest to Thicket/Dense bush 95% of conversion area removed and allocated back to Thicket/dense bush Assumption is that only 5% of the conversion of indigenous forest to thicket occurs and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Thicket/Dense bush). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. Data suggested there was no change but not all areas were assessed therefore to be more conservative it was assumed that 5% of change could have occurred. Indigenous forest to Natural woodland 95% of conversion area removed and allocated back to Natural woodland Assumption is that only 5% of the conversion of indigenous forest to natural woodland occurs and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Natural woodland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. Thicket/Dense bush to Natural woodland 95% of the converted area is reverted back to Natural woodland Assumption is that only 5% of the original conversion area is actually due to real change and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Natural woodland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps. This assessment indicates this area is very large and the whole area could not be assessed but it was indicated that "it is difficult to locate areas of real change in a class that is so extensive and in which interpretation changes probably overwhelmingly dominate any possible real changes." For this reason "overwhelmingly" was assumed to be 95%, however this assumption will need to be investigated in more detail in future. 274 Thicket/Dense bush to Shrubland 85% of the converted area is reverted back to Shrubland Assumption is that only 15% of the original conversion area is actually due to real change and that the 2018 map is more accurate therefore 80% of the converted area is allocated back to the 2018 classification (Shrubland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps. This assessment provides 6 examples of areas that were assessed and in one of these areas the change was assessed to be real whereas in the other examples the change was rather due to a change in interpretation as opposed to a real change. An assumption was therefore made that only 15% of the change may be due to real change while the rest was reallocated back to shrubland (to remain consistent with the assumption that the 2018 classification was better). Indigenous forest to grassland 80% of conversion area removed and allocated back to grasslands Assumption is that only 20% of the conversion of indigenous forest to grassland occurs and that the 2018 map is more accurate therefore 80% of the converted area is allocated back to the 2018 classification (Grassland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that most of these conversions were due to a change in interpretation and not due to real change. There were, however some area that were indicated to be lot due to clearing of alien invasives and so instead of the lower 10% it was assumed that 20% of the conversion still occurred. Losses were also indicated to be due to fire, but fire losses are already included in the biomass loss calculations so do not need to be included in the area as this would be double counting. This loss of biomass and land-use change due to fire could be something that applies to other land categories and so this possible double counting will be investigated further in the next inventory. 275 Thicket/Dense bush to Grassland 95% of the converted area is reverted back to Grassland Assumption is that only 5% of the original conversion area is actually due to real change and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Grassland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps. This assessment provides 6 examples of areas that were assessed and in most cases it was indicated the area was not due to real change but a change in interpretation. There was a small patch to the south of Polokwane where there may be a gradual loss of woody vegetation, therefore the assumption of 5% change was retained. Natural woodland to Shrubland 95% of the conversion area removed and allocated back to Shrubland Assumption is that only 5% of the original conversion of natural woodland to shrubland occurs and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Shrubland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. Natural woodland to Grassland 95% of the converted area is reverted back to Grassland Assumption is that only 5% of the original conversion area is actually due to real change and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Grassland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps. This assessment indicates that most of the change is due to an improved interpretation/classification but there is mention that there is a possibility of some area around rural settlements where wood harvesting could be occurring, so it was assumed that 5% of the original change may be due to actual change. Shrubland to Natural woodland 95% of the conversion area removed and allocated back to Natural woodland Assumption is that 5% of the conversion occurs while 95% is due to reclassification. It is assumed that the 2018 map is more accurate therefore the converted area is allocated back to the 2018 classification (Natural woodland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. 276 Grassland to Natural woodland 95% of the conversion area removed and allocated back to Natural woodland Assumption is that 5% of the conversion of Shrubland to Natural woodland occurs and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Natural woodland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. Grassland to Shrubland 95% of conversion area removed and allocated back to Shrubland Assumption is that 5% of the conversion of Grassland to Shrubland occurs and that the 2018 map is more accurate therefore 95% of the converted area is allocated back to the 2018 classification (Shrubland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that these conversions were due to a change in interpretation and not due to real change. Shrubland to grassland 50% of conversion area removed and allocated back to grasslands Assumption is that 50% of the conversion of Shrubland to grassland occurs and that the 2018 map is more accurate therefore 50% of the converted area is allocated back to the 2018 classification (Grassland). The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that some of the conversions are due to seasonal change and others could be real change. Some of the changes are suggested to possibly be due to fire. Losses due to fire are incorporated in the biomass losses. It is therefore assumed that only 50% of the conversions actually occur but this can be investigated further in the next inventory and the overlay with burnt area assessed. Settlements to mines Assumed no conversion occurs, all areas allocated back to mines These changes are "highly unlikely" so reverted back to the 2018 class as 2018 assumed to be more accurate. Based on the presentation by Mark Thompson (GTI) titled "2020SANLC Technical Presentation), 2020 SANLC Data Launch, 29th June 2021. Indigenous forest and thicket to plantation Assumed no conversion occurs and the areas are allocated back to plantations These changes are assumed highly unlikely so reverted to back to the 2018 land cover (plantations). Based on discussions with forestry industry, also GTI presentation on the 2020 SANL map (2022) indicates this to be highly unlikely. 277 Wetlands, agricultural lands and settlements to plantation Assumed no conversion occurs and the areas are allocated back to plantations These changes are assumed highly unlikely so reverted to back to the 2018 land cover (plantations). Based on discussions with forestry industry. Conversions between natural land classes Assumed only 5% of the conversion occurs and the other 95% is allocated back to land class in 2018. Assumption is that 5% of the conversion of the class occurs and that the 2018 map is more accurate therefore the other 95% of the converted area is allocated back to the 2018 classification. The 2018 LC Assessment report (DEA 2019) compared the new 2018 Sentinel LC maps with the older 1990/2014 Landsat LC maps and the assessment showed that conversions between natural vegetation classes were mostly due to an improved classification rather than actual change. There was no specific analysis on these land class changes in the report but based on the findings for the other natural land classes the same assumption (that 90% of the change was not real change but rather a change in classification) was applied. Conversion of forest land to waterbodies Assumes that a conversion of forest land to waterbody is highly unlikely therefore all the conversion area is allocated back to waterbody area These changes are assumed highly unlikely so reverted to back to the 2018 land cover (waterbodies). Conversion of waterbodies to settlements Assumes the conversion of waterbodies to settlements is highly unlikely therefore the conversion area is allocated back to settlements. These changes are assumed highly unlikely so reverted to back to the 2018 land cover (settlements). Based on the presentation by Mark Thompson (GTI) titled "2020SANLC Technical Presentation), 2020 SANLC Data Launch, 29th June 2021. Conversion of settlements to cultivated land Assumes the conversion of settlements to cultivated land is highly unlikely therefore the conversion area is allocated back to cultivated lands. These changes are assumed highly unlikely so reverted to back to the 2018 land cover (cultivated lands). Based on the presentation by Mark Thompson (GTI) titled "2020SANLC Technical Presentation), 2020 SANLC Data Launch, 29th June 2021. 278 Conversion of shrublands, grasslands, wetlands, cultivated land, and settlements to indigenous forests Assumes that lower biomass land types cannot be converted to indigenous forest in 2 years therefore revert these changes back to original land types These changes are logically not really possible due to the slow growth of trees Conversion of shrublands, grasslands, wetlands, cultivated land, and settlements to thicket Assumes that lower biomass land types cannot be converted to thicket/dense bush in 4 years therefore revert these changes back to original land types These changes are logically not really possible due to the slow growth of trees 279 Adjustments to 2018-2020 change map In the 2018 to 2020 change data only the unlikely changes mentioned in Table 6.17 were implemented. In an addition to this an attempt was made to make adjustments for seasonal changes, i.e. natural land areas that show changes but these are not permanent. This was done by taking the natural land class change areas from 2014-2018 and intersecting them with the opposite change areas in the 2018-2020 map. The areas that overlapped were assumed to be seasonal change and not permanent change and these areas were reverted back to the 2020 class. For example, the area of woodland converted to grassland in 2014-2018 was intersected with the area of grassland that was converted back to woodland in 2018-2020. The overlap area was assumed to therefore be non- permanent and the area was reverted back to the woodland class. The land change classes that were included in this analysis were Indigenous forests/Thickets, Thickets/Woodlands, Woodland/Grassland, Woodland/Low shrubland, Grassland/Low shrubland, Grassland/Barren land, and Low shrubland/Barren land. As more maps become available this analysis and assessment of seasonal variation can be improved on. Verification There is an urgent need to conduct further validation of the change data as this is the largest limitation to the LULUCF inventory. The previous review highlighted that validation data, perhaps using Collect Earth, was required and DFFE has started training the inventory team on this technique. Currently a targeted area of South Africa is being tested and then the validation process will be considered for inclusion in the development of maps going forward. More frequent datasets would also provide further accuracy and DFFE GIS Department has setup a system (Computer Automated Land Cover (CALC)) for generating the 20 class land cover maps every 2 years from Sentinel 2 data. A full detailed land cover map is expected to be produced every 4 years. As more maps are produced the natural variability will be distinguished from the permanent change. This system can be used in future to provide more quantitative data for excluding non-permanent changes. Land use change matrix and annual change areas The land change matrix for the most recent change map (2018-2020) before and after corrections are provided in Table 6.18 and Table 6.19, respectively. This rate of change was used to extrapolate the land areas in 2022. 280 Table 6.18: Land change (ha) matrix for South Africa between 2018 and 2020 prior to corrections. Land categories 2018 Indigenous Forest Thicket / Dense Bush Natural woodland Planted Forest Shrubland Grassland Waterbodies Wetlands Barren Land Eroded Land Cultivated Permanent Orchards Cultivated Permanent Vines Annuals Pivot Irrigated Annuals Non - Pivot Cultivated Subsistence Built - Up Residential All Built - Up Smallholdings Built - Up Commercial Built - Up Industrial Mines 2020 Indigenous Forest 425 300 4 1 775 978 142 4 145 54 158 67 0 0 0 0 4 0 192 2 1 0 48 Thicket / Dense Bush 2 959 634 259 314 20 11 341 23 447 497 256 325 3 237 0 116 317 438 678 0 67 19 36 Natural woodland 1 510 677 412 13 434 202 189 261 389 3 737 247 12 449 949 59 888 8 891 4 241 13 1 978 31 313 141 060 22 625 87 2 118 1 694 4 084 Planted Forest 832 4 068 7 411 1 974 867 6 227 63 454 473 3 137 1 459 59 256 0 6 771 108 2 891 160 27 75 358 Shrubland 120 9 855 95 376 3 191 27 608 050 2 206 915 7 821 447 1 775 487 20 632 38 39 189 2 388 14 21 0 2 12 6 749 Grassland 4 820 155 742 2 622 842 62 302 2 578 791 30 128 218 29 468 10 496 239 662 49 786 241 13 993 14 076 15 500 340 18 5 749 6 370 32 585 Waterbodies 32 312 8 922 413 13 484 17 022 2 063 968 6 598 18 891 378 35 13 50 1 231 109 392 82 15 98 3 446 Wetlands 308 413 1 226 2 734 3 099 15 450 8 117 1 019 293 2 796 1 6 1 20 177 55 134 52 16 32 339 Barren Land 168 2 559 34 946 1 114 1 697 277 218 837 26 530 753 10 574 723 31 11 1 16 156 109 35 0 3 46 981 Eroded Land 0 4 3 464 86 20 977 56 047 460 1 778 354 137 0 0 2 3 66 0 0 0 0 46 Cultivated Permanent Orchards 13 1 518 12 679 2 563 1 483 2 751 17 166 474 1 274 721 45 4 654 17 109 499 96 137 0 0 7 Cultivated Permanent Vines 0 86 1 192 28 2 341 323 6 18 184 0 23 140 110 32 114 0 61 29 0 0 6 Annuals Pivot Irrigated 1 1 431 11 051 210 2 481 13 891 39 389 338 48 303 94 829 476 41 055 232 55 72 0 0 129 Annuals Non- Pivot 97 4 280 112 932 5 055 26 856 239 211 689 5 521 11 101 273 8 213 125 19 845 10 133 175 12 210 1 113 1 593 0 0 2 684 Cultivated Subsistence 97 923 33 197 169 276 47 312 325 372 5 211 3 090 362 0 308 2 410 1 793 512 1 831 640 1 0 299 281 Built-Up Residential All 616 3 973 35 572 1 980 1 218 16 039 490 149 1 645 126 12 0 45 958 4 030 2 908 277 96 124 14 1 365 Built-Up Smallholdings 5 0 32 38 0 11 145 50 0 0 19 0 5 72 315 250 240 548 92 165 58 Built-Up Commercial 0 22 890 15 20 3 525 13 12 59 0 4 0 0 0 3 68 53 75 906 1 75 Built-Up Industrial 0 39 935 41 874 3 804 74 30 367 2 61 2 18 333 15 148 150 3 89 540 610 Mines 74 18 1 909 826 10 062 19 573 2 693 369 1 476 114 8 4 211 4 439 442 737 26 50 406 245 783 Table 6.19: Land change (ha) matrix for South Africa between 2018 and 2020 after corrections. Land categories 2018 Indigenous Forest Thicket / Dense Bush Natural woodland Planted Forest Shrubland Grassland Waterbodies Wetlands Barren Land Eroded Land Cultivated Permanent Orchards Cultivated Permanent Vines Annuals Pivot Irrigated Annuals Non - Pivot Cultivated Subsistence Built - Up Residential All Built - Up Smallholdings Built - Up Commercial Built - Up Industrial Mines 2020 Indigenous Forest 426 167 0 1 775 978 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Thicket / Dense Bush 0 1 030 403 71 457 20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Natural woodland 1 510 611 028 14 496 252 189 242 475 2 610 874 12 449 949 59 888 8 891 4 241 13 1 978 31 313 141 060 22 625 87 2 118 1 694 4 084 Planted Forest 0 0 7 411 1 975 281 6 227 63 454 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Shrubland 120 9 855 52 891 3 191 28 788 948 570 022 7 821 447 1 001 743 20 632 38 39 189 2 388 14 21 0 2 12 6 749 Grassland 4 820 155 742 1 800 058 62 302 2 189 423 33 037 083 29 468 10 496 0 49 786 241 13 993 14 076 15 500 340 18 5 749 6 370 32 585 Waterbodies 0 0 0 0 13 484 17 022 2 065 714 6 598 18 891 378 35 13 50 1 231 109 392 82 15 98 3 446 Wetlands 308 413 1 226 2 734 3 099 15 450 8 117 1 022 844 2 796 1 6 1 20 177 55 134 52 16 32 339 Barren Land 168 2 559 34 946 1 114 936 145 100 831 26 530 753 11 589 979 31 11 1 16 156 109 35 0 3 46 981 282 Eroded Land 0 4 3 464 86 20 977 56 047 460 1 778 354 199 0 0 2 3 66 0 0 0 0 46 Cultivated Permanent Orchards 13 1 518 12 679 2 563 1 483 2 751 17 166 474 1 275 214 45 4 654 17 109 499 0 0 0 0 7 Cultivated Permanent Vines 0 86 1 192 28 2 341 323 6 18 184 0 23 140 111 32 114 0 0 0 0 0 6 Annuals Pivot Irrigated 1 1 431 11 051 210 2 481 13 891 39 389 338 48 303 94 829 599 41 055 232 0 0 0 0 129 Annuals Non- Pivot 97 4 280 112 932 5 055 26 856 239 211 689 5 521 11 101 273 8 213 125 19 845 10 134 266 12 210 0 0 0 0 2 684 Cultivated Subsistence 97 923 33 197 169 276 47 312 325 372 5 211 3 090 362 0 308 2 410 1 794 059 0 0 0 0 299 Built-Up Residential All 616 3 973 35 572 1 980 1 218 16 039 0 149 1 645 126 12 0 45 958 4 030 2 915 931 96 124 14 1 365 Built-Up Smallholdings 5 0 32 38 0 11 0 50 0 0 19 0 5 72 315 250 243 208 92 165 58 Built-Up Commercial 0 22 890 15 20 3 525 0 12 59 0 4 0 0 0 3 68 53 76 052 1 75 Built-Up Industrial 0 39 935 41 874 3 804 0 30 367 2 61 2 18 333 15 148 150 3 90 041 610 Mines 74 18 1 909 826 10 062 19 573 2 693 369 1 476 114 8 4 211 4 439 442 0 0 0 0 246 225 283 Annual change by soil and land type To determine the area of each land class in a particular soil type, the soil datasets (DEA, 2015) were extracted using the national boundary to represent South Africa only. Each dataset was re-projected into the same projection as the land cover datasets (UTM 35s). Each dataset was resampled to a 30 m x 30 m pixel size to match the land cover datasets. Once the 1990 and 2018 land cover datasets and the soil datasets were processed into the same projection and pixel size, they were merged to generate a land cover change dataset within each soil category. An output table was then generated, and annual areas calculated in a similar manner to that mentioned above. Annual change was assumed to remain the same over the entire time series and land areas also incorporated the 20-year default conversion period as described in section Error! Reference source not found.. S ince land parcels are not being tracked it was assumed that the land was always converted from a land remaining category. This assumption is a simplified assumption as in reality much of the converted land is likely to be converted from previously converted land. Ways of improving on this assumption are currently being sought. 6.3.4.3 Incorporating the 20-year transition period Each land type is divided into land remaining land and land converted to that land class. IPCC states that land remains in the land converted to category for the default 20-yr period. After 20 years the converted land moves to the land remaining land category. In this inventory the 20-year transition period was included for each land category. It was assumed that the same annual rate of change occurred prior to 1990, therefore the area of land converted to and land remaining land for each land type in 1990 was calculated as: LC i 1990 = ALC i 1990 * 20 yrs LRL i 1990 = A i 1990 - LC i 1990 Where: LC i 1990 = area (ha) of land in land converted to land category i in 1990; ALC i 1990 = annual total area (ha) converted to land category i in 1990; LRL i 1990 = area (ha) of land in the land remaining in the land category i in 1990; A i 1990 = total area of land in category i in 1990. For subsequent years these categories were calculated as follows: LC i t = LC i t-1 + ALC i t - ALC i (t-20) LRL i t = LRL i t-1 + ALC i t – ACF i t 284 Where: LC i t = area (ha) of land in land converted to land category i in year t; LC i t-1 = area (ha) of land in land converted to land category i in year t-1; ALC i t = annual total area(ha) converted to land category i in year t; ALC i (t-20) = annual total area (ha) converted to land category i in year t-20; LRL i t = area (ha) of land in the land remaining in the land category i in year t; LRL i t-1 = area (ha) of land in the land remaining in the land category i in year t-1; ACF i t = annual total area (ha) converted from land category i to another land category in year t. This approach assumes that all the converted land is removed from the “land remaining” category which is unlikely to be true as the previously converted land could also be converted to another land use. To account for this the “land remaining” areas from 1990- 2014 was overlaid with the “land remaining areas” between 2014-2018, and this was also done for 2018-2020. This provide the fraction by which the “land remaining” category was reduced and therefore what fraction of the converted area was from “land remaining” and therefore what fraction came from already converted land. On average about 35% of the land conversions in Forest lands, Grassland and Wetlands came from “land remaining”, while this went up to about 47% for Cropland and Other lands. For Settlements the average was 57%, while the rest of the conversions came from already converted land areas. A fraction was therefore incorporated into the equation which indicated what fraction of the land conversion area was removed from “land remaining” category and, therefore, what fraction was removed from the “converted land” category. A fraction was included for the period 2014-2018 and another for 2018-2020. Extrapolation to 2022 It was assumed that the annual change between 2018 and 2020 continued to 2022. This assumption will be corrected in the next inventory when the SANLC data for 2022 is available. Forest land (4.A.) 6.4.1 Category description Reporting in this category covers emissions and removals from above-ground and below- ground biomass, litter, deadwood and mineral soils. The category included indigenous forests, plantations/woodlots, thickets/dense bush, and woodlands/open bush. As in the previous inventory the plantations were sub-divided into Eucalyptus sp., softwood sp., 285 acacia (wattle) and other plantation species. Softwoods were further divided into sawlogs, pulp and other as the growth and expansion factors of these plantations differed. Eucalyptus grandis and Other Eucalyptus species were separated. Changes in biomass included wood removal, fuelwood collection, and losses due to disturbance. Harvested wood was included for plantations, while fuelwood collection was estimated for all forest land and included under Woodlands as it was not possible to split it by forest type. All emissions from the burning of fuelwood for energy or heating purposes were reported as part of the energy sector. In plantations, disturbance from fires and other disturbances were included, while for all other subcategories only disturbance from fire was included due to a lack of data on other disturbances. Emissions from Harvested wood products were included under 4.G. This category reports emissions and removals from the categories Forest land remaining forest land and Land converted to forest land (new forest established, via afforestation or natural succession, on areas previously used for other land-use classes). Calculations are carried out on the basis of a 20-year transition period in that once a land area is converted it remains in the converted land category for 20 years. Biomass burning CH 4 and N 2 O were also included here instead of being included under the Biomass Burning category (3C1) under Aggregated and non-CO 2 emissions (3C) in Agriculture as was the case in the previous inventory. This is to bring the inventory into alignment with the CRT tables required for reporting under the ETF. 6.4.2 Methodological issues Carbon changes in the Forest land category were determined with a Tie 2 methodology for biomass, DOM and SOC for both the “land remaining” and the “converted land” categories. In the previous inventory Forest land remaining Forest land was identified as a key category and, based on the IPCC decision tree the Tier 2 approach is suitable for this category. A Gain-Loss approach was used for all forest land categories. The Stock- Difference approach was considered for plantations, but due to the fact that (a) detailed age-class population data from DFFE was only available from 2001 (therefore stock change data from 2002 only) and (b) land conversion losses were not fully incorporated into this methodology at this stage the Stock-Difference approach was used as verification and is discussed in more detail in section 6.4.5. Once these issues are resolved the Stock- Difference approach can be incorporated into the inventory. 6.4.2.1 Biomass Carbon gains 286 Natural forest lands remaining forest lands are classed as either undisturbed (or primary) or disturbed (secondary forest) to make use of different growth rates (Table 6.20). In the absence of data on specific areas of primary and secondary forests, it was assumed that primary forests are those that fall into protected areas. According to Statistics SA (StatsSA, 2021) 40%, 13% and 15% of indigenous forests, thickets and woodlands were protected. Annual above-ground biomass growth in primary indigenous forests, thickets and woodlands are considered to be zero (IPCC, 2019), which is in line with the findings of Glenday (2007). For secondary forests the growth rates in Table 6.20 were applied and were assumed to grow until they reach their maximum biomass. Root to shoot ratios (Table 6.21) were utilized to include the below ground biomass. The IPCC 2006 default value of 0.47 t C per t dm -1 (IPCC 2006, Table 4.3) was used for the carbon fraction of dry matter of all Forest lands. Table 6.20: Biomass growth or accumulation rates for the forest land categories. Land class Forest type Biomass growth rate (t C/ha/yr) Data source Other verification IPCC default (t C/ha/yr) Indigenous forest Primary (undisturbed) 0 Glenday, 2007 0.42 – 0.85 (subtropical dry forest in Africa >20yrs) Secondary (disturbed) 1.39 Calculated (AGB/60yrs – as Glenday, 2007 indicates growth of 50-65yrs) Glenday (2007) provided values between 1.14 and 1.84 tC/ha/yr) 0.56 – 1.18 (subtropical dry forest in Africa <20yrs) Thicket/ dense bush Primary (undisturbed) 0 Glenday, 2007 0.42 – 0.85 (subtropical dry forest in Africa >20yrs) Secondary (disturbed) 1.14 Glenday, 2007 0.56 – 1.18 (subtropical dry forest in Africa <20yrs) Woodlands Primary (undisturbed) 0 Glenday, 2007 0.42 – 0.85 (subtropical dry forest in Africa >20yrs) Secondary (disturbed) 0.9 Scholes & Walker, 1993 Hoffman & Franco, 2009 0.56 – 1.18 (subtropical dry forest in Africa <20yrs) Table 6.21: Root:shoot ratios for the forest land categories. 287 Land class Root:shoot ratio Reference Other verification IPCC default Indigenous forest 0.27 Glenday, 2007 Mangwale et al. 2017 FRA for South Africa (FAO, 2021) provided a value of 0.25 0.24 – 0.56 (subtropical dry forests, IPCC 2006) Thicket/dense bush 0.38 Glenday, 2007 Mills et al., 2005 Powell, 2009 Mills & Cowling, 2006 0.24 – 0.56 (subtropical dry forests, IPCC 2006) Woodlands 0.25 FAO, 2021 DEA, 2015 DEFF, 2020 For plantations a Tier 2 method is applied to calculate biomass gains. The plantation area provided in the land cover datasets is overestimated according to the FSA data, therefore in the calculation files a correction was made so that the FSA plantation area (FSA, 2019) was used for calculating gains. Any excess mapped plantation area was allocated to woodlands. It should be noted that data for 2020 onwards was not available at the time of completing this inventory and these years were extrapolated using the IPCC extrapolation technique. The data will be corrected in future inventories once the data becomes available. MAI curves were obtained from Forestry SA. The MAI at rotation age for each of the products (sawlogs, pulp, mining timber, poles and other uses) per each of the 5 plantations species (Pines, E.grandis, other Eucalyptus species, Wattle and other hardwood species) were weighted based on the area under production for each species and product. The overall MAI therefore varied slightly each year but data for 2022 is shown in Table 6.22. The BCEFs values were calculated by using the equations given in Dovey et al. (2021) which estimates the BCEFs from the volume data. Stock volumes (m 3 /ha) were determined from the MAI for each age class multiplied by the age of the stand. The BCEFi values were derived from the BCEFs data by analysing the 2006 IPCC Guideline default values and applying a ratio for BCERi to BCEFs. For softwood the BCEFi was determined to be 78% of the BCEFs, while for the other species it was 60%. The area weighted BCEFs and BCEFi for 2022 are provided in Table 6.22 but the values did vary slightly from year to year due to the changes in the areas of the different species and the areas of each age class. The root to shoot ratio was then applied to include the below-ground biomass. Table 6.22: Factors applied for plantation data in 2022 with IPCC default values for comparison. Softwoods (e.g. Pines) Eucalyptus grandis Other Eucalyptus sp. Acacia sp. (Wattles) Other hardwood sp. 288 Mean annual increment (m3/ha/yr) Sawlogs 15.2 17.5 17.8 17.8 Pulp 16.6 17.9 18.4 9.8 18.9 Mining timber 18.4 18.4 9.3 18.4 Poles 17.0 17.9 15.4 9.3 18.9 Weighted avg 15.5 17.9 18.3 9.9 18.5 2019 Refinement default values 12 - 18 18 - 24 12-14 (general) 22-28 (E.nitens) 10 - 12 BCEFs Weighted avg 0.924 0.997 1.053 1.046 0.801 2006 IPCC default (temperate) 0.6 – 1.0 0.8 – 2.6 0.8 – 2.6 0.7 – 1.9 0.7 – 1.9 BCEFi Weighted avg 0.721 0.598 0.632 0.628 0.481 2006 IPCC default (temperate) 0.6 1.3 1.3 0.9 0.9 BCEF R Weighted avg 0.595 0.713 0.705 0.990 0.671 2006 IPCC default (temperate) 0.83 1.89 1.89 1.55 1.55 Ratio of below- ground biomass to above ground biomass 0.28 0.24 0.24 0.28 0.26 Carbon losses Harvest losses Loss of carbon from harvested wood was calculated for plantations only and followed the IPCC equation 2.12. Harvested wood data was obtained from Timber Statistics report from the Department of Forestry at DFFE and from Forestry SA (FSA, 2019). The industry conversion factors provided by FSA were used to convert between tonnes and m 3 . The BCEF R (Table 6.22) were determined from the relationship between BCEF R and BCEFi provided in IPCC 2006 Guidelines. The harvested wood included the fuelwood and charcoal as it is all removed as whole trees. All losses due to harvesting were allocated to Forest land remaining forest land as it was assumed that recently converted land would not be harvested due to the longer harvest cycle. Fuelwood collection losses Loss of carbon from fuelwood removals was calculated using IPCC equation 2.13 (IPCC 2006 Guidelines). There is very little information on how this amount is split between the various vegetation types, therefore, the whole amount was allocated to woodlands/open bush with no removal from forests and thickets. All losses due to fuelwood collection are 289 allocated to the Forest land remaining forest land as there was insufficient data to provide a split on the losses between remaining and converted lands. The amount of fuelwood collected was determined from the number of households using fuelwood for cooking and heating (Statistics SA, 2021). It was assumed that there was overlap between the houses that use wood for heating and cooking and so the higher value (households for heating) was taken to be the total number of households using wood. An average household fuel wood consumption rate of 3.5 t per household per year (Shackleton et al., 2022; Twine and Holdo, 2016) was applied. This may be overestimated as it is indicated that the households that use wood but also have electricity use an average of 2.9 t per household per year. Data on which households have both electricity and fuelwood was not available but can be investigated further in the next inventory. Some of the fuelwood comes from plantations and this removal was already accounted for under harvested wood, therefore the fuelwood and charcoal removals from plantations was subtracted from the determined fuelwood consumption. In a review it was suggested that losses due to charcoal production also be included. There is no national data on charcoal production, therefore the charcoal production data from FAOStat was utilised. The wood consumption in m 3 was determined based on the assumption that 9 tons of wood are required to make 1 ton of charcoal (Garland et al., 2015). This consumption was then added to the fuelwood consumption. Some fuelwood would also be coming from the harvesting of alien invasive species, however this is another complex issue which needs to be considered for incorporation in future inventories. A wood density of 0.65 t dm/m 3 (Glenday, 2007) was applied to convert the volume to mass. The fuelwood consumption numbers are within the range of the value provided by the FAOStat and a UNEP report (UNEP, 2019). The household fuelwood consumption estimates showed a slight increase between 2000 and 2004, but then decline to 2016. The decline is attributed to the increased electrification, however from 2017 onwards household wood consumption starts to stabilise as even households that have electricity continue to use wood. There is an increase in charcoal production in 2003 and again in 2018 which leads to an increase in total fuelwood consumption data in these two years. The charcoal data is from FAO and, other than these two increases, remains level in the in-between years. These increases will need to be investigated in future and a better source of country specific charcoal production data needs to be identified to assist in the interpretation of the data. Disturbance losses The loss of carbon from disturbance was calculated following 2006 IPCC Equation 2.14. Only disturbance losses for fires was included, except for forest plantations where other disturbances were also included. 290 For fire disturbance losses the burnt area data is determined as described in section 6.1.3.6. Fire disturbance included both controlled and wildfire as there was no data to enable the split between the two. Burnt area is provided for the total area of each land class and in order to split this into land remaining land and land converted to forests the fraction of burnt area was calculated and then applied to the land remaining and the converted land areas. The above-ground biomass (B W ) of each forest land category was determined from published literature and are provided in Table 6.23. Literature provided biomass data for various forest types and the forest area data from the Vegetation Map of South Africa (Mucina and Rutherford, 2011) enabled an area weighted above ground biomass value to be determined for indigenous forests. Thickets have been shown to be degraded with degraded thickets having a lower carbon biomass. To incorporate this into the biomass estimates the fraction of thicket area that was intact, heavily degraded and moderately degraded was taken from Lloyd et al. (2002). Literature provided data on intact and degraded thicket biomass data and the degraded values were applied to the heavily degraded category, while the biomass for the moderately degraded area was taken to be an average of the intact and heavily degraded value. In the SANLC maps mangroves are included under thickets and biomass data was taken from Johnson eta al. (2020) and Steinke et al. (1995). An area weighted above ground biomass values was then determined for the thicket category. For Woodlands the SANLC data includes two classes, namely dense forest and woodland, and open woodland. Literature data was obtained for each category and an area weighted above ground biomass values was determined. The above-ground biomass data for forest plantations was determined from the age class and MAI data provided by Department of Forestry and the Forestry SA (FSA, 2019). This was combined with BCEFs data to estimate above ground biomass for each species each year. An area weighted biomass value was calculated, and this value varied slightly across the years due to changes in species composition and age structure. Table 6.23: Above ground biomass for the various forest land categories. Forest land category Data applied in the inventory IPCC default values Above ground biomass (t dm/ha) SD Reference Indigenous forests 175.1 (Southern Afrotemperate) 162.0 (Northern Afrotemperate) 253.5 (Southern Mistbelt) 218.8 (Northern Mistbelt) 103.4 (Scarp) 175.6 (Coastal) 80.0 (Sand) Adie et al. 2013 Mensah et al. 2016 Glenday, 2007 35.1 – 65.2 (subtropical Africa, IPCC 2019 Refinement) 20 – 200 (Subtropical Africa, IPCC 2006) 291 178.0 (Weighted average) 57.9 Thickets 59.3 (Intact thickets) 17.6 (Degraded thickets) 121.2 (Mangroves) 28.2 (Weighted average) 12.2 Glenday, 2007 Van der Vyver et al., 2013 Powel, 2009 Van der Vyver & Cowling, 2019 Johnson et al. 2020 Steinke et al. 1995 Woodlands 39.7 (Dense forest & woodland) 21.7 (Open woodland) 27.2 (Weighted average) 3.3 Glenday, 2007 Mograbi et al. 2015 Lembani, 2018 Wessels et al. 2013 Colgan et al. 2012 Scholes & Walker, 1993 Nickless et al., 2011 Cresswell et al., 1982 Shackleton & Scholes, 2011 Rutherford, 1079 Woomer, 1993 Gander, 1994 Plantations 95.3 – 103.2 (Softwoods) 56.4 – 75.1 (E.grandis) 54.0 – 63.1 (Other Eucalyptus) 45.5 – 57.8 (Wattle sp.) 103.2 – 147.1 (Other hardwoods) 76.5 – 83.2 (Weighted average) Calculated from Timber Statistics Reports (DAFF) and Forestry SA data (FSA, 2019) 15 – 70 (subtropical Africa, IPCC 2019 Refinement) 10 – 150 (Subtropical Africa, IPCC 2006) There is no country specific data on the fraction of biomass lost (fd) during the fire disturbance, so the combustion fractions provided by 2019 Refinements of the 2006 IPCC Guidelines were used as a proxy. For indigenous forests and thickets the value of 0.45 for all other temperate forests was applied, while for woodlands the value was 0.56 (average of early and mid-season burns for savanna woodlands). The fraction of biomass burnt for plantations was determined from actual forestry data (FSA, 2019; DAFF, 2021). This provided data on the area damaged during fire and other disturbances and the extent of the damage. Forest plantations were therefore the only category for which other disturbances losses were included. Disturbance matrices were developed for fire and other disturbances for both hardwoods and softwoods. FSA data reports on the severity of the disturbance, therefore disturbance matrices were developed for disturbances where there was (a) slight damage, (b) serious damage but where wood was salvaged and (c) severe damage where all biomass was lost. For slight damage it was assumed that half of the non-utilisable biomass would be burnt. The non- 292 utilisable component was determined from the Dovey et al. (2021) equation for hardwoods and softwoods. In the case where there was serious damage but wood was salvaged it was assumed that all the non-utilisable biomass and an additional 10% was lost, while for severe damage with no recovery it was assumed all biomass was lost. The overall average fraction (fd) of plantation biomass lost to fires and other disturbances was 0.661 and 0.363, respectively. Losses due to disturbance were calculated for both the Forest land remaining forest land and land converted to forest land by applying the percentage disturbance area to each of the land sub-categories. Gains and losses due to land conversions The 2006 IPCC Guideline equations 2.15 and 20.16 were utilised for estimating emissions from biomass changes on converted land. The conversions also included conversions between forest types but then these losses were included under the “land remaining” category. The biomass changes in land converted to forest lands involves the determination of gains and losses, but also initial biomass changes due to the conversion. Biomass gains and losses for land converted from other land types to forest lands are calculated as for forest lands remaining forest lands (described above). Initial change in biomass carbon stocks on the converted land is added to the equation. The carbon stocks on the land before conversion were determined from above- and below-ground biomass data provided in the relevant land category sections. Land being converted to plantations is assumed to be cleared and therefore the biomass values immediately after conversion were assumed to be zero. For land being converted to the natural forest land classes (indigenous forests, thickets and woodlands) it was assumed that there is no clearing and that the biomass slowly increases, at the annual growth rate for that forest, from the original land carbon stock to the final land carbon stock. 6.4.2.2 Deadwood and litter Land remaining A Tier 1 approach is applied to deadwood and litter for forest land where it is assumed there is no change in DOM in forest land remaining forest land. Change, however, did occur where one forest type was converted to another and these changes were included under “land remaining” but were estimated as described for land conversions. Land conversions For land that was converted to a forest land the initial DOM is given in the relevant land category section, and the final DOM for the forest land is shown in Table 6.24. Changes in DOM due to land conversions were estimated using 2006 IPCC Guidelines equation 2.23. 293 If there is a gain in DOM this is assumed to occur over the 20-year default period, while losses are assumed to occur within the year of conversion. Table 6.24: Deadwood and litter biomass for forest lands. Data applied in the inventory Comparison with other data DOM (t C/ha) Reference DOM from updated NTCSA (t C/ha) 2006 IPCC Guidelines (t C/ha) Indigenous forests Deadwood: 6.37 Litter: 3.82 Glenday, 2007 (Area weighted average of coastal scarp, coastal lowland and dune forest) 4.67 Broadleaf litter: 2 – 3 Needleleaf litter: 4.1 (Subtropical values) Thickets Deadwood and litter (intact): 4.52 Deadwood and litter (degraded): 1.17 Weighted average: 1.35 Glenday, 2007 Mills et al., 2005 Powell, 2009 Van der Vyver, 2018 2.34 Woodlands Deadwood: 1.00 Litter: 2.10 Glenday, 2007 (average of wooded grassland and disturbed woodland) 1.01 Plantations Litter: 2.38 Dames et al., 1998 2.26 6.4.2.3 Mineral soils Soil emissions were calculated as explained in section 6.1.3.3. As soils are not a key category the 2006 IPCC Guideline default stock change factor of 1 was applied for all forest categories. 6.4.2.4 Non-CO 2 gases from biomass burning Emissions from biomass burning were determined from burnt area data (see section 6.1.3.6), fuel load, combustion factor and an emission factor. The fuel load was the above- ground biomass and the DOM data for the relevant forest category, while the combustion factors were sourced from the 2006 IPCC Guidelines Table 2.6. The all “other” temperate forest value was applied to indigenous forests and thickets, while the average of the early and mid/late season factors for savanna woodlands was applied to the Woodland category. The fd factor for fire losses in plantations (described in section 6.4.2.1) was used as the combustion factor for forest plantations. Emission factors were taken from 2006 294 IPCC Guidelines Table 2.5. The extra tropical forest values were applied to indigenous forests, thickets and plantations, while the savanna values were applied to Woodlands. 6.4.3 Uncertainty Activity data uncertainty Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). The mapped areas of indigenous forests, thickets, woodlands and plantations have uncertainties of ±17%, ±22%, ±26% and ±6%, respectively. An additional 5% was added to this for indigenous forests, and 10% for woodlands and thickets to account for uncertainty on the corrections. The uncertainty for converted areas was taken to be the combined uncertainty (applying the propagation of errors as indicated in 2006 IPCC Guidelines) for the two land areas. The results of this approach indicate the converted lands have a slightly higher uncertainty than the individual land types, and this is considered to be reasonable. Harvested wood uncertainty is around ±2% (expert opinion) as the forestry companies measure the harvested wood so it is fairly accurate data. The uncertainty lies in whether all the harvested wood is accounted for as not all small growers report to Forestry SA. The fuelwood removal data is very uncertain as it was determined from household statistics on number of households using wood (±5.6% uncertainty for rural households and ±32% for urban households based on Shackelton et al., 2022), a single wood density value (±5% based on data from IPCC, 2006) and an average household wood consumption value (±40% based on Shackelton et al., 2022). The burnt area data determined from MODIS collection 6 has an uncertainty of <6% (Boschetti et al., 2021) and the soil maps are estimated to have an uncertainty of ±10%. The overall activity data uncertainty for forest land remaining forest land was estimated at ±22.8% and ±17.3% for CO 2 emissions and non-CO 2 emissions, respectively. For land converted to forest land the uncertainty estimates were ±30.8% and ±26.7%, respectively. Emission factor uncertainty The uncertainty on biomass gains was estimated from uncertainties provided in the literature scientific literature mentioned in Table 6.20 on carbon accumulation and in Table 6.21 on root to shoot ratios. The BCEF were assumed to have the same uncertainty as the biomass accumulation data. Carbon fraction uncertainty is ±6% (IPCC, 2006). In terms of carbon losses, the wood density value is estimated to have an uncertainty of around 5% based on data provided in IPCC 2006. Since the DOM and SOC data have greater uncertainty than the biomass data an uncertainty of ±20% is assigned to these categories. The CO 2 emission factor uncertainty for forest lands remaining forest land was 295 estimated at ±57%, while for land converted to forest lands it was ±62%. For CH 4 and N 2 O uncertainties were ±35% and ±37% respectively for forest land remaining forest land. 6.4.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however some corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets were consistent through the time-series. 6.4.5 Category specific QA/QC and verification All general QC listed were completed for this category. Land areas were checked. Carbon emission factors were compared to literature, and to IPCC values. Where possible outputs were compared to the NTCSA (DEA, 2015; DEFF, 2020). Data could not be compared at the land class level as the land class information in the DEFF (2020) report was based on biomes as opposed to the land categories in the SANLC, but the total data for the various biomass pools was compared and shown to be similar in range. Plantation data Plantation area and fuelwood consumption data was compared to FAO data. Plantation emissions were estimated with the Stock-Difference approach for verification. Age class data for the 5 plantation species groups (Pines, Eucalyptus grandis, Other Eucalyptus species, Acia species, and Other hardwood species) was obtained from DFFE Timber Statistics reports. Data was available for 2021 to 2019. MAI curves for each species (obtained from Forestry SA) were used to determine MAI at each age. Stock volume was estimated from the MAI and age of the trees. The stock volume was combined with the BCEFs value (determine for each species and age class using the equation provided by Dovey et al., 2021) to estimate carbon biomass stock per age class. These were then added to determine the total carbon stock per year. As the carbon stock is determined from area, it does not actually account for losses due to damage (fire or other disturbances). Disturbances losses, as determined for the Gain- Loss method, were therefore subtracted from the total carbon stock to get the overall carbon stock per species per year. The change in carbon stock was then determined by subtracting the initial year carbon stock from the final year carbon stock. The area of plantations changes from year to year 296 therefore an area correction 23 , as described by IPCC, was applied. Ideally the area should be the same and then the carbon gains or losses due to land conversions in that area are accounted for. The area correction means that only the plantation area is being considered and so the gains and losses due to land conversion to/from other land classes is not accounted for. This is one area where the Gain-loss and Stock-difference approach applied in this inventory differ and is one of the reasons why the Gain-loss method was included in the inventory instead of the Stock-difference. The carbon change determined by the Stock-Difference method had a similar trend to the estimates based on the Gain-Loss method, except that there were some variations between years (Figure 6.7). It is noted that between 2008 and 2016 there seems to be a mismatch in the years but there is no clear explanation for this. The Stock-difference method shows a sharp decline in carbon in 2012 and increase in 2013 which is not seen in the Gain-loss data. These peaks are due to large changes in TUP area. Effectively the carbon losses in the Stock-difference method are based on the area of TUP, whereas in the Gain-loss method the amount of wood harvested is taken into account. The Harvested wood and the TUP area show differing patterns which causes much of the variation between the Gain-loss and Stock-difference method. Overall the two methods produce values in a similar range, but the differences will be explored further in the next inventory. 23 Area corrected carbon stock difference = ((Final carbon stock/Final plantation area) - (Initial carbon stock/Initial plantation area)) x Final plantation area. 297 Figure 6.7: Carbon change for plantations estimated by the Gain-loss and Stock-difference methods. 6.4.6 Category-specific recalculations The following main improvements led to a recalculation of the Forest land emissions: • Inclusion of 1990-2014 and 2018-2020 land change maps • Introduction of land area corrections for seasonal variation • Updated biomass data • Updated BCEF for plantations • Inclusion of age class data for plantations • Updated household fuelwood consumption factor • Use of country specific biomass data for fuel loads • Inclusion of non-CO 2 emissions from biomass burning The improvements produced the same trends as the previous inventory but increased the sink 3-fold comparing the data in 2020. In the previous inventory the indigenous forest was a small source in most years, but with the improvements in this inventory the sink increased 2-fold and it is now a small sink in half of the years. The biggest sink being in 2016, as reflected in the previous inventory. The biggest changes were in thickets, as 298 were to be expected, since thickets showed the largest changes and reclassification in the land cover maps. The improvements in the mapping and the updated biomass factors changed the original thicket source into a sink across all years. Thickets contributed 72.2% to the overall change in the 2020 estimate since the last inventory. The inclusion of country specific BCEF factors and age class data also led to an increased sink in the forest plantations. The enhancement of the sink was greater between 2003 and 2010. In the previous inventory plantations were seen to be a source of emissions in most years, while in this inventory plantations show small increases and decreases around zero between 2009 and 2019. The sink is shown to increase from 2019 onwards, however this is extrapolated data due to plantation data only being available until 2019. This will be corrected in the next inventory. Minor changes in biomass data were made to woodlands, therefore very little change was seen in woodlands, except the change in land cover map for 2018-2020 led to an increased sink in these years. In 2020 woodlands contributed 22.6% towards the 2020 recalculated value. 6.4.7 Category-specific planned improvements The most critical factor in the LULUCF sector is the land use change data, particularly relating to thickets. The general land use change improvement plan will contribute significantly to improving this aspect of Forest land. Specific improvements for Forest land are to further assess the Stock-difference data for plantations and consider this for inclusion in the next inventory. Further data on litter for the various forest categories will be sought to start improving the DOM pool data as all pools are highlighted as being key for forest conversion. Croplands (4.B) 6.5.1 Category description The Cropland category covers perennial crops (orchards and vineyards) and annual crops (pivot irrigation, non-pivot and subsistence crops). Reporting in the cropland category covers emissions and removals of CO 2 from mineral soils, and from above- and below-ground biomass and litter. Croplands include annual commercial crops, annual semi-commercial or subsistence crops, orchards, and viticulture. This category reports emissions and removals from the category cropland remaining cropland (cropland that remains cropland during the period covered by the report) and the land converted to 299 cropland category. Calculations are carried out on the basis of a 20-year transition period in that once a land area is converted it remains in the converted land category for 20 years. 6.5.2 Methodological issues In the previous inventory both the “Cropland remaining cropland” category and the “Land converted to Cropland” category were identified as key categories and carbon changes should, based on the IPCC decision tree, be calculated with a Tier 2 or 3 approach. For Cropland remaining cropland there is still insufficient data to determine biomass and litter changes for annual crops using a Tier 2 so a Tier 1 methodology was applied. For perennial crops country specific growth and loss factors were included to estimate biomass and litter emissions or removals with a Tier 2 approach. Soil emissions for all categories were estimated with a Tier 2 approach. The annual crops category has, therefore, been identified for improvement in the next inventory as a Tier 2 method should be applied to this category. Emissions and removals from all pools of the “converted land” category were estimated with a Tier 2 method which is suitable for this category. Where there were conversions between crop types (i.e. annual crop converted to a perennial crop) a Tier 2 “converted land” approach was applied but the emissions and removals were allocated to “Cropland remaining cropland”. 6.5.2.1 Biomass Carbon gains in land remaining A Tier 1 approach was applied where annual crops were assumed to be in equilibrium, so biomass gains and losses were only accounted for in perennial crops. For gains the area of perennial crops was multiplied by a biomass accumulation rate (Table 6.25). Carbon losses in land remaining Losses accounted were those from biomass burning and harvest losses. For harvest losses the maturity cycle was 30 years and so the area was divided by 30 to divide the crops into age classes. Every year it was assumed then that the oldest age class was harvested. The only burning that was taken to occur in croplands is the pre-harvest burning of sugarcane. Sugarcane, however falls under the annual croplands and since equivalence was assumed for annual crops “land remaining” these CO 2 emissions did not need to be included. The non-CO 2 emissions from sugarcane burning are included under “Burning of Agricultural Residues”. 300 Table 6.25: Biomass and growth factors for the various cropland categories. Inventory biomass data Comparison to other data sets Inventory growth data AGB (t dm/ha) SD Reference Root to shoot ratio SD Reference Total biomass (t dm/ha) IPCC default biomass (t dm/ha) NTCSA (t dm/ha) Biomass growth rate (t C/ha/yr) References Annual non- pivot crops 8.6 Area weighted average calculated from production, yield and harvest index data (DLRRD, 2022; Stats SA, 2021; DEA, 2015) 0.22 Varies slightly for each crop type (2019 IPCC 2006 Refinement) and this was an area weighted average 10.0 10.0 (annual crops, IPCC 2006 Table 5.9) 5.83 NA Annual pivot crops 9.94 Subsistence crops 6.5 Assumed to be 75% of commercial 7.8 7.20 Orchards 80.9 DEA, 2015 0.42 Scandellari et al., 2016 114.88 134.0 (temperate conditions, IPCC 2006 Table 5.1) 17.45 1.80 Carbon stock divided by 30 yr harvest cycle Vineyards 29.8 DEA, 2015 0.42 Zhang et al., 2021 42.32 12.89 0.66 301 Gains and losses due to land conversions For “land converted to croplands” the 2006 IPCC Guideline equations 5.2 and 5.3 were applied. A tier 2 approach was adopted where the initial change in biomass carbon stocks on the converted land is added to the equation. Carbon gains and losses are calculated as for Cropland remaining cropland, with only the woody perennial crops being included. Losses are also only for fire disturbance. The carbon stock change due to the removal of biomass from the initial land use (i.e. ΔC CONVERSION ) is only calculated for the area of lands undergoing a conversion in a given year, and in subsequent years it is zero. The carbon stocks on the croplands before conversion are the values associated with the initial vegetation type (Table 6.25). The biomass stocks immediately after conversion are assumed to be zero, as it is assumed all land is cleared before being converted to cropland. A default time period of 20 years is applied to carbon stock increases for perennial crops (i.e. it is assumed the carbon stock will increase from the initial carbon stock to the final carbon stock over a period of 20 years). For annual crop only 1 years’ worth of growth is accounted for, after which the biomass is assumed to be at equilibrium. This is also the case for losses in annual crops. Carbon losses are assumed to occur within the year of conversion. For perennial crop the loss occurs across the total converted land area instead of the annual converted area. Where there was conversion between the various crop types the same approach as for “land converted to croplands” was applied, however the emissions are allocated to the “land remaining land” category. 6.5.2.2 Litter Land remaining The Tier 1 assumption for the litter pool is that the stocks in Cropland remaining cropland are not changing over time, therefore litter changes are reported to be zero. This was applied to areas where the crop type did not change, however, there were conversions between the various crop types so changes in litter were calculated for these areas using 2006 IPCC Guidelines equation 2.23 (stock-difference approach). Converted land For areas that are converted from another land to cropland, the litter carbon stock data from the initial (see relevant land category sections) and final land types (Table 6.26) was in the 2006 IPCC Guidelines equation 2.23. The litter data was sourced from the NTCSA (DEFF, 2020) which provided spatial carbon density maps. These maps were intersected with the SANLC data to provide an area averaged estimate of the litter for each crop type. 302 If there was a gain in litter carbon this was assumed to occur over the 20-year default period, while losses were assumed to occur within the year of conversion. Table 6.26: Litter data applied in the inventory for the various cropland categories. Data applied in the inventory Litter (t dm/ha) Reference Annual non-pivot crops 3.40 NTCSA (DEFF, 2020) Annual pivot crops 2.72 Subsistence crops 1.36 Orchards 6.86 Vineyards 9.80 6.5.2.3 Mineral soils Changes in mineral soil carbon were determined as described in section 6.1.3.3. Cropland stock change factors for management, input and land use were determined from data reported in Moeletsi et al. (2015) and Tongwane et al. (2016). Management and inputs differ between the crop types, therefore the data was weighted by crop area. Crop areas were obtained from DALRRD, national statistics (Statistics SA), Crop Estimates Committee, and FAO. The management and input data was combined with the 2019 Refinement of 2006 IPCC Guidelines default stock change factors and climate data to determine the stock change factors for each crop type (Table 6.27). The 2019 Refinement data was selected as it has greater disaggregation than the 2006 Guidelines. These factors were assumed to remain constant throughout the time period due to a lack of annual management data. The values applied in the inventory for the various crop types and land types is presented in Table 6.6. Table 6.27: Stock change factors for the specific crops in South Africa. Crop type Stock change factors Land use (F LU ) Management (F MG ) Inputs (F I ) Dry climate Barley 0.76 1 0.98 Cabbage 1 1.04 Cotton 1 0.51 Drybeans 1.001 0.99 303 General vegetables 1 1.04 Groundnut 1 1.01 Legumes 1 1.01 Lucerne 1 0.92 Maize 1.003 0.95 Onions 1 1.03 Other field crops 1 0.96 Other fodder crops 1 0.53 Other oil seeds 1 0.99 Other summer cereal 1.003 0.95 Other winter cereals 1.001 0.99 Potato 1 1.04 Silage 1.002 0.95 Sorghum 1 1.00 Soybean 1 0.56 Sugarcane 1 0.96 Sunflower 1 0.87 Teff 1.002 0.53 Tobacco 1 1.02 Tomato 1 1.02 Wheat 1.001 1 General annual crop 1.003 0.95 Fallow land 1.13 0 Pasture 1.13 0.51 6.5.2.4 Non-CO 2 gases from biomass burning Non-CO 2 emissions from croplands were not included here but rather under “Agricultural burning of Crop Residues” as the only burning that was considered to occur in croplands was the pre-harvest burning of sugarcane residues. 6.5.3 Uncertainty Activity data uncertainty Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). Cropland areas are determined from crop boundary data so uncertainty is low (±1% on perennials and ±7% on annual crops). The uncertainty for converted areas was taken to be the combined uncertainty (applying the propagation of errors as indicated in 2006 IPCC Guidelines) for the two land areas. The results of this approach 304 indicate the converted lands have a slightly higher uncertainty than the individual land types, and this is considered to be reasonable. The soil maps are estimated to have an uncertainty of ±10%. The overall activity data uncertainty for cropland remaining cropland was estimated at ±9.9%. For land converted to cropland the uncertainty was estimated at ±19.8%. Emission factor uncertainty Biomass data was determined from actual yield and production data from commercial crops so has a small uncertainty for the individual crops (estimates at ±5%) but the average value overall crops had a slightly higher uncertainty (±10%). Litter and SOC data have greater uncertainty than the biomass data, particularly since they were derived from the NTCSA overlay, and therefore an uncertainty of ±20% was assumed for these categories. Emission factor uncertainty for CO 2 was estimated at ±22% for croplands. 6.5.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets for Croplands were consistent across the time-series. 6.5.5 Category specific QA/QC and verification The cropland planted area from agricultural statistics, which reports commercial crops only, was compared to the pivot and non-pivot annual crop area from the LC maps and it was found that the planted area (5.7 million ha in 2020) was much less than the total cropland area reported in the SANLC (11.5 million ha in 2020). On average the agricultural abstract area is 50% of that reported in the SANLC. The arable land area report by StatsSA Agricultural 2017 census was 7.6 million ha which is closer to the area report by the agricultural abstracts. The area reported by FAO very closely matches what is reported in the agricultural abstracts. On the other hand, the SANLC data is developed from crop boundary data and the SANLC crop area data is shown to have an uncertainty of 7% which is the second lowest (after perennial crops) of all the land types. The difference will need to be investigated in further detail with the DALRRD and will be incorporated as part of the land change improvement plan. Dry mater yield calculated from area and production data were compared to estimates from the literature and in most cases the data matched well. The calculated yield values seemed to be overestimated for vegetables compared to the literature, but this may have been because the various vegetables were grouped together thereby creating high 305 uncertainty for this category. In future, once data becomes available, the different types of vegetables could maybe be considered. Country specific planted area, production and yield data were also compared to FAOStat data for South Africa. 6.5.6 Category-specific recalculations The following main improvements led to a recalculation of the Cropland emissions: • Inclusion of 1990-2014 and 2018-2020 land change maps • Introduction of land area corrections for seasonal variation • Updated biomass data (making use of harvest index data) • Inclusion of a weighted average biomass accumulation rate which varied across the time-series due to variations in yield • Inclusion of below-ground harvest losses • Increase of harvest cycle from 25 to 30 years for orchards and vineyards due to updated information • Assumption that no biomass burning occurs in agriculture except pre-harvest burning of sugarcane. These emissions were not included here but rather under burning of agricultural residue. The improvements led to a doubling of the Cropland source between 2000 and 2014. Between 2015 and 2018 the source peaked around 12 000 Gg CO 2 eq, but then declined again to levels shown between 2000 and 2014. The incorporation of the different land change maps was responsible for this change between 2015 and 2018. The main contributor to this source was an increase in conversion of perennial crops to annual (non-pivot) crops and the conversion of forest lands (mainly woodlands and plantations) to annual and subsistence crops. Subsistence crops and annual non-pivot crops were found to be a source of emissions rather than a sink, while orchards were found to be a smaller sink. These were the main changes contributing to the increased source in Croplands. 6.5.7 Category-specific planned improvements The inventory will be improved over time by sourcing more crop specific biomass data. The management system classification for the various crops will be improved by following the decision tree given in IPCC 2006 Guidelines, chapter 5, figure 5.1. Further the planned land change improvement plan will also lead to improvements for Croplands. 306 Grasslands (4.C) 6.6.1 Category description The Grassland category includes all grasslands, managed pastures and rangelands. The IPCC does recommend separating out improved grasslands so an attempt was made in this inventory to include improved and degraded grasslands. This section deals with emissions and removals of CO 2 in the biomass, litter and mineral soil carbon pools. Estimates are provided for Grasslands remaining grasslands and land converted to grasslands. CO 2 emissions from biomass burning of grasslands were not reported since emissions are largely balanced by the CO 2 that is reincorporated back into the biomass via photosynthetic activity. Emissions were only reported for the change area in the first year of conversion. Biomass burning CH 4 and N 2 O were included here instead of being included under the Biomass Burning category (3C1) under Aggregated and non-CO 2 emissions (3C) in Agriculture as was the case in the previous inventory. This is to bring the inventory into alignment with the CRT tables required for reporting under the ETF. 6.6.2 Methodological issues A Tier 1 approach was applied for Grasslands remaining grasslands. In the previous inventory this category was identified as a key category in the level assessment (at the bottom of the list) but not in the trend assessment. According to the methodology decision tree (2006 IPCC Guidelines, Figure 2.2) data should be collected to apply a Tier 2 for key categories. For grasslands this would require information on grassland management (such as degraded area, degree of degradation, area of improved grassland) and there is currently no sustainable source for this data, therefore the Tier 1 approach was applied. 6.6.2.1 Biomass Carbon gains in land remaining According to the IPCC Tier 1, the change in biomass is only estimated for woody vegetation because for annual grasses the increase in biomass stocks in a single year is assumed to equal the biomass losses in that same year. For low shrublands the accumulation rate is 1/20 of the carbon stock and therefore for “land remaining” it was assumed that this category has reached its equilibrium and therefore there is no more growth. The rule of equivalence was therefore also applied to low shrublands. 307 Carbon losses in land remaining Only disturbance losses due to fire (wildfire and controlled fire) were included as there was no data on other disturbances. The carbon losses in annual grasses and low shrublands under “land remaining” was not reported, as the carbon released during combustion is assumed to be reabsorbed by the vegetation during the next growing season. Where there was land-use change between the grasslands and low shrublands, carbon stock changes were reported under Grasslands remaining grasslands. Fire disturbance losses were only accounted for in land converted to low shrubland using 2006 IPCC Guideline Equation 2.14. Above-ground and below-ground biomass were included in these losses and the fraction of biomass lost due to fire were taken to be the same fraction as the combustion factor (Table 6.31). Gains and losses due to land conversions Land converted to grassland was identified as a key category in the previous inventory with the level and trend assessment and therefore a Tier 2 approach (2006 IPCC Guideline equations 5.2 and 5.3) was applied. For land converted to grasslands only the biomass increase and losses for shrubs were included for the annual area undergoing change, while in annual grasslands carbon stocks were assumed to be in balance and not included in the annual gain calculation. Converted lands remain in the converted category for a period of 20 years. Carbon gains and losses were calculated for the woody low shrubland component. The carbon stock change due to the removal of biomass from the initial land use (i.e. ΔC CONVERSION ) was only calculated for the area of lands undergoing a conversion in a given year, and in subsequent years it was zero. The biomass values for the initial land types are given in the relevant land category sections, while biomass data for the grassland categories are provided in Table 6.28. For low shrublands, which consist of fynbos, succulent karoo and nama karoo vegetation, biomass data was obtained from the literature and then an area weighted average was obtained based on area data provided in the SANLC maps (15% fynbos, 17% succulent karoo, 43% nama karoo and 25% other shrublands). It was assumed that only croplands and plantations were cleared before being converted to a grassland, while all other conversions were slow transitions and not abrupt changes. 6.6.2.2 Litter Land remaining The Tier 1 assumption for the litter pool is that the stocks in grasslands remaining grasslands are not changing over time, therefore litter changes are reported to be zero. 308 This was applied to areas where the grassland category did not change, however, there were conversions between the various grassland categories so changes in litter were calculated for these areas using 2006 IPCC Guidelines equation 2.23 (stock-difference approach). Converted land For land converted to grasslands the changes in litter were determined from the litter data in the various land types (see the relevant land section) with the 2006 IPCC Guidelines equation 2.23. the litter stock in grasslands is provided in Table 6.29. As with the cropland litter data, this litter biomass data was sourced from the NTCSA (DEFF, 2020). It is assumed that the change occurs slowly over the 20-year default transition period. 309 Table 6.28: Above ground biomass factors for grassland categories. Data applied in the inventory IPCC default AG biomass (t dm/ha) SD Reference Root to shoot ratio SD Reference Total biomass (t dm/ha) Grasslands 2.0 (Average) 0.24 Snyman, 2006 Snyman & Fouche, 1991 Snyman, 2005 Mills et al., 2005a O’Connor et al. 2001 Gander, 1994 2.6 Snyman, 2005 6.7 6.1 (warm temperate dry, IPCC 2006) Low shrublands 8.7 (Weighted average) 0.61 Milton, 1990 Rutherford and Westfall, 1986 Rutherford, 1978 Van Wilgen, 1982 Mills et al, 2005a Anderson et al., 2010 Werger and Morris, 1991 O’Farrell, 2005 0.8 Mills et al., 2005a Malan and Snyman, 2008 15.8 NA Eroded land 0 Assumed not to have biomass NA 0 NA 310 Table 6.29: Litter data applied in the inventory for the grassland categories. Data applied in the inventory Litter (t dm/ha) Reference Grasslands 1.55 NTCSA (DEFF, 2020) Low shrublands 2.72 Eroded land 0 Assumed to be zero 6.6.2.3 Mineral soils A value of 1 was applied as the carbon stock change factor for Grassland. As in the previous inventory improved and degraded grasslands were incorporated. The 2018 and 2020 land cover maps do not have any division for grasslands in term of degradation, however the land cover maps for 1994/95 (Fairbanks et al., 2000) had degraded and improved lands incorporated. These maps indicated that 0.45% of grasslands were improved. Matsika (2007) researched degradation in grasslands and showed that 26.7% of grasslands had low degradation, 58.7% moderate degradation and 14.6% had high degradation. Unfortunately, spatial data for this could not be incorporated due to not all the data being available and also the maps were all for different years and scales making it hard to combine. Since the data was not spatial the percentage improved and degraded from Matsika (2007) was combined with the IPCC default stock change factors to obtain weighted average management stock change factor for grasslands (Table 6.30). The grassland management data is only once-off data therefore it was assumed, for now, that the amount improved and degraded has remained constant over the whole time-series. Table 6.30: Stock change factors for grasslands in South Africa. Grassland type Stock change factors Land use (F LU ) Management (F MG ) Inputs (F I ) WTD climate Grasslands 1 0.85 1 Low shrublands 1 1 1 6.6.2.4 Non-CO 2 gases from biomass burning Emissions from biomass burning were determined from burnt area data (see section 6.1.3.6), fuel load, combustion factor and an emission factor. The fuel density (Table 6.31) 311 was determined from the above-ground biomass and litter in the various grassland categories. The combustion factor (C f ) for grasslands was determined from IPCC 2006 default values. Archibald et al. (2010) indicated that grassland burns are mainly in August with 80% of burning occurring at this time of the year. Therefore, the IPCC 2006 early and late season burn factors for sub-tropical grasslands were weighted based on this to get a weighted average C f for grasslands. For low shrublands, IPCC 2006 provided a factor for fynbos and then the general shrubland factor was applied to other shrublands. In this way a weighted average was obtained by using the areas data as indicated for biomass. Emission factors were taken from 2006 IPCC Guidelines Table 2.5. Table 6.31: Fuel density and combustion fractions for the various grassland classes. Vegetation class Fuel density (t/ha) Combustion factor Fuel consumption (t/ha) Value Source Value IPCC default Grasslands 3.5 0.88 Weighted average IPCC 2006 seasonal data 3.1 2.1 (early season burn) 5.2 (late season burn) Low shrublands 11.4 0.91 Weighted average 10.4 12.9 for fynbos 14.3 (all shrublands) 6.6.3 Uncertainty Activity data uncertainty Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). Grasslands have the highest uncertainty (55%) followed by shrublands (225) and eroded land (13%). An additional 10% was added to this to account for uncertainty on the corrections. The uncertainty for converted areas was taken to be the combined uncertainty (applying the propagation of errors as indicated in 2006 IPCC Guidelines) for the two land areas. The burnt area data determined from MODIS collection 6 has an uncertainty of <6% (Boschetti et al., 2021) and the soil maps are estimated to have an uncertainty of ±10%. The overall activity data uncertainty for grasslands remaining grasslands was estimated at ±25.8%, while for converted lands it was ±19.1%. Emission factor uncertainty 312 Uncertainty on growing stock was taken from the literature provided in Table 6.28 and were ±12%. Disturbance losses in industrialised countries have a default uncertainty of ±15%, so a ±20% uncertainty is assumed. Since the litter and SOC data have greater uncertainty than the biomass data an uncertainty of ±20% was assigned to these categories. Emission factor uncertainty for CO 2 was estimated at ±30.7%, while for CH 4 and N 2 O it was ±39% and 48%, respectively. The non-CO 2 emission factor uncertainties were derived from the IPCC 2006 default values (IPCC, 2006). 6.6.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however some corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets were consistent through the time-series. 6.6.5 Category specific QA/QC and verification The only category specific QA/QC checks that were undertaken for grasslands was the comparison of biomass data with the NTCSA (DFFE, 2020). The biomass data for low shrublands may be overestimated in the inventory as the values are higher than reported in the NTCSA, although they do still fall within the shrubland ranges reported in IPCC 2006. Further data will be required to improve this data. 6.6.6 Category-specific recalculations The following main improvements led to a recalculation of the Grassland emissions: • Inclusion of 1990-2014 and 2018-2020 land change maps • Introduction of land area corrections for seasonal variation • Updated biomass data • Inclusion of non-CO 2 emissions from biomass burning The improvements led to a reduced sink in the Grasslands category between 1990 and 2014, and this changed to a source from 2015 onwards. Part of this change was in land conversions, therefore improvements in other land category biomass data also contributed to changes in the Grassland category. Pure grasslands were indicated to be a source in this inventory rather than a sink (due mostly to the conversion of woodlands to grasslands), but the opposite was true for low shrublands. The largest contributor to the low shrubland sink was the conversion of bare ground to low shrublands. Both of these conversion categories, woodlands to grasslands and bare ground to shrublands, are 313 subjected to large seasonal variations. These may therefore be improved in future with the land change improvement plan. 6.6.7 Category-specific planned improvements Besides the planned improvements around the land cover maps and actual change detection (i.e. removal of seasonal variation changes), further biomass data for low shrublands will be sought to reduce the uncertainty on this dataset. Grasslands remaining grasslands are shown to be a key category and this category only includes the low shrubland data as pure grasslands are seen to be in equilibrium. Wetlands (4.D) 6.7.1 Category description Waterbodies and wetlands are the two sub-divisions in the wetland category and are defined in GTI (2015). Mangroves are not included under wetlands as these are within the thicket category. The wetland category is therefore only herbaceous (annual) vegetation. The wetlands are not separated into coastal or inland wetlands and since organic soils were assumed to be insignificant the wetlands were assumed to be on mineral soils. It is noted that this is a broad assumption and that a recent Blue Carbon Study (Raw et al., 2020) indicated there is a small amount of organic soils, this assumption and data will be updated in the next inventory. In this inventory the CO 2 emissions as well as the N 2 O and CH 4 emissions from wetlands were incorporated. Biomass burning CH 4 and N 2 O were also included here instead of being included under the Biomass Burning category (3C1) under Aggregated and non- CO 2 emissions (3C) in Agriculture as was the case in the previous inventory. This is to bring the inventory into alignment with the CRT tables required for reporting under the ETF. 6.7.2 Methodological issues Wetlands were not identified as a key category in the previous inventory, therefore a Tier 1 approach is suitable. 6.7.2.1 Biomass 314 Carbon gains and losses in land remaining According to IPCC Tier 1 gains and losses are in equilibrium in annual vegetation, so there are no changes in Wetlands remaining wetlands unless there was a change from a waterbody with no vegetation to a wetland which has vegetation. In this case gains and losses were included and estimated as for land conversions. Carbon gains and losses due to land conversions For land conversions a Tier 2 approach (2006 IPCC Guideline equations 5.2 and 5.3) was applied. For land converted to wetlands the annual wetland growth and losses are only accounted for on the annual change area, even though the area remains in the land converted category for 20 years. The carbon stock change due to the removal of biomass from the initial land use (i.e. ΔC CONVERSION ) is only calculated for the area of lands undergoing a conversion in a given year, and in subsequent years it is zero. It is assumed that only croplands are cleared before being converted to a wetland, while all other conversions are slow transitions and not abrupt changes. Wetland biomass data was sourced from the literature. An above ground biomass value of 14.2 t dm/ha were determined for salt marshes (Els, 2019) and 15.8 t dm/ha for macrophytes, reeds and sedges wetlands (Els, 2019; Glenday, 2007). A weighted average was determined (15.1 t dm/ha) by using an area based on the fraction of the two types of wetlands (DFFE, 2021). The root to shoot ratio of 0.96 provided in the IPCC Wetland Supplement (IPCC, 2014) was applied to determine below ground biomass. For carbon losses due to fire disturbance the fraction burnt was taken to be 0.7 from the 2006 IPCC Guidelines Table 2.6 for tropical wetlands. 6.7.2.2 Litter Land remaining The Tier 1 assumption for the litter pool is that the stocks in wetlands remaining wetlands are not changing over time, therefore litter changes are reported to be zero. This was applied to areas where the wetland category did not change, however, there were conversions between wetlands and waterbodies so changes in litter were calculated for these areas using 2006 IPCC Guidelines Equation 2.23 which is the same as the approach for land conversions. Land conversions 315 The litter stock for Wetlands was determined to be 2.34 t dm/ha. This was estimated from the overlay of the SANLC data with the NTCSA data (DFFE, 2020). For land converted to wetlands the changes in litter are determined from the litter data in the various land types (see the relevant land section) with the 2006 IPCC Guidelines equation 2.23. It was assumed that the change occurs slowly over the 20-year default transition period. 6.7.2.3 Mineral soils Changes in mineral soil SOC in Wetlands was calculated as described in section 6.1.3.3. 6.7.2.4 CH 4 and N 2 O emissions from wetlands CH 4 emissions from Wetlands were calculated following equation 5.1. of the IPCC Wetland Supplement (IPCC, 2014; Chapter 5) where the wetland area was multiplied by the 2006 IPCC Guideline default CH 4 emission factor of 235 kg CH 4 /ha/yr. This emission factor was much higher that the country-specific value 55.9 kg CH 4 /ha/yr provided by Kruger et al. (2012). The country study indicated that the values was lower than found in other studies and could be due to the time of the year. A study by Otter et al. (2000) provided an emission factor of 255 kg CH 4 /ha/yr which is much closer to the IPCC default value of 235 kg CH 4 /ha/yr. As the Kruger et al. (2012) did not see to be representative of all wetlands and the Otter et al. (2000) data being much higher the default IPCC value was selected until more data is collected to validate these emission factors. IPCC does not provide a method for estimating N 2 O emissions from Wetlands so the same equation as used for CH 4 was applied. The only emission factor found was that from Kruger et al. (2012). Even though the data from this study does not appear to representative of all wetlands it is the only data available thus the emission factor of 1.4 kg N 2 O/ha/yr (Kruger et al., 2012) but understanding that this value may be an underestimate. As more data becomes available this emission factor will be updated. 6.7.2.5 Non-CO 2 gases from biomass burning Emissions from biomass burning were determined from burnt area data (see section 6.1.3.6), fuel load, combustion factor and an emission factor. The fuel density (18 t dm/ha) was determined from the above-ground biomass and litter, while the combustion factor (C f ) was assumed to be the same as grasslands. Emission factors were taken from 2006 IPCC Guidelines Table 2.5 and the grassland values were applied. 6.7.3 Uncertainty Activity data uncertainty 316 Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). The burnt area data determined from MODIS collection 6 has an uncertainty of <6% (Boschetti et al., 2021) and the soil maps were estimated to have an uncertainty of ±10%. The overall activity data uncertainty for wetlands remaining wetlands was estimated to be ±11.6% and was ±21.5% for land converted to wetlands where the uncertainty was a combination of uncertainty of each land type. Emission factor uncertainty The uncertainty for biomass was estimated to be similar to Grasslands. Disturbance losses in industrialised countries have a default uncertainty of ±15%, so a ±20% uncertainty is assumed. Since the litter and SOC data have greater uncertainty than the biomass data an uncertainty of ±20% was assigned to these categories. Emission factor uncertainty for CO 2 was estimated at ±30%, while for CH 4 and N 2 O it was ±39% and 48%, respectively. The non-CO 2 emission factor uncertainties were derived from the IPCC 2006 default values (IPCC, 2006). 6.7.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however some corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets were consistent through the time-series. 6.7.5 Category specific QA/QC and verification No category specific checks were undertaken for Wetlands. 6.7.6 Category-specific recalculations The main improvements for the Wetlands category that led to recalculations for the time series were: • Inclusion of 1990-2014 and 2018-2020 land change maps • Updated biomass data • Updated CH 4 emission factor for emissions from saturated or flooded areas • Inclusion of non-CO 2 emissions from biomass burning The improvements caused a 4-fold increase in the sink and this was mainly due to the change in the CH 4 emission factor for wetland. The change in land cover areas and biomass data led to a reduction in emissions from Wetlands remaining wetlands, an 317 increase in emissions from the conversion of forest lands to wetlands, and a reduction in the sink due to the conversion of grasslands to wetlands. 6.7.7 Category-specific planned improvements There are no specific planned improvements for this category. Settlements (4.E) 6.8.1 Category description Settlements include all formal built-up areas, in which people reside on a permanent or near-permanent basis. It includes transportation infrastructure as well as mines. Changes in the extent of urban areas between 1990 and 2020 (increase of 6.7%) may not be as locally significant as expected as the settlements category includes peripheral smallholding areas around the main built-up areas; and these tend to be the first land- use that is converted to formal urban areas, before further expansion into natural and cultivated lands. Settlements were divided into: • Residential • Smallholdings • Commercial • Industrial and • Mines Residential and smallholdings were identified as either being vegetated (tree, bush or low shrub) or bare. Emissions and removals of CO 2 in the biomass, litter and mineral soil carbon pools for each of these categories was estimated, but dead wood was excluded due to insufficient data. Non-CO 2 emissions from biomass burning were also included for each category. Estimates are provided for both Settlements remaining settlements and land converted to settlements. 6.8.2 Methodological issues Settlements remaining settlements were identified as a key category in the trend analysis in the previous inventory. There is insufficient data to carry out a Tier 2 on Settlements remaining settlements, however a more detailed categorisation was included to try to include more detail and reduce uncertainty. 318 6.8.2.1 Biomass Carbon gains To include more detail into this category the detailed SANLC classes, which identified areas covered with tree, bush, low shrubland and bare ground, was used. The change in biomass was only estimated for woody areas because for annual grasses the increase in biomass stocks in a single year is assumed to equal the biomass losses in that same year. Biomass gains were estimated using 2006 IPCC Guidelines Equation 2.9. Even though an area may have been identified as having trees, it was assumed that the density was not as great as in woodlands. Therefore, the lower carbon biomass from shrublands was applied to areas that had trees. Area with shrubs were assumed to have biomass that is half that of the areas with tree. Areas identified as having low shrubs or grasses were allocated the biomass value from grasslands. The carbon accumulation rate for wooded areas was estimated from the biomass and a 20 year growth cycle. Bare areas did not have any biomass. Based on this biomass data and the areas from the SANLC 2020, weighted average biomass values were estimated for the 5 settlement classes (Table 6.32). The values were compared to the data derived from the NTCSA (DFFE, 2020) and were found to be lower than the NTCSA, however the report did indicate that the settlement data may be overestimated as the LiDAR had difficulty distinguishing vegetation from buildings. Carbon losses The carbon losses from fire disturbance in settlements was estimated with 2006 IPCC Guidelines Equation 2.14. Where there was land-use change between the settlement categories, carbon stock changes were reported under Settlements remaining settlements. Losses were estimated from biomass data and a fraction of vegetation that was burnt. This fraction was taken to be 0.74 which is an average of fraction lost for grasslands and woodlands as settlements contain both grass and wood components. Carbon gains and losses for land conversions Land converted to Settlements was estimated using a Tier 2 approach (2006 IPCC Guideline equations 5.2 and 5.3). For land converted to settlements only the biomass increase for shrubs were included for the annual area undergoing change, while in annual grasslands carbon stocks were assumed to be in balance and not included in the annual gain calculation. Converted lands remain in the converted category for a period of 20 years. The carbon stock change due to the removal of biomass from the initial land use (i.e. ΔC CONVERSION ) was only calculated for the area of lands undergoing a conversion in a given year, and in subsequent years it was zero. The biomass values for the initial land types are given in the relevant land category sections, while biomass data for the settlement categories are provided in Table 6.32. It was assumed that only croplands and plantations 319 were cleared before being converted to a grassland, while all other conversions were slow transitions and not abrupt changes. Table 6.32: Biomass growth and accumulation rates for the various settlement categories. Land class Biomass (t C/ha) Biomass accumulation rate (t C/ha/yr) AG Biomass (DFFE, 2020) (t C/ha) Residential 3.38 0.07 5.44 Smallholdings 4.00 0.11 4.77 Commercial 3.36 Non-woody so biomass accumulated in year 5.75 Industrial 3.36 6.23 Mines 0 0 2.53 6.8.2.2 Litter Land remaining The Tier 1 assumption for the litter pool is that the stocks in Settlements remaining settlements are not changing over time, therefore litter changes are reported to be zero. Land conversions Changes in litter were calculated using 2006 IPCC Guidelines equation 2.23 and data provided in Table 6.33. Litter stocks for other land classes are provided in the relevant land category sections of this report. As mines were assumed not to have any vegetation, there is also no litter. It was also assumed that change occurred slowly over the 20-year default transition period. Table 6.33: Litter data applied in the inventory for each of the settlement land classes. Litter (t dm/ha) Reference Residential 2.96 NTCSA (DFFE, 2020) Smallholdings 2.4 Commercial 2.35 Industrial 4.3 320 6.8.2.3 Mineral soils Changes in mineral SOC were determined using the methods described in section 6.1.3.3. The Settlement mineral soil carbon stocks were assumed equal to the reference values (i.e. the stock change factors for management and input are equal to 1). This factor was assumed to remain constant for the period 2000 to 2020, and this can be improved in future inventories if data becomes available. 6.8.2.4 Non-CO 2 gases from biomass burning Emissions from biomass burning were determined from burnt area data (see section 6.1.3.6), fuel load, combustion factor and an emission factor. The fuel density (18 t dm/ha) was determined from the above-ground biomass and litter, while the combustion factor (C f ) was assumed to be the same as for low shrublands and grasslands (Table 6.34). Emission factors were taken from 2006 IPCC Guidelines Table 2.5 and the grassland values were applied. Table 6.34: Fuel density and combustion fractions for the various settlement classes. Land class Fuel density (t/ha) Combustion factor Fuel consumption (t/ha) Value Source Value IPCC default Residential 5.73 0.89 Weighted average of low shrubland and grassland 5.10 2.1 – 14.3 for grasslands and shrublands Smallholding 6.03 0.89 5.47 Commercial 4.34 0.88 Same as grasslands 3.82 Industrial 6.29 0.88 Same as grasslands 5.54 6.8.3 Uncertainty Activity data uncertainty Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). The burnt area data determined from MODIS collection 6 has an uncertainty of <6% (Boschetti et al., 2021) and the soil maps were estimated (by expert opinion) to have an uncertainty of ±10%. The overall activity data uncertainty for settlements remaining settlements was ±38.0% and was ±105.3% for land converted to settlements where the uncertainty is a combination of uncertainty of each land type. The greatest uncertainty was land areas with residential areas having the highest uncertainty of ±21.7%, while smallholdings had an uncertainty of ±12.8%. Converted land obviously 321 had higher uncertainty that the land remaining land categories and the conversion of grasslands to settlements had the highest uncertainty (±67.7). Emission factor uncertainty Growing stock or gains were indicated to have a ±30% uncertainty for non-industrialised countries. Disturbance losses in industrialised countries have a default uncertainty of ±15%, so a ±20% uncertainty is assumed. Since the litter and SOC data have greater uncertainty than the biomass data an uncertainty of ±20% is assigned to these categories. Emission factor uncertainty for CO 2 was estimated at ±31%, while for CH 4 and N 2 O it was ±39% and 48%, respectively. The non-CO 2 emission factor uncertainties were derived from the IPCC 2006 default values (IPCC, 2006). 6.8.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however some corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets were consistent through the time-series. 6.8.5 Category specific QA/QC and verification No category specific quality checks were completed for Settlements. 6.8.6 Category-specific recalculations The main improvements for the Settlement category that led to recalculations for the time series were: • Inclusion of 1990-2014 and 2018-2020 land change maps • Inclusion of disaggregated settlement categories • Updated biomass data • Inclusion of non-CO 2 emissions from biomass burning The improvements caused the Settlements to change from a slight sink to a slight source of emissions. The updated biomass factors are the main reason for the reduction in the Settlement remaining settlement sink. Between 2015 and 2018 there was an increase in the conversion of woodlands to settlements and during this time the source increased 5- fold, but emissions returned to the 2014 levels from 2019 onwards. These changes were directly related to the change in the land use maps. 322 6.8.7 Category-specific planned improvements There are no specific planned improvements for this category. Other lands (4.F) 6.9.1 Category description Other land includes bare soil, rock, and all other land areas that do not fall into the other land classes. This category includes emissions and sinks for land converted to other lands. There are assumed to be no changes in the Other land remaining Other land category. For the land converted to other land category the biomass, litter and soil carbon changes are included. Burning was assumed not to occur on bare ground therefore no non-CO 2 emissions were included for this category. 6.9.2 Methodological issues 6.9.2.1 Biomass Carbon gains and losses in land remaining Bare ground is void of vegetation, therefore there are no gains or losses for Other lands remaining other lands. Carbon losses due to land conversions The change in carbon stocks in biomass due to land conversions was estimated following the Tier 2 approach (2006 IPCC Guideline equations 5.2 and 5.3). The carbon stock change due to the removal of biomass from the initial land use (i.e. ΔC CONVERSION ) is only calculated for the area of lands undergoing a conversion in a given year, and in subsequent years it is zero. The initial biomass carbon stocks are found in the relevant land category sections. 6.9.2.2 Litter Since there is no vegetation there is also no litter. On Other lands remaining other lands. Changes in litter were calculated using data provided in Table 6.33, as well as the litter stock for other lands (provided in the relevant land category sections) in the 2006 IPCC Guidelines equation 2.23. 323 6.9.2.3 Mineral soils Changes in mineral SOC were determined using the methods described in section 6.1.3.3. 6.9.3 Uncertainty Activity data uncertainty Uncertainty for the land areas were provided by the SANLC 2020 accuracy assessment (DFFE, 2021). The soil maps were estimated (by expert opinion) to have an uncertainty of ±10%. The overall activity data uncertainty for other lands remaining other lands was ±27.3% and was ±34.5% for land converted to other lands where the uncertainty is a combination of uncertainty of each land type. Emission factor uncertainty There was a ±15% uncertainty on the soil data for Other lands, but no uncertainty for biomass or DOM as there is no vegetation on these lands. 6.9.4 Time-series consistency The land change maps had some inconsistency in that different satellite data was used, however some corrections were made to bring these two datasets into alignment. The inconsistency was between 2014 and 2018. All other datasets were consistent through the time-series. 6.9.5 Category specific QA/QC and verification No category specific quality checks were completed for Other lands. 6.9.6 Category specific recalculations The main improvement to this category was the inclusion of 1990-2014, 2014-2018 and 2018-2020 land change maps. The recalculation of the emission estimates across the time-series led to a 27% increase in the source from this category between 2000 and 2014. Between 2018 and 2022 the emissions increased 3-fold compared to the previous inventory and this is due to the incorporation of the new land change maps. 6.9.7 Category specific planned improvements 324 There are no specific planned improvements for this category. Harvested wood products (3.D.1) 6.10.1 Category description Much of the wood that is harvested from forest land, cropland and other land types remains in products for differing lengths of time. This section of the report estimates the contribution of these harvested wood products (HWPs) to annual CO 2 emissions or removals. HWPs include all wood material that leaves harvest sites. 6.10.2 Methodological issues 6.10.2.1 Activity data All the data on production was obtained from Forestry SA (FSA, 2018) from 1990 (Figure 6.8), while export data was obtained from FAOSTAT. The production data was split into sawlogs and veneer logs, and pulpwood. Sawlogs and veneer data then had to be split into sawnwood and wood-based panels and this was done using the fraction sales of timber to sawn timber and panel products (FSA, 2019). Data was only available up until 2019 so the last few years were extrapolated using a linear function. 325 Figure 6.8: Production data from Forestry SA with comparison to FAO data. 6.10.2.2 Emission estimation method The HWP contribution was determined by following the updated guidance provided in the 2013 IPCC KP Supplement (IPCC, 2014). One of the implications of Decision 2/CMP.7 is that accounting of HWP is confined to products in use where the wood was derived from domestic harvest. Carbon in imported HWP is excluded. The guidelines also suggest that it is good practice to allocate the carbon in HWP to the activities afforestation (A), reforestation (R) and deforestation (D) under Article 3 paragraph 3 and forest management (FM) under Article 3 paragraph 4. For South Africa, there is insufficient data to differentiate between the harvest from AR and FM, it is conservative and in line with good practice to assume that all HWPs entering the accounting framework originate from FM (KP Supplement, Chapter 2, p 2.118). Equation 2.8.1 and 2.8.2 in the KP Supplement were applied to estimate the annual fraction of feedstock for HWP production originating from domestic harvest and domestically produced wood pulp as feedstock for paper and paperboard production. The resulting feedstock factors were applied to KP Supplement Equation 2.8.4 to estimate the HWP contribution of the aggregate commodities sawnwood, wood-based panels and paper and paperboard. First order decay 326 Transparent and verifiable data were available for sawnwood, wood-based panels and paper and paperboard, but no country-specific information for Tier 3 was available so a Tier 2 first order decay approach (Equation 12.1 in 2006 IPCC Guidelines) was applied to estimate the HWP contribution. As a proxy in the Tier 2 method it is assumed that the HWP pools are in steady state at the initial time (t 0 ) from which the activity data start. This means that as a proxy ΔC(t 0 ) is assumed to be equal to 0 (this was taken to be 1990 (S.Ruter, pers. comm.)) and this steady state for each HWP commodity category is approximated using KP Supplement Equation 2.8.6. 6.10.3 Uncertainty The activity data was obtained from Forestry SA from 1990 and is consistent over the time-series. Uncertainties for activity data and parameters associated with HWP variables are provided in the IPCC Guidelines (IPCC 2006, Volume 4, p. 12.22). Production and trade data have an uncertainty of ±50% since 1961, while the product volume to product weight factors and oven-dry product weight to carbon weight have uncertainties of ±25% and ±10%, respectively. There was also a ±50% uncertainty on the half-life values. 6.10.4 Time-series consistency All data sources were consistent across the time-series. 6.10.5 Category specific QA/QC and verification Forestry production data was compared to the data on FAOSTAT. Both data sets had similar trends and in some years the data sets matched. FAO data between 1990 and 2008 were generally slightly higher than the Forestry data and from 2010 onwards the FAO data was slightly lower. Overall, the country specific data was deemed to be more accurate. To verify the emissions from HWP the emissions were also calculated using the other 2006 IPCC methodologies, namely stock change, production, atmospheric flow and simple decay approach. The approach applied in this inventory is equivalent to a production approach. The Stock-change approach had exactly the same trend as the current inventory across the time series, but the method produced a sink that was twice as large as the current inventory (Figure 6.9). The Atmospheric approach produced a much larger sink, but again with the same trends. In this approach the sink was greater between 2000 and 2010, after which the sink was approximately 6-fold greater than the current inventory. The Production approach produced a greater sink between 2000 and 327 2009 (in between the stock change and atmospheric approach values) but matches the current values between 2009 and 2010. After 2010 the Simple decay approach produced values the same as the Stock change approach. The Simple decay approach produced the same values as the Production Approach. Figure 6.9: Comparison of HWP emissions estimated with the different IPCC methods. In all cases the 2006 IPCC approaches had much larger sinks between 2000 and 2010, but one of the reasons for this is that the FAO datasets for some of the required data (such as fuel wood and other industrial round wood) were not complete and had some gaps during this time period. The data for the various approaches is more aligned between 2010 and 2022. The differences are also related to the implementation of the steady state at the initial time (in 1990) in the current inventory method. The comparison suggests that the HWP sink could be underestimated, and this need further evaluation. This also supports the development of a project to conduct a more detailed assessment of the HWP pool. 6.10.6 Category specific recalculations 328 No recalculations were performed for this category. 6.10.7 Category specific planned improvements As was stated in the previous inventory a more detailed assessment of HWP is required to include a wider range of products. This has not yet been done as funds would need to be made available for such a study. This is also supported by the verification process which highlighted the possible underestimate of the HWP sink. References Adie H., Rushworth I., Lawes M.J., 2013. Pervasive, long-lasting impact of historical logging on composition, diversity and above ground carbon stocks in Afrotemperate forest. Forest ecology and management, 310: 887-895. Anderson P.M.L., Hoffman M.T., O’ Farrell P.J., 2010. Above ground perennial plant biomass across an altitudinal and land-use gradient in Namaqualand, South Africa. South African Journal of botany, 76: 471-481. Archibald S., Scholes R.J., Roy D., Roberts G., 2010. Southern African fire regimes as revealed by remote sensing. International Journal of wildland fire, 19: 861-878. Colgan M.S., Asner G.P., Levick S.R., Martin R.E., Chadwick O.A., 2012. Topo-edaphic controls over woody plant biomass in South African savannas. Biogeosciences, 9:1809-1821. DEA, 2015. Improvement of the Greenhouse Gas Emission Inventory for the Agriculture Sector, Pretoria: DEA. DEA, 2015a. 2013-2014 South African National Land-Cover Dataset, Pretoria: DEA. DEA, 2016. 1990 South African National Land-Cover Dataset, Pretoria: DEA. DEA, 2019. South African National Land Cover 2018 Report & Accuracy Assessment, Pretoria: DEA. DEA, 2019a. 2018 South African National Land-Cover Change Assessment, Pretoria: DEA. DEFF, 2020. National Terrestrial Carbon Sinks Assessment Technical Report. Pretoria: s.n. 329 DEFF, 2021a. GHG emissions and removals baseline: Scoping study for a blue carbon sinks assessment in South Africa, Pretoria: s.n. DFFE, 2021. South African National Land Cover 2020 Report & Accuracey Assessment, Pretoria: DEA. Dovey, S., du Toit, B. and Crous, J., 2021. Tier 2 above-ground biomass expansion functions for South Africn plantation forests. Southern Forests, 83: 69-78. Els, J., 2019. Carbon and nutrient storage of the Swartkops Estuary salt marsh and seagrass habitats. MSc, Nelson Mandela University, Port Elizabeth, South Africa. Fairbanks, D.H.K., Thompson, M.W., Vink, D.E., Newby, T.S., van der Berg, H.M. and Everards, D.A. 2000. The South African Land-cover Characteristics Database: A synopsis of the landscape. South African Journal of Science. FSA, 2019. South African Forestry and Forest Product Industry Facts 1980 – 2019, Forestry South Africa, KwaZulu-Natal. Gandar M., 1994. Afforestation and woodland management in South Africa. Afforestation and woodland management. Giglio, L., Boschetti, L., Roy, D., Humber, M. and Justice, C., 2018. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, Volume 217, pp. 72-85. Glenday, J., 2007. Carbon Storage and Sequestration Analysis for the eThekwini Environmental Services Management Plan Open Space System. Durban: eThekwini Municipality Environmental Management Department. Accessed 10 February 2020 at: http://assaf.org.za/files/2010/04/EESMPcarbonstock-report2006.pdf GTI, 2015. 1990 – 2013-14 South African National Land-cover change. Department of Environmental Affairs, Pretoria. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). ed. Japan: IGES. IPCC, 2014. 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands. Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M. and Troxler, T.G. ed. Switzerland: IPCC. IPCC, 2014a. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Chang. R.K. Pachauri and L.A. Meyer ed. Geneva, Switzerland: IPCC. IPCC, 2019. 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., 330 Ngarize, S., Osaka, A., Pyrozhenco, Y., Shermanau, P. and Federici, S. ed. Switzerland: IPCC. Johnson, J.L., Raw, J.L. and Adams, J.B., 2020. First report on carbon storage in a warm- temperate mangrove forest in South Africa. Estuarine, Coastal and Shelf Science, 235: 106566. Kruger J.P., Gerold G., Beckedahl H., Jungkunst H.F., 2012. Effect of environmental conditions on methane and nitrous oxide fluxes of two South African wetlands. Volume, 33:66-88. Lembani R.L., 2018. Assessing the effects of land use/land cover changes on carbon storage of natural forest ecosystems in Southern Africa: a remote sensing approach. Lloyd, J.W., van den Berg, E. & Palmer, A.R. 2002. Patterns of transformation and degradation in the thicket biome, South Africa. TERU report no: 39. University of Port Elizabeth, Port Elizabeth, South Africa. Malan, P.J. and Snyman, H.A., 2008. Dry matter production and partitioning of two palatable karoo shrubs. International Grassland Congress Proceedings. Mangwale, K., Shackleton, C. and Sigwela, A., 2017. Changes in forest cover and carbon stocks of teh coastal scarp forests of teh Wild Coast, South Africa. Southern Forests, 79(4), pp. 305-315. Mensah S., Veldtman R., du Toit B., Kakai R.G., Seifert T., 2016. Aboveground Biomass and Carbon in a South African Mistbelt Forest and the Relationships with Tree Species Diversity and Forest Structures. Forests, 7-79. Meraka Institute, 2019. Advanced fire information system, South Africa burnt area statistics. Pretoria, South Africa. Mills, A. a. C. R., 2006. Rate of carbon sequestration at two thicket restoration sites in teh Eastern Cape, South Africa. Restoration Ecology, 14(1), pp. 38-49. Mills, A., Cowling, R., Fey, M., Kerley, G., Donaldson, J., Lechmere-Oertel, R., Sigwela, A., Skowno, A. and Rundel, P., 2005. Effects of goat pastoralism on ecosystem carbon storage in semi-arid thichet, Eastern Cape, South Africa. Austral Ecology, Volume 30, pp. 797-804. Moeletsi, M.E., and Tongwane, M.I., 2015. Methane and Nitrous Oxide Emissions from Manure Management in South Africa. Animals, 5, 193–205. 331 Mograbi, P.J., Erasmus, B.F.N., Witkowski, E.T.F., Asner, G.P., Wessels, K.J., Mathieu, R., Knapp, D.E., Martin, R.E. and Main, R., 2015. Biomass increases go under cover: Woody vegetation dynamics in South African rangelands. PLoS ONE 10 (5). Nel, W., Boelhouwers, J., Borg, C-J., Cotrina, J., Hansen, C., Haussmann, N., Hedding, D., Meiklejohn, K., Nguna, A., Rudolph, E., Sinuka, S. and Sumner, P., 2020. Earth science research on Marion Island (1996-2020): A synthesis and new findings. South African Geographical Journal. NFA, 1998. National Forest Act, 1998 (Act 84 of 1998). Office of the President, South Africa. O'Connor, T.G., Haines, L.M. and Snyman, H.A., 2001. Influence of precipitation and species composition on phytomass of a semi-arid African grassland. J.Ecology, 89: 850- 860. Otter L.B., Scholes M.C., 2000. Methane sources and sinks in a periodically flooded South African savanna. Powell, M., 2009. Restoration of degraded subtropical thickets in the Baviaanskloof Megareserve, South Africa, MSc Thesis: Rhodes University. Scandellari F., Caruso G., Liguori G., Meggio F., Palese A.M., Zanotelli D., Celano G., Gucci R., Inglese P., Pitacco A., Tagliavini M., 2016. A survey of carbon sequestration potential of orchards and vineyards in Italy. Schulze, R. and Schutte, R., 2018. Identification and mapping of soils rich in organic carbon in south africa as a climate change mitigation option, Pretoria: DEA, GIZ Contract No: 83258289. Shackleton C., Sinasson G., Adeyemi O., Martins V., 2022. Fuelwood in South Africa Revisited: Widespread Use in a Policy Vacuum. Sustainability, 14:11018. Shackleton, C.M. and Scholes, R.J., 2011. Above ground woody community attributes, biomass and carbon stocks along a rainfall gradient in the savannas of the central lowveld, South Africa.S.A.J.Bot, 77: 184-192. Smith, V. and Mucina, L., 2006. Vegetation of Subantarctic Marion and Prince Edward Islands. In: L. a. R. M. Mucina, ed. The Vegetation of South Africa, Lesotho and Swaziland. Pretoria: SANBI, pp. 698-723. Snyman, 2005. Rangeland degradation in a semi-arid South Africa - 1: Influence on seasonal root distribution, root/shoot ratios and water-use efficiency. J.Arid Environ. 60 (3): 457-481 332 Snyman, H.A. 2006. Estimating grasland production loss due to fire from a semi-arid climate. S.A.J. Animal Science, 5: 38-41. Snyman., H.A. and Fouche, H.J. 1991. Production and water-use efficiency of semi-arid grasslands of South Africa as affected by feld condition and rainfall. Water SA, 17:263 - 268. StatisticsSA, 2021. Natural capital 2: Accounts for protected areas, 1900 to 2020. Report D0401.2. Pretoria, South Africa. Tongwane M., Mdlambuzi T., Moeletsi M., Tsubo M., Mliswa V., Grootboom L., 2016. Greenhouse gas emissions from different crop production and management practices in South Africa. Environmental Development, 19:23-35. Twine W.C., Holdo R.M., Fuelwood sustainability revisited: integrating size structure and resprouting into a spatially realistic fuelshed model. Journal of applied ecology, 53:1766-1776. UNEP, 2019. Review of woodfuel biomass production and utilization in Africa. Nairobi, Kenya. Van de Vyver M.L., 2018. Factors affecting effective ecological restoration of Portulacaria afra (spekboom)-rich subtropical thicket and aboveground carbon endpoint projections. Van der Vyver, M. & Cowling, R.M. 2019. Aboveground biomass and carbon pool estimates of Portulacaria afra (spekboom)-rich subtropical thicket with species-specific allometric models. Forest Ecology and Management, 448:11-21. Van der Vyver, M.L., Cowling, R.M., Mills, A.J. and Difford, M. 2013. Spontaneous return of biodiversity in restored subtropical thicket: Portulacaria afra as an ecosystem engineer. Restoration Ecology: 1-9. Wessels, K.J., Colgan, M.S., Erasmus, B.F.N., Asner, G.P., Twine, W.C., Mathieu, R., van Aardt, J.N.A., Fisher, J.T. and Smith, I.P.J. 2013. Unsustainable fuelwood extraction from South African savannas. Environ. Res. Lett. 8. (https://iopscience.iop.org/article/10.1088/1748-9326/8/1/014007/pdf) Zhang L., Xue T., Gao F., Wei R., Wang Z., Li H., Wang H., 2021. Carbon Storage Distribution Characteristics of Vineyard Ecosystems in Hongsibu, Ningxia. Plants, 10:1199. 333 Chapter 7: Waste (CRT sector 5) Sector overview The waste sector covers greenhouse gas emissions from the following sources: • Solid Waste Disposal • Biological Treatment of Solid Waste • Open Burning of Waste • Wastewater Treatment and Discharge 7.1.1 Overview of shares and trends in emissions The total emissions from the Waste sector for 2022 are shown in Table 7.1. The majority of Waste sector emissions are from wastewater treatment and discharge accounting for 45% of the emissions, followed by Solid waste disposal accounting for 42%. Emissions from Biological treatment of solid waste and Open burning of waste only contribute 12% and 2% respectively. An overview of the Waste emissions trends for the period 2000-2022 can be seen in Figure 7. Waste sector emissions have increased by 44.1% since 2000. Figure 7.12: Trend in emissions from Waste sector, 2000 – 2022 0,00 5 000,00 10 000,00 15 000,00 20 000,00 25 000,00 Waste Emissions Gg CO2e Solid Waste DisposalBiological treatment of solid waste Incineration and open burning of wasteWastewater Treatment and Discharge Total Waste 334 Emissions from all waste categories have increased since 2000. For wastewater treatment and discharge while the overall emissions increased, emissions for industrial wastewater treatment and discharge have decreased by 8.6% as seen in Table 7.1. Table 7.1: Summary of the change in emissions from South Africa’s Waste sector between 2000 and 2022. Source Categories Emissions (Gg CO2e) Change (%) 2000 2022 2000- 2022 5 - Waste 14 362.7 20 698.4 44.1 5A - Solid Waste Disposal 4 505.3 8 596.0 90.8 5B - Biological Treatment of Solid Waste 1 492.2 2 530.4 69.6 5C - Incineration and Open Burning of Waste 152.6 325.4 113.2 5C1 - Waste Incineration NE NE NE 5C2 - Open Burning of Waste 152.6 325.4 113.2 5D - Wastewater Treatment and Discharge 8 212.6 9 246.7 12.6 5D1 - Domestic Wastewater Treatment and Discharge 2 723.5 4 231.2 55.4 5D2 - Industrial Wastewater Treatment and Discharge 5 489.1 5 015.5 -8.6 5E-Other (please specify) NE NE NE 7.1.2 Overview of methodology and completeness Table 7.2 shows a summary of methods and tier levels used for the waste inventory. Table 7.2: Summary of methods and emission factors for the Waste sector and an assessment of the completeness of the Waste sector emissions. GHG Source and sink category CO 2 CH 4 N 2 O Details Method applied Emission factor Method applied Emission factor Method applied Emission factor A Solid waste disposal NA NA T1 DF NA NA Tier 1 FOD model was used. 335 B Biological treatment of solid waste NA NA T1 T1 2006 IPCC GL C Incineration and open burning of waste T1 DF T1 DF T1 DF 2006 IPCC GL D Wastewater treatment and discharge NA T1 DF, CS T1 DF 2006 IPCC GL 7.1.3 Improvements and recalculations Recalculations were performed for the Waste sector because of the following reasons: • Updated the population data with the latest Statistics South Africa data • Change from SAR to AR5 GWPs • Updated COD values for various industries by reverting back to IPCC defaults due to variability in country specific COD • Correction of units for biological treatment of solid waste category The recalculations show a change in the trend throughout the time series (Figure 7). The emissions are lower after the recalculations compared to the previous inventory. Figure 7.3: Change in Waste emission estimates due to recalculations since 2000 submission. 7.1.4 Key categories in the Waste sector 0 5 000 10 000 15 000 20 000 25 000 20002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022 Emissions Gg CO2e Previous inventoryCurrent inventory 336 The key categories for the Waste sector as determined by level (L) and trend (T) assessment are shown in Table 7.3. Table 7.3: Key categories in the Waste sector. IPCC Code Category GHG Criteria 5D2 Industrial Wastewater Treatment and Discharge CH 4 L, T 5A Solid Waste Disposal CH 4 L, T 7.1.5 QA/QC process and verification QA/QC measures in Table 7.4 below were used for the waste sector calculation files. Table 7.4: General QA/QC checks completed for the Waste sector. QA/QC principle Check Accuracy Activity data source Accuracy Correct units Accuracy Unit carry through Accuracy Method validity Accuracy Calculations check Accuracy Uncertainties Accuracy Double counting Accuracy Correct GWP Accuracy Notation keys Accuracy, Completeness, Consistency, Transparency Trend check Accuracy, Transparency Emission factor applicability Accuracy, Transparency, Consistency, Completeness Recalculations Completeness, Comparability Sub-category completeness Consistency Time series consistency Transparency Documentation Transparency, Completeness, Consistency, Accuracy Colour coding Accuracy, Comparability Cross check data Accuracy Spot checks Transparency, Consistency Data source referencing Transparency Links to source data Transparency Raw primary data 337 QA/QC principle Check Accuracy QA review 7.1.6 Planned improvements. The most challenging task in estimating GHG emissions in South Africa was the lack of specific-activity and emissions factor data. As a result, estimations of GHG emissions from both Solid waste and Wastewater sources were largely computed using default values suggested in IPCC 2006 Guidelines and, therefore, margins of error were large. No specific improvements are planned; however South Africa has identified the following areas to be considered in the improvement plan: • Obtain data on the quantities of waste disposed of into managed and unmanaged landfills including its composition. • Improve the classification of landfill sites. • Improve the reporting of economic data (e.g., annual growth) to include different population groups. The assumption that GDP growth is evenly distributed (using a computed mean) across all the population groups is highly misleading and leads to exacerbated margins of error. • Obtain information on population distribution trends between rural and urban settlements as a function of income; and • Conduct a study to trace waste streams and obtain more information on the bucket system which is still widely used in South Africa. • Collect data on CH 4 recovery at SWDS based on metering data. Solid Waste Disposal (5.A) 7.2.1 Category description Waste streams deposited into managed landfills in South Africa comprise waste from households, commercial businesses, institutions, and industry. In this report only the organic fraction of the waste in solid disposal sites was considered as other waste stream components were assumed to generate insignificant quantities in landfills. The types of waste included are municipal waste, industrial waste, and sewage sludge waste. Furthermore, only GHG’s generated from managed disposal landfills in South Africa were included, as data on unmanaged sites are not documented and the sites are generally shallow. A periodic survey is still needed to assess the percentage share of unmanaged 338 sites and semi-managed sites. Generating this information is central to understanding methane generation rates for different solid waste disposal pathways. According to the 2006 IPCC guidelines, emissions from flaring are however not significant, as the CO 2 emissions are of biogenic origin and the CH 4 and N 2 O emissions are very small, so good practice in the waste sector does not require their estimation. In addition, according to the guidelines, it is good practice to only estimate and report CH 4 recovery when references documenting the amount of CH 4 recovery are available. Reporting based on metering of all gas recovered for energy and flaring or reporting of gas recovery based on the monitoring of produced amount of electricity from the gas (considering the availability of load factors, heating value and corresponding heat rate, and other factors impacting the amount of gas used to produce the monitored amount of electricity) is consistent with good practice. 7.2.2 Methodological issues The methodology for calculating GHG emissions from Solid waste is consistent with the IPCC Tier 1 FOD Model (IPCC, 2006). This method utilizes a dynamic model driven by landfill data. It assumes that the degradable organic component (degradable organic carbon, DOC) in waste decays slowly throughout a few decades, during which CH 4 and CO2 are formed. If conditions are constant, the rate of CH 4 production depends solely on the amount of carbon remaining in the waste. As a result, emissions of CH 4 from waste deposited in a disposal site are highest in the first few years after deposition, then gradually decline as the degradable carbon in the waste is consumed by the bacteria responsible for the decay. 7.2.2.1 Activity data Input data includes population data (StatsSA, 2023), waste generation rates, GDP (World bank), annual waste generation, population growth rates, emission rates, half-lives of bulk waste stream (default value for the half-live is 14 years), rate constants, Methane correction factor (MCF), degradable carbon fraction (DCF), as well as other factors described in the IPPC Guidelines, Volume 5, Chapter (IPPC, 2006). Notably, due to a lack of published specific-activity data for many of these parameters in South Africa, the default values suggested in the IPCC Guidelines were applied (Table 7.5). The FOD method requires data to be collected or estimated for historical disposals of waste over a period of 3 to 5 half-lives in order to achieve an acceptably accurate result. It is therefore good practice to use disposal data for at least 50 years as this time frame provides an acceptably accurate result for most typical disposal practices and conditions. Therefore, the activity data used comprised waste quantities disposed of into managed landfills from 1950 to 2022, covering a period of about 75 years (satisfying the condition 339 for a period of five half-lives). Population data for the period 1950 to 2001 was sourced from United Nations population statistics (UN, 2012). Statistics South Africa population data was used for the period 2002 to 2022 (StatsSA, 2023). Waste generation rates for industrial waste were estimated using GDP values sourced from the World Bank for period 2000 to 2022. 7.2.2.2 Emission factors The emission factors for the FOD model are provided in Table 7.5. Table 7.5: IPCC default factors utilized in the FOD Model to determine emissions from solid waste disposal. Factor Sub-category Value Unit DOC (degradable organic carbon) Bulk MSW 0.2 Weight fraction (wet basis) Industrial waste 0.15 Sludge waste 0.05 DOCf (fraction of DOC dissimilated) 0.05 Fraction Methane generation rate constant Bulk MSW 0.05 Years -1 Industrial waste 0.05 Sewage sludge 0.06 Methane correction factor (MCF) Unmanaged, shallow 0.4 Unitless Unmanaged, deep 0.8 Managed 1 Managed, semi-aerobic 0.5 Uncategorized 0.6 Fraction of methane in generated landfill gas (F) 0.5 Fraction Oxidation factor (OF) 0 Unitless 7.2.3 Uncertainty Among the chief limitations of the FOD methodology is that even if activity data improved considerably, the limitations of the data, or lack thereof, of previous years will still introduce a considerable degree of uncertainty. On the other hand, the estimated waste generations derived from previous years, back to 1950, will remain useful in future estimations of GHGs as they will aid in taking into account half-lives. Uncertainty in this category (Table 7.6) is due mainly to the lack of data on the characterization of landfills, as well as of the quantities of waste disposed in them over the medium to long term. An uncertainty of ±30% is typical for countries that collect waste-generation data on a regular basis (IPCC 2006 Guidelines, Table 3.5). Another 340 source of uncertainty is that methane production is calculated using bulk waste because of a lack of data on waste composition, therefore, uncertainty is more than a factor of two (DEAT, 2009). For the purpose of the bulk waste estimates, the whole of South Africa is classified as a “warm temperate dry” climate zone, even though some landfills are located in dry tropical climatic conditions. Table 7.6: Uncertainties associated with emissions from South Africa’s solid waste disposal. Gas Activity data and emission factors Uncertainty % Source CH 4 Total municipal solid waste ±30 IPCC 2006 Fraction of MSW sent to SWDS More than a factor of two Total uncertainty of waste composition More than a factor of two DOC ±20 DOC f ±20 MCF ±10 Fraction of CH 4 in generated landfill gas ±5 Methane recovery ±50 7.2.4 Time series consistency The FOD methodology for estimating methane emissions from solid waste requires a minimum of 48 years’ worth of historical waste disposal data. However, in South Africa, waste disposal statistics are not available. In addition, periodic waste baseline studies do not build time-series data. Hence, population statistics sourced from the (StatsSA; 2023) provided consistent time-series activity data for solid waste disposal. Time series consistency is ensured by use of consistent model parameters and datasets for the calculations of emissions estimates. Where changes in datasets or model parameters occur, a full time series recalculation is undertaken. 7.2.5 Category specific QA/QC and verification General QA/QC measures in Table 7.4 were used for this category. 7.2.6 Category specific recalculations 341 The population data was updated from 2002 -2022 with the latest data from Statistics South Africa. The updated data led a slight increase in emissions by just over 1%. 7.2.7 Category specific planned improvements Planned improvements include: • Collection of actual quantities of waste disposed into landfill sites for period 2000 – 2022. • Conducting a detailed analysis of methane recovery from the National Climate Change Response Database, which captures valuable data from mitigation and adaptation projects for future GHG estimates from landfills. Biological Treatment of Solid Waste (5.B) 7.3.1 Category description Biological treatment of solid waste considers waste treated through composting and anaerobic digestion of organic waste. In this inventory only the emissions from Biological treatment of Industrial Solid Waste have been taken into account. Biological treatment of Municipal Solid Waste has been excluded due to lack of information. 7.3.2 Methodological issues A tier 1 method, with default emission factors from the 2006 IPCC Guidelines was applied to calculate emissions for this category. 7.3.2.1 Activity data The amount of waste treated by biological treatment of solid waste was obtained from a study that was done to by DFFE (Wastewater Treatment Pathways Study;2018)). Extrapolation was used to derive data for 2018-2022. The activity data is shown in Table 7.7. Table 7.7: Activity data for biological treatment systems. 342 Period Biological Treatment System Types of Waste Total Annual amount treated by biological treatment facilities 3 (Gg) CH 4 Recovered 2000 Composting Industrial 2677 0 2001 Industrial 2781 0 2002 Industrial 2955 0 2003 Industrial 2866 0 2004 Industrial 3096 0 2005 Industrial 3215 0 2006 Industrial 3333 0 2007 Industrial 3622 0 2008 Industrial 3957 0 2009 Industrial 3803 0 2010 Industrial 4052 0 2011 Industrial 4156 0 2012 Industrial 4315 0 2013 Industrial 4386 0 2014 Industrial 4409 0 2015 Industrial 4335 0 2016 Industrial 4357 0 2017 Industrial 4099 0 2018 Industrial 4741 0 2019 Industrial 4842 0 2020 Industrial 4188 0 2021 Industrial 4896 0 2022 Industrial 4997 0 2000 Anaerobic digestion at biogas facilities Industrial 5662 10.8 2001 Industrial 5732 10.9 2002 Industrial 5838 11.1 2003 Industrial 5808 11.0 2004 Industrial 5923 11.3 2005 Industrial 5888 11.2 2006 Industrial 5729 10.9 2007 Industrial 5980 11.4 2008 Industrial 5415 10.3 2009 Industrial 7118 13.5 2010 Industrial 8324 15.8 2011 Industrial 5631 10.7 2012 Industrial 5726 10.9 2013 Industrial 5691 10.8 2014 Industrial 6260 11.9 2015 Industrial 5698 10.8 2016 Industrial 5753 10.9 2017 Industrial 5683 10.8 343 Period Biological Treatment System Types of Waste Total Annual amount treated by biological treatment facilities 3 (Gg) CH 4 Recovered 2018 Industrial 6078 11.5 2019 Industrial 6054 11.5 2020 Industrial 5752 10.9 2021 Industrial 5999 11.4 2022 Industrial 6005 11.4 7.3.2.2 Emission factors The default emission factors for composting and anaerobic treatment at biogas facilities are shown in Table 7.8. Table 7.8: Emission Factors for Biological Treatment of Solid Waste CH 4 Emission Factors (g CH 4 /kg waste treated) N 2 O Emission Factors (g N 2 O/kg waste treated) Composting 10 0.6 Anaerobic Digestion at Biogas Facilities 2 Assumed negligible 7.3.3 Uncertainty The estimation of the uncertainties of methane emissions from Biological treatment of solid waste was carried out using the IPCC level 1 method (IPCC, 2006). 7.3.4 Time-series consistency The time series for Biological treatment of solid waste is complete and consistent from 2000-2022. 7.3.5 Category specific QA/QC and verification 344 General QA/QC measures in Table 7.4 were used for this category. 7.3.6 Category specific recalculations Recalculation were performed for this category as The QA process identified an incorrect emission factor discovered during the QA process was corrected. due to a comment that made during the review process where The QA process highlighted that the conversion of the emission factor from gCH 4 /kg waste to GgCH 4 /Gg waste was incorrect so this was updated and recalculations for the entire time-series was conducted. The recalculations led to a 0.2% increase in the emissions compared to the previous inventory. 7.3.7 Category specific planned improvements In future inventories, it is planned to improve data collection of waste treated by biological treatment. Incineration and open burning of waste (5.C) 7.4.1 Category description According to the IPCC guidelines, Open burning of waste can be defined as the combustion of unwanted combustible materials such as paper, wood, plastics, textiles, rubber, waste oils and other debris in nature (open-air) or in open dumps, where smoke and other emissions are released directly into the air without passing through a chimney or stack. Open burning can also include incineration devices that do not control the combustion air to maintain an adequate temperature and do not provide sufficient residence time for complete combustion. In South Africa Open burning of waste is considered in two cases: • Where population do not have access to formal waste collection services. It is assumed that this portion of the population burn its waste openly. • Small percentage of formal landfill sites who practice open burning to manage waste volumes at SWDS. In this source category only the emissions from Open burning have been included and emissions associated with Incineration of waste are not yet considered. 345 7.4.2 Methodological issues A Tier 1 approach, with default IPCC 2006 emission factors, was applied in the calculation of CO 2 , CH 4 and N 2 O emissions from open burning. The amount of MSW open- burned was determined using Equation 5.7 of the IPCC 2006 Guidelines (IPCC, 2006; vol 5, chapter. 5; pg. 5.16). 7.4.2.1 Activity data The activity data for the calculation of MSW are described in section 7.2.2. The fraction of population carrying out open-burning was estimated at 9% (Expert Judgement). CO2 emissions were calculated for the different waste types using the IPCC default breakdown. 7.4.2.2 Emission factors The emission factors for Open burning of waste are provided in Table 7.9. Table 7.9: Emission factors for estimating emissions from open burning of waste. Sub-category Value Unit Source Dry matter content Food Garden Paper Wood Textile Nappies Plastics, other inert 0.4 0.4 0.9 0.85 0.8 0.4 0.9 fraction IPCC 2006 Fraction of carbon in dry matter Food Garden Paper Wood Textile Nappies Plastics, other inert 0.38 0.49 0.46 0.5 0.5 0.7 0.03 fraction IPCC 2006 Fraction of fossil C in total carbon Food Garden Paper Wood Textile 0 0 0.01 0 0.2 fraction IPCC 2006 346 Sub-category Value Unit Source Nappies Plastics, other inert 0.1 1.0 Oxidation factor 0.58 fraction IPCC 2006 CH 4 emission factor 6500 g/t MSW IPCC 2006 N 2 O emission factor 150 G N 2 O/t waste IPCC 2006 7.4.3 Uncertainty 7.4.3.1 Activity data uncertainty Uncertainties associated with CO 2 emission factors for Open burning of waste depend on uncertainties related to fraction of dry matter in waste open-burned, fraction of carbon in the dry matter, fraction of fossil carbon in the total carbon, combustion efficiency, and fraction of carbon oxidized and emitted as CO 2 . A default value of ±40% is suggested by IPCC 2006. 7.4.3.2 Emission factor uncertainty Uncertainties on default N2O and CH4 emission factors have been estimated to be ±100%. 7.4.4 Time series consistency The time series is consistent as the activity data source is the same throughout the time series. 7.4.5 Category specific QA/QC and verification General QA/QC measures in Table 7.4 were used for this category. 7.4.6 Category specific recalculations Recalculations for this category were done due to change in data source for the population data. The recalculations resulted in a decrease in emissions compared to the previous inventory. 347 7.4.7 Category specific planned improvements An improvement planned for this category is collection of activity data on amounts of waste incinerated, by category of waste and the technologies used to incinerate waste in South Africa. Wastewater Treatment and Discharge (5.D) 7.5.1 Domestic wastewater treatments and discharge (5.D.1) 7.5.1.1 Category description Wastewater originates from a variety of domestic, commercial and industrial sources and may be treated on site (uncollected), sewered to a centralised plant (collected) or disposed untreated nearby or via an outfall. Domestic wastewater is defined as wastewater from household water use. Wastewater treatment contributes to anthropogenic emissions, mainly CH 4 and N 2 O. The generation of CH 4 is due to anaerobic degradation of organic matter in wastewater from domestic, commercial, and industrial sources. The organic matter can be quantified using biological oxygen demand (BOD) values. Unlike solid waste, organic carbon in wastewater sources generates comparatively low quantities of CH 4 . This is because even at very low concentrations, oxygen considerably inhibits the functioning of the anaerobic bacteria responsible for the generation of CH 4 . N 2 O is produced from nitrification and denitrification of sewage nitrogen, which results from human protein consumption and discharge. 7.5.1.2 Methodological issues In South Africa, most of the wastewater generated from domestic and commercial sources is treated through municipal wastewater treatment systems (MWTPs). Domestic and commercial wastewater CH 4 emissions mainly originate from septic systems and centralised treatment systems such as MWTPs. Because of the lack of national statistics on the quantities of Biological Oxygen Demand (BOD) generated from domestic and commercial sources in South Africa annually, the yearly estimates were determined using the IPPC 2006 default Tier 1 method. 348 Activity data The activity data for this category which is total amount of organically degradable material in the wastewater was calculated using population data from Statistics South Africa (Stats SA,2023) and IPCC default BOD. IPCC default correction factor of 1 was used for the factor I. Emission factors Methane emissions from Domestic Wastewater Treatment Default population distribution trends between rural and urban settlements as a function of income was sourced from the 2006 IPCC Guidelines. Generally, it is good practice to express BOD product as a function of income, however, this information is not readily available in South Africa. The emissions factors for different wastewater treatment and discharge systems were taken from the 2006 IPCC Guidelines (Table 7.10) as was the data on distribution and utilization of different treatment and discharge systems (Table 7.11). Table 7.10: Emission factors for different wastewater treatment and discharge systems Type of treatment or discharge Maximum CH 4 producing capacity (BOD) CH 4 correction factor for each treatment system Emission factor (kg CH 4 /kg BOD) (MCF) (kg CH 4 /kg BOD) Septic system 0.6 0.5 0.30 Latrine – rural 0.6 0.1 0.06 Latrine – urban low income 0.6 0.5 0.30 Stagnant sewer (open and warm) 0.6 0.5 0.30 Flowing sewer 0.6 0.0 0.00 Other 0.6 0.1 0.06 None 0.6 0.0 0.00 Table 7.11: Distribution and utilization of different treatment and discharge systems Income group Fraction of population income group Type of treatment or discharge pathway Degree of utilization (kg CH 4 /kg BOD) (Tij) Rural 0.39 Septic tank 0.10 Latrine – rural 0.28 349 Sewer stagnant 0.10 Other 0.04 None 0.48 Urban high-income 0.12 Sewer closed 0.70 Septic tank 0.15 Other 0.15 Urban low-income 0.49 Latrine – urban low income 0.24 Septic tank 0.17 Sewer (open and warm) 0.34 Sewer (flowing) 0.20 Other 0.05 Nitrous oxide emissions from Domestic Wastewater Treatment The default values provided by the IPCC Guidelines were used in estimating the potential growing trends of N 2 O emissions from the wastewater treatment systems. This was due to the lack of specific-activity data for South Africa. For the per capita protein consumption, a value of 27.96 kg/person/yr was applied throughout the time series (FAO, 2017). 7.5.1.3 Uncertainty An analysis of the results for methane emissions suggest that the likely sources of uncertainties may be due to the input data. These include uncertainties associated with South African population estimates provided by Statistics South Africa (StatsSA, 2023), the presumed constant country BOD production of about 37 g person-1 day-1 from 2001 to 2022, and the lack of data on the distribution of wastewater treatment systems in South Africa. It is recommended that, in future inventories, a detailed study on the input parameters merits careful consideration to minimize the uncertainty level. In turn, this approach would improve the reliability of the projected methane estimates from wastewater sources. 7.5.1.4 Time series consistency The time series is complete and consistent from 2000-2022. 7.5.1.5 Category specific QA/QC and verification General QA/QC measures in Table 7.4 were used for this category. 7.5.1.6 Category specific recalculations Recalculations were performed for this category due to the following changes: 350 • change in data source for the population data. The recalculations were done for the entire time series. • The factor was changed from 1.25 to 1 due to industrial wastewater being estimated separately therefore there is no commercial and industrial wastewater co discharged to domestic wastewater. The recalculations resulted in a decrease in emissions compared to the previous inventory. 7.5.1.7 Category specific planned improvements The following improvement is planned for this category: • Collection of wastewater related activity data for period 2000 – 2022 taking into account different wastewater treatment pathways in South Africa. 7.5.2 Industrial wastewater treatment and discharge (5.D.2) 7.5.2.1 Category description In South Africa, it is common practice for major industrial facilities to have comprehensive in-plant or on-site wastewater treatment systems for purposes of compliance with regulatory standards for wastewater treatment and discharge. Industrial and commercial wastewater treated anaerobically on-site (uncollected) or sewered (co-discharged) to a centralized plant (collected) or disposed untreated nearby waterbodies, generates and is a source of methane (CH 4 ) (IPCC 2006). 7.5.2.2 Methodological issues The Tier 1 default IPCC methodology was used to estimate CH 4 emissions from industrial wastewater treatment using national parameters on production tonnage per sector; wastewater generated per ton of production; average concentration of Chemical Oxygen Demand (COD); and discharge pathways for the period 2000-2017 (IPCC, 2006, equation 6.4). Emissions of methane from wastewater and their sludge generated in treatment systems were considered. A waste improvement study implemented in the year 2019, collected actual activity data for the period 2000-2017 for all the sectors of wastewater and biological solid waste, followed by developing a forecasting model for the period 2018-2035 for industrial wastewater. 351 Activity data The collected data for the industrial wastewater sectors included the following parameters: • Production tonnage per sector. • Wastewater generated per ton of production. • Average concentration of COD; and • Discharge pathways The production tonnage per sector data for the period 2000 – 2017 was obtained from a study done by DFFE on the Sanitation Pathways for industrial wastewater. For the period 2018-2022 the activity data was then extrapolated for the following sectors: • Alcohol Refining • Starch Production • Beer and Malt • Vegetable Oils • Dairy Products • Fish Processing • Vegetables Fruits and Juices • Meat and Poultry • Organic Chemicals • Petroleum Refining • Plastic and Resins • Soap and Detergents • Wine and Vinegar For the dairy products sector, data for the period 2018-2022 was obtained from Milk SA Lacto data report (Milk SA, 2023) which is published yearly. For the sugar industry, data for the period 2018-2022 was obtained from South Africa Sugar Association (SASA, 2023) and South African Sugar Technologists` Association (SASTA, 2023) on their production reports which are published yearly. For the Pulp and paper industry, data for the 2000- 2015 and period 2019-2022 was obtained from the Paper Manufacturers Association of South Africa (PAMSA, 2023) and for the period 2016-2018 data was interpolated. The COD data is a combination of IPCC defaults and country specific from the Sanitation Pathways study. 352 Methane recovery in the calculation was not taken into account in view of the lack of information on projects for the collection and utilization of methane in facilities for the treatment of industrial wastewater. The maximum methane producing capacity of CH 4 (Bo) The calculation used the value of B 0 by default 0.25 g CH 4 / g COD (IPCC, 2006). Methane Correction Factor The MCF values and emission factors applied are given in Table 7.12, while the COD values are provided in Table 7.13. Table 7.12: MCF values and emission factors Type of treatment or discharge CH 4 correction factor for each treatment system Emission factor (MCF) (kg CH 4 /kg BOD) Aerobic treatment plant 0.1 0,025 Sea River 0.1 0,025 Anaerobic Lagoon (shallow) 0.2 0,05 Aerobic Lagoon (ATP not well managed) 0.3 0,075 Sea River Untreated 0.1 0,025 Anaerobic Reactor 0.8 0,2 Anaerobic Lagoon 0.2 0,05 Table 7.13: Average COD values used to estimate CH 4 emissions from industrial wastewater. Industry Type Chemical Oxygen Demand (COD) Alcohol Refining 11 Starch Production 10 Beer and Malt 2.9 Vegetable Oils 1 Dairy Products 2.7 Fish Processing 2.5 Vegetables Fruits and Juices 5 Meat and Poultry 4.1 Organic Chemicals 3 353 Petroleum Refining 1 Plastic and Resins 1.45 Soap and Detergents 0.85 Pulp and Paper 9 Sugar 6 Wine & vinegar 1.5 Emission factors These are given in Table 7.12. 7.5.2.3 Uncertainty The estimation of the uncertainties of methane emissions from industrial wastewater was carried out using the IPCC level 1 method (IPCC, 2006). 7.5.2.4 Time series consistency The time series is complete and consistent from 2000-2022. 7.5.2.5 Category specific QA/QC and verification General QA/QC measures in Table 7.4 were used for this category. 7.5.2.6 Category specific recalculations Recalculations were done for industrial wastewater treatment due change of COD for the following industries: • Starch Production • Beer and Malt • Pulp and Paper • Vegetables Fruits and Juices • Wine and Vinegar The COD was changed to revert to IPCC defaults due to variability in the country specific COD. The recalculations resulted in an increase on the emissions compared to the previous inventory. 7.5.2.7 Category specific planned improvements 354 In future inventories, it is planned to continue collecting more detailed data on the applied technologies of wastewater treatment in various industries the use of the mandatory GHG reporting regime. Work will also continue the analysis and assessment of the data reported through the SAGERS to enhance the accuracy of the emissions from this category. References DEA, 2012. National Waste Information Baseline Report. Department of Environmental Affairs, Pretoria. Godfrey, L.K., Strydom, W, Muswema, A, Oelofse S, Roman H and Mange M. 2015. The formal South African Waste Sector: Its contribution to the economy, employment and innovation. In: 22nd Waste Management Conference and Exhibition, Somerset west, Western Cape, South Africa, 6-10 October 2014. IPCC, 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Eggleston H.S., Buendia L., Miwa K., Ngara T. and Tanabe K. (eds). ed. Japan: IGES. MilkSA,2023. Lacto Data. [Online] Available at: https://milksa.co.za/taxonomy/term/38 [Accessed 29 November 2023]. PAMSA,2023. Statistics. [Online] Available at: https://thepaperstory.co.za/statistics/ [Accessed 29 November 2023]. SASA,2023. . [Online] Available at: https://sasa.org.za/facts-and-figures/ [Accessed 29 November 2023]. SASTA, 2023. Congress Proceedings. [Online] Available at: https://sasta.co.za/congress- proceedings/ [Accessed 29 November 2023]. Stats SA (Statistics South Africa). 2023. Census 2022: October 2023. (Statistical release P0301.4). https://census.statssa.gov.za/assets/documents/2022/P03014_Census_2022_St atistical_Release.pdf Date of access: 22 Nov. 2023. 355 Appendix A: Key category analysis Table A.1: Level assessment on emissions excluding LULUCF for South Africa (2000) with the key categories highlighted in green. Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Solid Fuels CO 2 185 027.4 0.379 38% 1.A.3.b Road Transportation CO 2 37 452.4 0.077 46% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 254.1 0.066 52% 1.A.2 Manufacturing Industries and Construction CO 2 28 630.6 0.059 58% 1.B.3 Other emissions from energy production CO 2 28 146.6 0.058 64% 3.A.1.a.ii Non-dairy Cattle CH 4 27 776.4 0.057 69% 1.A.4 Other Sectors - Solid Fuels CO 2 22 051.8 0.045 74% 2.C.1 Iron and Steel Production C C C 77% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 14 272.3 0.029 80% 5.A Solid Waste Disposal CH 4 14 056.0 0.029 83% 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O 9 574.6 0.020 85% 3.A.2 Sheep CH 4 8 121.1 0.017 87% 2.C.2 Ferroalloys Production C C C 88% 1.A.4 Other Sectors - Liquid Fuels CO 2 7 515.1 0.015 90% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 489.1 0.011 91% 3.A.1.a.i Dairy Cattle CH 4 3 884.1 0.008 92% 2.A.1 Cement Production C C C 92% 1.B.1.a Coal mining and handling CH 4 3 117.4 0.006 93% 1.B.3 Other emissions from energy production CH 4 2 949.5 0.006 94% 5.D Wastewater Treatment and Discharge - Domestic CH 4 2 601.5 0.005 94% 1.A.1 Energy Industries - Gaseous Fuels CO 2 2 307.1 0.005 95% 356 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.3.a Domestic Aviation CO 2 2 305.7 0.005 95% 3.A.4 Goats CH 4 1 990.8 0.004 96% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 1 908.0 0.004 96% 3.G Liming CO 2 1 820.0 0.004 96% 2.B.2 Nitric Acid Production C C C 97% 2.C.3 Aluminium Production C C C 97% 5.B Biological Treatment of Solid Waste CH 4 1 066.6 0.002 97% 3.D.2 Indirect N 2 O Emissions From Managed Soils N 2 O 1 056.5 0.002 97% 2.C.3 Aluminium Production C C C 97% 3.B.1.a.ii Non-dairy Cattle N 2 O 818.5 0.002 98% 1.A.1 Energy Industries - Solid Fuels N 2 O 764.1 0.002 98% 1.B.2 Oil and Natural Gas CO 2 752.0 0.002 98% 3.B.1.a.ii Non-dairy Cattle CH 4 701.8 0.001 98% 3.B.1.a.i Dairy Cattle CH 4 599.1 0.001 98% 3.B.4 Poultry N 2 O 557.3 0.001 98% 1.A.3.c Railways CO 2 555.5 0.001 98% 2.B.1 Ammonia Production C C C 99% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 466.2 0.001 99% 1.A.3.b Road Transportation N 2 O 466.0 0.001 99% 2.B.6 Titanium Dioxide Production C C C 99% 2.A.2 Lime Production C C C 99% 5.B Biological Treatment of Solid Waste N 2 O 425.6 0.001 99% 1.A.4 Other Sectors - Biomass N 2 O 345.6 0.001 99% 357 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.3.b Road Transportation CH 4 330.6 0.001 99% 3.H Urea Application CO 2 297.3 0.001 99% 3.B.5 Indirect N2O Emissions From Manure Management N 2 O 291.9 0.001 99% 1.A.4 Other Sectors - Biomass CH 4 282.4 0.001 99% 3.B.3 Swine CH 4 256.2 0.001 99% 3.B.1.a.i Dairy Cattle N 2 O 230.0 0.000 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 209.8 0.000 99% 1.A.2 Manufacturing Industries and Construction N 2 O 194.1 0.000 100% 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 188.5 0.000 100% 3.B.2 Sheep N 2 O 159.2 0.000 100% 5.C Incineration and Open Burning of Waste CH 4 156.6 0.000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 3.A.4 Horses CH 4 136.1 0.000 100% 3.B.4 Poultry CH 4 112.7 0.000 100% 3.B.2 Sheep CH 4 108.8 0.000 100% 2.C.6 Zinc Production C C C 100% 1.A.1 Energy Industries - Liquid Fuels N 2 O 97.7 0.000 100% 1.A.2 Manufacturing Industries and Construction CH 4 95.2 0.000 100% 1.A.4 Other Sectors - Solid Fuels N 2 O 91.1 0.000 100% 2.B.1 Ammonia Production C C C 100% 2.A.3 Glass Production C C C 100% 1.B.1.c Solid fuel transformation CO 2 64.4 0.000 100% 3.B.4 Goats N 2 O 59.2 0.000 100% 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 58.9 0.000 100% 358 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.3.e Railways N 2 O 56.4 0.000 100% 3.F Field burning of agricultural residues CH 4 55.5 0.000 100% 1.A.1 Energy Industries - Solid Fuels CH 4 53.8 0.000 100% 3.A.3 Swine CH 4 53.0 0.000 100% 1.B.1.c Solid fuel transformation CH 4 46.3 0.000 100% 3.A.4 Mules & Asses CH 4 45.9 0.000 100% 5.C Incineration and Open Burning of Waste N 2 O 34.2 0.000 100% 3.B.4 Goats CH 4 33.7 0.000 100% 1.B.1.a Coal mining and handling CO 2 30.6 0.000 100% 3.B.3 Swine N 2 O 29.6 0.000 100% 5.C Incineration and Open Burning of Waste CO 2 18.6 0.000 100% 1.A.3.a Domestic Aviation N 2 O 16.7 0.000 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 15.9 0.000 100% 2.C.5 Lead Production C C C 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 13.9 0.000 100% 3.F Field burning of agricultural residues N 2 O 13.6 0.000 100% 1.A.4 Other Sectors - Gaseous Fuels CO 2 12.9 0.000 100% 1.A.4 Other Sectors - Liquid Fuels CH 4 7.7 0.000 100% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 7.4 0.000 100% 1.A.4 Other Sectors - Solid Fuels CH 4 6.4 0.000 100% 2.C.2 Ferroalloys Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 4.2 0.000 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 4.1 0.000 100% 359 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 2.1 0.000 100% 2.B.5 Carbide Production C C C 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 1.5 0.000 100% 1.A.1 Fuel combustion - Energy Industries - Gaseous Fuels CH 4 1.1 0.000 100% 1.A.1 Fuel combustion - Energy Industries - Gaseous Fuels N 2 O 1.1 0.000 100% 1.B.1.c Fugitive emissions from Solid Fuels - Solid fuel transformation N 2 O 0.9 0.000 100% 1.A.3.e Railways CH 4 0.9 0.000 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 0.6 0.000 100% 1.A.3.a Domestic Aviation CH 4 0.4 0.000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 1.A.4 Other Sectors - Gaseous Fuels CH 4 0.0 0.000 100% 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0.0 0.000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 0.0 0.000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0.0 0.000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0.0 0.000 100% 1.A.5 Other (Not specified elsewhere) - Biomass CH 4 0.0 0.000 100% 1.A.5 Other (Not specified elsewhere) - Biomass N 2 O 0.0 0.000 100% 1.B.2 Oil and Natural Gas CH 4 0.0 0.000 100% 1.B.2 Oil and Natural Gas N 2 O 0.0 0.000 100% 2.A.4 Other Process Uses of Carbonates C C C 100% 2.B.5 Carbide Production C C C 100% 2.B.7 Soda Ash Production C C C 100% 2.B.10 Other C C C 100% 2.B.10 Other C C C 100% 360 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 2.C.1 Iron and Steel Production CH 4 0.0 0.000 100% 2.F.2 Foam Blowing Agents F-gases 0.0 0.000 100% 2.F.3 Fire Protection F-gases 0.0 0.000 100% 2.F.4 Aerosols F-gases 0.0 0.000 100% 2.G Other Product Manufacture and Use CO 2 0.0 0.000 100% 2.B.2 Nitric Acid Production C C C 100% 2.B.2 Nitric Acid Production C C C 100% 2.B.8 Hydrogen Production C C C 100% 2.C.2 Ferroalloys Production C C C 100% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 0.0 0.000 100% 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 0.0 0.000 100% C=Confidential data 361 Table A.2: Level assessment on emissions including LULUCF for South Africa (2000) with the key categories highlighted in green. Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Solid Fuels CO 2 185 027,4 0,315 31% 1.A.4 Other Sectors - Solid Fuels CO 2 43 592,2 0,074 39% 1.A.3.b Road Transportation CO 2 37 448,9 0,064 45% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 254,1 0,055 51% 1.B.3 Other emissions from energy production CO 2 28 146,6 0,048 56% 3.A.1.a.ii Non-dairy Cattle CH 4 27 776,4 0,047 60% 4.A.2.b Grassland converted to forest land – all pools CO 2 -24 956,0 0,042 64% 4.A.1.a Forest land remaining forest land – biomass (above and below) CO 2 -18 241,8 0,031 68% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CO 2 17 680,3 0,030 71% 2.C.1 Iron and Steel Production C C C 73% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 12 737,8 0,022 75% 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O 9 574,6 0,016 77% 4.C.2.e Other land converted to Grassland – all pools CO 2 -9 026,6 0,015 79% 4.C.2.a Forest land converted to Grassland – all pools CO 2 8 864,9 0,015 80% 3.A.2 Sheep CH 4 8 121,1 0,014 81% 2.C.2 Ferroalloys Production C C C 83% 4.D.1 Wetlands Remaining Wetlands CH 4 6 990,6 0,012 84% 4.F.2.c Grassland converted to other land – all pools CO 2 6 879,7 0,012 85% 1.A.4 Other Sectors - Liquid Fuels CO 2 6 259,8 0,011 86% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 489,1 0,009 87% 5.A Solid Waste Disposal CH 4 4 505,3 0,008 88% 3.A.1.a.i Dairy Cattle CH 4 3 884,1 0,007 89% 2.A.1 Cement Production C C C 89% 362 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.B.3 Other emissions from energy production CH 4 2 949,5 0,005 90% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 2 613,7 0,004 90% 4.A.2.a Cropland converted to forest land – all pools CO 2 -2 451,3 0,004 91% 1.A.3.a Domestic Aviation CO 2 2 315,9 0,004 91% 1.A.1 Energy Industries - Gaseous Fuels CO 2 2 307,1 0,004 91% 5.D Wastewater Treatment and Discharge - Domestic CH 4 2 257,3 0,004 92% 4.B.2.b Grassland converted to Cropland – all pools CO 2 2 141,6 0,004 92% 4.G Harvested Wood Products CO 2 -2 106,2 0,004 92% 4(V) Biomass Burning CH 4 2 009,8 0,003 93% 3.A.4 Goats CH 4 1 990,8 0,003 93% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CO 2 1 976,7 0,003 93% 1.B.1.a Coal mining and handling CH 4 1 894,6 0,003 94% 3.G Liming CO 2 1 820,0 0,003 94% 4.B.2.a Forest land converted to Cropland – all pools CO 2 1 736,3 0,003 94% 4.C.2.b Cropland converted to Grassland – all pools CO 2 -1 699,5 0,003 95% 4.D.2 Land Converted to Wetlands CH 4 1 655,1 0,003 95% 4.C.1.a Grassland remaining Grassland – biomass (above and below) CO 2 -1 531,4 0,003 95% 2.B.2 Nitric Acid Production C C C 95% 4(V) Biomass Burning N 2 O 1 321,7 0,002 96% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 1 289,7 0,002 96% 4.A.2.e Other land converted to forest land – all pools CO 2 -1 225,3 0,002 96% 2.C.3 Aluminium Production C C C 96% 5.B Biological Treatment of Solid Waste CH 4 1 066,6 0,002 97% 3.D.2 Indirect N 2 O Emissions From Managed Soils N 2 O 1 056,5 0,002 97% 363 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 2.C.3 Aluminium Production C C C 97% 4.A.2.c Wetland converted to forest land – all pools CO 2 -949,7 0,002 97% 3.B.1.a.ii Non-dairy Cattle N 2 O 818,5 0,001 97% 4.F.2.a Forest land converted to other land – all pools CO 2 780,7 0,001 97% 1.A.1 Fuel combustion - Energy Industries - Solid Fuels N 2 O 764,1 0,001 97% 1.B.2 Oil and Natural Gas CO 2 752,0 0,001 98% 4.E.2.a Forest land converted to settlements – all pools CO 2 707,6 0,001 98% 3.B.1.a.ii Non-dairy Cattle CH 4 701,8 0,001 98% 4.D.2 Land Converted to Wetlands CO 2 616,1 0,001 98% 3.B.1.a.i Dairy Cattle CH 4 599,1 0,001 98% 3.B.4 Poultry N 2 O 557,3 0,001 98% 1.A.3.c Railways CO 2 555,5 0,001 98% 2.B.1 Ammonia Production CO 2 485,3 0,001 98% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 466,2 0,001 98% 1.A.3.b Road Transportation N 2 O 466,0 0,001 98% 1.A.4 Other Sectors - Solid Fuels N 2 O 449,5 0,001 99% 2.B.6 Titanium Dioxide Production C C C 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 428,1 0,001 99% 4.D.1 Wetlands Remaining Wetlands N 2 O 426,4 0,001 99% 2.A.2 Lime Production C C C 99% 5.B Biological Treatment of Solid Waste N 2 O 425,6 0,001 99% 4.A.2.d Settlements converted to forest land – all pools CO 2 -424,4 0,001 99% 1.A.3.b Road Transportation CH 4 330,5 0,001 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 327,3 0,001 99% 364 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 3.H Urea Application CO 2 297,3 0,001 99% 3.B.5 Indirect N 2 O Emissions From Manure Management N 2 O 291,9 0,000 99% 4.E.1.a Settlements remaining Settlements – biomass (above and below) CO 2 -275,9 0,000 99% 3.B.3 Swine CH 4 256,2 0,000 99% 4.B.1.a Cropland remaining Cropland – biomass (above and below) CO 2 248,5 0,000 99% 4.B.2.c Wetland converted to Cropland – all pools CO 2 246,3 0,000 99% 1.A.4 Other Sectors - Solid Fuels CH 4 235,8 0,000 99% 3.B.1.a.i Dairy Cattle N 2 O 230,0 0,000 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 220,7 0,000 99% 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 188,5 0,000 99% 4.E.2.c Grassland converted to settlements – all pools CO 2 177,0 0,000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 175,7 0,000 100% 3.B.2 Sheep N 2 O 159,2 0,000 100% 4.C.2.c Wetland converted to Grassland – all pools CO 2 151,7 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 3.A.4 Horses CH 4 136,1 0,000 100% 0 Wetlands converted to other land (all pools) CO 2 134,2 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels N 2 O 130,0 0,000 100% 5.C Incineration and Open Burning of Waste CH 4 114,1 0,000 100% 3.B.4 Poultry CH 4 112,7 0,000 100% 3.B.2 Sheep CH 4 108,8 0,000 100% 2.C.6 Zinc Production C C C 100% 4.D.2 Land Converted to Wetlands N 2 O 101,0 0,000 100% 1.A.1 Energy Industries - Liquid Fuels N 2 O 97,7 0,000 100% 365 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 2.B.1 Ammonia Production C C C 100% 2.A.3 Glass Production C C C 100% 1.B.1.c Solid fuel transformation CO 2 64,4 0,000 100% 4.E.2.b Cropland converted to settlements – all pools CO 2 -61,3 0,000 100% 4.D.1 Wetlands Remaining Wetlands CO 2 59,6 0,000 100% 3.B.4 Goats N 2 O 59,2 0,000 100% 1.A.3.e Railways N 2 O 56,4 0,000 100% 3.F Field burning of agricultural residues CH 4 55,5 0,000 100% 1.A.1 Energy Industries - Solid Fuels CH 4 53,8 0,000 100% 3.A.3 Swine CH 4 53,0 0,000 100% 1.B.1.c Solid fuel transformation CH 4 49,6 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CH 4 49,3 0,000 100% 3.A.4 Mules & Asses CH 4 45,9 0,000 100% 4.A.1.b Forest land remaining forest land – dead organic matter CO 2 34,9 0,000 100% 3.B.4 Goats CH 4 33,7 0,000 100% 3.B.3 Swine N 2 O 29,6 0,000 100% 4.C.2.d Settlements converted to Grassland – all pools CO2 -29,6 0,000 100% 5.C Incineration and Open Burning of Waste N 2 O 24,9 0,000 100% 1.B.1.a Coal mining and handling CO 2 18,6 0,000 100% 4.B.1.c Cropland remaining Cropland – soil (mineral) CO 2 -17,8 0,000 100% 4.B.2.d Settlements converted to Cropland – all pools CO 2 17,4 0,000 100% 1.A.3.a Domestic Aviation N 2 O 16,8 0,000 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 15,9 0,000 100% 2.C.5 Lead Production C C C 100% 366 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 4.C.1.b Grassland remaining Grassland – dead organic matter CO 2 -14,9 0,000 100% 3.F Field burning of agricultural residues N 2 O 13,6 0,000 100% 5.C Incineration and Open Burning of Waste CO 2 13,6 0,000 100% 1.A.4 Other Sectors - Gaseous Fuels CO 2 12,9 0,000 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 12,9 0,000 100% 1.B.2 Oil and Natural Gas CH 4 12,0 0,000 100% 4.B.2.e Other land converted to Cropland – all pools CO 2 -11,2 0,000 100% 4.E.2.e Other land converted to settlements – all pools CO 2 -9,8 0,000 100% 4.F.2.b Cropland converted to other land – all pools CO 2 9,2 0,000 100% 4.F.2.d Settlements converted to other land – all pools CO 2 8,7 0,000 100% 4.E.1.b Settlements remaining Settlements – dead organic matter CO 2 8,5 0,000 100% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 7,4 0,000 100% 1.A.4 Other Sectors - Liquid Fuels CH 4 6,9 0,000 100% 0 Wetlands converted to settlements (all pools) CO 2 6,4 0,000 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 5,3 0,000 100% 2.C.2 Ferroalloys Production C C C 100% 4.B.1.b Cropland remaining Cropland – dead organic matter (dead wood and litter) CO 2 3,8 0,000 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 2,9 0,000 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels N 2 O 2,6 0,000 100% 2.B.5 Carbide Production C C C 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 1,5 0,000 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CH 4 1,4 0,000 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 1,1 0,000 100% 1.A.1 Energy Industries - Gaseous Fuels CH 4 1,1 0,000 100% 367 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Gaseous Fuels N 2 O 1,1 0,000 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CH 4 1,0 0,000 100% 1.B.2 Oil and Natural Gas N 2 O 1,0 0,000 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels N 2 O 0,9 0,000 100% 1.B.1.c Solid fuel transformation N 2 O 0,9 0,000 100% 1.A.3.e Railways CH 4 0,9 0,000 100% 1.A.3.a Domestic Aviation CH 4 0,4 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0,1 0,000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0,1 0,000 100% 1.A.4 Other Sectors - Gaseous Fuels CH 4 0,0 0,000 100% 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0,0 0,000 100% 2.A.4 Other Process Uses of Carbonates CO 2 0,0 0,000 100% 2.B.5 Carbide Production C C C 100% 2.B.7 Soda Ash Production C C C 100% 2.B.10 Other C C C 100% 2.B.10 Other C C C 100% 2.C.1 Iron and Steel Production C C C 100% 2.F.2 Foam Blowing Agents F-gases 0,0 0,000 100% 2.F.3 Fire Protection F-gases 0,0 0,000 100% 2.F.4 Aerosols F-gases 0,0 0,000 100% 2.G Other Product Manufacture and Use CO 2 0,0 0,000 100% 2.B.2 Nitric Acid Production C C C 100% 2.B.2 Nitric Acid Production C C C 100% 368 Code Category Title GHG 2000 Estimates Level Assessment Cumulative Total 2.B.8 Hydrogen Production C C C 100% 2.C.2 Ferroalloys Production C C C 100% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 0,0 0,000 100% 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 0,0 0,000 100% 4.A.1.c Forest land remaining forest land – soil (mineral) CO 2 0,0 0,000 100% 4.C.1.c Grassland remaining Grassland – soil (mineral) CO 2 0,0 0,000 100% 4.E.1.c Settlements remaining Settlements – soil (mineral) CO 2 0,0 0,000 100% 4.F.1.a Other land remaining Other land – biomass (above and below) CO 2 0,0 0,000 100% C=Confidential data 369 Table A.3: Level assessment on emissions excluding LULUCF for South Africa (2022) with the key categories highlighted in green. Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Solid Fuels CO 2 191 064,0 0,399 40% 1.A.3.b Road Transportation CO 2 50 190,1 0,105 50% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 790,2 0,068 57% 3.A.1.a.ii Non-dairy Cattle CH 4 24 499,2 0,051 62% 1.B.3 Other emissions from energy production CO 2 23 477,0 0,049 67% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 19 009,9 0,040 71% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CO 2 18 424,4 0,038 75% 1.A.4 Other Sectors - Liquid Fuels CO 2 13 710,5 0,029 78% 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O 9 382,9 0,020 80% 5.A Solid Waste Disposal CH 4 8 596,0 0,018 82% 2.C.2 Ferroalloys Production C C C 83% 3.A.2 Sheep CH 4 6 377,5 0,013 85% 2.C.1 Iron and Steel Production C C C 86% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CO 2 5 415,7 0,011 87% 2.A.1 Cement Production C C C 88% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 015,5 0,010 89% 1.A.4 Other Sectors - Solid Fuels CO 2 4 381,2 0,009 90% 3.A.1.a.i Dairy Cattle CH 4 3 639,7 0,008 91% 5.D Wastewater Treatment and Discharge - Domestic CH 4 3 506,9 0,007 92% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 3 292,6 0,007 92% 1.B.3 Other emissions from energy production CH 4 2 933,3 0,006 93% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 2 579,8 0,005 93% 370 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 2 544,3 0,005 94% 3.G Liming CO 2 1 860,7 0,004 94% 5.B Biological Treatment of Solid Waste CH 4 1 735,7 0,004 95% 1.B.1.a Fugitive emissions from Solid Fuels - Coal mining and handling CH 4 1 672,4 0,003 95% 3.A.4 Goats CH 4 1 578,6 0,003 95% 2.C.3 Aluminium Production C C C 96% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 1 114,5 0,002 96% 3.D.2 Indirect N2O Emissions From Managed Soils N 2 O 995,2 0,002 96% 3.B.1.a.ii Non-dairy Cattle N 2 O 927,2 0,002 96% 3.B.4 Poultry N 2 O 857,8 0,002 96% 2.B.6 Titanium Dioxide Production C C C 97% 1.A.1 Energy Industries - Gaseous Fuels CO 2 801,9 0,002 97% 5.B Biological Treatment of Solid Waste N 2 O 794,7 0,002 97% 1.A.1 Energy Industries - Solid Fuels N 2 O 789,1 0,002 97% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 724,3 0,002 97% 2.A.2 Lime Production C C C 97% 1.A.3.a Domestic Aviation CO 2 683,9 0,001 98% 3.B.1.a.ii Non-dairy Cattle CH 4 681,5 0,001 98% 1.A.3.b Road Transportation N 2 O 646,5 0,001 98% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 609,1 0,001 98% 3.H Urea Application CO 2 584,7 0,001 98% 3.B.1.a.i Dairy Cattle CH 4 561,4 0,001 98% 1.A.4 Other Sectors - Gaseous Fuels CO 2 525,7 0,001 98% 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 515,7 0,001 98% 371 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 503,0 0,001 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 495,5 0,001 99% 2.B.2 Nitric Acid Production C C C 99% 1.A.3.c Railways CO 2 361,6 0,001 99% 1.B.1.c Solid fuel transformation CO 2 349,1 0,001 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 340,9 0,001 99% 3.B.5 Indirect N2O Emissions From Manure Management N 2 O 325,2 0,001 99% 1.A.3.b Road Transportation CH 4 322,8 0,001 99% 1.B.1.c Solid fuel transformation CH 4 250,7 0,001 99% 5.C Incineration and Open Burning of Waste CH 4 243,3 0,001 99% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 227,2 0,000 99% 3.B.1.a.i Dairy Cattle N 2 O 215,5 0,000 99% 2.B.1 Ammonia Production C C C 99% 3.B.3 Swine CH 4 208,9 0,000 99% 1.A.4 Other Sectors - Solid Fuels N 2 O 207,1 0,000 99% 2.A.3 Glass Production C C C 99% 3.B.4 Poultry CH 4 175,4 0,000 100% 3.A.4 Horses CH 4 168,0 0,000 100% 1.A.4 Other Sectors - Solid Fuels CH 4 161,8 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels N 2 O 150,0 0,000 100% 2.A.4 Other Process Uses of Carbonates C C C 100% 3.B.2 Sheep N 2 O 143,5 0,000 100% 2.C.3 Aluminium Production C C C 100% 2.B.1 Ammonia Production C C C 100% 372 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Liquid Fuels N 2 O 112,1 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 3.B.2 Sheep CH 4 87,6 0,000 100% 2.F.3 Fire Protection F-gases 87,5 0,000 100% 2.B.8 Hydrogen Production C C C 100% 1.B.2 Oil and Natural Gas CO 2 69,6 0,000 100% 2.G Other Product Manufacture and Use CO 2 65,7 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CH 4 63,0 0,000 100% 1.A.1 Energy Industries - Solid Fuels CH 4 55,6 0,000 100% 5.C Incineration and Open Burning of Waste N 2 O 53,1 0,000 100% 3.B.4 Goats N 2 O 49,2 0,000 100% 3.F Field burning of agricultural residues CH 4 47,0 0,000 100% 3.A.4 Mules & Asses CH 4 45,8 0,000 100% 3.A.3 Swine CH 4 43,2 0,000 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 42,9 0,000 100% 2.B.5 Carbide Production C C C 100% 1.A.3.e Railways N 2 O 33,3 0,000 100% 5.C Incineration and Open Burning of Waste CO 2 28,9 0,000 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 27,4 0,000 100% 3.B.4 Goats CH 4 27,1 0,000 100% 3.B.3 Swine N 2 O 24,2 0,000 100% 2.B.10 Other C C C 100% 2.F.4 Aerosols F-gases 18,3 0,000 100% 1.B.1.a Coal mining and handling CO 2 16,4 0,000 100% 373 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.4 Other Sectors - Liquid Fuels CH 4 14,8 0,000 100% 1.B.2 Oil and Natural Gas CH 4 13,7 0,000 100% 3.F Field burning of agricultural residues N 2 O 11,5 0,000 100% 1.B.2 Oil and Natural Gas N 2 O 11,0 0,000 100% 2.C.5 Lead Production C C C 100% 2.B.7 Soda Ash Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels N 2 O 5,7 0,000 100% 1.A.3.a Domestic Aviation N 2 O 5,0 0,000 100% 1.B.1.c Solid fuel transformation N 2 O 4,7 0,000 100% 2.B.5 Carbide Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CH 4 2,7 0,000 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CH 4 2,7 0,000 100% 2.B.2 Nitric Acid Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels N 2 O 2,6 0,000 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 2,5 0,000 100% 2.F.2 Foam Blowing Agents F-gases 2,1 0,000 100% 2.B.2 Nitric Acid Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 1,7 0,000 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 1,3 0,000 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 1,0 0,000 100% 1.A.3.e Railways CH 4 0,6 0,000 100% 1.A.1 Energy Industries - Gaseous Fuels CH 4 0,4 0,000 100% 1.A.1 Energy Industries - Gaseous Fuels N 2 O 0,4 0,000 100% 1.A.4 Other Sectors - Gaseous Fuels CH 4 0,3 0,000 100% 374 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0,3 0,000 100% 1.A.3.a Domestic Aviation CH 4 0,1 0,000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0,1 0,000 100% 2.C.2 Ferroalloys Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0,1 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 2.C.2 Ferroalloys Production C C C 100% 2.B.10 Other C C C 100% C=Confidential data 375 Table A.4: Level assessment on emissions including LULUCF for South Africa (2022) with the key categories highlighted in green. Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Solid Fuels CO 2 191 064,0 0,297 30% 1.A.3.b Road Transportation CO 2 50 190,1 0,078 37% 4.A.2.b Grassland converted to forest land – all pools CO 2 -47 358,8 0,073 45% 4.A.1.a Forest land remaining forest land – biomass (above and below) CO 2 -33 242,4 0,052 50% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 790,2 0,051 55% 3.A.1.a.ii Non-dairy Cattle CH 4 24 499,2 0,038 59% 1.B.3 Other emissions from energy production CO2 23 477,0 0,036 62% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 19 009,9 0,030 65% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CO 2 18 424,4 0,029 68% 4.F.2.c Grassland converted to other land – all pools CO 2 18 120,9 0,028 71% 1.A.4 Other Sectors - Liquid Fuels CO 2 13 710,5 0,021 73% 4.C.1.a Grassland remaining Grassland – biomass (above and below) CO 2 13 167,6 0,020 75% 4.C.2.a Forest land converted to Grassland – all pools CO 2 10 864,7 0,017 77% 4.C.2.e Other land converted to Grassland – all pools CO 2 -10 027,5 0,016 79% 3.D.1 Direct N2O Emissions From Managed Soils N 2 O 9 382,9 0,015 80% 5.A Solid Waste Disposal CH 4 8 596,0 0,013 81% 2.C.2 Ferroalloys Production C C C 83% 3.A.2 Sheep CH 4 6 377,5 0,010 84% 2.C.1 Iron and Steel Production C C C 85% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CO 2 5 415,7 0,008 85% 2.A.1 Cement Production C C C 86% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 015,5 0,008 87% 376 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 4.A.2.a Cropland converted to forest land – all pools CO 2 -4 993,1 0,008 88% 4.D.1 Wetlands Remaining Wetlands CH 4 4 597,6 0,007 88% 1.A.4 Other Sectors - Solid Fuels CO 2 4 381,2 0,007 89% 4.D.2 Land Converted to Wetlands CH 4 4 099,3 0,006 90% 3.A.1.a.i Dairy Cattle CH 4 3 639,7 0,006 90% 5.D Wastewater Treatment and Discharge - Domestic CH 4 3 506,9 0,005 91% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 3 292,6 0,005 91% 1.B.3 Other emissions from energy production CH 4 2 933,3 0,005 92% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 2 579,8 0,004 92% 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 2 544,3 0,004 93% 4.C.2.b Cropland converted to Grassland – all pools CO 2 -2 297,8 0,004 93% 4(V) Biomass Burning CH 4 2 098,6 0,003 93% 4.B.2.b Grassland converted to Cropland – all pools CO 2 1 894,3 0,003 94% 3.G Liming CO 2 1 860,7 0,003 94% 5.B Biological Treatment of Solid Waste CH 4 1 735,7 0,003 94% 1.B.1.a Fugitive emissions from Solid Fuels - Coal mining and handling CH 4 1 672,4 0,003 94% 3.A.4 Goats CH 4 1 578,6 0,002 95% 4.A.2.e Other land converted to forest land – all pools CO 2 -1 447,3 0,002 95% 4.B.2.a Forest land converted to Cropland – all pools CO 2 1 423,5 0,002 95% 4(V) Biomass Burning N 2 O 1 328,6 0,002 95% 4.A.1.b Forest land remaining forest land – dead organic matter CO 2 -1 161,7 0,002 95% 2.C.3 Aluminium Production C C C 96% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 1 114,5 0,002 96% 377 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 4.A.2.c Wetland converted to forest land – all pools CO 2 -1 073,4 0,002 96% 3.D.2 Indirect N 2 O Emissions From Managed Soils N 2 O 995,2 0,002 96% 3.B.1.a.ii Non-dairy Cattle N 2 O 927,2 0,001 96% 3.B.4 Poultry N 2 O 857,8 0,001 96% 2.B.6 Titanium Dioxide Production C C C 97% 4.A.2.d Settlements converted to forest land – all pools CO 2 -805,6 0,001 97% 1.A.1 Energy Industries - Gaseous Fuels CO 2 801,9 0,001 97% 5.B Biological Treatment of Solid Waste N 2 O 794,7 0,001 97% 1.A.1 Energy Industries - Solid Fuels N 2 O 789,1 0,001 97% 4.F.2.a Forest land converted to other land – all pools CO 2 761,6 0,001 97% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 724,3 0,001 97% 2.A.2 Lime Production C C C 97% 1.A.3.a Domestic Aviation CO 2 683,9 0,001 97% 3.B.1.a.ii Non-dairy Cattle CH 4 681,5 0,001 98% 1.A.3.b Road Transportation N 2 O 646,5 0,001 98% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 609,1 0,001 98% 3.H Urea Application CO 2 584,7 0,001 98% 3.B.1.a.i Dairy Cattle CH 4 561,4 0,001 98% 4.D.2 Land Converted to Wetlands CO 2 544,8 0,001 98% 1.A.4 Other Sectors - Gaseous Fuels CO 2 525,7 0,001 98% 4.E.2.a Forest land converted to settlements – all pools CO 2 525,5 0,001 98% 4.C.1.b Grassland remaining Grassland – dead organic matter CO 2 -518,3 0,001 98% 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 515,7 0,001 98% 378 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 503,0 0,001 98% 4.C.2.c Wetland converted to Grassland – all pools CO 2 -501,7 0,001 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 495,5 0,001 99% 4.E.1.a Settlements remaining Settlements – biomass (above and below) CO 2 -437,2 0,001 99% 2.B.2 Nitric Acid Production C C C 99% 1.A.3.c Railways CO 2 361,6 0,001 99% 1.B.1.c Solid fuel transformation CO 2 349,1 0,001 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 340,9 0,001 99% 3.B.5 Indirect N 2 O Emissions From Manure Management N 2 O 325,2 0,001 99% 1.A.3.b Road Transportation CH 4 322,8 0,001 99% 4.D.1 Wetlands Remaining Wetlands N 2 O 287,8 0,000 99% 4.E.2.c Grassland converted to settlements – all pools CO 2 268,5 0,000 99% 4.D.2 Land Converted to Wetlands N 2 O 256,6 0,000 99% 1.B.1.c Solid fuel transformation CH 4 250,7 0,000 99% 5.C Incineration and Open Burning of Waste CH 4 243,3 0,000 99% 4.B.2.c Wetland converted to Cropland – all pools CO 2 242,9 0,000 99% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 227,2 0,000 99% 3.B.1.a.i Dairy Cattle N 2 O 215,5 0,000 99% 2.B.1 Ammonia Production C C C 99% 3.B.3 Swine CH 4 208,9 0,000 99% 1.A.4 Other Sectors - Solid Fuels N 2 O 207,1 0,000 99% 4.D.1 Wetlands Remaining Wetlands CO 2 203,1 0,000 99% 2.A.3 Glass Production C C C 99% 379 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 4.G Harvested Wood Products CO 2 -181,4 0,000 99% 3.B.4 Poultry CH 4 175,4 0,000 100% 3.A.4 Horses CH 4 168,0 0,000 100% 1.A.4 Other Sectors - Solid Fuels CH 4 161,8 0,000 100% 4.B.1.a Cropland remaining Cropland – biomass (above and below) CO 2 156,9 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels N 2 O 150,0 0,000 100% 2.A.4 Other Process Uses of Carbonates C C C 100% 3.B.2 Sheep N 2 O 143,5 0,000 100% 0 Wetlands converted to other land (all pools) CO 2 133,1 0,000 100% 2.C.3 Aluminium Production F-gases 125,9 0,000 100% 2.B.1 Ammonia Production CH 4 113,7 0,000 100% 1.A.1 Energy Industries - Liquid Fuels N 2 O 112,1 0,000 100% 4.C.2.d Settlements converted to Grassland – all pools CO 2 -110,2 0,000 100% 4.B.2.e Other land converted to Cropland – all pools CO 2 -106,6 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 3.B.2 Sheep CH 4 87,6 0,000 100% 2.F.3 Fire Protection F-gases 87,5 0,000 100% 4.E.2.b Cropland converted to settlements – all pools CO 2 -81,4 0,000 100% 2.B.8 Hydrogen Production C C C 100% 1.B.2 Oil and Natural Gas CO 2 69,6 0,000 100% 2.G Other Product Manufacture and Use CO 2 65,7 0,000 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CH 4 63,0 0,000 100% 4.B.1.b Cropland remaining Cropland – dead organic matter CO 2 -61,0 0,000 100% 380 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.1 Energy Industries - Solid Fuels CH 4 55,6 0,000 100% 5.C Incineration and Open Burning of Waste N 2 O 53,1 0,000 100% 3.B.4 Goats N 2 O 49,2 0,000 100% 3.F Field burning of agricultural residues CH 4 47,0 0,000 100% 3.A.4 Mules & Asses CH 4 45,8 0,000 100% 3.A.3 Swine CH 4 43,2 0,000 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 42,9 0,000 100% 2.B.5 Carbide Production C C C 100% 1.A.3.e Railways N 2 O 33,3 0,000 100% 5.C Incineration and Open Burning of Waste CO 2 28,9 0,000 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 27,4 0,000 100% 3.B.4 Goats CH 4 27,1 0,000 100% 4.B.2.d Settlements converted to Cropland – all pools CO 2 -24,8 0,000 100% 3.B.3 Swine N 2 O 24,2 0,000 100% 2.B.10 Other C C C 100% 2.F.4 Aerosols F-gases 18,3 0,000 100% 1.B.1.a Coal mining and handling CO 2 16,4 0,000 100% 4.B.1.c Cropland remaining Cropland – soil (mineral) CO 2 -16,4 0,000 100% 4.E.2.e Other land converted to settlements – all pools CO 2 -15,8 0,000 100% 1.A.4 Other Sectors - Liquid Fuels CH 4 14,8 0,000 100% 1.B.2 Oil and Natural Gas CH 4 13,7 0,000 100% 3.F Field burning of agricultural residues N 2 O 11,5 0,000 100% 1.B.2 Oil and Natural Gas N 2 O 11,0 0,000 100% 381 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 4.E.1.b Settlements remaining Settlements – dead organic matter CO 2 -9,0 0,000 100% 0 Wetlands converted to settlements (all pools) CO 2 7,9 0,000 100% 4.F.2.d Settlements converted to other land – all pools CO 2 7,5 0,000 100% 2.C.5 Lead Production C C C 100% 2.B.7 Soda Ash Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels N 2 O 5,7 0,000 100% 1.A.3.a Domestic Aviation N 2 O 5,0 0,000 100% 1.B.1.c Solid fuel transformation N 2 O 4,7 0,000 100% 2.B.5 Carbide Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CH 4 2,7 0,000 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CH 4 2,7 0,000 100% 2.B.2 Nitric Acid Production C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels N 2 O 2,6 0,000 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 2,5 0,000 100% 4.F.2.b Cropland converted to other land – all pool CO 2 2,2 0,000 100% 2.F.2 Foam Blowing Agents F-gases 2,1 0,000 100% 2.B.2 Nitric Acid Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 1,7 0,000 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 1,3 0,000 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 1,0 0,000 100% 1.A.3.e Railways CH 4 0,6 0,000 100% 1.A.1 Energy Industries - Gaseous Fuels CH 4 0,4 0,000 100% 1.A.1 Energy Industries - Gaseous Fuels N 2 O 0,4 0,000 100% 382 Code Category Title GHG 2022 Estimates Level Assessment Cumulative Total 1.A.4 Other Sectors - Gaseous Fuels CH 4 0,3 0,000 100% 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0,3 0,000 100% 1.A.3.a Domestic Aviation CH 4 0,1 0,000 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0,1 0,000 100% 2.C.2 Ferroalloys Production C C C 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0,1 0,000 100% 2.B.8 Petrochemical and Carbon Black Production C C C 100% 2.C.2 Ferroalloys Production C C C 100% 2.B.10 Other C C C 100% 2.C.1 Iron and Steel Production C C C 100% 2.C.6 Zinc Production C C C 100% 4.A.1.c Forest land remaining forest land – soil (mineral) CO 2 0,0 0,000 100% 4.C.1.c Grassland remaining Grassland – soil (mineral) CO 2 0,0 0,000 100% 4.E.1.c Settlements remaining Settlements – soil (mineral) CO 2 0,0 0,000 100% 4.F.1.a Other land remaining Other land – biomass (above and below) CO 2 0,0 0,000 100% C=Confidential data 383 Table A.5: Trend assessment on emissions excluding LULUCF for South Africa (2000 - 2022) with the key categories highlighted in green. Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.A.4 Other Sectors - Solid Fuels CO 2 43 592,2 4381,207 31% 31% 1.A.3.b Road Transportation CO 2 37 448,9 50190,053 10% 42% 2.C.1 Iron and Steel Production C C C C 49% 1.A.4 Other Sectors - Liquid Fuels CO 2 6 259,8 13710,545 6% 55% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 12 737,8 19009,890 5% 60% 1.A.1 Energy Industries - Solid Fuels CO 2 185 027,4 191063,951 5% 65% 1.B.3 Other emissions from energy production CO 2 28 146,6 23477,024 4% 68% 5.A Solid Waste Disposal CH 4 4 505,3 8596,000 3% 72% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CO 2 1 976,7 5415,712 3% 74% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 0,0 3292,625 3% 77% 3.A.1.a.ii Non-dairy Cattle CH 4 27 776,4 24499,238 3% 80% 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 0,0 2544,278 2% 82% 3.A.2 Sheep CH 4 8 121,1 6377,534 1% 83% 1.A.3.a Domestic Aviation CO 2 2 315,9 683,915 1% 84% 1.A.1 Energy Industries - Gaseous Fuels CO 2 2 307,1 801,878 1% 85% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 2 613,7 1114,480 1% 87% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 1 289,7 2579,793 1% 88% 5.D Wastewater Treatment and Discharge - Domestic CH 4 2 257,3 3506,877 1% 89% 2.A.1 Cement Production C C C C 90% 2.B.2 Nitric Acid Production C C C C 90% 2.C.3 Aluminium Production C C C C 91% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CO 2 17 680,3 18424,352 1% 92% 384 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.B.2 Oil and Natural Gas CO 2 752,0 69,565 1% 92% 5.B Biological Treatment of Solid Waste CH 4 1 066,6 1735,718 1% 93% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 7,4 609,096 0% 93% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 254,1 32790,163 0% 94% 1.A.4 Other Sectors - Gaseous Fuels CO 2 12,9 525,721 0% 94% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 489,1 5015,537 0% 95% 3.A.4 Goats CH 4 1 990,8 1578,606 0% 95% 2.B.6 Titanium Dioxide Production C C C C 95% 5.B Biological Treatment of Solid Waste N 2 O 425,6 794,658 0% 95% 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 188,5 515,689 0% 96% 3.B.4 Poultry N 2 O 557,3 857,768 0% 96% 3.H Urea Application CO 2 297,3 584,663 0% 96% 1.B.1.c Solid fuel transformation CO 2 64,4 349,124 0% 96% 2.B.1 Ammonia Production C C C C 97% 2.A.2 Lime Production C C C C 97% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 466,2 724,274 0% 97% 3.A.1.a.i Dairy Cattle CH 4 3 884,1 3639,745 0% 97% 1.A.4 Other Sectors - Solid Fuels N 2 O 449,5 207,055 0% 97% 1.B.1.a Coal mining and handling CH 4 1 894,6 1672,412 0% 98% 1.B.1.c Solid fuel transformation CH 4 49,6 250,689 0% 98% 1.A.3.c Railways CO 2 555,5 361,554 0% 98% 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O 9 574,6 9382,894 0% 98% 1.A.3.b Road Transportation N 2 O 466,0 646,549 0% 98% 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 327,3 502,957 0% 98% 385 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.A.4 Other Process Uses of Carbonates C C C C 98% 5.C Incineration and Open Burning of Waste CH 4 114,1 243,318 0% 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 220,7 340,897 0% 99% 2.A.3 Glass Production C C C C 99% 3.B.1.a.ii Non-dairy Cattle N 2 O 818,5 927,249 0% 99% 2.C.6 Zinc Production C C C C 99% 2.F.3 Fire Protection F-gases 0,0 87,504 0% 99% 2.B.8 Hydrogen Production C C C C 99% 1.A.4 Other Sectors - Solid Fuels CH 4 235,8 161,810 0% 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 428,1 495,486 0% 99% 2.G Other Product Manufacture and Use CO 2 0,0 65,711 0% 99% 3.B.4 Poultry CH 4 112,7 175,419 0% 99% 3.D.2 Indirect N 2 O Emissions From Managed Soils N 2 O 1 056,5 995,231 0% 99% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 175,7 227,192 0% 99% 3.B.3 Swine CH 4 256,2 208,933 0% 99% 2.C.3 Aluminium Production C C C C 99% 3.G Liming CO 2 1 820,0 1860,710 0% 99% 2.B.8 Petrochemical and Carbon Black Production C C C C 100% 3.B.1.a.i Dairy Cattle CH 4 599,1 561,405 0% 100% 2.B.5 Carbide Production C C C C 100% 3.B.5 Indirect N 2 O Emissions From Manure Management N 2 O 291,9 325,159 0% 100% 3.A.4 Horses CH 4 136,1 167,973 0% 100% 5.C Incineration and Open Burning of Waste N 2 O 24,9 53,142 0% 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 15,9 42,852 0% 100% 386 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.B.1 Ammonia Production C C C C 100% 1.A.1 Energy Industries - Solid Fuels N 2 O 764,1 789,069 0% 100% 1.A.3.e Railways N 2 O 56,4 33,335 0% 100% 3.B.2 Sheep CH 4 108,8 87,598 0% 100% 3.B.1.a.ii Non-dairy Cattle CH 4 701,8 681,523 0% 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels N 2 O 130,0 150,010 0% 100% 2.B.10 Other C C C C 100% 2.F.4 Aerosols F-gases 0,0 18,255 0% 100% 1.B.3 Other emissions from energy production CH 4 2 949,5 2933,252 0% 100% 3.B.2 Sheep N 2 O 159,2 143,505 0% 100% 5.C Incineration and Open Burning of Waste CO 2 13,6 28,909 0% 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 12,9 27,421 0% 100% 3.B.1.a.i Dairy Cattle N 2 O 230,0 215,536 0% 100% 1.A.1 Energy Industries - Liquid Fuels N 2 O 97,7 112,053 0% 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CH 4 49,3 62,980 0% 100% 1.A.3.a Domestic Aviation N 2 O 16,8 4,953 0% 100% 1.B.2 Oil and Natural Gas N 2 O 1,0 11,010 0% 100% 3.B.4 Goats N 2 O 59,2 49,208 0% 100% 3.A.3 Swine CH 4 53,0 43,218 0% 100% 3.F Field burning of agricultural residues CH 4 55,5 47,006 0% 100% 2.C.5 Lead Production C C C C 100% 1.A.4 Other Sectors - Liquid Fuels CH 4 6,9 14,778 0% 100% 1.A.3.b Road Transportation CH 4 330,5 322,809 0% 100% 3.B.4 Goats CH 4 33,7 27,052 0% 100% 387 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.B.7 Soda Ash Production C C C C 100% 3.B.3 Swine N 2 O 29,6 24,169 0% 100% 2.B.5 Carbide Production C C C C 100% 2.C.2 Ferroalloys Production C C C C 100% 1.B.1.c Solid fuel transformation N 2 O 0,9 4,671 0% 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 5,3 1,703 0% 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels N 2 O 2,6 5,703 0% 100% 2.B.2 Nitric Acid Production C C C C 100% 1.B.1.a Coal mining and handling CO 2 18,6 16,430 0% 100% 2.F.2 Foam Blowing Agents F-gases 0,0 2,104 0% 100% 3.F Field burning of agricultural residues N2O 13,6 11,534 0% 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 2,9 0,987 0% 100% 1.A.1 Energy Industries - Solid Fuels CH 4 53,8 55,582 0% 100% 1.B.2 Oil and Natural Gas CH 4 12,0 13,691 0% 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CH 4 1,0 2,735 0% 100% 2.B.2 Nitric Acid Production C C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels N 2 O 0,9 2,588 0% 100% 2.C.2 Ferroalloys Production C C C C 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CH 4 1,4 2,706 0% 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 1,5 2,535 0% 100% 1.A.1 Energy Industries - Gaseous Fuels CH 4 1,1 0,391 0% 100% 1.A.1 Energy Industries - Gaseous Fuels N 2 O 1,1 0,370 0% 100% 1.A.3.a Domestic Aviation CH 4 0,4 0,131 0% 100% 1.A.3.e Railways CH 4 0,9 0,602 0% 100% 388 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.A.4 Other Sectors - Gaseous Fuels CH 4 0,0 0,264 0% 100% 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0,0 0,250 0% 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 1,1 1,321 0% 100% 3.A.4 Mules & Asses CH 4 45,9 45,796 0% 100% 2.C.2 Ferroalloys Production C C C C 100% 2.B.10 Other C C C C 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0,1 0,114 0% 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0,1 0,108 0% 100% C=Confidential data 389 Table A.6: Trend assessment on emissions including LULUCF for South Africa (2000 - 2022) with the key categories highlighted in green. Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.A.4 Other Sectors - Solid Fuels CO 2 43 592,2 4381,207 19% 19% 4.A.2.b Grassland converted to forest land – all pools CO 2 -24 956,0 -47358,835 11% 30% 4.A.1.a Forest land remaining forest land – biomass CO 2 -18 241,8 -33242,410 7% 37% 4.C.1.a Grassland remaining Grassland – biomass CO 2 -1 531,4 13167,621 7% 44% 1.A.3.b Road Transportation CO 2 37 448,9 50190,053 6% 50% 4.F.2.c Grassland converted to other land – all pools CO 2 6 879,7 18120,881 5% 56% 2.C.1 Iron and Steel Production C C C C 60% 1.A.4 Other Sectors - Liquid Fuels CO 2 6 259,8 13710,545 4% 64% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CO 2 12 737,8 19009,890 3% 67% 1.A.1 Energy Industries - Solid Fuels CO 2 185 027,4 191063,951 3% 70% 1.B.3 Other emissions from energy production CO 2 28 146,6 23477,024 2% 72% 5.A Solid Waste Disposal CH 4 4 505,3 8596,000 2% 74% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CO 2 1 976,7 5415,712 2% 76% 2.F.1.a Refrigeration and Air conditioning - refrigeration and stationary ACs CO 2 e 0,0 3292,625 2% 77% 3.A.1.a.ii Non-dairy Cattle CH 4 27 776,4 24499,238 2% 79% 2.F.1.b Refrigeration and Air conditioning - mobile ACs CO 2 e 0,0 2544,278 1% 80% 4.A.2.a Cropland converted to forest land – all pools CO 2 -2 451,3 -4993,079 1% 81% 4.D.2 Land Converted to Wetlands CH 4 1 655,1 4099,344 1% 82% 4.D.1 Wetlands Remaining Wetlands CH 4 6 990,6 4597,609 1% 84% 4.C.2.a Forest land converted to Grassland – all pools CO 2 8 864,9 10864,683 1% 85% 4.G Harvested Wood Products CO 2 -2 106,2 -181,444 1% 86% 3.A.2 Sheep CH 4 8 121,1 6377,534 1% 86% 390 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.A.3.a Domestic Aviation CO 2 2 315,9 683,915 1% 87% 1.A.1 Energy Industries - Gaseous Fuels CO 2 2 307,1 801,878 1% 88% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CO 2 2 613,7 1114,480 1% 89% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 1 289,7 2579,793 1% 89% 5.D Wastewater Treatment and Discharge - Domestic CH 4 2 257,3 3506,877 1% 90% 4.A.1.b Forest land remaining forest land – dead organic matter CO 2 34,9 -1161,677 1% 90% 2.A.1 Cement Production C C C C 91% 2.B.2 Nitric Acid Production C C C C 92% 4.C.2.e Other land converted to Grassland – all pools CO 2 -9 026,6 -10027,477 0% 92% 2.C.3 Aluminium Production C C C C 92% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CO 2 17 680,3 18424,352 0% 93% 1.B.2 Oil and Natural Gas CO 2 752,0 69,565 0% 93% 5.B Biological Treatment of Solid Waste CH 4 1 066,6 1735,718 0% 93% 4.C.2.c Wetland converted to Grassland – all pools CO 2 151,7 -501,689 0% 94% 2.D.2 Non-energy Products from Fuels and Solvent Use - Paraffin wax use CO 2 7,4 609,096 0% 94% 4.C.2.b Cropland converted to Grassland – all pools CO 2 -1 699,5 -2297,795 0% 94% 1.A.1 Energy Industries - Liquid Fuels CO 2 32 254,1 32790,163 0% 95% 1.A.4 Other Sectors - Gaseous Fuels CO 2 12,9 525,721 0% 95% 4.C.1.b Grassland remaining Grassland – dead organic matter CO 2 -14,9 -518,338 0% 95% 5.D Wastewater Treatment and Discharge - Industrial CH 4 5 489,1 5015,537 0% 95% 3.A.4 Goats CH 4 1 990,8 1578,606 0% 96% 4.A.2.d Settlements converted to forest land – all pools CO 2 -424,4 -805,639 0% 96% 2.B.6 Titanium Dioxide Production C C C C 96% 5.B Biological Treatment of Solid Waste N 2 O 425,6 794,658 0% 96% 391 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.D.1 Non-energy Products from Fuels and Solvent Use - Lubricant use CO 2 188,5 515,689 0% 96% 4.B.2.a Forest land converted to Cropland – all pools CO 2 1 736,3 1423,508 0% 96% 3.B.4 Poultry N 2 O 557,3 857,768 0% 97% 3.H Urea Application CO 2 297,3 584,663 0% 97% 1.B.1.c Fugitive emissions from Solid Fuels - Solid fuel transformation CO 2 64,4 349,124 0% 97% 2.B.1 Ammonia Production C C C C 97% 2.A.2 Lime Production C C C C 97% 5.D Wastewater Treatment and Discharge - Domestic N 2 O 466,2 724,274 0% 97% 4.B.2.b Grassland converted to Cropland – all pools CO 2 2 141,6 1894,320 0% 97% 3.A.1.a.i Dairy Cattle CH 4 3 884,1 3639,745 0% 97% 1.A.4 Other Sectors - Solid Fuels N 2 O 449,5 207,055 0% 98% 1.B.1.a Coal mining and handling CH 4 1 894,6 1672,412 0% 98% 4.A.2.e Other land converted to forest land – all pools CO 2 -1 225,3 -1447,324 0% 98% 1.B.1.c Solid fuel transformation CH 4 49,6 250,689 0% 98% 1.A.3.c Railways CO 2 555,5 361,554 0% 98% 3.D.1 Direct N 2 O Emissions From Managed Soils N 2 O 9 574,6 9382,894 0% 98% 4.E.2.a Forest land converted to settlements – all pools CO 2 707,6 525,481 0% 98% 1.A.3.b Road Transportation N 2 O 466,0 646,549 0% 98% 1.A.5 Other (Not specified elsewhere) - Solid Fuels N 2 O 327,3 502,957 0% 98% 4.E.1.a Settlements remaining Settlements – biomass CO 2 -275,9 -437,241 0% 98% 4.D.2 Land Converted to Wetlands N 2 O 101,0 256,582 0% 98% 2.A.4 Other Process Uses of Carbonates CO 2 0,0 147,261 0% 99% 4.D.1 Wetlands Remaining Wetlands CO 2 59,6 203,124 0% 99% 4.D.1 Wetlands Remaining Wetlands N 2 O 426,4 287,769 0% 99% 392 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 5.C Incineration and Open Burning of Waste CH 4 114,1 243,318 0% 99% 4.A.2.c Wetland converted to forest land – all pools CO 2 -949,7 -1073,396 0% 99% 1.A.5 Other (Not specified elsewhere) - Solid Fuels CH 4 220,7 340,897 0% 99% 2.A.3 Glass Production C C C C 99% 3.B.1.a.ii Non-dairy Cattle N 2 O 818,5 927,249 0% 99% 2.C.6 Zinc Production C C C C 99% 4.B.2.e Other land converted to Cropland – all pools CO 2 -11,2 -106,553 0% 99% 4.B.1.a Cropland remaining Cropland – biomass (above and below) CO 2 248,5 156,878 0% 99% 4.E.2.c Grassland converted to settlements – all pools CO 2 177,0 268,546 0% 99% 4(V) Biomass Burning CH 4 2 009,8 2098,636 0% 99% 2.F.3 Fire Protection F-gases 0,0 87,504 0% 99% 4.C.2.d Settlements converted to Grassland – all pools CO 2 -29,6 -110,166 0% 99% 2.B.8 Hydrogen Production C C C C 99% 1.A.4 Other Sectors - Solid Fuels CH 4 235,8 161,810 0% 99% 4.D.2 Land Converted to Wetlands CO 2 616,1 544,821 0% 99% 1.A.3.d Domestic Navigation - Liquid Fuels CO 2 428,1 495,486 0% 99% 2.G Other Product Manufacture and Use CO 2 0,0 65,711 0% 99% 4.B.1.b Cropland remaining Cropland – dead organic matter CO 2 3,8 -61,020 0% 99% 3.B.4 Poultry CH 4 112,7 175,419 0% 100% 3.D.2 Indirect N 2 O Emissions From Managed Soils N 2 O 1 056,5 995,231 0% 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CO 2 175,7 227,192 0% 100% 3.B.3 Swine CH 4 256,2 208,933 0% 100% 2.C.3 Aluminium Production C C C C 100% 4.B.2.d Settlements converted to Cropland – all pools CO 2 17,4 -24,787 0% 100% 393 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 3.G Liming CO 2 1 820,0 1860,710 0% 100% 2.B.8 Petrochemical and Carbon Black Production C C C C 100% 3.B.1.a.i Dairy Cattle CH 4 599,1 561,405 0% 100% 2.B.5 Carbide Production C C C C 100% 3.B.5 Indirect N 2 O Emissions From Manure Management N 2 O 291,9 325,159 0% 100% 3.A.4 Horses CH 4 136,1 167,973 0% 100% 5.C Incineration and Open Burning of Waste N 2 O 24,9 53,142 0% 100% 1.A.1 Energy Industries - Liquid Fuels CH 4 15,9 42,852 0% 100% 2.B.1 Ammonia Production C C C C 100% 1.A.1 Energy Industries - Solid Fuels N 2 O 764,1 789,069 0% 100% 1.A.3.e Railways N 2 O 56,4 33,335 0% 100% 3.B.2 Sheep CH 4 108,8 87,598 0% 100% 3.B.1.a.ii Non-dairy Cattle CH 4 701,8 681,523 0% 100% 4.E.2.b Cropland converted to settlements – all pools CO 2 -61,3 -81,417 0% 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels N 2 O 130,0 150,010 0% 100% 2.B.10 Other C C C C 100% 4.F.2.a Forest land converted to other land – all pools CO 2 780,7 761,614 0% 100% 2.F.4 Aerosols F-gases 0,0 18,255 0% 100% 4.E.1.b Settlements remaining Settlements – dead organic matter CO 2 8,5 -8,964 0% 100% 1.B.3 Other emissions from energy production CH 4 2 949,5 2933,252 0% 100% 3.B.2 Sheep N 2 O 159,2 143,505 0% 100% 5.C Incineration and Open Burning of Waste CO 2 13,6 28,909 0% 100% 1.A.4 Other Sectors - Liquid Fuels N 2 O 12,9 27,421 0% 100% 3.B.1.a.i Dairy Cattle N 2 O 230,0 215,536 0% 100% 394 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 1.A.1 Energy Industries - Liquid Fuels N 2 O 97,7 112,053 0% 100% 1.A.2 Manufacturing Industries and Construction - Solid Fuels CH 4 49,3 62,980 0% 100% 1.A.3.a Domestic Aviation N 2 O 16,8 4,953 0% 100% 1.B.2 Oil and Natural Gas N 2 O 1,0 11,010 0% 100% 3.B.4 Goats N 2 O 59,2 49,208 0% 100% 3.A.3 Swine CH 4 53,0 43,218 0% 100% 3.F Field burning of agricultural residues CH 4 55,5 47,006 0% 100% 2.C.5 Lead Production C C C C 100% 1.A.4 Other Sectors - Liquid Fuels CH 4 6,9 14,778 0% 100% 1.A.3.b Road Transportation CH 4 330,5 322,809 0% 100% 4.F.2.b Cropland converted to other land – all pools CO 2 9,2 2,240 0% 100% 4(V) Biomass Burning N 2 O 1 321,7 1328,568 0% 100% 3.B.4 Goats CH 4 33,7 27,052 0% 100% 2.B.7 Soda Ash Production C C C C 100% 4.E.2.e Other land converted to settlements – all pools CO 2 -9,8 -15,823 0% 100% 3.B.3 Swine N 2 O 29,6 24,169 0% 100% 2.B.5 Carbide Production C C C C 100% 2.C.2 Ferroalloys Production C C C C 100% 1.B.1.c Solid fuel transformation N 2 O 0,9 4,671 0% 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels N 2 O 5,3 1,703 0% 100% 4.B.2.c Wetland converted to Cropland – all pools CO 2 246,3 242,865 0% 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels N 2 O 2,6 5,703 0% 100% 2.B.2 Nitric Acid Production C C C C 100% 1.B.1.a Coal mining and handling CO 2 18,6 16,430 0% 100% 395 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.F.2 Foam Blowing Agents F-gases 0,0 2,104 0% 100% 3.F Field burning of agricultural residues N 2 O 13,6 11,534 0% 100% 1.A.5 Other (Not specified elsewhere) - Liquid Fuels CH 4 2,9 0,987 0% 100% 1.A.1 Fuel combustion - Energy Industries - Solid Fuels CH 4 53,8 55,582 0% 100% 1.B.2 Oil and Natural Gas CH 4 12,0 13,691 0% 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels CH 4 1,0 2,735 0% 100% 2.B.2 Nitric Acid Production C C C C 100% 1.A.2 Manufacturing Industries and Construction - Gaseous Fuels N 2 O 0,9 2,588 0% 100% 0 Wetlands converted to settlements (all pools) CO 2 6,4 7,901 0% 100% 2.C.2 Ferroalloys Production C C C C 100% 4.B.1.c Cropland remaining Cropland – soil (mineral) CO 2 -17,8 -16,351 0% 100% 1.A.2 Manufacturing Industries and Construction - Liquid Fuels CO 2 1,4 2,706 0% 100% 4.F.2.d Settlements converted to other land – all pools CO 2 8,7 7,517 0% 100% 0 Wetlands converted to other land (all pools) CO 2 134,2 133,120 0% 100% 1.A.3.d Domestic Navigation - Liquid Fuels N 2 O 1,5 2,535 0% 100% 1.A.1 Fuel combustion - Energy Industries - Gaseous Fuels CH 4 1,1 0,391 0% 100% 1.A.1 Fuel combustion - Energy Industries - Gaseous Fuels N 2 O 1,1 0,370 0% 100% 1.A.3.a Domestic Aviation CH 4 0,4 0,131 0% 100% 1.A.3.e Railways CH 4 0,9 0,602 0% 100% 1.A.4 Other Sectors - Gaseous Fuels CH 4 0,0 0,264 0% 100% 1.A.4 Other Sectors - Gaseous Fuels N 2 O 0,0 0,250 0% 100% 1.A.3.d Domestic Navigation - Liquid Fuels CH 4 1,1 1,321 0% 100% 3.A.4 Mules & Asses CH 4 45,9 45,796 0% 100% 2.C.2 Ferroalloys Production C C C C 100% 396 Code Category Title GHG 2000 Estimates 2022 Estimates Contribution to Trend Cumulative Total 2.B.10 Other C C C C 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels CH 4 0,1 0,114 0% 100% 1.A.5 Other (Not specified elsewhere) - Gaseous Fuels N 2 O 0,1 0,108 0% 100% 2.B.8 Petrochemical and Carbon Black Production C C C C 100% 2.C.1 Iron and Steel Production C C C C 100% 4.A.1.c Forest land remaining forest land – soil (mineral) CO 2 0,0 0,000 0% 100% 4.C.1.c Grassland remaining Grassland – soil (mineral) CO 2 0,0 0,000 0% 100% 4.E.1.c Settlements remaining Settlements – soil (mineral) CO 2 0,0 0,000 0% 100% 4.F.1.a Other land remaining Other land – biomass CO 2 0,0 0,000 0% 100% C=Confidential data 397 Appendix B: Uncertainty analysis Table B.1: Overall uncertainty analysis including LULUCF for 2000 to 2022. IPCC Category Gas Base year emission s/ removals (2000) Year t emission s/ removals (2017) Activity data uncertainty Emission factor/estimation parameter uncertainty Combined uncertainty Contribution to variance in Year t Uncertainty in trend in national emissions introduced by EF/ estimation parameter uncertainty Uncertainty in trend in national emissions introduced by activity data uncertainty Uncertainty introduced into the trend in total national emissions Gg CO2e Gg CO2e (-)% (+)% (-)% (+)% (-)% (+)% Lower (%) Upper (%) % % % 1A1a Electricity and Heat Production CO2 185 128.6 196 116.1 3.00% 5.00% 7.00% 7.00% 7.62% 8.60% 0.1174% 0.1498% 0.33% 3.00% 0.09% 1A1b Petroleum Refining CO2 4 005.3 1 078.7 3.00% 5.00% 7.00% 7.00% 7.62% 8.60% 0.0000% 0.0000% 0.04% 0.02% 0.00% 1A1c Manufacture of Solid Fuels and Other Energy Industries CO2 30 454.7 27 461.1 3.00% 5.00% 7.00% 7.00% 7.62% 8.60% 0.0023% 0.0029% 0.02% 0.42% 0.00% 1A1a Electricity and Heat Production CH4 53.9 61.3 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A1b Petroleum Refining CH4 2.8 0.6 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A1c Manufacture of Solid Fuels and Other Energy Industries CH4 14.2 36.9 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A1a Electricity and Heat Production N2O 764.4 799.9 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0002% 0.0002% 0.01% 0.01% 0.00% 1A1b Petroleum Refining N2O 4.2 0.8 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A1c Manufacture of Solid Fuels and Other Energy Industries N2O 94.3 100.7 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 398 1A2 Manufacturing Industries and Construction CO2 20 946.7 26 419.9 5.00% 10.00% 7.00% 7.00% 8.60% 12.21% 0.0027% 0.0055% 0.10% 0.81% 0.01% 1A2 Manufacturing Industries and Construction CH4 51.7 68.4 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A2 Manufacturing Industries and Construction N2O 133.5 158.3 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.01% 0.00% 0.00% 1A3a Civil Aviation CO2 2 315.9 683.9 5.00% 5.00% 1.50% 1.50% 5.22% 5.22% 0.0000% 0.0000% 0.00% 0.01% 0.00% 1A3b Road Transport CO2 37 448.9 50 190.1 5.00% 5.00% 2.00% 2.00% 5.39% 5.39% 0.0038% 0.0038% 0.06% 0.77% 0.01% 1A3c Railways CO2 555.5 361.6 5.00% 5.00% 2.00% 2.00% 5.39% 5.39% 0.0000% 0.0000% 0.00% 0.01% 0.00% 1A3d Water-Borne Navigation CO2 428.1 495.5 5.00% 5.00% 3.00% 3.00% 5.83% 5.83% 0.0000% 0.0000% 0.00% 0.01% 0.00% 1A3a Civil Aviation CH4 0.4 0.1 5.00% 5.00% 70.00% 70.00% 70.18% 70.18% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3b Road Transport CH4 330.5 322.8 5.00% 5.00% 9.00% 9.00% 10.30% 10.30% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3c Railways CH4 0.9 0.6 5.00% 5.00% 9.00% 9.00% 10.30% 10.30% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3d Water-Borne Navigation CH4 1.1 1.3 5.00% 5.00% 50.00% 50.00% 50.25% 50.25% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3a Civil Aviation N2O 16.8 5.0 5.00% 5.00% 70.00% 50.00% 70.18% 50.25% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3b Road Transport N2O 466.0 646.5 5.00% 5.00% 70.00% 72.00% 70.18% 72.17% 0.0001% 0.0001% 0.03% 0.01% 0.00% 1A3c Railways N2O 56.4 33.3 5.00% 5.00% 70.00% 72.00% 70.18% 72.17% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A3d Water-Borne Navigation N2O 3.1 3.6 5.00% 5.00% 40.00% 140.00 % 40.31% 140.09% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A4a Commercial/Institu tional CO2 20 516.6 12 037.7 5.00% 10.00% 7.00% 7.00% 8.60% 12.21% 0.0006% 0.0011% 0.11% 0.37% 0.00% 1A4b Residential CO2 26 941.0 3 495.5 5.00% 10.00% 7.00% 7.00% 8.60% 12.21% 0.0000% 0.0001% 0.33% 0.11% 0.00% 1A4c Agriculture/Forestr y/Fishing/Fish Farms CO2 2 407.3 3 084.3 5.00% 10.00% 7.00% 7.00% 8.60% 12.21% 0.0000% 0.0001% 0.01% 0.09% 0.00% 1A4a Commercial/Institu tional CH4 7.8 11.3 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A4b Residential CH4 232.3 162.1 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.01% 0.00% 0.00% 1A4c Agriculture/Forestr y/Fishing/Fish Farms CH4 2.6 3.5 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.00% 0.00% 0.00% 399 1A4a Commercial/Institu tional N2O 79.8 27.7 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.01% 0.00% 0.00% 1A4b Residential N2O 376.9 200.2 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.03% 0.01% 0.00% 1A4c Agriculture/Forestr y/Fishing/Fish Farms N2O 5.7 6.8 5.00% 10.00% 75.00% 75.00% 75.17% 75.66% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1A5a Stationary CO2 15 527.3 20 351.6 3.00% 5.00% 7.00% 7.00% 7.62% 8.60% 0.0013% 0.0016% 0.09% 0.31% 0.00% 1A5a Stationary CH4 223.6 342.0 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0000% 0.0000% 0.02% 0.01% 0.00% 1A5a Stationary N2O 332.7 504.8 3.00% 5.00% 75.00% 75.00% 75.06% 75.17% 0.0001% 0.0001% 0.03% 0.01% 0.00% 1B1a Coal mining and handling CO2 18.6 16.4 10.00% 10.00% 63.00% 63.00% 63.79% 63.79% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1B1a Coal mining and handling CH4 1 894.6 1 672.4 10.00% 10.00% 63.00% 63.00% 63.79% 63.79% 0.0006% 0.0006% 0.02% 0.05% 0.00% 1B1c Solid fuel transformation CO2 64.4 349.1 10.00% 10.00% 63.00% 63.00% 63.79% 63.79% 0.0000% 0.0000% 0.04% 0.01% 0.00% 1B1c Solid fuel transformation CH4 49.6 250.7 10.00% 10.00% 63.00% 63.00% 63.79% 63.79% 0.0000% 0.0000% 0.03% 0.01% 0.00% 1B1c Solid fuel transformation N2O 0.9 4.7 10.00% 10.00% 63.00% 63.00% 63.79% 63.79% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1B2a Oil CO2 752.0 69.6 25.00% 25.00% 75.00% 75.00% 79.06% 79.06% 0.0000% 0.0000% 0.10% 0.01% 0.00% 1B2a Oil CH4 12.0 13.7 25.00% 25.00% 75.00% 75.00% 79.06% 79.06% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1B2a Oil N2O 1.0 11.0 25.00% 25.00% 75.00% 75.00% 79.06% 79.06% 0.0000% 0.0000% 0.00% 0.00% 0.00% 1B3 Other Emissions from Energy Production CO2 28 146.6 23 477.0 25.00% 25.00% 75.00% 75.00% 79.06% 79.06% 0.1814% 0.1814% 0.50% 1.80% 0.03% 1B3 Other Emissions from Energy Production CH4 2 949.5 2 933.3 25.00% 25.00% 75.00% 75.00% 79.06% 79.06% 0.0028% 0.0028% 0.02% 0.22% 0.00% 2A1 Cement Production CO2 3 870.6 5 022.7 30.00% 30.00% 35.00% 35.00% 46.10% 46.10% 0.0028% 0.0028% 0.10% 0.46% 0.00% 2A2 Lime Production CO2 426.1 693.7 30.00% 30.00% 6.00% 6.00% 30.59% 30.59% 0.0000% 0.0000% 0.00% 0.06% 0.00% 2A3 Glass Production CO2 74.4 191.4 1.00% 3.00% 1.00% 3.00% 1.41% 4.24% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2A4a Ceramics CO2 0.0 34.3 1.00% 85.00% 1.00% 5.00% 1.41% 85.15% 0.0000% 0.0000% 0.00% 0.01% 0.00% 2A4b Other Uses of Soda Ash CO2 0.0 31.5 1.00% 85.00% 1.00% 5.00% 1.41% 85.15% 0.0000% 0.0000% 0.00% 0.01% 0.00% 2A4d Other CO2 0.0 81.5 15.00% 85.00% 1.00% 5.00% 15.03% 85.15% 0.0000% 0.0000% 0.00% 0.02% 0.00% 400 2B1 Ammonia Production CO2 485.3 209.4 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B1 Ammonia Production CH4 87.4 113.7 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B2 Nitric Acid Production CO2 0.0 1.7 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B3 Nitric Acid Production CH4 0.0 2.6 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B2 Nitric Acid Production N2O 1 405.8 364.3 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B5 Carbide Production CO2 2.0 37.4 5.00% 5.00% 10.00% 10.00% 11.18% 11.18% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B5 Carbide Production CH4 0.0 4.6 5.00% 5.00% 10.00% 10.00% 11.18% 11.18% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B6 Titanium Dioxide Production CO2 437.6 813.8 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B7 Soda Ash Production CO2 0.0 6.4 5.00% 5.00% 0.00% 0.00% 5.00% 5.00% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B8f Carbon Black production CO2 138.6 100.6 15.00% 15.00% 15.00% 15.00% 21.21% 21.21% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B8f Carbon Black production CH4 0.1 0.1 85.00% 85.00% 85.00% 85.00% 120.21 % 120.21% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B8g Hydrogen Production CO2 0.0 79.1 2.00% 2.00% 2.00% 2.00% 2.83% 2.83% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B10 Other CO2 0.0 19.3 5.00% 5.00% 10.00% 10.00% 11.18% 11.18% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2B10 Other CH4 0.0 0.0 5.00% 5.00% 10.00% 10.00% 11.18% 11.18% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2C1 Iron and Steel Production CO2 15 334.4 6 307.2 5.00% 10.00% 25.00% 25.00% 25.50% 26.93% 0.0014% 0.0015% 0.44% 0.19% 0.00% 2C1 Iron and Steel Production CH4 0.0 0.0 5.00% 10.00% 25.00% 25.00% 25.50% 26.93% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2C2 Ferroalloys Production CO2 8 079.1 8 080.6 5.00% 5.00% 5.00% 25.00% 7.07% 25.50% 0.0002% 0.0022% 0.03% 0.12% 0.00% 2C2 Ferroalloys Production CH4 4.4 0.1 5.00% 5.00% 25.00% 25.00% 25.50% 25.50% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2C2 Ferroalloys Production N2O 0.0 0.1 5.00% 5.00% 25.00% 25.00% 25.50% 25.50% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2C3 Aluminium Production CO2 1 091.3 1 133.5 1.00% 1.00% 5.00% 5.00% 5.10% 5.10% 0.0000% 0.0000% 0.00% 0.00% 0.00% 401 2C3 Aluminium Production PFCs 1 025.0 125.9 1.00% 1.00% 15.00% 15.00% 15.03% 15.03% 0.0000% 0.0000% 0.03% 0.00% 0.00% 2C5 Lead Production CO2 15.1 7.2 10.00% 10.00% 50.00% 50.00% 50.99% 50.99% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2C6 Zinc Production CO2 108.4 0.0 10.00% 10.00% 50.00% 50.00% 50.99% 50.99% 0.0000% 0.0000% 0.01% 0.00% 0.00% 2D1 Lubricant Use CO2 188.5 515.7 10.00% 20.00% 50.00% 50.00% 50.99% 53.85% 0.0000% 0.0000% 0.04% 0.03% 0.00% 2D2 Paraffin Wax Use CO2 7.4 609.1 10.00% 20.00% 50.00% 50.00% 50.99% 53.85% 0.0001% 0.0001% 0.07% 0.04% 0.00% 2F1 Refrigeration and Air Conditioning HFCs 0.0 5 836.9 25.00% 25.00% 25.00% 25.00% 35.36% 35.36% 0.0022% 0.0022% 0.32% 0.45% 0.00% 2F2 Foam Blowing Agents HFCs 0.0 2.1 50.00% 50.00% 25.00% 25.00% 55.90% 55.90% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2F3 Fire Protection HFCs 0.0 87.5 25.00% 25.00% 25.00% 25.00% 35.36% 35.36% 0.0000% 0.0000% 0.00% 0.01% 0.00% 2F4 Aerosols HFCs 0.0 18.3 25.00% 25.00% 25.00% 25.00% 35.36% 35.36% 0.0000% 0.0000% 0.00% 0.00% 0.00% 2G1 Use of Electrical equipment SF6 0.0 65.7 25.00% 25.00% 25.00% 25.00% 35.36% 35.36% 0.0000% 0.0000% 0.00% 0.01% 0.00% 3A1 Enteric fermentation - cattle CH4 31 660.5 28 139.0 17.19% 17.19% 13.82% 13.82% 22.06% 22.06% 0.0203% 0.0203% 0.05% 1.48% 0.02% 3A2 Enteric fermentation - sheep CH4 8 121.1 6 377.5 13.07% 13.07% 30.80% 30.80% 33.46% 33.46% 0.0024% 0.0024% 0.09% 0.25% 0.00% 3A3 Enteric fermentation - swine CH4 53.0 43.2 50.25% 50.25% 20.00% 20.00% 54.08% 54.08% 0.0000% 0.0000% 0.00% 0.01% 0.00% 3A4d Enteric fermentation - goats CH4 1 990.8 1 578.6 20.62% 20.62% 35.50% 35.50% 41.05% 41.05% 0.0002% 0.0002% 0.02% 0.10% 0.00% 3A4e Enteric fermentation - horses CH4 136.1 168.0 7.07% 7.07% 40.00% 40.00% 40.62% 40.62% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3A4f Enteric fermentation - mules and asses CH4 45.9 45.8 11.18% 11.18% 40.00% 40.00% 41.53% 41.53% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3A4h Enteric fermentation - other game CH4 0.0 0.0 0.00% 0.00% 0.0000% 0.0000% 0.00% 0.00% 0.00% 402 3B1 Manure management - cattle CH4 1 300.9 1 242.9 17.19% 17.19% 12.00% 12.00% 20.97% 20.97% 0.0000% 0.0000% 0.00% 0.07% 0.00% 3B2 Manure management - sheep CH4 108.8 87.6 13.07% 13.07% 16.00% 16.00% 20.66% 20.66% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B3 Manure management - swine CH4 256.2 208.9 50.25% 50.25% 20.00% 20.00% 54.08% 54.08% 0.0000% 0.0000% 0.00% 0.03% 0.00% 3B4d Manure management - goats CH4 33.7 27.1 20.62% 20.62% 47.75% 47.75% 52.01% 52.01% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B4e Manure management - horses CH4 0.0 0.0 7.07% 7.07% 30.00% 30.00% 30.82% 30.82% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B4f Manure management - mules and asses CH4 0.0 0.0 11.18% 11.18% 30.00% 30.00% 32.02% 32.02% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B4g Manure management - poultry CH4 112.7 175.4 20.62% 20.62% 20.00% 20.00% 28.72% 28.72% 0.0000% 0.0000% 0.00% 0.01% 0.00% 3B4h Manure management - other game CH4 0.0 0.0 0.00% 0.00% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3A2a Manure management - cattle N2O 1 048.5 1 142.8 43.20% 43.20% 50.00% 100.00 % 66.08% 108.93% 0.0003% 0.0008% 0.03% 0.15% 0.00% 3A2c Manure management - sheep N2O 159.2 143.5 32.95% 32.95% 50.00% 100.00 % 59.88% 105.29% 0.0000% 0.0000% 0.00% 0.01% 0.00% 3B3 Manure management - swine N2O 29.6 24.2 59.37% 59.37% 50.00% 100.00 % 77.62% 116.30% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3A2d Manure management - goats N2O 59.2 49.2 36.46% 36.46% 50.00% 100.00 % 61.88% 106.44% 0.0000% 0.0000% 0.00% 0.01% 0.00% 403 3B4e Manure management - horses N2O 0.0 0.0 31.22% 31.22% 50.00% 100.00 % 58.95% 104.76% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B4f Manure management - mules and asses N2O 0.0 0.0 32.08% 32.08% 50.00% 100.00 % 59.41% 105.02% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B4g Manure management - poultry N2O 557.3 857.8 44.16% 44.16% 50.00% 100.00 % 66.71% 109.32% 0.0002% 0.0005% 0.07% 0.12% 0.00% 3B4h Manure management - other game N2O 0.00% 0.00% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3B5 Indirect N2O from manure management N2O 291.9 325.2 89.47% 89.47% 100.00 % 101.00 % 134.18 % 134.93% 0.0001% 0.0001% 0.01% 0.09% 0.00% 3D1a Inorganic fertilisers N2O 1 732.1 1 713.6 15.00% 15.00% 100.00 % 120.00 % 101.12 % 120.93% 0.0016% 0.0023% 0.02% 0.08% 0.00% 3D1b Organic fertilisers N2O 643.4 775.6 41.18% 41.18% 100.00 % 120.00 % 108.15 % 126.87% 0.0004% 0.0005% 0.04% 0.10% 0.00% 3D1c Urine and dund deposited by grazing animals N2O 6 179.9 5 295.9 53.57% 53.57% 100.00 % 230.00 % 113.44 % 236.16% 0.0190% 0.0823% 0.26% 0.87% 0.01% 3D1d Crop residues N2O 426.9 977.7 91.24% 91.24% 100.00 % 120.00 % 135.37 % 150.75% 0.0009% 0.0011% 0.15% 0.27% 0.00% 3D1e FSOM N2O 592.2 620.1 5.00% 5.00% 100.00 % 230.00 % 100.12 % 230.05% 0.0002% 0.0011% 0.03% 0.01% 0.00% 3D2a Atmospheric deposition N2O 995.4 921.6 203.94 % 203.94 % 100.00 % 120.00 % 227.13 % 236.62% 0.0023% 0.0025% 0.00% 0.58% 0.00% 3D2b Nitrogen leaching and runoff N2O 61.1 73.6 58.45% 58.45% 100.00 % 82.00% 115.83 % 100.70% 0.0000% 0.0000% 0.00% 0.01% 0.00% 3F Field burning of agricultural residues CH4 55.5 47.0 21.21% 21.21% 40.00% 40.00% 45.28% 45.28% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3F Field burning of agricultural residues N2O 13.6 11.5 21.21% 21.21% 27.00% 27.00% 34.34% 34.34% 0.0000% 0.0000% 0.00% 0.00% 0.00% 3G Liming CO2 1 820.0 1 860.7 75.00% 75.00% 50.00% 50.00% 90.14% 90.14% 0.0015% 0.0015% 0.02% 0.43% 0.00% 404 3H Urea application CO2 297.3 584.7 10.00% 10.00% 50.00% 50.00% 50.99% 50.99% 0.0000% 0.0000% 0.03% 0.02% 0.00% 4A1 Forest land remaining forest land CO2 -18 206.9 -34 404.1 22.82% 22.82% 56.79% 56.79% 61.20% 61.20% 0.2334% 0.2334% 2.12% 2.40% 0.10% 4A2 Land converted to forest land CO2 -30 006.8 -55 678.3 30.79% 30.79% 62.15% 62.15% 69.36% 69.36% 0.7852% 0.7852% 3.68% 5.25% 0.41% 4A1 Forest land remaining forest land CH4 1 008.7 1 138.7 18.53% 18.53% 35.16% 35.16% 39.75% 39.75% 0.0001% 0.0001% 0.01% 0.06% 0.00% 4A2 Land converted to forest land CH4 500.1 475.6 27.92% 27.92% 29.95% 29.95% 40.95% 40.95% 0.0000% 0.0000% 0.00% 0.04% 0.00% 4A1 Forest land remaining forest land N2O 535.6 568.7 18.53% 18.53% 37.05% 37.05% 41.42% 41.42% 0.0000% 0.0000% 0.01% 0.03% 0.00% 4A2 Land converted to forest land N2O 352.7 340.6 27.92% 27.92% 31.56% 31.56% 42.14% 42.14% 0.0000% 0.0000% 0.00% 0.03% 0.00% 4B1 Cropland remaining cropland CO2 234.6 79.5 9.99% 9.99% 21.79% 21.79% 23.97% 23.97% 0.0000% 0.0000% 0.01% 0.00% 0.00% 4B2 Land converted to cropland CO2 4 130.5 3 429.4 19.88% 19.88% 22.05% 22.05% 29.68% 29.68% 0.0005% 0.0005% 0.02% 0.21% 0.00% 4C1 Grassland remaining grassland CO2 -1 546.3 12 649.3 28.10% 28.10% 30.72% 30.72% 41.64% 41.64% 0.0146% 0.0146% 0.94% 1.09% 0.02% 4C2 Land converetd to grassland CO2 -1 739.2 -2 072.4 21.27% 21.27% 30.72% 30.72% 37.37% 37.37% 0.0003% 0.0003% 0.03% 0.13% 0.00% 4C1 Grassland remaining grassland CH4 501.5 578.7 27.17% 27.17% 39.10% 39.10% 47.61% 47.61% 0.0000% 0.0000% 0.01% 0.05% 0.00% 4C2 Land converetd to grassland CH4 77.6 127.2 20.40% 20.40% 39.10% 39.10% 44.10% 44.10% 0.0000% 0.0000% 0.00% 0.01% 0.00% 4C1 Grassland remaining grassland N2O 433.3 500.0 27.17% 27.17% 47.60% 47.60% 54.81% 54.81% 0.0000% 0.0000% 0.01% 0.04% 0.00% 4C2 Land converetd to grassland N2O 67.5 110.6 20.40% 20.40% 47.60% 47.60% 51.79% 51.79% 0.0000% 0.0000% 0.00% 0.01% 0.00% 4D1 Wetland remaining wetland CO2 59.6 203.1 11.62% 11.62% 30.00% 30.00% 32.17% 32.17% 0.0000% 0.0000% 0.01% 0.01% 0.00% 405 4D2 Land converted to wetland CO2 616.1 544.8 21.51% 21.51% 30.00% 30.00% 36.92% 36.92% 0.0000% 0.0000% 0.00% 0.04% 0.00% 4D1 Wetland remaining wetland CH4 6 990.6 4 597.6 8.53% 8.53% 39.10% 39.10% 40.02% 40.02% 0.0018% 0.0018% 0.17% 0.12% 0.00% 4D2 Land converted to wetland CH4 1 655.1 4 099.3 20.23% 20.23% 39.10% 39.10% 44.02% 44.02% 0.0017% 0.0017% 0.21% 0.25% 0.00% 4D1 Wetland remaining wetland N2O 426.4 287.8 8.53% 8.53% 47.60% 47.60% 48.36% 48.36% 0.0000% 0.0000% 0.01% 0.01% 0.00% 4D2 Land converted to wetland N2O 101.0 256.6 20.23% 20.23% 47.60% 47.60% 51.72% 51.72% 0.0000% 0.0000% 0.02% 0.02% 0.00% 4E1 Settlement remaining settlement CO2 -267.4 -446.2 18.16% 18.16% 30.72% 30.72% 35.69% 35.69% 0.0000% 0.0000% 0.01% 0.02% 0.00% 4E2 Land converted to settlements CO2 819.9 704.7 18.92% 18.92% 30.72% 30.72% 36.08% 36.08% 0.0000% 0.0000% 0.00% 0.04% 0.00% 4E1 Settlement remaining settlement CH4 3.3 2.0 16.11% 16.11% 39.10% 39.10% 42.29% 42.29% 0.0000% 0.0000% 0.00% 0.00% 0.00% 4E2 Land converted to settlements CH4 0.6 0.8 17.89% 17.89% 39.10% 39.10% 43.00% 43.00% 0.0000% 0.0000% 0.00% 0.00% 0.00% 4E1 Settlement remaining settlement N2O 2.9 1.7 16.11% 16.11% 47.60% 47.60% 50.25% 50.25% 0.0000% 0.0000% 0.00% 0.00% 0.00% 4E2 Land converted to settlements N2O 0.5 0.7 17.89% 17.89% 47.60% 47.60% 50.85% 50.85% 0.0000% 0.0000% 0.00% 0.00% 0.00% 4F1 Other land remaining other land CO2 0.0 0.0 27.29% 27.29% 15.00% 15.00% 31.14% 31.14% 0.0000% 0.0000% 0.00% 0.00% 0.00% 4F2 Land converted to other land CO2 7 812.5 19 025.4 34.45% 34.45% 15.00% 15.00% 37.57% 37.57% 0.0269% 0.0269% 0.38% 2.01% 0.04% 4G Harvested wood products CO2 -2 106.2 -181.4 10.00% 10.00% 10.00% 10.00% 14.14% 14.14% 0.0000% 0.0000% 0.04% 0.01% 0.00% 5A Solid Waste Disposal CH4 4 505.3 8 596.0 50.00% 50.00% 40.00% 40.00% 64.03% 64.03% 0.0159% 0.0159% 0.38% 1.32% 0.02% 5B Biological Treatment of Solid Waste CH4 1 066.6 1 735.7 50.00% 50.00% 50.00% 50.00% 70.71% 70.71% 0.0008% 0.0008% 0.08% 0.27% 0.00% 406 5B Biological Treatment of Solid Waste N2O 425.6 794.7 50.00% 50.00% 50.00% 50.00% 70.71% 70.71% 0.0002% 0.0002% 0.04% 0.12% 0.00% 5C2 Open Burning of Waste CO2 13.6 28.9 50.00% 50.00% 40.00% 40.00% 64.03% 64.03% 0.0000% 0.0000% 0.00% 0.00% 0.00% 5C2 Open Burning of Waste CH4 114.1 243.3 50.00% 50.00% 100.00 % 100.00 % 111.80 % 111.80% 0.0000% 0.0000% 0.03% 0.04% 0.00% 5C2 Open Burning of Waste N2O 24.9 53.1 50.00% 50.00% 100.00 % 100.00 % 111.80 % 111.80% 0.0000% 0.0000% 0.01% 0.01% 0.00% 5D1 Domestic wastewater treatment and discharge CH4 2 257.3 3 506.9 50.00% 50.00% 40.00% 40.00% 64.03% 64.03% 0.0027% 0.0027% 0.12% 0.54% 0.00% 5D1 Domestic wastewater treatment and discharge N2O 466.2 724.3 50.00% 50.00% 90.00% 90.00% 102.96 % 102.96% 0.0003% 0.0003% 0.06% 0.11% 0.00% 5D2 Industrial wastewater treatment and discharge CH4 5 489.1 5 015.5 50.00% 50.00% 40.00% 40.00% 64.03% 64.03% 0.0054% 0.0054% 0.01% 0.77% 0.01% 462 205.4 435 827.7 1.46% 1.57% 0.80% Uncerta inty in total invento ry 12.08% 12.51% Trend uncertainty 8.93% C = Confidential 407 Appendix C: Summary emission tables for 2022 Table C.1: Summary emission table for South Africa for 2022. IPCC 2006 category Emissions and removals Net CO 2 CH 4 N 2 O HFCs PFCs SF6 NOx CO NMVOC Total GHGs (Gg) (Gg CO 2 e) (Gg) (Gg CO 2 e) Emissions (incl. LULUCF) 335 992.1 2 652.2 73.4 5 944.8 125.9 65.7 106.6 2 241.9 211.3 435 827.7 Emissions (excl. LULUCF) 392 138.4 2 258.6 65.6 5 944.8 125.9 65.7 1.6 57.2 0.0 478 887.5 1 - Energy 365 688.0 210.0 9.4 374 072.4 1.A - Fuel Combustion Activities 341 775.9 36.1 9.4 NE NE NE 345 274.5 1.B - Fugitive emissions from fuels 23 912.1 173.9 0.1 NE NE NE 28 797.9 1.C - Carbon dioxide Transport and Storage NE NE 2 - Industrial Processes and Product Use 23 976.1 4.3 1.4 5 944.8 125.9 65.7 30 598.0 2.A - Mineral Industry 6 055.1 NE 6 055.1 2.B - Chemical Industry 1 267.7 4.3 1.4 1 753.1 2.C - Metal Industry 15 528.5 0.0 0.0 NE 125.9 15 654.6 2.D - Non-Energy Products from Fuels and Solvent Use 1 124.8 NE NE 1 124.8 2.E - Electronics Industry NE NE NE NE NE 2.F - Product Uses as Substitutes for Ozone Depleting Substances NE 5 944.8 NE 5 944.8 2.G - Other Product Manufacture and Use NE NE NE 65.7 65.7 2.H - Other NA NA NA NA 3 - Agriculture 2 445.4 1 362.2 48.8 1.6 57.2 53 518.7 3.A – Enteric fermentation 1 298.3 36 352.1 3.B – Manure management 62.2 9.6 4 284.5 3.C – Rice cultivation NO NO 3.D – Agricultural soils NA 39.2 NE NE NE 10 378.1 3.E – Prescribed burning of savannas IE IE IE IE IE IE 408 3.F – Field burning of agricultural residues 1.7 0.0 1.6 57.2 NE 58.5 3.G – Liming 1 860.7 1 860.7 3.H – Urea application 584.7 584.7 3.I – Other carbon-containing fertilisers NE NE 4 - LULUCF -56 146.3 393.6 7.8 105.1 2 184.7 211.3 -43 059.8 4.A – Forest land -90 082.4 57.7 3.4 57.7 1 421.2 150.1 -87 558.8 4.B - Cropland 3 508.9 IE IE IE IE IE 3 508.9 4.C - Grassland 10 576.8 25.2 2.3 42.8 713.2 55.3 11 893.4 4.D - Wetland 747.9 310.6 2.1 4.4 47.6 5.7 9 989.2 4.E - Settlements 258.5 0.1 0.0 0.2 2.7 0.2 263.6 4.F – Other land 19 025.4 NO IE NO NO NO 19 025.4 4.G – Harvested wood products -181.4 -181.4 5 - Waste 28.9 682.1 5.9 20 698.4 5.A - Solid Waste Disposal 307.0 NE 8 596.0 5.B - Biological Treatment of Solid Waste 62.0 3.0 2 530.4 5.C - Incineration and Open Burning of Waste 28.9 8.7 0.2 325.4 5.D - Wastewater Treatment and Discharge 304.4 2.7 9 246.7 5.E – Other NO NO NO NO Memo items International bunkers 3 673.2 0.1 0.1 3 703.8 International aviation 2 311.0 0.0 0.1 2 328.2 International water-borne transport 1 362.1 0.1 0.0 1 375.7 Multilateral operations NA NA NA NA 409 Table C.2: Summary Energy sector emission table for South Africa for 2022. Categories Emissions (Gg) Emissions CO 2 CH 4 N 2 O NOx CO NMVOCs SO 2 (Gg CO 2 e) 1 - Energy 365 688 210 9 0 0 0 0 374 061 1.A - Fuel Combustion Activities 341 776 36 9 0 0 0 0 345 275 1.A.1 - Energy Industries 224 656 4 3 0 0 0 0 225 656 1.A.1.a - Main Activity Electricity and Heat Production 196 116 2 3 NE NE NE NE 196 977 1.A.1.a.i - Electricity Generation 196 116 2 3 NE NE NE NE 196 977 1.A.1.a.ii - Combined Heat and Power Generation (CHP) NE NE NE NE 0 1.A.1.a.iii - Heat Plants NE NE NE NE 0 1.A.1.b - Petroleum Refining 1 079 0 0 NE NE NE NE 1 080 1.A.1.c - Manufacture of Solid Fuels and Other Energy Industries 27 461 1 0 NE NE NE NE 27 599 1.A.1.c.i - Manufacture of Solid Fuels 27 461 1 0 NE NE NE NE 27 599 1.A.1.c.ii - Other Energy Industries NE NE NE NE NE NE NE NE 1.A.2 - Manufacturing Industries and Construction 26 420 2 1 0 0 0 0 26 647 1.A.2.a - Iron and Steel 1 626 0 0 NE NE NE NE 1 630 1.A.2.b - Non-Ferrous Metals 786 0 0 NE NE NE NE 789 1.A.2.-ab - 1A2 Remainder 24 007 2 1 NE NE NE NE 24 228 1.A.2.c - Chemicals IE IE IE NE NE NE NE IE 1.A.2.d - Pulp, Paper and Print IE IE IE NE NE NE NE IE 1.A.2.e - Food Processing, Beverages and Tobacco IE IE IE NE NE NE NE IE 1.A.2.f - Non-Metallic Minerals IE IE IE NE NE NE NE IE 1.A.2.g - Transport Equipment IE IE IE NE NE NE NE IE 1.A.2.h - Machinery IE IE IE NE NE NE NE IE 1.A.2.i - Mining (excluding fuels) and Quarrying IE IE IE NE NE NE NE IE 1.A.2.j - Wood and wood products IE IE IE NE NE NE NE IE 1.A.2.k - Construction IE IE IE NE NE NE NE IE 410 1.A.2.l - Textile and Leather IE IE IE NE NE NE NE IE 1.A.2.m - Non-specified Industry IE IE IE NE NE NE NE IE 1.A.3 - Transport 51 731 12 3 0 0 0 0 52 744 1.A.3.a - Civil Aviation 684 0 0 NE NE NE NE 689 1.A.3.a.i - International Aviation (International Bunkers) 2 311 0 0 NE NE NE NE 2 328 1.A.3.a.ii - Domestic Aviation 684 0 0 NE NE NE NE 689 1.A.3.b - Road Transportation 50 190 12 2 NE NE NE NE 51 159 1.A.3.b.i - Cars NE NE NE NE 0 1.A.3.b.i.1 - Passenger cars with 3-way catalysts NE NE NE NE 0 1.A.3.b.i.2 - Passenger cars without 3-way catalysts NE NE NE NE 0 1.A.3.b.ii - Light-duty trucks NE NE NE NE 0 1.A.3.b.ii.1 - Light-duty trucks with 3-way catalysts NE NE NE NE 0 1.A.3.b.ii.2 - Light-duty trucks without 3-way catalysts NE NE NE NE 0 1.A.3.b.iii - Heavy-duty trucks and buses NE NE NE NE 0 1.A.3.b.iv - Motorcycles NE NE NE NE 0 1.A.3.b.v - Evaporative emissions from vehicles NE NE NE NE 0 1.A.3.b.vi - Urea-based catalysts NE NE NE NE 0 1.A.3.c - Railways 362 0 0 NE NE NE NE 395 1.A.3.d - Water-borne Navigation 495 0 0 NE NE NE NE 500 1.A.3.d.i - International water-borne navigation (International bunkers) 1 362 0 0 1 376 1.A.3.d.ii - Domestic Water-borne Navigation 495 0 0 NE NE NE NE 500 1.A.3.e - Other Transportation NE NE NE NE 0 1.A.3.e.i - Pipeline Transport NE NE NE NE NE NE NE NE 1.A.3.e.ii - Off-road IE IE IE NE NE NE NE NE 1.A.4 - Other Sectors 18 617 6 1 0 0 0 0 19 029 1.A.4.a - Commercial/Institutional 12 038 0 0 NE NE NE NE 12 077 1.A.4.b - Residential 3 496 6 1 NE NE NE NE 3 858 1.A.4.c - Agriculture/Forestry/Fishing/Fish Farms 3 084 0 0 NE NE NE NE 3 095 411 1.A.4.c.i - Stationary 3 084 0 0 NE NE NE NE 3 095 1.A.4.c.ii - Off-road Vehicles and Other Machinery IE IE IE NE NE NE NE NE 1.A.4.c.iii - Fishing (mobile combustion) IE IE IE NE NE NE NE NE 1.A.5 - Non-Specified 20 352 12 2 0 0 0 0 21 198 1.A.5.a - Stationary 20 352 12 2 NE NE NE NE 21 198 1.A.5.b - Mobile NE NE NE NE 0 1.A.5.b.i - Mobile (aviation component) NE NE NE NE NE NE NE NE 1.A.5.b.ii - Mobile (water-borne component) NE NE NE NE NE NE NE NE 1.A.5.b.iii - Mobile (Other) NE NE NE NE NE NE NE NE 1.A.5.c - Multilateral Operations 1.B - Fugitive emissions from fuels 23 912 174 0 0 0 0 0 28 787 1.B.1 - Solid Fuels 366 69 0 0 0 0 2 289 1.B.1.a - Coal mining and handling 16 60 NE NE NE NE 1 689 1.B.1.a.i - Underground mines 16 60 NE NE NE NE 1 689 1.B.1.a.i.1 - Mining 13 48 NE NE NE NE 1 369 1.B.1.a.i.2 - Post-mining seam gas emissions 3 11 NE NE NE NE 320 1.B.1.a.i.3 - Abandoned underground mines NE NE NE NE NE NE NE 1.B.1.a.i.4 - Flaring of drained methane or conversion of methane to CO2 NE NE NE NE NE NE NE 1.B.1.a.ii - Surface mines 0 0 NE NE NE NE 0 1.B.1.a.ii.1 - Mining 0 0 NE NE NE NE 0 1.B.1.a.ii.2 - Post-mining seam gas emissions 0 0 NE NE NE NE 0 1.B.1.b - Uncontrolled combustion and burning coal dumps NE NE NE NE NE NE NE NE 1.B.1.c - Solid fuel transformation 349 9 0 NE NE NE NE NE 1.B.2 - Oil and Natural Gas 70 0 0 0 0 0 0 94 1.B.2.a - Oil 70 0 0 NE NE NE NE 94 1.B.2.a.i - Venting NE NE NE NE NE 1.B.2.a.ii - Flaring NE NE NE NE NE 1.B.2.a.iii - All Other NE NE NE NE 0 412 1.B.2.a.iii.1 - Exploration NE NE NE NE 0 1.B.2.a.iii.2 - Production and Upgrading NE NE NE NE 0 1.B.2.a.iii.3 - Transport NE NE NE NE 0 1.B.2.a.iii.4 - Refining NE NE NE NE 0 1.B.2.a.iii.5 - Distribution of oil products NE NE NE NE 0 1.B.2.a.iii.6 - Other NE NE NE NE 0 1.B.2.b - Natural Gas NE NE NE NE 0 1.B.2.b.i - Venting NE NE NE NE 0 1.B.2.b.ii - Flaring NE NE NE NE 0 1.B.2.b.iii - All Other NE NE NE NE 0 1.B.2.b.iii.1 - Exploration NE NE NE NE 0 1.B.2.b.iii.2 - Production NE NE NE NE 0 1.B.2.b.iii.3 - Processing NE NE NE NE 0 1.B.2.b.iii.4 - Transmission and Storage NE NE NE NE 0 1.B.2.b.iii.5 - Distribution NE NE NE NE 0 1.B.2.b.iii.6 - Other NE NE NE NE 0 1.B.3 - Other emissions from Energy Production 23 477 105 0 0 0 0 0 26 410 1.C - Carbon dioxide Transport and Storage 0 0 0 0 0 0 1.C.1 - Transport of CO2 0 0 0 0 0 0 1.C.1.a - Pipelines NE NE NE NE NE NE 1.C.1.b - Ships NE NE NE NE NE NE 1.C.1.c - Other (please specify) NE NE NE NE NE NE 1.C.2 - Injection and Storage 0 0 0 0 0 0 1.C.2.a - Injection NE NE NE NE NE NE 1.C.2.b - Storage NE NE NE NE NE NE 1.C.3 - Other 0 0 0 0 0 0 413 Table C.3: Summary IPPU sector emission table for South Africa for 2022. Categories Emissions (Gg) Emissions CO 2 CH 4 N 2 O HFCs PFCs SF6 NOx CO NMVOCs SO 2 (Gg CO 2 e) 2. Industrial processes and product use 24 056.8 6.6 2.7 5 944.8 125.9 0.0 0.0 0.0 0.0 0.0 26 594.8 2A Mineral Industry 6 055.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6 055.1 2A1 Cement production 5 022.7 NE NE NE NE 5 022.7 2A2 Lime production 693.7 NE NE NE NE 693.7 2A3 Glass Production 191.4 NE NE NE NE 191.4 2A4 Other Process Uses of Carbonates 147.3 NE NE NE NE 147.3 2A4a Ceramics 34.3 NE NE NE NE 34.3 2A4b Other Uses of Soda Ash 31.5 NE NE NE NE 31.5 2A4c Non Metallurgical Magnesia Production 0 NE NE NE NE 0 2A4d Other (please specify) (3) 81.5 NE NE NE NE 81.5 2A5 Other (please specify) (3) NE NE NE NE NE 2B Chemical Industry 1 348.4 6.6 2.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 246.8 2B1 Ammonia Production C C NE NE NE NE C 2B2 Nitric Acid Production C NE NE NE NE C 2B3 Adipic Acid Production NE NE NE NE NE NE 2B4 Caprolactam. Glyoxal and Glyoxylic Acid Production NE NE NE NE NE NE 2B5 Carbide Production C NE NE NE NE NE C 2B6 Titanium Dioxide Production C NE NE NE NE C 2B7 Soda Ash Production C NE NE NE NE C 2B8 Petrochemical and Carbon Black Production C C NE NE NE NE NE NE NE NE C 2B8a Methanol NO NO NO NO NO NO NO 414 2B8b Ethylene NO NO NO NO NO NO NO 2B8c Ethylene Dichloride and Vinyl Chloride Monomer NO NO NO NO NO NO NO 2B8d Ethylene Oxide NO NO NO NO NO NO NO 2B8e Acrylonitrile NO NO NO NO NO NO NO 2B8f Carbon Black C C NE NE NE NE C 2B8g Hydrogen Production C NO NE NE NE NE C 2B9 Fluorochemical Production NE NE NE NE NE NE NE NE NE NE NE 2B9a By-product emissions (4) NE NE NE NE NE NE 2B9b Fugitive Emissions (4) NE NE NE NE 0.0 2B10 Other (Please specify) (3) NE NE NE NE 0.0 2C Metal Industry 15 528.5 23.8 0.0 0.0 125.9 0.0 0.0 0.0 0.0 0.0 15 654.4 2C1 Iron and Steel Production 6 307.2 0.0 NE NE NE NE 6 307.2 2C2 Ferroalloys Production 8 808.6 0.0 NE NE NE NE 8 808.6 2C3 Aluminium production 1 133.5 125.9 NE NE NE NE 1 259.3 2C4 Magnesium production (5) 0.0 NO NO NO NO NO 0.0 2C5 Lead Production 7.2 NE NE NE NE 7.2 2C6 Zinc Production 0.0 NE NE NE NE 0.0 2C7 Other (please specify) (3) 0.0 NE NE NE NE 0.0 2D Non-Energy Products from Fuels and Solvent Use (6) 1 124.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 124.8 2D1 Lubricant Use 515.7 NE NE NE NE 515.7 2D2 Paraffin Wax Use 609.1 NE NE NE NE 609.1 2D3 Solvent Use (7) NE NE NE NE 0.0 2D4 Other (please specify) (3). (8) NE NE NE NE 0.0 2E Electronics Industry 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2E1 Integrated Circuit or Semiconductor (9) NE NE NE NE NE NE NE NE 2E2 TFT Flat Panel Display (9) NE NE NE NE NE NE NE 415 2E3 Photovoltaics (9) NE NE NE NE NE NE 2E4 Heat Transfer Fluid (10) NE NE NE NE NE NE 2E5 Other (please specify) (3) NE NE NE NE NE 2F Product Uses as Substitutes for Ozone Depleting Substances 0.0 0.0 0.0 5 944.8 0.0 0.0 0.0 0.0 0.0 0.0 5 944.8 2F1 Refrigeration and Air Conditioning 0.0 0.0 0.0 5 836.9 NE NE NE NE NE NE 5 836.9 2F1a Refrigeration and Stationary Air Conditioning 3 292.6 NE NE NE NE 3 292.6 2F1b Mobile Air Conditioning 2 544.3 NE NE NE NE 2 544.3 2F2 Foam Blowing Agents 2.1 NE NE NE NE 2.1 2F3 Fire Protection 87.5 NE NE NE NE NE 87.5 2F4 Aerosols 18.3 NE NE NE NE 18.3 2F5 Solvents NE NE NE NE NE NE NE 2F6 Other Applications (please specify) (3) NO NO NO NO NO NO NO 2G Other Product Manufacture and Use 0.0 0.0 0.0 0.0 0.0 65.7 0.0 0.0 0.0 0.0 65.7 2G1 Electrical Equipment NE NE NE NE NE 65.7 NE NE NE NE 65.7 2G1a Manufacture of Electrical Equipment NE NE NE NE NE NE NE 2G1b Use of Electrical Equipment NE 65.7 NE NE NE NE 65.7 2G1c Disposal of Electrical Equipment NE NE NE NE NE NE NE 2G2 SF6 and PFCs from Other Product Uses NE NE NE NE NE NE NE NE NE NE NE 2G2a Military Applications NE NE NE NE NE NE NE 2G2b Accelerators NE NE NE NE NE NE NE 2G2c Other (please specify) (3) NE NE NE NE NE NE NE 2G3 N2O from Product Uses NE NE NE NE NE NE NE NE NE NE NE 2G3a Medical Applications NE NE NE NE NE NE 416 2G3b Propellant for pressure and aerosol products NE NE NE NE NE NE 2G3c Other (Please specify) (3) NE NE NE NE NE NE 2G4 Other (Please specify) (3) NE NE NE NE NE 2H Other 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2H1 Pulp and Paper Industry NE NE NE NE 0.0 2H2 Food and Beverages Industry NE NE NE NE 0.0 2H3 Other (please specify) (3) NE NE NE NE 0.0 417 Table C.4: Summary Agriculture sector emission table for South Africa for 2022. Emissions Total emissions (Gg) (Gg CO 2 e) CO 2 CH 4 N 2 O NOx CO NMVOCs 3 - Agriculture 2 445.4 1 385.3 50.7 671.9 32.6 38.9 53 518.67 Livestock 0.0 1 360.5 8.4 0.0 0.0 0.0 40 636.63 3.A - Enteric Fermentation 0.0 1 298.3 0.0 0.0 0.0 0.0 36 352.11 3.A.1 - Cattle 1 005.0 28 139.0 3.A.1.a - Dairy Cows 130.0 3 639.7 3.A.1.b - Other Cattle 875.0 24 499.2 3.A.2 - Sheep 227.8 6 377.5 3.A.3 - Swine 1.5 43.2 3.A.4 – Other 3.A.4.a- Buffalo NO NO 3.A.4.b - Camels NO NO 3.A.4.d - Goats 56.4 1 578.6 3.A.4.e - Horses 6.0 168.0 3.A.4.f - Mules and Asses 1.6 45.8 3.B - Manure Management 0.0 62.2 8.4 0.0 0.0 0.0 4 284.5 3.B.1 - Cattle 44.4 4.3 2 385.7 3.B.1.a - Dairy Cows 20.1 0.8 776.9 3.B.1.b - Other Cattle 24.3 3.5 1 608.8 3.B.2 - Sheep 3.1 0.5 87.6 3.B.3 - Swine 7.5 0.1 208.9 3.B.4 – Other 3.B.4-a Buffalo NO NO NO 3.B.4-b Camels NO NO NO 3.B.4.d - Goats 1.0 0.2 27.1 3.B.4.e - Horses NO NO NO 3.B.4.f - Mules and Asses NO NO NO 418 3.B.4.g - poultry 6.3 3.2 175.4 3.B.5 – Indirect N 2 O emissions 1.2 325.2 3.C - Rice cultivation NO NO NO NO 3.D - Agricultural soils 0.0 39.2 2.7 1 093.9 47.7 53.4 10 378.1 3.D.1-Direct N 2 O Emissions from managed soils 35.4 9 382.9 3.D.2-Indirect N 2 O Emissions from managed soils 3.8 995.2 3.E - Prescribed burning of savannas IE IE IE IE 3.F-Field burning of agricultural residues 1.7 0.0 57.2 1.6 NO 1.7 58.5 3.G - Liming 1 860.7 1 860.7 3.H - Urea application 584.7 584.7 3.I - Other carbon containing fertilisers NE NE NE NE 3.J - Other NO NO NO NO 419 Table C.5: Summary LULUCF sector emission table for South Africa for 2022. Emissions Total emissions (Gg) (Gg CO 2 e) CO 2 CH 4 N 2 O NOx CO NMVOCs 4 - LULUCF -56 146.3 393.6 7.8 105.1 2 184.7 210.9 -43 059.8 4.A - Forest land -90 082.4 57.7 3.4 57.7 1 421.2 150.1 -87 558.8 4.A.1 - Forest land Remaining Forest land -34 404.1 40.7 2.1 36.7 981.7 109.5 -32 696.7 4.A.2 - Land Converted to Forest land -55 678.3 17.0 1.3 21.0 439.5 40.6 -54 862.1 4.A.2.a - Cropland converted to Forest Land -4 993.1 1.5 0.1 2.0 40.2 2.1 -4 918.5 4.A.2.b - Grassland converted to Forest Land -47 358.8 14.0 1.1 17.6 363.1 34.7 -46 684.6 4.A.2.c - Wetlands converted to Forest Land -1 073.4 0.6 0.0 0.6 15.5 1.8 -1 044.4 4.A.2.d - Settlements converted to Forest Land -805.6 0.4 0.0 0.4 10.6 1.1 -785.9 4.A.2.e - Other Land converted to Forest Land -1 447.3 0.4 0.0 0.5 10.0 1.0 -1 428.7 4.B - Cropland 3 508.9 IE IE IE IE IE 3 508.9 4.B.1 - Cropland Remaining Cropland 79.5 IE IE IE IE IE 79.5 4.B.2 - Land Converted to Cropland 3 429.4 IE IE IE IE IE 3 429.4 4.B.2.a - Forest Land converted to Cropland 1 423.5 IE IE IE IE IE 1 423.5 4.B.2.b - Grassland converted to Cropland 1 894.3 IE IE IE IE IE 1 894.3 4.B.2.c - Wetlands converted to Cropland 242.9 IE IE IE IE IE 242.9 4.B.2.d - Settlements converted to Cropland -24.8 IE IE IE IE IE -24.8 4.B.2.e - Other Land converted to Cropland -106.6 IE IE IE IE IE -106.6 4.C - Grassland 10 576.8 25.2 2.3 42.8 713.2 55.3 11 893.4 4.C.1 - Grassland Remaining Grassland 12 649.3 20.7 1.9 35.0 584.0 45.8 13 728.0 4.C.2 - Land Converted to Grassland -2 072.4 4.5 0.4 7.8 129.2 9.4 -1 834.6 4.C.2.a - Forest Land converted to Grassland 10 864.7 2.5 0.2 4.3 71.9 4.9 10 997.5 4.C.2.b - Cropland converted to Grassland -2 297.8 0.6 0.1 1.1 17.6 1.4 -2 265.2 4.C.2.c - Wetlands converted to Grassland -501.7 0.2 0.0 0.4 6.2 0.5 -491.1 4.C.2.d - Settlements converted to Grassland -110.2 0.1 0.0 0.2 3.6 0.3 -103.5 4.C.2.e - Other Land converted to Grassland -10 027.5 1.1 0.1 1.8 29.9 2.3 -9 972.3 420 4.D - Wetlands 747.9 310.6 2.1 4.4 47.6 5.3 9 989.2 4.D.1 - Wetlands Remaining Wetlands 203.1 164.2 1.1 2.3 38.6 3.0 5 088.5 4.D.2 - Land Converted to Wetlands 544.8 146.4 1.0 2.1 8.9 2.3 4 900.7 4.D.2.c.i - Forest Land converted to wetlands 171.9 37.9 0.3 0.5 8.9 0.7 1 299.2 4.D.2.c.ii - Cropland converted to wetlands -13.8 9.6 0.1 0.1 0.0 0.2 272.5 4.D.2.c.iii - Grasslands converted to wetlands 417.7 94.0 0.6 1.3 0.0 1.3 3 213.4 4.D.2.c.iv - Settlements converted to wetlands 8.5 2.3 0.0 0.0 0.0 0.0 76.3 4.D.2.c.v - Other Land converted to wetlands -39.5 2.7 0.0 0.0 0.0 0.0 39.4 4.E - Settlements 258.5 0.1 0.0 0.2 2.7 0.2 263.6 4.E.1 - Settlements Remaining Settlements -446.2 0.1 0.0 0.1 2.0 0.1 -442.6 4.E.2 - Land Converted to Settlements 704.7 0.0 0.0 0.0 0.8 0.1 706.1 4.E.2.a - Forest Land converted to Settlements 525.5 0.0 0.0 0.0 0.3 0.0 526.0 4.E.2.b - Cropland converted to Settlements -81.4 0.0 0.0 0.0 0.1 0.0 -81.2 4.E.2.c - Grassland converted to Settlements 268.5 0.0 0.0 0.0 0.3 0.0 269.2 4.E.2.d - Wetlands converted to Settlements 7.9 0.0 0.0 0.0 0.0 0.0 7.9 4.E.2.e - Other Land converted to Settlements -15.8 0.0 0.0 0.0 0.0 0.0 -15.8 4.F - Other Land 19 025.4 NO NO NO NO NO 19 025.4 4.F.1 - Other land Remaining Other land 0 NO NO NO NO NO 0.0 4.F.2 - Land Converted to Other land 19025.37 NO NO NO NO NO 19 025.4 4.F.2.a - Forest Land converted to Other Land 761.61443 NO NO NO NO NO 761.6 4.F.2.b - Cropland converted to Other Land 2.2401785 NO NO NO NO NO 2.2 4.F.2.c - Grassland converted to Other Land 18120.881 NO NO NO NO NO 18 120.9 4.F.2.d - Wetlands converted to Other Land 133.12041 NO NO NO NO NO 133.1 4.F.2.e - Settlements converted to Other Land 7.5173288 NO NO NO NO NO 7.5 4.G - Harvested wood products -181.4 NA NA NA NA NA -181.4 421 Table C.6: Summary Land sector emission table for South Africa for 2022. Categories Activity Data Net carbon stock change and CO2 emissions Net CO 2 emissions (Gg CO 2 ) Total Area (ha) Thereof: Area of organic soils (ha) Biomass Dead organic matter Soils Increase (Gg C) Decrease (Gg C) Net carbon stock change (Gg C) Carbon stock change (Gg C) Net carbon stock change (Gg C) Net carbon stock change in mineral soils (2) (Gg C) Carbon loss from drained organic soils (Gg C) 4 - LULUCF 123 562 851 NE 47 471.4 34 674.5 12 796.9 1 926.8 1 926.8 539.5 NE -55 964.9 4.A - Forest land 27 756 880 NE 41 955.1 19 632.9 22 322.1 1 826.8 1 826.8 419.0 NE -90 082.4 4.A.1 - Forest land Remaining Forest land 16 848 814 NE 25 227.5 16 161.4 9 066.1 316.8 316.8 0.0 NE -34 404.1 4.A.2 - Land Converted to Forest land 10 908 066 NE 16 727.6 3 471.5 13 256.0 1 509.9 1 509.9 419.0 NE -55 678.3 4.A.2.a - Cropland converted to Forest Land 1 005 784 NE 1 303.3 344.9 958.4 120.9 120.9 282.4 NE -4 993.1 4.A.2.b - Grassland converted to Forest Land 9 229 456 NE 14 504.0 2 887.0 11 616.9 1 294.8 1 294.8 4.3 NE -47 358.8 4.A.2.c - Wetlands converted to Forest Land 232 530 NE 334.7 95.3 239.5 30.1 30.1 23.1 NE -1 073.4 4.A.2.d - Settlements converted to Forest Land 193 200 NE 263.8 70.0 193.8 25.9 25.9 0.0 NE -805.6 422 4.A.2.e - Other Land converted to Forest Land 247 096 NE 321.8 74.4 247.4 38.2 38.2 109.1 NE -1 447.3 4.B - Cropland 14 298 177 NE 1 824.9 2 150.7 -325.8 139.2 139.2 -770.4 NE 3 508.9 4.B.1 - Cropland Remaining Cropland 11 303 557 NE 812.2 855.0 -42.8 16.6 16.6 4.5 NE 79.5 4.B.2 - Land Converted to Cropland 2 994 619 NE 1 012.7 1 295.7 -283.0 122.6 122.6 -774.8 NE 3 429.4 4.B.2.a - Forest Land converted to Cropland 917 367 NE 388.6 599.3 -210.7 17.0 17.0 -194.6 NE 1 423.5 4.B.2.b - Grassland converted to Cropland 1 922 251 NE 573.4 650.3 -76.9 97.2 97.2 -537.0 NE 1 894.3 4.B.2.c - Wetlands converted to Cropland 80 563 NE 18.1 45.4 -27.3 4.2 4.2 -43.2 NE 242.9 4.B.2.d - Settlements converted to Cropland 34 990 NE 5.8 0.2 5.6 1.1 1.1 0.0 NE -24.8 4.B.2.e - Other Land converted to Cropland 39 449 NE 26.9 0.7 26.2 3.0 3.0 -0.1 NE -106.6 4.C - Grassland 61 859 432 NE 3 458.3 8 569.4 -5 111.0 293.8 293.8 1 932.7 NE 10 576.8 4.C.1 - Grassland Remaining Grassland 49 110 515 NE 1 293.6 4 884.7 -3 591.2 141.4 141.4 0.0 NE 12 649.3 423 4.C.2 - Land Converted to Grassland 12 748 917 NE 2 164.8 3 684.6 -1 519.9 152.4 152.4 1 932.7 NE -2 072.4 4.C.2.a - Forest Land converted to Grassland 6 490 577 NE 460.6 3 278.5 -2 817.8 -142.2 -142.2 -3.1 NE 10 864.7 4.C.2.b - Cropland converted to Grassland 1 638 753 NE 161.3 46.0 115.3 11.4 11.4 499.9 NE -2 297.8 4.C.2.c - Wetlands converted to Grassland 574 317 NE 58.8 68.4 -9.6 16.9 16.9 129.6 NE -501.7 4.C.2.d - Settlements converted to Grassland 326 200 NE 23.6 4.6 18.9 11.4 11.4 -0.3 NE -110.2 4.C.2.e - Other Land converted to Grassland 3 719 070 NE 1 460.5 287.1 1 173.4 254.9 254.9 1 306.5 NE -10 027.5 4.D - Wetlands 3 100 156 NE 0.0 158.9 -158.9 15.1 15.1 -60.2 NE 747.9 4.D.1 - Wetlands Remaining Wetlands 2 423 513 NE 0.0 45.9 -45.9 1.5 1.5 -11.0 NE 203.1 4.D.2 - Land Converted to Wetlands 676 643 NE 0.0 113.0 -113.0 13.6 13.6 -49.2 NE 544.8 4.D.2.c.i - Forest Land converted to wetlands 14 549 NE 0.0 29.2 -29.2 2.2 2.2 -19.8 NE 171.9 4.D.2.c.ii - Cropland converted to wetlands 46 692 NE 0.0 3.9 -3.9 -0.2 -0.2 7.9 NE -13.8 424 4.D.2.c.iii - Grasslands converted to wetlands 495 844 NE 0.0 78.7 -78.7 10.7 10.7 -45.9 NE 417.7 4.D.2.c.iv - Settlements converted to wetlands 24 695 NE 0.0 1.0 -1.0 0.2 0.2 -1.5 NE 8.5 4.D.2.c.v - Other Land converted to wetlands 94 862 NE 0.0 0.0 0.0 0.7 0.7 10.1 NE -39.5 4.E - Settlements 3 644 453 NE 233.0 381.3 -148.2 37.1 37.1 40.6 NE 258.5 4.E.1 - Settlements Remaining Settlements 2 465 619 NE 168.3 49.1 119.2 2.4 2.4 0.0 NE -446.2 4.E.2 - Land Converted to Settlements 1 178 833 NE 64.7 332.2 -267.5 34.6 34.6 40.6 NE 704.7 4.E.2.a - Forest Land converted to Settlements 387 473 NE 24.8 176.1 -151.3 8.0 8.0 0.0 NE 525.5 4.E.2.b - Cropland converted to Settlements 145 332 NE 7.7 26.0 -18.3 3.8 3.8 36.7 NE -81.4 4.E.2.c - Grassland converted to Settlements 592 721 NE 30.8 125.5 -94.7 21.1 21.1 0.3 NE 268.5 4.E.2.d - Wetlands converted to Settlements 19 851 NE 0.5 4.4 -3.9 0.3 0.3 1.4 NE 7.9 4.E.2.e - Other Land converted to Settlements 33 457 NE 0.8 0.2 0.6 1.4 1.4 2.3 NE -15.8 4.F - Other Land 12 903 753 NE 0.0 3 781.4 -3 781.4 -385.2 -385.2 -1 022.2 NE 19 025.4 425 4.F.1 - Other land Remaining Other land 9 246 448 NE 0.0 0.0 0.0 NE 0.0 4.F.2 - Land Converted to Other land 3 657 305 NE 0.0 3 781.4 -3 781.4 -385.2 -385.2 -1 022.2 NE 19 025.4 4.F.2.a - Forest Land converted to Other Land 176 235 NE 0.0 125.7 -125.7 -17.5 -17.5 -64.6 NE 761.6 4.F.2.b - Cropland converted to Other Land 38 226 NE 0.0 0.8 -0.8 -0.1 -0.1 0.3 NE 2.2 4.F.2.c - Grassland converted to Other Land 3 294 173 NE 0.0 3 649.5 -3 649.5 -367.4 -367.4 -925.1 NE 18 120.9 4.F.2.d - Wetlands converted to Other Land 148 183 NE 0.0 5.2 -5.2 -0.2 -0.2 -30.9 NE 133.1 4.F.2.e - Settlements converted to Other Land 487 NE 0.0 0.1 -0.1 0.0 0.0 -1.9 NE 7.5 426 Table C.7: Summary Waste sector emission table for South Africa for 2022. Categories Emissions [Gg] Total emissions CO 2 CH 4 N 2 O NOx CO NMVOCs SO 2 (Gg CO 2 e) 5 - Waste 28.91 682.05 5.93 NE NE NE NE 20 698.43 5A - Solid Waste Disposal 307.00 NE NE NE NE 8 596.00 5A1 - Managed Waste Disposal Sites 307.00 NE NE NE NE 8 596.00 5A2 - Unmanaged Waste Disposal Sites NE NE NE NE NE NE 5A3 - Uncategorised Waste Disposal Sites NE NE NE NE NE NE 5B - Biological Treatment of Solid Waste 61.99 3.00 NE NE NE NE 2 530.38 5C - Incineration and Open Burning of Waste 28.91 8.69 0.20 NE NE NE NE 325.37 5C1 - Waste Incineration NE NE NE NE NE NE NE NE 5C2 - Open Burning of Waste 28.91 8.69 0.20 NE NE NE NE 325.37 5D - Wastewater Treatment and Discharge 0.00 304.37 2.73 NE NE NE NE 9 246.69 5D1 - Domestic Wastewater Treatment and Discharge 125.25 2.73 NE NE NE NE 4 231.15 5D2 - Industrial Wastewater Treatment and Discharge 179.13 NE NE NE NE 5 015.54 5E - Other (please specify) NE NE NE NE NE 427 Appendix D: Reference and sectoral fuel consumption The figures below show the comparison between the Reference and Sectoral Approach for 2000-2020 should this in line with the latest available data from the energy balance. Figure D.1: Comparisons between the solid fuel consumption determined by the reference and sectoral approaches, 2000 – 2020. 428 Figure D.2: Comparisons between the liquid fuel consumption determined by the reference and sectoral approaches, 2000 – 2020. Figure D.3: Comparisons between the gaseous fuel consumption determined by the reference and sectoral approaches, 2000 – 2020.

Draft 9th National Greenhouse Gas Inventory Report

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