The change in forest cover is a result of the changed areas losing sufficient tree cover to be reclassified as dense scrub or parkland, rather than conversion to settlement land or agriculture, therefore changes in soil carbon stocks do not occur. Def orestation in the Cayman Islands arises from mangrove conversion to urban development (Jurn et al. 2018) and occurs on The UK has assessed the available information on wildfires in the OTs and CDs, and will repeat this procedure for future i nventories. The procedure included reviewing available published government data on fire occurrence and contact with local experts for all CDs and OTs, which brought up no relevant information. We also conducted searches on global fire data portals (Global Forest Watch, Global Fire Emissions Database, and Global Wildfire Information System (GWIS)). All of these portals record zero occurrences of fires and zero burnt area for 2002-2019 for the Cayman Islands, Jersey, Guernsey and the Isle of Man. The GWIS portal does record a total of 11 fires between 2002 and 2019 in the Falkland Islands with an estimated total area of 5,024 ha based on pixel size, although the total burnt area is

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 934 Due to the lack of publicly available data in wildfires in the OTS and CDS we estimate the extent of biomass burning using geographical proxy burning rates. The pro rata area of grassland wildfires in the UK was used for grassland biomass burning in the Crown Dependencies, Cayman Islands and Falkland Islands. The estimated rate of grassland wildfire occurrence in the Falklands, using the geographical proxy, is 944 ha p.a., which has been assessed as a plausible annual area by local experts. The pro rata area of forest wildfires in the UK was used for forest biomass burning in the Crown Dependencies, and the pro rate for Cuba was used for the Cayman Islands. Bermuda fires statistics reported no wildfire area for either forest or grassland. There is no forest in the Falkland Islands so no wildfire occurrence there. The estimated rate of grassland wildfire occurrence in the Falklands, using the geographical proxy, is 944 ha p.a., which has been assessed as a plausible annual area by local experts. Information on the area of each IPCC land category, dominant management practices, land use change, soil types and climate types were compiled for each OT/CD from statistics and personal inventories to be constructed for the OT/CDs for all land use categories . The assumptions and uncertainty and may not capture all relevant activities, but given the size of the territories any missing sources are likely to be small. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 935 Territory LULUCF category Time period Reference Personal communication from Isle of Man Department of Agriculture, Fisheries and Forestry FAO (2010) Global Forest Resources Isle of Man Isle of Man Agricultural and Horticultural completed by all farmland occupiers on an Isle of Man Digest of Economic and Social Statistics 4B, 4C 2012 - 2013 Isle of Man Digest of Economic and Social Statistics 4B, 4C 2014 The Isle of Man in Numbers 4E 1991-1994 Isle of Man Ecological Habitat Survey, Phase 1 Report (Sayle et al, 1995) 4D 1990-present Evans et al. 2017 and subsequent updates Guernsey 4A 1990-2010 FAO Global Forest Resources Assessment 2010: Guernsey 4A, 4B, 4C, 4D, 4E 1998/9, 2005, 2018 Guernsey Habitat Survey (1998, 2010, 2018), Sustainable Guernsey 2005, 2009; Guernsey Jersey 4A 1990-2010 FAO Global Forest Resources Assessment 2010: Jersey 4B 1990 - 2018 Jersey Agricultural Statistics 4A, 4B, 4C, 4D, 4E 2006, 2008, 2012, 2015 Jersey In Figures 2006-present Department of Mineral Resources, personal communication FAO Global Forest Resources Assessment 2010: Falkland Islands 4B, 4C 1991-present Falkland Islands Agricultural Statistics 4D 1991-present Falkland Islands 2016 Census 4E 1990-2005 Falkland Islands Environment and Planning Department, personal communication projections 2006 - 2016 Stanley Town Plan, Environmental Planning Dept, Falkland Islands Government. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 936 Territory LULUCF category Time period Reference Cayman Islands 4A, 4B, 4C, 4D, 4E 2013 Cayman Island Compendium of Statistics- Land Cover 2013; Agricultural Land Capability of the 4E 1965-2013 Jurn et al.
(2018); Information provided by Cayman Islands Government to Aether for GHGI Bermuda 4A, 4B, 4D, 4E 1989-present Bermuda Biodiversity Study37, Bermuda Environmental Statistics Compendium Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda Mineral soil 95 95 95 87 35 47 37

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 937 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 938 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda No change in SOC No change in SOC No change in SOC N/A

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 939 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 940 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda N/A N/A N/A N/A The grassland area No change in SOC No change in SOC No change in SOC N/A

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 941 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda N/A N/A T1 tropical dry forest N/A N/A Default (-0.25 tC/ha) N/A N/A N/A N/A N/A N/A Tier 1 EFs with EF2 N/A N/A N/A N/A Peat extraction N/A N/A N/A N/A T1 tropical dry forest

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 942 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda N/A N/A N/A N/A Only dry forest is N/A N/A N/A N/A It is assumed that

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 943 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 944 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda N/A N/A N/A N/A Area assumed to

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 945 Isle of Man Guernsey Jersey Falkland Islands Cayman Islands Bermuda

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page946 A 3.4.12 Uncertainty analysis of the LULUCF sector The purpose of carrying out uncertainty analysis within the LULUCF inventory is to quantify where the largest sources of errors lie, and to identify areas to be targeted in future work so as to reduce the uncertainties. In the 1990-2010 inventory report (2012) a sensitivity analysis of the whole of the existing inventory methodology was undertaken, applying uncertainty quantification more widely and rigorously to all model parameters and empirical conversion factors, in order to quantify the impact of those uncertainties on the inventory. This work was revisited in 2019/2020 (Henshall and Watterson 2020, not yet published), to re-assess the uncertainties and sensitivities associated with the forest land modelling and update the uncertainties for the non-forest parts of In 2010 p arameterisation of the forest model was assessed as the second largest source of uncertainty. This has been addressed with the move to CARBINE, as 19 tree species are now modelled instead of the two used in previous submissions. Results from the National Forest Inventory (NFI) and small woods dataset will also provide additional information on carbon stocks in trees (e.g. Forestry Commission 201538). The choice of soil carbon model and its parameters are also important because the time course of the flux following land use change may be quite different, depending on the equations used to represent this, and how carbon is distributed between fast - and slow -turnover pools. The choice of forest model is less important, la rgely The updated uncertainty assessment of the Forest Land inventory and Harvested Wood Products (Henshall and Watterson 2020) (outputs derived from the CARBINE model) undertook a Monte Carlo analysis. The probability density functions (PDFs) assigned to the various CARBINE input parameters were based on information from the literature and expert judgement. A selection of 100 sets of input parameters were generated using Latin hyperc ube sampling, as this was considered to be the minimum number of model runs to get a reasonable estimate of the uncertainty considering the number of parameters adjusted. The analysis of the carbon emissions/removals predicted by the model gave an uncerta inty estimate at the 95% confidence interval of approximately 25% for both forest land and harvested wood products (rounded to the nearest 5%) in both the base year (1990) and the latest inventory year and 20% when both categories are considered in conjunction. This is smaller than what was assumed in the previous uncertainty analysis (around 40% uncertainty for those categories). While this may come in part from the improvement in the stratification of forest land obtained by the change of forest model, this is likely mostly a technical re-evaluation from a more up to date analysis of uncertainties range for the different parameters and the absence of characterisation of structural uncertainties in the model. In relative terms, carbon net emissions estimates for land converted to forest are assessed as considerably more uncertain (85% in the 1990 and 145% in 2020) than for forest remaining forest (20% in both 1990 and 2020). The activity data uncertainty for biomass, dead wood, litter and mineral soils is as sessed as being 1%, but as 5% for forest 38 This survey is preliminary and the carbon stocks have been estimated using the same relationships and calculation parameters that underlie CARBINE; they are therefore not an independent validation of the

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page947 In 2010 and the updated assessment the area undergoing land use change was assessed as being the single biggest uncertainty in the inventory.
This uncertainty in the land use change areas has been addressed by the development of a new vector -based approach (see Section A 3.4.3.1), combining multiple sources of land use data and land cover data from administrative, survey and earth observation datasets. This has allowed better estimation of the uncertainties associated with the activ ity data and enabled the separate calculation of the uncertainties associated with carbon stock changes in non -forest Land remaining in the same land category and Land converted to another land category. The activity data uncertainty for Land remaining in the same category is relatively stable across the 1990 -2021 time series, but more variable for Land converted to other land categories, due to the complex contribution of different data sources in the data assimilation across the inventory time period. Uncertainties for activities on organic soils were updated in the 1990-2021 submission as part of the revision of Tier 2 emission factors. In general, compared to the previous inventory submission, the overall uncertainties did not LULUCF sub-category Variable CO2 CH4 N2O 4A1 Forest remaining Forest Forest biomass and mineral soils 20%a 4A1 Forest remaining Forest Forest organic soils 21%a 4A2 Land converted to Forest Forest biomass and mineral soils 86-146 %a 4A2 Land converted to Forest Forest organic soils 86-146 %a 4A Forest Land/4(I) Forest fertilisation 34%a 4A Forest Land/4(II) Mineral soil drainage 140%b 4A Forest Land/4(II) Organic soil drainage 95% c 97% c 4A Forest Land/4(III) N2O from land use change 135% b 4A Forest Land/4(V) Biomass burning 54%d 54% d 54% d 4B1 Cropland remaining Cropland LUC mineral soil carbon change 22-24%e 4B1 Cropland remaining Cropland LUC biomass carbon 36-37%e 4B1 Cropland remaining Cropland Cropland management (soils and 4B1 Cropland remaining Cropland Organic soils 62% c 4B2 Forest Land converted to Cropland LUC mineral soil carbon change 33-153%e 4B2 Forest Land converted to Cropland LUC biomass carbon 36-154%a 4B2 Forest Land converted to Cropland LUC DOM carbon stock change 36-154%a 4B2 Forest Land converted to Cropland Organic soils 60-161%c 4B2 Grassland converted to Cropland LUC mineral soil carbon change 18-24%e 4B2 Grassland converted to Cropland LUC biomass carbon 34-37%e 4B2 Grassland converted to Cropland Organic soils 62%c 4B2 Settlements converted to Cropland LUC mineral soil carbon change 186-196% e

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page948 LULUCF sub-category Variable CO2 CH4 N2O 4B2 Settlements converted to Cropland LUC biomass carbon 188-197% e 4B2 Settlements converted to Cropland Organic soils 62% c 4B Cropland/4(II) Organic soil drainage- deep 75% c 61% c 4B Cropland/4(II) Organic soil drainage- wasted 92% c 80% c 4B Cropland/4(III) N2O from land use change 36%a 4B Cropland/4(V) Biomass burning (wildfires and controlled burning after deforestation) 4C1 Grassland remaining Grassland LUC mineral soil carbon change 27-30%e 4C1 Grassland remaining Grassland LUC biomass carbon 39-42%e 4C1 Grassland remaining Grassland Grassland management (biomass) 13% b 4C1 Grassland remaining Grassland Organic soils 31-32% c 4C2 Forest Land converted to Grassland LUC mineral soil carbon change 42-156%e 4C2 Forest Land converted to Grassland LUC biomass carbon 36-155%a 4C2 Forest Land converted to Grassland LUC DOM carbon stock change 36-155%a 4C2 Forest Land converted to Grassland Organic soils 31-32%c 4C2 Cropland converted to Grassland LUC mineral soil carbon change 21%e 4C2 Cropland converted to Grassland LUC biomass carbon 35%e 4C2 Cropland converted to Grassland Organic soils 31-32%c 4C2 Settlements converted to Grassland LUC mineral soil carbon change 135-142%e 4C2 Settlements converted to Grassland LUC biomass carbon 138-145%e 4C2 Settlements converted to Grassland Organic soils 31-32%c 4C Grassland/4(II) Organic soil drainage 34-36% c 46% c 4C Grassland/4(III) N2O from land use change 135% b 4C Grassland/4(V) Biomass burning (wildfires and controlled burning after deforestation) 4D Wetlands Peat extraction offsite emissions 32%a 4D Wetlands Organic soils (extraction and rewetting) 43-44% c 4D Wetlands/4(II) Organic soil drainage 47-50% c 118-121% 4D Wetlands Flooded land biomass carbon 75% b 4D2 Forest Land converted to Other LUC biomass and DOM loss after 4D Wetlands/4(V) Biomass burning (controlled burning 4E1 Settlements remaining Settlements LUC mineral soil carbon change 67-73%e 4E1 Settlements remaining Settlements LUC biomass carbon 72-79%e 4E1 Settlements remaining Settlements Organic soils 183% c

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page949 LULUCF sub-category Variable CO2 CH4 N2O 4E2 Forest Land converted to Settlements LUC mineral soil carbon change 32-150% e 4E2 Forest Land converted to Settlements LUC biomass and DOM carbon stock 4E2 Forest Land converted to Settlements Organic soils 183% c 4E2 Cropland converted to Settlements LUC mineral soil carbon change 87-119% e 4E2 Cropland converted to Settlements LUC biomass carbon 91-121% e 4E2 Cropland converted to Settlements Organic soils 183% c 4E2 Grassland converted to Settlements LUC mineral soil carbon change 70-129% e 4E2 Grassland converted to Settlements LUC biomass carbon 76-132% e 4E2 Grassland converted to Settlements Organic soils 183% c 4E Settlements/4(II) Organic soil drainage 75% c 158% c 4E Settlements/4(III) N2O from land use change 135% b 4E Settlements/4(V) Biomass burning (wildfires and controlled burning after deforestation) 4G HWP Harvested wood products 15-19%a 4 Indirect N2O (atmospheric deposition) 240% b 4 Indirect N2O (leaching) 163% b a Updated uncertainty assessment (Henshall and Watterson, 2020); b IPCC default; c Analysis of uncertainties for the reporting of drainage and rewetting of peatlands recommended by Evans et al. 2017 (see Section A 3.4.7.3) and revised in 2022; d Uncertainty assessment 2012, e Uncertainty assessment A 3.5.1 Solid Waste Disposal on Land (5A) Because waste sent to landfill i s now evaluated using individual waste consignments by EWC code, there is no need to make assumptions regarding waste composition, other than for two waste categories. These EWC codes are 19.12.12 (residues from waste sorting) and 20.03.01 (mixed municipal waste). Wastes with these codes were allocated in accordance with the findings In absence of more up to date information this waste consumption data is considered to be representative of UK national circumstances.
Data on DOC, DOCf and material compositions are The model allocates waste to two types of landfill – old, closed sites which last received waste in 1979, and modern engineered landfills that came into operation from 1980. Only these latter sites have gas management systems. The old closed sites have no gas control. generated, recovered, used for power generation, flared, oxidised and emitted to the atmosphere

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page950 Material 19.12.12 (residues from waste sorting) 20.03.01 (mixed municipal waste Haz waste and batteries 1.1% 0.9% Carpet, underlay & furniture 7.0% 5.0% Bricks, plaster and soil 7.9% 4.1% Other non-combustible 1.7% 1.5%

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page952 Paper and Card 5% 65% 15% 15% 9% 61% General industrial waste 5% 65% 37% Food and Abattoir 15% 70% 70% 5% 11% 11% 36% 7% 6% 18% Construction and demolition 5% 65% 30% Miscellaneous process waste 5% 65% 20% Textiles / Carpet and Underlay 5% 65% 20% 0% 15% 15% 0% 0% 0% 0% Sanitary 5% 65% 65% 0% 0% 47% 0% 0% 0% 0% Year England Scotland Wales Northern Ireland Total Year England Scotland Wales Northern Ireland Total 1946 71.2 9.0 4.6 2.4 87.2 1984 77.2 8.5 4.6 2.6 92.8 1947 71.5 9.0 4.6 2.4 87.4 1985 77.2 8.4 4.6 2.6 92.8

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page953 Year England Scotland Wales Northern Ireland Total Year England Scotland Wales Northern Ireland Total 1948 71.7 9.0 4.6 2.4 87.7 1986 77.3 8.4 4.6 2.6 92.9 1949 72.3 9.0 4.6 2.4 88.4 1987 77.3 8.3 4.6 2.6 92.9 1950 72.9 9.1 4.6 2.4 89.1 1988 77.4 8.3 4.6 2.6 92.9 1951 73.6 9.1 4.6 2.5 89.8 1989 77.4 8.3 4.6 2.6 92.9 1952 74.2 9.1 4.7 2.5 90.5 1990 77.7 8.3 4.7 2.6 93.3 1953 74.2 9.1 4.7 2.5 90.4 1991 77.7 11.3 4.7 2.6 96.3 1954 74.2 9.0 4.6 2.4 90.3 1992 77.7 12.2 4.7 2.6 97.2 1955 74.2 9.0 4.6 2.4 90.3 1993 77.6 14.0 4.6 2.6 98.8 1956 75.7 9.1 4.7 2.5 92.0 1994 77.6 15.9 4.6 2.6 100.7 1957 77.2 9.2 4.8 2.5 93.7 1995 81.8 15.0 4.9 2.8 104.5 1958 78.6 9.3 4.8 2.6 95.4 1996 80.7 15.0 4.8 2.8 103.3 1959 80.1 9.5 4.9 2.6 97.1 1997 81.1 14.0 4.8 2.8 102.7 1960 81.5 9.6 5.0 2.6 98.8 1998 75.0 11.9 4.5 2.6 93.9 1961 81.1 9.7 4.9 2.7 98.4 1999 69.3 10.9 4.1 2.4 86.6 1962 80.9 9.6 4.9 2.7 98.0 2000 67.4 11.2 4.0 2.3 84.9 1963 84.5 10.0 5.1 2.8 102.4 2001 71.4 8.9 4.2 2.4 86.9 1964 84.6 9.9 5.1 2.8 102.4 2002 66.8 8.2 3.9 2.3 81.3 1965 85.7 10.0 5.1 2.8 103.6 2003 65.4 7.9 3.8 2.2 79.4 1966 85.3 9.9 5.1 2.8 103.2 2004 64.9 7.8 3.8 2.2 78.7

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page954 Year England Scotland Wales Northern Ireland Total Year England Scotland Wales Northern Ireland Total 1967 85.0 9.8 5.1 2.8 102.7 2005 60.0 7.1 3.5 2.0 72.6 1968 84.8 9.7 5.1 2.8 102.4 2006 64.9 7.1 4.0 2.0 78.1 1969 84.0 9.6 5.0 2.8 101.4 2007 60.7 7.4 3.2 1.9 73.2 1970 83.8 9.5 5.0 2.8 101.0 2008 53.9 6.1 2.9 1.6 64.5 1971 82.8 9.3 4.9 2.7 99.8 2009 44.0 4.7 2.5 1.1 52.3 1972 81.8 9.2 4.8 2.7 98.5 2010 43.6 4.6 2.3 1.0 51.4 1973 81.3 9.1 4.8 2.7 97.9 2011 44.7 4.7 2.2 1.0 52.5 1974 79.9 9.0 4.8 2.6 96.3 2012 41.8 4.5 2.2 1.1 49.6 1975 80.1 9.0 4.8 2.6 96.5 2013 41.1 4.1 2.2 1.1 48.4 1976 78.8 8.8 4.7 2.6 94.9 2014 41.3 4.1 1.5 1.3 48.2 1977 78.4 8.8 4.7 2.6 94.5 2015 43.9 4.2 1.3 1.6 51.0 1978 78.8 8.8 4.7 2.6 95.0 2016 44.7 3.7 2.0 1.9 52.3 1979 78.7 8.8 4.7 2.6 94.7 2017 45.4 3.8 1.8 1.7 52.7 1980 78.6 8.7 4.7 2.6 94.6 2018 44.1 3.7 1.4 1.5 50.7 1981 77.0 8.5 4.6 2.5 92.7 2019 45.9 3.0 1.1 1.5 51.4 1982 77.1 8.5 4.6 2.5 92.7 2020 39.8 2.6 1.0 1.4 44.8 1983 77.1 8.5 4.6 2.6 92.8 2021 43.2 3.4 1.1 1.6 49.3 Chapter 7, taking account of recovery and oxidation.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page955 1990 93.25 2,709 33 1% 33 1% 0 0% 268 10% 2408 89% 1991 96.32 2,752 50 2% 50 2% 0 0% 270 10% 2432 88% 1992 97.18 2,797 90 3% 90 3% 0 0% 271 10% 2436 87% 1993 98.85 2,837 107 4% 107 4% 0 0% 273 10% 2457 87% 1994 100.75 2,878 124 4% 124 4% 0 0% 275 10% 2479 86% 1995 104.50 2,939 135 5% 135 5% 0 0% 280 10% 2524 86% 1996 103.26 2,983 170 6% 170 6% 0 0% 281 9% 2532 85% 1997 102.70 3,022 218 7% 218 7% 0 0% 280 9% 2524 84% 1998 93.85 3,038 278 9% 278 9% 0 0% 276 9% 2484 82% 1999 86.63 3,032 394 13% 394 13% 0 0% 264 9% 2374 78% 2000 84.85 3,028 500 17% 500 17% 0 0% 253 8% 2275 75% 2001 86.92 3,040 566 19% 566 19% 0 0% 247 8% 2227 73% 2002 81.28 3,021 599 20% 598 20% 1 0% 242 8% 2180 72% 2003 79.38 2,981 723 24% 723 24% 0 0% 226 8% 2032 68% 2004 78.71 2,937 874 30% 874 30% 0 0% 206 7% 1857 63% 2005 72.59 2,870 926 32% 926 32% 0 0% 194 7% 1750 61% 2006 74.83 2,753 950 35% 944 34% 6 0% 180 7% 1622 59% 2007 73.21 2,645 989 37% 987 37% 2 0% 166 6% 1490 56% 2008 64.51 2,528 1065 42% 980 39% 85 3% 146 6% 1316 52% 2009 52.33 2,400 1112 46% 1015 42% 97 4% 127 5% 1159 48% 2010 51.38 2,278 1200 53% 1066 47% 134 6% 108 5% 971 43%

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page956 2011 52.54 2,159 1178 55% 1075 50% 103 5% 98 5% 883 41% 2012 49.55 2,041 1124 55% 1042 51% 82 4% 92 4% 825 40% 2013 48.43 1,929 1144 59% 1035 54% 109 6% 78 4% 706 37% 2014 48.15 1,820 1142 63% 1007 55% 136 7% 68 4% 610 34% 2015 50.98 1,716 1065 62% 974 57% 90 5% 65 4% 587 34% 2016 52.31 1,627 1007 62% 941 58% 67 4% 62 4% 558 34% 2017 52.75 1,544 916 59% 857 56% 59 4% 63 4% 565 37% 2018 50.70 1,468 837 57% 783 53% 53 4% 63 4% 568 39% 2019 51.45 1,396 773 55% 725 52% 49 3% 62 4% 560 40% 2020 44.84 1,325 757 57% 699 53% 58 4% 57 4% 511 39% 2021 49.31 1272 732 58% 662 52% 70 5% 54 4% 486 38% a. Methane generated is based on the MELMod model. b. Methane captured is the sum of methane used for power generation and methane flared. c. Methane used for power generation is calculated from official figures on landfill gas electricity generation (Digest of UK Energy Statistics (BEIS, 2016), in GWh/year, assuming a net calorific value for methane of 50 GJ/tonnes and a conversion efficiency between methane use and electricity export of 30% rising to 36%, which includes parasitic losses and on-site use of electricity, e.g. for gas blowers, leachate treatment and site offices. d. Methane flared is calculated from site-specific data provided by the Environment Agency at regulated sites for 2009 to 2013, from SEPA for 2013, from a study carried out during 2014, and from site-specific data provided voluntarily by site operators. e. Methane oxidised is based on the IPCC default oxidation factor of 10%, applied to methane remaining after subtraction of the amount captured. f. Methane emitted = (methane generated – methane captured) x (1-oxidation factor). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page957 A 3.5.1.3 Overseas Territories and Crown Dependencies For the overseas territories and crown dependencies, the IPCC landfill model is used. Wher e available, country -specific waste generation and composition data have been applied and appropriate defaults chosen e.g. taking into account climatic variation. There are no landfill emissions for Region Methodology Activity data MCF DOC k value 2005 total MSW to landfill data and percentage that is plastics, other inert. Prior to 2005: flat-lined Isle of Man IPCC Landfill Model 2004 all waste incinerated for energy from waste. Prior to 2004: population and IPCC default waste managed, semi-aerobic (results from Bermuda IPCC Landfill Model Total MSW to landfill (Environmental IPCC default values; no information on management system so assume

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page958 Region Methodology Activity data MCF DOC k value Cayman Islands IPCC Landfill Model 2000 Total MSW to landfill Health). Prior to 2000: flat-lined 2000 IPCC default values; landfill sites are lined and managed to some degree, Falkland Islands IPCC Landfill Model 1998: Halcrow Report.
Other flat-lined after advice in personal communication from environmental

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 959 A 3.5.2 Biological Treatment of Solid Waste (5B) The annual amount of waste treated in the composting process (fresh weight; fw) are reported in 2006 3,664,578 339,909 110,618 2007 3,893,614 390,804 542,678 2008 3,720,016 441,622 629,269 2009 4,655,911 492,393 438,011 2010 4,802,584 496,077 1,282,060 2011 4,882,131 500,008 1,898,570

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 960 2012 5,322,707 503,005 1,719,118 2013 5,423,868 505,833 2,138,366 2014 6,064,095 509,350 2,439,341 2015 5,820,954 513,040 2,740,317 2016 6,088,865 517,007 3,041,292 2017 6,175,704 519,898 3,342,268 2018 5,692,141 522,778 3,643,244 2019 5,458,284 525,570 3,944,219 2020 4,912,301 527,590 4,245,195 2021 5,272,205 530,215 4,546,171 Anaerobic digestion – MBT (Mg)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 961 Anaerobic digestion – MBT (Mg) A 3.5.3 Waste Incineration (5C)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 962

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 963 a Note that all MSW incinerators were either closed or converted to extract power by 1997. In the latter case they were then considered to be power generation and so emissions were reported in 1A1a. 1995 0.143 0.984 2.205 0.143 0.032 2.002 5.374

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 964 Note that all MSW incinerators were either closed or converted to extract power by 1997. In the latter case they were then considered to be power generation and so emissions were reported in 1A1a. NE and NO are notation keys as defined in FCCC Decision 24/CP.19 paragraph 37. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 965 A 3.5.4 Wastewater Handling (5D) A 3.5.4.1 5D1 Domestic and Commercial Wastewater Handling and Sludge Disposal Process stage Process type unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Total Sludge kt tds 1344 1393 1483 1768 1655 1597 1688 1687 1670 1713 Population Equivalent million 69.1 70.1 71.2 71.4 70.3 73.0 73.8 74.7 71.6 71.1 Additional Treatment Digested kt tds 491.8 531.0 517.5 903.8 954.3 759.6 659.5 598.0 600.7 579.0 Advanced Digested kt tds 0.0 0.0 57.9 290.3 358.2 515.1 711.9 833.5 857.3 893.3 Composted kt tds 0.0 0.0 9.2 13.5 23.8 6.5 1.7 4.9 0.7 0.6 Disposal route Land kt tds 595.8 670.4 768.6 1384.9 1372.4 1454.9 1553.8 1569.9 1580.4 1616.0 Landfill kt tds 120.8 113.8 83.0 130.9 33.7 0.4 0.4 0.4 0.4 0.6 Incineration kt tds 68.8 81.7 235.5 252.6 249.3 141.8 133.7 116.9 89.0 96.1 Sea kt tds 558.3 527.5 395.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Where tds is total dissolvable solids; this is assumed to be comparable to Biochemical Oxygen Demand (BOD) Process stage Process type unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Mechanical treatment and storage1 kt/Mt tds 2.70 2.70 2.70 2.70 2.70 2.70 2.70 2.70 2.70 2.70 Additional Treatment Digested2 kt/Mt tds 18.10 18.10 18.10 18.10 17.83 15.90 13.37 13.03 12.26 11.90 Advanced Digested kt/Mt tds 4.53 4.53 4.53 4.53 4.53 4.52 4.53 4.52 4.52 4.51 Composted kt/Mt tds 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 966 Process stage Process type unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Disposal route Land3 kt/Mt tds IE IE IE IE IE IE IE IE IE IE Landfill3 kt/Mt tds IE IE IE IE IE IE IE IE IE IE Incineration3 kt/Mt tds IE IE IE IE IE IE IE IE IE IE Sea4 kt/Mt tds 60.00 60.00 60.00 60.00 60.00 60.00 60.00 60.00 60.00 60.00 Total5 kt/Mt tds 33.13 31.29 24.49 12.71 13.98 11.73 9.84 9.56 9.43 9.08 1. All waste is mechanically treated and stored, so the emission factor is applied to total sludge. 2. Implied emission factor after methane capture. 4. Not an IEF, this is the default IPCC factor for sea, river and lake discharge. 5. For information, IEF when dividing total emissions by total activity.
Process stage Process type 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Mechanical treatment and storage1 2.12 2.34 2.94 4.77 4.47 4.31 4.56 4.56 4.51 4.62 Additional Treatment Digested2 8.90 9.61 9.37 16.36 17.02 12.08 8.82 7.79 7.37 6.89 Advanced Digested - - 0.26 1.32 1.62 2.33 3.22 3.77 3.87 4.03 Composted - - 0.01 0.02 0.04 0.01 0.00 0.01 0.00 0.00 Disposal route Farmland3 IE IE IE IE IE IE IE IE IE IE Landfill3 IE IE IE IE IE IE IE IE IE IE Incineration3 IE IE IE IE IE IE IE IE IE IE Sea4 33.50 31.65 23.74 - - - - - - - Total5 44.52 43.60 36.32 22.47 23.15 18.73 16.60 16.12 15.75 15.55

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 967 Data Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Estimated population connected to private 1417.20 1440.65 1438.70 1465.31 1525.80 1577.78 1605.82 1606.95 1613.66 1613.77 BOD value applied g/person/day 53.21 54.42 57.04 67.77 64.43 59.92 62.65 61.80 63.83 65.97 A 3.5.4.2 5D2 Industrial Wastewater Handling and Sludge Disposal Industry Units 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Alcohol refining kt 458 469 446 552 577 645 649 725 653 700 Beer & malt kt 7,788 7,983 6,709 7,211 6,571 6,664 6,230 5,821 5,507 7,101 Coffee kt 11 11 63 74 78 83 85 90 92 96 Dairy Products kt 6,755 6,924 6,172 6,223 8,594 9,363 9,254 8,667 8,261 8,673 Fish processing kt 187 191 362 363 272 230 183 174 160 159 Iron and Steel manufacturing kt 235 214 177 98 301 835 203 205 216 247 Meat & Poultry kt 2,640 2,706 2,617 2,855 2,656 3,244 3,725 3,578 3,729 3,810 Organic chemicals kt 132 158 129 65 1,184 438 336 303 297 540 Petroleum Refineries kt 66,972 86,131 81,129 80,145 68,599 57,577 54,871 55,407 45,262 45,485 Nitrogen fertiliser kt 853 1,021 1,021 1,021 1,075 1,762 2,139 2,977 3,590 3,853 Plastics & resins kt 964 1,413 1,334 1,741 1,650 1,823 1,690 1,723 1,682 1,871

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 968 Industry Units 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Pulp & Paper kt 8,640 8,625 8,222 7,957 6,787 7,241 6,804 6,647 6,689 7,280 Soap & detergents kt 126 151 210 253 282 260 105 116 188 235 Starch production kt 22 23 10 57 187 87 101 126 125 123 Sugar Refining kt 35 35 40 25 21 18 15 13 13 13 Vegetable Oils kt 295 302 288 346 411 612 397 386 419 372 Vegetables, Fruits & Juices kt 995 1,020 1,068 1,085 808 893 1,041 904 738 871 Wine & Vinegar kt 545 559 604 702 685 2,007 2,548 1,710 845 912 A 3.6.1 Overview of Data Sources Fuel use data for Isle of Man, Guernsey and Jersey are assumed to be included in UK national energy statistics (see Section 1.1.2.2), so fuel thought to be used in these territories are split out from UK total consumption unless otherwise stated in Section A 4.2.1. Activity data including fuel use data for other territories are obtained from government departments for those territories, • Department of Statistics; • Cayman Department of Environment Sustainable Development Unit; and, • Falkland Environmental Department, Policy & Economic Development Unit. Activity and emissions data estimates from LULUCF sources and sinks have been researched via the FAOSTAT database, to supplem ent data available from the OTs. The LULUCF data for Cayman Islands from FAOSTAT (FAO, 2018) indicates zero emissions, using Tier 1 methods. The data sources and methodologies used for other sectors are described in the main methodology sections of the NIR. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 969 A 3.6.2 Activity and Emissions Data Sector 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 1. Energy 1.49 1.66 1.73 1.4 1.3 1.15 1.22 1.3 1.04 1.01 2. Industrial Processes and Product Use 0.00178 0.00476 0.0177 0.0353 0.0549 0.0627 0.0619 0.0591 0.0554 0.0552 3. Agriculture 0.184 0.188 0.195 0.182 0.167 0.16 0.152 0.15 0.15 0.147 5.
Waste 0.117 0.119 0.122 0.121 0.105 0.0884 0.0807 0.078 0.0729 0.0732 Total 1.77 1.94 2.04 1.72 1.62 1.44 1.49 1.56 1.30 1.26 Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Aviation spirit TJ 95.6 109 157 163 85.8 44.8 28.9 18 2.35 17.5 Burning oil TJ 2890 3370 4630 3980 3760 3210 3400 3220 3280 3340 Clinical waste Mt 0.000588 0.000588 0.000588 0.000588 0.000588 0.000588 0.000596 0.0006 0.000482 0.000486 Coal TJ 329 210 149 100 8.2 8.84 8.81 8.81 8.81 8.81 Fuel oil TJ 6360 7870 5890 1070 1210 819 893 1710 222 211

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 970 Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Gas oil TJ 3370 3630 3610 3070 2220 1880 2050 1900 1580 1750 Natural gas TJ 3190 3810 3520 4100 4880 4730 3480 Petrol TJ 3540 3420 3250 3290 2880 2540 2330 2290 1960 2050 0.000295 0.000685 0.000811 0.000813 0.000733 0.000777 Category 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Dairy 15,888 15,729 16,186 13,127 11,511 10,846 11,192 10,807 10,687 10,300 Non-dairy 28,663 28,333 29,176 31,719 28,821 26,643 23,927 22,893 23,718 23,053 Sheep 151,764 160,228 176,259 151,103 138,251 133,666 126,029 128,599 124,647 123,963 Pigs 4,854 5,411 4,609 1,148 4,086 2,861 2,188 2,154 2,391 1,966 Poultry 84,048 46,481 46,448 58,160 54,400 62,916 59,485 59,734 60,408 60,271 Goats 333 347 376 141 288 539 458 484 452 435 Horses 2,785 2,785 2,785 2,822 3,236 2,891 2,536 2,591 2,591 2,591

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 971 Territory 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Isle of Man 0.0380 0.0383 0.0345 0.0284 0.0244 0.0227 0.0226 0.0221 0.0220 0.0218 Guernsey 0.0029 0.0030 0.0026 0.0024 0.0024 0.0025 0.0025 0.0024 0.0023 0.0023 Jersey 0.0081 0.0085 0.0083 0.0072 0.0070 0.0069 0.0068 0.0067 0.0061 0.0061 Sector 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 1. Energy 1.32 1.31 1.43 1.6 1.57 1.72 1.67 1.67 1.44 1.59 2. Industrial Processes and Product Use 0.000342 0.00208 0.011 0.0755 0.192 0.342 0.41 0.406 0.388 0.415 3. Agriculture 0.282 0.272 0.256 0.228 0.189 0.186 0.179 0.185 0.174 0.188 5. Waste 0.123 0.107 0.0877 0.0878 0.136 0.115 0.121 0.124 0.13 0.137 Total 1.82 1.78 1.88 2.09 2.31 2.47 2.49 2.5 2.24 2.44 Fuel Fuel Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Aviation turbine fuel TJ 3990 2850 2950 2630 2870 2590 2840 2900 1280 990 Burning oil TJ 63.5 78.6 90.5 117 106 130 128 138 134 118

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 972 Fuel Fuel Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Fuel oil TJ 1970 2060 3600 4140 4300 4410 4660 4720 4510 4570 Gas oil TJ 8070 8770 9050 9660 10300 12000 10700 10100 9080 10300 Natural gas TJ 1.47 1.82 2.1 2.63 3.13 3.44 3.17 3.23 3.16 2.97 Petrol TJ 2830 2880 3210 2540 2940 3040 3410 3620 2690 3120 0.000208 0.000652 0.000692 0.000761 0.000641 0.000694 Lubricants TJ 63.9 76 75.8 73.8 69.1 59.8 58.1 59.6 44.4 47.9 Petroleum waxes kg 83,500 71,700 58,400 139,000 72,700 88,100 75,300 95,400 81,300 77,200 Category 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Dairy Cattle 2,161 1,862 1,911 1,145 868 675 598 595 580 583 Non-dairy Cattle 5,256 4,861 5,077 7,845 6,360 4,748 4,772 4,860 5,003 4,883 Sheep 739,999 717,571 669,905 580,864 478,625 482,131 462,245 476,867 444,834 487,237 Goats 405 867 1,286 1,704 2,251 1,986 2,775 2,939 2,963 2,989 Horses 2,217 2,069 1,703 1,417 1,269 1,223 1,221 1,227 1,184 1,195 Swine 1,116 1,174 1,376 1,384 1,233 1,058 1,485 1,579 1,502 1,489

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 973 Category 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Poultry 15,319 14,664 20,890 27,164 32,293 39,458 30,019 31,805 33,844 33,584 Deer 0 0 0 0 184 287 339 358 378 400 Territory 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Bermuda 0.0006 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 0.0007 Cayman Islands 0.0007 0.0009 0.0010 0.0011 0.0012 0.0010 0.0011 0.0011 0.0012 0.0012 Falkland Islands 0.0963 0.0930 0.0870 0.0764 0.0638 0.0636 0.0612 0.0630 0.0590 0.0642 Sector 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 1. Energy 0.216 0.214 0.240 0.275 0.286 0.357 0.321 0.295 0.236 0.255 2. Industrial Processes and Other Product Use 0.000 0.001 0.002 0.012 0.028 0.050 0.056 0.056 0.055 0.056 3. Agriculture - - - - - - - - - -

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 974 Sector 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 5.
Waste 0.007 0.009 0.012 0.001 0.003 0.002 0.002 0.002 0.002 0.002 Total 0.224 0.224 0.254 0.288 0.317 0.409 0.379 0.352 0.292 0.313 Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Aviation spirit TJ (net) 3 - - - - - - - - - TJ (net) 809 722 540 840 695 873 800 927 415 643 Clinical waste kt - - - - 5.43 0.92 1.07 1.07 1.07 1.07 Charcoal TJ (net) - 0 0 0 0 0 0 2 3 3 DERV TJ (net) 229 200 417 975 1,026 2,210 2,291 1,760 1,462 1,938 Fuel oil TJ (net) 35,956 36,499 61,925 90,757 104,465 101,866 99,845 95,021 97,851 99,832 Gas oil TJ (net) 4,539 5,052 6,953 9,495 11,379 13,714 13,265 10,066 8,264 7,470 LPG TJ (net) 1,140 1,140 1,140 1,140 1,140 1,140 1,199 1,272 1,252 1,291 MSW TJ (net) 327 379 481 - - - - - - - Natural gas TJ (net) - - - - - - - 1,780 3,388 4,288 Petrol TJ (net) 895 765 1,059 1,061 989 915 1,036 945 945 986 Mt 0.03 0.02 0.01 0.03 0.02 0.02 0.02 0.02 0.02 0.02

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 975 Unit 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Lubricants TJ (net) 68 66 57 56 52 47 45 44 34 36 Territory 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Isle of Man 36.5 39.7 40.8 0 0 0 0 0 0 0 Guernsey 56.7 56.7 56.7 56.7 35.0 33.1 27.2 4.16 3.72 2.59 Jersey N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A Territory 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Cayman Islands 33.8 33.8 33.8 144 94.1 63.3 108 136 136 113 Falklands Islands 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Bermuda 64.5 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 976 ANNEX 4: National Energy Balance The UK energy balance is produced and published annually by the Department of Energy Security and Net Zero in the Digest of UK Energy Statistics – DUKES. This is available online following sections explain how the energy balance is used for the UK inventory for individual fuel types, and how the data are supplemented with other statistics that may lead to deviations from The UK energy statistics (detailed breakdown) are presented on a mass basis for liquid and solid fuels, and on a gross energy basis for gaseous fuels (including derived gases). The UK inventory is calculated using these data directly, and for the purposes of reporting in the CRF and NIR, activity data and emission factors are converted to energy units, on a net basis. The scope of the UK energy balance, as shown below, is fuel use in the United Kingdom and its Crown Dependencies (Jersey, Guernsey and the Isle of Man), as described i n the NIR Section The fuel use estimates for Overseas Territories (OTs) are not included within DUKES, and are obtained through direct communications with the respective government contacts in each of the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 977 Production 736 0 44,737 0 31,323 12,829 16,979 0 0 106,604 Imports 3,197 781 45,705 26,927 48,223 5,380 0 2,471 0 132,683 Exports -743 -8 -37,029 -20,001 -6,507 -388 0 -358 0 -65,035 Marine bunkers 0 0 0 -2,071 0 0 0 0 0 -2,071 Stock change [note 4] +1,786 +144 +159 +1,616 +99 +4 0 0 0 +3,808 Total supply 4,976 916 53,573 6,470 73,137 17,825 16,979 2,113 0 175,990 Statistical difference [note 5] -16 +0 +2 -17 -336 0 0 -83 0 -450 Total demand 4,992 916 53,571 6,488 73,473 17,825 16,979 2,196 0 176,440 Transfers 0 +20 -627 +613 +532 -557 -7,077 +7,077 0 -19 Transformation -3,750 194 -52,944 52,116 -24,241 -11,570 -9,903 19,300 1,591 -29,207 Electricity generation -1,680 -401 0 -353 -21,837 -11,358 -9,903 19,300 0 -26,231 Major power producers -1,668 0 0 -117 -19,625 -5,740 -9,903 16,129 0 -20,924 Autogenerators -12 -401 0 -235 -2,212 -5,618 0 3,171 0 -5,307 Heat generation -4 -1 0 -40 -2,404 -212 0 0 1,591 -1,069 Petroleum refineries 0 0 -53,268 52,923 0 0 0 0 0 -345 Coke manufacture -1,174 1,095 0 0 0 0 0 0 0 -79 Blast furnaces -805 -621 0 0 0 0 0 0 0 -1,426 Patent fuel manufacture -88 122 0 -59 0 0 0 0 0 -24 Other [note 6] 0 0 324 -356 0 0 0 0 0 -32 Energy industry use 0 392 0 3,447 4,458 0 0 1,734 334 10,365 Electricity generation 0 0 0 0 0 0 0 1,066 0 1,066 Oil and gas extraction 0 0 0 633 3,682 0 0 53 0 4,367 Petroleum refineries 0 0 0 2,814 236 0 0 401 334 3,786

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 978 Coal extraction 0 0 0 0 5 0 0 28 0 33 Coke manufacture 0 142 0 0 0 0 0 1 0 143 Blast furnaces 0 250 0 0 38 0 0 15 0 303 Patent fuel manufacture 0 0 0 0 0 0 0 0 0 0 Pumped storage 0 0 0 0 0 0 0 53 0 53 Other 0 0 0 0 497 0 0 115 0 612 Losses 0 147 0 0 342 0 0 2,262 0 2,751 Final consumption 1,241 591 0 55,770 44,965 5,697 0 24,577 1,257 134,099 Industry 881 426 0 2,572 9,181 1,672 0 7,318 706 22,756 Unclassified 0 0 0 1,629 0 549 0 0 0 2,178 Iron and steel 13 426 0 10 436 2 0 194 0 1,081 Non-ferrous metals 17 0 0 8 255 0 0 343 0 622 Mineral products 399 0 0 188 1,453 336 0 499 0 2,875 Chemicals 32 0 0 101 1,792 116 0 1,263 212 3,515 Mechanical engineering etc 8 0 0 0 1,020 0 0 482 0 1,511 Electrical engineering etc 4 0 0 1 312 0 0 499 0 815 Vehicles 33 0 0 154 433 12 0 302 0 935 Food, beverages etc 37 0 0 120 1,725 77 0 941 7 2,907 Textiles, leather etc 39 0 0 44 256 0 0 216 0 556 Paper, printing etc 54 0 0 33 352 285 0 787 4 1,514 Other industries 240 0 0 41 720 294 0 1,681 483 3,460 Construction 5 0 0 242 427 0 0 111 0 786 Transport 10 0 0 41,593 84 1,463 0 475 0 43,625 Air 0 0 0 5,176 0 0 0 0 0 5,176 Rail 10 0 0 552 0 0 0 401 0 963

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 979 Road 0 0 0 35,229 84 1,463 0 74 0 36,850 National navigation 0 0 0 636 0 0 0 0 0 636 Other 350 122 0 6,140 35,323 2,563 0 16,784 551 61,833 Domestic 328 122 0 2,489 27,377 1,127 0 9,411 260 41,115 Public administration 14 0 0 713 3,015 56 0 1,423 79 5,299 Commercial 4 0 0 1,596 3,937 1,229 0 5,616 206 12,588 Agriculture 0 0 0 920 114 133 0 334 4 1,505 Miscellaneous 5 0 0 421 880 18 0 0 1 1,325 Non energy use 0 43 0 5,466 377 0 0 0 0 5,886 Note 1 Includes all manufactured solid fuels, benzole, tars, coke oven gas and blast furnace gas. Note 2 Includes colliery methane. Note 3 Includes geothermal, solar heat, and heat pumps. Note 4 Stock fall (+), stock rise (-). Note 5 Primary supply minus primary demand. Note 6 Back-flows from the petrochemical industry. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 980 The fuels data are taken from DUKES - the Digest of UK Energy Statistics (BEIS, 20 21), so the fuel definitions and the source categories used in the NAEI reflect those in DUKES. IPCC Guidelines (IPCC, 2006) lists fuels that should be considered when reporting emissions. relate to the fuels listed in the IPCC Guidelines. In most cases the mapping is obvious but there are a few cases where some explanation is required.
Category IPCC Fuel Name NAEI Fuel Name Aviation Gasoline Aviation Spirit Jet Kerosene Aviation Turbine Fuel1 (ATF) Liquefied Petroleum Gases Liquefied Petroleum Gas (LPG) Refinery Gas Other Petroleum Gas (OPG) Other Other Petroleum Products Refinery Miscellaneous Patent Fuel Solid Smokeless Fuel (SSF) Blast Furnace Gas Blast Furnace Gas

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 981 Category IPCC Fuel Name NAEI Fuel Name Gaseous Natural Gas Natural Gas Other Fuels Municipal Solid Waste Municipal Solid Waste Industrial Scrap Tyres Scrap Tyres Other Primary Solid Biomass Straw Poultry Litter, Meat & bone meal Other liquid biofuels Liquid Biofuels 1 Includes fuel that is correctly termed jet gasoline. 3 Coal-water slurry used in some power stations 4 Unrefined natural gas used on offshore platforms and some power stations 5 IPCC Guidelines (IPCC, 2006) specifies coal seam methane is included in Natural Gas. A 4.2.1 Reallocations of energy data and differences from UK energy The main source of energy consumption data used in the UK inventory is the Digest of UK Energy Statistics (DUKES; BEIS, 20 22). This annual publication gives detailed sectoral energy consumption broken down by fuel type and covering the entire time period of the inventory. In many cases, these data are used directly in the inventory without modification. However, there are instances where the activity data used are not based directly on DUKES, instead utilising alternative data sources which provide supplementary information to the al location of fuel to individual sectors and sources. In general, the UK inventory totals by fuel are kept consistent with the DUKES national totals for each fuel. There are some exceptions where the UK total may be different to that presented in DUKES due to different scopes and reporting requirements. The reasons for any deviations from use of DUKES data in the inventory are discussed within the source category methodological descriptions in Section 3 of the main report. The main reasons for reallocations or modifications • To account for differences in geographical scope (e.g. to account for energy use in OTs)

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 982 • To make best use of EU ETS data (this data is only used indirectly in producing UK energy • To utilise other operator reported data (e.g. direct to the Inventory Agency , or to environmental or industry regulators). • When bottom-up models are available providing fuel consumption data on a more granular level and are considered to be a higher quality estimate by the Inventory Agency. and how the energy data for the major fuels in the UK inventory are reconciled against the energy demand data from DUKES. The tables 1. Where fuels are re-allocated between sectors, but the overall annual fuel consumption across all UK sectors is kept consistent with the data in DUKES; and 2. Where deviations are made to DUKES figures for total UK consumption of a given fuel, and in which source categories these deviations are made. The Inventory Agency presents data below for the fuel allocations for coal, natural gas, fuel oil, gas oil (including DERV) and petroleum gases (LPG, OPG) for the latest inventory year. Together these fuels constitute the majority of the UK inventory 1A sector emissions total. Deviations to the energy balance are made in consultation with the authors of the energy Total coal use within the GHG inventory is consistent with the DUKES total and in most cases, coal use at the sectoral level is also consistent with the DUKES data. However, there are several instances where operator -reported data, either via trade associations such as the Mineral Products Association, or through EU ETS, indicates slight differences from the DUKES statistics. In those cases, the Inventory Agency deviates from DUKES to ensure higher accuracy for those source categories. Overall, however, the DUKES demand total is regarded as complete and accurate and therefore the 1A2gviii Other Industrial Combustion is used as the ‘residual’ source category, to deliver exact reconciliation between GHGI activity and the DUKES demand total. estimates for the latest inventory year. 2.635 Power Stations 1A1ai 2.635 0.000 Autogenerators 0.019 Autogenerators (inc.

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 983 1.544 Coke manufacture 2C1 1.486 0.058 Operator-provided data Blast furnaces 1.059 Blast furnaces 1B1b 1.059 - Coal extraction - Collieries - combustion 1A1ciii - - Other industries 0.500 Other industrial 1A2gviii 0.545 -0.046 Industrial combustion Iron and steel 0.021 Iron and steel - 1A2a 0.021 - Operator-provided data 1A2b 0.029 0.000 Industrial combustion 0.605 Cement processes 1A2f 0.620 -0.015 Cement and lime sector 1A2c 0.050 - Industrial combustion food beverages 0.055 Food & drink, tobacco 1A2e 0.055 - Industrial combustion Paper printing 0.094 Pulp, Paper and Print 1A2d 0.094 -0.000 Industrial combustion Sub-total 1.354 Sub-total ∑1A2 1.414 -0.061 Some operator data Rail 0.014 Rail transport 1A3c 0.014 - Domestic 0.480 Domestic combustion 1A4bi 0.480 0.000

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 984 1A4ai 0.023 -0.004 Some reallocation from Commercial 0.005 Miscellaneous 1A4ai 0.012 -0.000 Some reallocation from Miscellaneous 0.007 1A4ci - - Agriculture - Agriculture (mobile & TOTAL 7.279 TOTAL 1A2e 7.279 -0.000 Fully reconciled to Rows are shaded to help illustrate reconciliation between sectors. Data for natural gas use is largely taken directly from DUKES and the national total is consistent between the inventory and the energy statistics, other than a small additional use of natural gas at a number of (international) gas pipeline inter -connectors and also on the Isle of Man (IoM) which is added to the inventory, as natural gas use on IoM is not included in DUKES demand totals.
Operator estimates for ammonia production (both fuel and feedstock), and ETS data for gas separation plant lead to minor reallocations of the DUKES data, these are summarised below 741,650 Power Stations (UK) 1A1ai 741,518 132 This includes fuel referred - Power Stations (CDs) 1A1ai 2,458 -2,458 Isle of Man gas use is 1A1cii 146,379 -7,251 EUETS natural gas use in Coal extraction 195 Collieries - combustion 1A1ciii 195 0 Blast furnaces 1,438 Blast furnaces 1A2a 1,438 0 Other 18,781 Gas production 1A1ciii 19,807 -1,025 From discussion with

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 985 21,710 Petroleum Refineries 1A1b 39,698 -17,988 Re-allocation in the GHGI Autogenerators 82,609 Autogeneration (inc. 1A4ai 51 Re-allocation in the GHGI Subtotal 105,246 Subtotal 105,227 19 Agriculture 4,469 Agriculture - stationary Commercial 164,285 Miscellaneous 1A4ai 197,515 GHGI total is the sum of Miscellaneous 33,265 - - Domestic 1,034,583 Domestic combustion 1A4bi 1,035,419 -836 Isle of Man gas use not Subtotal 1,370,199 Subtotal 1,367,798 2,401 Domestic gas use on Iron and steel 17,977 Iron and steel - - Cement processes 1A2f 4,876 Other 187,787 Other industrial Subtotal 392,835 Subtotal 377,194 15,641 Whole-sector NEU 14,256 Ammonia production 2B1 20,980 -

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 986 Subtotal 14,256 Subtotal 29,824 -15,568 Offset by industrial Losses 12,913 Losses - 12,913 GHGI doesn’t report a TOTAL 2,796,642 TOTAL 2,791,838 4,804 GHGI doesn’t report a 2,791,838 -8,109 GHGI allocation for Autogenerators 964 Collieries - combustion 1A1ciii 964 - - TOTAL 977 0 TOTAL 977 - - Exact reconciliation Rows are shaded to help illustrate reconciliation between sectors. Note that DUKES activity data is originally in gross energy terms. Reconciliation has been calculated by net terms using a net/gross ratio derived from sources external to DUKES (i.e. from information provided by the GB’s gas network operators). For shipping, a major research project was completed in 2017 and the results were incorporated from the 2018 submission onwards. The estimated total fuel oil consumption derived from this research is greater than as reported for shipping in DUKES, and any deviations from the national navigation sector are considered additional and are not reconciled elsewhere in the inventory. Additional sectoral deviations are also made to account for known use of fuel oil in power stations, 0.052 Power Stations 1A1ai 0.050 0.001 EU ETS data used for Autogenerators 0.029 Autogenerators (inc. 1A2b - 0.029 Fuel reallocated to iron

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 987 1A1ciii - 0.050 Fuel oil allocated to oil Other industries 0.054 Other industrial 1A2gviii 0.116 -0.062 Calculated as a Iron and steel 0.005 Iron and steel - 1A2a 0.023 -0.018 Increased to account food beverages 0.002 Food & drink, Paper printing 0.001 Pulp, Paper and 0.157 -0.081 GHGI allocates residual - Shipping - coastal 1A3d 0.158 - - Fishing vessels 1A4ciii 0.008 - Sub-total - Sub-total 0.172 -0.172 Bottom-up shipping

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 988 Marine bunkers 0.597 Marine bunkers Memo 0.591 0.006 Shipping between the Commercial 0.021 Miscellaneous Agriculture 0.005 Agriculture (mobile & 1.119 -0.166 Higher overall reported Rows are shaded to help illustrate reconciliation between sectors. Gas oil is used in both off-road transport and machinery diesel engines, and as a fuel for stationary combustion. The varied use of this fuel and the complexity of the supply chain complicates the means of allocating consumption across the wide range of se ctors that use the fuel in the inventory. DUKES provides a breakdown of gas oil consumption in different economic sectors, but the data resolution in DUKES does not distinguish between use of the fuel for stationary combustion and off-road machinery, a distinction which is necessary for the inventory. There is also a significant quantity of fuel reported in DUKES as ‘unclassified’ (a higher proportion for gas oil than most other fuels), which means that the UK energy statistics team are currently unable to identify the end users of this portion of fuel use. This reflects that the UK energy statistics team face similar challenges to the inventory team in identifying and allocating gas oil to the many To address the inventory req uirement to distinguish mobile from stationary fuel use, t he GHGI estimates consumption of gas oil and emissions for off-road machinery using a bottom-up method based on estimates of population and usage of different types of machinery , as described in Section 1.1 , MS 6 . However, this has led to a situation where the total amount of gas oil consumption across sectors exceeds that which is available as given in DUKES in many years, To address the issue of GHGI bottom -up estimates of fuel consumption being higher than fuel available in the DUKES balance, the inventory team deploys a 6 -step method for deciding how fuel is allocated between sources as 1. Separate out sources which should not be factored into the reconc iliation (e.g., because they are well understood, like rail, or because the NAEI are deviating from DUKES for that sector, such as national navigation , which is discussed further below ), and establish the quantity of gas oil which is available for the sectors being reconciled

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 989 2. Always use the de minimis data from established data such as ETS on stationary fuel use. It is understood that this will in many cases be an underestimate of stationary fuel use. 3. Always use NRMM fuel use which coincides with DUKES all ocations after removing the 4. If there is not enough fuel in the DUKES balance to allocate to bottom-up NRMM estimates, then scale the amount which exceed each DUKES sector. This effectively means that where the bottom-up estimates agree with DUKES, then there is no scaling, and there is more scaling when there is more disagreement 5.
When there is enough fuel for all bottom-up estimates from the NAEI, split the remaining fuel by the residual of each sector which is not thought to be NRMM capped at the DUKES 6. If there is still fuel left in ‘unallocated’ after all fuel has been allocated in the previous step, then allocate this remainder to ‘other industry’ In order to apply step 3 in this process, we are req uired to map machinery types to DUKES sectors. Many machinery types are not exclusively used by single sectors, so in these cases we have mapped the machinery types to the sectors where we expect the largest use of those been discussed and agreed with the UK energy statistics team. Agriculture Agriculture We would expect almost all gas oil used in the agriculture sector to be for use in agricultural machinery, and this equipment is unlikely to be captured in any other sectors. Airport Commercial Gas oil in the UK energy statistics specifically excludes road diesel, and so any gas oil reported as delivered to airports would not be considered a road fuel and allocated to the transport sector, instead it would be allocated to the airport operator, which would be considered ‘commercial’. This equipment is unlikely to be captured in any other sectors. Construction Construction We would expect almost all gas oil used in the construction sector to be for use in construction machinery, and this equipment is unlikely to be captured in any other sectors. Forklifts Commercial Likely used across almost all sectors to some degree, although many more intensive users (such as retailers, distributors) would be considered ‘commercial’ organisations by the energy users (road diesel or petrol) is used for this equipment instead of gas oil. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 990 Generators Other industry Likely used across almost all sectors to some degree, and there are no clear sectors which would dominate use. These are allocated to ‘other industry’ in the absence of a clear sector Minerals We would expect almost all gas oil used in the minerals sector to be for use in mining and quarrying machinery, and this equipment is unlikely to be captured in any other sectors. Other industry This category includes a very wide range of machinery, typically with small engines (such as <37kW). It would not add much value to look into these machinery types on a case -by- case basis to better allocate them to end users, so instead these are simply allocated to ‘other industry’. Commercial This is s pecifically transport refrigeration units which are independently powered; this does not include the main engine for modes of transport carrying transport refrigeration units. While many sectors such as food and drink industries will use this equipment, we expect the largest users to be retailers and distributors, which would be considered ‘commercial’ by the National navigation in DUKES is defined as “ Fuel oil and gas/diesel oil delivered, other than under international bunker contracts, for fishing vessels, UK oil and gas exploration and production, coastal and inland shipping and for use in ports and harbours”. Therefore, this sector would be expected to include fuel use for port machinery, and this equipment is unlikely to be captured in any other sectors. Waste Commercial The UK energy stats team indicated that waste management organisations would be categorised as ‘commercial’ in DUKES. This equipment is unlikely to be captured in any other sectors. Steps 2-6 of this process are illustrated in the below two figures, which presented the quantities of fuel in each stage of the reconciliation calculations. Note • The first three columns represent the input data, the final column the final allocation used, and the other columns present the calculations done to determine how fuel is allocated; • The first column ‘Bottom-up de minimis’ is equivalent to step 2 in the reconciliation process • The fourth column ‘Overlap between NRMM and DUKES’ is equivalent to step 3 in the reconciliation process described above; • The seventh column ‘remaining fuel assigned to NRMM’ is equivalent to step 4 in the reconciliation process described above; • The ninth column ‘remaining fuel assigned to stationary’ is equivalent to step 5 in the reconciliation process described above; • The tenth column ‘residual unclassified fuel allocated to other industry’ is equivalent to step 6 in the reconciliation process described above;

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 991 • When you sum up the columns for steps 2-6, you will have the final allocations presented • In most cases the colour scheme has pairs where the paler bars relate to mobile combustion, and the darker bars relate to stationary combustion; • At the top of the third column ‘DUKES’, is ‘unclassified’ fuel. This is free for the inventory team to reallocate to whichever sector our bottom -up estimates indicate are • At the top of se cond column bottom-up NRMM’ are port machinery, which are excluded from the reconciliation process. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 992

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 993

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 994 In addition to off -road mobile machinery, gas oil allocations are also required for other sources such as vessels on the UK’s inland waterways (Walker et al, 2011). Research into fuel use on inland waterways indicates that not all vessels with diesel engines use gas oil, but that some also use road diesel; this may also apply to other off-road machinery sources, especially those that consume small amounts of fuel on an irregular basis, e.g. for private or recreational use rather than commercial use.
There are also inconsistencies in terminology used to define types of fuel; the research indicated that the terms “gas oil”, “red diesel” and “diesel” are used interchangeably by fuel suppliers and consumers and this confuses the situation when considering fuel allocations As with fuel oil, the introduction of the results of a major research project into the shipping sector in the 2018 submission, whereby Automatic Identification System (AIS) data was used to calculate shipping movements along the coast of the UK and the Crown Dependencies, however suggested that gas oil consumption reported by DUKES for national navigation is an underestimate. As a result, total gas oil use (not including DERV) deviates from DUKES as any further consumpti on in the national navigation sector are considered additional to DUKES and are not reconciled elsewhere in the inventory. Note that in DUKES, port machinery fuel use is included as part of national navigation, and therefore, similar to the estimates of gas oil use for shipping, the bottom- up estimates for fuel consumption by port machinery also deviates from DUKES and are not reconciled elsewhere in the inventory. 0.058 Power Stations 1A1ai 0.059 -0.001 Autogenerators 0.028 Autogenerators (inc. 1A2b - 0.028 Fuel reallocated to iron Oil gas extraction 0.533 Upstream Oil/Gas - Refineries - combustion 1A1b - - Other industries 1.487 Other industrial 1A2gviii 0.069 1.419 Calculated as a residual Iron and steel 0.004 Iron and steel - 1A2a 0.003 0.001 Data provided by Non ferrous metals 0.007 Non-ferrous metal Cement processes 1A2f 0.005 - Data provided by Chemicals 0.080 Chemicals food beverages 0.041 Food & drink, tobacco Paper printing 0.031 Pulp, Paper and Print

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 995 Road 21.727 Road transport 21.593 0.134 Reduced to offset Rail 0.506 Rail transport 1A3c 0.477 0.029 Inventory Agency National navigation 0.584 Inland and small Fishing vessels 1A4ciii 0.140 Naval shipping 1A5b 0.164 Marine bunkers 1.332 Marine bunkers Memo item 1.332 0.000 Domestic 0.123 Domestic combustion 1A4bi 0.123 0.000 1A4bii 0.011 -0.011 Inventory Agency 1A4ai 0.024 0.253 Fuel use offset to account Commercial 0.513 Miscellaneous 1A4ai 0.050 0.702 Fuel use offset to account Miscellaneous 0.239 Agriculture 0.374 Agriculture (stationary 1A4ci - 0.374 Fuel use offset to account 1A4cii 1.503 -1.503 Inventory Agency TOTAL 27.944 TOTAL 28.896 - 0.952 Overall the GHGI reports Rows are shaded to help illustrate reconciliation between sectors For petroleum gases (LPG, OPG), the total fuel use in the inventory is greater than the national statistics in several years, to reflect information from other sources (such as EU ETS data) that indicate potential under -reports in the UK energy statistics. These modifications to the energy

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 996 the petrochemicals sector, and fuel use for upstream oil and gas production. Liquefied Petroleum Gas (LPG) – in DUKES this fuel is reported as propane and butane Iron and steel 0.000 Iron and steel - Other industry 0.320 Other industrial Road transport 0.058 Road transport 1A3bv 0.059 -0.001 Other Petroleum Gases (OPG) – includes Refinery Fuel Gas (RFG). In DUKES, reported as Ethane, Other gases and 1A1b 1.960 -0.356 Refinery (and on-

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 997 0.178 0.178 Backflows from (combustion) 2B8g 1.019 -1.019 Sequences of shaded rows indicate categories which are grouped for purposes of data reconciliation, and should be considered together. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 998 The UK conducts an annual analysis of how the GHGI reference approach compares to what the UK energy statistics team submit to the IEA. This acts a QA step for the UK GHGI balance, and aids transparency of why these data might differ for different reporting obligations. The fossil Primary fuels Crude oil 1,929,413.8 1,929,354.1 59.7 0.0% liquids 116,775.1 119,706.9 -2,931.8 -2.5% IPCC default NCV used for GHGI RA calculations, which differs from the NCV used in the calculations that provide an estimate for the IEA return by about 2.5%. fuels Gasoline -228,159.4 -231,522.8 3,363.4 -1.5% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return. In 2021 the Overseas territories imported 3,445 TJ of gasoline, which explains almost the entirety of Jet kerosene -9,795.9 -19,313.0 9,517.1 -97.2% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return. The scope of international bunkers is also different, as the IEA return will exclude Overseas Territories as part of the UK's coverage for the IEA return, but the OTs are included in the UK's submission. This difference is likely exaggerated this year due to large changes in the aviation sector's behaviour due to the pandemic impacting the different approaches to estimating international / domestic splits that are used for IEA and GHGI reporting. kerosene 31,884.8 31,757.7 127.1 0.4% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return. In 2021 the Overseas territories imported 118 TJ of other kerosene, which explains almost the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 999 oil 413,721.3 400,449.7 13,271.6 3.2% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return. In 2021 the Overseas territories imported 13,090 TJ of Gas/diesel oil, which explains almost the oil -100,380.1 -105,683.7 5,303.7 -5.3% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return.
In 2021 the Overseas territories imported 4,800 TJ of residual fuel oil, which explains most of this In addition to this, the boundary of what is considered international journeys or not differ due to journeys between the UK and OTs being considered domestic under -11,812.6 -12,786.9 974.3 -8.2% Imports are expected to be higher in the GHGI due to the inclusion of Overseas territories, which won't be included in the IEA return. In 2021 the Overseas territories imported 886 TJ of LPG, which explains most of this difference. Ethane 54.0 0.0 54.0 100.0% Suspected that this is a result of rounding in the IEA report, where these small values might be rounded to zero. Naphtha -12,270.8 -12,354.1 83.3 -0.7% Bitumen 44,469.2 43,760.6 708.6 1.6% 0.7% difference in NCV. The rest of the difference appears to be a difference in stock change between the IEA return Lubricants -2,118.8 -3,016.1 897.3 -42.3% The difference appears to be a difference in stock change between the IEA return and the UK energy statistics. coke -4,712.2 -4,783.9 71.7 -1.5% feedstocks 77,560.0 75,520.8 2,039.2 2.6% Mostly explained by a 2.4% difference in choice of NCV. Other oil -26,194.6 -27,359.2 1,164.6 -4.4% Mostly explained by a 3.3% difference in choice of NCV. There is also a small quantity of additional fuel estimated to be imported for use in OTs in the GHGI compared to the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1000 total 2,218,433.9 2,183,730.1 34,703.8 1.6% Most of the difference can be put down to additional imports of fuel associated with OT consumption in the GHGI compared to the IEA, with additional differences around differences in bunkers fuel allocations and fossil Primary fuels Anthracite 16,988.9 0.0 16,988.9 100.0% The submission to the IEA doesn't differentiate between Anthracite and other bituminous coal. Coking coal 72,297.2 72,446.9 -149.7 -0.2% 109,103.2 128,925.1 -19,821.9 -18.2% The submission to the IEA doesn't differentiate between Anthracite and other bituminous coal. Additionally, the weighted NCVs used differ between the IEA return and the patent fuel 7.8 -28.3 36.1 464.6% Suspected rounding differences in the small numbers being totals 236,759.2 239,696.3 -2,937.1 -1.2% Primarily the impact of differences in weighted NCVs for (dry) 2,764,071.3 2,754,907.2 -9,164.1 0.33% Primarily driven by a more precise gross-net value used in the GHGI based on industry data, which is 0.3% different. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1001 2,764,071.3 2,754,907.2 -9,164.1 0.33% Primarily driven by a more precise gross-net value used in the GHGI based on industry data, which is 0.3% 74,657.1 59,943.0 14,714.1 19.7% There is normally a ~5% or so difference in the CV assumptions between the GHGI and the IEA, however this is exaggerated this year due to recalculations being made to industrial waste reported to the IEA which are yet to be implemented in published UK energy statistics used for the Peat 72.8 0.0 72.8 100.0% The UK energy statistics team do not estimate peat balances, the GHGI make this estimate via alternative data Total 5,293,994.2 5,281,566.4 12,427.8 0.2% There are 2 key reasons for 1) Geographical coverage, the UK GHGI includes OTs, which are not included in the IEA returns. 2) Differences in conversion factors used to convert from a mass basis to an energy basis or a gross to net 3) Recalculations in the IEA data which is yet to be implemented in published UK energy statistics

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1002 ANNEX 5: Additional Information to be Considered as Part of the Annual

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1003 ANNEX 6: Comparison of Inventory This Annex describes the verification of the reported UK emissions through comparison with UK emissions estimated through the use of atmospheric observations and modelling. Comparison of the UK GHGI (Greenhouse Gas Inventory) with emission estimates calculated using atmospheric observations is considered to be best practice by the UNFCCC as it allows for an independent assessment of the GHG emissions from the UK using a comprehensively different approach. Significant differences in the emissions estimated using the two methods are a means of identifying areas worthy of further investigation, for example as occurred with a re-assessment of the emissions of HFCs for refrigeration. In order to provide a comparison to the UK GHGI, BEIS (UK government department of Business, Energy and Industrial Strategy) supported the establishment and maintenance of a high -quality remote observation station at Mace Head (MHD) on the west coast of Ireland as pa rt of the Advanced Global Atmospheric Gases Experiment (AGAGE) (Prinn et al. , 2018). The station reports high-frequency concentrations of the key greenhouse gases and is under the supervision of Prof. Simon O’Doherty of the University of Bristol (O’Doherty et al. 2004, 2014, Stanley et al. BEIS extended the measurement programme in 2012 with three new tall tower stations across the UK, collectively called the UK DECC (Deriving Emissions linked to Climate Change) Tacolneston (TAC) near Norwich; Ridge Hill (RGL) near Hereford; and Tall Tower Angus (TTA) near Dundee, Scotland (decommissioned in 2015). Two additional stations, Heathfield (HFD) in Southern England and Bilsdale (BSD) in North Yorkshire, were established through the NERC GAUGE (Greenhouse gAs UK and Global Emissions) programme. BSD replaced TTA in 2015 in the UK DECC network and is funded by DESNZ (formerly BEIS). A fire at BSD in August 2021 destroyed the tower and measurements have therefore been discontinued until a replacement tower is available. HFD is supported by the National Physical Laboratory (NPL). Methane (CH4), carbon dioxide (CO 2), nitrous oxide (N 2O) and s ulphur hexafluoride (SF 6) are measured at all stations across the UK DECC network. The hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) are measured at MHD and TAC, and nitrogen trifluoride (NF 3) is only measured at MHD within the DECC network. With permission of the data providers, observations were also obtained • Carnsore Point (CSP) on the east coast of Ireland (200 5-2010), funded by the Irish Environmental Protection Agency (F-gases).
• Weybourne (WAO) in East Anglia, England (2013 -present) ICOS site supported by the University of East Anglia and the National Centre for Atmospheric Sciences (CH4). UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1004 • Cabauw (CBW) in the Netherlands (1993 -2019) supported by the Netherlands Organisation for applied scientific research (TNO), funded by the Dutch Min istry of Infrastructure and Water Management . Glass flasks are analysed at the University of • Jungfraujoch (JFJ) in the Swiss Alps (2007-present) supported by the Federal Office for the Environment (FOEN) through the project HALCLIM/CLIMGAS-CH, by the International Foundation High Altitude Research Stations Jungfraujoch and Gornergrat (HFSJG), and by the Swiss Federal Laboratories for Materials Science and Technology (Empa) (CH4, F- • Monte Cimone (CMN) in the Italian Apennine mountains (2007-present) supported by the University of Urbino (CH4, F-gases). • Taunus (TOB) in central Germany (2013-present) supported by the Goethe University • Trainou (TRN) in France (2007-present) ICOS site operated by Laboratoire des Sciences du Climat et de l'Environnement (LSCE) (CH4). • Lutjewad (LUT) in The Netherlands (2007-present) ICOS site operated by University of Groningen - Centre for Isotope Research (RUG -CIO), University of Groningen (RUG) For the global hemispheric concentration analysis, data were kindly provided from the Kennaook/Cape Grim observatory (CGO) in Tasmania, Australia by CSIRO ( The Commonwealth Scientific and Industrial Research Organisation , an Australian Government The Met Office, under contract to DESNZ, employs the Lagrangian dispersion model NAME (Numerical Atmospheric dispersion Modelling Environment) (Ryall et al. 1998, Jones et al. 2007) driven by three -dimensional modelled meteorology to interpret the observations. NAME determines the history of the air arriving at each station at the time of each observation. Estimates of UK emissions are made by firstly estimating the underlying backgr ound trend (Northern Hemisphere, mid-latitude, atmospheric concentrations with the short -term impact of regional pollution removed from the data) and secondly by modelling the impact of where the air has passed over on route to the observation stations at a regional scale. A methodology called Inversion Technique for Emission Modelling (InTEM) has been developed that uses a minimisation technique, Non -Negative Least Squares (NNLS) (Lawson and Hanson, 1974), to determine the emission distribution that most a ccurately reproduces the observations (Manning et al. 2003, 2011, 2021 and Arnold et al. 2017). For each reported gas, the Northern (estimated using observations from MHD) and Southern (estimated using observations from CGO) Hemisphere background concentr ations and the UK emission estimates are presented. InTEM uses all of the available observations. Two-year inversion windows are used up until 2013 due to the paucity of UK observations prior to this year. Each inversion is performed for a two-year period and then the period is incremented by one-year e.g. 1989 – 1990, 1990 – 1991 etc., from which a mean for each year is estimated . From 2013, with the additional data from the other stations (principally TAC), the inversion time window is shortened to one year or smaller ( 1 month for CH 4, N2O and SF 6 as all of the DECC network stations measure these gases). For CH4, 2-month windows were used from 2007-2013 due to the availability of additional European observations during this time. The geographical spread of the UK DECC (and other stations) network allows the spatial distribution of the emissions across the UK to be better constrained by InTEM. The InTEM

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1005 estimates of UK emissions using the atmospheric observations are compared to the reported GHGI estimates. For each gas the InTEM estimated geographical distribution is presented as an average for 2019-2022. For CH4 and N2O these are shown as seasonal averages for 2019-2022. The time-series of UK emissions from 1990 are given showing the comparison between the GHGI and InTEM (2-year and 1-year or monthly) estimates. The uncertainty of the InTEM estimates are calculated through the Bayesian framework. The GHGI uncertainties have been linearly interpolated between the reported 1990 and 2 021 Approach 2 uncertainty values given in the 2022 submission of the UK NIR. As the values in the GHGI should represent a 95% confidence interval, the uncertainties have been halved so they are comparable to ±1σ, consistent with the error bars presented in this chapter. The most recent GHGI uncertainty assessment is not completed in time to allow for inclusion in the analysis presented in this chapter, but as the assessments for uncertainty by gas do not generally change substantially between submissions, the use of the previous year’s GHGI uncertainty values is a All of the comparisons have been made in units of CO 2-equivalence using global warming potentials (GWP) over a 100 -year timeframe (IPCC 5th assessment), consistent with the val ues used throughout this document. and (b) N2O, estimated from MHD, Ireland observations are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents As with all of the background plots for each different gas, it shows how the overall atmospheric concentration of the gas in question is changing as a result of global emissions and atmospheric loss processes. For CH 4, the underlying background trend is positive. In 2022, the Northern Hemisphere (NH) concentration grew by 15.2 ppb, the SH by 18.4 ppb. The CH4 emission estimates made for the UK using the InTEM methodology are compared to the to note that although the UK GHGI CH4 emissions are estimated to have fallen since 1990, the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1006 global atmospheric concentration of CH4 has increased, indicating that global emi ssions of CH4 are still outperforming the global natural removal of CH4 from the atmosphere. CH4 has a natural (biogenic) component and it is estimated that 22% of the annual global emission is released from wetlands (Nilsson et al. 2001). Usually, natural emissions are strongly dependent on a range of meteorological factors such as temperature and also growth and decay cycles.
Such non-uniform emissions will add to the uncertainties in the InTEM modelling, although in northwest Europe the natural emissions are thought to be small compared to the anthropogenic emissions (<5%, Bergamaschi et al 2005). Due to the relatively strong local (within 20km) influence of emissions at some of the stations , observations taken when local emissions are thought to be significant (poor atmospheric mixing, low boundary layer heights, low wind speeds, stable atmospheres) have been removed from the InTEM analysis. eq yr-1 (a) from 1990 and (b) from 2006. GHGI estimates are shown in black. InTEM 2-site 2-year estimates are shown in green (±1σ). InTEM full network, 1-month annualised, estimates are shown in blue (±1σ). InTEM 7-site, 2-month annualised, estimates are shown in red (±1σ). UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1007 eq month-1 from 2006. GHGI estimates are shown in black. InTEM 2- month estimates are shown in red (±1σ). InTEM 1-month estimates are The UK GHGI trend is monotonically downwards from 1990 whereas, in contrast the InTEM trend is relatively flat from 1993 onwards. Prior to 2007 the InTEM estimates are based only on MHD and CBW observations. The reasons for the large discrepancy in the 1990s and 2000s are not known and given the paucity of observations it is difficult to un pick further. The large drop in the GHGI is estimated to be dominated by a reduction in waste and coal mining emissions. From 2007 observations from TTA, JFJ, CMN, LUT and TRN become available. From 2012, observations from TAC, RGL, HFD, BSD and WAO from the UK DECC network are available. The InTEM estimates show a modest (-1.5 % yr-1) annual decline 2013-2021, similar to the GHGI network of observations do not show a strong seasonal cycle in UK CH4 emissions, whereas the CH4 emissions (average over 2019-2022) as estimated by InTEM per season, Winter (Dec-Feb), Spring (Mar-May), Summer (Jun -Jul) and Autumn (Sep -Nov), each of the plots show elevated emissions in highly populated areas. Scotland is poorly resolved due to the paucity of

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1008 2019-2022 by (a) Spring (b) Summer (c) Autumn and (d) Winter. The observation stations are shown as blue triangles. Major cities are onwards. The background trend is monotonic and positive. The Northern Hemispheric background concentration increased by 1.3 ppb in 2022. The main activities in Europe resulting in the release of N 2O agricultural practices resulting in emissions from soils (~75%), industrial processes (~4%), energy (~10%), waste (5%) and LULUCF (6%) (UNFCCC 2020 emission estimat es for EU+UK). The amount emitted from soils has significant uncertainty and has a diurnal and seasonal release cycle. It is driven by the availability of nitrogen, temperature and the soil moisture content. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1009 eq yr-1 (a) from 1990 and (b) from 2012. GHGI estimates are shown in black. InTEM 1-site, 3-year estimates are shown in green (±1σ), InTEM full network, 1-month, annualised, estimates are shown in blue (±1σ). The annual InTEM estimates (2012-2021) are, on average, 5.4 ± 0.9 Tg CO2-eq yr-1 higher than those reported by the GHGI. Neither the InTEM estimates (2012-2021) or the GHGI (2012-2021) are showing a statistically significant trend in UK N 2O emissions. The GHGI estimates show a sharp decline ( ~9 Tg CO 2-eq) between 1998 and 1999 in line with the introduction of clean technology at an adipic acid plant in Wilton, northeast England. It is estimated that they cut their emissions of N2O by 90%, from 12 Tg CO2-eq yr-1 to around 1.6 Tg CO2-eq yr-1. The improved network of observations from 2012 onwards allows a very strong seasonal cycle (average peak there is a peak in UK emissions in spring -summer (on average 2.7 Tg CO2-eq mth -1) and a minimum in the winter months (on average 1.3 Tg CO2-eq mth-1), aligned with the traditional fertiliser application period. There is however a strong year -to-year variability in this seasonal pattern demonstrating the impact of varying meteorology on the emissions of N 2O. The spatial emission pattern than for CH4. The nature of the N 2O emissions (related to fertiliser app lication and subsequent rainfall and runoff) challenges the InTEM assumption of uniformity of release both in time (within the monthly InTEM timeframe) and space (within the InTEM grid resolution). Also, the point of release to the atmosphere may not be coincidental with the activity ultimately responsible for generating the N2O e.g. the N2O, or its precursors, may be transported from its source, for example by rivers, prior to its release to the atmosphere. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1010 eq month-1 from 2012. GHGI estimates are shown in black. InTEM 1- month estimates are shown in blue (±1σ). 2019-2022 by (a) Spring (b) Summer (c) Autumn and (d) Winter. The observation stations are shown as blue triangles. Major cities are

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1011 134a, HFC-143a, HFC-152a, HFC-23, HFC-32, HFC-227ea, HFC-245fa, HFC-365mfc and HFC- 43-10mee) in Tg CO2-eq yr−1, combined using the AR5 GWP values. The GHGI is shown in black and InTEM annualised 2 -year in blue and the 1 -year in orange. The total InTEM HFC is consistently lower than the GH GI, with the best agreement between 2008 -2011. Looking at the 2-year data, from 2013 the InTEM estimate starts to decrease, dropping more sharply from 2017 to 2020 before levelling off in 2021. The 1 -year data is broadly consistent but has a peak in emissions in 2017 which is not seen in the more averaged 2 -year results. The 1-year estimates for 2020, 2021 and 2022 suggest that the previously decreasing trend has ceased and total HFC emissions have remained static for the last three years. Note, the GHGI reports uncertainty for the HFCs collectively, they are not available for the individual gases.
HFC-152a, HFC-23, HFC-32, HFC-227ea, HFC-245fa, HFC-365mfc and HFC-43-10mee) in Tg CO2-eq yr−1 from the GHGI (black) and InTEM, annualised 2-year inversion (blue) and 1-year inversion (orange). Note HFC-43-10mee (< 0.2 Tg CO2-eq yr−1) is included from 2011 when the observations start. The uncertainty bars represent ±1σ. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1012 estimated from MHD, Ireland observations are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents unratified data. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1013 estimated from MHD, Ireland observations are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents unratified data. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1014 in Tg CO2-eq yr-1 from 1990. GHGI estimates are shown in black. InTEM 2-year estimates are shown in blue (±1σ), InTEM 1-year estimates are

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1015 in Tg CO2-eq yr-1 from 1990. GHGI estimates are shown in black. InTEM 2-year estimates are shown in blue (±1σ), InTEM 1-year estimates are

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1016 2019-2022. The observation stations are shown as blue triangles. Major cities are shown as red circles. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1017 2022. The observation stations are shown as blue triangles. Major cities from 1995 onwards. The background trend is monotonic and positive, in 20 22 the Northern Hemisphere background increased by 6.2 ppt. the period 1990 onwards. The GHGI shows a stronger increase in emission compared to the InTEM estimates. The InTEM estimates have risen at about 70% of the rate of the GHGI. Between 1995 and 2011 the trend o f the GHGI and InTEM is in agreement but the InTEM estimates are consistently lower than the GHGI estimates, with the difference well outside the InTEM uncertainty range, except for 1995 to 1997. From 2011, the InTEM 2 -year estimate diverges from the GHGI and starts to gradually decline until 2018, whereas the GHGI rises until 2015 after which it starts to slowly decline. From 2018 both the GHGI and InTEM decline more quickly, with InTEM still estimating approximately 70% of the GHGI value. From 2020, the 2 -year InTEM estimate levels off and plateaus in 2021. The 1-year InTEM results show much greater year-to-year variation and have an unexplained peak in 2017. The results for 2019-2021 are closely aligned with the 2-year results and the 2022 value indicates that the previous steady decrease in emissions may have ended. Since the implementation of phase three of the EU MAC directive (2006), on 1st January 2017, the use of fluorinated greenhouse gases with GWPs100 higher than 150 (mainly HFC-134a)

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1018 in all new models of cars and light vans sold in the EU has been banned. The impact of this policy is seen both in InTEM and the GHGI. UK in kg km−2 yr−1, for the four -year period 2019 -2022. The variable grid resolution of InTEM produces a patchwork of different resolutions dependant on source s ignal at the measurement site. For HFC-134a, the highest emissions are generally focused on the more populated areas, with the highest emission region appearing over London and in the south of the UK with significant emissions also indicated in the populated regions around Birmingham, Leeds and Manchester. from 2004 onwards. The hemispheric background trend is positive, in 20 22 the Northern Hemisphere background increased by 1.8 ppt. InTEM emission estimates for the UK for HFC -143a for the period 2004 onwards are shown in GHGI, increased year -on-year from the early 1990s until a peak in 2012-2013, and thereafter estimated a gradual decline. The InTEM estimates show reasonable agreement with the GHGI, though is slightly higher between 2005 and 2011. Between 2012 -2013 there is excellent agreement, and then InTEM follows the slope of the declining GHGI closely but is slightly lower from 2013-2017. After 2017, the InTEM estimates decline much more rapidly than the GHGI and is approximately 60% of the GHGI by 2019. The decline in InTEM estimates between 2019 and 2020 is very similar to the decline in the GHGI, maintaining the InTEM estimate as approximately 60% of the GHG I in 2020. InTEM emission estimates plateau from 2020 to 2022, indicating a levelling off of the previously sharply declining emissions of HFC-143a. UK in kg km−2 yr−1, for the four-year period 2019-2022. The highest emissions are generally focused around populated regions, with the highest emission area estimated to be over London and in the highly populated regions around Birmingham and further north to Manchester, in a similar distribution to that seen for HFC-134a. from 1998 onwards. The background trend is monotonic and exponentially increasing, in 2022 the Northern Hemisphere background increased by 4.1 ppt. InTEM emission estimates for the UK for HFC -125 for the period 1999 onwards are shown in 125 from the UK increased significantly from the 1990s. The InTEM estimates are slightly higher than the GHGI between 1999-2004, but between 2005-2013 there is excellent agreement. From 2014-2017, the 2-year InTEM estimates start to decline in contrast to the GHGI which continues to rise. The 1 -year model largely follows the 2 -year data with the exception of a much higher estimate in 2017. The GHGI starts to decrease from 2017, but not as rapidly as InTEM. By 2020 the InTEM estimate is approximately 70% of the GHGI. The InTEM estimates level off from 2020, with both 2021 and 2022 values being higher than the 2020 value. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1019 in kg km −2 yr−1, for the four -year period 2019 -2022.
Similar to HFC -134a and HFC -143a, the highest emissions are generally found in the more populated areas, with the highest emission region estimated to be over the London area. 2004 onwards. The background trend is monotonic and positive, in 20 22 the Northern Hemispheric background increased by 4.7 ppt. (d). The InTEM 2-year emission estimates match the GHGI estimates extremely well up until 2013. Between 2014-2021 the agreement is less good with the InTEM estimates not rising as rapidly as the GHGI. The InTEM 2 -year estimates continue to increase throughout the period. The InTEM 1-year estimate shows more year -to-year variability and has a peak in 2017. The overall trend in the 1-year data is an increasing one, but at a slower rate than the GHGI. in kg km −2 yr−1, for the four -year period 2019 -2022. As with the previous HFCs, the highest emissions are generally found in the more populated areas, with the highest emission region from 2007 onwards. There is a positive trend in the background; in 2022 the Northern Hemispheric baseline increased by 0.2 ppt. by InTEM. The trend in emission estimates is similar between the GHGI and InTEM, although the InTEM estimates decline more rapidly than the GHGI. The InTEM 2021 and 2022 estimates shows a levelling off in the decline in emissions. The estimate for 2021 is less than a third of the magnitude of the GHGI emission estimate for 2021. UK in kg km−2 yr−1, for the four -year period 2019-2022. The geographic distribution appears to largely follow population with the notable exception of the area around TAC and in North Wales or North West England . This is thought to be due to local e missions, probably from fire suppression maintenance and manufacture, and is worthy of further investigation. from 2007 onwards. There is a positive trend in the background; in 2022 the Northern Hemispheric background increased by 0.1 ppt. A 6.11 (f)) have significant uncertainty and are consistently lower than the GHGI estimates, with the exceptio n of 2008 when there is reasonable agreement . The GHGI estimates show a significant decline between 2007 to 2008 and then a steady annual increase up to and including 2019, before a decrease in 2020 and 2021. The 2-year InTEM estimates show a sharp decrease

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1020 from 2008 to 2012, after which emissions increase steadily until 2018, after which they remain approximately level. The 1-year InTEM estimates follow the same trend as the 2 -year, as would be expected, however there is high year-to-year variability in the estimated emissions of this gas making it hard to conclude whether the downturn in 2022 is significant or not. UK in kg km −2 yr−1, for the four -year period 2019 -2022. HFC -245fa emissions follow the distribution of population over the UK. from 2008 onwards. The background trend is monotonic and positive, in 20 22 the Northern Hemispheric background increased by 0.9 ppt. from (a) 1990 and (b) 2004 using a different scale. The InTEM estimates from 2009 onwards are higher than the emissions estimated by the GHGI until 2020 when they agree, before rising again in 2021 and 2022. It should be noted however that the InTEM uncertainties are large and often extend down to zero. From the observations it is clear that some intermittent emission s of HFC- in kg km−2 yr−1, for the four-year period 2019-2022. The levels of HFC-23 are fairly uniform over the UK, with areas of higher emissions indicated near Hull on the east coast of England, around Liverpool and North Wales and in Northern Ireland. from 2004 onwards. There was a positive trend in the Hemispheric backgrounds up until 2019 but no growth was estimated in the Northern Hemisphere in 2022. InTEM 2-year estimates show a similar response. The InTEM 2-year estimates are larger than the GHGI from 2005 to 2007, and then lower from 2009 onwards, but matches the rise in emissions from 20 12 to 2015 before declining sharply. The 1 -year InTEM estimates follow the same trend but have higher values in 2014 and 2016. The InTEM 2-year estimates level off from 2019 to 2021 and are under half the magnitude of the GHGI. The 1 -year emission estimate for 2022 shows a further decrease. UK in kg km−2yr−1, for the four-year period 2019-2022. The levels of HFC -365mfc emission are relatively uniform over England and Wales, areas of higher population do show slightly enhanced emissions. Higher emissions are indicated on the near continent in Belgium. onwards. The Northern Hemispheric background concentration shows a strong rise from the mid- 1990s until 2008, then a much-reduced annual increase until 2012. Between 2012-2017 a small decline is observed (Simmonds et al ., 2016), followed by a rise , peaking in 2019. The Northern

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1021 and Southern Hemispheric differences are exacerbated for this gas because of its relatively short atmospheric lifetime (1.6 years). In 2022 the Northern Hemispheric background increased by 0.3 for the period 1990 onwards. Between 1999-2008 and from 2012 onwards, the GHGI estimates are significantly larger than those estimated from InTEM. It is also interesting to note that the increasing trend from 2010 to 2018 in the UK GHGI is in contrast with a much flatter InTEM trend followed by a sharp decline from 2017 to 2022. The InTEM estimate is < 20% of the GHGI in UK in kg km−2 yr−1, for the four-year period 2019-2022. Similar to HFC-134a, the highest emissions are generally located in the more populated areas, with the highest emission region estimated to 10mee from 2011 onwards. There is a positive trend in the background , in 2022 the Northern Hemispheric baseline concentration increased by 0.01 ppt.
GHGI estimates are initially in agreement with those estimated by InTEM, but the GHGI then has a small rise to 2015 and 2016, just as the InTEM emissions drop sharply to a low point in 2014 and 2015, before rising back up until 2017, before dropping monotonically. Throughout the InTEM A 6.14 (d) and is largely similar to the other HFCs, i.e. spread by population. 218, PFC-318) in Tg CO2-eq yr−1. The GHGI is shown in black and InTEM annualised 2 -year in blue and the 1 -year in orange. The InTEM PFC estimate is considerably higher than the GHGI until 2011 when the agreement starts to improve. From 2018 to 2021 the InTEM estimates are approximately a factor of two higher than the GHGI estimate. The 1 -year InTEM result for 2021 and 2022 indicates a sharp rise in total PFC. Note, the GHGI reports uncertainty for the PFCs collectively, uncertainties are not available for the individual gases. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1022 318) in Tg CO2-eq yr−1 from the GHGI (black) and InTEM, annualised 2- year inversion (blue) and 1-year inversion (orange). The InTEM uncertainty bounds represent 1-σ. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1023 estimated from MHD, Ireland observations are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents unratified data. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1024 eq yr-1 from 1990. GHGI estimates are shown in black. InTEM 2-year estimates are shown in blue (±1σ), InTEM 1-year estimates are shown

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1025 2022. The observation stations are shown as blue triangles. Major cities from 2004 onwards. The background trend is positive , in 20 22 the Northern Hemispheric background concentration increased by 0.9 ppt. aluminium production plant in the UK. The InTEM uncertainty ranges are large and the estimates are approximately double the GHGI from 2005 to 2010. InTEM estimates then gradually fall more in line with the GHGI although the estimated uncertainties are still large and often extend down to ze ro, probably because the majority of emissions come from intermittently emitting point sources. Overall there is reasonable agreement between the GHGI and the InTEM estimates over the last ten years, however it does look as if the slight decreasing trend from 2018 in the GHGI is at odds with a slightly increasing trend emerging in the InTEM estimates. in kg km−2 yr−1, for the four-year period 2019-2022. The spatial distribution of PFC-14 shows sources in the NW of England, south of Dublin and on the near-continent. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1026 from 2004 onwards. The background trend is monotonic and positiv e and in 2022 the Northern Hemispheric background concentration increased by 0.1 ppt. The UK InTEM estimates have large uncertainties that generally overlap with the GHGI estimates few years, though with high uncertainty. The 2019 -2022 geographical spread of emissions around Belfast, Northern Ireland, and Northeast England. There are also sour ces on the near - from 2004 onwards. The bac kground trend is monotonic and positive and in 2022 the Northern Hemispheric background concentration increased by 0.02 ppt. 6.17 (c)), though the very clear fall in UK GHGI emissions between 2005 and 2008 is replicated by the InTEM estimates. There is better agreement from 2013, with the InTEM estimates remaining largely flat whilst the GHGI showing a strong year -to-year variation, most notably in 2017. The InTEM 1-year estimate shows a sharp increase from 2020 to 2022. The 2019 -2022 is clearly in Northwest England, the home of the UK’s only PFC-218 production facility. from 2011 onwards. The background trend is monotonic and positive and in 2022 the Northern Hemisphere background concentration increased by 0.1 ppt. The UK InTEM estimates are between 0.03 and 0.12 Tg CO 2-eq yr-1 significantly higher than InTEM estimated quantities have large uncertainties , sometimes extending down to zero be seen in Northwest of England, in the west of Scotland, in Northern Ireland and in Ireland to the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1027 and (b) NF3 estimated from MHD, Ireland observations are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents unratified hexafluoride (SF6) from 2004 onwards. The background trend is monotonic and positive and in 2022 the Northern Hemispheric background concentration increased by 0.4 ppt. yr-1 from (a) 1990 and (b) 2012. GHGI estimates are shown in black. InTEM 2-year estimates are shown in green (1σ), InTEM annualised 1- month estimates are shown in blue (1σ). a steep decline until 2016. From 2002 until 2009 the GHGI shows a steady decline from ~1.2 Tg CO2-eq yr-1 to ~0.7 Tg CO2-eq yr-1, a rise in 2010 and then a slow decline u p to 2015 and a rise

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1028 until 2018. Whilst there is poor agreement between the GHGI and InTEM from 2004 -2015, from 2016 there is good agreement especially from 2018-2020 following the decreasing trend. Looking in individual months e.g. Dec 2016. There is no evidence of a strong seasonal cycle in UK SF 6 emissions, al though there are specific months with very elevated estimated emissions. The observations themselves reveal some very large, short -lived, pollution events that are worthy of further investigation. The estimated spatial distribution of emissions 2019 -2022 from InTEM is single emission source. The main reported sources of emissions in the UK are from use in high- voltage switchgear which is widespread across the UK. yr-1 from 2012. GHGI estimates are shown in black. InTEM 1-month estimates are shown in blue (1σ). UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1029 2019-2022. The observation stations are shown as blue triangles. Major cities are shown as red circles. trifluoride (NF3) from 2014 onwards.
The background trend is monotonic and positive, and the Northern Hemispheric atmospheric concentration increased by 0.3 ppt in 2022. NF3 is only measured at MHD, JFJ and CBW (2021 & 2022 only). The InTEM emission estimates 4-year average InTEM emission map (2019 -2022). NF3 emissions are very low and the most significant source is seen around Dublin in Ireland. NF3 has been introduced in the semiconductor industry as a more efficient fluorine source than PFC-116. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1030 yr-1 from 2012. GHGI estimates are too small to see. InTEM 2-year estimates are shown in blue (1σ), InTEM 1-year estimates are shown 2019-2022. The observation stations are shown as blue triangles. Major cities are shown as red circles. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1031 High precision, high frequency measurements of carbon dioxide (CO 2) are made across the UK DECC network. The Northern Hemisphere background trend is positive; in 2022 it increased by 2.5 ppm. The background has a strong seasonal cycle due to the influence of the biosphere with a maximum in early spring and a minimum in late summer. The CO2 observed has three principle 3. Non-anthropogenic (natural) estimated from MHD, Ireland observations (data obtained from LSCE equipment through a data sharing agreement) are shown in red, and background Southern Hemisphere monthly concentrations from CGO, Tasmania are shown in blue. Grey shading represents unratified data. Since plants both produce CO2 through respiration and absorb CO2 through photosynthesis, the CO2 flux from vegetation has a strong diurnal and seasonal cycle and switches from production to absorption on a daily basis. This unknown natural (biogenic) component of the observed CO 2 is significant when compared to the anthropogenic (man -made) component and cannot be ignored. It is difficult to use CO2 measurements directly in an inversion to estimate anthropogenic emissions because (a) it is not possible to distinguish between biogenic and anthropogenic CO2, and (b) the diurnally varying biogenic CO2 flux is at odds with a key assumption of the inversion method, namely that emissions do not strongly vary in time over the inversion time -window (say monthly). Methods are under development to attempt to over -come these challenges, such the use of isotopic observations, through ratios with respect to anthropogenic carbon mono xide

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1032 (CO), and tracking at what time of day air passes over the ground and using biogenic process models. The uncertainties associated with each of these methods are predicted to be significant. The estimated uncertainties in the CO2 GHGI are very small compared to inversion results. Work is on-going to seek to improve our methods of verifying inventory CO2 emission estimates. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1033 ANNEX 7: Analysis of EU/UK ETS Data This annex summarises the analysis of the 2021 UK Emissions Trading System (UK ETS) energy and emissions data that is used within the compilation of the UK GHG inventory. The UK ETS replaced the UK’s participation in the EU Emissions Trading System at the end of the transition period on 31 December 202040, as such data for 2005 to 2020 is part of the EU ETS and data for 2021 onwards is part of the UK ETS. The ETS data are used to inform activity data estimates for heavy industry sectors, carbon dioxide emission factors of UK fuels within those sectors, and for comparison of fuel allocations to specific economic sectors against data presented in the Digest of UK Energy Statistics (DUKES), published by the Department of Business, Energy and Industrial The ETS data are used in the UK GHGI compilation as • ETS raw data on energy and emission estimates are processed and checked to enable integration of the activity data, implied emission factors and installation emission estimates as far as practicable within the UK GHG inventory compilation. Emission sources reported in ETS are allocated to inventory fuels and source codes, outliers are identified, and clarifications of data inconsistencies are sought with the regulatory agencies; • ETS activity data are closely compared against the UK national energy balance (DUKES) published by BEIS, and any inconsistencies are researched, seeking to resolve these through consultation with BEIS wherever possible; • The verified ETS data provides up to date high quality fuel compositional analysis of UK fuels, and these data are used to improve inventory emission estimates across the highly energy intensive sources such as power stations, refineries, cement kilns, and oil and gas • The ETS dataset for offshore oil and gas installations are checked to assess data consistency in emissions reporting between the ETS and the EEMS dataset which is also used within the UK GHGI compilation; • Overall, the Inventory Agency approach seeks to minimise data discrepancies between EU/UK ETS and the GHGI as far as practicable, in order that the derivation of traded and non-traded emission estimates from the UK GHGI are as accurate as possible. Close consistency between the ETS and GHGI is an important aspect of the development of a complete and consistent evidence base for policy development and tra cking progress towards UK GHG reduction targets in the non -traded sector under the EU Effort Sharing The scope of reporting under EU ETS increased from the 2013 dataset onwards. Phase II of the EU ETS ran from 2008-2012 inclusive.
Phase III reporting began in 2013, with some new emission sources and new installations reporting for the first time on their GHG emissions; in particular, the 40 There are also 5 power generating stations in Northern Ireland that do not currently participate in the UK E TS as they remain as participants in the EU ETS, under the terms of the Northern Ireland Protocol, to protect the operation of the Single Electricity Market

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1034 definition of combustion has now been extended to cover installations such as furnaces, driers, and other plant where heat is used directly. A handful of industrial process sources of CO 2 are also included from 2013, such as soda ash production. In the UK, the changes in reporting in Phase III are most significant for the chemicals sector, where the scope of reporting is larger than previously and now encompasses both new industrial process emission sources, and additional energy use. There is also a notable shift towards estimation methods that are based on mass balance calculations (e.g. for chemical manufacturing ) within the UK operator reporting to EU ETS. Other sectors with significant increases in reporting are food and drink manufacture, where installations such as driers, ovens etc. were included for the first time thus adding to the emissions from boilers and CHP plant that were reported in previous years, and roadstone coating, a sector which has not been present in the dataset before. Analysis of the phase III data enabled the Inventory Agency to improve estimates of emissions from the combustion of waste residues and process off -gases within the chemical and petrochemical sectors (which are all reported under IPPU sector 2B10), as well as to generate improved estimates for the IPPU component of several specific manufacturing processes, such as for soda ash (2B7), and titanium dioxide (2B6). In addition, following a review of methodology for all IPPU sources, EU/UK ETS data for phase II onwards has been used to improve emission estimates for glass production (2A3), brickmaking (2A4) and reductant use in electric arc furnaces The key findings from the analysis and use of the EU/UK ETS data • In the 2021 ETS dataset, a very high coverage of Tier 3 emissions data is evident for all fuel use in the power sector, and for solid, liquid and waste -derived fuels used in the cement and lime sectors. The proportion of Tier 3 data is somewhat lower for refinery fuel use, but still sufficiently high for the ETS to be considered the most reliable data available. All of the fuel quality data for these sources and fuels are therefore used within the UK GHGI, as the ETS fuel quality data is the most representative dataset available to inform UK carbon dioxide emission factors in the inventory; • ETS emissions data from refineries are higher than estimates derived from DUKES activity data for all but two years within the time series, with a discrepancy evident in OPG emissions. Consultation with the industry trade association, UKPIA, and cross -checking with their data shows that the ETS data are felt to more accurately reflect estimates of CO2, and therefore UK GHGI estimates are based on EU/UK ETS data rather than refinery fuel use data reported in the UK energy balance; • There are a range of other activity data discrepancies when compared to DUKES within the oil & gas, cement and lime, other industry and iron and steel sectors. Revisions to fuel allocations within the UK GHGI have been implemented for a number of sources, whilst further research is needed in some instances to clarify the issues where the reporting format of EU /UK ETS does not map explicitly to energy balance and GHG inventory • EU/UK ETS data for fuel use at chemical and petrochemical production facilities has helped to identify and quantify the combustion of process off-gases that are derived from Natural Gas Liquid (NGL) feedstock to petrochemical production processes, and from combustion of carbon-containing process residues. Analysis of “fuel gas” calorific v alues and carbon content informs the calculations to estimate emissions from NGL -derived

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1035 1A1a Power stations - coal, fuel oil 1A1a Power stations – petcoke ✓ Some additional data is 1A1b Refineries – petcoke & OPG ✓ EU/UK ETS figures only 1A1b Refineries – natural gas ✓ Upstream oil and gas production – Gas oil, natural gas, LPG, OPG 1A1c Gas industry – natural gas ✓ Integrated steelworks ✓ ✓ Use of various EU/UK ETS fuels, activity data for others 1A1c Collieries – Colliery methane ✓ 1A2g Industry - petcoke & waste ✓ No alternative data available 1A2g Industry – colliery methane ✓ 2A1 Cement ✓ Data used is actually from

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1036 2B8g Ethylene & other petrochemicals ✓ 2C1 Electric arc furnaces - reductants ✓ A 7.2.1 EU/UK ETS Data and GHG Inventories The UK ETS and EU ETS data provide annual estimates of fuel use and fuel quality data from the most energy intensive sites in the UK, and provides a source of data - or can be used to cross- check data held in the UK Greenhouse Gas Inventory (GHGI) - to inform the carbon contents of current UK fuels. The EU ETS has operated since 2005, and data has been available on an annual basis since this time across major UK industrial plants. The UK ETS replaced the UK’s participation in the EU ETS at the end of the transition period on 31 December 2020. The data reported under the EU/UK ETS includes quantities of fuels consumed (or other activity data for process sources of CO2), carbon contents of fuels and other inputs, calorific values (fuels only) and emissions of carbon dioxide, all presen ted by installation and by emission source.
Activity data are also given for many biofuels, although emissions of CO2 from these fuels are not included in the emissions data. This is useful though, since PI/SPRI/WEI/NIPI emissions data for CO2 often include biocarbon as well as fossil carbon, and the EU/UK ETS data on biofuels helps to explain differences between CO 2 emissions reported in EU /UK ETS and in those regulator inventories. EU/UK ETS data for individual installations are treated as commercially co nfidential by the UK regulatory authorities and so only aggregated emissions data are reported in inventory As part of the UK’s annual reporting requirements to the MMR and UNFCCC, the UK must include a comparison of the EU /UK ETS data against the national inventory dataset within the National Inventory Report. Furthermore, the analysis of the inventory against the EU /UK ETS dataset is coming under increasing scrutiny due to the development of domestic GHG reduction targets that are based on non-traded41 emissions data only, and the growing need to understand the UK non- traded sector emissions for future reporting under the Effort Sharing Decision. The EU/UK ETS dataset helps to improve the UK GHG inventory in a number of • Identifying new sources, therefore improving completeness; 41 All GHG emissions that are regulated within the EU/UK ETS are defined as “traded” emissions, whilst all other GHG emissions are defined as “non-traded”. The EU Effort Sharing Decision has lead to the UK adopting a new target for GHG reductions by 2020 for all of the non-traded emissions (i.e. everything outside of EU ETS), and progress towards this target will be monitored through the UK GHG inventory. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1037 • Helping assess true levels of uncertainty in fuel- and sector-specific data; • Providing fuel quality data and oxidation factors for complex processes; • Providing information on process-specific emissions that are not apparent from the • Reducing uncertainty in the GHGI; and • Acting as a source of quality assurance to inventory data. In the 1990-2021 inventory cycle, the Inventory Agency has updated and extended the EU /UK ETS analysis conducted for inventory compilation, using the 2021 UK ETS dataset, which is the first since exiting the European Union . This annex presents a comprehensive review of the seventeenth year of ETS data, indicating where the data have already be en used in the improvement of the GHGI, as well as highlighting outstanding issues which could be investigated further, with potential for further revision and improvement of the GHGI. The Inventory Agency has also been provided with details of all offshore oil and gas installations from EU/UK ETS data since 2011, which are regulated by BEIS OPRED. Access to these detailed data has enabled a more thorough review of the fuel/gas quality and reported emissions from combustion and flaring sources at offshore installations, and has directly improved the completeness and accuracy of the sector estimates within the UK GHGI. The analysis of the EU /UK ETS data for use in the UK GHGI necessitates a detailed review of the available data, in order to ensure correct interpretation and application of the available data. The study team prioritises effort to the sources and sites that are the most significant in UK GHGI terms, and/or where data reporting discrepancies have been identified from previous work. For those sectors where EU/UK ETS data are used in the GHGI, it is important to review emission factors from all major installations to ensure that any outliers are identified and checked prior to their inclusion in inventory calculations. Wherever possible, consistent assumptions are made when interpreting data across all years of the EU/UK ETS. For instance ensuring that each site is allocated to the same inventory sector in each year (unless there is reason to change it – some industrial combustion plants in recent years have been converted into power stations, and so these sites do need to be allocated to different sectors in different years), and that there is consistency in the way in which site -specific names for fuels are interpreted across the entire period. The information on the EU /UK ETS method “Tier” used for each of the data dictates whether they are used in inventory compilation. The highest tier EU/UK ETS data are assumed to be subject to the lowest level of uncertainty, and so only tier 3 and tier 4 emission factor data are used. Occasionally there are internal inconsistencies in the EU/UK ETS data between the data on consumption of a given fuel and emissions from the use of that fuel. These need to be resolved before the data can be used in the UK GHGI. A s emissions data are verified, we cross-check the detailed emissions data against the final verified emissions for each site. As a general rule it is found that the most appropriate solution to inconsistencies is to assume that the EU /UK ETS emissions data are correct as EU /UK ETS reporting requirements are well regulated, and that it is the activity data that need to be amended A 7.2.2 Scope of the EU/UK ETS and Implications for the GHG There are a number of limitations to the EU/UK ETS data that affect the data usefulness in GHG inventory compilation,

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1038 • The EU/UK ETS data are only available from 2005 onwards, whilst the UK GHG inventory reports emission trends back to 1990. The additional information that EU/UK ETS provides (e.g. year-specific emission factors for many fuels in energy intensive sectors) helps to reduce the uncertainties in inventory emission estimates for the later years, but care is needed where revisions to the time series are made back to 2005.
A consistent approach to inventory compilation across the time series is a key tenet of IPCC good practice guidance, and care is needed to ensure that the use of EU ETS data does not introduce a systematic reporting step-change in the UK GHGI; • Further to this point, it is important to note that the scope of EU ETS reporting has evolved through the years, from Phase I (2005 to 2007) into Phase II (2008 to 2012 data) and now to Phase III (2013 onwards). The comparability of EU ETS data for many sectors is poor between these three phases. For example, many cement kilns did not report to EU ETS until Phase several sectors including cement were reporting under Climate Change Agreements and were opted-out of EU ETS during Phase I. Therefore, in several sectors, more complete coverage of EU ETS reporting is evident in Phase II and data from 2008 onwards are therefore much more useful for UK GHGI reporting. The scope of coverage of chemical industry emissions has gone through two step changes – in 2008, and again in 2013, and some sectors (such as roadstone coating) only appear for the first time in the 2013 EU ETS data. Less significantly for the GHGI, many small installations, mostly in the public sector, were removed from EU ETS at the end of phase I. It is vital that the GHGI takes full account of such changes and that UK inventory data do not include trends that merely result from the increase (or decrease) in scope of EU/UK ETS. The changes in EU ETS scope have made the data set increasingly useful, and there are now five years’ worth of Phase II data, eight years of data under Phase III, and the first year of UK ETS, hence the EU /UK ETS dataset is now an important source of information for the UK • In the UK during EU ETS Phases I and II, the regulators adopted a “medium” definition of the term “combustion”, and as a result there were many sectors where fuel use in specific types of combustion unit were not included in the EU ETS reporting scope until the start of Phase III (2013). Examples of this include flaring on chemi cal sites, and fuel use in heaters, dryers, fryers and stenters in industry sectors such chemicals, food and drink, textiles, paper and pulp. Hence the total fuel use and GHG emissions from these sectors have typically been under-reported within the EU ETS historically compared with the UK inventory, with many sites and sources excluded from the scope of EU ETS. However, the ETS data for these sectors is also incomplete both in Phase II , Phase III and UK ETS because small installations are not covered by EU/UK ETS. Therefore, while the change in scope for combustion installations in phase III is a positive step, it has relatively little impact on the data used in GHGI compilation. Some Phase III data has been used to improve the estimates of emissions from combustion of process wastes / off-gases in the chemical and petrochemical sector in the recent submissions. • Phase III also brought an increased scope for industrial process sources of CO2, and data appeared for the first time for soda ash production, and titanium dioxide manufacture. • When using the EU /UK ETS data, assumptions and interpretations are required to be made regarding the fuel types used by operators; assumptions are made on a case by case basis depending on knowledge of the site or industry and expert judgement. Operators are free to describe fuels as they wish in their returns, rather than choosing from a specific list of fuels, and so assumptions occasionally need to be made where the fuel type used is not clear from the operator’s description. This issue was more significant

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1039 in the earlier years of EU ETS reporting, with operators often using terms such as “Fuel 1”. The assumption then made about fuel type was based both on the other data the operator provided on the fuel such as calorific value, but also by comparison with later data for the same site, since operators now tend to use more recognisable fuel names, and the use of wholly ambiguous terms is now very rare. • The direct use of EU /UK ETS data (e.g. fuel use data by sect or) to inform UK GHGI estimates is limited to where the EU /UK ETS is known to cover close to 100% of sector installations. For example, the EU /UK ETS is regarded as representative and 100% comprehensive in coverage of refineries, power stations (except in the case of some small power stations burning biomass, gas oil, or burning oil as the main fuel), integrated steelworks, cement and lime kilns, soda ash plant, titanium dioxide plant, petrochemical works and glassworks (container, flat, wool & continuous f ilament fibre only – small lead glass and frit producers are not included). Coverage is very close to 100% for brickworks and tileries. For many other industrial sectors (such as chemicals, non -ferrous metals, food and drink, engineering) the EU/UK ETS is not comprehensive and therefore the data • EU/UK ETS Implied Emissions Factors (IEFs) can be used within the UK GHGI, but only where the evidence indicates that EU/UK ETS data are representative of the sector as whole and provides more comprehensive and accurate data than alternative sources. The key criteria to consider in the assessment of EU/UK ETS IEF usefulness is the percentage of annual fuel use by sector where operator estimates use Tier 3 emission factors. • Review of the EU/UK ETS IEFs for different fuels across different sites provides a useful insight into the level of Tier 3 reporting within different sectors, the progression of higher- Tier reporting within EU/UK ETS through the time series and the level of variability in fuel quality for the different major fuels in the UK.
As a general rule, those energy -intensive sectors with near 100% coverage in EU /UK ETS also report a very high proportion of emission factor data at Tier 3. Those sectors with incomplete coverage tend to report most emission factor data below Tier 3. As a result, in all cases where the level of sectoral coverage is high, the quality of reported data is also sufficiently high to be used with confidence in the UK inventory. A 7.2.3 Limitations of EU/UK ETS Data Integration with GHG Despite detailed research there remain some fundamental limitations in the use of EU /UK ETS data within national inventories where the sector allocation of energy use and emissions cannot be resolved against the national energy statistics that underpin the GHG inventory compilation. One key example is that of the division between fuel use in aut ogeneration (or heat generation) and direct fuel use within a specific sector. For example, based on the data available from EU/UK ETS, it is impossible to differentiate between gas use in autogeneration on a chemical installation, and gas used directly to heat chemical production processes. In this example, the allocation of EU/UK ETS energy use and emissions between 1A2c (chemicals) and 1A2f (autogenerators) is uncertain, and therefore comparison of EU/UK ETS and GHGI estimates is uncertain. The EU/UK ETS data are not sufficiently detailed and transparent to enable accurate allocation, and so in all cases fuels and emissions are allocated to the industry sector, and not to autogeneration. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1040 It is worth noting here that the UK energy statistics are also subject to some uncertainty, however small, and that there is likely to be more uncertainty in estimates at industrial sector-level, rather than at more aggregated levels. For example, while fuel producers and suppliers will be able to quantify total fuel demand with a high level of certainty, it would be far more difficult for them to estimate fuel use by specific industrial sectors. This will be reflected in the quality of UK energy statistics which are used to estimate emissions from 1A2c etc. We consider that a high proportion of fossil fuel use by the UK chemical industry will be included in the EU/UK ETS, on the basis that most industrial chemical processes will require sufficiently large combustion installations to exceed the threshold for EU/UK ETS. Therefore, we consider that it is reasonable to assume that EU/UK ETS emissions for the chemical sector should cover most of the sector and therefore be similar in magnitude to those estimated from UK energy statistics and even, given the uncertainty in fuel a llocation for autogeneration, to exceed them. For other sectors such as metals, paper, and food and drink, we would assume that the level of sectoral coverage by the EU/UK ETS would be lower, so that emission estimates based on EU /UK ETS would probably be lower than those based on energy statistics, even taking into account the uncertainty regarding autogeneration. BEIS provided the detailed UK ETS regulator data from the Environment Agency, Natural Resources Wales, Scottish Environment Prot ection Agency , Northern Ireland Environment Agency, and the Offshore Petroleum Regulator for Environment and Decommissioning in May 2022, and the Inventory Agency industrial emissions experts progressed the analysis, combining the datasets to generate a UK -wide UK ETS dataset. The work built on analysis conducted in previous years, as the EU ETS has been in place since 2005, but this latest analysis, while focussing on the latest year of data, did involve review of the data for earlier years, to ensure a consistent approach to the interpretation of energy and emissions data across the time series. The initial step in the analysis is the allocation of all sites in the dataset to one of the economic sectors as reported within the DUKES Commodity Balance tables . Next, the reported fuels for every UK installation have to be allocated to one of the GHGI fuel names, which are also aligned with the fuel types reported within DUKES. This enables a direct comparison of UK ETS fuel totals against sector fuel allocations within DUKES and therefore used within the GHGI. Most of the allocations have been made as part of previous years’ work, and do not need to be revisited. However, several new installations included in the 20 21 UK ETS data had to be allocated to DUKES’ se ctors, and all of the fuel data for 20 21 also has had to be allocated to DUKES/GHGI fuel types. In a very small number of cases, we have revised data for earlier years, for example when it has become apparent that existing assumptions are likely to be inco rrect. The allocation process does rely upon some expert judgement, with the Ricardo team using the reported UK ETS fuel names as well as the reported fuel quality data such as calorific values and carbon emission factors in order to make the fuel -type allocation for each entry in the UK ETS spreadsheet. The allocation is, occasionally, quite uncertain, particularly with the allocation of petroleum-based fuels such as the GHGI fuel categories LPG, OPG, gas oil and fuel oil, often because of the use of abbre viations or other slightly ambiguous names for fuels within the UK ETS reporting system. Cross -checking of data across the time series for each installation has been used to ensure as much consistency in fuel allocations as possible, although in some cases, operators of installations use different fuel terminology in different years, and the possibility of the use of different fuels in different years at a site cannot always be ruled out. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1041 The quality checking and allocation process is very resource -intensive and essentially an open- ended task for such a large dataset, and hence the Inventory Agency focuses on the highest emitters and the known “problem” sites and fuel types.
Where uncertainties arise in allocations, the most important allocation decisions ar e copied across to the BEIS DUKES team, for their information and input, as ultimately the UK ETS analysis by the Inventory Agency is taken into account to some degree within the compilation of DUKES for the following year. As a data verification step, th e installation emissions (broken down by fuel) from the EU ETS regulator spreadsheets are then compared against the total installation emissions for 2021 on the European Union Transaction Log (EUTL) which is a central website that holds the verified EU ETS emissions totals for all EU installations in the scheme. The UK ETS Public Reports website holds the equivalent Compliance Report on emissions for the UK ETS. We have noted that for some sites the regulator data does not match the EUTL /Compliance Report datasets, and therefore some “residual” emissions allocations are generated, from the difference between these datasets and regulator information. In cases where these residual emissions are large, these are fed back to the regulator contacts, for their consideration and to request any insights into the likely fuels that the residual emissions should be allocated against. Minor residual emissions are ignored for the purposes of the analysis reported here. A final data set is then available for fuel combustio n emission sources, which includes the • Fuel Carbon Emission Factor; and The Inventory Agency then combines the data by sector and/or fuel category to provide data for comparison against GHGI emissions data, and energy statistics published in DUKES. In this way, • provide improved CO 2 emission factors for highly energy -intensive in dustrial sectors covered by the GHGI through the use of verified data; • provide a comparison with UK energy statistics, allowing the identification of inconsistencies between EU/UK ETS and DUKES; • Identify any emission sources that are not contained in the GHGI. The analysis of the UK ETS data for all onshore facilities was completed by May 20 22 and provided to the BEIS team of energy statisticians, in time for them to consider the UK ETS dataset during compilation of the UK energy balance for 2021, as published within DUKES (published in The UK ETS data for offshore oil and gas installations was provided in May 2022 and were used directly in the compilation of emission estimates for the upstream oil and gas sector, after the UK energy balance had been compiled by BEIS. Access to these UK ETS data for offshore facilities provided more fuel-specific information (GCV, carbon content) to help improve completeness and accuracy of the upstream oil and gas estimates in the UK GHGI, augmenting the EEMS dataset

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1042 which is a more comprehensive dataset (i.e. EEMS covers more emission sources than UK ETS) but does not provide the same level of fuel-specific data. The coverage of the EU ETS data has changed over the years for which data are available. Major changes have been outlined in Section A 7.1, and these changes in scope have an impact on the usefulness of data for some sectors, with data generally being more complete for Phase II (2008-12) and Phase III (2013-2020) of EU ETS. In addition, smaller com bustion installations in the industrial, commercial and public sectors are outside the scope of EU ETS, and in fact coverage was decreased after 2007 due to the exemption of certain 'small emitters' from the UK EU ETS. For some source sectors in the GHGI, the EU ETS data therefore only includes a small proportion of the sector and the EU ETS data are not useful to directly inform the GHGI. The following GHGI source sectors are well represented in the EU/UK ETS data sets in the UK, with all UK installations • Power stations burning coal, gas, and fuel oil as the principal fuel; • Coke ovens & Integrated steelworks; • Cement kilns (from Phase II onwards); and • Lime kilns (from Phase II onwards) • Glassworks - container, flat, wool & continuous filament glass fibre subsectors only (from • Brickworks and other sites manufacturing heavy ceramic goods (from Phase II onwards) • Titanium dioxide and soda ash manufacture (from Phase III onwards). However, GHGI sectors such as industrial combustion, autogeneration, and public sector combustion are only partially represented in the EU/UK ETS data. An indication of the actual level sector which are included in the ETS dataset for 2005 and 20 21 are given, together with the Inventory Agency’s estimate of the total number of installations in that sector throughout the UK Power stations (fossil fuel, > Power stations (fossil fuel, < Power stations (nuclear) 12 12 7 7

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1043 Combustion – iron & steel industry 11 200a 25 200a Combustion – other industry 171 5000a 445 5000a Combustion – commercial sector 28 1000a 100 1000a Combustion – public sector 169 1000a 93 1000a Glassworks (flat, special, container Soda ash & titanium dioxide 0 4 4 4 a These estimates are ‘order of magnitude’ figures, based on expert judgement of the inventory team, to show that the number of installations in the UK is likely to be considerably higher than the number of installations reporting in b Numbers of brickworks are not certain in 2005 but will have been significantly higher than in 2008 (when there were about 70) since many brickworks were closed or mothballed in the second half of 2007. All brickworks are believed to Data are included in EU /UK ETS for all coke ovens, refineries, sinter plant and blast furnaces. stations are not included in the UK ETS data for 20 21, these are all small (in most cases, very small diesel-fired plant supplying electricity to Scottish islands).
In comparison, coverage is quite poor in 2005 for cement and lime kilns (due to CCA participants opting out during Phase I) and for combustion processes (due to CCA/UKETS opt-outs and the fact that numerous combustion plant are too small to be required to join the EU /UK ETS). All cement kilns and all lime kilns are included in 2021. Coverage of glassworks and brickworks was very limited during Phase I, but since 2008 has been very all large glassworks have been included since 2008, and all but one brickworks were included in Phase II, with that remaining site being added for Phase III. UK totals for brickworks are subject to some uncertainty however, and may be revised in future should more data be obtained. Both soda ash plant and both plants manufacturing titanium dioxide via the chloride process have only been included in EU ETS since the start of Phase III. For most emission sources the level of detail given in the EU/UK ETS data matches well with the structures of the GHGI, allowing comparison of like with like. Only in the case of coke ovens and integrated steelworks is this not the case, since the EU/UK ETS reporting format does not provide a breakdown of emissions for the sectors reported within the i.e. estimates of emissions from coke ovens, blast furnaces and sinter plants are not provided explicitly. However, for these sectors, additional detailed analysis, including the coll ection of other industry data, has allowed for far greater use of EU/UK ETS data for the inventory. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1044 A 7.3.1 EU/UK ETS Data Use in the UK GHGI The use of EU/UK ETS data in the UK GHGI may conveniently be divided into two • Instances where activity data and, in most cases, emission totals as well are taken from • Instances where emission factors only are taken from EU /UK ETS and then used in the UK GHG Inventory with activity data from other sources such as DUKES. A 7.3.2 Activity and Emissions Data A 7.3.2.1 Crude Oil Refineries The comparison of EU /UK ETS emissions data against GHGI data based on DUKES fuel use allocations for petcoke, natural gas, fuel oil and OPG use is inconsistent to varying degrees in different years. Previous EU ETS analysis indicated that petcoke data in DUKES were too low; the BEIS energy statistics team have investigated this matter with the refinery operators and have revised data for a number of sites that had been misreporting through the DORS system used to compile DUKES. In recent years, therefore, the EU and UK ETS and DUKES data are closely consistent for petcoke use by refineries. Data inconsistencies between DUKES and EU/UK ETS remain for other fuels, however, in some cases, this will be due to misallocation of fuel use data within the EU/UK ETS analysis, where fuel names are unclear, e.g. “fuel gas” could be interpreted solely as refinery use of OPG or to also cover the use of natural gas as a back-up fuel within the refinery fuel gas system. The fuel oil activity data in most years is around 10% higher in EU ETS than in DUKES. Natural gas is a relatively minor fuel in the sector; whilst the EU ETS allocations indicate an over -report in DUKES, there is considerable uncertainty over the allocations of gases in the EU ETS dataset, as noted above. However, DUKES data for natural gas used in autogeneration includes some fuel burnt at refineries, thus the difference between refinery fuel use as given in EU ETS, and that derived from DUKES data can be reduced by taking this into account. Consu mption of naphtha reported in DUKES as “unclassified industry” is allocated to refineries as the only known consumers in the UK. However, in the case of OPG, there is typically an under-report in DUKES, allocated to OPG for those years (2004 onwards except 2005, 2012) where UKPIA and EU ETS data indicates that DUKES data are too low. Note that the GHGI estimates also include the assumption that all of the OPG allocation to “autogenerators” within the DUKES commodity balance tables (in the column “Other gases”) is used within the refinery sector. Consultation with the BEIS DUKES team has indicated (Personal Communication, Evans, 2010) that the “Other gases” column in the Commodity Balance tables is the OPG on the refinery basis, with CHP plant on site allocated to the autogeneration line. We have therefore retained thi s assumption in the current analysis, including the autogenerator allocation of “other gases” within the refinery sector. To resolve the refinery sector under-report, we have compared DUKES data against EU/UK ETS data, and also considered the total carbon dioxide emissions for the refinery sector provided annually by UKPIA. At the installation level, the UKPIA and EU /UK ETS data show very close consistency for recent years (typically within 1%). The close consistency of the EU /UK ETS and UKPIA data further strengthens the case for using EU /UK ETS data as the primary dataset to inform the UK GHG inventory, in preference to the DUKES energy statistics. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1045 At the fuel-specific level, the greatest disparity is evident in the reporting of OPG use at refineries; the reporting disparity has therefore been resolved through a top -down emissions comparison between DUKES -derived data and the best available operator data from EU /UK ETS (2005 onwards) and UKPIA (pre-2005), with the difference between the two then allocated to OPG use in the UK GHGI. UK inventory estimates of emissions for the sector are therefore aligned with EU ETS totals back to 2005, and with UKPIA data prior to 2005, unless the estimates derived from DUKES data are higher than those from UKPIA or EU ETS (i.e. in 2005 and 2012).
No deviations from UK energy statistics have been made prior to 2004, as the data from UKPIA and GHGI estimates based on DUKES are closely consistent with the DUKES-derived data being slightly higher; therefore, a conservative app roach is adopted, using DUKES -derived GHG The time series of emissions data and the additional OPG and petroleum coke emissions data (where DUKES data are low) for the sector are shown below. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1046 1 For 2005 onwards, the EU/UK ETS data are verified by third parties and regarded as the best available sector estimates; prior to 2005 the best available operator emissions data are from the trade association, UKPIA. There is some level of uncertainty in the allocation of fuels in EU ETS to specific “DUKES” fuels, although the OPG use in refineries seems to be reported quite consistently as “Refinery Gas”, “Refinery Off -Gas”, or “OPG/RFG”. The BEIS DUKES team have reviewed the year to year consistency of OPG use in refineries through the DORS system. A 7.3.2.2 Natural Gas Use by Downstream Gas Supply Installations The EU/UK ETS data includes natural gas use by large gas compressor and storage sites that operate on the UK gas transmission and distribution network, as well as the three operational LNG terminals and a small number of other downstream gas industry sites. The gas use reported in EU /UK ETS for these sites from Phase II onwards has been notably Use, Other). This has been evident in the traded / non -traded analysis for the gas supply sector in the UK and Devolved Administrations GHGI. As this gas use arises from the downstream network, the Inventory Agency and the BEIS DUKES team consider that the DUKES data indicate a small misallocation of gas use, r ather than a gap in reported gas use. For 2005 to 2019, therefore, the EU ETS data for this source are used within the UK GHG inventory, and the overall gas use data are balanced by reducing the allocation of gas use to “other industrial combustion" (IPCC source 1A2g); the EU ETS data since 2005 shows good consistency with the data from DUKES for earlier years. The increased gas use for this sector based on EU /UK ETS data is expected to still be a small under-report for the sector as a whole, as the EU /UK ETS scope only includes around 35 of the larger gas compressors, LNG terminals and storage sites on the UK network, and it is likely that additional gas use on smaller sites also occurs. However, the Inventory Agency has no data to A 7.3.2.3 Other Industry OPG use There are a number of “other industry” sites where OPG use has been allocated by the Inventory Agency from EU/UK ETS data, where the fuel is defined as either a specific gas (e.g. ethane, propane, butane) or more generic terms such as “OPG”, “High Pressure Refinery Gas”, “Low Pressure Refinery gas”, “fuel gas” or “RFG/OPG/ROG” within the EU ETS forms. In refinery complexes, the use of RFG for autogeneration (for the refinery and/or for co -located plant) is reported within the energy ba lance (allocated to “OPG”). At a number of other UK installations, commodities that are used initially as feedstocks in chemical and petrochemical production (e.g. naphtha, ethane, LPG, gas oil) are allocated to “non -energy use” in the UK

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1047 energy statistics; any subsequent use of process off-gases (derived from these NEU feedstocks) as a fuel is not reflected in DUKES. Therefore, the Inventory Agency uses other data from industry, primarily from EU /UK ETS, to generate estimates of the use of such secondary fuels. For a small number of sites, consultation with the DUKES team, regulators and operators has clarified that the EU/UK ETS energy and emissions data are the best available dataset for use in In the 1990 -2021 inventory cycle, EU /UK ETS da ta for fuel use at petrochemical production facilities helped to identify and quantify the combustion of process off-gases that are derived from Natural Gas Liquid (NGL) feedstock to petrochemical production processes. Analysis of “fuel gas” calorific values and carbon content informs the calculations to estimate emissions from NGL - derived gases and other residues. A 7.3.2.4 Industrial Processes The EU/UK ETS dataset contains data on several industrial processes for which alternative data sources are either unavailab le or of low quality. The EU /UK ETS data therefore constitute the most reliable set of emissions data for these processes and are used in the UK inventory. In almost all cases, the EU/UK ETS activity data are difficult to use directly, largely because different operators provide activity data and emission factors on a different basis (e.g. some may provide input material and emission factors on a consumption basis, others will provide production data and emission factors on a production basis). Therefore, f or all of the industrial process sources, the EU/UK ETS emissions data are adopted, and activity data are generally back-calculated from the emissions using a suitable IPCC emission factor. The industrial process sources where EU/UK ETS data have been used to generate estimates of emissions included within the UK GHGI in this submission, • Emissions from the manufacture of lime. UK activity data for limestone and dolomite consumption in lime production would yield much lower emission estimates than i s suggested by EU/UK ETS returns therefore, as a conservative approach, the EU/UK ETS data are used instead. Activity data are back-calculated using the IPCC default factor for lime production. See Section 4.3 for further details. • Emissions from the use of carbonates in the manufacture of glass. As with lime production, the available data on consumption of limestone and dolomite for glass production are suspect, being very inconsistent across the time series, and so EU/UK ETS data are used in the generation of the inventory time series, as detailed in Section 4.4.
• Emissions from the use of clays, carbonate minerals and other additives in the manufacture of bricks and roofing tiles, as detailed in Section 4.5. The EU/UK ETS data are very detailed, with sepa rate lines for different input materials such as different types of clay, carbonate minerals used in the bricks or in scrubbers used to abate fluoride emissions, and coke oven coke/petroleum coke used as an additive in certain bricks. UK brick production data are used as activity data. • Estimates for emissions from the use of limestone in flue-gas desulphurisation (FGD) plant for the years 2005 -2021 are taken from EU /UK ETS data, because UK activity data (for gypsum produced from the FGD plant) are incomplet e for those years. Activity data for 1990-2004 are available from non-ETS sources and are back-calculated from the EU ETS CO2 emissions for 2005 onwards assuming an emission factor of 253 kg CO 2 per tonne gypsum produced (which is based on an assumed 100% conversion of limestone and SO2

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1048 • EU ETS Phase III saw the introduction of data for soda ash manufacturing sites and EU ETS data, and CO2 emissions reported for earlier years in the PI, are used as the basis of UK inventory emissions data for that sector. See Section 4.12 for more details. • Titanium dioxide production was also included in Phase III of the EU ETS, but full data for the UK plant are not included in the data set provided, and so emission estimates are generated using an alternative, more conservative method. • For 2020, a stand -alone model was developed to estimate carbon emissions from the operation of Electric Arc Furnaces (EAFs). The objective of the model was to derive a time series of Emission Factors (EFs) per unit stee l production to estimate annual GHG emissions, 1970 – latest year, for inclusion in the UK GHGI, to ensure completeness and to address any risk of under -reporting when comparing the UK GHGI against other data sources, including the EU/UK ETS. See Section 4.16 for further details. A 7.3.3 Implied Emission Factors by autogenerators. The percentage of emissions based on Tier 3 emission factors is given (Tier 3 factors are based on fuel analysis and are therefore more reliable than emission factors based on default values), as well as the average emission factor for EU/UK ETS emissions based on (Emission Factors in kt / Mt for Coal & Fuel Oil, kt / Mth for Gases) Year Fuel % Tier 3 Average Carbon Emission

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1049 Year Fuel % Tier 3 Average Carbon Emission

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1050 Year Fuel % Tier 3 Average Carbon Emission a It is not possible to distinguish between fuel oil and waste oil in the EU/UK ETS data, so all emissions have been b Plant operated as a power station after 2012 and included in the figures for power stations burning coal The EU /UK ETS data shown are regarded as good quality data, since a high proportion of emissions are based on Tier 3 emission factors (i.e. verified emissions based on fuel analysis to ISO17025). The factors are also very consistent across the time-series, which would be expected for this sector. As shown in Section 3, the EU/UK ETS data for power stations also cover almost

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1051 all UK installations in this sector, and certainly cover all of the installations which burn coal, fuel A few power stations burn small quantities of petroleum coke as well as coal. One supplies data to ETS for coal/petroleum coke blends i.e. there are no separate emissions data or carbon factors for the coal and the petroleum coke at that site. We therefore back-calculate the coal IEF in those blends by using an assumed default for the petroleum coke carbon content and more detailed activity data on the constituents of the fuel blends, obtained directly from the operator. The EU/UK ETS based emission factors presented above for power stations are used directly as factors were only used for about half of the sector’s emissions reported in EU ETS. Small quantities of sour gas were burnt at one power station in 2005-2007 and 2009 and EU ETS Tier 3 emission factors are available and therefore used. [Due to the confidentiality of the data, the emission factors are not shown]. Prior to 2005, the emission factors for these sectors are based on the methodology established by Baggott et al, 2004, since it has been concl uded that this represents the most reliable approach. The EU ETS factors for coal-fired autogenerators are slightly different to the factors for the power stations in that, although the EU ETS data are exclusively Tier 3, they only represent about 80 - 90% of total fuel used by the sector. A 7.3.3.2 Crude Oil Refineries The main fuels in refineries are fuel oil and OPG and emissions also occur due to the burning off of ‘petroleum coke’ deposits on catalysts used in processes such as catalytic cracking. In the latter case, emissions in the EU /UK ETS are not generally based on activity data and emission factors but are instead based on direct measurement of carbon emitted. This is due to the technical difficulty in measuring the quantity of petroleum coke burnt an d the carbon content.
Refineries also use natural gas, although it is a relatively small source of emissions compared to Factors in kt / Mt for Fuel Oil and kt / Mth for OPG and Natural Gas) Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1052 Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1053 Average Carbon Emission Factor There has been some variation in the proportion of Tier 3 reporting for all three fuels, which will adversely affect the quality of the emission factors, although coverage is still in excess of 50% for Emission factors for fuel oil generated from EU/UK ETS data have been adopted in the GHGI, with the exception of data for 2005, where Tier 3 methods were used for only 25% of fuel. Carbon factors can be derived for OPG based on moderate levels of Tier 3 reporting for 2005 - 2007 and 2011 -2019 but 80 % for 2008 -2010, which gives us a high confidence in the representativeness of the carbon factors for 2008 -10. There is some uncertainty regarding the allocation of EU ETS fuels to the OPG fuel category, and the derived emission factors do cover a wider spread of values than for many other fuels in EU ETS. However, this reflects the nature of this fuel, and the data for all years have been used in the inventory. Carbon factors for natural gas are based on a low % of Tier 3 reporting until 2008; in 2008 to 2010 over 90% of gas use is reported at Tier 3 and over 80% in 2011 and 2013 -2019. Within the UK GHGI, the EU ETS factors for 2008 to 2016 are used directly, whilst emission factors for earlier years are derived from gas network operator gas compositional analysis. EU ETS emission data for petroleum coke are higher in 2005-2010, when compared against the estimates derived from DUKES activity data and the industry-recommended emission factor. This is especially noticeable for 2005, where the petroleum coke consumption given in DUKES would have to be more than 100% carbon in order to generate the carbon emissions given in the EU ETS. Consultation with BEIS energy statisticians has identified that the figures given in DUKES are subject to uncertainty and hence t he EU ETS data are used directly within the UK GHGI for

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1054 A 7.3.3.3 Integrated Steelworks & Coke Ovens coke ovens. The data exclude one independent coke oven which calculated emissions using a detailed mass balance approach which makes it more difficult to assess the data in the same way as the other installations. This site closed at the end of 2014. (Emission Factors in kt/Mt for solid & liquid fuels, kt/Mth for gases) Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1055 Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1056 Average Carbon Emission Factor Most of the ETS data for coke ovens and steelworks are now used in the GHGI, although not the emission factors shown above. Instead, the Inventory Agency have used the EU /UK ETS data and other detailed, site-specific and fuel-specific data, provided by the process operators to refine the carbon balance model used to generate emission estimates for the sector. Details of the revisions to the carbon balance model can be found in the research report from the 2013 -2014 inventory improvement programme (Ricardo-AEA, 2014). Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1057 Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1058 The EU/UK ETS dataset also provides a detailed breakdown of cement sector process emissions from the decarbonisation of raw materials during the clinker manufacturing process. These data are useful to compare against statistics provided by the Mineral Products Asso ciation (MPA) regarding clinker production and the non -combustion emissions associated with UK cement production. The MPA data on clinker production are commercially confidential. The two data sets show significant differences for 2005 -2007; however, the EU ETS data cover only a fraction of the sector, so differences might be expected. From 2008 onwards, there is close agreement (average of 0.5% difference) between the two data sets. The coal IEF data across the time series are also fairly consistent, other than in 2006 where the ETS value is very much lower than in other years. Because of the good agreement in both activity data and emission factors for 2008 onwards, the industry-wide estimates provided by the MPA and used within the GHGI show very close comparison with the EU ETS estimates. The difference between the EU/UK ETS and 2008 2010 2015 2017 2018 2019 2020 2021 8,288 5,783 6,537 6,555 6,500 6,621 5,804 6,269 8,250 5,785 6,532 6,549 6,507 6,627 5,817 6,271 (reported under IPCC sector 2A1) from UK cement kilns. kilns. Unlike cement kilns, which often burn a variety of fuels, many lime kilns burn just a single fuel, often natural gas. The data below exclude coke oven coke used in lime kilns at soda ash plant since these kilns were not covered by EU ETS until Phase III, and the small number of sites make the data confidential in any case. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1059 Mt for Solid Fuels and kt / Mth for Gases) Average Carbon Emission Factor *Coal used in the lime industry in the UK includes a proportion of anthracitic coal, and hence some of these IEFs are notably higher than for coal used in other sectors of UK industry. The EU ETS data for lime kilns vary across the time series, both in terms of the proportion of emissions based on Tier 3 factors, and in the emission factors themselves.
EU ETS based factors are currently used for coal and petroleum coke from 2008 onwards, as the EU ETS data do include all lime kilns burning those fuels and almost all those data are Tier 3 and hence are regarded as EU ETS data for natural gas use in the lime industry does cover all installations burning this fuel, however the proportion of emissions based on Tier 3 factors is very low. Therefore, the EU ETS emission factors are not used in the UK GHGI, and the emission factors for natural gas continue to be based on the methodology given in Baggott et al, 2004. are used within the UK GHG inventory. The lime industry can be sub -divided into those installations where lime is the primary product, and carbon dioxide is an unwanted by -product; and those installations where both lime and carbon dioxide are utilised. The latter include kilns in the sugar industry (where carbon dioxide is used in the purification stages) and soda ash

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1060 production (where carbo n dioxide is combined with other chemicals to produce sodium carbonate), and in these kilns, the carbon dioxide from decarbonisation of the limestone or dolomite feedstock is assumed to be fully consumed in the process, rather than emitted to These factors compare with a theoretical emission factor based on the stoichiometry of the lime manufacturing process of 214 kt / Mt lime, assuming use of pure limestone. We note that the EU ETS factors are all lower than the theoretical emission factor and this is despite some use of dolomitic limestone in the UK industry which would be expected to further increase the emission factor above the 214 kt/Mt lime factor. The EU/UK ETS data are subject to third party verification, and therefore the emissions data are assumed to be accurate. It is assumed that the reason for this deviation from the theoretical emission factor is due to the production activity data being inflated by either the products containing some proportion of slaked lime (i.e. hydrated product and hence containing a lower proportion of carbon than pure lime) a nd/or other additives to the lime product which decrease the % carbon content of the lime product. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1061 A 7.3.3.6 Other Industrial Combustion At first sight, the data for coal looks like it should be reliable enough to be used in the GHGI with 90% or more of emissions based on Tier 3 factors in each year , with the exception of 2010 . However, it must be recalled that numerous smaller industrial consumers will not be represented in EU/UK ETS and that the EU/UK ETS data are not fully representative of UK fuels as a whole – see Section A 7.3 for details. This is also true for ETS data for fuel oil and natural gas but here, in addition, very little of the ETS data are based on Tier 3 factors. Therefore, none of these data have been used directly in the compilation of the GHGI estimates. Combustion Plant (Emission Factors in kt / Mt for Coal & Fuel Oil, kt / Average Carbon Emission Factor 2005 Fuel oil 48 864.7 879.0

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1062 Average Carbon Emission Factor

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1063 Average Carbon Emission Factor * No emissions where reported under Tier 3 for this fuel for 2021 in the reported UK ETS data. Emission factors can also be derived from EU/UK ETS where a high percentage of Tier 3 analysis is evident, for a number of other minor fuels. Due to the very low number of sites that report data for each fuel type, these ETS-derived emission factors are confidential and are not tabulated here. The source/activity combinations for which EU/UK ETS emission factor data are used within the inventory • Other industrial combustion / petroleum coke • Other industrial combustion / waste solvents • Other industrial combustion / colliery methane The ETS-derived emission factors for colliery methane for each year (2005-2021) are also applied to all other sources using these fuels. A comparison has been made between the GHGI emissions data and those of the UK ETS for 2021. The comparison excludes LULUCF and emissions from 1.A.3.a civil aviation. The comparison shows that the UK ETS covers approximately 23% of the total UK GHGI emissions. This translates to 30% for total UK CO2 emissions. There are a variety of factors that contribute to such discrepancy between the two datasets that is primarily related to the coverage of data in the UK ETS. In the energy industries category (1.A.1), the UK ETS excludes public electricity and heat generation (1.A.1.a) using Municipal Solid Waste (MSW). The UK ETS dataset does not cover all the emissions from the manufacture of solid fuels and other energy industries (1.A.1.c). There are also differences between the allocation of emissions and scope of the ETS and GHGI for iron and steel sector that may contribute to the differences. Another source of discrepancy arises from the emissions associated with the manufacturing industries and construction (1.A.2). The allocation of emissions to the ETS does not necessarily match those of the GHGI for the iron and steel sector (1.A.2.a). Almost all of the non-ferrous metals sector in the UK are small and therefore not covered by the ETS. The same issue also

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1064 applies to the food sector (1.A.2.e). Furthermore, the GHG inventory figures do exclude emissions as the result of autogeneration (reported instead in 1.A.2.g). Most UK sites in the other industry sector (1.A.2.g) are small and excluded from the ETS. The GHG inventory figures also include fuels used throughout industry for autogeneration, whereas in the ETS these emissions will be split between 1.A.2.c, 1.A.2.d, 1.A.2.e and 1.A.2.g.
Finally, GHG inventory figures include certain minor fuels burnt at cement and lime kilns which will be included in 1.A.2.f. in the ETS data. With regards to fugitive emissions from fuels (1.B), the allocation of emissions in UK ETS to 1.A.1.c, 1.B, 1.A.2 and 2.C.1 is not exactly comparable to GHG inventory. The high percentage of emissions under the ETS reflects the high proportion of upstream oil and gas flaring emissions that make up the UK GHGI, since all UK offshore rigs and oil and gas terminals are With regards to nitrous oxide (N2O) emissions from nitric acid production (2.B.2) activities, figures for the installations in the UK ETS dataset do not seem to match the N2O emissions reported by the operators directly to both the Inventory Agency, and to the regulator for inclusion in the Pollution Inventory (covering emissions from industrial sites subject to EPR 2012 in England) and PRTR. Within the UK ETS reporting, it is not always clear which emissions are of N2O and which are of CO2 from combustion of off-gases. It is therefore likely that the ETS figures include emissions of CO2 that would be reported in the GHG inventory under 2.B.1. We note that the overall GHGI figures for 2.B.1 (CO2) and 2.B.2 (N2O) slightly exceed those in the UK ETS, and so we believe that the GHG inventory figures are accurate or even conservative. For the full analysis, please see the ‘Consistency of reported GHG emissions_2023.xlsx’

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1066 emissions/removals from LULUCF (Mt CO2eq) – National Statistics NC category 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 Energy supply 283.5 240.5 224.7 232.6 208.4 145.9 104.6 96.2 84.5 86.9 Business 111.9 109.9 112.9 104.7 88.6 81.3 78.3 77.0 73.9 75.3 Transport 128.6 130.1 133.2 136.0 125.0 123.9 125.0 123.1 99.3 109.5 Public 16.0 15.7 13.8 12.2 9.5 8.0 7.8 7.7 7.2 7.5 Residential 80.3 81.6 88.6 85.5 87.3 67.1 68.3 65.5 65.2 69.4 Agriculture 54.4 53.6 52.3 51.0 48.4 49.2 48.3 48.1 46.6 47.9 processes 55.4 47.0 26.4 20.6 12.7 12.7 10.3 10.5 9.5 10.3 management 72.1 75.2 67.7 52.8 31.5 21.2 20.8 20.6 19.0 18.7 Total 813.4 762.3 725.8 698.7 613.0 510.4 464.8 450.4 406.3 426.5 emissions/removals from LULUCF (Mt CO2eq) – National Statistics Gas 1990 1995 2000 2005 2010 2015 2018 2019 2020 2021 HFCs 12.1 15.6 6.9 8.6 11.2 12.9 12.4 11.8 11.0 10.3 PFCs 1.5 0.5 0.5 0.4 0.3 0.3 0.1 0.2 0.2 0.2 Total 813.4 762.3 725.8 698.7 613.0 510.4 464.8 450.4 406.3 426.5 44 Land use, land use change and forestry

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1067 This Annex explains the concept of an end user emissions (sometimes also referred to as “final user emissions”, summarises the end user calculation methodology with examples, and contains tables of greenhouse gas emissions according to the end user from 1990 to 2021. The end user sectoral categories used are consistent with tho se used in the National Communications (NC) to the UNFCCC. The sectoral categories in the NC are derived from the UNFCCC reporting guidelines on national communications45.. The purpose of the end user calculations is to allocate emissions from fuel and ele ctricity producers to the energy users - this allows the emission estimates for a consumer of energy to include the emissions from the production of the fuel or electricity they use. The UNFCCC does not require end user data to be included in the UK’s Nati onal Inventory Report. These data have been included to provide DESNZ with information for their policy support The tables in this Annex present summary data for UK greenhouse gas emissions for the years 1990-2021, inclusive. These data are updated annually to reflect revisions in the methods used to estimate emissions, and the availability of new information within the inventory. These recalculations are applied retrospectively to earlier years to ensure a consistent time series and this accounts for any differences in data published in previous reports. Emissions presented in this chapter show emissions from the UK only, consistent with the BEIS The end user46 calculations allocate emissions from fuel producers to fuel users. The end user calculation therefore allows estimates to be made of emissions for a consumer of fuel, which also include the emissions from producing the fuel the consumer has used. The emissions included in the end user categories can be illustrated with an example of two end users - the residential sector and road • Emissions in the residential end user category 1. All direct emissions from domestic premises, for example, from burning gas, coal or 45 See page 84 of UNFCCC Guidelines contained in FCCC/CP/1999/7 available 46 An end user is a consumer of fuel for useful energy. A ‘fuel producer’ is someone who extracts, processes or converts fuels for the end use of end users. Clearly there can be some overlap of these categories but here the fuel uses categories of the UK BEIS publication DUKES are used, which enable a distinction to be made. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1068 2. A portion of indirect emissions used by domestic consumers power stations generating electricity; emissions from refineries including refining, storage, flaring and extraction; emissions from coal mines (including emissions due to fuel use in the mining industry itself and fugitive emissions of methane from the mines); and emissions from the extraction, storage and distribution of mains gas. • Emissions in the road transport end user category 1. Direct emissions from motor vehicle exhausts. 2. A p ortion of indirect emissions refineries producing motor fuels, including refining, storage, flaring and extraction of oil; the distribution and supply of motor fuels; and power stations generating the electricity used by electric vehicles. Fuel and electricity producers also require the use of energy which comes from other producers.
Therefore, in the process of reallocating emissions to the end user, emissions are allocated from one to the other and the n are reallocated to end users. This circularity results in an iterative approach being used to estimate emissions from categories of end users. consuming electricity are responsible for a proportion of the emissions from power st ations they are also responsible for emissions from collieries, and some of these emissions in turn come from electricity generated in power stations and from refineries. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1069 The approach for estimating end user emissions is summarised in the three steps 1. Emissions are calculated for each sector for each fuel. 2. Emissions from fuel and electricity producers are then distributed to those sectors that use the fuel according to the energy content47 of the fuel they use (these sectors can include other fuel producers). This distribution is based on inventory fuel consumption data and DUKES electricity consumption data. 3. By this stage in the calculation, emissions from end users will have increased and those from fuel and electricity producers will have decreased. The sum of emissions from fuel producers and power stations in a particular year as a percentage of the total emissions is then calculated. If this percentage, for any year, exceeds a predetermined value (In the model used to determine emissions from end users, the value of this percentage can be adjusted. The tables presented later in this Annex were calculated for a convergence at 0.001%) the process continues at Step 2. If this percentage matches or is less than the predetermined value, the calculation is finished. Convergence occurs as the fuel flows to the end users are much greater than fuel flows amongst While a direct solution could possibly be used it was decided to base the calculation on an iterative • This can be implemented in the database structures already in existence for the UK • It can handle a wide range of flows and loops that occur without any of the limits that other • The same code will cover all likely situations and will be driven by tabular data stored in The following example illustrates the methodology used to calculate emissions according to end users. The units in this example are arbitrary. users, residential, industry and commercial. The following assumptions have been made for • The only fuels used are coal and electricity; • Coal is the only source of carbon emissions (released from burning coal in power stations to produce electricity and from burning coal in the home for space heating); and • Commerce uses no coal and so has zero ‘direct’ emissions. 47 If calorific data for the fuels is not available then the mass of fuel is used instead. This is the case for years prior

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1070 of electricity consumed (blue arrows). In this example the coal extracted by the colliery is burnt in the power station to produce electricity for the end users. Industrial and residential users also directly burn coal. Although the coll iery uses electricity produced by the power station, it is not considered to be an end user. The colliery is a ‘fuel producer’ as it is part of the chain that extracts, processes and converts fuels for the end

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1071 Residential Industrial Commercial Unallocated emissions as percentage of total Mass 100 100,000 50,000 100,000 0 25,000 25,000,000 12,500,000 25,000,000 0 10,000 50,000 100,000 100,000 Initial 70 70,000 35,000 70,000 0 40.02 175,070 Emissions after Iteration step 1 2,692 28 48,476 96,951 26,923 1.55 175,070 2 1 1077 49,020 98,039 26,934 0.62 175,070 3 41 1 49,227 98,454 27,348 0.02 175,070 4 0 17 49,235 98,470 27,348 0.01 175,070 5 1 0 49,238 98,477 27,355 0 175,070 6 0 0 49,239 98,477 27,355 0 175,070 The initial carbon emissions are 70% of the mass of coal burnt. The emissions from the power stations are distributed to the other sectors by using the • (Electricity used by that sector)/(total electricity used minus own use by power stations); • Similarly, for the colliery emissions the following factor is used; and • (Energy of coal used by that sector)/(total energy of coal consumed used minus own use At the end of iteration step one, the commerce sector has 26,923 tonnes of carbon emissions allocated to it, mainly derived from power stations. Emissions allocated to the residential and industry sectors have also increased over their initial allocations. However, collieries and power stations still have some e missions allocated to them (these come from each other) and so the reallocation process is repeated to reduce these allocations to zero – these two sectors are not end users. The total unallocated (in this example, equal to the total emissions from collieries and power stations) falls in each iteration until the emissions are consistently allocated across the sectors. In this example, six iterations are needed to achieve a consistent allocation across the The sum of emissions allocated to the secto rs (175,070 tonnes of carbon) remains unchanged from the initial allocation to the allocation in the sixth iteration. This check is an important quality control measure to ensure all emissions are accounted for during the end user calculations. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1072 sectors considered in the end user example the end of the end user calculation. The direct emissions of carbon are from the combustion of coal in the sectors. The direct and end user emissions are from two distinct calculations and must be considered independently – in other words, the direct and end user emissions in each sector must not be summed. The sum of all the direct emissions and the sum of the end user emissions, There are relatively large direct emissions of carbon from power stations, residential and industry sectors.
The end user emissions from the power stations and the colliery are zero because these two sectors are not end use rs. The carbon emissions from these two sectors have been reallocated to the residential, industrial and commercial sectors. This reallocation means the end user emissions for the residential and industrial sectors are greater than their ‘direct’ emissions. each of these groups, source categories are distributed by the total energy consumption of a group of fuels. For example, for the coal group, the emissions of four source categories are distributed to end users according to the energy use of anthracite and coal combined. Colliery Power Station Residential Industrial Commercial

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1073 End user group Emission sources to be reallocated Iron and steel – flaring Blast furnace gas 2. Coal Closed Coal Mines Coal Coal storage and transport Anthracite 3. Natural gas Gas leakage Natural gas Upstream Gas Production - flaring Upstream Gas Production - fuel Upstream Gas Production - Gas terminal Upstream Gas Production - Offshore Well Upstream Gas Production - venting Upstream oil and gas production - combustion at gas separation plant Upstream Gas Production - fugitive Well exploration (unconventional gas): all Onshore natural gas production Upstream Gas direct process 4. Electricity Autogeneration - exported to grid Electricity

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1074 End user group Emission sources to be reallocated 5. Petroleum Petroleum processes Aviation spirit Refineries - combustion Aviation turbine fuel Upstream Oil Production - flaring Biodiesel Upstream Oil Production - fuel Upstream Oil Production - Offshore Oil Upstream Oil Production - Offshore Well Upstream Oil Production - Oil terminal Upstream Oil Production - Onshore Oil Upstream Oil Production - venting Gas oil Oil transport pipelines (to shore) Lubricants Oil transport pipelines (onshore) LNG Oil transport road tankers LPG Onshore oil production (conventional) Naphtha Upstream Oil direct process Upstream Oil Production - fugitive Oil fuel combustion Refinery miscellaneous Oil Gas Flaring Vaporising oil Onshore oil gas flaring Abandoned oil wells (offshore)

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 1075 End user group Emission sources to be reallocated 6. Solid Smokeless Fuels Solid Smokeless fuel production Solid Smokeless Fuels 7. Town gas Town gas manufacture Town Gas 8. Charcoal Charcoal production Charcoal Comments on the calculation methodology used to allocate emissions according to the end • Emissions are allocated to end users on the basis of the proportion of the total energy produced that is used by a given sector. This approach is followed to allow for sectors such as petroleum where different products are made in a refinery; • Some emissions are allocated to an “exports” category. This is for emissions within the UK from producing fuels, (for example from a refinery or coal mine), which are subsequently exported or sent to bunkers for use outside the UK. Therefore, these emissions are part of the UK inventory even if the use of the fuel produces emissions that cannot be included in the UK inventory because it takes place outside the UK; • No allowance is made for the emission from the production of f uels or electricity outside the UK that are subsequently imported; • Some of the output of a refinery is not used as a fuel but used as feedstock or lubricants. This is not currently treated separately and the emissions from their production (which are small) are allocated to users of petroleum fuels. This is partly due to lack of data in the database used to calculate the inventory, and partly due to the lack of a clear, transparent way of separating emissions from the production of fuels and from the production of non- • End user emissions are estimated for aviation in four domestic take -off and landing, international take-off and landing, domestic cruise and international cruise. This enables both IPCC and UNECE categor ies to be estimated from the same end user NAEI source sectors and activity names are also shown, as it is necessary to subdivide some IPCC categories.