Emissions for fuel use that cannot be allocated to these industries are reported under 1A2g. According to the 2006 IPCC Guidelines (GLs), electricity generation by companies primarily for their own use is autogeneration, and the emissions produced should be reported under the industry concerned. However, most National Energy Statistics (including those of the UK) report fuels used by industry for electricity generation as a separate category. The UK statistics attempts to report this as far as possible according to the IPCC methodology by placing The sectoral estimates reported under 1A2a to 1A2g include fuels reported in the national energy statistics for ‘heat generation’. These are fuels that are used by sites that generate heat for other users e.g. many UK paper mills, and chemical manufacturers are supplied with steam from a separate combustion plant run on a neighbouring site by a different operator. The re-allocation from the heat generation category to industry sectors is made on the basis of estimates provided by UK energy statisticians. UK NID 2025 (Issue 1) Ricardo Page 137 Activity DUKES (DESNZ, 2024a), cement sector fuel use estimates (MPA, 2024) and installation-specific activity data from UK ETS e.g. for lime kilns (EA, Emission Where available, operator-reported EFs from UK ETS are used for high- emitting source sectors. Other UK CS CEFs are taken from the 2004 Carbon Factors Review (Baggott et al., 2004). Defaults for non -CO2 gases are derived from IPCC (IPCC 2006). An accompanying document “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. For most source estimates, the inventory method uses national energy statistics and applies country-specific factors for CO 2 (Tier 2), and default factors (typically from IPCC) for other DUKES provides most of the energy activity statistics, including a breakdown between industrial combustion categories (although the level of granularity across the time series is not consistent). A number of approaches are used to fine tune the allocation of energy use under the different subcategories to maximise consistency with other datasets such as UK ETS, industrial data (e.g. from trade associations) and other estimates in the GHG inventory (e.g. the off-road machinery model). These approaches are listed • Fuel use in cement kilns (1A2f) is collected from process operators, via the Mineral Products Association (MPA). These data are not complete for the earlier part of the time series, so some assumptions have to be made to fill these gaps (see assumptions). Reallocations are sometimes made between cement and other subcategories compared with DUKES, to account for known fuel uses; • Fuel use in lime kilns (1A2f) is estimated based on EU and UK ETS data. All lime kilns are included in the scope of EU ETS from 2008 – 2020, and in the UK ETS from 2021, so there is a full set of fuel data for 2008-2022, with incomplete data for the years 2005- 2007. For the earlier part of the time-series, fuel use is estimated by extrapolation from the EU ETS data using lime production estimates; • Balancing of energy consumption data between 1A2 and other source categories, to accommodate source-specific AD from other data sources (e.g. operator data, UK and EU ETS) in preference to DUKES data. Key examples of fuel re-allocations in 1A2 AD for natural gas for gas network operators (i.e. gas use re -allocation between 1A2 and 1A1c); AD for oils for power stations (i.e. gas and fuel oil re -allocations between • Analysis of UK and EU ETS data indicates that there are several installations which use petroleum coke as a fuel, where there is no such allocation of petroleum coke as a fuel for that source in DUKES. The Inventory Agency therefore re -allocates some petroleum coke from the non-energy use estimate in DUKES to address this reporting discrepancy and align emission estimates in 1A2f and 1A2g with UK and EU ETS. This re-allocation increases the overall reporting of petroleum coke as an emissive energy • Analysis of UK and EU ETS data has identified several chemical and petrochemical manufacturers that utilise carbon -containing process off -gases and residues as fuel sources. Consultation with industry and with the DESNZ energy statistics team has clarified that in DUKES the delivery of feedstock materials to chemical and petrochemical sites are reported as non -energy use, with no subsequent reporting in

UK NID 2025 (Issue 1) Ricardo Page 138 DUKES of the use of process off-gases as an energy source in these industries. The ETS data are therefore used to derive inventory estimates to account for this use of feedstock-derived process gases, which are reported as “other petroleum gas” use within the inventory, in addition to DUKES allocations to fuel use in these sectors. However, in accordance with the 2006 GLs, these emissions are reported under source category 2B8 (see IPPU chapter) rather than 1A2; and, • Separation of gas oil used for stationary and mobile machinery is based on data on populations of mobile equipment, or train or ship movements etc. The approach developed for allocating gas oil between different source categories is described in Emission factors for carbon are almost exclusively derived from country specific data. Site - specific data, (including both UK / EU ETS data, and data provided by process operators directly or via industrial trade associations) is aggregated up to generate factors for a small number of sectors. Sector -wide factors are derived in other cases based usually on the methods described in Baggott et al, 2004. In the case of coal-fired autogeneration, EU ETS-based factors are available for 2005 -2011. This sector was dominated by a single plant that supplied electricity to a large aluminium smelter until 2012. Originally, the UK inventory used a combination of EU ETS factors for 2005 onwards, and factors from Baggott et al, 2004 for earlier years but this resulted in a large step change in the emission factor between 2003 and 2005.
The Inventory Agency reviewed the EU ETS data for the one installation (Lynemouth s melter), which exhibits a very stable CEF across all reported years (2005 to 2011) and concluded that it was very unlikely that this plant would have used significantly different quality coal in 2003 from that used in 2005. Therefore, to improve the inventory time series consistency, the Inventory Agency now extrapolates CEFs from the EU ETS back to 1990 and applies the 2011 value for subsequent years. Emission factors for waste oils are based on the analysis of 8 samples of waste oils collected from UK sites in 2003. The factors for coke and other manufactured fuels are based on carbon balance approaches (see MS 4 for coke). Emission factors for methane and nitrous oxide are largely IPCC defaults. An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission factors used in the energy sector, including a full list of references56 • Breakdown of fuel use for cement from the MPA data are not available for 1991-1999, and so fuel usage for these years must be interpolated between the 1990 and 2000 data, considering changes in cement clinker production in each year; and, • Combined Heat and Power (CHP) systems where all of the electricity is fed into the public supply are classified as power stations and excluded from estimates described Allocation of industrial electricity • The UK’s statistical data for autogenerators relate to fuels used for electricity generation by companies primarily for their own consumption. This includes CHP systems where electricity is used by the generator. The UK methodology allocates all autogeneration to 1A2g (as no other sub -categorisation is available). Coal use by autogenerators was previously allocated to 1A2b since until 2013, almost all of the coal is known to have been used in a power station operated by an aluminium producer, which supplied electricity to their smelter operation. The smelter closed in 2012 and since then the power station has supplied electricity to the national grid and coal used at that site is now allocated to 1A1a. This autogenerator coal use is now allocated to

UK NID 2025 (Issue 1) Ricardo Page 139 1A2g, for consistency with reporting for other fuels. Estimates have also been introduced from the 2025 submission for oil based autogeneration. • The large change in the quantity of coal burnt by autogenerators between 2012 (when consumption was over 1,000,000 tonnes) to 2013 (33,000 tonnes) and then 2014 onwards (less than 20,000 tonnes) has a marked impact on the time-series for the CH4 IEF reported for 1A2gviii in the CRT. Since the factors applied for autogeneration and non-autogeneration use of coal are quite different, there are large step changes in the time-series over the 2012-2015 period as a result. Autogeneration has been included separately for oils for the first time in the 2025 submission, this is included in 1A2gviii. Autogenerators use of coal has also been reallocated to 1A2gviii for consistency with other fuels. These are reallocations which d o not have a notable impact on the national total but will impact the sectoral disaggregation of emissions. Revisions to DUKES also impact emissions in 1A2, notably there have been revisions to natural gas and burning oil in 1A2gviii which outweigh the impact of the changes related to autogeneration as described above. Use of biofuels has been separated out from fossil fuels and is the subject of MS 21, and so recalculations related to changes to the treatment of biofuels are not described here. Improvements (completed and planned) For the 2025 submission, estimates have been made for fuel oil and gas oil use for autogenerators, and these emissions are reported under 1A2gviii. These estimates are based on available data in the UK energy statistics. As part of this improvement, coal u se in autogeneration has been reallocated from 1A2b to 1A2gviii for consistency with other fuels. Planned/Ongoing: Emission factors and activity data remain under annual review. Specific QA/QC and validation exercises relevant to these source categories • the comparison of the reference/sectoral approach; and, • comparison of UK and EU ETS data with DUKES and data direct from industry The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Code of Practice for Statistics57. The UK ETS data, is subject to its own QA process, defined and managed by the competent Differences in data sources across the time series are noted in the method approach section above. The Inventory Agency seeks to identify and address any inconsistencies in the inventory time series, such as those arising from revisions to the energy balan ce data that may be implemented within DUKES for recent years only, through meetings with the key data providers. The use of carbon EFs derived from the UK and EU ETS, which are available only from 2005 onwards, is considered the best available data for re cent years in many source categories; the carbon EF data prior to 2005 were also derived from analysis of UK fuels, either from data submissions from fuel users or fuel suppliers, and so the method is consistent across all years noting that the level of detail, frequency of reporting and QA/QC underpinning the data prior to EU ETS is generally lower than from 2005 onwards, and hence the uncertainty of estimates in the earlier years is higher than for recent years. UK NID 2025 (Issue 1) Ricardo Page 140 Uncertainties for both activity and emission factors are based on expert judgement. The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. Uncertainties in fuel use statistics are typically low. The carbon emission factors are based on UK specific data. Since there is a direct link between the carbon emitted and the carbon content of the fuel, it is possible to estimate CO 2 emissions accurately. Non-CO2 emissions are dependent on a greater number of parameters and are largely based on defaults.
As such, the uncertainties are higher, but since the emissions are smaller, this does not have a significant impact on the overall uncertainty of total GHG emissions. UK NID 2025 (Issue 1) Ricardo Page 141 MS 4 Iron and steel, and coke manufacture Relevant Categories, source names 1A2a: Iron, and steel - combustion plant coke oven coke only 2C1b: Iron and steel – flaring Iron and steel - combustion plant (coke oven gas and blast furnace gas only) Blast furnace gas, Coal, Coke, Coke oven gas, Coke produced, Colliery methane, Dolomite, Fuel oil, Gas oil, Limestone, Liquefied Petroleum Gas (LPG), Natural gas This MS covers the carbon balance approach used for integrated steelworks and independent coke manufacture. Integrated steelworks use the blast furnace/basic oxygen furnace route to Most UK coke is produced at coke ovens associated with the UK's three integrated steelworks, although one independent coke manufacturer also existed until closure at the end of 2014. The Teesside steelworks was closed in September 2015 and the two coke ove ns at Scunthorpe steelworks were closed in early 2016 and mid -2023. At the end of 2023, there was only coke oven left in the UK. The last operational coke oven in the UK was shut in early 2024, the data for this coke oven will be presented in the 2026 subm ission. Four other coke ovens were in existence in 1990 but closed in the years up to 2005 due to closure of two integrated steelworks and other coke consumers, such as the UK's only lead/zinc smelter in covered by the inventory. Coke production emissions are reported under 1A1ci (combustion) Year Coke ovens Integrated steelworks Electric arc steelworks

UK NID 2025 (Issue 1) Ricardo Page 142 Year Coke ovens Integrated steelworks Electric arc steelworks The carbon balance method described in this method statement covers the use of coke oven coke, blast furnace gas and coke oven gas as fuels throughout the iron and steel industry, whereas the use of primary fossil fuels in boilers and heat treatment or melting furnaces is described in the method statement for 1A2. All fuels used in coke ovens, sinter plant, and blast furnaces are included in the carbon balance. The key processes and related emission activities covered by this method statement are 1. Coke oven coke is produced by heating coking coal in ovens in order to drive off volatiles which are collected as gases (coke oven gas, used as a fuel to heat the ovens) or liquids (coal tars and benzole, recovered for use in chemicals manufacture and other processes). The solid residue is coke oven coke, which is used as a fuel for sintering, as a reductant in blast furnaces, or sold for use in other industrial processes. Emissions of greenhouse gases resulting from combustion to heat the coke ovens are re ported in 1A1c, whereas fugitive emissions of methane from the coke ovens are reported in 2. Integrated steelworks convert iron ores into steel using the three processes of sintering, pig iron production in blast furnaces and conversion of pig iron to steel in basic oxygen furnaces. Emissions from integrated steelworks are estimated for these three processes, as well as other minor processes such as slag processing. 3. Sintering involves the agglomeration of raw materials for the production of pig iron by mixing these materials with fine coke (coke breeze) and placing it on a travelling grate where it is ignited. The heat produced fuses the raw materials together into a porous material called sinter. Emissions from sintering are reported in 2C1d. UK NID 2025 (Issue 1) Ricardo Page 143 4. Blast furnaces are used to reduce the iron oxides in iron ore to iron. They are continuously charged with a mixture of sinter, fluxing agents such as limestone, and reducing agents such as coke, fuel oil and coal. Hot air is blown into the lower part of the furnace and reacts with the reducing agent, producing carbon monoxide, which 5. Gas leaving the top of the blast furnace has a high heat value because of the residual CO content and is used as a fuel in the steelworks. Molten iron and liquid slag are withdrawn from the base of the furnace. The most significant greenhouse gas emissions to occur directly from the blast furnace process are the combustion gases from the 'hot stoves' used to heat the blast air. 6. These generally use blast furnace gas, together with coke oven gas and/or natural gas as fuels. These emissions are now reported under CRT category 2C1b, in line with reviewer recommendations. Gases emitted from the top of the blast furnace are collected and emissions should only occur when this gas is subsequently used as fuel. These emissions are allocated to the process using them. However, some blast furnace gas is lost, and the carbon content of this gas is also reported under CRT 7. Pig iron has a high carbon content derived from the coke used in the blast furnace. A substantial proportion of this must be removed to make steel and this is done in the basic oxygen furnace. Molten pig iron is charged to the furnace and oxygen is blown through the metal to oxidise carbon and other contaminants. As a result, carbon monoxide and carbon dioxide are emitted from the furnace and are collected for u se as a fuel. As with blast furnace gases, some losses occur, and these losses are reported with blast furnace gas losses under CRT category 2C1. In DUKES, basic oxygen furnace gas is combined with blast furnace gas and so separate figures for production and use of the two gases are not given. 8. The fuels derived in coke ovens and integrated steelworks are used in boilers and in heat treatment or melting furnaces and CO 2 emissions from these energy uses are calculated using emission factors derived using the carbon balance.
Activity Main sources of activity data (fuel use, production data) are DUKES (DESNZ, 2024a), ISSB annual statistics (ISSB, 2024), installation - specific activity data from EU/UK ETS (EA, NRW, both 2024), operator information for integrated steelworks (Tata Steel and British Steel, both Emission Input parameters for the carbon balance method are derived from EU/UK ETS data or operators of integrated steelworks (reference as for AD). Other UK CS CEFs are derived from the 2004 Carbon Factors Review (Baggott et al., 2004). EFs for non-CO2 gases are predominantly IPCC defaults (IPCC 2006), Baggott et al., 2004. An accompanying spreadsheet “Energy_background_data_uk_ 2025.xlsx” lists all emission information for common activity data sources. The carbon balance for the combined coke ovens and integrated steelmaking processes is based on tracking the carbon through four successive stages – coke making, sintering, pig iron production, and basic oxygen steel production. At each stage carbon is inp ut as fuels and/or feedstocks; carbon leaves in products; is emitted to air or removed as waste products. UK NID 2025 (Issue 1) Ricardo Page 144 listing main inputs and coal → coke + coke oven gas + benzole & tars + fugitive carbon emission coke + limestone + iron ore → sinter + carbon emission sinter + coke + other reducing agents → pig iron + blast furnace gas pig iron + scrap + dolomite → steel + slag + basic oxygen furnace gas The outputs that are allowed to vary, and therefore used to ensure that the overall carbon balances, are coke, blast furnace gas and basic oxygen furnace gas. outputs of carbon (expressed as CO2) given for the year 2023 as an example. Note that there is one negative value in the diagram because the figures take into account imports, exports, and stock changes. For some years, DUKES does not have sufficient coke oven coke to account for all known uses and so the GHGI has to deviate from DUKES by assuming a higher a Other adjustments includes statistical differences (-20 kt CO2), imports (-5349 kt CO2), exports (0 kt CO2), stock changes (-1451 kt CO 2), fugitive emissions from coke ovens reported as methane (12 kt CO 2), adjustments for natural gas added to coke oven gas (-53 kt CO2), carbon stored in dusts (+58 kt CO2). 3,036 kt CO2 5,891 kt CO2 1,068 kt CO2

UK NID 2025 (Issue 1) Ricardo Page 145 Emission estimates for limestone and dolomite added to sinter plants, blast furnaces, and oxygen furnaces are based on industry consumption data (Iron & Steel Statistics Bureau, 2024) and carbon contents from the operators (Tata Steel, British Steel, both 2024 and SSI Steel 2015), and based on their UK and EU ETS reporting (EA, NRW, both 2024). Emissions of CH4 and N2O are estimated using IPCC 2006 default emission factors. A detailed description of the carbon balance methodology has been given in Ricardo -AEA, GHG Inventory Use of EU ETS Data - Iron & Steel Sector, Chemical Industry Feedstock Use, April 2014 (available for download on the NAEI website62) and so only a brief summary of assumptions is given here. The carbon balance method requires the carbon content in input fuels and feedstocks to be estimated using consumption data and carbon contents for each fuel or feedstock. The balance is then used to distribute that carbon amongst the various derived fuels, products and wastes from the coke ovens and steelmaking processes. The total emission of CO2 is therefore dependent upon the assumptions made about the quantity of carbon in inputs, and in the main input – coking coal – in particular. The carbon content o f coking coal and blast furnace coal has, in recent years, been measured by operators as a result of their need to collect data for EU/UK ETS reporting purposes, and operators have also been able to supply high quality measurement-based data for the carbon contents of derived fuels, coal tars, benzole, limestone, dolomite, steel scrap, and steel product. The EU/UK ETS data indicate that the carbon contents of fuels do not vary greatly from one year to another and therefore, for earlier years, where EU/UK ETS data are not available, carbon factors are assumed to be the same as for those years where EU/UK ETS data are available. For each fuel, the average carbon content is calculated for years with EU/UK ETS reporting, and these values then used for the The operators also supply data on the consumption and production of fuels and these data should be consistent with UK energy statistics. This is largely so, but in a couple of instances where the UK statistics seem to underestimate consumption of a particular fuel in a particular year, we have used the operators' data instead. For example, operator data for the consumption of coking coal in coke ovens for the years 2003 -2023 is mostly lower than the figures given in DUKES, and the operator data are used in preference. The coal consumption figures for other industrial use are also modified by an equal and opposite amount so that overall coal consumption in the GHGI is the same as in DUKES. DUKES also excludes a small quantity of coke oven gas generated at one steelworks which is then supplied as a fuel to a co-located process, and so we have used operator data on this fuel in the inventory. In this case, it would not be appropriate to maintain consistency with overall UK demand figures in DUKES (since this fue l is missing from DUKES, not classified to a different sector). Finally, some small deviations are made for 2009, where operator data on consumption of coal and coke oven coke in blast furnaces are somewhat higher.
The changes to coal are treated as misallocations in DUKES (so UK totals for coal consumption are adhered to), whereas for coke oven coke, it is necessary to increase UK consumption to above the level given in DUKES, There have been minor changes to the underlying energy statistics (DUKES) data for 2023 that have resulted in minor recalculations for this sector. 62

UK NID 2025 (Issue 1) Ricardo Page 146 There have been no changes to the methodology for this version of the inventory, and no improvement work is planned, though all input data and assumptions are kept under review. Specific QA/QC and validation exercises relevant to these source categories • the comparison of the reference/sectoral approach; • comparison of inventory estimates based on the carbon balance, with EU/UK ETS data and detailed emission estimates provided by the operators; • comparison of DUKES data with industry-reported activity data (e.g. from ISSB); • comparison of carbon emission factors derived from the carbon balance, with IPCC default emission factors; and, • checks on the time -series consistency of carbon emission factors generated by the The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Code of Practice for Statistics57. EU ETS data is subject to its own QA process. A bilateral exchange was undertaken in May 2015 with the Inventory Agency from Germany, which included a review of the revisions to the iron and steel sector method in the 2014 All activity data used are available for the full time series of the estimates. Carbon factors for key inputs such as coking coal and blast furnace coal are available from operators only for some recent years (2005-2014 and more recently from 2021-2023 in the case of coking coal, 2007-2014 for other fuels) so the same values must be assumed to be appropriate in earlier years. Data are not available for 2015 to 2020, partly due to the Teesside works closing in September 2015, and the sale of the Scunthorpe w orks to a new operator in early 2016, so 2014 values for some parameters have been assumed to be correct for 2015 -2020 as well. While this does introduce some additional uncertainty for parts of the time-series, the assumed factors for coking coal and blast furnace coal, and the derived factors for coke oven coke, coke oven gas and blast furnace gas for these years are all within the ranges suggested in the IPCC Note that the implied emission factor for 1B1b is very sensitive to the weighting of emissions between coke manufacture and other solid fuel transformation; this is discussed further in MS Uncertainties for both activity and emission factors are based on expert judgement. The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. Uncertainties in fuel use statistics are typically low. The carbon emission factors are based on UK specific data. Since there is a direct link between the carbon emitted and the carbon content of the fuel, it is possible to estimate CO2 emissions accurately. UK NID 2025 (Issue 1) Ricardo Page 147 MS 5 Other stationary combustion Relevant Categories, source names 1A4ai: Miscellaneous industrial/commercial combustion Railways - stationary combustion 1A4ci: Agriculture - stationary combustion Miscellaneous industrial/commercial combustion Anthracite, Burning oil, Coal, Coke, Fuel oil, Gas oil, LPG, Natural gas, Peat, Petroleum coke, This method statement covers emissions from fuel combustion by non -industrial sectors including commercial, agricultural, public and residential sectors. Most stationary plants are small-scale, apart from a few large installations providing energy for large commercial or public sector buildings (e.g. banks, hospitals, schools, sport centres). Emissions from stationary railway sources are reported under 1A4a where the fuel is used in stationary combustion of burning oil and fuel oil to heat buildings, as wel l as natural gas combustion. This gas usage may include fuel used for electricity generation for own use by the railway sector. The ‘miscellaneous’ source includes energy use by a range of other users including the sewage and refuse disposal sector, and fuels used by television and radio broadcasters. DUKES (DESNZ, 2024a) Emission Baggott et al., 2004, IPCC, 2006 An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. Emissions for this category are calculated based on multiplying activity data by an emission factor. Activity data are taken directly from DUKES, with a few exceptions (see assumptions and observations). A full list of emission factors is included in Annex 5.1. Carbon emission factors are largely UK specific, whereas non-CO2 emissions use default emission factors. To improve the accuracy of emissions estimates for air quality pollutants, fuel use in domestic appliances is modelled to account for the ages and types of appliances used. However, for greenhouse gases, a single emission factor is applied across all appliance types, and the total modelled fuel use is normalised to DUKES. As such the method applied to model the fuel use by appliance type is not detailed here but is described in the UK’s Informative Inventory Report

UK NID 2025 (Issue 1) Ricardo Page 148 The source representing public sector combustion includes emissions from stationary combustion at military installations, which should ideally be reported under 1A5a Stationary. However, we do not currently have separate data for the military fuel component. Bottom-up estimates are made for a number of categories using gas oil (railways, off -road machinery etc.). In order to reconcile the gas oil used in these categories with the total in DUKES, reallocations (subtractions) are made from other categories, incl uding AD used for the estimates of emissions from stationary combustion in 1A4. These deviations from DUKES Activity data estimates for domestic sector use of fuels derived from petroleum coke are based on estimates provided by industry experts (CPL, 2015). There have been no changes to methods. Revisions to DUKES and other input data have been incorporated into the inventory. The impact of changes is set out in Chapter 10.
Improvements (completed and planned) No methodological improvements have been made directly to the categories detailed in this method statement. Improvements related to the treatment of biofuels are described in MS 21 This source category is covered by the general QA/QC of the greenhouse gas inventory in Annex 4. Fuel combustion estimates are verified through the comparison of the reference and The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Official Statistics Code of Practice57. For gas oil, bottom up estimates are made for various sources, which leads to changes in the sectoral allocations within DUKES. There are no official top down statistics to verify the bottom up statistics, however, the totals are reconciled with DUKES. Pet roleum coke and peat data are outside of DUKES, but are small emission sources included for completeness. Emission factors and activity data are taken from consistent data sets, there are no time series Uncertainties for both activity and emission factors are based on expert judgement. The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. There are no additional official statistics to compare the category specific fuel use for 1A4 with, as such it is difficult to verify the activity data allocations in DUKES. As such the uncertainty for the sources included in this MS will be higher than for power stations, for example. Uncertainties in total fuel use statistics are typi cally low. The carbon emission factors are based on UK specific data. Since there is a direct link between the carbon emitted and the carbon content of the fuel, it is possible to estimate CO 2 emissions accurately. Non -CO2 emissions are dependent on a greater number of parameters, and are largely based on defaults. As such, the uncertainties are higher, but since the emissions are smaller, this does not have a significant impact on the overall uncertainty of total GHG emissions. UK NID 2025 (Issue 1) Ricardo Page 149 Relevant Categories, source names 1A2giii : Mining and Quarrying 1A2gv : Construction machinery 1A2gvii: Generators, Waste, and Other Industrial machinery 1A3eii : Aircraft - support vehicles, Refrigerated Transport, Sea Ports 1A4bii : House and garden machinery 1A4cii : Agriculture/Forestry/Fishing - mobile machinery In 2022 Defra commissioned Ricardo to conduct a detailed Government-supported machinery population and usage survey with industry stakeholders, including evaluating the findings for their use in the inventory. Following review, agreed implementation of the data into an updated version of the off-road machinery model has been conducted, containing revised activity and population data. As such, there are now two models, the previous approach covering house and garden, and the current model for all other off-road machinery. Emissions are estimated for different types of portable or mobile equipment powered by diesel, gas oil, LPG, or petrol driven engines. These range from machinery used in agriculture such as tractors and combine harvesters; industry such as portable generat ors, forklift trucks and air compressors; construction such as cranes, bulldozers and excavators; domestic lawn mowers and aircraft support equipment. In the inventory they are grouped into the following • Mining and quarrying – reported under 1A2giii; • Aircraft support machinery, Sea Port machinery, and Refrigerated Transport – reported • Construction – reported under 1A2gv; • Generators, Waste, and Other Industry – reported under 1A2gvii • Forklifts– reported under 1A4aii; • Domestic house & garden – reported under 1A4b; and • Agricultural machinery (includes forestry) – reported under 1A4c. Netcen, 2004, ONS, DfT, Eurostat, UKMY, OHEEG, AEA, BEIS Projections (personal communication), CAA Emission Baggott et al., 2004, EMEP/ EEA Guidebook, EU Non -Road Mobile

UK NID 2025 (Issue 1) Ricardo Page 150 An accompanying spreadsheet “Energy_background_data_uk_ 2025.xlsx” lists all emission information for common activity data sources. Method approach - House and Garden Machinery (and other machinery before 2022) In the previous approach, a Tier 3 methodology is used for calculating emissions from individual types of mobile machinery. Default machinery or engine -specific fuel consumption and emission factors (g/kWh) are taken from EMEP/ EEA Guidebook. For methane, emission factors for more modern machinery based on engine or machinery-specific emission limits for total hydrocarbons established in EU Non-Road Mobile Machinery Directive are also included where available. The measures introduced to reduce total hydroca rbon emissions are assumed to affect methane emissions. Activity data are based on bottom -up estimates of machinery numbers and hours of use in 2004 (Netcen, 2004). Various proxy statistics are used as activity drivers for different groups of machinery typ es to estimate fuel consumption Emissions are calculated from a bottom -up approach using machinery - or engine -specific emission factors in g/kWh based on the power of the engine and estimates of the UK population and annual hours of use of each type of machinery. The emission estimates a re calculated using a modification of the methodology given in EMEP/ EEA Guidebook (2009). The population, usage and lifetime of different types of off -road machinery were updated following a study carried out by the Inventory Agency on behalf of the Department for Transport (Netcen, 2004). This study researched the current UK population, annual usage rates, lifetime and average engine power for a range of different types of diesel and petrol powered non-road mobile machinery. Additional information including data for earlier years were based on research by Off Highway Research (2000) and market research polls amongst equipment suppliers and trade associations by Precision Research International on behalf of the former DoE (Department of the Environment) (PRI, 1995, 1998). Usage rates from data published by Samaras et al (1993, 1994) were also used. The population and usage surveys and assessments were only able to provide estimates on activity of off -road machinery for 2004. These are one -off studies requiring intensive resources and are not updated on an Category Driver source Machinery types 5: number of households by size A simple turnover model is used to characterise the population of house and garden machinery type by age (year of manufacture/sale).
For older units, the emission factors used came mostly from EMEP/ EEA (2009) though a few of the more obscure classes were taken from Samaras & Zierock (1993). The load factors were taken from Samaras (1996). Emission factors for garden machinery, such as lawnmowers and chainsaws were updated following a review by Netcen (2004). For the air pollutants and for those equipment whose emissions are regulated by Directive 2002/88/EC or 2004/26/EC, the emission factors for a given unit were taken to be the maximum permitted by the directive at the year of manufacture. The emission regulations are quite complex in terms of how they apply to different machinery types. Each of the different

UK NID 2025 (Issue 1) Ricardo Page 151 machinery types was mapped to the relevant regulation in terms of implementation date and limit value. The trends in total hydrocarbon (THC) emissions across the emission regulation stages were applied to the trends in methane emissions as it is assumed th at measures to control THC emissions will also impact methane emissions. Estimation of Off -Road Machinery (Industrial, Airports, Agriculture (1A2gvii, 1A4cii, Databases and information for the current model were provided by industry trade associations and stakeholder groups including the Off Highway Engine and Equipment Group (OHEEG) the Agricultural Engineers Association (AEA), Department for Transport (DfT) an d the Construction and Agriculture Equipment Security and Registration (CESAR) database, and though these data were not designed for use in inventories, they provided valuable Machinery or engine-specific fuel consumption and emission factors (g/kWh) in the new model are based on stakeholder consultation and the 2019 EMEP/EEA Guidebook. The AEA were able to provide fuel consumption rates for different agricultural machinery type s in litres per hour based on telemetry data. Exception were agricultural telehandlers which were adjusted to align with fuel consumption rate implied by Guidebook factors. The current model was designed in ‘R’, an open -source software to accommodate the new activity data and used to calculate an estimate of emissions. Using activity data collated from OHEEG, AEA, DfT licensing statistics supplemented by various assumptions f rom expert judgement from discussions with AEA where data are missing, the model was constructed and used to calculate trends in fuel consumption and emissions, initially from agricultural machinery and then extended to other off-road machinery (excluding house and garden). Inventories for these machinery were grouped into portable generators, construction machinery, forklifts, machinery used in mining & quarrying, waste services, airport support and seaport support machinery and Transport Refrigeration Units (TRUs). Nearly a ll types of machinery use gas oil, but for some machinery types, a further breakdown in consumption and emissions is provided for machines running on petrol and LPG, where relevant. In the case of portable generators, cement mixers, cranes and various lifting equipment used in construction and industry, data from Eurostat PRODCOM statistics on sales/production of these equipment were used. ONS construction statistics and DESNZ energy statistics for other types of construction and industry machinery continue to be used. • Confidential data provided by the British Industrial Truck Association (BITA) have been adopted for trends in sales of forklifts. • For airport machinery, statistics on number of terminal passengers at UK airports • DfT port freight statistics have been used as proxies for trends in activities for • Trends in TRU activities were based on DfT statistics on licensed “insulated vans” In the current model, emissions from off-road agricultural machinery are calculated using the Tier 3 method in the EMEP/EEA Emissions Inventory Guidebook 2019 (The latest 2023 Guidebook does not contain any relevant changes, thus the model method remains unchanged). The equation to calculate the emissions is given 𝐸 = 𝑁 𝑥 𝐻𝑅𝑆 𝑥 𝑃 𝑥 𝐿𝐹 𝑥 (1 + 𝐷𝐹𝐴) 𝑥 𝐿𝐹𝐴 𝑥 𝐸𝐹𝐵𝑎𝑠𝑒

UK NID 2025 (Issue 1) Ricardo Page 152 E = mass of emissions of pollutant during inventory period (g), N = number of engines (units), DFA = deterioration factor adjustment, EFBase = Base emission factor (g/kWh). The emission factors are taken from the 2019 Guidebook, as provided for different legislative stages and engine power ratings. The exception to this was for the smallest and largest machinery in the power bands <19 kW or greater than 560 kW for the other machinery types. For these machines, OHEEG noted that although off -road in these power classes were unregulated in Europe prior to Stage V (which was incorporated into the new model), nevertheless it was common for machines in these size classes to be fitte d with engines suitable for the US market, with the exception of >560 kW gensets. Thus, a significant proportion will have lower emissions than might be assumed for ‘unregulated’ engines as provided in the Guidebook. Thus, emission factors were changed to reflect US Tier 2 or Tier 4 regulations prior to Stage V. Tier 2 machines were phased into the market from 2006 for machinery for >560kW. Tier 4 is phased in from 2008 for those machines <19kW. In contrast to the previous approach, in the current off-road model, machinery can be grouped into more than one of eight sector types (excluding house & garden which remains in the previous approach), based on stakeholder feedback. Each machinery type can be placed into machinery types and sectors in the new off-road model. Airport Port Other Refrigeration Agriculture

UK NID 2025 (Issue 1) Ricardo Page 153 Airport Port Other Refrigeration Agriculture

UK NID 2025 (Issue 1) Ricardo Page 154 Airport Port Other Refrigeration Agriculture

UK NID 2025 (Issue 1) Ricardo Page 155 Airport Port Other Refrigeration Agriculture [1] [2] [3] [4] The assumptions made to estimate emissions from this source are described in the methods and approach section above. The drivers chosen are considered by expert judgement to be most appropriate among all the statistical data that are available.
A fuel reco nciliation procedure is followed for gas oil which takes account of consumption from all sources, as The main re-calculation is due to changes in fuel consumption for industrial and construction mobile machinery affecting 1A2gvii arising from the re -allocation of changed gas oil activity data in DUKES. The changes to this sector are made to retain fuel mass balance with DUKES and are affected by changes made to other sectors using gas oil. Other re-calculations arise • Revision to DUKES gas oil consumed in agriculture for years 2017 and 2018 which is a driver for the agricultural machinery sector. • Revision to ONS construction output data from 1997. UK NID 2025 (Issue 1) Ricardo Page 156 Defra have commissioned Ricardo to conduct a detailed Government -supported machinery population and usage survey with industry stakeholders and evaluating the findings for their This source category is covered by the general QA/QC of the greenhouse gas inventory in An expert judgement quality check has been done to verify that the amount of gas oil used by off-road machinery estimated from the bottom-up approach is neither excessively high nor low as a proportion of total UK gas oil available for consumption as given in DUKES. Although the bottom -up data for machinery population and usage is only available for one year, the proxy statistics used to generate the time -series are consistent across the time - Fuel consumption by these off -road machinery sources is not provided in DUKES and so is estimated for each machinery type from a bottom -up Tier 3 approach to derive machinery population and usage rates. An overall fuel balance taking account of consumption by other uses of gas oil, diesel and petrol ensures consistency with total consumption figures in DUKES. Various proxy data are used to establish a consistent time-series in activity rates, as The highest uncertainties are considered to be in the estimates for general industrial machinery as these cover a wide range of machinery types with multiple use applications, e.g. portable generators. Uncertainties in the trends for the other off-road sources (domestic house and garden, airport machinery and agricultural machinery) are considered to be smaller and less biased by the choice of proxy data. UK NID 2025 (Issue 1) Ricardo Page 157 Relevant Categories, source names 1D1a: International bunkers - Aviation Aviation turbine fuel (jet kerosene), Aviation spirit (aviation gasoline), Sustainable Aviation In accordance with the agreed guidelines, the UK inventory contains estimates for both domestic and international civil aviation. Emissions from international aviation are recorded as a memo item and are not included in national totals. Emissions from both the Landing and Take-Off (LTO) phase and the Cruise phase (including climb and descent) are estimated. Emissions of a range of pollutants are estimated in addition to the reported greenhouse gases. The method reflects differences between airports and the aircraft that use them. In addition to aircraft main engines exhaust, emissions from aircraft auxiliary power units are also included. A full description is given in Watterson et al. (2004). The method used to estimate emissions from military aviation can be found in MS 15. Activity CAA (2024a); CAA (2024b); DESNZ (2024a); DfT (2024) Emission Baggott et al. (2004); EMEP/EEA (2019); IPCC (1997); IPCC (2006) An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission factors used in the energy sector, including a full list of references 56. In addition, Annex 5.1 EMEP/EEA Guidebook aircraft categories. Estimates are based on IPCC Tier 3 method and use the number of aircraft movements broken down by aircraft type at each UK airport together with UK energy statistics. The methods used to estimate emissions from aviation require the following activity • Aircraft movements and distances travelled Detailed activity data has been provided by the UK Civil Aviation Authority (CAA). These data include aircraft movements broken down airport; aircraft type; whether the flight is international or domestic; and the next/last POC (port of call) from which sector lengths (great circle) have been calculated. The data covered all Air Transport Movements (ATMs)

UK NID 2025 (Issue 1) Ricardo Page 158 excluding air-taxi. The CAA also compiles summary statistics at reporting airports, which include air-taxi and non-ATMs. • Inland Deliveries of Aviation Turbine Fuel, Aviation Spirit, and Sustainable Aviation Total inland deliveries of aviation spirit, aviation turbine fuel and Sustainable Aviation Fuel (SAF) to air transport are given in DUKES (DESNZ, 2024a). This is the best approximation of aviation bunker fuel consumption available and is assumed to cover international, • Consumption of Aviation Turbine Fuel and Aviation Spirit by the Military These data are supplied by the Ministry of Defence (MoD). Military aviation estimates are included in MS 15 . The data for total fuel use for military aviation is used in the normalisation to the DUKES total. Calendar year activity data are derived from the data sources described above. International Flights from UK Airports, 1990-2023 Giga metres, or 109 metres Estimated emissions from aviation are based on data provided by the CAA and, for overseas territories, the DfT. Gm flown calculated from total flight distances for departures from UK and overseas territories airports. UK NID 2025 (Issue 1) Ricardo Page 159 A combination of national airport specific LTO factors (derived from local airport studies) and EMEP/EEA Eurocontrol cruise factors for generic aircraft are used. An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission factors used in the energy sector, including aviation, and associated references. Carbon emission factors are country specific, whereas defaults are used for other gases. The basic approach to estimating emissions from the LTO cycle is as follows. The contribution to aircraft exhaust emissions (in kg) arising from a given mode of aircraft operation (see list below) is given by the product of the duration (seconds) of the operation, the engine fuel flow rate at the appropriate thrust setting (kg fuel per second) and the emission factor for the pollutant of interest (kg pollutant per kg fuel).
The annual emissions total for each mode (kg per year) is obtained by summing contributions over all engines for all aircraft movements in the year. The time in each mode of operation for each type of airport and aircraft has been taken from individual air port studies. The time in mode is multiplied by an emission rate (the product of fuel flow rate and emission factor) at the appropriate engine thrust setting in order to estimate emissions for phase of the aircraft flight. The sum of the emissions from all the modes provides the total emissions for a particular aircraft journey. The modes considered • Take-off Roll (start of roll to wheels-off); • Initial-climb (wheels-off to 450 m altitude); • Climb-out (450 m to 1000 m altitude); • Approach (from 1000 m altitude); • Auxiliary Power Unit (APU) use after arrival; and Departure movements comprise the following LTO taxi -out, hold, take-off roll, initial- climb, climb-out and APU use prior to departure. Arrivals approach, landing-roll, taxi-in and APU use after arrival. Aircraft often take-off at reduced thrust (i.e. less than 100% thrust). Thrust setting for Take-off roll, Initial-climb, and Climb-out depend on airport and aircraft type and are derived from local airport studies. Thrust setting during Approach are 15% for the initial phase (above 600 ft) and 30% for the final phase (below 600 ft). Depending on airport and aircraft type, the Landing-roll often includes periods of reverse thrust at either at idle or 30%, the remainder of the time is at idle thrust setting. Other modes (Taxi and Hold) are at idle thrust. Idle thrust is nominally 7%, however an adjustment is made to the idle fuel flow to account for engine The approaches to estimating emissions in the cruise are summarised below. Cruise emissions are only calculated for aircraft departures from UK airports (emissions therefore associated with the departure airport), which gives a total fuel consumption compa tible with recorded deliveries of aviation fuel to the UK. This procedure prevents double counting of emissions allocated to international aviation. The EMEP/EEA Emission Inventory Guidebook (EMEP/EEA, 2019) provides fuel consumption and emission data for non -GHGs (NOx, HC and CO) for a number of aircraft

UK NID 2025 (Issue 1) Ricardo Page 160 cruise modes (climb cruise and descent). The data are given for a selection of generic aircraft type and for a number of standard flight distances. The breakdown of the CAA movement by aircraft type contains a more detailed list of aircraft types than in the EMEP/EEA Emission Inventory Guidebook. Therefore, each specific aircraft type in the CAA data has been assigned to a generic type in the Guideboo k. Details of this Piecewise linear regression has been applied to these data to give fuel consumption as a 𝐹𝐶_𝐶𝑟𝑢𝑖𝑠𝑒𝑑,𝑔,𝑝 = 𝑚𝑔,𝑝 × 𝑑 + 𝑐𝑔,𝑝 𝐹𝐶_𝐶𝑟𝑢𝑖𝑠𝑒𝑑,𝑔,𝑝 is the fuel consumption in cruise of pollutant 𝑝 for generic aircraft type 𝑔 and flight distance 𝑑 (kg) 𝑔 is the generic aircraft type 𝑝 is the pollutant (or fuel consumption) 𝑚𝑔,𝑝 is the slope of regression for generic aircraft type 𝑔 and pollutant 𝑝 𝑐𝑔,𝑝 is the intercept of regression for generic aircraft type 𝑔 and pollutant Estimates of CO2 were derived from estimates of fuel consumed in the cruise (see equation above) and the carbon contents of the aviation fuels. Methane emissions are believed to be negligible at cruise altitudes (IPCC, 2006). Estimates of N 2O have been derived from an emission factor recommended by the IPCC (IPCC, 1997)63 and the estimates of fuel consumed in the cruise (see equation above). publication DUKES are the national statistics on fuel consumption, and IPCC guidance states that national total emissions must be on the basis of fuel sales. Therefore, the estimates of emissions have been re-normalised based on the results of the comparison between the fuel consumption data in DUKES and the estimate of fuel consumed produced from the civil aviation emissions model, having first scaled up the emissions and fuel consumption to account for air-taxi and non-ATMs. The scaling is done separately for each airport to reflect the different fractions of air -taxi and non -ATMs at each airport and the different impacts on domestic and international emissions. Air -taxi and non-ATM fuel consumption estimates are not documented by Watterson et al. (2004), as this revision to methodology occurred after publication of the report. The aviation fuel consumptions presented in DESNZ DUKES include the use of both civil and militar y fuel, and the military fuel use must be subtracted from the DUKES total to provide an estimate of the civil aviation consumption. This estimate of civil aviation fuel consumption has been used in the fuel reconciliation. Emissions from flights originating from the overseas territories have been excluded from the fuel reconciliation process as the fuel associated with these flights is not included in DUKES. Emissions will be re-normalised each time the aircraft movement data are modified or data for anothe r year 63 T1 cruise factors in the 2006 IPCC Guidelines are not in units consistent with our methodology, and T2 LTO factors in the 2006 IPCC Guidelines are based on IPCC 1997 values anyway. UK NID 2025 (Issue 1) Ricardo Page 161 For aviation turbine fuel reconciliation is quite close; pre -normalised fuel estimates generally agree with DUKES within 5%. However, the reconciliation for aviation spirit is poor due to limited coverage of smaller flights by the CAA dataset. The following modifications are made to the CAA data in order to ensure complete geographical coverage of the inventory and full compliance with the IPCC definitions of • Flights between the UK and overseas territories are reclassified from international to • International flights with an intermediate stop at a domestic airport are considered international in the CAA aircraft movement data.
These are reclassified as having a domestic leg and an international leg in response to a recommendation from the UNFCCC centralised review in 2013; and • The CAA data have been supplemented with data from overseas territories, supplied There were minor revisions due to time series revisions in fuel consumption data from DUKES from 2009 to 2021. In 2022 there were recalculations due to revised activity data from Improvements (completed and planned) A number of improvements have been made to the model over recent years, to include findings from UK specific research. The 2024 inventory submission included improvements resulting from the adoption of taxi times and cruise factors from the 2023 EMEP/EEA Guidebook, and the introduction of SAF (Sustainable Aviation Fuel) as a new fuel type to reflect the addition of aviation biofuel use data in DUKES. A watching brief is kept on developments in emission factors and activity data for all modes of This source category is covered by the general QA/QC of the greenhouse gas inventory in Consistent data sets and methods are used across the full time series to ensure time series consistency. There was a dramatic reduction in aviation activity in 2020 and 2021 as a result It should be noted that since emissions of methane from engines consuming aviation spirit vary significantly, and that total use of this fuel in aviation is low, the time -series of implied emission factors of methane is subject to large year -on-year variations, including a notable change in methane IEF between 2009-2010. Uncertainties for both activity and emission factors are based on expert judgement. The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. Uncertainties in fuel use statistics are typically low. The carbon emission factors are based on UK specific data. Since there is a direct link between the carbon emitted and the carbon

UK NID 2025 (Issue 1) Ricardo Page 162 content of the fuel, it is possible to estimate CO 2 emissions accurately. Non-CO2 emissions are dependent on a greater number of parameters and are largely based on defaults. As such, the uncertainties are higher, but since the emissions are smaller, this does not have a significant impact on the overall uncertainty of total GHG emissions. UK NID 2025 (Issue 1) Ricardo Page 163 Relevant Categories, source names 1A3bi : Road transport - cars - cold start Road transport - cars - motorway driving Road transport - cars - rural driving Road transport - cars - urban driving 1A3bii : Road transport - LGVs - cold start Road transport - LGVs - motorway driving Road transport - LGVs - rural driving Road transport - LGVs - urban driving 1A3biii : Road transport - buses and coaches - motorway driving Road transport - buses and coaches - rural driving Road transport - buses and coaches - urban driving Road transport – HGV articulated – motorway driving Road transport – HGV articulated – rural driving Road transport – HGV articulated – urban driving Road transport – HGV rigid – motorway driving Road transport – HGV rigid – rural driving Road transport – HGV rigid – urban driving 1A3biv: Road transport - mopeds (<50cc 2st) - urban driving Road transport - mopeds (<50cc 2st) – lubricants use Road transport - motorcycle (>50cc 2st) - rural driving Road transport - motorcycle (>50cc 2st) - urban driving Road transport - motorcycle (>50cc 4st) - motorway driving Road transport - motorcycle (>50cc 4st) - rural driving Road transport - motorcycle (>50cc 4st) - urban driving 1A3bv: Road transport - all vehicles LPG use Petrol (gasoline), Diesel (DERV), Liquefied Petroleum Gas (LPG), Natural Gas (NG) This MS includes all fuel related emissions from road transport. Emissions from Urea consumption are reported under IPPU and detailed in Chapter 4. UK NID 2025 (Issue 1) Ricardo Page 164 Activity DfT (traffic data, vehicle licensing statistics, ANPR data, MOT data). Data on petrol and diesel fuels consumed by road transport in the UK are taken from the Digest of UK Energy Statistics (DUKES) published by DESNZ and corrected for consu mption by off -road vehicles and the fuel consumed by the Crown Dependencies included in DUKES. Emission COPERT 5.8, EMEP/EEA 2024 Emission Inventory Guidebook. An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. A Tier 3 methodology is used for calculating exhaust emissions from passenger cars (1A3bi), light goods vehicles (1A3bii), heavy goods vehicles, buses and coaches (1A3biii) and Petrol and diesel vehicle fuel consumption and emissions are estimated for individual vehicle types from a bottom-up approach using an array of traffic statistics and exhaust emission and fuel consumption factors representing the real -world performance of vehicles. These estimates are reconciled to national energy consumption statistics from DUKES. This approach provides estimates that are consistent with the IPCC 2006 Guidelines and includes inherent QA/QC in the comparison of bottom-up traffic activity related estimates and top-down Emissions from vehicles running on LPG are estimated on the basis of national figures derived from DUKES, detailing the consumption of this fuel in road transport. The CO2 emissions from LPG consumption cannot be broken down by vehicle type because there are no reliable figures available on the total number of vehicles or types of vehicles running on this fuel. It is believed that many vehicles running on LPG are cars and vans converted by their owners and that these conversions are not necessarily reported to vehicle licensing agencies. Figures from DUKES suggest that the consumption of LPG is only a small percentage (0.31%) of the total amount of fuels consumed by road transport and vehicle licensing data suggest a similar percentage of all light duty vehicles run on LPG. Very few vehicles in the UK run on natural gas. Vehicle licensing data in the UK suggest that less than 0.15% of all road vehicles run on NG in 2023. Figures from DUKES suggest that the consumption of NG is around 0.16% of the total fuel consumed by road transport in 2023.
Traffic-based emission an overview A Tier 3 method is used to calculate fuel consumption and emissions from different types of petrol and diesel vehicles using detailed traffic and fleet information before a final fuel reconciliation is done. Details of the methodology are given in a separate report “Methodology for the UK’s Road Transport Emissions Inventory” (Brown et al., 2018) which will be updated periodically covering any new methodological changes for both greenhouse gases and air pollutants. This describes the very detailed information available on road transport activities in the UK and how these are used in estimating the road transport inventory. Only a brief overview of the approach used and the activity data and emission factors specific to the greenhouse gases in the current inventory are provided in this report. Fuel consumption and emissions of CH 4 and N 2O, as well as the indirect GHGs and air pollutants, NMVOCs, NO x, CO and SO 2, from individual vehicle types are calculated from measured emission factors expressed in g/km and road traffic and fleet composition statistics from the Department for Transport. The emission factors are from the COPERT 5.8 (Emisia,

UK NID 2025 (Issue 1) Ricardo Page 165 2024) and EMEP/EEA Guidebook 2023 – Update 2024 expressed as equations relating emission factor to average vehicle speed or road type for different vehicle types compliant with different legislative emission standards (Euro standards). The type of emissions • Hot exhaust emissions from the vehicle exhaust when the engine has warmed up to its normal operating temperature. • Cold start the excess emissions that occur when a vehicle is started with its engine below its normal operating temperature. For NMVOCs, evaporative emissions of fuel vapour from petrol -fuelled vehicles are also Emissions are calculated for vehicles of the following • Petrol Light Goods Vehicles (Gross Vehicle Weight (GVW) ≤ 3.5 tonnes); • Diesel Light Goods Vehicles (Gross Vehicle Weight (GVW) ≤ 3.5 tonnes); • Rigid-axle Heavy Goods Vehicles (GVW ≥ 3.5 tonnes); • Articulated Heavy Goods Vehicles (GVW ≥ 3.5 tonnes); Total emission rates (as well as fuel consumption) are calculated by multiplying emission factors in g/km with annual vehicle kilometre figures for each of these vehicle types on different types of roads. This procedure is followed to derive the initial bottom -up estimate of fuel consumption and implied fuel -based emission factors for CH 4 and N2O by vehicle category before the normalisation to fuel sales is carried out. Activity data for traffic-based emission Hot exhaust emission factors are dependent on average vehicle speed and therefore the type of road the vehicle is travelling on. Average emission factors are combined with the number of vehicle kilometres travelled by each type of vehicle on rural roads, h igher speed motorways/dual carriageways and different types of urban roads with different average speeds. The emission results are combined to yield emissions on each of these main road • Rural single carriageway; and DfT estimates annual vehicle kilometres (vkm) for the road network in Great Britain by vehicle type on roads classified as motorways, trunk, principal and minor roads in urban and rural areas (DfT, 2024a). DfT provides a consistent time series of vehicle k m data by vehicle and road types going back to 1993 for the 2023 inventory, taking into account any revisions to historic data. The vkm data are derived by DfT from analysis of national traffic census data involving automatic and manual traffic counts. Add itional information discussed later (e.g. Automatic Number Plate Recognition data) (DfT, 2022) are used to provide the breakdown in Vehicle kilometre data for Northern Ireland by vehicle type and road class were provided by the Department for Regional Development, Northern Ireland, Road Services (DRDNI, 2016). This gave a timeseries of vehicle km data from 2008 to 2014. To create a timeseries of vehicle km data for 1990 to 2007, the vehicle km data from DRDNI (2013) was used. The data were scaled up or down based on the ratio of the data for 2008 between DRDNI (2016) and DRDNI

UK NID 2025 (Issue 1) Ricardo Page 166 (2013) for the given vehicle type and road type considered. Data for 2015 -2023 were not available for the current inventory compilation and thus they were extrapolated from 2014 vehicle km data for Northern Ireland based on the traffic growth rates between 2014 and 2023 in Great Britain. Motorcycle vehicle km data were not available for Northern Ireland so they were derived based on the ratio of motorcycles registered in Northern Ireland relative to Great Britain each year. The ratios were then applied to t he motorcycle vehicle km activity data for Great Britain. Information about the petrol/diesel split for cars and LGVs in the traffic flow are based on licensing data for Northern Ireland as provided by DfT (2024d).
The Northern Ireland data has been combined with the DfT data for Great Britain to produce 3.10 shows the time-series of total UK vehicle kilometres by vehicle and road type for selected Billion vkm 199064 2000 2005 2010 2015 2021 2022 2023 Petrol cars urban 142.2 134.8 118.1 98.0 89.4 79.2 85.4 86.5 rural 140.9 134.1 127.6 110.5 95.6 86.3 95.1 97.4 m-way 49.3 53.0 48.9 41.9 34.5 33.0 39.0 40.0 Diesel cars urban 5.8 26.0 40.2 52.8 64.9 55.7 57.6 55.2 rural 6.1 28.3 47.8 66.6 90.1 78.9 83.0 81.2 m-way 2.8 14.7 25.3 33.5 45.8 31.2 35.3 34.5 Electric cars urban 0.0 0.0 0.1 0.0 0.2 3.3 5.8 10.7 rural 0.0 0.0 0.1 0.0 0.3 3.4 6.4 12.0 m-way 0.0 0.0 0.0 0.0 0.1 1.4 2.7 5.1 Petrol LGVs urban 11.2 4.5 2.1 1.4 1.0 0.8 0.8 0.8 rural 11.6 5.4 2.6 1.7 1.3 1.3 1.4 1.4 m-way 4.0 2.1 1.1 0.6 0.6 0.6 0.6 0.6 Diesel LGVs urban 5.7 15.1 20.2 21.8 25.0 25.9 26.2 25.8 rural 5.9 18.3 25.2 29.5 34.7 40.7 43.1 42.9 m-way 2.0 7.2 10.0 11.2 14.4 18.1 19.6 19.7 Electric LGVs urban 0.0 0.0 0.0 0.0 0.0 0.3 0.4 0.6 rural 0.0 0.0 0.0 0.0 0.0 0.4 0.7 1.0 m-way 0.0 0.0 0.0 0.0 0.0 0.2 0.3 0.5 LPG LGVs urban 0.0 0.1 0.5 0.4 0.3 0.2 0.2 0.3 rural 0.0 0.1 0.7 0.6 0.5 0.4 0.4 0.6 m-way 0.0 0.1 0.3 0.2 0.2 0.2 0.2 0.3 64 Prior to 1993, a different definition was used for urban and rural areas; areas were defined as 'built-up'/'non-built-up'. ‘Non- built-up’ roads were those with a speed limit of more than 40mph, and ‘built-up’ roads were those with a speed limit of 40mph or

UK NID 2025 (Issue 1) Ricardo Page 167 Billion vkm 199064 2000 2005 2010 2015 2021 2022 2023 Rigid HGVs urban 4.5 3.9 4.0 3.2 3.0 2.5 2.5 2.4 rural 7.1 7.2 7.5 6.7 6.4 5.9 5.9 5.6 m-way 3.7 4.2 4.2 4.1 3.9 3.9 3.9 3.8 Artic HGVs urban 1.1 1.1 1.1 0.8 0.9 1.0 1.0 1.0 rural 4.4 5.2 5.4 5.1 5.3 6.1 6.2 6.0 m-way 4.7 7.4 7.9 7.5 8.4 9.5 9.4 9.2 Buses urban 2.4 3.0 3.1 3.0 2.7 1.8 1.9 1.8 rural 1.7 1.7 1.5 1.6 1.4 1.0 1.1 1.1 m-way 0.6 0.5 0.5 0.5 0.4 0.1 0.2 0.3 M/cycle urban 3.3 2.3 2.9 2.5 2.2 2.3 2.6 2.6 rural 2.0 2.0 2.2 1.8 1.9 1.6 1.8 1.8 m-way 0.3 0.4 0.4 0.4 0.4 0.2 0.3 0.3 Total 423.3 482.7 511.2 507.9 535.9 497.4 541.1 553.1 The road classification and traffic speed assignments have been developed to improve the representation of total road transport emissions and their spatial distribution. Speed limit classification data has been assigned to OS Openroads geometry based on th e length weighted median speed limit for each road link. The underlying speed limit dataset has been provided by Basemaps for Great Britain (Speed Limit Data Basemap, 2021). The vehicle speeds assigned to each category were derived from an analysis of GPS vehicle speed observations (Teletrac Navman, 2021) for England provided by DfT. The observed average speeds for England were applied across the UK. The vehicle speeds are used to derive the emission factors for each vehicle and road type from the emission factor-speed relationships available for different pollutants. Vehicle kilometre data based on traffic surveys does not distinguish between the type of fuels the vehicles are being run on (petrol and diesel) nor on their age. The inventory uses the Automatic Number Plate Recognition (ANPR) data provided by DfT (2022) to define petrol and diesel mix in the car fleet on different road types (urban, rural and motorway), leading to the vehicle km data for petrol and diesel cars on different road types in on different road types (urban and rural major/minor roads, and motorways) and regions. They cover various vehicle and road characteristics such as fuel type, age of vehicle, engine size, vehicle weight and road types. The road transport fleet turnover model is developed to calculate the composition of the UK vehicle fleet and road transport emissions over a time-series from 1990 to 2023. The model is based on a comprehensive set of vehicle licensing (DfT, 2024d) and annual mileage data from MOT (Ministry of Transport) records provided by DfT (2024e), covering years between 2007 and 2023 (licensing data back to 1994 and MOT data also available for 2023). These have been supplemented with additional DfT data from the Conti nuing Survey of Road Goods Transport (CSRGT) and National Travel Survey and used to develop revised vehicle survival rate and mileage with age profiles that vary by year and have been used to update the NAEI’s fleet turnover model. The model is used to cal culate a consistent time -series in the composition of the fleet in terms of the proportion of vehicle kilometres travelled by vehicles of different Euro emission standards from 2005 to 2023. UK NID 2025 (Issue 1) Ricardo Page 168 Vehicle licensing statistics and mileage data are used to define trends • The breakdown in vkm of cars, mopeds and motorcycles by engine size category. • The breakdown in vkm by rigid HGVs, artic HGVs, buses and coaches by vehicle The year-of-first registration of a vehicle determines the type of emission regulation that the vehicle complies with. These have entailed the successive introduction of tighter emission control technologies. Although emission standards do not apply to CH4 and N2O, technologies designed to control the regulated pollutants such as hydrocarbons and NOx affect these GHG Detailed information on the fleet composition in London is regularly provided by Transport for London (TfL, 2024). The inventory pays particular attention to the unique features of the bus, taxi, HGV and LGV fleets in London.
This is primarily so as to be able to account for measures taken to reduce emissions and improve air quality in London through the introduction of the London Low Emission Zone and Ultra Low Emission Zone both introduced in stages. The expanded area of the ULEZ up to, but not including the North and South Circular Roads, was introduced in 2021, is also taken into account. The inventory also takes account of the early introduction of certain emission standards and additional voluntary measures, such as incentives for HGVs to upgrade engines and retrofit with particle traps, to reduce emissions from road vehicles in the UK fleet. This was based on advice from officials in DfT. Fuel Consumption Factors for Vehicle Fuel consumption is calculated for each vehicle type using the fuel consumption -speed relationships given in COPERT 5.8 and the EMEP/EEA Emissions Inventory Guidebook (2023). This includes a method for passenger cars which applies a year-dependent ‘real-world’ correction to the average type-approval CO2 factor weighted by new car sales in the UK from 2005 to 2023. The new car average type-approval CO2 factors for cars in different engine size bands were provided by the Society of Motor Manufacturers and Traders (SMMT, 2024). The real-world uplift uses empirically -derived equations in the Guidebook that take account of average engine capacity and vehicle mass. As mentioned previously, the bottom -up fuel consumption is reconciled to DUKES fuel consumption. Using the Guidebook factors with fleet composition data and average speeds on different road types, fleet average fuel consumption factors for each main vehicle category are shown in Source 1990 2000 2005 2010 2015 2021 2022 2023 Petrol cars 79.9 71.7 68.1 65.9 62.3 57.1 55.9 55.1 Diesel cars 60.5 59.2 59.8 60.8 58.5 57.8 57.5 57.3 LGVs 99.0 81.9 75.7 74.4 73.5 72.7 72.2 71.7 HGVs 234.6 218.1 230.2 230.3 230.1 232.5 232.2 231.9 coaches 316.7 288.0 282.0 275.1 271.3 272.0 269.6 267.4 motorcycles 57.9 57.3 50.5 46.0 43.7 38.9 38.0 37.2 CO2 can be calculated from the carbon content of the fuel and the fuel used (calculated as above). Carbon emission factors for petrol, diesel and LPG are set out in “Energy_background_data_uk_2025.xlsx”. UK NID 2025 (Issue 1) Ricardo Page 169 CH4 and N2O Emission Factors for Vehicle Types The emission factors for N 2O and CH 4 for all vehicle types in g/km are based on the recommendation of the Emissions Inventory Guidebook (EMEP, 2023) derived from COPERT 5.8. Tables showing the emission factors for different vehicle types, Euro standards and road types are provided in Annex A in the road transport inventory report by Brown et al (2018). This also shows the cold start emission factors for N2O emissions from petrol cars and LGVs Nitrous oxide emissions were a problem with early generation petrol cars fitted with three-way catalysts, being formed as a by -product on the catalyst surface during the NO x reduction process. Emission factors have been declining with successive Euro standards since the first generation of catalysts for Euro 1, presumably due to better catalyst formulations as well as reductions in fuel sulphur content which also reduces N2O emissions. The fuel sulphur content of road fuels has been steadily declining since 2000 with the requirements of the European Fuel Quality Directive and is now less than 10ppm since January 2009 according to the Directive 2009/30/EC. Factors for HGVs and buses have been increasing with more recent Euro standards (Euro IV-VI). This is most likely due to the fitting of selective catalytic reduction (SCR) systems on the exhaust system for controlling NOx emissions. Road transport is a relatively unimportant emitter of CH 4, only being produced as a consequence of incomplete combustion, but largely controlled by catalysts on petrol vehicles. Tighter regulations on hydrocarbon emissions from petrol and diesel vehicles have led to reductions in CH4 emissions with the introduction of successive Euro standards. mg/km. These factors are weightings according to the distances travelled by the mix of Euro classes in the fleet each year as well as the proportions of kilometres travelled at different speeds and therefo re with different emission factors. These factors also include the contribution from cold start emissions. Gas Source 1990 2000 2005 2010 2015 2021 2022 2023 CH4 Petrol cars 111.8 57.0 32.7 17.0 12.1 9.8 9.5 9.6 DERV cars 19.7 8.4 4.4 2.1 0.9 0.4 0.3 0.3 LGVs 80.7 26.0 9.0 3.7 1.4 0.5 0.4 0.4 HGVs 86.2 70.1 66.3 37.4 13.6 6.2 5.5 5.2 coaches 149.0 116.6 94.1 54.8 27.0 9.1 7.4 6.3 motorcycles 207.9 182.0 140.9 90.9 66.3 47.4 41.5 37.5 N2O Petrol cars 8.2 11.1 7.7 2.7 1.6 1.2 1.2 1.2 DERV cars - 3.9 5.8 6.3 6.0 6.1 5.7 5.6 LGVs 5.5 5.2 6.1 6.2 6.0 5.7 5.4 5.2 HGVs 35.2 16.9 10.0 18.0 35.7 43.2 41.5 41.3 coaches 35.2 18.2 10.3 13.5 22.6 33.1 32.2 32.4 motorcycles 1.9 1.9 1.8 1.7 1.7 1.7 1.8 1.8 Using the CH4 and N2O emissions and fuel consumption calculated from the traffic data, it is possible to derive implied fuel-based emission factors of CH4 and N2O (in g/kg fuel) for each vehicle type in each year which is used in conjunction with the normalised fuel consumption

UK NID 2025 (Issue 1) Ricardo Page 170 (see below) to estimate their emissions. This ensures all pollutant emissions are consistent Fuel reconciliation with national statistics and normalisation The ‘bottom-up’ calculated estimates of petrol and diesel consumption described above are compared with DESNZ figures for total fuel consumption in the UK published in DUKES. The total amounts in DUKES are adjusted to remove the small amount of consumption by inland waterways, off -road machinery and consumption in the Crown Dependencies, and are combined with the estimated lubricant oil consumption.
For a valid comparison with DUKES which covers only fossil fuel petrol and diesel, the amount of petrol and d iesel displaced by biofuel consumption, based on data from the Renewable Transport Fuel Obligation (RTFO), has been used to correct the calculated consumption of petrol and diesel. Natural gas is not included in this reconciliation process, however it is a very small proportion of total road transport fuel use. A small proportion of LGV consumption of diesel in the bottom-up model is displaced by LPG as well, which is described on the following page. This comparison shows a small difference between the bottom-up estimated fuel consumption and DUKES-based figures. In order to be consistent with the IPCC methodologies and ensure that the fuel consumption data matches national statistics, it is necessary to adjust the calculated estimates for individual vehicle types by using a normalisation process to ensure the total calculated consumption of petrol and diesel equals the DUKES-based figures. consumption) to the figures in DUKES based on total fuel sales of petrol and diesel in the UK, allowing for off -road consumption. In the earlier part of the time -series, there was a greater deviation from the DUKES figures with the maximum deviation being at 16% (for Petrol, in 2015). When the ratio tends to be towards 1, it indicates a better agreement with fuel sales data. The bottom-up method for petrol consumption in 2023 was 1 08.1% and for diesel was 109.4% of the value from DUKES. The normalisation process introduces uncertainties into the fuel consumption estimates for individual vehicle classes even though the totals for road transport are known with high accuracy. Petrol fuel consumption calculated for each vehicle type was scaled up by the same proportions to make the total consumption align with DUKES. The same procedure was used to scale up diesel consumption by each vehicle type. Passenger cars consume the vast majority of petrol, so one would expect that DUKES provides a relatively accurate description of the trends in fuel consumption by petrol cars. This suggests the gap in the early part of the inventory time-series between DUKES and bottom-up estimates is due to inaccuracies in the estimation of fuel consumption by passenger cars during the 1990s. The fuel consumption, normalised to DUKES in the manner described above, is used to calculate CO2 emissions for each vehicle type. For CH4 and N2O, the year-dependent implied fuel-based emission factors derived from the traffic data are combined with the normalised fuel consumed by each vehicle type with the amount of displaced biofuel added to the DUKES total. This is so that these non-CO2 emissions cover all the fuel consumed by the road vehicles, including the biofuel, and not just the fossil-fuel amounts included in DUKES. UK NID 2025 (Issue 1) Ricardo Page 171 Calculated petrol and diesel fuel consumption are based on traffic movement and fuel consumption factors summed for different vehicle types. DUKES figures for these fuels are based on fuel sales in the UK. Emission factors for CH4 and N2O emissions from LPG consumption Carbon emissions from LPG consumption are calculated from the total LPG consumption given in DUKES and fuel-based factors set out in “Energy_background_data_uk_2025.xlsx”. Consumption of LPG is relatively small in the UK (0.31% of all road fuels in 2023) and there are no reliable data on the number or types of vehicles running on LPG. Vehicle licencing statistics suggests that 0.08% of all light duty vehicles ran on LPG in 2023. Assuming all the LPG consumed in the UK is by Light Goods Vehicles, the amount of LPG consumed was used to estimate the number of vehicle km travelled by LGVs using LPG. Emissions of CH4 and N2O from consumption of LPG were then calculated from the vehicle km data and emission factors (expressed as g of pollutant per km) available from the EMEP/EEA Emissions Inventory Guidebook (2023) covering all types of light duty vehicles. Further details are given in Brown et al (2018). Lubricant consumption by the unintended combustion in vehicle engines is estimated using the method from the EMEP/EEA Emissions Inventory Guidebook (2023). These consumption estimates were used to calculate CO 2 emissions from lubricant combustion in road vehicle engines and are reported in IPCC sector 2D1 ( Section 4.22) except for lubricants use by 2 - stroke mopeds, which is deemed to be intentional fuel use and hence reported in IPCC sector Emissions of CH 4 and N2O also arise from lubricant combustion in engines. However, the exhaust emission factors for these gases will include the contribution of lubricants as well as the main fuel to the pollutant emissions when the vehicles were tested. Hence, the emissions of CH4 and N2O (and other air pollutants) from lubricants are included implicitly in the hot exhaust emissions calculated for each vehicle and fuel type. Treating emissions of these pollutants separately would lead to a double count. UK NID 2025 (Issue 1) Ricardo Page 172 Overseas Territories and Crown Dependencies Fuel consumption data for 1A3b were obtained from national statistics for all Overseas Territories and Crown Dependencies. This was disaggregated into fuel consumption by vehicle type by assuming a UK fleet breakdown and assuming the relative mileage of ea ch vehicle type is the same as non-London urban UK. UK-specific emission factors were applied to the disaggregated fuel consumption. There are many assumptions made, using expert judgement, in the Tier 3 approach and these are referred to in the detailed road transport inventory methodology report by Brown et al Emissions of direct greenhouse gases are calculated on the basis of fuel sold (and not vkm travelled) and are consistent with UK energy statistics. For CO2, the assumptions have little effect on total road transport emissions as this is based on fuel sales figures in DUKES, but the assumptions used during the normalisation process affect the distribution of emissions between vehicle types. In particular, the procedure used to is important as all vehicle types have a similar share of diesel consumption.
For CH4 and N2O emissions, the diesel normalisation method assumed has a direct effect on emission estimates as emissions per unit of fuel consumed vary for each vehicle type. A sensitive parameter in the emission calculations of CH 4 and N 2O for petrol cars is the assumption made about the proportion of the fleet with catalyst systems that have failed, for example due to mechanical damage or failure of the lambda sensor. Following discussions with DfT, it is assumed that the failure rate is 5% per annum for all Euro standards and that up to 2008 only 20% of failed catalysts were rectified properly, but those that were rectified were done so within a year of failing. From 2009, a change in the repair rate is taken into account for Euro 3 and above petrol LDVs assuming all failed vehicles are rectified properly due to the introduction of EU Regulations Controlling Sale and Installation of Replacement Catalytic Converters. Further details are given in Brown et al (2018). Other key assumptions that affect CH4 and N2O emissions • Application of vehicle speeds measured on a sample of roads to cover the whole road • Distances covered by petrol car engines not fully warmed up in calculation of cold start • All LPG is consumed by light goods vehicles. Time-series emissions revisions are mainly a result of the update to use COPERT 5.8 emission factors and methodology, whereas the previous inventory used COPERT 5.6. This has included the introduction of new non-zero emission factors for CH4 from Euro 3 standard leading to higher weighted factors with relative changes increasing over time. Furthermore, the cold start methodology in the calculation of Euro 1 factors being applied to Euro 2 - 5 petrol LDVs has been updated in the latest version of the guidebook. This led to fuel consumption This source category is covered by the general QA/QC of the greenhouse gas inventory in

UK NID 2025 (Issue 1) Ricardo Page 173 An internationally established Tier 3 method is used consistent with IPCC Guidelines and EMEP/EEA Emissions Inventory Guidebook approaches. The Method Approach section has described a comparison between the bottom -up, traffic-based approach for calculating fuel consumption and the total fuel sales figures provided in DUKES; the agreement is within 16% The traffic data (vkm) and fleet composition data are provided by DfT and have been assessed by the UK Statistics Authority and confirmed as National Statistics. DfT statistics are accompanied by a Statement on Quality Strategy Principles and Processes65. Emission factors and fuel consumption factors are from standard IPCC and EMEP/EEA Inventory Guidebooks and COPERT. These are peer-reviewed sources. There are no time -series issues. Time-series consistency is ensured by the use of DUKES fuel consumption and use of continuous, consistent vkm traffic data from DfT. The uncertainty analysis is set out in Annex 2. The reconciliation between bottom-up and top- down approaches gives a high level of confidence in the calculated emissions for road transport. There is greater uncertainty in the division in CO2 emissions between vehicle types. There are greater uncertainties in the emission factors for CH 4 and N2O because of limited emission factor measurements, in particular for more recent vehicle technologies and emission standards entering service. The main sources of uncertainties in the activity data affecting the CH4 and N2O inventories are in the division of diesel fuel consumption between vehicle types and the uncertainty in the fuel consumption factors that determine how much CH4 and N2O emissions are scaled to be consistent with national fuel consumption. There are also uncertainties affecting the emission estimates for CH 4 and N 2O in the on -road fleet composition, catalyst failure rates, trip lengths (for estimating cold start emissions). 65 Statement on Quality Strategy Principles and Processes, Department for Transport, available

UK NID 2025 (Issue 1) Ricardo Page 174 Relevant Categories, source names This MS includes emissions from gas oil used to power trains and from the consumption of coal used to power steam trains. The methodology for gas oil is based around three categories of railway freight, intercity and regional. Stationary combustion in the rail sector is included in MS 5. Most of the electricity used by the railways for electric traction is supplied from the public distribution system, so the emissions arising from its generation are reported under 1A1a Public Electricity. DUKES, Office of Rail and Road (ORR) National Rail Trends Yearbook (NRTY), ORR data portal and Network Rail Open Data Platform Emission Baggott et al. (2004), 2006 IPCC Guidelines, EMEP/EEA 2023, DfT’s Rail Emissions Model (REM; DfT 2012b), AP -42 (USEPA), Rail Safety and An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. Emissions are calculated based on AD x EF. Coal consumption data has been obtained from DUKES. Estimates have been made for 2005 onwards and are believed to be due to consumption by heritage trains. No coal use is allocated to railways in DUKES for earlier years, it is assumed that this is included within other reporting The UK GHGI reports emissions from trains that run on gas oil in three freight, intercity and regional. Emissions from these are reported under the IPCC category 1A3c Apart from the relevant activity data updates for 2023 all main aspects of the inventory remain consistent with 2022 inventory. In the 2019 inventory, emission estimates were improved based on recent work for the UK’s Rail Safety Standards Board (RSSB) to develop new emission factors that better reflect actual operation of the diesel engine) and account for the

UK NID 2025 (Issue 1) Ricardo Page 175 non-linear relationships between engine power output and emissions of pollutants (RSSB For Great Britain, vehicle kilometre data for intercity and regional trains has been obtained from REM for 2009 to 2011, 2014 and 2018 and then estimated for other years from the Office of Rail and Road (ORR) National Rail Trends Yearbook (NRTY) and data portal (ORR, 2024).
Adjustments to the diesel vehicle kilometres for 2019 -2023 were made to account for the introduction of new bi-mode passenger trains. Actual train kilometre data for diesel freight train movements in 2019 is available and this has been s caled forward to 2023 and back to 2005 using the trend in net tonne km from the ORR NRTY and data portal. Train kilometre data for freight trains is also available for 2004 and, similarly, data is scaled back to the start of the time series using information on the trend in net tonne km of freight moved. Gas oil consumption by passenger and freight trains is obtained from the 2011 NRTY for the period 2005-2009 and from ORR’s data portal for the years 2011 -2022. No data is available for the years 1970 -2004 and 2010, and data for 2023 had not yet been releas ed at time of compilation. Therefore, fuel consumption for these years was estimated based on the trend in For Northern Ireland, train kilometre data and fuel consumption data are provided by Translink, the operator of rail services in the region. Carbon and nitrous oxide emissions are calculated using fuel-based emission factors and the total fuel consumed. The CEF for coal is derived from the 2006 IPCC guidelines whilst the CEF for gas oil is taken from Baggott et al. (2004). The N 2O emission factor was updated in the 2020 inventory to utilise the new value provided in the 2019 EMEP / EEA Guidebook for the rail sector (and which has not changed in the 2023 EMEP / EEA Guidebook). Emissions of other pollutants are based on the vehicle / train kilometre estimates, and emission factors for different train classes. For coal-fired steam trains, US EPA emission factors for hand -stoked coal-fired boilers are used to estimate non-CO2 emissions. These are considered most appropriate for the type of coal-fired boilers on heritage trains. In recent years diesel passenger train kilometres have steadily increased from 220 million km in 2000 to almost 256 million km in 2019. In 2020 diesel passenger train kilometres declined to 186 million km, primarily as a result of reduced services due to t he Covid-19 pandemic. Diesel passenger train kilometres then increased in 2021 to 208 million km but remained lower than years prior to 2020 due to the continuing effects of the pandemic. In 2022, activity levels are estimated to have remained similar to t he previous year due in part to industrial action across the UK and also due to a switch to more electric passenger trains on the network. In 2023 diesel passenger train kilometres increased to 218 million km as passenger traffic further recovered. The amo unt of freight moved has declined steadily since 2013 as a result of a substantial decline in the amount of coal hauled and then due to the Covid-19 pandemic. The amount of freight moved in 2020 was around 68% of the amount estimated for 2013 but increased to around 75% of the 2013 value in 2021 before reducing to around 70% of the 2013 value in both 2022 and 2023. Recalculations of emissions were the result of one or a combination of the • Revision of the ORR fuel consumption figures for passenger and freight for 2021 • Use of the ORR fuel consumption figures for 2022 now that data is available

UK NID 2025 (Issue 1) Ricardo Page 176 • Incorporation of revisions to DUKES annual values of fuel density for 2005 to 2022 • Updates to regional passenger vehicle kilometres fleet fractions for 2021 and 2022. Improvements (completed and planned) Various emissions-related projects are currently being undertaken by the rail industry in the UK which may lead to further improvements in the NAEI rail emission estimates in future years. This source category is covered by the general QA/QC of the greenhouse gas inventory in Coal use in heritage railways is not reported in DUKES for all years. For the years in which no activity data are reported, emissions are reported as “IE.” Consultation with the DUKES team has indicated a high level of confidence in total coal use for the UK. As such although no data are available to allocate emissions to rail for earlier years in the time series, this does not represent an under report in the UK inventory. Gas oil consumed by the rail sector is estimated based on the change in train / vehicle kilometres prior to 2005 and for 2010 and 2023. However, the total amount of gas oil consumed in the UK is thought to be reliable and therefore this does not represent an under / over report in the UK inventory as a whole. The uncertainty analysis is set out in Annex 2. The main uncertainties for the rail sector relate to the poor emission factor data across all sources and the lack of detailed train kilometre data

UK NID 2025 (Issue 1) Ricardo Page 177 MS 10 National navigation and fishing Relevant Categories, source names This MS includes emissions from UK domestic and crown dependency coastal shipping and fishing, including fishing outside UK territorial waters . Emissions from inland waterways are covered in MS 12 , and shipping between the UK and OTs (classified as domestic) are UK Maritime and Coastguard Agency, DfT Maritime Statistics (2024c), MMO Fishing statistics (MMO, 2024), DUKES (DESNZ 2024a), Emission IMO (2015). An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. The shipping emissions model applied uses 2014 high resolution terrestrial Automatic Identification System (AIS) vessel movement data supplied by the UK Maritime and Coastguard Agency. This methodology meets and exceeds the requirements of a Tier 3 methodology set out in the EMEP EEA Emissions Inventory Guidebook 2019 and the requirements for reporting national greenhouse gas emissions to the UNFCCC under the 2006 IPCC Guidelines.
The new methodology carries out an emission calculation specific to each vessel and for each point of the vessel’s voyage around the UK coast that is tracked with AIS The receivers capture a number of smaller vessels and voyages such as movements to and from off-shore oil and gas rigs, and journeys to/from crown dependencies. The approach also uses a detailed set of port statistics for different vessel categories as pro xies for estimating activities in years back to 1990 and forward to 2023 from the 2014 base year. A significant step in the process is identifying whether a vessel movement is a UK domestic movement, and reported under 1A3dii, or part of an international voyage calling in the UK reported as a Memo item under 1A3di. Details of the methodology are given in the report by Scarbrough et al (2017) and only a

UK NID 2025 (Issue 1) Ricardo Page 178 The model methodology estimates the Heavy Fuel Oil (HFO) and Marine Diesel Oil (MDO) fuel consumption for each AIS position message down -sampled to 5 -minute temporal resolution. The calculation takes into account where available the individual vessel characteristics of main engine power, engine speed and load, and makes bottom -up assumptions for auxiliary engines. The fuel and emissions are estimated for each AIS message to cover the time period until the next AIS message, which is often 5 minutes, but in cases where the vessel travels at or outside the range of the terrestrial AIS receivers, may be longer or much longer. Many assumptions for the modelling have been drawn from the International Maritime Organization’s (IMO) Third Greenhouse Gas Study (IMO, 2015). The emissions are calculated separately for each vessel and for each AIS data point assuming that the vessel continues to combust fuel and emit pollution at the same rate until the subsequent AIS message. The fuel consumption and emission factors are tailo red to the specific vessel that is identified in the AIS dataset. The factors account • The fuel type assumed to be used by the vessel, the known engine type and engine • The rated power of the engines, which are either known from a third party vessel characteristics database, or estimated based on other known or reported vessel characteristics (e.g. vessel length). • The actual power demands on the main engines for each AIS message, expressed as a function of reported and designed vessel speed, and reported and designed vessel • The location and type of the vessel, i.e. whether the vessel is in a Sulphur Emission Control Area (SECA), whether the vessel is at berth, and whether the vessel is a In those cases where part of a voyage is not captured within the range of the terrestrial AIS dataset (defined as a gap in AIS coverage of 24 hours), allocation assumptions have been based on vessel type. Specifically, if cargo or passenger vessel journeys had a gap between AIS messages of greater than 24 hours, these vessels were assumed to have been on UK international voyages if they had started or finished at a UK port. For the remaining vessel types, which includes offshore industry vessels, fishing fl eets and service vessels, voyages were assumed to be UK domestic if the AIS dataset showed the vessel had started and finished at a UK port, regardless of the length of time of any gaps in AIS coverage. The detailed Tier 3 approach used in Scarbrough et al. (2017) is able to distinguish fuel consumption and emissions between domestic movements from one UK port to another and UK international movements between a UK port and a port overseas. This enables the correct activities and emissions to be allocated to the NFR14 category 1A3dii Domestic Water-borne The Scarbrough et al. (2017) inventory excluded emissions and fuel consumption from military vessel movements which are not captured in the AIS movements database. Naval shipping emissions are reported separately using fuel consumption data supplied by the Ministry of Defence (MoD). Emissions from these vessels are covered in MS 14. The Scarbrough et al. (2017) study did not cover small tugs and service craft used in estuaries, private leisure craft and vessels used in UK rivers, lakes and canals as these were not captured in the AIS data. These were captured in the estimates for inland waterways described Commercial fishing vessels were captured by AIS data, including those that eventually leave the UK to fish in overseas waters, before returning later so emissions could be calculated in

UK NID 2025 (Issue 1) Ricardo Page 179 the same way as for other domestic navigation and reported separately under 1A4ciii. Although these fishing vessels go out of range of UK shore-based terrestrial AIS data capture, the time period between successive AIS messages from these vessels is known corresponding to the times when the vessels first go out of range on route to their fishing destination to the point when they return. Time-series trends in activity data The approach to estimate emissions for historical years before 2014 and years after 2014 uses DfT port statistics as proxies for activity levels. This is detailed further in section 3 of Scarbrough et al. (2017). Overall, there are 15 vessel categories tha t are each mapped to a DfT port statistic. This includes separating cargo or freight commodity types. The statistical time-series cover all years back from 2014 to 1990 and forward to the most recent year of statistics (currently 2023). In many cases, mult iple statistical series need to be used if no complete series is available to cover the entire period to 1990. The specific statistical series • PORT0201 Domestic UK major port freight traffic by cargo type and direction, • SPAS0101 UK international sea passenger movements, by port and port from • SPAS0201 UK domestic sea passenger movements by type of from 2003 SPAS0201. Pre-2003 trend uses the approach described in Entec (2010).
Cruise Same approach as used for the Ferry-pax only vessel category Yes 2010-2023: MMO UK Sea Fisheries Annual Statistics – Chapter 3 Landings. Pre- 2010 trend uses the approach described in Entec (2010). UK NID 2025 (Issue 1) Ricardo Page 180 petroleum production, imports and exports; Indigenous production of Note 1 – pre-2000 trend uses the approach described in Entec (2010). The model assumes that there is a fuel switch from HFO to MDO as a result of the tightening in 2015 of the SECA fuel sulphur limit from 0.5% to 0.1%. This assumption is made on the basis of evidence that low sulphur heavy fuel oil was available to comply w ith the SECA fuel sulphur limits of 1.5% to 2010 and 1% from 2010 (IMO, 2010). The requirement that vessels at berth from 2010 use fuel which complies with a sulphur limit of 0.1% implies the need for MDO. Therefore, in the back casted inventory prior to 2010, any vessels that would have used HFO, save for the at berth requirement of 0.1% S fuel, are assumed prior to 2010 to use HFO. The source of the raw emission factors used for CO 2, CH4 and N2O is given in section 2.2.8 of Scarbrough et al. (2017). Fuel -based emission factors in kg/tonne fuel are used and may differ by engine type and/or fuel type. The fuel -based CO 2 emissions factors for main and auxiliary engines are the same as assumed in IMO (2015) and are based on MEPC 63/23, Annex 8: • 3,114 kg CO2/tonne fuel • 3,206 kg CO2/ tonne fuel Methane emission factors for diesel -fuelled engines, steam boilers and gas turbines are the same as used in IMO (2015). These are derived from IVL (2004) which states that CH 4 emissions are approximately 2% the magnitude of NMVOCs. Therefore, the CH 4 factors are derived by multiplying the NMVOC factors by 2%. Values of methane emission factors are 0.04-0.06 kg/tonne fuel depending on engine type. The N2O emission factors are taken from IMO (2015). N2O emission factors are unaffected by fuel sulphur content but do differ slightly between HFO and MDO. Values for HFO are 0.16 kg/tonne fuel and are 6% less for MDO. Emission factors for other pollutants are given in section 2.2.8 of Scarbrough et al. (2017). These emission factors also derive from IMO (2015). Emission factors remain constant over the time -series. However, vessels using HFO in a SECA are assumed to switch to using MDO from 2015 onwards, with an SO2 emission factor reduction of 90% (from 1% S HFO to 0.1% MDO) accordingly. Fuel consumption is calculated for each vessel based on the characteristics of the vessel, engine type, movement and draught for the 2014 activity dataset received. It is expected that shipping transport efficiency increases over time in response to financial and regulatory drivers. For all vessels it is assumed that the efficiency of sea transport improves by 1% per year from 2014 onwards to account for lower fuel co nsumption per unit

UK NID 2025 (Issue 1) Ricardo Page 181 (tonne or container or passenger) transported and more fuel efficient new vessels compared i.e. 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑖𝑛𝑑𝑒𝑥𝑦 = 0.99(𝑦−2014) Further details on how this value was derived are given in section 3.2.3. of Scarbrough et al. (2017). The current inventory therefore implies a small improvement in the fuel efficiency of the fleet from the 2014 base to 2023. The coverage of vessels captured by the AIS receivers is considered complete for this sector. Small vessels which do not have AIS transmitters, such as small recreational craft and service vessels, are captured in the inventory for inland waterways. The ma in assumption concerns the allocation of a vessel movement to UK domestic or international for a cargo or passenger vessel starting or finishing at a UK port when it goes out of AIS signal range, based on the gap between AIS messages being greater or less than 24 hours. Minor changes due to revisions in the driver data used. No change at the source and activity Improvements (completed and planned) A major methodology change was undertaken for the 2018 submission based on detailed AIS vessel movement data, updated DfT port statistics and updated emission factors. This major change in methodology took two years to develop and introduce to the UK inven tory. An update to this work is expected to be introduced for the 2026 inventory submission, using a more recent base year (2019) based on newer AIS data. This source category is covered by the general QA/QC of the greenhouse gas inventory in Annex 4. The new approach that has been adopted is described in detail in Scarbrough et al (2017) and has been peer-reviewed by BEIS (now DESNZ), Defra, DfT as well as presented to experts in the maritime industry. Scarbrough et al. (2017) also reports on validation with other estimates of shipping emissions given in the literature covering the same geographical area of the North Sea and English Channel. The time -series for national navigation and fishing is derived from trends in port activity statistics for different vessel types. Some of these show an increase in activities over time, others a decrease in activities over the time-series for different vessel types. The approach assumes that a switch from HFO to MDO occurs as a result of the tightening in 2015 of the North Sea and English Channel SECA fuel sulphur limit from 0.5% to 0.1%. This break in the time-series is not considered to be a time-series consistency issue. The uncertainty analysis is set out in Annex 2. The uncertainty in the bottom up calculated estimates of fuel consumption in 2014 is considered to be less than the allocation of fuel to national navigation provided in DUKES and more representative of UK domestic shipping activities as defined in the IPCC 2006 Guidelines.
UK NID 2025 (Issue 1) Ricardo Page 182 Further consideration of uncertainties in the approach is given in Scarbrough et al (2017), particularly with respect to the allocation of a vessel movement to domestic or international when the vessel goes out of AIS range. However, overall, the emission calculations are estimated to have relatively low uncertainty for most large vessels which are responsible for 85% of total emissions. Scarbrough et al. (2017) also report that the model estimates compare well with those from other European shipping invent ories when comparisons are made on a Additional uncertainty is introduced through the use of proxy statistics to develop the time - series. The uncertainty in the carbon emission factor is considered low, whereas the uncertainties for non-CO2 gases are higher. UK NID 2025 (Issue 1) Ricardo Page 183 MS 11 Shipping between UK and OTs Relevant Categories, source names 1A3d: Shipping between UK and Gibraltar Shipping between UK and OTs (excl. Gibraltar) This MS includes estimates of emissions from shipping movements between the UK and the Overseas Territories. These were not included in the new methodology for domestic shipping developed by Scarbrough et al. (2017) (described in MS 10) and are therefore calculated separately. These are included as domestic emissions for UNFCCC reporting, and reported DfT (personal communication), OT port authorities (personal communications), EMEP/EEA 2019 Emission Scarbrough et al. (2017) and based on IMO (2015) An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. The total fuel consumed by vessels moving between the UK and each OT is calculated as the sum of all fuel consumed by freight and passenger vessels. This is calculated separately for movements from the UK to each OT and from each OT to the UK. There are no published data on the number and types of voyages between the UK and overseas territories (OTs). However, officials at the UK Department for Transport (DfT, 2023f) were able to interrogate their ports database which forms the basis of the less detailed information published in DfT’s Maritime Statistics. This included information on freight shipping movements and passenger vessel movements. Additional information on passenger vessel movements were gathered from individual OT port authorities. For freight shipping, the DfT were able to provide the number of trips made between a UK port and an OT port by each unique vessel recorded. The information provided the type of vessel and the departure and arrival port. Figures were provided for all years between 2000 The information on the type of vessel combined with information from the EMEP Emissions Inventory Guidebook 2019 was used to • the average cruise speed of the vessel;

UK NID 2025 (Issue 1) Ricardo Page 184 • the average main engine power (in kW); and • the specific fuel consumption factor (g/kWh). DfT were unable to provide the detailed port data for years before 2000. The individual OT port authorities also did not have this information. The trends in fuel consumption calculated for all UK international shipping from 1990 to 2000 (based on less detailed UK port statistics) were used to define the trend in fuel consumption for shipping between the UK and OTs over For passenger vessels , the information held by OT port authorities indicated the only movements were by cruise ships (i.e. not ferries). Data from DfT was used for the years 2013- 2017 (DfT, 2018a). Detailed movement data were held by the port authority of Gibraltar listing all voyages departing to or arriving from the UK from 2003 to 2012 66. The DfT also held information on the number of UK port arrivals by cruise ships from the OTs, but only between 1999 and 2004. This is unpublished information and was provided via direct communication Information held by the other OTs indicated that none had any cruise ship sailings with the UK logged. The data held by DfT showed the majority of sailings were from Gibraltar and the data were consistent with the information provided by the Gibraltar port authority. However, the DfT data also showed a total of 3 arrivals from the Falkland Islands between 1999 and 2004. No cruise ship information was available before 1999 from either DfT or the individual OT port authorities. Trends in the total number of passengers on cruises beginning or ending at UK international short sea passenger movements, by port and port 1950 – 2009) were used to define the trend in fuel consumption by cruise ships between the UK and OTs over these This information was combined to show the total number of cruise ship movements between the UK and OTs from 1999 to 2017. Data was unavailable upon request from DfT for calendar years 2018 and 2019. To estimate the fuel consumption for passenger vessels for 2018 and 2019, the mean fuel consumption of the previous five years (2013 – 2017) by OT was used. For 2020 to 2023, the estimated fuel consumption values were based on the trend of “UK All Cruise Passengers" from the SPAS0101 DfT data set, relative to the index value of 1 in 2019. Distance Distances for each voyage for freight and passenger were taken from and These two websites have tools to calculate route distance by specifying the departure and arrival ports. Using the distance, average speed, engine power and fuel consumption factor it was possible to calculate the amount of fuel consumed for every voyage made. All fuel used for voyages between the UK and OTs is assumed to be fuel oil. The emission factors used are average factors implied by Scarbrough et al. (2017) for all vessels involved in international voyages from or to a UK port from/to a non-UK destination. All fuel used for voyages between the UK and OTs is assumed to be fuel oil as it is cheaper to run and so will be the preferred choice where vessels don’t need to use gas oil to meet emission limits within Sulphur Emission Control Areas.
Also, only the larger ships will tend to do these long-distance journeys, and these larger ships use fuel oil as it is a heavier fuel and 66

UK NID 2025 (Issue 1) Ricardo Page 185 a larger engine is required to use it efficiently. Emission factors are assumed to be the average of all vessels involved in UK international voyages. Data provided by various data sources are assumed to be complete. There have been minor recalculations (less than 1%) to the activity in 2021 due to revised vessel type splits as a result of updated DfT port activity data. Improvements (completed and planned) This emission source was introduced in response to the UNFCCC ERT in 2012. Improvements may be considered in the scope of wider improvements to shipping planned for the inventory This source category is covered by the general QA/QC of the greenhouse gas inventory in Annex 4. There are no official statistical data sets available to verify the information provided for the calculation of these estimates. They are considered to be the best available data. The method approach section above details which years data were available for. Gaps have been filled for the early part of the time -series based on other statistics, to ensure that the inventory is complete for all years. The uncertainty analysis is set out in Annex 2. The uncertainty in this particular source is high although the contribution to the total inventory is low and as such, it does not warrant further research. Estimates are included for completeness, following a recommendation from the

UK NID 2025 (Issue 1) Ricardo Page 186 Relevant Categories, source names 1A3d Inland goods-carrying vessels Motorboats / workboats (e.g. canal boats, dredgers, service boats, tourist boats, river Personal watercraft e.g. jet ski Sailing boats with auxiliary engines The category 1A3dii Waterborne Navigation includes emissions from fuel used for small passenger vessels, ferries, recreational watercraft, other inland watercraft, and other gasoline- fuelled watercraft. Methods for estimating emissions for these small vessels are presented separately here as they are calculated using different approaches to other marine emissions Walker et al (2011), ONS Social Trends, Visit England, OECD Stat, DfT Maritime Statistics (elaborated under Method approach, below). Emission EMEP/EEA 2009, IMO 2015 An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. The Guidelines recommend national energy statistics be used to calculate emissions, but if these are unavailable then emissions should be estimated from surveys of fuel suppliers, vessel movement data or equipment (engine) counts and passenger and cargo tonnage counts. The UK has no separate nati onal fuel consumption statistics on the amount of fuel used by inland waterways in DUKES. However, they are included in the overall marine fuel statistics. A Tier 3 bottom -up approach based on estimates of population and usage of different types of inland waterway vessels is used to estimate their emissions. In the UK, all emissions from inland waterways are included in domestic shipping totals. The methodology applied to derive emissions from the inland waterways sector uses an approach consistent with the 2016 EMEP/EEA Emissions Inventory Guidebook (EMEP, Emissions from individual vessel types are calculated using the following 𝐸 = ∑ 𝑁 × 𝐻𝑅𝑆 × 𝐻𝑃 × 𝐿𝐹 × 𝐸𝐹𝑖

UK NID 2025 (Issue 1) Ricardo Page 187 𝐸 = mass of emissions of pollutant i or fuel consumed during inventory period, 𝑁 = source population (units), 𝐻𝑃 = average rated horsepower, 𝐸𝐹𝑖 = average emissions of pollutant i or fuel consumed per unit of use (e.g. • a categorisation of the types of vessels and the fuel that they use (petrol, DERV or gas • numbers for each type of vessel, together with the number of hours that each type of • data on the average rated engine power for each type of vessel, and the fraction of this (the load factor) that is used on average to propel the boat; and • g/kWh fuel consumption factors and fuel-based emission factors. The inland waterways class is divided into four categories and sub -categories (Walker et al, • Sailing Boats with auxiliary engines; • Motorboats / Workboats (e.g. dredgers, canal, service, tourist, river boats); o recreational craft operating on inland waterways; o recreational craft operating on coastal waterways; • Personal watercraft i.e. jet ski; and • Inland goods carrying vessels. A bottom-up approach was used based on estimates of the population and usage of different types of craft and the amounts of different types of fuels consumed. Estimates of both population and usage were made for the baseline year of 2008 for each type of v essel used on canals, rivers and lakes and small commercial, service and recreational craft operating in estuaries / occasionally going to sea. For this, data were collected from stakeholders, including the British Waterways, DfT, Environment Agency, Maritime and Coastguard Agency (MCGA), As it was only possible to estimate population and activities for one year (2008), proxy statistics were used to estimate activities for different groups of vessels for other years in the household expenditure on "Recreation and culture"67. No data were available for this dataset after 2009, therefore a second dataset was used to estimate the activity from 2010: OECD - ‘Final consumption expenditure of household, UK, • Commercial passenger/tourist craft – Visit Britain, Visitor Attraction Trends in England 2023, Full "Total England Attractions"69. 67 68 69

UK NID 2025 (Issue 1) Ricardo Page 188 • Freight – DfT - Waterborne transport in the goods lifted and moved by traffic type, One of these three proxy data sets was assigned to each of the detailed vessel types covered in the inventory and used to define the trends in their fuel consumption from the 2008 base year estimate to all other years in the inventory. The fuel-based emission factors used for all inland waterway vessels for CH4 were taken from the EMEP/EEA 2009. Emission factors for carbon are from Baggott et al, 2004. For N2O, the emission factor for fuel oil is taken from EMEP/EEA 2009.
For N 2O from diesel and gas oil, the emission factor used is 0.15 g /kg fuel, consistent with the emission factor used in MS 10 A key assumption made is that privately owned vessels with diesel engines used for recreational purposes use DERV while only commercial and service craft and canal boats use gas oil (Walker et al., 2011). Some smaller vessels also run on petrol engines. Walker at al. (2011) and Murrells et al. (2011) had previously drawn attention to the potential overlap between the larger vessels using the inland waterways and the smaller vessels in the shipping sectors (namely tugboats and chartered and commercial fish ing vessels), and the judgement and assumptions made to try to avoid such an overlap. This potential overlap was reconsidered in light of the methodology for domestic shipping (Scarbrough et al. (2017), since certain types of vessels operating at sea close to shore that were previously included in the inland waterways sector of the inventory were now captured in the AIS data. Hence their emissions are included under coastal shipping described above and by Scarbrough et al. (2017). These vessels were considered to be passenger vessels with >12 passengers and 3 or more engines operating in estuaries, tugs, cranes, and chartered and commercial fishing vessels. To avoid a double count, the activities for these vessels were therefore removed from the inland waterways database. Revised ONS data has led to revisions from 2010, with the largest revision in private leisure Improvements (completed and planned) Emission factors and activity data are kept under review. Improvements may be considered in the scope of wider improvements to shipping planned for the 2026 inventory submission. This source category is covered by the general QA/QC of the greenhouse gas inventory in The bottom up analysis for this source category was carried out for one year, and the time - series is generated using proxy statistics, as set out in the method approach section, above. Consistent time-series of proxy statistics, where available, have been used to estimate the 70

UK NID 2025 (Issue 1) Ricardo Page 189 inland waterways activities time -series. For private watercraft, two data sets have been combined. Where the two data sets overlap, there is a correlation in the trend. The combination of these data sets does not introduce any time consistency issues. The uncertainty analysis is set out in Annex 2. There are no official statistics for the population of vessels, the total fuel consumption or the annual usage of the vessels. There may also be some overlap in definitions between small coastal shipping and inland waterways. UK NID 2025 (Issue 1) Ricardo Page 190 Relevant Categories, source names 1D1b: Marine Shipping - international IPCC definition This method statement covers estimates of international marine bunkers which are reported as a Memo item and not included in the UK totals. DUKES (DESNZ, 2024); Emission Scarbrough et al. (2017) and based on IMO (2015) An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. Fuel consumption for international shipping is taken directly from DUKES figures for international marine fuel bunkers, as discussions with DESNZ indicate that there is higher confidence in the DUKES estimates of the international ‘marine bunkers’ fuel sales data than the portion allocated to national navigation. As such, the marine bunkers fuel statistics in DUKES are used without further adjustment as the activity data for emissions from the international navigation Memo item under 1A3di. The consequence of having emissions for national navigation and inland waterways (1A3dii), fishing (1A4ciii) and naval (1A5b) based on a bottom -up method derived from vessel activity and of having emissions for international navigation (1A3di) based on DUK ES data for international bunkers is that the total marine fuel consumption exceeds that given in DUKES for national navigation plus marine bunkers. In some years, the fuel consumption for national navigation and inland waterways (1A3dii), fishing (1A4ciii ) and naval (1A5b) alone exceeds the total given in DUKES for national navigation plus marine bunkers. Notwithstanding uncertainties in the modelling approach which were discussed by Scarbrough et al. (2017), one possible reason for this difference is that a significant proportion of domestic voyages in the UK are taken by vessels that fuelled overseas. This amount of “fuel tankering” national navigation and for consistency with the IPCC 2016 Guidelines definition of domestic shipping, the UK prefers to use the higher bottom-up estimates for the domestic sources to be included in the national totals, particularly as they are based directly on vessel activities. and military shipping, fishing, inland waterways, international shipping and voyages from the

UK NID 2025 (Issue 1) Ricardo Page 191 UK to the OTs for selected years since 1990. Fuel consumed in the OTs and for voyages from 1990 1.89 0.23 0.03 1.14 0.82 0.82 0.008 1.39 2000 1.96 0.18 0.03 1.14 0.80 0.80 0.011 0.93 2005 1.64 0.19 0.04 0.89 0.78 0.78 0.009 1.16 2010 1.41 0.17 0.04 0.95 0.57 0.57 0.011 1.85 2015 1.41 0.16 0.05 1.67 0.18 0.18 0.009 0.83 2016 1.38 0.17 0.05 1.77 0.17 0.17 0.010 0.88 2017 1.35 0.17 0.05 1.67 0.17 0.17 0.011 0.77 2018 1.37 0.16 0.05 1.63 0.16 0.16 0.009 0.81 2019 1.37 0.15 0.05 1.59 0.16 0.16 0.009 0.68 2020 1.18 0.14 0.02 1.33 0.15 0.15 0.006 0.54 2021 1.22 0.14 0.02 1.33 0.16 0.16 0.006 0.59 2022 1.19 0.13 0.03 1.29 0.15 0.15 0.011 0.66 2023 1.14 0.14 0.04 1.35 0.15 0.15 0.013 0.58 Emissions for international shipping (1A3di) were calculated by multiplying the fuel consumption calculated above with an implied emission factor for international vessel movements. The emission factors used are average factors implied by Scarbrough et al. (2017) for all vessels involved in international voyages from or to a UK port to/from a non-UK destination. The source of these factors is as described in MS 10 for national navigation and The activity data for the International navigation Memo item 1A3di in this inventory is based solely on figures in DUKES for international fuel bunkers.
It reflects emissions from UK international marine fuel sales whereas the emissions for national naviga tion and inland waterways (1A3dii) and fishing (1A4ciii) reflect the amount of fuel used for domestic navigation The main observation is that with international shipping fuel consumption and emissions being based on DUKES, and with fuel consumption and emissions for domestic marine activities being derived from vessel activities, the total marine fuel consumption implied by the inventory exceeds the amount available according to DUKES. UK NID 2025 (Issue 1) Ricardo Page 192 This aspect has been discussed with the UK national energy statistics team at BEIS The shipping methodology described above and in MS 10 leads to a different fuel use allocation for national navigation marine fuels compared with the allocations in the national energy statistics (DUKES) and submissions to IEA/EUROSTAT. There are only minor changes in activity (<0.01%) due to revised activity data in 2021 and Improvements (completed and planned) Improvements may be considered in scope as part of wider improvements planned for shipping in the 2026 inventory submission. This source category is covered by the general QA/QC of the greenhouse gas inventory in Time-series consistency is ensured by using fuel consumption data for international fuel bunkers taken directly from the latest version of DUKES in all years. Fluctuations reflect any fluctuations in the bunker fuel figures in DUKES. The uncertainty analysis is set out in Annex 2. Uncertainty for international bunkers is not

UK NID 2025 (Issue 1) Ricardo Page 193 Relevant Categories, source names Emissions from military shipping are reported separately under IPCC code 1A5b. Emission Scarbrough et al. (2017) and based on IMO (2015) An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. Emissions are calculated using a time-series of naval fuel consumption data (naval diesel and marine gas oil) provided directly by the Sustainable Development team of the MoD (MoD, 2024). Data are provided on a financial year basis and are amended to deriv e figures on a Implied emission factors derived for international shipping vessels running on marine distillate oil (MDO) from Scarbrough et al. (2017) were assumed to apply for military shipping vessels. It is assumed that emission factors for international shipping vessels apply to military vessels. Improvements (completed and planned) Emission factors and activity data are kept under review. Improvements may be considered in scope as part of wider improvements planned for shipping in the 2026 inventory submission. This source category is covered by the general QA/QC of the greenhouse gas inventory in The time-series is generated from consistent data sets for all years, there are no known issues

UK NID 2025 (Issue 1) Ricardo Page 194 The uncertainty in the fuel use estimates is low since these are taken directly from the MoD. The carbon factors of fuel used by naval vessels would be known with low uncertainty, but default factors for CH4 and N2O taken from international shipping vessels and their relevance to naval vessel engines are much more uncertain. UK NID 2025 (Issue 1) Ricardo Page 195 Relevant Categories, source names Aviation spirit, aviation turbine fuel Emissions from military aviation are reported separately under IPCC code 1A5b. Emission Baggott et al., 2004, IPCC, 1997. An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. LTO data are not available for military aircraft movements, so a simple, Tier 1 approach is used to estimate emissions from military aviation. The estimate of military emissions is made using military fuel consumption data (MoD, 2024) and IPCC (1997) 71. The military fuel data include fuel consumption by all military services in the UK. An earlier data set (MoD, 2015) also includes fuel shipped to overseas garrisons and casual uplift at civilian airports; these data have been extrapolated assuming consta nt consumption at the level the latest data indicates to generate a complete time-series. Most fuel use for military aviation is included in the DUKES totals. Military aircraft consumption data provided directly by the Sustainable Development and Continuity Division of the Defence Fuels Group of the MoD (MoD, 2024) is subtracted from DUKES to ensure there is no double counting (see Annex 5.2). Fuel use for casual uplift is considered to be outside of DUKES. Improvements (completed and planned) No improvements to this method are currently planned. Emission factors and activity data are 71 T1 cruise factors in the 2006 IPCC Guidelines are not in units consistent with our methodology, and T2 LTO factors in the 2006 IPCC Guidelines are based on IPCC 1997 values anyway. UK NID 2025 (Issue 1) Ricardo Page 196 This source category is covered by the general QA/QC of the greenhouse gas inventory in The time-series is generated from consistent data sets for all years, there are no known issues The uncertainty in the fuel use estimates is low since these are taken from a reliable source. Carbon emission factors are based on country specific data, whereas the non-CO2 gases are reliant on defaults, which can lead to higher uncertainties. UK NID 2025 (Issue 1) Ricardo Page 197 MS 16 Coal mining and handling Relevant Categories, source names 1B1ai2: Coal storage and transport In 2023 there were only eight small deep-mining collieries licensed to operate in the UK. The UK coal industry has been in decline for many years and during 2015 the last large deep coal mines closed, and this is reflected in an over 99% reduction in UK deep-mined coal production since 2015, according to UK energy statistics (DESNZ, 2024). None of the remaining mines are large enough to warrant investment in methane drainage and recovery systems used to collect and burn mine gas to raise power; until 2015 there were still operational deep mines in the UK that did capture and utilise methane. A further 3 open -cast coal mines were also operating in the UK in 2023. This is compared with 188 deep mining collieries and 126 open- cast mines operating in 199072.
Activity All activity data on coal production at open cast and deep mines is from DUKES (DESNZ, 2024), except for production at licensed mines during 1990-1995 (only) which are from an industry reference (Barty, 1995). Emission Operator reported data on methane emissions from deep mines are available for many years of the inventory time -series (1998-2014), and are used to derive CS EFs (UK Coal, 2015; Coal Authority, 2015), in conjunction with UK energy statist ics from DUKES (DESNZ, 2024). However, due to the closure of all UK large deep mines there are no operator-reported emissions data for all years from 2015. Up to and including 2014, many UK deep mines were operating and, for a high proportion of those deep mines, data are available at the mine -specific level on coal production, methane drainage, methane used in gas engines and methane emitted to atmosphere. From these data, mine - specific methane emission factors are derived for (i) total methane released fr om the mining activity (i.e. including the methane that is available for use in gas engines), and (ii) the total methane emitted to atmosphere (i.e. after having subtracted the amount of methane used in gas engines). In deriving inventory estimates for 2015, the mine-specific emission factors from 2014 (UK Coal, 2015) were applied to 2015 data on mine -specific production (Coal Authority, 2016), taking account of where those deep mines were still utilising methane in engines. For 2016 72

UK NID 2025 (Issue 1) Ricardo Page 198 onwards, with UK deep-mined coal production only at a handful of small mines with no methane capture and utilisation, the inventory estimates are derived based on the UK weighted -average emission factor from 2014 (UK Coal, 2015) excluding any mitigation of methane in gas engines, applied to the 2016 to 2023 UK activity data on coal production in deep mines (DESNZ, 2024). Methane EFs from mining operations from UK research are used to estimate emissions from open -cast mines and licensed mines (both from Will iams, 1993), and emissions from coal storage and transport (Bennett et al, 1995). An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. Emissions are calculated from saleable coal production statistics for open -cast and deep - mined coal, taken from DUKES (DESNZ, 2024). For all sources, UK-specific emission factors are applied, which in the early part of the time -series are derived from peri odic industry publications, and for later years (1998 onwards) are primarily derived from company -specific or mine -specific reporting of methane emissions by mine operators. Industry -wide colliery methane utilisation data are taken from DUKES (DESNZ, 2024). From 1990 -1995, a small number of privately -owned mines classified as “deep mines” operating in the UK were shallower and smaller than Government-owned deep mines. These mines were licensed by the UK Government and in all years produced less than 3% of tot al UK deep -mined coal, whilst the majority of deep mines were Government -owned and operated. The Watt Committee Report #28 (Williams, 1993) indicates that these smaller licensed mines emitted less methane than the nationalised deeper mines, and therefore t he aggregate emission factor for the early part of the time-series is slightly lower. Activity data for production at licensed mines is taken from Barty (1995), with the activity data for non-licensed mines calculated by difference from the UK deep -mine coal production total in UK energy Emission factors for methane from deep-mined coal production are taken 1990-1992 Bennett et al (1995) was a study on deep mines which produced estimates of emissions for the period 1990-93. This was a period over which significant numbers of mines were being closed, hence the range in emission factors from 10 to 13.1 kg CH4 per tonne coal 1990-1995 The methane emission factor of 1.36 kg CH4/tonne coal produced at licensed, shallow mines is from Williams (1993). 1993-1997 No time-series of emissions data or industry research for deep -mined mines are available for 1993-97, and therefore the 1998 factor from operator reporting at deep mines (see below) is used. The combination of this 1998 factor for deep -mined coal and the lower factor for licensed, shallow mines operating to 1995 leads to a variable aggregate factor during 1998-2014 The emission factors for UK mines in 1998 -2014 are based on operator measurements of the methane extracted by the mine ventilation systems for all collieries operated by UK Coal (UK Coal, 2015) and for collieries owned by other operators that report methane utilisation and venting data (Coal Authority, 2015). Not all UK collieries provide data on methane utilisation and venting. The emission factor derived from the sites that provide data is applied across all UK production at deep-mined sites. The proportion of UK production that is covered by the reporting collieries ranges from 77% in 1998 to 96% in 2004 and 2007,

UK NID 2025 (Issue 1) Ricardo Page 199 and was around 90% from 2008 to 2012, but following closures fell back to 78% in 2014 and no mine-specific data from operators are available for 2015, 2016 nor 2017. In 2015, only data on the production of coal at the UK’s large deep mines was available (Coal Authority, 2016). In order to maintain time -series consistency of the method, the Inventory Agency used the mine -specific production data from 2015 and applied the emiss ion factors derived from the 2014 dataset for each of the large deep mines. From 2016 onwards, as all of the UK’s major deep mines had already closed, total UK coal production declined to its lowest level across the time -series. Again, to maintain time -series consistency, the Inventory Agency applied the 2014 emission factor derived for all UK coa l deep-mined extraction but discounting any methane mitigation as none of the remaining small mines have any systems to capture and use the eluted methane in gas engines. This is reflected in the increase in the IEF from 2016 onwards.
Methane extracted at deep mines is either emitted into the atmosphere or utilised for energy production; the gas is not flared for safety reasons. Data provided by colliery operators provides mine-specific annual data on the mass of • vented to atmosphere, fan drift (A); • drainage to surface (B); and • utilisation of methane in electricity generation I. The total methane vented to atmosphere from these sites that report the methane vented drained and utilised is therefore calculated as “A + B – C”. For the non -reporting sites that are typically smaller with no methane utilisation, the EF is derived from the sites that do report (from the vented and drained methane). Annual data (methane generation, methane utilisation, coal production) are obtained from mine operators. In 2005 there were 7 mines that reported methane emissions, then 6 in 2006, 5 in 2007 to 2010, 4 in 2011-12 and only three in 2013 and 2014. For these mines the aggregate emissions of methane (before any utilisation in gas engines) has been used together with the annual production data to derive an “unabated” methane IEF that is regarded as the most representative factor to apply to the production data from the smaller non -reporting (of emissions) UK deep coal mines. Therefore, total methane emission estimates for deep-mined coal in the UK from 1998 onwards are calculated as D = the sum of methane emissions reported (after any utilisation in gas engines) by the (typically larger) UK deep coal mines that can provide annual methane emission estimates; E = UK total deep -mined coal production from DUKES – Annual coal production at all sites F = IEF for unabated methane emissions, based on reported methane emissions data from sites included in D (i.e. methane elution before any utilisation) / production at the sites included The decline in methane emissions in recent years in the UK reflects both the decline in UK deep-mined coal production and the increase in uptake of technology to utilise coal mine methane to generate electricity. The emission factor for methane from coal storage and transport factor of 1.16 kg CH 4 per tonne of coal produced is only applied to deep mined coal production and is taken from industry research, Bennett et al (1995). UK NID 2025 (Issue 1) Ricardo Page 200 The emission factor for methane emissions from open-cast coal production of 0.34 kg CH4 per tonne of coal production is taken from industry research, Williams (1993). The total production of saleable coal is derived from the DUKES statistics. Where coal is upgraded to saleable form, some coal is rejected in the form of coarse discards containing high mineral matter and also in the form of unrecoverable fines. Typically, around 20% of the weight of the raw coal feed is lost through these preparation processes, as per the 2006 IPCC guidelines. Raw coal production is therefore estimated by increasing the amount of ‘saleable coal’ by the fraction lost through washing. The total emissions from open-cast mining are based on measurements of the total methane content of freshly sampled coal cores from open-cast sites from the three main producing regions in the UK. These data are used to generate the total emission factor for all open-cast coal production, regardless of the stage at which this emission takes place. • Open cast coal emission As noted in the method section, the CS EF for CH4 emissions from open cast coal production are based on analysis of the total methane content of freshly sampled coal cores and these EFs reflect the total methane emissions for all open -cast coal produced, regardless of the stage at which this emission tak es place. i.e. it is assumed in the UK GHGI that all of the measured methane content of the coal is released prior to combustion, and these emissions are all allocated within 1B1ai2 open-cast coal mining (Mining activities). This is consistent with the 1996 IPCC GLs method where country -specific data are used, in section 1.7.2.4, Equation 5 and the text on page 1.11“: "In most cases, if the Tier 2 approach is used to estimate methane emissions from surface mines, post -mining emissions from surface-mined coals are assumed to be zero." Furthermore, the UK approach is consistent with the general equation for estimating fugitive emissions from surface coal mining presented in sectio n 4.1.4 of the 2006 GLs, as the UK EF comprises all methane in the coal produced that could be released at any stage post -mining. As a result, the UK estimate for open -cast coal mining activities is likely to be an over - estimate, as some methane will be re tained within the coal up to the point of combustion, especially for lump coal used in domestic grates, where desorption of the methane is much slower than for fine coal processed for use in other sources such as power stations. The basis for this open -cast coal production factor also explains why the EF on methane from coal storage and transport (see paragraph above) is only applied to the activity of deep -mined coal in the UK, rather than to the total UK coal production data; to apply it to open -cast production also would introduce a double - • Other In the UK energy balance, there is an additional line for coal production which is for “other” sources of coal into the UK economy, which are typically very small numbers (95 kt in 2013 and zero since 2014) and represent coal obtained from slurries, ponds and rivers. We therefore include the activity data from other sources of coal within the UK energy balance, as part of the overall supply of coal as reported in the production source, as it is not coal that has been abstracted from open -cast or deep • Decline in emissions from deep -mined coal . The 76% reduction in emissions reported in 1B1ai1 since 2015, is fully consistent with the almost complete closure of UK deep-mined coal production.
Between 2018 and 2019, deep mined coal production increased from 24kt to 99kt as the Aberpergwm Colliery started up production again

UK NID 2025 (Issue 1) Ricardo Page 201 As part of the compilation of the 2025 submission, further consultation with the operators of the Aberpergwm Colliery was undertaken to investigate the figures they submit to the PI. In the previous submission, these values were identified as being higher than expected and that they warranted a comprehensive review before being incorporated. As a result, it was determined that the PI values were valid and as such they have been used in this submission. The effect on the total 1B1ai1 emissions in 2022 is an increase of 277 kt of CO2e with similar recalculations present for the previous years in which Aberpergwm was in operation mining anthracitic coal for the water treatment sector. Activity data for coal production in deep -mined and open -cast mines in the UK are quality - checked through comparison of data reported within DUKES and data reported directly by the UK Coal Authority, which provides regional and UK totals of coal production. The information provided directly by colliery operators regarding their methane recovery systems are also checked against the data published by DESNZ on coal mine methane projects in the UK (which encompasses both operating and closed / abandoned mines with coal mine methane The factors for coal mining are all based on UK industry research. Emission factors from coal storage and transport, licensed mines and from open-cast mines do not vary through the time series; in each case the same factor is applied to the UK activity in every year. For deep-mined coal emissions there is a variable emission factor across the time series, derived from operator reporting and reflecting the changing methane management practices within UK collieries, especially to increase methane capture and oxidation for power -raising in recent years, leading to a gradually declining methane emission factor per unit coal produced since the early 2000s. The variability in the factor also reflects the changes in production from different mines that have different methane management practices, as for some UK collieries the capture and use of methane has not proved cost -effective and therefore the technology is not uniformly implemented. The variability of the time series of emission factors represents changes in UK coal mining, and not time series consistency issues. As described in the methodology section above, the latest years of reporting (since 2015) the Inventory Agency has extrapolated the latest EFs from 2014 in order to maintain time-series consistency of the method, in light of the decline in the industry and the cessation of any operator -reported data on methane elution, utilisation (in gas engines) and emissions to atmosphere. The uncertainty in the coal production statistics is low, since these are based on national statistics. The emission factors applied are country specific, and in some cases based on mine specific data, and therefore the uncertainty is lower than using defa ult literature values. Additional uncertainty is introduced through the application of emission factors based on a sub-set of mines to represent full UK coal production, but we note that the total UK deep mined production where a methane elution factor is applied based on data from other sites is typically smaller sites that together produce (for many years in the time series) only around 10% of UK coal. However, we also note that the proportion of UK production at non-reporting deep mines has grown due to recent closures to 28%, 22% and 15% in 2013, 2014, and 2015 respectively and now 100% in 2016 to 2023. Therefore, the overall uncertainty of deep-mined coal methane emissions is higher for these years, but it this is set against the context that deep mined coal

UK NID 2025 (Issue 1) Ricardo Page 202 emissions only account for less than 0.39% of total methane emissions in the UK in 2023, whilst open-cast coal mining only accounts for 0.01% of total UK methane emissions. UK NID 2025 (Issue 1) Ricardo Page 203 Relevant Categories, source names Modelled emissions of releases of residual methane within coal seams from abandoned coal mines, including from the flooding of mine seams following closure. Methane emissions from closed coal mines are accounted for within category 1B1ai3 of the UK inventory. Emission estimates are based on a study funded by DECC (WSP, 2011) which updated research from 2005 (White Young Green, 2005) • reflect the UK trend in mine closures and re-openings driven by fluctuations in energy prices since the 2005 research; and • improve the representation of methane recovery and utilisation at closed collieries (Colliery methane combustion emissions are reported in the energy sector, 1A). Methane emissions from closed mines reach the surface through many possible flow vents, old mine entries, diffuse emission through fractured and permeable strata. Direct measurement of the total quantity of gas released from abandoned mines is not practical. Data for 32 mines closed between 1990 and 2015, and 121 mines closed before 1990 are included in the model. The model also includes projections, which can be changed to account for mine closures occurring earlier or later than predicted. Note that all majo r UK deep coal mines were closed by the end of 2015, leaving a very small number of operating small mines. The abandoned mines model was updated to reflect this for the 2017 submission, and there has been no further update to the model in the 202 5 submission. Methane utilisation has increased significantly across the time-series, up to a maximum of 94% in 2004. WSP, 2011 and White Young Green, 2005 An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. The UK model was developed in 2005 (White Young Green, 2005) and revised in 2011 (WSP, 2011).
The 2011 study used the same method, updating data for mine closures during 2005- The model generates both historic and projected methane emission estimates from closed UK coal mines, combining two separate sets of calculations to estimate emissions • coal mines that were closed before 2005 and included in the 2005 update; and • mines that were not included in the 2005 update, including mines closing or predicted to close between 2004 and 2028. UK NID 2025 (Issue 1) Ricardo Page 204 The model uses a relationship between emissions and the quantity of the underlying methane gas within the abandoned mine workings, including site -specific considerations of the most appropriate decay model for the recently closed mines. The model calculates methane reserves for all UK coalfields that are not totally flooded from 1990 with projections to 2050. The gas reserves are calculated by totalling all the gas quantities in individual coal seams likely to have been disturbed by mining activity. To enable calculation of the reserves over time, the rise in water levels in the abandoned mines due to water inflow has been calculated based on industry consultation, with a date estimated for each of the mines to be fully flooded; as mine workings become flooded they cease to release significant amounts of methane to the surface. The development of the model has drawn on industry monitoring to measure methane emission from vents and more diffuse sources, including measurement of the flow rate and methane concentrations of vented mine gases. The industry knowledge of these methane sources has increased greatly in the UK over the last 10 years as the technology to capture and utilise the methane for power generation has developed alongside new economic incentives to utilise the mine methane in this way. Monitoring of more diffuse sources involves the collection of long-term gas samples to measure any increases in background atmospheric methane level in the locality. Methane flows measured by both methods showed a general increase with the size of the underlying gas reserve. The data indicate an emission of 0.74% of the reserve per year as a suitable factor to apply to the methane reserve data in order to derive methan e emission estimates for abandoned UK coalfields for 1990 to 2050, and this factor is applied within the model to derive the UK emission estimates. Estimates have been made for both deep-mined and open-cast coal. WSP (2011) derived estimates for historic methane emissions from closed coal mines and also generated projections to 2050, based on forecasts for UK coal mining activity and industry information on the quantity of underlying methane gas and expected rates of flooding of each mine following closure. The 2023 emission estimates in the 2025 UK GHGI submission were therefore taken from the projections of emissions within the 2011 WSP report, with the emission profiles through time for all major UK deep mines recently closed (i.e. since the study in 2011) brought forward to the actual date of closure, as all such mines were closed by the end of 2015. Each large deep mine within the model has a profile of projected flooding and emissions of methane upon closure; the closure dates in the model have now bee n fixed to the actual dates rather than projected dates, and the emissions of methane following closure are therefore now occurring for every deep mine, and will diminish over time. Following the rapid decline of the UK deep -mined coal industry, this sourc e is now the most significant emission source in 1B1, and accounts for 0.8% of total UK methane emissions in 2023. The emissions from all abandoned mines are included within the 1B1ai3 source category. All large deep mines in the UK have now closed, and therefore this source category will decline in significance in future years. There were no recalculations or method changes to the closed coal mines source category in this submission. All remaining large deep coal mines closed by the end of 2015, and the model was modified to reflect the closures in deriving estimates for the 2017 submission; no further updates to the model have been made for subsequent submissions, as the modelled future profile of emissions across the sector is unchanged. UK NID 2025 (Issue 1) Ricardo Page 205 Improvements (completed and planned) No improvements to this method are currently planned. The model is periodically reviewed and updated. However, as all large deep mines in the UK that contribute significantly to this emission source are now closed, the emissions trend is diminishing through time and in 2023 only accounts for 0.8% of the UK methane inventory total, this source is considered a low priority for future improvement work. This source category is covered by the general QA/QC of the greenhouse gas inventory in Annex 4. WSP (2011) was subject to review by a steering committee, and final sign off by DECC. The research also includes benchmarking of UK specific estimates with other inventories to ensure that the method used remains appropriate for the UK. No time-series consistency issues have been identified. The uncertainty in the emissions from this source was assessed as part of WSP (2011). The uncertainty assessment indicated a range of ±17% to ±41% over the period 1990-2050. This level of uncertainty is in line with IPCC guidance on Tier 2 and Tier 3 methodologies. This considered the uncertainty in the future mine closure dates, gas reserve estimates, the annual methane emissions rate as % of gas reserve, the open cast mine methane emissions factor and the methane utilisation factor.
UK NID 2025 (Issue 1) Ricardo Page 206 MS 18 1B2 Oil refining, storage and distribution (1B2aiv to v) and natural gas distribution (1B2biii to v) Relevant Categories, source names 1B2ai: Onshore, offshore oil well exploration; Oil offshore well testing 1B2aii: Upstream oil fugitives, direct processes; Onshore oil production (conventional); Oil other fugitives, direct processes 1B2aiii: Upstream oil onshore, offshore oil loading; Oil transport 1B2avi: Abandoned oil onshore, offshore 1B2bi: Unconventional gas well exploration; Onshore, offshore gas well exploration; Gas offshore well testing 1B2bii: Onshore natural gas production (conventional); Onshore natural gas gathering 1B2biii: Upstream gas fugitives, direct processes; Gas other 1B2bvi: Abandoned gas onshore, offshore 1B2c1i: Upstream oil venting; Oil venting 1B2c1ii: Upstream gas venting; Gas venting 1B2c2i: Upstream oil flaring; Oil flaring 1B2c2ii: Upstream gas flaring; Gas flaring All fugitive releases from oil and gas production, excluding leakage from gas transmission and distribution. Distribution of oil products is not described since there are no direct GHG The activity data for well exploration and also for wells abandoned are the number of wells (drilled or abandoned, accordingly) per year. The activity data for well testing is the mass of flared gas arising from the well tests, whilst the activity data for all other gas flaring is also the mass of gas flared. The activity for the oil transport source categories is the annual mass of crude oil transported The activity data for source categories in 1B2b2 is the annual volume of natural gas

UK NID 2025 (Issue 1) Ricardo Page 207 For other source categories the methods are based on the sum of reported emissions and there are no associated activity data. These source categories across 1B2 cover fugitive emissions arising from the exploration, production, transportation, processing and storage of liquid and gaseous fuels. It excludes fuel combustion emissions associated with upstream oil and gas exploration and production which are reported within 1A1cii Oil and Gas Extraction, the method for which is presented in MS2. The UK has been producing oil and gas, predominantly offshore in the North Sea, for decades, and there are several hundred oil and gas platforms that have been operating across the time series. Oil and gas exploration activities (well drilling, completions, well testing) lead to fugitive and flaring emissions; similarly at the end of the production life of each well there may be emissions from abandoned wells, whether they are plugged or not, and from purging of infrastructure during decommissioning. Proces s treatments (e.g. acid gas treatment) and fugitive releases give rise to GHG emissions during the production phase at offshore facilities and in subsequent material processing at onshore terminals to prepare oil and gas products [Emissions from leakage during gas transmission and distribution, and the point of use are Offshore oil and gas is transported to onshore terminals via pipelines and marine tankers. Emissions of CH4 and NMVOC occur during loading of oil into the ship's tanks and subsequent unloading. Similarly, CH 4 and NMVOC occur when storage tanks are loaded or unloaded, whether these be at oil terminals or a different onshore location. There are also a small number of onshore oil and gas wells in the UK. This is a very small component of the upstream sector. Fugitive emissions do arise from well drilling, the production of oil and gas, flaring of waste gases, from well abandonment and fr om the transfers of oil product to rail and road tankers for deliver to oil terminals and refineries. Shale gas reserves have been identified and some preliminary testing to explore the prospects for shale gas production has occurred during the inventory time series, but there is no active exploration or production of shale gas currently in the UK. Activity EEMS ( DESNZ OPRED, 2024), DUKES (DESNZ, 2024a), IED/PRTR- reported data (EA, NRW and SEPA, 2024) EU/UK ETS data (DESNZ, 2024b), UKOOA (2005), Fuels Industry UK (2024), PPRS data (DESNZ, 2024c), DTI Brown Book (DTI, 2004), WONS data (NSTA, 2024). Emission EEMS (DESNZ OPRED, 2024), EU/UK ETS (DESNZ, 2024b), UKOOA (2005), IPCC 2006 Guidelines (IPCC, 2006), 2019 Refinement to the An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission information for common activity data sources. 73 This can be found as one of the additional documents to the NID Note that there can be a delay between the NID being published on the NAEI website after official submission. UK NID 2025 (Issue 1) Ricardo Page 208 The Inventory Agency has researched and analysed all available activity and emissions data for the UK upstream oil and gas sector across the time series during a recent inventory improvement project (Thistlethwaite et al, 2022). The inventory methods for s ources across 1B2 draw upon the best available data from the sector, through a range of reporting mechanisms that have been developed by UK regulatory agencies across the time series. A more detailed summary of the oil and gas sector improvement project is presented in Annex 5.1.1.6. The methods developed impact both fuel combustion emissions reported in 1A1cii (see MS 2) and all upstream oil and gas fugitive emission sources reported in 1B2. An overview of the data sources and methods used to derive estimates for the categories Note that in the UK there are different regulatory mechanisms that govern the activities (i) offshore oil & gas exploration and production, (ii) onshore conventional oil & gas exploration and production, and (iii) onshore unconventional shale gas explo ration (of which, there is no current production). These different regulatory systems dictate data availability for upstream oil and gas activities, which impacts on the best available method for each sector of the inventory; for example, the data availabl e for offshore rigs is more detailed (source -specific) than for onshore terminals (where only some resolution of emissions by source is feasible).
It is important to note however that despite these different methods and data availability, the UK Inventory Agency does report in a time series consistent manner for each source, and that the UK inventory is complete for all emission sources, with the exc eption that there are no data available currently to estimate emissions from oil and gas well blowouts other than for the 2012 blow-out at the Elgin platform, as outlined below. 1B2a1 Oil Exploration Onshore wells, all IPCC Tier 1 (2019 Refinement) method. Emission = #wells drilled x IPCC default EF (per conventional oil well) Offshore well testing, 1990-1997: UKOOA 2005 (Tier 2). Assumptions to derive time series using well drilling time series. Offshore well testing, 1998 – Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024), which includes well testing emissions per facility, per year. Tier 2/3 as EFs are from EEMS 1B2a2 Oil Production Onshore oil Hybrid Tier 2 method. ∑ Large + small sites. Larger sites, Emissions = ∑operator emissions per wellsite (EA, 2024) Smaller sites, Emissions = Production AD (PPRS) x EF derived from larger sites, IEF from (PI emissions / PPRS production data) Offshore oil production, 1990-1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends; residual category for CH4. Offshore oil production, 1998 - Latest ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024). Tier 3 as emissions for process emissions are derived from operator reporting; fugitives derived

UK NID 2025 (Issue 1) Ricardo Page 209 Oil terminals, 1990-1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends; residual category for CH4. Oil terminals, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI, SPRI (EA, SEPA, 2024). Tier 3 as process emissions are based on operator reporting; fugitives derived from EEMS guidance EFs. 2011 - only RI data, so estimates modelled on previou s years share of RI total; source is also 1B2a3 Oil Transport Offshore oil loading, all IPCC Tier 1 (2019 Refinement). Emission = OTL production (PPRS/BB) x IPCC default EF ( assumes no Onshore oil loading, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Onshore oil loading, 1998-latest ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 2/3 as emissions for oil loading are derived from operator reporting and use of EEMS guidance EFs (CS). 2011 - only RI data, so estimates modelled on previous years share of RI total; Seal Sands data Onshore oil transport (pipelines), all years : IPCC Tier 1 (2019 Refinement). Emission = Wytch Farm production (PPRS/BB) x IPCC Onshore oil transport (road/rail), all years : IPCC Tier 1 (2019 Refinement). Emission = Onshore production less Wytch Farm Oil terminal storage, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Oil terminal storage, 1998 – latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 2/3 as emissions for oil storage are derived from operator reporting and use of EEMS guidance EFs (CS). 2011 - only RI data, so estimates modelled on previous years share of RI total. 1B2a6 Oil - Other Oil wells abandoned, all IPCC Tier 1 (2019 Refinement) method. Emission = #wells abandoned per year (cumulative) x IPCC default EF (per well abandoned where it is unknown if plugged or not) The AD are taken from the NSTA wellbore database search facility. The AD comprise all historic oil and gas wells, so the estimates include IPCC Refinement states that leaks from abandoned offshore wells are assumed to be 2% of those onshore, as 98% of the gases are dissolved in the water column. This assumption is applied in the method for Onshore conventional gas wells, all IE, reported within 1B2a1, as there are no AD specific to gas wells drilled, only oil and gas

UK NID 2025 (Issue 1) Ricardo Page 210 Onshore unconventional gas well drilling : Activity only evident in 2011, 2012, 2014, 2018 and 2019. ∑operator emissions per facility, based on operator and regulator information. Offshore well testing, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on #wells drilled per year, from NSTA. Offshore well testing, 1998 – Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024), which includes well testing emissions per facility, per year. Tier 2/3 as EFs are from EEMS Onshore gas production, all years : IPCC Tier 1 (2019 Refinement) Emission = natural gas produced (Mm 3) x IPCC default EF (for onshore activities with higher-emitting technologies and practices) Onshore gas gathering, all years : IPCC Tier 1 (2019 Refinement) Emission = natural gas produced (Mm 3) x IPCC default EF (for onshore activities with higher-emitting technologies and practices) Offshore gas production, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends; installation -level direct process emissions from key sites (i.e. Elgin, Rough) based on their gas production trends. Residual category for CH4 and NMVOC. Offshore gas production, 1998- Latest ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024). Tier 3 as process emissions are based on operator reporting; fugitives derived from EEMS guidance EFs. Also includes one -off estimate of emissions from Elgin blow-out, 2012, based on Lee et al publication from aircraft monitoring of Gas terminals, 1990-1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends; installation -level estimates of process emissions from key sites (i.e. SAGE, CATS) based on gas throughput. Gas terminals, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 3 as process emissions are based on operator reporting; fugitives derived from EEMS guidance EFs. 2011 - only RI data, so estimates modelled on previous years share of RI total. Gas terminal storage, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends.
Gas terminal storage, 1998 – latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 2/3 as emissions for gas storage are derived from operator monitoring and use of EEMS guidance EFs (CS). 2011- only RI data, so estimates modelled on previous years share of RI total. Gas wells abandoned, all years : Included Elsewhere. Reported within 1B2a6, as there are no AD specific to gas wells abandoned, only oil and

UK NID 2025 (Issue 1) Ricardo Page 211 Offshore oil venting, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Offshore oil venting, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024). Tier 3: venting emissions are based on operator monitoring. Oil terminals venting, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Oil terminals venting, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 3: venting emissions are based on operator monitoring. 2011- only RI data, so estimates modelled on previous years Offshore gas venting, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends. Offshore gas venting, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024). Tier 3: venting emissions are based on operator monitoring. Gas terminals venting, 1990-1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends. Gas terminals venting, 1998 - Latest year : ∑operator emissions per facility, based on EEMS (DESNZ OPRED, 2024) and PI/SPRI (EA, SEPA, 2024). Tier 3: venting emissions are based on operator monitoring. 2011- only RI data, so estimates modelled on previous years Offshore oil flaring, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Offshore oil flaring, 1998- Latest ∑operator emissions per facility, based on EU/UK ETS (DESNZ, 2024b), EEMS (DESNZ OPRED, 2024). Tier 3: flaring emissions are based on operator reporting. CEFs from flare gas sampling, analysis, 98% oxidation factor. Oil terminals flaring, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on crude oil production trends. Oil terminals flaring, 1998 - Latest year : ∑operator emissions per facility, based on EU/UK ETS (DESNZ, 2024b), EEMS (DESNZ OPRED, 2024). Tier 3: flaring emissions are based on operator reporting. CEFs from flare gas sampling, analysis, 98% oxidation factor. Offshore gas flaring, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends. Offshore gas flaring, 1998 - Latest year : ∑operator emissions per facility, based on EU/UK ETS (DESNZ, 2024b), EEMS (DESNZ OPRED, 2024). Tier 3: flaring emissions are based on operator reporting. CEFs from flare gas sampling, analysis, 98% oxidation factor. Gas terminals flaring, 1990 -1997: UKOOA 2005 (Tier 2). Time series based on natural gas production trends. Gas terminals flaring, 1998 - Latest year : ∑operator emissions per facility, based on EU/UK ETS (DESNZ, 2024b), EEMS (DESNZ OPRED,

UK NID 2025 (Issue 1) Ricardo Page 212 2024). Tier 3: flaring emissions are based on operator reporting. CEFs from flare gas sampling, analysis, 98% oxidation factor. [Where BB = DTI Brown Book “Development of the Oil and Gas Resources of the UK”] Key data sources for the 1B2 inventory methods • Oil and gas operators submit annual source -specific emission estimates to the Environmental and Emissions Reporting System (EEMS) , regulated by DESNZ Offshore Petroleum Regulator for Environment and Decommissioning (DESNZ • Industry survey data from 1990 onwards were used to derive a time -series of sector estimates from 1990 to 2003, reported to UK Government studies by the trade association (UKOOA, 2005). Whilst the data resolution per source is limited in this dataset, it is the best available information to inform estimates prior to the EEMS • Well testing emissions estimates on an installation -specific basis are also included within the EEMS datasets from 1998 onwards at all sites of offshore exploration activities within UK’s territorial waters, including data on both activity and emission factors of excess gas that is flared or released to the atmosphere. Emissions released at the seabed are not included in estimates; it is assumed that any such releases will dissolve in the water column without subsequent release to the atmosphere. • Onshore well drilling for oil and gas exploration leads to fugitive emissions. These are estimated using a Tier 1 method from the 2019 IPCC Refinement, with the number of wells drilled per year obtained from the North Sea Transition Authority’s Well Operations Notification Syst em (WONS). Abandoned wells also release fugitive methane and NMVOC, and again a 2019 IPCC Refinement Tier 1 method is applied, using NSTA wellbore database information on the number of wells abandoned. • Annual reporting of emissions by pollutant aggregated across all emission sources under the IED/PRTR reporting system to the UK environmental regulatory agencies (i.e. EA, NRW, SEPA) are available for onshore sites only (i.e. including oil and gas terminals but excluding all offshore oil and gas installations). These data are available from 1998 in England and Wales and f or 2002 and 2004 onwards in Scotland and include emission estimates for a suite of GHG and air quality pollutants including CO2, CH4 and N2O. Whilst there is a high level of complete reporting from onshore oil and gas terminals, operators only report to these Regulatory Inventories (RIs) where the annual pollutant emission is above a reporting threshold; hence there is very little RI data reported by operators of onshore well sites in the UK, where emissions are low. • For 1995 to 2009, all terminals reported source -specific emission estimates to the EEMS system. For combustion and flaring sources, the EEMS dataset for this period includes mass-based activity data, and emission estimates for GHG and air quality pollutants.
Since 2010 onshore terminals report voluntarily to EEMS, but the reporting is incomplete ac ross UK terminals; reporting to EEMS was deemed a duplication of the reporting requirements under IED/PRTR and hence was no longer a mandatory duty on terminal op erators. Hence from 2010 onwards the EEMS dataset is not a comprehensive record of emissions from onshore terminals. The UK GHGI estimates are based on the IED/PRTR data in conjunction with EU/UK ETS data, which provides emissions of CO2 from combustion and flaring sources. • Under the Energy Act and Petroleum Act, upstream oil and gas operators are mandated to report on their economic activities to the North Sea Transition Authority (NSTA). Whilst these data are not specific to environmental reporting, they do provide

UK NID 2025 (Issue 1) Ricardo Page 213 a full time series of useful activity data such as UK oil production and gas production; detailed data per oil and gas field are now reported via the Petroleum Producers Reporting System (PPRS), which began in 2000. Prior to PPRS, the same data were published in annual statistical releases such as the historic DTI publication “ The Development of the Oil and Gas Resources of the UK” , known as the Brown Book, which ceased to be published in 2004. Together these resources provide a full, time - series consistent dataset on activity for the sector, as well as a wealth of installation - level detail. They are used in inventory method development primarily to support the use of IPCC good practice gap -filling techniques, as they provide a useful proxy dataset of activity to help address reporting gaps per source, per installation and/or at • The Inventory Agency continues to investigate ways in which methane and NMVOC emissions from oil and gas well blow outs can be estimated, although there is no IPCC methodology for such events, and only limited data are available in the UK with which to estimate emissions. The Inventory Agency has researched the UK Health and Safety Executive (HSE) hydrocarbon release datab ase, and there is only one major blow-out event recorded by upstream operators and reported to the HSE across the time series, which was the Elgin blow-out of 25th March to 16th May 2012. In this case, the Inventory Agency has estimated the total methane and NMVOC released by the blowout and added it to the fugitive emission estimates for the year 2012. The UK emission estimate is based on reported daily methane flow-rate observations taken on 5 days during the blow-out period (30th March, 3rd April, 17th April, 24th April and 4th May 2012). These estimated flow rates of methane are derived from air sampling and analysis by research aircraft flights, as reported by Lee et al. (2018)74.The Inventory Agency will continue to research data from UK academic and industry researchers, regulators and operators, in order to develop emission estimates for any similar historic • All UK onshore production to date is from conventional oil and gas wells. These installations are all regulated under the IED/PRTR by UK environmental regulators (EA, SEPA) and the operators report on annual methane emissions and these estimates are aggregated and included within the UK GHGI under 1B2a2. Howev er, the Inventory Agency has sought to estimate methane emissions from onshore shale gas exploration activities, though to date there has been no production of natural gas from unconventional shale gas resources in the UK. Since 2010 there have been twelve shale gas wells spudded in the UK. None of these sites are actively producing gas and none of the shale gas sites are yet under IED/PRTR regulation by the EA or SEPA, and there are no annual reported emission estimates by operators from their initial well spudding and completion activities. Also, as there has been no production of natural gas at any of these sites, there is no activity data available to chapter of the IPCC 2006 Guidelines. Therefore, the Inventory Agency has consulted with the regulatory agencies (NSTA, EA) and obtained one -off reports, where available, from each unconventional gas well site. o The EA has confirmed that 12 wells were spudded during the period of August 2010 to January 2019 in England and that no extraction of products has occurred. It has been confirmed with the Regulator that all shale wells are now abandoned, and permits surren dered or suspended, with no foreseeable exploration and production activity75. 74 Atmos. Meas. Tech., 11, 1725-1739, 2018. 75 Personal communication with the Environment Agency, March 2021

UK NID 2025 (Issue 1) Ricardo Page 214 o One report to the EA provides methane emission estimates during a nitrogen gas lift operation at one of the larger shale gas well sites; review of the other sites identified that several wells did not strike any hydrocarbon deposits. o Based on the limited data available, a conservative estimate of emissions for methane from the well exploration activities has been made and added to the UK GHGI; methane emissions are estimated to peak at 60t CH 4 in 2010 and 2011, with well drilling emissions in 2014 of 20t CH4, and 25t CH4 in 2019. o Further information can be made available to an ERT, to access the NAEI research report which contains commercial in confidence details on a site -by- site basis (Thistlethwaite, Gorji and Passant, 2021). The source resolution in the UKOOA 2005 data reference is limited across the 1990 -1997 period, and a series of proxy datasets have been used to derive time -series estimates per source back to 1990. These are described in more detail in Annex 5.1.1.6. The resolution of data by source type within the EEMS dataset is such that fugitive emission sources are typically reported aggregated for each installation, without any further information on the specific source/unit. Further, the emissions reported from gas terminals are aggregated across all sources under the IED/PRTR reporting system.
These national circumstances of data availability mean that the UK inventory data cannot be disaggregated to separate fugitive emissions from gas processing units, from ot her fugitives, such as tie -ins to transmission systems, acid gas removal units, other connectors, flanges and pipeline infrastructure. Hence the emissions from all of these sources are reported together under 1B2biii. The 2019 Refinement method has been applied as the better resolution of fugitive EFs in the 2019 Refinement enables the UK to address the emissions from the onshore gas production and gathering sector. UK production is a very small part of the upstream ind ustry and predominantly takes place at small gas well sites that do not typically report emissions (e.g., of hydrocarbons) to UK regulatory mechanisms (e.g., to the Pollution Inventory of the Environment Agency). Therefore, applying the 2019 Refinement met hod to the total UK onshore gas production AD ensures that the inventory is complete. Implementing mitigation practices / technologies is unlikely to be cost -effective at small gas well sites and so the Inventory Agency has applied the 2019 Refinement EFs for "activities occurring with higher- emitting technologies and practices". Noting that the 2019 Refinement is based on the latest scientific research, this is considered to be the most representative approach for estimating We note that the emissions reported in 1B2b3 (natural gas processing) do not include any pollutant emissions that are also reported in 1B2b2, i.e., there is no double -counting of emissions. The 1B2b2 emissions are all associated with onshore production at well sites and the associated gathering systems. The 1B2b3 emissions are based on separate data streams of production and treatment at (i) offshore dry gas and associated gas platforms/FPSOs, and (ii) UK onshore gas terminals that process the gas from offshore sources and inject the gas to the National Transmission System. There have been minor recalculations to estimates for 2022, due to revaluating the number of wells drilled during onshore exploration and inclusion of further EEMS data on gas flaring at a

UK NID 2025 (Issue 1) Ricardo Page 215 Improvements (completed and planned) Following the oil and gas sector improvement project (Thistlethwaite et al , 2022), which assessed all available UK data to improve the quality of the UK GHGI submission across 1A1cii and 1B2, and is described in more detail in Annex 5.1.1.6, emission factors and activity The Inventory Agency will maintain dialogue with regulators and industry experts in order to seek any new data on emissions from oil and gas well blowouts, and to follow -up on the reporting of well testing within the new EEMS system. The Inventory Agency will also maintain a watching brief on the development of the shale gas industry, in order to ensure that if the industry does start to produce gas in the UK, that the Inventory Agency will have access to information to allow emission estimates to be derived for The EEMS dataset quality system is managed by the regulatory agency (DESNZ OPRED) and developed in conjunction with the trade association, Offshore Energies UK (OEUK). EEMS uses an online reporting system with controls over data entry, together with guidance notes provided to operators to provide estimation methodology options and emission factors for specific processes. The IED/PRTR system is similar to EEMS, but regulated by the onshore environment agencies (E A, NRW, SEPA); it also has operator guidance on emission estimation and reporting, and a system of annual checks on data submitted by operators, by a Site Inspector / Process Engineer assigned by the regulator to manage the performance and compliance assessments for each installation. The data reported under IED/PRTR however are installation-wide, rather than source-specific. The EU/UK ETS dataset quality system is managed by the regulatory agency (DESNZ OPRED). The reporting standards of the ETS currently operating in the UK are consistent with the EU ETS, with estimation methods that operators may use defined within their permit. The data are third party verified and submitted to the regulator. The Inventory Agency combines UK energy statistics, the EEMS data, EU/UK ETS and IED/PRTR data to derive the oil and gas sector estimates, then conducts time series consistency checks using other sector activity data from PPRS and the Brown Book to identify and address any potential data reporting gaps or outliers. Where the EU/UK ETS or IED/PRTR data are inconsistent with the EEMS data, the Inventory Agency works with DESNZ OPRED and facility operators to help determine the best available data for each source to ensure that the reported data are complete for each installation. The Inventory Agency also conducts time- series consistency checks to identify missing sites or sources, and for those sources where the EEMS data includes emissions and activity data the Inventory Agency reviews the time - series of implied emission factors to identify outliers. Any sites or sources where the quality checks identify gaps, outliers or inconsistent reporting between different regulatory systems are resolved in consultation with regulators and operators, as required. The emission estimates for the offshore industry are based predominantly on the EEMS dataset for 1998 onwards, whilst emission estimates for 1990-1997 are based on sector data submitted to UK Government (UKOOA, 2005). The method is compromised by the lack of source -specific data for the 1990 -1997 period, where only aggregate emissions data across all sources in 1A1cii and 1B2 are available from the industry submissions to UK Government; this coincides with a period wher e consultation with the BEIS (now DESNZ) energy statistics team has confirmed that that the UK energy

UK NID 2025 (Issue 1) Ricardo Page 216 statistics were not gathering complete data for all oil and gas terminals.
Wherever possible the Inventory Agency has filled data gaps with operator-reported estimates and applied IPCC good practice gap -filling methods to ensure that the time series consis tency is as good as practicable given the available data; this is possible as there are a defined number of installations that are active in this sector and their activities (and emissions) are generally well documented with gaps in data being relatively minor. Further, the Inventory Agency has conducted time series consistency checks between the aggregated emissions reported in the 1990 -2003 data submission (UKOOA 2005) and the installation-level EEMS and NAPs data (BEIS OPRED, 2022), across the overlap years of 1998 to 2003. This analysis shows close consistency, indicating that the scope of reporting in the UKOOA 2005 dataset is consistent with the later installation -level EEMS data. Further details are presented in Annex 5.1.1.6. We further note that whilst the emission estimates specific to fuel combustion in 1990 -1997 are uncertain, that the total emissions across all upstream oil and gas sources (∑1A1cii, 1B2) in the UK GHGI are aligned with the industry submission to UK Government (UKOOA, 2005) and hence are regarded as the most accurate data available. The EEMS dataset (DESNZ OPRED, 2024) provides a consistent time -series of emission estimates for many facilities and sources, but since 2010 the reporting by onshore terminals is voluntary. Furthermore, whilst the EEMS data quality appears to be improving over recent years, the completeness of EEMS data for specific facilities and sources is still subject to uncertainty; reporting gaps appear to be systematic for some facilities, such as frequent non- reporting of oil loading / unloading emissions at some terminals. For this reason, the Inventory Agency has moved away from using EEMS data for the estimates of oil loading, and instead uses the PPRS activity data on oil production at OTLs, in conjunction with industry EFs, as this method delivers a more complete and time series consistent estimate. The Inventory Agency continues to work with the regulatory agency, DESNZ, in the continued development of emission estimates from this sector. Uncertainties for both activity and emission factors are based on expert judgement, informed by the understanding of the available data, the level of uncertainty that is accepted within the reporting systems (e.g. UK ETS) and the likelihood of error compen sation across the UK The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. Emissions data taken from the EEMS reporting system 1998 onwards are considered to be high quality, emissions data for other years are subject to greater uncertainties. As noted above, while the uncertainty per source may be high due to the limited source resolution in the UKOOA 2005 dataset, the overall uncertainty in the sum of emissions across 1A1cii and 1B2 is much lower and is based on the best available data for that period. UK NID 2025 (Issue 1) Ricardo Page 217 Relevant Categories, source names 1B2biv: Natural Gas (transmission leakage) 1B2bv: Natural Gas (distribution leakage) Natural Gas (leakage at point of use) Leakage from gas transmission and distribution, leakage at the point of use The UK GHG inventory includes estimates of methane and carbon dioxide emissions from natural gas leakage from the downstream gas supply network, including releases high pressure transmission network; distribution network; gas leaks at point of use. Annual activity data and gas compositional analysis are provide d by National Gas, four companies (formed in 2005) that operate the low-pressure gas distribution networks within Great Britain, and three gas suppliers in Northern Ireland. Activity Natural gas leakage data in energy and mass units, from the UK downstream natural gas network National Grid, Cadent Gas, SGN, Northern Gas Networks, Wales & West, and Phoenix Gas and AD for gas use in domestic and commercial sectors from DUKES (DESNZ, 2024a) are used to generate leakage at point of use estimates. Emission Natural gas compositional data (mass % data nitrogen, carbon dioxide, methane, ethane, propane, i -butane, n-butane, neo-pentane, i- pentane, n -pentane, hexanes+) supplied by the GB gas network operators as listed above. UK estimates o f natural gas consumption within each Local Distribution Zone (LDZ) are used to generate a weighted-average UK compositional analysis of natural gas consumed annually. From 2007 these data are available from Long Term Development Plans published by each of the gas network operators; earlier data by LDZ are based on Local Authority -level consumption estimates aggregated into LDZs (CLARE database, 2012). EFs for the gas leakage at point of use are derived from UK data on gas fitting performance and assumptions regarding unit operational cycles, An accompanying spreadsheet “Energy_background_data_uk_2025.xlsx” lists all emission factors used in the energy sector, including a full list of references56. The leakage estimates are calculated using separate methodologies to

UK NID 2025 (Issue 1) Ricardo Page 218 1. Natural gas leaks from the high -pressure transmission mains (National Grid Gas); (reported under 1B2b4 Transmission) 2. Natural gas leaks from the low -pressure distribution network, medium pressure gas mains, Above Ground Installations (AGIs), AGI working losses and interference (Cadent Gas, SGN, Northern Gas Networks, Wales & West, Phoenix Gas, Firmus Energy); (Reported under 1B2b5 Distribution) 3. Other losses of natural gas at the point of use (DESNZ DUKES, UK research); (Reported under 1B2b5 Distribution) For methods 1 and 2 above, from 2004 onwards the gas network operators provide annual gas leakage estimates on a mass basis, providing a breakdown of emissions across all 14 regional gas networks in the UK, which are called Local Distribution Zones (LDZs). National Grid operates the high-pressure natural gas transmission network; Cadent Gas operates 5 of the LDZs; Northern Gas Networks operates 2 LDZs; SGN operates 3 LDZs; Wales and West Utilities operates 3 LDZs; Phoenix Gas, SGN and Firmus Energy together supply gas within the Northern Ireland network. In addition, each of the gas network operators provides annual natural gas compositional analysis for their networks.
Prior to 2004, the data on gas leakage (activity data and compositional analysis) was all provided by British Gas, which operated all of the UK networks before the industry was privatised. The information on methane losses from the high pressure transmission system (1B2b4) are estimated by National Grid (NG) based on (i) periodic fugitive emission surveys for the National Transmission System (NTS), compressor stations and LNG terminals, (ii) NG records of intentional venting actions on the network, (iii) estimation of fugitive emissions from AGIs using Marcogaz model and (iv) estimation of emissions as the result of incomplete combustion using compressor station turbine specification and site running hours. These data have not been available for every year across the time -series, with only two data points in the 1990s, annual data from 2000 -2004, and for 2011 -2023, with data for other years estimated using interpolation (2005-2010) and extrapolation (early time-series). The UK GHG inventory estimates for 1B2b5 (distribution leakage) are based on the aggregate of mass of gas leaked across all networks (low pressure mains and other losses), with the methane content of the natural gas based on compositional analysis from all of the gas The activity data reported in the CRT for these sources are the final UK annual gas demand data. These data are not used within the GHG inventory estimation method, but are presented to enable IEFs to be derived, to aid comparability of the UK estimates wi th those of other The UK gas network operators use a common industry leakage model to derive their annual estimates of gas leakage from the low and medium pressure distribution systems. The UK gas network leakage model was developed by British Gas and uses factors and assum ptions on leakage rates for different types of gas mains and installations, based on measurements and surveys conducted in 1992 and 2002, with annual updates to maintain the representation of the UK gas network infrastructure (such as length and type of pi pelines and other units) and reflect the rolling programme of network replacement. Historical data for the leakage from the low-pressure distribution network and other losses is based on studies from British Gas in the early 1990s (British Gas, 1993; Williams, 1993). Natural Gas Compositional Data Data on the methane and NMVOC content of natural gas have been provided by contacts within British Gas Research for 1990-1996 and by UK Transco from 1997 to 2005 (Personal Dave Lander, 2008), and from the gas network operators from 2006 onwards. UK NID 2025 (Issue 1) Ricardo Page 219 NMVOC content for 2001-2003 has been estimated by interpolation due to a lack of data; CO2 compositional data from 2004 onwards are derived from annual compositional analysis by gas network operators, whilst the 1990 -2003 data have been extrapolated back from the 2004 Each of the gas network operators obtain their compositional analysis from a central system of data logging from the automated sampling and analysis network that was operated previously under the Transco ownership, prior to the network being opened up to g reater The calculation of the reported Great Britain (GB) average gas composition is derived from the sum -product of the annual Local Distribution Zone (LDZ) compositional data and the estimated gas consumption through each of the LDZs. No gas composition data ha ve been provided by any operator in Northern Ireland, and in the absence of data, the average composition of the gas available in GB’s network is assumed to be applicable in Northern Ireland. The estimates of gas consumption within each LDZ are based, from 2007 onwards, on LDZ throughput data presented within Long Term Development Statements by each of the gas network operators; prior to 2007 these data are unavailable, and the best available data to inform the UK weighted average composition are sub -national gas use statistics at local authority level (then aggregated to LDZs) which are published by DESNZ annually and processed for UK Local Authority CO2 emission estimates via the CLARE database. The gas infrastructure in Northern Ireland is much newer than in the rest of the UK, as the gas pipeline (from Scotland) was only commissioned in 1999. Since then, the gas network has continued to develop across Northern Ireland. Annual estimates of gas le akage from 2005 onwards have been provided by the main gas operator (Phoenix Gas, 2024; SGN, 2024; Firmus Energy, 2024), and the data for 1999 to 2004 have been extrapolated back from the Gas Leakage at the Point of Use The third inventory estimation methodology is used to determine estimates of natural gas leakage at the point of use, and these estimates are also reported in 1B2b5. Leakages are estimated for a range of different appliances that use gas, combined with national statistics on natural gas consumption in the domestic and commercial sectors (DESNZ, 2024). Methane releases are assumed to be “ Not Occurring ” from these appliances, based on consultation with technical experts that advise the UK Government for the CHP QA scheme (Personal R Stewart, 2011). Larger boilers typically operate almost permanently once ignited (particularly if used for steam-raising) with little or no cycling from on to off states. Furthermore, releases of un -burnt natural gas are strictly controlled in industrial locations for safety reasons. Domestic Heating, Water Heating Boilers and cooking Methane emissions from pre-ignition losses of gas appliances domestic properties are based on activity data from Energy Consumption in the UK (DESNZ, 2024) which provides a time - series of gas use for heating, water heating and cooking in the domestic sector, using a series of assumptions regarding the size of units, number of units, age of units, gas flow rates, air flow rates, delays to ignition, operation times from used to determine the percentage of gas that is not burned.