Other fossil fuel mostly consists of wastes, but the nature of waste means that three is a lot of scope for differences in choices of calorific values, gross-net conversions, biogenic/non-biogenic splits, carbon contents, and what is classified as waste (e.g., the Inventory Agency is aware that in earlier years UK energy stats excludes some scrap tyres, but in more recent years they are included). These parameters can additionally var y by end use. The Inventory Agency have not yet managed to fully trace how each of these differences might be contributing to the observed differences between the RA and SA for other fossil fuels in CRF Table1A(c). The overall comparison between the Refere nce Approach (RA) and the Sectoral Approach (SA) indicates that on average the RA estimates are 0.1% lower than the SA estimates. Overall, the SA-RA comparison shows that there is close consistency between the SA and RA datasets for the UK, and provides verification of the reported SA emission estimates for 1A. 48 Note that the average deviation (in this case 1.6%) is the average of the absolute values of (RA/SA-1) for each year, whereas the average % difference (in this case 1.4%) would be the average of (RA/SA-1). Average deviation is always greater than or equal to absolute value of the average % difference. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 131 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Sectoral Approach 1A (Mt CO2) 550.9 561.4 546.5 532.9 524.0 514.6 533.0 512.8 520.3 513.6 Reference Approach (Mt CO2) 546.7 561.8 551.0 532.9 524.6 515.5 532.3 510.5 519.1 515.4 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Sectoral Approach 1A (Mt CO2) 523.8 536.1 521.7 529.9 530.3 527.8 525.9 516.0 504.8 462.5 Reference Approach (Mt CO2) 527.3 533.8 518.6 527.1 528.8 529.8 527.4 515.1 504.7 460.4 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Sectoral Approach 1A (Mt CO2) 480.5 440.2 457.4 442.8 403.6 389.1 371.3 359.4 352.8 338.9 Reference Approach (Mt CO2) 477.6 434.2 455.5 443.1 402.7 391.1 372.3 360.0 352.7 338.1 Sectoral Approach 1A (Mt CO2) 300.7 323.5 Reference Approach (Mt CO2) 298.8 323.1

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 132 International Bunker Fuels (memo item) International bunker emissions (international aviation and shipping) are not included in the national total but are reported separately. These estimates are consistent with the Tier 3 method adopted for aviation and described in MS 7 and the Tier 3 method adopted for shipping as described in MS 13. The methods for the calculation of international bunker fuels are presented in the relevant method statements. Each year the Inventory Agency confirms that the UK energy balance is consistent with data submitted to EUROSTAT and IEA and that the total fuel consumption used for the GHG estimates is consistent with the UK energy balance. For marine bunkers, the UK GHG estimates are based on the bottom -up analysis from the BEIS shipping inventory (Scarborough et al., 2017). This leads to a different total fuel use allocation for m arine fuels from the allocations in the national energy statistics (DUKES) and submissions to Feedstock and Non-Energy Use of Fuels The methodology for estimating emissions from fuels used for non-energy purposes is set out in the relevant sections of this NIR. A summary of the method, including all non -energy uses The UK energy statistics (BEIS, 2022a) contain an allocation for non-energy use for each fuel in the commodity balance tables. The UK inventory estimates emissions from fuels, including emissions arising from non-energy uses. In some cases, the inventory estimate for non-energy use does not agree with the DUKES allocation, and reallocations are made between energy and non-energy use for inventory reporting, if inventory estimates lead to more conservative emission estimates. In 2013, the Inventory Agency carried out research into non-energy uses of fuels; this was followed up by the DECC (now DESNZ as of 2023; also was BEIS between 2016 and 2023) energy statistics team during 2014, and a series of revised allocations were introduced in the Digest of UK Energy Statistics 2014 (DECC, 2014), improving consistency between the inventory and the UK energy statistics. The activity data used for the national inventory and any deviations from the UK energy balance are presented and explained in The evidence that the Inventory Agency uses to make estimates for NEU • annual reporting by plant operators (e.g. , UK ETS returns, which include data on the use of process off-gases in the chemical and petrochemical production sector); • periodic surveys or research by trade associations / research organisations / environmental regulators, such as to assess the fate of coal tars and benzoles, petroleum coke or waste oils, or the impact of regulations on solvents, waste, product • information from literature sources on the estimated split of stored to emitted carbon (and therefore CO 2) related to use of chemical feedstocks, including other country NIRs, where UK-specific information is not available. In many cases, the energy statistics allocate fuels to non-energy use that are used in chemical and petrochemical production processes where • fossil carbon-containing off-gases are used for combustion in facility boilers; or • products containing the “stored” carbon are subsequently used / partly combusted / disposed and degraded with some proportion of the “stored carbon” in products ultimately emitted to atmosphere. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 133 In other instances, the allocation of fuels to “non -energy use” in the UK energy balance is contrary to other statistical evidence from industry or surveys that the Inventory Agency has access to in the compilation of the national inventory. For example, p etroleum coke for residential use was not recorded in the national energy data, nor was industrial use prior to 2008, and so use has been made of other data for both industrial and domestic sector consumption.
Evidence from environmental reporting and from research indicates that several industries use petroleum coke directly as a fuel or process input (e.g. : cement kilns, chemical manufacturing processes, domestic fuel manufacturers), and that petroleum coke is supplied as a fuel for the residential market. Use of UK Energy Statistics in the GHG inventory The main source of official national statistics and energy balances data used in the UK inventory is the Digest of UK Energy Statistics (BEIS, 2022a), hereafter referred to as DUKES. This annual publication gives detailed sectoral energy consumption broken down by fuel type, covering the entire period relevant to the inventory. In many cases, these data are used directly in the inventory without modification. However, the activity data used to derive emission estimates in the UK inventory may not exactly match the fuel consumption figures given in DUKES and other national statistics. This occurs for one of four • Data in DUKES and other national statistics are not always available to the level of detail required for inventory reporting. For example, activity data within DUKES do not distinguish between fuel used in stationary and mobile combustion units. Emissions from these distinct types of appliances have to be separately reported in the inventory and furthermore they may exhibit very different combustion characteristics (for non - CO2 gases) and therefore require application of different emission factors in the UK • Data in DUKES and other national statistics are subject to varying levels of uncertainty, especially at the sector-specific level, and in some cases alternative data suggesting higher fuel consumption are available from other sources, which we use in preference. For example, the UK ETS indicates higher fuel use for several high - emitting industrial sectors which is used in preference to DUKES data. • DUKES and other national statistics do not include any data for a given source. For example, DUKES does not provide any information on secondary fuels such as process off-gases that are derived from petroleum feedstocks and are commonly used as fuels in petrochemical and chemical industries. • Where the DUKES team make improvements to national energy statistics, they typically do not revise the full time series of data; usually, DUKES data a re typically retrospectively revised for up to the 5 most recent years. This can lead to step changes in the DUKES time -series that are due to methodological differences rather than reflecting real changes in fuel use . Therefore, to ensure time series cons istency of reported emissions, the Inventory Agency works with the DESNZ (formerly BEIS) energy statistics team to derive a defensible historic time series back to at least 1990 The rationale for those modifications or deviations from DUKES data that are made, and the sources of alternate data are discussed in the sections detailing methodology for each CRF source category that follow Section 3. A summary of modifications by fuel is given in Annex The modifications described above involve changes to the sector -level estimates of fuel use used in the UK inventory, when compared with the original source data from DUKES. As a

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 134 general rule, the overall demand for each fuel in the UK inventory is kept co nsistent with the overall demand for that fuel in DUKES; the Inventory Agency approach is such that in almost all cases, any modifications to the sector allocation of DUKES data are matched by an equal and opposite allocation change in another sector, to ensure a zero net change in fuel demand relative to DUKES. Annex 4 includes a series of tables that demonstrate this consistency between the UK inventory and DUKES. There are some exceptions to the general rule of consistency with DUKES, for petroleum coke and for OPG, where other statistical evidence indicates that the energy balance data for fuel combustion sources may be too low, and where re-allocations of fuel use from the “non-energy use” lines in DUKES are made by the Inventory Agency (see Annex 4). Apart from DUKES, the main other data source used for fuel use estimates in the inventory is the installation -level data available for processes covered by the UK Emissions Trading System (UK ETS) (BEIS, 2022b), which has been analysed and compared with the data from DUKES. Further details of the analysis of the UK ETS and use of the data within the UK GHG inventory are given in Annex 7 . Further fuel consumption data are taken from the Environmental Emissions Monitoring System (EEMS) data set (BEIS OPRED, 2022) and from data supplied by the UK Mineral Products Association (MPA, 20 22), and from the UK solid fuel supply sector (CPL, 2015). These are used to modify fuel use and emission estimates for 1A1c, 1A2f, and 1A4b respectively, and are described more ful ly in the sections below that deal with those source categories. Fuel use estimates for transport sources also rely upon data taken from DUKES, with some further detail provided from other sources. Combustion of biomass and other biofuels is includ ed in the UK energy statistics and in the UK inventory. The inventory considers the possible use of such fuels in all subsectors of CRF 1A. The UK energy statistics reports biomass activity data that are complete for all UK consumption, and these are presented in the inventory reported across many source sectors. Greenhouse gas emissions including CO2 are estimated for these fuels and presented in the relevant sections of the CRF. The CO2 emissions from biomass are, however, not included in the total UK emissions from fuel combustion and are instead recorded as a memo item.
Emissions of N 2O and CH 4 from biomass combustion are included within the UK inventory totals although in the case of emissions from use of biofuels in road transport or biogases blended with natural gas, the emissions are not reported separately, and are instead included in the emissions reported for the primary fuel these are blended with (such as petrol for bioethanol, or natural gas for biogases). For fuels that contain both fossil carbon and biogenic carbon, the CO2 emissions are reported to reflect the split between these components. The details for the relevant fuels are set out • CO2 emissions are split between other fossil fuels and biomass. CH4 and N2O, and the total activity data, are reported as other fossil fuels. • Natural A small percentage of biogas is incorporated into the UK natural gas grid. The CO2 emissions are split between the gaseous fuels and biomass categories within the CRF. All activity data and non -CO2 emissions are reported in the gaseous fuels • Fossil component of liquid there is a fossil-carbon component of some liquid biofuels (e.g., the methyl group in FAME is derived from fossil feedstock). The activity

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 135 data, and CO2 from biomass are reported under biomass and the fossil CO2 emission is reported under other fossil fuels. The impact of biomass use on carbon stocks in the UK is recor ded in the LULUCF sector; biomass imported into the UK will affect the LULUCF sector in the country from which the When fuels are combusted, a small proportion of the carbon in the fuel is not fully oxidized. For example, unburnt carbon can remain in the ash left after combustion of coal. Emission estimates for CO2 need to take account of any carbon in fuels that remains long -term in this In the UK Inventory, it is assumed that unoxidized carbon is only significant for solid fuels. For gaseous and liquid fuels, although some carbon might not be oxidized fully during combustion (for example emitted as VOC or particulate matter), based on discussions with fuel suppliers, it is assumed than any indefinite storage of unoxidized carbon will be sufficiently trivial to be ignored. For solid fuels, UK -specific assumptions are employed, either based on expert Solid Coal All others All 0% 0% Solid Anthracite 1A4b All 0% 0%

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 136 a Expert judgements provided by UK fuel producers and fuel users (see Baggott et al, 2004). b Calculated from site-specific UK and EU ETS returns for all UK coal -fired power stations except in 2005 and 2016 where no information is available for one site. c From the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, unless otherwise stated. Currently in the UK, CO 2 emitted from flue gases is not captured and stored. This source is The rest of the energy chapter is structured using a series of inventory compilation method statements in order to group together categories where the source data and methods are similar, minimising unnecessary repetition of method descriptions and improving the clarity of stationary combustion, mobile combustion and fugitive sources. MS number IPCC categories Source categories Power stations, Public heat production, refineries (including emissions from flaring at refineries) and other energy industries (collieries, gas production, nuclear fuel production). MS 2 1A1cii Upstream oil and gas production - combustion MS 3 1A2 Manufacturing industries and construction (excluding iron and steel use of derived fuels, and off-road machinery) Iron and steel, and coke manufacture MS 5 1A4ai, 1A4bi, 1A4ci Other stationary combustion

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 137 MS number IPCC categories Source categories Memo item International aviation MS 10 1A3d, 1A4ciii Shipping – coastal, and fishing in UK waters MS 11 1A3d Shipping between UK and Gibraltar, and between UK and OTs MS 13 Memo item International shipping MS 16 1B1ai, 1B1aii, 1B1a2i Coal mining and handling (excluding closed coal mines) MS 17 1B1a1iii Closed coal mines MS 18 1B2 1B2 Natural gas distribution (1B2biv to v). Note that emissions from Natural Gas Production (1B2b2) are reported as ‘IE’, and aggregated in reporting under Natural Gas Processing (1B2b3). Note also that emissions from refinery flaring are included Note that emissions for 1B2c1iii and 1B2c2iii, venting or flaring for oil and gas combined are reported as ‘IE’ a nd aggregated in reporting in oil or gas venting or flaring (1B2c1i, 1B2c1ii, 1B2c2i, MS 19 1B2biv, 1B2bv Natural Gas leakage – transmission, distribution, point of use MS20 1A1ci, 1B1b Solid smokeless fuel production, charcoal production MS 1 Power stations, refineries, and other energy industries Relevant Categories, source names 1A1aiii: Public heat production 1A1ciii: Collieries, gas production and nuclear fuel production

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 138 Burning oil, Coal, Colliery methane, Fuel oil, Gas oil, Landfill gas, Liquid bio-fuels, LPG, MSW, Naphtha, Natural gas, OPG, Orimulsion, Petrol, Petroleum coke, Poultry litter, Refinery miscellaneous, Scrap tyres, Sewage gas, Sour gas, Straw, Waste oils and Wood Note that emissions reported under 1A1b include those from flaring at refineries. The operator- reported emiss ions from refineries include both combustion and flaring sources; it is not possible to disaggregate the data accurately for all installations and years. Hence the UK does not include any emissions from refinery flaring under 1B2c. [Note that this MS coke production, smokeless solid fuel production (both MS 4) and upstream oil and gas production (MS 2).] This Method Statement (MS) includes information about UK power stations, public heat production, refineries, and other energy industries. fossil fuels used by the UK electricity supply industry are bituminous coal and natural gas. The number of coal stations has decreased markedly across the time series, and the number of gas fired stations peaked in 2012 but has decreased slightly since then.
The share of total UK electricity generated in 20 21 was 2.0% from coal and 40.7% from ga s. Nuclear stations generated a further 14.1%, and almost all of the remainder was generated from renewables or non-thermal sources such as wind and hydro (43.2%). Biomass is being burnt at an increasing number of power generati on sites to help electricity generators meet Government targets for renewable energy production. These sites use poultry litter, straw, or wood as the main fuel, whilst many coal -fired power stations have increased the use of biofuels such as short -rotation coppice to supplement the use of fossil fuels. Electricity is also generated in a large number of engines running on biogas at landfill sites and sewage treatment works. CO 2 emissions associated with biofuel combustion are estimated and reported as memo items, but not included in the energy sector; these emissions will be reflected in the LULUCF carbon stocks of the country producing the fuel. Emissions of other greenhouse gases from biofuel use are estimated and included in the national inventory totals, in accordance with IPCC guidance on the treatment of biofuel-derived emissions. Electricity is also generated at an increasing number of Energy from Waste (EfW) installations in the UK. Formerly classed as municipal solid waste (MSW) incinerators, all such installations have since the late 1990s been required to be fitted with boilers to raise power and heat, and their emissions are therefore reported under CRF source category 1A1 (electricity generation), rather than 5C (Waste Incineration). Prior to 1997 at least some MSW was burnt in older installations without energy recovery. Gas oil Gas Waste Biomass Biogas Nuclear 1990 44 8 12 1 2 0 Unknowna 19 1995 23 8 13 18 4 2 Unknowna 16

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 139 Gas oil Gas Waste Biomass Biogas Nuclear aNumber of power stations for early years is unknown although emissions are reported, biogas consumption is bFigure for 2021 not published in DUKES, estimated equal to previous year The UK had 8 operating refineries during 20 21, of which 2 were small specialist refineries employing simple processes such as distillation to produce solvents or bitumen on ly. The remaining 6 complex refineries are much larger and produce a far wider range of products including refinery gases, petrochemical feedstocks, transport fuels, gas oil, fuel oils, lubricants, and petroleum coke. The crude oils processed, refining techniques, and product mix will differ from one refinery to another, influencing the energy use and emissions from the sector. A seventh large crude oil refinery ceased operation in November 2014, and four other major refineries in operation in 1990 closed between 1997 and 2010. Year Crude oil refineries Specialist refineries

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 140 Year Crude oil refineries Specialist refineries Some of the crude oil and natural gas input to the refineries comes from a large number of offshore installations in UK waters, together with a small number of onshore production facilities. Emissions estimates from these activities are described in MS 1 and MS 18. Coal is extracted in the UK from deep mines and open-cast sites. The production of coal has been in rapid decline in the UK and levels of UK activity are far lower in recent years than in 1990. The last large UK deep mine closed in 2015 and so production of deep-mined coal was negligible in 2016-2021. Emissions from combustion at UK collieries are covered in this MS. Fugitive emission estimates from these mining and extraction activities are included in MS 15 and MS Nuclear fuel production is a very minor user of fossil fuel in the UK, and is included in this MS. Activity DUKES (BEIS, 2022a), UK and EU ETS (BEIS, 2022b) Emission Carbon factors are predominantly derived from UK and EU ETS data (2005 onwards), from refinery sector reporting (UK Petroleum Industry Association, 2022) and from the 2004 Carbon Factors Review (Baggott et al., 2004), with some solid fuel factors derived f rom UK research (Fynes and Sage, 1994); non-CO2 EFs are predominantly IPCC defaults An accompanying spreadsheet “Energy_background_data_uk_202 3.xlsx” lists all emission factors used in the energy sector, including a full list of references49. The justification for use of several references, such as UK and EU ETS, the 2004 Carbon Factors Review and Fynes and Sage, are presented in Annex 3.1.3. The calculation of direct greenhouse gases for the sources covered by this MS The sources of emission factors and activity data are summarised under “key data sources” above, with a full list of emission factors set out in “Energy_background_data_uk_2023.xlsx”. The activity data are taken from DUKES, noting the exceptions set out under Assumptions & observations, below. Annex 4 describes the energy balance for the UK and how this is used for the inventory, and any deviations from these data. • Power stations - gas oil / fuel oil / burning oil activity data : DUKES reports less fuel oil burnt by power producers than is reported by operators either directly to the 49 This can be found as one of the additional documents to the NIR . Note that there can be a delay between the NIR being published on the NAEI website after official submission. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 141 Inventory Agency or via the UK Emissions Trading System (UK ETS). For some years this is also true of gas oil, and in the case of burning oil, DUKES does not give any figures for any year. For each oil therefore, we take the larger of either the DUKES is reallocated from industry (1A2) to power stations to ensure con sistency with the operator data, while maintaining consistency with the overall UK fuel consumption data • Coal-fired power stations – oxidation factors (OF). All UK coal -fired power stations report to UK ETS and present installation -specific data on coal composition (carbon 99%.
The factors presented in “Energy_background_data_uk_2023.xlsx” are the factors including consideration of the oxidation factor. The data for recent years is taken from installation-specific analysis through UK ETS, and from the underlying data we can derive the weighted average oxidation factor across UK coal -fired power stations. The data for earlier years is all taken from the Carbon Factors review in 2004. The data may be low compared to the IPCC default, but they are base d on country- specific analysis and the CEF is consistently low across the time series. For 1990 - 2004, the assumed oxidation factor for power station coal is 0.98. For 2005 onwards, CS oxidation factors are derived from the UK ETS data. These UK ETS data indicate that 0.98 is a defensible estimate. • Power stations – The activity data reported in the UK inventory is a combination of non -biodegradable (fossil) and biodegradable wastes and we apply IPCC default carbon factors for each type of waste. • Refineries - OPG activity As noted in the recalculation justification & summary of change section below, for OPG, discrepancies in activity data are evident between UK ETS and DUKES. Based on data from UK ETS and the refinery trade association, UKPIA, potential under-reports were identified in the UK energy balance data for the refinery sector from 2004 onwards, although not in all years. The Inventory Agency takes the conservative approach of using the higher fuel consumption data for each year. The estimat es for 2004 in the UK GHGI are therefore based on data supplied directly to the Inventory Agency by the UK Petroleum Industry Agency (UKPIA) data, whilst the data for 2006 -2011 and 2013-2020 are based on UK ETS data. Data from DUKES are used for 2005 and 2 012. Prior to 2004 the UK GHGI emission estimates based on DUKES energy data are closely consistent with UKPIA sector estimates, and • Refineries - Petroleum coke activity Similar to the issue noted above for OPG, comparison of the AD presented in DUKES versus the AD reported via the UK ETS indicates for several years that the DUKES AD are under -reported. The UK GHGI estimates from refinery petroleum coke use are therefore based on the higher value of DUKES or UK ETS and applying the EF for petroleum coke provided by UKPIA; UK ETS data are higher (and therefore used in the GHGI, deviating from DUKES) for all years 2005 to 2010 and again in 2013 and 2015-2018. In 2011, 2012, 2014, and 2019- 21 the DUKES data are higher than UK ETS and are therefore retained; we note, however that this is a possible over-report and leads to UK GHGI emission estimates for the sector as a whole being higher than UK ETS totals in 2012. The Inventory Agency retains this approach in order to use UK ETS emission estimates as a de - minimis, and taking a conservative approach to deriving the time series of refinery emissions. Note that the UK GHGI estimates for the refinery sector are also higher than the UK ETS figures for 2005: this is because DUKES reports higher consumption of other fuels (including fuel oil and natural gas) than given in UK ETS, rather than due to differences for OPG and petroleum coke as in 2012. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 142 • Public Heat Production – Data for landfill gas and sewage gas combustion, reported in DUKES under the categories unclassified, and public administration, respectively, are allocated to public heat production for the NAEI. Activity data revisions • Revisions to DUKES in 2019 and 2020. No method changes have been made a nd none of the DUKES revisions are notable. Quantitative information on recalculations is included in Chapter 10. Improvements (completed and planned) Recalculations and updates completed as described above. Planned/Ongoing: Emission factors and activity data remain under annual review. Specific QA/QC and validation exercises relevant to these source categories • The Inventory Agency conducts extensive quality checks on the operator-reported UK ETS data emissions, AD, EFs, NCVs. The QC assesses the fuel quality data, time-series consistency of reported data by installation, detailed source -specific UK ETS data against the installation-wide total emissions reported to the EU Transaction Log and UK Compliance Reports, and comparisons between DUKES and EU ETS AD to identify and resolve any potential mis -allocations or under -reports in the DUKES dataset. Findings are discussed with the DESNZ energy statistics team and (where necessary) the UK ETS regulators and/or operators. This process has led to many significant improvements in UK GHGI accuracy; • The comparison of the reference/sectoral approach; • A bilateral exchange with Denmark in 2015, providing peer review and quality assurance in updating to 2006 Guidelines; and • A bilateral exchange with Germany in 2014, providing peer review and quality assurance of the energy sector and refinery estimates. (Ricardo-AEA, 2014). The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Code of Practice for Statistics 50. The UK ETS data, is subject to its own QA process, defined and managed by the competent authority and compliant with UK rules. Activity data for petroleum coke and OPG consumption in refineries are based on DUKES data for certain years, and data direct ly from UK ETS or trade association (UKPIA) for other years in the time series. This is described in the method approach section above. The differing data sources have been used to ensure a consistent complete coverage of emissions from refineries, addressing under-reports in DUKES and ensuring the time series consistency is 50 Available from

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 143 For some sources and fuels, carbon emission factors are taken from Baggott et al., for the period 1990-2003, and from UK and EU ETS for 2005 onwards (2004 is interpolated). This makes best use of available data and the time series trend of EFs shows a smooth transition between data sources.
We note that the key data providers that informed the 2004 Carbon Factors Review are the same operators of high-emitting plants (i.e. power stations, refineries, cement kilns, iron, and steel works) that subsequently provide data to the UK ETS. Therefore, whilst the UK ETS data provides a larger dataset of more detailed, installation -specific fuel composition and hence carbon emission factors for recent years, the underlying source data available prior to EU ETS comes from the same operators. This means that, despite use of a smaller dataset prior to the availability of EU ETS data, the time series consistency of this 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. MS 2 Upstream oil and gas production – fuel combustion Relevant Categories, source names • Upstream oil fuel combustion • Oil fuel combustion • Upstream gas fuel combustion • Gas fuel combustion Gas oil, Fuel gas51 (‘Natural Gas’) This source category comprises emissions from the combustion of all fuels (excluding fuel used for vessel propulsion) including producers’ own fuel gas and purchased fuels such as diesel, through all phases of exploration, development, production and decommissioning for all upstream oil and gas installations on the UK Continental Shelf (UKCS) and o nshore, i.e. including at offshore assets (platforms, Floating Production Storage and Offloading vessels - 51 In UK upstream facilities where fuel gas is used, the gas is predominantly methane and similar in composition to natural gas but also typically contains more higher-chain hydrocarbons (e.g. ethane, propane, butane, C5 and above) and often also higher levels of CO2 and sulphur compounds compared to natural gas that is provided to downstream users via the National Transmission System (NTS) after processing at gas terminals. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 144 FPSOs, and Mobile Offshore Drilling Units - MODUs), at onshore terminals and at onshore 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. As they have high power demands to run the exploration and production operations, most platforms include large gas turbines that are run off a proportion of the fuel gas produced on-site, with smaller supplementary engines, heaters and other units that may burn fuel gas UK GHGI methods are implemented to derive separate estimates (ii) offshore platforms, FPSOs and MODUs. The different methods per installation type reflects the difference in source datasets between onshore and offshore facilities due to different regulatory systems in the UK for the respective installations. Each installation is allocated to either upstream oil or upstream gas production according to the North Sea Transition Authority ( NSTA) definitions of the fields/terminals producing/treating the oil or gas. Activity Gas oil data are primarily taken from DUKES (BEIS, 202 2a); fuel gas (‘natural gas’) data are derived from operator reporting to the UK ETS (2005-) (BEIS, 2022b) and EEMS (1998-) (BEIS OPRED, 2022) reporting systems and quality checked again st UK energy statistics in DUKES (BEIS 2022a). Fuel use estimates for pre -EEMS years, i.e. 1990 -1997, are derived from sector data submitted to UK Government based on operator reporting (UKOOA, 2005). Emission Carbon factors for fuel gas (‘natura l gas’) are derived from operator - reporting to UK ETS (BEIS, 2022b) and EEMS (BEIS OPRED, 202 2), supplemented by operator data for the earlier years in the time -series (UKOOA, 2005); the carbon factor for gas oil is derived from the 2004 Carbon Factors Review (Baggott et al, 2004). Methane and nitrous oxide EFs are based on operator reporting via EEMS from 1998 onwards; these EFs are also extrapolated back to 1990. An accompanying spreadsheet “Energy_background_data_uk_202 3.xlsx” lists all emission information for common activity data sources. 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 method for 1A1cii draws 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. 52 This can be found as one of the additional documents to the NIR Note that there can be a delay between the NIR being published on the NAEI website after official submission. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 145 A more detailed summary of the oil and gas sector analysis is presented in Annex 3.1.6. The methods developed impact both fuel combustion emissions in 1A1cii and all upstream oil and gas fugitive emission sources reported in 1B2 (see MS 18). Across all years of the time series, the fuel use estimates presented in DUKES are incomplete and operator-reported data are used to deliver a complete and accurate inventory estimate. (For further details see the section ‘Assumptions and Observations’.) The key activity and emissions datasets used across the time series • 1990-1997: Inventory agency estimate derived from the UKOOA 2005 aggregated estimates of GHG emissions presented for all offshore and onshore production emissions.
AD estimated from the emissions data, assuming that the sector-wide EFs from 1998 are representative for earlier years for all fuels (diesel, fuel gas); • 1998-2003: EEMS operator-reported fuel combustion emission and activity estimates per installation, from all offshor e mobile and fixed installations and all onshore terminals, supplemented by analysis of the UK ETS National Allocation Plan (NAP) • 2004: EEMS operator -reported fuel combustion emission and activity estimates per installation, offshore and onshore; • 2005-2010: UK ETS (CO2) and EEMS (all GHGs) operator -reported fuel combustion emission and activity estimates per fixed installation, offshore and onshore. EEMS data for all mobile offshore units; • 2010-Current: UK ETS (CO2) and EEMS operator-reported fuel combustion emission and activity estimates per fixed offshore installation; EEMS data for all mobile offshore units; UK ETS (CO 2) operator -reported fuel combustion emission and activity estimates per onshore terminal. Since 1998 there are installation -level d ata reported to UK Government, including fuel combustion estimates that are based predominantly on installation -specific carbon emission factors for fuel gas; this is therefore a Tier 3 method using the aggregate of reported data across all UK installation s, and based on a large dataset of fuel compositional analysis and operator reported emission estimates. Prior to 1998 the source data reported to Government are more aggregated, based on sector surveys and (in some years) the use of proxy data (oil and g as production statistics) to estimate the activity data. An industry submission to Government in 2005 (UKOOA, 2005) provided a comprehensive estimate of sector emissions from 1990 to 2003 based on aggregated UK operator reporting, and hence this is a Tier 2 method. Since 2005, the activity and emissions data for combustion are primarily derived from UK ETS (BEIS, 2022b) reporting for those installations that report to UK ETS, and from EEMS (BEIS OPRED, 2022) data for the sites that fall below the reporting threshold for UK ETS. Analysis has shown that there is a small systematic under -report for fuel gas use by the oil and gas sector for recent years of the time series in the UK energy statistics, DUKES (BEIS, 2022a). Where the fuel combustion emissions are reported for an installation via both EEMS and UK ETS, the UK ETS data are regarded as better quality as they are subject to Third Party verification, as part of the requirements of the trading scheme. However, the scope of reporting under UK ETS is not as complete as EEMS; mobile offshore units (e.g. drilling units) do not fall within UK ETS scope and a number of smaller offshore platforms also report only to EEMS as they do not meet the UK ETS threshold for combustion unit capacity. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 146 Onshore oil and gas terminal operators reported fuel combustion estimates via EEMS from 1998 to 2010. Since 2010, terminal operators are not mandated to report to EEMS and most have ceased to do so, as they are already required to report installation-wide annual emission estimates under the IED/PPC reporting systems to onshore regulators. The UK ETS data provide complete estimates for fuel use at all onshore oil and gas terminals from 2005 The UK ETS CO2 data for high emitting source streams are based on source-stream-specific fuel analysis (i.e. compositional analysis to derive carbon content, NCV) and the assumption that the fuel is 100% oxidised; for example on most oil and gas platforms the estimates of emissions from fuel gas use within turbines, engines, heaters and other units are based on sampling and analysis of the carbon content of the fuel gas. As such the UK ETS data are considered highly accurate; they provide a rich and detailed dataset that exhibits a range of variability in the fuel gas across installations and across the time series. For 1998 to 2004 inclusive, the combustion activity and emissions were reported by all upstream oil and gas installations, offshore and onshore, via EEMS (BEIS OPRED, 202 2). The oil and gas operators subsequently conducted more detailed analysis, to review activity data and carbon emission factors, in the course of developing the National Allocation Plans (Phase I NAP, Defra 2005) 53, in the years leading up to the EU ETS. The ac curacy of the 1998-2003 data was improved through this process in order to ensure accurate emission allocations per installation in the first phase of EU ETS which ran from 2005 to 2007. To derive the UK GHGI estimates for combustion in 1998 to 2004, the Inventory Agency has reviewed the EEMS and NAPs data during the recent oil and gas improvement project and used the best available data per installation. For 1990 to 1997 there are more aggregated data available from industry reporting (UKOOA 2005) that are used to inform the UK GHGI estimates. The 1990 and 1991 CO2 estimates are based on company reported data, with CH 4 and N 2O estimates derived by the Inventory Agency, applying EFs from operator reporting in later years (under EEMS). Data for 1995 to 1997 were compiled from operator reporting under a system with a similar reporting structure to EEMS, but data are only available from across the whole industry, rather than per installation, and hence are somewhat less transparent. The sector estimates for 1992 to 1994 are based on modelling by the oil and gas trade association, using oil and gas production data as a proxy, scaling emissions between the reported data in 1991 and 1995. The fuel combustion in the sector is a minor source of emissions of methane and nitrous oxide. Operators report estimates to EEMS, predominantly applying defaults from operator guidance for fuel gas combustion or gas oil combustion. The inventory estimates are based on the operator-reported estimates from EEMS for 1998 onwards; th e estimates in 1990 -1997 are based on EFs rolled back from EEMS 1998 data.
The DUKES commodity balance tables are regarded as high quality and complete for most fuels and sectors, where the fuel allocations are based on fuel sale s data (from tax records, from annual and periodic surveys), surveys of fuel suppliers and producers, import and export data. However, for the upstream oil and gas sector a high proportion of fuel use (and hence combustion emissions) arise from operators’ own use of fuels (mainly fuel gas, a mixture of methane and other hydrocarbons) that are generated and used on site and are therefore not ‘bought and sold’ (unlike most fuel use across the UK economy), nor are they metered or 53 EU Emissions Trading Scheme, Approved Phase I National Allocation Plan 2005-2007, Defra (2005) phase_1/phasei_nap/phasei_nap.aspx

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 147 delivered through a system (e. g. pipeline network) where inputs and outputs are routinely monitored to track fuel use / sales to recharge the suppliers. The DUKES long-term trends in producers’ own fuel gas use by the upstream sector54 exhibit a ~20% single year step -change from the year 2000 to 2001 that the UK Government (then DECC) energy statistics team confirmed was due to the more complete data capture after the Petroleum Production Reporting System (PPRS) was implemented (DECC, 2012. Personal communication) and was not a ‘real’ change in fuel use. Prior to PPRS the data capture mechanisms in place under -reported the sector fuel use, with data gaps indicated by UK Government energy statisticians for fuel gas use at gas terminals and at oil terminals. This has informed our method choice to deviate from UK energy statistics and to use the industry reported data for emission estimates in the 1990s (i.e. the UKOOA 2005 dataset) in preference as they are the more accurate, complete dataset. Further, the UK energy statistics are still in complete in recent years for fuel gas use, as confirmed during the oil and gas improvement project through analysis of the own gas use reported by UK terminals and consultation with the BEIS energy statistics team. Consultation with BEIS energy statistics (BEIS, 2021. Personal communication) has confirmed that the fuel gas use reported within PPRS and UK ETS from oil terminals is not included in the DUKES data for ‘oil and gas extraction’ use of ‘natural gas’. Hence the UK GHGI method deviates from DUKES, using the operator-reported (and Third Party verified) UK ETS data on fuel gas use as it is regarded as the most complete and accurate dataset for the oil and gas sector. This is a continuation of the method from the 2021 submission. DUKES (BEIS, 2022a) reports gas oil use for the upstream oil and gas sector since 2005 but not for earlier years in the time series; the operator data from EEMS (1998 -2004) and from UKOOA (1990-1997) shows that gas oil has been used by the sector throughout the time series. Therefore, the UK GHGI uses the operator -reported estimates directly for 1990-2004 and the DUKES data for 2005 onwards, which are based on operator returns to EEMS. We note that when the operators’ own fuel gas is the primary fuel for generating heat and power in upstream facilities , it is formed predominantly of methane and has a similar composition to natural gas processed at gas terminals and provided to downstream users in the UK via the National Transmission System (NTS) . However, fuel gas frequently ha s a greater proportion of higher-chain hydrocarbons (e.g. ethane, propane, butane, C5 and above) than natural gas, as well as higher levels of CO 2 and sulphur compounds. Tables in Annex 3.1.6 set out the fuel compositional data for the fuel gas, illustrati ng the variability of CEFs, densities and NCVs of the fuel gas across the UK upstream oil and gas sector. All of the emissions are reported under ‘natural gas’ use in 1A1cii, reflecting the allocation of the fuel gas to the natural gas commodity balance in DUKES. Emissions from OTs and CDs are ‘Not Estimated’ for this source. There is no oil or gas production in any of the OTs and CDs, and only limited well drilling and initial exploration activity (i.e. well testing) in waters around the Falklands Islands in 1998, in 2010, 2012 and 2015. There are no fuel use estimates specific to those exploration activities; it is assumed that any fuel use is accounted for within the Falklands energy balance data. Emission factors for N2O for 1A1cii are higher than the IPCC default range, and this issue has been noted by previous UNFCCC expert review teams. The factors applied in the UK inventory are based on operator-reported data from predominantly offshore oil & gas facilities using fuel gas, which is mainly natural gas or associated gas from oil production. These operator data 2000-2001-2002 64,634 – 65,555 – 78,457 – 79,364. The step change 2000 to 2001 is a 19.7% apparent increase, but reflect better data capture (Personal BEIS, 2012)

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 148 are considered to be more representative of combustion emissions at UK installations than There have been no major recalculations to estimates. Improvements (completed and planned) The oil and gas sector improvement project (Thistlethwaite et al , 2022) has 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 3.1.6. Emission factors and activity data remain under review. Further improvements may be achieved if it becomes possible to obtain more resolved data on fuel gas quality per installation, to improve the assumptions for NCVs and density of fuel gas. The current method applies the best available data from PPRS but this is a separate data reporting mechanism to the EEMS and UK ETS datasets. If a more comprehensive NCV dataset directly from e.g. the UK ETS data reporting, were to become available, this may help to improve data quality.
However, we note that this would not alter the emission totals, but it would slightly improve the accuracy of the AD and EFs. We note that the recent oil and gas improvement project has fully expl ored all available data for the early part of the time series and we see no practicable opportunity to improve the Specific QA/QC and validation exercises relevant to these source categories • The comparison of the reference/sectoral approach; • Comparison of EEMS, UK ETS and DUKES activity data for fuel (natural) gas combustion. The data underpinning DUKES estimates are gathered via the PPRS which presents facility-level activity data that are compared against EEMS and UK ETS to identify and reconcile any data inconsistencies; • Comparisons between EEMS and UK ETS, to review installation-specific activity data and emissions data (and hence implied IEFs for each site and source) to identify any possible gaps in the EEMS dataset, using UK ETS as a de-minimis. The UK ETS data typically covers a smaller scope of activities on a given installation, but the data quality (AD, EFs) are third -party verified, whereas the EEMS dataset should be a comprehensive record of all combustion activities on upstream oil and gas installations but the data are subject to less rigorous QC; • Comparisons of total emissions data reported by each onshore oil and gas installation via the Regulatory Inventories (RIs) to assess time -series consistency and completeness of reporting, comparing CO2 emissions data against those presented in UK ETS (and EEMS if the terminal reports to EEMS also). The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Code of Practice for Statistics. UK ETS data is subject to its own QA process. 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 where consultation

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 149 with the BEIS energy statistics team has confirmed that that the UK energy 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 consistency 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 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 3.1.6. In order to validate the data estimates, the Inventory Agency has derived estimates of fuel gas use per unit production for oil production and gas production back to 1990. There is a general trend to higher fuel gas use per unit production a cross the time series, reflecting the higher energy demands to extract materials from increasingly depleted oil and gas fields, although this trend is not always continuous year to year as some fields cease production and others come on stream. The total fuel gas use is 1.54 TJ net per kt crude oil production and 0.841 TJ net per Mm3 gas production in 1990 whereas by the end of the 1990s the figures are 1.56 and 0.842 in 2000, with increases evident to over 1.60 and around 1.00 by 2002. These figures can on ly be regarded as indicative given the variability in emissions intensity production evident across the UKCS and limited data resolution in the early 1990s, but they do indicate that the derived estimates of fuel use for 1990 are lower than data for later years and of a similar order of magnitude, which is as expected. 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. 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 compensation across the UK The uncertainty analysis set out in Annex 2 provides details of these uncertainty values. Uncertainties in fuel use statistics are typically low. However, we note (as outlined above) that there are known data gaps in national statistics across the time -series and less detailed emissions data available for the 1990-1997 period, and hence uncertainties for the estimates in 1990 are higher than for recent years where much more detailed and complete operator - reporting of activity and emissions are evident. 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 unc ertainties are higher, but since the emissions are smaller, this does not have a significant impact on the overall uncertainty of total GHG emissions.
UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 150 MS 3 Manufacturing industries and construction (excluding iron and steel use of derived fuels, and off-road machinery) Relevant Categories, source names 1A2a – Iron and Steel (combustion) 1A2b - Non-Ferrous Metal (combustion), Autogeneration - exported to grid (coal), 1A2d - Pulp, Paper and Print (combustion) 1A2e - Food & drink, tobacco (combustion) 1A2f - Cement production – combustion, Lime production - non decarbonising 1A2gviii - Other industrial combustion, Autogeneration - exported to grid (gas), Autogenerators Biogas, Biomass, Burning oil, Coal, Coke, Coke oven gas, Colliery methane, Fuel oil, Gas oil, LPG, Lubricants, Natural gas, OPG, Petroleum coke, Scrap tyres, SSF, Waste, Waste oils, This MS cov ers the use of fossil fuels for heat and power production in industry. Estimates cover fuel used throughout industry and including from both large and small installations. Larger installations are included in the UK ETS (2021) and EU ETS (2008 – 2020), but there are large numbers of small industrial plants which are not. Sectoral emissions for iron and steel, non-ferrous metal, chemical, paper, food and drink, and mineral industries are reported under 1A2a to 1A2f. Emissions for fuel use that cannot be allo cated to these industries are According to the 2006 IPCC 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 for natural gas. The UK inventory attempts to report this as far as possible according to the IPCC methodology by placing emission estimates in 1A2g, except for where further information is available to allow the allocation to another source category. The sectoral e stimates 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 NIR 2023 (Issue 1) Ricardo Energy & Environment Page 151 Activity DUKES (BEIS, 202 2a), cement sector fuel use estimates (MPA, 2021) 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_20 23.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. The full breakdown is available for all categories under 1A2 for coal, natural gas, fuel oil and gas oil. Other fuels such as LPG, coke and burning oil cannot be split within 1A2 and are therefore allocated solely under 1A 2g due to a lack of any data on sectoral use in DUKES. 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 • Fuel use in cement kilns (1A2f) is collected from process operators, via the Mineral Products Association (MPA). These data are not complete for all of 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 2008-2021, 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 oth er 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 carbo n-containing process off -gases and residues as fuel

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 152 sources. Consultation with industry and with the BEIS 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 DUKES of the use of process off-gases as an energy source in these industries.
The EU 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 f actors 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 smelter), 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_2023.xlsx” lists all emission factors used in the energy sector, including a full list of references49 • 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 u sed 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 gas- fired autogeneration to 1A2g (as no other sub -categorisation is available) while coal use by autogenerators is 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 153 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. The coal use by autogenerators since 2013 is very low (only 0.2% of total UK coal demand in 2020) compared with earlier years, because of the re-allocation of this one site, but emissions are still reported in 1A2b, in the absence of any information on the nature of the remaining small users. • 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 1A2b in the CRF. 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. Emissions from the combustion of biogas have been disaggregated from category 1A2gviii to the individual industry categories. This has not led to an overall change to the inventory, but a reallocation between the categories is evident. This utilises the data available within DUKES and is consistent with the methodology described here. An improved estimate has been generated for NRMM, which is described in MS6. This has impacted the allocation of gas oil between categories. The reallocation approach is reported There are also minor revisions to DUKES which lead to small changes to the categories included in this Method Statement, for the latest 2-3 years. Improvements (completed and planned) 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 Statistics50. 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 balance 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 availabl e data for recent 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 NIR 2023 (Issue 1) Ricardo Energy & Environment Page 154 Uncertainties f or 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. 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, 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 one of the two coke ovens at Scunthorpe steelworks was closed in early 2016 so, at the end of 2016, there were two coke ovens left in the UK, both at steelworks. 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 numbers of coke ovens and steelworks vary over the period covered by the inventory. Coke production emissions are reported under 1A1ci (combustion) and 1B1b (fugitive). UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 155 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 reported 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 t o 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. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 156 3. Sintering involves the agglomeration of raw materials for the production of pig iron b y 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. 4. Blast fu rnaces 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 mono xide, 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 CRF 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 CRF 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 an d other contaminants. As a result, carbon monoxide and carbon dioxide are emitted from the furnace and are collected for use as a fuel. As with blast furnace gases, some losses occur and these losses are reported with blast furnace gas losses under CRF cat egory 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 (BEIS, 2022a), ISSB annual statistics (ISSB, 202 2), installation-specific activity data from EU ETS (EA, NRW, both 202 2), operator information for integrated steelworks (Tata Steel and British Steel, both 2022) Emission Input parameters for the carbon balance m ethod are derived from EU 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_ 2023.xlsx” lists all emission information for common activity data sources. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 157 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 basi c oxygen steel production. At each stage carbon is input as fuels and/or feedstocks; carbon leaves in products; is emitted to air or removed as waste products. 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 2020 as an example. Note that there is one negative value in the diagram because the figures tak e 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 158 a Other adjustments includes statistical differences (+6 kt CO2), imports (-3414 kt CO2), exports (0 kt CO 2), stock changes ( -620 kt CO2), fugitive emissions from coke ovens reported as methane ( 15 kt CO2), adjustments for natural gas added to coke oven gas (-53 kt CO2), carbon stored in dusts (+23 kt CO2). 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, 2022) and carbon contents from the operators (Tata Steel, SSI Steel, both 2015), and based on their UK and EU ETS reporting (EA, NRW, both 2022). 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 website55) 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 55 ETSEU 3,036 kt CO2 5,891 kt CO2 1,068 kt CO2

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 159 dependent upon the assumptions made about the quantity of carbon in inputs, and in the main input – coking coal – in particular. The carbon content of 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 ETS reporting purposes, and operators have also been able to s upply high quality measurement-based data for the carbon contents of derived fuels, coal tars, benzole, limestone, dolomite, steel scrap, and steel product. The EU 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 ETS data are not available, carbon factors are assumed to be the same as for those years where EU ETS data are available. For each fuel, the average carbon content is calculated for years with EU ETS reporting, and these values then used for the earlier years. 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 -2020 is mostly higher 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 w ith overall UK demand figures in DUKES (since this fuel 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 some what 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, The main recalculation is the reallocation of emissions from blast furnace gas and coke oven gas from 1A2a to 2C1b, this has no impact on the national total. 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 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 carb on 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 Statistics50. 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 160 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 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 onwards, partly due to the Teesside works closing in September 2015, and the sale of the Scunthorpe works to a new operator in ear ly 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 deriv ed factors for coke oven coke, coke oven gas and blast furnace gas for these years are all within the ranges suggested in the IPCC 2006 Guidelines. 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 fac tors 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. 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, Biogas, Biomass, Burning oil, Charcoal, Coal, Coke, Fuel oil, Gas oil, LPG, Natural gas, Peat, Petroleum coke, SSF, Straw, Wood 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 well as natural gas combustion. This gas usage

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 161 may include fuel used for electricity generation for own use by the railw ay 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 (BEIS, 2022a) Emission Baggott et al., 2004, IPCC, 2006 An accompanying spreadsheet “Energy_background_data_uk_20 23.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 3. Carbon emission factors are largely UK specific, whereas non-CO2 emissions use default emission factors. The source representing public sector combustion includes emission s 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 g as 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, including AD used for the estimates of 1A4. These deviations from DUKES are presented in Annex 4. 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. The following summarises the • Any revisions to DUKES and other input data have been incorporated into the inventory, including revisions to natural gas in 2020 • Data for biogas and biomass combustion have been disaggregated across all relevant reporting categories, based on DUKES (formerly all reported in 1A2gviii), affecting categories 1A4ai and 1A4ci, but with no impact on the national total. The impact of changes is set out in Chapter 10. Improvements (completed and planned) Improvements made to this category include the disaggregation of biomass and biogas data, which has led to recalculations in this category but not to the national total. The model for domestic combustion has been upgraded to contain more detail on the technology types and ages used for fuel combustion, as well as to split out dry, wet and seasoned wood. These changes have been made to improve the inventory for air quality pollutants, and as there is no differentiation in the emission factors for greenhouse gases, this has not had any impact on the emissions reported here. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 162 No further improvements to this method are currently planned. Emission factors and activity This source category is covered by the general QA/QC of the gr eenhouse gas inventory in Section 1.3.3. Fuel combustion estimates are verified through the comparison of the reference The energy AD used in these estimates that come from DUKES are subject to the UK Statistics Authority’s Official Statistics Code of Practice50. 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 typically low. The carbon emission factors are based on UK specific data. Since there is a direct link between the carbon emitted a nd 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. Relevant Categories, source names 1A2gvii : Industrial off-road mobile machinery 1A3eii : Aircraft - support vehicles 1A4aii : Commercial off-road mobile machinery 1A4bii : House and garden machinery 1A4cii : Agriculture/Forestry/Fishing - mobile machinery

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 163 Defra commissioned Ricardo Energy & Envi ronment 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 versi on 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 new model for all other off -road Emissions are estimated for different types of portable or mobile equipment powered by diesel or petrol driven engines. These range from machinery used in agriculture such as tractors and combine harvesters; industry such as portable generators, 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 four main • Industrial off-road (includes construction and quarrying) – reported under 1A2gvii; • Aircraft support machinery – reported under 1A3e; • Domestic house & garden – reported under 1A4b; and • Agricultural machinery (includes forestry) – reported under 1A4c. Netcen, 2004, ONS, UKMY, OHEEG, AEA, BEIS Projections (personal Emission Baggott et al., 2004, EMEP/ EEA Guidebook, EU Non -Road Mobile An accompanying spreadsheet “Energy_background_data_uk_ 2023.xlsx” lists all emission information for common activity data sources. 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 hydrocarbon 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 types to estimate fuel consumption Emissions are calculated from a bottom -up approac h 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 are 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 re searched the current UK population, annual usage rates, lifetime and average engine power for a range of different types of diesel and petrol

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 164 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 machinery types was mapped to the relevant regulation in t erms 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 that 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 new 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) and 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 types 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 new 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 from expert judgement from discussions with AEA whe re data are missing, the model was constructed

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 165 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 all types of machinery use gas o il, 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[1] statistics on sales/production of these equipment were used. ONS[2] construction statistics and BEIS 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 [3] continue DfT port freight statistics [4] have been used as proxies for trends in activities for machinery Trends in TRU activities were based on DfT statistics [5] on licensed “insulated vans” vehicle In the new model, emissions from off-road agricultural machinery are calculated using the Tier 3 method in the EMEP/EEA Emissions Inventory Guidebook 2019. The equation to calculate 𝐸 = 𝑁 𝑥 𝐻𝑅𝑆 𝑥 𝑃 𝑥 𝐿𝐹 𝑥 (1 + 𝐷𝐹𝐴) 𝑥 𝐿𝐹𝐴 𝑥 𝐸𝐹𝐵𝑎𝑠𝑒 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 tha t although off -road in these power classes were unregulated in Europe prior to Stage V (which was incorporated into the new model),

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 166 nevertheless it was common for machines in these size classes to be fitted 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 St age 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 new 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 NIR 2023 (Issue 1) Ricardo Energy & Environment Page 167 Airport Port Other Refrigeration Agriculture

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 168 Airport Port Other Refrigeration Agriculture

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 169 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 reconciliation procedure is followed for gas oil which takes account of consumption from all sources, as The IEFs for CH 4 are significantly lower in naei21 compared to naei20. This is due to the application of 2019 Guidebook emission factors for naei21. For 2 -stroke and 4-stroke petrol- fuelled engines, the base emission factors used in in naei21 are the Tier 3 emission facto rs factors for the engine size class “100 <=S <225 cc” (or referred to as size code “SN3” in the Guidebook) have been used, as this is the most conservative assumption. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 170 For naei20, these were based on emission factors developed as part of a large-scale off-road improvement programme over 10 years ago. For petrol -fuelled NRMM in 2020, for example, in naei20, the vast majority of emissions came from generators with an emis sion factor of 2 g/kWh. For naei21, the emission factor from the 2019 Guidebook applied is 0.43 g/kWh; The IEFs for CH 4 are significantly lower in naei21 compared to naei20. This is due to the application of 2019 Guidebook emission factors for naei21. The base emission factors used For naei20, these were based on emission factors developed as part of a large-scale off-road improvement programme over 10 years ago. For gas oil-fuelled NRMM in 2020, for example, in naei20, the vast majority of emissions came from “generator sets 5 -100 kW” with an emission factor of 0.05 g/kWh for all stages. For naei21, the emission factor from the 2019 Guidebook applied is typically lower than this, but significantly, also varies by regulation stage. Therefore, the IEFs in naei21 decrease relative to the naei20 EFs moving towards the later 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 as a driver for the agricultural machinery sector. • Revision to ONS construction output data from 1997. Improvements (completed and planned) Defra have commissioned Ricardo Energy & Environment to conduct a detailed Government- supported machinery population and usage survey with industry stakeholders and evaluating the findings for their use in the inventory. 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 botto m-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 -

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 171 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. See Section 3.2.4 for information. An overall fuel balance taking account of consumption by o ther 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 explained in this section. 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. Relevant Categories, source names International bunkers - Aviation Aviation turbine fuel (jet kerosene), Aviation spirit (aviation gasoline) 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 (2022a); CAA (2022b); BEIS (2022a); DfT (2022) Emission Baggott et al.
(2004); EMEP/EEA (2019); IPCC (1997); IPCC (2006) An accompanying spreads heet “Energy_background_data_uk_2023.xlsx” lists all emission factors used in the energy sector, including a full list of references 49. In addit ion, Annex 3 EMEP/EEA Guidebook aircraft categories. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 172 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 Auth ority (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) 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 and Aviation Spirit Total inland deliveries of aviation spirit and aviation turbine fuel to air transport are given in DUKES (BEIS , 2022a ). This is the best approximation of aviation bunker fuel consumption available and is assumed to cover international, domestic and military use. • 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, 1990-2018

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 173 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. 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_2023.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 airport studies. The time in mode is multiplied by an emission rate (the produ ct 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 or 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. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 174 Idle thrust is nominally 7%, however an adjustment is made to the idle fuel flow to account for The approaches to estimating emissions in the cruise are su mmarised 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 compatible 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 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 Guidebook.
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) 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 BEIS DUKES include the use of both civil and military 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 175 aviation fuel consumption has been used in the fuel reconciliation. Emissions from flights originating from the overseas territories have b een 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 another year 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 follo wing 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 fro m 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 rec ommendation from the UNFCCC centralised review in 2013; and • The CAA data have been supplemented with data from overseas territories, supplied There were minor recalculations from 2017 to 2020 due to revised activity data from Heathrow. These led to minor redistributions of activity amongst sources mostly resulting in changes of Improvements (completed and planned) A number of improvements have been made to the model over recent years, to include findings from UK s pecific research. The 2022 inventory submission included improvements resulting from the adoption of cruise factors from the 2019 EMEP/EEA Guidebook and the assignment of aircraft to the new EMEP/EEA cruise categories . There have also been improvements to helicopter emissions resulting from the adoption FOCA data. 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 inventor y 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 t ime-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. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 176 Uncertainties for both activity and emission factors are based on expert judgement. Th e 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 emitt ed 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.
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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 177 Petrol (gasoline), Diesel (DERV), LPG This MS includes all fuel related emissions from road transport. Emissions from Urea consumption are reported under IPPU and detailed in Chapter 4. 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 BEIS and corrected for consumption by off-road vehicles and the fuel consumed by the Crown Dependencies included in DUKES. Emission COPERT 5.4, EMEP/EEA 2019 Emission Inventory Guidebook. An accompanying spreadsheet “Energy_background_data_uk_20 23.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 a re 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 (0.19%) 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. The UK inventory does not currently estimate emissions from vehicles running on natural gas. The number of such vehicles in the UK is extremely small, with most believed to be running in captive fleets on a trial basis in a few areas. Estimates are not made as there are no separate figures from BEIS on the amount of natural gas used by road transport. The small amount of gas that is used in the road transport sector would currently be allocated to other sources in

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 178 DUKES, and therefore the omission of this source does not represent an underestimate in the 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 de tailed 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 b rief 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 .4 (Emisia, 2020) and EMEP/EEA (2019 ) Emissions Inventory Guidebook, 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 bot tom-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 , higher speed motorways/dual carriag eways and different types of urban roads with different average

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 179 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, 2022a). DfT provides a consistent time series of vehicle km data by vehicle and road types going back to 1993 for the 20 21 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. Additional information discussed lat er (e.g. Automatic Number Plate Recognition data) (DfT, 2022b) are used to provide the breakdown 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 (2013) for the given vehicle type and road type considered. Data for 2015 -2021 were not available for the current inventory compilation and thus they were extrapolate d from 2014 vehicle km data for Northern Ireland based on the traffic growth rates between 2014 and 2021 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 the 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 (2022d). The Northern Ireland data has been combined with the DfT data for Great Britain to produce 3.12 shows the time-series of total UK vehicle kilometres by vehicle and road type for selected Billion vkm 199056 2000 2005 2010 2015 2019 2020 2021 Petrol cars urban 142.2 134.8 118.1 97.8 89.2 88.0 70.3 78.5 rural 140.9 134.1 127.6 110.5 95.5 101.1 76.5 86.0 m-way 49.3 53.0 48.9 41.7 34.3 36.3 26.6 32.8 Diesel cars urban 5.8 26.1 40.3 53.1 65.1 68.6 52.7 56.6 rural 6.1 28.3 47.8 66.6 90.2 98.0 72.1 78.7 m-way 2.8 14.7 25.2 33.6 45.9 46.8 29.5 31.3 Petrol LGVs urban 11.2 4.5 2.1 1.2 1.0 0.9 0.8 0.8 rural 11.6 5.4 2.6 1.7 1.3 1.4 1.2 1.3 m-way 4.0 2.1 1.1 0.6 0.6 0.6 0.5 0.6 56 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 NIR 2023 (Issue 1) Ricardo Energy & Environment Page 180 Billion vkm 199056 2000 2005 2010 2015 2019 2020 2021 Diesel LGVs urban 5.7 15.2 20.7 22.3 25.4 26.4 24.2 26.1 rural 5.9 18.4 25.9 30.1 35.2 40.9 36.6 41.2 m-way 2.0 7.2 10.3 11.4 14.7 17.0 15.7 18.3 Electric cars urban 0.0 0.0 0.0 0.0 0.2 1.2 1.6 3.2 rural 0.0 0.0 0.0 0.0 0.3 1.5 1.9 3.9 m-way 0.0 0.0 0.0 0.0 0.1 0.6 0.7 1.5 Electric LGVs urban 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.2 rural 0.0 0.0 0.0 0.0 0.0 0.1 0.2 0.3 m-way 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.1 Rigid HGVs urban 4.5 3.9 4.0 3.2 3.0 2.5 2.4 2.5 rural 7.1 7.2 7.5 6.7 6.4 6.1 5.5 5.9 m-way 3.7 4.2 4.2 4.1 3.9 4.0 3.6 3.9 Artic HGVs urban 1.1 1.1 1.1 0.8 0.9 1.0 0.9 1.0 rural 4.4 5.2 5.4 5.1 5.3 5.9 5.6 6.1 m-way 4.7 7.4 7.9 7.5 8.4 9.0 8.9 9.5 Buses urban 2.4 3.0 3.1 3.0 2.7 2.3 1.6 1.8 rural 1.7 1.7 1.5 1.6 1.4 1.2 0.9 1.0 m-way 0.6 0.5 0.5 0.5 0.4 0.4 0.1 0.1 M/cycle urban 3.3 2.3 2.9 2.5 2.2 2.2 1.9 2.3 rural 2.0 2.0 2.2 1.8 1.9 1.8 1.5 1.6 m-way 0.3 0.4 0.4 0.4 0.4 0.4 0.2 0.2 Total 423.3 482.7 511.3 507.8 535.9 566.4 444.4 497.3 In the current inventory, a new road classification and traffic speed assignments is 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 the 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 (2022b) 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 181 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. In the current inventory, a new fleet turnover model i s developed that calculates the composition of the UK vehicle fleet and road transport emissions over a time-series from 1990 to 2021. The new model is based on a new, more comprehensive and up-to-date set of vehicle licensing (DfT, 2022d) and annual milea ge data from MOT (Ministry of Transport) records provided by DfT (2022e), covering years between 2007 and 2021 (licensing data back to 1994 and MOT data also available for 2021). These have been supplemented with additional DfT data from the Continuing Sur vey 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 calculate 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 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 regul ation 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, 2022) 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 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 Fuel Consumption Factors for Vehicle Fuel consumption is calculated for each vehicle type using the fuel consumption -speed relationships given in COPERT 5 .4 and the EMEP/EEA Emissions Inventory Guidebook (2019). 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 2021. 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, 2022). The real-world uplift uses empirically -derived equations in the Guidebook that take account of average engine capacity and vehicle mass. 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 182 Source 1990 2000 2005 2010 2015 2019 2020 2021 Petrol cars 55.7 55.0 55.1 55.2 53.6 50.6 50.3 49.7 Diesel cars 53.8 53.2 54.0 54.9 53.2 52.7 52.7 52.6 LGVs 74.5 69.6 66.9 66.2 66.1 66.0 66.0 65.8 HGVs 221.6 208.0 220.8 223.4 226.4 231.6 233.6 233.7 coaches 294.5 270.4 267.3 262.0 255.6 249.4 252.2 250.7 motorcycles 35.6 36.9 35.8 38.8 39.8 38.4 37.4 37.4 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_2023.xlsx”. CH4 and N2O Emission Factors for Vehicle Types The emis sion 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, 2019) derived from COPERT 5.4. 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 mo re 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 d istances travelled by the mix of Euro classes in the fleet each year as well as the proportions of kilometres travelled at different speeds and therefore with different emission factors. These factors also include the contribution from cold start emissions. Gas Source 1990 2000 2005 2010 2015 2019 2020 2021 CH4 Petrol cars 125.4 64.4 36.8 19.3 13.9 11.7 12.0 11.7

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 183 Gas Source 1990 2000 2005 2010 2015 2019 2020 2021 DERV cars 20.1 8.7 3.1 1.1 0.4 0.2 0.2 0.1 LGVs 89.9 28.8 8.8 2.9 1.0 0.5 0.4 0.4 HGVs 88.1 73.2 69.2 39.2 14.6 7.9 7.1 6.6 coaches 152.3 121.7 98.9 57.8 29.0 13.1 11.2 9.5 motorcycles 233.0 206.3 158.7 105.5 82.6 68.1 65.9 65.9 N2O Petrol cars 9.3 12.5 8.7 3.1 1.8 1.3 1.4 1.3 DERV cars - 4.1 6.1 6.7 6.5 6.3 6.4 6.4 LGVs 6.1 5.6 6.6 6.6 6.4 6.0 6.1 6.0 HGVs 35.9 17.7 10.5 18.9 38.2 44.1 45.7 46.1 coaches 35.9 19.1 10.8 14.3 24.4 32.2 34.3 35.0 motorcycles 2.2 2.1 2.1 2.0 1.9 1.9 2.0 2.0 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 (see below) to estimate their emissions. This ensu res 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 BEIS figures for total fuel c onsumption 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. For a valid comparison with DUKES which covers only fossil fuel petrol and diesel, the amount of petrol and diesel displaced by biofuel consumption has been used to correct the calculated consumption of petrol and diesel. 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 normalisa tion 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 DUK ES figures with the maximum deviation being at 16% (for DERV, in 1994). 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 2021 was 100.9% and for diesel was 101.8% 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 184 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. 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 fro m LPG consumption are calculated from the total LPG consumption given in DUKES and fuel-based factors set out in “Energy_background_data_uk_2023.xlsx”. Consumption of LPG is relatively small in the UK (0. 19% of all road fuels in 20 21) and there are no rel iable data on the number or types of vehicles running on LPG. Vehicle licencing statistics suggests that 0.10% of all light duty vehicles ran on LPG in 2021. 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 (20 19) 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 (2019).
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 -

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 185 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 o ther 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. Overseas Territories and Crown Dependencies Fuel consumption data for 1A3b were obtained from national statistics for all Overseas Territories and Crown Dependencies. Fleet composition data were available for all territories and used to disaggregate the fuel consumption data. More detailed fleet composition data for the UK were used to further disaggregate the fuel consumption data in order to apply UK - 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 catalys t 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 we re 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 introdu ction 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. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 186 Time-series emissions revisions are mainly a result of the decrease in vehicle kilometres following a revision to the DfT estimates of vehicle kilometres travelled on minor roads57. 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 An internationally established Tier 3 method is used consistent w ith 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 Processes58. 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 emissi on estimates for CH 4 and N 2O in the on -road fleet composition, catalyst failure rates, trip lengths (for estimating cold start emissions). Relevant Categories, source names 57 frequently-asked-questions#how-have-these-revisions-affected-total-road-traffic-in-great-britain 58 Statement on Quality Strategy Principles and Processes, Department for Transport, available

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 187 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 0.
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 DUKES, Office of Rail and Road (ORR) National Rail Trends Yearbook (NRTY), ORR data portal and Network Rail Open Data Platform Emission EMEP/EEA 2019, DfT’s Rail Emissions Model (DfT 2012b), AP -42 (USEPA), Rail Safety and Standards Board (RSSB; 2020a) An accompanying spreadsheet “ Energy_background_data_uk_2023.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- 2021 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 categories. For the indirect GHG emissions, US EPA emission factors for hand-stoked coal- fired boilers are used to estimate emissions from coal-fired steam trains. The UK GHGI reports emissions from trains that run on gas oil in three freight, intercity and regional. Emissions from these ar e reported under the IPCC category 1A3c Apart from the relevant activity data updates for 2021 all main aspects of the inventory remain consistent with 2020 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 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 ORR’s National Rail Trends Yearbook (NRTY) and data portal (ORR, 2022). Adjustments to the diesel

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 188 vehicle kilometres for 2019, 2020, and 2021 were made to account for the introduction of new bi-mode passenger trains. Train kilometre data for diesel freight train movements in 2019 is available and this has been scaled forward to 2021 and back to 2005 using the trend in net tonne km from ORR’s NRTY and data portal. Train kilometre data for freight trains is also available for 2004 and , similarly, data is scaled bac k 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 -2021. No data is available for the years 1970-2004 and 2010. Therefore, fuel consumption for these years was estimated based on the trend in train kilometres. 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 Fynes & Sage (1994) whilst the CEF for gas oil is taken from Baggott et al. (2004). The N2O emission factor was updated in the 2020 inventory to utilise- the new value provided in the 2019 EMEP / EEA Guidebook for the rail 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 emissions. These are considered most appropriate for the type of coal-fired In recent years diesel passenger train kilometres have steadily increased from 220 million km in 2000 to approximately 255 million km in 201 8; however, in 2019 this metric declined to approximately 248 million km as new bi-mode trains were introduced and declined further to 186 million km in 2020, primarily as a result of reduced services due to the Covid 19 pandemic. Diesel passenger train kilometres then increased slightly in 2021 to 205 million km, but remained much lower than previous years due to the ongoing effects of the pandemic. This trend is generally reflected in the passenger train fuel consumption data. The amount 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 The use of all available historic freight moved data from the Office of Road and Rail data has resulted in small recalculations of freight emissions for some years in the period 1982-2003. Improvements (completed and planned) Various emissions-related projects are currently being undertaken by the rail indu stry 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 189 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 oi l consumed by the rail sector is estimated based on the change in train / vehicle kilometres prior to 2005 and in 2010 and 2019. 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 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 (2022c), MMO Fishing statistics (MMO, 2022), DUKES (BEIS 2022a), Scarbrough Emission IMO (2015). An accompanying spreadsheet “ Energy_background_data_uk_2023.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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 190 Coastguard Agency. This methodology meets and exceeds the requirements of a Tier 3 methodology set out in the EMEP EEA Emissions Inventory Guidebook 201 9 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 proxies for estimating activities in years back to 1990 and forward to 2021 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 The model methodology estimates the Heavy Fuel Oil (HFO) and Marine Diesel Oil (MDO) fuel consu mption 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 aux iliary 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 tailored 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 fleets and service vessels, voyages

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 191 were assumed to be UK domestic if the AIS dataset showed the vessel had star ted 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 oversea s waters, before returning later so emissions could be calculated in 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 tim e period between successive AIS messages from these vessels is known corresponding to the times when the vessels first go out of range o n 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 that are each mapp ed 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 2021). In many cases, multiple statistica l 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 192 SPAS0201. Pre-2003 trend uses the approach described in Entec (2010). Cruise Same approach as used for the Ferry-pax only vessel category ✓ 2010-2021: MMO UK Sea Fisheries Annual Statistics – Chapter 3 Landings. Pre- 2010 trend uses the approach described in Entec (2010). 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 with 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 u sed 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 193 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 onw ards to account for lower fuel consumption per unit (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 2021. 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 main 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 inventory. An update to this work is expected to be introduced into the inventory in 2024 or 2025, using a more recent base year based on newer AIS data. This source category is covered by the general QA/QC of the greenhouse gas inventory in Section 1.6.
The new approach that has been adopted is described in detail in Scarbrough et

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 194 al (2017) and has been peer-reviewed by BEIS, 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 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 calculate d 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. Further consideration o f 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 inventories 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. 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 195 DfT (personal communication), OT port authorities (personal communications), EMEP/EEA 2016 Emission Scarbrough et al. (2017) and based on IMO (2015) An accompanying spreadsheet “ Energy_background_data_uk_2023.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, 2021d) 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 r ecorded. 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 2016 was used to • the average cruise speed of the vessel; • 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 59. 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 59

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 196 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 and 2021, 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 Distance Distances for each voyage for freight and passenger w ere taken from This has a tool t o 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 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 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 to the activity in 2020 as it is now assumed there is non- zero activity in 2020. There has also been a minor upwards increase in the weighted emission factors in 2020 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 2024 or 2025. This source category is covered b y the general QA/QC of the greenhouse gas inventory in Section 1.6. 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 197 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 o ther 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 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_2023.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 supplie rs, vessel movement data or equipment (engine) counts and passenger and cargo tonnage counts. The UK has no separate national fuel consumption statistics on the amount of fuel

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 198 used by inland waterways in DUKES. However, they are included in the overall mar ine 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 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. g/kWh). • 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 vessel used on canals, rivers and lakes and small commercial, service and recrea tional 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 possibl e 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 199 household expenditure on "Recreation and culture"60;. No data were available for this dataset after 2009, therefore a second dataset was used to estimate the activity from 2010: OECD.Stat - ‘Final consumption expenditure of household, UK, Recreation and culture)61; • Commercial passenger/tourist craft – Visit Britain, Visitor Attraction Trends in England 2021, Full "Total England Attractions"62. • 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 consump tion 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 fishing 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 includ ed 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 revision being a 4.2% decrease in private leisure craft in 2020. Updated data for visitor trends has led to an increase of 4.8% in activity for commercial passenger/tourist craft in 2019. Revised data has led to a very minor increase in freight vessels activity in 2019 and 2020 too. 60 61 62 63

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 200 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 2024 or 2025 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 approac h section, above. Consistent time-series of proxy statistics, where available, have been used to estimate the inland waterways activities time -series. For private water craft, two data sets have been combined. Where the two data sets overlap, there is a co rrelation 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. Relevant Categories, source names 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 (BEIS, 2022); Emission Scarbrough et al. (2017) and based on IMO (2015) An accompanying spreadsheet “ Energy_background_data_uk_2023.xlsx” lists all emission information for common activity data sources. UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 201 Fuel consumption for international shipping is taken directly from DUKES figures for international marine fuel bunkers, as discussions with BEIS indicate that the re 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 DUKES 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. 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 in ventory 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 navigation 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. 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 (<3%) due to revised activity data. and military shipping, fishing, inland waterways, international shipping and voyages from the

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 202 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 0.96 0.57 0.57 0.011 1.83 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 Improvements (completed and planned) Improvements may be considered in scope as part of wider improvements planned for 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 a nalysis is set out in Annex 2. Uncertainty for international bunkers is not

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 203 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_2023.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 dir ectly by the Sustainable Development team of the MoD (MoD, 2022). Data are provided on a financial year basis and are amended to derive 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. Revised MoD data has led to a 16% and 7.5% decrease in activity in 2019 and 2020 respectively. There are only minor (<0.4%) recalculations to emission factors due to updated DfT port statistics used to derive the time-series. 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 2024 or 2025. This source category is covered by the general QA/QC of the greenhouse gas inventory in

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 204 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 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. 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 spr eadsheet “Energy_background_data_uk_2023.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, 2022) and IPCC (1997). The military fuel data include fuel consumption by al l 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 constant 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, 2022) is subtracted from DUKES to ensure there is no double counting (see Annex 4). Fuel use for casual uplift is considered to be outside of DUKES. There have been no method changes and revised fuel use statistics from the MoD. Improvements (completed and planned)

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 205 No improvements to this method are currently planned. Emission factors and activity data are This source category is covered by the general QA/QC of the greenhouse gas inventory i n 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. MS 16 Coal mining and handling Relevant Categories, source names 1B1a1ii : Coal storage and transport In 2021 there were only seven 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 an over 99% reduction in UK deep -mined coal production since 2015, according to UK energy statistics (BEIS, 2022). 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 2021. This is compared with 188 deep mining collieries and 126 open -cast mines Activity All activity data on coal production at open cast and deep mines is from DUKES (BEIS, 2022), except for production at licensed mines during 1990-1995 (only) which are from an industry reference (Barty, 1995). Emission Operator reported data on methane emi ssions from deep mines are available for many years of the inventory time -series (1998-2014), and 64

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 206 are used to derive CS EFs (UK Coal, 2015; Coal Authority, 2015), in conjunction with UK energy statistics from DUKES (BEIS, 2022). 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 from the mining activity (i.e. including the methane that i s 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 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 2021 UK activity data on coal production in deep mines (BEIS, 2022). Methane EFs from mining operations from UK research are used to estimate emissions from open -cast mines and licensed mines (both from Williams, 1993), and emissions from coal storage and transport (Bennett et al, 1995). An accompanying spreadsheet “Energy_background_data_uk_2023.xlsx” lists all emission information for common activity data sources. Emissions are calculated from saleable coal pr oduction statistics for open -cast and deep - mined coal, taken from DUKES (BEIS, 2022). For all sources, UK -specific emission factors are applied, which in the early part of the time -series are derived from periodic 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 (BEIS, 2022). 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 total 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 the 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 207 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, 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 coal 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 e ngines. 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 (C). The total methane vented to atmosphere from these sites that report the metha ne 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

UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 208 D = the sum of methane emissions repor ted (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 bo th 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). 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 emissi on 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 takes place. i.e. it is assumed in the UK GHG I that all of the measured methane content of the coal is released prior to combustion, and these emissions are all allocated within 1B1a2i open-cast coal mining (Mining activities).