Tyres contain a mixture of natural and synthetic rubbers, and so where waste tyres are used as a fuel, the emission estimates take into account that only some of the carbon emitted is derived from fossil fuels. Some propane / butane mixtures are used as a propellant in aerosols and are emitted as VOC.
The UK inventory contains estimates of these VOC emissions, combined with emissions of It is assumed that all gas oil used for non -energy purposes is used as a feedstock material, and consultation with DECC (now DESNZ) energy statisticians supports this (Personal Will Spry, DECC Energy Statistics team, 2014). A possible alternative use would be in explosives, but consultation with the Health and Safety Executive, who regulate the UK explosives industry, has confirmed that no UK installations manufacture explosives using gas oil or fuel oil as a feedstock (HSE, 2013).
Lubricants are listed separately in the UK energy statistics and are used in vehicles and in machinery. The inventory includes estimates of emissions of carbon due to oxidation of lubricants during use, and also includes estimates of emissions from the combustion of waste lubricants and UK GHG inventory estimates of the quantities of lubricants burnt as fuels are based on data from Recycling Advisory Unit, 1999; BLF/UKPIA/CORA, 1994; Oakdene Hollins Ltd, 2001 & ERM,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 758 2008, as well as recent research to access information regarding the UK market for waste oils and the impact of European Directives to consolidate industrial emission regulations such as the Waste Incineration Directive (Oil Recycling Association, 2010).
Estimates of waste oil combustion are derived for the following source • 1A2f Other (unclassified) industry. The estimated emissions for other industry assume that waste oils are used by two road- stone coating plant and garages.
Other sectors may use waste oils as a fuel or as a reductant, but research to date pro vides no compelling evidence that there is a significant gap in the UK inventory for waste oil use by industrial operators. The emission trends from power station use of waste lubricants reflect the fact that the Waste Incineration Directive (WID) had a profound impact on the market for waste oil, used as a fuel.
It is assumed that no waste oil was burnt in power stations for the years 2006 -2008, on the basis that the classification of waste oil as a fuel would have led to users being subject to the requirements of WID. In 2009 a Quality Protocol 6 was introduced that allowed compliant fuel produced from waste oils to be burned as non -waste and this has encouraged a resumption in the consumption of waste oil-derived fuels from 2009 onwards.
Carbon dioxide emission estimates for the oxidation of lubr icants within vehicle engines and machinery, and the use of waste oils for energy are all based on a single carbon emission factor derived from analysis of the elemental composition of a series of UK -sourced samples of waste oil (Passant, 2004). The UK inventory adopts the IPCC Tier 1 methodology for lubricant use i.e.
assuming that 20% of all lubricants are oxidized during use. This assumption is used for the various sub-categories of lubricant use (including road, rail, marine, off -road and air transport) In the UK, bitumen is used only for applications where the carbon is stored.
By far the most important of these is the use of bitumen in road dressings. The inventory does assume that a very small proportion of the carbon in the bi tumen itself is emitted as VOC during road -stone coating but does not include any estimates of direct carbon emissions from uses of bitumen.
Industry consultation in 2013 (UK Petroleum Industries Association, 2013; Refined Bitumen Association, 2013) has confirmed that there are no emissive applications of bitumen in the UK. Around 85% of bitumen is used in road paving, with the remaining proportion used almost entirely in the manufacture of weather-proofing materials.
Coal-tars and benzol e are by -products of coke ovens. Consultation with the operators of coal ovens (Tata, 2013) and also the UK company that refines and processes coal tars and benzole (Koppers UK, 2013) has confirmed that all of these materials are collected, refined and processed into a range of products that are not used as fuels.
The carbon within coal tars and oils are entirely used within chemical processes. In some cases, the carbon is processed into anodes used in the ferrous and non-ferrous metals industries and then used (in the UK and overseas) within emissive 6

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 759 applications.
The UK inventory already includes estimates of emissions from UK consumption of carbon anodes within these industries, using methods based on UK metal production statistics. Based on the evidence from process operators, the Inventory Agency allocates all of the reported coal tars and oils to Non Energy Use, i.e.
assuming that all carbon is stored and there are no GHG emissions from this source -activity. The Digest of UK Energy Statistics (BEIS, 20 22) also report the use of tars and benzole entirely to Non Energy Use.
Coal-tar pitch is used in the manufacture of electrodes, together with petroleum coke and a proportion of the carbon ultimately emitted, but details of input materials are scarce; emissions of carbon from these sources are included in the inventory attributed to petroleum coke. This may introduce a small mis-allocation of emissions between petroleum coke and coal oils and tars, but does not affect the UK inventory emissions total.
Natural gas is used as a chemical feedstock for the manufacture of ammonia and formerly for methanol as well, though production of the latter ceased in 2001. Emissions occur directly as a result of a) combustion of natural gas used to power the steam reforming process that is required for manufacture of both ammonia and methanol; b) oxidation of gas in the steam reforming, producing CO2 which in the case of ammonia production is not needed and is instead emitted.
The emissions are reported under 2B1 for ammonia and 2B8 for methanol. Most of the emissions from feedstock use of natural gas in ammonia production are at source, i.e.
waste gases containing carbon are emitted directly from the ammonia plant. Up until 2001, some was exported to a neighbouring methanol plant and here converted into methanol, and this CO2 is treated as stored.
Further CO 2 is captured and sold for use elsewhere, for example, in carbonated drinks and this CO2 is assumed all to be emitted in the UK. A 3.1.3.6 Other Oil (industrial spirit, white spirit, petroleum wax, miscellaneous White Spirit and Special Boiling Point (SBP) spirits are used exclusively for non -energy solvents; SBP sp irits are used for industrial applications where quick drying times are needed (e.g.
adhesives and other coatings) while white spirit is used as a solvent for decorative paint, as a cleaning solvent and for other applications. Estimates of VOC emissions are included in the UK inventory, but no estimates are made of direct emissions of carbon from these products, as they are regarded as “not occurring”. The only emissions from this group of petroleum feedstock that are included in the UK GHG inventory are the releases of carbon from petroleum waxes which are reported under 2D2. These The evidence from industrial reporting of fuel use and from periodic surveys of fuel producers that use petroleum coke to produce domestic fuels (including smokeless fuels) indicates that the allocation of petroleum coke to combustion activities in the UK energy balance is an under - estimate across all years. Therefore, the Inventory Agency generates revised estimates for all combustion activities and effectively re-allocates some of the petroleum coke reported in DUKES as non-energy use to energy-related emission sources in the UK inventory. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 760 Within the UK inventory, petroleum coke is included for the following energy and non -energy • 1A1a: Power station use of petroleum coke, primarily within blends with coal at a small number of UK facilities; in some years only, • 1A1b: Refinery emissions from regeneration of catalysts; • 1A2f: Cement industry use of petroleum coke as a fuel; • 1A2g: Other industry use of petroleum coke as a fuel; • 1A4b: Petroleum coke use within domestic fuels; • 2A4: Use in brick manufacture (reported combined with other emissions e.g. from use of carbonate minerals in brickmaking; • 2B6: Use in chemicals manufacturing; • 2C1: Carbon emissions from electrodes used in electric arc furnaces and ladle arc furnaces and petroleum coke added to furnaces as a carbon source; • 2C3: Carbon emissions from anode use in primary aluminium production; and • 2D3: Petroleum coke used for non-energy applications not included elsewhere. The UK energy balance tables in DUKES contain data on the energy use in power stations (1A1a) and refineries (1A1b), although the former are only available for 2007 onwards, and both sets of data do not always agree with the available activity data from UK ETS. The remaining energy uses in industrial combustion (1A2f, 1A2g) and the domestic sector (1A4b) are not included in DUKES. The UK Inventory Agency therefore makes independent estimates of the consumption of petroleum coke in all of these sectors. Petroleum coke is burnt in cement kilns (1A2f) and has been burnt in some years at a handful of power stations (1A1a). A few other large industrial sites (1A2g) have also used the fuel. Good estimates of the consumption of petroleum coke by these large sites a re available from the operators themselves, from trade associations and from UK ETS data (from 2005 onwards). Fuel grade petroleum coke is also used as a domestic fuel (both smokeless and non-smokeless types, reported in 1A4b). The Inventory Agency uses data supplied by the UK fuel supply industry to estimate petroleum coke consumption for domestic fuels across the time series, from 1990 to the latest year; these estimates are broadly consistent with fuel use data published in earlier editions of DUKES for a few years in the late 1990s. Carbon deposits build up with time on catalysts used in refinery processes such as catalytic cracking. These deposits need to be burnt off to regenerate the surface area of the catalyst and ensure continued effectiveness of the catalyst; emissions from this process are reported within DUKES since 1998 and UK ETS since 2005. Catalyst regeneration is treated in the inventory as use of a fuel (since heat from the process is used) and are reported under 1A1b. Estimates of carbon released from electrodes and anodes during metal processes are estimated based on operator data and reported in 2C1 and 2C3. Petroleum coke content of these electrodes and anodes is estimated based on operator data and literature sources such as Bes t available techniques reference documents (BREF notes). UK ETS data also show that some petroleum coke is added to electric arc furnaces as a carbon source, and the emissions from this use are also reported in 2C1. UK ETS data are also used for emission e stimates for brickmaking, which include a component from petroleum coke. Finally, petroleum coke is used in the manufacture of titanium dioxide, with emission estimates generated from UK ETS and other operator data. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 761 Based on data from DUKES we believe that there is some additional non-energy use of petroleum coke for most years; we assigned this residue to 2D3 and assume that it is all eventually emitted. The consumption estimates for industrial users of petcoke as a fuel or in industrial processes are associated with low uncertainty as they are primarily based on operator reported data within the UK ETS or other regulatory reporting mechanisms. Whilst it is conceivable that other sectors may also use petroleum coke as a fuel, there is no evidence from resources such as UK ETS and Climate Change Agreement reporting that this is the case in the UK. The remaining petroleum coke consumption given in DUKES is therefore assumed to be used in various unidentified non- energy uses, all of which are assumed to be emissive. The estimates of petroleum coke used to generate fuels for the domestic sector are associated with higher uncertainty as they are based on periodic consultation with fuel suppliers to that market, and expert judgement of stakeholders. production, imports and exports of petroleum coke, which together provide more inform ation on the nature of the UK consumption of petroleum coke.
These data cover three distinct types of petroleum coke – catalyst coke, produced and consumed at refineries only (so no import/export or supply of fuel to other UK sectors), and then two product s made in a refinery process known as fuel grade (green) coke and anode -grade coke, with the former being used as a fuel, and the latter being a calcined 7 version of the former, used in various non -energy processes. Consultation with the DECC (now DESNZ) energy statistics team and the only UK refinery with a coking process (DECC, 2013) has confirmed that the UK produces only anode -grade coke, and exports will also be anode -grade coke, whilst imports will be fuel grade coke for use as a cost - effective fuel source or raw material for production processes under NEU. Carbon factors for petroleum coke use are derived from industry-specific data (including UK ETS fuel analysis) in the case of cement kilns (MPA, 20 22), power stations and other industrial s ites (EA, 2022; SEPA, 2022). The petroleum coke factor for refinery consumption is based on trade association analysis conducted as part of the 2004 Carbon Factors Review (UKPIA, 2004) while the factor for domestic consumption is based on compositional analysis of samples of petroleum coke sold as domestic fuels (Loader et al, 2008). These factors do show quite a large variation from sector to this is probably primarily a reflection of the different requirements of fuels for different sectors (higher quality, higher carbon for some, less so for others). The highest carbon factor is for ‘petroleum coke’ burnt in sector 1A1b, but this fuel is actually of a different nature from the fuel burnt as petroleum coke in sectors 1A1a, 1A2f and 1A4b. In the cas e of 1A1b, the fuel is a build -up of carbon on catalysts used in various refinery process units, while in the other three cases, the petroleum coke is a solid by - product of a totally different refinery process (coking) which has different characteristics. A 3.1.3.8 Carbon Storage Import-Export balance for Carbon-containing The analysis within the UK energy statistics or GHG inventory compilation system cannot accurately account for the variable (over time) import -export balance of carbon -containing materials in the UK economy. For example, where the Inventory Agency accounts for the carbon emissions from scrap tyres burned in cement kilns, power stations, incinerators and so on within 7 Calcined petroleum coke is a processed petroleum coke that has a very high carbon content; the resulting fuel is somewhat similar to coke oven coke

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 762 the inventory estimates or from the incineration of plastics or synthetic fibres, there is no way of tracing the quantity that is derived from imported tyres/plastics/fibres. The reported estimate of the fate of the reported NEU of fuels from the UK energy balance is based on an assumed “closed system”, whereby we acc ount for all emissions from carbon - containing products and fuel types that are allocated as NEU as if they are derived from the fuel statistics in the UK energy balance. In reality, the source of the carbon emitted from feedstock and NEU of fuels will part ly be carbon from imported materials, with UK feedstock carbon also exported and emitted elsewhere. EMEP/EEA Aircraft Type CAA Aircraft Types

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 763 EMEP/EEA Aircraft Type CAA Aircraft Types

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 764 EMEP/EEA Aircraft Type CAA Aircraft Types

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 765 EMEP/EEA Aircraft Type CAA Aircraft Types * Scaled to account for later aircraft developments An overview of the time series of estimates of gas leakage at the point of use, together with overall

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 766 Use, including cooking appliances, gas fires and boilers Units 1990 1995 2000 2005 2010 2015 2019 2020 2021 417 470 561 587 608 429 428 431 463 532 479 462 448 401 414 400 415 446 191 171 165 160 144 148 143 149 160 22,182 24,190 27,525 28,447 29,089 22,104 21,819 22,018 23,642 538 688 697 699 636 515 535 501 515 5999 7680 8863 8386 7802 7316 7,383 6855 7,317 ktCH4 1.02 0.94 0.86 0.85 0.8 0.78 0.80 0.83 0.89 ktCH4 0.76 0.83 0.94 0.98 1 0.76 0.75 0.76 0.81 ktCH4 0.83 1.06 1.09 1.05 1 0.91 0.97 0.91 0.96

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 767 Units 1990 1995 2000 2005 2010 2015 2019 2020 2021 2.61 2.83 2.9 2.88 2.8 2.46 2.52 2.49 2.65 A 3.1.6 Upstream oil and gas production (1A1cii, 1B2) The UK has recently completed an oil and gas sector improvement project (Thistlethwaite et al, 2022) which has led to method improvements and recalculations affecting the fuel combustion emissions, reported under 1A1cii, and the fugitive emission estimates, reported under 1B2. The research was commissioned to improve the accuracy and completeness of the UK inventory and to make use of improved oil and gas sector data availability in recent years, such as the North Sea Transition Authority’s (NSTA) new national online data repository ‘Open Data’, which includes field-level oil and gas production data. In addition, the 2019 Refinement to the 2006 IPCC Guidelines for National GHG Inventories (‘the 2019 Refinement’) includes several new or updated inventory methods for the estimation of fugitives from oil and gas production; several of these new methods have been applied in the UK GHGI as they provide the best available basis for accurate and complete UK GHGI estimates, reflecting UK circumstances.
The research (i) a review of the new and emerging datasets, (ii) a critical review of pre-existing reports and data used to inform estimates across the inventory time series, and (iii) consideration of the 2019 Refinement suite of inventory methods for fugitive emissions, including to address any reporting gaps by applying the new methods. This NIR annex text provides an insight into the key data sources used to derive inventory estimates, a summary of the inventory methods that have been developed for use in the 2022 UK GHGI submission onwards and a summary of the recalculations arising from the project. For more information, please refer to the Thistlethwaite et al (2022) project report. A 3.1.6.2 Scope of Upstream Oil and Gas Source Categories in the UK Inventory The scope of emissions from the upstream oil and gas sector in the UK comprise a wide range of emission sources that are reported within the Common Reporting Format (CRF) tables • 1A1cii (fuel combustion emissions); and • 1B2 (fugitive emissions, including from flaring and venting). For the early part of the inventory time series (i.e.1990 to 1997) the emissions data available to inform UK inventory estimates is limited in detail due to the limited source resolution in early industry-wide reporting. Since the inception of EEMS reporting in 1998, there are annual operator emission reports per source per facility. As a result, the ability to generate a consistent time series of emissions per source from 1990 onwards is compromised. Source-specific estimates have been derived by the Inventory Agency through the use of IPCC good practice gap-filling techniques to provide estimates back to 1990; through access to and use of new data to estimate the emission trends across 1990-1997 the oil and gas improvement project has led to improved time series consistency for many sources. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 768 However, the assurance of time series consistency for the sector as a whole may only properly be assessed at an aggregate level (i.e. across 1A1cii and 1B2 combined). The precise source allocation of emission estimates in the 1990-1994 period is subject to higher uncertainty than in the rest of the time series, but at an aggregate level the sector-wide estimates are based on the best available data from Government and industry and analysis indicates them to be time series consistent with data post-1997 at that aggregate level. In developing methods for all sources in the upstream sector, there are several changes over time in data availability to address, most notably for UK energy statistics (due to changes in reporting requirements and data gathering systems managed by the UK Government over the period since 1990) and for atmospheric emissions data reporting. A 3.1.6.3 UK Regulatory Landscape and Key Data Sources (Also see Thistlethwaite et al (2022) project report section 2.2.) The UK regulatory landscape for the oil and gas exploration and production sector is complex, with financial, energy and environmental reporting obligations across a range of onshore and offshore reg ulators. There are separate regulations (and regulatory agencies) governing the requirements for permits to operate or perform certain activities (e.g. well drilling, production activities, flaring, venting) and company reporting of activity data (e.g. pro duction data) and environmental emissions data. As a result, there are numerous permitting and data reporting systems in place across the sector that may provide useful data to inform inventory estimates; systems for onshore installations (well sites, term inals) often differ from those for offshore installations. Furthermore, some data reporting mechanisms provide a high degree of source resolution in annual (or more frequent) operator reporting, whilst others provide no source resolution but rather present activity and/or emissions totals per year per field or per installation. The scope and detail of data available varies considerably across the time series, which reflects the evolution of regulations in the UK and consequent changing reporting requirements on plant operators. There are long-standing data collection and reporting systems evident for activity data, such as from UK energy statistics and from the regulations governing oil exploration and production; even these however exhibit changes in scope, completeness and resolution through For example, at the end of the 1990s there was an overhaul to the reporting to oil and gas regulators regarding oil and gas production, venting and flaring, as a new system, the Petroleum Producers Reporting System (PPRS) was implemented from 2000 onwards, to replace systems that had previously informed the UK Government statistical annual called “Development of the Oil and Gas Resources of the UK ”, known universally as the DTI Brown Book, production of which ceased from 2004. Much more granular data are now available from the PPRS system than were published in the Brown Book, although analysis of aggregate data across the overlap years (2000 to 2003) between the PPRS and the Brown Book indicates a highly consistent overall scope of Therefore, a key challenge to compile accurate and complete inventory activity and emissions estimates is to assess the scope and quality of data reported across these mechanisms and determine how best to integrate them. The U K inventory improvement project has enabled the Inventory Agency to review the data in detail, consult with key stakeholders and to identify where there are high quality data that should be prioritised for use for specific emission sources, and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 769 where there are opportunities to use inter -comparisons (between reporting mechanisms) to validate or improve (e.g. gap-fill) inventory data. Key regulatory and data reporting mechanisms that help to inform UK inventory estimates • Emissions Trading Scheme s (UK ETS, formerly EU ETS): Operators of upstream installations submit annual estimates of CO2 emissions from combustion of fuels (i.e. fuel gas and diesel) since 2005, and from flaring since 2008. The scope of reporting includes all high emitting offshore and onshore fixed installations, and reporting is per installation (i.e.
per platform, FPSO or terminal); the UK ETS scope does not include smaller sites (e.g. onshore well sites and smaller offshore platforms) where the annual combustion and flaring emissions fall below the UK ETS threshold, and it also excludes mobile installations such as drilling units. Data are subject to Third Party verification checks and the system is managed via UK regulatory agencies for onshore (i.e. EA, SEPA, NRW) and offshore (DESNZ OPRED). The UK ETS provides a large, detailed dataset that includes the mass or volume of fuel burned or flared, the NCV, carbon emission factors, oxidation factors. The monitoring and reporting methods agreed across the sector include assumptions such as that flaring efficiency is 98%; sampling and compositional analysis of fuel gas samples is required for high emitting source streams. • EU ETS National Allocation Plans (NAPs) for Phase I and Phase The NAPs for EU ETS Phase I (combustion sources only) and Phase II (combustion and flaring) were prepared in the early 2000s in order to enable trading scheme allocations to reflect the recent historical emissions per installation. The NAPs data present installation totals of CO2 emissions for 1998 to 2003, with no breakdown by source or by fuel; however, due to the different scope of the NAP I and NAP II, an assessment of the emissions from all combustion and from all flaring per installation can be calculated (i.e. flaring by difference between NAP I and NA P II). NAPs data were based on operator activity data and installation-level fuel gas sampling and analysis, to improve the accuracy compared to previous estimates where default carbon emission factors had been applied (e.g. within EEMS reporting) by some operators. Where oil or gas fields were scheduled to cease production pre-EU ETS (which began in 2005), the NAPs excluded the emission estimates from installations for those production streams, to ensure that the NAPs did not over - • Environmental and Emissions Monitoring System (EEMS) 8: EEMS is an emissions reporting system managed by OPRED to accommodate statutory reporting obligations such as those under PPC/IED for reporting of GHG and air quality pollutants from combustion installations above 50MWth. Scope of reporting is from offshore fixed and mobile installations (i.e. it encompasses platforms, FPSOs, mobile drilling units), and includes reporting from the smaller platforms that may fall below the UK ETS reporting threshold. Operators submit annual returns of emissions of CO 2, CH4, N2O, NMVOCs, NOX, CO and SO2 as well as activity data (where appropriate) in tonnes per year. Activity and emissions are reported per source, per installation, i.e. with separate estimates provided for emission sources that may occur on the installation, fuel combustion (fuel gas, diesel consumption), gas flaring, gas venting, well testing, fugitives, direct process sources (e.g. acid gas treatment) and from oil loading. Operators of onshore oil and gas facilities and terminals are not mandated to use the EEMS system but re port their total emissions to the Regulator Inventories (RIs) of the onshore regulatory agencies in England, Scotland and Wales. The data in the RIs is less granular than EEMS as it is 8

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 770 not broken down by source (see below). Up to 2010, however, the onshore terminals did voluntarily report emission estimates per source to EEMS. • Pollution Prevention and Control Regulations / Industrial Emissions Directive (PPC/IED): All onshore terminals and most other onshore facilities (e.g. Natural Gas Liquid processing plant, onshore well sites, transit terminals where crude oil and oil products are stored and transferred between vessels, terminals, refineries, other sites) report to the relevant Regulator Inventory (RI) according to their location. The onshore installations are regulated by the EA (in England), SEPA (in Scotland) and NRW (in Wales). Under the terms of PPC permits, operators submit annual emission estimates per pollutant for all emissions sources (combined) within the boundary of the permitted installation. These annual emission submissions are verified by the regulatory agencies onshore and are then published on public registers. However, for onshore facilities the resolution of emissions data per source is not available, with a single value for each pollut ant per facility. The scope of pollutant reporting is as per EEMS (above), but there are pollutant reporting thresholds which limit the completeness of operator reporting, i.e. annual returns to the RIs may not provide any estimate of pollutant emissions i f the operator determines that the sum of emission across all sources falls below the reporting threshold. In addition, reporting of activity data (e.g. fuel use data, production or throughout data, flaring or venting data) is not required under PPC/IED; • Petroleum Production Reporting System (PPRS): The NSTA’s Petroleum Production Reporting System (PPRS) collects monthly data from operators of onshore and offshore hydrocarbon production in the UK Continental Shelf (UKCS), per oil or gas field and per terminal. The PPRS data provides useful activity data for inventory purposes, such as crude oil and/or gas production per month, own gas use, venting and flaring volumes, and in some cases there are other useful parameters reported such as gas density, gas NCV. The PPRS data are not collected with environmental reporting in mind; they are the basis for DESNZ (formerly BEIS) energy statistics reporting for e.g. crude oil production, dry gas and associated gas production, Natural Gas Liquid (NGL) production, as well as statistics on gas flaring and gas venting volumes. The high level of resolution of data (to field level) and the reporting of similar units (fields or terminals) enables ready analysis of key data that can support inventory estimates; for example, th e sum of production at all Offshore Tanker Loader oil fields (i.e. oil fields not connected to pipelines, and hence reliant of crude oil export via shuttle tankers) directly provides an activity dataset for the annual transfers of crude oil to shuttle tank ers, and onwards to refineries and terminals. The data are available since the inception of the PPRS in 2000.
Whilst the production data are aggregated and published, most of the data in the PPRS reports are not public domain and were provided to the Inventory Agency solely for the purposes of the inventory • DTI annual statistical publication “Development of Oil and Gas Resources of the United Kingdom”, known historically as the DTI Brown Until 2004 the UK Department of Trade and Industry published annual statistics for the upstream oil and gas sector, which brought together statistics from upstream operators that were then rolled into the PPRS reporting system (above) from 2000 onwards. The scope of data reported in those annual publications is similar to the data that can now be derived from the PPRS system, and similarly it underpins the long-term oil and gas production time series that are included in the Digest of UK Energy Statistics (DUKES). Whilst the PPRS data are more granular (e.g. monthly data), for the overlapping years (2000 -2003) there is close consistency, even at the field -level aggregate annual production data. The Inventory Agency reviewed the DTI Brown Book information across 1990-2003, which includes more detail and qualitative information used to establish material flow mapping from oil/gas fields to platforms/FPSOs and then onto specific oil and/or gas terminals. This is critical information to enable the development of the field to installation to term inal mapping that

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 771 is needed to aggregate and compare field-level Brown Book/PPRS data against reported activity and emissions data. As a result, the inventory has been able to perform cross - comparisons to help identify where there may be data gaps or double-counts, and to build a more detailed understanding of production and emissions sources across the UKCS. For example, the Brown Book notes where an installation offshore is not connected to a gas export line, which we then expect to see in the emissions d atasets as a high flaring • Digest of UK Energy Statistics (DUKES): DUKES is one of the primary input datasets to the UK GHGI and the Inventory Agency consulted extensively with energy statistics leads for the upstream oil and gas sector during the oil and gas improvement project. The main points requiring clarification were the relationship between the “clean, final” data that are presented in DUKES and the upstream data inputs from systems such as the PPRS. DUKES includes numerous data time series that are ultimately derived from the upstream datasets outlined above, including data on UK crude oil production, gas production, and on the energy consumption across the sector, which is (in most years) limited to data entries for “oil and gas extraction” for two natural gas and gas oil. In addition, DUKES presents data such as GCVs and NCVs for “natural gas produced” as well as for “natural gas consumed” (i.e. in downstream sectors). There are some data gaps evident within DUKES for some of the historic data, which all previous UK GHGI submissions have also sought to address, the most significant being an under -report in fuel gas activity data presented in DUKES up to the inception of PPRS in 2000. The oil and gas improvement project has provided an opportunity to revisit the estimates for actual fuel gas use, based on analysis of other datasets and testing of the trends reported in different reporting • UKOOA 2005 oil and gas sector data The EEMS reporting system (see above) was developed from an emissions reporting system developed during the 1990s by the UK Offshore Operators Association (UKOOA) in conjunction with the offshore regulator (now OPRED) and managed by a team of consultants that conducted company surveys, data gatheri ng and generated a database of emission estimates. This dataset from 1995 onwards was able to generate source -specific estimates for the sector, in a format closely comparable to the subsequent format of EEMS. Data for 1990 -1994 were estimated and reported to UK Government based on industry surveys in 1990 and 1991, together with an analysis of the production trends across all years. Subsequently the industry conducted further analysis of key emission sources, such as to derive more accurate carbon emission factors per installation, through the process to develop the National Allocation Plans (see above) to underpin allocations per installation for the EU ETS. The 1990-2003 dataset (originally based on the early industry surveys, 1990 -1994, the 1995-1997 data, and then the first few years of EEMS reporting, 1998-2003) were re- analysed to reflect the improvements in industry knowledge, and reported to UK Government in 2005. The UKOOA 2005 data submission has been used in part to inform previous UK GHG inventor y estimates, primarily to inform some of the fugitive source estimates. The oil and gas improvement project has enabled a re -analysis of the data alongside the other datasets that are now available for the early part of the time series. Together with the time series (sector wide and per installation) of oil and/or gas production, and well drilling activity data, the Inventory Agency has used the UKOOA 2005 dataset and IPCC good practice methods to derive estimates per source for the sector back to • NSTA Well Data records, Well Operations Notification System (WONS): The North Sea Transition Authority (NSTA), established in 2016, is the regulator responsible for managing the UK’s well consent system for the oil and gas exploration sector. The NSTA

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 772 manages the data records 9 from well drilling activity (from well spudding, to testing, completions) and well status (e.g. when wells are suspended or abandoned by operators). The Inventory Agency consulted with the NSTA throughout the improvement project in order to access data held in the transactional databases used to manage the consent process, but it was not possible to develop suitable queries to extract useful annual data from these resources.
This may become possible in future, however the existing onlin e data resources for well drilling activity provides a good indication of the level of exploration activity on the UKCS across the time series, with data on numbers of wells drilled per year. A 3.1.6.4 Oil and Gas sector data pre-processing (Also see Thistlethwaite et al (2022) project report section 3.1.) The raw data from the reporting systems outlined above requires pre-processing in order to derive a dataset in a suitable format and with data labels added to enable (i) data from parallel reporting mechanisms to be compared per installation, and (ii) the inventory calculations to be performed. The pre-processing of the raw data • Aggregation and labelling of data from multiple years of reporting to develop a dataset in a consistent format, with data labels added to facilitate subsequent data processing within inventory models (e.g. spreadsheets, databases, coded models). Data labels year of activity / emission, unit of activity / emission, and numeric identifiers to represent the installation or emission source / activity / pollutant. • Initial data consistency checking and ‘cleansing’ to identify and correct data gaps and/or outliers that may affect the accuracy of subsequent calculations, e.g. to apply range checks on input data to identify where a parameter (e.g. fuel gas density, NCV) has been reported at the wrong order of magnitude. • Initial data validation checks and enhancements, for example to conduct time series consistency checks through cross -comparison with other datasets, and to derive other useful parameters for use in inventory methods, e.g. unit conversions / other data transformations to derive weighted-average parameters across a source/sector to apply in The sections below describe the key raw data pre-processing steps and checks that the Inventory Agency has conducted to generate data for input into the source-specific inventory methods. NSTA data (on oil and gas production, own gas use, flaring and venting) is gathered in the monthly operator returns within the PPRS; these data are gathered per individual oil or gas field, i.e. at the level of each individual geological formation that has been developed for production. All of the environmental data reporting, through EEM S or UK ETS, is at the level of the top -side installation, i.e. per oil or gas platform, mobile drilling unit or FPSO. Both types of dataset exhibit data quality problems (or potential problems) such as data reporting gaps and outliers, and both the production / activity and emissions datasets have notable step- changes in data availability across the time series. Inter -comparison of the NSTA/PPRS and EEMS or UK ETS datasets enables gaps and outliers to be checked, corrected where necessary, and uncertainties minimised. To do this, the Inventory Agency researched documentation (e.g. DTI Brown Book section “ Review of Fields in Production and Under Development ”) and online information to develop a mapping to link each geological oil or gas field to the platfor m or FPSO that receives and processes the oil and gas. In many cases the mapping is a 1-1 relationship with 9

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 773 low uncertainty. In cases where there was some uncertainty in the mapping, e.g. fields that may seek clarifications and corrections. This pre-processing step enables text (such as names of fields and installations) to be linked to numeric values for simpler data processing in databases and other models. The process of developing this mapping has also significantly enhanced the information resources available to the UK GHG inventory team, as the research has led to development of a resource of information to aid the understanding of the pipeline networks, out lier oil platforms/FPSOs that are not linked to gas export pipelines (and hence are likely to conduct more gas flaring), and those OTLs where oil loading emissions are expected to be reported within EEMS. Installation-level Data Labelling Similar to the item above, the management of data from numerous reporting systems for a given installation requires the development of a series of translation tables that enable links to be made and calculations performed to compare and/or integrate data from those multiple data sources, to derive “the best” emission estimates per installation per source to minimise inventory uncertainty. Over time, the upstream installations may be opened / closed / mothballed, they may be sold to a new operator, have a change of name, a change of permit reference, they may re -locate (e.g. FPSOs may service one area of production and then be re-deployed to a new area), or they may be divested (one site sold and split into several smaller parts, with different operators and permits) or merged. Furthermore, underpinning regulations and guidance to operators evolves over time and hence the consistency of data reported year to year may change. All of these potential changes to raw data provision may lead to difficulties for inventory compilers in accurately tracking emissions from a consistent scope of emission sources per installation over time. Hence for each installation, clear labelling of input data sources is needed, to provide the requisite references and audit trail for the input data , and to allow querying of the data to check for potential changes in scope. To enable the data tracking, comparisons and (ultimately) the appropriate use of the data in inventory calculations, the Inventory Agency has developed a series of data translation tables to document the data sources and enable the linkages and comparisons to be performed within The development of these data translation tables and detailed enquiry of reported data from across the time series has helped to identify numerous errors and inconsistencies in the data used in previous inventory submissions. For example, it has led to revisions in some site allocations between reporting under “oil production” or “gas production” IPCC source categories, leading to (in general) equal and opposite recalculations between the oil and gas sectors.
Through the research and consultation with industry, the Inventory Agency has also reviewed and updated the scope of installations that are “upstream” oil and gas sites, including the identification and removal of some double counts with downstream or other industrial sites. For example, one LNG terminal and one power plant (previously considered part of an adjacent terminal) were included within the scope of upstream estimates in previous submissions, and also the associated fuel use and emissions were included in other inventory sectors (i.e. 1A1ci and 1A2gviii in those Activity and Emissions Data Pre-Processing UK The reported CO2 emissions (and underlying AD and EFs) from the UK ETS are from a very limited sub-set of inventory (mostly key) source categories, • Upstream oil production; Upstream gas production

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 774 o Fuel Fuel gas or Diesel The allocation of the UK ETS data to flaring, fuel gas combustion or diesel combustion is then conducted manually by the Inventory Agency, through review of the reported parameters (activity data, emission factors, oxidation factors, NCVs) and the accompanying text descriptions provided • An oxidation factor (OF) of 98% is used for flaring 10; an OF of 100% is used for • Diesel use is identified through returns indicating source type “combustion: commercial standard fuels”, source stream description “Gas/diesel oil” or “Diesel”, and a CO 2 EF for • Fuel gas is identified through returns indicating source type “combustion: other gaseous and liquid fuels”, source stream description may be wide range of names but typically includes “fuel gas” or “export gas”. The activity data, emission factors and NCVs show a wider range of variability, with typically EFs in the range ~2.5 to 2.8 tCO2 per tonne The Inventory Agency has access to detailed UK ETS (formerly EU ETS) data available from 2013 onwards (i.e. Phase III of EU ETS) and for some earlier years back to 2005, and hence there is a relatively large, detailed dataset and the emission sources and fuel types / qualities per installation show good time series consist ency. For some earlier EU ETS years the Inventory Agency does not have access to fully detailed data (i.e. information per source, per fuel, including EFs, NCVs) but does have the (public domain) EU Transaction Log emission totals per installation, and EEMS reporting for offshore installations which does present data split between combustion and flaring sources also. A similar, but simpler, data allocation process as applied for the UK ETS data is conducted for the EEMS data reporting, in order to ali gn the reported data to installation codes and to UK inventory source categories and fuels / activities. The EEMS data reporting documentation assigns each line of data to one emission source from a defined list of sources, together with the operator name, facility name and type (fixed or mobile). The annual emissions data and activity data (“Total use”) are all presented in mass units (tonnes). The EEMS emission sources are used in the inventory for both upstream oil or gas installations, and • Gas in either turbines, engines or heaters, each with different default EFs per pollutant. Scope of CO2, NOX, N2O, SO2, CO, CH4, NMVOC. • Diesel consumption : Scope and resolution of data reported is the same as for gas consumption. Notably a high proportion of the diesel use is reported as used in engines • Fuel Oil consumption : Scope and resolution of data reported is the same as for gas consumption. Reporting of fuel oil use is limited to a small number of sites and years. • Gas flaring : Scope of CO 2, NO X, N 2O, SO 2, CO, CH 4, NMVOC. Sub - categorisations of flaring (e.g. gross, routine operations, maintenance, upsets/other) are used by some operators but does not appear to be reported consistently. • Gas Scope of pollutants is CO 2, CH4, NMVOC. As with flaring, sub - categorisations of venting (e.g. gross, maintenance, operational, emergency) are used by some operators but does not appear to be reported consistently. 10 Shaw et al., 2022. Through the analysis of emission plumes in the North Sea between 2018 and 2019, it was found that 98.4% of carbon in flared gas is converted to CO2

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 775 • Well Reported under either Emission Category Oil or Gas, defining whether the well being drilled was for oil or gas exploration. Scope of CO2, NOX, N2O, SO2, CO, CH 4, NMVOC. The EEMS operator guidance indicates that the emissions are primarily due to the flaring of gases as the liquid and gaseous materials eluted from a well test are separated, with the liquids collected for disposal. • Fugitive emissions : Scope of CO 2, CH 4, NMVOC. The vast majority of reported fugitives are described (sub -source) as gross, but in some cases more details are provided of the precise source (e.g. valves, connectors, open-ended pipes). • Direct Scope of CO 2, NOX, N2O, SO2, CO, CH4, NMVOC. Many of the direct process entries have further information provided to clarify the source, which are sour gas vent, thermal oxidiser, acid gas treatment, amine regeneration, • Oil Scope of CH4, NMVOC. Analysis of the time series of EEMS data shows that the reporting of this source is inconsistent with many sites only reporting the source intermittently. This source is only reported by OTLs, and not by upstream gas producers nor oil sites connected to pipelines. • Storage Scope of CH 4, NMVOC. This source was used in the earlier years of EEMS reporting by the terminal operators. Since 2010 when reporting to EEMS was deemed not to be a mandatory requirement for terminal operators (as they also report to the RIs), this source is not reported consistently in EEMS. all historic EEMS data, i.e. from 1998 onwards. These data are then quality checked, e.g.
time series checks to identify gaps and outliers in AD and EFs, compar ed against the UK ETS data (for flaring and combustion sources) and applied within the inventory source category National Allocation Plans (NAP): During the oil and gas improvement project, t he Inventory Agency analysed the UK’s National Allocation Plan for Phase s I and II of the EU ETS and compared them against the original EEMS data submissions, to assure and/or improve the accuracy of the data for the upstream sector in the 1998 -2003 period. Further information about this process and its effect can be found in 2022 submission of the UK GHGI Annual Report11. Since 2000, operators have submitted monthly data returns from individual oil and gas fields, and from oil, dry gas and associated gas terminals; these data are useful to inform or quality check inventory estimates. The PPRS data are confidential and have not been made available previously for use in the UK inventory development. The Inventory Agency is now granted annual access to the data and reviews in detail to identify opportunities to improve inventory estimates. The monthly reports are available for defined unit types ( oil, dry gas, associated gas; oil, gas, offshore loaders, onshore loaders), with a consistent scope of data fields reported by each operator per unit type. The Inventory Agency has critically reviewed the data across the time series, to check for time series consistency, look at data reporting gaps per terminal or oil or gas fields, and assess how best to use PPRS data to improve the accuracy and completeness of We note that these PPRS data underpin the UK energy statistics for the sector, with quality checks and data gap-filling conducted by the DESNZ energy statistics team. The oil and gas improvement project has afforded a useful parallel analysis of the data; in many cases the Inventory Agency has been able to reproduce the data that is published in DUKES and hence understand more 11

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 776 completely the processing that is conducted and the scope of data that is used to inform energy statistics, including not only the annual fuel use totals but also useful other parameters such as fuel calorific values and densities. Deviations from UK energy statistics based on the analysis from PPRS are detailed in the inventory methodology sections, including (i) total upstream oil and gas fuel gas use, (ii) fuel gas NCVs, and (iii) oil loading activity data from Offshore Tanker Loaders (OTLs). The analysis of the PPRS datasets per unit type implemented numerou s data checks (e.g. time series consistency, outlier identification, internal consistency checks such as mass balance on material flows though the terminals), gap-filling and aggregation of data to compare against other datasets, such as the industry summa ry data presented in DUKES or other DESNZ statistical outputs. This detailed “deep dive” analysis enabled the Inventory Agency to assess the overall data quality per PPRS report, and to better understand the scope and potential usefulness of the different monthly returns, the parameters reported and the expected internal consistencies for each PPRS report. Once lessons had been learned and (for example) acceptable ranges of parameters identified, more automated approaches were developed to conduct data cleansing of the raw data, identifying data gaps or outliers, and applying assumptions to derive a revised, more complete and internally consistent dataset for subsequent use in inventory methods. The PPRS data provide a detailed insight into the variable quality of the products and the eluted gases at each site, which in turn reflect the variability of the geological formations across the different areas in the UKCS and the changes over time as production trends have shifted across the many individual oil and gas fields. The PPRS data that have been used in the inventory • Time series of field, installation and sector-wide crude oil and natural gas production data, used primarily as a proxy dataset to address reporting gaps, i.e. to help identify wh ere emissions data may be missing from EEMS, and in some cases estimating emissions in a missing year using production trends as the proxy to indicate activity and emission • Time series of fuel gas density and calorific values, derived for the diffe rent types of installation and fuel gas, to reflect whether the origin of the fuel gas was a dry gas field / installation / terminal, or associated gas from an oil field / installation / terminal. • Time series of production from Offshore Tanker Loaders (OTLs) to underpin the oil loading fugitive emissions from transfers of crude oil to shuttle tankers, for transport to shore. The tables below present the fuel quality data that have been derived from PPRS, EU ETS and EEMS data across the time series, including the CO 2 EF per TJ (net), NCV and density of fuel gas in four offshore oil installations, offshore gas installations, oil terminals and gas terminals. The variable fuel gas composition across the different sub -sectors of the industry is based on the annual weighted averages of operator-reported data from each UK installation and reflects the different composition of the untreated fuel gases that are encountered at the different stages of upstream oil and gas production.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 777 Installation Units 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Oil field tCO2/Tjnet 64.5 64.5 64.5 64.5 64.5 65.1 64.8 66.4 65.3 64.1 Gas field tCO2/Tjnet 59.0 59.0 59.0 59.0 59.0 59.5 59.3 60.7 60.0 58.2 Oil terminal tCO2/Tjnet 65.7 65.7 65.7 65.7 65.7 63.4 64.1 67.3 67.6 66.7 Gas terminal tCO2/Tjnet 58.6 58.6 58.6 58.6 58.6 56.5 57.1 60.0 59.7 58.5 Installation Units 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Oil field tCO2/Tjnet 64.2 63.4 63.8 64.4 64.1 63.1 62.9 63.6 63.4 64.0 Gas field tCO2/Tjnet 58.6 58.3 57.4 58.1 58.1 59.0 58.1 58.1 58.0 57.2 Oil terminal tCO2/Tjnet 66.6 68.8 67.5 67.8 67.4 67.4 67.3 67.2 64.4 65.2 Gas terminal tCO2/Tjnet 57.6 57.4 57.3 57.6 56.8 57.0 56.5 58.8 57.6 57.5 Installation Units 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Oil field tCO2/Tjnet 63.2 65.4 64.0 62.9 65.3 64.5 63.9 63.7 64.1 63.0 Gas field tCO2/Tjnet 57.5 58.2 60.4 62.0 58.3 59.1 59.3 57.4 58.3 58.5 Oil terminal tCO2/Tjnet 67.2 70.2 68.7 66.5 68.3 67.9 66.9 67.3 67.3 66.5 Gas terminal tCO2/Tjnet 57.2 57.8 59.4 57.6 57.1 57.2 57.5 57.9 58.1 57.3 Oil field tCO2/Tjnet 63.0 61.4 Gas field tCO2/Tjnet 59.0 59.9 Oil terminal tCO2/Tjnet 66.3 66.2 Gas terminal tCO2/Tjnet 56.2 57.0

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 778 Installation Units 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Oil field GJ/tonne 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 Gas field GJ/tonne 45.7 45.7 45.7 45.7 45.7 45.7 45.7 45.7 45.7 45.7 Oil terminal GJ/tonne 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 41.7 Gas terminal GJ/tonne 46.1 46.1 46.1 46.1 46.1 46.1 46.1 46.1 46.1 46.1 Installation Units 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Oil field GJ/tonne 41.7 42.1 41.5 41.3 41.5 41.7 41.7 41.5 41.7 41.5 Gas field GJ/tonne 45.7 46.1 45.8 45.8 46.0 45.1 45.6 45.4 45.2 46.0 Oil terminal GJ/tonne 41.7 42.1 41.5 41.3 41.5 41.7 41.7 41.5 41.7 41.5 Gas terminal GJ/tonne 46.1 46.8 46.2 45.9 46.1 45.5 46.1 46.3 45.9 46.2 Installation Units 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Oil field GJ/tonne 41.9 40.6 41.2 41.8 40.8 41.0 41.7 41.6 41.4 41.9 Gas field GJ/tonne 46.2 45.6 44.2 45.6 45.8 45.3 45.2 46.4 45.8 45.5 Oil terminal GJ/tonne 41.9 40.6 41.2 41.8 40.8 41.0 41.7 41.6 41.4 41.9 Gas terminal GJ/tonne 46.4 45.9 44.7 46.0 46.2 46.3 46.5 46.4 45.7 46.0 Oil terminal GJ/tonne 42.1 42.1 Gas terminal GJ/tonne 46.2 46.2

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 779 PPRS vs DTI Brown Book Once the PPRS data quality checking and cleansing was completed, the Inventory Agency conducted further data quality checks focusing on the time series consistency of field-level oil and (dry or associated) gas production data between the 1990- 2003 datasets from the DTI Brown Book compared against the 2000 onwards PPRS data. This comparison indicted that the overlap years of 2000-2003 show very close consistency for all crude oil production data, not only at the overall level (as summarised below), but also for each individual field. The close comparability of the overlapping years in the two datasets gives a high level of confidence that the data reported across the time series from the two data sources are on a consistent basis and scope. There are l arger differences evident of around 1 -2% for the gas production data, but no systematic difference and an average difference of only 0.8% across the four years, again indicating that there are no step -changes in the scope of gas production data UKOOA 2005 submission to UK The oil and gas improvement project afforded the Inventory Agency the time and resources to evaluate the UK Offshore Operators’ Association (UKOOA). For each emission source, the Inventory Agency has (i) assessed the data quality in the UKOOA 2005 dataset against the EEMS and NAPs data for the “overlap” years of 1998 to 2003, to identify any key outliers or step changes in the data, and (ii) developed a time series per inventory emission source back to 199 0 using the best available data and applying IPCC good Installation Units 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Oil field kg/sm3 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 Gas field kg/sm3 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 Oil terminal kg/sm3 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 0.86 Gas terminal kg/sm3 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 0.76 Installation Units 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Oil field kg/sm3 0.86 0.86 0.86 0.86 0.85 0.86 0.85 0.86 0.85 0.85 Gas field kg/sm3 0.76 0.75 0.76 0.76 0.75 0.77 0.76 0.75 0.76 0.75 Oil terminal kg/sm3 0.86 0.86 0.86 0.86 0.85 0.86 0.85 0.86 0.85 0.85 Gas terminal kg/sm3 0.76 0.75 0.76 0.76 0.75 0.77 0.76 0.75 0.76 0.75 Installation Units 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Oil field kg/sm3 0.84 0.86 0.85 0.85 0.86 0.85 0.85 0.84 0.85 0.85 Gas field kg/sm3 0.75 0.76 0.78 0.76 0.75 0.76 0.75 0.75 0.75 0.76 Oil terminal kg/sm3 0.84 0.86 0.85 0.85 0.86 0.85 0.85 0.84 0.85 0.85 Gas terminal kg/sm3 0.75 0.76 0.78 0.76 0.75 0.76 0.75 0.75 0.75 0.76

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 780 practice gap -filling methods . Further information about this process can be found in 2022 submission of the UK GHGI Annual Report12. A 3.1.6.5 Oil and Gas sector methods under 1B2 Method statement 18 presents an overview of the data sources and methods developed and applied in the UK GHGI for upstream oil and gas sector fugitive source categories that are reported in 1B2.
This annex presents additional details per source category. For all sources, the individual assessment of uncertainties (as presented in Thistlethwaite et al, 2022) have been used to inform the uncertainty parameters per category, per gas, as presented in Annex A2.3. 1B2a1: Oil Exploration; 1B2b1: Gas Exploration • Onshore conventional oil well exploration • Onshore unconventional gas well exploration The initial phases of exploration for oil and gas resources lead to fugitive emissions of GHGs; these sources occur prior to production, including prospecting, exploratory well drilling, well testing, completion, field and well development. In the UK the main emission source is in the well testing phase offshore, where wells are drilled and tested to assess the available resources, the field depth, pressure and so on to assess the feasibility of extracting the oil or gas. During the well tests, the produce d fluids are separated, water and oil collected, and the gases are flared. These activities may be conducted directly from existing platforms, or from Mobile Drilling Units (MODUs), and all UK operators report their well testing emission estimates to EEMS. The 2019 Refinement (Energy Volume, Fugitives Chapter page 4.48) notes that there are no EFs for offshore well drilling / exploration activities and that these emissions “ are thought to be negligible ”; we interpret this to mean that the fugitive leaks from the initial phases of well drilling may be assumed to be negligible and/or dissolve in the water There are a small number of onshore oil wells in the UK; there are limited emissions data reported by operators within the IED/PPC regulatory inventories as often the level of annual emissions of GHGs from these well sites fall below the reporting threshold. The NSTA Well Operations Notification System (WONS) includes reports on annual well drill ing activity, and these data can be used to derive GHG emission estimates from the exploration phase, using the method set out In 2021 BEIS commissioned a separate study to estimate GHG emissions from unconventional gas well drilli ng. Very minor emissions of methane are reported in 1B2b1 from the exploratory drilling conducted in the UK during 2010 to 2019, however t here was no subsequent gas • Carbon dioxide, methane, nitrous oxide, oxides of nitrogen, carbon monoxide, sulphur dioxide, NMVOCs, particulate matter 12

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 781 • IPCC 2019 Refinement Tier 1 Emission = AD x Default EF • Activity Number of conventional oil wells drilled per year. These data on wells drilled onshore area available across the time o 1990 to 1993: DTI Brown Book 2001, Appendix 4; o 1994 to 1999 data from DTI Brown Book 2004; and o 2000 onwards from the NSTA Well Operations Notification System (WONS) 13 annual reports on drilling activity • Emission Factor(s): Default (D) EFs from IPCC, mass of pollutant emitted per conventional Offshore Oil Well Testing and Offshore Gas Well testing • UK industry Tier 2/3 method, utilising the facility -level EEMS data for 1998 onwards, the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of well testing activity for 1990 -1994 through extrapolation back from 1995 using well drilling statistics. The EEMS dataset specifies if the well test was for oil or gas. • The EEMS data (BEIS, 202 2a) present the AD in tonnes (of gases flared) and the emissions of individual gases CO2, CH4, N2O, NOX, NMVOC, CO, SO2. • UK GHGI emissions = ∑ operator emissions data per pollutant • EFs for each pollutant are EF = ∑ operator emissions / ∑ activity data Onshore Unconventional Gas Well Exploration • UK GHGI emissions = ∑ operator emissions data per pollutant • Information obtained direct from the regulatory agency, the EA, for each of the 12 well sites spudded during 2011-2019; none of these wells have gone into production. Method Assumptions and Observations • There is no unconventional oil exploration and production in the UK. The method applied to the onshore conventional oil sector is taken from the 2019 Refinement and addresses a minor gap in UK regulatory reporting, as the well operators onshore seldom exceed the reporting threshold for IED/PPC reporting dur ing the exploration phase. There is a small risk of a minor double-count if for some of the larger well sites the operators have included some well drilling/exploration emission estimates in their annual submissions to regulators (which are used in the method outlined below for onshore oil production emissions). • Well testing emission estimates on an installation -specific basis are included within the EEMS datasets from 1998 onwards at all sites of offshore exploration activities within UK’s territorial waters, including data on both activity and emission factors of excess gas that is flared or released to the atmosphere. Emissions released at the seabed are not included in estimates; it is assumed that any such releases will dissolve in the water column without subsequent release to the atmosphere. • In the EEMS dataset there is no separate reporting of emissions from well drilling, completions and testing; it is assumed that any releases of gases at the seabed during drilling or completions will dissolve in the water column, whereas any fugitive releases on the rigs are reported within EEMS. The Inventory Agency has consulted with the Co - ordinating Lead Author of the 2019 IPCC Refinement, Energy Fugitives, and national expert in oil and gas emissions inventory reporting, and confirmed that there are no default 13

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 782 data to estimate well drilling and completion emissions in offshore production; therefore, the UK inventory estimates are considered to be accurate as they based on the best available operator-reported data, complete and consistent with the IPCC Guidelines. • In the UK there are no known omissions. The addition of estimates for onshore oil well exploration address a minor gap in previous UK submissions.
There is a risk that operators offshore ma y not report their oil or gas well testing activity to EEMS; mobile drilling units by their nature are deployed across different production regions of the world and hence they may appear and disappear from the EEMS reporting year to year, which makes it di fficult to evaluate the completeness of EEMS over the time series. However, we have no evidence that under -reporting occurs. The UK inventory includes estimates from drilling activities at unconventional gas sites during 2010-2019, which total <60 tCH4 in any one year; see Method Statement 18 for details. • The onshore oil production method is Tier 1, applying default EFs from the 2019 IPCC Refinement which are associated with high uncertainty (cited as -12.5% to +800%). It is a minor source in the UK context and hence does not impact significantly on overall inventory uncertainty. The oil and gas well testing EFs that operators typically apply in their EEMS returns are taken from operator guidance that was last updated for this source in 2008, based on UK industry research. There is some uncertainty that the carbon emission factors from that research are representative of the carbon content of the eluted gases from all oil and gas wells across the UKCS, given the range in crude oil, associated gas and dry gas compositional analysis that is noted from different installations reporting from different production areas on the UKCS. However, the data are UK -specific EFs, derived from analysis of fluids from UKCS production historically. • Time Series The underlying data (well drilling numbers) for the onshore oil exploration source is time series consistent. The offshore oil and gas well testing reporting by operators has been to a consistent reporting mechanism since ~1995. The 1990-1994 data are extrapolated using IPCC good practice methods, i.e. proxy data on well drilling to deliver a time series consistent dataset as far as is practicable. Scope for future research and improvement • To conduct drilling activities, offshore operators are required to report to NSTA under the Energy Act / Petroleum Act, request drilling consents, submit data to the NSTA WONS portal and also apply for Consent to Locate to a given oil or gas field. Through analysis of information on Consent to Locate and PETS EIA directions, it may be feasible to check on the completeness of reporting to EEMS by MODUs, i.e. to ensure that all operating MODUs have reported to EEMS, and to gap -fill where needed. However, operators are only required to obtain an NSTA flaring consent and an EIA Direction for extended well tests (i.e. well tests scheduled to run for longer than 96 hours) and not for standard well tests and hence there may not be a complete list from NSTA to use to validate the • The EFs applied in the EEMS system for oil and gas well testing have not been reviewed by the industry for >10 years; they may or may not be accurate and representative for the well testing practices and drilling activities in new production areas of the UKCS in recent years. To improve accuracy and ensure that the UK estimates are based on current EFs, new research and/or monitoring would need to be conducted. • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • As noted above, the EFs applied for onshore oil exploration are associated with high uncertainty; the 2019 Refinement cites a range of -12.5% to +800% of the stated EF. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 783 • The oil and gas well testing EFs are based on UK industry research from ~15 years ago. The GHG emissions are dominated by CO2, which is closely linked to the carbon content of the flared gases. Based on many years of EEMS and EU ETS reporting of combustion of gas from the UKCS, the gas content can vary considerably, but the overall average CEF does not. The well testing EFs uncertainty is therefore estimated to be quite low, at ±10%. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 784 1B2a2: Oil Production & Upgrading • Offshore oil Direct Processes • Offshore oil Other fugitives • Oil Direct processes • Oil Other fugitives • Onshore conventional oil production These emission sources cover the release of fugitive gases from the processing units on upstream facilities, where the produced fluids are extracted, treated (e.g. to remove acid gases), separated to allow the onwards delivery or use of liquids (crude oil, condensate) and gases. The emissions arise from leaks on the platform / FPSO / terminal infrastructure, from pipes, flanges, connectors, compressors, dehydrators, separators and other units. In the UK the reporting of fugitive releases by operators tends to fall into two (i) several installations report “direct process” emissions that are usually due to the treatment of acid gases which are processed or flared / incinerated leading (usually) to additional releases of CO 2 and other gases such as SO 2 (e.g. Tartan Alpha, Piper Bravo, Kinneil Terminal); and (ii) all offshore facilities and oil terminals report operational fugitive releases from leaking infrastructure, which are usually estimated based on an inventory of all of the equipment on the facility (i.e. counts of flanges, pipelines, connectors, compressors and so on) and UK industry EFs (from EEMS) on leaks per year per piece of Onshore oil production sites also e xhibit similar fugitive releases but for most sites the level of annual emissions is below the reporting threshold for IED/PPD regulatory inventories, and hence an alternative method is needed to address that reporting gap. • Carbon dioxide, methane, nitrous oxide, oxides of nitrogen, carbon mono xide, sulphur • For CH4 and NMVOC, a hybrid method that uses UK operator data where they are reported and gap-filling for sites that do not report.
For CO2 and N2O there is no operator reporting of any emissions data and hence an IPCC Tier 1 method is Emission = AD x • Activity Over the time series there are 47 oil well sites active, and for each we have an annual volume of crude oil produced from industry reporting to NSTA and its o 1990 to 2003: DTI Brown Book 2004; o 2004 onwards from the PPRS system of monthly reporting. • Emission Factor(s): For the larger sites, such as Wytch Farm, Scampton North, Singleton and Cold Hanworth, there are operator reported estimates of CH 4 and NMVOC available from the PI, and these are used directly. For the re maining sites, CH 4 and NMVOC estimates are gap-filled using their reported production data and the weighted-average EF from the sites reporting to the PI, i.e. derived by dividing the sum of reported emissions by the sum of production at sites that reported emissions. This is effectively a Tier 2 method, For CO 2 and N 2O, for all sites the method uses the IPCC default EFs from the 2019 Refinement for sites with high emitting technologies and practices; this EF is selected on

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 785 the basis that whilst there is a regulatory system in place in the UK, these are small producing sites where implementing mitigation techniques are unlikely to be economic to apply. We further note that these are very small producers and the impact on the UK GHGI totals of the choice of default EF is almost negligible; if they were significant emitters they Offshore Oil Direct Processes and Fugitives • UK industry Tier 2/3 method, utilising the facility -level EEMS data for 1998 onwards, the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of direct process and fugitive emissions for 1990-1994 through extrapolation back from 1995 using crude oil production statistics. A small number of installations account for the direct process sources and in those cases the time series of their estimated annual oil production or throughput was used to estimate the process emissions. • The EEMS data (BEIS, 202 2a) present the AD in tonnes (of all gases released) and the emissions of individual gases CO 2, CH 4, N 2O, NO X, NMVOC, CO, SO 2. Emissions of fugitives (rather than direct process emissions) are dominated by CH 4 and NMVOC, with some reporting of CO2 also evident. • UK GHGI emissions = ∑ operator emissions data per pollutant Oil Terminal Direct Processes and Fugitives • The method is as described for offshore units above, i.e. a UK industry Tier 2/3 method, utilising the facility -level EEMS data for 1998 to 2010 (when most terminals ceased reporting to EEMS), the industry -wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of direct process and fugitive emissions for 1990 -1994 through extrapolation back from 1995 using crude oil production statistics. • For onshore terminals, the annual submissions to the PI/SPRI are verified by the regulatory agency, whereas EEMS data are not. Therefore, to align the inventory totals to these verified data, across all years where PI/SPRI > EEMS totals per pollutant, the inventory method allocates the residual emissions to this source category. Further, for 2011 onwards, where the only data reported are from the PI/SPRI, the inventory method across all sources aligns to the total reported to the PI/SPRI and estimates of direct process and fugitive emissions are modelled based on previously reported source estimates and the trend in annual emissions per pollutant, per installations. • This source category is also used for residual emissions once all other source estimates have been made, for the 1990-1997 dataset. The UKOOA 2005 dataset provides source- specific estimates back to 1995, and the 1990 -1994 estimates per so urce are modelled (see other method descriptions across 1A1cii and 1B2) using proxy data. CO 2 and N2O arise primarily from fuel combustion and gas flaring. For methane and NMVOC, the allocation of emissions across a range of sources is especially uncertain for 1990-1994; it is unknown whether the reported emissions from industry were from process sources, fugitive leaks, material storage or from venting. Our approach is to estimate specific allocations of methane and NMVOC from direct processes, storage and venting, and allocate the rest to “other fugitives” and report them here. Method Assumptions and Observations • For process and fugitive sources where the EEMS emissions data are provided without any underlying AD and EF information, the UK inventory metho d is to aggregate those operator-reported data and conduct QC against other reported data (such as production data to identify when installations start and cease production) to ensure completeness. • Fugitive emissions reported within EEMS are typically aggr egated for each installation, without any further information on the specific source/unit. Similarly, emissions reported

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 786 under IED/PPC to the PI/SPRI by terminal operators are aggregated across all sources on the defined installation. These national circum stances of data availability mean that the UK inventory data cannot be disaggregated to separate fugitive emissions from oil and gas processing units, from other fugitives, such as acid gas removal units (except where these are specifically identified as “direct process” sources), other connectors, flanges and pipeline infrastructure. The transparency of the underlying operator calculations is limited, and QC of the data focuses on time series consistency per installation. • The time series of estimates is heavily influenced by reported data from a relatively small number of installations. As noted in the method overview, a number of sites have additional processing requirements due to, for example, the incidence of acid gases from the upstream oil fields. The UK GHGI trend is therefore influenced significantly by the production trends at those installations.
As with all sources, there is greater uncertainty regarding the estimates at the start of the time series due to the limited data resolution in the UKOOA 2005 dataset, but IPCC good practice gap-filling techniques have been used to deliver a plausible time series per source. • The CH4 and NMVOC method for onshore oil well sites uses operator reported emissions for larger sites and then applies an assumption that the smaller non-reporting sites operate at a similar EF of emissions per unit production. Scope for future research and improvement • The method is reliant on the operator reporting to EEMS; in order to test against an IPCC default or other methodology (such as the fugitives methodology developed through research in Norway in recent years) would require significant investment to gathering more detailed data about the infrastructure on UK platforms, FPSOs and terminals. To develop a more compr ehensive Tier 2 method would require UK regulators and industry to generate more detailed activity and emissions data through either annual submissions or • For terminals there is an opportunity to update the requirements within IED/PPC permits (e.g. in response to the latest BREF notes) to include additional operator reporting (annual or periodic) of source -specific estimates, to supplement the installation -wide emission estimates that are currently reported to the PI/SPRI. Additional data (including AD or contextual info on e.g. production) would provide transparency of the source -specific emissions, and remove the need for assumptions to be applied to estimate the allocation of total emissions across fugitives, venting, storage, combustion etc, improving accuracy and opportunities to conduct QC. • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • The EFs applied for onshore oil production are associated with high uncertainty; the 2019 Refinement indicates that CO2 EF uncertainties are around ±30%, whilst the range for N2O • In the latest year and considering the relative contributions to emission estimates per pollutant and the underlying methods and EFs, our expert judgement is that the activity data uncertainty is around 5 to 10% and the EF uncertainties are around 30% for CO 2,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 787 • Oil transport pipeline (onshore) • Oil transport road and rail tankers The transfer of oil from the upstream production installations to refineries and terminals leads to fugitive emissions of hydrocarbons due to venting and leakage from pipelines, marine tankers, rail and road tankers. In the UK, these emissions arise (i) crude oil production and offshore loading from OTLs to shuttle tankers; (ii) off-loading of crude oil from oil tankers to onshore terminals and refineries; (iii) transfer of crude oil via pipelines from offshore platforms and FPSOs to onshore (iv) onshore loading of crude oil to road or rail tankers at onshore well sites; and (v) the subsequent oil unloading from road/rail tankers at onshore terminals. Under the IED/PPC reporting scope for onshore terminals, the items (ii), (v) and the onshore pipeline component of (iii) are already accounted for, and further any fugitives from the offshore end of oil pipelines under (iii) are covered within the scope of operator reporting of fugitive releases two sets of EFs for shuttle tanker ships to account for those operating abatement equipment (“VRU”) and those that do not. These EFs are based on Norwegian research; information from the industry indicates that North Sea shut tle tankers operate across the UK and Norwegian Continental Shelf production area, and hence the 2019 IPCC Refinement EFs are regarded as representative of UK circumstances. Loading emissions are influenced by many contributing factors the comp osition and temperature of the crude oil; the design and operation of the loading system; whether the vessel cargo tanks contain HC gases, inert gases or a mixture of these when the loading operation starts; and (for offshore loading) the wave heights and weather conditions during loading. • Methane, NMVOC and carbon dioxide • IPCC 2019 Refinement Tier 1 Emission = AD x Default EF • Activity Over the time series there are 33 offshore installations that service the crude oil from oil fields that are OTLs, and for each we can derive an annual volume of crude oil produced across the time series, from industry reports to NSTA and DTI, and the field - o 1990 to 2003: DTI Brown Bo ok. [1990-1994, BB 1995 Annex 6; 1995 -1997, BB 2000 Appendix 9; 1998 -2000, BB 2001 Appendix 9; 2001 to 2003, BB 2004 o 2004 onwards from the PPRS system of monthly reporting per field, aggregated across all fields and months per installation. • Emission Factor(s): Default (D) EFs from IPCC. EF units are mass of pollutant emitted per o Shuttle tankers (no VRU): 0.065 t CH4 /1000 m3; 1.10 t NMVOC /1000 m3

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 788 • UK industry Tier 2/3 method, utilising the facility-level EEMS data for 1998 to 2010 (when most terminals ceased reporting to EEMS), the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of onsh ore oil loading emissions for 1990 -1994 through extrapolation back from 1995 using crude oil production statistics. • The EEMS data (BEIS, 2022a) present the AD in tonnes of crude oil received from shuttle tankers at the terminal and the emissions of individual gases in tonnes, CH4 and • UK GHGI emissions = ∑ operator emissions per pollutant • For 2011 onwards, where installations continued to report to EEMS (e.g. Nigg, Flotta reported to 2014) then these data are used. For other sites where th e only data reported are from the Pollution Inventory (PI) or the Scottish Pollutant Release Inventory (SPRI), there is no source resolution of reported emissions, only a total per pollutant per year per site is reported.
The inventory method across all sources aligns to the total reported to the PI/SPRI and an estimate of oil loading emissions has been modelled based on previously reported source estimates and site total. These estimates have been augmented through operator consultation, for example with t he ConocoPhillips Seal Sands oil terminal environmental manager (ConocoPhillips, 2019. Personal Communication) who provided a breakdown of total reported NMVOC emissions. Oil transport pipeline (onshore) • IPCC 2019 Refinement Tier 1 Emission = AD x Default EF • Activity data : There is only one onshore production site where the level of annual production warrants the investment in a pipeline to a nearby terminal, and that is the 91 km 16” diameter pipeline from Wytch Farm to Hamble terminal , via Fawley refinery. The annual production of crude oil at Wytch Farm is published via the historic DTI Brown Book, o 1990 to 2003: DTI Brown Book. [1990 -1992, BB 1995 Annex 6; 1993 -1994, BB 2008 Annex 6; 1995-1997, BB 2000 Appendix 9; 1998-2000, BB 2001 Appendix 9; 2001 to 2003, BB 2004 Appendix 9.] o 2004 onwards from the PPRS, through annual aggregation of monthly reported • Emission Factor(s): Default (D) EFs from IPCC. EF units are mass of pollutant emitted per o 0.0054 t CH4 /1000 m3; 0.00049 t CO2 /1000 m3; 0.054 t NMVOC /1000 m3 Oil transport road and rail tankers (onshore) • IPCC 2019 Refinement Tier 1 Emission = AD x Default EF • Activity The annual production of crude oil at all onshore well -sites is published via the historic DTI Brown Book, and now via the PPRS. The AD here is the total for all onshore fields less that for Wytch Farm, where the product is transferred via pipeline (see o 1990 to 2003: DTI Brown Book. [1990 -1992, BB 1995 Annex 6; 1993 -1994, BB 2008 Annex 6; 1995-1997, BB 2000 Appendix 9; 1998-2000, BB 2001 Appendix 9; 2001 to 2003, BB 2004 Appendix 9.] o 2004 onwards from the PPRS, through annual aggregation of monthly reported • Emission Factor(s): Default (D) EFs from IPCC. EF units are mass of pollutant emitted per

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 789 o 0.025 t CH4 /1000 m3; 0.0023 t CO2 /1000 m3; 0.25 t NMVOC /1000 m3 Method Assumptions and Observations • Offshore loading of crude oil is a key source category for NMVOCs in the UK inventory, and therefore a higher-Tier approach has been sought. We note that operators do report emission estimates from oil loading at offshore assets in EEMS, but that the data show significant inter -annual variability in scope with some installations only reporting periodically and other known Offshore Tanker Loaders (OTLs) not reporting at all, indicating that EEMS data for this source are not complete. In the absence of reliable operator-reported emissions, PPRS activity data has been used along with the IPCC 2019 Refinement emission factor th at assumes no abatement equipment (such as a VRU) is used on shuttle tankers while they are loaded. • The activity data required for estimates of emissions of hydrocarbons from oil loading offshore is the annual mass of crude oil production at UKCS platforms or FPSOs that are not connected to oil pipelines and hence the crude oil is transported to shore using shuttle tankers. The operator reporting in EEMS includes activity data for the mass of crude oil transferred per year. However, the NSTA PPRS data for O TLs provides an alternative dataset via the monthly returns per OTL field on crude oil production which can be aggregated to the installation (i.e. platform or FPSO) level using the field to installation mapping. We note that the PPRS data are underpinned by statutory reporting obligations whilst EEMS is a voluntary reporting system for the oil loading source. As noted above, comparison of EEMS against PPRS and subsequent consultation with operators via the OPRED team confirmed that the EEMS -reported data b y offshore operators are Gas Liquids production, which reports an aggregated time series of mass (in kt) of crude oil production at OTLs per year. The DUKES da ta is derived from the NSTA PPRS data and shows close consistency in most recent years. However, the DUKES data is derived based on a calculation method that considers disposals and stock changes month to month within the tankers; our analysis indicates th at in most years this provides very similar estimates to a direct aggregation of the reported mass of production per month per indicates a much lower level of OTL production when compared against the aggregate of crude oil production data in the PPRS dataset; comparison of the PPRS vs. DUKES data at the field and installation level, shared with the BEIS (now DESNZ) energy statistics team, shows that production at three BP oil fields West of Shetland are significantly under- reported in the DUKES time series. Hence to deviate from the UK energy statistics in these mid-time-series years to use the higher PPRS data is justified and was agreed with BEIS; this is important to ensure that the 2005 Base Year for NMVOC reporting is accurate. • The outcome of this analysis indicates that the PPRS activity data are the most complete and accurate dataset for the UK inventory method, rather than the EEMS or data per field, and we have identified which oil fields are OTLs and can hence derive an aggregate total; the overlap years (2000 -2003) between the Brown Book and the PPRS show very close consistency and hence we are confident that the UK inventory method has a time series consistent activity data time series, using the Brown Book and PPRS data together from 1990 to latest year.
• The scope of reporting of fugitive emissions at offshore installations addresses any leaks at the offshore end of oil pipelines, whilst leaks under -sea we assume to be dissolved in the water column and any leaks at the onshore terminal receiving end of the pipelines will be reported under the scope of PPC/IED annual returns. Hence, we do not consider that the 2019 Refinement method for fugitive emissions from oil transport via pipelines is

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 790 appropriate for the UK GHGI as it would introduce a double-count. We note that there is a risk that applying the pipelines method to the onshore production at Wytch Farm may introduce a small double count where fugitive leaks occur at Wytch Farm or at Hamble terminal and are already included wit hin their annual reported emissions to the PI; however, the pipeline is on land rather than under -sea and hence any leaks at connections, compressors on the route are otherwise a gap in the UK GHGI. Hence the estimates are likely conservative but address a minor completeness issue. • Across all of these transport fugitive sources, there is scarce data from UK sources to inform a country-specific EF; further, the many parameters that influence actual emissions (e.g. sea and weather conditions) make the accurat e characterisation of this emission source highly uncertain. For the offshore loading source, there is the EEMS 2008 operator guidance which presents EFs that are derived from research in the UK in the 1990s; however we note that the 2019 Refinement EFs are derived primarily from research in the North Sea production area by the Norwegian authorities. The fleet of shuttle tankers that service the Norwegian sector also service UK installations and hence we consider that the 2019 Refinement EFs are the more recent data, based on circumstances similar to the UK and hence are the best available option. • In deriving the offshore loading OTL activity data, we note that the crude oil production in the UK share of the median -line oil field, Statfjord, is processed and exported from a platform in Norwegian waters, and hence we have omitted the Statfjord production data in the UK GHGI activity data across the time series, as the emissions arise in Norwegian • The method described above is the recommended approach to derive both CH 4 and NMVOC emissions from these emission sources, but we note that to apply the new methods for NMVOCs is a decision for Defra. • The onshore loading emissions dataset from EEMS for the small number of UK oil terminals shows clear step-changes in the NMVOC EFs applied by individual operators, which reflect the deployment of mitigation at each site over the years. Step-changes down are notable for NMVOC Kinneil Terminal (2003 -4); Sullom Voe (2008 -9); Flotta (2010-11); Seal Sands (2009-10). The default EFs in EEMS are hence not representative for onshore loading at oil terminals, where more stringent controls are now in place, due to the risk to local receptors of high NMVOC emissions at terminal ports and oil storage Scope for future research and improvement • There is scope for UK research into the EFs applied for all sources in this section of the industry. We note that, for example, in the update of onshore facility permits to operate under PPC/IED that the onshore regulators (EA and SEPA) have the opportunity to request that plant operators provide further insight into the source -specific estimates of pollutants within the boundary of the defined installation. This would be especially helpful to improve the evidence base for the origin of fugitive NMVOC and CH 4 emissions, not only for oil loading but across all sources. This type of data is likely to be gathered already by operators; however, we note that there are a range of measurement options available to operators to estimate fugitive hydrocarbons, and a standard method applied across all UK installations would be needed to generate a more accurate and comprehensive • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 791 • As noted above, the EFs are associated with high uncertainty; the 2019 Refinement cites a range of ±100% of the EFs for CH4 and CO2 from oil transport by pipelines, and -50% to +200% for NMVOC. The uncertainty range for oil transport by road and rail tankers is similar with a range of ±50% of the EFs for CH4 and CO2 and -50% to +200% for NMVOC. For offshore oil loading to shuttle tankers with or without VRUs the uncertainty range for CH4 is cited as ±50%; no data are provided for NMVOC for that source. • Noting the IPCC default uncertainty ranges above and the data limitations as regards no source-specific data reported by onshore terminal operators, our expert judgement is that overall the uncertainties for this group of sources is ±50% for methane, which is the only significant GHG emission, and similar for other gases. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 792 1B2a4: Refining / Storage; 1B2b4: Natural Gas Transmission and Storage The storage of oil in onshore terminal tank farms leads to relatively low releases of hydrocarbons as the tanks breathe and minor fugitive releases occur. In the UK the regulation of NMVOC emissions in particular has led to mitigation of such sources through closed -loop tank filling and storage syst ems, floating roofs and so on. There are similar, even less significant, fugitive emission sources for hydrocarbons from storage of fluids at many UK gas terminals, which also lead to NMVOC emissions and very low releases of CH4.
Emissions from oil and gas terminals are reported under the scope of IED/PPC annual returns to UK regulators (EA and SEPA), but as with other sources there are no source -specific estimates [The methods for downstream sources such as fugitives from refining of mineral oil or from gas transmission networks are reported in the NIR Energy Chapter, Method Statement 19.] Oil Terminal Storage and Gas Terminal Storage • Tier 2/3 method, utilising the facility -level EEMS data for 1998 to 2010 (when most terminals ceased reporting to EEMS), the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005). Estimates of oil terminal storage emissions for 1990 -1994 are deriv ed through extrapolation back from 1995 using crude oil production statistics; a similar method is used to estimate gas terminal storage using gas production statistics as a proxy. • The EEMS data (BEIS, 2022a) present the AD in tonnes of fluids stored at t he oil or gas terminal and the emissions of individual gases in tonnes, CH4 and NMVOC. • UK GHGI emissions = ∑ operator emissions per pollutant • For 2011 onwards where the only data reported are to the PI/SPRI, there is no source resolution of reported emissions; only a total per pollutant per year per oil or gas terminal is reported. The inventory method across all sources aligns to the total reported to the PI/SPRI and an estimate of storage emissions has been modelled based on previously reported source estimates and the trend in annual site emission totals. • Oil terminals that report storage emissions in EEMS Flotta, Sullom Voe, Nigg, • Gas terminals that report storage emissions in EEMS Barrow North, Theddlethorpe, Dimlington, Easington. Method Assumptions and Observations • There is a very limited dataset to inform estimates from these minor sources across both oil and gas terminals, but the historic EEMS data do consistently show that total emissions of CH4 are almost negligible; NMVOC emissions are slightly more significant. Scope for future research and improvement • There is scope for UK research into the EFs applied for all sources in this section of the industry, but we note that given the relative insignificance of these sources that this is not a priority for improvement in future. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 793 • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • Noting the data limitations as regards no source-specific data reported by onshore terminal operators, our expert judgement is that overall, the uncertainty for this source is ±50% for CH4 and NMVOC. These sources are very low emitters; hence i n the context of uncertainties across the upstream oil and gas sector they are immaterial. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 794 1B2a6: Additional/Other Oil Fugitives • Abandoned Oil Wells (onshore) • Abandoned Oil Wells (offshore) • IPCC 2019 Refinement Tier 1 Emission = AD x Default EF • Activity Number of wells abandoned per year (cumulative), derived from the NSTA public wellbore search facility, downloads-and-publications/well-data • Emission Factor(s): Default (D) EFs from IPCC. EF units are mass of pollutant emitted per well abandoned per year. IPCC Refinement 2019 pr ovides Tier 1 emissions factors for plugged, unplugged and both types for onshore and offshore oil wells. Method Assumptions and Observations • That each well, once abandoned, continues to emit low levels of hydrocarbons in each subsequent year, and that the IPCC default EFs are representative of UK circumstances. • Over the history of onshore oil and gas production in the UK, there has been an evolution of post-operational practices as regulation has increased; older wells are unlikely to have been capped, whereas more recently all wells abandoned are required to be capped to minimise risk of hydrocarbon leakage. • The NSTA has not been able to provide analysis of the wells dataset to present the specific information on the year in which each well was abandoned. The NSTA well status is listed according to when the well was drilled. Therefore, we have assumed, given the large number of wells drilled and abandoned over time, that the records of wells drilled that are subsequently abandoned (in any future year) is a good proxy for the actual number of wells abandoned in a given year. • (Inherent in the IPCC method) The emissions of hydrocarbons for offshore wells that are abandoned is estimated to be only 2% that compared to onshore wells, as the IPCC Refinement Tier 1 method states that it is assumed that 98% of hydrocarbons released will dissolve in the water column and not be emitted to atmosphere. • As the activity dataset is available only for all oil and gas wells aggregated, the method applies the same EFs to the full estimate of all abandoned oil and gas wells; hence emissions that ideally ought to be reported under 1B2b for leaks from abandoned gas wells are included here. The EF for oil wells is assumed to be applicable for gas wells also. • This is a minor source and not a key category for methane emissions and hence a Tier 1 method is proportionate. The UK re gulatory system for mining and oil production and after-care requirements for former production sites is such that only low levels of seepage of hydrocarbons is expected. We note that whilst there are academic studies in the UK to research the rate of leakage of methane from individual abandoned oil and gas well sites, there are no country specific EFs available and hence no Tier 2 method option. Therefore, to apply the IPCC 2019 Refinement default is the best available dataset to address what would otherwise be a minor completeness issue in the UK GHGI. • In the UK there are no known omissions, the scope of reporting is complete.
We note that NMVOC emissions m ay occur from these sources, notably from abandoned onshore oil wells, though no EFs for NMVOC are available from UK research

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 795 nor IPCC or EMEP/EEA inventory guidance;. There is no known activity of oil production in any OT or CD. There have been a small number of exploratory drilling campaigns offshore in the waters around the Falkland Islands, but no subsequent production and well • The method is Tier 1 using detailed AD for the UK and methods from the 2019 IPCC Refinement. The EFs are associated with high uncertainty (as high as -99 to 150% of the stated emission factor). • Time Series Annual NSTA data on oil wells drilled and their current status is available across the time series, including whether wells are suspended or abandoned, via the public wellbore status search facility. The method is therefore tim e series Scope for future research and improvement • The Inventory Agency will continue to engage with NSTA to seek a solution that may enable us to derive a time series of wells abandoned in each year. • Research to improve the understanding of well abandonment and capping would enable an improvement to the method to apply the IPCC default EFs (or other EFs) that are specific to (i) capped wells and (ii) uncapped wells, rather than using default EF provided for when capping is unknown (currently applied to the full activity data). • There are no default EFs for NMVOC or specific hydrocarbons (e.g. benzene) in either IPCC nor EMEP-EEA guidebooks; there may be suitable EFs in other literature sources. • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • As noted above, the EFs are associated with high uncertainty; the 2019 Refinement cites a range of -99 to 150% of the stated EFs. The tier 1 method involves large uncertainties both in factor selection and also in determining whether an abandoned well has been plugged or not after decommissioning due to data limitations. • The method complies with IPCC 2019 guideline for fugitive emissions from abandoned offshore and onshore oil wells. The Tier 1 approach has been applied as Tier 2 or 3 approaches are not available. We note that EFs for abandoned wells ha ve high uncertainty. Activity data for this source are counts of total abandoned onshore and offshore wells in each year of the time series. • Available information on abandoned wells do not indicate a clear distinction between abandoned oil and abandoned gas wells regarding practices or emission rates. Thus, all the EFs for 1.B.2.A/B.VII in IPCC 2019 are developed from data for both abandoned oil and gas wells. The EFs of abandoned wells are split into either “plugged” (or, properly decommissioned per regulations) and “unplugged” well sub-segments. If insufficient data on plugging practices is available to disaggregate activity data in such a way, the default EF for all type wells is to be used. More limited data are available on offshore wells and disaggregated (i.e. plugged versus unplugged) factors for offshore abandoned wells are developed in IPCC 2019 from onshore wells data considering that most methane (around 98%) from offshore abandoned wells is dissolved in marine water. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 796 • Onshore natural gas production (conventional) • Onshore natural gas gathering These emission sources cover the release of fugitive gases from sources from the gas wellhead through to the delivery of gas to processing plants (where necessary), or to the connections to the National Transmission System. UK gas production onshore is limited to a small number of well sites, all conventional (i.e. no fracturing) and hence fugitives arise mainly from any leaks around the wellhead and through infrastructure (pipes, connectors, dehydrators, compressors). • Carbon dioxide, methane, nitrous oxide and NMVOC Onshore natural gas production (conventional) • IPCC Tier 1 Emission = AD x Default EF • Activity Annual volume of natural gas (million m3) produced, obtained from industry reporting to NSTA, DESNZ and their predecessors (DTI, DECC, BEIS): o 1990 to 1998: DTI Brown Book. [1990, BB 1995 Appendix 7; 1991-1992, BB 1996 Annex 7; 1993-1995 BB 1998 Appendix 7; 1996-1998 BB 2001 Appendix 10; 1999 onwards is from DUKES Annex F2 o 1999 onwards from DUKES Annex F.2. • Emission Factor(s): Default (D) EFs from IPCC. EF units are mass of pollutant emitted per EFs for Natural Gas Production Segment, 1B2b2. Onshore activities occurring with higher- emitting technologies and practices. o 4.09 t CH4 / Mm3; 1.45 t CO2 / Mm3; 0.98 t NMVOC / Mm3; 0.000025 t N2O / Mm3 • Method identical to the method presented above for onshore natural gas production Tier 1 EFs for Natural Gas Production Segment, 1B2b2. Onshore activities occurring with higher-emitting technologies and practices. o 3.20 t CH4 / Mm3; 0.35 t CO2 / Mm3; 0.77 t NMVOC / Mm3; 0.000006 t N2O / Mm3 Method Assumptions and Observations • There is a very limited dataset to inform estimates from these minor sources from the UK onshore gas production sector, as there are no reported data to the Pollution Inventory. • The annual level of fugitive releases per well site is below the reporting threshold for IED/PPC regulatory inventories, and the UK industry does not produce any country specific EFs or estimates of fugitive leaks; hence to apply the IPCC 2019 Refinement Tier 1 default method is proportionate to address what would otherwise be a minor completeness issue in the UK GHGI. • In the UK there are no known omissions, the scope of reporting is complete. We note that there are no EFs for GHG nor NMVOC from UK research or the • The method is Tier 1 using detailed AD for the UK and methods from the 2019 IPCC Refinement; hence uncertainties are high %s of very small emission estimates.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 797 • Time Series Annual natural gas production onshore data is available across the time series, via the UK energy statistics and previous annual statistics publications (DTI Brown Book). The method is therefore time series consistent. Scope for future research and improvement • There is scope for UK research into the EFs applied for all sources in this section of the industry, but we note that given the relative insignificance of these sources that this is not a priority for improvement in future. • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • The EFs applied for onshore natural gas production are associated with high uncertainty; the 2019 Refinement indicates that CH4 and CO2 EF uncertainties are around ±20%, whilst the range for N2O is -10% to +1000% and for NMVOC is -75% to +250%. • The EFs applied for onshore natural gas gathering are associated with high uncertainty; the 2019 Refinement indicates that CH4 and CO2 EF uncertainties are around ±10%, whilst the range for N2O is -10% to +1000% and for NMVOC is -75% to +250%. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 798 • Offshore gas Direct Processes • Offshore gas Other fugitives • Gas Direct processes • Gas Other fugitives These emission sources cover the release of fugitive gases from the processing units on upstream facilities, where the produced fluids are extracted, treated (e.g. to remove acid gases), separated to allow the onwards delivery or use of gas and condensate. The e missions arise from leaks on the platform / FPSO / terminal infrastructure, from pipes, flanges, connectors, compressors, dehydrators, separators and other units. In the UK the reporting of fugitive releases by operators tends to fall into two (i) several installations report “direct process” emissions that are usually due to the treatment of acid gases which are processed or flared / incinerated leading (usually) to additional releases of CO2 and other gases such as SO2 (e.g. Elgin, Rough BD, Markham, and gas SAGE-St Fergus, Barrow, CATS, Point of Ayr, Theddlethorpe); and (ii) all offshore facilities and gas terminals report operational fugitive releases from leaking infrastructure, which are usually estimated based on an i nventory of all of the equipment on the facility (i.e. counts of flanges, pipelines, connectors, compressors and so on) and UK industry EFs (from EEMS) on leaks per year per piece of equipment. • Carbon dioxide, methane, nitrous oxide, oxides of nitrogen, sulphur dioxide, carbon Offshore Gas Direct Processes and Fugitives14 • Tier 2/3 method, utilising the facility-level EEMS data for 1998 onwards, the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of direct process and fugitive emissions for 1990-1994 through extrapolation back from 1995 using natural gas production statistics. A small number of installations account for the direct process sources; emissions are dominat ed by CO 2 arising from sour gas treatment/venting and amine regeneration at the Elgin platform and from Rough BD platform. The time series of the annual gas production at each installation was used to estimate process emissions in • The EEMS data (BEIS, 2022a) present the AD in tonnes (of all gases released) and the emissions of individual gases CO 2, CH 4, N 2O, NO X, NMVOC, CO, SO 2. Emissions of fugitives (rather than direct process emissions) are dominated by CH 4 and NMVOC, with some reporting of CO2 also evident. • UK GHGI emissions = ∑ operator emissions data per pollutant Gas Terminal Direct Processes and Fugitives • The method is as described for offshore units above, i.e. a UK industry Tier 2/3 method, utilising the facility -level EEMS data for 1998 to 2010 (when most terminals ceased reporting to EEMS), the industry -wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of direct process and fugitive emissions for 1990 -1994 through 14 An additional source reported in the UK GHGI as a fugitive emission is the emissions from the 2012 Elgin blow-out. A country- specific method was applied here, based on reported daily methane flow-rate observations taken on 5 days over the blow-out period. [This method was developed in previous research and is noted here for completeness.] Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 799 extrapolation back from 1995 using na tural gas production statistics. The installations at SAGE-St Fergus and CATS terminals opened in 1992 and 1993 respectively; the Inventory Agency has estimated process releases back to those years (and zero emissions in 1990). • For onshore terminals, the a nnual submissions to the PI/SPRI are verified by the regulatory agency, whereas EEMS data are not. Therefore, to align the inventory totals to these verified data, across all years where PI/SPRI > EEMS totals per pollutant, the inventory method allocates t he residual emissions to this source category. Further, for 2011 onwards, where the only data reported are from the PI/SPRI, the inventory method across all sources aligns to the total reported to the PI/SPRI and estimates of direct process and fugitive em issions are modelled based on previously reported source estimates and the trend in annual emissions per pollutant, per installations. • This source category is also used for residual emissions once all other source estimates have been made, for the 1990-1997 dataset. The UKOOA 2005 dataset provides source- specific estimates back to 1995, and the 1990 -1994 estimates per source are modelled (see other method descriptions across 1A1cii and 1B2) using proxy data. CO 2 and N2O arise primarily from fuel combustion and gas flaring. For methane and NMVOC, the allocation of emissions across a range of sources is especially uncertain for 1990-1994; it is unknown whether the reported emissions from industry were from process sources, fugitive leaks, material storage or f rom venting.
Our approach is to estimate specific allocations of methane and NMVOC from direct processes, storage and venting, and allocate the rest to “other fugitives” and report them here. Method Assumptions and Observations • For process and fugitive so urces where the EEMS emissions data are provided without any underlying AD and EF information, the UK inventory method is to aggregate those operator-reported data and conduct QC against other reported data (such as production data to identify when installations start and cease production) to ensure completeness. • Fugitive emissions reported within EEMS are typically aggregated for each installation, without any further information on the specific source/unit. Similarly, emissions reported under IED/PPC to t he PI/SPRI by terminal operators are aggregated across all sources on the defined installation. These national circumstances of data availability mean that the UK inventory data cannot be disaggregated to separate fugitive emissions from oil and gas processing units, from other fugitives, such as acid gas removal units (except where these are specifically identified as “direct process” sources), other connectors, flanges and pipeline infrastructure. The transparency of the underlying operator calculations is limited, and QC of the data focuses on time series consistency per installation. • The time series of estimates is heavily influenced by reported data from a relatively small number of installations. As noted in the method overview, a number of sites have additional processing requirements due to, for example, the incidence of acid gases from the upstream gas / condensate fields. The UK GHGI trend is therefore influenced significantly by the production trends at those installations. As with all sources, the re is greater uncertainty regarding the estimates at the start of the time series due to the limited data resolution in the UKOOA 2005 dataset, but IPCC good practice gap-filling techniques have been used to deliver a plausible time series per source. Scope for future research and improvement • The method is reliant on the operator reporting to EEMS; in order to test against an IPCC default or other methodology (such as the fugitives methodology developed through research in Norway in recent years) would require significant investment to gathering more detailed data about the infrastructure on UK platforms, FPSOs and terminals. To develop a more comprehensive Tier 2 method would require UK regulators and industry to

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 800 generate more detailed activity and emissions data through either annual submissions or • For terminals there is an opportunity to update the requirements within IED/PPC permits (e.g. in response to the latest BREF notes) to include additional operator reporting (annual or periodic) of source -specific estimates, to supplement the installation -wide emission estimates that are currently reported to the PI/SPRI. Additional data (including AD or contextual info on e.g. production) would provide transparency of the source -specific emissions, and remove the need for assumptions to be applied to estimate the allocation of total emissions across fugitives, venting, storage, combustion etc, improving accuracy and opportunities to conduct QC. • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • In the latest ye ar and considering the relative contributions to emission estimates per pollutant and the underlying methods and EFs , our expert judgement is that the activity data uncertainty is ~2-5% and the EF uncertainties are ~10% for CO 2, 50% for CH4 and

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 801 1B2c1i: Upstream Oil Production, Venting; 1B2c1ii Upstream Gas Production, Venting • Upstream oil venting • Upstream gas venting This source category comprises emissions from the venting of waste gases that arise through production activities for all upstream oil and gas installations on the UK Continental Shelf (UKCS) and onshore, i.e. including at offshore assets (platforms and FPSOs) and at onshore terminals. Venting re leases comprise discharges of waste gas streams and process by -products, either through intentional releases or in emergencies; operators report a wide range of emissions as venting such as solution gas emissions from storage tanks, purging and blowdowns, pressure relief releases and disposal of waste gases or off-specification products where there is no option to flare. In operator reporting via EEMS, venting sub -sources emergency, maintenance • Carbon dioxide, methane, NMVOC and (rarely) nitrous oxide Offshore oil Venting and Offshore gas Venting • UK industry Tier 2/3 method, utilising the facility -level EEMS data for 1998 onwards, the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of gas venting emissions for 1990-1994 through extrapolation back from 1995 using crude oil (for oil sites) or natural gas (for gas sites) production statistics. • The EEMS data (BEIS, 2022a) present the emissions of individual gases CO 2, CH4, and NMVOC with occasional reporting of other gases such as N 2O, NOX and CO. The EEMS reporting of activity data is inconsistent; in most cases the EEMS AD are the sum of the mass of the individual gases, but in others no AD are reported. In the UK GHG inventory model and reporting outputs, we simply aggregate the emissions data per pollutant across all sites and report that as the emission and the EF, with AD = 1. • UK GHGI emissions = ∑ operator emissions per pollutant = EF ; AD = 1 Oil Venting and Gas Venting • The method is as above, except that most terminals ceased to report emissions to EEMS beyond 2010 and hence for 2011 onwards, where the only data reported are from the PI or SPRI, there is no source resolution of reported emissions, only a total per pollutant per year per site.
The inventory method for 2011 onwards therefore aligns to the total reported to the PI/SPRI across all sources, and an estimate of venting emissions has been modelled based on previously reported source estimates and the trend in annual site Method Assumptions and Observations • EEMS data for venting are provided as emissions data without any underlying activity and emission factor information. The UK inventory method is to aggregate those operator - reported data and conduct QC against other reported data (such as production data to identify when installations start and cease production) to ensure completeness of reporting. In a small number of cases, operators may report gases other than CO 2, CH4

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 802 and NMVOC under venting in EEMS; where there are reports of small amounts of N 2O, NOX and CO reported as venting in EEMS, these data are included in the inventory, assuming that there are some waste combustion gases recorded as vented, e.g. from maintenance activities. This happens rarely and the mass of these gases is always very low; they may be misallocated, but it is a minor issue. • In the UK there are no known omissions, the scope of reporting is complete. Time-series checks by the Inventory Agency are used to assess the completeness of reporting each year; there are a small number of terminals that regularly report notable venting emissions, whilst offshore there are tens of installations that report notable venting of hydrocarbons (methane and NMVOC), and a small number (Elgin, Shearwater, Brae only in recent years) that report venting of CO2. Onshore terminals that routinely report notable venting emissions Flotta, Theddlethorpe, SAGE -St. Fergus, Shell-St Fergus, Barrow and Bacton. • The method is Tier 2/3 across the time series, using the best available data from operator reporting throughout. Noting that in many cases the operator estimates are not presented via an “activity” and “emission factor” but rather are direct estimates of the gases vented from monitoring of the gas throughput and an assumed ga s composition, the accuracy is hard to evaluate. Where there are installation-specific processes (e.g. acid gas stripping) that lead to high emissions of vented gases (e.g. Shearwater and Elgin often encounter high -CO2 produced gases that cannot be flared; several terminals vent the process gases from fuel gas treatment facilities) the composition of the gases is monitored by operators. Smaller -scale vented emissions may be estimated through engineering calculations and default data on gas composition. • Time Series The method is compromised by the lack of fully detailed 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. However, the Inventory Agency has conducted validation checks across the UKOOA dataset versus EEMS data in overlap years, which indicates good time series consistency, and the Inventory Agency has deployed gap -filling methods consistent with the 2006 IPCC Guidelines to develop time series consistent reporting per source category back to the early 1990s. Therefore, the time series consistency is as good as practicable, given the limited resolution of the available industry emissions and activity data. Scope for future research and improvement • The method is reliant on the operator reporting to EEMS. The PPRS monthly reports also include data on venting. Comparisons of PPRS and EEMS data during this project have indicated that for many sites there is good correlation betw een EEMS and PPRS, whilst for other sites there are gaps in the PPRS data where EEMS includes venting estimates. This indicates that PPRS is not always reliable for QC of EEMS and/or to inform better estimates. The NSTA has recently begun to consider revisions to the system of flare and vent consents, and there may be scope to establish better quality routine reporting of gas venting through the PPRS system, which could then provide an additional data source or • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • Uncertainties of emissions reported are based on expert judgement, informed by the understanding of the available data and the likelihood of error compensation across all UK • In the latest year of the time series, the uncertainty for venting is estimated to be ±5% for CO2, 100% for CH4, whilst in the Base Year (1990) the uncertainty is assumed to be ±20%

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 803 for CO2 and 100% for CH4 due to the more limited information available from industry and assumptions applied to estimate venting emissions. • The limited alternative data against which the EEMS data can be validated undermines confidence in the accuracy and completeness of the venting estimates. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 804 1B2c2i: Upstr eam Oil Production, Gas Flaring; 1B2c2ii Upstream Gas Production, Gas • Upstream oil gas flaring • Onshore oil gas flaring • Upstream gas gas flaring This source category comprises emissions from the flaring of waste gases that arise through production activities for all upstream oil and gas installations on the UK Continental Shelf (UKCS) and onshore, i.e. including at offshore assets (platforms, FPSOs, MODUs), at onshore terminals and at onshore production sites. The gases may need to be flared to address operational issues (e.g. excess gas supply), structural issues (e.g. some platforms/FPSOs that produce crude oil and associated gas do not have any gas export line), safety issues. In operator reporting by offshore operators to OPRED, via EEMS, flaring sub -sources routine operations, gross, maintenance, upsets / other. Flaring of gases is also conducted at oil and gas terminals, again to manage waste gas and maintain operational and safety standards across the sites. For all offshore production sites and terminals, gas flaring emissions are reported by operators under UK ETS (formerly EU ETS) since 2008, i.e.
from EU ETS Phase 2 onwards, and within EEMS from Onshore oil well sites are smaller production sites in the UK context but do still conduct a small amount of gas flaring during production; separate flaring estimates are made for these sites, for The flaring of waste gases during well exploration and testing is reported separately under the 1B2a1 and 1B2b1 IPCC source categories for oil and gas well testing respectively. This enables a distinction to be made between emissions from exploration activities, and emi ssions from • Carbon dioxide, methane, nitrous oxide, oxides of nitrogen, carbon mono xide, sulphur The emission estimates across the time series are based on the sum of the best available data from upstream oil and gas operators, onshore and offshore. The method since 1998 is essentially a Tier 3 method, aggregating installation -level activity and emission estimates . Estimates for 1990-1997 are based on lower resolution source data but are still a Tier 2 method, using industry- wide estimates from the trade association (UKOOA 2005) . This data was derived from operator surveys through the 1990s and assumes that carbon emission factors from gas flaring from 1998 are representative for earlier years. Offshore oil Gas Flaring and Offshore gas Gas Flaring and Oil Gas Flaring and Gas Gas Flaring • UK industry Tier 2/3 method, utilising the facility-level UK ETS data for 2008 onwards and EEMS data for 1998-2007, the industry-wide sector estimates for 1995 to 1997 (UKOOA 2005) and an estimate of gas flaring emissions for 1990-1994 through extrapolation back from 1995 using crude oil (for oil sites) or natural gas (for gas sites) production statistics. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 805 • The EEMS data (BEIS, 2022a) present the AD of gas flaring in tonnes and the emissions of individual gases CO 2, CH 4, N 2O, NO X, NMVOC, CO, SO 2. The UK ETS (formerly EU ETS) data provide the AD of gas flared in tonnes together with t he carbon emission factor and verified CO2 emissions total per flaring source per installation. 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 flared gas composit ion across installations. Reporting to both EEMS and UK ETS is underpinned by the sector -wide assumption of • UK GHGI emissions = ∑ operator emissions data per pollutant • Activity data = ∑ operator activity data (tonnes): IEF = Emissions / AD Onshore oil Gas Flaring • IPCC Tier 2 Emission = AD x Country Specific EF • Activity Annual mass of gas flared at onshore oil production facilities, obtained from industry reporting to NSTA, DESNZ and their predecessors (DTI, DECC, BEIS): o 1990 to 1999: Estimates of mass flared derived from the reported volumes of gas flared by DTI at onshore fields, scaled according to the mass and volume data for flaring at offshore fields, i.e. assuming similar gas density; o 2000 onwards from monthly returns under PPRS for onshore loader fields. • Emission Factor(s): The EF derived for offshore oil production per year is applied to the onshore flaring AD, as the best estimate of emissions per unit mass gas flared, as there are no operator-reported emissions data nor EFs from these smaller onshore well sites. Method Assumptions and Observations • Note that where the gas flaring 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. • The estimates of methane emissions from gas flaring are amongst the most uncertain of all estimates of GHGs from the upstream oil and gas sector. The EEMS operator guidance methane EF and the accepted UK ETS sector-wide methodology (to estimate CO 2 emissions under UK ETS) are based on a sector -wide assumption that the oxidation of flared gases is 98%. There is no routine monitoring and reporting of the performance of flares to industry regulators. Consultation with operators and regulators indicates that there is a variable approach by operators to track, monitor and resolve issues such as unlit flares, which will instead be cold venting flare gases. During such events, methane emissions will be much higher and carbon dioxide emissions much lower than the estimates reported based on the measurement of the amount of gas to flare and applying the 98% oxidation factor assumption. While the analysis of emission plumes in the North Sea between 2018 and 2019 found that the assumption is reasonable (Shaw et al., 2022), we note that just a small under -performance in flare efficiency below the 98% industry assumption will lead to a significant increase in methane emissions and an under-report in inventory estimates (e.g. a 96% flare efficiency equates to double the reported methane • Gas flaring is a minor source of emissions of nitrous oxide. Operators report estimates to EEMS, predominantly applying defaults from operator guidance, and hence this is essentially a Tier 2 approach . T he Inventory Agency gap-fills reported data where necessary, using the default EF. • Gas flaring at onshore well sites is a small component of total flaring emissions, e.g. in 2020 it is estimated to account for 0.6% of total flaring GHG emissions. The available data for this source is limited to activity data across the time series, with assumptions applied

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 806 to use the EF from offshore oil production facilities and to derive the AD in the early part of the time series. This component of the gas flaring estimates is therefore subject to greater uncertainty than the well-documented other sources (offshore and at terminals). • In the UK there are no known omissions, the scope of reporting is complete.
The Inventory Agency draws upon a range of data sources to ensure completeness (and accuracy), using UK ETS supplemented by EEMS data for smaller installations that fall below the UK ETS reporting threshold. • The method is Tier 2/3 across the time series, using the best available data from operator reporting throughout. In the UK there has been a high level of flare gas compositional analysis to inform UK ETS allocations (from the National Allocation Plans from 1998 onwards) and subsequently in all operator submissions to UK ETS. Further, the stringent monitoring and reporting and other QAQC requirements of the UK ETS system gives confidence that the reported mass of flare gas sent to flare per installation per year is highly accurate. As noted above, the biggest source of potential inaccuracy in GHG estimates is the assumption across all operator reporting that flare oxidation efficiency is 98%; deviation from that assumed level of oxidation will impact both the methane and • The 1990-1997 data are based on the UKOOA 2005 report to UK Government, which took account of the work in the National Allocation Plans to derive better installation -level carbon emission factors but are based on more limited industry surveys from the early 1990s and hence are associated with higher uncertainty than the later data. • Time Series The method is compromised by the lack of fully detailed 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. Therefore, the time series consistency is “as good as possible” given the limitations of the available Specific QA/QC and validation exercises relevant to these source categories • Comparisons between EEMS and UK ETS, to review installation-specific activity data and CO2 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 quality (AD, EFs) are third -party verified and hence regarded as the more accurate • Comparisons of total emissions data reported by each onshore oil and gas installation via the Pollution Inventory/Scottish Pollutant Release Inventory/Welsh Emissions Inventory 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). Scope for future research and improvement • A priority for further research is to develop a more rigorous and comprehensive evidence base for flare performance at all upstream installations, especially for those that operate offshore in potentially harsh conditions and with more limited opportunitie s for flare stack maintenance. Emission estimates are currently very sensitive to the assumption that 98% of flared gas is oxidised and, while recent evidence has supported this (Shaw et al., 2022), uncertainty remains high. Confidence could be improved by obtaining measurement data on the performance of different flare stack types (enclosed or open flare designs etc.) and the development of more rigorous and consistent operator monitoring and reporting systems to track when flares are operational, when they are unlit, and the volume/mass of flare gas passed to the flare stack during these different periods of operation. Consultation

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 807 with industry would also be useful in understanding the changes in flaring practises over • The uncertainty parameters applied at category-gas level are presented in Annex 2.3. • Uncertainties for both AD and EFs 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 all UK • In the latest year of the time series, the AD uncertain ty for gas flaring is estimated to be ±5%, whilst in the Base Year (1990) the AD uncertainty is assumed to be ±20% due to the more limited information available from industry and assumptions applied to estimate • Across the time series, the CO2 EF uncertainty is estimated to be ±5% whilst the uncertainty in the EFs for both methane and nitrous oxide are estimated to be ±100% across all installations, reflecting the uncertainty in oxidation factor assumption (for methane) and the widespread use of a default EF (for nitrous oxide). • Uncertainties in flaring AD are typically low. However, we note (as outlined above) that there are different operator flare stack monitoring (lit/unlit) practices evident (across the time series) and also that there are less detailed activity and emissions data available for the 1990-1997 period. Hence uncertainties for the estimates in 1990 are higher than for recent years where much more detailed and complete operator -reporting of activity and • The CO2 EFs are based on UK -specific data, from sampling and compositional analysis of gas sent to flare. Despite the uncertainty regarding the assumed gas flaring oxidation factor, across the sector the uncertainty of the CO2 EF is still expected to be low, however the uncertainty of the CH4 EF is considered to be high. There is currently no additional information for this sector in this Annex. Note that the references for this section are included in Section 17.4.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 808 Livestock Category 1990 1995 2000 2005 2010 2015 2020 2021 Total cattle 12,125 11,760 11,048 10,698 10,014 9,785 9,429 9,445 - dairy cows 2,848 2,603 2,336 2,003 1,839 1,906 1,853 1,856 - all other cattle 9,277 9,157 8,713 8,695 8,175 7,879 7,577 7,589 Sheep 45,475 44,233 43,154 36,140 31,724 34,032 33,427 33,641 Pigs 7,548 7,627 6,482 4,862 4,468 4,739 5,069 5,323 Total poultry 138,381 142,267 169,773 173,909 163,842 167,579 182,882 189,853 - laying hens 33,624 31,837 28,687 29,544 28,751 28,311 31,067 30,788 - broilers 73,944 77,177 105,689 111,475 105,309 107,056 120,047 126,584 Total horses 570 684 1,006 1,036 1,024 978 932 927 - horses kept on agricultural holdings 202 273 287 346 312 283 236 231 - professional horses 62 62 70 91 91 87 87 87 - domestic horses 305 348 649 599 621 608 608 608 Goat 98 75 74 95 93 101 112 111

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 809 A 3.3.1 Enteric Fermentation (3A) kg CH4/head/year kg CH4/head/year Cattle Dairy cows 123.97 38.26 Dairy replacements >1 year 51.37 5.94 Dairy calves <1 year 43.66 3.83 Beef females for slaughter 49.17 5.79 Heifers for breeding 48.51 6.34

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 810 A 3.3.2 Manure Management (3B) A 3.3.2.1 Methane emissions from animal manures Manure Handling System Methane Conversion Factor % Deep bedding/farmyard manure – cattle, pigs 17 Deep bedding/farmyard manure – sheep 2.0 Anaerobic digestion - cattleb 3 Anaerobic digestion - poultryb 1.5 aNo differentiation is made between crusted and non-crusted slurry storage bValues used for the anaerobic digestion of livestock manures are based on the values used in the German inventory

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 811 A 3.3.2.2 Nitrous Oxide emissions from Animal Waste Management Systems Livestock Category 1990 1995 2000 2005 2010 2015 2020 2021 Dairy cows 85.5 87.5 93.5 102.4 104.5 111.1 118.2 118.7 Other cattlea 39.8 41.2 43.2 44.7 45.7 45.7 44.5 44.4 Sows 23.6 22.5 21.4 20.6 20.8 21.1 21.1 21.1 Gilts 15.5 15.5 15.5 15.2 13.6 12.0 11.7 11.7 Boars 28.8 27.4 26.1 24.5 22.0 19.4 18.9 18.9 Fatteners > 80 kg 20.2 19.3 18.4 17.2 15.4 13.5 13.2 13.2 Fatteners 20-80 kg 14.6 13.9 13.2 12.4 11.1 9.9 9.6 9.6 Weaners (<20 kg) 4.6 4.4 4.2 4.0 4.0 4.0 4.0 4.0 Ewes 8.3 8.5 8.6 8.6 8.3 8.6 8.8 8.8 Rams 11.3 11.4 11.4 11.3 11.1 11.2 11.4 11.3 Lambs 3.7 3.8 3.9 4.1 4.0 4.2 4.2 4.3 Goats 8.4 8.4 8.4 8.4 8.4 8.4 8.4 8.4 – horses kept on agricultural holdings Deer 29.3 29.3 29.3 29.3 29.3 29.3 29.3 29.3

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 812 Livestock Category 1990 1995 2000 2005 2010 2015 2020 2021 Laying hens 0.87 0.84 0.80 0.77 0.76 0.74 0.74 0.73 Broilers 0.64 0.59 0.55 0.49 0.40 0.30 0.28 0.28 Turkeys 1.50 1.59 1.68 1.75 1.77 1.78 1.78 1.78 Pullets 0.42 0.39 0.36 0.34 0.35 0.36 0.36 0.36 Breeding flock 1.16 1.13 1.10 1.09 1.11 1.14 1.14 1.14 Ducks 1.30 1.41 1.52 1.57 1.40 1.23 1.20 1.20 Geese 1.30 1.41 1.52 1.57 1.40 1.23 1.20 1.20 Other poultry 1.30 1.41 1.52 1.57 1.40 1.23 1.20 1.20 a Weighted average for all other cattle categories

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 813 System Daily Spread Solid storage/Deep All other cattle 19 12 20 49 1 Other livestock Goats 0 0 8 92 0 Poultry All poultry 0 33 51 4 12

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 814 a) Quantities of poultry manure incinerated as kt and expressed as % of broiler and turkey manure for each Devolved Administration 1990 2000 2005 2010 2015 2020 2021 b) Amounts of poultry litter exported from Northern Ireland to be incinerated in 2015 2016 2017 2018 2019 2020 2021 Amount of poultry litter sent from Northern Ireland for incineration in 1,160 4,650 2,026 0 0 3,163 3,163 Amount of poultry litter sent from Northern Ireland for incineration in 7,403 5,142 4,127 0 4,871 6,169 6,169 c) Direct N2O Emission Factors for Animal Waste Management Systems Emission source EF (% of total Slurry – solid floor 0 N/A IPCC 2006 Slurry – slatted floor 0.2 Factor of 2 IPCC 2006 FYM systems 2.0 Factor of 2 UK measurement (at Slurry – slatted floor 0.2 Factor of 2 IPCC 2006

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 815 Emission source EF (% of total FYM systems 2.0 Factor of 2 UK measurement (at Sheep manure management (FYM) 0.5 Factor of 2 IPCC 2006 Layer manure management 0.5 Factor of 2 UK measurement (at Broiler manure management 0.5 Factor of 2 UK measurement (at 2.0 Factor of 2 Based on cattle/pig Turkeys manure management 0.5 Factor of 2 UK measurement (at Other poultry manure management 0.5 Factor of 2 UK measurement (at 2 Factor of 2 UK measurement (at Anaerobic digestion 0 N/A IPCC 2006

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 816 A 3.3.3 Agricultural Soils (3D) 1990 1995 2000 2005 2010 2015 2020 2021 1990 1995 2000 2005 2010 2015 2020 2021 to soils (kt N/y) 19,140 46,083 49,792 140,684 790,836 10,229,798 20,837,501 20,905,684 0.0002 0.0005 0.0006 0.0017 0.0093 0.1202 0.2448 0.2456 0 0 0 596 916,078 13,234,801 20,392,925 20,778,690 0 0 0 0.00001 0.01440 0.20798 0.32046 0.32652

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 817 1990 1995 2000 2005 2010 2015 2020 2021 0 0 0 894,125 3,711,425 18,752,915 28,685,445 29,625,445 0 0 0 0.01405 0.05832 0.29469 0.45077 0.46554 0 0 670 345,720 377,545 2,153,916 3,216,419 3,246,569 0 0 0.00001 0.00543 0.00593 0.03385 0.05054 0.05102

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 818 Emission source EF (% of total N) Uncertainty Data source Livestock slurry 0.7475 SE 0.17328 Topp et al., in prep Livestock solid manure (FYM) 0.33 SE 0.07 Topp et al., in prep Poultry manure 1.01 SE 0.15 Topp et al., in prep Sewage sludge 1.0 0.3 – 3.0 IPCC 2006 Manure-based digestate 0.7475 SE 0.17328 Topp et al., in prep Non-manure based digestate 1.0 0.3 – 3.0 IPCC 2006 Crop residues 1.0 0.3 – 3.0 IPCC 2006 N mineralisation 1.0 0.3 – 3.0 IPCC 2006 Histosols - cropland 16.28 kg N2O-N/ha SE 4.423 Histosols – intensive grassland 7.39 kg N2O-N/ha SE 1.642 Cattle urine 0.629 SE 0.0930 Topp et al., in prep Cattle dung 0.193 SE 0.0212 Topp et al., in prep Sheep urine 0.315 SE 0.0658 Topp et al., in prep, IPCC Sheep dung 0.097 SE 0.0150 Topp et al., in prep, IPCC Outdoor goats, horses and deer 0.3 0.0 – 1.0 IPCC 2019 Outdoor pigs and poultry 0.4 0.0 – 1.4 IPCC 2019 Crop Type Crop area, ha Fertiliser,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 819 Crop Type Crop area, ha Fertiliser, Spring oats 23,756 1.6 Potatoes Winter oats 9,983 0.7 Sugar beet 95,249 6.6 Spring barley 12,898 1.1 Maize 95,815 6.2 Spring barley (malting) 425,867 42.2 Grain maize 14,762 0.9 Spring barley (non-malting) 306,088 29.6 Forage maize 116,469 6.7 Winter barley 7,944 1.2 Rootcrops for Winter barley (malting) 70,076 8.8 Leafy forage Winter barley (non-malting) 326,872 47.0 Other fodder Wheat (milling) 655,352 127.7 Vegetables Wheat (non-milling) 1,127,309 199.7 Vegetables Minor cereals 70,819 7.9 Vegetables Spring oilseed rape 6,735 1.1 Soft Fruit 7,483 0.4 Winter oilseed rape 294,863 50.6 Top Fruit 23,216 1.4 Linseed 40,907 3.7 Miscanthus 7,829 0.1 Field beans (harvested dry) 186,861 0.2 Willow (short Field peas (harvested dry) 60,732 0.1 Other field Fruit (mixed top & soft fruit) 12 0.0 Wine grapes 2,762 0.2 Permanent grass 6,070,944 287.1 Temporary

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 820 Year ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N 1990 2,014 183 635 90 882 140 148 184 390 226 1,606 166 5,316 108 1991 1,981 189 553 90 841 142 148 188 440 225 1,607 168 5,314 107 1992 2,067 185 515 89 784 141 151 175 421 197 1,582 157 5,266 95 1993 1,759 182 518 90 649 134 143 187 377 177 1,581 146 5,261 100 1994 1,811 189 481 95 627 145 138 194 404 182 1,455 170 5,375 110 1995 1,859 193 504 97 689 144 144 176 354 187 1,407 170 5,375 108 1996 1,977 185 519 93 749 140 149 171 356 190 1,393 166 5,338 105 1997 2,036 191 519 93 840 143 133 166 445 199 1,403 147 5,266 103 1998 2,045 182 484 91 769 136 131 187 506 193 1,302 156 5,365 99 1999 1,847 186 631 99 548 143 148 154 493 197 1,226 180 5,449 101 2000 2,086 191 539 107 589 148 138 159 393 192 1,226 142 5,363 90 2001 1,635 193 783 114 462 150 137 160 444 205 1,205 130 5,584 84 2002 1,996 193 555 112 546 153 129 154 436 198 1,243 135 5,519 77 2003 1,836 197 621 107 455 148 118 149 549 194 1,200 129 5,683 75 2004 1,990 187 587 98 420 144 121 144 498 193 1,246 116 5,620 71 2005 1,870 184 553 97 384 139 113 140 588 196 1,193 108 5,711 65

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 821 Year ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N ‘000 ha kg/ha N 2006 1,836 176 494 96 388 132 117 144 568 182 1,137 106 5,967 60 2007 1,830 179 515 92 383 132 112 131 674 184 1,176 98 5,965 55 2008 2,080 176 616 93 416 134 114 154 598 185 1,141 91 6,036 45 2009 1,814 186 749 98 411 137 118 161 581 179 1,262 86 6,081 47 2010 1,939 190 539 97 382 141 114 132 642 194 1,231 99 5,925 53 2011 1,969 192 611 97 359 140 120 153 705 195 1,278 92 5,877 51 2012 1,992 194 618 99 385 147 123 134 756 187 1,357 91 5,799 50 2013 1,615 184 903 107 310 145 114 166 715 174 1,390 96 5,802 54 2014 1,936 191 651 106 429 145 115 140 675 191 1,396 100 5,824 51 2015 1,832 194 659 105 442 148 105 155 652 191 1,167 97 6,078 49 2016 1,823 186 683 105 439 146 114 130 579 182 1,144 94 6,118 50 2017 1,792 183 754 101 423 149 121 133 562 180 1,144 96 6,135 49 2018 1,748 183 751 100 387 143 117 135 583 188 1,152 95 6,178 52 2019 1,816 185 710 97 453 145 120 153 530 183 1,193 95 6,207 49 2020 1,389 170 1,074 100 312 141 119 117 381 173 1,181 94 6,122 49 2021 1,790 184 745 98 405 141 113 115 307 171 1,217 95 6,071 47

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 822 Spring oats 0.46 0.5 NA NA 0.25 Winter oats 0.46 0.5 NA NA 0.25 Spring barley 0.52 0.5 NA NA 0.22 Spring barley (malting) 0.52 0.5 NA NA 0.22 Spring barley (non-malting) 0.52 0.5 NA NA 0.22 Winter barley 0.52 0.5 NA NA 0.22 Winter barley (malting) 0.52 0.5 NA NA 0.22 Winter barley (non-malting) 0.52 0.5 NA NA 0.22 Wheat (milling) 0.50 0.5 NA NA 0.23 Wheat (non-milling) 0.50 0.5 NA NA 0.23 Minor cereals 0.49 0.5 NA NA 0.23 Oilseed rape 0.30 1 NA NA 0.35 Spring oilseed rape 0.30 1 NA NA 0.35 Winter oilseed rape 0.30 1 NA NA 0.35 Linseed and Flax 0.38 0.5 NA NA 0.35 Field beans and peas combined NA 1 1.13 0.85 0.19 Potatoes (maincrop) NA 1 0.10 1.06 0.20

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 823 Potatoes (seed or earlies) NA 1 0.10 1.06 0.20 Sugar beet NA 1 1.07 1.54 0.20 Grain maize NA 1 1.03 0.61 0.22 Forage maize NA 0.15 1.03 0.61 0.22 Root crops for stockfeed NA 0.15 1.07 1.06 0.20 Leafy forage crops NA 0.15 0.30 0.00 0.35 Other fodder crops NA 0.1 NA NA 0.35 Vegetables (not-differentiated) NA 1 0.30 0.00 0.35 Vegetables (brassicas) NA 1 0.30 0.00 0.35 Vegetables (legumes) NA 1 0.30 0.00 0.35 Vegetables (other non-legumes) NA 1 0.30 0.00 0.35 Other horticultural crops NA 1 0.30 0.00 0.35 Soft Fruit NA 1 0.20 0.00 0.35 Miscanthus NA 1 1.00 0.00 0.35 Willow (short rotation coppice) NA 1 1.00 0.00 0.35 Other field crops 0.52 0.5 NA NA 0.22 Wine grapes NA 1 0.20 0.00 0.35 Fruit (mixed top & soft fruit) NA 1 0.20 0.00 0.35 aWhere ‘NA’ appears in the Harvest Index column, it indicates that the IPCC 2006 method was used; bwhere ‘NA’ appears in the IPCC slope or intercept column, it means that the Harvest Index approach was used

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 824 Crop Below Ground N, kg N/[t DM] Crop Residue Above Ground Spring barley (malting) 14.0 6.7 Spring barley (non-malting) 14.0 6.7 Winter barley (malting) 14.0 6.7 Winter barley (non-malting) 14.0 6.7 Field beans and peas combined 8.0 8.0 Potatoes (seed or earlies) 14.0 17.3

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 825 Crop Below Ground N, kg N/[t DM] Crop Residue Above Ground Root crops for stockfeed 14.0 12.6 Vegetables (not-differentiated) 12.0 26.1 Vegetables (brassicas) 12.0 38.4 Vegetables (legumes) 22.0 23.2 Vegetables (other non-legumes) 22.0 16.7 Other horticultural crops 22.0 26.1 Willow (short rotation coppice) 11.0 0.3 Fruit (mixed top & soft fruit) 11.0 8.1

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 826 1990 1995 2000 2005 2010 2015 2020 2021 38.84 49.62 63.99 75.58 81.87 83.99 82.07 81.10 0.05 0.07 0.06 0.06 0.06 0.06 0.90 1.60 0.61 0.78 1.01 1.19 1.29 1.32 1.29 1.27 0.00075 0.00116 0.00098 0.00099 0.00093 0.00096 0.01414 0.02506

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 827 1990 1995 2000 2005 2010 2015 2020 2021 0.14 0.18 0.23 0.27 0.29 0.30 0.29 0.29 0.75 0.96 1.23 1.46 1.58 1.62 1.60 1.59 1990 1995 2000 2005 2010 2015 2020 2021 Area of histosols – Cropland, ha 197,352 196,205 195,091 193,304 191,775 190,422 189,123 188,863 Area of histosols - Intensive grassland, ha 194,972 191,047 187,343 184,336 182,458 181,160 179,951 179,716 Direct N2O emissions from histosols - Cropland, kt N2O/y 5.05 5.02 4.99 4.94 4.91 4.87 4.84 4.83 Direct N2O emissions from histosols – Intensive grassland, kt N2O/y 2.26 2.22 2.18 2.14 2.12 2.10 2.09 2.09 Total N2O emissions from histosols, kt N/y 7.31 7.24 7.17 7.09 7.02 6.97 6.93 6.92

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 828 A 3.3.3.4 Atmospheric deposition of NOx and NH3 1990 1995 2000 2005 2010 2015 2020 2021 Amount of volatilized N, kg/yr Total N2O emissions from volatilized N (kt N2O/y) 3.01 2.81 2.70 2.57 2.50 2.75 2.59 2.65 1990 1995 2000 2005 2010 2015 2020 2021 Amount of N lost through leaching and runoff, kg/yr Total N2O emissions from N lost through leaching and runoff, kt N2O/y 6.38 6.11 6.18 5.78 5.70 6.00 5.19 5.51 1990 1995 2000 2005 2010 2015 2020 2021 Amount of limestone applied to soils, t/yr 1,632,538 1,350,922 1,386,120 2,603,325 2,366,733 1,942,627 2,205,401 2,392,724 Amount of dolomite applied to soils, t/yr 625,385 665,824 456,362 492,982 481,331 343,255 308,198 257,385

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 829 1990 1995 2000 2005 2010 2015 2020 2021 Total CO2 emissions from application of limestone (kt CO2/y) 718 594 610 1,145 1,041 855 970 1,053 Total CO2 emissions from application of dolomite (kt CO2/y) 298 317 218 235 229 164 147 123 1990 1995 2000 2005 2010 2015 2020 2021 Amount of urea applied to soils, t/yr 399,483 243,280 194,039 254,642 349,229 478,591 318,586 343,760 Total CO2 emissions from application of urea (kt CO2/y) 293 178 142 187 256 351 234 252

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 830 A 3.3.6 Distribution of manure in different management systems A review of the literature on livestock housing and manure management practices conducted by Ken Smith (ADAS) as part of Defra project AC0114 (Smith, 2012), updated with survey data on manure spreading practices from the British Survey of Fertiliser Practice15, and data provided directly by DAERA statistics for Northern Ireland (Peter Cottney, pers.
comm.) was used as the basis for developing the 1990 to 2021 timeseries of livestock housing and manure management practices for each country (England, Wales, Scotland and Northern Ireland) from which a weighted average was derived for the UK.
Detailed practice-specific data are applied at a country scale for each livestock category for the livestock housing, manure storage and manure application phases of the manure management continuum. Estimates for these activity data across the timeseries are derived from a number of routine and ad-hoc surveys including the Defra Farm Practices Surveys16 and published manure management surveys (Smith et al., 2000, 2001a, 2001b).
Tonnages of poultry litter incinerated in each year were obtained directly from MREL17, with tonnage exported from Northern Ireland to be incinerated in Scotland and England provided by DAERA. data provided by the National Non-Food Crops Centre18 annual deployment report, a database listing operational, under-construction and proposed anaerobic digestion plants in the UK.
Information in the database includes location, capacity, feedstock (inputs) types and feedstock quantities in five manure, crops, crop wastes, food and other. Although co-digestion of two or more feedstocks is commonly practiced, for the purpose of the emission calculations each is treated individually.
Manure as a feedstock is further categorised as cattle, pig, poultry, equine and miscellaneous animal. For inventory calculations, the categories of equine and miscellaneous animal (i.e.
not specifically identified) are summed and reallocated to cattle, pig and poultry based on the relative proportions of total manure (leaving housing) for those livestock types. Similarly, as cattle is not further defined, the relative proportions from the dairy and beef sectors are assumed to be in proportion to the total quantity of manure managed for those sectors.
Within each livestock sector, for each livestock subcategory, the same proportions are applied to manure quantity going to anaerobic digestion as are applied to that sector as a whole. For cattle and pigs, slurry and farmyard manure are assumed to be equally applicable for anaerobic digestion, so have the same proportional implementation.
Within the sheep sector, all (100%) manure from the ewe housing period is managed as solid farmyard manure with additions of straw bedding. All (100%) manure from the housing period is stored for a period of several months as a field heap on grass before spreading to land.
Therefore, there is a nitrate leaching loss from all stored sheep manure (estimated according to the measurements of Nicholson et al., 2011) and there is no need to assume that leachate from manure heaps stored on concrete pads is collected and managed differently. Based on Roderick (2001) survey of 2,649 flocks managed by members of the National Sheep Association from across the United Kingdom, the inventory calculations assume that 75% of ewes associated with lowland systems, 60% with upland systems, and 40% with hill systems 15 Most recent 16 Defra Farm Practices 17 Melton Renewable Energy Ltd., formerly 18 National Non-Food Crops

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 831 are housed in the last weeks of pregnancy.
From the same survey, we assumed that housed breeding ewes are housed for 42 days prior to lambing, and that neither the ewe nor new-born lamb are housed post lambing. Further detail on management housing for other activities (e.g.
shearing) were obtained from a postal survey of 697 farms in England and Wales (Defra, 2004; Dauven and Crabb, 1998; Webb et al., 2001). A 3.3.7 Description of the UK Agriculture Inventory Model The agricu lture inventory model was developed as part of the Defra funded Improvement Programme in order to meet the range of UK Government reporting and policy evidence requirements, and to enable the UK to meet international and national reporting obligations and mitigation targets across GHGs and air quality pollutants.
A 3.3.7.1 International Conventions The primary purpose of the UK agriculture inventory model is to produce annual inventories of ammonia (NH3) and GHG (nitrous oxide, N2O; methane, CH4; and carbon dioxide, CO2) emissions from all UK agricultural sources in compliance with international inventory methodological and reporting guidelines. The methods deployed in the model are consistent with the IPCC guidelines for national GHG inventories and the EMEP-EEA guidance for air pollutant emission inventories.
The inventory model and system has been developed under contract to the UK Department for Environment, Food and Rural Affairs (Defra) and is operated in co -ordination with the UK’s Inventory Agency to the Department for Energy Security and Net Zero (DESNZ) which is the UK’s designated Single National Entity under the UN Framework Convention on Climate Change The agriculture inventory model outputs are designed to meet the UK’s international reporting obligations under several conventions, including the reporting of GHG emissions under the UNFCCC Reporting Guidelines and to the Kyoto Protocol, and reporting of air pollutant emissions under the Gothenburg Protocol. Ammonia emissions are reported under the Convention on Long- Range Transboundary Air Pollution (CLRTAP) and the National Emission Ceilings Regulation A 3.3.7.2 National and Sub-national Commitments In addition to the international reporting outputs, the agriculture inventory model delivers emission inventory and projections data as underpinning evidence for use within national and sub-national reporting, progress/target-tracking and policy development, including to track emissions against UK national GHG targets and carbon budgets, Devolved Adminis tration (DA) GHG targets and budgets, and Local Authority (LA) level datasets.
The same data, assumptions and methods that are used for UK national inventory compilation and reporting are also used to inform the DA inventories (GHG and AQ) as well as Local Authority GHG inventories. Further, the agriculture inventory outputs include detailed emission maps for across the UK, in particular to support air quality modelling to assess the sources and impacts of ammonia (NH3) emissions.
This integrated approach e nsures that where better data or information (e.g. regarding local farm practices, uptake of mitigation measures) becomes available, the subsequent improvement in method accuracy to better -reflect local circumstances also ripples through all of the sub -national to This wide range of reporting and policy evidence requirements has informed the design and scope of the UK agriculture model, and indeed continues to shape the evolution of the UK agriculture model, through improvements to refine inventory data and methods in order to address ever - increasing demands for more detailed, accurate GHG and ammonia (NH3) data, at a higher level of resolution (e.g.
per region/locality, per livestock or crop sub-category, per waste management stream, per mitigation action and so on). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 832 The inventory outputs are applied within analytical tools across Government, including the Defra Scenario Modelling Tool (SMT) which enables policy analysts to test mitigation strategies as the UK seeks to achieve N ational Emission Ceiling Reduction targets in 2030 and beyond for ammonia (NH3) alongside developing Net Zero GHG pathways for the sector in each region of A 3.3.7.3 National and Local Circumstances The model is designed to be sensitive not only to changes i n key activity data such as livestock numbers and crop areas, but also to changes in UK farming practices and the uptake of mitigation measures, and at a local level to reflect local (i.e.
not “average national”) circumstances. The UK agriculture inventory model is structured to enable representation of the key underlying driving variables of the GHG and ammonia (NH 3) emissions, including soil types, climate, livestock and cropping characteristics and farm management practices including the update of specific mitigation methods.
Cross-cutting analysis of the UK agriculture sector, using farm survey data, seeks to identify farms by type within a 10x10km grid square; this includes characterisation of the farms according to the primary sources of economic incom e (e.g. arable, sheep, dairy, poultry, mixed) and whether the farm location (e.g.
remote upland) is within a less favoured economic area. This initial analysis then informs some of the assumptions applied in the source- specific inventory methods, for examp le regarding production systems and waste management practices, the technology and mitigation techniques that are most commonly deployed per farm type, the likely level of investment in fertiliser use and so on.
The model also includes detailed representation of uncertainties consistent with the UK inventory- wide uncertainty analysis, in order to highlight and prioritise opportunities for future improvement. A 3.3.7.4 Territories, Sources, Pollutants, Years Note that the UK Agriculture Inventory as described in this document covers all of the agriculture sector emission estimates from sources in the UK only (i.e.
England, Wales, Scotland and Northern Ireland); it does not generate emission estimates from the agriculture sector in the UK Crown Dependencies (CDs), i.e. Jersey, Guernsey and the Isle of Man, nor in the UK Overseas Territories (OTs), which include the Falkland Islands, Bermuda, the Cayman Islands, the Virgin Islands, Montserrat and Gibraltar.
The development of evidence through field trials and subsequent development of higher -Tier methods have been focused on the UK territory alone, and to use UK national statistics and other sector data to deliver high quality emission estimates. The GHG and air quality pollutant emission estimates for CDs and OTs are based on much more limited activity data and evidence, applying simpler methods, often IPCC Tier 1 methods (i.e.
applying default Emission Factors). As such the CD and OT emission estimates are associated with higher uncertainty, but these simpler meth ods reflect the much lower levels of activity and emissions in CDs and OTs compared to the UK, and the lower level of evidence to inform higher- tier methods, such as EFs that reflect local circumstances in the CDs and OTs.
The agriculture model does not generate emission estimates from fuel combustion in the sector, neither in stationary sources nor in mobile machinery. These fuel combustion emission sources are reported under the Energy sector of the UK inventory, with methods described in the National Inventory Report and Informative Inventory Report for GHG and air quality pollutants respectively.
The data and methods applied are consistent with IPCC and EMEP-EEA Energy sector inventory guidance, applying country-specific or default EFs to the activity data that are available from UK energy statistics. The agriculture model described here generates the emission estimates from sources that are as described within the AFOLU volume of the IPCC inventory guidance, i.e.
from livestock sources (enteric fermentation; waste management) and crop sector sources (agricultural soils emissions including from fertiliser use, liming and urea applications; field burning). Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 833 The agriculture inventory model also does not cover the emission sources and sinks from changes in soil carbon stocks and GHG emissions associated with specific land management or land use changes; these are estimated and reported in the LULUCF section of the UK GHG inventory.
Further the agriculture sector model does not cover pre - or post -farm gate GHG emissions associated with agricultural operations (e.g. fertiliser manufacture, processing of livestock feeds, transport and processing of foodstuffs, etc.).
Such emissions, where they occur in the UK, are accounted for in other sections of the UK GHG inventory (e.g. Energy, Industrial processes) and are not broken down in sufficient detail as to allocate them specifically to the agriculture industry; emissions from the production of fertilisers / feeds etc.
that are imported for use in the UK are reported within their country of origin. The scope of pollutants reported is described per emission source throughout this document and comprises GHGs (N2O, CH4, CO2) and air quality pollutants including ammonia (NH3), oxides of nitrogen (NOx) and non-methane volatile organic compounds (NMVOCs).
Consistent with the international reporting requirements, the UK inventory time series of emission estimates that are generated through the agriculture model are focused on annual emission estimates from 1990 to the latest year for which data are available. In the UK, the Base Year for GHGs (excluding F -gases) under the UNFCCC is 1990, whilst the Base Year for air quality pollutant targets under NECR is 2005.
The UK inventory does also continue to develop longer - term trend es timates, including of ammonia (NH 3) emissions from 1970 onwards but the data quality and resources applied to those earlier years is such that they should be regarded as A 3.3.7.5 Summary of the Model Structure The model structure and data flows are described in detail later in this Annex, but in summary the core engine of the model is written in C# language using stored queries, calling on an SQL server database which contains all of the pre -processed Activity Data and Emission Factor data per source category. The coded model comprises the calculations for all major emission sources of the agriculture sector in the UK, with a separate calculation module developed for each source.
The code per source draws on source-specific input activity tables that hold input data at a high level of disaggregation, including geo -referencing. The model generates source -specific output tables per 10 by 10 km grid cell which can be aggregated according to the required reporting taxonomy such as the IPCC Common Reportin g Format (CRF) tables for GHGs, Nomenclature for Reporting (NFR) tables for ammonia (NH 3) and other air quality pollutants, for UK national statistics or for reporting against UK, DA, LA targets, including • enteric methane from livestock; • manure management methane and nitrous oxide emissions from livestock dairy, beef, sheep, swine, poultry, goats, horses and deer; • direct and indirect nitrous oxide emissions from synthetic N fertiliser, organic N (e.g ., animal manure, sewage sludge and digestate) applied to grassland and arable crops, and • nitrous oxide emissions from urine and dung N deposited on pasture from dairy and beef cattle, sheep, swine, poultry, goats, horses and deer.
Activity data are collated, and calculations are performed per year from 1990 to the latest inventory year, per source category, per farm type, at a 10 by 10 km grid cell resolution; the results are aggregated across the four constituent countries (England, Wales, Scotland and Northern Ireland) to derive UK totals. The choice of grid size reflects uncertainty in agricultural holdings that are variously geo -referenced by parish, holding office or field centroids, but is principally justified by the confidentiality re strictions placed on the mapping of inventory input and output

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 834 The high level of data resolution per grid cell, per farm type enables the UK agriculture inventory model to perform emission estimation calculations bottom-up to reflect local parameters such as climate (long term temperature, rainfall) and soil type, as well as modelling the farm management practices that reflect the economic status per farm type and local farm survey responses.
The aggregation of data (e.g. livestock numbers, fertiliser use, crop production) by farm type across a 10 by 10 km grid ensures that data for specific farm holdings are not outputted, to protect commercially sensitive raw data in line with statutory data protection obligations.
Outside of the scope of the mo del are the nitrous oxide emissions from mineralisation and histosols and carbon dioxide emissions from liming and the application of urea to soils; these source categories in the UK inventory are estimated using Tier 1 methods, within MS Excel spreadsheet models. They are not derived “bottom-up” within the main UK agriculture model but are estimated for UK-wide emissions per source.
The UK agriculture inventory data management and reporting is conducted in accordance with the requirements of prevailing legislation, including the General Data Protection Regulation (GDPR). In the context of the inventory data and methods, under GDPR there must be no public data disclosure of data pertaining to an individual farm holding.
The procedures for handl ing of holding-level data from farm surveys are agreed via agreements with the data owners. Holding level data are not shared around the agriculture inventory consortium, to minimise risk of accidental data release.
Task owners hold the raw survey data on secure servers. Data are processed, checked and then aggregated (e.g.
to per grid cell, pre RFT type) prior to distribution to sector leads for source -specific data QC and calculations. The aggregated inventory outputs ensure that activity or emission estimates from individual holdings cannot be isolated even in the most detailed mapping outputs.
A 3.3.7.7 Design, development, acceptance, verification and validation of the UK The UK agriculture inventory model is bespoke in design, tailored to UK-specific data availability and resolution, the latest scientific understanding that reflects UK circumstances (e.g. soils, climate), production systems and farming practices.
Consistent with inventory good practice guidance, the methods for key source categories (i.e. t hose sources that contribute most to the level or trend of inventory estimates) are higher -Tier methods that apply UK -specific emission factors, whereas minor sources are estimated using simpler methods where international default factors are applied; this approach is proportionate and efficiently minimises the overall uncertainty The methods are developed to represent UK territorial emission sources and do not cover agricultural sources in Crown Dependencies nor Overseas Territories; m ethods for these territories are simpler, typically Tier 1, reflecting their low level of emissions compared to UK and that detailed activity data and information on local farming practices/conditions are scarce for The model was develop ed to accommodate scientific research findings from an extensive programme of UK field studies, following a significant investment over several years by Defra, via the GHG Research platform19 which led to numerous published research papers to establish the best source methods and UK emission factors to accurately reflect emissions on UK farms.
The UK Climate Change Act 2008 and the associated Carbon Budgets process motivated a major investment in the inventory compilation and accounting system to ensure that it was fit for purpose. Critically the objective was to ensure the UK's GHG inventory accurately reflects the 19

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 835 unique structure of the UK industry, and that progress made by farmers to improv e production efficiency was adequately captured in the calculations.
The work supported comprehensive measurements of UK livestock and soils emissions, gathering pre-existing academic and industry data sets and combining with bespoke additional measurement s to fill knowledge gaps. The higher-tier inventory methods are based • Around 150 site -years of data over a range of soil types, climates, fertiliser regimes and • Around 3,400 existing records plus 5,600 new measurements of m ethane emissions from ruminant livestock complete with data on diet, management etc.
• All data collection was audited and calibrated by the UKs National Physical Laboratory This UK dataset constitutes one of the largest single database s of agricultural em issions measurements collected globally and has been used as a source of data to revise the IPCC’s standard emissions factors for livestock. Throughout the development process Defra engaged a stakeholder group of industry representatives to steer the progr amme.
This included significant provision of expertise and data to underpin the inventory method development. The inventory improvement programme focussed on those areas of the inventory with largest uncertainties, and those that had been recommended by pr evious Expert Review Teams to convert to higher Tiers.
The implementation of the model was initiated in 2016, with the code developed and extensively tested per source method via an iterative process of code development including unit testing of code secti ons, validated against separate calculations performed in MS Excel by the team of UK sector experts, i.e. via extensive reperformance testing.
Through this process the model has been extensively tested and calibrated against the results from field trials and other research. Further, in the development and acceptance of source-specific methods, prior to their implementation within the UK inventory, the method publications were peer-reviewed by leading UK academics, and subsequently several key category methods have been subject to bilateral reviews with inventory experts from countries with similar climatic conditions and farming practices, including from France, Ireland and Germany.
Limitations of the current model, aside from the geographical limits indicated above, include that for several source methods the algorithms have been developed such that the impacts of specific mitigation measures will be represented in the outputs, however there are (in some cases) scarce data on the national uptake of these measures and limited information on the impacts of mitigation measures. Therefore, some of the model code is currently latent, awaiting the development of improved data streams to inform mitigation uptake and impacts across the UK.
Mitigation actions represented in the inventory model are described later in this Annex, as one of the key data inputs to the modelling. In addition, the temporal resolution of the modelling is limited although several of the source category methods do use activity data that are available at monthly intervals in order ✓ Beef and dairy cattle numbers vary across the year, and these are reflected in the modelling with calculations performed at monthly resolution; ✓ Emissions from fertiliser application are based on monthly estimates of fertiliser use, in order to reflect the climatic conditions (and their impact on emissions), especially during The model is designed and built for use (updates and analytical runs) by experienced domain experts with an understanding of the sector and also the modelling framework.
In order to enable other stakeholders to access the detailed outputs from the agriculture inventory model, user - friendly outputs and interfaces have b een developed, such as the Output Viewer software (described later in this Annex) which is made available to Defra and DA inventory and policy leads

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 836 Sections later in this Annex set out the key data sources, the pre-processing of survey data and then the details of each source method including the activity data, assumptions and emission factors, and the level of resolution in the method implementation, e.g. livestock age ranges, breed types, farm types and so on.
Where appropriate, links are provided to key publications to present A 3.3.7.8 Roles and Responsibility The agriculture inventory work programme is managed and funded by Defra, as the UK Government Department with policy responsibility for farming, food and rural affairs. Defra contracts out the delivery of the inventory via a competitive tender process, typically every 3 -4 years.
The agriculture inventory is currently (@2023) produced by a consortium of organisations, led by Rothamsted Research (RRes). The roles and responsibilities of key organisations are Defra as the overall management lead is the inventory Commissioner and Approver.
The Rothamsted Research project manager is the overall inventory Lead Analyst, with other sector experts in ADAS, Cranfield University, SRUC and UKCEH acting as Lead Analyst for individual sectors. Ricardo has an over-arching QAQC role across the consortium, acting as the Assurer.
Other key stakeholders associated with the agriculture inventory are DESNZ as the UK GHGI Single National Entity and two steering groups that UK Government has established to manage inventory improvements and sign -off, and oversee the development of sci entific, technical,

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 837 modelling and policy evidence components of the UK the National Inventory Steering Committee (NISC, for GHGs) and the Air Quality Inventory Steering Committee (AQISC, for air quality pollutants). These steering groups include representation from across UK Government Departments, from the Devolved Administrations of Scotland, Wales and Northern Ireland, and from other agencies and organisations, industry, academia and local government.
The outputs from the agriculture invento ry complement the work in other sectors to generate comprehensive UK inventories of anthropogenic emissions to atmosphere for GHGs and air quality pollutants, for onwards reporting to the UNFCCC, UNECE and other international organisations. Where necessary to ensure internal consistency, the agriculture inventory experts liaise directly with the inventory compilers of other sectors including for Land Use, Land Use Change and Forestry (LULUCF) and Waste Management.
A 3.3.7.9 Inventory Model Development of UK Evidence The UK agriculture inventory system has been developed over a decade of investment in field trials, desk-based research and new modelling methods by Defra and the DA governments. The investment in UK scientific understanding and method developm ent was initiated through the Agricultural GHG Inventory Research Platform, via a large consortium of UK academic and research organisations in three projects led by Rothamsted Research (N 2O inventory improvement), IBERS (CH4 inventory improvement), and ADAS (Data mining) under contract to The UK Government investment has substantially improved the UK-specific emissions evidence base, leading to development of new higher Tier UK-specific calculation methodologies, emission factors and sector representation in the inventory emission estimates for the agriculture sector.
The inventory development programme culminated in the development of a bespoke agriculture inventory model with a fully revised structure to enable representation of the key underlying driving variables of the GHG emissions, including soils, climate, livestock and cropping characteristics and farm management practices including the uptake of specific mitigation methods. The model also includes detailed representation of uncertainties, allowing full uncertainty analyses to highlight areas for future improvement.
The UK inventory undergoes continuous improvement to reflect developments in source data and scientific understanding, for example when inventory guidance is upda ted to improve methods, new AD or EFs become available, or to add new emission sources and/or to reflect new mitigation actions or changes to UK farming practices. • • • • A 3.3.7.10 Rationale for Agriculture Inventory Model Design The UK agriculture inventory model was designed to meet the UK and sub-national reporting and policy requirements, to provide industry sector-specific emission estimates and to account for the impact of changing management practices within the industry on emission estimates.
The UK has therefore developed a combination Tier 2 and Tier 3 approaches for all key emission categories, in which country-specific soil and climatic conditions, management practices and emission factors are all considered in the emission calculation. The inventory methods include, for livestock systems, a nitrogen mass-flow approach that accounts for nitrogen losses and transformations from the point of excretion by the animal through the manure management continuum, application to soil and the relevant losses to the atmosphere and water courses.
Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 838 The model is designed to enable the inventory calculations to be performed at the level of 10 by 10 km grid squares in order to improve accuracy (through more accurate representation of local conditions of climate, soil type etc.) and sensitivity to changes in local ac tivity data (livestock numbers, areas of grassland and crop production) and local management practices and mitigation uptake (where data allows).
The sum per source category across all of the 10 by 10 km grid squares is then aggregated to deliver devolved country (i.e. England, Scotland, Wales and Northern Ireland) and UK inventory totals.
The total crop areas and livestock numbers are spatially disaggregated on a fixed regular grid of 10 by 10 km cells covering the terrestrial area of the United Kingdom. There are 3,040 terrestrial cells that are aligned with the Ordnance Survey National Grid for Great Britain with an origin at the south-west corner of the cell located 400 km west and 100 km south of 49oN 2oW.
Each grid cell is assigned a unique number (CE LL). The reference grid excludes the islands of Jersey, The indexed crop areas and livestock numbers are also disaggregated by ‘Representative Farm Type’ (RFT) that is aligned to the MAFF (1993) methodology for defining ‘Robus t Farm Types’ that are based on the farm enterprise that contributes the majority (more than two thirds) of an estimated business ‘Standard Output’ (SO), based on surveyed crop areas and livestock numbers and standard output coefficients.
There are ten rep resentative farm types (Cereal, General Cropping, Horticulture, Pig, Poultry, Less Favoured Area Cattle and Sheep, Lowland Cattle and Sheep, Dairy, Mixed, and Other). The total crop areas and livestock numbers are disaggregated by ‘Sector Type’ that are al igned to the generic crop (Crop and Grass) and livestock (Beef, Dairy, Sheep, Pig, Poultry and Minor) types reported in the UK inventory.
Each sector is represented by one or more ‘Item Type’ that are the specific crop and livestock types. For example, the sheep sector is disaggregated between More detailed data resolution has been implemented for specific source categories to further Dairy For example to improve the representation of dairy production systems, within each grid cell the livestock numbers are derived on a monthly basis for a series of production systems and sub -systems dairy cows and heifers (productive animals following their first calving); Dairy heifers in calf (previously non-lactating animals, in calf for the first time); Dairy replacements (all young animals > 1year which are being kept as replacements for the milking herd); Dairy calves (all calves < 1 year which will go on to become dairy replacements); Dairy bulls (bulls kept on dairy farms).
Within each spatial cell and system/sub-systems described above, certain production parameters will be associated with the particular livestock sub -category at a monthly resolution for a given year. For example, for dairy cows and heifers the parameters required will be calving time, mean daily milk yield, mean milk fat content, mean milk protein content, mean live weight, mean weight gain (or loss) and mean pregnancy rate (the proportion of mature females that will go through gestation in a year).
For dairy heifers in calf, the same parameters will be required excepting mean daily milk yield and fat content, and for the other categories the same parameters excepting mean milk yield and fat content and mean pregnancy rate. Additional data needs will also include transfers from the dairy to beef herd (e.g.
dairy cows that still support beef calf production). Local holding survey data and assumptions on management practices per RFT in a given grid cell are used to assign calculation par ameters that reflect housing and manure management practices, for example to reflect dairy cattle housed on slurry -based or a litter -bedded loose housing system.
The proportion of dairy cattle within each system/sub -system/category associated with each hou sing type on an annual basis is required for each year as is the proportion that has access to an outdoor collecting or feeding yard. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 839 Within each spatial cell, the areas of grassland for grazing and silage production and the areas of other forage crops ass ociated with dairy production are required, together with a monthly breakdown of amount of fertiliser nitrogen applied, by fertiliser type.
The extent to which any fertiliser additives are used (e.g. urease or nitrification inhibitors) may also be modelled , subject The dairy examples are presented to describe the model design; full details of the data and methods per source category are described within the National Inventory Report.
The level of disaggregation permits different assump tions to be applied within the inventory methods across the agricultural sector, for example to take account of the different economic circumstances (e.g. farms within Less Favoured Area status) which may determine the typical management practices, level of investment in mitigation action uptake.
The allocation of farms to RFTs also enables the modelling to take account of different practices that are expected, e.g. for the waste management and housing options of cattle on a predominantly beef or dairy or m ixed Regarding the emissions from agricultural soils, the spatial resolution of the model combined with the parameters held in the physical database (see the Raw Data Inputs section of this Annex for details) enable the climatic conditions (rainfall, temperature), soil types and elevation per grid cell to be considered, where appropriate, within calculations to estimate nitrous oxide and ammonia The model is designed to perform calculations per year and per country (England, Wales, Scotland, Northern Ireland) at a 10 by 10 km grid resolution, calling on the underlying data for that grid square of climate, soil, farm types, as well as reflecting different production systems and mitigation actions (from survey information, where availabl e) and fertiliser applications by type.
All these localised datasets result in localised EFs per source category-pollutant; these are then aggregated across regions / areas, e.g. per Local Authority, per devolved Country, for the UK as a whole.
The resulta nt dataset exhibits region -specific EFs and is sensitive to local information on e.g. uptake of mitigation actions or changes to production and management systems.
A 3.3.7.11 Overview of the Agriculture Inventory Model Data Flow This section describes the UK agriculture data flow • Compilation of activity data tables per source category • Core model calculations, within the C# model • Outputs, expert review, inventory finalisation The schematic below provides an overview of the whole process; more detailed flow charts are presented throughout this section. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 840 The UK agriculture inventory compilation and reporting cycle is compressed into a relatively short time-window each year.
This is necessitated by the fact that the key year “n” data that are required for the annual updates typically only become available during June-July in year “n+1”, and the UK releases and to underpin international reporting outputs requires that final data are ready for the Autumn NISC meeting, typically around the 3 rd week in November; prio r to that date, the agriculture inventory data (together with all documentation of data references, reasons for recalculations and so on) must be provided to the UK Inventory Agency to combine with the other sector data, for the purposes of overall UK inventory analysis, checking and preparation of Inventory Compilation Activity April, May Data requests sent out; data supply / GDPR agreements revised and updated between the inventory team and key data providers. JAS/C and CTS datasets may be received during these months.
June CPH data inputs updated, where necessary, for use within CTS processing (UKCEH) UKCEH check and update the CPH data; send to Cranfield when finalised. CTS data cleaning, pre-processing.
(Cranfield) Cranfield aim to deliver draft processed CTS data for year n, by end of June. July CTS data quality checks , conducted by sector experts.
(Rothamsted, ADAS and Any outliers, gaps are flagged, revised, CTS data updated by Cranfield. Dairy sector data prepared, checked and sent to UKCEH (by mid -July) to enable the RFT allocation to be conducted.
(Rothamsted)

Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 841 Inventory Compilation Activity Data processing of the JAS/C and finalised CTS to conduct the RFT allocation , complete all location and LFA datasets (UKCEH) UKCEH aim to complete all of the JAS/C and RFT data processing by the end of July. BSFP detailed database of survey responses typically provided by mid-July.
(Defra) ADAS pre-processes and QCs the BSFP data, re -stratifies the BSFP data to generate more accurate GB fertiliser use data for year n, based on actual holding level response August Prepare and load Base database tables (ADAS) ADAS aims to complete this task over late July / early August. Preparation of all sector Activity D ata tables (All sector Rothamsted, ADAS, All sector data aim to be prepared by mid -August to enable QC and first model runs to All initial C# model runs, output draft sector data (ADAS) ADAS aims to run, QC and circulate draft results by the end of August.
September Sector expert quality checking of draft model outputs. (All sector Rothamsted, ADAS, Cranfield), including cross -sectoral QC with other UK Inventory Agency teams, crop and grassland areas per DA vs LULUCF; sewage sludge spreading data vs waste sector; poultry litter fate QC vs energy sector.
Any errors, gaps, outliers are checked, justified or corrected and Activity Data tables updated, model code re -run as required to finalise the data per source cate gory- NAEI output file preparation (Rothamsted) Once all sector data are finalised, Rothamsted generate aggregated outputs in inventory-ready formats, for integration by the UK Inventory Agency into the UK Sector methods, recalculations, references, explanation of trends (All sector Rothamsted, ADAS, Cranfield) All sector leads draft the key information that is needed to present to UK Government / NISC to explain all changes / improvements in methods, data and resultant recalculations, and prepare updated documentation for the purposes of the NIR and IIR. Draft Scheduler sheets (Rothamsted) QC of Scheduler sheets (All sector Rothamsted, ADAS, Cranfield) The output Scheduler sheets are generated for ease of use / data accessibility, primarily for UK Government inventory data users.
Engagement with UNFCCC expert reviews of the UK inventory (All, as required) October Documentation of methods; preparation of reporting outputs , e.g. CRF Reporter The Inventory Agency combines the agriculture sector with the rest of the UK inventory data; the agriculture inventory experts focus on preparing formal reporting outputs.
November National Inventory Steering Committee meeting (Rothamsted, ADAS) Sector leads present to the NISC Advisory panel on agriculture inventory improvements, latest data for the next UK submission to the UNFCCC on GHG emissions. Contributions to national and sub -national reports, including the N IR, IIR, DA reports, mapping reports (All, led by Rothamsted).
Stakeholder engagement / QA activities , such as post -submission review, dialogue with Defra, DAs and the farming sector to identify potential inventory improvements. Other Detailed Methodological Descriptions A3 UK NIR 2023 (Issue 1) Ricardo Energy & Environment Page 842 The UK inventory model calculations are driven by data held in a number of databases; one of these is a static database of physical data per 10 by 10 km grid cell that holds data on soil types and climatic data, whilst the annual updates are driven by annual survey data that are updated and extended each cycle inventory cycle.
These are described below. Physical geography, elevation, soil and climate - description The physical database holds data on soils and climate, as well as defining the administrative boundaries and the geo-referencing for all agricultural holdings across the inventory reference 10 by 10 km grid cells.
Agricultural holdings are also geo-referenced by country and Less Favoured Area status according to the location of the majority of their land area with respect to digitised vector boundaries. Agricultural holdings can only be located in one cell, country and Less Favoured Area type.
The choice of grid size reflects u ncertainty in agricultural holdings that are variously geo-referenced by parish, holding office or field centroids, but is principally justified by the confidentiality restrictions placed on the mapping of inventory input and output datasets, specifically derived from the June Agricultural Survey. Confidentiality restrictions prevent the mapping of summary statistics for fewer than 5 or 10 holdings, depending on the data source.
Each grid cell that is located within England is assigned to one of nine Government Office Regions (GORs); the country boundaries define grid cell allocations to England, Scotland, Wales and Northern Ireland. All agricultural holdings are geo-referenced with respect to the Less Favoured Areas (LFA) designations (Disadvantaged and Sev erely Disadvantaged Areas).
The LFA designation is used within source category methods to aid characterisation of farm system types. Common land may be grazed by cattle and sheep but is not reported by the June Agricultural Survey, which reports only land that is owned and managed by an individual; areas of registered common land within England, Wales and Northern Ireland, and the area of forage claims on common grazings in Scotland are geo-referenced across the inventory 10 by 10 km grid cell.
The average altitude (m) above sea level of each inventory grid cell has been calculated from the Ordnance Survey Panorama dataset, under the Open Data licence, at a source spatial resolution of 50 by 50 m and elevation resolution of 10 m; these data are also held in the physical database. Soil properties, notably field capacity, plant available water and percent clay and organic carbon content are used as input to inventory calculations of grass growth and nitrogen leaching following application of fertilisers, manage d manures and excreta returns.
The UK inventory uses the RB209 (MAFF, 2000) soils classification, based on soil texture and depth to rock, with seven soil types (light sandy, shallow, medium, deep clay, deep fertile silty, organic, peaty). For each country, soil scientists from the James Hutton Institute (Scotland), National Soils Resources Institute University (England and Wales) and the Agri -Food and Biosciences Institute (Northern Ireland) applied the RB209 soil typology to national digital soils dataset s. The resulting United Kingdom map of RB209 soil types was spatially intersected with the reference 10 by 10 km grid to provide a statistical summary of the percent of the agricultural land area located on each of the RB209 soil types.
The same national digital soils datasets were also used to calculate the percent of the soil area within each grid cell (excluding peaty soils) that is alkaline, based on topsoil measurements of pH (≥ 7), which is used in calculations of ammonia (NH 3) emissions from Long-term annual and monthly average rainfall and air temperatures were obtained from the Met Office. Met Office UKCP09 baseline average datasets are for the period 1981 to 2010 (Jenkins et al., 2008).
The UKCP09 data are available at 5 b y 5 km resolution and summarised to the reference 10 by 10 km grid cells used in the agriculture inventory model. Daily time -series of measured rainfall derived from the UK synoptic network for the MORECS model (Hough and Jones, 1997) were obtained from the Met Office across the 1981 to 2010 time series at 40 by 40 km resolution.