Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations

We present an updated version of the Global Fuel Exploitation Inventory (GFEI) for methane emissions 20 and evaluate it with results from global inversions of atmospheric methane observations from satellite (GOSAT) and in situ platforms (GLOBALVIEWplus). GFEI allocates methane emissions from oil, gas, and coal sectors and subsectors to a 0.1° x 0.1° grid by using the national emissions reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC) and mapping them to infrastructure locations. Our updated GFEI v2 gives annual emissions for 2010-2019 that incorporate the most recent UNFCCC national reports, 25 new oil/gas well locations, and improved spatial distribution of emissions for Canada, Mexico, and China. Russia's oil/gas emissions decrease by 83% in its latest UNFCCC report while Nigerian emissions increase sevenfold, reflecting changes in assumed emission factors. Global gas emissions in GFEI v2 show little net change from 2010 to 2019 while oil emissions decrease and coal emissions slightly increase. Global emissions in GFEI v2 are lower than the EDGAR v6 and IEA inventories for all sectors though there is considerable variability in the comparison 30 for individual countries. GFEI v2 estimates higher emissions by country than the Climate TRACE inventory with notable exceptions in Russia, the US, and the Middle East. Inversion results using GFEI as a prior estimate confirm the lower Russian emissions in the latest UNFCCC report but Nigerian emissions are too high. Oil/gas emissions are generally underestimated by the national inventories for the highest emitting countries including the US, Venezuela, Uzbekistan, Canada, and Turkmenistan. Offshore emissions in GFEI tend to be overestimated. Our updated GFEI v2 35 provides a platform for future evaluation of national emission inventories reported to the UNFCCC using the newer generation of satellite instruments such as TROPOMI with improved coverage and spatial resolution. It responds to recent aspirations of the Intergovernmental Panel on Climate Change (IPCC) to integrate top-down and bottom-up information into the construction of national emission inventories.


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Countries under the Paris Agreement must set goals for mitigating greenhouse gas emissions through nationally determined contributions (NDCs). The NDCs often include mitigation targets for methane based on national inventories of current methane emissions from different sectors (COP 2021;COP 2016). These national methane emission inventories are submitted to the United Nations Framework Convention on Climate Change (UNFCCC) and form the framework for methane climate policy. But they may have large uncertainties, particularly for the oil/gas sector. These uncertainties are particularly relevant to current climate policy as many world countries commit to a 30% reduction in methane emissions by 2030 (European Commission, 2021), including major oil/gas producers like the United States (US), Canada, Nigeria, and Iraq.
Inverse analyses of atmospheric methane observations offer an independent check on the inventories (Bergamaschi 50 et al., 2009) but require spatially resolved inventory information that is generally not available from UNFCCC reports. Here we provide this information in a global gridded (0.1° x 0.1°) representation of the UNFCCC-reported national emission inventories for fuel exploitation (oil, gas, and coal) emissions in 2010-2019, updating our previous work for 2016 (Scarpelli et al., 2020a). We compare these national inventories to recent inversions of satellite (GOSAT) and in situ (GLOBALVIEWplus) atmospheric methane observations, and draw implications for 55 improving the inventories.
Oil/gas activities are currently estimated to account for 22% (84 Tg a -1 , range 72-97 Tg a -1 ) of global anthropogenic methane emissions in 2017 according to emission inventories compiled by the Global Carbon Project (Saunois et al., 2020). The potential for economical mitigation makes the oil/gas sector an attractive target for emission reductions 60 (Alvarez et al., 2018). Individual countries report oil/gas methane emissions to the UNFCCC as part of their national inventories using 'bottom-up' methods that apply emission factors (e.g., mass of methane emitted per unit volume of oil produced) to source activity data (e.g., volume of oil produced per year). Annex I countries must report emissions every year by oil/gas subsector (e.g., oil production). Non-Annex I countries are not required to report emissions every year or by subsector, and many use default emission factors from the Intergovernmental Panel on National inventories submitted to the UNFCCC do not in general provide the spatial resolution needed for the exploitation of top-down information. An exception is the United Kingdom (UK) which provides a finely gridded yearly inventory (Defra and BEIS, 2021). A number of studies have spatially allocated national inventories for 80 specific years to enable inversions of atmospheric data including for Australia (Wang and Bentley, 2002), Switzerland (Hiller et al., 2014), the US , Mexico (Scarpelli et al., 2020b), and Canada (Scarpelli et al., 2021a). Scarpelli et al. (2020a) constructed the Global Fuel Exploitation Inventory (GFEI) for 2016 that spatially allocates national oil, gas, and coal methane emissions reported to the UNFCCC to a 0.1° x 0.1° grid, and supplements information for non-reporting countries. This inventory has been used as prior estimate in a number 85 of inversions Shen et al., 2021;Lu et al., 2021;Qu et al., 2021;Western et al., 2021).
Here we update GFEI to 2019 (Scarpelli et al., 2021b) using more recent national emissions submitted to the UNFCCC (2021), describe the 2010-2019 national emission trends based on the UNFCCC reports, and interpret the results from global inversions of atmospheric methane observations using GFEI as prior estimate. We use the 90 bottom-up information embedded in GFEI, including infrastructure locations, to identify the processes that drive discrepancies between the bottom-up and inversion estimates. Our work provides a step towards the aspiration of IPCC (2019) to integrate top-down and bottom-up information in the construction of national inventories for climate policy.
2 Updated Global Fuel Exploitation Inventory (GFEI v2) 95 2.1 GFEI v1 Scarpelli et al. (2020a) constructed the Global Fuel Exploitation Inventory version 1 (GFEI v1) at 0.1° x 0.1° grid resolution by disaggregating the national UNFCCC methane emission reports to oil/gas/coal emission subsectors and then allocating subsector emissions to the appropriate infrastructure locations within each country including wells, processing plants, compressor stations, pipelines, storage facilities, refineries, and coal mines. GFEI v1 was 100 constructed for 2016 and includes separate gridded emission data for each oil/gas subsector and emission process (leakage, venting, flaring). Annex I countries report emissions to the UNFCCC annually and by subsector so these emissions are used as reported. Non-Annex I countries are only required to report total emissions by sector and do not report every year. Scarpelli et al. (2020a) partitioned non-Annex I emissions by subsector and updated emissions to 2016 using a combination of IPCC emission factors (IPCC, 2006) and oil/gas activity data from the US Energy 105 and Information Administration (EIA). They also incorporated more detailed emission estimates, when available, from the most recent National Communications and Biennial Update Reports of the top-emitting (above 1 Tg a -1 ) non-Annex I countries. In North America, GFEI v1 used the gridded inventories from Sheng et al. (2017) for oil/gas in Canada and Mexico, and Maasakkers et al. (2016) for oil/gas/coal in the US, to distribute the UNFCCC-reported national emissions.

Construction of GFEI v2
Here we update GFEI to provide annual emissions for 2010-2019 using the most recent national reports to the UNFCCC as of September 2021 combined with new infrastructure information. We refer to this updated inventory as version 2 (v2; Scarpelli et al., 2021b). For GFEI v2, we do not use UNFCCC national reports if the most recent report is dated prior to 2000 which most notably includes Iraq. The UNFCCC (2020) data include yearly emissions from Annex I countries for 2010-2019, and updated emissions for a number of non-Annex I countries. We use updated EIA (2021) activity data to partition non-Annex I emissions to subsectors as necessary and to adjust emissions to the desired year. For the top-emitting non-Annex I countries (emissions above 1 Tg a -1 ), we continue to use additional emissions information from National Communications and Biennial Update Reports submitted to the UNFCCC. This includes Nigeria, for which emissions were below 1 Tg a -1 in GFEI v1 but are much higher in the most recent National Communication to the UNFCCC (Federal Republic of Nigeria, 2020).
We start from the same spatial infrastructure information as Scarpelli et al. (2020a)  Mexico (Scarpelli et al., 2020b), Canada (Scarpelli et al., 2021a), and coal in China .

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Annex I countries report 'other' oil/gas emissions which Scarpelli et al. (2020a) allocated 50% to wells and 50% to pipelines. For GFEI v2, we distribute 'other' emissions to oil/gas subsectors and their corresponding infrastructure relative to the contribution of each subsector to total oil/gas emissions. The US and Canada are exceptions where we instead attribute all 'other' oil/gas emissions to oil/gas production based on national inventories (EPA, 2020; . Figure 1 shows GFEI v2 methane emissions at 0.1° x 0.1° grid resolution for 2019, totaling 26 Tg a -1 for oil, 22 Tg a -1 for gas, and 33 Tg a -1 for coal. Global emissions by sector and oil/gas subsector are compiled in Table 1. Figure 2 shows emissions for the top emitting countries with China, the US, and Russia together accounting for 39% of global gas emissions and 79% of global coal emissions while oil emissions more distributed among the top emitting 140 countries. GFEI v2 oil and gas production emissions are 32% and 15% lower, respectively, than in GFEI v1 (Table   1), mainly because of downward revision of Russia's national emissions in its latest UNFCCC (2021) report. Global coal emissions do not change significantly between v1 and v2 for the same year. Figure S1 shows a comparison of emissions in GFEI v2 and GFEI v1 for 2016, aggregated to 2° x 2.5° grid 145 resolution for visibility. Differences reflect changes to national emissions based on UNFCCC reporting, as well as

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The low emission factors shown in Fig. 3 for some Middle East countries could reflect modern infrastructure, high rates of production per well, and widespread associated gas capture and high efficiency flaring. The dominance of offshore production in countries like Norway and Qatar may also contribute to low emission factors. The order of magnitude decrease in Russian oil emissions and increase in Nigerian oil emissions between GFEI v1 and v2 (Table  2)  Coal emissions show a steady decrease in the US and an increase in Russia. Figure 5 shows global oil, gas, and coal emissions for GFEI along with the most recent estimates from the EDGAR v6 inventory (Crippa et al., 2021;European Commission, 2021) and from the International Energy Agency (IEA) 220 inventory (IEA, 2021). The IEA inventory does not include coal emissions. GFEI v1 has higher oil emissions than the other bottom-up inventories, mostly attributable to the high Russian emissions mentioned previously. Global emissions in EDGAR and IEA are higher than GFEI v2 for all sectors but with considerable variability between countries including in the sign of the difference as shown in Fig. S2.

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Here we examine results from two recent global inversions of atmospheric methane observations that used GFEI v1 as a prior estimate of emissions Qu et al., 2021), to determine what insights can be gained toward improving the bottom-up inventories and arbitrating the differences between the inventories. We focus our discussion on oil/gas emissions because of the difficulty for these inversions to quantify coal emissions in China . This difficulty is due in part to poor spatial allocation of Chinese emissions since corrected in GFEI v2 235 .

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The contributions of oil/gas sectors and subsectors to the total posterior non-wetland emissions optimized by the inversions can be inferred from the inversion results by applying a summation matrix ( ): where * = ' ( ' ) " is the pseudo inverse matrix (Calisesi et al., 2005). Here " % is a posterior state vector of oil/gas emissions per grid cell, country, or globally; and % is the corresponding averaging kernel matrix. is constructed by using the prior estimates of the oil/gas sector/subsector contributions to emissions in individual grid 275 cells, and summing those nationally or globally. We use GFEI v1 at the native 0.1° x 0.1° resolution to better resolve boundaries in estimates of national emissions, but the coarse resolution of the inversions is still a limitation for small countries and for oil/gas emissions near country borders. Figure S3 shows posterior oil/gas emissions and averaging kernel sensitivities for the Lu21 and Qu21 inversions.
to lead to higher DOFS, but this is offset by the use of 8 years of both satellite and in situ data in Lu21, with the inclusion of the in situ data increasing DOFS by 25% compared to the GOSAT-only result. Figure 5 shows global oil, gas, and coal emissions from the inversions and Table 1 gives further detail for oil/gas subsectors. Global gas emissions in Lu21 and Qu21 are 23-36% higher than GFEI v2 with higher emissions for all gas subsectors (Table 1). Averaging kernel sensitivities are high for upstream gas activities (production and processing) but low for gas transmission and distribution. Lu21 and Qu21 estimate much lower gas emissions 290 compared to EDGAR and IEA estimates (Fig. 5), and averaging kernel sensitivities are sufficiently high that this difference cannot be simply attributed to the lower prior estimate. Global oil emissions in Lu21 are slightly lower than GFEI v1 (7% lower) while Qu21 emissions are much lower (34%) and in better agreement with GFEI v2, mostly due to decreases in Russian oil emissions. Global oil emissions in EDGAR and IEA are in between the Lu21 and Qu21 estimates.  Figure 6 shows the national oil/gas emissions in the two inversions and the different bottom-up inventories. We also 300 compare to annual country-level oil/gas methane emission estimates in the Climate TRACE inventory (Reuland et al., 2021). Both inversions use GFEI v1 as a prior estimate, so results are directly relevant to evaluating the national reports to the UNFCCC. The averaging kernel sensitivities in Fig. 6 indicate the dependence of the inversion results on the prior estimate (1 = totally independent, 0 = totally dependent). Even when averaging kernel sensitivities are low the sign of the corrections relative to GFEI v1 is informative. There are some large discrepancies between Lu21 305 and Qu21, generally for countries with low averaging kernel sensitivities in Qu21. An additional concern with Qu21 is the strong prior constraint on wetland emissions that may lead to aliasing of wetland emissions adjustments to oil/gas when there is spatial overlap (such as Russia and Canada). We therefore focus on the Lu21 results but add the perspective from the Qu21 results when appropriate.  Table 2 shows Lu21 oil/gas emissions by country for the top-emitting countries which account for 82% of Lu21 global oil/gas emissions. Upstream oil/gas activities (oil/gas production and gas processing; Table S1 and S2) have

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Russia accounts for 25% of global oil/gas emissions in the Lu21 inversion, with a national total of 15.8 Tg a -1 . This is lower than GFEI v1 (24.9 Tg a -1 ), used as prior estimate, but still higher than the other bottom-up inventories including GFEI v2 for 2016 (4.3 Tg a -1 ). Averaging kernel sensitivities for Russia are relatively low in the Lu21 inversion because the high latitude oil/gas emissions are difficult to observe. Thus the inversion results are strongly 330 influenced by the high prior estimate from GFEI v1, and are not consistent with the much lower estimate in GFEI v2. The Qu21 inversion gives lower oil/gas emissions for Russia compared to all bottom-up inventories but we suspect that this reflects their non-optimization of wetlands, which have substantial overlap with oil/gas emissions in 340 Russia.
Lu21 find higher oil/gas emissions for the US and Canada compared to GFEI v1 with high averaging kernel sensitivities for both countries. Many past studies in the US have found an underestimate of oil/gas emissions in the US national inventory (Alvarez et al., 2018;Omara et al., 2018;Cui et al., 2019;Maasakkers et al., 2019Maasakkers et al., , 2021 345 Rutherford et al., 2021) and similar underestimates have been shown for Canada's national inventory (Johnson et al., 2017;Atherton et al., 2017;Baray et al., 2018Baray et al., , 2021Chan et al., 2020;MacKay et al., 2021;Tyner and Johnson, 2021). These underestimates are not addressed in the more recent versions of the national inventories as used in GFEI v2 ( Table 2). The subsector emissions distribution for Canada in GFEI v2 shows a large underestimate of gas transmission emissions compared to Lu21 but better agreement for gas production (Table S1 350 and S2). Qu21 agree with Lu21 for the US but find much lower emissions for Canada; this again likely reflects errors in satellite observations at high latitudes with spatial overlap between oil/gas and wetland emissions (Scarpelli et al., 2021a).
Lu21 and Qu21 find large underestimates of oil/gas emissions in the national inventory of Turkmenistan despite its 355 use of oil production emission factors at the higher end of the IPCC range (Ministry of Nature Protection of Fig. 6), with the greatest underestimates in the south-central part of the country which contains most of the country's oil/gas production and gas processing infrastructure. The underestimate is larger for GFEI v2 than for GFEI v1 because it uses a more recent UNFCCC report (Uzhydromet, 2021) that estimates 37% lower national oil/gas emissions. The IEA, EDGAR, and TRACE inventories are even lower than GFEI. The higher resolution results of Qu21 feature an offset between the underestimate in the south-central part of the country and a slight overestimate in 365 the western part (Fig. S3). GFEI allocates most of Uzbekistan's gas transmission and processing emissions uniformly along pipelines due to a lack of facility data, and this may not properly account for the density of gas processing sources in central Uzbekistan. Venezuela

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The inversions find that GFEI overestimates emissions around the Persian Gulf (Fig. S3) Individual countries may estimate offshore oil/gas production emissions using lower emission factors like those 390 provided by the IPCC (2006), but these emissions are often aggregated in national reports making it difficult for GFEI to differentiate between onshore and offshore wells for spatial allocation of national emissions.
Lu21 find lower emissions than the EDGAR v6, IEA, and Climate TRACE inventories for a number of countries including Nigeria, Iraq, Kuwait, and Qatar which all have high averaging kernel sensitivities (Fig. 6), though 395 country estimates are limited due to the coarse resolution for small countries like Kuwait an Qatar. Similar to these inventories, GFEI v2 overestimates emissions in Iraq based on the use of IPCC Tier 1 methods and in Nigeria based on its most recent UNFCCC report.

Conclusions
We have updated the Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal

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There is considerable interest in using satellite observations of atmospheric methane to evaluate and improve the national inventories used for climate policy. The scope of this work was limited by the sparsity of the GOSAT observations and the coarse resolution of the global inversions. New satellite observations from TROPOMI now provide much higher data density though there are still large regional biases in the early-generation methane 425 retrievals . As the TROPOMI data improve , they will prompt finer-resolution inversions to better quantify emissions on national scales and resolve the regional contributions from individual activities. Inverse analyses of TROPOMI data to evaluate the national methane emission inventories reported by individual countries to the UNFCCC, as enabled here by the GFEI spatial gridding, may enable efficient monitoring of national methane emissions from space in pursuit of climate policy.
Data/Code availability. GFEI v2 emission grids for 2019 by sector and subsector are available for download from the Harvard Dataverse at https://doi.org/10.7910/DVN/HH4EUM (Scarpelli et al., 2021b). The 2010-2018 emission grids are available upon request. The code used for inventory creation is available upon reasonable request.

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Author contributions. TRS and SG compiled the datasets and created the inventory with assistance from MPS. DJJ conceived of and provided guidance for the project. XL, ZQ, and YZ provided inversion data and assisted with data analysis. FR and DG provided TRACE inventory data and feedback on interpretation. TRS prepared the manuscript with contributions from all coauthors.

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Competing interests. The authors declare that they have no conflict of interest.