Global satellite survey reveals uncertainty in landfill methane emissions

As a regulatory agency, evaluating and improving estimates of methane (CH4) emissions from the San Francisco Bay Area is an area of interest to the Bay Area Air Quality Management District (BAAQMD). Currently, regional, state, and federal agencies generally estimate methane emissions using bottom-up inventory methods that rely on a combination of activity data, emission factors, biogeochemical models and other information. Recent atmospheric top-down measurement estimates of methane emissions for the US as a whole (e.g., Miller et al., 2013) and in California (e.g., Jeong et al., 2013; Peischl et al., 2013) have shown inventories underestimate total methane emissions by ~ 50% in many areas of California, including the SF Bay Area (Fairley and Fischer, 2015). The goal of this research is to provide information to help improve methane emission estimates for the San Francisco Bay Area. The research effort builds upon our previous work that produced methane emission maps for each of the major source sectors as part of the California Greenhouse Gas Emissions Measurement (CALGEM) project (http://calgem.lbl.gov/prior_emission.html; Jeong et al., 2012; Jeong et al., 2013; Jeong et al., 2014). Working with BAAQMD, we evaluate the existing inventory in light of recently published literature and revise the CALGEM CH4 emission maps to provide better specificity for BAAQMD. We also suggest further research that will improve emission estimates. To accomplish the goals, we reviewed the current BAAQMD inventory, and compared its method with those from the state inventory from the California Air Resources Board (CARB), the CALGEM inventory, and recent published literature. We also updated activity data (e.g., livestock statistics) to reflect recent changes and to better represent spatial information. Then, we produced spatially explicit CH4 emission estimates on the 1-km modeling grid used by BAAQMD. We present the detailed activity data, methods and derived emission maps by sector. In total, we estimate the anthropogenic emissions for BAAQMD to be 116.4 Gg (1 Gg = 109 g) CH4/yr, with a likely uncertainty of ~ 50% or more (e.g., NRC, 2010; US-EPA, 2015). Including the emissions from wetland (Jeong et al., 2013), the total CH4 emission estimate for BAAQMD is 120.1 Gg CH4/yr. Table 1 summarizes the estimated CH4 emissions for 2011 by sector. The sectors were categorized following those that are used in recent regional emission quantification studies (e.g., Jeong et al., 2013; Peischl et al., 2013; Wecht et al., 2014). However, we note that this result is marginally lower than the top-down estimate of 240 ± 60 Gg CH4/yr (at 95% confidence) reported by Fairley and Fischer (2015), suggesting some combination of systematic error in the top-down estimate, underestimation of emissions from known sources, or as yet unidentified sources may be present. With respect to the relative contributions from different source sectors, the CH4 emissions from the region are dominated by urban activities. Landfill emissions represent 53% of the District’s total emission followed by livestock (16%) and natural gas (15%). These three dominant sectors account for 84% of the total anthropogenic emission in BAAQMD. This suggests that mitigation efforts need to focus on these three sources. Figure 1 shows the gridded anthropogenic CH4 emissions on the BAAQMD’s 1-km grid. In general, the spatial pattern of emissions follows the density of population while strong point sources are also distributed in the rural areas of the District. Detailed methods and emissions for each sector and county are described in the following sections.

Methane emissions along biomethane and biogas supply chains are underestimated

Landfills are one of the major sources of methane (CH 4 ) emissions. Prediction of CH4 emissions from landfills is important in estimating power generation potential and greenhouse gas (GHG) emissions from landfills. The most widely used landfill gas (LFG) models developed based on the first order decay (FOD) reaction do not take into account changing waste composition and landfill site conditions in methane estimations. The aim of this study was therefore to develop a LFG model for estimation of methane emissions from landfills in Lagos metropolis. Field investigations were carried out to determine waste composition, waste disposal rates and site conditions relevant for methane emissions estimation. Waste composition studies were conducted and waste fractions were divided into rapidly, moderately and slowly degrading. The output of the model was verified with the US EPA Landfill Gas Emission model (Land GEM). Results revealed maximum CH 4 emissions estimated occurred at the end of landfill’s closure. Methane generation potential (𝑳𝒐) and methane generation rate (𝒌) parameters were dependent on waste composition and site conditions. Model verification also showed methane emissions peaked at the end of landfill’s closure for both models and variation in modelling parameters by Land GEM model resulted in significant change in methane emissions. Keywords : Methane emissions, Landfills, Municipal solid waste, landfill gas, Land GEM model

Mitigating climate change by abating coal mine methane: A critical review of status and opportunities

Published estimates of methane emissions from atmospheric data (top-down approaches) exceed those from source-based inventories (bottom-up approaches), leading to conflicting claims about the climate implications of fuel switching from coal or petroleum to natural gas. Based on data from a coordinated campaign in the Barnett Shale oil and gas-producing region of Texas, we find that top-down and bottom-up estimates of both total and fossil methane emissions agree within statistical confidence intervals (relative differences are 10% for fossil methane and 0.1% for total methane). We reduced uncertainty in top-down estimates by using repeated mass balance measurements, as well as ethane as a fingerprint for source attribution. Similarly, our bottom-up estimate incorporates a more complete count of facilities than past inventories, which omitted a significant number of major sources, and more effectively accounts for the influence of large emission sources using a statistical estimator that integrates observations from multiple ground-based measurement datasets. Two percent of oil and gas facilities in the Barnett accounts for half of methane emissions at any given time, and high-emitting facilities appear to be spatiotemporally variable. Measured oil and gas methane emissions are 90% larger than estimates based on the US Environmental Protection Agency's Greenhouse Gas Inventory and correspond to 1.5% of natural gas production. This rate of methane loss increases the 20-y climate impacts of natural gas consumed in the region by roughly 50%.

We employed a global high-resolution inverse model to optimize the CH4 emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH4 yr−1 and 232.5 Tg CH4 yr−1, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH4 yr−1, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH4 yr−1), the United States (25.9 ± 8.3 Tg CH4 yr−1), Russia (13.2 ± 9.3 Tg CH4 yr−1), India (12.3 ± 6.4 Tg CH4 yr−1), and Canada (12.2 ± 5.1 Tg CH4 yr−1). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries.

Achieving the Paris Agreement 1.5 C target requires a reversal of the growing atmospheric concentrations of methane, which is about 80 times more potent than CO 2 on a 20-year timescale. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report stated that methane is underregulated, but little is known about the effectiveness of existing methane policies. In this review, we systematically examine existing methane policies across the energy, waste, and agriculture sectors. We find that currently only about 13% of methane emissions are covered by methane mitigation policies. Moreover, the effectiveness of these policies is far from clear, mainly because methane emissions are largely calculated using potentially unrepresentative estimates instead of direct measurements. Coverage and stringency are two major blind spots in global methane policies. These findings suggest that significant and underexplored mitigation opportunities exist, but unlocking them requires policymakers to identify a consistent approach for accurate quantification of methane emission sources alongside greater policy stringency. ll

This study establishes a regional inverse framework to refine methane (CH4) emission inventories for Melbourne, Australia. Methane is a long-lived greenhouse gas and the second most significant contributor to radiative forcing from greenhouse gases after carbon dioxide. Improved understanding of methane emissions from different sectors in Australia is necessary to focus and prioritise mitigation efforts and to track progress towards emissions reduction; however, methane emissions are uncertain, especially at fine resolution (urban and regional scales) needed for mitigation. Moreover, improving predictions of atmospheric methane mole fractions requires precise and accurate emission estimates; However, previous studies indicate a mismatch between current emission estimates and atmospheric observations.Here, we use a combination of surface atmospheric measurements of methane and an inversion approach based on Bayes’ theorem to improve urban-scale methane emission estimates for Melbourne, Australia. Our inversion system is a Python-based four-dimensional variational (Py4DVar) data assimilation system. Due to lack of local methane inventories, prior emission estimates for Melbourne are compiled from globally-accessible datasets, including (1) anthropogenic emissions from the Emissions Database for Global Atmospheric Research (EDGAR), (2) fire emissions from the Global Fire Assimilation System (GFAS) dataset and (3) biogenic emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). Boundary condition adjustments are made using Kennaook/Cape Grim continuous in-situ CH4 mole fraction measurements and the Whole Atmosphere Community Climate Model (WACCM) dataset. The boundary condition adjustments are necessary to develop the efficiency of the regional inversion. The main goal of our inversion system is to provide more precise estimates of regional methane emissions. Independent satellite measurement comparisons are used to assess the system.The comparison with assimilated data shows improvements in modelling methane mole fraction at the suburban Aspendale site with a bias reduction from ~70 ppb (prior) to ~3 ppb (posterior). Our detailed investigations indicate that although the prior results in a reasonable match of modelled mole fraction with observations, the EDGAR dataset does not provide a realistic spatial pattern for the main anthropogenic sources (enteric fermentation and landfills) around Melbourne. The possibility of improving the spatial distribution of the prior emissions has been tested using available local/global datasets, including national maps of livestock and landfills. Eventually, to obtain more comprehensive improved emission inventories in Melbourne, more CH4 mole fraction observational data are required in this area. The results of this study are being used to expand the methane monitoring network for Melbourne.

Methane (CH4) is the second greatest contributor to climate forcing after carbon dioxide (CO2).  Methane has a considerably shorter atmospheric lifetime compared to CO2 (12 yr c.f. 300-1000 yr) but a higher warming potential in the atmosphere (GWP100yr 28, (IPCC, 2014)). Most anthropogenic emissions come from landfills, wastewater treatment plants, leaks in the fossil fuel supply chain and ruminant livestock.  The reduction of anthropogenic methane emissions is key to maintaining the feasibility of the Paris Agreement. The Global Methane Pledge launched at COP26 aims to reduce methane emissions by 30% relative to 2020 by 2030. Urban areas are an ideal target to reduce methane emissions given that they account for around 20% of the total emissions whilst they occupy only 3% of the land surface. Urban methane mitigations plans are proven to have a high impact reducing GHG emissions and bringing co-benefits in public health through improvements in air quality.Australia is a signatory to both the Paris Agreement and the Global Methane Pledge and have an important potential emission reduction in urban areas. Melbourne is the second most populous city in Australia with over 5 million people (around 1/5 of Australia’s population) and it is projected to become the most populated by 2050. A recent study attempted to improve methane emission inventories for Melbourne using an inversion system, global emission data and atmospheric measurements (Shahrokhi , 2022).  Their results showed that current emission datasets do not accurately represent the spatial distribution and total estimates of methane emissions over Melbourne. Hence, an improved emission inventory is required for Melbourne. This will reduce the uncertainty and limitations of current methane emission estimates and support the formulation of effective emissions mitigation plans. Essential to this is the expansion of the Melbourne urban observational network, which is currently too sparse to accurately detect emissions. Here we present our preliminary progress on the development of a comprehensive methane observation network. This project aims to combine different measurement techniques to achieve a better representation of methane mole fraction variability in the Melbourne region, to inform inverse modelling estimates of emissions. We use a combination of mobile and stationary ground observations in key parts of the city to better capture and represent methane emissions. Future work includes the comparison of high precision analysers with low-cost sensors, improvement of source attribution by measurements of methane isotopes and other tracers, and the use of “AirCore” technology to obtain vertical methane profiles. ReferencesIPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.Shahrokhi, N. 2022. Regional Methane Inversion for Melbourne, Australia, using in-situ measurements. Online poster [accessed 10 Jan 2022]. Available from: https://agu2022fallmeeting-agu.ipostersessions.com/default.aspx?s=2F-7B-00-39-98-DC-28-09-C3-90-81-25-D7-44-E2-D2

Fossil-fuel-related emissions of methane, a potent greenhouse gas, have been miscalculated and may be twice as high as previously thought. Researchers say the emissions have been at this high level for the past three decades (Nature 2016, DOI: 10.1038/nature19797). However, the researchers find that total fossil-fuel-related methane emissions, although previously underestimated, remained relatively stable between 1985 and 2013, despite an increase in oil and natural gas drilling and production activities. The study provides another piece in a puzzle of global methane emission sources and emission levels. Methane is the primary component of natural gas and a by-product of oil and coal production and use. It is also released through agricultural practices and decay of organic material. The hydrocarbon has a global warming potential 28 to 36 times that of carbon dioxide over 100 years, according to the Environmental Protection Agency. Researchers used a combination of atmospheric measurements and a detailed

Abstract. Monitoring the spatial distribution and trends in surface greenhouse gas (GHG) fluxes, as well as flux attribution to natural and anthropogenic processes, is essential to track progress under the Paris Agreement and to inform its global stocktake. This study updates earlier syntheses (Petrescu et al., 2020, 2021, 2023), provides a consolidated synthesis of CH4 emissions using bottom-up (BU) and top-down (TD) approaches for the European Union (EU), and is expanded to include seven additional countries with large anthropogenic and/or natural emissions (the USA, Brazil, China, India, Indonesia, Russia, and the Democratic Republic of the Congo (DR Congo)). Our aim is to demonstrate the use of different emission estimates to help improve national GHG emission inventories for a diverse geographical range of stakeholders. We use updated national GHG inventories (NGHGIs) reported by Annex I parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2023 and the latest available biennial update reports (BURs) reported by non-Annex I parties. Comparing NGHGIs with other approaches highlights that different system boundaries are a key source of divergence. A key system boundary difference is whether anthropogenic and natural fluxes are included and, if they are, how fluxes belonging to these two sources are partitioned. Over the studied period, the total CH4 emission estimates in the EU, the USA, and Russia show a steady decreasing trend since 1990, while for the non-Annex I emitters analyzed in this study, Brazil, China, India, Indonesia, and DR Congo, CH4 emissions have generally increased. Quantitatively, in the EU the mean of 2015–2020 anthropogenic UNFCCC NGHGIs (15±1.8 Tg CH4 yr−1) and the mean of the BU CH4 emissions (17.8 (16–19) Tg CH4 yr−1) generally agree on the magnitude, while inversions show higher emission estimates (medians of 21 (19–22) Tg CH4 yr−1 and 24 (22–25) Tg CH4 yr−1 for the three regional and six global inversions, respectively), as they include natural emissions, which for the EU were quantified at 6.6 Tg CH4 yr−1 (Petrescu et al., 2023). Similarly, for the other Annex I parties in this study (the USA and Russia), the gap between the BU anthropogenic and total TD emissions is partly explained by the natural emissions. For the non-Annex I parties, anthropogenic CH4 estimates from UNFCCC BURs show large differences compared to the other global-inventory-based estimates and even more compared to atmospheric ones. This poses an important potential challenge to monitoring the progress of the global CH4 pledge and the global stocktake. Our analysis provides a useful baseline to prepare for the influx of inventories from non-Annex I parties as regular reporting starts under the enhanced transparency framework of the Paris Agreement. By systematically comparing the BU and TD methods, this study provides recommendations for more robust comparisons of available data sources and hopes to steadily engage more parties in using observational methods to complement their UNFCCC inventories, as well as considering their natural emissions. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, future development needs to resolve knowledge gaps in the BU and TD approaches and to better quantify the remaining uncertainty. TD methods may emerge as a powerful tool to help improve NGHGIs of CH4 emissions, but further confidence is needed in the comparability and robustness of the estimates. The referenced datasets related to figures are available at https://doi.org/10.5281/zenodo.12818506 (Petrescu et al., 2024).

Abstract. Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study investigated Finnish CH4 emissions from 2000–2021 using bottom-up and top-down approaches. We evaluated the ability of a global atmospheric inverse model CarbonTracker Europe – CH4 to estimate CH4 emissions within a single country. We focused on how different priors and their uncertainties affect the optimised emissions and showed that the optimised anthropogenic and natural CH4 emissions were strongly dependent on the prior emissions. However, while the range of CH4 estimates was large, the optimised emissions were more constrained than the bottom-up estimates. Further analysis showed that the optimisation aligned the trends of anthropogenic and natural CH4 emissions and improved the modelled seasonal cycles of natural emissions. Comparison of atmospheric CH4 observations with model results showed no clear preference between anthropogenic inventories (EDGAR v6 and CAMS-REG), but results using the highest natural prior (JSBACH–HIMMELI) agreed best with observations, suggesting that process-based models may underestimate CH4 emissions from Finnish peatlands or unaccounted sources such as freshwater emissions. Additionally, using an uncertainty estimate based on a process-based model ensemble for natural CH4 emissions seemed to be advantageous compared to the standard uncertainty definition. The average total posterior emission of the ensemble from one inverse model with different priors was similar to the average of the ensemble including different inverse models but similar priors. Thus, a single inverse model using a range of priors can be used to reliably estimate CH4 emissions when an ensemble of different models is unavailable.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ~25 km &times; 25 km resolution, using spatially allocated national UNFCCC reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up estimate (+103 % for gas, +53 % for waste, +49 % for livestock, &minus;14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0&ndash;5.5) Tg a^&minus;1; best estimate and uncertainty range), Turkmenistan (4.4 (2.8&ndash;5.1) Tg a^&minus;1), Saudi Arabia (4.3 (2.4&ndash;6.0) Tg a^&minus;1), Algeria (3.5 (2.4&ndash;4.4) Tg a^&minus;1), Egypt (3.4 (2.5&ndash;4.0) Tg a^&minus;1) , and Turkey (3.0 (2.0&ndash;4.1) Tg a^&minus;1). Most oil/gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil/gas emission hotspots in Turkmenistan, Algeria, Oman, and offshore in the Persian Gulf. We show that oil/gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil/gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil/gas emission inventories. We examined the methane intensity, defined as the upstream oil/gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil/gas sector, and using as reference the 0.2 % target set by industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through capture of associated gas, modern infrastructure, and concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil/gas upstream emissions and a 26 % decrease of total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ~25 km &times; 25 km resolution, using spatially allocated national UNFCCC reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up estimate (+103 % for gas, +53 % for waste, +49 % for livestock, &minus;14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0&ndash;5.5) Tg a^&minus;1; best estimate and uncertainty range), Turkmenistan (4.4 (2.8&ndash;5.1) Tg a^&minus;1), Saudi Arabia (4.3 (2.4&ndash;6.0) Tg a^&minus;1), Algeria (3.5 (2.4&ndash;4.4) Tg a^&minus;1), Egypt (3.4 (2.5&ndash;4.0) Tg a^&minus;1) , and Turkey (3.0 (2.0&ndash;4.1) Tg a^&minus;1). Most oil/gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil/gas emission hotspots in Turkmenistan, Algeria, Oman, and offshore in the Persian Gulf. We show that oil/gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil/gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil/gas emission inventories. We examined the methane intensity, defined as the upstream oil/gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil/gas sector, and using as reference the 0.2 % target set by industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through capture of associated gas, modern infrastructure, and concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil/gas upstream emissions and a 26 % decrease of total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.

<strong class="journal-contentHeaderColor">Abstract.</strong> We use 2019 TROPOMI satellite observations of atmospheric methane in an analytical inversion to quantify methane emissions from the Middle East and North Africa at up to ~25 km &times; 25 km resolution, using spatially allocated national UNFCCC reports as prior estimates for the fuel sector. Our resulting best estimate of anthropogenic emissions for the region is 35 % higher than the prior bottom-up estimate (+103 % for gas, +53 % for waste, +49 % for livestock, &minus;14 % for oil) with large variability across countries. Oil and gas account for 38 % of total anthropogenic emissions in the region. TROPOMI observations can effectively optimize and separate national emissions by sector for most of the 23 countries in the region, with 6 countries accounting for most of total anthropogenic emissions including Iran (5.3 (5.0&ndash;5.5) Tg a^&minus;1; best estimate and uncertainty range), Turkmenistan (4.4 (2.8&ndash;5.1) Tg a^&minus;1), Saudi Arabia (4.3 (2.4&ndash;6.0) Tg a^&minus;1), Algeria (3.5 (2.4&ndash;4.4) Tg a^&minus;1), Egypt (3.4 (2.5&ndash;4.0) Tg a^&minus;1) , and Turkey (3.0 (2.0&ndash;4.1) Tg a^&minus;1). Most oil/gas emissions are from the production (upstream) subsector, but Iran, Turkmenistan, and Saudi Arabia have large gas emissions from transmission and distribution subsectors. We identify a high number of annual oil/gas emission hotspots in Turkmenistan, Algeria, Oman, and offshore in the Persian Gulf. We show that oil/gas methane emissions for individual countries are not related to production, invalidating a basic premise in the construction of activity-based bottom-up inventories. Instead, local infrastructure and management practices appear to be key drivers of oil/gas emissions, emphasizing the need for including top-down information from atmospheric observations in the construction of oil/gas emission inventories. We examined the methane intensity, defined as the upstream oil/gas emission per unit of methane gas produced, as a measure of the potential for decreasing emissions from the oil/gas sector, and using as reference the 0.2 % target set by industry. We find that the methane intensity in most countries is considerably higher than this target, reflecting leaky infrastructure combined with deliberate venting or incomplete flaring of gas. However, we also find that Kuwait, Saudi Arabia, and Qatar meet the industry target and thus show that the target is achievable through capture of associated gas, modern infrastructure, and concentration of operations. Decreasing methane intensities across the Middle East and North Africa to 0.2 % would achieve a 90 % decrease in oil/gas upstream emissions and a 26 % decrease of total anthropogenic methane emissions in the region, making a significant contribution toward the Global Methane Pledge.