metadata: authors: 0: author: Dasgupta,Susmita
1: author: Lall,Somik V.
2: author: Wheeler,David
count: World
docna: 0: docna: Identifying and Monitoring Priority Areas
            for Methane Emissions Reduction
docty: Policy Research Working Paper
abstracts: cdata!: This paper identifies high-priority
            areas for methane emissions reduction and estimates recent
            emissions changes in those areas using atmospheric
            concentration data from the European Space
            Agency&#x2019;s Sentinel-5P satellite platform. The
            modeling approach is illustrated with three case studies:
            landfills in Spain (Madrid), irrigated rice production in
            India (Karnal district, Haryana state), and oil production
            in Iraq (Al Amarah district, Maysan governorate). For each
            case, the paper estimates two change models by fixed
            effects: the monthly trend in methane concentration from
            January 2019 to November 2022, and the difference between
            mean concentration in 2022 and the previous three years.
            The paper estimates the change models for 775 high-priority
            areas and finds that cases with decreasing methane emissions
            are outnumbered four to one by cases with increasing
            emissions.  The paper also analyzes trends in high-priority
            areas for seven major methane source sectors (agricultural
            soils, livestock, gas, oil, coal, landfills, and wastewater)
            and finds only two where emissions decreases outnumber
            increases (gas and oil). Among World Bank income groups,
            decreases outnumber increases in high-income economies but
            increases are hugely dominant in the other three groups. The
            paper concludes with a presentation of summary emissions
            trend reports for all 775 high-priority areas, with
            accompanying maps and an Excel file. As satellite-based
            monitoring becomes more widely employed, such reports will
            provide a useful template for judging further progress
            toward fulfillment of the Global Methane Pledge.
display_title: Identifying and Monitoring Priority
            Areas for Methane Emissions Reduction
listing_relative_url: /research/2023/04/34034418/identifying-monitoring-priority-areas-methane-emissions-reduction
topicv3: Climate Change Mitigation m1327079 579,Greenhouse Gas Accounting m1327770 582
publishtoextweb_dt: 2023-04-03T00:00:00Z
dois: 10.1596/1813-9450-10391
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 95558
1: author_key: 124534
2: author_key: NA
model_extractions: Policy Research Working Paper 10391 Identifying and Monitoring Priority Areas for Methane Emissions Reduction _Susmita Dasgupta Somik Lall David Wheeler_ Development Economics Development Research Group April 2023
Policy Research Working Paper 10391

# **Abstract**

This paper identifies high-priority areas for methane emissions reduction and estimates recent emissions changes in those areas using atmospheric concentration data from the European Space Agency’s Sentinel-5P satellite platform. The modeling approach is illustrated with three case studies: landfills in Spain (Madrid), irrigated rice production in India (Karnal district, Haryana state), and oil production in Iraq (Al Amarah district, Maysan governorate). For each case, the paper estimates two change models by fixed effects: the monthly trend in methane concentration from January 2019 to November 2022, and the difference between mean concentration in 2022 and the previous three years. The paper estimates the change models for 775 high-priority areas and finds that cases with decreasing methane emissions are outnumbered four to one by cases with increasing emissions. The paper also analyzes trends in high-priority areas for seven major methane source sectors (agricultural soils, livestock, gas, oil, coal, landfills, and wastewater) and finds only two where emissions decreases outnumber increases (gas and oil). Among World Bank income groups, decreases outnumber increases in high-income economies but increases are hugely dominant in the other three groups. The paper concludes with a presentation of summary emissions trend reports for all 775 high-priority areas, with accompanying maps and an Excel file. As satellite-based monitoring becomes more widely employed, such reports will provide a useful template for judging further progress toward fulfillment of the Global Methane Pledge.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Identifying and Monitoring Priority Areas for Methane Emissions Reduction**

**Susmita Dasgupta* Somik Lall David Wheeler World Bank**

The research is funded by the Knowledge for Change Trust Fund, administered by the World Bank.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

- Authors’ names in alphabetical order.
# **1. Introduction**

The World Meteorological Organization forecasts that the current greenhouse gas (GHG) emissions trend will increase global temperature 3-5 degrees C by 2100 (Reuters 2018). This would far overshoot the 2-degree limit pledged by the 2015 Paris Climate Accord (COP 21) and might have a catastrophic impact (Steffen et al. 2018; World Bank 2012). In response, several industrial nations pledged very steep emissions reductions at the Leaders’ Summit on Climate in April 2021. The response deepened at COP 26 in Glasgow with the introduction of the Global Methane Pledge, whose 122 current participating nations have joined a collective effort to reduce global methane (CH4) emissions at least 30 percent from 2020 levels by 2030. The Pledge highlights the particular importance of methane reduction: “Methane is a powerful but shortlived climate pollutant that accounts for about half of the net rise in global average temperature since the pre-industrial era. Rapidly reducing methane emissions from energy, agriculture and waste ... is regarded as the single most effective strategy to keep the goal of limiting warming to 1.5 ̊C within reach while yielding co-benefits including improving public health and agricultural productivity.”<sup>1</sup> The Global Methane Pledge is a laudable commitment by the international community, and broad-based participation will build political support for emissions reduction. At the same time, a rapid transition may be assisted by an evidence-based global effort that identifies the highestpriority CH4 emissions sites and mobilizes the latest technology to monitor their progress. This paper attempts to contribute with a multi-level approach. First, we perform a spatial analysis of global CH4 emissions to assess potential gains from priority site identification. Then we use the World Bank’s gridded database of satellite-measured methane concentrations at 5-km resolution to establish a template for tracking methane emissions reduction at priority sites. We demonstrate its potential effectiveness with case studies for large sources of methane emissions from energy, agriculture and waste. Then we extend the template to create and analyze emissions trend reports for 775 high-priority global sites.

The remainder of the paper is organized as follows. Section 2 analyzes the global distribution of methane emissions sources using “bottom-up” estimates from the Emissions Database for Global Atmospheric Research (EDGAR).<sup>2,3</sup> In Section 3, we use the results to identify high-priority sites where rapid reduction of methane emissions would accelerate the global transition. Section 4 develops a strategy for sampling satellite-measured CH4 concentrations in sites that account for 50% of global methane emissions. Section 5 introduces our monitoring approach with illustrative case studies for landfills (Spain), irrigated rice production (India) and oil production (Iraq). In Section 6, we extend our approach to 775 high-priority areas and use the results to analyze the distribution of emissions trends globally, by source sector, and by World Bank income group. Section 7 presents the priority-area reports in detailed tables, maps and an accompanying Excel file, while Section 8 summarizes and concludes the paper.

> 1 https://www.globalmethanepledge.org/#about

> 2 https://edgar.jrc.ec.europa.eu/

> 3 EDGAR’s “bottom-up” approach estimates emissions by combining sectoral activity data with broadly-calibrated emissions factors (Crippa et al. 2020; Solazzo et al. 2021).

3
# **2.  The Global Distribution of Methane Emissions**

The global standard for “bottom-up” methane emissions reporting is the Emissions Database for Global Atmospheric Research (EDGAR (2022)), which combines detailed sectoral activity data with emissions factors that are adjusted for broad regional differences (Crippa et al. 2020; Solazzo et al. 2021). EDGAR reports estimated emissions<sup>4</sup> by sector in raster files with a resolution of 0.10 degrees (approximately 10 km), yielding information for 6,480,000 grid cells. Table 1 reports global totals from these files by sector in 2018, the latest year for which complete estimates are available. Table entries are displayed in descending order of emissions, which total 375.2 million tons.<sup>5</sup> For expositional clarity and simplicity, we confine our analysis to the seven sectors that account for 90% of global methane emissions. Two are in agriculture (Enteric Fermentation, Agricultural Soils), three in energy (Gas, Coal, Oil Exploitation) and two in the waste sector (Waste Water Handling, Solid Waste Landfills).

Priority site selection is most effective when the spatial distribution of emissions is dominated by relatively few large sources. We assess the potential effectiveness of priority-setting in this case by calculating total methane emissions for countries’ Level 1 administrative units (GADM1s). We expand the dataset to include emissions from countries’ marine exclusive economic zones (EEZs), because many oil and gas operations are offshore. We tabulate estimated emissions in descending order, calculate the cumulative percentage of total emissions, and divide the results into deciles. For example, the first decile comprises the largest-emitting areas whose total emissions account for 10% of the global total.

Figure 1 provides a summary table and graph for emissions from 3,812 areas that comprise Level 1 ADM1 and EEZ units. The tabIe reveals a highly-skewed spatial distribution; in the top decile, 7 areas account for 10% of global methane emissions. In the next four deciles, 10% of emissions are accounted for by 14,18, 28 and 40 areas, respectively. In summary, only 2.8% (107) of the 3,812 areas account for 50% of global methane emissions.

Emissions deciles for all areas are mapped in Figure 2, which shows that the 39 areas in the top 3 deciles (accounting for 30% of total methane emissions) are concentrated in relatively few countries. Table 3 displays these areas in descending order of emissions, along with sectoral percent shares. Among the 11 countries represented, China accounts for 17 areas, followed by India and Brazil (6 each), Pakistan and the US (2), and Qatar, the Russian Federation, Saudi Arabia, the Islamic Republic of Iran, Nigeria and Indonesia (1).

> 4 For brevity, we refer to anthropogenic emissions as emissions in this paper.

> 5 As a greenhouse gas, methane is far more potent than CO2.  Applying the standard conversion factor (25), total methane emissions are the equivalent of 9.4 Gt CO2.

4
Table 3 summarizes Table 2 in a cross-tabulation of dominant aggregate sectors by country. Of the 39 areas in the top 3 deciles, methane emissions are dominated by agriculture in 23, fuel in 15 and waste in 1. Agricultural emissions have important roles in 4 countries (China, Brazil, India and Pakistan), while fuel-based emissions dominate in the remaining 7 (United States, Indonesia, Islamic Republic of Iran, Nigeria, Qatar, Russia, Saudi Arabia).

**Table 1: Global methane emissions, 2018**<sup>**6**</sup>

|**EDGAR Sector**|**Emissions**<br>**(mt) 2018**|**Cum.**<br>**%**|
|---|---|---|
|Enteric Fermentation|108.4|28.9|
|Waste Water Handling|44.4|40.7|
|Fuel Exploitation - Gas|43.6|52.3|
|Agricultural Soils|37.9|62.4|
|Fuel Exploitation - Coal|36.7|72.2|
|Solid Waste Landfills|34.9|81.5|
|Fuel Exploitation - Oil|32.2|90.1|
|Manure Management|12.3|93.4|
|Energy For Buildings|12.3|96.7|
|Oil Refining and Transformation Industry|6.3|98.3|
|Agricultural Waste Burning|2.0|98.9|
|Solid Waste Incineration|1.5|99.3|
|Road Transportation No Resuspension|1.0|99.5|
|Combustion For Manufacturing|0.7|99.7|
|Power Industry|0.5|99.8|
|Chemical Processes|0.3|99.9|
|Iron And Steel Production|0.2|100.0|
|Fossil Fuel Fires|0.2|100.0|
|Railways Pipelines Off-Road Transport|0.0|100.0|
|Total|375.2||
|CO2 Conversion Factor|25.0||
|C02 Equivalent|9,381.2||
|**Source: EDGAR (2022)**|||

> 6 For detailed sector definitions and descriptions, see: EDGAR Sector-Specific Gridmaps, 1970 – 2021 https://edgar.jrc.ec.europa.eu/dataset_ghg70#sources IPCC 2006 Reporting Guidance and Tables

> https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_8_Ch8_Reporting_Guidance.pdf

5
**Figure 1:  Global CH4 emissions from level-1 administrative units and EEZs, by decile**

<!-- Start of picture text --> CH4<br>250<br>Cum. (mt)<br>Decile Areas Areas 2018<br>200<br>1 7 7 31.8<br>2 14 21 34.2<br>150<br>3 18 39 32.2<br>4 28 67 34.6<br>100<br>5 40 107 33.1<br>6 69 176 33.2<br>50<br>7 124 300 33.3<br>8 232 532 33.2<br>0<br>9 492 1024 33.3<br>10 20 30 40 50 60 70 80<br>10 2788 3812 33.2<br>Total Methane Emissions Decile<br>Areas in Decile (GADM1 + EEZ)<br><!-- End of picture text --> 6
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# **Table 2:  Top 3 deciles, global CH4 emissions – Level-1 administrative units and EEZs**

|||||||Se|ctor Shar|es (%) of|CH4 Emis|sions||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||||Agricul|ture||Fuel||W|aste|
||||CH4|||||||Solid|Waste|
||||(mt)||Enteric|Ag.||||Waste|Water|
|Decile|State/Province|Country|2018|Top Sectors|Ferment.|Soils|Gas|Oil|Coal|Landfills|Handling|
|1|Shanxi|China|8.93|Fuel|1.2|0.1|0.6|0.2|93.5|1.6|2.9|
|1|Texas|United States|4.27|Fuel|21.0|0.5|59.6|10.2|0.8|5.8|2.1|
|1|Punjab|Pakistan|4.05|Agriculture|66.8|13.6|1.5|0.1|0.0|5.8|12.3|
|1|Henan|China|3.86|Fuel|13.3|6.2|0.6|0.0|54.6|9.9|15.4|
|1|Shandong|China|3.74|Fuel|11.1|2.3|1.9|0.6|58.3|10.0|15.8|
|1|Uttar Pradesh|India|3.62|Agriculture|56.5|13.9|0.9|0.3|0.8|2.6|24.9|
|1|Kalimantan Timur|Indonesia|3.39|Fuel|0.2|1.5|4.3|2.4|90.5|0.2|0.9|
|2|Hunan|China|3.16|Agriculture|8.6|58.0|0.2|0.0|8.2|8.2|16.9|
|2|Anhui|China|3.11|Agriculture|7.4|35.0|0.5|0.0|36.8|7.3|12.9|
|2|Khuzestan|Iran, Islamic Rep.|2.67|Fuel|1.5|0.4|42.7|53.8|0.0|0.3|1.3|
|2|Guangdong|China|2.59|Agriculture|9.9|42.1|0.9|0.0|3.2|17.2|26.6|
|2|Sichuan|China|2.58|Agriculture|17.2|37.6|1.0|0.0|8.6|12.4|23.1|
|2|Ash Sharqiyah|Saudi Arabia|2.41|Fuel|1.2|0.0|36.9|56.2|0.0|2.6|3.1|
|2|Jiangsu|China|2.39|Agriculture|3.8|47.6|1.1|0.1|13.6|13.6|20.3|
|2|Jiangxi|China|2.28|Agriculture|7.8|59.9|0.6|0.0|8.7|7.7|15.3|
|2|São Paulo|Brazil|2.24|Waste|36.2|0.1|1.7|1.4|0.1|21.7|38.8|
|2|Minas Gerais|Brazil|2.19|Agriculture|73.8|0.3|0.9|0.2|0.0|9.6|15.4|
|2|Guangxi|China|2.18|Agriculture|24.3|47.3|0.8|0.0|3.1|7.7|16.9|
|2|North Dakota|United States|2.16|Fuel|6.8|0.0|77.1|15.1|0.9|0.0|0.1|
|2|Madhya Pradesh|India|2.13|Agriculture|64.7|7.5|0.3|0.0|7.8|1.6|18.0|
|2|Kemerovo|Russian Federation|2.09|Fuel|0.6|0.0|1.5|0.1|94.4|2.4|1.0|
|3|Sind|Pakistan|2.07|Agriculture|54.7|10.1|11.0|8.7|1.7|4.7|9.1|
|3|Maharashtra|India|2.05|Agriculture|50.7|6.4|2.3|1.0|5.5|2.4|31.7|
|3|Hebei|China|2.04|Fuel|16.0|4.0|1.9|0.2|40.2|14.2|23.4|
|3|Hubei|China|2.01|Agriculture|9.9|56.3|1.6|0.1|1.5|10.6|20.1|
|3|Mato Grosso|Brazil|1.98|Agriculture|94.9|0.9|0.2|0.0|0.0|1.7|2.3|
|3|Heilongjiang|China|1.96|Fuel|10.9|22.0|2.1|0.8|41.5|7.0|15.8|
|3|Rajasthan|India|1.95|Agriculture|73.6|0.7|2.3|0.8|0.3|1.6|20.6|
|3|West Bengal|India|1.92|Agriculture|44.0|28.2|0.3|0.0|6.5|2.2|18.8|
|3|Qatar_EEZ|Qatar|1.77|Fuel|0.0|0.0|80.3|19.7|0.0|0.0|0.0|
|3|Mato Grosso do Sul|Brazil|1.70|Agriculture|93.9|0.2|0.3|0.0|0.0|1.6|3.9|
|3|Odisha|India|1.68|Agriculture|36.1|25.0|0.6|0.1|18.7|1.1|18.4|
|3|Goiás|Brazil|1.64|Agriculture|88.3|0.4|0.2|0.1|0.0|4.3|6.7|
|3|Liaoning|China|1.64|Fuel|7.5|14.4|2.8|0.8|46.7|9.6|18.3|
|3|Yunnan|China|1.57|Agriculture|33.6|25.9|1.3|0.0|3.8|11.2|24.3|
|3|Zhejiang|China|1.56|Agriculture|2.5|58.7|1.0|0.1|0.9|14.8|22.0|
|3|Nigeria_EEZ|Nigeria|1.56|Fuel|0.0|0.0|15.3|84.7|0.0|0.0|0.0|
|3|China_EEZ|China|1.54|<br>Fuel|0.0|0.0|69.4|30.6|0.0|0.0|0.0|
|3|Rio Grande do Sul|Brazil|1.53|8<br>Agriculture|73.5|4.0|1.8|1.4|2.1|6.8|10.5|
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# **3.  The Political Geography of Methane Monitoring**

# **3.1 The Spatial Distribution of Methane Emissions**

Methane emissions come primarily from fossil fuel extraction and processing, livestock grazing, irrigated rice production and waste disposal. Table 4 shows that the spatial distribution of methane emissions sites is hugely skewed. Of 6.5 million cells in the EDGAR 10 km global raster, only 142 (0.002% of total cells) account for 10% of global methane emissions and 21,057 (0.325%) account for 50%.<sup>7</sup>

**Table 4:  Spatial clustering of global methane emissions**

|EDGAR<br>Decile|Cells|Cum.<br>Cells|Cum.<br>%|
|---|---|---|---|
|1|142|142|0.002|
|2|578|720|0.011|
|3|1,775|2,495|0.039|
|4|5,817|8,312|0.128|
|5|12,745|21,057|0.325|
|6|22,364|43,421|0.670|
|7|37,919|81,340|1.255|
|8|64,334|145,674|2.248|
|9|124,529|270,203|4.170|
|10|6,209,797|6,480,000|100.000|

Figure 4 displays the global distribution of the 21,057 cells that account for 50% of methane emissions. The map reveals widely-scattered cell clusters in all global regions except South and East Asia, which also have very large clusters in some subregions. Figure 4 motivates a cluster-concentrated strategy, because the cost-effectiveness of emissions-reducing interventions rises with spatial clustering.<sup>8</sup> Table 4 poses a corollary challenge for the global community: The Global Methane Pledge is only likely to succeed if significant reductions can be achieved in the spatial clusters that account for 50% of global methane emissions. Beyond those clusters, methane emissions sites become progressively more scattered and interventions more costly.

# **3.2 Local Accountability**

This paper highlights potential benefits from combining “top down” satellite monitoring of emissions changes with “bottom up” estimates of emissions magnitudes. However, these benefits can only be realized by effectively-administered interventions. Effective administration will in turn require the informed participation of governing institutions at the appropriate geographic scale. In this case the best match is provided by the second-level administrative units whose territories include important methane emissions sites. Accordingly, this paper develops and illustrates a strategy for satellite

> 7 We include the full global grid in these calculations, because many large methane sources are offshore oil and gas extraction sites.

> 8 Monitoring, regulatory enforcement and abatement investment all tend to be lower-cost for activities that are concentrated in relatively small areas.

10
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monitoring of methane emissions from the high-priority second-level administrative units that account for 50% of total emissions.

Figure 5 provides a detailed illustration of our approach for an important methane emissions cluster in Iraq. Figure 5(a) overlays a map of Iraq governates with 10 km EDGAR cells whose methane emissions place them among 21,057 cells (Table 4) in the top five deciles (accounting for 50% of global emissions). The cells are color-coded by decile. For this exercise we highlight the emissions cluster in the east-central part of Maysan governate. Enlarging the provincial map (Figure 5(b)) reveals that the principal cluster is in one second-level administrative unit, Al Amarah district. Within this district the emissions cells are roughly clustered along a north-south central axis.

The key to effective monitoring in this context is mobilization of high-resolution Sentinel-5P observations within a methane emissions cluster area. The measurement area should be large enough to ensure a robust sample of high-quality satellite measures,<sup>9</sup> but small enough to ensure that area-average measures reflect cluster activities. For this exercise, we adopt the approach illustrated in Figure 5(c). Using the R programming language, we compute the convex hull that includes all EDGAR 5-decile sites in Al Amarah district. We construct a 20-km buffer around the hull to enlarge the sample of proximate Sentinel-5P observations. Then we intersect the buffered polygon with the boundaries of Al Amarah to define the sampling area (with blue boundary) for Sentinel-5P observations.

# **3.3 Global Monitoring Areas**

We extend the previous illustration to global coverage in a multi-step exercise. For terrestrial emissions, we adopt GADM level 2 as the default standard. For countries without GADM 2 units, we adopt the highest GADM level available (0 or 1). For emissions from offshore gas and oil sites, we create geographic units of comparable scale by intersecting countries’ exclusive economic zones (EEZs) with a global grid whose cells have sides of 250 km. In all, this combined terrestrial/offshore exercise yields 51,976 global geographic units (GGUs). We compute total EDGAR emissions in the latest available year for each GGU, order by total emissions, compute cumulative emissions, and divide units by decile. Table 5 displays the results.

**Table 5:  Global geographic units by EDGAR emissions decile**

|Decile|GGUs|Cum.<br>GGUs|
|---|---|---|
|1|39|39|
|2|93|132|
|3|174|306|
|4|303|609|
|5|522|1,131|
|6|888|2,019|
|7|1,674|3,693|
|8|3,411|7,104|
|9|7,164|14,268|
|10|37,708|51,976|

> 9 Image quality can be degraded by extensive cloud cover or random technical problems.

12
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Table 5 shows that of 51,976 GGUs, 1,131 units in the top 5 deciles account for 50% of global methane emissions. For these high-priority GGUs, we simplify the monitoring problem by confining our exercise to emissions-intensive (5-decile) EDGAR cells in the set of 21,057 cells that account for 50% of global methane emissions (Table 4). Within each GGU, we compute the convex hull that includes all EDGAR 5-decile cells in the unit, construct a 20-km buffer around the hull, and intersect the buffered polygon with the GGU boundaries to define the sampling area for Sentinel-5P observations. This generates sampling areas for 1,001 high-priority GGUs.<sup>10</sup> Figure 5 maps these units, revealing significant representation for on- and offshore GGUs in all world regions.

# **4.  Sample Data**

We have constructed our atmospheric CH4 concentration database from observations by the ESA’s Sentinel-5P platform that typically number over 200,000 per day. We locate each observation in a 5-km global grid comprising 25,920,000 cells. The dominant component in each observation is the global stock of atmospheric CH4 molecules that have accumulated during the past decade. The second component is seasonal, determined by natural processes over the annual cycle. The third component is geographic: Emitted CH4 molecules tend to remain within their latitude bands of origin for significant periods before full global mixing.

The fourth observation component is determined by local anthropogenic CH4 emissions, which remain near their point of origin long enough to be distinguished from the three “background” components. We term this the “concentration anomaly” since it measures the local deviation from the global background CH4 concentration. As previously noted, its primary sources are emissions from agriculture, fossil fuel production and waste disposal.

Sentinel-5P (S5P) operates in a Sun-synchronous near-polar orbit with an equatorial crossing at 13:30 local solar time. It completes 14 orbits of the Earth per day, with a site revisit time of one day and a current spatial resolution of 5.5 x 3.5 km. We use the S5P L2 Offline georeferenced measures of XCH4 (the column average dry air mixing ratio of methane<sup>11</sup> ), corrected for bias associated with XCH4 dependence on surface albedo.<sup>12</sup> Recent global research by Sha et al. (2021) has shown that S5P CH4 measures correspond very closely to independent measures taken at ground stations maintained by TCCON (Total Carbon Column Observing Network).<sup>13</sup> Sentinel-5P’s bias-corrected XCH4 data exhibit a difference of

> 10 The remaining 5-decile GGUs do not contain any 5-decile EDGAR cells.

> 11 The total atmospheric column between the surface and the top of the atmosphere normalized to the corresponding dry air column.

> 12 For more technical details, see the documentation at

> https://sentinel.esa.int/documents/247904/3541451/Sentinel-5P-Methane-Product-Readme-File

> 13 Current TCCON sites are located in the United States, China, Canada, Germany, Poland, France, Japan, Australia, New Zealand, the Republic of Korea, Réunion, and Ascension Island.

14
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−0.26±0.56 % when compared with the corresponding TCCON data. Per the ESA’s recommendation, we use only pixels with quality values greater than 0.5. The data have been downloaded from the ESA’s AWS repository managed by MEEO (Meteorological Environmental Earth Observation).<sup>14,15</sup> We filter the bias-corrected XCH4 data for local concentration anomalies, or differences between observed and background CH4 at each point.<sup>16</sup> We calculate background CH4 using the methodology of Hakkarainen et al. (2019), which isolates local anomalies from the three background components described above. We compute the daily median XCH4 for each 10degree latitude band and linearly interpolate the result to each S5P observation with 1-degree resolution. Following Hakkarainen, we use the median as the representative value because it is not skewed by extreme observations. We subtract this background value to compute the local anomaly for each observation. Then we compute monthly mean values of concentration anomalies for each 5-km grid cell in our database.

For this exercise, we overlay the 5-km grid with the sampling polygons for our high-priority GGUs. Numerous technical and atmospheric factors produce highly-varied sample counts. To ensure robust estimates, we confine our exercise to GGUs with at least 20 observations in each year.

Table 6 provides summary statistics on sample counts for the 775 GGUs that meet this condition. The median GGU has 2,033 S5P observations for the period 2019 – 2022, while the leastrepresented GGU has 92 observations and the most-represented has 77,011.

**Table 6:  Statistics for GGUs with minimum yearly S5P sample of 20**

GGU S5P Observations, 2019 - 2022 GGUs Min P10 P25 P50 P75 P90 Max 775 92 464 980 2,033 3,861 8,123 77,011

# **5.  Priority Area Monitoring – Illustrations**

This exercise aims to use methane data from the Sentinel-5P platform to track changes in emissions-intensive areas identified by EDGAR. In the previous section, we developed a methodology for sampling S5P data for those areas. We illustrate our approach for three areas dominated by important CH4 sources: Madrid Province, Spain [two large landfills]; Karnal District, Haryana State, India [irrigated rice production]; and Al Amarah District, Maysan Governorate,

> 14 https://www.meeo.it/

> 15 - The data are available in NetCDF format at the ESA’s AWS repository, https://meeo <u>s5p.s3.amazonaws.com/OFFL/L2__CH4, with filenames catalogued at</u> https://scihub.copernicus.eu/catalogueview/S5P/

> 16 For further discussion, see Dasgupta, Lall and Wheeler (2022, 2023).
Iraq [oil production]. We previously used Al Amarah to illustrate our methodology for identifying sampling areas.

# **5.1 Landfills in Madrid Province, Spain**

Recent technical research has used satellite data from Sentinel-5P and other sources to detect methane flares from large emissions sources (Maasakkers et al. 2022; Ouerghi et al. 2021). Particular attention has focused on emissions flares from two large landfills – Valdemingómez and Pinto -- southeast of Madrid, Spain (Tu et al. 2022; ESA 2021).

Figure 7 provides summary information about these two sites. Figure 7(a) displays the GGU (Madrid Province) identified for this exercise, along with the sampling area for Sentinel-5P data (bordered in blue).<sup>17</sup> Both landfill sites are identified in the figure. Figure 7 (b) displays satellite images of the two sites, along with the results of methane plume detection on August 20, 2021 (ESA 2021). Methane concentrations in the plume are colored from red (highest) to blue (lowest).

Figure 7 (c) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022. Our data corroborate the recent studies, identifying the landfill cells and their immediate neighbors as the highest-intensity areas in the Madrid GGU. The data also indicate that prevailing winds typically disperse emissions from the two sites toward the southwest.

Our 5-km database contains 3,300 observations for the GGU sampling area. Figure 8 plots the observations against time, with mean monthly observations superposed. The plot reveals significant annual variation in 2019 – 2021, with peaks in the first quarter of each year. We see no sign of a “covid effect” in these data, since the most notable variation is in 2019 and the annual series for 2020 looks very similar to the series in 2021. Although some monthly fluctuation remains in 2022, the previous first-semester “peaking” is not present.

This exercise aims to mobilize global GGU-level information like the Madrid data for monitoring methane emissions trends in high-priority areas. To date, satellite-based estimation of physical emissions volumes from methane plumes has relied on relatively sophisticated models that utilize local atmospheric data for specific periods (Maasakkers et al. 2022; Varon et al. 2019; Cusworth et al. 2021; Zhang et al. 2020.). Such tailored exercises are undoubtedly critical for monitoring the performance of individual methane emitters that commit to specific reductions in emissions. However, their data- and model-intensities have precluded global scale-up to date.

> 17 Per Section 3.3, this is the 20-km-buffered convex hull surrounding all GGU EDGAR points in the top 5 deciles for global methane emissions.

17
<!-- Start of picture text --> Methane Flares (a)<br>August 20, 2021<br>ts)<br>Valdemingémez Landfill ay =<br>/ ae<br>~ Cy) = - | Pee \<<br>, ) , A * pe pe = — S _ = =<br>fm > a er ~ - Soe<br>P<br>Pinto Landfill<br>5 Vis ‘<br>. oS<br>y at "<br>. ad ~ x ¢ “ ’<br>Cx c<br>=<br>Sentinel-SP /t ~s<br>Mean CH4 Anomalies atm) ‘<br>2019 -2022 (5 KM) a<br>we -0.9 - 0.35 h f<br>fi = “se _ s = =<br>Me e2- 13.15 ~ .<br><!-- End of picture text -->
<!-- Start of picture text --> ° °<br>o .<br>ro)<br>©'<br>.. . —<br>° = | ‘<br>oO r . ie ". . o's:* t. a "2t S*hseged3 e 5 trPir fie<br>" 1 \"etl ‘ie |<br>E fee MS: "rybpe '<br>ol. . ! 6 . . . * | ] | .<br>; . ; 3 Pai he Si ut “Hh; i<br>ve OES! io ee oe Be * a:<br>. e ‘ s r .<br>i : . : Hae 1<br>fo | .<br>=f<br>2019m1 2020m1 2021m1 2022m1 2023m1<br>Date<br>* CH4Anomaly =——— Monthly Mean Anomaly<br><!-- End of picture text -->
parameter (γ1). Model (2) provides more immediate feedback: The difference between the average CH4 anomaly in the most recent year and the average for all preceding years can be inferred from the sign, size and significance of parameter (δ1).<sup>18</sup> Although the Sentinel-5P platform provides the best available CH4 data, its coverage for our 5- km grid cells is not necessarily complete for all sample months. From an econometric perspective, this implies an unbalanced panel for estimation. Within the Madrid GGU, typical CH4 concentration anomalies can differ substantially across cells (see Figure 7c). To cite one possible consequence, a naive trend analysis could generate spuriously-positive results in cases where early-period observations are more numerous in lower-anomaly cells and later observations are more concentrated in higher-anomaly cells. We control for this possibility with a panel estimator that incorporates cell-specific fixed effects.

Table 7 reports estimates for the Madrid GGU from OLS, Random Effects and Fixed Effects models. In this case the two trend models align with very high significance in all cases. The monthly trend (γ1) is negative and highly significant, yielding an estimated decline of about 0.19 ppb/month (or about 9 ppb over four years). The final-year deviation is also negative and highly significant, yielding an estimated difference of about 3.5 parts per billion between 2022 and the previous three years.

**Table 7:  Trend model results, Madrid GGU**

|Dependent varia|ble: Monthly<br>|Mean CH4 An<br>|omaly<br>||||
|---|---|---|---|---|---|---|
||(1)|(2)<br>Panel|(3)<br>Panel|(4)|(5)<br>Panel|(6)<br>Panel|
||OLS|RE|FE|OLS|RE|FE|
|Month|-0.186***|-0.191***|-0.195***||||
||(-9.88)|(-10.32)|(-10.46)||||
|[Year = 2022]||||-3.343***|-3.503***|-3.635***|
|||||(-7.07)|(-7.56)|(-7.81)|
|Const.|5.929***|6.101***|6.194***|1.641***|1.710***|1.743***|
||(9.96)|(9.53)|(10.53)|(5.89)|(4.64)|(6.39)|
|N|3,300|3,300|3,300|3,300|3,300|3,300|

18 Results for models (1) and (2) can diverge in cases where a large discontinuity emerges in the most recent year. For example, a modest uptrend from 2019 to 2021 followed by a large decrease in 2022 can produce a small, positive, statistically-insignificant estimate for γ1 and a negative, statistically-significant estimate for δ1.

20
In summary, our results for the Madrid GGU indicate significant reduction in methane emissions during the past four years, coupled with continuing progress during the most recent year. While the Madrid landfills undoubtedly remain high methane emitters among Western European sites, the evidence from our database suggests that their emissions are at least headed in the right direction.

# **5.2 Irrigated Rice Production in Karnal, Haryana, India**

Our second case focuses on irrigated production of rice, the staple crop for the majority of the world’s population (Adhya et al. 2014). Table 1 shows that Agricultural Soils account for about 10% of global methane emissions, and irrigated rice production accounts for most of those (Smith, Reay and Smith 2021). In 2013, rice was harvested on 165 million hectares of land in 100 countries, with 90 percent of global production in Asia. Irrigated fields occupy about 80 million hectares and produce 75 percent of the global crop (FAO 2014; Fischer et al. 2014). Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methane-producing bacteria.

We identify rice paddy flooding months using RiceAtlas, a spatial database of global rice calendars developed by Laborte et al. (2017). RiceAtlas distinguishes up to three rice cultivation seasons and identifies the first flooding and harvest days for each season by day-of-year. We have chosen Karnal district in India for this case because it is identified by RiceAtlas as an area with only one of three potential rice cultivation seasons. This permits a clear illustration of the utility of the 5 km methane database for tracking methane emissions across seasons and years. In Karnal, fields are typically flooded from early summer through October.

Figure 9 provides summary information about Karnal. Figure 9(a) displays the GGU (Karnal District, Haryana) along with the sampling area for Sentinel-5P data (bordered in blue). Figure 9(b) draws on our 5-km global database to display mean methane concentration anomalies for the GGU sampling area from January 2019 to November 2022.

Our 5-km database contains 2,747 observations for the Karnal GGU sampling area. Figure 10 plots the observations against time, with mean monthly observations superposed. The plot reveals the close correspondence between rice paddy flooding months and annual peak periods for mean CH4 anomalies.

Table 8 reports estimates for the Karnal GGU from OLS, Random Effects and Fixed Effects models. Results for the two models align in all cases. For Karnal the monthly trend (γ1) is positive and highly significant, yielding an estimated increase of about 0.40 ppb/month (or about 19.2 ppb over four years). The final-year deviation is also positive and highly significant, yielding an estimated difference of about 7.8 parts per billion between 2022 and the previous three years.

21
<!-- Start of picture text --> (a)<br>Karnal District nth . se<br>Haryana State See T- aby: 4 =<br>North India Sonn} Wig<br>(b)<br>Sentinel-5P<br>Mean CH4 Anomalies<br>2019 -2022 (5 KM)<br>Me 20.31 - 21.32<br>We 21.33 - 22.26<br>em 25.76 - 27.22 i J<br>Pr Is<br><!-- End of picture text -->
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<!-- Start of picture text --> Al Amarah District<br>Maysan Governorate v=<br>Iraq le<br>Sentinel-5P<br>Mean CH4 Anomalies noone me Ud<br>2019 -2022 (5 KM) i a<br>We -24.02 8.11<br>Wm -8.1 - 1.93 Abies<br>ie 1.94 - 4.7<br>Ml 4.79 - 6.86<br>Me 10.47 - 12.17<br><!-- End of picture text -->
<!-- Start of picture text --> oS<br>©<br>oOo : + :<br>+ “in 4 Py E = °<br>Oo . . sd id i! gore H . nes ; .<br>qi 8 +o! e ' : ri : 7<br>Oo tryl]. . . on ' - : . "s : = :<br>. ee ! 3 = g« “s ly ra rae ee '<br>- e e’ © e's<br>oO e sd . ® .<br>5 e 6<br>=] . .<br>sf ¢ :<br>® .<br>Oo<br>=<br>2019m1 2020m1 2021m1 2022m1 2023m1<br>Date<br>* CH4Anomaly =——— Monthly Mean Anomaly<br><!-- End of picture text -->
# **6.  Global Trends for High-Priority GGUs**

In this section, we extend our sampling and modeling approach to global scale. Among 51,976 Global Geographic Units, we have identified 1,131 GGUs that account for 50% of global methane emissions in the EDGAR database. After limiting our exercise to GGU sampling areas with at least 20 Sentinel-5P observations per year, we estimate trend models (1) and (2) by fixed effects for 775 high-priority GGUs.

# **6.1 Overall Results**

Table 10 provides a summary that aggregates the econometric results into four categories: No Trend (statistically-insignificant results for both models); Mixed (statistically-insignificant results for one model or oppositely-signed but significant results for two models); Decreasing (significant negative results for both models); and Increasing (significant positive results for both). We find No Trend for 56 GGUs because neither model attains classical significance (p=0.05). Results are Mixed for 150 GGUs, Decreasing for 106 and Increasing for 453. In summary, a substantial majority of 775 GGUs have experienced unambiguous increases in CH4 emissions during the past four years, whether judged by the overall trend or change in the most recent year. The same picture emerges if we weight the regression results by EDGAR emissions.

**Table 10:  GGU estimation results (fixed effects)**

||||Emissions-<br>Weighted|
|---|---|---|---|
|Results|Count|%|%|
|No Trend|56|7.2|6.5|
|Mixed|160|20.6|23.3|
|Decreasing|106|13.7|13.1|
|Increasing|453|58.5|57.1|

# **6.2 Sectoral Results**

Table 11 explores the sectoral distribution of results weighted by EDGAR emissions. Soils and Livestock exhibit enormous disparities between Increasing and Decreasing cases (90.2 % vs. 0.6% for the former; 63.6% vs. 4.6% for the latter). The fuels sectors reveal striking differences, with a huge imbalance toward Increasing for Coal (66.7% vs. 5.1%) while Gas and Oil are both dominated by Decreasing (44.7% vs. 25.3% for the former; 23.8% vs. 10.6% for the latter). The two waste sectors have large imbalances toward Increasing (52.4% vs. 18.0% for Landfills; 61.0% vs. 18.3% for Wastewater). In summary, Oil and Gas exhibit more Decreasing results but the two sectors only account for 27.2% of EDGAR emissions. The remaining sectors all exhibit huge dominance for Increasing results.

26
# **Table 11:  GGU estimation results by sector (fixed effects)**

||% of Emi<br>|ssions-Wei<br>|ghted Estimatio<br>|n Results<br>|% of Priority<br>|
|---|---|---|---|---|---|
|Sector<sup>a</sup>|No Trend|Mixed|Decreasing|Increasing|Emissions|
|Soils|0.0|9.2|0.6|90.2|20.4|
|Livestock|2.9|28.8|4.6|63.6|19.9|
|Coal|5.7|22.5|5.1|66.7|25.1|
|Gas|4.5|25.5|44.7|25.3|14.5|
|Oil|26.7|38.9|23.8|10.6|12.7|
|Landfills|13.0|16.5|18.0|52.4|3.1|
|Wastewater<br>aSectoral abbrevi<br>Coal [Fuel Exploita<br>[Solid Waste Land|1.0<br>ations (from Ta<br>tion – Coal]; G<br>fills]; Wastewat|19.7<br>ble 1): Soils<br>as [Fuel Expl<br>er [Waste W|18.3<br>[Agricultural Soil<br>oitation – Gas]; O<br>ater Handling]|61.0<br>s]; Livestock [En<br>il [Fuel Exploita|4.2<br>teric Fermentation];<br>tion – Oil]; Landfills|

# **6.3 Results by World Bank Income Group**

Our results by World Bank income group (Table 12) suggest that global accountability has begun to affect methane emissions intensity in high-income countries. They have an imbalance toward Decreasing (34.6% vs. 24.0%), but they account for only 14.5 % of EDGAR emissions for our priority GGUs. The other three income groups have very large imbalances toward Increasing. The greatest imbalance (65.2% vs. 9.0%) is in upper-middle-income countries, which also dominate methane emissions (56.5% of the total).

**Table 12: GGU estimation results by World Bank income group**

||% of E<br>|missions-Wei<br>|ghted Estimation<br>|Results<br>|% of Priority<br>|
|---|---|---|---|---|---|
|Income Group|No Trend|Mixed|Decreasing|Increasing|Emissions|
|Low|4.9|27.5|15.1|52.6|1.7|
|Lower Middle|8.0|23.8|10.4|57.8|27.3|
|Upper Middle|6.2|19.6|9.0|65.2|56.5|
|High|5.0|36.4|34.6|24.0|14.5|

27
# **7. Reporting GGU Emissions Changes**

The models and results presented in this paper can offer potentially valuable information to participants in the Global Methane Pledge. Accordingly, we provide illustrative reports in tabular and graphical formats.

Table 13 reports results for EDGAR Decile 1 GGUs, which are dominated by Increasing in Coal (10 districts in China and Russia) and Livestock (4 districts in Pakistan and China). There are 2 other GGUs with Increasing (Soils in Chongqing, China; Landfills in Durango, Mexico). Only 2 GGUs have Decreasing, both in Gas (Aksu District, China and Al Gharbia District, UAE). Among the remaining GGUs in Table 13, 7 have Mixed results and 1 has No Trend. Complete results for GGUs in all five EDGAR deciles are provided in the accompanying Excel file<sup>19</sup> .

Figures 13 and 14 map our results for the top 4 EDGAR deciles.<sup>20</sup> Map icons are centered over their GGUs, color-coded by emissions change categories and bordered by color codes for dominant GGU sectors. To cite two examples: All Algerian icons are Decreasing with Gasdominated GGUs; all Sudanese icons are Increasing with Livestock-dominated GGUs.<sup>21</sup> Overall, the results can be roughly summarized as follows: Europe: Almost all Decreasing [Landfills, Wastewater] Russia: Rough parity in Increasing, Mixed, Decreasing [Oil, Gas, Coal] North Africa: Almost all Decreasing [Oil, Gas] Sahelian Africa: Rough parity in Increasing, Mixed [Livestock] Southern Africa: Mostly Mixed [Coal] Gulf Region: Decreasing or Mixed [Oil, Gas] Central Asia: Majority Decreasing [Gas] Pakistan: Majority Mixed [Livestock] India: Almost all Increasing [Livestock, Soils, Coal] Other South and Southeast Asia: Almost all Increasing [Soils, Livestock] China: Dominated by Increasing [Soils, Livestock, Coal, Wastewater] North America: Majority Increasing, along with Mixed [Oil, Gas, Landfills] Mexico: Increasing [Landfills] South America: Decreasing [Gas, Wastewater]<sup>22</sup>

> 19https://datacatalog.worldbank.org/int/search/dataset/0064309/methane_emissions_changes_by_edgar_decile

> 20 The maps are visually cluttered by spatial “overcrowding” when 5th-decile GGUs are also included.

> 21 To minimize clutter in the maps, we include only the names of the countries with EDGAR 1st-decile results.

> 22 One important caveat:  Our regional coverage is biased by the relative scarcity of clear imagery because of cloudiness in the West African coastal region, the Amazon and Central African rain forest regions, and large swathes of Southeast Asia.  Inspection of Figure 1 shows that numerous GGUs would be included for these areas if images were more plentiful.

28
**Table 13:  CH4 emissions changes, EDGAR Decile 1**

|**Country**|**State/Province**|**District**|**Sector**|**Model_1**|**Model_2**|**Overall**|
|---|---|---|---|---|---|---|
|Canada|Saskatchewan|Division No. 1|Oil|Decreasing|Increasing|Mixed|
|China|Beijing|Beijing|Livestock|Increasing|Increasing|Increasing|
|China|Chongqing|Chongqing|Soils|Increasing|Increasing|Increasing|
|China|Henan|Zhengzhou|Coal|Increasing|Increasing|Increasing|
|China|Shandong|Jining|Coal|Increasing|Increasing|Increasing|
|China|Shandong|Tai'an|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Yangquan|Coal|Increasing|No Trend|Mixed|
|China|Shanxi|Changzhi|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Shuozhou|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Linfen|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Taiyuan|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Jincheng|Coal|Increasing|Increasing|Increasing|
|China|Shanxi|Luliang|Coal|Increasing|Increasing|Increasing|
|China|Xinjiang Uygur|Aksu|Gas|Decreasing|Decreasing|Decreasing|
|Iran, Islamic|||||||
|Rep.|Ilam|Dehloran|Oil|No Trend|No Trend|No Trend|
|Iran, Islamic|||||||
|Rep.|Khuzestan|Ahvaz|Oil|No Trend|Increasing|Mixed|
|Iraq|Diyala|Khanaqin|Oil|No Trend|No Trend|No Trend|
|Mexico|Durango|Durango|Landfills|Increasing|Increasing|Increasing|
|Pakistan|Punjab|Dera Ghazi Khan|Livestock|Increasing|Increasing|Increasing|
|Pakistan|Punjab|Bahawalpur|Livestock|Increasing|Increasing|Increasing|
|Pakistan|Punjab|Multan|Livestock|Increasing|Increasing|Increasing|
|Pakistan|Sind|Hyderabad|Livestock|Increasing|Decreasing|Mixed|
|Russian|||||||
|Federation|Kemerovo|Novokuznetskiy|Coal|Increasing|Increasing|Increasing|
|South Africa<br>United Arab|Mpumalanga|Nkangala|Coal|Decreasing|No Trend|Mixed|
|Emirates|Abu Dhabi|Al Gharbia|Gas|Decreasing|Decreasing|Decreasing|
|United States|North Dakota|McKenzie|Gas|Decreasing|Increasing|Mixed|

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# **8.  Summary and Conclusions**

This paper has identified high-priority sites for methane emissions reduction and introduced a global monitoring approach that employs data from the ESA’s Sentinel-5P (S5P) satellite platform. Drawing on geolocated S5P observations that typically number over 200,000 per day, we compute monthly mean methane concentration anomalies for each cell of a 5-km global grid. Our data span the period from January 2019 to November 2022. To highlight local accountability, our monitoring approach estimates trends in S5P atmospheric concentration data for districtlevel (GADM2) administrative units and scale-equivalent areas within national exclusive economic zones (EEZs) where offshore oil and gas extraction occur. These global geographic units (GGUs) comprise 51,976 areas. The spatial distribution of methane emissions is so highly skewed that 2.2% of these areas account for 50% of anthropogenic methane emissions in “bottom-up” estimates provided the Emissions Database for Global Atmospheric Research (EDGAR). We focus on these 1,131 high-priority GGUs, drawing on EDGAR to identify their dominant sources of emissions from seven sectors (Agricultural Soils, Livestock, Gas, Oil, Coal, Landfills and Wastewater).

We illustrate our monitoring approach with three GGU case studies: landfills in Spain (Madrid); irrigated rice production in India (Karnal district, Haryana state); and oil production in Iraq (Al Amarah district, Maysan governorate). Each case employs S5P data from the GGU subregion that includes all EDGAR emissions points in the point set that accounts for 50% of global emissions. These points are often spatially clustered, and our approach ensures that our trend models focus on areas where emissions are most highly concentrated.

In each case, we estimate two trend models by fixed effects. The first model estimates the monthly trend for the period January 2019 – November 2022, while the second focuses on recent history by estimating the difference between mean concentrations in 2022 and the previous three years.

We extend this approach to 775 high-priority GGUs that have sufficient S5P observations for robust trend estimation. For each GGU, we provide a prototype monitoring report that incorporates the direction and significance of parameter estimates for the two models, along with the dominant source sector from EDGAR. We aggregate the econometric results into four categories: No Trend (statistically-insignificant results for both models); Mixed (statisticallyinsignificant results for one model, or significant but opposite-signed results for the two models); Decreasing (significant negative results for both models); and Increasing (significant positive results for both).

Overall, we find that about 30% of the GGUs have No Trend or Mixed results, about 13% are Decreasing and 57% are Increasing. These results hold for both “raw” estimates and estimates weighted by EDGAR-estimated emissions. We find striking differences across sectors, with Increasing emissions dominant in five sectors and Decreasing emissions in two (Gas and Oil). For
World Bank income groups, we find moderate dominance for Decreasing GGUs in high-income economies and heavy dominance for Increasing GGUs in the other three groups.

In the final section of the paper, we summarize our results and provide an accompanying Excel file with monitoring reports for 775 GGUs, separated into five deciles by estimated EDGAR emissions. For each GGU, we report separate results for the two models, the composite result in the four previously-defined categories, and the dominant source sector.

We also illustrate our results with global maps, with each GGU represented by color-coded icons that display the composite results and dominant source sectors. These maps provide graphic illustrations of the challenges that confront the Global Methane pledge. Although some regions have many Decreasing results (e.g., Western Europe, North Africa, the Gulf Region), most are dominated by Increasing results across several sectors.

We close with two observations. First, the current state of the art enables us to estimate the significance and direction of trends for GGUs, but not the magnitudes of emissions reductions. In consequence, even in areas where Decreasing results dominate, the _levels_ of emissions may remain high for extended periods. Second, our results provide a cautionary reminder of the challenges ahead. We will know that the Global Methane Pledge is beginning to succeed when future versions of Figures 13 and 14 are dominated by Decreasing (Blue) icons. At present, that is far from the case.

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