Global Methane Emitters Tracker methodology

This article is part of the Global Methane Emitters Tracker, a project of Global Energy Monitor.

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Coal mines Oil and gas extraction Oil industry pipelines LNG Terminals

The Global Methane Emitters Tracker (GMET) includes 1) annual methane emissions estimates from fossil fuel assets using emission factors applied to GEM’s fossil fuel infrastructure datasets and 2) infrastructure attribution information for publicly available, remotely-sensed methane plumes. In addition, the GMET research team maintains a companion sheet of coal mine methane (CMM) mitigation projects across the globe built using the Global Methane Initiative’s global project list and linked to specific coal mines.

Methodology

GMET uses asset level infrastructure data in Global Energy Monitor’s Global Coal Mine Tracker, Global Oil and Gas Extraction Tracker, and Global Gas Infrastructure Tracker as well as other non-GEM datasets for both methane plume attribution information and annual asset emissions estimations.

Asset level infrastructure datasets are created using the following data sources:

• Government data on individual units, country energy and resource plans, and government websites tracking extraction permits and applications
• Reports by state-owned and private companies
• News and media reports
• Local non-governmental organizations tracking extraction permits and operations
• On-the-ground contacts who can provide first-hand information about a project

Plume Attribution

GEM creates infrastructure attribution information for a subset of all publicly available remotely sensed methane plumes. These subsets were prioritized based on whether the plumes were likely associated with a known GEM infrastructure asset, and whether they have been publicly analyzed through other initiatives.

Plume data are provided by Carbon Mapper and the International Methane Emissions Observatory. GEM’s process of producing methane plume attribution information involves visually comparing plume origin locations, reported by the plume data provider, to Google Earth imagery and GEM’s infrastructure location and operational databases. For a numerical and geographical summary of which plumes GEM has reviewed, see the summary tables for the tracker. More detailed information on the coverage can also be found below

1. Plume Attribution Information Coverage

As of the December 4, 2025 data release, GEM has analyzed publicly available methane plumes selected through three different methods, summarized as follows. Method one implemented a new methodology noted as "bulk review" in the "Workflow version" column in GMET, and includes an update of some plumes initially reviewed in the queries below. Methods two and three are noted as "original" in same column.

1. Every publicly available, high-resolution methane plume provided by Carbon Mapper and the International Methane Emissions Observatory located within the known or estimated boundary of a GEM coal mine (closed and non-closed) as of August 15th, 2025 (for Carbon Mapper plumes) and July 21st, 2025 (for IMEO plumes)
2. Every Carbon Mapper methane plume from the NASA EMIT sensor located within 10.5 km of a GEM coal mine, bioenergy plant, oil or gas plant, hydropower plant, coal plant, or liquified natural gas terminal which were observed by the satellite as of March 29th, 2024.
3. All observations from the plane-based AVIRIS-NG and Global Airborne Observatory instruments in California (2020-2022), Louisiana and the Western Gulf, New Mexico, Pennsylvania, and Texas (excluding the Permian) as of May 14th, 2023.

2. Attribution Methodology

2.1 “Bulk review” methodology

Bulk review involved two stages: 1) creating and validating coal mine boundaries and 2) visually inspecting methane plumes inside boundaries and identifying their potential infrastructural sources.

Creating and validating coal mine boundaries

GEM researchers identified detailed coal mine boundaries and potential CMM sources for 174 coal mines. This research supports the United Nations Environmental Programme’s International Methane Emissions Observatory (IMEO) Steel Methane Programme in developing a database of metallurgical CMM emissions and sources. More information about this project can be found here.

GEM analysts identified a strong linear correlation between the detailed coal mine boundary areas and coal mine sizes in the Global Coal Mine Tracker (GCMT). This relationship supported the creation of estimated radial boundaries based on GCMT mine sizes for the remaining 6,782 coal mines in the GCMT.  Gaps in GCMT mine sizes were estimated using the following, in order:

1. The median detailed boundary area for mine type and country

2. The median GCMT mine size for mine type and country

3. The median detailed boundary research area for mine type across all countries

Mine types include underground, surface, and underground and surface mines.

Visually inspecting methane plumes inside boundaries and identifying their potential infrastructural sources.

GEM researchers analyzed 1,708 methane plumes, detected by public high resolution remote sensing satellites and planes, for intersections with mine boundaries. In many areas of the world, coal mines boundaries abut or overlap one another. For this reason, GEM researchers took a tiered approach to creating attribution information:

Mine scale-attribution information

1. A plume in a researched boundary was assigned to the mine if it was visually attributable to coal mine infrastructure
2. A plume that overlapped with only one estimated boundary if it was visually attributable to a coal mine of an appropriate type (surface, underground, etc.)
3. A plume that overlapped with multiple estimated boundaries if it was associated with a mine feature that could be connected to one of the mines, or if the other mines could be ruled out based on mine type (i.e. a plume over a ventilation shaft would not be associated with a surface mine).

Mine feature attribution information

Mine infrastructure categories and subcategories that were developed for the detailed boundary and CMM source research were assigned to plumes. Researchers reviewed Google Earth Pro historical satellite imagery, finding the most recent date for a basemap image that immediately preceded the methane plume’s observation date. In cases where historical imagery was greater than a year older than the plume data, or where the available historical satellite imagery was obscured, an alternative imagery date immediately following the plume’s observation was recorded as well.

Validation approach

To validate the above approach, radial estimated mine boundaries were first generated for the 174 mines for which detailed boundaries had been identified. Methane plumes inside of the radial estimated boundaries were then counted. Of the 527 plumes that fell within only one estimated boundary, 91% matched the coal mine assignment generated using manually researched boundaries.

2.2 Previous plume attribution methodology

This methodology was utilized in prior data releases, and applies to the plumes marked “Original” in the “Workflow version” column in GMET.  Plume attribution information created for the older two queries were done manually following a protocol similar to Rafiq et al. (2020). This involves visually inspecting points representing the origin of each plume observation along with raster images of the methane detections to both high-resolution Google Earth basemap satellite imagery and a variety of infrastructure datasets enumerated below.

The RGB images associated with the plume images provided by Carbon Mapper were compared to the Google Earth basemap imagery to ensure that the infrastructure used for attribution existed at the time of the emission. Carbon Mapper provides both plume origins (the approximate location of the observed plumes on the earth surface) and plume sources (their best estimation of the infrastructural source of the plume) for each image. Each plume origin and image were analyzed both individually and in context with other plumes which Carbon Mapper had determined were interconnected (i.e., the points represented multiple observations of the same infrastructural source.) Each plume observation was reviewed by at least two GEM analysts.

GEM provides attribution information in the following fields within GMET: Nearest Government Well ID, GEM Infrastructure Name, Type of Infrastructure, Nearby California VISTA and other Government ID assets, and Notes. Note that because infrastructure can change hands regularly, that the operator assigned to the asset in each of the databases presented here may not match the owner/operator at the time of the plume’s emission.

• Nearest Government Well ID: In every United States state except Pennsylvania, this field provides an American Petroleum Institute (API) number. We use API Number here as a stand-in for the whole wellpad–not necessarily the well itself. Plumes are assigned a value in this field if the plume origin is within 10m of a well with no other obvious sources of methane, or if the plume is within a clearly defined wellpad. Because these IDs are used to represent the entire wellpad, it is possible that the actual source of the emission is a storage tank, pipeline leading from the well, or another well on the wellpad. For multiwell pads, the ID of the closest well with government data is provided, unless otherwise indicated in the Notes field.
• Type of Infrastructure: These categories of infrastructure are drawn largely from the definitions used by California’s Statewide methane emissions inventory, Vista-CA, definitions, with a few changes. These changes include: 1) wellpad in GMET refers to either an oil or gas well or infrastructure (storage tank, flare stack, etc.) located within a clearly defined wellpad 2) dairies and livestock facilities are combined 3) power plants which were attributed to an asset in GEM's Global Oil and Gas Plant Tracker or Coal Plant Tracker were identified as either coal or gas plants. Types of infrastructure are identified through a combination of review of GEM’s datasets, visual inspection of Google Earth satellite imagery, and comparison with additional government and company data sources detailed in the notes column.
• GEM Infrastructure Name: This field contains the name of the GEM asset in which the plume origin falls. For assets identified through GEM’s Global Coal Mine Tracker (GCMT), Global Coal Plant Tacker (GCMT), Global Bioenergy Power Tracker (GBPT), Global Oil and Gas Plant Tracker (GCPT), and Global Gas Infrastructure Tracker (GGIT), plumes are attributed if they are located within the visually estimated footprint of the asset contained within these databases. Note that for some California plumes, GEM assets may not be referenced if they have been attributed to a Vista-CA asset. For the Global Oil and Gas Extraction Tracker (GOGET), plumes which have been attributed to a particular wellpad have been associated with the appropriate GOGET oil and gas extraction area containing the well. Note that the ownership data within GOGET may be more or less recent than the emissions date of the plume, i.e., the company listed as operator for a GOGET asset containing the plume may not have been the operator at the time of emission.
• Nearby California VISTA and other Government ID assets: California plumes whose origins fall within a Vista-CA asset (for the infrastructure types represented by polygons) or who are located on the same facility identified in Vista-CA (for infrastructures represented by points) are assigned the ID for the asset within Vista-CA. For Vista-CA oil and gas fields, note that in rare cases the plume, even if it falls within a Vista-CA field, may not be related to oil and gas production. Refer to the qualitative notes for more details in these instances. For plumes in the Western Gulf, this field is also used to identify the lease area the plume falls within, using the AGT_NUMBER for that lease area.
• Notes: GEM researchers made qualitative observations of the plume’s location and the surrounding infrastructure. Each set of notes was reviewed by at least two GEM analysts. For instances where attribution is obvious (e.g., the plume falls directly over an Oil & Gas Facility identified in the Vista-CA data) notes are not provided. Broadly, the notes field offers the justification GEM researchers used when making attributions. They are particularly useful for identifying instances where definite attributions were challenging: e.g., where there was no visible infrastructure in the basemap and a possible underground pipeline may have been responsible, or when a plume observation is between multiple pumpjacks or wellheads. Additionally, they can provide more detail on the specific equipment from which a plume may originate, such as a flarestack on a wellpad. Notes are reviewed to ensure that there is consistency among plumes believed to be originating from a single source (though the exact wording may vary between plume observations).

Non-GEM databases used in original plume attributions, by state

Below is a list of all of the non-GEM databases we used in the original plume attribution research protocol in each state. In all US states, for pipelines we relied on observations of infrastructure visible in the Google Earth imagery. Plumes which are above areas with no visible infrastructure and may be connected to an underground pipeline are described as such in the notes column.

• California: The attribution data we drew on for California included all publicly available information contained within the 2019-12-17 Vista-CA dataset. This includes power plants, refineries, natural gas fueling stations, natural gas stations, oil and gas fields, processing plants, storage fields, feed lots, digesters, dairies, landfills, composting sites, wastewater treatment plants, and oil and gas wells (though for these we use a more recent version of the underlying California Geological Energy Management (CalGEM) well data, downloaded 6/15/2023). Ohio. We downloaded oil and gas well and wellpad data from the Ohio Department of Natural Resources on 9/12/2023.
• Louisiana and the Western Gulf: Louisiana Oil and Gas wells were downloaded from the Louisiana Department of Natural Resources Strategic Online Natural Resources Information System (SONRIS) on 9/20/2023. Federal offshore platforms and lease polygons were downloaded from the Bureau of Ocean Management (BOEM) on 9/12/2023
• Pennsylvania: Pennsylvania well data were downloaded from Pennsylvania Department of Environmental Protection on 5/24/2023
• Texas: Texas wells were downloaded from the Texas Railroad Commission on 8/27/2023.
• Ohio: Ohio wells and wellpads were downloaded from the Ohio Department of Natural Resources on 9/12/2023

• New Mexico: New Mexico wells were downloaded from the New Mexico Oil and Natural Gas Revenue Database on 2/21/2024

In addition to these specific datasets, for some plumes we could identify a specific facility source with high likelihood based on their published location in government or company reports (e.g. air quality permits, company websites, etc.) This detail is included on a per-plume basis in the “Notes” column in GMET, with citations. As above, it is possible that the owner/operator of the facility as listed may not have been responsible for the plume if the facility had changed hands.

Coal mining methane emissions

On coal mining emissions, GEM has relied on its methodology developed in 2022 through the Global Coal Mine Tracker.

CMM mitigation research

GEM researchers maintain a CMM companion sheet which provides details about global CMM mitigation projects and links them to coal mines in GEM’s Global Coal Mine Tracker (GCMT). The companion sheet builds on the Global Methane Initiative’s (GMI) International CMM Project List, which was last updated in July 2024 and is the most comprehensive known list of global CMM mitigation efforts. In December 2024, GEM researchers began to expand on the GMI’s list by linking mitigation projects to mines in the GCMT and categorizing them based on an adapted version of the International Energy Agency’s designations of CMM mitigation projects: “flaring,” “utilization,” “VAM utilization,” “VAM destruction,” or “flaring and utilization.” In a December 2025 update to the CMM companion sheet, two additional data attributes were added to indicate (1) whether pre-mining methane drainage occurred at the site of the mitigation project, and (2) whether CMM utilization projects involved the use of captured methane on- or offsite of the relevant coal mine. Further details are available in the standalone CMM Mitigation Projects Companion Sheet methodology wiki.

Estimating Methane Emissions From Transmission Pipelines

The gas pipelines analyzed in GMET are drawn from GEM’s Global Gas Infrastructure Tracker (GGIT) data released in December 2024. In order to estimate emissions, we use a Tier 1 estimation drawn from the 2019 Refinement to the IPCC (Table 4.2.4i, Tier 1 Emission Factors for Gas Transmission and Storage segment.) GEM does not yet compile comprehensive data on leak detection and repair (LDAR) across pipeline operators, and so uses the default emissions factor of 4.1 (-20%/+30%) tonnes per kilometer. For pipelines with robust LDAR systems this will be an overestimate, as the emissions factor for pipelines which have extensive LDAR and >50% dry seals for centrifugal compressors have an IPCC Tier 1 emissions factor of 2.8 (-20%/+30%). Therefore, we have explicitly named this attribute in the data as the emissions for pipelines assuming no LDAR, if operational. We include estimates for non-operational pipelines so that users can estimate the potential emissions of the pipelines if they were to be used. For details on how pipeline lengths are estimated, refer to the GGIT methodology.

Estimating Methane Emissions From LNG terminals

This methodology estimates the annual methane emissions occurring at LNG terminals through liquefaction (the process of cooling pipeline natural gas until it condenses into a liquid for transport and export) and regasification (the process of heating liquid natural gas back into a gaseous form upon import.) These estimates do not include the methane emissions at other stages of the LNG supply chain (e.g. upstream natural gas production and midstream transmission, tanker emissions, or downstream consumption.)

Emissions factors for regasification (.018% of total throughput) and liquefaction (.070% of total throughput) were drawn from Innocenti et al. (2023). The authors developed these emissions factors through a voluntary measurement protocol with three liquefaction and two regasification sites. Because of the voluntary nature of their operators participation, other researchers suggest these emissions factors may be underestimates. Additionally, while Innocenti et al. (2023) caution against using these factors for annualization, we include them in GMET because this study is one of the few to provide empirical and measurement-based emissions factors estimates. The Innocenti et al. (2023) average emissions factors were multiplied against the “Capacity” field in the GGIT database for export and import terminals as appropriate. Emissions are calculated this way for every terminal regardless of status (operating, proposed, cancelled, etc.) As such, these figures should be interpreted as the estimated annual emissions if the terminal were to operate at its full nameplate capacity.

Estimating Potential Methane Emissions From Oil and Gas Field Reserves

Broadly, these are back-of-the-envelope estimates created by multiplying the quantity of Proved & Probable (2P) reserves within each GOGET asset against a regionally specific emissions factor. The emissions factors used in this estimation are based on proxies chosen from the Oil Climate Index Plus Gas (OCI+) and are specific to fuel type (either oil, gas, or condensate). Proxies were selected based on their proximity to the GOGET asset and their fuel type, detailed in the “EF Deriviation Method” column in GMET. For GOGET assets reporting quantities in other fuel categories (e.g. the less descript “hydrocarbons”) estimates are not developed.

Importantly, the emissions factor chosen here represents just Scope 1 emissions directly related to oil and gas production (i.e. not indirect emissions related to energy consumption, end-consumer use, etc.) Therefore, the values produced here are best interpreted along the lines of: “If these entire reserves were extracted under current operating procedures, what would the methane emissions be from their production?”. This line of reasoning is similar to the one developed by Heede & Oreskes (2016) except here we focus only on methane, and include Probable reserves.

There are important limitations to using a sophisticated modeling framework like OCI+ – which builds off of component- and equipment-level data of oil and gas facilities and is validated against remotely sensed methane data — and extrapolating it to a back-of-the-envelope estimate like the one provided here. For one, country-level processes may not drive differences in emissions between oil and gas extraction areas, particularly if there is significant in-country geological variation between regions, differences in the number and proportion of components (valves, thief hatches, etc.), and work processes (liquid unloadings, workovers, etc.). Additionally, methane emissions in oil and gas production are driven in large part by super-emitters originating in accidental releases (see Alvarez et al., 2018). Especially at scales as small as the field level, these stochastic processes are unlikely to remain static year-to-year, particularly under changing leak detection and repair regimes. Because of these limitations, the estimates presented here should be construed as exploratory.

Oil and Gas Extraction Areas - Connecting to Climate TRACE’s Estimations

The oil and gas fields are drawn from GOGET. For methane analysis purposes, we have added three attributes in the data in order to relate these assets to the oil and gas extraction areas within the 2024 Climate TRACE database, which itself was built on the Rocky Mountain Institute’s Oil Climate Index plus Gas (OCI+). Researchers matched the GOGET assets to TRACE using metadata and spatial proximity Where the GOGET assets differ from the TRACE fields, they are typically more granularly defined, particularly for unconventional fields in the United States and Canada. The emissions and emissions factor columns in GMET therefore do not correspond directly with the GOGET assets, but rather the 2024 TRACE assets which encompass each GOGET field.

The granularity of the GOGET assets is part of why the 2024 TRACE field emissions measurements were appended here, rather than applying an emissions factor to the GOGET fields themselves. Official emissions factors, such as those from the EPA Greenhouse Gas Emissions Inventory, tend to undercount methane emissions from oil and gas production by roughly two-fold, and they become less reliable at smaller scales owing to the stochastic behavior of super-emitters (See more in the literature underlying the OCI+/TRACE model here Rutherford et al., 2021). Detailed component and equipment counts within each GOGET field would be necessary to perform simulations such as those undergirding the OCI+/TRACE emissions estimates.