Executive Summary
Standalone overview of the fact sheet designed to quickly grasp key points
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Fact Sheets
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Executive Summary & Full Fact Sheet
Full Fact Sheet
All Key facts, data, statistics, and information about methane in one fact sheet
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Sector Fact Sheets
Agriculture Facts
For the Agriculture sector, key facts, data, statistics, and information about methane
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Fossil Fuel Facts
For the Fossil Fuels sector, key facts, data, statistics, and information about methane
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Waste Facts
For the Waste sector, key facts, data, statistics, and information about methane
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Data Sources
The most reliable sources for methane emissions data
The Global Methane Budget 2000-2020 is considered the most recent, comprehensive and reliable source of methane emissions data available at this time. The Budget averages data from both top-down and bottom-up inventories. 4 main bottom-up inventories alongside 7 top-down climate models were evaluated to arrive at a common quantification of methane sources and sinks.
Emissions for methane have higher levels of uncertainty compared to fossil fuel emissions from CO2, as the latter results from estimations of combustion by products of relatively well-known fuels, whereas methane emissions come from sources that have higher variabilities. For example, there is high variability in methane emission factors per barrel of oil or tonne of coal, as the emissions can vary significantly by the source oil well or coal mine. Emissions from waste depend on the composition of the waste, anaerobic conditions, and even temperature and humidity; emissions from rice vary on production systems, organic load, and even fertilizer use; and emissions from livestock depend on production system, animal genetics and age, animal health or feed, and environmental factors.
Review global, geographical and sectorial approaches
The most recent global synthesis of methane emissions data comes from the Global Methane Budget 2000-2020, which evaluated the four main bottom-up inventories alongside 7 top-down climate models to arrive at a common quantification of methane sources and sinks.
Since there is more variation between individual models (+/- 50 Mt) than there is between years (~2 Mt/year), it is more accurate to use Global Methane Budget estimates at the global level than to reference more recent data from a singular inventory. The Global Methane Budget is the most recent and best estimate available at this time.
The most recent global synthesis of methane emissions data comes from the Global Methane Budget 2000-2020, which evaluated the four main bottom-up inventories alongside 7 top-down climate models to arrive at a common quantification of methane sources and sinks.
Since there is more variation between individual models (+/- 50 Mt) than there is between years (~2 Mt/year), it is more accurate to use Global Methane Budget estimates at the global level than to reference more recent data from a singular inventory. The Global Methane Budget is the most recent and best estimate available at this time.
The most recent global synthesis of methane emissions data comes from the Global Methane Budget 2000-2020, which evaluated the four main bottom-up inventories alongside 7 top-down climate models to arrive at a common quantification of methane sources and sinks.
Since there is more variation between individual models (+/- 50 Mt) than there is between years (~2 Mt/year), it is more accurate to use Global Methane Budget estimates at the global level than to reference more recent data from a singular inventory. The Global Methane Budget is the most recent and best estimate available at this time.
Inventories
List of Bottom-Up and Top-Down Inventories
Bottom-Up Inventories
Bottom-up inventories are produced by combining activity data (e.g., number of landfills) and emissions factors (e.g., estimated emissions per landfill). They provide more comprehensive estimates at the country- and sub-sector levels but likely underestimate emissions in many countries and sectors because they rely on self-reported data from countries and industries, which introduces bias. Poorly developed inventories also limit the estimation of potential mitigation opportunities, as they cannot capture mitigation implementation costs. It is important to note that all bottom-up inventories rely partly on country-reported data, which partly relies on industry-reported data, as well as default emissions factors when country-specific emissions factors are unavailable. These biases will be reduced in the future as data quality improves.
| Inventory | Institution | References |
|---|---|---|
| Community Emissions Data System (CEDS) | Joint Global Change Research Institute at the Pacific Northwest National Laboratory and University of Maryland | Hoesly et al. (2018) O’Rourke et al (2021) |
| Emissions Database for Global Atmospheric Research (EDGAR) | European Commission Joint Research Centre (EC-JRC) and Netherland’s Environmental Assessment Agency (PBL | Crippa et al. (2023) |
| IIASA GAINS v4.0 | International Institute for Applied Systems Analysis (IIASA) | Höglund-Isaksson et al., (2020) |
| U.S. EPA | United States Environmental Protection Agency | USEPA (2019) |
Top-Down Inventories
Top-down inventories are derived by atmospheric measurements of CH4 concentrations and are used to constrain bottom-up models. While top-down inventories are increasingly being used to estimate country-level and sub-sector emissions, they often are not able to provide fully comprehensive details for all countries and sub-sectors.
| Inventory | Institution | References |
|---|---|---|
| Carbon Tracker- Europe CH4 | Finnish Meteorological Institute (FMI) | Tsuruta et al. (2017) |
| LMDz-CIF | Climate and Environment Sciences Laboratory (LSCE) | Thanwerdas et al. (2022a) |
| LMDz-PYVAR | Climate and Environment Sciences Laboratory (LSCE) | Zheng et al. (2018a, 2018b, 2019) |
| MIROC4-AСТМ | Japan Agency for Marine-Earth Science and Technology (JAMSTEC) | Patra et al. (2018); Chandra et al. (2021) |
| NISMON-CH4 | Japan National Institute for Environmental Studies (NIES) / Japan Meteorological Research Institute (MRI) | Niwa et al. (2022) |
| NIES-TM- FLEXPART (NTFVAR) | Japan National Institute for Environmental Studies (NIES) | Maksyutov et al. (2020) Wang et al. (2019a) |
| TM5-CAMS | Dutch Organisation of Applied Scientific Research (TNO) / Vrije Universiteit Amsterdam(VU) | Segers et al. (2022) |
REFERENCES
Citations from external sources
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- Stavert, Ann R., et al. “Regional trends and drivers of the global methane budget.” Global Change Biology 28.1 (2022): 182-200.
- Global Methane Budget 2000-2020, Table 3
- EDGARV8.0
- IPCC, 2021. Figure SPM.2
- Rotz, C. A., et al. “Fifty years of environmental progress for United States dairy farms.” Journal of Dairy Science 107.6 (2024): 3651-3668.
- Capper, Judith L., and Dale E. Bauman. “The role of productivity in improving the environmental sustainability of ruminant production systems.” Annu. Rev. Anim. Biosci. 1.1 (2013): 469-489.
- Stavert et al. (2021)
- Global Methane Assessment 2022
- Figure 2
- Section 4.2
- Shindell et al., in preparation.
- Global Methane Assessment 2021
- Executive Summary
- Section 4.2
- Page 87
- Page 98
- FAO, 2022. Global Livestock Environmental Assessment Model v3.0
- FAO, 2023
- FAOSTAT Climate Change: Agrifood systems emissions, Emissions Totals
- Table 2. Pathways towards lower emissions – A global assessment of the greenhouse gas emissions and mitigation options from livestock agrifood systems. Rome.
- IEA, Methane Tracker Database, IEA, Paris, Licence: CC BY 4.0.
- U.S. EPA, 2024. New Data Show U.S. Oil & Gas Methane Emissions Over Four Times Higher than EPA Estimates, Eight Times Greater than Industry Target
- Höglund-Isaksson, L., et al. Technical potentials and costs for reducing global anthropogenic methane emissions in the 2050 timeframe -results from the GAINS model. (2020). Environmental Research Communications, 2(2), 025004.
- Romanzini, Eliéder P. et al. “Water-Based Supplementation Technology for Grazing Cattle in the Tropics: A Large- Scale Commercial Case Study.” Appl. Sci. 2025, 15, 851.
- Schnürer A., Sun C. et al. (2025). Reducing methane emissions by developing low-fumarate high-ethanol ecofriendly rice. Mol. Plant (2025).
- IEA 2024
- Strategies to reduce emissions from coal supply
- China
- Tracking pledges, targets and action
- Tate (2022), Bigger than Oil or Gas?
- EDF (2023)
- Kaza, Silpa; Yao, Lisa C.; Bhada-Tata, Perinaz; Van Woerden, Frank. 2018. What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. Urban Development;. © Washington, DC: World Bank. License: CC BY 3.0 IGO.