Methodology
Country selection
To ensure comprehensive coverage of global methane emissions, while balancing feasibility, we applied three criteria to select countries:
- Countries where the cumulative sum of methane emissions makes up more than 90% of global methane emissions in both 2020 (base year for the Global Methane Pledge) and 2023 (most recent year with widely available data).
- Countries with rapidly growing methane emissions – i.e. an annual average growth rate exceeding 5% over the past three to five years.
- Major fossil fuel producers (defined as the top ten producers for oil, gas, and coal).
Based on these criteria, we identified a final list of 72 countries:
Afghanistan, Algeria, Angola, Argentina, Australia, Azerbaijan, Bangladesh, Bolivia, Brazil, Cambodia, Cameroon, Canada, Chad, China, Colombia, Ecuador, Egypt, Ethiopia, the European Union, France, Germany, India, Indonesia, Iran, Iraq, Italy, Japan, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Mali, Mexico, Mongolia, Morocco, Myanmar, Nepal, New Zealand, Niger, Nigeria, Norway, Oman, Pakistan, Paraguay, Peru, Philippines, Poland, Qatar, Republic of Congo, Romania, Russia, Saudi Arabia, Somalia, South Africa, South Korea, South Sudan, Spain, Sudan, Tanzania, Thailand, Turkey, Turkmenistan, Uganda, Ukraine, United Arab Emirates, United Kingdom, United States, Uruguay, Uzbekistan, Venezuela and Vietnam.
Emissions database
We selected four databases to explore methane emissions, in order to improve coverage, consistency and cross-validations across countries and sectors: CEDS, EDGAR, PRIMAP-hist, and national inventories submitted to the UNFCCC.
The PRIMAP dataset combines several published datasets to produce a comprehensive time series of annual greenhouse gas emissions for all countries. Its HISTCR scenario prioritises emissions data officially reported to the UNFCCC. It fills gaps – for both years and sectors – from third-party sources such as the Carbon Dioxide Information Analysis Center (CDIAC), Energy Institute, FAOSTAT, and the EDGAR database.
In contrast, the EDGAR database uses globally harmonised activity data, often derived from international databases like PRIMAP. While PRIMAP’s HISTCR scenario uses more detailed Tier 2 and Tier 3 methodologies – incorporating national emission factors that are reviewed by the UNFCCC and are internally robust – EDGAR applies a Tier 1 approach based on default IPCC emission factors to ensure global consistency. As a result, there can be significant differences between the two, as EDGAR does not use the country-specific emission factors, activity data, and technologies reflected in the higher-tier methodologies used by PRIMAP.
In the CEDS methodology, default estimates are firstly sourced from activities involved in key sectors with help of third-party databases. It mainly sources energy statistics from the International Energy Agency (IEA), emission factors derived from Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) for combustion data, non-combustion data from the Emission Database for Global Atmospheric Research (EDGAR), and carbon dioxide emissions from the Carbon Dioxide Information Analysis Center (CDIAC). Default estimates are scaled to match existing national and regional emissions inventories where available.
Finally, country-reported inventories submitted to the UNFCCC – in accordance with the 2006 IPCC guidelines and its subsequent refinements – are data that drawn primarily from Biennial Transparency Reports (BTRs), National Inventory Reports (NIRs), and Common Reporting Format (CRF) tables submitted by Parties. These inventories include detailed, sectoral methane emissions estimates compiled using nationally appropriate activity data and country-specific or IPCC default emission factors.
Targets database
To establish an overview of government commitments, we compiled a database of methane-related targets. This captures both the inclusion of methane in countries’ Nationally Determined Contributions (NDCs) and identifying which countries have set methane-specific targets. We employed a large language model (LLM) to assist in reviewing country submissions to the UNFCCC and researched additional information on government commitments, followed by a thorough expert review. The resulting targets were organised and classified in a database based on their sectoral scope, greenhouse gas coverage, type of target, and target year.
Policies Database
We developed a multi-stage approach to collect and categorise methane policies in a consistent and comparable way. We employed a large language model (LLM) to support the initial policy identification and classification, followed by a thorough expert review. To ensure consistency, we developed a codebook that standardised how policies were categorised across the categories we track: sector, subsector, instrument type, mitigation type, implementation status and date, and whether a policy directly or indirectly targets methane emissions. The complete codebook is included in Annex B of our October 2025 report. We also developed detailed instructions to train the LLM to effectively identify and classify policies.