Sector	Category	Abbr.	Years	Spatiotemporal Resolution	Developer	Contributor	Email	Dataset Summary
Energy	Fuel combustion activities	FCA	2011-2023	Annual, national	Qiang Liu	Fei Teng	tengfei@tsinghua.edu.cn	The dataset on China's energy-related methane emissions includes fossil fuel combustion methane emissions (in Excel) and fugitive methane emissions from coal, oil, and gas production (in GeoTIFF). While combustion emissions are estimated at the national level, fugitive emissions are provided at a 0.1° × 0.1° spatial resolution. This dataset can serve as prior information for top-down methane emission inversion studies and assist in identifying emission hotspots. Compiled using a combination of dynamic facility-level data and advanced estimation methodologies, the dataset captures the impacts of facility-level operational changes on both spatial and temporal emission patterns, providing up-to-date emission characteristics. The raw data used to develop this dataset is publicly accessible, including sources such as annual announcements of coal mine production capacities, national coal mine gas-level identification, the Global Energy Monitor database, and relevant literature.
	Solid fuels	SOF	2011-2022	Annual, 0.1°
	Oil and natural gas	ONG	2011-2022	Annual, 0.1°
Agriculture, forestry and other land use (AFOLU)	Enteric fermentation	ENF	1980-2023	Annual, 0.1°	Yuanyi Gao	Xuhui Wang	gyy@stu.pku.edu.cn	The Gridded Livestock Emission Dataset of China (GLEDCv1) offers a grid-level perspective on non-CO₂ greenhouse gas emissions from livestock in China since 1980. Specifically, the methane (CH₄) emissions from enteric fermentation cover 12 types of livestock: dairy cattle, non-dairy cattle, buffalo, camels, horses, donkeys, mules, goats, sheep, pigs, poultry, and rabbits. The data is formatted as GeoTIFF, featuring an annual temporal resolution and a spatial resolution of 0.1°. Developed using the Tier 2 method from the "2019 Revised 2006 IPCC Guidelines for National Greenhouse Gas Inventories," the dataset relies on baseline data from the Chinese National Agricultural Censuses (for the year 1996, 2006, and 2016). It integrates various sources, including the China Rural Statistical Yearbook, the National Bureau of Statistics database, and county-level livestock activity data gathered from literature, while also employing the GLW World Livestock Gridding dataset for gridding constraints. By utilizing a multi-source fusion of localized databases, this dataset adheres to the latest inventory methodologies and demonstrates good overall data quality.
	Manure management	MNM	1980-2023	Annual, 0.1°	Yuanyi Gao	Xuhui Wang	gyy@stu.pku.edu.cn	The Gridded Livestock Emission Dataset of China (GLEDCv1) offers a grid-level perspective on non-CO₂ greenhouse gas emissions from livestock in China since 1980. Specifically, the methane (CH₄) emissions from manure management cover 12 types of livestock: dairy cattle, non-dairy cattle, buffalo, camels, horses, donkeys, mules, goats, sheep, pigs, poultry, and rabbits. The data is formatted as GeoTIFF, featuring an annual temporal resolution and a spatial resolution of 0.1°. Developed using the Tier 2 method from the "2019 Revised 2006 IPCC Guidelines for National Greenhouse Gas Inventories," the dataset relies on baseline data from the Chinese National Agricultural Censuses (for the year 1996, 2006, and 2016). It integrates various sources, including the China Rural Statistical Yearbook, the National Bureau of Statistics database, and county-level livestock activity data gathered from literature, while also employing the GLW World Livestock Gridding dataset for gridding constraints. By utilizing a multi-source fusion of localized databases, this dataset adheres to the latest inventory methodologies and demonstrates good overall data quality.
	CH₄ uptake by soils	NCS	1980-2023	Annual, 0.1°	Yingxuan Wang, Lijun Yu	Xuhui Wang, Tingting Li	MeMo v1.0: xuhui.wang@pku.edu.cn CH4MODFG: litingting@mail.iap.ac.cn	The China Soil Methane Uptake Dataset provides spatiotemporal distribution data on soil methane uptake from 1980 to 2023. The dataset is simulated using two soil methane uptake models (CH4MODFG and MeMo v1.0), with data formatted in GeoTIFF. The temporal resolution is annual, and the spatial resolution is 0.1°. The CH4MODFG model uses ERA5 data as input and simulates the spatiotemporal distribution of soil methane uptake in Chinese forests and grasslands. The MeMo v1.0 model uses ERA5 data, methane concentration data from CAMS, and published soil property data as inputs, providing unsaturated soil methane uptake rates for spatial grid cells across China. The simulation results from both models offer long-term, high-resolution estimates of soil methane uptake in China and provide comprehensive insights into the spatiotemporal patterns of soil methane uptake in the country.
	CH₄emissions from biomass burning	BMB	2012-2023	Annual, 0.1°	Zhengyang Lin	Xuhui Wang	xuhui.wang@pku.edu.cn	The China Wildfire Emission Dataset (ChinaWED) is a specialized wildfire emission dataset for studies in China. It is derived from burned area data and emission factors, with records commencing in January 2012 and continuously updated. The burned area data integrates fire information from the MODIS burned area product and FIRMS VIIRS S-NPP active fire records. Combustion efficiency is determined by fixed thresholds corresponding to land cover types. Emission factors are sourced from previous studies conducted in countries and regions within and around China. Fuel loads are measured using a high-resolution aboveground biomass product. ChinaWED has refined the calculation of burned areas and emission factors specifically for China, particularly accounting for the significant number of agricultural fires. In comparison to other global fire emission datasets, ChinaWED reports higher greenhouse gas (GHG) emissions than those based solely on burned area data but lower than those based on fire radiative power (FRP).
	Rice cultivations	RCV	1980-2023	Annual, 0.1°	Lijun Yu, Shihua Li	Wen Zhang, Wenping Yuan	CH4MOD: zhw@mail.iap.ac.cn IBIS-CH4: yuanwp@pku.edu.cn	The gridded dataset of methane emissions from rice paddies in China is based on the bottom-up process-based models CH4MOD and IBIS-CH4. The dataset include methane emissions for different rice cropping systems, namely double-season rice, middle rice, and single late rice. The data is provided in GeoTIFF format, with an annual temporal resolution and a spatial resolution of 0.1°. These two process-based models utilized high-resolution rice distribution maps of China from 2017 to 2022 (spatial resolution: 30m × 30m) and data on rice harvest area from the National Bureau of Statistics since 1980 to simulate methane emissions during the rice growing season. The IBIS-CH4 model takes into account two primary microbial processes of methane production (acetoclastic methanogenesis and hydrogenotrophic methanogenesis) to simulate methane emissions from rice paddies. The CH4MOD model comprehensively considers the impact of agricultural management measures such as irrigation and straw returning on methane emissions.
	Aquaculture	ACT	1980-2023	Annual, provincial	Liangliang Zhang	Xuhui Wang	xuhui.wang@pku.edu.cn	The ChinaAquaEmis Dataset provides a comprehensive assessment of non-CO₂ emissions from provincial freshwater aquaculture in China from 1980 to 2024. The dataset, compiled from the China Fisheries Statistical Yearbook and 358 non-CO₂ gas flux observations across seven major aquaculture regions, quantifies annual CH₄ emissions for five aquaculture systems: ponds, lakes, reservoirs, paddy fields, and ditches.
	Wetlands	WLD	1980-2022	Annual, 0.1°	Han Xiao, Tingting Li, Qiuan Zhu	Wenping Yuan, Tingting Li, Qiuan Zhu	IBIS: yuanwp@pku.edu.cn CH4MODwetland: litingting@mail.iap.ac.cn TRIPLEX-GHG: zhuq@hhu.edu.cn	The Chinese wetland CH₄emission dataset describe the temporal spatial resolution of Chinese wetland CH₄emissions. The datasets used 3 process-based models (CH4MODwetland, TRIPLEX-GHG and IBIS), driven by uniform meteorological and soil data. The wetland distribution is based on the Random Forest model and WAD2M datasets, and adjusted by the third national land resource survey. This dataset is a unique long-term wetland CH₄emission dataset on national scale with high temporal spatial resolution.
	Lakes	LAK	2000-2023	Annual, 0.1°	Shilong Luan	Wenping Yuan	yuanwp@pku.edu.cn	CH₄emissions from lakes and reservoirs are in GeoTIFF format, with a temporal resolution of years and a horizontal spatial resolution of 0.1°. The original emission data are collected from literature, and the areas of lakes and reservoirs are respectively based on the Chinese Reservoir Dataset and the Chinese Lake Dataset. Using machine learning, we first construct the environmental elements in the water body (pH, DO, DOC, CODMn, TN, TP, Tur, and EC), and then further derive the CH₄emissions from lakes and reservoirs.
	Reservoirs	RSV
Waste	Solid waste disposal	SWD	2000-2023	Annual, 0.1°	Haoyu Zhang, Ying Cui, Jicui Cui, Hui Wang	Ziyang Lou	louworld12@sjtu.edu.cn	The China Regional Waste Landfill Dataset covers key information of CH₄emissions from landfills. The data are stored in GeoTIFF format with a temporal resolution of one year and a horizontal spatial resolution of 0.1°. The dataset integrates the National Urban and Rural Construction Statistical Yearbook, on-site research and literature, and ensures the accuracy and reliability of the data by making localized corrections to the IPCC FOD model factors, and then using the Clean Development Mechanism (CDM) monitoring report for aggregate constraints and data validation.
	Wastewater treatment and discharge	WTD	2000-2022	Annual, 0.1°	Huiwen Yang	Xu Zhao	yanghuiwen1117@163.com	The sewage treatment methane discharge data set includes two parts: domestic sewage treatment methane discharge and industrial sewage treatment methane discharge. The data is in GeoTIFF format, the time resolution is 1 year, and the horizontal space resolution is 0.1°. The original data information comes from the Yearbook of China's Environmental Statistics (2000-2022), the detailed data of China's sewage plants from 2014 to 2018, and the detailed data of China's industrial enterprises from 2000 to 2013. The calculation method refers to IPCC2019 and adopts the emission factor method for accounting.