Sector	Category	Abbr.	Years	Spatiotemporal Resolution	Developer	Contributor	Email	Dataset Summary
Energy	Power industry	ENE	1985, 1995-2023	Annual, 0.1°	Minqi Liang	Wenping Yuan	yuanwp@pku.edu.cn	Energy sector N₂O emission data set, including electricity, oil refining, manufacturing, transport, construction, fuel fugitives, a total of 6 elements. The data is in GeoTIFF format, with a temporal resolution of one year and a horizontal spatial resolution of 0.1°. The original data comes from the energy balance table in the China Energy Statistical Yearbook. The IPCC guideline method is used to calculate the annual N₂O emissions at provincial level, etc. The EDGAR grid distribution is used to divide the spatial pattern.
	Oil refineries and transformation industry	REF-TRF
	Combustion for manufacturing	IND
	Transport	TRP
	Energy for buildings	RCO
	Fuel exploitation	PRO	1985, 1995-2023	Annual, 0.1°	Minqi Liang	Fei Teng	tengfei@tsinghua.edu.cn
Industrial processes and product use (IPPU)	Nitric acid production	NAP	2002-2023	Annual, 0.1°	Minqi Liang	Wenping Yuan	yuanwp@pku.edu.cn	IPPU sector N₂O emission data set, including nitric acid production and adipic acid production, a total of 2 elements. The data is in GeoTIFF format, with a temporal resolution of one year and a horizontal spatial resolution of 0.1°.  The IPCC guideline method is used to calculate the annual N₂O emissions. Activity data were derived from the CDM projects and capacity or production information provided by plants.
	Adipic acid production	AAP
Agriculture, forestry and other land use (AFOLU)	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 nitrous oxide (N₂O) 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.
	Natural soils	NTS	1980-2023	Annual, 0.1°	Zimeng Li, Minqi Liang, Qiuan Zhu	Songbai Hong, Wenping Yuan, Qiuan Zhu	data-driven model: songbaih@pku.edu.cn IBIS-MicN: yuanwp@pku.edu.cn TRIPLEX-GHG: zhuq@hhu.edu.cn	The natural soil N₂O emission dataset encompasses N₂O emissions from forests and grasslands. This dataset offers a high spatial-temporal resolution gridded estimate of China's natural soil N₂O emissions from 1980 to 2023 in GeoTIFF format, with a yearly temporal resolution and a spatial resolution of 0.1°. The emissions were simulated using Random Forest modeling (data-driven model), IBIS-MicN model and TRIPLEX-GHG model. The Random Forest models were constructed based on 319 in-situ records of N₂O fluxes. The IBIS-MicN model incorporates four microbial N₂O-producing processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification, and denitrifier denitrification, as well as the nitrogen cycling related microbial dynamics. The TRIPLEX-GHG model is designed to simulate the production, consumption, and transportation of N₂O by integrating the processes of nitrification, denitrification, and diffusion that occur within soil layers. Validation efforts conducted at 52 sampling sites have demonstrated that the TRIPLEX-GHG model effectively represents the interannual and seasonal variations in N₂O flux across different biomes.
	N₂O emissions 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).
	Managed soils	NMS	1980-2023	Annual, 0.1°	Zimeng Li	Songbai Hong	songbaih@pku.edu.cn	The dataset of China's cropland N₂O emissions encompasses emissions from six sources: cropland fertilization, nitrogen deposition, crop residue incorporation, pasture fertilization, nitrogen mineralization, and nitrogen leaching. This dataset offers a high spatial-temporal resolution grid-based estimation of China's cropland N₂O emissions spanning from 1980 to 2023 in GeoTIFF format, with a yearly temporal resolution and a spatial resolution of 0.1°. It was derived from 1,705 in-situ observations and the Random Forest algorithm utilizing the Emission Factor approach.
	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  N₂O emissions for five aquaculture systems: ponds, lakes, reservoirs, paddy fields, and ditches.
	Lakes	LAK	2000-2023	Annual, 0.1°	Shilong Luan	Jing Wei	weij53@mail.sysu.edu.cn	Inland waters linking terrestrial and marine ecosystems play a crucial role in maintaining the biogeochemical cycling of water, carbon (C), and nitrogen (N). Contrary to the conventional wisdom that recognize inland waters as passive pipes which transfer materials conservatively, accruing evidence suggests that metabolization of organic matter within inland waters can produce considerable quantities of N₂O for emission. N₂O emissions 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 N₂O emissions from lakes and reservoirs.
	Reservoirs	RSV
Waste	Wastewater treatment and discharge	WTD	2000-2022	Annual, 0.1°	Huiwen Yang	Xu Zhao	yanghuiwen1117@163.com	The sewage treatment nitrogen oxide discharge data set includes two parts: domestic sewage treatment nitrogen oxide discharge and industrial sewage treatment nitrogen oxide 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.