A comparative study of anthropogenic CH 4 emissions over China based on the ensembles of bottom-up inventories

Abstract. Atmospheric methane (CH 4 ) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, contributes considerably to the global anthropogenic CH 4 emissions by rice cultivation, ruminant feeding, and coal production. Understanding the characteristics of China's CH 4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH 4 emissions. This study provides a comprehensive comparison of China's anthropogenic CH 4 emissions by synthesizing the most current and publicly available datasets (13 inventories). The results show that anthropogenic CH 4 emissions differ widely among inventories, with values ranging from 44.4–57.5 Tg CH 4  yr −1 in 2010. The discrepancy primarily resulted from the energy sector (27.3 %–60.0 % of total emissions), followed by the agricultural (26.9 %–50.8 %) and waste treatment (8.1 %–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s but increased significantly thereafter, with annual average growth rates (AAGRs) of 2.6 %–4.0 % during 2000–2010 but slower AAGRs of 0.5 %–2.2 % during 2011–2015, and the emissions became relatively stable, with AAGRs of 0.3 %–0.8 %, during 2015–2019 because of the stable emissions from the energy sector (mainly coal production). Spatially, there are large differences in emissions hotspot identification among inventories, and incomplete information on emission patterns may mislead or bias mitigation efforts for CH 4 emission reductions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions and reducing the uncertainty in bottom-up inventories. Data used in this article are available at https://doi.org/10.6084/m9.figshare.12720989 (Lin et al., 2021).