Spatial-temporal distribution and multiple driving mechanisms of energy-related CH₄ emissions in China

China's energy-related methane (CH4) emissions account for >19.03% of global energy sector emissions, posing a significant threat to China's endeavors in mitigating climate change. However, research on the spatial-temporal patterns, drivers, and mitigation policies remains limited. This study aims to address this gap by building an innovative research framework to reveal the spatial and temporal distribution characteristics and drivers of energy-related CH4 emissions across 30 Chinese provinces from 2010 to 2019, combining Standard Deviation Ellipsometry (SDE), Exploratory Spatial Data Analysis (ESDA), Markov Chain (MC) and Spatial Durbin Model (SDM). Key findings are: (1) The emissions exhibit a distinct clustering characteristic, with emissions clustered towards the "Northeast-Central-Northwest" direction over time. (2) There is significant positive spatial autocorrelation among emissions. As L-L agglomeration type provinces have shifted to L-H agglomeration type, the uneven distribution pattern of emissions will be intensified. (3) The emissions display path dependency, maintaining 84% steady-state probability. High-emitting provinces surrounded by medium-high or high emitters have higher downward shift probabilities. (4) The emissions have negative spillover effects on neighboring regions, suggesting the presence of competitive/substitution relationships among provinces regarding CH4 emission sources or mitigation measures. This research enhances understanding of China's energy-related CH4 emissions patterns and drivers, enriching the emission reduction research system. It provides new perspective for policymakers to develop adaptive policies.