Mitigating carbon emissions in China’s power sector: Spatial patterns, driving factors, and strategies from 2000 to 2020

• Power sector’s CO 2 emissions were calculated at the provincial level in China. • An inverted U-shaped exists between economic growth and power sector’s CO 2 emissions. • The drivers for spatial variability of power sector’ CO 2 emissions were analyzed. • Regional CO 2 emissions reduction strategies were proposed in the power sector. Power sector CO 2 emissions (PSCE) are a key ecological pressure indicator, directly reflecting anthropogenic stress on the environment and influencing national carbon neutrality goals. This study constructs a multi-dimensional indicator framework to analyze PSCE across 30 Chinese provinces from 2000 to 2020. We integrate the STIRPAT model, spatial LMDI decomposition, and K-means clustering to quantify emission drivers and identify regional patterns. Nine driving-factor indicators are employed, covering emission coefficients, fossil energy structure, thermal power efficiency, nuclear and renewable energy contributions, electricity supply–demand balance, electricity intensity, economic development, and population size. Results indicate that economic growth, electricity intensity, fossil fuel generation efficiency, and the share of thermal power are the main emission drivers. The Environmental Kuznets Curve (EKC) is validated, suggesting emissions could decline as income rises if cleaner energy and efficiency measures are adopted. Spatial decomposition reveals significant regional disparities, influenced by factors such as population, development level, energy intensity, and renewable energy adoption. K-means clustering identifies five regional PSCE types, with provinces like Inner Mongolia and Shanxi showing high reduction potential. These findings emphasize the need for differentiated, region-specific mitigation strategies to support China’s dual carbon goals.