Measurement, regional disparities, and spatiotemporal evolution of rural energy poverty in China

Energy poverty greatly affects environmental sustainability and diminishes residents' quality of life and social fairness, especially in rural areas. In this study, using longitudinal data from 30 Chinese provinces between 2010 and 2023, we establish a Rural Energy Poverty (REP) evaluation system based on three dimensions: energy use level, energy use structure, and energy use capability. We apply the entropy weighting method to calculate the Rural Energy Poverty Index (REPI). The Dagum's Gini coefficient, kernel density estimation, and Markov chains are used to analyze its spatial and temporal evolution and regional differences. The results indicate that REP in China has generally decreased over time, with the greatest improvements in energy use capacity and the least in energy use structure. Spatially, a pattern emerges: “lower in the east and south, higher in the west and north.” The overall regional differences are expanding, with interregional differences primarily driving inequality, and the largest intra-regional disparity observed in western China. The absolute difference of REP in overall China, the eastern region, and the central region is narrowing, while the absolute difference in the western region is expanding. In addition, REP exhibits significant spatial autocorrelation, with transition probabilities strongly influenced by neighboring areas, demonstrating “low–low” and “high–high” clustering patterns consistent with club convergence effects. Therefore, it is crucial to promote access to clean energy, improve rural energy use capacity, and strengthen coordinated energy management across regions.