Energy Poverty in China: Measurement, Regional Inequality, and Dynamic Evolution

  • Zhiyuan Gao
  • , Ziying Jia
  • , Chuantong Zhang*
  • , Shengbo Gao
  • , Xinyi Yang
  • , Yu Hao*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Against the backdrop of China’s transition from the eradication of absolute poverty toward the pursuit of common prosperity, equitable access to energy has become an increasingly important policy concern. This study develops a multidimensional framework to assess energy poverty from three interrelated dimensions: energy use level, energy structure, and energy capability. Using panel data for 30 provincial-level regions from 2005 to 2020, a provincial energy poverty index (EPI) is constructed based on the entropy-weighting approach. The spatial and temporal dynamics of energy poverty are examined using Moran’s I, the Dagum Gini decomposition, kernel density estimation, and spatial Markov chain analysis. The results reveal several key patterns. (1) Although energy poverty has declined nationwide, it remains pronounced in parts of western, central, and northeastern China. (2) Energy poverty exhibits significant spatial clustering, with high-poverty clusters concentrated in resource-dependent regions such as Shanxi and Inner Mongolia, while low-poverty clusters are mainly located along the eastern coast. (3) Regional disparities follow an inverted U-shaped trajectory over time, with east–west differences constituting the primary source of overall inequality. (4) Moreover, the evolution of energy poverty displays strong path dependence and club convergence. These findings highlight the need to strengthen dynamic monitoring and governance mechanisms, promote region-specific clean energy development, and enhance cross-regional coordination to support energy security and green transformation under China’s “dual-carbon” objectives.

Original languageEnglish
Article number143
JournalEnergies
Volume19
Issue number1
DOIs
Publication statusPublished - Jan 2026

Keywords

  • energy poverty
  • regional disparity
  • spatial Markov chain
  • spatial clustering

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