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<p>Improving energy–environment efficiency (EEE) is crucial for achieving a low-carbon economy and addressing climate change. As the largest energy user and emitter, China faces significant pressure to enhance its efficiency performance. This study employs data from 282 prefecture-level cities in China between 2007 and 2022. It applies an extended SBM-DEA model, spatial autocorrelation analysis, the Dagum Gini coefficient (DGC), and Tobit regression to systematically evaluate EEE’s temporal and spatial evolution, regional disparities, and driving mechanisms. Though regional disparities persist, the results show steady improvement in overall efficiency. The western region shows the highest efficiency, followed by the central region, while the eastern region lags. The widening east–west gap is the primary source of inequality, with super-variance density contributing most in the long term. Spatial analysis highlights clustering patterns of “high-high” and “low-low” cities, reflecting resource and governance homogeneity. Regression results indicate that economic development, technological investment, and innovation capacity significantly enhance efficiency, whereas external openness exerts a negative influence. Environmental regulation effects remain limited. These findings suggest that differentiated regional policies, stronger clean energy innovation, improved green finance, and cross-regional cooperation are key to advancing China’s EEE.</p>

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Spatial-temporal evolution and determinants of energy environmental efficiency in China: an integrated analytical approach

  • 期刊论文

  • 发表日期：

    2025年9月19日

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  • Wenzhong Lu,
  • Songyu Jiang,
  • Hao Zhu

摘要

Improving energy–environment efficiency (EEE) is crucial for achieving a low-carbon economy and addressing climate change. As the largest energy user and emitter, China faces significant pressure to enhance its efficiency performance. This study employs data from 282 prefecture-level cities in China between 2007 and 2022. It applies an extended SBM-DEA model, spatial autocorrelation analysis, the Dagum Gini coefficient (DGC), and Tobit regression to systematically evaluate EEE’s temporal and spatial evolution, regional disparities, and driving mechanisms. Though regional disparities persist, the results show steady improvement in overall efficiency. The western region shows the highest efficiency, followed by the central region, while the eastern region lags. The widening east–west gap is the primary source of inequality, with super-variance density contributing most in the long term. Spatial analysis highlights clustering patterns of “high-high” and “low-low” cities, reflecting resource and governance homogeneity. Regression results indicate that economic development, technological investment, and innovation capacity significantly enhance efficiency, whereas external openness exerts a negative influence. Environmental regulation effects remain limited. These findings suggest that differentiated regional policies, stronger clean energy innovation, improved green finance, and cross-regional cooperation are key to advancing China’s EEE.