Spatio-temporal patterns and determinants of energy efficiency across China’s provinces

Datos Bibliográficos

ID7397196
AutoresMin Wan (0000-0003-4527-7292), De-Wei Yang (0000-0001-9771-4269), Shuai Zhang (0000-0002-5059-6688), Ji Yijia, Zhang Junmei, Meng Haishan, Hang Yang (0000-0001-5647-0018)
Año2025
Volumen10
Páginas101021-101021
Fecha de publicación2025-07-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSustainable Futures (JOURNAL)
Identificadores de la revistaISSN: 2666-1888
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.sftr.2025.101021
OpenAlexW4412448542
IdiomaEN
Citas recibidas2
Referencias citadas47

Improving energy efficiency is a critical pathway for facilitating energy transition and mitigating climate change. This study employs the Super-SBM model to measure provincial energy efficiency in China from 2010 to 2022, followed by the Global Malmquist-Luenberger (GML) index to decompose dynamic changes in efficiency and examine regional disparities. Furthermore, a panel data regression model is utilized to identify key influencing factors. The findings reveal that: (1) China's energy efficiency exhibited an overall upward trend during the study period, though significant interprovincial and interregional disparities persisted. (2) High-efficiency regions demonstrated a pronounced clustering effect, particularly in South, East, and Central China, while Northwestern, Southwestern, and Northeastern regions remained low-efficiency zones, indicating distinct polarization characteristics. (3) The GML index exhibited notable fluctuations but remained above 1 in most provinces, suggesting sustained improvements in energy efficiency across China. (4) Urbanization level and environmental regulations were positively correlated with energy efficiency, whereas industrial structure, energy consumption patterns, government intervention, and trade openness exerted negative effects. These results provide valuable insights for policymakers to formulate region-specific strategies aimed at enhancing energy efficiency and accelerating the low-carbon transition

China · Economic geography · Geography · Efficiency Analysis Using DEA · Energy, Environment, Economic Growth · Environmental Impact and Sustainability