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ORIGINAL RESEARCH article

Front. Environ. Sci.

Sec. Environmental Economics and Management

Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1625233

This article is part of the Research TopicMoving Towards Sustainable Development: Exploring the Impact of Land-Use Policies on Land Green Utilization EfficiencyView all 24 articles

Spatiotemporal Evolution Characteristics and Influencing Factors of China's Interprovincial Industrial Green Efficiency Based on the Super-Efficiency SBM Model

Provisionally accepted
  • School of Food and Chemical Engineering, Beijing Technology and Business University, Beijing, China

The final, formatted version of the article will be published soon.

With the rapid development of the economy and excessive resource consumption, improving industrial green efficiency is a crucial pathway to achieving sustainable industrial development. Based on the super-efficiency SBM model and using provincial panel data from 2005 to 2022, this study measures China's industrial green efficiency index. It constructs an indicator system with 36 specific indicators selected from seven dimensions: industrial governance, economic foundation, factor input, technological efficiency, environmental governance, natural factors, and pollutant emissions. The study employs the geographical detector method to conduct an in-depth analysis of the driving effects of industrial green efficiency. The key findings are as follows: (1) Since 2005, China's industrial green efficiency index has exhibited an initial increase followed by a slight decline, dropping from 0.668 to 0.623. (2) The pace of China's green transition has been gradually accelerat-ing, yet significant disparities in green efficiency indices remain among provinces, with particularly pronounced gaps between the developed eastern regions and the underdeveloped western regions. (3) The Moran's Index indicates that 77.4% of China's provinces show a positive spatial correlation between their industrial green efficiency and that of neighboring provinces, with this proportion on the rise. Strong spatial clustering is observed in Shanghai, Zhejiang, and Fujian, while spatial dispersion is noted in Hebei and Guangdong. (4) Industrial governance has the most significant driving effect on industrial green efficiency, followed by economic foundation (1.153), factor input (0.772), technological efficiency (0.637), environmental governance (0.567), natural factors (0.338), and pollutant emissions (0.239). (5) At present, economic development, industrial upgrading, capital investment, and green finance are the key driving forces behind industrial green efficiency. The negative impact of pollutant emissions has been gradually decreasing, while the role of technological innovation, though important, has shown a marginal diminishing trend in the later stages. Additionally, the digital economy's contribution to industrial green efficiency has been steadily increasing.

Keywords: Super-efficiency SBM model, Industrial green efficiency, digital economy, Spatial pattern, Industrial governance, sustainable development

Received: 08 May 2025; Accepted: 29 Sep 2025.

Copyright: © 2025 Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jinyu Wang, wjinyu@cqu-edu.cn

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