ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Economics and Management
Can the Central Environmental Protection Inspection Improve Urban Energy Utilization Efficiency?
Provisionally accepted- 1School of Finance, Central University of Finance and Economics, Beijing, China
- 2School of Public Finance and Taxation,Southwestern University of Finance and Economics, Chengdu, China
- 3Graduate School of Business Administration, Hitotsubashi University, Tokyo, Japan
- 4School of Economics, Yunnan University, Kunming, China
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Using a panel dataset of Chinese cities, this paper evaluates the impact of the Central Environmental Protection Inspection (CEPI) on urban Energy Utilization Efficiency by employing a staggered difference-in-differences design. The analysis yields four key findings. First, CEPI significantly improves cities' Energy Utilization Efficiency, and this result remains robust across a battery of empirical checks, including sensitivity analyses for deviations from the parallel trends assumption, estimation using imperfect instrumental variables, and heterogeneous treatment effects. Second, mechanism analysis suggests that CEPI enhances urban innovation capacity, discourages the entry of pollution-intensive firms, and mitigates capital misallocation. Third, heterogeneity analysis reveals that the positive effects of CEPI on Energy Utilization Efficiency are more pronounced in resource-based cities, cities with weaker environmental enforcement, and those with higher levels of green financial development. Fourth, extending the analysis using a Double Machine Learning framework, we further find that CEPI contributes to improvements in cities' inclusive green growth. Taken together, these findings offer new insights for developing economies—such as China—seeking to improve Energy Utilization Efficiency through institutionalized environmental regulatory mechanisms.
Keywords: Central environmental protection inspection, Energy utilization efficiency, Difference-in-differences, Imperfect Instrumental Variables, Double machine learning
Received: 30 Aug 2025; Accepted: 30 Oct 2025.
Copyright: © 2025 Tang, Zhong, Zuo and Xie. 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: 
Yu  Zhong, 2662697379@qq.com
Zhizhao  Zuo, changjiang_bei@outlook.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
