ORIGINAL RESEARCH ARTICLES
Front page. Public Health
Abs. Environmental Health and Exposome
Volume 12 – 2024 |
doi: 10.3389/fpubh.2024.1415309
Provisionally accepted
- 1
Institute of Big Data, Zhongnan University of Economics and Law, Wuhan, China
- 2
School of Mathematics and Statistics, Zhongnan University of Economics and Law, Wuhan, China
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Horizontal ecological compensation (HEC) has the potential to promote inclusive green growth in cities. Using the multi-level difference-in-differences (DID) method, this study investigates the impacts of HEC policies as a quasi-natural experiment. Panel data are analyzed; the data refer to 87 cities in the Yangtze River basin from 2007 to 2020. The results show that HEC policies contribute significantly to inclusive green growth, with consistent effects across different estimators. The moderating effect test shows that urban industrial pollution levels and green innovation are important pathways through which HEC policies influence inclusive green growth. Further analysis shows that the positive effects of HEC are more pronounced in watersheds with high commercialization and in downstream regions, suggesting that HEC may exacerbate regional differences in inclusive green growth. This study provides insights for China and other developing countries seeking to promote inclusive green growth in cities and achieve sustainable development goals.
Keywords:
horizontal ecological balance, ecological balance, inclusive green growth, unfair environment, green development
Receive:
10 April 2024;
Accepted:
14 August 2024.
Copyright:
© 2024 Wang, LI, Xiao, and Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). Use, distribution, or reproduction in other forums is permitted provided the original author(s) or licensor are credited and the original publication in this journal is cited in accordance with accepted academic practice. Use, distribution, or reproduction not in accordance with these terms is not permitted.
* Correspondence:
Hengli Wang, Institute of Big Data, Zhongnan University of Economics and Law, Wuhan, China
Disclaimer:
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