Environmental credit constraints and pollution reduction: Evidence from China's blacklisting system for environmental fraud

Danyang Di, Guoxiang Li, Zhiyang Shen, Malin Song and Michael Vardanyan
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Danyang Di: Nanjing University of Finance & Economics
Guoxiang Li: NNU - Nanjing Normal University
Malin Song: Anhui University of Finance and Economics

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Abstract: Environmental performance-based credit mechanisms are among the tools policymakers can use to influence the polluting firms' behavior. In this study, we use China's blacklisting system for environmental fraud as a quasi-natural experiment to analyze the pollution-reducing effect of environmental credit constraints (ECCs). We operationalize our approach using a sample of 287 Chinese cities for the period 2008–2018 and find that ECCs help reduce emission intensity—a result that is both statistically significant and robust. Furthermore, our analysis suggests that ECCs can motivate producers to increase investment in technological innovation and optimize their factor allocation structure to improve green total factor productivity, thereby helping reduce their environmental impact. We demonstrate that the ECC-based schemes could be particularly effective in helping reduce pollution in regions with high enterprise credit dependence and relatively heavy presence of the manufacturing industry. In addition, these pollution-reducing effects are significant in regions with relatively strict environmental regulation. Hence, we argue that environmental credit systems could help policymakers provide polluting companies with additional incentives to voluntarily cut their emission levels and thus offer opportunities for diversifying the strategies policymakers can use to mitigate adverse environmental impacts.

Date: 2023-08
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Published in Ecological Economics, 2023, 210, pp.107870. ⟨10.1016/j.ecolecon.2023.107870⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04277540

DOI: 10.1016/j.ecolecon.2023.107870

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