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

Author

Listed:
  • Danyang Di

    (Nanjing University of Finance & Economics)

  • Guoxiang Li

    (NNU - Nanjing Normal University)

  • Zhiyang Shen

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux])

  • Malin Song

    (Anhui University of Finance and Economics)

  • Michael Vardanyan

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - ULCO - Université du Littoral Côte d'Opale - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

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.

Suggested Citation

  • Danyang Di & Guoxiang Li & Zhiyang Shen & Malin Song & Michael Vardanyan, 2023. "Environmental credit constraints and pollution reduction: Evidence from China's blacklisting system for environmental fraud," Post-Print hal-04277540, HAL.
  • Handle: RePEc:hal:journl:hal-04277540
    DOI: 10.1016/j.ecolecon.2023.107870
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    Cited by:

    1. Zhao, Yujie & Yang, Yuanyuan & Hua, Min & Chan, Kam C., 2024. "Social credit scoring system and corporate pollution governance: Insights from China's Social Credit System Construction," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    2. Peng, Hui & Ying, Huanqin & Wang, Xuerui & Lu, Yaobin & Wang, Shouyang, 2025. "Gains or losses: Can grassland ecological compensation policy alleviate the decoupling of welfare from wealth?," Structural Change and Economic Dynamics, Elsevier, vol. 74(C), pages 538-555.
    3. Zhang, Kun & Zhu, Pei-Hua & Qian, Xiang-Yan, 2024. "National information consumption demonstration city construction and urban green development: A quasi-experiment from Chinese cities," Energy Economics, Elsevier, vol. 130(C).
    4. Muhammad Zaheer Akhtar & Khalid Zaman & Muhammad Azhar Khan, 2024. "Governance, foreign investment, and growth: the impact of governance indicators, foreign direct investment, economic expansion, and industrialization on carbon emissions," SN Business & Economics, Springer, vol. 4(12), pages 1-25, December.
    5. Gao, Feng & Lin, Yijie & Zhang, Xuanming & Li, Shanhong & Lv, Yanqin, 2023. "Interconnectedness between land resource misallocation and environmental pollution: Exploring the sustainable development potential in China," Resources Policy, Elsevier, vol. 86(PB).
    6. Zhang, Renjie & Zhu, Guiyi, 2024. "Green public procurement and firms' pollution emissions: Does demand-side environmental policy matter?," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1958-1978.
    7. Shen, Yanyan & Guo, Feng & Li, Zhen, 2024. "Unintended effects of tax-sharing adjustments on firms' pollution emissions: Evidence from China," Energy Economics, Elsevier, vol. 139(C).
    8. Zhao, Xiaomeng & Chen, Yinna & Si, Deng-Kui & Jiang, Cun-Yuan, 2024. "How does environmental legislation affect enterprise investment preferences? A quasi-natural experiment based on China's new environmental protection law," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 834-855.
    9. Ren, Yi-Shuai & Klein, Tony & Jiang, Yong & Liu, Pei-Zhi & Weber, Olaf, 2025. "Dynamic connectedness between crude oil futures and energy industrial bond credit spread: Evidence from China," Energy Economics, Elsevier, vol. 143(C).
    10. Jia, Zhijie & Wen, Shiyan, 2024. "Interaction effects of market-based and incentive-driven low-carbon policies on carbon emissions," Energy Economics, Elsevier, vol. 137(C).
    11. Lai, Aolin & Li, Zhenran & Hu, Xiurong & Wang, Qunwei, 2024. "Does digital economy improve city-level eco-efficiency in China?," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 1198-1213.
    12. Chen, Jia & Wang, Ning & Lin, Tongzhi & Liu, Baoliu & Hu, Jin, 2024. "Shock or empowerment? Artificial intelligence technology and corporate ESG performance," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 1080-1096.

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