Abstract
Pollution control policies have achieved remarkable results in China. However, industrial firms still lack enthusiasm for technological innovation to reduce pollution and endogenous motivation for controlling pollution, which pose severe obstacles to industrial pollution control. The top-runner scheme can stimulate the pollution control behaviors of firms to a certain extent. Therefore, the top-runner scheme is combined with economic incentive policies to design an appropriate incentive scheme in this paper, theoretical research is conducted on the selection standards and incentive methods, and four industries mainly responsible for pollution in a developed province of China are investigated as case studies. The results show that (1) the selection standard that stipulates the rewarded quotas instead of the discharge amount achieved higher emission reduction efficiency; (2) tax cuts are the best incentive for top-runners, and (3) the top-runner incentive scheme achieves a greater reduction effect in high-pollution industries than low-pollution industries. Value-added tax exemption for the top-runner, as determined by the rewarded quotas, contributes to increasing the enthusiasm from firms to participate in and improve pollution control measures.
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Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This research is supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China. Project No. 17XNB025.
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Conceptualization: Xiangbo Xu and Dunhu Chang; methodology: Dunhu Chang and Donghui Gao; formal analysis and investigation: Dunhu Chang, Donghui Gao, and Ying Ju; writing—original draft preparation: Dunhu Chang and Donghui Gao; writing—review and editing: Xiangbo Xu; funding acquisition: Dunhu Chang; resources: Dondhui Gao, Xiaoyue Shen, and Xin Wang; supervision: Xiangbo Xu.
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Chang, D., Gao, D., Xu, X. et al. Top-runner incentive scheme in China: a theoretical and empirical study for industrial pollution control. Environ Sci Pollut Res 28, 29344–29356 (2021). https://doi.org/10.1007/s11356-021-12561-1
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DOI: https://doi.org/10.1007/s11356-021-12561-1