A Stackelberg game model with tax for regional air pollution control

Ran Jiang, Laijun Zhao, Lei Guo, Qin Wang, Yujing Xie and Jian Xue

Journal of Management Analytics, 2023, vol. 10, issue 1, 1-21

Abstract: The command-and-control regulation is likely inefficient and costly. This study investigates a regional pollution control scheme with tax (RPCST) under which the central government sets the tax rate under a given pollutant reduction quota and local governments determine their pollution removal rates based on the central government’s policy. First, a one-leader-multi-follower (OLMF) Stackelberg game model is formulated, in which the central government is the leader and the local governments are the followers. Then, a procedure based on bilevel programming and relaxation method is applied to solve the OLMF model. Finally, a case study analyzing the SO2 reduction of the Yangtze River Delta in China is conducted to demonstrate the effectiveness of the RPCST. The results show that RPCST works better than the current command-and-control scheme. Our analysis provides a guideline for governments to design optimal tax schemes to effectively solve the regional air pollution crisis.

Date: 2023
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DOI: 10.1080/23270012.2022.2089062

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