EMISSION-MITIGATION-DRIVEN EXPORT TAX REBATE POLICY: AN EVOLUTIONARY GAME ANALYSIS

Export tax rebates are a common policy in global trade. With improvements to manufacturing techniques and economic development levels, export tax rebate policies are transitioning from being driven by economic objectives to being driven by environmental objectives through the alignment of export performance with environmental protection. However, existing studies lack the consideration of trade-offs between environmental objectives and economic objectives, and rarely analyze the relationship between production strategies of enterprises and export rebate rates. To formulate an efficient export tax rebate policy, an evolutionary game model is established to reveal the game relationship between the government and enterprises when export tax rebates with different policy goals are implemented. Furthermore, the impacts of export tax rebates on social welfare under different evolutionary strategies are analyzed. According to the results, enterprises fail to produce clean products in the case where economic objectives have priority. In the case where environmental objectives have priority, the output of clean products is directly related to the export tax rebate rate. In addition, the optimal tax rebate rate depends on the environmental benefits and the weights on the environmental and economic objectives. The innovation of this study lies in using the optimal dynamic response mechanism and the replication dynamic equation evolution mechanism to analyze the government-enterprise game problem in the export tax rebate mechanism according to different learning ability of the government and the enterprise. Meanwhile, we consider the influence of the weight of economic and environmental goals on both strategic choice and social welfare. The findings provide a valuable reference for designing and optimizing export tax rebate policies, especially for countries that implement export tax rebate policies and desire to improve the environment urgently.