Using evolutionary game theory to study governments and manufacturers' behavioral strategies under various carbon taxes and subsidies

Governments of both developed and developing countries are monitoring the growing problems of environmental pollution, resource consumption, and energy shortages. They use carbon taxes to discourage manufacturing that is not eco-friendly, and subsidizes to encourage low-carbon production methods. In this research, the evolutionary game theory is applied to examine the behavioral strategies of the manufacturers in response to various combinations of carbon taxes and subsidies considering that the manufactured products have no distinctly low-carbon characteristics. First, we developed an evolutionary game theory model of the interaction between governments and manufacturers based on static carbon taxes and subsidies. Then we examined the evolutionary stable strategy (ESS) of the governments and manufacturers under different constraints. Second, we analyzed the evolutionary behaviors of the governments and manufacturers under three additional models: dynamic taxes and static subsidies, static taxes and dynamic subsidies, and dynamic taxes and dynamic subsidies. Finally, we used a simulation to compare the results of all the models to determine the optimal carbon tax and subsidy mechanism. The results showed that the static carbon tax and subsidy mechanism implemented by the governments cannot provide the needed positive impact on manufacturers decision-making. Of the three dynamic carbon tax and subsidy mechanisms, the bilateral dynamic tax and subsidy mechanism is more effective, and it provides more incentives for manufacturers to adopt low-carbon manufacturing. The carbon taxes levied by governments are proved more effective to encourage low-carbon manufacturing than governments subsidize the low-carbon technology. Manufacturers' behavioral strategy is influenced mainly by the governmental policies, to which governments also need to make some dynamic strategy adjustments in response. (C) 2018 Elsevier Ltd. All rights reserved.