Carbon tax revenue recycling strategy choices under macroeconomic uncertainty based on low-carbon technology progress

Carbon tax recycling strategies can effectively mitigate the adverse effects of carbon tax policies, yet existing research still faces challenges in designing effective carbon tax recycling strategies. This study constructs a three-party random evolutionary game model involving local governments, enterprises, and residents to examine how governments should design appropriate carbon tax refund strategies under macroeconomic uncertainty, considering different development scenarios of low-carbon technologies. Using python programming language, we solve the random replicator dynamics equation to visualize the evolutionary outcomes. The experiments indicate that the choice between low-carbon subsidies and income subsidies should depend on the maturity, evolutionary rate, and diffusion rate of low-carbon technologies. (1) Only considering the maturity of low-carbon technologies, income subsidies should be prioritized when the technology is in its conceptual phase or has a significant market share. As the technology reaches commercialization potential, low-carbon subsidies become more appropriate. (2)The evolution rate and diffusion rate of low-carbon technologies also influence the strategies of local governments. A high evolution rate favors low-carbon subsidies, while a high diffusion rate suggests income subsidies. These findings provide a strong theoretical foundation and practical guidance for policymakers in formulating carbon tax policies and income recovery strategies.