Optimizing environmental regulation policies for digital transformation in energy enterprises in China: An evolutionary game theory approach

Abstract

In response to the global consensus on achieving carbon reduction, China has introduced a series of policies aimed at accelerating the digital transformation of energy enterprises. However, these policies have revealed shortcomings such as deficiency in regulation methods and insufficient integration of regulation with technologies. This study applies evolutionary game theory (EGT) to evaluate the impacts of different environmental regulatory policies on the digital transformation and verifies the effectiveness of the theory in policy optimization. Utilizing modified real-world data, the study quantitatively examines the effects of alterations in various parameter combinations on players' strategic choices. The results indicate that: (1) increasing the regulatory intensity above 0.8, the carbon tax rate and penalties can promote the digital transformation, stimulating the low-carbon development in energy sector; (2) an interesting finding is that tax incentives such as carbon tax refund ratio can exacerbate enterprises’ reliance on government compensation, thereby slowing down their transition process; (3) this study highlights the optimal service provision intensity for technological service providers (TSP) is 0.9, which can expedite system evolution towards the ideal state and foster the construction of a favorable digital regulatory environment. The study provides valuable references for optimizing regulatory policies and promoting digital transformation to realize the decarbonization goal.