Evolutionary game analysis of chemical enterprises' emergency management investment decision under dynamic reward and punishment mechanism

The safe operation and management of chemical enterprises remains a critical area of focus, and encouraging companies to proactively invest in emergency management is a significant practical challenge for government supervision. In this study, we combine prospect theory, mental accounting, and evolutionary game theory to establish a bounded-rationality hypothetical model for all actors (enterprises, government, and neighboring residents) involved in the emergency management investments of chemical enterprises. We analyze the evolutionary stable strategies for government departments, neighboring residents, and enterprises under static and three dynamic reward and punishment mechanisms, followed by a simulation analysis. Our findings indicate that: (1) the emergency management investment decisions of chemical enterprises are influenced by multiple factors, not only shaped by external elements but also governed by the decision-makers' own competency factors; (2) the application of a static reward and punishment mechanism by government departments, in the absence of evolutionary stable strategies, fails to exert a substantial restraining effect on enterprises; (3) the adoption of a dynamic reward and punishment mechanism effectively compensates for the limitations of static mechanisms, with the dynamic reward and dynamic punishment mechanism proving to be the optimal choice, outperforming other alternatives.