Evolutionary Game and Simulation Analysis of Production Safety Regulation in Chemical Enterprises

Safety supervision is identified as a crucial tool for encouraging safe production within chemical enterprises, yet the existing safety supervision methods often struggle to deter unsafe behaviors, leaving these enterprises susceptible to safety accidents. The current literature, predominantly based on evolutionary game theory, largely focuses on optimizing supervision methods while lacking effective guidance for enterprises to ensure rule compliance. Furthermore, this research predominantly centers on the analysis of two key stakeholders using static reward and punishment strategies, neglecting other potential participants and dynamic reward and punishment strategies. To address these gaps, this paper introduces an evolutionary game model encompassing the three primary stakeholders in chemical production safety supervision: government regulators, chemical enterprises, and employees. The study assesses the stability of these three subjects under static reward and punishment strategies, dynamic punishment strategies, and dynamic reward and punishment strategies. In conjunction with the system dynamics model, numerical simulations are utilized to analyze shifts in stakeholders' decision-making behavior across different scenarios. Simulation results show that, under the static mechanism, there is no evolutionary equilibrium solution for the three-game subjects. While increasing reward and punishment coefficients can temporarily enhance enterprise compliance, it also escalates system volatility. The linear dynamic punishment mechanism can mitigate subject volatility but does not yield optimal evolutionary results. Finally, a novel nonlinear dynamic punishment-reward mechanism is proposed, effectively controlling the instability within the game scenario and making compliant production the optimal strategic choice for chemical enterprises.