A system dynamics model integrated evolutionary game theory and hybrid appraisal mechanisms for industrial pollution control

Effectively controlling industrial pollution is a critical global challenge, yet the strategic interaction between local and central governments often leads to unstable or unpredictable policy enforcement, creating significant difficulties for long-term environmental governance. In this study, we developed an evolutionary game model within a system dynamics (SD) framework to analyze the strategic behaviors of local and central governments. We proposed and compared three mechanisms, the assessment mechanism for ecological progress (AMFEP), the ecological governance supervision and punishment mechanism (EGSPM), and a hybrid model combining both, to stabilize the evolutionary path. Simulation results demonstrated that while the basic model and individual mechanisms often failed to converge, the hybrid model successfully drove the system to a stable equilibrium, increasing the probability of local governments implementing environmental regulation strategies from 0.2 to 0.167. This finding indicated that a combined appraisal mechanism is significantly more effective in guiding local governments toward proactive environmental enforcement while central governments maintain a lighter supervisory role, offering a robust and practical policy tool for sustainable industrial pollution management.