Collaborative Supervision for Sustainable Governance of the Prepared Food Industry in China: An Evolutionary Game and Markov Chain Approach

The rapid growth of China’s prepared food industry has created new opportunities for industrial upgrading, but it has also intensified concerns regarding product quality, supervision gaps, and the long-term sustainability of governance structures. In response to these challenges, this study develops a tripartite evolutionary game model involving local governments, enterprises, and consumers, and further integrates a Markov chain framework to capture stochastic disturbances and long-run state transitions. This dynamic–stochastic modeling approach enables an examination of how collaborative supervision evolves under varying regulatory intensities, compliance costs, and consumer reporting costs. The results show that (1) multi-actor collaborative supervision substantially increases firms’ incentives to operate honestly and reinforces positive feedback loops between regulators and consumers; (2) excessive regulatory, compliance, or reporting costs weaken system stability and reduce policy effectiveness; (3) aligning regulatory intensity with penalty mechanisms accelerates the system’s convergence toward a stable equilibrium, balancing industrial development with food safety objectives; and (4) Markov chain simulations confirm the robustness and long-term stationarity of the governance system. Overall, this study provides a dynamic and evidence-based framework for designing sustainable and resilient regulatory mechanisms in the prepared food industry. The findings offer practical guidance for advancing Sustainable Development Goals (SDGs) 3, 12, and 16 through improved food safety, responsible production, and stronger institutional governance.