An Evolutionary Game Study of Brownfield Pollution Governance Under Uncertain Media Supervision

Effective remediation of contaminated brownfields requires coordinated decisions among regulators, developers, and communities; however, existing environmental engineering research lacks a framework to capture how uncertaint y in media supervision influences multistakeholder behavioral d y namics. To address this gap, this study develops a tripartite evolutionar y game model that links supervision uncertainty, incentive and penalty structures, and stakeholder behavior in brownfield pollution governance. Policy-based social network analysis identifies key actors and provides evidence for model development. Probabilistic media supervision is modeled as an external factor influencing reputational risks and regulatory responses. Numerical simulations spanning the latent, development, and recovery stages of public opinion, along with sensitivity analyses of key parameters, are used to investigate how governance conditions influence the evolution of cooperative remediation. Results indicate that limited transparency and weak institutional pressure push the s y stem toward a passive and low-efficiency equilibrium. Moderate and time-varying media supervision promotes early regulator y action, increases resident engagement, and slows the decline in cooperative behavior. Performance-linked subsidies and differentiated compensation help sustain remediation efforts, whereas penalties generate rapid but short-lived deterrence. These findings clarify how reputational mechanisms and incentive structures influence remediation performance, highlighting the importance of maintaining transparency, aligning subsidies with verifiable progress, and sustaining enforcement to prevent strategic backsliding. Although parameterized in the Chinese context, the model is adaptable to diverse regulatory settings and serves as a transferable tool for managing contaminated sites under uncertainty. By incorporating uncertain media supervision into a multistakeholder evolutionary framework, this study advances decision support for contaminated-site governance in environmental engineering practice.