Analysis of multi-agent greenwashing governance in China: A stochastic evolutionary game perspective

The greenwashing behaviors (GWBs) in contemporary enterprises exhibit characteristics of widespread prevalence, low concentration, and skilled concealment, rendering traditional governance models gradually ineffective. This challenge is compounded when local governments, driven by economic pressures or political considerations, choose to ignore GWBs during the territorial supervision process. Accordingly, this study constructs a tripartite game model—considering both scenarios with and without central government regulation—based on the strategic interactions between local governments, enterprises, and the public. Findings indicate: (1) Public participation in supervision (PPS) is crucial for curbing GWBs, as high PPS levels can unravel collusion between local governments and enterprises; (2) Higher environmental political incentives tend to reduce local government supervision of GWBs; (3) The central government's adoption of an innovative punishment-reward mechanism combining static and dynamic elements, can help maintain the governance system in an ideally stable state; (4) The robustness of effective GWBs governance endures amid stochastic perturbations when establishing a rational multi-agent cooperative regulatory framework, with enterprises' strategic choices being less affected by such random disturbances. Our model, by incorporating real-world scenarios of public exposure to corporate GWBs, better adapts to the regulatory dilemma. It provides a low-cost and feasible solution to address greenwashing issues in China.