Social "win-win" promotion of green housing under the four-subject evolutionary game

The development of green housing in China has been primarily driven by the government in a top-down manner. To ensure successful promotion and maximize economic benefits, it is crucial for government departments to accurately understand the payment behavior of residents, the key demand-side participants in China's green housing market. Additionally, realtors stand to benefit from better understanding residents' preferences and employing more accurate marketing strategies, leading to mutually beneficial outcomes for the economy, environment, and society. This study incorporates the main stakeholders of green housing (government, realtors, and residents) into a four-party evolutionary game model, introducing a "virtual game party" (virtual government) under different policy scenarios: no policy, incentive policy, and mandatory policy. The Matlab simulation results indicate that the greater the level of neglect by the government, the more inclined it becomes to adopt incentive or mandatory policies. As the government's penalties become more stringent, real estate developers are more likely to choose to engage in the development and construction of green housing. This, in turn, influences the decisions of the government and residents. Higher government subsidies lead to a greater likelihood of real estate developers choosing to develop green housing, and residents are more inclined to opt for paying for green housing. This also encourages the government to promote green housing initiatives. When the subsidy coefficient offered by real estate developers increases, their likelihood of choosing to develop green housing grows, but at the same time, residents become less likely to opt for paying for green housing. A higher trust coefficient leads to residents being more likely to choose to purchase green housing, which further drives the government and real estate developers to construct and promote green housing. Finally, the study provides corresponding policy recommendations based on the research findings.