Evolutionary dynamics of homeowners' energy-efficiency retrofit decision-making in complex network.

Understanding homeowners' energy-efficiency retrofit (EER) decision-making is a critical priority for reducing the adverse environmental impacts of the building sector and promoting a sustainable consumption transition. Existing research lacks attention to the dynamics and social interactions in the decision-making process of homeowner EER adoption. This paper applies the complex network-based evolutionary game approach with agent-based modeling to construct an evolutionary dynamics model for homeowners' EER adoption decision-making. Through simulation experiments, this paper examines the effects of various key factors, including government incentives, retrofit costs, retrofit uncertainty, and network size, on the evolution of EER adoption. The results suggest that government incentives facilitate EER adoption, but their effects require a sufficiently long period of policy implementation and extensive social interaction to be realized. Reducing retrofit costs is a robust and effective way to encourage EER adoption, especially when uncertainty is high. Retrofit uncertainty has a significant impact on the adoption evolution. Increased uncertainty can hinder adoption decisions. In particular, the combination of high uncertainty and incentives is prone to lead to incentive failure. The increase in network size contributes to EER adoption, but attention needs to be paid to the impact of potential incentive redundancy in large-scale networks.

Exploring the coevolution of residents and recyclers in household solid waste recycling: Evolutionary dynamics on a two-layer heterogeneous social network.

China's household solid waste recycling system has long faced the challenge of recycling formalization. This process is affected by the complexity of the interdependence of the recycler and resident decisions. The aim of this study was to gain a better understanding of the coevolution of residents' and recyclers' recycling decisions. To this end, this study applied agent-based modeling and network-based evolutionary game methods to construct evolutionary game models based on a two-layer social network. The two layers of heterogeneous social networks depicted the connections between residents and recyclers, respectively. Residents and recyclers choose either formal or informal recycling strategies within layers according to evolutionary game theory. Waste flows and cash flows underlie the interdependent coevolution between the two layers. Using this model, the effects of resident subsidies, recycler subsidies, and regulatory policies on the coevolution of residents and recyclers were simulated. The results showed that the impact of policies on recycling systems relies on long-term social interaction. The trends in the strategic evolution of residents and recyclers were similar, and emerged from their interdependence and mutual influence. Resident-oriented subsidy incentives can promote formal recycling stably and positively. However, recycler subsidies and regulatory policies have opposite marginal promotion effects, as is reflected by the fact that the evolution of formal recycling is insensitive to high recycler subsidies and sensitive to high-intensity regulatory policies. These findings provide a more comprehensive insight into the development of recycling systems and inform the design of waste management policies.