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.

The effective subsidy policies for new energy vehicles considering both supply and demand sides and their influence mechanisms: An analytical perspective from the network-based evolutionary game.

The development of new energy vehicles (NEVs) cannot be separated from the support of subsidy policies. However, the effectiveness of different subsidy policies remains to be verified. To investigate a more effective way of NEV subsidy and maximize the effect of subsidy policies, this study proposes two subsidy strategies, namely, consistent subsidy and adaptive subsidy, and constructs a network-based evolutionary game model for NEV diffusion. The effects of different subsidy policies are then comprehensively evaluated from the supply and demand sides, and their internal influence mechanisms are further investigated. Results show that: 1) from the supply side, subsidy for both policy achieves the highest NEV diffusion, but subsidy for enterprises is more efficient; 2) from the demand side, NEV diffusion increases NEV sales in the same proportion. Surprisingly, the increase in NEV diffusion rate benefits traditional vehicle manufacturers by expanding their average market demand; 3) from the cost-benefit analysis, the adaptive subsidy is more efficient than consistent subsidy; 4) The higher the initial benefits of NEV enterprises, the higher the level of NEV diffusion. The government should implement the adaptive subsidy and focus on providing subsidies to NEV enterprises to increase the NEV diffusion rate and achieve efficient resource allocation.

A data-driven group emergency decision-making method based on interval-valued intuitionistic hesitant fuzzy sets and its application in COVID-19 pandemic.

The outbreak of Corona Virus Disease 2019 (COVID-19) makes people more concerned about the validity and timeliness of emergency decision making. When an emergency occurs, it is difficult for decision makers (DMs) to give accurate assessment information in the early stage due to the urgency of time, the incompleteness of information, and the limitations of DMs' cognition and knowledge. Hence, we use interval-valued intuitionistic hesitant fuzzy sets rather than exact numbers to better characterize the fuzziness and uncertainty of emergencies. In addition, the Internet has become a major platform for the public to express their opinions or concerns, so we can collect the user-generated content on social media to help DMs determine appropriate emergency decision-making criteria which are the premise and basis of scientific decisions. However, there is likely to be some correlation between the obtained criteria. To this end, we first extend the Bonferroni mean (BM) operator to the interval-valued intuitionistic hesitant fuzzy environment, and propose three interval-valued intuitionistic hesitant fuzzy BM operators to capture the interrelation of fuzzy input variables, including an interval-valued intuitionistic hesitant fuzzy BM operator, a simplified interval-valued intuitionistic hesitant fuzzy BM operator, and a simplified interval-valued intuitionistic hesitant fuzzy weighted BM (SIVIHFWBM) operator. Then, a new group emergency decision-making method based on the SIVIHFWBM operator and social media data is proposed, and the specific steps of ranking all emergency plans are put forward. Moreover, our method is applied to evaluate emergency plans for the prevention and control of COVID-19. Finally, the effectiveness and feasibility of the method are verified by the sensitivity analysis, validity test, and comparative analysis.