How to promote municipal household waste management by waste classification and recycling? A stochastic tripartite evolutionary game analysis.

Municipal household waste management (MHWM) is crucial for urban governance and sustainable development. Currently, Chinese governments at all levels are making great effort to implement MHWM through waste classification and recycling (WCR). However, the major participants in WCR, such as urban residents, property management companies and government departments, may act in their self-interest and hinder the MHWM goals. Therefore, it has become an imperative initiative to promote MHWM by effectively coordinate their conflicts of interest. Considering that external factors with complexity and uncertainty may also affect participants' behaviors, we develop a stochastic tripartite evolutionary game to model behavioral interaction of the three participants. Then we derive theoretical results and simulate different scenarios to examine the effects of key factors on participants' strategy evolution. The results show that stochastic interference, cost reduction and rules simplification contribute to WCR of MHWM, and reward and performance improvement benefits have different incentive effects. Moreover, credit-based punishment and disclosure of non-compliance are more effective than financial-based penalty. To promote MHWM, policymakers should not only simplify classification rules, lower participation cost and enhance credit-based punishment system, but also encourage anonymous report and apply well-designed financial-based incentive and penalty.

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.

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.

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.

Promotion Policies for Electric Vehicle Diffusion in China Considering Dynamic Consumer Preferences: A Network-Based Evolutionary Analysis.

An improved understanding of how policies can promote the diffusion of electric vehicles (EVs) is critical to achieving sustainable development. Previous studies of EV diffusion dynamics have paid insufficient attention to consumer preferences. In this paper, a network-based evolutionary game model considering dynamic consumer preference is constructed to study EV diffusion. Through numerical experiments, the evolutionary processes and results of various promotion policies, including carbon taxes, production subsidies, purchase subsidies, and information policy on EV diffusion, are simulated. In particular, this paper explores the differentiated effects of supply-side policies and demand-side policies. The simulation results indicate that: (1) The effectiveness of promotion policies is sensitive to the size of the manufacturer network, and large networks can dampen periodical fluctuations in diffusion rates. (2) Supply-side carbon taxes and subsidies facilitate a steady diffusion of EVs. However, compared with the sustained effectiveness of subsidies, carbon taxes may inhibit the rapid penetration of EVs. (3) Implementing purchase subsidies in the early stages of diffusion is more effective than production subsidies, but the potential uncertainty of demand-side subsidies should be noted. (4) The impact of information policy on the evolutionary trend of EV diffusion is pronounced but is a longer-term impact, requiring a long enough implementation horizon.

Superdiffusion criteria on duplex networks.

Diffusion processes widely exist in nature. Some recent papers concerning diffusion processes focus their attention on multiplex networks. Superdiffusion, a phenomenon by which diffusion processes converge to equilibrium faster on multiplex networks than on single networks in isolation, may emerge because diffusion can occur both within and across layers. Some studies have shown that the emergence of superdiffusion depends on the topology of multiplex networks if the interlayer diffusion coefficient is large enough. This paper proposes some superdiffusion criteria relating to the Laplacian matrices of the two layers and provides a construction mechanism for generating a superdiffusible two-layered network. The method we proposed can be used to guide the discovery and construction of superdiffusible multiplex networks without calculating the second smallest Laplacian eigenvalues.

Study on Multi-Agent Evolutionary Game of Emergency Management of Public Health Emergencies Based on Dynamic Rewards and Punishments.

In the context of public health emergency management, it is worth studying ways to mobilize the enthusiasm of government, community, and residents. This paper adopts the method of combining evolutionary game and system dynamics to conduct a theoretical modeling and simulation analysis on the interactions of the behavioral strategies of the three participants. In response to opportunistic behavior and inadequate supervision in the static reward and punishment mechanism, we introduced a dynamic reward and punishment mechanism that considers changes in the social environment and the situation of epidemic prevention and control. This paper proves that the dynamic reward and punishment mechanism can effectively suppress the fluctuation problem in the evolutionary game process under static scenarios and achieve better supervision results through scenario analysis and simulation experiments.

A new SAIR model on complex networks for analysing the 2019 novel coronavirus (COVID-19).

Nowadays, the novel coronavirus (COVID-19) is spreading around the world and has attracted extremely wide public attention. From the beginning of the outbreak to now, there have been many mathematical models proposed to describe the spread of the pandemic, and most of them are established with the assumption that people contact with each other in a homogeneous pattern. However, owing to the difference of individuals in reality, social contact is usually heterogeneous, and the models on homogeneous networks cannot accurately describe the outbreak. Thus, we propose a susceptible-asymptomatic-infected-removed (SAIR) model on social networks to describe the spread of COVID-19 and analyse the outbreak based on the epidemic data of Wuhan from January 24 to March 2. Then, according to the results of the simulations, we discover that the measures that can curb the spread of COVID-19 include increasing the recovery rate and the removed rate, cutting off connections between symptomatically infected individuals and their neighbours, and cutting off connections between hub nodes and their neighbours. The feasible measures proposed in the paper are in fair agreement with the measures that the government took to suppress the outbreak. Furthermore, effective measures should be carried out immediately, otherwise the pandemic would spread more rapidly and last longer. In addition, we use the epidemic data of Wuhan from January 24 to March 2 to analyse the outbreak in the city and explain why the number of the infected rose in the early stage of the outbreak though a total lockdown was implemented. Moreover, besides the above measures, a feasible way to curb the spread of COVID-19 is to reduce the density of social networks, such as restricting mobility and decreasing in-person social contacts. This work provides a series of effective measures, which can facilitate the selection of appropriate approaches for controlling the spread of the COVID-19 pandemic to mitigate its adverse impact on people's livelihood, societies and economies.

Environmental Governance Cooperative Behavior among Enterprises with Reputation Effect Based on Complex Networks Evolutionary Game Model.

This paper first portrays the equilibrium payoff of enterprise's cooperation of environmental governance based on the Cournot model. Secondly, the evolutionary game model in complex networks is adopted to depict the evolution of environmental governance cooperative behavior among enterprises. Further, the evolutionary process of environmental governance cooperative behavior of enterprises is simulated considering the supervision behavior of government and the reputation evaluation behavior of environmental social organization. The results show that the cooperation level of enterprise group under self-organization condition will reach a low level; the supervision of government can enhance the cooperation level of enterprise group with high betrayal tempatation while it has limited effect on enterprise group with low betrayal tempatation. The reputation evaluation behavior of environmental social organization can realize reputation effect to improve the the cooperation level of enterprise group with high betrayal tempatation. The enhance of reputation sensitivity can optimize equilibrium distribution of reputation and it can strengthen the reputation effect on cooperation level. Based on the analysis above, the suggestions to effectively improve cooperation level are given.