## 2 Literature Review
However, most of the previous studies only considered the application of intelligent technologies by enterprises during the COVID-19 pandemic to solve the problems of supply chain disruptions and production shutdowns, and few scholars have considered the problem of productive pollution by smart supply chain enterprises. With the development concept of the circular economy, the transformation to green supply chains has become an inevitable trend of supply chain development, and green supply chain management can effectively alleviate the pollution problem of enterprises. In recent years, the application of game theory to study green supply chain management is a current research hotspot. [PERSON] and [PERSON] [34] analyzed the main influencing factors of green behavior of supply chain enterprises by constructing a game model of green behavior of supply chain enterprises. [PERSON] et al. [35] applied the evolutionary game model to study a two-level green supply chain consisting of green suppliers and green manufacturers, and they analyzed various internal and external factors affecting the emission reduction behaviors of both sides of the game. [PERSON] et al. [36] used a game model to analyze the optimal innovation strategy choice of a recycling supply chain among two innovation paths: green autonomous innovation and green imitation innovation. [PERSON] et al. [37] constructed a game-theoretic model to study the effect of social preferences on supply chain management performance. Some other scholars have studied the evolutionary game process among the participants of corporate pollution events based on green supply chain management. For example, [PERSON] et al. [38] used evolutionary game theory to construct an evolutionary game model of green operation model of a pig supply chain, and they analyzed the evolutionary path and influence mechanism of a green operation model of the pig supply chain for the problems of environmental surface source pollution brought by the pig farming industry in China. [PERSON] et al. [39] studied sustainability theory in combination with supply chain management and coordination in order to ensure the sustainability of ecosystems, constructing an evolutionary game approach that seeks synergistic alliances between environmental and commercial benefits by establishing coordination between producers and retailers to judge their strategies for triggering green practices. [PERSON] et al. [40] modeled the contrast between government goals and producer goals using two swarm evolutionary game theory approaches to study government policies and the implementation of incentives to influence producers' pollution activities. From the above study, we found that the implementation of smart supply chain and green supply chain management can be a good solution to the imbalance of supply and demand in the market of essential goods and the pollution problem of enterprises during the COVID-19 pandemic, which provides the theoretical support for this paper to select the enterprise type of green smart supply chain enterprises. However, most scholars have studied the evolutionary process among participants in enterprise pollution events in non-emergency situations,and fewer studies have studied the evolution of participants in enterprise pollution events in emergency situations such as the COVID-19 pandemic. In addition, few scholars have considered the game process among participants in enterprise pollution events in both public panic buying and green smart supply chains. To summarize the above, existing studies can provide some theoretical and methodological support for the study of public panic buying and enterprise pollution problems during the COVID-19 pandemic. However, there are some shortcomings. (1) For the study of panic buying, on the one hand, most previous studies lacked a consideration of quantitative models of subjective perceived behavior as a finite rational public [16; 17; 18; 19]. In recent years, prospect theory has been mostly used to portray the public's perceived value of items [24; 25].