## 5 Numerical Simulations
### Effects of Parameter Changes on Evolutionary Stable Strategy Combinations  
1.1 Transformation from Steady State \(O_{4}(0,0,1)\) to Ideal Steady State \(O_{6}(1,0,1)\) and Sensitivity Analysis of Related Parameters  
The parameters are set as follows when Scenario 3(a) is satisfied (the parameter satisfies the \(O_{4}\) equilibrium point condition):\(R_{S}^{G}=3.5,C_{S}^{G}=6,R_{SS}=3,V_{AB}=6,V_{PB}=8,C_{S}=10\)\(C_{G}=15,\omega_{1}=0.6,C_{P}=5,C_{G}^{G}=10,R_{U}=12,R_{-U}=15,R_{G}=9\). The initial parameter settings satisfy the following three conditions mentioned in Scenario 3(a): \((1-\omega_{1})V_{PB}\)\(<\)\(C_{P}\), \(R_{S}^{G}+C_{G}\)\(<\)\(R_{U}\) and \(R_{S}^{G}+C_{S}^{G}\)\(<\)\(C_{S}\). Its stabilization condition from the equilibrium condition of Scenario 3(a) to the ideal equilibrium point can be achieved by adjusting the parameters. (1) Considering the effect of \(R_{S}^{G}\), realizing the transition from \(O_{4}(0,0,1)\) to \(O_{6}(1,0,1)\), and studying the effect of \(R_{S}^{G}\) on the evolution of each subject's strategy, at this point, keeping the other parameters constant and letting \(R_{S}^{G}=\{3.5,5.5:8\}\), we obtain Figure 5. (2) Considering the effect of \(C_{S}^{G}\), realizing the transition from \(O_{4}(0,0,1)\) to \(O_{6}(1,0,1)\), and studying the effect of \(C_{S}^{G}\) on the evolution of each subject's strategy, at this point, keeping the other parameters constant and letting \(C_{S}^{G}=\{6,6.6,8\}\), we obtain Figure 6.  
As can be seen from Table 4, from satisfying Condition 2 to satisfying Condition 3, \(R_{S}^{G}\uparrow\) or \(C_{S}^{G}\uparrow\) can be transformed from stable state \(O_{4}(0,0,1)\) to ideal stable state \(O_{6}(1,0,1)\), and the evolutionary stability strategy of green smart supply chain enterprises is from \(x=0\) to \(x=1\), as shown in Figures 5 and 6. It is worth noting that the meanings of the abbreviations in the graphs are as follows: SSCE is the abbreviation for green smart supply chain enterprises, and lg is the abbreviation for local governments. The meanings in Figures 5-14 are the same. From Figure 5, it can be seen that the low subsidy given by the local government to green smart supply chain enterprises will cause them to choose to respond negatively, while the lack of relief from high pollution prevention funds will also inhibit them from choosing to respond positively. With the increase in subsidies for positive responses, the strategy of green smart supply chain enterprises will change from passive response to active response, and at the same time, it can speed up the evolution of their choice to the actively respond strategy, prompting green smart supply chain enterprises to actively respond in the early stage of the COVID-19 pandemic. However, the excessive subsidies have caused fluctuations in the strategies of local governments, and the decisions of supply chain enterprises change with those of the local governments. In reality, in order to effectively  
Figure 5: The influence of \(R_{S}^{G}\) on the evolution of each subject’s strategy. Figure 6: The influence of \(C_{S}^{G}\) on the evolution of each subject’s strategy. manage the problems arising from the COVID-19 pandemic, local governments often need to take various emergency measures, and excessive financial subsidies will lead to high fiscal deficits of local governments, making it difficult to make decisions that are conducive to epidemic prevention and control due to financial pressure. For relaxed supervision, as the local government's response weakens, some green smart supply chain enterprises have the opportunity to speculate and change their response measures, leading to fluctuations in the strategies of green smart supply chain enterprises. Meanwhile, it further demonstrates the influence of \(R_{S}^{G}\) in Propositions 1 and 5 on the strategies of local governments and green smart supply chain enterprises.