*3.3. DID Model Baseline Analysis*

The analysis of the DID model was mainly carried out by observing the influence of DID variables. To avoid multicollinearity problems, the grouping variable group and the policy implementation variable post were not used here. We used four models constructed with different dependent variables, and the fit effect of the model was at the normal level, except for Model 2, in which the R<sup>2</sup> was around 0.2 to 0.3. In terms of variable action, the control variables we selected had obvious effects on the dependent variables, in which the variable temp and the variable speed had stable negative effects—that is, the higher temperature, the faster the speed, and the better the air quality. However, the action mechanism of the variable humidity was relatively complex, with significant positive effects on PM2.5 and CO, but with negative effects on PM10 and SO2. However, our core variable DID had significant negative effects in all four models; that is, the implementation of the policy improved the ambient air quality. Considering that the range of values of different indicators varies, it is normal that the coefficient size varies greatly, and it is more meaningful for us to compare the significance of the variables. From the significance level, the policy affected SO2 and PM10 greatly, while the effects on PM2.5 and CO were relatively weak. The results are shown in Table 7.




**Table 7.** *Cont.*

t-statistics in parentheses. \*\*\* *p* < 0.01, \*\* *p* < 0.05, \* *p* < 0.1.
