*4.1. Parallel Trend Test*

The hypothesis of parallel trends is that there was no significant difference between the control and experimental groups before the policy occurred. After the policy implementation, there were significant differences between the experimental group and the control group. We here constructed the dummy variables using the dates of the first three events and tested them. Because the previous period is too close to the policy, the results of the previous three and two periods prevailed, namely the before\_3 and before\_2 variables in our results. From the results (Table 8), the before\_3 variable had a nonsignificant negative effect in all four models, while the direction of before\_2 was positive and negative, but overall, the effect was not significant, so the four dependent variables passed the parallel trend test. From the trend of change (Figure 3), there was indeed a downward trend from before\_1 to after 2, which also proves the effectiveness of the policy.


**Table 8.** Parallel trend test results.

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

#### *4.2. Placebo Test*

The idea for the placebo test in this study was to advance the policy implementation date by one year, until 1 January 2017, and to re-run the DID model (These four models are the did models constructed with the four indicators of PM2.5, PM10, CO and SO2 as the dependent variables.). Based on the results, the effect of DID was no longer significant in the PM2.5 and CO models. However, PM10 still has a significant negative effect in SO2, which is not much different from the original model (Table 9). This indicates that the two measures of PM2.5 and CO passed the placebo test, while PM10 and SO2 failed the placebo test, but does not mean that the policy has no effect on the latter two. In previous analyses, namely in parallel trend tests and DID models, we concluded that policy does

have a significant impact on environmental indicators. Combined with the analysis results of the trend chart part, we can believe that the ambient air index itself has a downward trend, which is not caused by a separate policy, but the result of a series of relevant policies and institutional provisions. The policy of this study has a relatively more obvious effect on PM2.5 and CO, while accelerating and promoting the effect on PM10 and SO2, that is, accelerating the existing downward trend.

**Figure 3.** Coefficient trend change.


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