**3. Result**

## *3.1. Basic Result Analysis*

We take the AQI as an example to make the trend change map of environmental indicators (Figure 2). In the long term, the control group has a slight downward trend, but the trend is relatively limited. The experimental group was affected by the policy, so the change range is large, especially in the second year affected by the policy; in 2019, the AQI had a significant decline, reflecting the impact effect of the policy. Despite the subsequent rebound, it was still lower than the 2018 level and also significantly lower than the control sample level of that year.

**Figure 2.** The AQI time-trend.

If the time range is set from 2017–2019, one year before and after the policy occurred, the results of Table 2 are as follows. There was a significant downward trend before the policy, in which the average value of the experimental group was −1.659 and the average value of the control group was 0.820. The range of decline in the experimental group was significantly greater than that of the control group, reaching twice that of the control group, reflecting the impact of the policy implementation.

For the specific impact of policy implementation on the four dependent variables, we used an independent sample *t*-test method. The *t*-test tests whether the variable is significant over all periods. The first was for the whole sample, including the experimental and control groups. From the results of the *t*-test (Table 3), the *p*-value of the four indicators was less than 0.05, and the sample with a group value of 1 after implementation was less than the sample before implementation, which shows that the policy implementation reduced the index value and had significant differences. Secondly, a further *t*-test was performed for the samples from the experimental group (Table 4). For the experimental group samples, the conclusion did not differ from the overall sample, and the samples

after the implementation decreased significantly compared with the samples before the implementation. Moreover, it was more obvious in the first two indicators, with PM2.5 decreasing by 11.37 and 14.9 in the experimental group. In the PM10 index, the overall sample decreased by 36.44, and the experimental group sample decreased by 41.57. Therefore, the impact of the policy is obvious and significant.


**Table 2.** The change values of different cities before and after the policies.

**Table 3.** *T*-test results for the whole sample.


**Table 4.** *T* test results of the experimental group.


#### *3.2. PSM Model Results*

The role of PSM is to reduce endogeneity problems and reduce the bias between samples. Here we take group as the grouping variable, *speed* and *temp* among the control variables as the characteristic variables, and AQI as the output variable for PSM processing (Table 5).


**Table 5.** Changes in the sample size before and after PSM.

One might ask, why is the humidity variable not included? The first reason is that the two grouped samples were so close in *humidity* that the bias itself could be considered small. The second reason was that, after our attempt to add the variable of *humidity*, we found that the bias between samples did not shrink but increased, so joining became meaningless. From the PSM results (Table 6), we used variables *speed* and *temp* as conditions for sample matching, and after the last matching samples, the bias was reduced by 31.8% and 90.5%, respectively, which was successful and effective from the purpose perspective.


