*4.3. Dynamic Effect of the Coal to Gas and Electricity Policy*

For the test of dynamic effect, the main design ideas refer to Zhu et al. [29]. The biggest difference is the different data dimensions. This study by Zhu et al. used daily data as the analysis data, while our study used weekly data as the main data. Autumn and winter were also used by Zhu et al. [29]. as the policy implementation cycle, specifically from the fortieth week to the tenth week of the second year. That model is different from the DID model of our subject. The Zhu et al. subject model belongs to the time-invariant DID model, while our model belongs to the time-varying DID model, that is, DID in which the individual's policy implementation status will change within a year. In terms of test methods, the *DID* variable and dummy variable forms were also used. As shown in Table 10, the *week1*, and *week2* variables represent the first two weeks, and the *week3* and *week4* variables represent the last two weeks.


**Table 10.** Dynamic test result.

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

From the results (Table 10), the *DID* variable still had a significant negative effect on PM10 and SO2, while the effect of the other two indicators was not significant. As for the dynamic effect we are concerned about, the dummy variables in the first two weeks all had a significant negative effect, proving that the policy had a strong impact in the initial stage. The effect in the last two weeks was very weak, and only in SO2 was there still a significant negative effect. This shows that at the end of the implementation of the policy, or near the end of the stage, its effect was greatly reduced, and it was far less obvious than in the initial stage of the policy.
