*4.2. Parallel Trend Analysis*

A parallel trend is a prerequisite for the DID model. It means that there is no systematic difference in carbon emission trends between the two groups before the policy, or, even if there are differences, the differences are fixed. Therefore, we followed Li et al. (2016) [27] and Zhu and Xu (2022) [5], and constructed our model as:

$$\text{LnCO}\_{2\text{c},t} = \alpha + \beta\_{\text{t}} \times \text{Treat} \times \text{D}^{\text{léar}(\text{t})} + \mathcal{Q} \times \text{Control}\_{\text{c},\text{t}} + \delta\_{\text{c}} + \mu\_{\text{t}} + \varepsilon\_{\text{c},\text{t}} \tag{2}$$

DYear(t) is year dummy variables, and it is equal to 1 when year is t. For example, D2006 is equal to 1 when year is 2006, and 0 otherwise. Therefore, the parameters of β<sup>t</sup> identify <sup>t</sup> year policy effects. To avoid Treat <sup>×</sup> DYear(t) collinearity, we use the policy year (i.e., 2011) as the base year. The estimation results are presented in Figure 2. We can see that there is no pre-policy effect (before 2011), indicating that our identification satisfies the parallel trend assumption. Furthermore, the policy has a strong continuity effect.

**Figure 2.** Dynamic effects of the City Cluster.
