*3.2. Empirical Design*

The purpose of this study is to examine the effect of city cluster policy on CO2 emissions. As a classical method for policy evaluation, the difference-in-differences (DID) model has been widely adopted by most scholars, and we also use this method. This method can examine the difference in CO2 emissions before and after the city cluster policy implementation, and assess the average effect of city cluster policy on carbon emissions. The benchmark model is as Formula (1).

$$\text{LnCO}\_{2\text{c},\text{t}} = \alpha + \beta \times \text{Treat}\_{\text{c}} \times \text{Post}\_{\text{t}} + \mathcal{Q} \times \text{Control}\_{\text{c},\text{t}} + \delta\_{\text{c}} + \mu\_{\text{t}} + \varepsilon\_{\text{c},\text{t}} \tag{1}$$

where c is the city and t is the year. Independent variable LnCO2c,t indicates the carbon emissions of city c in year t. Our dependent variable is the city cluster policy (Treat\*Post). The coefficient on Treat\*Post, β, is the one with the main interest. Thus, β reflects the impact of the city cluster policy on carbon emissions. A negative β implies that a city cluster policy will reduce carbon emissions in cities. Control is our control variable. δ<sup>c</sup> and μ<sup>t</sup> are city-fixed effect and year-fixed effect, respectively, and εc,t is a random error term.

#### *3.3. Variables*
