3.3.1. Independent Variables

Our independent variable consists of city-level CO2 emissions (LnCO2). Unfortunately, only provincial-level CO2 data and county-level CO2 data are available in the Carbon Emission Accounts & Database. Meanwhile, we have also noticed that county-level CO2 is measured by light intensity, not real CO2 emissions. Given such two dimensions of CO2 data structure, this paper uses two methods to construct city-level CO2 emissions. One method uses county-level CO2 emission data directly added to the city level [26]. Another method uses county-level CO2 data to calculate the proportion of each city in its province. Based on this proportion, provincial-level CO2 is allocated to each city by this proportion weight, and the weighted city CO2 is constructed. We would use weighted city

CO2 emissions from the second method in the benchmark regression. We also use the CO2 emissions from the first method in the robustness test.

#### 3.3.2. Dependent Variables

Our dependent variable is the city cluster policy (Treat\*Post). It is an interaction item between Treat and Post. The Treat variable equals one in the city cluster list. The city cluster list in the Yangtze River economic belt contains the Chengyu city cluster (Nation), the Dianzhong city cluster (Region), the Yangtze River city cluster (World), the Yangtze middle river city cluster (Nation), the Qianzhong city cluster(Region), and zero otherwise. Post equals one in a year that is equal to or larger than 2011, and zero otherwise.
