5.1.1. Productivity Effect

In this section, we will examine whether the city cluster improves city productivity through the city scale effect, which reduces carbon emissions. Based on the article of Chen et al. (2022) [30], we measure an index of the city's total factor productivity (TFP) and examine whether the city cluster has an impact on city productivity based on model (3).

$$\text{TFP}\_{\mathsf{c},\mathsf{t}} = \mathsf{x} + \mathsf{\beta} \times \text{Treat}\_{\mathsf{c}} \times \text{Post}\_{\mathsf{t}} + \mathsf{\mathcal{Q}} \times \text{Control}\_{\mathsf{c},\mathsf{t}} + \delta\_{\mathsf{c}} + \mu\_{\mathsf{t}} + \varepsilon\_{\mathsf{c},\mathsf{t}} \tag{3}$$

The empirical results are shown in Column (1) of Table 6. The core explanatory variable Treat\*Post is significantly positively correlated with the explained variable TFP at the confidence level of 5%. The TFP level of the city increased by 22% after cities were classified as the city cluster. Compared to other cities, China's Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei region have more advanced infrastructure development, providing a more favorable environment for the flow of production factors. This urban network further creates a scale effect and promotes urban productivity.


**Table 6.** Mechanism analysis of the effect of city cluster policy on CO2 emissions.

Note: *t* statistics are shown in parentheses; \*\*\*, \*\*, and \* represent significance at the 1%, 5%, and 10% levels, respectively.

#### 5.1.2. Technological Innovation Effect

In this section, we will examine whether the city cluster enhances city innovation, which reduces carbon emissions, through city knowledge spillovers. Therefore, based on the article of Du et al. (2021) and Lyu et al. (2019) [31,32], we take city R&D input as a proxy variable of city innovation and will examine whether the city cluster has an impact on city innovation based on model (4).

$$\text{LnRD}\_{\text{c,t}} = \alpha + \beta\_{\text{t}} \times \text{Treat}\_{\text{c}} \times \text{Post}\_{\text{t}} + \mathcal{Q} \times \text{Control}\_{\text{c,t}} + \delta\_{\text{c}} + \mu\_{\text{t}} + \varepsilon\_{\text{c,t}} \tag{4}$$

The empirical results are shown in Column (2) of Table 6. It can be seen that the core explanatory variable Treat\*Post is significantly positively correlated with the explained variable LnRD at the confidence level of 5%. The city R&D input increased by 61.1% after cities were classified as the city cluster. It can be seen that the flow of production factors brought by the city cluster does significantly enhance the knowledge spillover

effect, and cross-regional knowledge spillover creates favorable conditions for improving innovation efficiency.

## 5.1.3. Industrial Structure Optimization

In this section, we will examine whether the city cluster improves city industrial structure upgrading by reducing the proportion of secondary industries, which reduces carbon emissions. Therefore, based on the article of Liu et al. (2021) [33], we measured an index of the rationalization of industrial structure, which can reflect the coupling degree of the element inputs and outputs.

Formula (5) will be used to measure the rationalization degree of industrial structure.

$$\text{ISO}\_{\text{i,t}} = \sum\_{\text{j=1}}^{3} \frac{\text{Y}\_{\text{i\text{jt}}}}{\text{Y}\_{\text{it}}} \ln \left( \frac{\text{Y}\_{\text{i\text{jt}}}}{\text{Y}\_{\text{it}}} / \frac{\text{l}\_{\text{i\text{jt}}}}{\text{L}\_{\text{it}}} \right) \tag{5}$$

where i is the city, t is the year, and j is the industry. Variable yijt indicates the carbon emissions of the industry j in city i and year t. Variable Yit indicates the gross of the industry of city c in year t. Variable lijt indicates the number of employees of the industry j in city i and year t. Variable Lit indicates the total number of employees of city c in year t. Obviously, the closer ISO is to 0, the higher the coupling degree between the allocation and output ratio of employees in the three industries is and the more reasonable the industrial structure is. On the contrary, the industrial structure is unreasonable.

We will examine whether the city cluster has an impact on city industrial structure upgrading based on model (6).

$$\text{ISCO}\_{\text{C},\text{t}} = \alpha + \beta\_{\text{t}} \times \text{Treat}\_{\text{C}} \times \text{Post}\_{\text{t}} + \mathcal{O} \times \text{Control}\_{\text{c,t}} + \delta\_{\text{c}} + \mu\_{\text{t}} + \varepsilon\_{\text{c,t}} \tag{6}$$

The empirical results are shown in Column (3) of Table 6. It can be seen that the core explanatory variable Treat\*Post is significantly negatively correlated with the explained variable ISO at the confidence level of 10%. The ISO index decreased by 0.6% after cities were classified as the city cluster. Therefore, the optimization of a city system stemming from the development of the city cluster will promote the industrial structure upgrading.
