# The Impact of Polycentric Structure on CO\({}_{2}\) Emissions: Evidence from China
## 4 Discussion  
Our primary empirical findings suggested that polycentric structures contributed to CO\({}_{2}\) abatement, aligning with previous studies that found increased CO\({}_{2}\) efficiency [11] and decreased CO\({}_{2}\) concentrations [4]. Monocentric structures, which partly represent resource agglomeration in cities, tend to exacerbate emissions [30]. In contrast, polycentric structures can mitigate agglomeration diseconomies, promote market integration, and optimize energy efficiency [10; 31], ultimately leading to CO\({}_{2}\) reduction. Our results build upon existing knowledge by using panel data from Chinese city regions and conducting comprehensive robustness tests. Green technology and energy consumption had been identified as potential mechanisms linking polycentric structures to CO\({}_{2}\) emissions. On the one hand, polycentric spatial structure is conducive to the green economy, which is inevitable for green technology [32; 33]. In the meantime, green technology is widely accepted as being environmentally friendly and helping with CO\({}_{2}\) reduction [34]. On the other hand, with the optimization of the polycentric structure, mega-cities form internal subcenters represented by industrial agglomeration, and hence strengthen the level of specialization. Therefore, in the context of coordinated industrial development, the polycentric structure promotes green technology. Furthermore, the polycentric spatial structure might reduce CO\({}_{2}\) through energy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\  & **Green Technology** & & & & **Energy Consumption** & \\
**Variables** & **FE** & **FE\_DK** & **FE\_BS** & **FE** & **FE\_DK** & **FE\_BS** \\ \hline Q & 0.173 *** & 0.173 *** & 0.173 *** & \(-\)0.121 ** & \(-\)0.121 ** & \(-\)0.121 ** \\  & (3.10) & (3.55) & (2.85) & (\(-\)2.36) & (\(-\)1.63) & (\(-\)2.21) \\ Control & YES & YES & YES & YES & YES & YES \\ City FE & YES & YES & YES & YES & YES & YES \\ Year FE & YES & YES & YES & YES & YES & YES \\ Observations & 4286 & 4286 & 4286 & 2375 & 2375 & 2375 \\ R-squared & 0.877 & 0.877 & 0.877 & 0.654 & 0.654 & 0.654 \\ \hline \hline \end{tabular} Notes: t-values are in parentheses; **, and *** represent the 5%, and 1% significance levels, respectively; the coefficients of the control variables (i.e., the secondary industry as a percentage of GDP, foreign direct investment, population density, the percentage of budget fiscal expenditure and technology expenditure on GDP, and the ratio of the employed population), year and time dummies are not reported to save space.  
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Table 7: The impacts of green technology and energy consumption on CO\({}_{2}\) emissions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **(1)** & **(2)** & **(3)** & **(4)** \\
**Variables** & **PCM** & **Non-PCM** & **Resource-Based** & **Non-Resource-Based** \\ \hline Q & \(-\)0.064 & \(-\)0.052 *** & \(-\)0.020 & \(-\)0.055 *** \\  & (\(-\)0.17) & (\(-\)2.66) & (\(-\)0.55) & (\(-\)2.78) \\ Control & YES & YES & YES & YES \\ City FE & YES & YES & YES & YES \\ Year FE & YES & YES & YES & YES \\ Observations & 507 & 4006 & 1693 & 2820 \\ R-squared & 0.920 & 0.951 & 0.945 & 0.949 \\ \hline \hline \end{tabular} Notes: t-values are in parentheses; *** represent the 1% significance levels, respectively; the coefficients of the control variables (i.e., secondary industry as a percentage of GDP, foreign direct investment, population density, the percentage of budget fiscal expenditure and technology expenditure on GDP, and the ratio of the employed population), year and time dummies are not reported to save space.  
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Table 8: Heterogeneity analysis for different features of cities. consumption. One plausible reason is that polycentric development could affect energy efficiency [35] and, hence, influence energy consumption. Meanwhile, energy consumption is prone to CO2 emissions [36].