# The Impact of Polycentric Structure on CO\({}_{2}\) Emissions: Evidence from China
## 5 Conclusions  
Fueled by the urgency of achieving the Sustainable Development Goals (SDGs), addressing the rise in CO2 emissions has become a pressing concern. This study delved into the role of sustainable urban development in mitigating CO2 emissions by investigating the impact of polycentric structures on CO2 emissions in 278 Chinese cities from 1999 to 2017. We employed a two-way fixed effect model to estimate our benchmark results and constructed IVs to address endogeneity concerns. Our findings revealed that polycentric structures contributed to CO2 emissions reduction by promoting green technology and lowering energy consumption. Notably, the positive impact of polycentric structures on CO2 emissions was only significant in cities that were not classified as municipalities or resource-based cities. Our study marginally contributed to the theory. First, we figured out a robust relationship between polycentric structures and CO2 emissions, accounting for potential biases from policy interferences, selection bias, and core variable measurements. Second, we proposed two innovative pathways that mediated the impact of spatial structures on CO2 emissions. Third, we considered the heterogeneous effects of administrative hierarchy and resource dependence in this context. From our findings, we derived practical implications for achieving SDGs. First, polycentric structures offer a viable approach for developing countries to regulate CO2 emissions. When it comes to China specifically, some cities in China should gradually build polycentric spatial structures. Second, government departments should implement strategies related to green technology and energy consumption to regulate CO2 emissions and achieve SDGs. Third, urban planning and CO2 mitigation strategies should be tailored to individual cities, with a focus on establishing more environmental regulations in resource-based cities and municipalities. In closing, we acknowledge some limitations and avenues for future research. First, our study used county-level data to calculate the morphological polycentric index at the city level, overlooking the functional dimensions of polycentric structures. Future studies should explore these functional dimensions further. Second, our analysis focused on the Pareto index in relation to city size balance, suggesting that future research could examine the impact of spatial balance on CO2 emissions. Lastly, while our study area is China, future studies could investigate the heterogeneous effects of diverse countries and delve into other aspects of the SDGs to gain a more comprehensive understanding of the topic. Conceptualization, J.W. and X.Z. ; methodology, X.Z. ; software, J.W. and X.Z. ; formal analysis, W.D. ; data curation, X.Z. ; writing--original draft preparation, J.W. and W.D. ; writing--review and editing, Z.S. ; visualization, W.D. ; supervision, Z.S. and X.O. ; project administration, Z.S. and X.O. ; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was jointly supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (Grant No. CBAS2022 IRP04), the National Natural Science Foundation of China (Grant No. 42171291), the Key R&D Program Projects in Hainan Province (grant number ZDYF2020192), and the Key Research and Development Program of Guangxi (Guike-AB220305060). **Data Availability Statement:** The data on nighttime light are openly available in the Prolonged Artificial Nighttime-Light Dataset of China at [[https://doi.org/10.11888/Sociocco.tptdc.271202](https://doi.org/10.11888/Sociocco.tptdc.271202)]([https://doi.org/10.11888/Sociocco.tptdc.271202](https://doi.org/10.11888/Sociocco.tptdc.271202)). The data on CO\({}_{2}\) emissions are openly available at [[https://www.ceads.net/data/county/](https://www.ceads.net/data/county/)]([https://www.ceads.net/data/county/](https://www.ceads.net/data/county/)) (accessed on 8 May 2023).