Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster

Hetian Qin; Qiuhao Huang; Ziwei Zhang; Yu Lu; Manchun Li; Lang Xu; Zhenjie Chen

doi:10.1016/j.scitotenv.2019.05.352

Carbon dioxide emission driving factors analysis and policy implications of Chinese cities: Combining geographically weighted regression with two-step cluster

Sci Total Environ. 2019 Sep 20:684:413-424. doi: 10.1016/j.scitotenv.2019.05.352. Epub 2019 May 24.

Authors

Hetian Qin¹, Qiuhao Huang², Ziwei Zhang³, Yu Lu⁴, Manchun Li⁵, Lang Xu⁶, Zhenjie Chen⁷

Affiliations

¹ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China. Electronic address: mg1727066@smail.nju.edu.cn.
² School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. Electronic address: qhhuang@nju.edu.cn.
³ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China. Electronic address: mg1827085@smail.nju.edu.cn.
⁴ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China. Electronic address: mg1827064@smail.nju.edu.cn.
⁵ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. Electronic address: limanchun@nju.edu.cn.
⁶ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China. Electronic address: mg1627075@smail.nju.edu.cn.
⁷ School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China; The Key Laboratory of the Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China. Electronic address: chenzj@nju.edu.cn.

PMID: 31154214
DOI: 10.1016/j.scitotenv.2019.05.352

Abstract

Global warming and climate change have become a serious environmental problem and China's carbon emissions are currently the highest in the world. Cities are the main sources of carbon emissions and the key to solving these problems. Therefore, research on reducing carbon dioxide emissions is a matter of concern. In this study, a spatial autocorrelation analysis was performed to understand the spatial characteristics of carbon dioxide emissions in 171 Chinese cities. Then, stepwise and geographically weighted regressions were used to explore the processes that drive carbon dioxide emissions in Chinese cities. A two-step cluster was used to classify Chinese cities into different categories based on the degree of impact of each driver. The results showed that there is a spatial aggregation relationship between urban carbon dioxide emissions. High-high clusters mainly occur in the Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations, while low-low clusters occur in the central, western, and southwestern cities. Among all variables, freight volume, per capita gross domestic product, population density, and the proportion of secondary industries correlate positively with carbon dioxide emissions, whereas the number of buses per 10,000 people correlates negatively with carbon dioxide emissions. The geographically weighted regression model provided more detailed results and revealed the spatial heterogeneity of the effects of the different drivers. The impact of population, economic factors, and industrial factors in the eastern region is significantly greater than that in the central and western regions. Freight volume and public transport have the most significant impact in the northeast region. The clustering results showed that cities can be divided into four types. These findings provide a reference and policy suggestions for how cities in different regions should reduce carbon dioxide emissions.

Keywords: Carbon dioxide emissions; Chinese cities; Driving factors; Geographically weighted regression; Two-step cluster.