Qin Hetian, Huang Qiuhao, Zhang Ziwei, Lu Yu, Li Manchun, Xu Lang, Chen Zhenjie
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.
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.
Sci Total Environ. 2019 Sep 20;684:413-424. doi: 10.1016/j.scitotenv.2019.05.352. Epub 2019 May 24.
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.
全球变暖和气候变化已成为一个严重的环境问题,且中国目前是世界上碳排放最高的国家。城市是碳排放的主要来源,也是解决这些问题的关键所在。因此,关于减少二氧化碳排放的研究备受关注。在本研究中,进行了空间自相关分析以了解中国171个城市二氧化碳排放的空间特征。然后,运用逐步回归和地理加权回归来探究驱动中国城市二氧化碳排放的过程。采用两步聚类法根据各驱动因素的影响程度将中国城市划分为不同类别。结果表明,城市二氧化碳排放之间存在空间集聚关系。高高聚类主要出现在京津冀和长江三角洲城市群,而低低聚类出现在中部、西部和西南部城市。在所有变量中,货运量、人均国内生产总值、人口密度和第二产业比重与二氧化碳排放呈正相关,而每万人公交车数量与二氧化碳排放呈负相关。地理加权回归模型提供了更详细的结果,并揭示了不同驱动因素影响的空间异质性。东部地区人口、经济因素和产业因素的影响明显大于中部和西部地区。货运量和公共交通在东北地区的影响最为显著。聚类结果表明城市可分为四种类型。这些研究结果为不同地区的城市应如何减少二氧化碳排放提供了参考和政策建议。