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作者

Can Cui;Bofeng Cai;Guoshu Bin;Zhen Wang

来源

CITIES,Vol.105,Issue1,Article 102818

语言

英文

关键字

Urban agglomerations;Cities;CO2 emissions;Spatial;China

作者单位

School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning, Beijing 100012, China;College of Civil Engineering and Architecture, Guilin University of Technology, Guilin 541004, China;College of Resources and Environment, Huazhong Agricultural University, Wuhan 430072, China;School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China;Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning, Beijing 100012, China;College of Civil Engineering and Architecture, Guilin University of Technology, Guilin 541004, China;College of Resources and Environment, Huazhong Agricultural University, Wuhan 430072, China

摘要

With fast urbanization and economic growth in recent years, China's urban agglomerations (UAs) thrive, at the cost of surging CO2 emission. Although UAs have been individually examined for their CO2 emissions, a nation-wide investigation and comparison of the UAs in China is lacking. To reveal the low-carbon development performance of the major UAs in China, we identify the clustering patterns of CO2 emissions of 14 UAs using the spatial autocorrelation analysis on city-level CO2 emissions and investigate the scaling effect among the cities within UAs. Results show that from 2005 to 2015, UAs in eastern and central China were high-high (the center and the surroundings featured high levels of CO2 emission) clustered in total emissions, whereas low-low clustered in per-unit-GDP emission. In western and southern China, UAs like Chengdu-Chongqing showed a high-low clustering pattern in total emissions but a low-low clustering pattern in per-capita emission. Scaling effects behind the emission patterns are different; the Yangtze-River-Delta, the Beibu-Gulf and the Guangdong-Hong Kong-Macao UAs were more efficient at emission reduction with the cities' rising scales, while cities of the Beijing-Tianjin-Hebei UA and the Chengdu-Chongqing UA performed less efficiently. Low-carbon pathways are discussed based on spatial patterns and scaling effects of UAs' emissions.