摘要
城市群建设是推动区域经济发展的重要形式,依靠城市群内的网络关系实现城市群的协同减排是建立城市群绿色发展的重要路径。该文基于社会网络分析方法对中国三大城市群的碳排放空间关联网络的结构特征及其影响因素进行分析。结果表明:京津冀城市群的网络密度较小、网络等级度较大,长三角城市群的网络密度最大,网络等级度较小,珠三角城市群网络密度和网络等级度均较大,三大城市群的网络效率均较高,维持在0.7左右的水平;京津冀城市群的空间关联关系主要受到地理位置和技术创新的影响,长三角城市群碳排放的空间关联主要受到地理位置、产业结构、人口密度和城镇化水平影响,珠三角城市群的地理位置、产业结构和城镇化水平对其碳排放的空间关联具有显著影响。
The construction of urban agglomerations is an important form of promoting regional economic development.It is an important path to establish green development of urban agglomeration to realize collaborative emission reduction based on the network relationship within urban agglomeration.According to the social network analysis method,this paper analyzes the structural characteristics and influencing factors of carbon emissions’spatial correlation network of three urban agglomerations in China.The results show that the network density of Beijing-Tianjin-Hebei is relatively small,the network hierarchy are high.The network density of Yangtze River Delta is the biggest,the network hierarchy is relatively small.The network density and network hierarchy of Pearl River Delta are relatively high.The network efficiency of three major urban agglomerations is high and maintain at a level of about 0.7.The spatial correlation of Beijing-Tianjin-Hebei is mainly affected by the differences in geographic location and technological innovation.The spatial correlation of carbon emissions in Yangtze River Delta is mainly affected by geographic location,industrial structure,population density and urbanization level differences.The geographic location,industrial structure and urbanization level of cities in Pearl River Delta have significant impact on the spatial correlation of carbon emissions.
作者
李爱
王雅楠
李梦
王博文
陈伟
LI Ai;WANG Yanan;LI Meng;WANG Bowen;CHEN Wei(College of Economics and Management,Northwest A&F University,Yangling 712100,China)
出处
《环境科学与技术》
CAS
CSCD
北大核心
2021年第6期186-193,共8页
Environmental Science & Technology
基金
国家社会科学基金项目(20CJY023)。
关键词
碳排放
社会网络分析
城市群
空间关联网络
carbon emissions
social network analysis
urban agglomeration
spatial correlation network
