Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis
Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis
复制标题
成渝城市群CO2排放空间关联性及效应评价：来自社会网络分析的定量证据
DOI：
10.3390/su11010001
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Chen, Baiyu
中科院分区：
文献类型：
--
作者：
Song, Jinzhao;Feng, Qing;Chen, Baiyu
城市群是一种既有的城市空间格局，在促进区域经济增长的同时，也促进了城市CO2排放分布的空间关联和依赖。探索这种空间关联和依赖性有利于实施有效且协调的区域层面二氧化碳减排政策。利用IPAT模型计算了成渝城市群2005-2016年的CO2排放量，并利用社会网络分析方法实证研究了成渝城市群CO2的空间结构格局和关联效应。研究结果表明：（1）该地区CO2排放的空间结构是一个复杂的网络结构，在样本期内，CO2排放关联度稳步上升，网络稳定性持续增强;（2）该地区城市的中心性可分为三类：成都、重庆被定义为第一类，第二类涵盖德阳、绵阳、宜宾、南充，第三类包括自贡、遂宁、眉山、广安--这一类的城市数量呈上升趋势;（3）全球CO2排放网络分为成都周边、川南、川东北和渝西4个亚区，这4个亚区的CO2溢出效应最大;（4）全球CO2排放网络密度较高，显著降低了区域排放强度，缩小了区域间差异。中心度越高的网络，其辐射强度越低。
Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO2 reduction. This study calculated CO2 emissions from 2005–2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO2 emission in the area is a complex network pattern, and in the sample period, the CO2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan—the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO2 is greatest; and (4) the higher density of the global network of CO2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.