City-level environmental performance and the spatial structure of China's three coastal city clusters

被引:7
|
作者
Wu, Dan [1 ,2 ]
Lie, Yuying [3 ]
Liu, Li [4 ,5 ,6 ]
Cheng, Ziye [4 ]
Zhang, Yujie [4 ]
Yang, Yuxiang [1 ]
Xiao, Wenjun [4 ]
Li, Suli [4 ]
Luo, Guangyang [4 ]
Wang, Zhen [7 ]
机构
[1] Hainan Univ, Sch Publ Adm, Haikou, Peoples R China
[2] Hainan Univ, Hainan Univ UC Davis Joint Res Ctr Energy & Transp, Haikou 570228, Peoples R China
[3] Sun Yat Sen Univ, Sch Environm Sci & Engn, Guangzhou, Peoples R China
[4] South China Univ Technol, Sch Environm & Energy, Guangzhou 510006, Peoples R China
[5] South China Univ Technol, Guangdong Prov Key Lab Solid Wastes Pollut Control, Guangzhou 510006, Peoples R China
[6] South China Univ Technol, Key Lab Pollut Control & Ecosyst Restorat Ind Clus, Minist Educ, Guangzhou 510006, Peoples R China
[7] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China
关键词
Green total factor productivity; Spatial network; City; City cluster; TOTAL FACTOR PRODUCTIVITY; POLLUTION HAVEN HYPOTHESIS; INDUSTRIAL CLUSTERS; AGGLOMERATION; EFFICIENCY; TRANSPORTATION; COMPETITION; NETWORKS; URBANIZATION; COOPERATION;
D O I
10.1016/j.jclepro.2023.138591
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Cities' environmental efficiency (measured in terms of green total factor productivity, GTFP) have been found in the existing literature to correlate spatially. However, the spatial structure of the interrelations between cities' GTFP have not yet been determined. In this study, we attempted to characterize city-level spatial networks of GTFP by investigating China's major three coastal city clusters: Jing-Jin-Ji (JJJ), Yangtze River Delta (YRD), and Pearl River Delta (PRD). We estimated the GTFP of 38 cities within these three coastal city clusters by applying a super-efficiency slack-based measure (SBM) within a data envelopment analysis (DEA) approach, constructed a spatial network of GTFP for each city cluster based on gravity model, traced out the centralities and subgroups of each GTFP network by conducting social network analysis, and then investigated the socioeconomic factors correlating to GTFP interrelation between cities. The results are as follows: First, the average GTFP in the three clusters increased from 2010 to 2021, as did the industrial value added in these regions. In addition, average GTFP increased the most in PRD region relative to the other two clusters. Second, centers of the spatial network of GTFP exist in these three regions. These are as follows: Beijing, Tianjin, and Langfang in JJJ region; Wuxi, Suzhou, and Yangzhou in YRD region; and Guangzhou and Foshan in PRD region. In the JJJ and YRD regions, the composition of the subgroups containing cities with greater closeness of GTFP underwent significant changes between 2010 and 2021, whereas the composition of the subgroup in the PRD region did not change as much. Third, differences in terms of industrial structure and distance between two cities are key underlying factors that influence the interrelation of GTFP between these cities. The characteristics of the spatial networks of GTFP in the three city clusters imply that improving the balance between the central cities and other noncentral cities to alleviate the misallocations between cities and economic resources could result in an increase in the overall efficiency of economic growth and environmental improvement.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] City-level environmental performance in China
    Wang Y.
    Shah S.A.A.
    Zhou P.
    Energy, Ecology and Environment, 2018, 3 (3) : 149 - 161
  • [2] The environmental effect of technical change: A spatial model with city-level data in China
    Le, Peng
    Ouyang, Yaofu
    Shi, Dan
    CHINESE JOURNAL OF POPULATION RESOURCES AND ENVIRONMENT, 2021, 19 (01) : 22 - 33
  • [3] The environmental effect of technical change: A spatial model with city-level data in China
    Peng Li
    Yaofu Ouyang
    Dan Shi
    Chinese Journal of Population,Resources and Environment, 2021, (01) : 22 - 33
  • [4] Domestic environmental impacts of OFDI: City-level evidence from China
    Liu, Yishuang
    Liu, Wei
    Zhang, Xiaoling
    Dong, Hanmin
    Zhao, Zhihui
    Zhang, Zhan
    INTERNATIONAL REVIEW OF ECONOMICS & FINANCE, 2024, 89 : 391 - 409
  • [5] City-level climate change mitigation in China
    Shan, Yuli
    Guan, Dabo
    Hubacek, Klaus
    Zheng, Bo
    Davis, Steven J.
    Jia, Lichao
    Liu, Jianghua
    Liu, Zhu
    Fromer, Neil
    Mi, Zhifu
    Meng, Jing
    Deng, Xiangzheng
    Li, Yuan
    Lin, Jintai
    Schroeder, Heike
    Weisz, Helga
    Schellnhuber, Hans Joachim
    SCIENCE ADVANCES, 2018, 4 (06):
  • [6] City-level emission peak and drivers in China
    Shan, Yuli
    Guan, Yuru
    Hang, Ye
    Zheng, Heran
    Li, Yanxian
    Guan, Dabo
    Li, Jiashuo
    Zhou, Ya
    Li, Li
    Hubacek, Klaus
    SCIENCE BULLETIN, 2022, 67 (18) : 1910 - 1920
  • [7] The inequality of city-level energy efficiency for China
    Zhang, Ning
    Zhou, Mengling
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2020, 255 (255)
  • [8] A city-level evaluation of the development of a Beautiful China
    Guan, Yang
    Xiao, Yang
    Zhang, Nannan
    Chu, Chengjun
    Xiong, Shangao
    JOURNAL OF CLEANER PRODUCTION, 2025, 486
  • [9] Is environmental regulation effective in China? Evidence from city-level panel data
    Hao, Yu
    Deng, Yuxin
    Lu, Zhi-Nan
    Chen, Hao
    JOURNAL OF CLEANER PRODUCTION, 2018, 188 : 966 - 976
  • [10] Immigration and Firm Performance: a city-level approach
    Teruel Carrizosa, Mercedes
    Segarra Blasco, Agusti
    INVESTIGACIONES REGIONALES-JOURNAL OF REGIONAL RESEARCH, 2009, (15) : 111 - 137