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Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

Author

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  • Jinzhao Song

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Qing Feng

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Xiaoping Wang

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Hanliang Fu

    (School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China)

  • Wei Jiang

    (Department of Ecosystem Science and Management, Pennsylvania State University, Forest Resources Building, University Park, PA 16802, USA)

  • Baiyu Chen

    (College of Engineering, University of California Berkeley, Berkeley, CA 94720, USA)

Abstract

Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO 2 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 CO 2 reduction. This study calculated CO 2 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 CO 2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO 2 emission in the area is a complex network pattern, and in the sample period, the CO 2 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 CO 2 is greatest; and (4) the higher density of the global network of CO 2 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.

Suggested Citation

  • Jinzhao Song & Qing Feng & Xiaoping Wang & Hanliang Fu & Wei Jiang & Baiyu Chen, 2018. "Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2018:i:1:p:1-:d:191900
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    as
    1. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    2. Manchin, Miriam & Orazbayev, Sultan, 2018. "Social networks and the intention to migrate," World Development, Elsevier, vol. 109(C), pages 360-374.
    3. Sun, Fengrui & Yao, Yuedong & Chen, Mingqiang & Li, Xiangfang & Zhao, Lin & Meng, Ye & Sun, Zheng & Zhang, Tao & Feng, Dong, 2017. "Performance analysis of superheated steam injection for heavy oil recovery and modeling of wellbore heat efficiency," Energy, Elsevier, vol. 125(C), pages 795-804.
    4. Liu, Zhijian & Liu, Yuanwei & He, Bao-Jie & Xu, Wei & Jin, Guangya & Zhang, Xutao, 2019. "Application and suitability analysis of the key technologies in nearly zero energy buildings in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 329-345.
    5. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    6. Kang, Jidong & Zhao, Tao & Liu, Nan & Zhang, Xin & Xu, Xianshuo & Lin, Tao, 2014. "A multi-sectoral decomposition analysis of city-level greenhouse gas emissions: Case study of Tianjin, China," Energy, Elsevier, vol. 68(C), pages 562-571.
    7. Matsumoto, Hidenobu, 2004. "International urban systems and air passenger and cargo flows: some calculations," Journal of Air Transport Management, Elsevier, vol. 10(4), pages 239-247.
    8. Zhou, Ya & Shan, Yuli & Liu, Guosheng & Guan, Dabo, 2018. "Emissions and low-carbon development in Guangdong-Hong Kong-Macao Greater Bay Area cities and their surroundings," Applied Energy, Elsevier, vol. 228(C), pages 1683-1692.
    9. Wei Liu & Jie Xu & Jie Li, 2018. "The Influence of Poverty Alleviation Resettlement on Rural Household Livelihood Vulnerability in the Western Mountainous Areas, China," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    10. Murat Celik, H. & Guldmann, Jean-Michel, 2007. "Spatial interaction modeling of interregional commodity flows," Socio-Economic Planning Sciences, Elsevier, vol. 41(2), pages 147-162, June.
    11. Hanliang Fu & Zhaoxing Li & Zhijian Liu & Zelin Wang, 2018. "Research on Big Data Digging of Hot Topics about Recycled Water Use on Micro-Blog Based on Particle Swarm Optimization," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    12. Zhang, Junjie & Yu, Biying & Wei, Yi-Ming, 2018. "Heterogeneous impacts of households on carbon dioxide emissions in Chinese provinces," Applied Energy, Elsevier, vol. 229(C), pages 236-252.
    13. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    14. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    15. Yong Wang & Han Zhao & Fumei Duan & Ying Wang, 2018. "Initial Provincial Allocation and Equity Evaluation of China’s Carbon Emission Rights—Based on the Improved TOPSIS Method," Sustainability, MDPI, vol. 10(4), pages 1-27, March.
    16. Leibowicz, Benjamin D. & Krey, Volker & Grubler, Arnulf, 2016. "Representing spatial technology diffusion in an energy system optimization model," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 350-363.
    17. Pingxing Li & Wei Sun, 2018. "Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China," Sustainability, MDPI, vol. 10(9), pages 1-13, August.
    18. Sun, Fengrui & Yao, Yuedong & Li, Xiangfang, 2018. "The heat and mass transfer characteristics of superheated steam coupled with non-condensing gases in horizontal wells with multi-point injection technique," Energy, Elsevier, vol. 143(C), pages 995-1005.
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