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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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    Cited by:

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    3. Hongkuan Zang & Lirong Zhang & Ye Xu & Wei Li, 2020. "Dynamic Input–Output Analysis of a Carbon Emission System at the Aggregated and Disaggregated Levels: A Case Study in the Northeast Industrial District," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    4. Jie Chang & Pingjun Sun & Guoen Wei, 2022. "Spatial Driven Effects of Multi-Dimensional Urbanization on Carbon Emissions: A Case Study in Chengdu-Chongqing Urban Agglomeration," Land, MDPI, vol. 11(10), pages 1-19, October.
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    7. Baiyu Chen & Yi Kou & Daniel Zhao & Fang Wu & Shaoxun Liu & Alvin Chia & Liping Wang, 2020. "Calculations on stopping time and return period," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(2), pages 537-550, March.
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