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Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China’s Three Urban Agglomerations as Examples

Author

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  • Feipeng Guo

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Zifan Wang

    (School of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, China
    Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Shaobo Ji

    (Sprott School of Business, Carleton University, Ottawa, ON K1S 5B6, Canada)

  • Qibei Lu

    (School of International Business, Zhejiang International Studies University, Hangzhou 310023, China)

Abstract

Nowadays, driven by green and low-carbon development, accelerating the innovation of joint prevention and control system of air pollution and collaborating to reduce greenhouse gases has become the focus of China’s air pollution prevention and control during the “Fourteenth Five-Year Plan” period (2021–2025). In this paper, the air quality index (AQI) data of 48 cities in three major urban agglomerations of Beijing-Tianjin-Hebei, Pearl River Delta and Yangtze River Delta, were selected as samples. Firstly, the air pollution spatial correlation weighted networks of three urban agglomerations are constructed and the overall characteristics of the networks are analyzed. Secondly, an influential nodes identification method, local-and-global-influence for weighted network (W_LGI), is proposed to identify the influential cities in relatively central positions in the networks. Then, the study area is further focused to include influential cities. This paper builds the air pollution spatial correlation weighted network within an influential city to excavate influential nodes in the city network. It is found that these influential nodes are most closely associated with the other nodes in terms of spatial pollution, and have a certain ability to transmit pollutants to the surrounding nodes. Finally, this paper puts forward policy suggestions for the prevention and control of air pollution from the perspective of the spatial linkage of air pollution. These will improve the efficiency and effectiveness of air pollution prevention and control, jointly achieve green development and help achieve the “carbon peak and carbon neutrality” goals.

Suggested Citation

  • Feipeng Guo & Zifan Wang & Shaobo Ji & Qibei Lu, 2022. "Influential Nodes Identification in the Air Pollution Spatial Correlation Weighted Networks and Collaborative Governance: Taking China’s Three Urban Agglomerations as Examples," IJERPH, MDPI, vol. 19(8), pages 1-17, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:8:p:4461-:d:789022
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    References listed on IDEAS

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    Cited by:

    1. Jiancheng Li, 2023. "Structural Characteristics and Evolution Trend of Collaborative Governance of Air Pollution in “2 + 26” Cities from the Perspective of Social Network Analysis," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    2. Zhaoxian Su & Yang Yang & Yun Wang & Pan Zhang & Xin Luo, 2023. "Study on Spatiotemporal Evolution Features and Affecting Factors of Collaborative Governance of Pollution Reduction and Carbon Abatement in Urban Agglomerations of the Yellow River Basin," IJERPH, MDPI, vol. 20(5), pages 1-20, February.
    3. Wei Zhou & Feipeng Guo, 2022. "Precise Supervision of Enterprise Environmental Protection Behavior Based on Boolean Matrix Factorization under Low Carbon Background," IJERPH, MDPI, vol. 19(13), pages 1-17, June.
    4. Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.

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