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Spatial Correlation of Industrial NO x Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis

Author

Listed:
  • Shurui Jiang

    (School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Xue Tan

    (State Grid Energy Research Institute Co., LTD, Beijing 102209, China)

  • Yue Wang

    (School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Lei Shi

    (School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Rong Cheng

    (School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Zhong Ma

    (School of Environment and Natural Resources, Renmin University of China, Beijing 100872, China)

  • Genfa Lu

    (State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210093, China)

Abstract

The Chinese government has identified air pollution transmission points in Beijing–Tianjin–Hebei region and its surrounding areas under 2 + 26 initiative. This study introduces a modified Gravity Model to construct the spatial correlation network of industrial NO x in 2 + 26 policy region from 2011 to 2015, and further explores network characteristics and socioeconomic factors of this spatial correlation network by Social Network Analysis. Results indicate significant correlation of industrial NO x emission in 2 + 26 policy cities. The spatial correlation network of industrial NO x has remained stable within 5 years, implying no pollution exacerbation of interregional transmission. According to the effect of output and input in the correlation network of industrial NO x , cities in 2 + 26 policy region can be categorized into four types: high-high, high-low, low-low, and low-high, as each should adopt the corresponding strategies for emission reduction. Shijiazhuang, Liaocheng, Cangzhou, Heze and Handan should be key monitored during implementation of emission reduction. Taiyuan, Hebi, Langfang, Tangshan and Yangquan, should give priority to local emission reduction although less associated with other cities, based on city type and current emission situation. Environmental regulation and geographical distance have significant influence on the spatial correlation network of industrial NO x , of which the indicator of environmental regulation difference matrix has become significantly negative since 2014, while the indicator of geographical effect has been significantly positive all along. Urban industrial emission has significant correlation between cities with distance of 0–300 km, while no significant correlation between cities with distance exceeding 300 km.

Suggested Citation

  • Shurui Jiang & Xue Tan & Yue Wang & Lei Shi & Rong Cheng & Zhong Ma & Genfa Lu, 2020. "Spatial Correlation of Industrial NO x Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2289-:d:332697
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    References listed on IDEAS

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    Cited by:

    1. Siyao Li & Qiaosheng Wu & You Zheng & Qi Sun, 2021. "Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry," Sustainability, MDPI, vol. 13(4), pages 1-19, February.

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