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Analysis of the Social and Economic Factors Influencing PM2.5 Emissions at the City Level in China

Author

Listed:
  • Han Huang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

  • Ping Jiang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

  • Yuanxiang Chen

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

Abstract

Respirable suspended particles (PM2.5) are one of the key components of haze, which not only causes a variety of lung, intestinal, and vascular diseases, but also affects cognitive levels. China is facing the challenge of severe PM2.5 concentrations, especially in urban areas with a high population density. Understanding the key factors that influence PM2.5 concentrations is fundamental for the adoption of targeted measures. Therefore, this study used the Logarithmic Mean Divisia Index (LMDI) method to identify the key factors influencing PM2.5 concentrations in 236 cities in northeastern, western, central, and eastern China. The findings were as follows. The emission intensity (EI) played an important suppressing role on PM2.5 concentrations in all cities from 2011–2020. The energy intensity (EnI) inhibited PM2.5 concentrations in 157 cities; the economic output (EO) stimulated PM2.5 concentrations in some less economically developed regions; and population (P) spurred PM2.5 concentrations in135 cities, mainly concentrated in developed eastern cities. This study provides a whole picture of the key factors influencing PM2.5 concentrations in Chinese cities, and the findings can act as the scientific basis and guidance for Chinese city authorities in formulating policies toward PM2.5 concentration reduction.

Suggested Citation

  • Han Huang & Ping Jiang & Yuanxiang Chen, 2023. "Analysis of the Social and Economic Factors Influencing PM2.5 Emissions at the City Level in China," Sustainability, MDPI, vol. 15(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16335-:d:1288664
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    References listed on IDEAS

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    1. Yan, Dan & Ren, Xiaohang & Kong, Ying & Ye, Bin & Liao, Zangyi, 2020. "The heterogeneous effects of socioeconomic determinants on PM2.5 concentrations using a two-step panel quantile regression," Applied Energy, Elsevier, vol. 272(C).
    2. Wang, Juan & Li, Ziming & Wu, Tong & Wu, Siyu & Yin, Tingwei, 2022. "The decoupling analysis of CO2 emissions from power generation in Chinese provincial power sector," Energy, Elsevier, vol. 255(C).
    3. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    4. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    5. Zhang, Wei & Wang, Nan, 2021. "Decomposition of energy intensity in Chinese industries using an extended LMDI method of production element endowment," Energy, Elsevier, vol. 221(C).
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