IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16335-d1288664.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oluwatoyin J. Gbadeyan & Joseph Muthivhi & Linda Z. Linganiso & Nirmala Deenadayalu, 2024. "Decoupling Economic Growth from Carbon Emissions: A Transition toward Low-Carbon Energy Systems—A Critical Review," Clean Technol., MDPI, vol. 6(3), pages 1-38, August.
    2. Jiasha Fu & Fan Wang & Jin Guo, 2024. "Decoupling Economic Growth from Carbon Emissions in the Yangtze River Economic Belt of China: From the Coordinated Regional Development Perspective," Sustainability, MDPI, vol. 16(6), pages 1-24, March.
    3. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    4. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.
    5. Yuqing Zhou & Haibin Liu, 2023. "Temporal and Spatial Distribution of Ozone and Its Influencing Factors in China," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    6. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2007. "Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea," Energy Policy, Elsevier, vol. 35(6), pages 3226-3235, June.
    7. Ronald E. Miller & Umed Temurshoev, 2017. "Output Upstreamness and Input Downstreamness of Industries/Countries in World Production," International Regional Science Review, , vol. 40(5), pages 443-475, September.
    8. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    9. GUPTA Monika, 2019. "Decomposing The Role Of Different Factors In Co2 Emissions Increase In South Asia," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(1), pages 72-86, April.
    10. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    11. Linghua Qiu & Junhao He & Chao Yue & Philippe Ciais & Chunmiao Zheng, 2024. "Substantial terrestrial carbon emissions from global expansion of impervious surface area," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    12. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    13. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    14. Zhen, Wei & Qin, Quande & Wei, Yi-Ming, 2017. "Spatio-temporal patterns of energy consumption-related GHG emissions in China's crop production systems," Energy Policy, Elsevier, vol. 104(C), pages 274-284.
    15. Mohlin, Kristina & Camuzeaux, Jonathan R. & Muller, Adrian & Schneider, Marius & Wagner, Gernot, 2018. "Factoring in the forgotten role of renewables in CO2 emission trends using decomposition analysis," Energy Policy, Elsevier, vol. 116(C), pages 290-296.
    16. Hwang, In Chang, 2013. "Stochastic Kaya model and its applications," MPRA Paper 55099, University Library of Munich, Germany.
    17. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.
    18. Congxin Li & Xu Zhang, 2022. "The Influencing Mechanisms on Global Industrial Value Chains Embedded in Trade Implied Carbon Emissions from a Higher-Order Networks Perspective," Sustainability, MDPI, vol. 14(22), pages 1-38, November.
    19. Ouyang, Xiaoling & Lin, Boqiang, 2015. "An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 838-849.
    20. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16335-:d:1288664. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.