IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i8p3152-d1373023.html

Simulation and Forecasting Study on the Influential Factors of PM 2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region

Author

Listed:
  • Dongxue Li

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Ying Shi

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Yingshan Sun

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Yingzhe Xing

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Rui Zhang

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

  • Jingxin Xue

    (School of Management, China University of Mining and Technology (Beijing), Beijing 100083, China)

Abstract

It is still necessary to regularly investigate the breakdown of socio-economic elements as a starting point for analyzing the effects of diverse human production activities on PM 2.5 intensity from industrial and regional viewpoints. In this paper, the emission factor model was adopted to measure PM 2.5 emissions in the Beijing–Tianjin–Hebei (BTH) region at the regional and industrial levels. The logarithmic mean Divisia index (LMDI) decomposition model was employed to analyze the factors affecting PM 2.5 emissions related to energy consumption in the BTH region at the regional and sectoral levels. Building on this foundation, a system dynamics (SD) model was established to make a prediction regarding PM 2.5 pollution in the BTH region in 2025. This study found that secondary industry was a major source of PM 2.5 emissions in the BTH region. Coal remained the main form of energy consumption in the BTH region. Secondly, the effect size of the factors affecting PM 2.5 intensity ranked in the order of energy intensity, energy structure, and industrial structure. Thirdly, in 2025, PM 2.5 emissions in the BTH region will decline appreciably, but there is still a certain gap in terms of meeting the targets of “the 14th Five-Year Plan” between the three provinces and cities. These results indicate that the BTH region should achieve the effective management of PM 2.5 pollution at the source through the following initiatives: it is necessary to carry out the continuous adjustment of energy structures to gradually increase the proportion of clean energy; we must steadily promote the decline in energy intensity reduction, and gradually strengthen scientific and technological innovation; and we must continue to promote the optimization of the industrial structure and increase the proportion of tertiary industry every year.

Suggested Citation

  • Dongxue Li & Ying Shi & Yingshan Sun & Yingzhe Xing & Rui Zhang & Jingxin Xue, 2024. "Simulation and Forecasting Study on the Influential Factors of PM 2.5 Related to Energy Consumption in the Beijing–Tianjin–Hebei Region," Sustainability, MDPI, vol. 16(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:8:p:3152-:d:1373023
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/8/3152/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/8/3152/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rafiq, Shuddhasattwa & Salim, Ruhul & Nielsen, Ingrid, 2016. "Urbanization, openness, emissions, and energy intensity: A study of increasingly urbanized emerging economies," Energy Economics, Elsevier, vol. 56(C), pages 20-28.
    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. Muhlis Can & Daniel Balsalobre-Lorente & Festus Fatai Adedoyin & Mehmet Mercan, 2025. "The impact of trade openness, export concentration and economic complexity on energy demand among G7 countries," Energy & Environment, , vol. 36(2), pages 638-659, March.
    2. Zhang, Hui & Zhou, Peng & Sun, Xiumei & Ni, Guanqun, 2024. "Disparities in energy efficiency and its determinants in Chinese cities: From the perspective of heterogeneity," Energy, Elsevier, vol. 289(C).
    3. Rao, Amar & Srivastava, Mrinalini & Parihar, Jaya Singh & Chavriya, Shubham & Hosen, Mosharrof, 2024. "Minerals at the crossroads: Economic policies, global trade, and renewable energy in the global South," Resources Policy, Elsevier, vol. 97(C).
    4. Shiwei Yu & Xing Hu & Xuejiao Zhang & Zhenxi Li, 2019. "Convergence of per capita carbon emissions in the Yangtze River Economic Belt, China," Energy & Environment, , vol. 30(5), pages 776-799, August.
    5. Ioannidis, Alexis & Chalvatzis, Konstantinos J. & Li, Xin & Notton, Gilles & Stephanides, Phedeas, 2019. "The case for islands’ energy vulnerability: Electricity supply diversity in 44 global islands," Renewable Energy, Elsevier, vol. 143(C), pages 440-452.
    6. Dong, Xiao-Ying & Hao, Yu, 2018. "Would income inequality affect electricity consumption? Evidence from China," Energy, Elsevier, vol. 142(C), pages 215-227.
    7. Chen, Zhongfei & Huang, Wanjing & Zheng, Xian, 2019. "The decline in energy intensity: Does financial development matter?," Energy Policy, Elsevier, vol. 134(C).
    8. He, Jingze & Wang, Feng, 2025. "Does urban agglomeration reduce carbon emissions in Chinese cities? New perspective on factor mobility," Energy Economics, Elsevier, vol. 143(C).
    9. Jun Zhao & Qingzhe Jiang & Kangyin Dong, 2021. "Income inequality and natural gas consumption in China: Do heterogeneous and threshold effects exist?," Australian Economic Papers, Wiley Blackwell, vol. 60(4), pages 630-650, December.
    10. Dong, Kangyin & Sun, Renjin & Hochman, Gal & Li, Hui, 2018. "Energy intensity and energy conservation potential in China: A regional comparison perspective," Energy, Elsevier, vol. 155(C), pages 782-795.
    11. Cong Khai Dinh & Quang Thanh Ngo & Trung Thanh Nguyen, 2021. "Medium- and High-Tech Export and Renewable Energy Consumption: Non-Linear Evidence from the ASEAN Countries," Energies, MDPI, vol. 14(15), pages 1-16, July.
    12. Dargahi, Hassan & Khameneh, Kazem Biabany, 2019. "Energy intensity determinants in an energy-exporting developing economy: Case of Iran," Energy, Elsevier, vol. 168(C), pages 1031-1044.
    13. Pang, Qinghua & Chen, Zhengxin, 2025. "Targeting SDG7 in G20 countries: A comparative study of clean and dirty energy performance," Energy, Elsevier, vol. 335(C).
    14. Hussain Ali Bekhet & Nor Salwati Othman & Tahira Yasmin, 2020. "Interaction Between Environmental Kuznet Curve and Urban Environment Transition Hypotheses in Malaysia," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 384-402.
    15. Jeyhun Mikayilov & Vusal Shukurov & Shahriyar Mukhtarov & Sabuhi Yusifov, 2017. "Does Urbanization Boost Pollution from Transport?," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1709-1718.
    16. Hanif, Imran & Faraz Raza, Syed Muhammad & Gago-de-Santos, Pilar & Abbas, Qaiser, 2019. "Fossil fuels, foreign direct investment, and economic growth have triggered CO2 emissions in emerging Asian economies: Some empirical evidence," Energy, Elsevier, vol. 171(C), pages 493-501.
    17. Wu, Haitao & Hao, Yu & Weng, Jia-Hsi, 2019. "How does energy consumption affect China's urbanization? New evidence from dynamic threshold panel models," Energy Policy, Elsevier, vol. 127(C), pages 24-38.
    18. Trinh, Hai Hong & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Vo, Diem Thi Hong, 2022. "Examining the heterogeneity of financial development in the energy-environment nexus in the era of climate change: Novel evidence around the world," Energy Economics, Elsevier, vol. 116(C).
    19. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    20. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2022. "Institutional quality and its spatial spillover effects on energy efficiency," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:16:y:2024:i:8:p:3152-:d:1373023. 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.