IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i11d10.1007_s10668-022-02586-y.html
   My bibliography  Save this article

System dynamics model of Beijing urban public transport carbon emissions based on carbon neutrality target

Author

Listed:
  • Lei Wen

    (North China Electric Power University)

  • Anqi Wang

    (North China Electric Power University)

Abstract

Since the 18th National Congress of Chinese Communist Party, ecological civilization construction has been incorporated into Five-sphere Integrated Plan, that is, to promote coordinated progress in the economic, political, cultural, social, and eco-environmental fields. The Chinese government has proposed the goal of “carbon peak” and “carbon neutrality”. Low-carbon transportation becomes the mainstream. To achieve carbon peak and carbon neutrality in public transport, this paper takes Beijing’s public transport sector as a case study, using system dynamics model with relevant policies and the current situation. It calculates and predicts carbon emissions under electric vehicle substitution scenario, clean energy generation, and carbon capture, utilization and storage scenario for the period of 2020–2060. The following main conclusions are obtained: Direct carbon emissions have peaked and started to decline based on the current measures, while indirect carbon emissions have peaked and started to decline under the suggested intervention. The direct carbon emissions peaked at 2.58005 × 106 tons in 2014. The indirect carbon emissions peaked at 1.59027 × 106 in 2025. They all achieved carbon neutrality under the suggested intervention. Electric vehicle substitution and carbon capture, clean energy generation, utilization and storage are the main factors of carbon emission reduction. The outcomes of this study can provide essential information for policy-makers to advance Beijing's future low-carbon development in public transport. The electric vehicle substitution, clean energy generation and carbon capture, utilization and storage provide a new perspective to research carbon emissions in the transport sector.

Suggested Citation

  • Lei Wen & Anqi Wang, 2023. "System dynamics model of Beijing urban public transport carbon emissions based on carbon neutrality target," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12681-12706, November.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:11:d:10.1007_s10668-022-02586-y
    DOI: 10.1007/s10668-022-02586-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02586-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02586-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Xue & Ma, Shoufeng & Tian, Junfang & Jia, Ning & Li, Geng, 2015. "A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO2 emissions: A case study of Beijing," Energy Policy, Elsevier, vol. 85(C), pages 253-270.
    2. Dehghan, Hamed & Amin-Naseri, Mohammad Reza & Nahavandi, Nasim, 2021. "A system dynamics model to analyze future electricity supply and demand in Iran under alternative pricing policies," Utilities Policy, Elsevier, vol. 69(C).
    3. Dixon, Peter B. & Rimmer, Maureen T. & Waschik, Robert, 2017. "Linking CGE and specialist models: Deriving the implications of highway policy using USAGE-Hwy," Economic Modelling, Elsevier, vol. 66(C), pages 1-18.
    4. Krook-Riekkola, Anna & Berg, Charlotte & Ahlgren, Erik O. & Söderholm, Patrik, 2017. "Challenges in top-down and bottom-up soft-linking: Lessons from linking a Swedish energy system model with a CGE model," Energy, Elsevier, vol. 141(C), pages 803-817.
    5. Koot, Martijn & Wijnhoven, Fons, 2021. "Usage impact on data center electricity needs: A system dynamic forecasting model," Applied Energy, Elsevier, vol. 291(C).
    6. Qian, Wuyong & Wang, Jue, 2020. "An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China," Energy, Elsevier, vol. 209(C).
    7. Wang, Haikun & Fu, Lixin & Bi, Jun, 2011. "CO2 and pollutant emissions from passenger cars in China," Energy Policy, Elsevier, vol. 39(5), pages 3005-3011, May.
    8. Lin, Tzu-Ping, 2010. "Carbon dioxide emissions from transport in Taiwan's national parks," Tourism Management, Elsevier, vol. 31(2), pages 285-290.
    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. Zhenxiang Cao & Liqing Peng, 2023. "The Impact of Digital Economics on Environmental Quality: A System Dynamics Approach," SAGE Open, , vol. 13(4), pages 21582440231, December.
    2. Zaman, Khalid & Shahbaz, Muhammad & Loganathan, Nanthakumar & Raza, Syed Ali, 2016. "Tourism development, energy consumption and Environmental Kuznets Curve: Trivariate analysis in the panel of developed and developing countries," Tourism Management, Elsevier, vol. 54(C), pages 275-283.
    3. Wang, Yadong & Wang, Delu & Shi, Xunpeng, 2023. "Sustainable development pathways of China's wind power industry under uncertainties: Perspective from economic benefits and technical potential," Energy Policy, Elsevier, vol. 182(C).
    4. Matteo Coronese & Davide Luzzati, 2022. "Economic impacts of natural hazards and complexity science: a critical review," LEM Papers Series 2022/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    5. Gupta, Monika & Bandyopadhyay, Kaushik Ranjan & Singh, Sanjay K., 2019. "Measuring effectiveness of carbon tax on Indian road passenger transport: A system dynamics approach," Energy Economics, Elsevier, vol. 81(C), pages 341-354.
    6. Ding, Song & Tao, Zui & Zhang, Huahan & Li, Yao, 2022. "Forecasting nuclear energy consumption in China and America: An optimized structure-adaptative grey model," Energy, Elsevier, vol. 239(PA).
    7. Solaymani, Saeed, 2019. "CO2 emissions patterns in 7 top carbon emitter economies: The case of transport sector," Energy, Elsevier, vol. 168(C), pages 989-1001.
    8. Yang, Qing & Zhang, Lei & Zhang, Jinsuo & Zou, Shaohui, 2021. "System simulation and policy optimization of China's coal production capacity deviation in terms of the economy, environment, and energy security," Resources Policy, Elsevier, vol. 74(C).
    9. Tsai, Kang-Ting & Lin, Tzu-Ping & Hwang, Ruey-Lung & Huang, Yu-Jing, 2014. "Carbon dioxide emissions generated by energy consumption of hotels and homestay facilities in Taiwan," Tourism Management, Elsevier, vol. 42(C), pages 13-21.
    10. Dandan Liu & Dewei Yang & Anmin Huang, 2021. "LEAP-Based Greenhouse Gases Emissions Peak and Low Carbon Pathways in China’s Tourist Industry," IJERPH, MDPI, vol. 18(3), pages 1-15, January.
    11. Nassios, Jason & Giesecke, James A. & Dixon, Peter B. & Rimmer, Maureen T., 2020. "What impact do differences in financial structure have on the macro effects of bank capital requirements in the United States and Australia?," Economic Modelling, Elsevier, vol. 87(C), pages 429-446.
    12. Li, Yu & Zheng, Ji & Li, Zehong & Yuan, Liang & Yang, Yang & Li, Fujia, 2017. "Re-estimating CO2 emission factors for gasoline passenger cars adding driving behaviour characteristics——A case study of Beijing," Energy Policy, Elsevier, vol. 102(C), pages 353-361.
    13. Babasola Osibo & Simisola Adamo, 2023. "Data Centers and Green Energy: Paving the Way for a Sustainable Digital Future," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 12(11), pages 15-30, November.
    14. Andersen, Kristoffer S. & Termansen, Lars B. & Gargiulo, Maurizio & Ó Gallachóirc, Brian P., 2019. "Bridging the gap using energy services: Demonstrating a novel framework for soft linking top-down and bottom-up models," Energy, Elsevier, vol. 169(C), pages 277-293.
    15. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    16. Lifang Zhang & Jianzhou Wang & Zhenkun Liu, 2023. "Power grid operation optimization and forecasting using a combined forecasting system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 124-153, January.
    17. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    18. William Wills & Emilio Lebre La Rovere & Carolina Grottera & Giovanna Ferrazzo Naspolini & Gaëlle Le Treut & F. Ghersi & Julien Lefèvre & Carolina Burle Schmidt Dubeux, 2022. "Economic and social effectiveness of carbon pricing schemes to meet Brazilian NDC targets," Post-Print hal-03500923, HAL.
    19. Yue Dou & Muhammad Shahbaz & Kangyin Dong & Xiucheng Dong, 2022. "How natural disasters affect carbon emissions: the global case," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(3), pages 1875-1901, September.
    20. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.

    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:spr:endesu:v:25:y:2023:i:11:d:10.1007_s10668-022-02586-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.