Skip to main content

Advertisement

SpringerLink
Log in

Future scenarios of China’s electric vehicle ownership: A modeling study based on system dynamic approach

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Promoting electric vehicles is an important way to achieve a low-carbon transport sector for the carbon peak and neutrality target. A better understanding of future scenarios of EV ownership is fundamental for more policy-relevant results. This study develops a system dynamic model for simulating EV ownership in China by considering the interactions among factors influencing the application of electric vehicles and synthetic scenarios designed. The result shows that under the baseline scenario, China’s EV ownership would increase by 36 times, from 4.3 to 157.2 million during 2020–2035, and the new EV sales will account for 60% of total car sales, meaning that it would reach the target of promoting electric vehicles set by the Chinese government. However, according to the scenario analysis, the efficient strategies to accelerate the promotion of EVs in China are speeding up the construction of charging facilities, improving battery lifespan, and reducing daily usage fees, which are estimated to increase the EV ownership in 2035 by 59.6%, 27.3%, and 26.8%, respectively. In comparison, the effect is not so obvious for measures like duty-free and subsidy policy. The developed model can offer policymakers a reliable tool and reference to assess the outcomes of policies to promote EV application ahead of their implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Azadeh, A., Neshat, N., Rafiee, K., & Zohrevand, A. M. (2011). An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: The case of Iran. Transportation Planning and Technology, 35(2), 221–240.

    Article  Google Scholar 

  • Bao, H., Guo, X., Liang, J., Lan, F., Li, J., Chen, G., & Mo, J. (2022). Relevance vector machine with optimal hybrid kernel function for electric vehicles ownership forecasting: The case of China. Energy Reports, 8, 988–997.

    Article  Google Scholar 

  • CATARC. (2021). China automotive industry yearbook 2021. China Automotive Technology & Research Center (CATARC).

  • Chen, X. (2020). Purchase tax exemption extending 2 years. Automotive Observer, 5, 78.

    Google Scholar 

  • CNBS. (2021). China statistical yearbook 2017–2021. National Bureau of Statistics of China (CNBS).

  • Dong, F., & Liu, Y. (2020). Policy evolution and effect evaluation of new-energy vehicle industry in China. Resources Policy, 67, 101655.

    Article  Google Scholar 

  • Gan, Y., Lu, Z., Cai, H., Wang, M., He, X., & Przesmitzki, S. (2020). Future private car stock in China: Current growth pattern and effects of car sales restriction. Mitigation and Adaptation Strategies for Global Change, 25(3), 289–306.

    Article  Google Scholar 

  • Guan, Y., Shan, Y., Huang, Q., Chen, H., Wang, D., & Hubacek, K. (2021). Assessment to China’s recent emission pattern shifts. Earth’s Future, 9(11), e2021EF002241.

    Article  CAS  Google Scholar 

  • Guo, J. (2020). Research on the actual operation of electric vehicles in China: The average daily travel of private passenger vehicles is about 65 kilometers. Accessed from 6 Jan 2022 https://zhuanlan.zhihu.com/p/150357157.

  • Hao, X., Lin, Z., Wang, H., Ou, S., & Ouyang, M. (2020). Range cost-effectiveness of plug-in electric vehicle for heterogeneous consumers: An expanded total ownership cost approach. Applied Energy, 275, 115394.

    Article  Google Scholar 

  • Hu, B., Na, L. I., Liu, Y., & Zheng, L. (2014). Prediction on private cars in Wuhan city based on system dynamics model. Journal of Wuhan University Technology, 36(1), 65–68.

    Google Scholar 

  • iResearch. (2021). White book of China's new energy vehicle industry 2020. Accessed from 6 Jan 2022 https://report.iresearch.cn/report_pdf.aspx?id=3713.

  • Jiang, S., Zhang, L., Hua, H., Liu, X., Wu, H., & Yuan, Z. (2021). Assessment of end-of-life electric vehicle batteries in China: Future scenarios and economic benefits. Waste Management, 135, 70–78.

    Article  Google Scholar 

  • Kester, J., Noel, L., Zarazua de Rubens, G., & Sovacool, B. K. (2018). Policy mechanisms to accelerate electric vehicle adoption: A qualitative review from the Nordic region. Renewable Sustainable Energy Reviews, 94, 719–731.

    Article  Google Scholar 

  • Koot, M., & Wijnhoven, F. (2021). Usage impact on data center electricity needs: A system dynamic forecasting model. Applied Energy, 291, 116798.

    Article  Google Scholar 

  • Li, K., Ma, T., Wei, G., Zhang, Y., & Feng, X. (2019). Urban Industrial water supply and demand: System dynamic model and simulation based on cobb-douglas function. Sustainability, 11(21), 5893.

    Article  Google Scholar 

  • Liu, X., Sun, X., Zheng, H., & Huang, D. (2021). Do policy incentives drive electric vehicle adoption? Evidence from China. Transportation Research Part A: Policy and Practice, 150, 49–62.

    Google Scholar 

  • Ma, L., Wu, M., Tian, X., Zheng, G., Du, Q., & Wu, T. (2019). China’s provincial vehicle ownership forecast and analysis of the causes influencing the trend. Sustainability, 11(14), 3928.

    Article  Google Scholar 

  • OECD. (2022). Real GDP long-term forecast. Organisation for Economic Co-operation and Development (OECD). Accessed from 6 Jan 2022 https://data.oecd.org/gdp/real-gdp-long-term-forecast.htm

  • Rietmann, N., Hügler, B., & Lieven, T. (2020). Forecasting the trajectory of electric vehicle sales and the consequences for worldwide CO2 emissions. Journal of Cleaner Production, 261, 121038.

    Article  CAS  Google Scholar 

  • SCPRC. (2020). New energy vehicle industry development plan 2021–2035. State council of the People's Republic of China (SCPRC). Accessed from 6 Jan 2022 http://www.gov.cn/zhengce/content/2020-11/02/content_5556716.htm.

  • SCPRC. (2021). Notice on subsidy policies for the promotionnew energy vehicles in 2022 State Council of the People's Republic of China (SCPRC). Accessed from 6 Jan 2022 http://www.gov.cn/zhengce/zhengceku/2021-12/31/content_5665857.htm.

  • UNDESA. (2019). World population prospects. United Nations Department of Economic and Social Affairs (UNDESA). Accessed from 6 Jan 2022 https://population.un.org/wpp2019/Download/Standard/Population/.

  • Wang, N., Tang, L., Zhang, W., & Guo, J. (2019). How to face the challenges caused by the abolishment of subsidies for electric vehicles in China? Energy, 166, 359–372.

    Article  Google Scholar 

  • Whelan, G. (2007). Modeling car ownership in Great Britain. Transportation Research Part A: Policy and Practice, 41(3), 205–219.

    Google Scholar 

  • Xiong, Y., & Wang, L. (2020). Policy cognition of potential consumers of new energy vehicles and its sensitivity to purchase willingness. Journal of Cleaner Production, 261, 121032.

    Article  Google Scholar 

  • Yuan, R., Guo, F., Qian, Y., Cheng, B., Li, J., Tang, X., & Peng, X. (2022). A system dynamic model for simulating the potential of prefabrication on construction waste reduction. Environmental Science and Pollution Research, 29(9), 12589–12600.

    Article  Google Scholar 

  • Yuan, X., & Cai, Y. (2021). Forecasting the development trend of low emission vehicle technologies: Based on patent data. Technological Forecasting Social Change, 166, 120651.

    Article  Google Scholar 

  • Zeng, Y., & Hesketh, T. (2016). The effects of China’s universal two-child policy. The Lancet, 388(10054), 1930–1938.

    Article  Google Scholar 

  • Zhang, X. W., & Chang, J. Y. (2012). Research on urban car ownership prediction based on PCA-BP neural network. Computer Simulation, 29(12), 376–379.

    Google Scholar 

  • Zhiyan Consulting. (2021). Analysis on the current situation and investment scale of China's new energy vehicle charging (changing) facilities in 2020. Accessed from 6 Jan 2022 https://www.chyxx.com/industry/202108/966512.html.

  • Zhu, Z., & Du, H. (2018). Forecasting the number of electric vehicles: A case of Beijing. IOP Conference Series Earth Environmental Science, 170, 042037.

    Article  Google Scholar 

Download references

Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (41901243; 41971259), the Social Science Foundation of Jiangsu Province of China (19GLC014), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (19KJB610018), the Philosophy and Social Science Foundation of the Jiangsu Higher Education Institutions of China (2019SJA0157), and the Startup Foundation for Introducing Talent of NUIST (2019r020).

Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, People’s Republic of China

    Fanyu Pu & Songyan Jiang

  2. College of Economics and Management, Nanjing Forestry University, Nanjing, 210037, People’s Republic of China

    Ling Zhang

Authors
  1. Fanyu Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Songyan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Songyan Jiang.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pu, F., Jiang, S. & Zhang, L. Future scenarios of China’s electric vehicle ownership: A modeling study based on system dynamic approach. Environ Dev Sustain 25, 10017–10028 (2023). https://doi.org/10.1007/s10668-022-02474-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-022-02474-5

Keywords

Search

Navigation