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Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios

Author

Listed:
  • Min Zhao

    (SILC Business School, Shanghai University, Shanghai 201800, China)

  • Yu Fang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Debao Dai

    (School of Management, Shanghai University, Shanghai 200444, China)

Abstract

Accurate forecasting of the power structure and sales volume of the automobile industry is crucial for corporate decision making and national planning. Based on the auto sales data from 2011 to 2022 compiled from the official website of the China Association of Automobile Manufacturers (CAAM), the total auto sales in China from 2023 to 2030 were firstly predicted using a combined GM (1,1), and quadratic exponential smoothing forecast model optimized by particle swarm algorithm. Subsequently, the vehicles were classified into the following four categories by power: traditional fuel vehicles, pure electric vehicles, plug-in hybrid vehicles, and hydrogen fuel cell vehicles. Then, based on vehicle sales data from 2015 to 2022, The Markovian model and the component data model based on hyperspherical transformation are used to predict the vehicle power structure from 2023 to 2030 under the natural evolution scenario and the consumer purchase intention dominant scenario, respectively. The results show that total vehicle sales in China are expected to reach 32.529 million units by 2030. Under the natural evolution scenario and the consumer purchase intention dominant scenario, China will achieve the planned target of 40% of the new car market in the sales of new energy vehicles in 2028 and 2026, respectively. By 2030, under the natural evolution scenario, the sales volume of traditional fuel vehicles in the new car market will be 54.83%, the proportion of pure electric vehicles will be 35.92%, the proportion of plug-in hybrid vehicles will be 9.23%, and the proportion of hydrogen fuel cell vehicles will be 0.02%. Under the consumer purchase intention dominant scenario, the proportions of the four power types are 36.51%, 48.11%, 15.28%, and 0.10%, respectively.

Suggested Citation

  • Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3985-:d:1076792
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    References listed on IDEAS

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    1. Wang, Kunlun & Zheng, Leven J. & Zhang, Justin Zuopeng & Yao, Hongjiang, 2022. "The impact of promoting new energy vehicles on carbon intensity: Causal evidence from China," Energy Economics, Elsevier, vol. 114(C).
    2. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Yong Zhang & Miner Zhong & Nana Geng & Yunjian Jiang, 2017. "Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    4. Ling-Ling Pei & Qin Li, 2019. "Forecasting Quarterly Sales Volume of the New Energy Vehicles Industry in China Using a Data Grouping Approach-Based Nonlinear Grey Bernoulli Model," Sustainability, MDPI, vol. 11(5), pages 1-15, February.
    5. Lin Ma & Manhua Wu & Xiujuan Tian & Guanheng Zheng & Qinchuan Du & Tian Wu, 2019. "China’s Provincial Vehicle Ownership Forecast and Analysis of the Causes Influencing the Trend," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    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. Lian Lian & Wen Tian & Hongfeng Xu & Menglan Zheng, 2018. "Modeling and Forecasting Passenger Car Ownership Based on Symbolic Regression," Sustainability, MDPI, vol. 10(7), pages 1-16, July.
    8. Minfeng Wu & Wen Chen, 2022. "Forecast of Electric Vehicle Sales in the World and China Based on PCA-GRNN," Sustainability, MDPI, vol. 14(4), pages 1-14, February.
    9. Zhu, Bangzhu & Wang, Kefan & Chevallier, Julien & Wang, Ping & Wei, Yi-Ming, 2015. "Can China achieve its carbon intensity target by 2020 while sustaining economic growth?," Ecological Economics, Elsevier, vol. 119(C), pages 209-216.
    10. Lin, Boqiang & Shi, Lei, 2022. "Do environmental quality and policy changes affect the evolution of consumers’ intentions to buy new energy vehicles," Applied Energy, Elsevier, vol. 310(C).
    11. Wang, Xiaoli & Huang, Lucheng & Daim, Tugrul & Li, Xin & Li, Zhiqiang, 2021. "Evaluation of China's new energy vehicle policy texts with quantitative and qualitative analysis," Technology in Society, Elsevier, vol. 67(C).
    12. Enci Liu & Jie Li & Anni Zheng & Haoran Liu & Tao Jiang, 2022. "Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    13. Yigang Wei & Zhichao Wang & Huiwen Wang & Yan Li & Zhenyu Jiang, 2019. "Predicting population age structures of China, India, and Vietnam by 2030 based on compositional data," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-42, April.
    14. Mingyang Zhang & Heyan Xu & Ning Ma & Xinglin Pan, 2022. "Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    15. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    16. Leonardo Bitencourt & Tiago Abud & Rachel Santos & Bruno Borba, 2021. "Bass Diffusion Model Adaptation Considering Public Policies to Improve Electric Vehicle Sales—A Brazilian Case Study," Energies, MDPI, vol. 14(17), pages 1-19, September.
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