The Frontiers of Society, Science and Technology, 2024, 6(3); doi: 10.25236/FSST.2024.060306.
Guo Jianqin1, Yan Xue2
1Zaozhuang University, Shandong, Zaozhuang, 277000, China
2School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China
Predicting the development prospect of new energy vehicles (NEVs) in China is of great significance for the rational planning of industrial policies, the improvement of industrial structure and industrial layout, and achieving the orderly development of NEVs market. Based on the Toyota hybrid electric vehicles (HEVs) diffusion time series data of 20 years, the author establishes bass diffusion model, estimates the corresponding parameters combining with the characteristics of China's industrial policy, and predicts the development trend of the NEVs industry in China in the three scenarios of maintaining the existing policy strength, policy backslide, and increasing policy strength. The results show that the target of 5 million vehicles ownership will be difficult to achieve in the existing policy strength and the government should adjust the policy strength in various aspects including subsidies to achieve it.
new energy vehicles; Bass model; predication; analogy method; scenario analysis
Guo Jianqin, Yan Xue. A Predicating Analysis of New Energy Vehicles Development Based on Bass Model. The Frontiers of Society, Science and Technology (2024), Vol. 6, Issue 3: 34-40. https://doi.org/10.25236/FSST.2024.060306.
[1] Tong, F., Lan, F. C., & Chen, J. Q. (2016). Analysis of factors affecting the new energy vehicle development and ownership forecast. Science and Technology Management Research, 36(17), 112-116.
[2] Jiang, Y. M., & Zhao, W. P. (2010). Application Research of Logistic Model in Private Car Ownership Prediction in China. Journal of Industrial Technological Economics, 29(11), 99-104.
[3] Wang, Z. Y., Wang, W., Dai, J. Z., et al. (2016). Forecasting of electric vehicle quantity based on the elastic coefficient and Shanxi vehicles ownership per thousand people method. Power System and Clean Energy, 32(05), 142-147.
[4] Massiani, J., & Gohs, A. (2015). The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies. Research in transportation economics, 50, 17-28.
[5] Ismail, Z., & Abu, N. (2013). New car demand modeling and forecasting using bass diffusion model. American Journal of applied sciences, 10(6), 536.
[6] Zhang, Q., & Liao, X. M. (2014). A Study on innovation diffusion of hybrid vehicles under the influence of patent citations. Machinery, 52, (07) 1-4.
[7] Zeng, M., Zeng, F. X., Zhu, X. L., et al. (2013). Forecast of Electric Vehicles in China Based on Bass Model. Electric Power, 46(1), 36-39.
[8] Liu, Y. Q., Wang, M., & Wang, J. Y. (2016). The predictive research on China's new energy vehicles market. Research on Economics and Management, 37(04), 86-91.
[9] Ren, B., Shao, L. N., & You, J. X. (2013). Development of a generalized Bass model for Chinese electric vehicles based on innovation diffusion. Soft Science, 27(04), 17-22.
[10] Ye, N., & Zhou, M. H. (2012). Analysis of influencing factors and promotion strategy of new energy vehicles adoption. Statistics & Decision, (18), 60-62.
[11] Yang, G. Z., M, Z. T., & Chai, M. (2013). System dynamics model and simulation based on improved Bass model. Statistics & Decision, (13), 21-24.
[12] Jensen, A. F., Cherchi, E., Mabit, S. L., et al. (2016).Predicting the potential market for electric vehicles. Transportation Science, 51(2), 427-440.
[13] Al-Alawi, B. M. , & Bradley, T. H. (2013). Review of hybrid, plug-in hybrid, and electric vehicle market modeling studies. Renewable & Sustainable Energy Reviews, 21(may), 190-203.