Forecasting and Impact Analysis of the Development Trends of China's New Energy Electric Vehicles Based on Time Series Causal Analysis

Qilian Ge; Enning Liu; Yingxin Qi

doi:10.54097/cn399g09

Authors

Qilian Ge
Enning Liu
Yingxin Qi

DOI:

https://doi.org/10.54097/cn399g09

Keywords:

ISM Model, Time Series Model, VAR Model, Granger Causality Test, LCA Model.

Abstract

New energy electric vehicles with numerous properties have experienced explosive growth in recent years. This research constructs an ISM model to analyze the key factors influencing the new energy vehicle market and develops both a Winters' additive model and an ARIMA model to forecast the development of the NEV industry from 2023 to 2033. The results show that, the development of the new energy vehicles in the following ten years as a growth trend. Moreover, Granger causality tests, conducted within the VAR framework, revealed that NEV sales growth and market share significantly impact policy effectiveness and market dynamics. Simultaneously, the environmental impact was assessed using a LCA model, which indicates that the development of new energy vehicles is beneficial to the ecological environment. The results contribute to the theoretical and empirical understanding of the NEV market, providing valuable insights for policy makers and industry stakeholders.

Downloads

Download data is not yet available.

References

[1] Şahinli M A. Potato price forecasting with Holt-Winters and ARIMA methods: A case study [J]. American Journal of Potato Research, 2020, 97(4): 336-346.

[2] Hansun S, Charles V, Indrati C R. Revisiting the Holt-Winters' additive method for better forecasting [J]. International Journal of Enterprise Information Systems (IJEIS), 2019, 15(2): 43-57.

[3] Sahai A K, Rath N, Sood V, et al. ARIMA modelling & forecasting of COVID-19 in top five affected countries [J]. Diabetes & metabolic syndrome: clinical research & reviews, 2020, 14(5): 1419-1427.

[4] Hamilton J D. Time series analysis [M]. Princeton university press, 2020.

[5] Kumar R, Kumar P, Kumar Y. Multi-step time series analysis and forecasting strategy using ARIMA and evolutionary algorithms [J]. International Journal of Information Technology, 2022, 14(1): 359-373.

[6] Shojaie A, Fox E B. Granger causality: A review and recent advances [J]. Annual Review of Statistics and Its Application, 2022, 9(1): 289-319.

[7] Amornbunchornvej C, Zheleva E, Berger-Wolf T Y. Variable-lag granger causality for time series analysis[C]//2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2019: 21-30.

[8] Anthony Bagherian, Analyzing the relationship between digitalization and energy sustainability: A comprehensive ISM-MICMAC and DEMATEL approach [J], Expert Systems with Applications, 2024, Volume 236.

[9] Weike Zhang, Does de-capacity policy enhance the total factor productivity of China's coal companies? A Regression Discontinuity design [J], Resources Policy, 2020, Volume 68.

[10] Zhang B, Lu Q, Wu P. Study on life-cycle energy impact of new energy vehicle car-sharing with large-scale application [J]. Journal of Energy Storage, 2021, 36: 102334.

[11] Yu R, Cong L, Hui Y, et al. Life cycle CO2 emissions for the new energy vehicles in China drawing on the reshaped survival pattern[J]. Science of the Total Environment, 2022, 826: 154102.

[12] Li L, Yue C, Ma S, et al. Life cycle assessment of car energy transformation: evidence from China [J]. Annals of Operations Research, 2023: 1-20.