Prospect and sustainability prediction of China's new energy vehicles sales considering temporal and spatial dimensions

Abstract

The development prospect and sustainability of new energy vehicles (NEVs) are facing numerous challenges under the coupling influence of various factors, which has become a major strategic issue in the automotive industry research within China. Establishing a prediction method to forecast the development trends of NEVs under the complex conditions is of great significance. This paper comprehensively addresses the relevant factors influencing the sales of NEVs over temporal depth and spatial breadth, including random white noise and lag effect resulting from lag operators of influencing factors. A multivariate forecasting approach utilizing the ARIMA model and its optimized model, alongside the GBDT model were adopted to forecast NEV sales in China. The findings demonstrate that: 1) NEVs have exhibited a trend of rapid growth, but are still susceptible to random interference and lag effects caused by influencing factors. Key technology breakthroughs, accessibility of charging infrastructure, and national policy support remain as primary drivers of NEVs development; 2) The ARIM–IO–LSTM model achieved high prediction accuracy with a goodness-of-fit R2 exceeding 90%, outperforming the ARIMA and ARIMA-IO models by 23.586% and 20.790%, respectively, while the ARIMA-IO model improved accuracy by 5.126% compared to ARIMA. The method of model combination and outlier incorporation have proved to be practical solutions for strengthening model accuracy. 3) In the next 3–5 years, the NEV industry will enter a stage characterized by steady progress with an upward trend. Adapting the intensity, scope, and direction of policy support, coupled with achieving technological breakthroughs and enhancing infrastructure construction, are essential for sustaining industrial development and attaining desired objectives.