Analysis and Prediction of New Energy Vehicle Development in China Based on Regression and Time Series Models

Abstract

The rapid expansion of new energy vehicles (NEVs) in China represents a vital element of the global transition toward sustainable transportation and carbon neutrality. This study develops a comprehensive modeling framework to quantitatively examine the main influencing factors and predict the future trajectory of China's NEV market. Grey relational analysis is first employed to determine that the density of public charging infrastructure and government subsidy policies exhibit the strongest correlation with NEV sales. Subsequently, an ARIMA (0,1,1) time series model is constructed and validated to forecast annual NEV sales in China for the period 2024-2033, revealing a consistent upward trend. To explore the global implications, a Vector Autoregressive (VAR) model is established, indicating a significant yet intricate relationship among the rise of NEVs, the traditional fuel vehicle industry, global oil prices, and associated R&D investments. Furthermore, a population competition model incorporating policy resistance factors is simulated to evaluate the potential impact of international trade barriers. The results show that although external resistance may decelerate growth, it cannot reverse the overall expansion of China's NEV industry. This research offers a solid theoretical foundation and data-driven insights for policymakers and industry stakeholders, emphasizing the necessity of continuous infrastructure investment and strategic policy support.