A Structured Review of Electric Vehicle Sales Research: Multi-Level Driving Factors and Forecasting Pathways over the Past Decade

Abstract

Under dual-carbon targets, electric vehicles (EVs) have become central to transport decarbonization, making EV sales a key indicator of market diffusion and policy effectiveness. Despite the growing body of research, studies on EV sales remain fragmented and lack systematic integration. This study provides a structured review of EV sales research published between 2016 and 2025. Based on searches in Scopus and Web of Science, 1518 records were identified, and 194 peer-reviewed journal articles were retained after a multi-stage screening process. Temporal analysis reveals a clear stage-based evolution of EV sales research, with limited publications prior to 2020 and a marked expansion after 2021. The literature is categorized into two main streams: (i) determinants of EV sales and (ii) forecasting approaches. For determinants, a macro–meso–micro analytical framework is developed to organize policy, market, and behavioral factors. For forecasting, quantitative analysis shows that econometric and statistical models remain dominant (54%), while machine learning (18%), behavior simulation (14%), hybrid models (8%), and deep learning (4%) are increasingly adopted. This indicates a gradual shift toward data-driven and model integration approaches. This review offers a structured synthesis of determinant mechanisms and forecasting paradigms, identifies methodological imbalances, and outlines future research directions toward improved multi-level integration and mechanism-based modeling of EV sales dynamics.