A Structured Review of Electric Vehicle Sales Research: Multi-Level Driving Factors and Forecasting Pathways over the Past Decade
電気自動車販売研究の構造的レビュー：過去10年間のマルチレベル要因と予測経路 (AI 翻訳)
Guo-Ying Han, Zonglin Li
🤖 gxceed AI 要約
日本語
本レビューは、2016～2025年のEV販売研究を構造的に整理し、1518件の文献から194件を選定。要因分析ではマクロ・メソ・ミクロの枠組みを提示し、予測手法では計量モデル優位（54%）ながら機械学習等の台頭を確認。今後の研究はマルチレベル統合とメカニズムベースのモデリングが課題。
English
This review systematically structures EV sales research from 2016-2025, selecting 194 papers from 1518 records. It develops a macro-meso-micro framework for determinants and finds econometric models dominant (54%) but machine learning (18%) and other methods rising. Future research should focus on multi-level integration and mechanism-based modeling.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はEV普及に政策的注力しており、本レビューの要因分析や予測手法の整理は、国内自動車メーカーや政策担当者にとって市場動向把握や戦略立案の参考となる。また、SSBJ開示におけるScope1・2削減経路の検討にも示唆を与える。
In the global GX context
As EV adoption accelerates globally under transport decarbonization targets, this review provides a comprehensive map of sales determinants and forecasting methods. It helps researchers and policymakers understand the evolving landscape, especially the shift toward data-driven models, and supports national and corporate strategy for EV transition.
👥 読者別の含意
🔬研究者:Provides a structured taxonomy of EV sales determinants and forecasting methods, highlighting gaps and future directions for multi-level integration.
🏢実務担当者:Offers insights into key factors driving EV sales and the latest forecasting approaches, useful for market analysis and strategic planning.
🏛政策担当者:Summarizes policy effectiveness and market drivers, helping to design evidence-based incentives and infrastructure plans.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/wevj17030122first seen 2026-05-05 21:51:51
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。