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<p>This empirical study examines electric vehicle (EV) penetration dynamics across OECD member states between 2010 and 2023. We construct a longitudinal panel by merging economic, infrastructural, and environmental metrics. Our analytical framework progresses through data preprocessing, principal component extraction, and cluster identification before implementing predictive architectures. We contrast traditional panel regression specifications with ensemble learning algorithms Random Forest, LightGBM, and XGBoost within a temporal forecasting structure accommodating cross-country variation. Generalized additive modeling enhances interpretation by revealing nonlinear relationships and discontinuities in adoption trajectories. Findings indicate ensemble techniques achieve superior forecasting performance compared to linear panel estimators, with pronounced advantages in multidimensional policy environments. National income levels, household purchasing power, and charging network density emerge as dominant adoption catalysts, while taxation frameworks and renewable generation capacity demonstrate context-dependent influence.</p>

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Predictive modeling of electric vehicle adoption in OECD nations: an empirical assessment

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Sadik Aden Dirir

摘要

This empirical study examines electric vehicle (EV) penetration dynamics across OECD member states between 2010 and 2023. We construct a longitudinal panel by merging economic, infrastructural, and environmental metrics. Our analytical framework progresses through data preprocessing, principal component extraction, and cluster identification before implementing predictive architectures. We contrast traditional panel regression specifications with ensemble learning algorithms Random Forest, LightGBM, and XGBoost within a temporal forecasting structure accommodating cross-country variation. Generalized additive modeling enhances interpretation by revealing nonlinear relationships and discontinuities in adoption trajectories. Findings indicate ensemble techniques achieve superior forecasting performance compared to linear panel estimators, with pronounced advantages in multidimensional policy environments. National income levels, household purchasing power, and charging network density emerge as dominant adoption catalysts, while taxation frameworks and renewable generation capacity demonstrate context-dependent influence.