The transportation sector is a major contributor to global greenhouse gas emissions, making electric vehicle (EV) adoption critical for decarbonization. This study investigates EV adoption determinants in Turkey using explainable machine learning, focusing on economic, infrastructure, and attitudinal factors while exploring driver behavior and fuel-efficiency awareness. Data from 304 participants were collected; after excluding undecided responses, the final analytical sample comprised 232 participants. Multiple algorithms (Random Forest, XGBoost, Logistic Regression, and SVM) were evaluated, addressing class imbalance via SMOTETomek. SHAP analysis identified policy-relevant predictors. Results reveal that EV adoption intentions are primarily driven by perceived cost impact, EV knowledge, and charging infrastructure accessibility, showing substantially stronger effects than driver behavior. Exploratory analysis indicates that aggressive driving correlates with lower fuel-efficiency awareness, whereas maintenance and eco-driving support higher awareness. The best-performing Random Forest model achieved 89.36% accuracy and a 0.9348 F1-score. Rather than claiming novelty in ML application, this study contributes an interpretable framework and emerging-market evidence contrasting economic/infrastructure factors against behavioral variables. Findings provide actionable insights for policy, highlighting cost-focused incentives, infrastructure deployment, and targeted awareness campaigns.
Şişman et al. (Tue,) studied this question.