This study presents a Bayesian framework for estimating electric vehicle (EV) conversion rates based on average daily vehicle kilometers traveled (ADVKT) in South Korea. Although maximizing the environmental benefits of EVs requires accounting for real-world driving patterns and vehicle usage, the current EV policies in South Korea largely focus on supply expansion and uniform subsidy schemes, with limited consideration of driver behavioral heterogeneity. Using 2023 national vehicle travel statistics and regional-level data, the study applies a Bayesian approach to estimate the posterior probability of EV conversion by ADVKT based on the ADVKT distributions of internal combustion engine vehicles (ICEVs) and EVs, with the overall EV conversion rate serving as the prior probability. The results reveal distinct conversion trends by vehicle type, usage, and region. Non-commercial passenger cars show peak conversion potential in the 70–75 km/day range across all regional classifications, supporting the feasibility of nationwide policies. In contrast, commercial vehicles (e.g., vans and trucks) exhibit more varied patterns, indicating the need for targeted approaches. A simulation-based validation demonstrates that the estimated conversion probabilities closely align with the observed distribution of EVs. These findings provide empirical guidance for distance-based EV subsidy design, charging infrastructure planning, and strategic vehicle targeting in South Korea’s transition to low-emission transport.
Byun et al. (Wed,) studied this question.