시나리오기법 및 선택실험법 기반 한국 전기차 시장 수요예측 연구

This study presents a scenario-based, long-term forecasting model of electric vehicle (EV) sales. A simulated choice experiment using the multinomial hybrid choice model was estimated, where consumer utility was dependent on vehicle type, total cost of ownership (TCO), and personal variables. Results show that lower TCO, lower range-anxiety, and greater environmental concern lead to greater choice probability for EVs. Using the parameters of the TCO variable and the intercept, an initial probability of choosing an EV over an internal combustion engine car was derived. To this initial estimate, macro factors were incorporated, including charging infrastructure, oil prices, battery technology, and government incentives. Charging infrastructure was modeled as the convenience factor; battery technology and government incentives were modeled as affecting vehicle costs; and oil prices as affecting the operating cost. Based on long-term changes in the macro factors, three scenarios were selected: the reference scenario, the low-battery cost scenario, and the high oil price scenario. The forecasting model shows that long-term changes in oil prices are much more important than long-term changes in battery cost in terms of determining the adoption of electric vehicles. This is because fuel cost, over time, has a bigger impact on TCO, whereas the importance of battery prices on TCO decreases. Results also indicate that convenience is the main inhibitor of EV adoption, implying that the government’s policy should transition from providing cash-back incentives to building better charging infrastructure.