LAUSR

Abstract A significant increase in carbon footprint and energy requirements over the decades raised concerns among governments and policymakers. One of the primary contributors to this menace is the automotive sector, which heavily relied on gasoline vehicles. Electric vehicles (EVs) seem to be one of the promising steps towards reducing the carbon footprint and make the transportation sector energy efficient. However, a good forecast of EV demand and the development of related resources are significant challenges for policymakers worldwide. We use various diffusion models, specifically Gompertz, Logistic, Bass, and Generalized Bass, to simulate future EV demand, and in the process, discover multiple insights. We predicted the EV sales of 20 major countries and identified the clusters with the best-fit model for each country based on the accuracy metrics, namely, mean absolute percentage error and mean absolute deviation. A comparative analysis across four different forecasting models provides a new direction to envisage energy requirements. The modelling of external variables like charging infrastructure with the Generalized Bass diffusion model further improves the utility of this study. Sensitivity analysis of the models further reveals different diffusion scenarios and possible policy measures to improve EV acceptances, especially in the presence of an uncertain environment like COVID-19.