Drivers of Electric Vehicle Demand in the United States: A Random Coefficient Logit Model Approach

Abstract

Abstract Research suggests that greenhouse gas (GHG) emissions contribute to global health and environmental degradation, mainly because of heavy reliance on fossil fuel-based transportation. Electric vehicles (EVs) have gained attention for their potential to reduce transportation-related GHG emissions. However, to fully understand the demand function of EVs, it is essential to examine the characteristics that make EVs attractive to customers. While much focus on increasing EV adoption has been placed on government incentives, understanding the specific characteristics of EVs that drive consumer adoption is equally important. Although the EV market has seen growth and some stability in recent years, what drives consumers to adopt EVs still needs to be explored. As EV adoption increases, it becomes critically important to understand the EV characteristics that influence consumer purchasing decisions. This study aims to employ a random coefficient logit model (BLP) to examine how EV characteristics, such as driving range, charging time, and price, influence consumer purchasing decisions and how consumer demographics, such as education attainment, place of residency, and income, affect EV consumer purchasing decisions. To achieve this research objective, we analyze county-level aggregate EV sales and demographic data in California from 2012 to 2022. Results suggest that income does not significantly impact price sensitivity. However, education level and urban residence influence consumer sensitivity to price changes. Specifically, the study found that highly educated consumers are more sensitive to price changes, while urban residents are less sensitive to price increases. Additionally, our findings reveal that income and education do not significantly influence preferences for increased EV driving range, whereas urban residents prefer increased driving range less than rural residents. Furthermore, results suggest that income and education do not influence preferences around charging times for EVs. Interestingly, urban residents are less sensitive to charging time than rural residents. Finally, our analysis suggests that preferences for EVs significantly depend on the EV characteristics, price, and consumer socio-demographic factors. Overall, this research highlights the importance of EV characteristics and price for EV adoption and provides valuable insights for policymakers and automakers in the EV industry