Are Individuals' stated preferences for electric vehicles (EVs) consistent with real-world EV ownership patterns?. (April 2021) - Jia, Wenjian; Chen, T. Donna
Highlights: Individual stated preferences and real-world EV ownership patterns are analyzed. Effects of age are inconsistent between the two study approaches. Effects of gender, education, and charging infrastructure are consistent. Combined analysis uncovers a more comprehensive picture of EV adoption patterns. Abstract: Previous studies on factors affecting electric vehicle (EV) adoption mainly rely on individual stated preference surveys or aggregate market share analyses. This paper applies both approaches to study EV adoption patterns in the state of Virginia to compare and contrast findings from the two methods. An individual-level vehicle fuel type choice model is developed based on a 2018 stated preference survey of 837 Virginia drivers. A county-level EV ownership model is developed using Department of Motor Vehicles' 2012–2016 vehicle registration data. Results show several consistent findings: 1) being male and having higher educational attainment have positive effects on EV adoption; 2) availability of DC fast charging stations is positively associated with EV adoption, particularly for battery electric vehicles (BEVs). However, the age effects are found to be inconsistent between the two study approaches: older individuals state negative preferences for EVs whereas counties with greater share of older populations are associated with more EV registrations. Additionally, the individual vehicle choice model complements the county-level EV ownership analysis by examining the effects of various vehicle-related attributes: 1) model results show positive effects of EV purchase incentives but negligible effects of EV annual use fees on EV preferences; 2) battery range is found to be significant for the utility of BEVs, but not for plug-in hybrid EVs; and 3) EV owners place greater importance on battery range and DC fast charging stations than non-EV owners. The combined analysis confirms several influential factors of EV adoption, and identifies instances when stated preference/interest are not consistent with real-world ownership patterns, which should be explicitly considered in EV policy making.