To Buy an Electric Vehicle or Not? A Bayesian Analysis of Consumer Intent in the United States

The adoption of electric vehicles (EVs) is considered critical to achieving climate goals, yet it hinges on consumer interest. This study explores how public intent to purchase EVs relates to four unexamined factors: exposure to EV information, perceptions of EVs' environmental benefits, views on government climate policy, and confidence in future EV infrastructure; while controlling for prior EV ownership, political affiliation, and demographic characteristics (e.g., age, gender, education, and geographic location). We utilize data from three nationally representative opinion polls conducted by the Pew Research Center between 2021 and 2023, and employ Bayesian techniques to estimate the ordinal probit and ordinal quantile models. Results from ordinal probit show that respondents who are well-informed about EVs, perceive them as environmentally beneficial, or are confident in development of charging stations are more likely to express strong interest in buying an EV, with covariate effects--a metric rarely reported in EV research--of 10.2, 15.5, and 19.1 percentage points, respectively. In contrast, those skeptical of government climate initiatives are more likely to express no interest, by more than 10 percentage points. Prior EV ownership exhibits the highest covariate effect (ranging from 19.0 to 23.1 percentage points), and the impact of most demographic variables is consistent with existing studies. The ordinal quantile models demonstrate significant variation in covariate effects across the distribution of EV purchase intent, offering insights beyond the ordinal probit model. This article is the first to use quantile modeling to reveal how covariate effects differ significantly throughout the spectrum of EV purchase intent.