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 (age, gender, education, and geographic location). We use data from three nationally representative opinion polls by the Pew Research Center 2021 2023 and 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 purchase interest, with covariate effects (CEs)−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 CE (19.0–23.1 percentage points), and the impact of most demographic variables is consistent with the literature. The ordinal quantile models demonstrate significant variation in CEs across the distribution of purchase intent, offering insights beyond the ordinal probit model. We are the first to use quantile modeling to reveal how CEs differ significantly throughout the spectrum of purchase intent.Keywords: Decarbonization, electric vehicle, ordinal probit, Pew Research, quantile regression, technology adoption.