Do Determinants of EV Purchase Intent vary across the Spectrum? Evidence from Bayesian Analysis of US Survey Data

TL;DR

This study tackles whether the determinants of EV purchase intent differ across its entire distribution by applying Bayesian ordinal probit and ordinal quantile models to three nationally representative Pew polls (2021–2023) and analyzing four key predictors plus standard covariates. The ordinal probit results show consistent positive associations for information exposure, environmental-benefit beliefs, and confidence in charging infrastructure, with negative associations for views that government climate action is excessive; prior EV ownership and demographic factors further shape intent. The ordinal quantile analysis reveals substantial heterogeneity: covariate effects strengthen at higher intent quantiles and weaken at lower ones, illustrating that determinants are not uniform across the distribution. These findings imply targeted information campaigns and credible, long-run infrastructure planning can meaningfully accelerate adoption, especially when combined with policy messaging tailored to skeptical groups.

Abstract

While electric vehicle (EV) adoption has been widely studied, most research focuses on the average effects of predictors on purchase intent, overlooking variation across the distribution of EV purchase intent. This paper makes a threefold contribution by analyzing four unique explanatory variables, leveraging large-scale US survey data from 2021 to 2023, and employing Bayesian ordinal probit and Bayesian ordinal quantile modeling to evaluate the effects of these variables-while controlling for other commonly used covariates-on EV purchase intent, both on average and across its full distribution. By modeling purchase intent as an ordered outcome-from "not at all likely" to "very likely"-we reveal how covariate effects differ across levels of interest. This is the first application of ordinal quantile modeling in the EV adoption literature, uncovering heterogeneity in how potential buyers respond to key factors. For instance, confidence in development of charging infrastructure and belief in environmental benefits are linked not only to higher interest among likely adopters but also to reduced resistance among more skeptical respondents. Notably, we identify a gap between the prevalence and influence of key predictors: although few respondents report strong infrastructure confidence or frequent EV information exposure, both factors are strongly associated with increased intent across the spectrum. These findings suggest clear opportunities for targeted communication and outreach, alongside infrastructure investment, to support widespread EV adoption.

Figures (3)

• Figure 1: Stacked bars displaying the percentage of observations corresponding to the four opinion categories for a selected list of covariates. In each panel, the first stacked bar shows the percentage of observations for 'not at all likely' and the cumulative percentage for 'not at all likely' and 'not too likely'. The second stacked bar shows the percentage of observations for 'somewhat likely' and the cumulative percentage for 'somewhat likely' and 'very likely'.
• Figure 2: A pictorial representation of the distribution of the latent variable $z$. The four probabilities P$(y_{i}=1)$, P$(y_{i}=2)$, P$(y_{i}=3)$ and P$(y_{i}=4)$ correspond to the responses: 'not at all likely', 'not too likely', 'somewhat likely', and 'very likely', regarding the intention to purchase an electric vehicle ($z_{i}$) for individual $i$ with mean $x'_{i}\beta$.
• Figure 3: The figure presents the covariate effects for some selected variables from the April 2021 and May-June 2023 surveys, spanning the 5th to 95th quantiles with 5th quantile increments. Legend: $\Delta \Pr(\textrm{Not at all likely})$: ; $\Delta \Pr(\textrm{Not too likely})$: ; $\Delta \Pr(\textrm{Somewhat likely})$: ; $\Delta \Pr(\textrm{Very likely})$: .