买不买电动汽车？美国消费者意向的贝叶斯分析（英）

About the AuthorsNafisa Lohawalais a fellow at Resources for the Future (RFF). She earned a PhDin economics at the University of Michigan after receiving a BS-MS dual degree ineconomics with a minor in computer science and engineering (algorithms) from theIndian Institute of Technology, Kanpur. Lohawala’s research lies at the intersection ofindustrial organization, energy economics, and public financeMohammad Arshad Rahmanis an associate professor in the Department ofEconomic Sciences at the Indian Institute of Technology Kanpur (IITK), India. Hisresearch interests include Bayesian Econometrics, Quantile Regression, DiscreteChoice Modeling, Markov chain Monte Carlo Techniques, Machine Learning, EnergyEconomics, and Applied Econometrics.AcknowledgementsWe thank Beia Spiller, Deep Mukherjee, and Joshua Linn for helpful commentsand suggestions, and the RFF communications team for their assistance withdissemination. All remaining errors are our own. About RFFResources for the Future (RFF) is an independent, nonprofit research institution inWashington, DC. Its mission is to improve environmental, energy, and natural resourcedecisions through impartial economic research and policy engagement. RFF iscommitted to being the most widely trusted source of research insights and policysolutions leading to a healthy environment and a thriving economy.Working papers are research materials circulated by their authors for purposes ofinformation and discussion. They have not necessarily undergone formal peer review.The views expressed here are those of the individual authors and may differ from thoseof other RFF experts, its officers, or its directors.Sharing Our WorkOur work is available for sharing and adaptation under an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. Youcan copy and redistribute our material in any medium or format; you must giveappropriate credit, provide a link to the license, and indicate if changes were made,and you may not apply additional restrictions. You may do so in any reasonablemanner, but not in any way that suggests the licensor endorses you or your use.You may not use the material for commercial purposes. If you remix, transform, orbuild upon the material, you may not distribute the modified material. For moreinformation, visithttps://creativecommons.org/licenses/by-nc-nd/4.0/. To Buy an Electric Vehicle or Not? A Bayesian Analysis ofConsumer Intent in the United StatesNafisa LohawalaResources for the Future, Washington DC, USAMohammad Arshad Rahman∗Department of Economic Sciences, Indian Institute of Technology Kanpur, India.AbstractThe adoption of electric vehicles (EVs) is considered critical to achieving climate goals, yet ithinges on consumer interest. This study explores how public intent to purchase EVs relates to fourunexamined factors (exposure to EV information, perceptions of EVs’ environmental benefits, viewson government climate policy, and confidence in future EV infrastructure) while controlling for priorEV ownership, political affiliation, and demographic characteristics (age, gender, education, andgeographic location). We use data from three nationally representative opinion polls by the PewResearch Center 2021–2023 and Bayesian techniques to estimate the ordinal probit and ordinalquantile models. Results from ordinal probit show that respondents who are well informed aboutEVs, perceive them as environmentally beneficial, or are confident in development of chargingstations are more likely to express strong purchase interest, with covariate effects (CEs)−a metricrarely 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 than10 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 quantilemodels demonstrate significant variation in CEs across the distribution of purchase intent, offeringinsights beyond the ordinal probit model. We are the first to use quantile modeling to reveal howCEs differ significantly throughout the spectrum of purchase intent.Keywords:Decarbonization, electric vehicle, ordinal probit, Pew Research, quantile regression,technology adoption.Acknowledgements:We thank Beia Spiller, Deep Mukherjee, and Joshua Linn for helpful comments and sug-gestions, and the RFF communications team for their assistance with dissemination.All remaining errors are ourCorresponding authorEmail addresses:nlohawala@rff.org(Nafisa Lohawala),marshad@iitk.ac.in(Mohammad Arshad Rahman) own.∗ 1. IntroductionTransportation is the largest source of greenhouse gas emissions in the United States, represent-ing 28 percent of the total emissions in 2022, with light-duty vehicles responsible for more than half(Environmental Protection Agency, 2024).Electric vehicles (EVs) are widely reg