Transportation is one of the primary contributors to U.S. carbon emissions, which is why encouraging drivers to switch to electric vehicles (EVs) is an important part of lowering those emissions. Financial incentives, like subsidies or tax rebates, have been shown to be effective at improving adoption of electric vehicles, but many incentive designs are economically inefficient and primarily benefit high-income drivers. Tax credits, for instance, benefit those who can afford the full up-front purchase price of an EV and are able to wait for the credit to arrive later at the end of the tax year. Moreover, available data on the effectiveness of incentive programs are largely historical, meaning they are predominantly based on the behavior of early adopters, who tend to be both whiter and wealthier than the population as a whole. This grant supports work by John Helveston of George Washington University who will field a survey that will measure consumer preferences for different EV financial incentive features to gain insight from a more diverse population than is reflected in currently available data. The survey will ask respondents to choose among different alternative options that can be used to encourage EV adoption. In this case, the survey will ask about different incentive design features, such as the amount of the incentive, how it is provided (for instance, as a sales tax exemption, tax credit, deduction, or rebate), who is providing it (such as a government entity or car dealer), and when it is provided (such as at the time of sale or during annual tax filing). The survey will be distributed to at least 2,000 U.S. vehicle buyers of varying age, income, and race via an online platform. It will also collect other relevant data, such as the size of the car that the respondent plans on purchasing or whether they are looking to purchase a new or used vehicle. The resulting data, which will be among the most detailed of its kind, will be used to analyze how alternative incentive design features (or combinations of features) might differentially affect consumer behavior across different demographic groups.