As California policy shifts towards decarbonizing transportation, it’s clear there will be full light-duty vehicle electrification in the coming decades. To ensure vulnerable groups are not left behind, this dissertation will develop complete population tools and models to estimate vehicle electrification burden, first-time electric vehicle adoption (EV) rates, and additional EV adoption rates in different types of households in California. Thus far, this dissertation has developed models for the latter two goals, utilizing surveys of EV adopters to estimate rates of first-time adoption and to estimate differences between those who purchase one EV from those who purchase multiple EVs. During the fellowship period, a complete population tool, i.e. a synthetic population, will be developed for the purpose of estimating the difficulty in electrifying vehicles in different types of households in California.
Synthetic populations are helpful in EV equity analyses because they allow researchers to examine multiple aspects of households and populations instead of a few aggregate statistics. This dissertation will develop a representative synthetic population of California including household and vehicle variables crucial to modeling EV adoption and equity. This tool will be used to estimate first-time adoption rates, identify groups likely to adopt multiple EVs, and detect groups that will find it difficult to adopt EVs at all. Finally, this work will define and develop a framework to determine vehicle electrification burden, which broadly will estimate the difficulty households face in acquiring, charging, and owning EVs.
