Understanding the effects of vehicle portfolios on plug-in electric vehicles (PEVs)-adopting households’ vehicle replacement decision: an application of vehicles transaction model

Research on plug-in electric vehicles (PEVs) adoption to date has focused on understanding consumers' vehicle purchase decisions as single vehicle transactions. This research expands on this decision-making process by employing a vehicle transaction model to account for vehicle portfolio preferences at the household level. By leveraging discrete choice modeling techniques, specifically mixed multinomial logistic regression, we evaluate a sample of two-vehicle and PEV-adopting Californian households' vehicle transactions from 2017 to 2020. Our results demonstrate that there are strong complementarities among certain vehicle classes. Namely, PEV-adopting households are more likely to pair a car with a large truck or a SUV than with another car. Fuel type complementarities are also observed as households prefer to own a PEV and an internal combustion engine vehicle (ICEV) rather than a PEV-PEV portfolio. We also investigate households' income elasticity of choice for PEVs by quantifying their income sensitivity to the capital costs and operating costs of their vehicle portfolios. The implications of our work are two-fold: by applying the vehicle transaction model to empirical data, we estimate households’ preference parameters for PEVs attributes and portfolios. These results contribute to the growing literature on the quantitative understanding of vehicle replacement decisions for PEV-adopting households. Our work also has implications for the projection of vehicle fleets, where understanding how households take vehicle portfolio complementarities into account is essential for future projections of vehicle fleets.