Location
Purdue Memorial Union
Event Description/Abstract
The Biden administration has recently stated as a major policy goal that 50% of all new vehicle sales will be electric by the year 2030. However, current electric vehicle (EV) uptake sits at about 2.4% of current vehicle sales. Additionally, the administration’s goal is more ambitious than current forecasts predict will occur. Given the gap between the administration’s goals and the current EV trends, it is necessary to better forecast how current trends will progress and better understand what factors influence these trends. The purpose of this research is two-fold: to create better forecasts of EV adoption at the zip code level and understand how strongly various factors influence adoption rates. To do so, we estimate EV adoption in California at the zip code level via a logistic diffusion model. We examine how adoption varies for battery electric vehicles (BEVs) versus plug-in hybrid EVs (PHEVs). Furthermore, we describe our current work to estimate how various factors – including sociodemographics, the built environment, and charging infrastructure – relate to the adoption parameters suggested by the forecast models. This research can provide valuable insights into adoption trends at a more local level and what factors may be best leveraged to promote adoption.
Included in
Forecasting Electric Vehicle Adoption at a Local Level
Purdue Memorial Union
The Biden administration has recently stated as a major policy goal that 50% of all new vehicle sales will be electric by the year 2030. However, current electric vehicle (EV) uptake sits at about 2.4% of current vehicle sales. Additionally, the administration’s goal is more ambitious than current forecasts predict will occur. Given the gap between the administration’s goals and the current EV trends, it is necessary to better forecast how current trends will progress and better understand what factors influence these trends. The purpose of this research is two-fold: to create better forecasts of EV adoption at the zip code level and understand how strongly various factors influence adoption rates. To do so, we estimate EV adoption in California at the zip code level via a logistic diffusion model. We examine how adoption varies for battery electric vehicles (BEVs) versus plug-in hybrid EVs (PHEVs). Furthermore, we describe our current work to estimate how various factors – including sociodemographics, the built environment, and charging infrastructure – relate to the adoption parameters suggested by the forecast models. This research can provide valuable insights into adoption trends at a more local level and what factors may be best leveraged to promote adoption.
