Development of the EEZ Mobility Modeling Tool to Guide the Equitable Installation of Public Charging Stations for Plug-in Electric Vehicles
Research Lead: Scott Moura
UC Campus(es): UC Berkeley
Problem Statement: Today, public electric vehicle (PEV) charging infrastructure is generally installed where there are significant numbers of consumers who have already purchased PEVs. This approach may initially seem logical, but it risks exacerbating environmental, transportation, and economic inequities as most current PEV owners are high-income, highly educated, and have off-street parking that can accommodate private charging equipment. The problem of charger access is magnified in areas with a higher number of multi-unit dwellings. Access to chargers is also worse in low-income and majority Black and Hispanic neighborhoods compared to high-income and majority White locations. If public charging infrastructure is consistently built based on past and present PEV ownership trends, and infrastructure availability is a major factor in choosing to purchase a PEV, then this creates a cycle that systematically excludes low-income communities of color. The current approach to identifying where to invest in public charging infrastructure fails to consider important factors like housing type, public transit availability, private charger availability, and commute patterns, which all play critical roles for achieving zero emissions mobility for all.
Project Description: This project will develop a modeling tool, "EEZ Mobility," for planning future placement and construction of electric charging infrastructure that provides equitable benefits to all. The model will use data from CalEnviroScreen—an analytical tool that can be used to identify communities that face pollution and socioeconomic disadvantage—in conjunction with census data, currently available charging infrastructure locations, PEV ownership rates, public transit availability, and commute patterns to robustly quantify the impact of increasing public charging infrastructure. The model will be able to estimate three key performance indicators: (i) economic benefit to PEV owners/drivers, (ii) economic benefit to infrastructure owners/operators, and (iii) emissions and health effects of increasing growth of PEVs compared to internal combustion engine vehicles. These metrics will capture the expected benefits of investing in public charger infrastructure by geographic areas, and our public-facing tool would provide robust information to guide the equitable distribution of public funds.
Status: In Progress