A Two-sided Model for EV Market Dynamics and Policy Implications

TL;DR

This paper models the indirect network effects between EV adoption and charging infrastructure, providing insights into policy impacts and future market projections in urban areas like Los Angeles.

Contribution

It introduces a two-sided regression model to quantify the EV and EVCS network effects and offers policy recommendations to meet future EV market targets.

Findings

A 1% increase in EVCS correlates with a 0.35% increase in EV sales.

Forecasts show current policies are insufficient to reach 80% EV market share by 2045.

Policy adjustments with increased incentives could bridge the market share gap.

Abstract

The diffusion of Electric Vehicles (EVs) plays a pivotal role in mitigating greenhouse gas emissions, particularly in the U.S., where ambitious zero-emission and carbon neutrality objectives have been set. In pursuit of these goals, many states have implemented a range of incentive policies aimed at stimulating EV adoption and charging infrastructure development, especially public EV charging stations (EVCS). This study examines the indirect network effect observed between EV adoption and EVCS deployment within urban landscapes. We developed a two-sided log-log regression model with historical data on EV purchases and EVCS development to quantify this effect. To test the robustness, we then conducted a case study of the EV market in Los Angeles (LA) County, which suggests that a 1% increase in EVCS correlates with a 0.35% increase in EV sales. Additionally, we forecasted the future EV market dynamics in LA County, revealing a notable disparity between current policies and the targeted 80% EV market share for private cars by 2045. To bridge this gap, we proposed a combined policy recommendation that enhances EV incentives by 60% and EVCS rebates by 66%, facilitating the achievement of future EV market objectives.