Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2024

Abstract

This study explores the socioeconomic disparities observed in the early adoption of Electric Vehicles (EVs) in the United States. A multiagent deep reinforcement learning-based policy simulator was developed to address the disparities. The model, tested using data from Austin, Texas, indicates that neighborhoods with higher incomes and a predominantly White demographic are leading in EV adoption. To help low-income communities keep pace, we introduced tiered subsidies and incrementally increased their amounts. In our environment, with the reward and policy design implemented, the adoption gap began to narrow when the incentive was equivalent to an increase in promotion from 20% to 30%. Our study’s framework provides a new means for testing policy scenarios to promote equitable EV adoption. We encourage future studies to extend our foundational study by adding specifications.

Keywords

electric vehicle, incentive, subsidy, equity, reinforcement learning

Discipline

Transportation | Urban Studies and Planning

Research Areas

Integrative Research Areas

Publication

Applied Sciences

Volume

14

Issue

5

First Page

1

Last Page

16

ISSN

2076-3417

Identifier

10.3390/app14051826

Publisher

MDPI

Additional URL

https://doi.org/10.3390/app14051826

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