Measurement of Regional Electric Vehicle Adoption Using Multiagent Deep Reinforcement Learning

Abstract

This study investigates socioeconomic disparities in the early adoption of Electric Vehicles (EVs) in the United States and proposes a solution using a multiagent deep reinforcement learning-based policy simulator. Testing this model with data from Austin, Texas, reveals that neighborhoods with higher incomes and predominantly White demographics lead in EV adoption. To address disparities, tiered subsidies were introduced, with increasing amounts for low-income communities. Results show that narrowing the adoption gap began when incentives increased from 20% to 30% promotion equivalent. This framework offers a novel approach to testing policy scenarios for promoting equitable EV adoption, with potential for further development and expansion in future studies.

Team

Seung Jun Choi Junfeng Jiao

Author Contributions

Conceptualization, S.J.C.; methodology, S.J.C.; validation, S.J.C.; formal analysis, S.J.C.; investigation, S.J.C.; resources, J.J.; data curation, S.J.C.; writing—original draft preparation, S.J.C.; writing—review and editing, J.J.; visualization, S.J.C.; supervision, J.J.; project administration, J.J.; funding acquisition, J.J. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.dfwcleancities.org/evsintexas, and https://www.socialexplorer.com/ explore-tables.

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For more information, please visit: https://www.mdpi.com/2076-3417/14/5/1826