Electric Vehicle Adoption
A Machine Learning Approach to Inform Policy Decisions and Combat Climate Change
DOI:
https://doi.org/10.58445/rars.1733Keywords:
Electric Vehicles, Machine Learning, Policymaking, Climate Change, Clean Energy, Governmental PolicyAbstract
To combat climate change, this paper investigates numerous factors influencing Electric Vehicle
(EV) adoption across U.S. states from 2014 to 2022 using various multivariate machine learning
models. After testing multiple combinations and iterations of Gradient Boosting Machines,
Decision Trees, Neural Networks, and Regressors; RandomForestRegressor, XGBoost,
GradientBoostingRegressor, BayesianRidge, and LinearRegression were selected as the
optimal ensemble model. These models analyzed the impact of ten socioeconomic,
technological, operational, political, and policy variables on EV registrations per 10,000 people
and were trained on a curated dataset utilizing multiple sources rather than a pre-existing
dataset. Models were evaluated for quality of fit and robustness through control analysis and
repeated iterations; along with sensitivity analysis using the linear regression model. Contrary to
conventional thinking, findings through this work show that availability of charging infrastructure
was the most significant factor driving EV uptake, outweighing in-kind and financial policy
incentives. Notably, a 20% increase in charging infrastructure was linked to a 3.67% increase in
EV ownership. Statistically significant coefficients were also found for gas prices and motor fuel
taxes. These results provide novel insights for policymakers about the best practices to spur
greater EV adoption, suggesting that a strategy prioritizing infrastructure development could
more effectively promote EV uptake and accelerate the transition to a sustainable,
emissions-free future.
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