U.S. Light-Duty BEV Adoption and Electricity Demand Projections, 2026–2040 — Data and Code

Description

This dataset accompanies a forthcoming paper in Energy Reports forecasting U.S. light-duty battery electric vehicle (BEV) adoption and incremental electricity demand for all 50 states and the District of Columbia, 2026–2040. The deposit includes the underlying historical BEV new-car sales share series at both the national level (2013–2025) and the state level for all 51 jurisdictions over the same period, the five state-level structural variables used in the ensemble OLS ceiling regression (urban share, charging infrastructure density, gasoline price, median household income, and ZEV-mandate indicator), the regression output for the four model specifications, and complete state-level projection outputs for three scenarios (conservative, base, accelerated) covering both BEV adoption and incremental electricity demand. The OLS ensemble regression, logistic projection, and bottom-up electricity demand model were implemented in Microsoft Excel; the national Bayesian MCMC estimation of the logistic saturation parameter was implemented separately in Python (PyMC 5.x). Vehicle-category-specific energy consumption parameters (annual vehicle miles traveled and kWh/mile) used in the demand model are reported in supplementary Table S4 of the accompanying paper.

Institutions

• Georgetown University

  District of Columbia, Washington

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