Factors Influencing Electric Vehicle Adoption: Coupling Machine Learning Models and Open-Source Data
Bibliographic record
Abstract
Electric vehicles (EVs) are increasingly promoted in many countries to reduce transportation-related greenhouse gas emissions, offering numerous environmental and economic benefits, such as lower tailpipe emissions and operating costs. However, predicting EV adoption entails access to comprehensive data sets. This study introduces a cost-effective alternative by leveraging open-source socioeconomic and demographic (SED) data from census records and machine learning (ML) models. Focusing on high-resolution geography at the dissemination area level, this study examines the association of SED characteristics, urbanization, annual vehicle kilometer traveled (VKT), and charging infrastructure to predict EV adoption. Ensemble ML models, particularly eXtreme Gradient Boosting, achieve superior predictive accuracy (up to 95%), with forward sequential feature selection identifying 18 key features that enhance model performance. Furthermore, Shapley Additive exPlanation analysis indicates that higher education, income, urbanization, and charging infrastructure availability are strong drivers of EV adoption. In contrast, high population density, longer VKT, and extended commuting durations pose barriers. This approach validates the existing determinants of EV uptake and introduces a scalable, reproducible framework for policymakers. This study demonstrates the feasibility of high-resolution spatial forecasting by leveraging publicly available data. In addition, it provides actionable insights to support targeted policies and infrastructure development to accelerate EV adoption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".