Assessment of intended electric vehicle charging behaviours during wildfire evacuations
Bibliographic record
Abstract
Electric vehicle (EV) adoption is a growing challenge for disaster planning, requiring resilient grids and strategies. With minimal research on EV user behaviour in an evacuation context, this study addresses this gap by developing a series of discrete choice models to understand the factors that impact EV charging behaviour in a hypothetical wildfire evacuation. Through a non-probability panel from the Canadian provinces of Alberta and British Columbia of people living in high/medium fire risk, we collected survey data (n = 1371) on intended actions, assuming a 400 km range EV. Results indicate diverse EV charging patterns, and no single charging location type nor one form of charging behaviour dominated across scenarios throughout the evacuation time period. Across all models, we found that EV ownership, a preference to reduce risk to property and family, intended evacuation choices, and past hazard experience influenced charging behaviour. Targeted grid improvements and strategic placement of both fixed and mobile charging stations would likely be sufficient to meet electricity demand from EVs in evacuations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".