Electric vehicles in emergencies and evacuations: a review of resilience and future research directions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Disasters often require large-scale evacuations, and damage key infrastructure (e.g., power, transportation). With growing electric vehicle (EV) adoption and electrification of transportation, governments and utilities may face significant power challenges during disasters, especially during the evacuation stage. Low state-of-charge, sporadic charging infrastructure, or power outages could significantly hamper safe and effective evacuations. Yet, EVs also offer possible resilience benefits to emergency response by more easily charging electronics or sending power back to the grid through vehicle-to-grid (V2G) technology. This paper focuses on the opportunities, benefits, and drawbacks of EVs in disasters and evacuations through a systematic review of current literature, reports, and sources. Overall, this review discovered EVs show promise as modes of transportation and mobile energy supply units. However, crucial challenges such as charging infrastructure locations, upfront cost of resilience technologies, and user behavior necessitate more dedicated research to overcome shortcomings and guide more realistic implementation of benefits.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it