Examining Emerging Risks of Vehicle Electrification in Emergency Medical Transport
Bibliographic record
Abstract
An increasing number of countries have begun to utilize electric ambulances (EAs) in emergency medical transport (EMT) to meet net-zero emission targets. However, the extended battery-recovery time and limited battery capacity of EAs pose significant risks to time-sensitive and efficiency-critical EMT. On the basis of this, we aim to examine the effect of battery recovery on the performance of the EMT system with EAs and explore the carbon-reduction benefits in deploying EAs compared to fuel-powered ones. We develop a queuing model to characterize the EAs using the EMT system with two battery-recovery strategies (plug-in charging and battery swapping) and derive its key performance indicators for risk assessment. The results illustrate that when the ambulance fleet is small and most of them are EAs, the throughput time for EMT increases significantly. However, with a larger ambulance fleet, incorporating EAs can deliver a level of transportation service comparable to that of the fuel-powered ambulances, especially when the battery-swapping strategy is employed. While the use of EAs raises the input costs, achieving a critical scale of EAs enables the reduced energy cost and the social cost of carbon to quickly offset the initial investment. Finally, this study proposes policy recommendations on the construction of battery-recovery infrastructure and the deployment scale and timing of vehicles, providing optimized solutions to balance the risks of using EAs with the safety of EMT.
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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 | 0.001 | 0.004 |
| 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".