Carbon Footprinting of North American Emergency Medical Services Systems
Bibliographic record
Abstract
OBJECTIVES: This study was undertaken to characterize the carbon emissions from a broad sample of North American emergency medical services (EMS) agencies, and to begin the process of establishing voluntary EMS-related emission targets. METHODS: Fifteen diverse North American EMS systems with more than 550,000 combined annual responses and serving a population of 6.3 million reported their direct and purchased ("Tier 2") energy consumption for one year. We calculated total carbon dioxide equivalent (CO(2)e) emissions using Environmental Protection Agency, Energy Information Administration, and locality-specific emission conversion factors. We also calculated per-response and population-based emissions. We report descriptive summary data. RESULTS: Participants included government "third-service" (n = 4), public utility model (n = 1), private contractor (n = 6), and rural rescue squad (n = 4) systems. Call volumes ranged from 800 to 114,280 (median 20,093; interquartile range [IQR] 1,100-55,217). Emissions totaled 46,941,690 pounds of CO(2)e (21,289 metric tons); 75% of emissions were from diesel or gasoline. For systems providing complete Tier 2 data, median emissions per response were 80.7 (IQR 65.1-106.5) pounds of CO(2)e and median emissions per service-area resident were 7.8 (IQR 4.7-11.2) pounds of CO(2)e. Two systems reported aviation fuel consumption for air medical services, with emissions of 2,395 pounds of CO(2)e per flight, or 0.7 pounds of CO(2)e per service-area resident. CONCLUSION: EMS operations produce substantial carbon emissions, primarily from vehicle-related fuel consumption. The 75th percentiles from our data suggest 106.5 pounds of CO(2)e per unit response and/or 11.2 pounds of CO(2)e per service-area resident as preliminary maximum emission targets. Air medical services can anticipate higher per-flight but lower population-based emissions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".