Carbon Footprinting of Emergency Medical Services Systems: A Proof-of-Concept Study
Bibliographic record
Abstract
OBJECTIVE: In this proof-of-concept study, we evaluated the availability of emergency medical services (EMS) system energy consumption data required to calculate a carbon footprint. METHODS: Two diverse North American EMS systems with more than 125,000 combined annual unit responses agreed to report their energy consumption for the last fiscal or calendar year using a data-collection tool based on Carbon Trust recommendations. They also identified the source of information (e.g., bills, logs, receipts), whether the amounts reported were directly measured or estimated, and whether any of the amounts were prorated from shared facilities (e.g., electricity for a shared office building). For this proof-of-concept study, we report only descriptive data about the availability of data and aggregate carbon emissions. RESULTS: Both systems reported diesel fuel, gasoline, and electricity consumption. One system used natural gas; one system used aviation fuel. Direct measurement of consumption using utility bills and statements was possible for these energy types. One system prorated natural gas and electricity usage; one system was able to estimate commercial air travel. Annual carbon dioxide (CO(2)) emissions for these two systems totaled 11.1 million pounds of CO(2). The largest source of CO(2) emissions was diesel fuel (39%), followed by electricity (23%). CONCLUSION: These EMS systems were able to provide the data necessary to determine their carbon footprints. Future research could include broader study to establish EMS-specific norms for carbon emissions, benchmarking of these metrics between different EMS systems, and the assessment of programs designed to reduce EMS carbon 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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".