Greenhouse gas emissions of a large, academic outpatient orthopedic center in the United States
Bibliographic record
Abstract
Introduction: Hospitals and health systems create pollution as a byproduct of their work improving people's personal health. Pollution can harm human health. As part of a broad effort to comprehensively quantify a health system's pollution, we started with one group of pollutants, greenhouse gases, at a freestanding outpatient orthopedic center (OC). Methods: OC has clinic rooms, imaging, administrative offices, and a small ambulatory surgery center. It was newly constructed and received LEED Silver certification in 2022. The Greenhouse Gas Protocol was used to categorize emissions into Scope 1 (direct), Scope 2 (indirect from purchased energy), and Scope 3 (supply and value chain) emissions for fiscal year 2023. Results: OC's total annual emissions were 11,049 metric tons of carbon dioxide equivalent (MTCO2e), with 2% from Scope 1, 17% from Scope 2, and 81% from Scope 3. Most Scope 3 emissions came from just three categories: patient transportation (52% of Scope 3 emissions), purchased goods and services (20%), and employee commuting (12%). Discussion: This initial study highlights the significant contribution of Scope 3 emissions to an outpatient center's greenhouse gas footprint. It specifically identifies patient travel as a major contributor to emissions; this is particularly important since patient travel is not always included in Greenhouse Gas Protocol healthcare assessments and patient travel can be mitigated in some circumstances by utilizing telemedicine. The emissions distribution across scopes is similar to other international hospitals, indicating generalizability, though the high proportion of patient travel emissions is unique to this outpatient-focused facility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".