Life cycle assessment of medical oxygen
Bibliographic record
Abstract
We use life cycle assessment to model the environmental impacts of medical oxygen supply to hospitals. Although medical oxygen accounts for only 1% of global liquid oxygen production, it serves life-saving purposes in the healthcare sector, which is increasingly grappling with its environmental burdens. Considering six indicators in the TRACI impact assessment method, we estimate the total environmental impacts of bulk liquid oxygen – by far the dominant supply pathway in North America – as follows: global warming potential of 0.49 kg CO2 eq., fossil fuel depletion of 0.90 MJ surplus, carcinogenic toxicity of 6.2 × 10−8 CTUh, non-carcinogenic toxicity of 2.1 × 10−7 CTUh, respiratory effects of 2.8 × 10−4 PM2.5 eq., and ecotoxicity of 15 CTUe, per oxygen bed day, assuming a flow rate of 2 L/min. These impacts are primarily driven by electricity used to produce liquid oxygen via cryogenic distillation. Alternatively, liquid oxygen can be converted to gaseous form and shipped to hospitals in cylinder format – with substantially increased environmental impacts from the additional container and transportation. Medical oxygen can also be produced in gaseous form via pressure swing adsorption technology, either in an on-site plant or in a portable oxygen concentrator at the patient bedside. These alternatives may modestly reduce environmental impacts compared to liquid oxygen production, though the lower purity oxygen produced is less prevalent in clinical practice. We highlight several key variables affecting the environmental impacts of each medical oxygen supply pathway, including the location of production facilities (and corresponding electricity grids), losses in the supply-chain, and clinical practice (such as the choice of oxygen purity and flow rate). Limitations of our study include a lack of primary empirical data collection and a cradle-to-gate scope that omits other aspects of oxygen therapy such as hospital energy and water use (allocated to the procedure), wastes generated, and the production of medical equipment and consumables used. Ultimately, the results of our study suggest that medical oxygen production accounts for less than 1% of the total carbon footprint of healthcare in Canada.
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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 | 0.000 |
| 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) | 0.003 | 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".