Evaluating a hospital’s carbon footprint – A method using energy, materials and financial data
Bibliographic record
Abstract
Background: Healthcare systems have to prepare for climate change’s health impact, while reducing healthcare’s contribution to global warming. Most evaluations of healthcare’s greenhouse gas emissions involve national level methodologies.Objective: As sustainability metrics become a key factor in hospital management, the paper describes a method for quantifying emissions at a large tertiary care hospital in Singapore.Methods: Hospital operational and financial data was used to determine the greenhouse gas effect of the hospital. Emission factors from government and academic sources were used for on-site and purchased energy emissions. Spend based emission factors from the environmentally-extended multiregional input-output (EE-MRIO) Eora database were used for other indirect emissions. This provided the total carbon footprint across the various scopes.Results:The hospital had an annual carbon footprint of 245,962 tonnes of carbon dioxide equivalents (CO2e). Scope 1 emissions accounted for 4,223 tonnes of CO2e, scope 2 for 38,380 tonnes of CO2e and scope 3 for 165,190 tonnes of CO2e. Operating carbon totalled 207,793 tonnes of CO2e, and 38,169 tonnes of scope 3 CO2e was attributed to capital expenditure projects. Medical equipment, pharmaceutical supplies and electricity were the largest contributors to the hospital’s carbon footprint.Conclusions: Identifying key areas contributing to emissions can enable targeted approaches in reducing a hospital’s carbon footprint, better preparing the hospital as the carbon economy evolves to include the healthcare sector.
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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 | 0.002 | 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) | 0.000 | 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".