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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".