Decarbonising Respiratory Care: The Impact of a Low‐Carbon Salbutamol Pressurised Metered‐Dose Inhalers
Bibliographic record
Abstract
ABSTRACT Background Rapid reductions in greenhouse gas (GHG) emissions are vital to combat climate change. Healthcare systems contribute ~5% of global GHG emissions, with pressurised metered‐dose inhalers (pMDIs) a significant contributor owing to their hydrofluorocarbon propellants. This study compared GHG emissions of a salbutamol pMDI using a low‐carbon propellant in clinical development, hydrofluoroalkane (HFA)‐152a, against current salbutamol inhalers. Methods Three ‘cradle‐to‐grave’ lifecycle analyses compared GHG emissions of salbutamol pMDIs with HFA‐152a and HFA‐134a, and salbutamol dry‐powder inhaler (DPI). Over 600 individual emission factors were calculated from > 2000 data points, and categorised into Active Pharmaceutical Ingredients Manufacture, Micronisation, Device, Formulation/Packaging, Use, Distribution and End‐of‐Life stages. 2023 manufacturing and supply data were collected from Algeria, Australia, Canada, France, Poland, Romania and Saudi Arabia. Carbon footprints were independently verified by the Carbon Trust. Results Mean total carbon footprint per salbutamol inhaler (based on 100‐year global warming potential [GWP100]) was 26.91, 2.06 and 0.69 kgCO 2 e for HFA‐134a pMDI, HFA‐152a pMDI and DPI, respectively. This equated to a 92% reduction in total emissions for HFA‐152a versus HFA‐134a. Most pMDI emissions came from the Use stage (HFA‐134a: 21.48 kgCO 2 e; HFA‐152a: 1.45 kgCO 2 e). Per actuation, emissions were 135, 10 and 11 gCO 2 e, respectively. For each pMDI, GHG emissions were similar across countries. Conclusions The low‐GWP propellant, HFA‐152a is expected to achieve > 90% reduction in the carbon footprint of salbutamol pMDI versus the currently used HFA‐134a. The development of salbutamol pMDI with HFA‐152a is a crucial step towards ensuring future patient access to salbutamol in a pMDI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".