Trends in greenhouse gas emissions from volatile anaesthetics in 41 countries: 2013–2023
Bibliographic record
Abstract
INTRODUCTION: Inhalational anaesthetics contribute to greenhouse gas emissions, leading to regulatory restrictions in some countries. This study analysed time trends of greenhouse gas emissions directly attributable to the use of volatile anaesthetic agents in 41 countries. METHODS: Sales data were obtained using data from IQVIA MIDAS® and national medicines agencies. We calculated the kilograms of carbon dioxide equivalents (based on global warming potential) per capita and percentage change in greenhouse gas emissions, from the emission of volatile anaesthetics from 2018 to 2023. RESULTS: Data were obtained for 41 countries, representing approximately 35% of the global population. Greenhouse gas emissions associated with volatile anaesthetic agents decreased in the 27 European Union nations and other 'western' countries included in the study (Australia, Canada, New Zealand, UK and USA), achieving in some cases below 0.5 kg of carbon dioxide equivalents per inhabitant. In contrast, several Asian countries showed a substantial increase in emissions, with South Korea and Japan reporting the highest values globally (approximately 2.5 kg of carbon dioxide equivalents per inhabitant). A secondary analysis restricted to European countries showed a 17-fold difference in per-capita carbon-equivalent emissions between the highest and lowest emitters, suggesting that recommendations on the use of volatile anaesthetic agents are implemented inconsistently. DISCUSSION: Our study highlighted large differences in the management of greenhouse gas emissions attributable to volatile anaesthetic use. While results show a decreasing trend in western countries, albeit with substantial variation, rising trends observed in many Asian countries may constitute a source of concern. The experience of nations that have phased out the highest impacting volatile anaesthetic agents show that reducing emissions below 0.5 kg of carbon dioxide equivalents per inhabitant is attainable. This should serve as a model for other systems, prompting implementation of educational initiatives and specific policies.
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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 | 0.000 | 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) | 0.003 | 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".