Revisiting the challenges of ozone depletion from a prospective LCA perspective
Bibliographic record
Abstract
Currently, the main focus of prospective LCA research is the assessment of climate change. Still, there is a lack of understanding regarding the specific challenges of other impact categories, for example, the ozone depletion potential (ODP). Therefore, this work presents a review of recent studies regarding current ozone layer trends, future ozone-depleting substance (ODS) life cycle modelling, and characterisation factors to define strategies for assessing the ODP in prospective LCA studies. It was found that the phase-out of ODS due to the Montreal Protocol is currently not well represented in background databases, potentially resulting in large overestimations of the ODP by banned substances. These overestimations will be more important for prospective studies as the use of banned substances decreases. The review has also shown that, to date, anthropogenic N2O emissions, instead of halocarbons, are the most important contribution to ozone depletion. However, the current standard characterisation models for ozone depletion have not yet covered these emissions. In addition, several interlinkages with climate change were found. Based on these insights, recommendations are given for future work to improve the quality of inventory modelling and ODP impact assessment in prospective LCA. For example, strategies for N2O characterisation in prospective LCA will require geographical, temporal and scenario-based differentiation, as the ODP of N2O depends on the atmospheric temperature, CO2, CH4 and chlorine levels. More generally, this work showcases the importance of analysing the challenges of prospective LCA for each impact category individually and collectively, due to potential interlinkages.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".