Technical note: A method for calculating offsets to ozone depletion and climate impacts of ozone-depleting substances
Bibliographic record
Abstract
Abstract. By phasing out production and consumption of most ozone-depleting substances (ODSs), the Montreal Protocol on Substances that Deplete the Ozone Layer (Montreal Protocol) has avoided consequences of increased ultraviolet (UV) radiation and will restore stratospheric ozone to pre-1980 conditions by mid-century, assuming compliance with the phaseout. However, several studies have documented an unexpected increase in emissions and suggested unreported production of trichlorofluoromethane (CFC-11) and potentially other ODSs after 2012 despite production phaseouts under the Montreal Protocol. Furthermore, because most ODSs are powerful greenhouse gases (GHGs), there are significant climate protection benefits in collecting and destroying the substantial quantities of historically allowed production of chemicals under the Montreal Protocol that are contained in existing equipment and products and referred to as ODS “banks”. This technical note presents a framework for considering offsets to ozone depletion, climate forcing, and other environmental impacts arising from occurrences of unexpected emissions and unreported production of Montreal Protocol controlled substances, as recently experienced and likely to be experienced again. We also show how this methodology could be applied to the destruction of banks of controlled ODSs and GHGs or to halon or other production allowed under a Montreal Protocol Essential Use Exemption or Critical Use Exemption. Further, we roughly estimate the magnitude of offset each type of action could provide for ozone depletion, climate, and other environmental impacts that Montreal Protocol Parties agree warrant remedial action.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.012 |
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".