More to offer from the Montreal protocol: how the ozone treaty can secure further significant greenhouse gas emission reductions in the future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Action under the Montreal Protocol has contributed to climate change mitigation for almost 35 years. The phase-out of ozone-depleting substances (ODS) has set the ozone layer on a path to recovery, protecting the world’s biosphere from harmful ultraviolet radiation. The 2016 Kigali Amendment to the Montreal Protocol is expected to avoid 5.6–8.7 gigatonnes of carbon-dioxide equivalent (GtCO2e) emissions of hydrofluorocarbons (HFC) per year by 2100, reducing the impact of HFCs on global average warming by up to 0.4°C. Despite its successes, unexpected emissions of phased out ODS – notably the chlorofluorocarbon, CFC-11 - have brought attention to shortcomings in the Protocol’s monitoring, reporting, verification and enforcement (MRV+E) which must be addressed to guarantee its controls are sustained. Meanwhile, additional significant mitigation could be achieved by accelerating the phase-down of HFCs under the Kigali Amendment, by tackling ODS and HFC emissions from leaking banks of equipment and products and by controlling feedstocks, which are not subject to Montreal Protocol phase-out controls. Recent scientific papers have linked almost 870 million tCO2 per year of greenhouse gases (GHG) and ODS to fluorochemical industrial processes and illegal fluorochemical production. Expanding the scope of the Montreal Protocol to address nitrous oxide (N2O), itself an ODS and GHG, would also contribute substantial ozone and climate benefits. This perspective essay discusses new and strengthened policy measures that governments can consider under the Montreal Protocol in order to maximize early, cost-effective reductions in emissions of non-CO2 greenhouse gases and ensure future implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it