Strengthening the Global Regulation of Hydrofluorocarbons under the Montreal Protocol
Bibliographic record
Abstract
The Montreal Protocol on Substances that Deplete the Ozone Layer was developed to address the concern of stratospheric ozone depletion. It has been highly effective in addressing its primary concern and it is widely considered to be one of the most successful international law agreements to date. However, the Montreal Protocol had the effect of replacing chemicals that have a high potential to deplete ozone with hydrofluorocarbons, which are powerful greenhouse gases and short-lived climate pollutants. This resulted in the increased consumption of hydrofluorocarbons, which was contributing to climate change. Following considerable resistance and debate, hydrofluorocarbons were added to the list of substances controlled by the Montreal Protocol in 2016. The chapter provides an overview of the global regulation of hydrofluorocarbons, focusing particularly on the Montreal Protocol. The chapter highlights the successes of the ozone regime as well as its weaknesses, in relation to hydrofluorocarbons, and considers ways in which the ozone regime could be further strengthened to ensure its continued success. The chapter emphasises the importance of co-ordinating measures under different international law regimes to effectively regulate hydrofluorocarbons.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".