HFC mitigation pathways compatible with 1.5 oC in the context of the Kigali Amendment
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) are widely used as cooling agents in refrigeration and air conditioning, as solvents in industrial processes, and as fire-extinguishing agents, foam blowing, and aerosol propellants. They have been used in large quantities as the primary substitutes for ozone-depleting substances regulated under the Montreal Protocol. HFC refrigerant gases are the fastest-growing atmospheric greenhouse gases and amongst the most potent. The Kigali Amendment (KA) to the Montreal Protocol is an international agreement to gradually reduce the consumption and production of HFCs. It is well established that full global compliance with the current KA phasedown schedule will not mitigate future emissions at levels consistent with the Paris Agreement target to limit global warming in this century to 1.5°C, compared to pre-industrial levels. Even though full compliance with the KA phasedown schedule is projected to achieve substantial avoided warming (0.2 to 0.4°C) by the end of this century but is insufficient to achieve a 1.5°C-consistent target. Therefore, in this study, we present HFC emissions in the pre-Kigali baseline scenario analyze HFC phase-down under the Kigali Amendment (KA), Maximum Technically Feasible Reduction (MTFR), and develop alternative pathways due to the enhanced ambition of parties to the Montreal Protocol to phase-down HFCs. However, in keeping with the Montreal Protocol’s history of strengthening through regular amendments, this study indicates that fast-tracking HFC phasedown under the KA will be consistent with the Paris Agreement targets following the example of accelerated phase-out of hydrochlorofluorocarbons (HCFCs) under the Montreal Protocol.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".