The Importance of Lifecycle Refrigerant Management in Climate and Ozone Protection
Bibliographic record
Abstract
Hydrofluorocarbons (HFCs) are widely used in refrigeration, air conditioning, heat pumps (RACHP), and various other applications such as aerosols, fire extinguishers, foams, and solvents. Initially, HFCs were adopted as the primary substitutes for ozone-depleting substances (ODSs) regulated under the Montreal Protocol. However, many HFCs are potent greenhouse gases, and as such subject to a global phasedown under the provisions of the Kigali Amendment to the Montreal Protocol. Managing the refrigerant bank of ODSs and HFCs throughout the equipment’s lifecycle—referred to as Lifecycle Refrigerant Management (LRM)—presents a significant challenge but also a significant climate action opportunity. LRM includes the leak prevention, recovery, recycling, reclamation, and destruction (RRRD) of refrigerants. This study employed the GAINS modeling framework to assess the ozone and climate benefits of LRM. The findings indicated that implementing robust LRM practices during the use and end-of-life stages of RACHP equipment could reduce ODS emissions by approximately 5 kt ODP (Ozone Depletion Potential) between 2025 and 2040, and HFC and hydrochlorofluorocarbon (HCFC) emissions by about 39 Gt CO2e between 2025 and 2050. The implementation of robust LRM measures in conjunction with the ongoing phasedown of HFCs under the Kigali Amendment can yield substantial additional climate benefits beyond those anticipated from the HFC phasedown alone.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".