Waste Management Strategies to Mitigate the Effects of Fluorinated Greenhouse Gases on Climate Change
Bibliographic record
Abstract
Fluorinated greenhouse gases (F-gases) are used for various applications, such as in refrigeration and air conditioning, as substitutes of the ozone-depleting substances. Their utilization has increased drastically over the last few decades, with serious consequences for global warming. The Kigali Amendment to the Montreal Protocol and several national and international legislations, such as the 2014 EU F-gas Regulation, aim to control the utilization and emissions of these gases. In the EU, the phase-down of hydrofluorocarbons (HFCs) is underway, with successive reductions in quotas up to 2050. Under this scenario, efficient strategies for managing the produced and already existing F-gases are of vital importance to guarantee that their effect on the environment is mitigated. Up to now, most of the F-gases recovered from end-of-life equipment or when retrofitting systems are either released into the atmosphere or destroyed. However, in order to put forward a cost-efficient adaptation to the F-gas phase-down, increasing separation and recycling efforts must be made. This critical review aims at providing a revision of the current F-gas management problems and strategies and providing an overview on the innovative strategies that can be applied to contribute to build a sustainable market under circular economy principles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".