Analysis and Implications of the Hydrofluorocarbon Allowance Allocation Systems in the European Union and United States
Bibliographic record
Abstract
Although hydrofluorocarbons （HFCs） do not deplete the stratospheric ozone layer， a few of these have high Global Warming Potentials （GWPs） ranging from 12 to 14 000. At present， these chemicals are widespread in air conditioners， refrigerators， aerosols， foams， and other products. This study systematically analyzed HFC allowance allocation systems and the practical experiences of the European Union and United States. The focus is on analyzing HFC control concepts， quota allocation mechanisms， and calculation rules in the EU and US， as well as the characteristics of the two systems. It has been observed that both the EU and US have adopted synergistic approaches to protect the ozone layer and address climate change. They jointly promote the implementation of the Kigali Amendment and achievement of climate neutrality goals， and reduce HFCs through economic measures and market mechanisms. Specifically， the EU was the first to adopt paid quotas， thereby strengthening the market constraints. Meanwhile， the US used free quotas with a quota transfer offset system， thereby flexibly utilizing market tools. Additionally， both have established quota-accounting methods based on the GWP values of substances， and the US has developed a toolbox for HFC quota allocation. China implemented the HFCs quota licensing system in 2024. The experiences of the HFC allowance allocation systems in the EU and US can provide references for China's compliance with the Kigali Amendment.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".