Key Drivers behind the Development of Fluorocarbons Destruction Infrastructure: A Case Study of Japan
Bibliographic record
Abstract
The Montreal Protocol has been ratified to progress phase-out of CFCs and HCFCs globally. HFCs have come into wide use as alternatives to CFCs and HCFCs, but as we know today, it was found that HFCs have a huge negative influence on global warming, and the Kigali Amendment to the Montreal Protocol entered into force to promote phase-down of HFCs. Since the enforcement of the Fluorocarbons Recovery and Destruction Law (F-gas law) in 2002, Japan has been undertaking fluorocarbons collection and destruction by environmentally-sound manners. However, no study has been reported investigates on how the Japanese fluorocarbons destruction infrastructure has been developed over the past several years. To analyze the development, we studied key drivers that contributed to encourage fluorocarbons collection from end of life electric appliances and to promote fluorocarbons destruction by environmentally and commercially sustainable technologies. We showed that recycling laws and the F-gas law have made progress in encourage fluorocarbons collection and destruction by making relevant stakeholders take physical and financial responsibilities for proper fluorocarbons disposal. This study also researched fluorocarbons destruction technologies that destruction operators used as of 2004 and 2019, and found that three specific destruction technologies have long been used practically in Japan. Finally, we discussed influencing factors that have made these technologies accepted, installed and practically used by fluorocarbons destruction operators. In conclusion, we identified that existence of political frameworks as well as application of fluorocarbons destruction technologies that are commercially sustainable and socially acceptable were key drivers behind the development of fluorocarbons destruction infrastructure in Japan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".