Estimation, and Framework Proposal of Greenhouse Gas Emissions of Fluorinated Substitutes for Ozone-Depleting Substances by Application Area in the Republic of Korea
Bibliographic record
Abstract
Since fluorine compounds have both high ozone depletion potential and high global warming potential, the study of hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) is crucial for climate change research. In this study, greenhouse gas (GHG) emissions from ozone-depleting industries in the Republic of Korea were estimated based on survey data on the use of fluorine compounds. This study is a response to the growing global attention to halocarbons that arose from the Kigali Amendment to the Montreal Protocol. Survey data on the consumption of fluorine compounds by application area were used to estimate emissions by applying the 2019 refinement IPCC Guidelines Tier 1a method. In addition, both the consumption ratio of fluorine compounds in the refrigeration and air conditioning application area and total fluorine compounds consumption by application area were compared with the values suggested by the UN Environment Programme to compare the current status with that in developing and developed countries. By comparing the derived GHG emissions with current emissions in the National Inventory Report, it was confirmed that 14,565 GgCO2eq of GHG emissions differed. In addition, through the replacement of fluorine compounds used as refrigerants, 14,422 GgCO2eq of the GHG emissions can be reduced.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".