A review of ozone-depleting substances and fluorinated greenhouse gases in China
Bibliographic record
Abstract
Ozone-depleting substances (ODS) and fluorinated greenhouse gases (F-GHGs) significantly affect ozone layer depletion and greenhouse effect, thereby profoundly affecting global environment change and climate change. They are trace halogenated atmospheric gases that international conventions related to global environmental and climate change aim to reduce and control. The long atmospheric lifespan of ODS and F-GHGs, coupled with their low concentrations and small fluctuation ranges and the wide array of sources and the emergence of new substitutes, poses substantial challenges for precise, comprehensive, and real-time atmospheric monitoring. Estimating the emissions of ODS and F-GHGs and evaluating compliance effectiveness are issues of high interest both in the scientific community and in environmental diplomacy. Since China acceded to the Montreal Protocol on Substances that Deplete the Ozone Layer, it has made outstanding contributions to the global reduction of ODS emission. In recent years, China has also actively promoted international actions on F-GHGs to play a leading role in addressing climate change. This paper summarizes China′s research on ODS and F-GHGs, focusing on measurement methods, ambient mixing ratios, and emission estimations. Over the past decade, controlling substances such as CFCs and HCFCs have shown a declining trend in China, indicating remarkable compliance effectiveness. However, there has been a noticeable increase in hydrofluorocarbons, perfluorinated compounds, and chlorinated solvents. This paper analyzes the current issues and challenges faced by China in research on ODS and F-GHGs and looks forward to potential areas of focus that may arise under the concerted effort to protect the ozone layer and tackle climate change.
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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 | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".