Establishment\nof HCFC-22 National–Provincial–Gridded\nEmission Inventories in China and the Analysis of Emission Reduction\nPotential
Bibliographic record
Abstract
Due to chlorodifluoromethane’s\n(CHClF<sub>2</sub>, HCFC-22)\ndual environmental impact on climate change and ozone depletion, its\nemissions have attracted international attention. In this study, a\nset of national–provincial–gridded (1° × 1°)\nemission estimation methods were built and applied to obtain the national,\nprovincial, and gridded emission inventories in China in 1990–2019.\nIn addition, the HCFC-22 emission reduction potential of different\nemission scenarios was analyzed. The results show that China’s\nHCFC-22 emissions reached a peak in 2017 and that the cumulative emissions\nin 1990–2019 were 1576.8 (1348.2–1819.0) kt (equivalent\nto 86.7 kt CFC-11 and 2854.1 Mt CO<sub>2</sub>). China’s HCFC-22\nemissions in the east were higher than those in the west, and the\nemissions in the south were higher than those in the north. Under\nthe control of the Montreal Protocol, China will reduce the cumulative\nemissions of 17 840.8 kt (avoiding 0.08° of global warming\nby 2056) in 2020–2056. If the disposal refrigerant can be effectively\nrecycled in the future, the HCFC-22 emission reduction in this period\nwill reach 18 020.3 kt. The established emission estimation\nmethods and obtained results can provide scientific and technological\nsupport for ozone layer protection and for addressing climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 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 teacher head, 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".