Inverse Modeling Revealed Reversed Trends in HCFC-141b Emissions for China during 2018–2020
Bibliographic record
Abstract
Having the highest ozone-depleting potential among hydrochlorofluorocarbons (HCFCs), the production and consumption of HCFC-141b (1,1-dichloro-1-fluoroethane, CH 3 CCl 2 F) are controlled by the Montreal Protocol. A renewed rise in global HCFC-141b emissions was found during 2017–2020; however, the latest changes in emissions across China are unclear for this period. This study used the FLEXible PARTicle dispersion model and the Bayesian framework to quantify HCFC-141b emissions based on atmospheric measurements from more sites across China than those used in previous studies. Results show that the estimated HCFC-141b emissions during 2018–2020 were on average 19.4 (17.3–21.6) Gg year –1, which was 3.9 (0.9–7.0) Gg year –1 higher than those in 2017 (15.5 [13.4–17.6] Gg year –1 ), showing a renewed rise. The proportion of global emissions that could not be exactly traced in 2020 was reduced from about 70% reported in previous studies to 46% herein. This study reconciled the global emission rise of 3.0 ± 1.2 Gg year –1 (emissions in 2020 – emissions in 2017): China’s HCFC-141b emissions changed by 4.3 ± 4.5 Gg year –1, and the combined emissions from North Korea, South Korea, western Japan, Australia, northwestern Europe, and the United States changed by −2.2 ± 2.6 Gg year –1, while those from other countries/regions changed by 0.9 ± 5.3 Gg year –1 .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".