Emissions of HCFC-22 and HCFC-142b in China during 2018–2021 Inferred from Inverse Modeling
Bibliographic record
Abstract
Hydrochlorofluorocarbons (HCFCs) are transitional substitutes for chlorofluorocarbons (CFCs). However, they still have the capacity to be ozone-depleting substances (ODSs). Therefore, they are scheduled to be phased out in China by 2030 under the Montreal Protocol. The emission estimates of HCFC-22 (CHClF 2 ) and HCFC-142b (CH 3 CClF 2 ) in China using atmospheric observations are lacking after 2017, making it hard to understand the effectiveness of the phase-out process of HCFCs in China. Here, we use flask and in situ measurements of HCFC-22 and HCFC-142b during 2018–2021 and inverse modeling to determine the emission magnitude and changes in China. It was determined that China’s emissions were 172 ± 40, 154 ± 39, 160 ± 22, and 155 ± 33 Gg yr –1 of HCFC-22 and 8.3 ± 1.8, 7.8 ± 1.6, 7.4 ± 1.7, and 7.9 ± 1.7 Gg yr –1 of HCFC-142b from 2018 to 2021, respectively. Top-down estimates show that HCFC-22 emissions in China were stable, while HCFC-142b emissions were decreasing during 2013–2021, although both substances were in the stage of being phased out during 2013–2021. This study reveals that 46 and 39% of the global HCFC-22 and HCFC-142b emissions, respectively, cannot be traced to certain countries in 2020. We suggest that more studies on HCFC emissions around the world in the future are needed to better safeguard the ozone layer recovery and climate mitigation by ensuring compliance with the Montreal Protocol during HCFC phase-out processes.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".