Estimate Gaps of Montreal Protocol-Regulated Potent Greenhouse Gas HFC-152a Emissions in China Have Been Explained
Bibliographic record
Abstract
1,1-Difluoroethane (HFC-152a) is a hydrofluorocarbon regulated by the Montreal Protocol, and its emissions in China are of concern as China will regulate HFC-152a in 2024. However, no observation-inferred top-down estimates were undertaken after 2017, and substantial gaps existed among previous estimates of China’s HFC-152a emissions. Using the atmospheric observations and inverse modeling, this study reveals China’s HFC-152a emissions of 9.4 ± 1.7 Gg/yr (gigagrams per year), 10.6 ± 1.8 Gg/yr, and 9.7 ± 1.5 Gg/yr in 2018, 2019, and 2020, respectively. In addition, we display an overall increasing trend during 2011–2020, which is in contrast to the decreasing and steady trend reported by the Emission Database for Global Atmospheric Research (EDGAR) and the Chinese government, respectively. Subsequently, we establish a comprehensive bottom-up emission inventory matching with top-down estimates and thus succeed in explaining the gaps among previous estimates. Furthermore, the contribution of China’s emissions to global HFC-152a emission growth increased from 15% during 2001–2010 to >100% during 2011–2020. An emission projection based on our improved inventory shows that the Kigali Amendment (KA) would assist in avoiding 1535.6–4710.6 Gg (251.8–772.5 Tg CO 2 -eq) HFC-152a emissions during 2024–2100. Our findings indicate relatively accurate China’s HFC-152a emissions and provide scientific support for addressing climate change and implementing the KA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".