Unexplained high and persistent methyl bromide emissions in China
Bibliographic record
Abstract
Methyl bromide (CH3Br) is an important ozone-depleting substance whose use is regulated under the Montreal Protocol. Quantifying emissions on the national scale is required to assess compliance with the Montreal Protocol and thereby ensure the timely recovery of the ozone layer. However, the spatial-temporal patterns of China’s national CH3Br emissions remain unclear. Here we estimate the national emissions of CH3Br in China during 2011−2020 using atmospheric observations at 10 sites across China combined with an inversion technique (top-down) and compare those with an updated inventory of identified emission sources (bottom-up). Measured CH3Br mole fractions are enhanced well above the background mole fractions, especially at sites in eastern China. Top-down emission estimates exceed bottom-up estimates by 5.5 ± 1.4 gigagrams per year, with the largest fraction (60%) of observationally derived CH3Br emissions arising from underestimated or unidentified emissions sources. This study shows the potential impacts of the unaccounted emissions on stratospheric ozone depletion, with implications for the Montreal Protocol. Methyl bromide (CH3Br) is an important ozone-depleting substance whose use is regulated under the Montreal Protocol. However, the spatial-temporal patterns of China’s national CH3Br emissions remain unclear. Here, the authors find that China’s top-down emission estimates exceed bottom-up estimates by 60%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".