Anthropogenic Chloroform Emissions from China Drive Changes in Global Emissions
Bibliographic record
Abstract
Emissions of chloroform (CHCl 3 ), a short-lived halogenated substance not currently controlled under the Montreal Protocol on Substances that Deplete the Ozone Layer, are offsetting some of the achievements of the Montreal Protocol. In this study, emissions of CHCl 3 from China were derived by atmospheric measurement-based “top-down” inverse modeling and a sector-based “bottom-up” inventory method. Top-down CHCl 3 emissions grew from 78 (72–83) Gg yr –1 in 2011 to a maximum of 193 (178–204) Gg yr –1 in 2017, followed by a decrease to 147 (138–154) Gg yr –1 in 2018, after which emissions remained relatively constant through 2020. The changes in emissions from China could explain all of the global changes during the study period. The CHCl 3 emissions in China were dominated by anthropogenic sources, such as byproduct emissions during disinfection and leakage from chloromethane industries. Had emissions continued to grow at the rate observed up to 2017, a delay of several years in Antarctic ozone layer recovery could have occurred. However, this delay will be largely avoided if global CHCl 3 emissions remain relatively constant in the future, as they have between 2018 and 2020.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".