Emissions of ozone-depleting carbon tetrachloride (CCl4) in China during 2011-2021 derived by top-down and bottom-up methods
Bibliographic record
Abstract
Carbon tetrachloride (CCl4) is a long-lived ozone-depleting substance that has been controlled by the Montreal Protocol on Substances that Deplete the Ozone Layer. Despite the global phase-out of production and consumption for dispersive uses of CCl4 since 2010, its global emissions have still been substantial and persistent for more than a decade, potentially delaying the recovery of the ozone layer, with ~30-40% of the sources of global CCl4 emissions remaining largely unknown.In this study, we focus on CCl4 emissions in China, a major contributor to global halocarbon emissions. We determined top-down CCl4 emissions in China over 2011-2021, using long-term atmospheric observations from a Chinese network (including AGAGE measurements made at one station) and an inverse modelling approach. We identified substantial and persistent emissions of CCl4 in China over the time period despite its complete phase-out, without a statistically significant decreasing trend in the emissions during the period. These CCl4 emissions in China are comparable to global total annual hydrochlorofluorocarbons (HCFCs) emissions in terms of the CFC-11-eq emissions in 2020. We also compiled a bottom-up CCl4 emission inventory for China, incorporating emission sources that have been proposed to be able to close the majority of the global CCl4 emissions budget from recent studies. We identified substantial CCl4 emissions from allowed feedstock use, from feedstock use for the renewed production of CFC-11 over 2013-2018, and from by-product emissions in chlorine-related processes. After considering these major sources, substantial top-down CCl4 emissions sources still remained unaccounted-for in China, which could account for more than half of the reported global unaccounted-for emission budget in 2014 and 2019. However, the magnitude of the unaccounted-for CCl4 emissions may have decreased in China over 2011-2021.
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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 | 0.000 | 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".