Bayesian modeling of HFC production pipeline suggests growth in unreported CFC by-product and feedstock production
Bibliographic record
Abstract
Observationally-derived emissions of ozone depleting substances must be scrutinized to maintain the progress made by the Montreal Protocol in protecting the stratospheric ozone layer. Recent observations of three chlorofluorocarbons (CFCs), CFC-113, CFC-114, and CFC-115, suggest that emissions of these compounds have not decreased as expected given global reporting of their production. These emissions have been associated with hydrofluorocarbon (HFC) production, which can require CFCs as feedstocks or generate CFCs as by-products, yet emissions from these pathways have not been rigorously quantified. Here, we develop a Bayesian framework to jointly infer emissions of CFC-113, CFC-114, and CFC-115 during HFC-134a and HFC-125 production. We estimate that feedstock emissions from HFC-134a production accounted for 90% (82-94%) and 65% (47-77%) of CFC-113 and CFC-114 emissions, respectively, from 2015-2019, while by-product emissions during HFC-125 production accounted for 81% (68-92%) of CFC-115 emissions. Our results suggest that unreported feedstock production in low- to middle-income countries may explain the unexpected emissions of CFC-113 and CFC-114, although uncertainties within chemical manufacturing processes call for further investigation and industry transparency. This work motivates tightened feedstock regulations and adds a reduction in CFC emissions to the benefits of the HFC phasedowns scheduled by the Kigali Amendment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".