Bayesian assessment of chlorofluorocarbon (CFC), hydrochlorofluorocarbon (HCFC) and halon banks suggest large reservoirs still present in old equipment
Bibliographic record
Abstract
Halocarbons contained in equipment such as air conditioners, fire extinguishers, and foams continue to be emitted after production has ceased. These “banks” within equipment and applications are thus potential sources of future emissions, and must be carefully accounted for in order to differentiate nascent and potentially illegal production from legal banked emissions. Here, we build on a probabilistic Bayesian model, previously developed to quantify chlorofluorocarbon (CFC-11, CFC-12, and CFC-113) banks and their emissions. We extend this model to a suite of banked chemicals regulated under the Montreal Protocol (hydrochlorofluorocarbon, HCFC-22, HCFC-141b, and HCFC-142b, halon 1211 and halon 1301, and CFC-114 and CFC-115) along with CFC-11, CFC-12, and CFC-113 in order to quantify a fuller range of ozone-depleting substance (ODS) banks by chemical and equipment type. We show that if atmospheric lifetime and prior assumptions are accurate, banks are most likely larger than previous international assessments suggest, and that total production has probably been higher than reported. We identify that banks of greatest climate-relevance, as determined by global warming potential weighting, are largely concentrated in CFC-11 foams and CFC-12 and HCFC-22 non-hermetic refrigeration. Halons, CFC-11, and CFC-12 banks dominate the banks weighted by ozone depletion potential (ODP). Thus, we identify and quantify the uncertainties in substantial banks whose future emissions will contribute to future global warming and delay ozone-hole recovery if left unrecovered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".