Australian chlorofluorocarbon (CFC) emissions: 1960–2017
Bibliographic record
Abstract
Environmental context Chlorofluorocarbons (CFCs) are potent greenhouse and stratospheric ozone depleting trace gases. Their atmospheric concentrations are in decline, thanks to global production and consumption controls imposed by the Montreal Protocol. In recent years, the rates of decline of CFC atmospheric concentrations, especially for CFC-11 (CCl3F), are not as large as anticipated under the Protocol, resulting in renewed efforts to estimate CFC consumption and/or emissions to possibly identify new or poorly quantified sources. Abstract Australian emissions of chlorofluorocarbons (CFCs) have been estimated from atmospheric CFC observations by both inverse modelling and interspecies correlation techniques, and from CFC production, import and consumption data compiled by industry and government. Australian and global CFC emissions show similar temporal behaviour, with emissions peaking in the late-1980s and then declining by ~10 % per year through to the present. Australian CFC emissions since 1978 account for less than 1 % of global emissions and therefore make a correspondingly small contribution to stratospheric ozone depletion. The current CFC emissions in Australia are likely from ‘banks’ of closed-cell foams, and refrigeration–air conditioning equipment now more than 20 years old. There is no evidence of renewed consumption or emissions of CFCs in Australia. The reduction in CFC emissions has made a significant contribution to reducing Australian greenhouse gas emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".