Plane trading : policies for reducing the climate change effects of international aviation
Bibliographic record
Abstract
Aviation is the fastest growing source of transport greenhouse gases, although it is still small in proportion to others. Yet emissions from international flights are not controlled by the Kyoto Protocol (KP) and there are no policy instruments directly addressing the problem. The International Civil Aviation Organisation (ICAO) is now developing policy options for reducing greenhouse gas emissions from aviation. This report reviews the current proposals and concentrates in particular on market-based options: levies and emissions trading. Policy makers are faced with three broad options for how aviation emissions should be addressed. The aviation industry favours a voluntary agreement to improve efficiency. The difficulties and inconsistencies resulting from this approach are discussed. Some form of emissions charge may be implemented at national, regional or global levels. This is the option which has been most discussed at the European level. The environmental effectiveness of such policies relies on the price signal stimulating both some reductions in demand for air travel and greater effort to increase fuel efficiency. Such policies also need to be implemented internationally, or the industry may relocate to avoid them. The potential of emissions trading policies and emissions caps is discussed. It is suggested that a technical solution to greenhouse gases from aviation would be the best environmental and economic solution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".