Influence of Carbon Pricing on Regional Aircraft and Route Network Design
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0463.vid Carbon pricing policies have become the de-facto standard policy of national and multi-national governments in an attempt to mitigate the affects of green house gasses on the environment. To date carbon pricing, either in the form of a carbon tax or an emissions trading scheme, has been implemented by more than 60 countries. Regional aircraft network operations will be challenged by the effect of carbon pricing policies as they increase the fuel price on domestic air travel. A System of Systems analysis is performed to determine the effect of carbon pricing on a regional airline network. This analysis includes route and passenger allocations while considering different combinations of a narrow body jet transport and turboprop aircraft. Trade-offs in network operation were analyzed using different turboprop configurations under three different carbon pricing scenarios for a representative regional network using data from Qantas Airways operating in Australia. While the carbon pricing policies increase the fuel price by up to 72%, results show that optimal allocation of airline resources in the network increase the operational cost and passenger ticket price only by up to 5.81%. This is achieved by changing the fleet compositions by up to 34%. Optimal route network configurations favored smaller but more efficient turboprops over higher capacity turboprops to improve payload range efficiency and reduce fuel burn.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".