Cost estimation of the use of low-carbon fuels in prospective scenarios for air transport
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2328.vid Using low-carbon energies is a major lever to reduce the CO2 emissions of aviation. Some low-carbon energy carriers consist in fuels that are drop-in and require few modifications to current aircraft, like biofuels and electrofuels. Hydrogen is another low-carbon fuel that would be relevant in the long term only since it requires significant aircraft modifications (non-drop-in fuel). In both cases, several production pathways exist with radically different impacts in terms of cost of production and life-cycle CO2 emissions. Literature is already exhaustive on prospective decarbonization scenarios for aviation and low-carbon fuel production cost estimates. In this paper, an open-source simulation framework named CAST is enhanced by a module that links low-carbon fuels production cost to their respective consumption in given scenarios. Hence, the cost of a custom decarbonization scenario is evaluated. Results show that the cost of the integration of low-carbon fuels in this scenario would represent around 40 % of airlines revenues in 2050, while the energy demand growth would necessitate important capital investments, regularly increasing to 130 Bn e in 2050. A sensitivity analysis shows that these cost estimates are subject to large uncertainties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".