Assessment of aircraft leasing efficiency: An airline perspective
Bibliographic record
Abstract
This research contributes to the theory of assessing aircraft leasing efficiency from an airline's perspective by revising factors influencing leasing decisions and developing an integrated approach to modelling aircraft leasing decisions. Despite the extensive research on aircraft leasing appraisal, modelling tools for informed decision-making require constant updates regarding the attributes to be included and approaches to uncertainty conditions. The key contribution of this research supports the ongoing discussion of the practical deployment of management science in the aircraft leasing field, aiding the industry in quickly adapting to changing market conditions. The research includes brief analyses of key performance indicators in the aircraft leasing industry to identify trends and a literature review to justify the current factors influencing leasing decisions. It is demonstrated that integrating operational modelling into aircraft leasing assessment can provide a data-driven justification for the importance of new, fuel-efficient aircraft in achieving sustainability goals and maintaining operational profitability. The sensitivity analysis further illustrates the robustness of the model and solution, and its capacity to generate scenario assessments reflecting varying conditions such as changes in demand, taxes, fuel costs, and other airline expenses. The simulation of a hypothetical example based on data from airlines’ business intelligence platforms reveals that significant initial fixed leasing payments, including taxes, can negatively impact an airline's ability to modernise its fleet through leasing. Fuel efficiency, zero customs duties and VAT on aircraft leasing, and sufficient capital to cover fixed leasing costs are the main drivers stimulating fleet modernisation and can be justified by a slight increase in expected profits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".