Is the aircraft leasing industry on the way to a perfect storm? Finding answers through a literature review and a discussion of challenges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the emergence of the first private aircraft leasing companies in the 1970’s, the airline industry has undergone tremendous changes. Supported by several decades of growing demands and rising world economies, the share of leased aircraft across airlines worldwide has grown steadily, exceeding 50% for the first time during the peak of the COVID-19 pandemic. Given that earlier research has quantified the optimal lease rate for an airline between 40% and 60%, the industry might be facing challenges soon — potentially counteracting the recent recovery from COVID-19. This study reviews the existing research on aircraft leasing; a subject which has been rather scattered in the literature for the past few decades. We summarize more than 70 scientific papers published on aircraft leasing and closely related subjects. Based on the dissection and categorization of existing studies, we derive a set of important challenges for the aircraft leasing industry, which should be addressed by the community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it