Balancing Carbon and Profitability in Aviation: A Risk and Policy Perspective
Bibliographic record
Abstract
This study examines the intricate relationship between carbon emissions and sustainable financial performance in the global airline industry, a sector increasingly scrutinized for its environmental impact. Building upon the win–win hypothesis, trade-off theory, and emerging perspectives on non-linear environmental–financial linkages, this study explores how firm profitability is influenced not only by emission intensity but also by contextual factors such as temperature anomalies and the adoption of Sustainable Aviation Fuel (SAF). Using panel data from 29 major airlines headquartered across seven global regions, the analysis reveals a curvilinear relationship: while increased emissions are initially linked to higher profitability, likely reflecting operational scale, excessive emissions may diminish financial returns. The findings also underscore the moderating role of temperature anomalies, which can intensify both the initial benefits and the subsequent costs of emissions. Furthermore, the adoption of SAF appears to mitigate the financial risks of emissions under heightened climate-related pressure. Although initially costly and negatively associated with profitability, SAF investment shows potential long-term benefits, suggesting a non-linear payoff structure. Overall, the findings suggest that firms in carbon-intensive industries must carefully calibrate environmental strategies and investments to achieve long-term financial resilience. The study offers new insight into how internal decisions and external pressures jointly shape the emissions–performance dynamic.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".