Analyzing the Environmental Efficiency of Global Airlines by Continent for Sustainability
Bibliographic record
Abstract
The study of environmental sustainability in the aviation industry mainly focuses on research targeting specific regions such as the United States, Europe, and China. However, for the environmental sustainability of the aviation industry, global airlines on all continents around the world must implement efficient environmental management. This study divides the world into six continents and attempts to verify environmental efficiency for airlines belonging to each continent. Using data from 2014 to 2018 of 31 global airlines, this study compares environmental efficiency in the aviation industry by continent and individual airline. Data envelopment analysis (DEA), which is actively used in efficiency studies was adopted as an analysis method. We find that, first, airlines in Europe and Russia have the highest environmental efficiency, and airlines in North America and Canada are the second highest, which can be a good benchmark for other airlines. Second, in technical efficiency (TE) values, airlines in Africa and the Middle East and Latin America generally have low efficiency; but, in the airlines in Africa and the Middle East, environmental efficiency is steadily improving slightly. In comparison, airlines in Latin America showed a decrease in environmental efficiency value, requiring a lot of effort and investment to improve efficiency. Third, for airlines in North America and Canada, the scale efficiency (SE) value was the lowest, even though there was a high level of overall environmental efficiency, indicating the need for efficiency improvement through economies of scale. This study has implications, in that, it suggests how airlines can perform efficient environmental management for sustainability according to the continent to which they belong.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".