Flying Towards Climate Failure: An Analysis of the Seven Biggest European Airline Groups
Bibliographic record
Abstract
Globally, aviation is a major contributor to rising greenhouse gas emissions (GHG). In recent years, annual emissions from aviation have increased by 4-5%, up to the start of the COVID crisis in 2020. Although the pandemic has led to a temporary decline in aviation emissions, air travel is projected to return to its skyrocketing pre-pandemic levels as early as 2024. Without political action to counter its growth prospects, the aviation industry will become one of the biggest emitting sectors globally and by 2050 it will have consumed up to a quarter of the global carbon budget for achieving the 1.5°C Paris Agreement goal.Under pressure for their skyrocketing emissions, some actors in the aviation sector have recently pledged to achieve net-zero emissions by 2050. But no company in the sector has pledged to effectively cut greenhouse gas emissions in order to achieve real-zero decarbonisation. Instead, the industry and political leaders are relying on excessive optimism about false or technological solutions, such as carbon offsetting, electric planes and alternative fuels that are either ineffective, harmful for the environment or a long way from being viable in the coming decades or easily available at the required volumes. Researchers have highlighted that these "technology myths" are stalling the necessary progress in climate policy for aviation. While other transport sectors, such as rail and road, can – to a certain extent – directly use electricity based on renewable sources such as solar and wind power, similar solutions do not yet exist for aviation. The goal of real-zero emissions will not be achieved without a significant reduction in flights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".