Air-Rail Alliances in the Context of Liability and Environmental Protection
Bibliographic record
Abstract
Since the deregulation of airlines in the 1970s and 80s the aviation industry has constantly tried to find new ways to engage with the increasingly competitive aviation market by expanding their outreach through strategic partnerships and global alliances. Over the past 10 years airlines have strengthened their partnerships with railway companies to offer more convenient connections for passengers to their hubs and link remote areas to their route network. These Air-Rail Alliances have helped airlines to stay competitive in the modern aviation market. This short paper will briefly examine two legal issues pertaining to Air-Rail Alliance namely whether an airline can be held liable in case of an accident during the train leg of the journey and how Air-Rail Alliances help airlines to meet their carbon emission reduction goals under the European Union Emissions Trading Scheme (EU ETS) and the ICAO Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Especially during the COVID-19 pandemic, Air-Rail Alliances proved to be a good vehicle for airlines to replace specific flights in their network. The paper will first explain basic terminology relating to the airline’s business before explaining the structure of Air-Rail Alliances in more detail. Afterwards, it will address the question as to whether an airline can be held liable in the case of an accident during the train leg of the journey. The paper will answer this question by arguing that airlines cannot be held liable under international aviation law but rather the train operator under the lex loci of the state in which the accident occurred. Finally, the paper will discuss the structure of both the EU ETS and CORSIA and argue that Air-Rail Alliances are a valuable tool for airlines to meet their CO2 reduction goals. It will be highlighted that both regimes are flexible and adaptive enough to take the unprecedented consequences of the COVID-19 pandemic into account and underline how the law can be adaptive in such a changing environment.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".