European Union and United Kingdom Competition Law Developments in the Aviation Sector: January to June 2021
Bibliographic record
Abstract
As a direct result of the Coronavirus disease 2019 (COVID-19) pandemic, and the support measures initiated by EU Member States to compensate airlines for lost revenues, a review of the EU competition law developments the first half of 2021 continues to be dominated by State aid cases. This article summarizes the main developments over the past six months and provides the reader with an overview of the details of each of these developments. The first six months of 2020 have continued to be dominated by State aid cases, be it decisions by the European Commission (‘Commission’) to approve State aid applications, appeals of those or previously decided State aid cases, or the handing down of judgments from the General court. It is to be expected that for years to come, State aid decisions will form a significantly greater part of the competition law diet. In the six-month period, looking only at COVID-19 pandemic related cases/decision: at least sixteen Commission State aid decisions related to compensating undertakings for the direct or indirect effects of the COVID-19 pandemic; twelve Commission State aid decisions were appealed to the General court (mostly by Ryanair); and we are now seeing a significant number of judgments from the General Court relating to some of the Commission’s COVID-19 State aid decisions. In this period, Ryanair scored equal numbers of successful and unsuccessful appeals. Taking a quick look at the State aid applications submitted to the Commission State, a number of measures were submitted under Article 107(2)(b) of the Treaty on the Functioning of the European Union (‘TFEU’), which enables the Commission to approve State aid granted to compensate specific companies or specific sectors for the damages directly caused by ‘exceptional occurrences’. Few could ever contest that the COVID-19 pandemic would not qualify as an ‘exceptional occurrence’ it was an extraordinary, unforeseeable event having a significant economic impact globally. As a result, the interventions by the Member States to compensate for the damages directly linked to the COVID- 19 pandemic were considered justified. The Commission is also empowered to assess State aid measures requested by Member States under Article 107(3)(b) TFEU, which enables it to approve State aid granted to promote the execution of an important project of common European interest or to remedy a serious disturbance in the economy of a Member State. Again, few would dispute that the COVID-19 pandemic caused serious disturbances in the economies of EU Member States. The final main mechanism to consider State aid is the Temporary Framework specifically set up by the Commission to enable the quick approval of State aid providing that the measures fell within defined parameters. In relation to mergers, there are only five mergers mentioned, of which one reference the abandonment out of the Air Canada/Air Transat transaction after an in-depth investigation lasting more than eleven months. It should be noted that as a direct result of commitments regularly imposed by the Commission in State aid decisions under the Temporary Framework, recipients of State aid (like Air France, TUI, Finavia, Riga Airport etc.) are in principle prohibited from acquiring shares in competing or related markets, and this may have a dampening effect on merger activities in the aviation sector.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.018 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".