Can airports be a catalyst for reducing aviation’s effect on the climate?
Bibliographic record
Abstract
The aviation ecosystem is increasingly coming under pressure to decarbonise from the public and governments. Airports are at the leading edge of transforming their operations, alongside airlines and aerospace companies. While, however, the levers to reduce Scope 1 and 2 airport emissions are well understood, how to address Scope 3 emissions (which account for the bulk of an airport’s emissions) remains a major challenge. This paper provides an overview of potential Scope 3 decarbonisation levers across aircraft operations, ground transport and infrastructure construction. The paper then assesses the potential impact on emissions, and ease of implementation of these levers, highlighting aircraft operations as the area with highest potential to reduce ecosystem emissions. Next, the paper looks at how some of these levers have been implemented in practice, based on the case study of Geneva Airport, focusing on supporting sustainable mobility, energy support for aircraft and financial incentives for airlines to use latest-generation aircraft. The paper then identifies and discusses key barriers that must be overcome, including the ability of airports to influence domains outside their direct control, competing governmental policies and need for investment, highlighting the need for collaboration between a wide range of stakeholders. Based on this, the paper suggests a number of actions that airports should take to satisfy stakeholders and catalyse the aviation ecosystem towards its goal of achieving net zero.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".