Resilience in the face of disruptions: Assessing the impacts of COVID-19 and geopolitical conflicts on global airport connectivity
Bibliographic record
Abstract
The COVID-19 pandemic and rising geopolitical tensions have severely disrupted global air transport, significantly impacting airport connectivity. This study analyzes the evolving patterns of global airport connectivity in response to these overlapping crises. On top of using a refined Global Airport Connectivity Index (GACI) applied at quarterly intervals from 2019 to 2024, we develop an international GACI metric to evaluate changes and regional disparities across distinct stages of disruption and recovery. The findings reveal that, while global airport connectivity has largely returned to pre-pandemic levels, recovery trajectories varied markedly across regions due to different pandemic response strategies, vaccination timelines, and conflict-related airspace closures. Specifically, the Middle East and North Africa experienced rapid recovery, whereas Northeast Asia and Eastern Europe lagged, hampered by prolonged border restrictions and geopolitical conflicts respectively. Furthermore, the results indicate that medium-sized and emerging international airports played a significant role in the overall market recovery. The study provides important insights into emerging structural shifts in the global air transport network, underscoring the necessity for adaptive, coordinated policies to enhance resilience against future disruptions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".