On the relationship between air connectivity and economic development: A comparative analysis of inequality evolution for 2000–2019
Bibliographic record
Abstract
Throughout the past decades, aviation has experienced a tremendous growth of passenger and cargo demand. This growth may come with problems however, when considering the Sustainable Development Goals set by the United Nations in 2015, particularly those related to the sustainable and fair use of resources and reduction of inequalities among countries. This study discusses the air connectivity inequalities of countries from various perspectives, covering topology-based and schedule-based connectivity indicators. We focus on the connectivity changes of countries between the years 2000 and 2019, i.e., before the impact of the COVID-19 pandemic. Results indicate that our aviation system is dominated by a few highly-developed countries in Western Europe and Northern America. While this dominance is gradually weakened, mainly due to the emergence of aviation hubs in the Middle East and the role of China as a hub in Asia, there need to be more efforts towards a homogeneous aviation infrastructure. Moreover, the flight connectivity between countries appears to be correlated to the Human Development Index growth value, but not significantly correlated with the potential travel demand between countries. Finally, detailed analysis on network-induced inequalities of connectivity reveal that countries are most unequally distributed concerning betweenness centrality but most equally according to closeness centrality. We believe that our study contributes towards a better understanding of sustainable aviation planning and raises important policy issues concerning the achievement of Sustainable Development Goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".