Impacts of the Covid-19 Pandemic on the Efficiency of Brazilian Domestic Air Transportation
Bibliographic record
Abstract
The Covid-19 pandemic has given rise to broad challenges in the air transportation sector by leading to the closure of borders and imposing restrictive measures taken immediately.At the same time, Brazil struggled to contain and better prepare to deal with the consequences of the pandemic.Given the context, this work aims to analyze the impact of Covid-19 on the efficiency of air transportation sector and evaluating the prospects of its recovery compared to the prepandemic level.The present study makes use of Data Envelopment Analysis methodology seeking to identify the technical efficiency of both passenger and cargo flights.The methodology was applied by adopting relevant input and output indicators.We confirmed the negative impacts on the sector suffered from the pandemic.Cargo flights in Brazilian domestic market experienced a larger loss than passenger flights.Moreover, the study shows the Brazilian market did not perform ideally to prevent impacts of the second wave of Covid-19.For governments and policy makers, they need to carefully consider the effects of policies to be implemented.Our research also provides decision-making factors to organizations and companies related to business performance.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| 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".