A review on thematic and chronological framework of impact assessment for green airports
Bibliographic record
Abstract
Environmental impact assessment comprises a set of analyses, and there are several subjects. However, the “airport impact research” subject has grown into a more integral and broader view. Green airports make this phenomenon more complex when analyzing different operational criteria. This study focused on easing the readers’ understanding of the impact analysis phenomenon more comprehensively. Moreover, the motivation behind this paper is to express the roots of the research subject on the green airport concept. Three domains are defined to classify the impact analysis, affecting each other while focusing on operational or capital investments in the airports. Spending a budget or an organizational function might negatively affect carbon dioxide emissions or further toxic disseminating natural resources. This study has examined 93 published scientific papers to show the distribution of the classified subjects on the three domains as Energy, Natural resources, and economic sustainability. Despite the common superiority of environmental topics, it has been seen that economic efficiency and the importance of the design subjects are the main predecessors in this review. Subjects have diverted into the environmental issues in the later years. Also, popular keywords have been presented to the audience’s attention, which counts the frequency in time.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".