A review on energy practices and indoor environmental quality (IEQ) of airports
Bibliographic record
Abstract
Airports are key infrastructure facilities bridging the building and transportation sectors. Their operations are expected to keep growing exponentially in the coming years, which could increase their energy consumption. Therefore, reducing energy consumption is crucial in line with decarbonization and sustainability goals of airports. This study provides a review on energy efficiency for airport facilities, focusing on, (1) energy and environmental benchmarking, (2) renewable energy sources, their associated risks, economic, and environmental impacts, (3) energy conservation strategies, (4) control strategies for airport operational systems, and (5) recommended indoor environmental quality (IEQ) conditions by international standards. Findings suggest that using single indicators for benchmarking assessment can be misleading, and comprehensive frameworks are suggested for accurate comparisons. Additionally, photovoltaic (PV) systems are the common integration of renewables in airports, while wind, geothermal, and biomass offer alternative energy solutions; each with specific challenges. Energy conservation strategies for airports included systems such as radiant floors and displacement ventilation (DV), façade improvements, and advanced hybrid HVAC solutions. Control strategies mostly focused on fuzzy logic control (FLC) and model predictive control (MPC). Furthermore, it was found that there exist gaps in addressing diverse comfort requirements within the airport population and spaces in existing IEQ standards. This study offers valuable insights and demonstrations of energy efficiency and IEQ improvement strategies that are suitable for airport facilities, given their unique nature. Findings can be leveraged by airport managers to achieve decarbonization goals, reduce energy costs, and enhance sustainability.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".