Green Grow the Airports: North American Airports are Finding More Ways to Reduce Their Environmental Impact
Bibliographic record
Abstract
This article presents a survey of steps taken by North American airports to reduce their impact on the environment. The industry’s efforts are being led by the European and North American regions of Airports Council International (ACI), the successor to Airport Operators Council International. ACI-North America has started an awards program to recognize achievements in the area. It also conducted a survey of airports, receiving 74 responses from airports that represent 60 percent of North American air traffic. Winnipeg’s new terminal is expected to be the first Canadian terminal with green building certification. Its design makes use of natural light and uses recycled construction materials and low-flow plumbing. An advanced demand management system will minimize use of cooling and heating systems. It is adding hybrid vehicles to its fleet, using low-sulfur diesel, and reducing idling. As a snow-belt airport, it is paying attention to reducing the impact of chemicals used in snow and ice control, collecting the maximum possible amounts of fluids and reducing surface water runoff. Logan opened the first certified green airport terminal, Terminal A, in 2005. It has produced a 12 percent annual saving in energy and a 36 percent saving in water. Logan’s consolidated rental car facility, to be finished in 2012, will be LEED-certified. Denver International is another airport, along with Toronto’s Pearson International, that is using more environmentally friendly ways to de-ice. Additional projects around North America are outlined. They cover recycling, solar power, wetland mitigation, and aircraft noise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".