Regional airports' environmental management: key messages from the evaluation of ten European airports
Bibliographic record
Abstract
In a modern society, connectivity is the basis for economic competitiveness, social reform, regional development and cultural exchange.City airports serving mature markets have already expanded to meet existing and future demand and the challenge for the airport industry is now focused on the development of the secondary and regional airports to accommodate further air transport demand.Consequently, regional airports attract the interest of investors by providing new business opportunities.Although airports bring significant benefits to local and national economy, their contribution to environment disturbance in local and global scale is significant.As a result of the growing environmental sensitivity, airport environmental management is a crucial element of the aviation industry development.This is for reasons related to the control of community and nongovernmental organisations (NGOs) complaints on one hand, and to meet the regional and national targets set by the civil aviation and local authorities on the other hand.Especially for regional airports, the need to identify the environmental issues is essential, because their business development is directly linked to disturbance in the environment and to the local/national communities' level of tolerance.Although environmental management process is crucial to regional airport development, there is little research related to measuring the efficiency and the performance of their environmental management systems.Nevertheless, not many regional airports, especially those serving fewer than 5 million passengers, annually, have set specific targets for their environmental performance.This paper presents the results of the evaluation of 10 European regional airports' environmental plans.Conventional wisdom is to provide some key messages, in order to improve the planning and decision process in airport environment management, as well as highlight some recommendations for further research in the future.The key research finding is that the national legislation framework and resident's ethics along with the airport business factions (such as the management scheme, the location topology and the airport size) are essential elements strongly related to regional airport's environmental efficiency.
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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 | 0.025 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".