Measuring the energy efficiency for airlines under the pressure of being included into the EU ETS
Bibliographic record
Abstract
Summary In 2008, European Union (EU) announced that from 2012, each international flight taking off and landing in EU would be given an emission permit. Therefore, the period of 2008–2012 can be regarded as a buffer period for global airlines. Although EU formally decides to exclude non‐EU airlines from the EU Emission Trading System on March 4, 2014, it is necessary to investigate the impacts of the policy on airline energy efficiency in this period. Airline energy efficiency is divided into three stages—operations stage, service stage, and sales stage—and Greenhouse gas emission is treated as an undesirable output of service stage. Two models, network range‐adjusted measure model with weak disposability and network range‐adjusted measure model with strong disposability, are established to evaluate the efficiencies of 22 international airlines from 2008 to 2012. The results show that (i) most airlines' efficiencies have decreased in the period, and the EU Emission Trading System is not effective for the efficiency improvement; (ii) the average efficiency of European airlines is almost the same as that of non‐European airlines; and (iii) the model with weak disposability is more reasonable in distinguishing efficiency differences, while strong disposability is a more reasonable way in treating undesirable outputs. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".