Implementing the Circular Economy Model to Air Cargo System
Bibliographic record
Abstract
Circular Economy (CE) is an alternative to the traditional linear economy model. It is a systematic sustainable development strategy that seeks to tackle the deleterious effects of environmental degradation and resource scarcity. It proposes different ways to reduce waste, derive energy from renewables, recover resources at the end of a products life cycle and recycle them back into the production chain thereby significantly reducing pollution. This study is a review of the rapidly increasing literature on Circular Economy and its implementation to the Air Cargo System (ACS). It first reviews the different concepts of CE and distinguishes it from the current linear model of taking resources, making goods, and discarding waste. The study then presents how the different principles of CE can be applied to the current model of Air Cargo system and suggests ways in which the present linear model can be transformed into a regenerative sustainable model. The focus here is to highlight the different areas in the Cargo handling system (CHS) where the policies of CE can be applied in its existing state. For example, the paints used to distinguish different types of locks and restraints in the CHS contain chromates and other volatile organic compounds. A chrome free alternative that allows for application of thinner films to save weight will significantly reduce the carbon footprint of the product. Once the locks reach the end of its life cycle, ecofriendly techniques like dustless blasting can be used for paint removal and the metals parts can be recycled. The paint can be broken down into nontoxic component using the emerging Bioremediation technology that use metabolic pathway-based approaches for detoxification of chemicals. Finally, the paper identifies the different challenges in the implementation of CE model to CHS and provides some suggestions for its development as part of an initiative to make Air Cargo a greener mode of transporting goods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".