Towards Standardising Methods for Reporting the Embodied Energy Content of Aerospace Products
Bibliographic record
Abstract
Within the aerospace industry there is a growing interest in evaluating and reducing the environmental impacts of products and related risks to business. Consequently, requests from governments, customers, manufacturers, and other interested stakeholders, for environmental information about aerospace products are becoming widespread. Presently, requests are inconsistent and this limits the ability of the aerospace industry to meet the informational needs of various stakeholders and reduce the environmental impacts of their products in a cost-effective manner. Energy consumption is a significant business cost, risk, and a simple proxy value for overall environmental impact. This paper presents the initial research carried out by an academic and industry consortium to develop standardised methods for calculating and reporting the embodied manufacturing energy content of aerospace products. Following an action research approach, three potential methods are identified and applied in a real manufacturing environment. Suitability for use across the aerospace value chain is assessed. The benefits, implementations issues, areas of data uncertainty, and differences in results are outlined. Results show companies could be over/under reporting the embodied manufacturing energy content of parts by a factor of 10. The subsequent business and EU policy implications for industry reporting and evaluating product risks are discussed. The paper concludes the novel research outcomes will be valuable to businesses and other interested stakeholders seeking to report or understand the embodied energy content of aerospace products and associated data uncertainty, as well as inform the development of future industry standards.
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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 | 0.298 | 0.349 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".