Life Cycle Analysis of Battery Modules to be Integrated into Hybrid/Electric Aircraft
Bibliographic record
Abstract
It is no question that emissions reduction is a priority in the aviation industry: in 2021, the International Civil Aviation Organization set a net-zero carbon emissions target for 2050. Achieving this goal will require a comprehensive strategy encompassing the development of more efficient propulsion systems, effective supply chain management, and more responsible end of life practices in the industry. Notably, sustainable development in hybrid/electric aviation for short to medium range flight will be paramount to reducing emissions. As batteries replace traditional fuel-based propulsion systems, it becomes imperative to better understand the emissions generated throughout the manufacturing and operation phases of these systems. This paper presents a life cycle analysis (LCA) for a battery module to be integrated into electric aircraft. The resulting LCA demonstrates that while retrofitting the aircraft with an electric propulsion system would mitigate some environmental impacts, such as acidification, ozone depletion, and particulate matter formation, it would still generate higher global temperature change and global warming risks than continuing with business as usual. Moreover, current battery technology severely limits the operating capabilities of present-day aircraft.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".