Ecodesign as a key concept for improving the life cycle environmental performance of proton-exchange membrane fuel cells
Bibliographic record
Abstract
Fuel cell and hydrogen (FCH) technologies are an important part of the energy transition in the EU, therefore the environmental impact assessment of FCH technologies is crucial for further policy decisions. Future FCH production will need to implement ecodesign actions to minimise the environmental impact. The EU-funded project eGHOST defines ecodesign actions for FCH technologies to support the FCH industry. In the case of a proton-exchange membrane fuel cell (PEMFC), four future product concepts are defined together with the associated inventories. For each product concept, a life cycle assessment was carried out and the resultant environmental profiles benchmarked against a reference PEMFC case representing the current state. The study covers the manufacturing and end-of-life phases, with certain materials, such as platinum, being recycled in a closed loop. The results show that environmental impacts can be significantly reduced by implementing ecodesign measures, in the case of climate change by up to 85%. Ecodesign actions related to the reduction of platinum content (lower platinum loading) were identified as top priority, but not the only ones to pay attention to.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".