Life Cycle Assessment of a Hydrogen and Fuel Cell RoPax Ferry Prototype
Bibliographic record
Abstract
Abstract Estimates for the greenhouse gas emissions caused by maritime transportation account for approx. 870 million tonnes of CO 2 tonnes in 2018, increasing the awareness of the public in general and requiring the development of alternative propulsion systems and fuels to reduce them. In this context, the project HySeas III is developing a hydrogen and fuel cell powered roll-on/roll off and passenger ferry intended for the crossing between Kirkwall and Shapinsay in the Orkney Islands in Scotland, a region which currently has an excess of wind and tidal power. In order to explore the environmental aspects of this alternative, a life cycle assessment from cradle to end-of-use using the ReCiPe 2016 method was conducted, contrasting the proposed prototype developed within the project against a conventional diesel ferry and a diesel hybrid ferry. The results show that the use of hydrogen derived from wind energy and fuel cells for ship propulsion allow the reduction of greenhouse gas emissions of up to 89% compared with a conventional diesel ferry. Additional benefits are lower stratospheric ozone depletion, ionizing radiation, ozone formation, particulate matter formation, terrestrial acidification and use of fossil resources. In turn, there is an increase in other impact categories when compared with diesel electric and diesel battery electric propulsion. Additionally, the analysis of endpoint categories shows less impact in terms of damage to human health, to the ecosystems and to resource availability for the hydrogen alternative compared to conventional power trains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".