Environmental implications of solid oxide fuel cell system for hydrogen sustainability
Bibliographic record
Abstract
Hydrogen, known for its high energy content and clean combustion, is promising in the energy transition. This study explores the environmental impact of a solid oxide fuel cell (SOFC) system. 1 kg of hydrogen production at 1 bar serves as the functional unit. The SOFC system generates hydrogen, electricity, and heat across five modes. Results indicate that the SOFC system achieves a global warming potential of 0.17–9.50 kg CO 2 -eq/FU using the system expansion method. Regional analysis shows that areas with high renewable electricity shares experience increased CO 2 emissions due to functional unit decision. The exergy allocation method is less sensitive to electricity sources and seasonal emission profiles than system expansion. Comparing eight production routes, the SOFC system using biomethane (−5.46 kg CO 2 -eq/FU) outperforms steam methane reforming (11 kg CO 2 -eq/FU) and biomass gasification (1.49 kg CO 2 -eq/FU). These insights are valuable for advancing renewable energy initiatives and effectively mitigating climate change. • Study a novel SOFC system design that generates electricity, hydrogen, and heat. • Details LCA results for climate change, ecosystem quality and human health impacts. • Evaluates emissions using different electricity sources, fuel types, and profiles. • Highlights benefits of system expansion and exergy allocation in LCA multifunctionality. • Provides new insight on hydrogen categorization against conventional methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".