An Integrated Decision Support Framework for the Assessment and Analysis of Hydrogen Production Pathways
Bibliographic record
Abstract
As fossil fuel reserves become depleted, alternative energy sources will be required. One of the preferred options for an energy vector for the transportation sector is hydrogen. Hydrogen is considered as a clean fuel since its end use in fuel cell is virtually emission-free with a resulting significant improvement on urban air quality. However, the life cycle environmental impacts of hydrogen use depend strongly on the primary energy resources used and the process chain between the feedstock and its end use. In this study, an integrated decision support framework that combines the analytic hierarchy process and a life cycle analysis (LCA) criterion is proposed for the assessment and ranking of hydrogen production pathways. The LCA criterion was divided into energy consumption, fossil fuel usage, and greenhouse gas emissions. The pathways use electrolysis and steam methane reforming (SMR) as the technology for the production of hydrogen. A total of 11 hydrogen production pathways are compared and ranked using the proposed framework. The overall ranking in this analysis was found to be (1) electrolysis using renewable resources as a source of energy, (2) SMR, and (3) electrolysis using electricity mixes or fossil fuel electrical generation. The most preferred pathway was found to be electrolysis with hydroelectric power as the source of energy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".