REVIEW AND EVALUATION OF HYDROGEN PRODUCTION METHODS FOR BETTER SUSTAINABILITY
Bibliographic record
Abstract
This paper examines various potential methods of hydrogen production using renewable and non-renewable sources and comparatively assesses them for environmental impact, cost, energy efficiency and exergy efficiency. The social cost of carbon concept is also included to present the relations between environmental impacts and economic factors. Some of the potential primary energy sources considered in this study are: electrical, thermal, biochemical, photonic, electro-thermal, photo-electric, and photo-biochemical. The results show that when used as the primary energy source, photonic energy based hydrogen production (e.g., photocatalysis, photoelectrochemical method, and artificial photosynthesis) is more environmentally benign than the other selected methods in terms of emissions. Thermochemical water splitting and hybrid thermochemical cycles (e.g. Cu-Cl, S-I, and Mg-Cl) also provide environmentally attractive results. Photoelectrochemical method and PV electrolysis are found to be least attractive when production costs and efficiencies are considered. Therefore, increasing both energy and exergy efficiencies and decreasing the costs of hydrogen production from solar based hydrogen production have a potential to bring them forefront as potential options. The energy and exergy efficiency comparisons indicate the advantages of fossil fuel reforming and biomass gasification over other methods. Overall rankings show that hybrid thermochemical cycles are primarily promising candidates to produce hydrogen in an environmentally benign and cost-effective way.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".