A systemic review of hydrogen supply chain in energy transition
Bibliographic record
Abstract
Targeting the net-zero emission (NZE) by 2050, the hydrogen industry is drastically developing in recent years. However, the technologies of hydrogen upstream production, midstream transportation and storage, and downstream utilization are facing obstacles. In this paper, the development of hydrogen industry from the production, transportation and storage, and sustainable economic development perspectives were reviewed. The current challenges and future outlooks were summarized consequently. In the upstream, blue hydrogen is dominating the current hydrogen supply, and an implementation of carbon capture and sequestration (CCS) can raise its cost by 30%. To achieve an economic feasibility, green hydrogen needs to reduce its cost by 75% to approximately 2 $/kg at the large scale. The research progress in the midterm sector is still in a preliminary stage, where experimental and theoretical investigations need to be conducted in addressing the impact of embrittlement, contamination, and flammability so that they could provide a solid support for material selection and large-scale feasibility studies. In the downstream utilization, blue hydrogen will be used in producing value-added chemicals in the short-term. Over the long-term, green hydrogen will dominate the market owing to its high energy intensity and zero carbon intensity which provides a promising option for energy storage. Technologies in the hydrogen industry require a comprehensive understanding of their economic and environmental benefits over the whole life cycle in supporting operators and policymakers.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".