Examining dynamics of hydrogen supply chains
Bibliographic record
Abstract
Hydrogen is poised to play a pivotal role in achieving net-zero targets and advancing green economies. However, a range of complex operational challenges hinders its planning, production, delivery, and adoption. At the same time, numerous drivers within the hydrogen value chain present significant opportunities. This paper investigates the intricate relationships between these drivers and barriers associated with hydrogen supply chain (HSC). Utilising expert judgment in combination Grey-DEMATEL technique, we propose a framework to assess the interplay of HSC drivers and barriers. Gaining insight into these relationships not only improves access to hydrogen but also foster innovation in its development as a low-carbon resource. The use of prominence scores and net influence rankings for each driver and barrier in the framework provides a comprehensive understanding of their relative significance and impact. Our findings demonstrate that by identifying and accurately mapping these attributes, clear cause-and-effect relationships can be established, contributing to a more nuanced understanding of the HSC. These insights have broad implications across operational, policy, scholarly and social domains. For instance, this framework can aid stakeholders in recognizing the range of opportunities available by addressing key barriers to hydrogen adoption.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".