Scaling green hydrogen: Production, storage, techno-economics and global perspectives
Bibliographic record
Abstract
Hydrogen has emerged as a key green energy carrier for deep decarbonisation, offering a viable pathway to reduce emissions from carbon-intensive industries while enabling greater integration of renewable energy source into the global energy system. This study provides a comprehensive review of green hydrogen production technologies, storage methods, and industrial applications, alongside the financial and regulatory landscape shaping its large-scale deployment. From techno-economic viewpoints, alkaline electrolysis offers cost advantages at approximately USD 270/kW compared with proton membrane exchange and solid oxide electrolysis. Storage technologies show levelised costs of USD 2.48–15.61/kg H 2 with scalability to gigawatt level, surpassing battery systems. Hydrogen adoption enables substantial decarbonisation in hard-to-abate sectors, with deployments estimated to cut more than 1 Mtonne CO 2 emissions annually in steelmaking and more than 100 ktonne in cement production. This study underscores the importance of international cooperation, outlining pathways for countries with abundant renewable resources (e.g., Canada, Australia) to emerge as major hydrogen producers, while nations with strong demand (e.g., Japan, South Korea) act as market catalysts. Finally, investment dynamics, government incentives, regulatory frameworks, and targeted policy recommendations are reviewed to provide a holistic perspective for building a resilient and sustainable hydrogen ecosystem.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".