Future clean hydrogen potential from surplus energy: A techno-economic analysis
Bibliographic record
Abstract
Several studies evaluate hydrogen production from renewable and nuclear energy surpluses, most of which focus on specific technologies or on the energy situation of particular countries. This study assesses the global potential surplus of solar, wind, and nuclear energy, along with the corresponding potential hydrogen production, during the period 2023–2050. The analysis focuses on three different years: 2023, 2030, and 2050, specifying hydrogen production, electrolyzer capital cost, and hydrogen cost for each one. Results are presented considering both 100 % and 50 % surplus utilization scenarios, accounting for a 2 % annual inflation rate in future projections. The findings indicate that hydrogen production from surplus energy could reach a cumulative total amount of 6000 Mt by 2050. This increase in production is expected to lower electrolyzer capital costs and drive hydrogen costs below US$ 2 per kilogram in 2050. Furthermore, the potential reduction in CO 2 emissions, achieved by replacing coal and natural gas with hydrogen for electricity production, is analyzed. The study concludes that utilizing surplus energy could provide a significant boost to the hydrogen economy and offer an additional pathway toward sustainable 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".