Is there a role for firm hydrogen-based electricity in future energy systems? A comparative analysis of firm low-carbon electricity options
Bibliographic record
Abstract
Global electricity demand is expected to double as economies decarbonize, posing a dual challenge for fossil-based electricity systems: meet rising demands and transition to low-carbon technologies. Initiatives around the world have begun exploring the use of low-carbon hydrogen in electricity systems; however, research on blue hydrogen in different grid contexts is limited. This paper analyzes blue hydrogen-based firm low-carbon electricity and compares it with alternatives of green hydrogen, nuclear, and natural gas with carbon capture and storage (CCS). A new analysis framework and long-term energy-systems model are applied to Alberta, Canada, a jurisdiction with heavy reliance on natural gas. Ninety-two scenarios representing different technology mixes, technology costs, and carbon pricing were analyzed from 2025 to 2050. Of the technologies to supply blue hydrogen, autothermal reforming (ATR) was the most effective considering cumulative cost and GHG abatement together; however, all assessed alternatives to blue hydrogen were more effective. ATRCCS-based scenarios reduced cumulative system-wide emissions by less than 5 % by 2050, and had the highest marginal abatement costs ($161–$371/t), whereas natural gas with CCS scenarios reduced at least two times more emissions at lower marginal abatement costs ($7–$86/t). The next lowest MACs were from nuclear scenarios ($64–$94/t), then green hydrogen scenarios ($102–$107/t), with 37–39 % and 29–42 % of emissions abatement, respectively. Overall, findings suggest a limited and low-value role for blue hydrogen in future electricity systems, given the available alternatives for providing low-carbon firm electricity. These findings should be considered by decision makers when developing policy, allocating funding, and designing technology support mechanisms. • New analytical framework and model for hydrogen in electricity sector transitions. • Comparison of blue and green hydrogen, nuclear, and natural gas with CCS. • Autothermal reforming was the most effective option for blue hydrogen. • No condition found in which blue hydrogen was more effective than alternatives. • Small modular nuclear and natural gas with CCS were optimal low-GHG technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".