Limited scientific evidence for decarbonization of energy end-uses and the challenges to learning and empowerment of green hydrogen niches - insights from Canada
Bibliographic record
Abstract
Supporting inappropriate uses for hydrogen can delay climate action and decarbonization efforts should limit hydrogen to difficult-to-electrify end-uses. The introduction of novel green hydrogen niches to markets requires learning about which end-uses are appropriate for hydrogen and the empowerment of these niches. This work identifies and collates scientific evidence of when to use hydrogen over electrification of end-uses. The hydrogen end-uses being empowered by legitimization through discourse and resource mobilization are assessed in investment advice, 11 government plans, and 47 policies in Canada. The findings confirm the inattention to when to electrify and when to use hydrogen, observed in the very limited scientific evidence of only two approaches to prioritization, the lack of harmonization between the approaches, and the lack of legitimization of this information. Although some hydrogen end-uses being empowered align with scientific evidence, the most appropriate set of hydrogen end-uses that could contribute to decarbonization are not being legitimized and empowered in Canada. More attention should be paid and resources allocated to developing and legitimizing robust and scientifically based evidence of when to electrify and when to use hydrogen for energy end-uses. This novel method is globally applicable to other emerging technologies and policy analysis.
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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 | 0.049 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".