The development of an assessment framework to determine the technical hydrogen production potential from wind and solar energy
Bibliographic record
Abstract
Electrolytic hydrogen produced from wind and solar energy is considered a long-term option for multi-sectoral decarbonization. The study objective is to develop a framework for assessing country-level hydrogen technical potential from wind and solar energy. We apply locational suitability and zonal statistical analyses methods in a geographic information system-based environment to derive granular insights on non-captive technically exploitable hydrogen potential in high-resource locations. Seven setback factors were considered for locational suitability, and integrated with modules developed for evaluating the wind and solar resource penetration from open-source theoretical renewable resource geospatial data and electricity-to-hydrogen conversion analyses. The technique applied in this study would be a relevant contribution to determining national and regional-wide electrolytic hydrogen production potentials in other jurisdictions with requisite adjustments to data and technical constraints. The results from the case study country, Canada – a major hydrogen-producing country – show that the technical hydrogen potentials from wind and solar energy are approximately 1,897 and 448 million metric tonnes per year, respectively, at least 6.3 times greater than global hydrogen demand in 2019. When we integrated locational data on enabling infrastructure, we discovered that the lack of access to power transmission lines in low-population-density areas of the country significantly reduces the exploitable wind- and solar-based hydrogen potential by over 80% and 6%, respectively. The findings of this study show that in the absence of spatial data on infrastructural constraints, the exploitable hydrogen potential in a jurisdiction can be overestimated, leading to improper guidance for policy and decision-makers.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".