Green hydrogen production potential in Turkey with wind power
Bibliographic record
Abstract
The present study aims to investigate the renewable energy-based hydrogen production potential using onshore and offshore wind power, along with the available undersea currents in Turkey. Wind energy potential varies based on the cities location, for both onshore and applicable offshore applications. Furthermore, undersea current turbines are considered for generating renewable energy potential. Proton Exchange Membrane (PEM) electrolyzers are considered for water splitting and hydrogen production. The total hydrogen production potential for Turkey is estimated to be 248.56 million tons. The onshore wind, offshore wind, and undersea current hydrogen production potentials are found to be 233.38, 15.17, and 6.65 million tons, respectively. In this regard, Erzurum, Van, Konya, and Sivas appear to be the cities with maximum hydrogen production potentials of 13.83, 12.81, 12.05, and 11.82 million tons, respectively. The hydrogen generation potentials for all Turkish cities are provided and discussed for a hydrogen economy platform. It may help promote Turkey to a hydrogen hub leadership position in the region through creating jobs supporting energy sector, and providing a sustainable future by establishing local, national, and international connections and networks. It furthermore gives a country-wide spectrum of how effective role the wind energy can play in paving the road for a sustainable energy country. The study results may serve as a reliable base for planning and strategizing purposes as required for the country and help create new energy policies for exploiting renewable energy resources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".