Evaluation of Green and Blue Hydrogen Production Potential in Saudi Arabia
Bibliographic record
Abstract
• Centralized largescale Green Hydrogen Production (GHP) in five cities are evaluated. • Blue Hydrogen Production (BHP) via SMR with and without CCS/U is evaluated. • LCOH and cash flow for both GHP and BHP are calculated via technoeconomic model. • An informative revealing comparison between GHP and BHP is presented. The Kingdom of Saudi Arabia has rich renewable energy resources, specifically wind and solar in addition to geothermal beside massive natural gas reserves. This paper investigates the potential of both green and blue hydrogen production for five selected cities in Saudi Arabia. To accomplish the said objective, a techno-economic model is formulated. Four renewable energy scenarios are evaluated for a total of 1.9 GW installed capacity to reveal the best scenario of Green Hydrogen Production (GHP) in each city. Also, Blue Hydrogen Production (BHP) is investigated for two cases of Steam Methane Reforming (SMR) with different percentages of carbon capture. The two BHP scenarios were compared with a base case scenario of hydrogen production from natural gas without CCS/U (gray hydrogen). The economic analysis for both GHP and BHP is performed by calculating the Levelized Cost of Hydrogen (LCOH) and cash flow. The LCOH for GHP range for all cities ($3.27/kg–$12.17/kg) ) with the lowest LCOH is found for NEOM city (50% PV and 50% wind) ($3.27/kg). LCOH for the three SMR cases are $0.534/kg, $0.647/kg, and $0.897/kg for SMR wo CCS/U, SMR 55% CCS/U, and SMR 90% CCS/U respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".