Comparative techno-environmental analysis of grey, blue, green/yellow and pale-blue hydrogen production
Bibliographic record
Abstract
Hydrogen holds immense potential to assist in the transition from fossil fuels to sustainable energy sources, but its environmental impact depends on how it is produced. This study introduces the pale-blue hydrogen production method, which is a hybrid approach, utilizing both carbon capture and bioenergy inputs. Comparative life cycle analysis is shown for grey, blue, green and pale-blue hydrogen using cumulative energy demand, carbon footprint (CF), and water footprint. Additionally, the integration of solar-powered production methods (ground-based photovoltaic and floating photovoltaic (FPV) systems) is examined. The results showed blue hydrogen [steam methane reforming (SMR) + 56% carbon capture storage (CCS)] was 72% less, green hydrogen gas membrane (GM) 75% less, blue hydrogen [SMR+90%CCS] 88% less, and green hydrogen FPV have 90% less CF compared to grey hydrogen. Pale-blue hydrogen [50%B-50%G], blue hydrogen (GM + plasma reactor(PR)) PV and blue hydrogen (GM + PR) FPV offset 26, 48 and 52 times the emissions of grey hydrogen. • Life cycle analysis: reduced CO 2 footprint of pale-blue, blue, and green H 2 vs. grey H 2 . • Pale-blue H 2 combines solar power, water electrolysis, carbon capture, and bioenergy. • Pale-blue and blue (gas membrane + plasma reactor) H 2 offsets 26-48X grey H 2 emissions. • Pale-blue H 2 consumes 81.8% lower energy than grey H 2 , with a CED of 16.6 kWh/kg H 2 . • FPV powered green H 2 has the lowest CED at 1.08 kWh per kg H 2 .
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".