Vihreän vedyn kasvihuonekaasupäästöjen arviointi Saksan markkinalle : kansainvälisten tuotantoreittien vertaileva tarkastelu RED III- ja ISO 19870 -standardeja käyttäen
Bibliographic record
Abstract
This master’s thesis evaluates the sustainability of different production chains of green hydrogen and its derivatives. A Microsoft Excel-based model was developed to compare green hydrogen, ammonia, methanol, and methane produced for German market. Calculations follow the Renewable Energy Directive III and ISO 19870, which are compared with each other and to a broader scope of ISO 14067 guidelines. Commissioned by Wärtsilä, the aim is to identify international production pathways for producing hydrogen and its derivatives that meet the EU Taxonomy's green financing threshold of 100 gCO₂e/kWh and 70% GHG savings relative to fossil fuels. The production scenarios include Canadian wind-powered ammonia, Middle Eastern solar powered hydrogen derivatives, and Finnish grid produced hydrogen transported to Germany. Key findings reveal the highest emissions arising from electricity source and the choice of transportation. All reviewed production chains utilizing renewable energy sources achieved at least the 70% GHG savings even under worst scenarios. These fuels can also be combusted in engines for electricity production with emissions below the EU Taxonomy threshold of 100 gCO₂e/kWh. However, hydrogen produced with current Finnish grid electricity does not meet the 70% GHG savings requirement in any scenario. Future decarbonization of the Finnish grid, reducing its emission intensity to 40 gCO2e/kWh, would allow compliance with the 70% GHG savings threshold under all scenarios, allowing for renewable hydrogen production using grid electricity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".