Biogene und fossile Kraftstoffproduktion an norddeutschen Hafenstandorten: ein ökonomischer, ökologischer und energetischer Vergleich der Wertschöpfungsketten
Bibliographic record
Abstract
The most significant challenge of the 21st century is probably the supply of the human population and economy with sustainable energy. Biomass to Liquid (BtL) fuels have the potential of being an expedient alternative to fossil fuels and could therefore contribute significantly to the needed energy turnaround. The work at hand analyses the economical, ecological and energetic competitiveness of import biomass based BtL fuel supply chains in comparison to a fossil fuel supply chain. Initially it is shown, which biomass is in compliance with the requirements of the BtL production process and therefore suitable to be used for the manufacturing of BtL fuels. Considering these requirements, empty fruit bunch pellets from Malaysia grounded rice husks from China, wood pellets from Canada and woodchips from Lithuania are the best suited feedstock alternatives. To capitalize on the advantages of imported biomass, the BtL production should be located at a seaport. In this work, the port of Brake/Unterweser is selected to be the production site for the BtL production and the port of Wilhelmshaven is selected to be the production site for the fossil fuel production. The supply chain comparison is done economically by cost comparison, ecologically by a greenhouse gas balance and energetically by an energy balance. Furthermore the influence of oil price changes and the introduction of CO2 certificate is examined by using a sensitivity analysis. The BtL supply chains are ecologically and energetically superior but economically inferior to the fossil fuel supply chain. If the economical, ecological and energetic results are instead weighed equally, five BtL supply chains have been identified that are superior to the fossil fuel supply chain. The comparison of the BtL supply chains shows, that wooden Biomass from Europe or the Atlantic region are best suited for an economically competitive BtL production. On the other hand, East Asian biomass is not suited for an economically competitive BtL production in Germany at the moment. The sensitivity analysis shows, that at an oil price of 105,02 $ bbl-1 or an CO2 certificate price of 8,20
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".