The Potential Contribution of Imported Biomass to Renewable Energy Targets in the EU–the Trade-off between Ambitious Greenhouse Gas Emission Reduction Targets and Cost Thresholds
Bibliographic record
Abstract
Wood pellets could potentially contribute to bioenergy demand in the European Union (EU). Market cost constraints as well as greenhouse gas (GHG) emission savings thresholds imposed by the European Commission however limit the potential use of pellets. A spatially explicit assessment of import potentials of both pellets and torrefied pellets, based on the growing stock of forestry biomass in the US, Canada, Brazil, Russia and Baltic States, was combined with an analysis of supply chain costs and emissions in order to analyse potentials as limited by different levels of costs and emission constraints. Results show that in case of GHG savings thresholds of 70%, 80% and 85% the total import potential is reduced to 61 to 24 and 1 Mt, respectively. The potential for torrefied pellets is larger in all cases, 44 Mt in the case of an 80% limit. Import potentials at cost limits of 200, 175, 150 and 125 €/t are reduced from 58 Mt to 52, 38 and 9 Mt pellets, respectively, with little difference between pellets and torrefied pellets. This work shows that spatially explicit variation in feedstock availability and logistics has a significant impact on total import potentials and must therefore be included in any assessment of bioenergy potential and trade.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".