Greenhouse gas performance of heat and electricity from wood pellet value chains – based on pellets for the Swedish market
Bibliographic record
Abstract
Abstract Increased bioenergy demand has triggered a discussion on the sustainability of solid biomass‐based fuels and a system for sustainability criteria has been discussed within the EU. This paper assesses the greenhouse gas (GHG) emissions for heat and electricity from selected wood pellet value chains for the Swedish market and the associated potential emissions reduction in relation to fossil fuels using a life cycle assessment (LCA) perspective, and in relation to the approach described in recent EU policy developments. Nine different wood pellet value chains for heat and/or power production in Sweden are assessed (including pellets from Sweden, Latvia, Russia, and Canada). Selected assumptions are varied in a sensitivity analysis. The total factory‐gate GHG emissions at the conversion facility for the wood pellet value chains studied, range between 2 and 25 g CO2‐eq/MJ pellets with Swedish pellets at the lower end, and Russian pellets using natural gas for drying the raw material at the higher end. Imported pellets from Latvia, Russia, and Canada that use biomass for drying may also reach relatively low levels of GHG emissions. The potential GHG reduction as compared to a certain fossil fuel default energy comparator is 64–98% for the electricity produced in the pellet value chains studied and 77–99% for the heat produced. Thus, many wood pellet value chains on the Swedish market will most likely be able to meet strict demands for sustainability from a GHG perspective. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 | 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) | 0.003 | 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".