The international logistics of wood pellets for heating and power production in Europe: Costs, energy‐input and greenhouse gas balances of pellet consumption in Italy, Sweden and the Netherlands
Bibliographic record
Abstract
Abstract The European wood pellet market is booming: concerns about climate change and renewable energy targets are predominant drivers. The aim of this analysis is to compare typical wood pellet chains from the purchase of the feedstock from sawmills to the conversion into heat or electricity. Cost structures, primary energy inputs and avoided greenhouse gas (GHG) emissions are reviewed. Three cases are defined: pellets for district heating (DH) in Sweden (replacing heavy fuel oil); bagged pellets for residential heating in Italy (natural gas); and Canadian pellets for electricity production in the Netherlands (coal). Supply may cost €110–€170 per tonne of delivered pellets, with the main cost factors being feedstock collection, drying and long‐distance ocean transportation (for Canadian pellets only). Largest avoided emissions are for power production (1937 kg CO2eq/tonne of pellets), followed by district heating (1483 kg). In relative terms, the GHG reduction varies from 81% for residential heating (with pre‐dried feedstock) to 97% for DH. Based on a wood‐pellet consumption of 8.2 million tonnes, the EU27 plus Norway and Switzerland avoided about 12.6 million tonnes of CO2 emissions in 2008. Concluding, wood pellets can achieve substantial GHG savings, especially when substituting coal for power production. However, wood pellets are relatively expensive, especially compared to coal. Only in the case of high oil prices, can the substitution of heating oil for DH be commercially viable. In most other cases, substitution is only possible with financial support from national governments, for example, feed‐in tariffs or carbon taxes. The commercial markets for CO2 emission rights may cover some costs, but their impact is still limited. Copyright © 2010 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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".