INTERNATIONAL BIOENERGY TRADE: EXAMPLES, TRENDS AND BARRIERS OBSERVED BY IEA BIOENERGY TASK 40
Bibliographic record
Abstract
The aim of this paper is to present a synthesis of the main developments and drivers of international bioenergy trade in IEA bioenergy task 40 member countries.The use of biomass for energy varies in these countries between a few percent of the national energy supply up to significant shares (e.g.15-25% in Finland, Sweden and Brazil).In many European countries such as Belgium, Finland, the Netherlands, Sweden and the UK and, imported biomass forms already a significant part of the total biomass use (between 21-43%).International bioenergy trade is growing rapidly, far beyond what was deemed possible only a few years ago.For example, wood pellets are currently exported by Canada, Finland and (to a small extent) Brazil and Norway, and imported by Sweden, Belgium, the Netherlands, and the UK.In the Netherlands and Belgium, pellet imports nowadays contribute to a major share to total renewable electricity production.Major drivers for international demand are the large resource potentials and relatively low production costs in e.g.Canada, and high demand for biomass due to various policy incentives in importing countries.However, developing the required logistic infrastructure both in exporting and importing countries is required to access larger physical biomass volumes and to reach other (i.e.smaller) endconsumers.Trade in bio-ethanol is another example of a rapidly growing international market.With the EU-wide target of 5.75% biofuels for transportation in 2010 (and the recently announced target of 10% in 2020), exports from Brazil and other countries to Europe are likely to rise as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".