Locational determinants for wood pellet plants: A review and case study of North and South America
Bibliographic record
Abstract
The European Union’s Renewable Energy Directive has led many electricity producers in Europe to use wood pellets in place of fossil fuels. North America has become one of the primary suppliers of wood pellets to Europe. This paper critically examines literature, economic models and data, as well as the supply chain and country risk factors, related to wood pellet production to anticipate where North and South American pellet mills should be built to meet Europe’s demand. Canada, the United States, and Brazil maintain the largest natural forest area, planted forest area, and industrial roundwood production; however, South American countries achieve faster plantation growth rates. The World Bank’s Logistic Procurement Index and IHS’s Country Risk Index were used to score and rank countries’ investment climates, based on their supply chain and risk factors. In this regard, the United States, Canada, and Chile performed best, in contrast to Venezuela, Bolivia, and Ecuador. When considering both wood supply and investment climates, the United States, Canada, and Chile were the most attractive countries to build a pellet mill, while countries, such as Argentina, Brazil, Colombia, Paraguay, and Peru present significant trade-offs between having significant wood resources and riskier investment climates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".