Analysis of the global production location dynamics in the industrial wood pellet market: an MCDA approach
Bibliographic record
Abstract
Abstract Industrial wood pellet demand and international trade have been growing rapidly, requiring producers to build new production facilities. The purpose of this paper is to illustrate the trade‐offs of different wood pellet production locations across the world within the next ten years and to improve the quality and viability of future wood pellet production location and supply chain decisions. To this end, a multi‐criteria decision analysis (MCDA) was performed. This approach enabled assimilation and synthesis of both qualitative and qualitative data of a comprehensive set of regions in which lies its originality. The following characteristics were indicated and assessed: feedstock (availability, competition, and pricing), investment climate, electricity prices, market potential, and logistics. Analysis of various scenarios resulted in a preference for Northern American, Austrian, Belgian, and German regions based either on superior feedstock pricing or logistic position, complemented by a stable investment climate. However, a scenario of high demand of ocean freight quickly diminished the performance of Western Canada and the USA, Brazil, and Chile. Austria, Belgium, and Germany remained most volatile for feedstock shortages. Overall, Austria, Estonia, Czech Republic, and Sweden seem more robust to perform well in different scenarios, which may prove crucial to limit risk exposure in an emerging market. Some more remote regions with huge feedstock potential (e.g. Russia) depend on the investment climate and logistical feasibility of these regions. © 2011 Society of Chemical Industry and John Wiley & Sons, Ltd
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".