The Ability of Cellulosic Ethanol to Compete for Feedstock and Investment with Other Forest Bioenergy Options
Bibliographic record
Abstract
The economic performance of cellulosic ethanol production from forest resources was contrasted with that of other bioenergy options: biopower, combined heat and power, wood pellets, and Fischer-Tropsch liquids (biomass-to-liquids). Techno-economic models were designed for each conversion facility and a trucking model was used to determine delivered feedstock cost in central British Columbia, Canada. Facilities and processes were analyzed for their sensitivity to feedstock type (sawdust/shavings, whole logs, pulp chips, harvest residues, and hog fuel), scale (0.05–4 million bone dry tonnes per year), and product revenue volatility (based on historical volatility of proxy fossil fuels). Internal rate of return (IRR) was used as the primary metric for economic performance comparison. Under the base case scenario, with 0.2 million bone dry tonnes per year in the form of whole logs (roundwood), all facilities had a negative IRR, indicating that dedicated bioenergy harvest may be economically challenging at current market prices. However, all bioenergy options, and in particular wood pellets, were more attractive when the feedstock was switched to mill or harvest residues. Despite a higher cost for feedstock delivery, IRR increased for all technologies except for wood pellets as facility scale increased. The revenue required for a 15% IRR highlighted the structural market and policy differences between ethanol, electricity, and pellets, which may make it difficult for cellulosic ethanol producers to compete for forest feedstocks and investment under the current regime.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".