Co-movement of prices between biofuel co-products in Canada: ethanol, electricity, and pellets
Bibliographic record
Abstract
The potential emergence of a second-generation ethanol industry in Canada will depend on future production technologies and prices. Financial viability might be improved by producing cellulosic ethanol with co-products such as lignin pellets or electricity. Financial returns to ethanol production will depend on price variability and possible price spillovers among ethanol and its co-products. We use a multivariate BEKK–GARCH approach to investigate past mean price interactions and volatility spillovers between ethanol and electricity prices. Wood pellets are investigated in a univariate framework because of data constraints. Results show substantial price interactions and volatility spillovers among these products. If a second-generation ethanol industry emerges, co-production of products from common feedstocks may strengthen already established relationships between the prices of these energy products. These conditions could create increased risk and the clustering of high–low price fluctuations among co-products. For investors, results suggest that risk reduction strategies should protect against correlated volatility. For policy makers, results suggest that policies that target one commodity may lead to unintended impacts on co-product(s). In sum, understanding links between markets is important for designing future policies and for insights into how a second-generation ethanol industry may emerge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".