Assessing the energy potential of agricultural bioenergy pathways for Canada
Bibliographic record
Abstract
We assessed agricultural bioenergy pathways using existing and newly developed life cycle energy analyses so as to compare the potential of these pathways to replace fossil fuel in Canada. Energy gains after subtracting life cycle fossil energy inputs (Eg) and the ratio of fuel energy to life cycle fossil energy input (ER) were calculated. Results varied widely, reflecting differences in regional yields and study assumptions. Grasses and coppiced willow processed to electricity and heat exhibited Egs of 29–117 GJ ha-1 yr-1 and ERs of 4-17. These crops processed to lignocellulosic ethanol showed Egs of 22–114 GJ ha-1 yr-1 and ERs of 5–13. Grain ethanol and oilseed biodiesel showed Egs from -15 to 32 GJ ha-1 yr-1 and ERs from 0.8 to 3.7. Assuming 20% of Canada’s cleared agricultural land could be dedicated to annual biofuel crops, grain ethanol or oilseed biodiesel could displace up to 10 or 50%, respectively, of national road gasoline or diesel demand. If instead 40% of cleared agricultural land could be dedicated to perennials, the feedstocks could displace up to 52% of road gasoline demand if proc essed to lignocellulosic ethanol or 100% of utilities’and industries’fossil fuel demand for electricity and steam production. Our analyses showed that a goal of fossil fuel displacement favors the production of perennial crops for electricity and heating. Key words: Biomass, life cycles, oilseed, grain, grass, coppice
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".