Life cycle greenhouse gas emissions of electricity generation from corn cobs in Ontario, Canada
Bibliographic record
Abstract
Abstract Policy initiatives have motivated a search for environmentally sustainable alternatives to fossil‐fuel‐based electricity generation. Agricultural residues such as corn cobs may be a suitable feedstock. A life cycle approach was used to estimate the greenhouse gas ( GHG ) emission impacts associated with the use of pellets produced from corn cobs as the sole fuel for the generation of electricity at a hypothetically retrofitted coal‐fired generating station in Ontario, Canada. Pellets are compared with current coal and hypothetical natural gas combined cycle ( NGCC ) facilities. A life cycle model and soil carbon model calibrated for the agricultural region of interest were combined to quantify the GHG emissions of the biomass product system. The corn cob product system's life cycle emissions (240 g CO 2 eq kWh −1 ) are 40% and 80% lower than those of the NGCC and coal product systems, respectively. If corn cobs are left in the field to decompose, some carbon is sequestered in the soil, thus their removal from the field and combustion at the generation station represents a net GHG emission, accounting for 60% of life cycle emissions. In addition to the GHG impacts of combustion, removing agricultural residues from fields may reduce soil health, increase erosion and affect soil fertility through loss of soil organic carbon and nutrients. Their sustainable use should therefore consider the maintenance of soil fertility over the long‐term. Nevertheless, the use of the feedstock in place of coal may provide substantial GHG emissions mitigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".