Environmental and Economic Evaluation of Bioenergy in Ontario, Canada
Bibliographic record
Abstract
We examined life cycle environmental and economic implications of two near-term scenarios for converting cellulosic biomass to energy, generating electricity from cofiring biomass in existing coal power plants, and producing ethanol from biomass in stand-alone facilities in Ontario, Canada. The study inventories near-term biomass supply in the province, quantifies environmental metrics associated with the use of agricultural residues for producing electricity and ethanol, determines the incremental costs of switching from fossil fuels to biomass, and compares the cost-effectiveness of greenhouse gas (GHG) and air pollutant emissions abatement achieved through the use of the bioenergy. Implementing a biomass cofiring rate of 10% in existing coal-fired power plants would reduce annual GHG emissions by 2.3 million metric tons (t) of CO2 equivalent (7% of the province's coal power plant emissions). The substitution of gasoline with ethanol/gasoline blends would reduce annual provincial lightduty vehicle fleet emissions between 1.3 and 2.5 million t of CO2 equivalent (3.5-7% of fleet emissions). If biomass sources other than agricultural residues were used, additional emissions reductions could be realized. At current crude oil prices ($70/barrel) and levels of technology development of the bioenergy alternatives, the biomass electricity cofiring scenario analyzed is more cost-effective for mitigating GHG emissions ($22/t of CO2 equivalent for a 10% cofiring rate) than the stand-alone ethanol production scenario ($92/t of CO2 equivalent). The economics of biomass cofiring benefits from existing capital, whereas the cellulosic ethanol scenario does not. Notwithstanding this result, there are several factors that increase the attractiveness of ethanol. These include uncertainty in crude oil prices, potential for marked improvements in cellulosic ethanol technology and economics, the province's commitment to 5% ethanol content in gasoline, the possibility of ethanol production benefiting from existing capital, and there being few alternatives for moderate-to-large-scale GHG emissions reductions in the transportation sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".