Life Cycle Emissions and Cost of Producing Electricity from Coal, Natural Gas, and Wood Pellets in Ontario, Canada
Bibliographic record
Abstract
The use of coal is responsible for (1)/(5) of global greenhouse gas (GHG) emissions. Substitution of coal with biomass fuels is one of a limited set of near-term options to significantly reduce these emissions. We investigate, on a life cycle basis, 100% wood pellet firing and cofiring with coal in two coal generating stations (GS) in Ontario, Canada. GHG and criteria air pollutant emissions are compared with current coal and hypothetical natural gas combined cycle (NGCC) facilities. 100% pellet utilization provides the greatest GHG benefit on a kilowatt-hour basis, reducing emissions by 91% and 78% relative to coal and NGCC systems, respectively. Compared to coal, using 100% pellets reduces NO(x) emissions by 40-47% and SO(x) emissions by 76-81%. At $160/metric ton of pellets and $7/GJ natural gas, either cofiring or NGCC provides the most cost-effective GHG mitigation ($70 and $47/metric ton of CO2 equivalent, respectively). The differences in coal price, electricity generation cost, and emissions at the two GS are responsible for the different options being preferred. A sensitivity analysis on fuel costs reveals considerable overlap in results for all options. A lower pellet price ($100/metric ton) results in a mitigation cost of $34/metric ton of CO2 equivalent for 10% cofiring at one of the GS. The study results suggest that biomass utilization in coal GS should be considered for its potential to cost-effectively mitigate GHGs from coal-based electricity in the near term.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".