SUSTAINABLE INNOVATIONS FOR ELECTRICITY GENERATION IN ONTARIO
Bibliographic record
Abstract
The electricity generation sector is far from sustainable; in Ontario, 77% of electricity consumed is generated from non-renewable sources such as coal, natural gas and nuclear. As a result, this sector contributes significantly to many environmental challenges including global warming, smog formation, and acid deposition. It is critical to improve the sustainability of electricity generation through the incorporation of sustainable design concepts. Sustainable design takes into account the environmental performance of a product or process over its entire life cycle (including design and development, raw material acquisition, production, use, and end-of-life). Innovative design has resulted in new technologies for electricity generation. Generating electricity from biomass is one of the alternative technologies which could have the potential to improve the sustainability of the electricity generation sector. In this research we examine various scenarios for displacing coal-based generation. Coal gasification is a mature technology and to replace some or all of the feedstock with biomass, a re-design of some portions of the electricity generation technology are required. The technical changes in the process depend on several issues including the physical and chemical characteristics of biomass. We evaluate the environmental performance of electricity generation from agricultural residues through conducting a life cycle inventory for three biomass-to-electricity scenarios for the Province of Ontario; 1) a 5% co-firing of agricultural residues with coal in existing coal plants, 2) a 15% co-firing of agricultural residues with coal in existing coal plants, and 3) a hypothetical power plant which produces electricity from 100% agricultural residues using biomass gasification technology. For comparison purposes, we analyze a current coal only option using plant specific data. We quantify life cycle energy use, greenhouse gas and air pollutant emissions for electricity. Our results suggest that on a life cycle basis electricity generated from biomass can achieve a reduction in greenhouse gas emissions of 4% (for the 5% biomass co-firing) to 96% (for the 100% biomass gasification) compared to the coal-only option. Similarly, reductions in air pollutant emissions (sulfur oxides, nitrogen oxides, and particulate matter) range from 4% to 98%. Our study indicates that life cycle analysis is a useful tool for assisting decision makers in the selection of more sustainable design options for future electricity generation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".