Potential for Thermo-Chemical Conversion of Solid Waste in Canada to Fuel, Heat, and Electricity
Bibliographic record
Abstract
The amount of municipal solid waste (MSW) generation in Canada was 34 million tonnes in 2018. Responsible waste management is challenging, but essential to protect the environment and to prevent the contamination of the ecosystem on which we rely. Landfilling is the least desirable option, and diversion through thermo-chemical conversion to value-added products is a good option for difficult-to-recycle waste. In this study, the amounts, moisture contents, heating values, and compositions of municipally collected solid waste produced in Canada are reported, a classification that is suitable for conversion purposes is proposed, and the potential for thermo-chemical conversion is determined. Much of the waste generated in Canada is suitable for being converted, and its potential for heat or electricity generation was determined to be 193 PJ/yr and 37 TWh/y, respectively. The GHG emissions that are saved through diversion from the landfill, while assuming the generated heat or electricity offsets natural gas combustion, gives a GHG reduction of 10.6 MMTCO2E/yr or 1.6% of Canada’s GHG emissions. The blending of waste in feedstocks can have varying effects on the amount of biogenic CO2 produced per unit energy in the feedstock, which is an important consideration for new projects. Other considerations include the heating values, moisture contents, and contaminant levels in the waste.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 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".