Electricity Production from Anaerobic Digestion of Household Organic Waste in Ontario: Techno-Economic and GHG Emission Analyses
Bibliographic record
Abstract
The first Feed-in-Tariff (FiT) program in North America was recently implemented in Ontario, Canada to stimulate the generation of electricity from renewable sources. The life cycle greenhouse gas (GHG) emissions and economics of electricity generation through anaerobic digestion (AD) of household source-separated organic waste (HSSOW) are investigated within the FiT program. AD can potentially provide considerable GHG emission reductions (up to 1 t CO(2)eq/t HSSOW) at relatively low to moderate cost (-$35 to 160/t CO(2)eq) by displacing fossil electricity and preventing the emission of landfill gas. It is a cost-effective GHG mitigation option compared to some other FiT technologies (e.g., wind, solar photovoltaic) and provides unique additional benefits (waste diversion, nutrient recycling). The combination of electricity sales at a premium rate, savings in waste management costs, and economies of scale allow AD facilities processing >30,000 t/yr to be cost-competitive against landfilling. However, the FiT does not sufficiently support smaller-scale facilities that are needed as a transition to larger, more economically viable facilities. Refocusing of the FiT program and waste policies are needed to support the adoption of AD of HSSOW, which has not yet been developed in the Province, while more costly technologies (e.g., photovoltaic) have been deployed.
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 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 | 0.001 | 0.002 |
| 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".