Cost-Benefit Analysis of Anaerobic Digestion in Southern Ontario: A Case Study
Bibliographic record
Abstract
This thesis explores the economic analysis of producing Renewable Natural Gas (RNG) through anaerobic digestion of organic waste available in Southern Ontario. The economic analysis employs Cost-Benefit Analysis (CBA) to evaluate the feasibility and profitability of implementing anaerobic digestion technologies. By comprehensively examining current practices, potential improvements, and economic viability, this study aims to provide RNG producers with insights into how this process can generate profits while contributing to sustainable waste management and energy production. The CBA method assesses the costs associated with anaerobic digestion, including installation, operation, and maintenance, alongside the benefits such as reduced waste disposal costs, greenhouse gas emission reductions, revenue from RNG sales, carbon credits, and tipping fees. By integrating environmental and economic perspectives, this research highlights the crucial role of this technology in advancing both environmental sustainability and economic development in Ontario. The findings suggest that with the proper use of feedstocks produced in Southern Ontario and supporting policies, anaerobic digestion could become a key solution for managing organic waste and generating clean energy, thereby supporting the province's environmental and economic goals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".