Evaluation of waste reduction and diversion as alternatives to landfill disposal
Bibliographic record
Abstract
Although the Region of Waterloo implements a waste recycling program as part of its compliance with the 2004 Ontario Waste Diversion Goal a large fraction of its municipal solid waste ends up in the landfill. Landfill waste disposal adversely impacts the environment through the release of air pollutants and greenhouse gases to the atmosphere, and through the generation of leachate that may contaminate water sources. Landfills also require large land areas, which limit their long-term sustainability. This paper presents a quantitative comparison of the environmental, economic, and social impacts of the current waste disposal program in the Region of Waterloo and of three waste management alternatives: (i) expansion of the organics collection program with biogas recovery, (ii) expansion of the recycling program, and (iii) incineration with energy recovery. Environmental impacts were evaluated by performing a life cycle analysis using the US EPA's Waste Reduction Model. Economic impacts were quantified using cost-benefit analysis; social impacts were evaluated using a previously developed scoring scheme. Finally, the overall impacts were ranked and analyzed using the Saaty's Analytical Hierarchy Process (AHP) to determine an optimal alternative to landfill disposal. Results indicate that incineration with energy recovery is ranked the highest overall in all three evaluation criteria categories. Incineration results in the greatest gas reductions (86%) and the lowest cost to implement. Incineration also ranks the highest in the social impacts ranking due to reductions in foul odors, potential for attracting disease vectors, and land requirements. Expanding the recycling collection improves greenhouse gas emissions by 41% of the current method; it also reduces disposal costs. Overall, all three alternatives are better than the current waste disposal method, and incineration is deemed the optimal waste management option for the Region of Waterloo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".