Carbon Footprint of Municipal Solid Waste Management in Guelph City, Ontario
Bibliographic record
Abstract
More light is being shed continually on the environmental impacts of municipal solid waste due to the increasing amounts of waste generated and the related greenhouse gas emissions. Emissions from MSW account for 20% of Canadian greenhouse gas (GHG) emissions and accordingly, waste legislation in Ontario demands high waste recovery and a moving towards a circular economy. This study evaluates the current municipal solid waste management in the City of Guelph and assesses possible alternative scenarios based on the associated GHG emissions. Waste Reduction Model (WARM) that was developed by the US-EPA has been used to quantify the GHG emissions produced over the entire life cycle of the MSW management scenario. Sensitivity analysis was also conducted to investigate the influence of some scenarios on the overall GHG emissions. It has been found that one ton of landfilled waste generates approximately 0.39 ton of carbon dioxide equivalent (CO 2 Eq). It was also found that the current solid waste scenario has a saving of 36086 million ton of CO 2 Eq (MCO 2 Eq). However, the results showed that the scenario with enhanced waste-to-energy, reduction at source and recycling has resulted in a high avoided emissions (0.74 kg CO 2 Eq/kg MSW). The anaerobic Digestion scenario caused the lowest avoided emissions of 0.39 kg CO 2 Eq/kg MSW. The net avoided emissions for reduction at source scenario were found to be the same as that found by the current scenario (0.4 kg CO 2 Eq/kg MSW). The sensitivity analysis of both reduction at source and recycling rates show a linear inverse proportional relationship with total GHG emissions reduction.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".