Impacts of COVID-19 on Municipal Solid Waste Systems in Ontario, Canada: A Retrospective Reflection of Learnings from Municipalities
Bibliographic record
Abstract
Over the last decade, Ontario’s waste management industry has been under immense strain that could be considered a crisis. Between high waste generation, rapidly depleting landfill space, and land availability to replenish this diminishing resource, effective waste diversion must be a priority for Ontario municipalities. In March 2020, the COVID-19 pandemic added yet another challenge to effective waste diversion as individuals attempted to cope with the pandemic. Between increased waste (such as single-use plastics from online shopping) and changes in waste operations, studies conducted towards the beginning of the pandemic showed the global influence of the COVID-19 virus on waste systems as the pandemic unfolded. This research aims to explore the impact of the COVID-19 pandemic on municipal solid waste management in Ontario. Specifically, this study will provide first-hand accounts from Ontario municipalities regarding their experiences managing municipal solid waste during the COVID-19 pandemic. Additionally, this study will examine these experiences through a retrospective lens and allow municipalities to provide their learnings and perspectives on the impact of the virus in a post-COVID-19 context. A survey that compared the experience of managing waste in a pre-, during, and post-COVID-19 context was sent out to 306 municipalities in Ontario. The data collected from the survey was triangulated with secondary waste tonnage data from 2019, 2020, and 2021 collected by the Resource Productivity and Recovery Authority. It was found that while Ontario had to make many of the same pandemic adaptations that were produced globally, the long-term impacts were not as severe as they were during the early onset of the pandemic. The COVID-19 pandemic impacted waste operations and policy more than waste generation and composition in Ontario.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".