A City-Wide Emissions Inventory of Plastic Pollution
Bibliographic record
Abstract
A global agreement on plastic should have quantitative reduction targets for the emissions of plastic pollution and regular measurements to track success. Here, we present a framework for measuring plastic emissions, akin to greenhouse gas emissions, and demonstrate its utility by calculating a baseline measurement for the City of Toronto in Ontario, Canada. We identify relevant sources of plastic pollution in the city, calculate emissions for each source by multiplying activity data by emission factors for each source, and sum the emissions to obtain the total annual emissions of plastic pollution generated. Using Monte Carlo simulations, we estimate that 3,531 to 3,852 tonnes (T) of plastic pollution were emitted from Toronto in 2020. Littering is the largest source overall (3,099 T), and artificial turf is the largest source of microplastic (237 T). Quantifying source emissions can inform the most effective mitigation strategies to achieve reduction targets. We recommend this framework be scaled up and replicated in cities, states, provinces, and countries around the world to inform global reduction targets and measure progress toward reducing plastic pollution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".