Tracking progress toward sustainable development goal 12 using Canadian industrial pollutants in waste
Bibliographic record
Abstract
Canada's National Pollutant Release Inventory (NPRI) is part of a global network of over 50 national Pollutant Release and Transfer Registries. These registries track industrial pollutant releases into the air, water, and land, and transfer and disposal of pollutants to various waste management practices. Despite the NPRI being a long-standing public dataset that can speak directly to Canadian progress on various reductions in chemical releases and waste generation sought by the United Nations' Sustainable Development Goal (SDG) 12, its data on pollutant transfers and disposals is so far overlooked in tracking SDG progress. This study aims to uncover the potential of this waste-related data to provide meaningful measures of progress toward SDG 12 using two case studies (on the Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and their Disposal and the Minamata Convention on Mercury) and a framework by the Organization for Economic Cooperation and Development to do so. The findings challenge the premise that progress in the current SDG 12.4 indicator (number of parties to international chemical agreements that transmit required information) also leads to progress on overall SDG 12.4 waste-related aims. This analysis sets a precedent for using publicly available PRTR data from any country to monitor progress toward SDG 12 waste-related objectives, opening up new possibilities for more accurate global tracking of this SDG. • Pollutant Release and Transfer Registries (PRTR) track industrial chemicals in waste. • PRTR data is used here to track progress toward UN SDG 12.4 waste-related aims. • Canadian transboundary pollutant movements are increasing in quantity. • PRTR data show a different progress story for SDG 12.4 than official indicators.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.026 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".