Effectiveness of the National Pollutant Release Inventory as a Policy Tool to Curb Atmospheric Industrial Emissions in Canada
Bibliographic record
Abstract
To curb greenhouse gas emissions and reduce atmospheric pollutants in Canada, many pieces of environment legislation are targeted at reducing industrial emissions. Traditional regulation prescribes penalties through fines to discourage industries from polluting, but, in the past two decades, alternative forms of environmental regulation, such as the National Pollutant Release Inventory (NPRI), have been introduced. NPRI is an information management tool which requires industries to self-report emissions data based on a set of guidelines determined by Environment and Climate Change Canada, a federal agency. The tool works to inform the public regarding industry emissions and provides a database that can be analyzed by researchers and regulators to inform emissions trends in Canada. These tools have been successful in other jurisdictions (e.g., United States and Australia). However, research assessing the U.S. Toxic Release Inventory suggests there are fundamental weaknesses in the self-reported nature of the data and incidences of under-reporting. This preliminary study aimed to explore NPRI in Canada and test its effectiveness against the National Air Pollutant Surveillance Network (NAPS), an air quality monitoring program administered by the federal government. While instances of under-reporting were undetected, this study identified areas of weakness in the NPRI tool and instances of increasing emissions across various industrial sectors in Canada.
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".