What Do We Learn from Emissions Reporting? Analytical Considerations and Comparison of Pollutant Release and Transfer Registers in the United States, Canada, England, and Australia
Bibliographic record
Abstract
Pollutant release and transfer registers (PRTRs) are becoming a popular measure for addressing industrial pollution in many countries. PRTRs require reporting of emissions from specific industrial sectors and making the information publicly available. This article suggests a framework for comparing PRTRs in order to determine whether they attain their declared goals and which factors, if any, influence their effectiveness. The challenges to such a comparison can be divided into three groups. The first refers to changes that are directly linked to the characteristics of PRTRs: both the changes within a specific system over time and variations among different systems. The second refers to parameters that affect the outcomes of the systems without being directly a part of them. The third involves the relations between the emissions reported to the PRTRs and the associated environmental risk. We suggest an approach that relies on relative comparison, commensurate with the unique characteristics of each PRTR, that compares their actual outcomes. Such an approach is necessary both due to significant variations among current PRTRs as well as for following the unique policy objectives that are manifested in different PRTRs. Application of this comparative approach in the United States, England, Canada, and Australia demonstrates significant differences in PRTR systems across countries and suggests that the mere presence of a PRTR may not lead to reduced industrial emissions. The analysis also demonstrates that emission reductions do not correlate with reductions in risk-related measures. The article proposes several simple modifications to the composition of current PRTR databases that may facilitate more accurate analysis of results and effective oversight of implementation.
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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 | 0.033 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".