Are Hazardous Substance Rankings Effective? An Empirical Investigation of Information Dissemination About the Relative Hazards of Chemicals and Emissions Reductions
Bibliographic record
Abstract
Problem definition: Whether information dissemination about chemical hazards drives managers at facilities to undertake corresponding environmental actions, remains an open question that has not been adequately examined in the literature. Academic/practical relevance: We fill this gap in the literature by empirically investigating reductions in chemical emissions by facilities in relation to changes in the assessed hazard levels of chemicals evidenced in periodically-updated public information. We also examine the moderating effects of operational leanness—an attribute that prior studies have shown to be associated with better environmental performance—in our setting wherein the assessed hazard levels of chemicals change over time. Methodology: We draw data from four U.S. sources—the Substance Priority List from the Agency for Toxic Substances and Disease Registry, the Toxics Release Inventory from the EPA, the National Establishment Time-Series, and Compustat. We employ a panel model with facility-chemical- and time-fixed effects. Results: We find that public information dissemination on chemical hazards is effective, as indicated by the significant association between increases in the assessed hazard levels of chemicals and greater subsequent emissions reductions. Specifically, we find that facilities reduce emissions by an additional 4.28% on average, and their use of source reduction increases by 3.07% on average when the relative assessed hazard level of a chemical increases compared to when it decreases. We find that, overall, leaner facilities outperform less lean facilities with respect to emissions reductions. However, when the assessed hazard level increases, less lean facilities increase their emissions reductions more than leaner facilities. Managerial implications: Our findings provide insights for managers prioritizing environmental actions, including the extent of emissions reductions achievable by practicing lean. Our results can also be leveraged by governmental/nongovernmental organizations to anticipate responses to informational updates on chemical hazards, depending on characteristics of the affected facilities.
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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 | 0.020 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".