Impact of EPA's Voluntary 33/50 Program on Pollution Prevention Adoption and Toxic Releases
Bibliographic record
Abstract
This paper evaluates the impact of 33/50 program on 33/50 releases and examine if this impact differed for ozone depleting chemicals and the rest 15 chemicals. It also examines the extent to which any reduction in 33/50 releases can be attributed to pollution prevention techniques (P2). Our analysis is conducted at the facility level, using facility level participation information and emissions data for the 1988-1995 periods. The data set includes 12,463 facilities eligible to participate in the program starting in 1991. Of these, 1033 facilities belonging to 1268 parent companies participated in the 33/50 program. Dynamic panel data models are used to incorporate facility specific unobserved effect, timing of participation and endogeneity of program participation and P2 adoption decisions. The results show that the program has different impact on ozone depleting chemicals and other releases. Regulatory pressure to comply with Montreal protocol motivated firms to reduce emissions during early stage of the program. When the immediate threat or regulation was reduced, participants appear to have focused on non-ozone depleting chemicals. The pollution prevention methods adopted also contributed to a reduction in toxic releases.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".