EFFECTIVENESS OF THE ENFORCEMENT OF INDUSTRIAL EMISSION STANDARDS IN
Bibliographic record
Abstract
Unfortunately, the empirical literature on the enforcement of industrial emissions standards refer to case studies in the developed world, mostly the U.S. and Canada. There does not exist any example of this type of empirical work for Latin America. In fact, Dasgupta, et al. (2001) and Wang et al. (2002) are the only examples of empirical studies of effects of inspections and fines on pollution levels and the determinants of the monitoring and enforcement activities of regulators, respectively, for a less developed country (China). This constitutes a very important shortcoming because Latin America has a long tradition in water pollution control laws, but both public opin-ion and papers that have analyzed environmental policy in the region have regarded them as poorly enforced. Furthermore, many resources are being devoted to developing new regulations and in-struments, but no effort is being made to assess the effectiveness of the existing ones. This paper contributes to fill this gap by empirically testing the effect of inspections and enforcement actions of the municipal and national governments on industrial plants ’ emissions of BOD5 in Montevideo, Uruguay. Results suggest that monitoring and enforcement activity by formal regulators did not have an important deterrent effect on reported BOD5 levels and the compliance status of the indus-
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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 | 0.009 | 0.035 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".