Does competition prevent industrial pollution? Evidence from a panel threshold model
Bibliographic record
Abstract
Abstract The objective of this paper is to assess the impact of competition on industrial toxic pollution by using, for the first time, a panel threshold model which allows evaluations of the main drivers of toxic releases under two different market regimes. The empirical analysis is based on a micro‐level panel dataset over the five‐year period 1987–2012. We show that this relationship is statistically significant and robust above and below the threshold, even after accounting for alternative specifications of market concentration. Specifically, we unmask an inverted V‐shaped relationship between market concentration and industrial pollution. We argue that the increasing non‐parametric regression line up to a certain concentration (threshold) level indicates a negative effect on facilities' emissions levels, whereas a decreasing line indicates a positive effect. This relationship provides new insights into environmental policy design towards abatement of industrial releases and sustainability. Finally, our empirical model remains robust under different specifications properly accounted for possible endogeneity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".