The Economics of Abatement Based on Explicit Technologies for Output Reduction and Control
Bibliographic record
Abstract
The contribution of this paper lies in deriving socially optimal abatement (pollution avoidance) explicitly from separate technologies for production as well as control, where ‘control’ refers to decomposition of pollutants into harmless matter. This would help to construct welfare maximizing interventions such as Pigouvian taxes, given that a firm would respond to such an intervention by undertaking ‘reduction’ of its output and ‘control’, the two constituents of socially optimal abatement. Two cases are considered in this paper: zero and positive marginal cost of control at zero level of control. Cost minimization of a targeted level of abatement implies that the first case results in positive levels of both ‘reduction’ and ‘control’. The second case is associated with reduction equaling abatement for abatement below or equal to a threshold level, and positive levels of ‘reduction’ and ‘control’ otherwise. Thus, low enough marginal damages would be associated with low socially optimal abatement facilitated only through reduction; otherwise, a high enough socially optimal abatement facilitated by ‘reduction’ as well as ‘control’ would result. Further, an increase in the efficiency of the control technology which lowers the mentioned threshold level might have no impact on the magnitude of socially optimal level of abatement when marginal damages are low enough.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".