Output and pollution abatement in a U.S. state emission function
Bibliographic record
Abstract
Abstract Using U.S. state-level data for the period 1973–1994, this study models the relationship between emissions, output and pollution abatement by defining an emissions function, in a manner that is consistent with the residual (emissions) generation mechanism and firms' optimizing behavior. It thus accounts for factors that were previously unaccounted for or addressed only individually. Applications using this comprehensive setting can offer more informed insights for policy-making, something that is particularly useful for developing countries that face the environmental degradation that comes together with the benefits of economic growth. Using nonparametric econometric techniques as well as threshold regression, the empirical results show that there is a positive nonlinear relationship between emissions and output, rejecting an inverted-U type of relationship between the two (the Environmental Kuznets Curve, or EKC). In the absence of abatement the relationship turns around, verifying the arguments in the literature that abatement is one of the driving forces for an EKC to emerge.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".