Output and pollution abatement in a U.S. state emission function
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it