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Record W3002298322 · doi:10.1162/rest_a_00904

Environmental Regulations and the Cleanup of Manufacturing: Plant-Level Evidence

2020· article· en· W3002298322 on OpenAlex
Nouri Najjar, Jevan Cherniwchan

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Review of Economics and Statistics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsCarleton UniversityWestern University
Fundersnot available
KeywordsAir quality indexQuality (philosophy)Falling (accident)Environmental scienceAir pollutionEnvironmental qualityNatural resource economicsPollutionBusinessEnvironmental engineeringEnvironmental economicsEconomicsMeteorologyGeographyEnvironmental healthEcology

Abstract

fetched live from OpenAlex

Abstract For much of the industrialized world, pollution from manufacturing has been falling despite increased output. We examine how air quality standards---a common environmental regulation---have contributed to this cleanup of manufacturing. We develop a general equilibrium model to show how air quality standards can lead to a cleanup by causing reductions in plant emission intensity, relative changes in plant output, and plant entry and exit. We provide quasi-experimental evidence from Canada to highlight the magnitude of these responses. Our results suggest that air quality standards explain just under 40% of the cleanup of manufacturing.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.217
Teacher spread0.156 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it