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Record W2574424969 · doi:10.1016/j.jinteco.2017.01.005

Trade liberalization and the environment: Evidence from NAFTA and U.S. manufacturing

2017· article· en· W2574424969 on OpenAlexaff
Jevan Cherniwchan

Bibliographic record

VenueJournal of International Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEconomicsFree tradeInternational economicsLiberalizationManufacturing sectorInternational tradeMarket economy

Abstract

fetched live from OpenAlex

The unobserved responses of individual polluters are often used to rationalize the aggregate effects of international trade on the environment. In this paper, I provide the first evidence of these responses. I estimate the effects of NAFTA on the emissions of particulate matter (PM 10 ) and sulfur dioxide (SO 2 ) from manufacturing plants in the United States. My findings suggest that trade liberalization led to significant reductions of these pollutants at affected plants. On average, nearly two-thirds of the reductions in PM 10 and SO 2 emissions from the U.S. manufacturing sector between 1994 and 1998 can be attributed to trade liberalization following NAFTA.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.898
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.206
Teacher spread0.182 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations348
Published2017
Admission routes1
Has abstractyes

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