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Record W3125076162

Transboundary Movement of Waste: Second-hand Markets and Illegal Shipments

2011· preprint· en· W3125076162 on OpenAlexfundno aff
Sophie Bernard

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersToulouse School of EconomicsConseil Régional, Île-de-FranceMcGill University
KeywordsBusinessStylized factDirectiveProduct (mathematics)ReusabilityFree movementPollutionInternational tradeWaste managementIndustrial organizationCommerceEnvironmental economicsNatural resource economicsEngineeringEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

In a stylized model of international trade, firms in the North indirectly export second-hand products to a representative firm in the South to be reused as intermediate goods, with potential trade gains. The level of reusability of waste products is a crucial choice variable in the North. This is because, in the presence of imperfect international monitoring, non-reusable waste can be illegally mixed with reusable waste. I explore the driving forces for illegal waste movement, with a particular focus on local waste regulations such as the EU's Directive on Waste Electrical and Electronic Equipment. Under mild conditions, it is shown that increasingly stringent regulations in the North induce Northern firms to reduce product reusability. Consequently, the flow of non-reusable waste to the South increases, magnifying the pollution haven effect.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.006
Scholarly communication0.0040.007
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.065
GPT teacher head0.262
Teacher spread0.197 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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