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Record W2342357080 · doi:10.1111/roie.12217

Renewable Resources, Pollution and Trade

2016· article· en· W2342357080 on OpenAlexaff
Horatiu A. Rus

Bibliographic record

VenueReview of International Economics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsExternalityEconomicsResource (disambiguation)Natural resource economicsWelfareRenewable resourceSmall open economyPollutionStock (firearms)MicroeconomicsRenewable energyEcologyMarket economyMacroeconomics

Abstract

fetched live from OpenAlex

Abstract Detrimental spillovers from industrial activity onto resource‐based productive sectors are very common, yet their effects remain understudied. While international trade often creates conditions for the over‐exploitation of open‐access renewable resources, it also provides opportunities for separating different productive sectors spatially. The existing literature suggests that a diversified exporter of the renewable resource good tends to lose from trade in both welfare and conservation terms as a result of over‐depletion, while the exporter of the non‐resource good gains. However, the resource stock externality of harvesting and the inter‐industry pollution externality often coexist in reality. In a small open economy framework, this paper shows that acknowledging their interaction changes the nature of the autarkic equilibrium and enriches the set of resource conservation and welfare outcomes from trade. Depending on the relative damage inflicted by the two industries on the environment, which in turn are functions of the pollution intensity and bioeconomic parameters, it is possible that the inter‐sectoral pollution externality persists and specialization in manufacturing is not optimal from a welfare perspective.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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 designTheoretical or conceptual
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

Citations14
Published2016
Admission routes1
Has abstractyes

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