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Record W2902711951 · doi:10.1561/102.00000096

International Environmental Agreements and Trading Blocks — Can Issue Linkage Enhance Cooperation?

2020· article· en· W2902711951 on OpenAlexaff
Effrosyni Diamantoudi, Eftichios S. Sartzetakis, Stefania Strantza

Bibliographic record

VenueStrategic Behavior and the Environment · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsConcordia University
Fundersnot available
KeywordsLinkage (software)BusinessInternational tradeBiologyGeneticsGene

Abstract

fetched live from OpenAlex

This paper examines the effect of designing international agreements that jointly determine environmental and trade policies on the participation level and aggregate welfare. This paper builds on the non-cooperative game approach of the International Environmental Agreements (IEAs) literature extending the basic model by introducing firms that trade in a global market. Countries choose the level of a tax on emissions and a tariff on imports: signatories enjoy tariff-free trade among themselves, impose a tariff to nonsignatories and a common emissions tax; nonsignatories levy a tariff on imports and a tax on domestic emissions. Resorting to numerical simulations, the paper shows increased participation to the joint agreement of around 70% of the total number of countries. These coalitions are not only much larger than the two-country coalition derived in the case without trade, but they also achieve substantial welfare improvements of around 60% of the welfare improvement the grand coalition provides over the coalition of two. The paper presents a series of numerical simulations to confirm the robustness of these results to changes in the parameters values.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.044
GPT teacher head0.209
Teacher spread0.165 · 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 teacher head, not a consensus.

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

Citations3
Published2020
Admission routes1
Has abstractyes

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