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Record W3123438200 · doi:10.1111/1540-5982.00155

Can cross–border pollution reduce pollution?

2002· article· fr· W3123438200 on OpenAlexvenueno aff
Sajal Lahiri, Michael S. Michael

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2002
Typearticle
Languagefr
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsPollutionWelfare economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

We develop a two–country model of foreign aid and cross–border pollution resulting from production activities in the recipient country. There is both private and public abatement of pollution, the latter being financed through emissions tax revenue and foreign aid. We characterize a Nash equilibrium in which the donor country chooses the amount of aid and the recipient chooses the fraction of aid allocated to pollution abatement and the emission tax rate. At this equilibrium, an increase in the donor’s perceived rate of cross–border pollution reduces emission levels. JEL Classification: Q28, F35, H41 Est–ce que la pollution trans–frontière peut réduire le niveau de pollution? Les auteurs développent un modèle à deux pays d’aide à l’étranger et de pollution trans–frontière résultant d’activités de production dans le pays qui reçoit l’aide. Il existe des efforts privés et publics pour réduire la pollution, ces derniers étant financés par les rentrées fiscales d’une taxe sur la pollution et par l’aide étrangère. On définit un équilibre à la Nash pour lequel le pays donateur choisit le montant de l’aide, et le pays récipiendaire choisit la fraction de l’aide étrangère qu’il allouera à la lutte à la pollution ainsi que le taux de taxation sur la pollution. A cet équilibre, un accroissement dans le taux de pollution trans–frontière perçu par le donataire réduit le taux de pollution.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.126
GPT teacher head0.214
Teacher spread0.088 · 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; both teacher heads agree on what is shown here.

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

Citations81
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Economics/Revue canadienne d économiqueSame topicFiscal Policy and Economic GrowthFrench-language works237,207