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Record W3155584140 · doi:10.1007/s10666-021-09768-4

Transboundary pollution control and competitiveness concerns in a two-country differential game

2022· article· en· W3155584140 on OpenAlexaff
Simone Marsiglio, Nahid Masoudi

Bibliographic record

VenueCINECA IRIS Institutial research information system (University of Pisa) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMemorial University of Newfoundland
FundersUniversità di Pisa
KeywordsDifferential gameHomogeneousDifferential (mechanical device)PollutionControl (management)EconomicsClimate policySet (abstract data type)Environmental policyBusinessEconomic systemNatural resource economicsClimate changeEcologyComputer science

Abstract

fetched live from OpenAlex

We analyze a transboundary pollution control problem in a heterogeneous two-country differential game setting in which regulators care for the implications of environmental policies on the competitiveness. We characterize the noncooperative and the cooperative solutions, showing that under both scenarios, in presence of competitiveness considerations, heterogeneous countries will generally set different carbon taxes. This suggests, while implementing a mitigation policy is necessary to combat climate change, a universally homogeneous policy may not be optimal. Moreover, when countries are symmetric, except for their degree of competitiveness concerns, under noncooperation introduction of such concerns lowers the abatement policies in both countries, however, the self-effect is stronger than the cross-effect. Nevertheless, under cooperation, an increase in country j's competitiveness concerns leads to more stringent policies in country i, while the self-effect could be either positive or negative. The latter result emphasizes the importance of cooperation to tackle pollution in the presence of competitiveness concerns.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.089
GPT teacher head0.286
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 teacher head, 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

Citations7
Published2022
Admission routes1
Has abstractyes

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