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Record W4384079131 · doi:10.1504/ijesd.2023.132088

Enhancing fairness in the Paris Agreement: lessons from the Montreal and Kyoto protocols and the path ahead

2023· article· en· W4384079131 on OpenAlexaboutno aff
Donia Mahabadi

Bibliographic record

VenueInternational Journal of Environment and Sustainable Development · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationKyoto ProtocolCompliance (psychology)Montreal ProtocolAgreementGreenhouse gasPolitical scienceSustainable developmentBusinessLawPsychology

Abstract

fetched live from OpenAlex

Broad participation and compliance are key elements of any successful international environmental agreement (IEA). Notwithstanding the participation of a significant number of countries in the Paris Agreement, the level of compliance remains challenging. This article investigates the potential role of fairness in enhancing compliance under the Paris Agreement. It draws lessons from the Montreal and Kyoto protocols that could assist the Paris Agreement in incentivising countries. The article discusses the operationalisation of the common but differentiated responsibilities and respective capabilities (CBDR-RC) principle in different treaties. A formulaic approach to interpreting the CBDR-RC principle and the imposition of restrictions on non-compliant parties could be effective ways of promoting compliance with the Paris Agreement. Besides, market-based solutions are considered as economic approaches to incentivising countries to meet the climate target. The importance of market-based solutions is supported by the findings from a worldwide survey among international delegates negotiating the Paris Agreement.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.041
GPT teacher head0.259
Teacher spread0.219 · 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 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

Citations6
Published2023
Admission routes1
Has abstractyes

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