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Record W2032106936 · doi:10.7202/1027236ar

Flexibility Mechanisms in the Kyoto Protocol: Constitutive Elements and Challenges Ahead

2014· article· en· W2032106936 on OpenAlexvenueno aff
Jaume Saura Estapà

Bibliographic record

VenueRevue générale de droit · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsKyoto ProtocolFlexibility (engineering)Clean Development MechanismGreenhouse gasUnited Nations Framework Convention on Climate ChangeClimate changeJoint ImplementationEuropean unionProtocol (science)Position (finance)ConventionInternational communityInternational tradeEnvironmental resource managementBusinessPolitical scienceEconomicsLawEcologyPolitics

Abstract

fetched live from OpenAlex

Climate change has become in the past decades one of the major global problems that humanity must face. In order to try to stop it, and eventually reverse it, the international community has adopted the United Nations Framework Convention on Climate Change (1992) and the Kyoto Protocol (1997, not yet in force). The Protocol sets quantified commitments for developed countries concerning the reduction of emissions of greenhouse gases, but also the possibility to comply with such commitments in a flexible manner, through three instruments: joint implementation, the clean development mechanism and emissions trading. The inclusion of additional instruments addressed to facilitate the curbing of emissions at a low cost, the so-called flexibility mechanisms , was a key element that allowed the final agreement to be reached. The paper describes briefly the main developments of the climate change regime and of each of these mechanisms. It then outlines their common constitutive elements, while underlining the aspects that remain unsolved, especially relating to their supplemental character to domestic action and the fact that any project approved under the JI or the CDM must provide a reduction in emissions that is additional to any that would otherwise occur. Throughout the examination of both the elements and challenges of the three mechanisms, the position and inputs coming from the European Union and its Member States within the climate change regime are also analysed.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.615

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 head0.114
GPT teacher head0.280
Teacher spread0.166 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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