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Record W4399339778 · doi:10.5547/01956574.45.2.kans

Dependence Structure among Carbon Markets around the World: New Evidence from GARCH-Copula Analysis

2024· article· en· W4399339778 on OpenAlexaboutno aff
Karishma Ansaram, Paolo Mazza

Bibliographic record

VenueThe Energy Journal · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCopula (linguistics)Autoregressive conditional heteroskedasticityEconometricsEconomicsFinancial economicsVolatility (finance)

Abstract

fetched live from OpenAlex

In this paper, we investigate the dependence structure among carbon markets globally through different copulas. The analysis examines the relationship between carbon prices being traded across different emission trading systems (ETS) worldwide. The novelty of our approach lies in assessing carbon allowances for both futures and spot prices across all the key carbon markets as well as the three Chinese carbon markets for the period from 2011 to 2019 for future prices and the period from 2015 to 2020 for spot prices. The results demonstrate an asymmetric relationship between most carbon markets. A low tail dependence was observed between the European Union ETS and Regional Greenhouse Gas Initiative ETS, California and Quebec carbon markets, while higher tail dependence was found in the Asian carbon markets. Furthermore, carbon markets that have linkage agreements, ongoing cooperation or are geographically close tend to have positive and higher tail dependence. Our findings suggest the formation of regional carbon clubs based on the dependence structure.

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 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 score0.177
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.218
Teacher spread0.198 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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