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Record W4388303269 · doi:10.1080/14786451.2023.2268857

Renewable energy consumption and carbon emissions in developing countries: the role of capital markets

2023· article· en· W4388303269 on OpenAlexaff
Daniel Ofori‐Sasu, Joshua Yindenaba Abor, George Nana Agyekum Donkor, Isaac Otchere

Bibliographic record

VenueInternational Journal of Sustainable Energy · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsCarleton University
Fundersnot available
KeywordsRenewable energyGreenhouse gasEnergy consumptionNatural resource economicsLow-carbon economyRenewable energy creditConsumption (sociology)EconomicsEnvironmental scienceFeed-in tariffEnergy policyEngineeringEcology

Abstract

fetched live from OpenAlex

This study examines the impact of capital market on the relationship between energy consumption and carbon emissions.By employing a system Generalised Methods of Moments (GMM) for a sample of 138 developing countries over the period, 1990-2020, we find a U-shaped reverse relationship between renewable energy consumption and carbon emissions.The study reveals that beyond a threshold of 71.03, renewable energy consumption tends to increase carbon emissions.Similarly, the initial levels of carbon emissions reduce the use of renewable energy but beyond a 2.5 level of carbon emissions, renewable energy consumption begins to increase.We find that both the stock market and bond market reduce carbon emissions and enhance the levels of renewable energy consumption.We provide evidence to support that the capital market enhances the negative impact of renewable energy consumption on carbon emissions, while the corporate bond market magnifies the reductive effect of carbon emissions on renewable energy consumption.

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 score0.577
Threshold uncertainty score0.634

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 studies0.0000.000
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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