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Record W4297323754 · doi:10.32479/ijeep.13435

The Link between Economic Growth and Sustainable Energy in G7-Countries and E7-Countries: Evidence from a Dynamic Panel Threshold Model

2022· article· en· W4297323754 on OpenAlexaboutno aff
Najia Saqib, Haider Mahmood, Aamir Hussain Siddiqui, Muhammad Asif Shamim‬‬‬‬‬

Bibliographic record

VenueInternational Journal of Energy Economics and Policy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
FundersPrince Sultan University
KeywordsNexus (standard)EconomicsSustainable growth rateChinaPanel dataSustainable developmentSustainable energyRenewable energyOrder (exchange)Developing countryDevelopment economicsNatural resource economicsEconomic growthGeographyPolitical scienceEconometrics

Abstract

fetched live from OpenAlex

The available literature on sustainable energy use and economic growth nexus yields conflicting conclusions, as the effect can be positive, negative, or insignificant. This research explores the causal link between sustainable energy use and economic growth in G7-countries (Japan, Canada, Germany, Italy, France, United Kingdom, and United States) and E7-countries (Russia, Brazil, Indonesia, China, Mexico, India, and Turkey) countries from 1990 to 2019. We discover that sustainable energy use and economic growth are proportional. Our results show that sustainable energy use positively affects economic growth if E7-countries exceed a specific threshold. It is detrimental to economic growth for the E7 countries' sustainable energy use to fall below a certain threshold. The use of sustainable energy has no significant impact on economic growth, although it does have a positive and noticeable impact in the G7 countries. In order for the countries of the G7 to see positive economic growth as a result of their investment in renewable energy, it is necessary for those nations to surpass a certain threshold in terms of their use of sustainable energy.

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 categoriesMeta-epidemiology (narrow)
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 score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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 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

Citations10
Published2022
Admission routes1
Has abstractyes

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