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Record W4367171961 · doi:10.18280/mmep.100244

The Influence of the Relationship Between the Economic Development of Countries Using Renewable Energy and the Relationship with Environmental Effects

2023· article· en· W4367171961 on OpenAlexvenueno aff
Iskandar Muda, Yersi-Luis Huamán-Romaní, Rubén Apaza Apaza, Henrry Wilfredo Agreda Cerna, Lucy Mariella García Vilela, Segundo Ramos Villalta Arellano, Linda-Katherine Carrillo-De la Cruz

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsRenewable energyEconomicsPositive relationshipNatural resource economicsEnergy (signal processing)Environmental economicsEconomic systemBusinessEcologyPsychologyMathematicsSocial psychology

Abstract

fetched live from OpenAlex

For economic growth and progress, nations must have access to renewable energy sources.The amount of energy a country uses affects its economic growth.According to studies by academics from around the world, economic growth is a major factor in the rate of global energy consumption growth.These studies were done globally.This was discovered because these two variables are linked.In the first part of our investigation, we analyzed previous studies and investigations on expanding economies and energy use.These studies and investigations examined the link between growing economies and energy use.These studies were conducted to better understand the relationship between growing economies and energy use.This article examines the relationship between economic growth and renewable energy use in Organization of Petroleum Exporting Countries (OPEC) member countries from 1990 to 2015.We analyzed this connection using relevant data.According to this study, the increased use of renewable energy in some OPEC countries is driving economic growth.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.183
Teacher spread0.153 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2023
Admission routes1
Has abstractyes

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