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Record W3204023892 · doi:10.3390/jrfm14100471

Causality between Energy Consumption and Economic Growth in the Presence of Growth Volatility: Multi-Country Evidence

2021· article· en· W3204023892 on OpenAlexvenueno aff
Gulasekaran Rajaguru, Safdar Ullah Khan

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCointegrationEnergy consumptionEnergy intensityVolatility (finance)Per capitaReal gross domestic productNexus (standard)MacroeconomicsSustainabilityEconometricsMonetary economics

Abstract

fetched live from OpenAlex

Falling energy intensity (increasing efficiency) is believed to be a result of more efficient production methods that have evolved over time, indicating overall sustainability in the production process. The objective of this study is to investigate the diminishing trend of energy intensity and the related volatilities in growth of energy consumption and income growth through the energy–growth nexus. The country specific long-run and short-run causal relationships among real energy consumption per capita, real GDP per capita, and the volatilities of growth in income and the growth in energy consumption are established using the method proposed by Yamamoto–Kurozumi within a cointegration framework in 48 countries. The overall findings suggest that energy intensity is falling, in conjunction with the existing evidence on the energy–growth nexus in most of the countries studied; hence, implicitly this confirms sustainability. The results based on volatility analysis show a significant decrease in energy use in response to increasing income growth volatility. The negative effects of income growth volatility on energy consumption are usually countered through compensation measures, with subsidies provided to households and producers in order to smooth the energy consumption behaviours in those economies.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.591

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 head0.030
GPT teacher head0.227
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.

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

Citations16
Published2021
Admission routes1
Has abstractyes

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