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The impacts of energy prices and technological innovation on the fossil fuel-related electricity-growth nexus: An assessment of four net energy exporting countries

2014· article· en· W2743293563 on OpenAlexaboutno aff
Fei Qin, Rajah Rasiah, JiaShen Leow

Bibliographic record

VenueJournal of Energy in Southern Africa · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsNexus (standard)ElectricityEconomicsFossil fuelGranger causalityNatural resource economicsConsumption (sociology)Energy consumptionShort runTechnological changeEconomyMacroeconomicsEconometricsEngineering

Abstract

fetched live from OpenAlex

This study uses annual data from 1974 to 2011 to examine the long-run and short-run relationships between fossil fuel powered electricity consumption, economic growth, energy prices and technological innovation for four net energy exporting countries. Canada, Ecuador, Norway and South Africa are chosen as the main research background in order to investigate how the development degree and economic dependence on energy exports affect the electricity-growth nexus. Based on the results drawing from the ARDL approach and the Granger causality test, economic growth positively influences the variation in fossil fuel powered electricity consumption in both the short-run and long-run for all four countries. The reverse causality from electricity consumption to economic growth is only evident in Ecuador and Norway. The degree of dependence on energy exports is a contributory factor of explaining the causality puzzle of the electricity-growth nexus. Given the fact that technological innovation does not benefit fossil fuel powered electricity generation, this paper suggests these net energy exporting countries to replace fossil fuel with more sustainable and effective sources in the electricity generation process.

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)0.003
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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