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Record W3007145192 · doi:10.1080/15567249.2020.1729900

Clean energy consumption and economic growth nexus: asymmetric time and frequency domain causality testing in China

2020· article· en· W3007145192 on OpenAlex
Andisheh Saliminezhad, Pejman Bahramian

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Sources Part B Economics Planning and Policy · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsQueen's University
Fundersnot available
KeywordsNexus (standard)Causality (physics)EconomicsEnergy consumptionConsumption (sociology)EconometricsChinaLinkage (software)MacroeconomicsChemistryComputer science

Abstract

fetched live from OpenAlex

This article examines the symmetric and asymmetric causal relationships between clean energy consumption and economic growth in time and frequency domains for China. The results of both symmetric and asymmetric causality analysis suggest that clean energy consumption does not cause economic growth. This implies that the level of clean energy consumption in China seems to be optimal and beyond this level, it does not affect the growth level of the country. However, examination of the causality linkage from economic growth to clean energy consumption indicates medium and long-run evidence of a frequency-based symmetric causal relationship. Our asymmetric analysis makes this relation clearer such that only the adverse shocks to economic growth lead to a decline in the clean energy consumption level. This inference is complemented with the estimated causal parameter of 0.13, indicating that a 1% decrease in economic growth results in a 0.13% reduction in the level of clean 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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
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 head0.028
GPT teacher head0.216
Teacher spread0.189 · 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