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Record W2403802104 · doi:10.17722/ijme.v6i1.821

CO2 Emission, Energy Consumption and Economic Development in Malaysia

2015· article· en· W2403802104 on OpenAlexvenueno aff
Hamizah bt Muhyidin, Md. Khaled Saifullah, Yap Su Fei

Bibliographic record

VenueInternational Journal of Management Excellence · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationGranger causalityEconomicsEnergy consumptionProduction (economics)Consumption (sociology)Time seriesCausality (physics)Short runEconometricsRenewable energyMacroeconomicsNatural resource economicsEngineeringMathematicsStatistics

Abstract

fetched live from OpenAlex

Environmental awareness and its relation to the development of economy have garnered increased attention in recent years. This study analyzes the long-run relationship between environment degradation, economic growth, total energy consumption and industrial production index growth in Malaysia from year 1970 to 2012. The time series data are estimated using Johansen and Julies Cointegration test and VECM Granger causality test. The empirical analysis suggests a long-run cointegration relationship between all series. Granger causality analysis indicates strong evidence of uni-directional Granger causality running from economic growth and industrial production index growth to total energy consumption in the long-run. Also, the result shows evidence of a bi-directional Granger causality between total energy consumption and CO2 emission. This situation suggests that a pollution abatement policies and higher investment to control for CO2 emission will not jeopardize the economic sustainability and industry output in the long run. This study suggests that previous policies should be complimented with increasing the efficiency of energy use by employing a fuel balancing strategies and promoting the use of renewable energy resources like bio-fuel, solar energy and wind.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.223
Teacher spread0.195 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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