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Record W2028138313 · doi:10.5539/ijef.v3n1p208

Assessing the Elasticities of Electricity Consumption for Rural and Urban Areas in Malaysia: A Non-linear Approach

2011· article· en· W2028138313 on OpenAlexvenueno aff
Hussain Ali Bekhet, Nor Salwati Othman

Bibliographic record

VenueInternational Journal of Economics and Finance · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsElectricityTariffPrice elasticity of demandEconomicsIncome elasticity of demandAgricultural economicsPopulationConsumption (sociology)Rural areaProxy (statistics)EconometricsMicroeconomicsStatisticsMathematicsDemographyInternational economics

Abstract

fetched live from OpenAlex

This paper investigates and estimates the price and income elasticity of electricity consumption and then compares the level of sensitivity of demand for electricity in the rural and urban areas in Malaysia. The non linear model was employed to estimate the elasticity of electricity consumption. The electricity demand has been estimated as a function of tariff, real GDP, gas price and population in the rural and urban population. The gas price has been used as a proxy to measure the level of sensitivity goods by using the annual data covering the 1980-2009 period. The results showed that the income elasticity ( was less than unity which indicated electricity as a necessity good to the people in Malaysia. The reaction of electricity consumption was found to be greater in the urban area compared to the rural area. The higher sensitivity of electricity consumption in the urban population was due to higher exposure to electricity appliances and facilities.

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)0.000
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 score0.143
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.235
Teacher spread0.209 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

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