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Record W4306918452 · doi:10.18280/ijsdp.170608

The Effects of Electricity Consumption on Electrification Access on Economic Growth in Papua Province, Indonesia

2022· article· en· W4306918452 on OpenAlexvenueno aff
Jonathan Cosmus Karay, Firmansyah Firmansyah, Fransiscus Xaverius Sugiyanto, Wahyu Widodo

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsElectrificationElectricityIndustrialisationConsumption (sociology)Human capitalEconomicsBusinessLabour economicsAgricultural economicsNatural resource economicsEconomic growthMarket economyEngineering

Abstract

fetched live from OpenAlex

The current research aims to analyze the influence of electricity and infrastructure on regional economy. The study empirically tested the impact of the independent constructs on electrification access and economic growth in Papua, Indonesia by using mediating role of electricity consumption. Independent variables examined in this study are electrical installations and electricity capital, while the dependent variables are measured by using labor absorption. This study specifically investigates the relationship between these variables with a cross-sectional study model, conducted at Papua Province, Indonesia with data from 2012 to 2016. The results show that electricity consumption in Papua Province is significantly influenced by electrical installation, household electricity capital and industrial electricity capital. Furthermore, electricity consumption in general affects employment. The test of the mediating variable shows the role of the consumption variable in the ratio of electrification and employment. Theoretical implication posed from the findings is about the relationship between economic growth and energy infrastructure which is more likely to attract both domestic and foreign investment in a region. The novelty of this research is to reveal the role of electrification in industrialization and employment.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.233

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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