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Record W2105336999 · doi:10.5539/ibr.v3n3p111

Energy Use in Agriculture Sector: Input-Output Analysis

2010· article· en· W2105336999 on OpenAlexvenueno aff
Hussain Ali Bekhet, Azlina Abdullah

Bibliographic record

VenueInternational Business Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsnot available
FundersUniversiti Tenaga NasionalTenaga Nasional Berhad
KeywordsSocial connectednessAgricultureElectricityEconomic sectorCoalEnergy sectorBusinessNatural resource economicsInput–output modelEnergy (signal processing)Fossil fuelEconomicsAgricultural economicsEconomyMacroeconomicsMathematicsEngineeringStatisticsGeographyWaste management

Abstract

fetched live from OpenAlex

Many sectors rely on energy as input to produce output. Even though the use of energy in agriculture sector is not as high as in other sectors, it is important to study the connectedness between the two sectors as there is no study done so far to show the linkages between them in Malaysia. Input-output analysis has been used to study the connectedness degree between the two sectors using input-output data for 1991-2000. The direct and total backward linkages analyses have shown that there is a significant increase in the use of energy in agriculture sector for the 1991-2000 period but the connectedness is still weak. Among the three energy-related sectors namely; crude oil, natural gas & coal, petrol & coal industries and electricity & gas, it was found that the agriculture sector depends more on inputs from petrol & coal industries as compared to the other two sectors. Based on these results, some policy implications have been proposed to help the decision-makers in economic planning especially on implementing policies related to energy and agriculture sectors.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.031
GPT teacher head0.320
Teacher spread0.289 · 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.

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

Citations46
Published2010
Admission routes1
Has abstractyes

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