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Record W582297232 · doi:10.5547/01956574.37.2.ajad

Sectoral Interfuel Substitution in Canada: An Application of NQ Flexible Functional Forms

2015· article· en· W582297232 on OpenAlexaffabout
Ali Jadidzadeh, Apostolos Serletis

Bibliographic record

VenueThe Energy Journal · 2015
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectricityEconomicsSubstitution (logic)Fossil fuelNatural gasOil and natural gasAggregate (composite)Fuel oilNatural resource economicsEcologyEngineeringWaste managementComputer scienceBiology

Abstract

fetched live from OpenAlex

This paper focuses on the aggregate demand for electricity, natural gas, and light fuel oil in Canada as a whole and six of its provinces—Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia—in the residential, commercial, and industrial sectors. We employ the locally flexible normalized quadratic (NQ) expenditure function (in the case of the residential sector) and the NQ cost function (in the case of the commercial and industrial sectors), treat the curvature property as a maintained hypothesis, and provide evidence consistent with neoclassical microeconomic theory. We find that the Morishima interfuel elasticities of substitution are in general positive and statistically significant. Our results indicate limited substitutability between electricity and natural gas, but strong substitutability between light fuel oil and each of electricity and natural gas in most cases.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.227
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations19
Published2015
Admission routes2
Has abstractyes

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