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Record W1844941061 · doi:10.1596/1813-9450-5026

On Interfuel Substitution: Some International Evidence

2009· article· en· W1844941061 on OpenAlexaff
Apostolos Serletis, Govinda R. Timilsina, Olexandr Vasetsky

Bibliographic record

VenueWorld Bank eBooks · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomicsSubstitution (logic)ElectricityCoalSubstitution effectConsumption (sociology)Natural resource economicsInternational economicsMacroeconomicsEconomyMicroeconomicsChemistryEngineering

Abstract

fetched live from OpenAlex

This paper estimates interfuel substitution elasticities in selected developing and industrialized economies at the national and sector levels. In doing so, it employs state-of-the-art techniques in microeconometrics, particularly the locally flexible normalized quadratic functional forms, and provides evidence consistent with neoclassical microeconomic theory. The results indicate that the interfuel substitution elasticities are consistently below unity, revealing the limited ability to substitute between major energy commodities (i.e., coal, oil, gas, and electricity). While the study finds some evidences of larger interfuel substitution potential in high-income economies as compared to that in the middle- and low-income economies in the industrial and transportation sectors, no such evidence is observed in the residential and electricity generation sectors or at the national level. The implication is that interfuel substitution depends on the structure of the economy, not the level of economic development. Moreover, a higher change in relative prices is needed to induce switching toward a lower carbon economy.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.009
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.002

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 head0.036
GPT teacher head0.227
Teacher spread0.192 · 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 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

Citations4
Published2009
Admission routes1
Has abstractyes

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