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Record W4390002024 · doi:10.9770/ird.2023.5.4(5)

Energy use tendencies in a resource-abundant country: the case of Canada

2023· article· en· W4390002024 on OpenAlexaboutno aff
Mustafa Naimoğlu, İsmail Kavaz

Bibliographic record

VenueInsights into Regional Development · 2023
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsRebound effect (conservation)EconomicsCointegrationEfficient energy useEnergy consumptionConsumption (sociology)Energy (signal processing)DilemmaEconometricsEnvironmental economicsPublic economicsMacroeconomicsEngineeringStatistics

Abstract

fetched live from OpenAlex

Today’s global energy agenda focuses especially on the fields of increasing energy demand, security of supply and climate change. This situation causes the energy efficiency phenomenon to be considered by policymakers seriously, and additionally to be developed strategies by determining targets in this field. In this sense, it is thought that developments in the field of energy efficiency will increase energy savings and reduce emissions caused by high consumption. On the other hand, the expected improvements in energy saving based on consumer behavior are less than anticipated. In measuring the mentioned dimension, one of the important parameters is defined as the rebound effect. This effect is considered as a dilemma that is frequently emphasized, especially in developed countries since there is a prevailing opinion that the developments in energy efficiency may not cause the expected results in savings. Therefore, it is extremely important to accurately measure the dimensions of the said effect in terms of both guiding policymakers in their strategies on energy efficiency and preventing waste of resources. This study tests the validity of the rebound effect for Canada using annual data from 1972 to 2019. In the study, the Fourier Engle-Granger Cointegration Test, which is one of the current econometric methods, was used, and then FMOLS, CCR and DOLS methods were utilized for the estimation of the short- and long-term coefficients. Empirical findings suggest that increases in energy efficiency in Canada increase energy consumption. Thus, it can be said that the rebound effect is valid for Canada.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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 designNot applicable
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

Citations10
Published2023
Admission routes1
Has abstractyes

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