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Record W2911773408

Carbon dioxide emissions, energy consumption and economic growth: The historical decomposition evidence from G-7 countries

2018· preprint· en· W2911773408 on OpenAlexaboutno aff
Mehmet Balcılar, Zeynel Abidin Özdemir, Hüseyin Özdemir, Muhammad Shahbaz

Bibliographic record

VenueRePEc: Research Papers in Economics · 2018
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsConsumption (sociology)Energy consumptionEmpirical evidenceNatural resource economicsDecompositionEconomyDevelopment economicsEcology
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates the relationship between carbon dioxide emissions, energy consumption and economic growth in the G-7 countries from a historical perspective. To this end, taking time varying interaction and business cycle into account, we use the historical decomposition method for the first time in the literature. Our results provide evidence that Canada, Italy, Japan and partly the United States need to sacrifice economic growth if they aim to reduce CO2 emissions by decreasing the fossil-based energy use. This situation is not valid since the early 1990s for France, throughout the analysis period for Germany and a few exceptions in all periods for the UK. Furthermore, empirical results provide evidence contrary to the EKC hypothesis for Canada, Germany, Japan, the UK and the US. We found BC-shaped and N-shaped curve for France and Italy, respectively. Although the EKC hypothesis is not valid for Germany and the UK, economic growth has no damaging effect on environmental quality. Also, this effect seems to be cyclical for the US. While the energy conservation theory is fully supported for Canada, it is strongly supported for France, Italy, Japan and the US with the exception of some periods. In addition to these findings, we find strong evidence to support the growth theory for all the G-7 countries.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.001
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 head0.042
GPT teacher head0.279
Teacher spread0.237 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

Explore more

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