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Record W2299282207 · doi:10.5539/eer.v6n1p23

Reduction of CO2 Emissions Due to Energy Use in Crete-Greece

2016· article· en· W2299282207 on OpenAlexvenueno aff
John Vourdoubas

Bibliographic record

VenueEnergy and Environment Research · 2016
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaGreenhouse gasFossil fuelCarbon dioxideEnergy consumptionRenewable energyNatural resource economicsEnergy intensityConsumption (sociology)Environmental scienceElectricityAgricultural economicsEmission intensityEnvironmental protectionPopulationEconomicsWaste managementChemistryEcologyEngineering

Abstract

fetched live from OpenAlex

Use of fossil fuels in modern societies results in CO2 emissions which, together with other greenhouse gases in the atmosphere, increase environmental degradation and climate changes. Carbon dioxide emissions in a society are strongly related with energy consumption and economic growth, being influenced also from energy intensity, population growth, crude oil and CO2 prices as well as the composition of energy mix and the percentage of renewable energies in it.The last years in Greece, the severe economic crisis has affected all sectors of the economy, has reduced the available income of the citizens and has changed the consumers’ behavior including the consumption of energy in all the activities. Analysis of the available data in the region of Crete over the period 2007-2013 has shown a significant decrease of energy consumption and CO2 emissions due to energy use by 25.90% compared with the reduction of national G.D.P. per capita over the same period by 25.45% indicating the coupling of those emissions with the negative growth of the economy. Carbon dioxide emissions per capita in Crete in 2013 are estimated at 4.96 tons. Main contributors of those emissions in the same year were electricity generation from fuel and heating oil by 64.85%, heating sector by 3.23% and transportation by 31.92%.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.284
Teacher spread0.245 · 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 designBench or experimental
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

Citations6
Published2016
Admission routes1
Has abstractyes

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