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Shifts in Energy Consumption Driven by Urbanization

2018· book· en· W2833361991 on OpenAlexaff
Perry Sadorsky

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsYork University
Fundersnot available
KeywordsUrbanizationConsumption (sociology)MilestoneEnergy consumptionEmpirical researchPopulationEconomic geographyEmpirical evidenceEconomicsDevelopment economicsNatural resource economicsEconomic growthGeographySociologySocial scienceEngineeringDemographyCartography

Abstract

fetched live from OpenAlex

The year 2007 marked an important milestone as, for the first time in history, the world’s urban population passed 50%. An increase in urbanization brings new opportunities and new challenges with respect to business, society, and the economy as increases in urbanization are associated with greater economic activity. One particular area of interest is how urbanization affects energy consumption. This chapter surveys recent theoretical and empirical contributions on the relationship between urbanization and energy consumption. The chapter first sets out the conceptual framework and some empirical observations on the relationship between energy consumption and urbanization. This is followed by sections that provide a more detailed review of the empirical evidence linking energy consumption with urbanization. The chapter concludes with some limitations from existing empirical studies, suggestions for future research, and policy implications.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.169
Teacher spread0.149 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations11
Published2018
Admission routes1
Has abstractyes

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