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Record W1712068010 · doi:10.1111/ajes.12072

The Effect of Urbanization and Industrialization on Energy Use in Emerging Economies: Implications for Sustainable Development

2014· article· en· W1712068010 on OpenAlexaff
Perry Sadorsky

Bibliographic record

VenueAmerican Journal of Economics and Sociology · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsYork University
Fundersnot available
KeywordsIndustrialisationUrbanizationEconomicsEnergy consumptionConsumption (sociology)Sustainable developmentNatural resource economicsFossil fuelEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Abstract This article investigates the impact of two important socio‐economic variables—urbanization and industrialization—on energy consumption in a panel of emerging economies. The results indicate that income increases energy consumption in both the long run and the short run. In the long run, urbanization decreases energy consumption, while industrialization increases it. Long‐run dynamics are important as evidenced by the estimated coefficient on the error correction term. These results have implications for sustainable development. Economic growth policies designed to increase income and industrialization will increase energy consumption. Since most energy needs in emerging economies are currently met by the burning of fossil fuels, economic growth and industrialization policies will be at odds with sustainable development.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.212
Teacher spread0.197 · 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

Citations161
Published2014
Admission routes1
Has abstractyes

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