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The Long Run Relationship between Economic Growth and Environmental Quality

2019· article· en· W2943394319 on OpenAlexaff
Redwan Ahmed, Gabriela Sabau, Morteza Haghiri

Bibliographic record

VenueAsian Journal of Agricultural Extension Economics & Sociology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPer capitaGross domestic productEconomicsAgricultural economicsEnvironmental pollutionProduction (economics)Environmental qualityCarbon dioxideEconomic growthEnvironmental scienceMacroeconomicsEnvironmental protectionPopulationChemistry

Abstract

fetched live from OpenAlex

One of the controversial debates in environmental economics, which began in the 1980s, is the relationship between environmental pollution and economic growth. The study investigated the relationship between per capita carbon dioxide emissions and gross domestic product per capita in 63 countries over 51 years during 1960 to 2010. Using a graphical analysis approach, the results of this study showed that the relationship between per capita carbon dioxide emissions and gross domestic product per capita amongst the sample data followed a sigmoid curve indicating that the per capita carbon dioxide emissions of a country increased when its economy transitioned from a labor-intensive technology to a capital-intensive one caused by an increase in the rate of economic growth. The results also showed that the amount of relative emissions varied amongst the countries. The variability could be imputed to the following reasons: (i) the heterogeneity in the structure of the economies, and (ii) the disparity in the mode of production used in the countries’ manufacturing processes.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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 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

Citations5
Published2019
Admission routes1
Has abstractyes

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