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Record W2114792797 · doi:10.5539/ijef.v5n2p86

Economic Growth and Environmental Sustainability: Empirical Evidence from East and South-East Asia

2013· article· en· W2114792797 on OpenAlexvenueno aff
Samsul Alam, Md. Nurul Kabir

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsKuznets curveSustainabilityEast AsiaPer capitaEnvironmental pollutionEconomicsIndex (typography)Per capita incomeGross domestic productControl variablePopulationSustainable developmentEnvironmental Sustainability IndexPollutionDevelopment economicsNatural resource economicsEconometricsGeographyEconomic growthEnvironmental protectionStatisticsEcologyMathematicsChinaDemography

Abstract

fetched live from OpenAlex

This study investigates the relationship between economic growth and environmental sustainability in the East and South-East Asian countries focused on the environmental Kuznets curve hypothesis, using data from environmental performance index (EPI) in 2010. Both pollution and eco-efficiency measures, two components of environmental sustainability, are considered as dependent variables while GDP per capita is used as an independent variable. Besides independent variable, the study also considers population density and civil and political liberty index (CIVLIB) as control variables and East and South-East Asia as a dummy variable. By using ordinary least square (OLS) method, this study reveals that while the increase of the GDP per capita appears to have positive impact on the pollution measures, it is found mix (both positive and negative) results on eco-efficiency measures. These findings prove the hypothesis of environmental Kuznets curve partially but not entirely. We conclude the paper by suggesting that the policy makers should give priority to the eco-efficiency measures along with pollution measures in order to ensure environmental sustainability in the process of economic 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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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