MétaCan
Menu
Back to cohort
Record W2101493860 · doi:10.5539/eer.v2n2p53

The Interplay and Feedback Mechanism between Environmental Pollution and Economic Growth in China

2012· article· en· W2101493860 on OpenAlexvenueno aff
Zhaogang Wang

Bibliographic record

VenueEnergy and Environment Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsVariance decomposition of forecast errorsEconomicsChinaCointegrationEnvironmental qualityPer capitaPer capita incomePollutionEnvironmental pollutionJohansen testEconometricsError correction modelEnvironmental scienceEnvironmental protectionGeography

Abstract

fetched live from OpenAlex

China’s rapid economic growth has given rise to serious clashes with environment protection needs. In order to address the dilemma between environment protection and economic growth, a thorough investigation into the relationship between the two is fundamental to the formulation and implementation of environment policies. This paper uses econometric analyses of time-series data on industrial pollution and GDP per capita between 1980 and 2008, examines the long-term dynamic equilibrium and interplay between environment quality and per capita income in China, and obtains the following points. First, there is cointegration between economic growth and environment pollution in China. Second, the increase of per capita GDP has evident lag effects on environment quality. Third, the simulated structure of Generalized Impulse Response Function shows that the responses of environment quality indicators to the impulse of per capita growth rate are mostly fluctuant. Fourth, the results of variance decomposition show that the contribution of pollution indicators to the variance decomposition of GDP per capita is relatively small.

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)0.002
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 score0.354
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEnergy and Environment ResearchSame topicEnergy, Environment, Economic GrowthFrench-language works237,207