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Record W2969044550 · doi:10.1177/0958305x19867082

CO <sub>2</sub> emissions converge in China and G7 countries? Further evidence from Fourier quantile unit root test

2019· article· en· W2969044550 on OpenAlex
Cuihong Ye, Yiguo Chen, Roula Inglesi‐Lotz, Tsangyao Chang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnergy & Environment · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsQuantileUnit rootUnit root testChinaPer capitaEconomicsQuantile regressionConvergence (economics)Unit (ring theory)Empirical evidenceEmpirical researchEconometricsAgricultural economicsEnvironmental scienceGeographyEconomic growthMathematicsStatisticsCointegrationDemographyPopulation

Abstract

fetched live from OpenAlex

G7 countries and China are considered not only the biggest energy producers globally but also the largest CO 2 emission groups of countries among the world. In this study, we apply the Fourier quantile unit root test to investigate whether CO 2 emissions converge in China and G7 countries using per capita CO 2 emissions data over 1950–2013. While traditional unit root test results indicate that per capita CO 2 emissions do not converge among these G7 countries and China, empirical results from the Fourier quantile unit root test point out that the CO 2 emissions did converge in Germany, Italy, and the United Kingdom. Although the results of this study do not find strong CO 2 emission convergence in the other five countries (i.e., Canada, France, Japan, the United States, and China), the CO 2 emissions did converge in certain quantiles for these five countries. Our empirical results have important policy implications for the governments of G7 countries and China to implement the effective energy policy to reduce the CO 2 emissions.

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.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.013
GPT teacher head0.189
Teacher spread0.176 · 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