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Record W2953771196

Economic Growth and Air Pollution Dynamics: The Case of Canada

2019· article· en· W2953771196 on OpenAlexaboutno aff
Achille Dargaud Fofack, Steve Yaw Sarpong, Damis Ferouz Kamna

Bibliographic record

VenueDergiPark (Istanbul University) · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCointegrationEconomicsError correction modelShock (circulatory)Causality (physics)Short runGranger causalityAir pollutionPollutionEconometricsVector autoregressionNatural resource economicsMacroeconomicsEcology
DOInot available

Abstract

fetched live from OpenAlex

The root cause of the most important issues facing humanity today could be traced back to environmental mismanagement or inequality. Thus, the aim of this paper is to examine the dynamic relationship between economic growth and air pollution in Canada using cointegration tests, vector error correction models and causality tests on annual data from 1960 to 2014. The cointegration tests reveal that there is a long-run relationship between economic growth, trade and air pollution in Canada. The results also show that in the long-run, economic growth and trade respectively have a positive and a negative impact on air pollution. The vector error correction models reveal that the coefficient associated with the error correction term is negative and significant. This means that, any shock disturbing the long-run equilibrium between economic growth, trade and air pollution will be corrected at a speed of 10 percent per year. As for the causality analyses, they show that economic growth does not cause air pollution in Canada.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.139
Teacher spread0.134 · 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 designTheoretical or conceptual
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

Citations4
Published2019
Admission routes1
Has abstractyes

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