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Record W6888813834 · doi:10.22054/jiee.2022.66047.1889

Persistence of Shocks on Environmental Degradation in Oil-Exporting Countries: Implications for Environmental Policymaking

2021· article· en· W6888813834 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEnergy, Environment, Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEcological footprintPersistence (discontinuity)Environmental degradationVariance (accounting)HomogeneousUnivariateEnvironmental policy

Abstract

fetched live from OpenAlex

The objective of this study is to investigate policy shocks to the ecological footprints of 33 oil-exporting countries for the period 1961-2017. For this purpose, we apply the panel stationarity tests with both sharp and smooth breaks developed by Bahmani-Oskooee et al. (2014) and Carrion-i-Silvestre et al. (2005) to test the persistence of shocks on environmental degradation. The overall results suggest that shocks to the ecological footprint as an indicator of environmental degradation in oil-exporting countries have temporary effects. In other words, the ecological footprint under the two assumptions of long-term homogeneous variance and long-term heterogeneous variance has a mean-reverting behavior. The results of the univariate test also reveal that the ecological footprint is stationary at a 10% significance level for all oil-exporting countries except Canada, Congo, Egypt, Indonesia, and Iran. This implies that policymakers should design effective long-run policies to reduce the ecological footprint in these countries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.217
GPT teacher head0.433
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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