Factors influencing carbon emissions under EKC scheme and the role of renewable energy in Gulf Cooperation Council countries
Bibliographic record
Abstract
Objectives: In the face of the rising challenges of climate change, lowering emissions has become a key driver of environmental sustainability and sustainable growth.This study examines the validity' Environmental Kuznets Curve (EKC) scheme for Gulf Cooperation Council (GCC) countries between 2000 and 2020 by considering different types of energy sources (total, non-renewable, renewable), urbanization, and trade openness.Methods/Approach: Therefore, we consider the issue of cross-sectional dependency within panel data by testing its presence and employing the CIPS test to inspect the unit root.The Pedroni, Kao and Westerlund panel data cointegration tests have also been used to check the presence of long-run linkages.In addition, we apply the fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and pooled mean group (PMG) techniques to explore the long-run dynamics between variables.Results: First, it is observed that the EKC hypothesis is established in the case of six countries of GCC using CO2 emissions.Second, the direct connection between economic complexity and environmental degradation is obtained.Third, energy consumption seems also to be negative and significant.Fourth, urbanization and trade openness contribute to increase CO2.Conclusions: Findings thus point to the fact that the promotion of energy contributes to reduce the harmful effect of economic complexity over dioxide carbon emissions as consequence of scale and composition effect.In this sense, the study suggests some noteworthy environmental policy implications to reduce the level of carbon dioxide emissions in Gulf Cooperation Council countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".