The Long-Run Relationship Between Renewable Energy Consumption, Non-Renewable Energy Consumption, Population, and Economic Growth in G20 Countries
Bibliographic record
Abstract
This study examines the impacts of renewable and non-renewable energy consumption, urban population, and population size on economic growth.This study utilizes the Panel Unit Root Test, Pedroni's Residual-based Cointegration Test, and Fully Modified Ordinary Least Square (FMOLS) techniques to analyze data from 2010 to 2021 for 19 countries within the Group of Twenty (G20).The cointegration test indicates that renewable energy consumption, nonrenewable energy consumption, urban population, and total population are long-term associated with GDP growth.According to FMOLS estimates, renewable energy consumption has a positive impact on GDP growth in G20 countries.The impact of renewable energy consumption on GDP growth varies; natural gas consumption does not affect GDP growth, but petroleum consumption significantly affects GDP growth in G20 countries.Furthermore, the study identifies the urban population as a control variable that harms GDP growth.The empirical evidence derived from this study posits that it would be judicious for G20 policymakers to proactively institute a suite of policies that invigorate the advancement of renewable energy sources, given that such a course of action has been demonstrated to engender a notable augmentation in GDP growth.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".