Renewable energy consumption, globalization, and economic growth shocks: Evidence from G7 countries
Bibliographic record
Abstract
This paper examines the asymmetric responses of renewable energy (RE) technology to globalization and economic growth shocks across the G7 countries using the Nonlinear Cointegrating Auto-Regressive Distributed Lag (NARDL) model. Our results indicate asymmetries across these countries and that positive shocks on globalization increase RE in Canada, Germany, the United Kingdom (UK), and the United States (US) while negative shocks decrease RE. However, both positive and negative globalization shocks promote RE consumption in Italy and Japan but decrease it in France. In contrast, both income shocks increase RE in France whilst positive income shocks increase RE in Canada, Germany, and Italy while negative shocks decrease RE. Positive income shock facilitates RE in the UK and the US while negative income shocks are detrimental to RE in Japan. Further analysis using panel cointegration, Fully Modified Ordinary Least Squares, and Panel Dynamic Ordinary Least Squares suggests that RE deployment in the G7 countries is mainly driven by positive shocks on income, globalization, and capital. We discuss the implications of the findings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".