Dynamic linkages between circular economy, green technologies, and energy transition under geopolitical shocks: Evidence from wavelet and non-parametric causality approaches
Bibliographic record
Abstract
This study examines the dynamic interlinkages between the circular economy (CE), green technologies (GT), and geopolitical risk (GPR) in shaping the global energy transition (ET). Using daily time-series data from July 2, 2019, to March 31, 2023, we apply rolling window wavelet correlation and non-parametric Granger causality to uncover how recent economic shocks, particularly the COVID-19 pandemic and the Russia–Ukraine conflict, alter these relationships. The results reveal that CE and GT exert a consistent positive influence on ET, while GPR tends to have a negative and asymmetric effect. These effects vary significantly across time horizons and quantiles, highlighting the need to account for non-linear and scale-dependent dynamics. Causality tests confirm the predictive power of CE, GT, and GPR for ET across most distributional segments. The findings offer actionable insights for designing resilient energy transition policies that integrate innovation, circularity, and geopolitical adaptability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".