Time-varying bidirectional causality between climate policy uncertainty and renewable energy investments
Bibliographic record
Abstract
Climate change poses a significant systemic risk in the twenty-first century, yet little attention has been paid to its interaction with renewable energy exchange traded funds (ETFs). This study employs the time-varying Granger causality approach to investigate the bidirectional causality between Climate Policy Uncertainty and Renewable ETFs, exploring how this relationship evolves over time. Monthly data spanning from January 2010 to June 2025 from the CPU index and the price of Renewable ETFs were used in this research. The results reveal a dynamic, time-varying, and asymmetric causal relationship between Climate Policy Uncertainty (CPU) and renewable energy ETFs. Strong causal effects are non-linear over time, with the influence of CPU on renewable ETFs intensifying after 2016, while the reverse causality from ETFs to CPU weakens after 2020. These findings emphasize the importance of exploring the relationship between CPU and renewable energy ETF prices. Understanding this interaction not only aids strategic decision-making and risk management for renewable energy investments but also fosters resilience against market fluctuations, driving the advancement of green finance initiatives. This study contributes to both climate change mitigation efforts and the development of sustainable finance strategies. • A time-varying Granger causality approach is used to investigate the causality between Climate Policy Uncertainty and Renewable ETFs ▪ Monthly data from January 2010 to December 2023 from the CPU index and the price of Renewable ETFs are used ▪ The results revealed that there is a weak causal relationship between CPU and Renewable ETFs ▪ Over time, the influence of CPU on renewable energy ETF prices becomes more pronounced
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".