Geopolitical risk and energy markets in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine the impact of geopolitical risk (GPR) on China's energy markets, focusing on carbon emission allowance prices, the clean energy stock index, the environmental–social–governance (ESG) 100 stock index, and the gas and oil stock index. Using a quantile-on-quantile regression with kernel regularized least squares methodology, we analyze weekly data from China from March 2, 2015, to December 26, 2022. Findings reveal that GPR negatively affects carbon market prices and ESG stocks, particularly when these markets are in weaker states. Conversely, clean energy stocks benefit from geopolitical uncertainties under favorable market conditions, while traditional energy stocks exhibit resilience and even strengthen due to their strategic importance during periods of heightened GPR. Moreover, GPR significantly drives energy market volatility, with amplified effects in high-volatility market conditions. This quantile-specific approach provides a nuanced understanding of how GPR influences energy assets, emphasizing the importance of tailored risk management strategies. Our findings highlight the necessity of integrating GPR assessments into investment decisions and policy frameworks to reduce the uncertainty affecting China's energy markets. • Examine the nonlinear and asymmetric impact of geopolitical risk on energy markets. • Use the quantile-on-quantile regression with kernel regularized least squares method. • We provide guidance on better managing risks in energy markets in China.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it