Researchers analysed relationships among oil-price shocks, climate-risk information and carbon prices in China.
What researchers studied
Researchers analysed relationships among oil-price shocks, climate-risk information and carbon prices in China.
The main carbon-price series came from the Guangdong emissions-trading pilot market.
Oil-price movements were separated into supply, demand and risk-related shocks.
A time-varying statistical model and frequency-domain analysis distinguished short-horizon from longer-horizon connectedness.
An XGBoost model with SHAP explanations examined predictive contributors to changing spillover intensity.
The Hubei regional carbon market provided a robustness comparison.
What they found
The analysis reported stronger short-term than long-term market connectedness.
The baseline short-horizon total connectedness index was 24.91, compared with 5.30 for the longer-horizon component.
The main short horizon covered one to ten trading days.
Physical and transition climate-risk information showed links in both directions with carbon prices.
Oil-related risk shocks were more relevant in the short term, while supply and demand shocks displayed stronger longer-horizon connections.
Exchange rates, Chinese economic-policy uncertainty and market liquidity emerged as important predictive variables.
The authors reported broadly similar qualitative patterns when they substituted Hubei carbon prices.
What it means
Carbon-market participants may need to distinguish immediate volatility from slower-moving changes in energy and climate expectations.
The findings suggest that energy prices, climate information and regional carbon markets are interconnected.
They could inform risk monitoring and scenario design for organisations exposed to several markets.
However, statistical spillovers do not prove that any particular market shock caused a subsequent change.
Predictive models can identify useful indicators without revealing structural economic mechanisms.
Important limitation
Guangdong and Hubei are regional pilots whose liquidity and coverage differ from China’s national emissions-trading system.
The climate-risk indices draw on global media information and may miss local developments.
SHAP feature contributions represent predictive associations rather than causal effects.
The findings are not reliable standalone forecasts of carbon prices or estimates of policy impacts.
Source Information
Study: Spillover effect between oil prices, climate risks, and carbon prices in China: dynamic effects and key drivers
Authors: Shanshan Zuo and Jianhong Yang
Journal / Source: Frontiers in Environmental Science
Published: 11 November 2026
