Predictive Effects of Climate Policy Uncertainty on Returns and Volatility of Carbon Emission Prices: The Case of China

This study investigates whether climate policy uncertainty (CPU) has predictive power over carbon price dynamics in China. Using daily data from 19 July 2021 to 29 December 2023, we apply a nonparametric quantile Granger causality approach to assess the effects of both domestic and global CPU on carbon price returns and volatility. Linear Granger causality tests reveal no significant average predictive relationship. However, the quantile-based analysis shows that CPU exerts significant and nonlinear causal effects across the entire distribution, with particularly strong influences on volatility during market downturns. Both national and global CPU indices consistently predict carbon price movements, though the effects of domestic CPU are generally stronger. Regional analysis across eight pilot markets highlights heterogeneity, with more mature markets such as Guangdong and Shanghai exhibiting stronger predictive relationships. Robustness checks controlling for trading activity confirm the persistence of these effects. The findings emphasise the importance of accounting for climate policy uncertainty when modelling carbon price behaviour, with implications for policymakers seeking to stabilise carbon markets and for investors developing risk management strategies in the face of uncertainty.