Risk spillovers from climate policy uncertainty to energy markets: Does climate policy stringency matter?
使用改进的高维时变参数向量自回归模型和双重机器学习方法，研究发现气候政策不确定性对全球能源市场存在显著风险溢出，且气候政策严格性有助于缓解这种风险。
This paper describes the complex risk contagion between climate policy uncertainty (CPU) and the global energy market utilizing an improved high-dimensional time-varying parameter vector autoregressive (HD-TVP-VAR) model. A dual machine learning (DML) approach is applied to test whether strict climate policies can mitigate the risk from CPU to energy market. The results indicate that the CPU is highly associated with the risk of energy markets. The risk spillover of CPU exhibits evident time-varying characteristics, and its impact on the energy markets of various countries varies. Furthermore, we find that climate policy stringency (CPS) plays a significant role in alleviating the risk from CPU to the energy market. These results provide valuable insights into the effective identification of CPU's risk contagion paths in global energy market.