Heterogeneous information transmission between climate policy uncertainty and Chinese new energy markets: A quantile-on-quantile transfer entropy method

This paper proposes a novel quantile-on-quantile transfer entropy (QQTE) method to quantify the heterogeneous nonlinear information transmission between climate policy uncertainty (CPU) and Chinese new energy markets under different conditions. Based on the daily data of CPU and three new energy stock indexes from January 4, 2005 to December 29, 2023, the bidirectional, asymmetric and dynamic characteristics of the nonlinear information transmission are observed. First, stronger intensity of information is transmitted from CPU to new energy markets. Second, the information transmission under normal conditions is weaker than that resulting from extreme events, especially regarding the impact of CPU on wind and solar markets when CPU experiences extreme negative fluctuations. In addition, the dynamic characteristics are verified using the rolling-window technique, and we find that the direction and strength of the information transmission are usually associated with policy fluctuations, such as the United Nations Climate Change conference, and external shocks, such as the financial crisis. For comparison, the strength of bidirectional transfer information between CPU and traditional energy markets is relatively larger with the occurrences of extreme shocks and is stable under the normal conditions. These results are crucial for understanding the complex interaction within the policy-energy system.