Nonlinear impact of climate transition risks on green stock performance: Perspectives from multiscale and lag effects

This study presents a novel examination of how climate transition risks, temperature variances, and the international crude oil market influence green stock performance in China. Utilizing signal decomposition and distributed lag nonlinear regression models on data from July 29, 2015, to June 30, 2022, we uncover a unique nonlinear and time-lagged relationship between climate risks and green stock returns. Our findings reveal that green stocks respond differently to climate and oil price changes under various market conditions, with long-term trends more affected than short-term movements. This highlights the critical role of temporal factors in financial analyses of climate-related risks and suggests that climate policies and market dynamics significantly shape investment and corporate strategies over time. The study offers fresh insights into green stock dynamics, stressing the need for nuanced understanding in financial decision-making amidst climate change and market volatility.