This analysis reveals the dynamic correlation of carbon prices and new energy indices, suggesting that seasonal factors significantly influence their relationship.
As the global consensus on carbon reduction targets continues to deepen, the relationship between carbon prices and new energy stock indices has become increasingly close. Revealing the evolutionary characteristics of the short-term correlation between carbon prices and new energy stock indices is particularly important. This study takes the Guangdong carbon price and the CSI Mainland New Energy Theme Index as research samples and adopts Pearson correlation analysis combined with the sliding window method to portray the correlation between the two. Next, we construct a complex network and research the evolution between the two under the influence of seasonal factors. The research results show that the correlation is complex and time-varying. Five key correlation modes govern how the correlation evolves, and their conversions exhibit self-stabilizing characteristics. The modes with high media capabilities in the network control the transitions between modes and provide early warning information for policymakers and enterprises. In addition, under the effect of different seasonal climates and performance cycles, the correlations reflect seasonal evolutionary characteristics. According to the regularities of correlation evolution between carbon prices and new energy stock indices, policymakers can adjust short-term policies flexibly, and enterprises can adapt their production and energy conversion strategies.
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Li et al. (2025) studied this question.
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