From carbon footprints to digital footprints: Return transmission between AI and renewable energy markets

Drawing on the perspective of financial integration, this study aims to determine the return transmission between AI stocks, AI tokens, and renewable energy stocks. Based on time‐varying parameter vector autoregression (TVP-VAR), the study examined the daily stock data from June 25, 2019, to August 15, 2025. The sample was decomposed into three segments for in-depth empirical analysis: full sample, pre-COVID-19, and post-COVID-19. The key results indicated a moderate level of return transmission between the variables of interest. During COVID-19, a medium level of shock transmission was observed in AI stocks and renewable energy stocks toward the AI tokens and GCEI. The dynamic analysis reflects the time-varying and asymmetrical transmission between the variables of interest. The findings infer managerial and policy-level implications for the financial market participants and enable them to make informed investment decisions. The findings remain instrumental in devising adequate diversification and hedging strategies to mitigate market uncertainty. The study enlightened the novel aspect of return transmission between AI stocks, AI tokens, and renewable energy markets, addressing AI technologies' static and dynamic connectedness and the renewable energy sector's financial dynamics. By employing a TVP-VAR model and segmenting the analysis into pre- and post-COVID-19, the study provides novel insights into the evolving roles of AI and renewable energy markets as transmitters and recipients of return spillovers.