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Purpose Sustainable development hinges on a crucial shift to renewable energy, which is essential in the fight against global warming and climate change. This study explores the relationships between artificial intelligence (AI), fuel, green stocks, geopolitical risk, and Ethereum energy consumption (ETH) in an era of rapid technological advancement and growing environmental concerns. Design/methodology/approach This research stands at the forefront of interdisciplinary research and forges a path toward a comprehensive understanding of the intricate dynamics governing green sustainability investments. These objectives have been fulfilled by implementing the innovative quantile time-frequency connectedness approach in conjunction with geopolitical and climate considerations. Findings Our findings highlight coal market dominance and Ethereum energy consumption as critical short- and long-term market volatility sources. Additionally, geopolitical risks and Ethereum energy consumption significantly contribute to volatility. Long-term factors are the primary drivers of directional volatility spillover, impacting green stocks and energy assets over extended periods. Additionally, SHapley Additive exPlanations (SHAP) findings corroborate the quantile time-frequency connectedness outcomes. Research limitations/implications This study highlights the critical importance of transitioning to sustainable energy sources and embracing digital finance in fostering green sustainability investments, illuminating their roles in shaping market dynamics, influencing geopolitics and ensuring the long-term sustainability required to combat climate change effectively. Practical implications The study offers practical sustainability implications by informing green investment choices, strengthening risk management strategies, encouraging interdisciplinary cooperation and fostering digital finance innovations to promote sustainable practices. Originality/value The implementation of the quantile time-frequency connectedness approach, in line with considering geopolitical and climate factors, marks the originality of this paper. This approach allows for a dynamic analysis of connectedness across different distribution quantiles, providing a deeper understanding of variable interactions under varying market conditions.
Mnif et al. (Thu,) studied this question.