Dynamic spillovers between ESG green bonds and AI equity indices: implications for higher education institutions

Purpose Higher education institutions (HEI) increasingly depend on sustainable financing to support long-term research, infrastructure development and innovation. As investment in artificial intelligence (AI) expands alongside institutional commitments to sustainability, understanding the interaction between AI-related financial markets and environmental, social and governance (ESG) Green Bond markets becomes increasingly important. This study aims to examine the time-varying and asymmetric causal relationship between AI equity markets and ESG Green Bond markets, with particular attention to its implications for sustainability-oriented investment decisions in higher education. Design/methodology/approach A quantile Granger causality framework is combined with a rolling-window approach to capture time-varying and distribution-dependent causal relationships. By conducting causality tests across multiple quantiles and sub-samples, the analysis identifies asymmetric and tail-dependent effects that are not observable with conventional mean-based Granger causality tests. Findings Results show that AI market movements consistently precede changes in ESG-Green Bond markets, while the reverse relationship is significant mainly in extreme quantiles. The rolling-window analysis reveals that causal links are significant during periods of market stress and insignificant under stable conditions, indicating state-dependent interactions between AI and ESG assets. Practical implications The findings suggest that HEI should consider market conditions when integrating AI-related investments with sustainability-focused financial strategies. In stable periods, AI and ESG assets may complement each other in long-term portfolios, while during volatile periods, stronger spillovers may reduce diversification benefits. Originality/value This study provides novel evidence on the asymmetric and time-varying relationship between AI and ESG financial markets. It highlights the importance of distribution-sensitive methods for informing sustainable investment decisions in higher education and contributes to understanding the dynamic nexus between technological innovation and sustainability-oriented finance.