The Global “Carbon-Energy-Intelligence” Framework: Decoding Cross-Market Interlinkages

The risk spillover theory posits that risks originating in one market can propagate and amplify systemic risk across interconnected markets. This study investigates the dynamic causality and interconnectedness by integrating the European Union's carbon market, the energy market (i.e., oil and renewable), and artificial intelligence (AI) through a “Carbon-Energy-Intelligence” framework. Employing a time-varying parametric vector autoregressive model with stochastic volatility, we confirm robust bidirectional causality between each asset pair, with these causal links becoming particularly pronounced during unforeseen events like geopolitical tensions. Specifically, AI exhibits a persistent positive correlation with the WTI spot series, enduring these effects over time. Yet the impact of AI on the renewable and carbon markets remains complex, with positive and negative responses observed. Our quantile connectedness analysis further corroborates that systemic risk spillovers intensify, especially during crises. These findings offer novel perspectives on cross-market spillovers and emphasize the crucial role of AI in supporting global decarbonization initiatives. • A novel framework, known the “Carbon-Energy-Intelligence” framework, is proposed. • The time-varying Granger causality test is introduced. • The tail risk contagion across these underlying assets is also investigated. • Tail risk is estimated using Conditional Autoregressive Value at Risk. • Extreme events are liable for tightening cross-market casualties and risk spillovers.