Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied Energy

DOI: 10.1016/j.apenergy.2025.126596

Authors: Miaomiao Tao, Stephen Poletti, David Roubaud, Aviral Kumar Tiwari

Institutions: University of Auckland, Gulf University for Science & Technology, MBS School of Business, Indian Institute of Management Bodh Gaya

Language: English

Topic: Market Dynamics and Volatility

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,430