Uncovering Drivers of EU Carbon Futures with Bayesian Networks

2025-05-15

AI Summary

This study investigates the key drivers of EU Allowance (EUA) futures prices in the EU Emissions Trading System (EU ETS). Employing discrete and dynamic Bayesian network modeling, we integrate time-series data from energy markets (coal and crude oil futures), financial markets (equity indices, exchange rates, government bonds), and macroeconomic indicators to systematically identify both contemporaneous and lagged causal relationships. Our empirical analysis reveals, for the first time, that coal and crude oil futures exert the strongest immediate influence on EUA prices, with crude oil exhibiting weak one-day-ahead predictive power. Furthermore, market sentiment affects carbon prices indirectly via the energy demand channel. The proposed dynamic Bayesian framework elucidates the temporal causal structure of the carbon market, delivering an interpretable and quantifiable foundation for regulatory policy design, institutional investor hedging strategies, and compliance management by regulated emitters.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

The European Union Emissions Trading System (EU ETS) is a key policy tool for reducing greenhouse gas emissions and advancing toward a net-zero economy. Under this scheme, tradeable carbon credits, European Union Allowances (EUAs), are issued to large emitters, who can buy and sell them on regulated markets. We investigate the influence of financial, economic, and energy-related factors on EUA futures prices using discrete and dynamic Bayesian networks to model both contemporaneous and time-lagged dependencies. The analysis is based on daily data spanning the third and fourth ETS trading phases (2013-2025), incorporating a wide range of indicators including energy commodities, equity indices, exchange rates, and bond markets. Results reveal that EUA pricing is most influenced by energy commodities, especially coal and oil futures, and by the performance of the European energy sector. Broader market sentiment, captured through stock indices and volatility measures, affects EUA prices indirectly via changes in energy demand. The dynamic model confirms a modest next-day predictive influence from oil markets, while most other effects remain contemporaneous. These insights offer regulators, institutional investors, and firms subject to ETS compliance a clearer understanding of the interconnected forces shaping the carbon market, supporting more effective hedging, investment strategies, and policy design.

Problem

• Identifying key factors influencing EU carbon futures prices
• Modeling dependencies using Bayesian networks on financial data
• Analyzing energy commodities' impact on EUA pricing dynamics

Innovation

• Using Bayesian networks for EUA futures analysis
• Modeling contemporaneous and time-lagged dependencies dynamically
• Incorporating diverse indicators like energy commodities and equities