Uncovering drivers of EU carbon futures with Bayesian networks

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel Bayesian network approach to model carbon price drivers, useful for further research on carbon market dynamics.

🏢実務担当者:Helps corporate sustainability teams understand key factors affecting carbon credit prices for better hedging and investment strategies.

🏛政策担当者:Offers insights into how energy markets and financial conditions influence carbon prices, informing ETS design and stability measures.

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.