A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data

Dinggao Liu, Liuqing Wang, Shuo Lin and Zhenpeng Tang ()
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Dinggao Liu: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Liuqing Wang: College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Shuo Lin: College of Economics and Management, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Zhenpeng Tang: College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China

Mathematics, 2025, vol. 13, issue 3, 1-23

Abstract: The European Union Emissions Trading System (EU ETS) serves as the cornerstone of European climate policy, providing a critical mechanism for mitigating greenhouse gas emissions. Accurate forecasting of the carbon allowance prices within the market is essential for policymakers, enterprises, and investors. To address the need for interval-valued time series modeling and forecasting in the carbon market, this paper proposes a Transformer-based multi-task learning framework that integrates online news and search engine data information to forecast interval-valued EU carbon allowance futures prices. Empirical evaluations demonstrate that the proposed framework achieves superior predictive accuracy for short-term forecasting and remains robust under high market volatility and economic policy uncertainty compared to single-task learning benchmarks. Furthermore, ablation experiments indicate that incorporating news sentiment intensity and search index effectively enhances the framework’s predictive performance. Interpretability analysis highlights the critical role of specific temporal factors, while the time-varying variable importance analysis further underscores the influence of carbon allowance close prices and key energy market variables and also recognizes the contributions of news sentiment. In summary, this study provides valuable insights for policy management, risk hedging, and portfolio decision-making related to interval-valued EU carbon prices and offers a robust forecasting tool for carbon market prediction.

Keywords: carbon prices; interval-valued time series; multi-task learning; sentiment analysis; interval forecasting; interpretability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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