System Engineering and Productivity

System Engineering and Productivity

A Data-driven Hybrid Approach for Examining the Factors Influencing the Price of EUA during Phase IV of the EUETS

Document Type : Research Paper

Authors
Nasser Safaie 1
Yasin Heidari Soochelmai 2
Majid Mirzaee Ghazani 3
1 Corresponding author: Assistant Professor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Ph.D. Student, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 AssociateProfessor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
10.22034/sep.2025.2050780.1253
Abstract
The price of EU Allowances (EUAs) serves as a mechanism for managing greenhouse gas emissions, influenced by various factors such as economic, financial, political, and other indicators. This study assesses the impact of 31 different energy, financial, and commodity indices on EUA prices. To achieve this, a hybrid data-driven approach is employed. Initially, the Autoregressive Integrated Moving Average (ARIMA) algorithm cleans the relevant data. Subsequently, the Variational Mode Decomposition (VMD) method decomposes the indices into intrinsic mode functions (IMFs). These IMFs are categorized into three-time scales: short-term, medium-term, and long-term. Next, by integrating Recursive Feature Elimination (RFE) and Random Forest (RF) with cross-validation, the most influential features across these time scales are selected. The results demonstrate that the model achieves higher accuracy in medium-term and long-term time scales. Forecasting the price fluctuations of EUAs using these hybrid approaches can contribute to more precise and timely decision-making in policy formulation and investment strategies.

Highlights

  • A hybrid approach was developed to predict fluctuations in EU Allowance prices.
  • Features such as the Dow Jones Industrial Index were identified as key factors.
  • Highly accurate predictions were achieved in medium- and long-term time scales.

Keywords
EU Allowances (EUAs)
Feature Selection
Data-Driven Hybrid Approach
Variational Mode Decomposition (VMD)
Recursive Feature Elimination (RFE)
Subjects

Copyright © Nasser Safaie. Yasin Heidari Soochelmai. Majid Mirzaee Ghazani

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

 

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System Engineering and Productivity
Volume 5, Issue 1 - Serial Number 14
Serial No. 14, Spring Quarterly
Spring 2025
Pages 113-134

  • Receive Date 15 January 2025
  • Revise Date 08 February 2025
  • Accept Date 17 February 2025
  • First Publish Date 17 February 2025
  • Publish Date 22 May 2025