ارائه یک رویکرد ترکیبی داده محور برای بررسی عوامل اثرگذار بر قیمت کربن طی فاز چهارم طرح تجارت آلایندگی اتحادیه اروپا

صفایی, ناصر; حیدری سوچلمائی, یاسین; میرزایی قزانی, مجید

doi:10.22034/sep.2025.2050780.1253

ارائه یک رویکرد ترکیبی داده محور برای بررسی عوامل اثرگذار بر قیمت کربن طی فاز چهارم طرح تجارت آلایندگی اتحادیه اروپا

نوع مقاله : پژوهشی

نویسندگان

ناصر صفایی ¹

یاسین حیدری سوچلمائی ²

مجید میرزایی قزانی ³

¹ نویسنده مسئول: استادیار، گروه مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

² دانشجوی دکتری، گروه مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

³ دانشیار، گروه مهندسی صنایع، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران

10.22034/sep.2025.2050780.1253

چکیده

قیمت کمک‌هزینه اتحادیه اروپا یک راهکار به‌منظور مدیریت انتشار گازهای گلخانه‌ای می‌باشد، عوامل بسیاری نظیر شاخص‌های اقتصادی، مالی، سیاسی و ... می‌تواند بر آن اثرگذار باشد. پژوهش پیش رو در نظر دارد تا میزان اثرگذاری 31 شاخص مختلف انرژی، مالی و کالاهای اساسی را بر آن بررسی نماید. ازاین‌رو با یک استفاده روش ترکیبی داده محور به بررسی این امر می‌پردازد. در ابتدا با استفاده از الگوریتم میانگین متحرک یکپارچه خود رگرسیون به پاک‌سازی داده‌های مربوط به آن می‌پردازد و پس‌ازآن با روش تجزیه حالت متغیر بخش‌های مختلف این شاخص‌ها را تجزیه می‌نماید و یک سری مد ذاتی به دست می‌آورد، پس‌ازآن این مدها در سه مقیاس زمانی کوتاه‌مدت، میان‌مدت و بلندمدت قرار می‌گیرند، سپس با استفاده از ادغام حذف بازگشتی ویژگی‌ها و جنگل تصادفی با در نظر گرفتن اعتبارسنجی متقابل اقدام به انتخاب بهترین ویژگی‌ها در این سه مقیاس گرفته می‌شود. نتایج به‌دست‌آمده نشان می‌دهد در مقیاس زمانی میان‌مدت و بلندمدت، مدل موفق به پیش‌بینی با دقت بیشتر می‌باشد و پیش‌بینی نوسانات قیمت کمک‌هزینه اتحادیه اروپا با استفاده از این رویکردهای ترکیبی، می‌تواند به تصمیم‌گیری‌های دقیق‌تر و به‌موقع درزمینه سیاست‌گذاری و سرمایه‌گذاری کمک کند.

تازه های تحقیق

یک رویکرد ترکیبی برای پیش‌بینی نوسانات قیمت کمک‌هزینه‌های اتحادیه اروپا توسعه داده شد.
ویژگی‌هایی مانند شاخص صنعتی داوجونز، به‌عنوان عوامل کلیدی شناسایی شدند.
پیش‌بینی‌هایی با دقت بالا در مقیاس‌های زمانی میان‌مدت و بلندمدت حاصل شد.

کلیدواژه‌ها

کمک‌هزینه اتحادیه اروپا

انتخاب شاخص‌های اثرگذار

رویکرد ترکیبی داده محور

تجزیه حالت متغیر

حذف بازگشتی ویژگی‌ها

موضوعات

داده‌کاوی و مدیریت داده‌ها

عنوان مقاله English

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

نویسندگان English

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

چکیده English

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.

کلیدواژه‌ها English

EU Allowances (EUAs)

Feature Selection

Data-Driven Hybrid Approach

Variational Mode Decomposition (VMD)

Recursive Feature Elimination (RFE)

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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