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Short-term European Union Allowance price forecasting with artificial neural networks

Author

Listed:
  • Agustín García

    (University of Extremadura, Spain)

  • Agustín García

    (Tufts University, United States)

  • Miguel A. Jaramillo-Morán

    (University of Extremadura, Spain)

Abstract

The European Union Emissions Trading Scheme (EU ETS) was created to reduce greenhouse gas emissions. Companies producing carbon emissions have to manage associated cash flows by buying or selling carbon allowances. Moreover, future carbon prices could affect company decision making on decarbonization technology investments. In this paper, we forecasted short-term future carbon allowance prices using an artificial intelligence tool: a neural network. The resulting mean error was 1.7617 %. This is indicative of very good performance for a time series whose evolution is influenced by subjective economic and political decisions. The inclusion in the forecasting model of variables possibly directly related to the evolution of the price of CO2 emission allowances did not improve prediction accuracy. Therefore, we can assume that emission allowances evolve following a random path. The neural network provided reliable predictions which agents selling or buying allowances can use to make their decisions.

Suggested Citation

  • Agustín García & Agustín García & Miguel A. Jaramillo-Morán, 2020. "Short-term European Union Allowance price forecasting with artificial neural networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(1), pages 261-275, September.
    • Handle: RePEc:ssi:jouesi:v:8:y:2020:i:1:p:261-275
    DOI: 10.9770/jesi.2020.8.1(18)
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    References listed on IDEAS

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    1. Bingxin Zeng & Lei Zhu, 2019. "Market Power and Technology Diffusion in an Energy-Intensive Sector Covered by an Emissions Trading Scheme," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    2. Julien Chevallier, 2013. "Carbon Price Drivers: An Updated Literature Review," International Journal of Applied Logistics (IJAL), IGI Global, vol. 4(4), pages 1-7, October.
    3. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    4. Lutz, Benjamin Johannes & Pigorsch, Uta & Rotfuß, Waldemar, 2013. "Nonlinearity in cap-and-trade systems: The EUA price and its fundamentals," Energy Economics, Elsevier, vol. 40(C), pages 222-232.
    5. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "Carbon price volatility: Evidence from EU ETS," Applied Energy, Elsevier, vol. 88(3), pages 590-598, March.
    6. Haoran Zhang & Rongxia Zhang & Guomin Li & Wei Li & Yongrok Choi, 2019. "Sustainable Feasibility of Carbon Trading Policy on Heterogenetic Economic and Industrial Development," Sustainability, MDPI, vol. 11(23), pages 1-18, December.
    7. Boersen, Arieke & Scholtens, Bert, 2014. "The relationship between European electricity markets and emission allowance futures prices in phase II of the EU (European Union) emission trading scheme," Energy, Elsevier, vol. 74(C), pages 585-594.
    8. Giovanni Marin & Marianna Marino & Claudia Pellegrin, 2018. "The Impact of the European Emission Trading Scheme on Multiple Measures of Economic Performance," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(2), pages 551-582, October.
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    Cited by:

    1. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    2. Jonathan Berrisch & Florian Ziel, 2021. "CRPS Learning," Papers 2102.00968, arXiv.org, revised Nov 2021.
    3. Berrisch, Jonathan & Pappert, Sven & Ziel, Florian & Arsova, Antonia, 2023. "Modeling volatility and dependence of European carbon and energy prices," Finance Research Letters, Elsevier, vol. 52(C).
    4. Berrisch, Jonathan & Ziel, Florian, 2023. "CRPS learning," Journal of Econometrics, Elsevier, vol. 237(2).

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    More about this item

    Keywords

    European Union Allowances (EUA); carbon allowance price; neural networks; time series forecasting;
    All these keywords.

    JEL classification:

    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • Q52 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Pollution Control Adoption and Costs; Distributional Effects; Employment Effects
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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