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Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries

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Listed:
  • Mohammad Hossein Rezaei
  • Milad Sadeghzadeh
  • Mohammad Alhuyi Nazari
  • Mohammad Hossein Ahmadi
  • Fatemeh Razi Astaraei

Abstract

CO2 emission depends on several parameters. Due to environmental issues, it is necessary to find influential factors on CO2 emission as one of the most critical greenhouse gases. Type of utilized fuels and their share in total primary energy consumption, Gross Domestic Product (GDP) as an indicator for economic activities and the share of renewable energies play key role in the amount of CO2 emission. In the present study, Group method of data handling (GMDH) is applied in order to model CO2 emission as a function of consumption of various fuels, renewable energies and GDP. Obtained data showed that GMDH is an appropriate approach to predict CO2 emission. Comparing between actual data and GMDH output indicates that the R-squared value for the proposed model is equal to 0.998 which shows its high accuracy. In addition, it is observed that the highest absolute error by using GMDH artificial neural network is lower than 4%. The absolute relative error for more than 66% of data is lower than 1% which is another criterion demonstrating acceptable accuracy of the proposed model.

Suggested Citation

  • Mohammad Hossein Rezaei & Milad Sadeghzadeh & Mohammad Alhuyi Nazari & Mohammad Hossein Ahmadi & Fatemeh Razi Astaraei, 2018. "Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(3), pages 266-271.
    • Handle: RePEc:oup:ijlctc:v:13:y:2018:i:3:p:266-271.
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    File URL: http://hdl.handle.net/10.1093/ijlct/cty026
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    Citations

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    Cited by:

    1. Ali Komeili Birjandi & Morteza Fahim Alavi & Mohamed Salem & Mamdouh El Haj Assad & Natarajan Prabaharan, 2022. "Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network [Energy and exergy analyses of single flash geothermal power plant at optimum separator temp," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 321-326.
    2. Ramezanizadeh, Mahdi & Ahmadi, Mohammad Hossein & Nazari, Mohammad Alhuyi & Sadeghzadeh, Milad & Chen, Lingen, 2019. "A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Xiaodong Li & Ai Ren & Qi Li, 2022. "Exploring Patterns of Transportation-Related CO 2 Emissions Using Machine Learning Methods," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    4. Behzad Maleki & Mahyar Ghazvini & Mohammad Hossein Ahmadi & Heydar Maddah & Shahaboddin Shamshirband, 2019. "Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network," Mathematics, MDPI, vol. 7(11), pages 1-12, November.
    5. Seyed Mohammad Seyed Alavi & Akbar Maleki & Ali Khaleghi, 2022. "Optimal site selection for wind power plant using multi-criteria decision-making methods: A case study in eastern Iran [Selection of optimal location and design of a stand-alone photovoltaic scheme," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1319-1337.

    More about this item

    Keywords

    CO2 emission; GDP; renewable energy; GMDH;
    All these keywords.

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