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A machine learning approach for predicting the relationship between energy resources and economic development

Author

Listed:
  • Cogoljević, Dušan
  • Alizamir, Meysam
  • Piljan, Ivan
  • Piljan, Tatjana
  • Prljić, Katarina
  • Zimonjić, Stefan

Abstract

The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach.

Suggested Citation

  • Cogoljević, Dušan & Alizamir, Meysam & Piljan, Ivan & Piljan, Tatjana & Prljić, Katarina & Zimonjić, Stefan, 2018. "A machine learning approach for predicting the relationship between energy resources and economic development," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 211-214.
    • Handle: RePEc:eee:phsmap:v:495:y:2018:i:c:p:211-214
    DOI: 10.1016/j.physa.2017.12.082
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    References listed on IDEAS

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    1. Agustin Alonso-Rodriguez, 1999. "Forecasting economic magnitudes with neural network models," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(4), pages 496-511, November.
    2. Agustin Alonso-Rodriguez, 1999. "Forecasting economic magnitudes with neural network models," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(2), pages 215-230, May.
    3. Alshehry, Atef Saad & Belloumi, Mounir, 2015. "Energy consumption, carbon dioxide emissions and economic growth: The case of Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 237-247.
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    2. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, vol. 167(C), pages 99-115.

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