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Measurements and determinants of extreme multidimensional energy poverty using machine learning

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  • Abbas, Khizar
  • Butt, Khalid Manzoor
  • Xu, Deyi
  • Ali, Muhammad
  • Baz, Khan
  • Kharl, Sanwal Hussain
  • Ahmed, Mansoor

Abstract

The contribution of this study is twofold. First, it calculates the depth, intensity, and degrees of energy poverty in developing countries using a multidimensional approach. The data analysis of 59 developing countries of Asia and Africa confirmed a widespread ‘severe’ energy poverty across multiple dimensions. The results revealed that Afghanistan, Yemen, Nepal, India, Bangladesh, and the Philippines in Asia and DR Congo, Chad, Madagascar, Niger, Sierre Leone, Tanzania, and Burundi in Africa were the most susceptible countries to extreme multidimensional energy poverty. Second, the study employed supervised machine learning algorithms to identify the most pertinent socioeconomic determinants of extreme multidimensional energy poverty in the developing world. The results of machine learning identified the accumulated wealth of a household, size and ownership status of a house, marital status of the main breadwinner, and place of residence of the main breadwinner to be the five most influential socioeconomic determinants of extreme multidimensional energy poverty. Therefore, the robust findings of an accurate assessment of extreme energy poverty and its socioeconomic determinants have policy significance to eradicate severe energy poverty by announcing additional incentives, allocating resources, and providing special assistance to those who are at the bottom.

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  • Abbas, Khizar & Butt, Khalid Manzoor & Xu, Deyi & Ali, Muhammad & Baz, Khan & Kharl, Sanwal Hussain & Ahmed, Mansoor, 2022. "Measurements and determinants of extreme multidimensional energy poverty using machine learning," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008805
    DOI: 10.1016/j.energy.2022.123977
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    6. Yuxiang Xie & E. Xie, 2023. "Measuring and Analyzing the Welfare Effects of Energy Poverty in Rural China Based on a Multi-Dimensional Energy Poverty Index," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    7. Arkadiusz Piwowar, 2022. "Energy Poverty as a Current Problem in the Light of Economic and Social Challenges," Energies, MDPI, vol. 15(22), pages 1-9, November.
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