Measurements and determinants of extreme multidimensional energy poverty using machine learning

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.