Examining the state of energy poverty in Rwanda: An inter-indicator analysis

This paper adopted an inter-indicator analytical approach to investigate the state of energy poverty in Rwanda. It used a nationally representative sample of 14458 households from Rwanda's Integrated Living Standard Survey conducted between October 2016 and October 2017. The first indicator ent...

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Autores principales:	Fydess Khundi-Mkomba, Akshay Kumar Saha, Umaru Garba Wali
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Household energy poverty index Multidimensional analysis Inter-indicator Rwanda Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/2f9eb93cb9c74e2698af536cdf5a7c63
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spelling	oai:doaj.org-article:2f9eb93cb9c74e2698af536cdf5a7c632021-12-02T05:03:14ZExamining the state of energy poverty in Rwanda: An inter-indicator analysis2405-844010.1016/j.heliyon.2021.e08441https://doaj.org/article/2f9eb93cb9c74e2698af536cdf5a7c632021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405844021025445https://doaj.org/toc/2405-8440This paper adopted an inter-indicator analytical approach to investigate the state of energy poverty in Rwanda. It used a nationally representative sample of 14458 households from Rwanda's Integrated Living Standard Survey conducted between October 2016 and October 2017. The first indicator entailed a multidimensional analysis of energy poverty using eleven pointers of energy deprivation. Each pointer was assigned a weight using principal component analysis to form a household energy poverty index. The paper also employed a ‘modified’ expenditure-based approach that emphasizes affordability and accessibility. This is the approach on which the second indicator of energy poverty was based. This constituted an examination of different levels of household income and energy expenditure patterns as well as the use of biomass for cooking. The results from the multidimensional analysis revealed that the most energy-poor households were concentrated in the southern (30.15%), western (27.69%) and northern (24.86%) provinces of Rwanda. In contrast, ‘the least energy-poor are mostly found in urban areas of the country. A cross-comparison with the second approach showed different magnitudes of energy poverty incidences. Nonetheless, similar trends were observed in terms of areas of concentration of energy poverty. Last, the results from multilevel binary logistic regressions showed that household size, income poverty, education level of the head of the family, rural location and Kigali residentship were determinants of energy poverty.Fydess Khundi-MkombaAkshay Kumar SahaUmaru Garba WaliElsevierarticleHousehold energy poverty indexMultidimensional analysisInter-indicatorRwandaScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08441- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Household energy poverty index Multidimensional analysis Inter-indicator Rwanda Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Household energy poverty index Multidimensional analysis Inter-indicator Rwanda Science (General) Q1-390 Social sciences (General) H1-99 Fydess Khundi-Mkomba Akshay Kumar Saha Umaru Garba Wali Examining the state of energy poverty in Rwanda: An inter-indicator analysis
description	This paper adopted an inter-indicator analytical approach to investigate the state of energy poverty in Rwanda. It used a nationally representative sample of 14458 households from Rwanda's Integrated Living Standard Survey conducted between October 2016 and October 2017. The first indicator entailed a multidimensional analysis of energy poverty using eleven pointers of energy deprivation. Each pointer was assigned a weight using principal component analysis to form a household energy poverty index. The paper also employed a ‘modified’ expenditure-based approach that emphasizes affordability and accessibility. This is the approach on which the second indicator of energy poverty was based. This constituted an examination of different levels of household income and energy expenditure patterns as well as the use of biomass for cooking. The results from the multidimensional analysis revealed that the most energy-poor households were concentrated in the southern (30.15%), western (27.69%) and northern (24.86%) provinces of Rwanda. In contrast, ‘the least energy-poor are mostly found in urban areas of the country. A cross-comparison with the second approach showed different magnitudes of energy poverty incidences. Nonetheless, similar trends were observed in terms of areas of concentration of energy poverty. Last, the results from multilevel binary logistic regressions showed that household size, income poverty, education level of the head of the family, rural location and Kigali residentship were determinants of energy poverty.
format	article
author	Fydess Khundi-Mkomba Akshay Kumar Saha Umaru Garba Wali
author_facet	Fydess Khundi-Mkomba Akshay Kumar Saha Umaru Garba Wali
author_sort	Fydess Khundi-Mkomba
title	Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title_short	Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title_full	Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title_fullStr	Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title_full_unstemmed	Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title_sort	examining the state of energy poverty in rwanda: an inter-indicator analysis
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/2f9eb93cb9c74e2698af536cdf5a7c63
work_keys_str_mv	AT fydesskhundimkomba examiningthestateofenergypovertyinrwandaaninterindicatoranalysis AT akshaykumarsaha examiningthestateofenergypovertyinrwandaaninterindicatoranalysis AT umarugarbawali examiningthestateofenergypovertyinrwandaaninterindicatoranalysis
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Examining the state of energy poverty in Rwanda: An inter-indicator analysis

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