Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Khundi-Mkomba, Fydess, Kumar Saha, Akshay, Wali, Umaru Garba
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642615/
https://www.ncbi.nlm.nih.gov/pubmed/34901498
http://dx.doi.org/10.1016/j.heliyon.2021.e08441
_version_ 1784609707000856576
author Khundi-Mkomba, Fydess
Kumar Saha, Akshay
Wali, Umaru Garba
author_facet Khundi-Mkomba, Fydess
Kumar Saha, Akshay
Wali, Umaru Garba
author_sort Khundi-Mkomba, Fydess
collection PubMed
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 Online
Article
Text
id pubmed-8642615
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-86426152021-12-09 Examining the state of energy poverty in Rwanda: An inter-indicator analysis Khundi-Mkomba, Fydess Kumar Saha, Akshay Wali, Umaru Garba Heliyon Research Article 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. Elsevier 2021-11-19 /pmc/articles/PMC8642615/ /pubmed/34901498 http://dx.doi.org/10.1016/j.heliyon.2021.e08441 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Khundi-Mkomba, Fydess
Kumar Saha, Akshay
Wali, Umaru Garba
Examining the state of energy poverty in Rwanda: An inter-indicator analysis
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8642615/
https://www.ncbi.nlm.nih.gov/pubmed/34901498
http://dx.doi.org/10.1016/j.heliyon.2021.e08441
work_keys_str_mv AT khundimkombafydess examiningthestateofenergypovertyinrwandaaninterindicatoranalysis
AT kumarsahaakshay examiningthestateofenergypovertyinrwandaaninterindicatoranalysis
AT waliumarugarba examiningthestateofenergypovertyinrwandaaninterindicatoranalysis