Understanding Household Fuel Choice Behaviour in the Amazonas State, Brazil: Effects of Validation and Feature Selection

Kojo Sarfo Gyamfi, Elena Gaura, James Brusey, Alessandro Bezerra Trindade and Nandor Verba
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Kojo Sarfo Gyamfi: Centre for Data Science, Coventry University, Coventry CV1 5FB, UK
Elena Gaura: Centre for Data Science, Coventry University, Coventry CV1 5FB, UK
James Brusey: Centre for Data Science, Coventry University, Coventry CV1 5FB, UK
Alessandro Bezerra Trindade: Department of Electricity, Federal University of Amazonas (UFAM), AM 69067-005 Manaus, Brazil
Nandor Verba: Centre for Data Science, Coventry University, Coventry CV1 5FB, UK

Energies, 2020, vol. 13, issue 15, 1-21

Abstract: Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This paper presents findings from surveys that were carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and more generally discuss the validity and robustness of such models in the context of statistical validation and evaluation metrics. Specifically for the Amazonas communities considered in this study, the research showed that the fuel choice determinants are the age of household, the number of people at meals each day, the number of meals daily, the community, education of the household head, and the income level of the household. Moreover, given the Brazilian policies related to energy and sustainability, this region is not likely to reach the Sustainable Development Goals proposed by United Nations for 2030.

Keywords: rural electrification; fuel stacking; fuel choice; multinomial logistic regression model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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