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, Nandor Verba

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
66 Downloads (Pure)


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.
Original languageEnglish
Article number3857
Number of pages21
Issue number15
Publication statusPublished - 28 Jul 2020

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • rural electrification
  • fuel stacking
  • fuel choice
  • multinomial logistic regression model
  • Rural electrification
  • Fuel choice
  • Fuel stacking
  • Multinomial logistic regression model

ASJC Scopus subject areas

  • Control and Optimization
  • Energy (miscellaneous)
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment


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