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Multidimensional energy poverty in Colombia: A department-level review from 2018 to 2022. | LitMetric

Multidimensional energy poverty in Colombia: A department-level review from 2018 to 2022.

Heliyon

Javeriana University, Faculty of Humanities and Social Sciences, Department of Legal and Political Science, Calle 18 No. 118-250, Cali, Colombia.

Published: July 2024

AI Article Synopsis

  • - The article evaluates energy poverty across Colombia from 2018 to 2022 using a Multidimensional Energy Poverty Index (MEPI) based on 28 variables across seven dimensions, as proposed by researchers Nussbaumer et al.
  • - Data was collected through the National Quality of Life Survey (ECV) managed by DANE, employing a weighting method that includes subjective assessments and an entropy technique for component variables.
  • - Findings indicate a rising trend in energy poverty, particularly in municipal capitals, with notable disparities between urban and rural areas and higher poverty levels in remote regions, guiding policymakers in evaluating the impact of current energy-related programs.

Article Abstract

This article aims to measure energy poverty in Colombia in its thirty-two departments and its capital city from 2018 to 2022, using a composite approach. To achieve this, a Multidimensional Energy Poverty Index (MEPI) was designed, according to the methodology proposed by Nussbaumer et al. (2012; 2013) [1,2]. Twenty-eight variables were used, which were distributed across seven dimensions, and recorded by the National Quality of Life Survey (ECV, Spanish acronym), administered by the National Administrative Department of Statistics (DANE) of Colombia. In addition, a nested weighting method was used to assign weights within the index. Subjective weights were given to the dimensions, and an entropy method was used for each of the component variables. The results show that energy poverty has an increasing trend in Colombia throughout the period, especially in the municipal capitals. There are significant differences between urban and rural areas in all territories, and the departments located in the most remote areas of the country have a higher energy poverty. This is consistent with the low population density, as well as with off-grid areas. The results obtained will allow decision makers to conduct a preliminary evaluation of the management and effects of the specific public policy programs and plans that have been implemented in the different territories of the country.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11315106PMC
http://dx.doi.org/10.1016/j.heliyon.2024.e34395DOI Listing

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