Developing a Fuzzy MCDA-Based Multidimensional Index to Measure Energy Poverty in Developing Countries

The COVID-19 pandemic highlighted the critical need for reliable energy access, underscoring energy poverty as a significant barrier to social well-being and sustainable development. This study develops a comprehensive multidimensional index to assess energy poverty in developing countries using a hybrid approach that integrates fuzzy set theory with Delphi, DEMATEL, and ANP methods. By evaluating 33 indicators across five dimensions—availability, adequacy, affordability, cleanability, and convenience—this study refines the list to 19 key indicators through expert consensus and constructs a cause-and-effect relationship among them. The analysis identifies electricity consumption per capita as the most critical indicator of energy poverty. The index is applied to 119 developing countries, revealing stark disparities, with Chile having the lowest and Chad the highest energy poverty scores. The findings emphasize the need for targeted interventions and policy measures to enhance energy access and reduce socioeconomic inequalities, thereby supporting the achievement of Sustainable Development Goal 7 (SDG 7). This study offers a robust framework for policymakers and researchers to address energy poverty effectively in the post-COVID-19 era.