Mazenda, Adrino2026-03-032026-03-032025-12Mazenda, A. 2025, 'Multidimensional energy poverty and food insecurity nexus in Gauteng and Western Cape provinces of South Africa', Energy Research & Social Science, vol. 130, art. 104460, pp. 1-12. https://doi.org/10.1016/j.erss.2025.104460.2214-6296 (print)2214-6326 (online)10.1016/j.erss.2025.104460http://hdl.handle.net/2263/108714DATA AVAILABILITY : The data is obtained from Statistics South Africa. General Household Survey 2021 [dataset]. Version 1. Pretoria: Statistics SA [producer], 2021. Cape Town: DataFirst [distributor], 2022. doi:10.25828/7h7t-df42Energy poverty and food insecurity are interconnected challenges that disproportionately affect vulnerable populations in low and middle-income countries. The paper utilised the Tobit regression model to examine the relationship between household food insecurity and various socioeconomic factors, and the Structural Equation Modelling (SEM) to examine the complex interactions between multidimensional energy poverty (MEP), (measured by the MEP fuzzy score) and food insecurity, measured by the Household Food Insecurity Access Scale (HFIAS), mediated by various socioeconomic factors. A total of 6484 households were analysed, categorised by region and gender, in both metropolitan and non-metropolitan areas of the Gauteng and Western Cape provinces of South Africa, using data from the 2021–2022 South African General Household Survey. The Tobit regression findings showed that higher household income consistently reduces food insecurity. A larger household size and increased MEP contribute to higher levels of food insecurity, with the effect of MEP being significant in the Western Cape. Social grant recipients remained more food insecure, suggesting persistent vulnerability. Spatial differences emerged, with higher food insecurity in metropolitan areas of the Western Cape but lower in Gauteng. The SEM analysis revealed that energy poverty has a direct and significant impact on food insecurity, with household income serving as the strongest mediating factor. Education, employment, and household size contributed modest indirect effects, while other socio-economic variables showed weak or inconsistent mediation. The paper highlights the importance of adopting integrated policy approaches that simultaneously address energy access and food security, with a focus on gender and spatial inequalities.en© 2025 The Authors. This is an open access article under the CC BY-NC-ND license 4.0.Energy povertyFood insecurityGenderSocioeconomic factorsMetropolitan and non-metropolitan areasMultidimensional energy poverty (MEP)Tobit analysisStructural equation modellingSouth Africa (SA)Structural equation modelling (SEM)Article