Rural energy poverty: An investigation into socioeconomic drivers and implications for off-grid households in the Eastern Cape Province, South Africa

Lesala, Mahali Elizabeth; Mukumba, Patrick; KeChrist, Obileke Article Rural energy poverty: An investigation into socioeconomic drivers and implications for off-grid households in the Eastern Cape Province, South Africa Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Lesala, Mahali Elizabeth; Mukumba, Patrick; KeChrist, Obileke (2025) : Rural energy poverty: An investigation into socioeconomic drivers and implications for off-grid households in the Eastern Cape Province, South Africa, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 5, pp. 1-16, https://doi.org/10.3390/economies13050128 This Version is available at: https://hdl.handle.net/10419/329408 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Angeliki N. Menegaki Received: 25 February 2025 Revised: 15 April 2025 Accepted: 5 May 2025 Published: 9 May 2025 Citation: Lesala, M. E., Mukumba, P., & KeChrist, O. (2025). Rural Energy Poverty: An Investigation into Socioeconomic Drivers and Implications for Off-Grid Households in the Eastern Cape Province, South Africa. Economies,13(5), 128. https://doi.org/10.3390/ economies13050128 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Rural Energy Poverty: An Investigation into Socioeconomic Drivers and Implications for Off-Grid Households in the Eastern Cape Province, South Africa Mahali Elizabeth Lesala * , Patrick Mukumba and Obileke KeChrist Renewable Energy Research Niche Area, Computational Science/Physics, University of Fort Hare, Alice 5700, South Africa; [email protected] (P.M.); [email protected] (O.K.) *Correspondence: [email protected] Abstract: Energy poverty is a significant barrier to sustainable development, limiting access to modern energy solutions and exacerbating socioeconomic inequalities in South Africa. This research identifies key socioeconomic factors contributing to energy poverty among off-grid households using the household-specific energy poverty line. A crosssectional study was conducted using a well-structured questionnaire among 53 households. The findings reveal significant gender disparities, with female-headed households being more vulnerable to energy poverty, which continues to subject them to economic hardship and social marginalization. Additionally, while larger households generally face higher energy demands, they were found to be less likely to experience energy poverty. The findings also challenge the ‘energy ladder hypothesis’ by showing that education, while potentially enabling better energy awareness, does not guarantee improved energy access in off-grid areas due to infrastructural limitations. Social grant dependency was found to be strongly correlated with energy poverty, underscoring the inadequacy of income transfers in addressing the systemic barriers to energy access. The findings emphasize the need for multidimensional, gender-responsive policy interventions that address both infrastructural and socioeconomic barriers to energy access, particularly in rural South Africa. These insights are crucial for developing targeted interventions to alleviate energy poverty and foster sustainable development in off-grid communities. Keywords: energy access; energy poverty; off-grid households; socioeconomic drivers 1. Introduction Energy poverty is widely understood as a form of deprivation from adequate essential energy services like heating, cooling, lighting, and household appliance power (Lozano & Taboada,2020). This issue is particularly acute in developing regions like ub-Saharan Africa, where millions of households lack both the financial means to afford modern energy services and the physical infrastructure needed for reliable access (Ritchie et al.,2019). Economic downturns, such as the global financial crisis and the 2020 COVID-19 pandemic, have worsened this situation, pushing over 100 million people into extreme poverty in the region (Masuku,2024;World Bank,2020). Consequently, poor and low-income households continue to struggle to meet basic energy needs such as cooking, heating, and lighting, limiting their opportunities for education, income generation, and overall socioeconomic development (Lesala et al.,2024). South Africa is no exception, with energy poverty deeply rooted in socioeconomic and geographic disparities limiting access to essential services and economic opportunities. Economies 2025,13, 128 https://doi.org/10.3390/economies13050128 Economies 2025,13, 128 2 of 16 Many rural and peri-urban areas in South Africa still lack access to clean energy. Even as about 86% of the population has access to electricity (Longe,2021), the remaining 14% who live primarily in rural areas still experience energy insecurity. Though income inequality does not act as a barrier to accessing energy, it has rendered modern energy services too expensive for many low-income households (Sarkodie & Adams,2020), with the poor spending about 27% of their income to energy (Lu et al.,2020;van Niekerk et al.,2022). The South African challenge is further worsened by the fact that policies aimed at alleviating energy poverty, such as free basic electricity (FBE), primarily benefit households already connected to the grid. As a result, off-grid rural communities remain excluded from such support, deepening their energy insecurity. The slow pace of grid expansion continues to disproportionately affect rural areas (Masuku,2024), leaving many communities without access to modern energy. Without targeted interventions, rural communities’ risk being trapped in a cycle of chronic energy poverty, posing serious challenges to South Africa’s broader development goals. However, for years, the discussion of energy poverty in South Africa has received limited attention. Energy poverty was often overshadowed by broader discussions on general poverty and national development challenges, treated as just another aspect of socioeconomic deprivation rather than a distinct challenge requiring targeted solutions. This approach overlooked the unique struggles of off-grid rural communities, where geographical isolation and infrastructural neglect further compound energy access issues. As a result, the literature surrounding energy poverty was sparse, with little focus on the specific experiences and needs of those living in energy-deprived conditions. However, recent years have witnessed a significant shift in this narrative. A growing body of research in South Africa has begun to highlight the significance of energy poverty and its multifaceted nature. This transition is particularly pertinent to the rising energy costs, which have placed a financial burden on households already struggling to make ends meet, while also affected by the frequent power outages or load-shedding that disrupts their daily activities (Isandla Institute,2024). As the urgency to address this phenomenon becomes evident, studies have begun to explore its various dimensions, including regional disparities, offering insights into its nature and the factors contributing to persistent energy deprivation. Notable works of Ye and Koch (2021) advanced the discussion by employing the household level energy poverty line to determine the both the prevalence and severity of energy poverty among South African households. Their results showed that South African households not only suffer from energy poverty but that the severity of energy poverty is disproportionately experienced by poor households. Building on this, Ye and Koch (2023) examined energy poverty from a multidimensional perspective and showed significant urban–rural inequalities in the access and affordability of energy. Their study noted that rural households have more difficulty accessing clean energy and are hit harder by affordability concerns. Mgwambani et al. (2018) utilized survey data from the community of Louisville in Mpumalanga and found that many households reported dissatisfaction with their energy sources since they were concerned about costs rising with a decrease in income, which contributed to the reliance on traditional fuels such as firewood for cooking, heating, and lighting. Studies by Masekela and Semenya (2021) and Netshipise and Semenya (2022) revealed similar trends in Ga-Malahlela and Thulamela (Limpopo), thus advocating for the adoption of traditional fuels. Their results showed that while electrification efforts continued, socioeconomic factors like low income, lack of education, and access to free basic electricity services persisted in promoting the use of firewood. Similarly, Oyekale and Molelekoa (2023) reported equivalent scenarios in the Western Cape and KwaZulu-Natal Economies 2025,13, 128 3 of 16 regions, notably with space heating, indicating that poor electricity access is prevalent in many areas of South Africa. Ismail and Khembo (2015) used data from the National Income Dynamics Survey (NIDS) carried out in 2012 and identified several important predictors of energy poverty, including household expenditure patterns, race, education, household size, and electricity access. Their findings also revealed that, despite progress in electrification, many households, particularly in rural areas, remain reliant on traditional fuels such as firewood due to affordability constraints and other socioeconomic factors. Ngarava et al. (2022) also revealed specific vulnerabilities, particularly among femaleheaded households in Black/African rural communities, where energy poverty is worsened by a combination of factors such as gender, race, and income inequalities, which compels greater reliance on traditional fuels. Ningi et al. (2020), on the other hand, using the Multidimensional Energy Poverty Index (MEPI), found that households in the Melani village in the rural areas of the Eastern Cape were generally energy secure. They found that energy security in this community was closely determined by marital status, household size, electricity affordability, and income sources. However, these results seem less reflective of off-grid, remote rural areas, where energy deprivation is more keenly felt due to the absence of modern energy infrastructure, and restrict their relevance to the off-grid, non-electrified communities. Dinis et al. (2023) expanded on this, particularly emphasizing its interaction with structural injustices and geographic isolation. Their analysis revealed that current definitions of energy poverty, as reflected in SDG indicators, do not effectively address the larger characteristics of affordability, dependability, and sustainability. They claim that weak policy frameworks that ignore systemic imbalances and a lack of modern energy infrastructure worsen energy scarcity in off-grid environments, disproportionately affecting rural, vulnerable people. Building on this, Lesala et al. (2023) explored the energy poverty of the off-grid remote community of the Upper Blinkwater. A compelling observation emerges from their findings, indicating that despite the lack of grid electricity, alternative energy sources like paraffin, liquified petroleum gas (LPG), and firewood have provided households in this community with some level of functional energy access and enabled them to meet some basic energy needs. However, relying on these sources does not equate to access to modern energy services. Such services are essential for driving socioeconomic transformation. This highlights a critical gap in understanding the reality of off-grid areas, where energy poverty is shaped not only by access but also by the quality and sustainability of energy sources. The findings in Lesala et al. (2023) challenge the common understanding of energy poverty, which typically centers on the absence of electricity and other modern energy services, without considering the broader dynamics in energy use in off-grid communities. This highlights the urgent need to further explore energy poverty in remote, off-grid communities. Such exploration would extend the conceptual boundaries of energy poverty and articulate the experiences of vulnerable rural communities completely disconnected from modern energy services. This study shifts the discourse from simplistic measures of energy access towards a more comprehensive understanding of the lived realities of households in off-grid communities. While many definitions focus on electricity affordability (Ngarava et al.,2022; Ningi et al.,2020;Ye & Koch,2021), energy poverty in this study is not merely understood as the absence of grid electricity, but also as a multifaceted condition in which households lack access to clean, affordable, and sustainable forms of energy necessary to meet basic needs such as cooking, lighting, and heating. Drawing from Lesala et al. (2023), where other energy sources like paraffin and LPG provide some degree of energy security, the definition also considers the quality, efficiency, and sustainability of the energy sources Economies 2025,13, 128 4 of 16 and the socioeconomic constraints that hinder the transition of households to modern energy systems. This approach moves beyond conventional binary definitions and is consistent with broader definitions that recognize both material deprivation, referring to the lack of necessary infrastructure, and capability deprivation, which is characterized by limited income, access, and choices as critical dimensions of energy poverty in off-grid communities. It highlights the importance of exploring the broader socioeconomic conditions that shape rural and off-grid energy realities. It is against this backdrop that the present study is undertaken, aiming to explore the factors influencing energy poverty in the remote, off-grid community of Upper Blinkwater. This study contributes to the body of literature by providing rare insight into the lived experiences and drivers of energy poverty in remote, marginalized communities that are often overlooked in national energy policy discourse and large-scale energy access surveys. It provides a broader and more reflective understanding of energy poverty in off-grid areas; as such, it informs policies that are better suited to the specific needs of similar communities. Additionally, the study supports broader efforts aimed at improving the quality of life for vulnerable populations by offering practical insights for policymakers seeking to expand energy access and alleviate energy poverty in remote areas. The remainder of this article is structured as follows: Section 2describes the methodology used in this study, including the data collection process and analytical framework. Section 3presents the results, highlighting the key determinants of energy poverty in Upper Blinkwater. Section 4offers a discussion of the findings, connecting them to broader policy and development implications. Finally, Section 5concludes the article with recommendations for targeted interventions and suggestions for future research on energy poverty in off-grid rural communities. 2. Methodology 2.1. Description of the Study Area The study was carried out in the small, isolated rural settlement known as Upper Blinkwater within the Raymond Mhlaba Municipality. Upper Blinkwater is situated at coordinates 32 ◦ 34 ′ 46.7 ′ ′ S and 26 ◦ 33 ′ 33.8 ′ ′ E at an elevation of approximately 900 m above sea level. The Municipality is characterized by dispersed settlements that make accessibility and infrastructure particularly challenging. As a result, poverty and unemployment levels are amongst the highest in Africa, with many households reliant upon social grants as their primary source of income (Ravanbach et al.,2019). Upper Blinkwater is home to some 67 households, mostly of Xhosa ancestry, housing approximately 254 people in total. The inspiration for selecting this community for this study stems from it being the first community to be identified by the Provincial government of the Eastern Cape as an intended beneficiary of a renewable energy pilot project to introduce a hybrid mini grid to meet rural electrification challenges in the Eastern Cape province, and in South Africa. Two extremely remote communities were identified, including Upper Blinkwater, which was ultimately selected due to its relatively easier accessibility. Although the project had not been implemented at the time of this study, early engagement with the community revealed a strong awareness of the limitations of their current energy sources and an expressed interest in modern energy services. As such, the project represents both the intervention and a point of reference for understanding the broader implications of energy poverty in remote areas. However, this study does not evaluate the outcomes of the mini-grid intervention but rather examines the determinants of energy poverty. Figure 1shows the location of the community of Upper Blinkwater, Raymond Mhlaba Municipality within the Amathole District Municipality in the Eastern Cape province within South Africa. Economies 2025,13, 128 5 of 16 Economies2025,13,xFORPEERREVIEW5of17  withintheAmatholeDistrictMunicipalityintheEasternCapeprovincewithinSouthAfrica.  Figure1.UpperBlinkwaterlocation.Source:(Kühneletal.,2021;Lesalaetal.,2024). 2.2.ResearchDesign Theresearchdesignofthisstudyisquantitative,whichallowsfordatatobesystematicallycollectedandanalyzed,andthusbeingabletoidentifyandquantifywhatattributescontributetoenergypovertywhilehighlightinghowtheymaybeimpactinghouseholdaccesstoandusageofenergy.Usingthismethodofresearchensuresgeneralizability, meaningthatresultsobservedinasmallsamplecanbegeneralizedtoalargergroupof peopleorwithincomparablesituations,makingthestudymorerelevantandpractical.As aresult,theconclusionscanbeconsideredvalidandreliable,whichinturnformsastrong basisforevidence-basedpolicyrecommendationsanddecision-making. 2.3.DataCollection Thedatacollectionprocessincludeddevelopingaquestionnairethroughathorough examinationoftheexistingliteratureonenergypovertyandhouseholdenergyconsumption.DataforthisstudywerecollectedinNovember2019.Thequestionnairecontained householddemographics,household-leveldataonenergyexpenditures,includingallexistingformsofenergy.Pre-testingwasconductedinthecommunityforquestionnaireand contentvalidityandthenecessaryadjustmentsweremadeaccordingly.Toensureunderstandingoftheprocess,rights,andexpectations,thesurveywasconductedinXhosa,the SouthAfrica Figure 1. Upper Blinkwater location. Source: (Kühnel et al.,2021;Lesala et al.,2024). 2.2. Research Design The research design of this study is quantitative, which allows for data to be systematically collected and analyzed, and thus being able to identify and quantify what attributes contribute to energy poverty while highlighting how they may be impacting household access to and usage of energy. Using this method of research ensures generalizability, meaning that results observed in a small sample can be generalized to a larger group of people or within comparable situations, making the study more relevant and practical. As a result, the conclusions can be considered valid and reliable, which in turn forms a strong basis for evidence-based policy recommendations and decision-making. 2.3. Data Collection The data collection process included developing a questionnaire through a thorough examination of the existing literature on energy poverty and household energy consumption. Data for this study were collected in November 2019. The questionnaire contained household demographics, household-level data on energy expenditures, including all existing forms of energy. Pre-testing was conducted in the community for questionnaire and content validity and the necessary adjustments were made accordingly. To ensure understanding of the process, rights, and expectations, the survey was conducted in Xhosa, the local language. Respondents were also made to understand that participation was entirely voluntary, and assured of respect for their privacy. Economies 2025,13, 128 6 of 16 Since the Upper Blinkwater community is small, we aimed to include all 67 households in the survey because it is challenging to obtain a representative sample due to the small population size. Sample size classes of this magnitude often result in even smaller samples, further limiting the generalizability of any conclusions drawn (Faber & Fonseca,2014; Korngiebel,2015). To address this limitation, Korngiebel (2015) recommends assessing the entire population to reduce distortion in the findings. Following this recommendation, all 67 households in Upper Blinkwater were considered eligible for the survey. However, due to availability constraints, interviews were conducted with 53 heads of household. Data were cleaned extensively after collection then analyzed using STATA software version 15. Descriptive statistics (frequency distributions, percentages, and mean values) were used in addition to regression analysis to investigate the main drivers of energy poverty in Upper Blinkwater. 2.4. Data Analysis The primary focus of this study is to identify the drivers of energy poverty. While previous research has already established the energy poverty status of households (Lesala et al.,2023), this study builds upon those findings by examining the key factors contributing to energy poverty. The Foster–Greer–Thorbecke (FGT) approach was used to determine whether households in this community were energy poor or not. First, FGT derives the energy poverty line and uses the per capita energy expenditure. Unlike the traditional fixed thresholds, such as the 10% expenditure rule, which assumes an equal energy burden across all households, and fails to account for variations in income levels, consumption patterns, and household sizes (Ye & Koch,2021), the FGT approach enables a more accurate and context-specific classification of energy poverty by accounting for differences in household income, size, and energy consumption patterns, rather than relying on one-size-fits-all thresholds. Although the FGT method not only identifies whether a household is energy poor but also captures varying degrees of deprivation, revealing both the depth and severity of energy poverty, for the purposes of this study, the focus is solely on determining whether or not a household is energy poor. This binary classification is essential for analyzing the socioeconomic and demographic factors that influence a household’s energy poverty status. Households falling below the FGT-derived energy poverty line, regardless of the extent of their deprivation, are therefore treated uniformly as energy poor in the subsequent regression analysis. To explore the underlying determinants of energy poverty, a probit regression model was employed. Given that the dependent variable is binary, indicating whether a household is energy poor or not, the probit model is well-suited for this type of analysis. It allows for the estimation of the probability that a household falls below the energy poverty line, based on a set of observed socioeconomic and demographic characteristics. The probit model was preferred over other binary response models such as logit models, due to its underlying assumption of latent variable formulation and its frequent application in welfare and poverty analysis literature, where the normal distribution is often considered more appropriate. Following Greene (2012), the model is specified as: Y* =∑n i=1βiXi+ϵ=β1X1+β2X2+· · · +βnXn+ϵ(1) where Y* represents the dependent variable, βi represents the parameter to be estimated, Xi represents specific household characteristics, and ϵ is the error term assumed to follow a standard normal distribution (ϵ~ N(0,1)). The observed outcome Y is binary: Y=(1 if Y* >0(indicating energy poverty), 0, otherwise Economies 2025,13, 128 7 of 16 The probability that a household falls below the established energy poverty line (P(Y=1)) is expressed as P(Y=1)=Φ(βX+ϵ)(2) P(Y=0)=1−Φ(βX+ϵ)(3) where P(Y = 1) represents the parameter to be estimated, that is, the probability that a household is below the energy poverty line (indicating poverty incidence), Φ is the cumulative distribution function of the standard normal distribution, and Y* is a latent variable indicating whether the expenditure of a particular household falls below the consumption poverty line. This probability indicates the likelihood that specific household features positively or negatively influence the risk of being energy poor. Within this framework, coefficients βi associate with each factor and represent the direction and magnitude of influence on energy poverty. Furthermore, post-estimation analysis, specifically marginal effects, was also performed to interpret the of all the independent variables on the probability of energy poverty which provides more straightforward insight into the possible targeted policymaking to reduce energy poverty of the community. Explicitly, the model variables are expressed in Table 1. These indicators provide a comprehensive understanding of how various socioeconomic factors influence households’ ability to access reliable and affordable energy in the Upper Blinkwater community. Table 1. Variable description. Variable Description Y Energy poverty status (1 if energy poor households; 0 otherwise) X1Gender of household head (1 if male, 0 for female) X2Age of household head (continuous variable) X3Household size (number of individuals in the household) X4Formal education of the household head (1 if formally educated, 0 if not) X5Employment status of the household head (1 if employed, 0 if not) X6Dwelling type (1 for brick house, 0 for mud house or other types) X7 Log of social grant amount (natural log of the amount received from social grants) To further test the interpretability and robustness of the probit model estimates, we computed the average marginal effects (AME) after estimation. Marginal effects quantify the change in the predicted probability of being energy poor for a one-unit change in each of the explanatory variables, when other variables are held constant. This approach not only complements the probit coefficients but also serves as a robust check by providing a more intuitive feel for the direction and magnitude of effects. 3. Results 3.1. Energy Expenditure Patterns Table 2presents an analysis of the energy expenditure patterns among households in the community. The data highlights the types of energy sources used and their associated costs, shedding light on the financial burden of energy access. Table 1provides a detailed breakdown of energy expenditure across different energy types, offering insights into the diversity of energy sources used and their associated costs. On average, households spend ZAR 248 (approximately USD 13) monthly on energy, with significant variation (standard deviation of ZAR 160, or USD 8.40), reflecting differing energy needs and access. The highest expenditure is on liquefied gas, with a mean of ZAR 104 (approximately USD 5.45), indicating its importance as a primary energy source for some households. This Economies 2025,13, 128 8 of 16 is followed by paraffin (ZAR 50 or USD 2.62) and wood (ZAR 49 or USD 2.57), suggesting that traditional and transitional fuels still play a role in meeting energy needs. Table 2. Household energy expenditure breakdown (ZAR and USD). Variable Mean (ZAR) Mean (USD) Std. Dev Wood Expenditure 48.50 2.57 21.70 Paraffin Expenditure 50.47 2.67 49.86 Liquefied Gas Expenditure 104.15 5.50 107.39 Candles Expenditure 34.42 1.87 21.57 Generator Expenditure 10.23 0.54 33.84 Total Energy Expenditure 247.75 13.09 160.49 Per Capita Energy Expenditure 92.40 4.88 78.17 Note: Conversion assumes an exchange rate of 1 USD ≈ 18.9 ZAR (prevailing rate at time of writing this article). Source: Authors’ computation, 2025. The low average expenditure on generators (ZAR 10 or USD 0.52) suggests that few households rely on this source, which is likely due to its high operating costs. On a per capita basis, households spend an average of ZAR 92 (approximately USD 4.82) on energy, with expenditures ranging from ZAR 10 (USD 0.52) to ZAR 316 (USD 16.54). These findings highlight the varying degrees of energy access and affordability within the community, which are critical for understanding and addressing energy poverty. 3.2. Energy Poverty Prevalence Table 3presents the results indicating the distribution of households based on their energy poverty status, providing a foundational understanding of the extent of energy poverty. This classification forms the basis for the subsequent analysis, which applies a probit regression model to examine the factors contributing to energy poverty in the community. Table 3. Energy poverty statistics. Energy Poverty Frequency Percentage Energy Poor 20 37.7% Not Energy Poor 33 62.3% Total 53 100% Source: Authors’ computation, 2025. The data showed that 37.7% of households were experiencing energy poverty. This included those who were severely or moderately energy poor. These households not only lacked access to modern energy sources but also faced other challenges like low or unstable income, large household sizes, and a heavy reliance on traditional fuels such as firewood. These factors made it harder for them to meet their basic energy needs. On the other hand, 62.3% of households (33 in total) were not classified as energy poor. This finding reflects that these households met their basic energy needs through consistent access to alternative sources such as paraffin and LPG. However, this does not mean they enjoy full energy security; instead, it indicates that they have managed to cope within the constraints of an off-grid environment, often through relatively higher per capita income levels, diversified income sources, or sustained support from social grants. Their ability to afford energy alternatives enables them to avoid severe deprivation, but they remain exposed to the limitations of using less efficient, less sustainable, and often more expensive energy sources. Thus, while these households are not suffering from the worst forms of Economies 2025,13, 128 15 of 16 Acknowledgments: We would like to express our sincere appreciation to the RNA Renewable (Wind-Biogas) of the Department of Research and Innovation at the University of Fort Hare for their academic support. We also extend our gratitude to the community of Upper Blinkwater, the local municipality, and government institutions, including the Department of Mineral Resources and Energy (DMRE), the Socioeconomic Development unit, and the Provincial Government of the Eastern Cape, for their invaluable support and contributions to this research. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript: AME Average marginal effects FBE Free basic electricity LPG Liquified petroleum gas MEPI Multidimensional Energy Poverty Index NIDS National Income Dynamics Survey SDGs Sustainable Development Goals STATA Statistics and Data Statistical Software USD United States Dollar ZAR Zuid-Afrikaanse Rand (South African Rand) References Chen, K., & Feng, C. (2022). Linking housing conditions and energy poverty: From a perspective of household energy self-restriction. International Journal of Environmental Research and Public Health,19(14), 8254. [CrossRef] [PubMed] Clancy, J. S., Skutsch, M., & Batchelor, S. (2003). The Gender-Energy-Poverty Nexus: Finding the energy to address gender concerns in development. Department for International Development (DFID). Dinis, M. A. P., Ngarava, S., Zhou, L., & Chari, M. M. (2023). Editorial: Sustainability challenges around energy poverty in an era of global environmental and economic crisis. In Frontiers in environmental science (Vol. 11). Frontiers Media SA. [CrossRef] Faber, J., & Fonseca, L. M. (2014). How sample size influences research outcomes. Journal of Orthodontics,19(4), 27–29. [CrossRef] [PubMed] Greene, W. H. (2012). Econometric analysis. Prentice Hall. Isandla Institute. (2024). Backyarding: Energy poverty and the shift to renewable energy. Isandla Institute. Ismail, Z., & Khembo, P. (2015). Determinants of energy poverty in South Africa. Journal of Energy in Southern Africa,26(3), 66–78. [CrossRef] Korngiebel, D. M. (2015). Addressing the challenges of research with small populations. American Journal of Public Health,105(9), 1744–1747. [CrossRef] Kühnel, S., Ahmed, T., Mehta, K., Ehrenwirth, M., Besner, R., Knepper, K., Ileka, H., Trinkl, C., & Zörner, W. (2021). Holistic approach to develop electricity load profiles for rural off-grid communities in sub-Saharan Africa. Proceedings-ISES Solar World Congress 2021, 348–361. [CrossRef] Lesala, M. E., Makaka, G., & Mukumba, P. (2024). Exploring off-grid energy choices: Household decisions in Upper Blinkwater, South Africa. Energies,17(14), 3556. [CrossRef] Lesala, M. E., Shambira, N., Makaka, G., & Mukumba, P. (2023). The energy poverty status of off-grid rural households: A case of the Upper Blinkwater community in the Eastern Cape Province, South Africa. Energies,16(23), 7772. [CrossRef] Longe, O. M. (2021). An assessment of the energy poverty and gender nexus towards clean energy adoption in rural South Africa. Energies,14(12), 3708. [CrossRef] Lozano, L., & Taboada, E. B. (2020). Demystifying the authentic attributes of electricity-poor populations: The electrification landscape of rural off-grid island communities in the Philippines. Energy Policy,145. [CrossRef] Lu, Y., Khan, Z. A., Alvarez-Alvarado, M. S., Zhang, Y., Huang, Z., & Imran, M. (2020). A critical review of sustainable energy policies for the promotion of renewable energy sources. Sustainability,12(12), 5078. [CrossRef] Masekela, M. E., & Semenya, K. (2021). Factors influencing the use of firewood post-electrification in rural South Africa: The case of Ga-Malahlela village. Journal of Energy in Southern Africa,32(3), 24–40. [CrossRef] Masuku, B. (2024). Rethinking South Africa’s household energy poverty through the lens of off-grid energy transition. Development Southern Africa,41, 467–489. [CrossRef] Economies 2025,13, 128 16 of 16 Mgwambani, S. L., Kasangana, K. K., Masekameni, D., Makonese, T., Gulumian, M., & Mbonane, T. P. (2018, April 3–5). Assessment of household energy poverty levels in Louiville, Mpumalanga, South Africa. International Conference on the Domestic Use of Energy (DUE), Cape Town, South Africa. [CrossRef] Mofokeng, P. P. (2021). Changing gender roles in the household: A case study of Lamontville in KwaZulu-Natal Province, South Africa [Masters dissertation, University of KwaZulu-Natal]. Netshipise, L. F., & Semenya, K. (2022). Evaluating factors influencing firewood consumption in households at the Thulamela Local Municipality, South Africa. Journal of Energy in Southern Africa,33(2), 48–62. [CrossRef] Ngarava, S., Zhou, L., Ningi, T., Chari, M. M., & Mdiya, L. (2022). Gender and ethnic disparities in energy poverty: The case of South Africa. Energy Policy,161, 112755. [CrossRef] Ningi, T., Taruvinga, A., & Zhou, L. (2020). Determinants of energy security for rural households: The case of Melani and Hamburg communities, Eastern Cape, South Africa. African Security Review,29(4), 299–315. [CrossRef] Nussbaumer, P. (2012). Energy for sustainable development—An assessment of the energy-poverty-development nexus [Ph.D. thesis, Universitat Autònoma de Barcelona]. OXFAM South Africa. (2024). Prospects for an eco-feminist just energy transition in South Africa (pp. 1–48). OXFAM. Oyekale, A. S., & Molelekoa, T. C. (2023). Multidimensional indicator of energy poverty in South Africa using the fuzzy set approach. Energies,16(5), 2089. [CrossRef] Pachauri, S., Mueller, A., Kemmler, A., & Spreng, D. (2004). On measuring energy poverty in Indian households. World Development, 32(12), 2083–2104. [CrossRef] Pachauri, S., Van Ruijven, B. J., Nagai, Y., Riahi, K., Van Vuuren, D. P., Brew-Hammond, A., & Nakicenovic, N. (2013). Pathways to achieve universal household access to modern energy by 2030. Environmental Research Letters,8(2), 024015. [CrossRef] Percept Actuaries and Consultants. (2023). Understanding gender inequality. Inclusive Society Institute, Western Cape, South Africa. Ramaswamy, S., Varghese, C., Kaveri, V., Mohan, S., & Anandan, M. (2024). Quest for clean energy: Critically assessing energy choices and ladder theory. IJNRD2401274 International Journal of Novel Research and Development,9(1), 574–584. Available online: www.ijnrd.org (accessed on 3 May 2025). Ravanbach, B., Makaka, G., Shambira, N., Ernest Van Dyk, E., Schultz, R. D., Vorster, F. J., Agert, C., Von Maydell, K., Hanke, B., Weigel, O., Maebe, S., Mcmaster, A., & Ntuta, F. (2019, November 25–27). A systematic approach to development of key performance indicators for PV hybrid mini-grids. 6th Southern African Solar Energy Conference, East London, South Africa. Ritchie, H., Rosado, P., & Roser, M. (2019). Access to Energy. Our World in Data, 1–16. Available online: https://ourworldindata.org/ energy-access#article-licence (accessed on 3 May 2025). Sarkodie, S. A., & Adams, S. (2020). Electricity access and income inequality in South Africa: Evidence from Bayesian and NARDL analyses. Energy Strategy Reviews,29, 100480. [CrossRef] Sovacool, B. K. (2012). The political economy of energy poverty: A review of key challenges. Energy for Sustainable Development,16(3), 272–282. [CrossRef] Sovacool, B. K., & Griffiths, S. (2020). Culture and low-carbon energy transitions. Nature Sustainability,3(9), 685–693. [CrossRef] van Niekerk, A., Kimemia, D., Seedat, M., & Annegarn, H. (2022). Energy impoverishment and burns: The case for an expedited, safe and inclusive energy transition in South Africa. South African Journal of Science,118(3–4), 13148. [CrossRef] [PubMed] World Bank. (2020). COVID-19 to add as many as 150 Million extreme poor by 2021: Press release NO. 2021/024/DEC-GPV. Available online: https://www.worldbank.org/en/news/press-release/2020/10/07/covid-19-to-add-as-many-as-150-million-extreme -poor-by-2021 (accessed on 3 May 2025). Ye, Y., & Koch, S. F. (2021). Measuring energy poverty in South Africa based on household required energy consumption. Energy Economics,103, 105553. [CrossRef] Ye, Y., & Koch, S. F. (2023). Towards accessibility or affordability? Multidimensional energy poverty across the South African urban–rural divide. Energy Research and Social Science,97, 103002. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.