Energy Poverty Index Research Articles

Pobreza Energética en Chorrera Mirador: Implementación y Monitoreo de un Sistema Híbrido Solar-Eólico

This work studies energy poverty in the community of Chorrera Mirador, located in Chimborazo, Ecuador. Energy poverty, defined as the inability to cover basic energy needs such as heating, cooking, and refrigeration, affects rural areas of Latin America. In Ecuador, this problem is exacerbated in rural areas, where more than half of the population suffers from it. The article proposes a methodology to measure energy poverty using the multidimensional energy poverty index (MEPI), which evaluates multiple conditions associated with this problem in households. The results reveal moderate energy poverty in the community, which justifies the implementation of a hybrid solar and wind renewable energy system (SHER) to mitigate it. The SHER was sized based on the daily energy demand and the solar and wind resources available in the area. A data collection system is included to monitor the voltage stability and power production of the system. The results demonstrate the functionality of the system and its ability to satisfy the energy demand of the community. The study concludes that the implementation of the SHER, together with the use of low-consumption lighting elements, contributes to mitigating energy poverty in the Chorrera Mirador community, improving the quality of life of its inhabitants. In addition, the data collection system allows for long-term analysis, which makes it easier to identify possible improvements and strategies to optimize its operation

Household Energy Poverty in European Union Countries: A Comparative Analysis Based on Objective and Subjective Indicators

The study aims to assess household energy poverty in European Union (EU) countries, comparing them based on the Objective Energy Poverty Index and the Subjective Energy Poverty Index. The Objective Energy Poverty Index is derived from indicators such as energy expenditure share, risk-of-poverty rate, and electricity prices. The Subjective Energy Poverty Index includes indicators such as the inability to keep the home adequately warm, arrears on utility bills, and bad housing conditions. Both indices aggregate the indicators mentioned above using equal and non-equal weighting approaches. The analysis uses country-level data from 2019 to 2023 sourced from Eurostat. The findings indicate considerable variation in household energy poverty across the EU, with more pronounced inequalities in subjective indicators than objective ones. Additionally, the study reveals a weak correlation between the Objective Energy Poverty Index and the Subjective Energy Poverty Index, leading to differing country rankings based on these indices. However, the choice of weights in constructing the energy poverty indices does not significantly impact a country’s energy poverty ranking. The paper also identifies countries where household energy poverty decreased in 2023 compared to 2019 and those where it increased. Regarding the Subjective Energy Poverty Index, Croatia and Hungary showed the most notable improvement in their rankings among European countries, while France, Germany, and Spain deteriorated their positions. According to the Objective Energy Poverty Index, Bulgaria, Croatia, Portugal, and Spain demonstrated the most significant improvement, whereas Greece experienced a considerable decline.

Improving energy-related efficiency towards SDG7 in China: What role does energy poverty play?

The improvement of energy-related efficiency has promoted energy-sustainable development in China; however, it may be inhibited by energy poverty. This paper constructs an index system to measure China's energy poverty. Subsequently, we evaluate energy-related efficiency towards SDG7 comprised of energy production efficiency and energy sustainable utilization efficiency using a dynamic two-stage data envelopment analysis model. Furthermore, we adopt fixed effect models to quantify the impact of energy poverty on improving energy-related efficiency and the underlying mechanism. The findings indicate that: (1) China's energy poverty situation gradually improves from 2011 to 2020. The energy-related system's performance is medium due to the poor performance in its energy production stage in 30 provinces during 2011–2020. Energy-related efficiency is greater in areas with a higher response to SDG7. (2) The impact of energy poverty on improving energy-related efficiency is significantly negative. Addressing energy poverty significantly improves energy-related efficiency in regions characterized by a high energy poverty index and low energy-related efficiency. However, it bears no impact in regions with a low energy poverty index and high energy-related efficiency. (3) The moderating effect of technological innovation effectively improves energy-related efficiency, whereas the marketization level has an inverse effect. Consequently, this paper suggests several policy implications.

Geospatial Analysis of the Distribution of Energy Poverty in the Residential Sector in the Valencian Community

This study addresses energy poverty, a situation where households are unable to afford the minimum energy services required to meet their basic needs, a problem with profound implications for the well-being of vulnerable populations. To identify the most affected areas, a geospatial analysis of energy poverty distribution in the Valencian Community (Spain) was conducted using a correlation study between the global vulnerability index and the energy poverty index, calculated from residential energy certificates. The results highlight areas of significant social, economic, demographic, and residential vulnerability, as well as regions with very high or very low energy consumption. Specifically, regions with high energy poverty and vulnerability indices were identified, enabling targeted interventions. The study concludes that interventions in these identified areas are essential to mitigate the adverse effects of energy poverty and improve living conditions for affected populations. This research offers a novel methodology for mapping regional energy poverty, surpassing previous studies in precision for identifying and addressing vulnerable areas.

Energy poverty and health in Turkey: Evidence from Longitudinal data

This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.

Assessing rural energy poverty and early warning based on long-run evolution for clean energy transition in China

Rural China grapples with pervasive energy poverty. This study aims to measure China's rural energy poverty and propose early warning strategies. It establishes a rural energy relative poverty evaluation system based on four dimensions: energy service effectiveness, consumption cleanliness, management integrity, and development sustainability. Using the Analytic Hierarchy Process-Criteria Importance Though Intercriteria Correlation-Technique for Order Preference by Similarity to Ideal Solution model, it calculates rural energy poverty indices for Chinese provinces, while ward cluster analysis sets regional and hierarchical early warning criteria. Findings indicate: (1) China's rural energy poverty index varies widely, with a low range of 0.49–0.52 and a high range above 0.65. The top 14 regions average a rural energy poverty index of 0.62. (2) Over 2015–2021, there is a 9.70% decrease in the index, indicating a general downward trend. While rural energy services' efficiency and management integrity improve, consumption cleanliness and development sustainability decline. (3) Spatially, energy poverty is higher in the west and north, notably lower in the east and south. The eastern coastal and central regions exhibit significantly lower poverty levels due to better economic foundations and leading energy transformations. (4) Nine provinces are red warning areas, witnessing declining sustainability but improving service effectiveness, consumption cleanliness, and management integrity. Weak links in energy poverty vary across regions in terms of service effectiveness, consumption cleanliness, management integrity, and development sustainability. This study enhances the rural energy poverty evaluation system and proposes regional, hierarchical, and phased early warning standards.

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

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.

Foreign direct investment, Green Technological Innovation and Energy Poverty: Empirical evidences from Sub-Saharan African countries

This study investigates the unconditional and conditional effects of Foreign Direct Investment (FDI) and Green Technology Innovation (GTI) on Energy Poverty (EP) in 11 Sub-Saharan African (SSA) countries from 2000 to 2020. Given that EP captures the share of the population without access to energy, it includes electricity, clean fuel and technology for cooking (CFTP), renewable energy consumption (RECP), and the multidimensional energy poverty index (MEPI) which is computed using the PCA method. Furthermore, this study considere the overall, urban and rural supsamples, and the data from the World Bank and OECD databases. This study employs the FGLS and the Driscoll- Kraay approaches. The findings reveal that FDI and GTI negatively affect both electricity poverty and CFT poverty, whereas these effects are significantly positives for both REC poverty and MEPI. This means that, in the long term, FDI and GTI allow the alleviation of both electricity and CFT poverty; conversely, FDI and GTI strengthen renewable energy and MEPI irrespectively of the residential area. However, FDI-EP nexus is significanty affected through GTI development. Moreover, FDI and GTI contribute to strengthening the rural-urban poverty gap in access to both electricity and CFT. In light of these findings, policy implications are presented.

Analyzing variability in urban energy poverty: A stochastic modeling and Monte Carlo simulation approach

Urban energy poverty remains a critical challenge affecting millions worldwide, with significant implications for socio-economic development and sustainability. This study employs Monte Carlo simulations to analyze the variability in energy poverty, as measured by the Multidimensional Energy Poverty Index (MEPI). Through this approach, we explored how different socio-economic predictors affect MEPI scores across five scenarios: economic growth and diversification, energy transition and technological innovation, climate change impacts and vulnerability, policy intervention and social safety nets, and stagnation and rising inequality. The sensitivity analysis revealed that household income, size, and food security are paramount in influencing urban energy poverty levels. Scenarios involving economic growth and technological innovation demonstrated positive impacts on reducing energy poverty, while scenarios of climate change and economic stagnation highlighted the vulnerabilities and widening disparities within populations. The results suggest that to effectively alleviate urban energy poverty, policy interventions must be comprehensive, targeting the identified influential factors. The study emphasizes the need for targeted policy interventions on social safety nets to alleviate energy poverty.

Is larger always lekker? A comparative analysis of South Africa's water user associations (WUAs) and catchment partnerships (CPs) and their impact on water, energy, and food (WEF) security

Complexities in water, energy, and food (WEF) governance provide varied systems and pathways that shape welfare outcomes. The biggest question relates to the scale and complexities in resource management and governance, which reaches a certain equilibrium to achieve maximum welfare benefits. This is a proposition that is assessed in this study which sought to compare the WEF welfare outcomes of jurisdictions under Water User Associations (WUAs) and Catchment Partnerships (CPs). The study used a multi-stage purposively sampled cross-sectional survey of 1184 households from Greater Taung and Magareng Local Municipalities (Vaalharts Water User Association) and Matatiele Local Municipality (uMzvimvubu Catchment Partnership) in South Africa. The Household Water Insecurity Experiences, Household Multi-Dimensional Energy Poverty Index and Household Food In-Access Scale, Min-Max normalization and Propensity Score Matching were used to analyze the data. There was association between jurisdiction and the level of WEF security. Households under the jurisdiction of a WUA experienced 13.8%, 6.8% and 9.3% higher levels of water, energy, and food insecurity, respectively. The study concludes that households under the jurisdiction of a CP had higher levels of WEF security, with complexities or lack thereof resulting in higher welfare outcomes. Therefore, larger is not always lekker. The study recommends either (i) scaling down of WUA to CP and/or (ii) diversifying by increasing the breadth and scope of stakeholders within WUA. The governance systems should be integrated in an evaluation to ascertain any impact on the welfare outcomes.

Energy Poverty and Democratic Values: A European Perspective

This paper explores the complex relationship between energy poverty and the maintenance of democratic values within the European Union (EU), suggesting that energy poverty not only impacts economic stability and health outcomes but also poses significant challenges to democratic engagement and equity. To measure energy poverty, a composite index is developed using the entropy method, which surpasses traditional measures focused solely on access to energy or its developmental implications. To assess the level of democratic governance in EU countries, the voice and accountability index (VEA), which is part of the World Governance Indicators compiled by the World Bank, is utilized. By analyzing EU data from 2006 to 2022, the findings suggest that a 1% improvement in VEA quality, represented by a coefficient of 0.122, is correlated with a notable improvement in the energy poverty index. This suggests that the EU should focus on enhancing transparency and public participation in energy decision-making, along with ensuring accountability in policy implementation. The research also differentiates between full and flawed democracies, noting that tailored approaches are needed. In full democracies, leveraging economic prosperity and trade is crucial due to their significant positive impacts on the energy poverty index. In contrast, in flawed democracies, enhancing governance and accountability is more impactful, as evidenced by a higher coefficient of 0.193. Strengthening legal and regulatory frameworks, improving regulatory quality, and ensuring public engagement in governance could substantially mitigate energy poverty in these contexts. In addition, this paper demonstrates that this relationship is influenced by factors such as income inequality, energy intensity, and trade openness.

Regional disparity in energy poverty: A spatial analysis of Odisha

Energy poverty is one of the major deprivations that has direct effect on the well-being of a household at micro level. At macro level, it also has significant effect and hinders the sustainable development of a nation. The binary measurement, commonly used to identify the energy poor population, has major drawbacks as it only takes accessibility of the services into consideration, whereas, in the contemporary period more emphasis should be on the affordability, quality, reliability and legal implications of energy services. The present study, therefore, tries to estimate the energy poverty condition by following a multi-dimensional approach and also tries to identify the actual energy poor by analysing the spatial disparity prevalent in the state of Odisha. A multidimensional framework is used to capture different aspects of energy provisioning at household level and an energy poverty index is constructed by assigning equal weights for the parameters. Spatial disparity is measured by considering proxies for the social parameters and following Gini’s coefficient of disparity measurement. The results indicated persistence of disparity in energy poverty at regional, inter and intra-district levels.

Estimations of Multidimensional Energy Poverty in Tanzania

This study assesses the level of multidimensional energy poverty in Tanzania. The study uses descriptive and inferential statistics based on data from the 2015-2016 Tanzania Demographic and Health Surveys (DHS), as they provide the most comprehensive datasets for this analysis. The analysis revealed that 74.1% of households were in rural areas, while the remaining 25.9% were in urban areas. Moreover, the study found that 80.6% of household heads were considered multidimensionally energy-poor. For inferential statistics, the Alkire-Foster estimates found that 81.2% of households in Tanzania were multidimensionally energy-poor, with higher rates of energy poverty in rural areas (94.2%) and among female-headed households (84.0%). Moreover, the findings show that the dimension of modern fuel contributes the most to the Multidimensional Energy Poverty Index (MEPI) (43.6%) compared to the asset (17.9%) and education (38.5%) dimensions. The study recommends that Tanzania invest in expanding access to modern cooking fuels and electricity, improving asset ownership, and enhancing educational opportunities to address multidimensional energy poverty. We recommend that the government emphasize and focus on both renewable energy and energy efficiency development in the country by implementing energy initiatives based on National Energy Policy 2015 to increase access to modern energy services and share renewable energies in the electricity generation mix to enhance availability, reliability, and security of supply.

Energy Poverty and Subjective Wellbeing: Empirical Analysis in Indonesia

As stated in the Sustainable Development Goals, energy poverty is a severe problem that is a priority for the United Nations to eradicate. In general, the study on energy poverty is concerned with objective indicators. More study still needs to evaluate energy poverty with subjective indicators, especially in Indonesia. This study aims to analyze the relationship between energy poverty as measured by the Multidimensional Energy Poverty Index which is proxied by happiness. Based on consumer theory that an individual's utility in meeting their needs will influence their satisfaction, where satisfaction is closely related to happiness, it is hypothesized that there is a relationship between energy poverty, where someone has limited access to energy, which will harm their happiness—estimated using the panel fixed effect method using 17,918 individual data obtained from IFLS4 (2007) and IFLS5 (2014) survey data. These findings suggest that energy poverty is detrimental to personal happiness. Using firewood for cooking, not having access to electricity, and not having a refrigerator or TV are signs of low energy and significantly impact happiness. Access to energy is very influential in making everyday life easier, increasing productivity and quality of life for individuals.

Multidimensional energy poverty: A study of its measurement, decomposition, and determinants in Indonesia

This study estimated Indonesian households' Multidimensional Energy Poverty Index (MEPI) using Alkire-Foster's multidimensional poverty concept to capture the incidence, intensity, decomposition, and changes over time. The study used monetary and non-monetary variables to identify the availability, accessibility, affordability, consumption, and deprivation of modern energy services; and compared existing affordability indicators. Redundancy, robustness, and sensitivity tests were conducted with three weighting schemes and deprivation cut-offs. The study decomposed and determined household-head (HH) socio-economic, demographic, and geographic factors for MEP using the Logit, Probit, Tobit, and Heckman Selection models. The results show that the low-income and high cost (LIHC) was the most robust affordability indicator, followed by the ten percent rule (TPR). The complement-frequency weighting scheme gave the smallest and most robust MEPI compared to equal and principal component analysis (PCA) weighting. Three alternative deprivation cut-offs can show households as “vulnerable,” “moderately,” or “severely” energy poor. The MEP incidence decreased, but its intensity remained high and increased. Energy-poor households were averagely deprived of 55–60 % of all weighted indicators. The lack of modern cooking services was the primary cause. MEPI differed by geographical location and HH gender, education, business field, and employment status. Policies that boost education levels, raise household income, and increase the availability, accessibility, and affordability of modern cooking technology in the rural, hinterland, or non-coastal forested locations in the eastern islands of Indonesia may minimize the number of households experiencing MEP.

Predictive Modeling of Energy Poverty with Machine Learning Ensembles: Strategic Insights from Socioeconomic Determinants for Effective Policy Implementation

This study aims to identify the key predictors of the multidimensional energy poverty index (MEPI) by employing advanced machine learning (ML) ensemble methods. Traditional energy poverty research often relies on conventional statistical techniques, which limits the understanding of complex socioeconomic factors. To address this gap, we propose an approach using three distinct ML ensemble models: extreme gradient boosting (XGBoost)‐random forest (RF), XGBoost‐multiple linear regression (MLR), and XGBoost‐artificial neural network (ANN). These models are applied to a comprehensive dataset encompassing various socioeconomic indicators. The findings demonstrate that the XGBoost‐RF ensemble achieves exceptional accuracy and reliability, with a root mean squared error (RMSE) of 0.041, an R‐squared (R2) of 0.975, and a Pearson correlation coefficient of 0.992. The XGBoost‐MLR ensemble shows superior generalizability, maintaining a consistent R2 of 0.845 across both the testing and training phases. The XGBoost‐ANN model balances complexity with predictive capability, achieving an RMSE of 0.056, an R2 of 0.954 in the testing phase, and an R2 of 0.799 in training. Significantly, the study identifies “Education,” “Food Consumption Score (FCS),” “Household Food Insecurity Access Scale (HFIA),” and “Dietary Diversity Score (DDS)” as critical predictors of MEPI. These results highlight the intricate relationship between energy poverty and factors related to food security and education. By integrating the insights from these ML models with policy initiatives, this study offers a promising new approach to addressing energy poverty. It highlights the importance of education, food security, and socioeconomic factors in crafting effective policy interventions.

Spatial and temporal patterns of energy aid and poverty in four African countries: Focusing on distributive and recognition justice

Energy poverty and unequal access to electricity are critical issues in African countries, underscoring the importance of understanding energy aid, a significant determinant, and its allocation. This study employs a framework of energy justice to investigate the spatial-temporal patterns of energy aid and the Multidimensional Energy Poverty Index (MEPI) over two decades (2001–2020) in four African countries: Nigeria, Tanzania, Ethiopia, and the Democratic Republic of Congo. We first explore the realm of distributive justice by scrutinizing where energy aid concentrates. Then, to tackle the domain of recognition justice, we analyze whether regions in need have benefited from energy aid through a comparison of regional MEPI and aid spending. The results indicate that energy aid is focused toward capital cities or urbanized areas, and analogous patterns are evident in electricity distribution and renewable energy sectors. The persistence of such patterns results in a temporal continuum of regional energy imbalances, with many marginalized groups not receiving sufficient aid. These findings highlight the importance of considering spatial and temporal aspects in pursuing energy justice while advocating for an equitable aid allocation to ensure an energy-just world.

Measurement of energy poverty in the Colombian Caribbean region: a comparative analysis

This research work is directed to analyze the level of energy poverty and its consequences on the quality of life of the population of the Colombian Caribbean region, by doing a comparison of the results obtained in that area with data regarding the population of Bogotá, capital of Colombia, and of the rest of the country. The method of meeting absolute energy needs was used to determine the energy poverty index at households (EPH). Results obtained indicate that EPH exceeds 60% in urban areas, and 96% in rural zones, where it was also evidenced a clear link between energy poverty and school dropout.

Cold, dark and malnourished: a cross-sectional analysis of the relationship between energy poverty and household burden of malnutrition in sub-Saharan Africa

ObjectiveEnergy poverty contributes to the general well-being of households; however, there is representational paucity on its role in household nutrition across sub-Saharan Africa (SSA). This study examined the influence of...

Does off-farm work alleviate rural households’ energy poverty in China? Comparative analysis based on livelihood patterns

Energy poverty is a global problem, particularly complex and severe in rural areas of developing countries. To address this issue, a theoretical framework is constructed based on the energy ladder theory and family altruism theory, to discuss the role of off-farm work in alleviating energy poverty for rural households. Using data from the Chinese Family Panel Studies, this study identifies the off-farm work patterns and multiple energy poverty index (MEPI) of different rural households, then analyzes the off-farm work and the distance of off-farm work on the MEPI of rural households by employing Oprobit model and conditional mixed process method and verifies the mediating effect of household income. The results show that local off-farm work significantly alleviates the MEPI among rural households. This effect is particularly pronounced among entrepreneurial households and those residing in communities with a higher level of commercial development. On the other hand, the MEPI alleviation effect of distant off-farm work is relatively modest, and this mitigation effect disappears among households with left-behind students and those living in communities with lower levels of commercial development. Furthermore, both forms contribute to the alleviation of MEPI by enhancing household income.