In many European countries, energy poverty is measured on the basis of real energy bills, as theoretical energy costs are hard to calculate. The UK is an exception—the data inputs for the Low Income-High Cost (LIHC) indicator are based on reasonable energy costs, these data are collected through specially designed surveys, often an intensive and costly procedure. Approaches which calculate energy needs are valid when energy bill data are unreliable or where households restrict consumption. In this analysis, energy poverty levels are evaluated for Greece, the municipality of Évora (Portugal), and the Basque Country (Spain): energy bills are modeled based on building energy performance data and other energy uses, and adjusted according to socio-demographic variables. To this end, equivalization weights are calculated using socio-economic data from the aforementioned southern European countries/regions. Data are analyzed to compare measurements with actual versus modeled bills using the Ten-Percent Rule (TPR) and Hidden Energy Poverty (HEP) against twice the median (2M) indicator, enhancing the identification of households with low energy consumption. In conclusion, theoretical energy needs can be combined with socio-demographic data instead of actual energy bills to measure energy poverty in a simplified way, avoiding the problem of targeting households that under consume.

Energy poverty is a widespread problem across Europe with serious socioeconomic, environmental, political and health implications. The primary contributing factors are related to low-income levels, high energy prices, and energy inefficient housing. However, household characteristics, political, and social circumstances, and other drivers such as severe weather conditions also play a vital role. Due to the complex nature of the problem, previous research efforts have usually focused on a limited number of factors. This paper comprises the first attempt to give a holistic picture of the problem of energy poverty, using Greece as a case study, by means of Fuzzy Cognitive Mapping (FCM). The FCM model provides an insight into the energy poverty system’s structure and function. Thus, it may prove useful to policy makers interested in developing and testing alternative measures for tackling energy poverty. There are, however, certain shortcomings and challenges, which should be taken into account for future work.