Life quality is an important indicator of sustainable development. Farmers’ quality of life strongly affects the farm’s viability. The main goal of this study was to identify the relationships between three main components of farmers’ quality of life: economic situation, living conditions, and mental comfort. In the first phase of the study, the theoretical model representing potential relationships between quality of life components was constructed, and in the second phase the relationships were verified with the use of the structural equation modelling method. The sample consisted of 600 farmers participating in the Polish Farm Accountancy Data Framework (FADN). In the analyses, data from the FADN database and data obtained during supplementary interviews with farmers were used. The analyses revealed that living conditions are significantly and positively correlated with the economic situation and mental comfort, while mental comfort turned out to be negatively correlated with the economic situation. It was concluded that future agricultural and rural development policies should be more focused on the social dimension of sustainable development than before, particularly considering the fact that a farmer’s higher engagement in the improvement of their family’s economic condition might result in lowering their mental comfort due to the stress and work overload.

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.

Research on household energy poverty is crucial for addressing basic human needs, highlighting the importance of conducting studies across different countries. This study examines energy poverty in the Visegrad Group countries using 2022 data from the EU Statistics on Income and Living Conditions survey, with the ‘inability to keep the home warm’ indicator as a measure. The study aims to identify socio-economic factors influencing energy poverty and examine how their impacts differ across the examined countries. Logistic regression, combined with machine learning techniques, was applied for this purpose. Furthermore, the study evaluates the classification results of logistic regression and three machine learning methods—CatBoost, Balanced Random Forests, and Extreme Gradient Boosting—on imbalanced data. It was found that, among the three machine learning methods used, Balanced Random Forests performed the weakest. Logistic regression, effective for our imbalanced data, complements the results and provides deeper insights into the socio-economic factors influencing energy poverty. The study found that Slovakia had the highest percentage of households vulnerable to energy poverty, while Czechia had the lowest. Income, household type, and the presence of disabled individuals were found to be important across all countries. However, other factors varied in their influence from one country to another, highlighting the need for country-specific analyses. Monitoring households’ exposure to energy poverty is a challenge for future social policy and the use of different methods provides an in-depth view of this complex issue.

The paper focuses on severe housing deprivation and its components—overcrowding and housing conditions problems. It is based on the European Union Statistics on Income and Living Conditions (EU-SILC) 2019 survey data. The paper analyzes this data on households in Poland—a country with significant problems in terms of housing deprivation. Three dimensions of housing deprivation are examined: experiencing only overcrowding, experiencing only housing conditions problems, and being severely housing deprived. The study aims to investigate three-dimensional housing deprivation depending on the urbanization level of living places and other socioeconomic characteristics of Polish households. The multinomial logit model was used to assess relative risk ratios for explanatory variables to achieve these purposes. It was found that some socioeconomic characteristics significantly influenced one dimension but not another. Moreover, the relative risk ratios for given characteristics sometimes are greater than one for a particular dimension and less than one for another. Specifically, a significant difference in housing conditions problems between households living in towns and rural areas was found. However, there are no significant differences in overcrowding and severe housing deprivation, given that all other explanatory variables are fixed. Furthermore, considering the relative risk as a ratio of the probability of experiencing the given dimension of housing deprivation and the probability of not being housing deprived at all, households in cities compared with rural households were more likely to be overcrowded and to experience severe housing deprivation but less likely to have housing conditions problems.

Food security (FS) is influenced by primarily financial but also sociodemographic factors. Identification of correlates of food insecurity (FI) is a crucial issue in the context of achieving sustainable development goals. The aims of the study were: (1) to recognize FI in the selected Central and Eastern European (CEE) countries, (2) to examine common socioeconomic and demographic characteristics for FI. The analysis used the set of eight-item FI indicators adopted by the Food and Agriculture Organization, applying the Gallup World Poll survey data from 2017 to 2019. Multinomial logistic regressions were used to examine FI at mild and moderate or severe levels compared with FS. Differences in the profiles of FI were observed in analyzed countries: Poland, Lithuania and Slovakia. Lithuanians experienced the lowest FS, and Slovaks the highest. The FI status was associated with education, gender, age, household composition and income. It was found that the impact of these factors was not the same in the examined countries. Differences in profiles of FI in CEE countries indicate the need to analyze the problem individually for each country. Identifying groups particularly vulnerable to FI may allow appropriate targeting of instruments counteracting FI and adapt them to people with different characteristics.