Lilia Karpinska

The recent energy crisis and the preceding COVID-19 pandemic impose danger on the well-being of the most vulnerable groups, causing the spread of energy poverty across Europe. This study examines the level and dynamics of energy poverty in 2019, 2021, and 2022, focusing on macro-regions (NUTS1) in 23 European countries. The data is aggregated from the EU Survey on Income and Living Conditions conducted with households in all EU countries. Following the Mazziotta-Pareto approach, we put forward a new comprehensive measure of energy poverty comprising several non-substitutable indicators with the possibility of conducting intertemporal comparisons. Latent hierarchical models are built to assess the variability within and between countries. Our results indicate that 2019–2022, European regions faced diverse levels and dynamics of energy poverty within countries. Bulgaria, Lithuania, Greece, and Portugal experienced the most hardship. We note substantial regional variability and opposite dynamics in Romania, Portugal, Italy, and Greece, which calls for a mindful regional cohesion policy. While our models provide evidence of poverty convergence across regions, national policies must balance regional disparities.

A key challenge of Fit for 55 will be reducing household emissions. For this, it is necessary to understand the factors that influence emissions and the specific role of household income. This study tests the validity of the environmental Kuznets curve (EKC) for households and links carbon emissions with income, housing, energy sources, and hidden energy poverty. We use individual household data from the Polish energy consumption survey complemented with data from a budget survey both compiled by Statistics Poland. The EKC is estimated in two scenarios, i.e. current energy mix, and zero-carbon electricity. Our model is based on the least trimmed squares technique. The study confirms the existence of the inverted U-shaped EKC in households, which is especially relevant for owners of small houses. This finding might be explained by the energy under-consumption often experienced by the poorest households, whereas wealthier households usually live in energy-efficient buildings and use less carbon-intensive fuels. The turning point is reached at 373 euros of income and 3425 kg of carbon emissions in a usual energy mix scenario compared to 560 euros and 5104 kg in a clean electricity scenario. Apart from income, household carbon emissions are interrelated with house size and hidden energy poverty, which should be in policymakers focus.

Objective: The author’s goal is to portray energy poor households in Poland based on several well-recognised and original indicators and some clustering techniques. In this study, I check whether the target population is identified correctly and I show possible directions in which the state energy poverty policy might evolve in this regard. Research Design & Methods: The ten-percent energy poverty measure, the ability-to-keep-home-warm, and the hidden-energy-poverty measures are used to examine the profiles of the energy poor. My source of data is the energy consumption module of the Household Budget Survey collected by the Polish statistical office in 2018. The statistical techniques include multiple linear regression, lasso regression, partitioning around medoids procedure, and hierarchical clustering, among other things. Findings: All indicators produce different rates of energy poverty, but they are consistent in describing the energy poor groups. Two similar clusters are obtained. The first group is composed mostly of retired single women occupying blocks of flats. The second group is represented mainly by working men living in families with children in stand-alone houses in remote areas. Implications / Recommendations: Although politicians might choose an energy poverty measure which gives the convenient level of energy poverty incidence, the profile of the target population does not change much. The above implies that regardless of the approach to estimating energy poverty, the profiles obtained in this study should be considered as a target population for policy actions. Contribution / Value Added: Energy poverty in Poland is often linked to low-stack emissions coming from the residential sector. The owners of single-family houses are the main target of many state programmes aimed at improving the air quality in the country as well as fighting energy poverty. In this study, I show that there are at least two target groups. The results are robust with regard to energy-poverty measuring.

Raising concerns about the effectiveness of the energy poverty policy actions in Poland, such as Clean Air and Stop Smog, brings forward the need to apply different strategies to identify the energy poor. More than 13.7% of Polish households were energy poor in 2018 according to the ability-to-keep-home-warm indicator. This study proposes enhancing the model-based approach to measure households’ energy poverty. Our goal is to assess regional vulnerability to energy poverty in Poland. The study relies on three national datasets and is conducted in two steps. The Energy Consumption Survey (2018) and the Household Budget Survey (2018) provide data for modeling household’s energy poverty in the first step. The Local Data Bank (2019) gives information on the potential factors increasing regional vulnerability to energy poverty evaluated in the second step. We apply multiple linear regression to identify energy-poor households and principal components analysis to examine the regions’ vulnerability factors. As a result, we produce several maps showing the spatial distribution of vulnerability to energy poverty in 380 Polish districts. Our results indicate that some northern, southern and eastern districts in Poland are primary targets of energy poverty policy actions.

A comparative analysis of energy poverty transitions and persistence can provide valuable suggestions for long-term policy actions. This study examines the dynamics of energy poverty in 17 European countries based on the longitudinal household data from the EU Survey on Income and Living Conditions, waves 2015–2018. The study pursues two goals. First, we explore households’ chances of transitioning in and out of energy poverty in each country following the discrete-time Markov process. On average, the probability to stay in energy poverty is 51.5%, and there is a lot of heterogeneity across countries. Households in Bulgaria, Greece, Romania, and Lithuania are quite close to the energy poverty trap. Second, we identify factors that help energy-poor households leave energy poverty. Demographic, technical, and socio-economic factors are the drivers in escaping energy poverty, which suggests common EU policy.