- Inability indicates the percentage of the total population who cannot keep their homes adequately warm.

- Arrears is the percentage of the total population with arrears on utility bills (heating, electricity, gas, water) on time due to financial difficulties.

- Leakages capture the percentage of the population living in a dwelling with a leaking roof, damp walls, floors, or foundation, or rot in window frames or floors.

In this case, the higher the value of the EP index, the higher the degree of energy poverty. The researchers give the inability to keep the home adequately warm a higher value as they argue that this indicator has greater importance, as proposed in the literature (Thompson and Neil, 2013; Bouzarovski and Tirado Herrero (2017)). According to this indicator, nearly half of the population is affected by at least some dimension of energy poverty (Figure 15). In this case, a significant share (seven out of 10) of the poor based on the at-risk of poverty are also energy poor (Figure 16).

<!-- Start of picture text --> Figure 15: Multidimensional energy poverty rate Figure 16: Overlap of at-risk of poverty and<br>(2019/20) multidimensional energy poverty rate (2019/20)<br>45.0 42.8 100%<br>40.0 0.504<br>35.0<br>50%<br>30.0 0.258<br>0.068<br>25.0<br>0.170<br>0%<br>20.0<br>15.0 Neither nor<br>10.0 Only energy poor<br>5.0<br>Only monetary poor<br>0.0<br>Energy and monetary poor<br>Energy poverty index<br><!-- End of picture text --> _Figure 16: Overlap of at-risk of poverty and multidimensional energy poverty rate (2019/20)_ _Source: Own estimates based on EU-SILC (2020)_

# 3.4. Big Data

## **We lastly experiment with big data to explore the potential of this data type to measure energy**

**poverty.** We note that this approach is experimental and should be cautiously taken. Nighttime lights have been used to measure urbanization dynamics (Zhou, 2012), population, local economic activity, and energy use (Goldblatt, 2016; Shi et al., 2014; N. Zhao,2017; Amaral et al., 2005; Townsend et al. 2010, Coscieme et al. 2014; Shi et al., 2014; Fehrer and Krarti (2018)). We follow the approach of Fehrer and Krarti (2018) and argue that nighttime light satellite data could be used as a proxy for building energy use intensity. The methodology presented in this study offers a replicable approach globally, providing opportunities for geospatial energy analysis without the challenges associated with disaggregated building energy use data collection. 42 We use the data made available by Li and Zhou (2017). Applying zonal statistics, we calculate the mean nighttime light data per municipality and plot it in Figure 17.

42 The study addresses the challenge of limited data for geospatial analysis of energy use in buildings by proposing the use of satellite-derived information, specifically nighttime light imagery from the VIIRS instrument aboard the SUOMI NPP satellite. Researchers evaluated this imagery as a predictor of building energy use intensity across states, counties, and cities in the United States. The findings indicate that nighttime lights can explain a significant portion of the variability in energy consumption, reaching up to 90%, depending on conditions and geospatial scale. The results are applied to generate detailed electricity and fuel consumption maps for the United States at a resolution of less than 200 square meters.

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