<mark>difficulty of capturing it with single indicators, several scholars propose to create multidimensional indicators. A recent review by S</mark> iksnelyte-Butkiene <mark>(2021) for 1994 to 2020 identified 34 indicators. For example, Day et al. (2016) embark on energy poverty from a capability framework. Similarly, Sokolowski et al. (2020) constructed a multidimensional indicator based on five dimensions of energy deprivation in Poland. Bollino and Botti (2017) generated a multidimensional energy poverty indicator based on five indicators from EU-SILC data, which is in line with this methodology. Similarly, Bouzarovski and Herrero (2016) constructed an energy poverty index for EU countries using three indicators from the EU-SILC (European Union, 2016). Pelz et al. (2018) evaluate several additional indicators that try to capture the multidimensional nature of energy poverty and evaluate their strengths and weaknesses. Further work exploring multidimensional energy poverty indicators in the EU is by Maxim et al. (2016) and Herrero and Bouzarovski (2014), among others. Other practical examples are from the Philippines (Mendoza et al., 2019), several countries in Asia (Lan et al., 2022), Africa (Nussbaum et al., 2012), and Uganda (Ssennono et al., 2021), among others.</mark>

**<mark>While multidimensional measures of energy poverty have gained increasing attention in the literature, these measures are also subject to downfalls.</mark>** <mark>Nussbaum et al. (2012) evaluate composite energy poverty metrics and conclude that these measures are useful but might be subject to important methodological shortfalls. While they help incorporate multidimensionality into one metric, there needs to be a consensus around which indicators to include, how to weigh them, and which thresholds to use for each subdimension to define energy poverty.</mark>

# 2.7. Big Data

**<mark>More recently, big</mark> and open-source data started to play a significant role in direct measures of energy consumption, whic** **<mark>h can also shed light on energy poverty and vulnerability.</mark>** <mark>Access to satellite data has facilitated the measurement of direct indicators without the necessity of implementing time-consuming home visits t</mark> o employ <mark>sensors or meters. X</mark> inyi et al. (2018) use freely available satellite data to construct a residential building stock model for energy consumption. Similarly, Fehrer and Krarti (2018) use nighttime light data to generate US electricity and fuel consumption maps. Sun et al. (2022) developed a building energy efficiency prediction algorithm to predict the energy efficiency of buildings by combining data from the Energy Performance Certificate (EPC) database and Google Street View (GSV) building façade images in the case of Glasgow. Another example is by Berger and Worlitschek (2018), who generate energy end-use data for heating in the residential sector by combining data on population, norm temperatures, energy supply, consumption data, and buildings. Table 2 summarizes this evidence.

_Table 2: Measurement strategies for energy consumption of buildings using open-source and satellite data – 4 examples_

|**_Authors/Source_**|**_Summary_**|**_Data_**|**_Place_**|
|---|---|---|---|
|Xinyi et al. (2018)|Definition<br>of<br>representative<br>buildings of a built-up residential<br>building stock, using satellite<br>images and cluster analysis<br>methods, to generate energy Use<br>Intensity (EUI)indicators|-<br>Satellite data (Google<br>Earth)<br>-<br>Ground survey|People’s<br>Republic of<br>China<br>(Districts)|
|Sun et al. (2022)|Application of deep learning-<br>based framework usingstreet|-<br>Energy<br>Performance<br>Certificate(EPC)<sup>32</sup>|Glasgow, UK|

32 This type of information is also gathered in Bulgaria, at least for new buildings and old buildings that undergo major renovations (iBroad, 2018).

19