**Our review reveals that direct measures of energy poverty are less used in the European Union, although the recent introduction of smart meters could change that.** Given the limited availability of datasets on temperature or energy services, direct measures are more challenging to implement. Gathering this data involves visiting households and taking stock of indoor temperatures, for example, a procedure that might be burdensome. In addition, it is challenging to select appropriate standards, given that they might be subjective by nature. Also, standards can vary by context and individual factors (Palma and Gouveira, 2022). One potential solution to the lack of reliable data on energy consumption is the introduction of smart meters (European Commission, 2023b). Although smart meters potentially increase access to timely and more accurate information on energy usage, they are still subject to shortfalls, such as energy sources that are not metered (e.g., wood, heating oil, coal) (Frei et al., 2021). Research to date has mainly relied on temperature sensor deployment (ibid).

**Based on our review, energy poverty indicators are mainly only available at aggregated levels; generating them at disaggregated levels would be useful as tackling energy poverty requires local policy engagement.** The European Commission recommends a local approach to tackle energy poverty, meaning that solutions must be adapted to national and local conditions. Therefore, it is worth generating more data and information on local (disaggregated) energy poverty. The geographical distribution and identification of energy poverty are critical for tackling energy poverty. The Energy Poverty Advisory Hub (2022) advises tackling energy poverty via a three-step process at the local level.<sup>21</sup> Given the local approach towards energy poverty, there is a need for indicators at the disaggregated geographic level. Palma and Gouveira (2022) review the literature on subnational analyses around energy poverty. They found that studies mainly focused on Southern Europe. While they do not mention any studies on Bulgaria, some generated insights could serve as inputs to generate geographically disaggregated analyses in Bulgaria.

**Although there is consensus that the geographical (subnational) distribution and identification of energy poverty is vital for mitigation, most subnational analyses rely on census data, and energy poverty indicators are often not collected as part of the population census.** According to the review by Palma and Gouveira (2022), most subnational studies rely on data collected as part of the national census, although energy poverty indicators are often not included. While other studies take advantage of HBS or EU-SILC for regional analyses, the data is usually only representative at the NUTS 1 or NUTS2 level (depending on the country). Therefore, direct estimates for higher disaggregation levels are unreliable (ibid). Small-area estimates of energy poverty are needed but not necessarily available. Alternatively, many studies incorporating subnational indicators included in the review by Palma and Gouveira (2022) rely on new surveys collected by researchers. Nevertheless, the increasing usage of big data and satellite data generates new opportunities for subnational analyses of energy poverty.<sup>22</sup> National research institutes, for example, might be able to share meteorological data and facilitate the creation of subnational energy poverty indicators based on climate data.

21 The first step is an energy poverty diagnosis. During this first phase, policy makers should try to understand local energy poverty more closely. The second step is the design of a Local Climate Plan, which is a plan of how to best tackle energy poverty by taking an integrated approach at the local level. It involves setting up a detailed strategy and defining short- and long-term goals. The last step is the implementation of the Local Climate Plan. 22 In this context, the World Bank is exploring the development of a methodology for small-area estimates of energy poverty, under a DG Regio Trust Fund. The project aims to establish a methodology to measure energy poverty at the sub-national level using small-area estimation techniques and assess data availability across selected member states. The methodology will use a specific indicator of energy poverty (e.g., utility arrears, the share of energy expenditures within the household budget) and will then develop and apply an appropriate empirical methodology to project estimates of energy poverty from survey data into census data.

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