The first attempts to establish EP also attempted to set the criteria for EP detection. This threshold value for energy expenditure is approximately 12 percent of household income, according to Isherwood and Hancock, who are the first names we come across in the literature (T. Emre 2020), (Isherwood 1979), (Liddell 2012). The 10% figure, which will unfailingly reach the same result in years of work, is in Boardman's book "Fuel Poverty, From Cold Homes to Affordable Warmth" published in 1991 (T. Emre 2020), (Boardman, Fuel Poverty: From Cold Homes to Affordable Warmth 1991), (Liddell 2012). Bradshaw said in 2008 that 71% of EP was already poor (T. Emre 2020), (Mahoney 2020), (Bradshaw 2008). This approach puts forward the idea that EP can be reached when the poor are detected. UK Department of Energy and Climate Change issued another document finding and using “the 10%” in 2010 (UK Department of Energy and Climate Change 2010), (T. Emre 2020). Although later, some specialists like Boardman and Dubois said that this study was a work that produced numbers to be nice, Hills said 10% criteria puts more population into the EP basket than reality and suggested additional criteria (T. Emre 2020), (Boardman, Fuel poverty synthesis: Lessons learnt, actions needed 2012), (Dubois 2012), (Hills 2012). Fahmy drew attention to his research about how many EP is in the category that meets the 10% criterion when pointing to Broadshaw's report, stating that 7% of consumers met the requirement (Fahmy 2011), (T. Emre 2020).
In today's circumstances, according to Boardman, real energy expenditure has risen uncontrollably, and even this threshold is difficult to meet (Boardman, Fuel poverty synthesis: Lessons learnt, actions needed 2012). According to Boardman, the lowest-income 30% of households in England invested 70% of the "required" normal energy expenditure in 2009 (T. Emre 2020), (Boardman, Fuel poverty synthesis: Lessons learnt, actions needed 2012). In the following studies, we can see that the guidelines were looked at in order to assess the EP cluster and that similar programs and pilot applications were completed. Costa-Campi at al confirmed energy poverty can be labelled through income level of the households (Maria T. Costa-Campi 2019). According to Li, Energy Development Index (EDI) and Multidimensional Energy Poverty Index (MEPI) were defined between 2002 and 2010 (Li 2014). As a different approach, Dagoumas emphasized unpaid invoices in its 2014 article (Dagoumas 2014). It is an important reminder that the main criterion should be the arrears on utility bills. Moore, for the first time, expressed the definition of absolute and relative poverty, which made him one of the most important names in the subject (Moore 2012).
While there is a description of spending over 10% of household income on energy expenses in the literature, according to Bouzarowski, one of the leading names in the field, it will be more fitting to define indicators that would represent the real situation in Eastern Block countries rather than this difficult to identify situation (S. T. Bouzarovski 2015). According to Bouzarovski, the energy-poor people spend more than usual on energy because of poor equipment (S. Bouzarovski 2012). This situation is like the older model vehicles consuming more fuel. Such finding was confirmed by the afford of Emeç et al of activities for the Turkish case (Emec 2015).
The EU Energy Poverty Observatory (EPOV), which will operate under the leadership of Thomson and Bouzarowski in 2016, has officially been put into operation. Not only does this center have access to data on EP metrics, but it also shares recent literature (T. Emre 2020). Thomson et al (Thomson 2017) identified three main methodologies of measurement:
-
Expenditure – where examinations of the energy costs faced by households against absolute or relative thresholds provide a proxy for estimating the extent of domestic energy deprivation;
-
Consensual approach – based on self-reported assessments of indoor housing conditions, and the ability to attain certain necessities relative to the society in which a household resides;
-
Direct measurement – where the level of energy services (such as heating) achieved in the home is compared to a cluster standard (Thema 2020).
According to them, researchers are mostly dependent on consensus evidence about the effects of energy poverty, such as power bill arrears and dampness in the household, and data accuracy must increase in order to progress the calculation of energy poverty on a European scale. This could involve amending existing variables so that they are more useful indicators, for instance changing from a binary response format to a Likert type scale to detect the frequency of the problems (Thomson 2017). In a 2017 article, Lenz and Grgurev claimed that people in certain EU countries, especially Bulgaria, Croatia, and Romania, were unable to adequately heat their homes. there are households in the EP cluster with a rate of 45% in Bulgaria, 14% in Romania, and 10% in Croatia (Lenz 2017: 7(2)). Bouzarovski, in his book published in 2018 (the book was written by Bouzarovski, Thomson, and Petrova and edited by Simcock), the subject was associated with energy prices, low household productivity conditions, and low income (S. Bouzarovski 2018). With a more comprehensive approach, it is impossible to deny that the problem has socioeconomic, regional, and political dimensions (T. Emre 2020)."Those who have an income below the threshold and consume above-average energy" (NEPIM, Wafzig,M.,Zimper,J. 2018) are described as "energy poor" in Austria. Seebauer at al studied on social housing policy to alleviate energy policy in Austria (Sebastian Seebauer 2019). Lakatos and Arsenopoulas studied a swot analysis for potential financial supporting programmes for the energy poverty population (Lakatos 2019). According to this study, 10%, etc. Such static methods can cause problems to be overlooked on a regular basis (T. Emre 2020).
Utility bill arrears are once again being highlighted by current sources. EPOV Member State Reports on Energy Poverty underlined the correlation between arrears on utility bills and affordability (EU Energy Poverty Observatory 2020). Regarding this report, it can be underlined that inability to keep the home adequately warm and arrears in utility bills trends are highly correlated. In this way, a simplification suggestion can be developed to focus on arrears on utility bills to catch the affordability cluster.
In Turkish literature, Bağdadioğlu analyzed the Household Budget Survey data in his report (Bağdadioğlu 2009). He proposed a 10% investment criterion for electricity and gas, a 3–5 percent criterion for water, and a 25% criterion for overall energy poverty, with no electricity poverty value calculated outside of the Southeastern Anatolia Region (Erdoğdu 2020). Kaygusuz stated that rural areas are the more vulnerable comparing economical conditions (K.Kaygusuz 2010). Ozcan et al offered a poverty cluster varying according to monthly income (Moore’s perspective) (Özcan 2013), (Eke 2018). The World Bank's 2015 report shows that energy poverty can exist in any consumer category. The percentage share of household electricity expenditures was divided into approximate categories for each consumption category in this study, and consumers were grouped according to monthly electricity consumption amounts. In this study, it was stated that “electricity expenditure in the lowest income group, which is the lowest income group for 100 kWh per month, which is accepted as the lowest consumption level, is 11% of the total household income. For the 150 kWh consumption group, this ratio is over 15% and some note that these values were 12% and 18% in the previous years” (World Bank 2015). The proposal to measure the threshold in terms of kWh electricity consumption was included in Tennakoon's report in 2008. In this study, the energy poverty threshold was found to be 120 kWh per month (D.Tennakoon 2008), (Eke 2018). Eke and Ayrancı devised an approach based on the provinces' average monthly consumption (Eke 2018). The threshold for energy poverty was set at 100 kWh in this method, and six provinces in the east and southeast Anatolia were listed as being energy poor. Selçuk et al stated accordingly the 2017 household budget survey data sets and their own survey results EP clusters cover ¼ of the total households, which means “Half of the lowest-income households face energy poverty”. It has been said that this rate was 36% of households in 2003 and decreased to 23% in 2017 (Selçuk 2019). PwC Report on EP stated each level of consumption has a unique EP threshold inside according to analysis through consumption data through 3 million consumers (T. Emre 2018).
In 2017, the Turkish government launched a new version of an existing social support system based on previous examples and best practices (Republic of Turkey Ministry of Family and Social Policy 2017). In 2019, Presidential Declaration derived support for about 2 million household electricity expenditure (Republic of Turkey Ministry of Family, Labour and Social Services 2019). Erdoğdu concluded that this figure is below the calculated number of poor households in his calculation based on 2018 TUIK data (Erdoğdu 2020). Parliamentary Records state that electricity utilities issued 330 million electricity bills to households in 2019. The 1.26% of this number was subject to power cut because of arrears. Utility bill arrears about 2 billion and 94 million TL (Grand National Assembly of Turkey 2020). According to the Turkish Minister of Energy and Natural Resources, 3.7 million consumers (out of 45 million) have had their power turned off due to utility bill arrears. The results for 2020 can represent a fluctuating EP population as well as an absolute EP population with over-use (Turkish Newspaper Sözcü 2021).
EPOV uses 28 different standards to determine EP (Thomson 2017). The number of criteria can be increased with today's technology, but their meaning should be questioned. The synchronicity of these data is a separate issue for indices like the European Union Statistics on Income and Living Conditions (SILC) and the European Energy Poverty Index (EEPI). The collection time of many evaluation criteria used from the relevant institutions and organizations is different and causes inconsistencies.
Thomson and Herrero stated that many data subject to be collected are not systematically evaluated. Moreover, “composite indices are hard to institutionalize both due to questions around how to assign weight-age and because the European Union (EU) policy-making environment prefers simplification of metrics” (Sareen 2020), (Espeland 1998), (Sebastien 2013). Mahoney stated that for EP level adjustment, which is tried to be determined over many indices, data collection is almost impossible and the accuracy of the collected data is questionable (Mahoney 2020). The most basic question is "what is the number of households?" Even the answer to the question is based on the figures or approaches of the official statistical institutions of the countries and is assumed correct.
Kose offered that poverty-health relationship id mostly related with the individuals economical conditions rather than regional conditions (T.Köse 2019).
The situation that has to be noticed above and above both of these issues is "utility bill arrears," which is the official indication of inability. The direct use of this data is a more simple and precise method of calculating the EP since it is based on energy demand, which is considered one of the most important human needs (Case name: Madan Lal v. State of Himachal Pradesh & ors. 2018).
While no official federal agency can say how many households exist in a region, the number of customers and invoices produced by all infrastructure firms, whether public or private, is certainly on a monthly basis. In case it is not used for heating, the invoice amount for electricity consumption does not make up the largest percentage of household expenses. Therefore, a debt for electricity and a power cut from debt is a sign of absolute poverty.
Poverty - Energy Poverty Relation
In literature, there are observations about the association between the general state of poverty and energy expenditure affordability, and these clusters are not the same. Poverty describes a lower socio-economic situation than energy poverty in the context. “Energy Poverty” might represent absolute or periodic inability for each consumer. In EP circumstances, households reduce their consumption for making economy, and this may affect their health since the living room temperature reduces in the cold winter days and nights. It should be understood that this state is long-lasting in cases of absolute poverty and absolute energy deprivation and that this situation periodically exits in the fluctuating state. Aside from the Law on protecting personal data, poor people do not want to be grouped psychologically. As a result, there are difficulties in reaching the list of the absolute poor. Since household income and expense statistics cannot be accessed simply, easily, or specifically due to secrecy concerns, and in order to avoid violations, absolute poverty reports are typically created by field visits and information gathering. The most obvious of these shreds of evidence are the debt bills.
Turkey, with its comprehensive social assistance program, must be one of the best examples of poverty/energy poverty support in the World. The boards of trustees founded in the provinces create the poor family list in the state registries, which is then created by local executive councils after on-site determinations (Republic of Turkey Ministry of Family and Social Policy 2017). The problem is all the personal information is secure even though the information allows bodies to support the families in possible needs. This means it is not possible to access data such as identity information, wage information, health information, asset information (title deed, rental income), expenditure information, debt information, credit information, bank records, which can be collected separately, within the same date range. Even if this data of all household members can be accessed, unregistered data are quite high. Non-public companies (distribution companies – DisCo) do not disclose certain types of information, especially trade secrets. Consumer payment habits, in their opinion, are an indication of a company's valuation process, which is very safe for an individual. We need to simplify the methodology and generate some policy-making activities in order to make the energy poverty cluster simpler, more basic, and comparable. The EPOV study emphasizes this condition, stating that debt bills are a significant source (EU Energy Poverty Observatory 2020).
Confirming the “problem of developed countries” approach (S. Bouzarovski 2018) for EP, other problems may arise in distribution regions, which have heterogeneous socio-economic structures. In these regions, illegal electricity consumption (non-technical loss & theft) is a problem. Psychologically, the inability afford to the energy used can turn into abuse and, the abuse can turn into habits and disrupt payment habits. This is how the proper payment habit turns into illegal electricity usage. The customer who cannot pay the electricity bill at the first time will make economy from consumption for a while, then accept that he cannot pay and switch to normal use, if he encounters an opportunity, he will start illegal use and eventually will come to the point of making illegal consumption more than he needs and wasting. This situation is a serious obstacle to determining the real need level.
Official statistical institutes prepare the socio-economical index of the regions. These indexes are useful for expressing poverty and EP for methodological purposes (EU-SILC is one of them (Sareen 2020)). In the Turkish case, the Turkish Ministry of Industry and Technology has released a study report with the name “2017 in the Provinces and Regions of the Socio-Economic Development Ranking Study-SEDI” (Regional Indicators of socioeconomic well-being) (Acar 2019). The Strong Principle Components Analysis approach was used to perform detailed analyses on 52 variables in the social, housing, education, health, competitive and creative capability, financial, accessibility, and quality of life clusters, and the provinces' growth indices were presented. This report is used in related studies as the most up-to-date and scientific source. According to this report, the development index of the provinces is between 4,051 points and − 1,788 points.
Definition of Electricity Arrears on Utility Bills and “debt” data
The term “debt” for electricity represents the receivable whose consumption is measured by a registered device, the invoice of which is made but not paid. The meter of an energy customer is read once a month, and an invoice is given. The invoice released has a 10-day due date. At the due date, the "receivable," which is the equivalent of consumption, becomes a "debt". After this date, a power-cut notice order is issued for the unpaid debt. According to the Electricity Distribution Company (DisCo) application, the notification can be sent using SMS, e-mail, etc. or the hard copy can be delivered to the address. If the debt has not been paid 10 days after the notification job order is issued, a power-cut job order is generated. According to this job order, when paying a loan, an individual whose power has been turned off will be charged an extra cut / open fee. Even then, if he does not pay his debt, execution proceedings are started.