*4.4. Germany-The Program for the Reduction of Interruptions in Electricity Supply*

Even in Europe's most powerful economy, Germany, there were 370,000 cases of disconnection from gas and power networks in 2017 alone. Implemented in the German state of North Rhine-Westfalia (in the fief of the Rhenish model of social market economy), the program includes energy suppliers, local authorities, institutions of social protection, but also non-governmental organizations. The program carries various activities such as offering advice to vulnerable consumers as well as providing legal representation in their relationship with energy suppliers and associated services, organizing debates, lobbying for domestic consumers, but also a series of other public relations activities. To date, the program was successful in offering legal and technical advice to a number of over 15,000 consumers, in preventing disconnection from the network for 80% of the households that benefited from advice and representation, in obtaining the revocation of over 60% of the already operated disconnections, and, ultimately, in raising public awareness about the fuel poverty issue [52].

### **5. Cross-Country Analysis**

In this part, a cross-country analysis regarding Granger causality between indicators representing all three dimensions was conducted. This is needed to see if there is a connection between indicators of the three dimensions, quality of life, energy poverty and renewables. The study starts from the hypothesis that there is a Granger causality between all selected variables. If one or more relations are found the number of indicators will be expanded in a future research in order to create a better connection chart between the three dimensions indicators. The selected indicators are the main used in each of the three dimensions. The period of the analysis is between 2010–2019 for the 28 EU member states. The indicators included in the analysis are:


In order to avoid autocorrelation, the data series were investigated for unit root using Levin, Lin & Chu t\*, Im, Pesaran and Shin W-stat, ADF-Fisher chi-squared, PP-Fisher chi-squared tests. For unit root tests, we used Schwarz info criterion, Newey-West automatic band width selection and Bartlett kernel.

To test causality relations between the variables, we started from the Granger (1969) hypothesis [53] that tested how much of the variable y may be deduced from the past values of x, and then asked if that, by adding the past values of x, we can obtain a better approximation of y. A variable y is Granger-caused by x when x improves the predictive capacity of y or when the past coefficients of variable x are statistically significant. It must be acknowledged that a two-way causality is a frequent case when x Granger causes y, and y Granger causes x.

In order to apply a Granger causality test, a Lag length 1 must be specified. Usually it is better to use more lags in order to get relevant information from the past. In our study, we tested the Granger causality relation between the variables for 1, 2 and 4.

After establishing the lag length, we estimated a bivariate regression of the form:

$$\mathbf{y}\_t = \mathbf{x}\mathbf{0} + \alpha \mathbf{y}\_{t+1} + \dots + \alpha \mathbf{y}\_{t-1} + \beta\_1 \mathbf{x}\_{t+1} + \dots + \beta\_1 \mathbf{x}\_{t-1} + \varepsilon\_t \tag{1}$$

$$\mathbf{x}\_{t} = \mathbf{x}\_{0} + \mathbf{a}\_{1}\mathbf{x}\_{t+1} + \dots + \mathbf{a}\_{l}\mathbf{x}\_{t-l} + \beta\_{1}\mathbf{y}\_{t+1} + \dots + \beta\_{l}\mathbf{y}\_{t-l} + \mu\_{t} \tag{2}$$

for each possible pair of (x,y) series of the group. F-statistic reported values were the Wald statistics for the consolidated hypotheses:

$$
\beta\_1 = \beta\_2 = \dots = \beta\_1 = 0 \tag{3}
$$

For each equation, the null hypothesis was that x did not Granger-cause y in the first regression and y does not Granger-cause x in the second regression.

In order to estimate the regression equation, it was started from the linear regression using the OLS method with panel data of the form:

$$\mathbf{Y}\_{\rm it} = \mathbf{f}(\mathbf{X}\_{\rm it}, \boldsymbol{\beta}) + \boldsymbol{\delta}\_{\rm i} + \mathbf{y}\_{\rm t} + \boldsymbol{\varepsilon}\_{\rm it} \tag{4}$$

This specific case implies a conditional mean linear specification so that we achieve:

$$\mathbf{Y\_{it}} = \alpha + \mathbf{X\_{it}'}\boldsymbol{\beta} + \boldsymbol{\delta\_i} + \mathbf{y\_t} + \boldsymbol{\epsilon\_{it}} \tag{5}$$

where Yit is the dependent variable, Xit is a regression vector k and it error terms for i = 1, 2, ... , M transversal units, observed for the dated periods t = 1, 2, ... , T. α is the general constant of the model while δ<sup>i</sup> and γ<sup>t</sup> represents the effects specific to transversal section or time period.

As Engle and Granger [54] pointed out that a linear combination of two or more non-stationary series may be stationary. If this happens the non-stationary series are said to be co-integrated. The test for co-integration Kao test [55] it was used because it is suited for panel data. In Table 4 results of the testing for unit root are presented for all variable included in the analysis.


**Table 4.** Unit root tests.

Source: authors calculation based on data from Eurostat.

After testing for the unit root, it can be observed that electricity prices for households (PR) was the only variable that did not have a unit root at level, while the inability to keep the home adequately warm (WARM), arrears on utility bills between (ARR), gross domestic product (GDP) and share of renewable energy in gross final energy consumption (RENEW) had a unit root at level, but they became stationary at the first difference. In this case the decision was to take all variables at the first difference.

Before analyzing the Granger pairwise causality tests results, the correlation matrix should be inspected for the analyzed variables. In Table 5, the correlation matrix is presented.


**Table 5.** Correlation matrix.

Source: authors calculation based on data from Eurostat.

As can be seen from the correlation matrix, there was a very strong positive correlation between gross domestic product (GDP) and share of renewable energy in gross final energy consumption (RENEW), and a strong negative correlation with the inability to keep the home adequately warm (WARM). This was expected as the renewable energy sources are most explored by high income countries and higher share of inability to keep warm can be observed in lower income countries. The level of arrears on utility bills between (ARR) has a very strong positive correlation with inability to keep the home adequately warm (WARM), a strong negative correlation with gross domestic product (GDP), a moderate negative correlation with share of renewable energy in gross final energy consumption (RENEW) and no correlation with electricity prices for households (PR). Electricity prices for households (PR) had a strong positive correlation with share of renewable energy in gross final energy consumption (RENEW) and very low negative correlation with the ability to keep the home adequately warm (WARM).

Although the correlation between variables can be observed, there is no way to know, at this stage, if there is a causality relation between the variables or if a different variable has a result-determining influence on both. To see if there is a causality link, the pairwise Granger causality test must be used.

Next, in Table 6, there are the results of the pairwise Granger causality test. To have a better understanding of the causality relation in time between variables, it was tested for Lag 1, 2 and 4.

After analyzing the results of the pairwise Granger causality tests, several causality links were found. Arrears on utility bills between (ARR) Granger-caused gross domestic product (GDP) with a significance of 10% at Lag 4, but not at Lag 1 and 2, and that electricity prices for households (PR) Granger-caused arrears on utility bills between (ARR), with a significance of 5% at Lag 1 and 4, but not 2, which means that there was a short-term shock of electricity price and a long-term influence. The inability to keep the home adequately warm (WARM) Granger-caused arrears on utility bills between (ARR) with a significance level of 1% for all three lag lengths examined. This result was expected as low-income households would accumulate arrears on utility for keeping warm, but not at a comfortable level.

Gross domestic product (GDP) Granger-caused electricity prices for households (PR) at a significance level of 5% for Lag4, but not for Lag 1 and 2. Hence, the level of GDP generated and influenced electricity prices after four periods. Electricity prices for households (PR) Granger-caused the inability to keep the home adequately warm (WARM) with a significance level of 5% for Lag 1, but not for 2 and 4, so the shock of price increase will be absorbed in first lag and will not create future influences as households adapt to it.


**Table 6.** Pairwise Granger causality test.

1% significance level, **5% significance level**, **10% significance level**; source: authors calculation based on data from Eurostat.

All relations presented until now are unidirectional and the only bidirectional relation was found between electricity prices for households (PR) and share of renewable energy in gross final energy consumption (RENEW) as PR Granger-caused RENEW with a significance level of 1% at Lag 1, but not on leg 2 and 4. RENEW Granger-caused PR with a significance level of 10% at Lag 2, but not on leg 1 and 4. Even though the causality relation is bidirectional this manifested at different lag lengths, and at different levels of significance. An increase of electricity prices could spike new investments in renewables at first lag, but the influence of renewables on electricity prices takes 2 lags and has lower probability.

Next the cointegration test is realized using Kao residual cointegration test for the analyzed variables presented in Table 7. In the test there were included 280 observations with no deterministic trend, lag-length selection based on SIC, Newey-West fixed bandwidth and Bartlett kernel.



Source: authors calculation based on data from Eurostat.

In addition, there are presented the results for ADF test equation on residuals using least square method with 1986 observations after adjustment.

#### **6. Subsidies-Sustainability's Motor in the Tackle Energy Poverty?**

In the last part of the study, an analysis of Romania's renewable sources potential was conducted also a comparison with the income data for each county.

Many experts believe that renewable energy is a solution that could solve the problem of energy poverty despite the challenges and limitations of using this energy source. Even if the generation of such an energy involves in some cases, higher costs than the production of traditional one, specialists emphasize the short-term and long-term benefits that its use induces. The implementation of renewable energy projects does not generate only disputes related to costs and investment financing, but also to their acceptance by local communities (given the negative externalities generated-like aesthetics, noise, biodiversity degradation, etc.). However, the renewable energy production projects could be a solution for the development of rural communities, which, in this way, providing the necessary energy, attracting the local labor force, thus ensuring the increase of the incomes of the local population [56–64].

Taking into account the current context regarding the access, in general of the European Union's population and in particular of Romania's people, to the energy sources for the creation of the minimum comfort in the households, it will be tried to define a model, for the future period which starts from the idea that, by implementing it, it will be able to lead to an improvement in the standard of living of vulnerable consumers affected by energy poverty.

The hypotheses from which it will be started the study are the following:


The most widely accepted indicators in practice and literature with a view to sizing the energy poverty phenomenon and targeting measures in order to combat it, consider a ratio between population incomes and household energy expenditure. In Romania, the only criterion used is the income per household, which leads to an incomplete understanding of the phenomenon. In the next lines of the article it will be presented an analysis of the statistical data, regarding the income of the population, for all the counties of the country.

Botosani, Vaslui, Calarasi and Giurgiu counties represent the extreme poles, with the lowest purchasing power. It follows in the ranking Suceava, Neamt, Vrancea, Buzau, Ialomita, Teleorman, Olt and Mehedinti a short distance from the first. The group of counties with purchasing power below the national average is completed by Satu-Mare, Maramures, Bistrita-Nasaud, Harghita, Covasna, Bacau, Iasi, Braila, Tulcea, Valcea, Dolj, Caras-Severin, Gorj, Salaj, Mures.

The counties located near the average in the country from the point of view of purchasing power are those that include cities in the development competition: Prahova, Arges, Constanta, Alba and Arad. In these counties, significant economic growths are foreshadowed, they serve as satellites of the big economic centers and benefit from the investments of the players who relocate their activities in the proximity of the big economic centers that become inadequate (Cluj, Timisoara and Brasov). All these cities occupy top places in the absorption of European funds and in the development of infrastructure. Arad has provided a large number of transport connections with the European road network, while Alba Iulia is the absolute national leader among the smart cities in the country, with most of the smart city projects implemented. The group of counties with high purchasing power begins with Brasov and Sibiu, "stars" on the map of the economic development of the country and the engines of the central

area of Romania. For several years here, a new industrial area of the country has been configured, attracting massive investments.

Brasov County has developed on several market segments, mainly on real estate and Business Service, due to the number of people with technical skills and language skills, the central geographic positioning, and the lower costs compared to other localities and the very good living conditions. At the same time, the county owns the most industrial parks in the country (10), after Prahova (15) and Cluj (11), and the development of the automotive and retail industry also generated an explosion of residential constructions. Thus, in 2017 was completed the largest number of homes in residential complexes in the post December '98 history of Brasov.

Sibiu County, in turn, has become a magnet for investors coming to Romania, being attractive to the auto and IT industry. The largest industrial employer in the county and the giant in the automotive industry-Continental-expanded its investment in 2018, followed by other big players (Kika Automation) who transfer their activities to this region.

Counties such as Cluj, Timis and Ilfov, in front of Bucharest are the traditional poles of development of the country (red areas) where the purchasing power is at least 20% above the country average. These areas keep their development rates stable and have the quality of "diffusers" of investments for the neighboring areas, making them positive corrections.

In general, the reasons for developing cities outside Bucharest are related to the cheap and educated workforce. The industries that have found the best opportunities in such cities are the automotive, IT and Business Service industries. Another important factor is the transport infrastructure.

According to GfK [65], it is estimated that Sibiu, Brasov, Arad, Constanta and Alba Iulia are the cities that will soon see a greater development than Bucharest, precisely because they have a good infrastructure, but also university centers that will form the workforce market (Figure 1). Last, but not least, another factor that changes the map of local development is the dynamics of costs-the classic development areas become expensive for new investors (the case of Cluj which has the most expensive industrial land in 2019 in the country), and this causes them to orient to less explored areas of the country.

**Figure 1.** The purchasing power in Romania-2019; Source [66].

In addition, the regional competition intensifies with the availability of European funds. In this regard, some counties have adopted strategies and alliances to boost the attractiveness of these funds to develop their infrastructure-as is the case of the "Western Alliance", an alliance between four counties (Cluj, Timis, Arad and Oradea)-meant to boost the attractiveness of financing, for regional development.

Solar energy is considered as a renewable energy source, as it is energy emitted by the sun. Solar energy can be used for generating electricity through solar cells (photovoltaics) or through solar thermal power stations (heliocentric); heating buildings directly or through heat pumps; heating buildings and produce hot water for consumption through solar thermal panels [65].

In order to increase sustainability and viability of the model, the proposal for government's factors was subsidization purchasing of solar panel systems for households in counties that have-according to the map of the sun-the benefits of this type of energy. For example, the counties that can benefit from such subsidies for solar panels are Prahova, Buzau, Ialomita, Olt, Dolj, Constanta, Calarasi, Giurgiu and Arges.

Romania is in an area with a good solar potential of 210 sunny days per year and an annual solar energy flow between 1000 kWh/m2/year and 1300 kWh/m2/year. From this total amount it is possible about 600 to 800 kWh/m2/year. The most important solar regions of Romania (Figure 2) are the Black Sea coast, Dobrogea de Nord and Oltenia, with an average of 1600 kWh/m2/year.

**Figure 2.** Map of sunshine in Romania (average between1994–2016). Source [67] https://www. fabricadecercetare.ro/regenerabil/.

Between the 1970s and 1980s, Romania was an important player in the solar energy industry, installing around 800,000 m2 (8,600,000 ft2) of low-quality solar panels, which placed the country in third place worldwide for total photovoltaic cell surface area. One of the most important solar projects was the installation of a 30 kW solar panel on the roof of the Polytechnic University of Bucharest, capable of producing 60 MWh of electricity per year.

Geothermal energy is energy stored in the form of heat beneath the solid layer of the earth's surface. The two fields of exploitation of geothermal energy are:


Referring to the map below, it has been found that the areas marked in gray shades have geothermal energy and are located in the west of the country (Bihor County, Arad, Timis and Satu Mare) and near Bucharest, more precisely in Ilfov County.

In order to increase the sustainability and viability of the model it was proposed to the governmental factors, the subsidization of the households in the counties that have, according to the map below, the benefits of this type of energy, for the purchase of some systems of valorization of this resource. For example, the counties that can benefit from such subsidies for green energy are: Bihor, Arad, Timis, Satu Mare and Ilfov. Geothermal energy can be used as a ventilation heating system with air conditioning systems to exploit geothermal energy on the surface.

In Romania, the Panonian Depression (Banat and western Apuseni mountains) is rich in geothermal deposits (Figure 3). In Timisoara there are geothermal resources with temperatures up to 80 ◦C.

**Figure 3.** Map of geothermal potential in Romania. Source: [68] https://www.researchgate.net/figure/ Repartition-of-geothermal-resources-in-Romania\_fig1\_312284159.

Wind power is a renewable energy source generated by wind power. The advantages of using wind energy are the following:


Wind turbines-also known as windmills-transform the kinetic energy of the wind into mechanical energy, which is in turn, further transformed into electricity. Electricity is produced by a system using a charging regulator and it is stored in different ways. Thus, through this model we propose a solution for subsidizing households in counties that have this type of energy. In this sense, the inhabitants of these counties will be able to benefit from low rates for electricity. For example, the areas that could benefit from such subsidies for the implementation of wind systems for electricity production are Constanta, Bistrita-Nasaud, Braila, Tulcea, Vaslui, Ialomita and Galati (Figure 4).

**Figure 4.** Map of the wind speed in Romania; Source [69] http://energielive.ro/energie-eoliana-hartade-vant-a-romaniei-potential-de-14-000-mw/harta-vant-romania/.

The current structure of the natural gas market in Romania comprises (Figure 5):


The internal market for natural gas has two components:


**Figure 5.** Map of the natural gas transmission network in Romania; Source [70] https://www.transgaz. ro/sites/default/files/uploads/users/admin/plan\_de\_dez\_2017\_-\_2026.pdf.

Following the analysis of the data presented above, the transformation and synthesis of the values may be observed n showing Table 8.The analysis is necessary in order to come to the demographic areas of the country and the potential of accessing the different alternative sources of energy of the consumption of natural eyes, recommendations can be issued in order to be able to method us and the technologies, to take care to have the main objective population in ensuring comfort in households, implicitly the phenomenon of energy poverty.

Based on the synthesized information, correlated proposals can be made, depending on the values of population incomes from different areas of the country and the potential of energy sources (natural gas vs. alternative energy sources), regarding the opportunity to subsidize different technologies that allow access to the energy needed for the households comfort in different areas of Romania.

Considering the geographic profile of Romania, it can be observed, from the analysis of the previous data, that there are areas where access to transport and distribution networks, for natural gas to consumers is impossible or possible, but with very high costs.

By superimposing the map with the national network of natural gas transmission and distribution buses with the other maps with energy potential from other sources, largely renewable and environmentally friendly, policies can be issued correlated with the level of population incomes, at government level and ministerial, regarding the support of the population, in a sustainable way, by granting subsidies for the implementation of technologies, that allow the access to the sources of alternative energy, that fight the phenomenon of energy poverty.

In addition, where possible, it is preferable to replace the consumption of natural gas with green or renewable energy sources, which are more environmentally friendly, leading of course to reducing the greenhouse effect on the planet.


**Table 8.** Information on population incomes and energy potential in Romania.

\* 1 Euro = 4.85 Lei. Source [66] https://www.gfk.com/ro/noutati/comunicate-de-presa/puterea-de-cumparare-aromanilor-a-crescut-in-2018-dar-odata-cu-ea-si-polarizarea-regionala.

For each county of the country, as shown in Table 9, alternative solutions for granting, subsidies are correlated with the incomes, in parallel and/or combined with the expansion of the natural gas transmission and distribution networks can be proposed to allow access easy for the population to source of energy.





Considering the above figure (Figure 6), it may be noticed that a very large number of wind projects-so the areas suitable for subsidizing this type of renewable energy-are found in Dobrogea, Moldova and Transylvania. In addition, photovoltaic projects are also a good alternative in areas where they can be installed, especially the southern areas of the country such as Danube Delta, Dobrogea and Romanian Plain. Renewable alternatives for geothermal projects can be found in Western Plain and Panonian Depression (Banat and Western Apuseni Mountains) and Romanian Plain, respectively Ilfov.

**Figure 6.** Interactive map of Romania's renewable energy projects; Source [67] https://www. fabricadecercetare.ro/regenerabil/.

The use of renewable energy sources-and the transition to the green economy-implies not only broad structural, institutional, technological, social, economic changes targeting the energy sector, transport, housing stock, equipment used by the population and companies in different fields, but also the behavior of consumers who must become more responsible in use of energy [68–77]. Modern societies must move towards an inclusive energy transition path that is achieved, on one hand, by reducing energy poverty, and on the other hand by controlling the impact of energy production and consumption on the environment through carbon emission [78–81].

#### **7. Conclusions**

Energy poverty is a dynamic and complex phenomenon with peculiarities depending on geographic location and level of development of each country. Energy poverty is generated by a combination of factors, namely low household incomes, high energy prices and poor access to the energy system for reasons other than lack of money and housing-specific energy shortages. Therefore, the financial situation of consumers, energy quality of the housing environment and the existence of a deficient energy system generate this phenomenon that occurs in both developed and developing countries, both in cold and warm areas.

Energy poverty is a multifaceted phenomenon because it affects different categories of people, involving numerous factors that generate consumer vulnerability. The heating and lighting needs of people-and the need for energy to power various appliances-are generated by the structure of households (the existence of several generations-the elderly and children with different needs), the state of health that could induce special requirements to ensure a certain temperature in the home or the professional status of household members (employees working from home, unemployed).

Solutions to solve this problem must be adapted to the specifics of each category of vulnerable consumers (urban or rural), and require the involvement of many categories of stakeholders such as local communities, NGOs, banks that must have adapted credit offers, public institutions which provides the legal framework, but also possible subsidies to encourage the production and use of renewable energy. As society develops, we will probably witness a paradigm shift in the sense of promoting and using the concept of well-being energy, so as to take into account not only the degree of satisfaction of needs, but also efficiency energy use.

Until such time as aggregate models or indicators will be able to sufficiently take all levels, dynamics and structure of energy poverty into account, we believe that an approach adapted to local specificities is necessary. For example, the same model of energy poverty and fuel poverty cannot be applied to a highly developed urban community in Europe, and at the same time, to a rural community in developing country, where households use woody biomass for heating and are often illegally connected to the electricity grid. Both in the effort to assess energy poverty and in trying to find solutions to these issues, a regional or local approach is needed that includes a series of indicators (in turn difficult to record and analyze) with reference to the cultural model, habits consumption, real incomes of households (not just declared or registered). Concerning the fight against energy poverty, on one hand, macro-solutions (i.e., the strategic approach, highlighted by the government policies to increase the level of energy independence by accessing and exploiting all available resources, obviously in environmentally friendly conditions) were highlighted. On the other hand, the micro-solutions (which focus on identifying vulnerable consumers and target their specific needs through joint efforts of local governments, civil society and economic agents) were also explored.

According to the results of the pairwise Ganger causality test, causality relations were found between pairs of variables. A causality circuit appeared: GDP Granger-caused electricity prices for households, electricity prices for households Granger-caused arrears on utility bills, arrears on utility bills Granger-caused GDP.

Taking into account the above, in the case study in Section 6-based on the synthesized information-proposals were made to access different sources of alternative energy. These were made in correlation with the population's income levels in the different areas of the country-and the potential of energy resources (natural gas vs. alternative energy sources), in terms of the opportunity to subsidize various technologies that allow access to the energy needed for the comfort of households in different areas of Romania. In these cases of accessing alternative energy sources (wind, sun, geothermal, etc.), even if the initial material efforts are high, the results are remarkable:


In conclusion, this case study, through the proposed solutions, brings a plus for the social framework and also for the environment.

**Author Contributions:** Conceptualization, A.N., M.P., J.D.M. and M.C.V.; methodology, A.N., M.P., J.D.M. and M.C.V.; software, M.C.V.; validation, M.C.V.; formal analysis, A.N., M.P. and J.D.M.; investigation, A.N.; resources, J.D.M.; data curation, A.N., M.P.; writing-original draft preparation, A.N., M.P., J.D.M. and M.C.V., writing-review and editing, A.N., M.P., J.D.M. and M.C.V.; visualization, A.N., M.P., J.D.M. and M.C.V.; supervision, A.N., M.P., J.D.M. and M.C.V.; project administration, M.P.; funding acquisition, A.N., M.P., J.D.M. and M.C.V. All authors have read and agreed to the published version of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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