*4.2. Results of the Panel Data Models*

In the proposed models, the determinants of energy consumption are estimated by using EU28-tested data panels for the period 1995–2014. The 17 developed models have been tested for the relevance of fixed effects, as well as to see if the random model is a better estimate than the fixed model. In this respect, the fixed effects test, as well as the Hausman test, was applied to both cross-sectional and period correlations, taking into account the null assumptions of each model presented in the methodology. The direction and magnitude of impacts of socio-economic and environmental determinants of primary and final energy consumption are presented and discussed in the following two sections [18].

#### 4.2.1. Determinants of the Primary Energy Consumption

In order to identify the variables that may have an impact on the primary energy consumption, several variables were used, by investigating multiple international databases, which were presented in the materials and methods section. Along these, there could be also other indicators with various influences both as a sign and as a magnitude depending on the combinations considered in the multiple regression models. The results obtained from Equation (1) are highlighted in Table 4.

The regression results of estimating the percentage change in primary energy consumption as an endogenous variable in Table 4 indicates that there is a primary energy cost that EU countries have to assume. All independent variables, which registered a negative sign of the coefficients in Table 4, have a negative relationship with the endogenous variable, which can be translated as a positive effect on reducing the energy consumption and mitigating climate change. Contrary, the positive signs indicate a positive relationship with the endogenous variable, which can be translated as a negative effect on reducing the energy consumption, as the effect is one of growth.

Model 8 and model 10 suggest a linear negative impact of the change of economic growth (GDP) on the dependent variable, in the sense that the 1% augmentation of GDP determined an increase of PEC by 0.38% and 0.32% during 1995–2014. Although the relationship is positive, the effect is considered negative because one of the objectives of sustainability is saving energy through conservation and energy efficiency.


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Note: Statistical significance, *p*-value, \* 10%, \*\* 5% or \*\*\* 1%. Source: [18].


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In contrast to the study of Çoban and Topcu [16], which identifies mixed relations between economic growth and energy use at EU27 level during 1990–2011, the results of the current economic growth impact on primary energy consumption in the EU28 during 1995–2014 suggest only linear relationships of intensification of the endogenous variable. However, if we consider the separate findings of Çoban and Topcu [16] in the case of the old member countries and the case of the new ones, then the results are similar to this study. Nevertheless, the magnitude of the change induced by economic growth cannot be compared with the study by Çoban as Topcu [16], as they reported the variables to the number of inhabitants achieving increases in energy use per inhabitant of 0.03–0.04% over a different period. In addition, the results are similar to the study by Saidi and Hammami [30], which notes the increase in energy consumption caused by GDP per capita. Thus, correlations with specialized researches in the case of the economic growth of the EU28 on primary energy consumption suggest that studying aggregates in a variable generates a clear, positive relationship with a high negative impact, increasing consumption as a state develops on the economic level.

The relationship between GDP and PEC suggest that, for the time being, the EU28 still needs to stimulate the decoupling of economic development from energy consumption. As other studies [10,24–26] demonstrate, this decoupling would not affect the further development of the member states. In addition, this lack of decoupling, which will intensify energy consumption, can be attributed to the new EU states (EU13), which still do not have the same level of economic development as the EU15 and therefore energy consumption will not diminish if economic growth will improve in the new member states, i.e., the EU13, including Romania, without strong active measures to change consumer patterns and achieve energy saving. However, in order to substantiate this statement, as Çoban and Topcu [16] realize, a future study could retest the models presented in Table 4 at EU15 and EU13 levels.

Moreover, the increase of energy consumption stimulates the growth of greenhouse gas emissions, which further contribute to climate change [66]. In this context, the aim of the EU decision makers could be to stimulate and evaluate the social welfare though other indicator than GDP. Further, one focus of the developed countries could be the implementation of green technologies, which are more energy efficient and register energy savings. In terms of developing countries, these could aim to increase their GDP by also adopting environmental and social-friendly practices, as these are viable in the context of the fast climate change.

At the same time, the change in GHG emissions, which growth in the analysed countries by 1%, has led to an increase of the primary energy consumption (PEC) by 0.75–0.80% in the period 1995–2014. This fact is given by the positive relationship between the influences of GHG emissions on primary energy consumption, which is presented in Table 4 through the + sign of the coefficients. This negative effect of stimulating energy consumption due to increased pollution, which supports the research hypothesis, is similar to the findings of Saidi and Hammami [30], who introduce part of the EU countries into their analysed group. Similarly, Wang et al. [11] show GHG emissions as a determinant of increasing energy consumption internationally. While the relationship between GHG emissions and energy consumption seems to be a bidirectional one, as shown in our analysis and that of [66], the decision makers should focus on reducing energy consumption through various measures, such as financial incentives or tax reductions on sustainable practices, promotion of the importance of both energy savings for the consumer sector and energy efficiency in all sectors of economy, changing the behaviour of consumers by applying punitive measures for those who do not comply with the regulations in the field, and so on.

Similarly, the energy mix from fossil fuels (FFE) and nuclear energy (NE) generated a 1% increase in the change in primary energy consumption by 0.008% and 0.006%. This result, in the case of the share of fossil fuel consumption, may be caused by their still intensive use. In the case of the share of renewable energy consumption (RE) it is observed that the effect is contrary to FFE and NE, i.e., positive, of diminishing the dependent variable, PEC, by 0.009–0.012%. The impact meaning and magnitude are similar in the case of the change in green electricity production (REL) and that from

nuclear sources (NEL). Thus, the benefit of reducing the primary energy consumption generated by the use of renewable energies is captured. This means that the decision makers should focus on developing public policies which aim at stimulating the production and use of renewable energy, bearing in mind as well the requirement of energy savings.

Contrary to the research hypothesis, the positive sign the oil price change (EP) in relation to the change in primary energy consumption shows that the 1% increase of oil price produced a growth of 0.007–0.081% of PEC during 1995–2014. Subsidizing the fossil fuel industry can explain this effect as well as the price of these non-renewable resources that do not include the negative externalities of the environment and human health. This is contrary to the findings of Çoban and Topcu [16], who identify a negative relationship in the EU27, between 1990 and 2011, a positive effect of lowering the use of energy per capita based on rising oil prices. Indeed, the differences in the analysis are related to the methodological approach, the data used, the time period considered and the target group. Thus, in the future, there is a need for reiterating this relationship for the EU28 and, again, its separate analysis for the EU15 and the EU13, with a view to homogenizing the groups.

The effect of changing the share of net energy imports as a share of the total energy used by a country (EIMP) on the dependent variable seems mixed, although the statistical significance only appears for the positive effect generated by the models 6 and 8. The positive effect of decreasing the consumption change of primary energy by increasing the share of net imports in energy consumption may be due to the promotion of the need for energy independence as well as the additional economic efforts of a country to source energy from imports, especially when the country owns the energy resources needed for energy security.

In order to deepen the impacts of the economic system indicators, other variables were tested. Thus, the changes in capital stock (K) and the change in gross capital formation as a share of GDP (K-GDP) lead to a negative effect on the change in primary energy consumption (PEC), in the sense that the increase of these indicators by 1%, it generates increases of PEC with 0.09–0.13% and 0.003–0.004%. This result is similar to the findings of Saidi and Hammami [30], who identify an increase in the use of energy per capita at the level of some 15 European countries due to capital stocks, but the relationship of this study is insignificant. Thus, the results in Table 4 suppress the statistical significance required for their validation. A lower negative effect also occurs in cases of change in financial development (C-GDP) and change in commercial opening (SE-GDP). Again, the impact of financial development on the rise in primary energy consumption is similar to that of Saidi and Hammami [30]. Also, the impacts of these two variables illustrated by the results are similar to the findings of Azam et al. [53]. Another indicator that captures commercial opening, but which has a positive effect on diminishing the change in primary energy consumption is the change in the external balance of goods and services as a share of GDP (EB-GDP), but its impact is very low, of −0.002%.

As with the impact on the change in the share of renewable energies in consumption, the change in the share of military expenditures in GDP (M-GDP) is not statistically significant on the change of the primary energy consumption, their correlation being inconclusive due to the mixed results recorded. Therefore, the research hypothesis that M-GDP growth would generate the augmentation of the primary energy consumption is not confirmed, producing a negative effect on the holistic system.

At the same time, contrary to the research hypothesis that R & D expenditure share (RD-GDP) in GDP generates a positive effect on the change in primary energy consumption in order to stimulate energy saving, the effect of this relationship is negative. A 1% increase in RD-GDP stimulated the endogenous variable enhancement by 0.058%. This surprising result indicates the failure of EU energy saving by 2014 and the possibility of a concave relationship between RD-GDP and PEC, as in the case of Kuznets. In addition, the same effect is observed with the change in the number of internet users (IU), whose 1% improvement causes a very small increase in the change in primary energy consumption by 0.0008%. However, the development of internet access implies the development of residential communications, indicating a better living and, implicitly, increasing energy consumption based on the use of electrical equipment and devices, confirming the results of Wang et al. [43].

The indicators considered for environmental assessment are either statistically insignificant, the case of changes in environmental taxes (ENVT), or they have opposite effects, the cases of changes in the weight of the agricultural area (AA) and the depletion of natural resources (NR-GDP). Although the research hypothesis implied a positive effect of ENVT on the change in primary energy consumption, the mixed and inconclusive results do not allow validation, nor its rejection. Instead, the negative impact of NR-GDP on PEC confirms the research hypothesis that energy consumption is growing as natural resources grow in exploitation, while the positive effect of AA on the endogenous variable rejects the hypothesis of research. Thus, as the change in the share of the agricultural area increases, the change in primary energy consumption decreases by 0.002%. This is surprising because the intensification of agricultural activities generates the increase in the use of energy. However, the development of green agricultural technologies can explain the situation identified at EU28 level.

Most social variables confirm the hypothesis of research. The most important social determinant, with a negative effect on the socio-ecological complex is the population. With a 1% increase in its change (POP), the primary energy consumption increased by 0.5%. This result is similar to that identified by Saidi & Hammami [30] generally applied for the estimation of energy use and shows that the population has a much more significant impact in the countries of Latin America than Europe. The same negative effect, but at a lower level, is also found for population density. In this respect, an increase of the population density (PD) by 1% determined an increase of the dependent variable by 0.001%. Also, the change in the degree of urbanization (UPOP) influenced only by 0.01% the primary energy consumption. This positive relationship, which sees a negative effect on the holistic system, is also indicated by Azam et al. [53] and Wang et al. [11], but for another geographic area, another period, and using another methodological approach, which of course has generated other magnitudes in the impact of urbanization on the increase in energy consumption. Obviously, agglomerations of the population intensify energy consumption, so the negative effect in this case proves the research hypothesis. Another significant negative impact is due to the change in the workforce (WF), whose 1% increase caused the increase in primary energy consumption by around 0.19% and 0.31%, depending on the factors of influence considered in the analysis.

Surprisingly, the increase in primary energy consumption is due to the rise of female decision makers (FM) as well as due to the increase in the share of educated workforce at least at tertiary level (TEDU). However, TEDU is statistically insignificant. Thus, it seems that the negative effects of these two indicators on the state of the holistic system must be carefully investigated in the future using other methodological approaches. As to the proportion of women's mandates in national parliaments, the effect is insignificant in statistical terms, but the meaning seems to indicate a negative effect posed by the positive correlation, i.e., a 1% increase in the proportion of mandates held by women in national parliaments (WP) led to an increase in primary energy consumption. The opposite, i.e., positive status, the effects of decreasing primary energy consumption by 0.10–0.15% and 0.01%, respectively, occurred due to the 1% increase in the share of the female population in the total population (FPOP), respectively the change in the share of health expenditure in GDP (H-GDP).

Most of the models are not auto correlated because Durbin–Watson is close to the value of two. They are also statistically relevant, and the amount of information recovered ranges from 20% to 78%.

### 4.2.2. Determinants of Final Energy Consumption

Another analysis was carried out on final energy consumption, with the aim of identifying variables that have a positive or negative influence on it. As in the previous cases, both economic variables and socio-ecological indicators of the holistic system were considered to best capture the relationships within it, the role of the energy sector and the evaluation of past policies. The results of the analysis based on Equation (2) are presented in Table 5.


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83

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The results of the final energy consumption determinants show both similarities and significant differences compared to those related to primary energy consumption.

In the case of GDP influences, the same linear trend of increasing the final energy consumption (FEC) was observed, but with the negative effect of 0.45–0.49%, which is approximately 10% higher than the primary energy consumption (PEC) case. In addition, ignoring statistical insignificance, model 3 even shows the hyperbola relationship between FEC and GDP. Similar effects to PEC occurred in the case of GHG emissions change (GHG), which 1% increases stimulated FEC growth by 0.53–0.74%, with a lower negative effect on PEC. This was somewhat expected, because the final energy consumption depends on the primary energy consumption.

In addition, the energy mix generated impact on the final energy consumption changes similar to those related to the change in primary energy consumption, except for the influence of the change in the share of nuclear energy consumption (NE), which generated statistically significant positive effect. Thus, the decrease of final energy consumption by 0.001% was caused by a 1% increase in NE. However, in the 7th model from Table 5, there is also a negative effect, statistically insignificant, which shows the necessity of further research. At the same time, both changes in fossil fuel consumption (FFE) as well as changes in the share of renewable energy in consumption (RE) have the same effects as in the primary energy determinants analysis: the negative effect of FFE caused the increase in FEC (0.006–0.007%) and the positive effect of RE generated decreases of FEC (−0.002–0.003%). In this case, the negative effect of the oil price change (EP) on the change in final energy consumption (FEC) contradicts the research hypothesis, according to which high oil prices cause a reduction in energy consumption. In this respect, the decrease of FEC by 0.019–0.069% occurred due to the increase in oil prices by 1%. This result can be explained by the diversification of the energy mix and the high possibilities of replacing oil with other energy sources.

Comparing the results in Table 4 with those in Table 5, it was found that there were identical results on the impact of some indicators on both primary and final energy consumption. These determinants with identical effects, previously interpreted, are as follows: Changes in net energy import (EIMP), changes in gross fixed capital formation (K-GDP), change in external balance (EB-GDP) and changes in health expenditure (H-GDP). At the same time, similar influences from capital stock changes (D\_LOG\_K) and commercial openness (SE-GDP) were observed. In the first case, the 1% increase in capital stock caused an increase of 0.03–0.05% in final energy consumption, the value being about half the value for primary energy consumption. In the second case, although a positive mathematical relationship was observed between SE-GDP and FEC, it generates a negative effect on the evolution of final energy consumption, but is not statistically significant.

What is interesting is the adverse impact of financial development on final energy consumption compared to primary energy consumption. Thus, the change in final energy consumption diminished with very low values of 0.0003–0.0004% amid a 1% improvement in financial development, thus indicating a positive effect on the trend of the endogenous variable at EU level during 1995–2014. This time, the research hypothesis was confirmed.

Contrary to the influence of primary energy consumption and the research hypothesis, the increase in the share of military expenditures in GDP (M-GDP) stimulated the reduction of the final energy consumption change by 0.01%, similar to the result recorded in the case of testing the regressions on GHG emissions. From the author's knowledge, there is no evidence of this relationship, but only the study by Jorgenson and Clark [56], which supports the increase of the ecological footprint by this indicator, i.e., the intensification of national consumption. An opposite effect on FEC, which once again rejects the research hypothesis, is caused by a change in the share of R & D expenditure, since improving it by 1% causes increases in final energy consumption by 0.04–0.08%.

Further, a 1% increase in the change of the share of the agricultural area (AA) led to an increase of the final energy consumption change by 0.003%, contrary to the influence on the primary energy consumption, but confirming the research hypothesis that the increase in agricultural area determines increased energy consumption as a result of agricultural activities. At the same time, a 1% increase of

the share of environmental taxes in GDP (ENVT) generated final energy savings between 0.01% and 0.02%, which confirms the research hypothesis.

Last but not least, the influence of the social variables on the final consumption of energy, which are similar to the effects of the primary energy consumption, were tested, except for the change in the population density, which, this time, inversely influences the endogenous variable, inducing a decrease in the final energy consumption with 0.0015%, contrary to the research hypothesis. However, strong impacts were identified in population change (POP) and labour change (WF). Thus, a population increase of 1% caused the final energy consumption to increase by 0.45%, while the increase of the labour force change by 1% caused the increase of the final energy consumption change by 0.23–0.34%. Another increase in the change in final energy consumption was caused by the increase in the share of the labour force with tertiary education in the total population (TEDU), the increase being about 0.001%. Although the research hypothesis is rejected, energy consumption has increased in this case, probably because of higher social welfare than other social categories, which induces the use of more energy-consuming equipment and technologies. The last tested variables do not present statistically significant values of the coefficients, but they can be interpreted in terms of meaning and influence they could have on final energy consumption. Thus, the increase of the proportion of women's mandates in national parliaments (WP) and the growth of Internet users (IU) appear to have increased final energy consumption. However, the existence of low values is noted, therefore, even if the effect would have been statistically relevant, the influence would have been almost insignificant.

Finally, the models estimated in Table 5 are statistically relevant according to the F Test. In terms of the Durbin–Watson test, which does not indicate autocorrelation for values close to two, the analysed models do not exhibit autocorrelation or, if present, cannot be determined by the Eviews software. The amount of information recovered is between 15% and 65%.

#### **5. Discussion**

Some studies [17] confirm the existence of a concave relationship between the economic development and the degradation of the natural environment, according to Kuznets' curve evolution, also a positive relationship is proven by other studies [22,23], while others [32,61] invalidate this relationship. Similarly, there are studies on the social dimension [3,36,60], which show that improving the conditions of the natural environment contributes directly to the improvement of social and institutional performance, components that are part of the social system of society. At the same time, the causality between economic development and social performance is shown, but the sign of the relationship is not conclusive [61]. Of course, in all these records, the role of the energy sector is undoubtedly important in mitigating climate change.

One of the purposes of the research was to analyse the factors of influence of energy consumption at EU28 level between 1995 and 2014. Several types of energy consumption were considered: The primary energy consumption and the final energy consumption. The models generated relevant information for both national and European policy makers as well as for the scientific literature on energy.

Table 6 summarizes the results of the analysis of the determinants of the endogenous variables considered.

First, the determinants with a strongly significant impact on the evolution of primary energy consumption are as follows: GHG emissions, GDP, size of human population and labour, capital accumulations and human feminine population. These strong influence factors have shown a positive relationship with primary energy consumption, with the exception of the human feminine population that has a negative relationship with the endogenous variable, which means that in the case of the EU28 the increase in the female population weight leads to a decrease of the energy consumption, thus stimulating the improvement of the state of the holistic system.


**Table 6.** Summary of the energy consumption determinants in EU 28, 1995–2014.

Note: Statistically significant relationship between endogenous and exogenous are denoted as follows: Positive + or negative −; ≈ Inconclusive relationship; !! Statistical insignificance. Source: [18].

In addition to the factors with a strong impact on the evolution of primary energy consumption, other determinants have also been analysed which have a very low influence on the endogenous variable. The latter recorded a positive relationship with the consumption of fossil fuels and nuclear energy, the oil price, the financial development, the research and development expenditures, the depletion of natural resources, the degree of urbanization, the population density, female decision makers and the number of Internet users.

By considering the negative impact of these factors on energy consumption, in the sense that it stimulates its increase, the policies should integrate sustainable principles and objectives of reducing them while increasing the social welfare. For example, while [59] indicate positive influence of financial development on reducing environmental impact, our results suggest an intensification of the energy consumption at EU level. In this case, as [59] states, financial developed countries benefit of innovative technologies and financial networks, which on long term stimulate diminution of environmental degradation. Although, initially, energy consumption and other use of resources could increase, the investments in green technologies are one of the best options for tackling climate change on long term. At the same time, being consistent with the findings of [43], the energy price rises seem to slowly intensify the energy consumption, which means that increasing the oil prices are not necessarily reducing the environmental impact. However, maybe aiming to stimulate the green initiative are more effective ways of tackling climate change. Further, the urbanization degree should be diminished by developing the sectors of health, education and services in rural area, as well as creating fast

linked infrastructure with the urban and peri-urban areas. In another example, the number of internet users would most likely increase in the future; however, technological development should focus on improving energy efficiency and energy use in production, transportation and consumption of these equipment and tools. In addition, although the proportion of women's mandates in national parliaments is a statistically insignificant determinant of energy consumption, its positive sign, along with that of women decision makers, indicate small intensification of energy consumption. While [59] found out that both the gender and the politic involvement of women influence the energy policies voting, the main influencing factor being the party affiliation, it seems like changing the views of the politic groups towards more green initiatives is more important.

In the same time, it was registered a negative relationship between primary energy consumption and the external balance of goods and services, the consumption of renewable energies and the health care expenditures, emphasizing the stimulation of the reduction in energy consumption. In this case, a policy measure should give, for example, tax reduction or other financial stimulus for reducing the energy consumption on all levels, as the traditional economic models of development and those based on consumption are not sustainable and intensify climate change. So, the developing countries should focus on not applying the pathways of developed countries, as Earth is already overstressed.

Then, a slightly different situation has been demonstrated in the impact of the final energy consumption determinants, which are statistically significant and more congenial in relation to the primary energy consumption factors. Again, the determinants with a strong statistically significant impact on the evolution of final energy consumption are as follows: GHG emissions, GDP, human population and labour, oil prices,R&D expenditure, capital accumulations and environmental taxes. Of these, with the exception of environmental taxes, all determinants had a positive relationship with final energy consumption, which means that in the case of the EU28 most of the high impact factors cause the intensification of the final energy consumption, which led, as a whole, the worsening of the state of the holistic system. It seems like policies which increase the environmental taxes stimulate the reduction of energy and, further, the climate change mitigation.

For assessing the macroeconomic perspective, it is also important to focus at microeconomic level, mainly at the factors that affect the decision to be friendly environmental and to invest in proper equipment. One of the factors that stimulate the green investments seems to be the length of the firm-bank relationship [67]. Other characteristics that encourage the green investment are related with the size, the age, the profitability and the innovative feature of the entity. It seems that more of these encourage entities to have a higher probability for investing in environmentally friendly equipment [67]. The problem regarding this type of information is that data on individual European EU firms are rare and incomplete and so, further studies should focus on it. The studies should look at the process of transforming micro-data into macro level observations in order to provide reliable information (for example R & D seems to encourage green equipment development, so smaller consumption of energy, while at macro level, theR&D expenditure from GDP seems to increase both the primary and the final consumption of energy). In Italy, facilitating the financing in green investment could create a win–win relationship for the environmental protection and it could provide long term values in features related with innovations, job opportunities or other social incentives [68]. On the other hand, uncertainty about the government policies, short term perspective of financial instruments and the lack of financial tailored to small-scale investment needs mitigates the effect of green financing opportunities. At the macroeconomic level, the growth of the economy has the highest net gain from a system where the stock market, mainly market capitalization of listed domestic entities, renewable energy-measured by shares of renewable energy in total final energy consumption-and non-hydro renewable electricity net generation, foreign direct investment inflows in GDP, domestic credit provided by financial sector in GDP, the Brent oil spot price and total greenhouse gas emissions are included [69]. Moreover, among the net determinants of the share of renewable energy are the credit market and the stock market. In China, fluctuations on oil prices also influence the energy structure. Considering the importance of oil

prices' fluctuations, options could be used to reduce the volatility of it and to quantify the benefit of using an active strategic reverse for volatility's mitigation [70].

In addition to the factors with a strong impact on the evolution of final energy consumption, other determinants have also been analysed which have a very low influence on the endogenous variable. Of these, fossil fuel consumption, agricultural land change, population density and tertiary education have led to increased final energy consumption, while renewable energy, nuclear power consumption, financial openness, net energy imports, the balance of external goods and services, military spending, and healthcare spending have led to a decline in final energy consumption. Future studies should also be developed to retest the impact of urbanization, commercial openness, internet users and women's influence in national parliaments on the evolution of final energy consumption at EU28 level as research has not produced conclusive findings in these cases.

Moreover, some variables registered statistical insignificance in relation with one type of energy consumption, such as military expenditures, internet users, environmental taxes, urbanisation and the tertiary education, as well as with the both types of endogenous variables analysed, such as trade openness and women in leading position. Although the military expenditure and environmental taxes stimulated the diminution of the finale energy consumption at EU level during 1995–2014, in terms of its correlation with primary energy consumption, it seems like statistical insignificance and mixed results are recorded. While the number of internet users, respectively the people with tertiary education, seems to increase the PEC, respectively the FEC, their effect on FEC and PEC is statistically insignificant, but with the same positive sign. These results are interesting as they indicate a higher use of energy when increasing the technological and the educational levels. Contrarily, while the increase of the urban population seems to increase the PEC, its effect on FEC is statistically insignificant, but with a negative sign, which means that the increase of urban population generated a diminution of the final energy consumption. This mixed result could be explained by the fact that, in the urban areas, the accommodations are smaller, being more apartments, which might consume less energy while the houses on rural areas could consume more energy. Statistically insignificant and with low impact, the trade openness seems to increase both types of energy consumption, based on the positive sign of its coefficients, such as in the models 1, 3 and 10. Similarly, the women in leading position seem to increase the energy consumption at the EU level, although some studies [71] show that their involvement seems to bring more environmental-friendly practices. Regarding the analysis conducted, it has been noticed that the combinations of factors influence differently the magnitude and sometimes the direction of the impacts of the determinants analysed on the endogenous variables and thus there is a methodological limitation of the research given that the multiple regressions do not allow the introduction of all available variables to evaluate the performance of the holistic system in a single model, because their effects would cancel each other out because of the lack of independence. However, the validity and diversity of results remains equally important to substantiate the need to improve both integrated and environmental energy policies. Nevertheless, it is proposed to carry out further analyses to apply new mathematical models based on complexity, such as those proposed by Marczyk [72] in the field of energy, as they allow the cumulative consideration of all available variables of a complex system, so such as the energy sector.

#### **6. Conclusions**

The relationship between economic, social and environmental factors, as a holistic system, and the consumption of energy (both primary and final) poses great interest for researchers around the world, the Chinese and Turkish researchers proving a remarkable amount of work placed in this field, but also for the authorities since reducing the non-renewable energy consumption is one of the UN sustainable development goals [12]. In addition, the scientific publication in this field has increased in the past years; the more the effects of climate change are becoming more obvious.

The panel models were conducted at EU level during 1995–2014 to estimate the determinants and their impact on both primary and final energy consumption. On one hand, it seems like the energy consumption is stimulated by increasing the GHG emissions, GDP, the use of fossil fuel sources, the oil price, the capital stock, research and development expenditures, agricultural area, natural resource depletion, the population and its density, as well as the labour force. On other hand, it seems like the energy consumption is diminished by increasing health expenditures, the female population, the external balance of goods and services, environmental taxes, as well as renewable energies. Thus, the decision makers on both a regional and a country level should focus on these results for introducing into the public policies the reduction of the determinants which increase the energy consumption for climate change mitigation by using punitive measures, while promoting the sustainable practices for better understanding of the impact at both individual and society levels.

Most of the models proposed in this study include a wider set of variables from those present in the literature, some of which have not been tested so far. Thus, this research is important because it indicated many historical trends of the components of the social, economic and environmental dimensions, which must be considered in future sustainable energy policies as well as in setting new targets and collaboration in the integrated energy-climate field. In the future, it is proposed to continue the research by studying the variables at each state level together with integration of the national energy policies. In conclusion, the study supports decision-makers in evaluating energy policies in the context of sustainability requirements by providing analyses of the determinants of energy consumption.

**Author Contributions:** The authors worked together for this research, but, per structure, conceptualization A.Z.; methodology, software validation and resources, A.Z., L.B. and M.C.D.; formal analysis, A.Z., C.I. and M.C.D.; writing—original draft preparation, and writing—review and editing, A.Z., M.C.D., L.B. and G.-R.L.

**Funding:** This research received no external funding.

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