Determinants of energy efficiency and energy consumption of Eastern Europe post-communist economies

Abstract

Energy consumption reduction and energy efficiency improvement are recognized as global priorities in the context of the green economy and sustainable development. In this paper, determinants of energy efficiency and energy consumption for the panel of 11 post-communist countries in the Eastern Europe during 1996–2013 are investigated. The stochastic frontier function approach and comparative analysis were used to examine long-run dynamic relations. The research results show that GDP growth is a key factor increasing both energy efficiency and energy consumption. The research results on energy efficiency relations show that CO₂ emissions per capita, a fixed capital and the share of industry in the economy are other important drives. In the context of per capita energy consumption growth, the factors of structural changes determined by industry share in the national economy and innovation concerned with development and implementation of high technologies are significant. The European Union accession and participation in the European energy policy promote to energy efficiency improvements in the post-communist countries while progress in governance and enterprise restructuring as measured by the European Bank for Reconstruction and Development is not important for energy efficiency and per capita energy consumption in the post-communist countries. According to the research results, energy efficiency policy in the sample countries should be aimed at providing further economic growth enhancing a positive impact of other factors and implementing energy efficiency projects.

Keywords

Energy efficiency energy consumption economic growth energy prices post-communist economies

Introduction

In the past four decades, energy efficiency issues have received increasing attention from policymakers and researchers. Efficient use of energy affects interests of all countries due to its significant impact on energy, economic, social and environmental national security. Recently, energy consumption reduction and energy efficiency improvement have been recognized as global priorities in the context of a green economy and sustainable development. However, scientific and political debates concerning factors of energy efficiency changes are still going on. While these factors form the base for identifying policy instruments set and measuring its efficiency, the correct identification of determinants becomes significant for territories and industries.

Many researchers investigate empirically the impact of different factors on energy consumption and energy efficiency of national economies, as well as the relationship between these indicators. Among the main factors of energy efficiency and energy consumption development, researchers identify economic growth,^1–10 energy prices,^1,10–15 structural and technological changes,^12,15–23 institutional reforms,^{12,15,17–18,20} investments,^18,24 population dynamics,^18,25 etc. Many studies indicate the interrelations between processes and factors of energy efficiency, energy consumption, economic growth and environmental pollution. These research trends are identified based on empirical studies conducted in the case of many countries and regions all over the world and in different periods.^{2–5,7,9,23,26–30} However, a contradiction of obtained scientific results complicates the formation of definitive guidelines for the implementation of effective sustainable energy policy in certain areas and countries.

Many investigations are devoted to determining factors that influence energy development of emerging market economies and particularly post-communist countries of Central and Eastern Europe.^10–12,15 The results of empirical studies often contradict one another, making it difficult to form grounded recommendations for governmental policy for energy efficiency improvements.

This study aims to identify the main determinants of energy efficiency and energy consumption processes for the panel of 11 post-communist countries in the Eastern Europe during 1996–2013. The distinguishing feature of the study is that the authors have taken into account both post-communist countries with significant results in energy efficiency (Lithuania, Latvia, Romania) and with medium (Poland, Check Republic, Slovenia, Slovak Republic) and low energy efficiency levels (Ukraine, Russia, Estonia, Belarus). The main contribution of this paper is the identification of common factors for chosen post-communist countries, which helps to form universal recommendations for policymakers to provide energy efficiency policy, green economy development and energy consumption decrease.

Also, unlike in previous papers, the authors chose the study period, which corresponds to the beginning of economic stabilization in all selected countries after the Soviet Union collapse and ends in the year preceding the beginning of a political conflict in the Crimea and actual change of Ukrainian territory. The chosen period gives the opportunity to get comparable results of analysis based on the World Bank and the European Bank for Reconstruction and Development (EBRD) data sets. The authors use stochastic frontier function approach as the main research method, which allows them to identify the key factors that determine the energy efficiency. The stochastic frontier approach proposed by Aigner et al.³¹ is considered to be a classical model to test the economic efficiency. According to Greene,³² within the use of deterministic frontier any error or imperfection in the specification of the model or measurement of its component variables might translate into increased inefficiency measures and on the contrary ‘…more appealing formulation holds that any particular firm faces its own production frontier, and that frontier is randomly placed by the whole collection of stochastic elements which might enter the model outside the control of the firm’. The use of stochastic frontier for panel data is widely discussed, for example, Behr³³ underlines that panel data ‘considerably improve the situation for estimating firm specific efficiency if some assumptions on the time path of inefficiencies are introduced’.

The main hypotheses tested within this paper are as follows:

energy efficiency of the post-communist countries depends on economic achievements, and better economic performance is associated with higher energy efficiency;

on the contrary, to the majority of developing economies, increase in natural gas prices is not an important factor for energy efficiency improvement in the post-communist countries due to the existence of long-term energy (gas) contracts for many post-communist countries in the past;

oil price is an important factor of energy efficiency improvement in the post-communist countries;

the wealthier the society becomes, the more amount of energy per capita it consumes due to the rebound effect in technological improvements;

institutional changes like the European Union accession and participation in the European energy policy should promote to energy efficiency improvements in the post-communist economies;

institutional changes like progress in governance and enterprise restructuring, as measured by EBRD, should promote to energy efficiency improvements in the post-communist economies.

The rest of the paper is organized as follows. The next section reviews the empirical literature for factors that influence on energy efficiency and energy consumption of national economies. Then the estimation methodology and data sources are specified. This is followed by a section that discusses the empirical results of the estimations. The final section contains conclusions and policy implications.

Literature review

Sustainable energy is based on principles of energy efficiency and green economy. The relevance and practical importance of these issues is confirmed by numerous scientific publications devoted to the study of direct and reverse impacts of energy consumption and energy efficiency on economic growth, identifying energy-saving potential of territories and factors affecting it, studying barriers to energy efficiency growth and investigating energy efficiency and energy-consumption trends. Nevertheless, the identification of the major determinants for energy efficiency increase and energy-consumption reduction can help to improve governmental and regional policy in this field and significantly contribute to reaching sustainable energy targets.

Energy efficiency determinants

Among the main factors that influence energy efficiency improvements, researchers identify economic growth, energy prices and structural changes, introduction of innovative technologies, investments in fixed assets, institutional changes and environmental contamination levels.^{8,10,12–15,17,18,20,24,34–36} Additional factors that are used to estimate energy efficiency are energy input structure, substitution among energy, labour and capital, trade, government regulation and others.^{10,20,35,37,38}

A number of studies^12–15,17 concluded that raising of traditional energy prices (such as gas, oil, coal, electricity) and decreasing prices for renewables via economic instruments is the main option for energy efficiency increase. This conclusion is based on the results of empirical research provided for countries of Central and Eastern Europe including the former Soviet Union,^12,15 China,^13,14 28 emerging market economies of Eastern Europe and 5 Western European Organization for Economic Co-operation and Development (OECD) countries,¹¹ 28 countries in Eastern Europe and Central Asia¹⁰ and others.

The growing threat of global warming causes increasing interplay of energy efficiency processes and environmental pollution. Limitations of CO₂ emissions for many countries, which are fixed in international documents, is an important factor that stimulates the energy efficient changes in national economies.^24,36

According to Birol and Keppler,¹⁷ the introduction of new technologies increases the productivity of each unit of energy. Nepal et al.¹⁵ confirmed this statement for emerging market economies that are the members of the Council of Europe Development Bank and the Commonwealth of Independent States countries and proved that reforms aimed at market liberalization, financial sector and most infrastructure industries drove energy efficiency improvements. Having studied 38 economies of the Union for Mediterranean countries, Esseghir and Khouni¹⁸ noted that energy efficiency increase can be achieved through innovations and clean technologies investment. Zeng et al.²³ found the same for China.

Economic growth is considered as a powerful driver for energy efficiency improvement according to the empirical results presented by Zhang.¹⁰ Higher per capita GDP can influence on other factors of energy efficiency providing possibilities for investing into new technologies and increasing capital stock.

Cornillie and Fankhauser¹² concluded that progress in enterprise restructuring caused by market-based reforms is an important driver for more efficient energy use in emerging market economies. Azadeh et al.¹⁶ and Wu et al.²² used the value added as a proxy for structural changes to access and optimize energy efficiency performance in energy-intensive sectors. Fan and Xia¹⁹ found that industry structure and technology improvements have major influences on energy efficiency processes as well as on energy consumption reduction.

Energy consumption drivers

Plenty of studies are devoted to the investigation of growth–energy consumption nexus. Nevertheless, their results are far from consensus. As mentioned in the study Shahbaz et al.,⁷ a unidirectional causality from economic growth to energy consumption was found for many countries all over the world. Among them are Germany and Italy,²⁷ Pakistan and Indonesia^3,6 Italy and Korea,⁹ France, Italy and Japan,^4,5 Canada,² Middle East countries,²⁹ etc. The reverse causality is reported for Canada, the UK, Germany, Sweden, Switzerland,⁵ G-7 countries,²⁸ the US²⁶ and China.⁷ Bidirectional causality between energy consumption and economic growth was found for Liberia,³⁰ Japan²⁷ and the Union for Mediterranean countries.¹⁸

Examining the relationship between Chinese aggregate production and consumption of coal, oil and renewable energy for the period 1977–2013 and 1965–2011, Bloch et al.¹ concluded that the main drivers of energy consumption are economic growth and prices changes, which have a reverse influence on energy consumption. Discovering interrelations between economic growth and energy consumption for 38 countries during 1980–2010, Esseghir and Khouni¹⁸ reported that for emerging market economies, the reduction in energy consumption could be attributed to structural change towards less-intensive economy due to decreasing heavy industry share. Based on 30 years empirical data, Geller et al.²⁰ confirmed the influence of well-designed energy policy and structural changes on energy consumption reduction for OECD countries. Apergis et al.²⁵ investigated energy consumption in OECD countries during the period of 1985–2011 and concluded that energy consumption is rising due to a rising population. Esseghir and Khouni¹⁸ included GDP, labour force and gross fixed capital formation as the main factors in the modified production function to investigate energy use. Liu et al.²¹ explained the dynamics of energy consumption for China and US economies in 1997–2007 by the influence of technological and industrial structure changes.

Summarizing the literature review, the following determinants of energy consumption can be identified: economic growth, energy prices, structural changes, increasing population, technological innovations, institutional reforms as well as classical factors of labour and capital. Among other reasons that cause energy consumption change, there are governmental energy policy and energy efficiency improvements, CO₂ emissions, financial development, international trade, etc.^{7,18,20,21,25,34,39} Nevertheless, there is still a lack of empirical studies investigating determinants of energy efficiency dynamic processes and energy consumption in the case of the post-communist countries.

Methodology and data

Using the World Bank and EBRD data,^40,41 on economic trends and countries’ energy development, the authors estimate the impact of various factors on the dynamics of energy efficiency and energy consumption for 11 selected post-communist countries of the Eastern Europe for the period 1996–2013.

When choosing a number of countries to study, the authors considered the following facts. The selected post-communist countries (Slovenia, Slovak Republic, Czech Republic, Romania, Poland, Lithuania, Latvia, Estonia, Belarus, Russian Federation and Ukraine) have common historical and strong economic roots. For a long time, the countries have had a centrally planned economy that defined the specifics of their economic development with historical emphasis on heavy industries and artificially low energy prices during the Soviet era. Even after Soviet Union collapse, these countries have preserved the dependence on energy resources especially on natural gas supplied from one of them – Russia. For a long time in the past, that dependence defined practical absence of world oil and gas prices influence on the post-communist countries due to the long-term energy contracts with Russia at fixed prices.

Over the past 25 years, the post-communist countries have passed a different path. Some of them have reached significant results in energy efficiency and economic growth of their national systems while the others were less successful in this field. Problems of energy efficiency development of the post-communist countries motivate the investigation of common factors that influence energy efficiency and energy consumption in order to create the basis for policy improvements at micro and macro level.

The period of the study (1996–2013) was chosen for the following reasons:

statistics for a number of post-communist countries (Lithuania, Latvia, Estonia, Belarus, Russian Federation and Ukraine) is available only since 1991. In that year, the Soviet Union collapsed and the former Soviet republics began to operate as independent states. However, after the Soviet Union collapse during next few years economies of the former republics were in crisis because of breaking economic ties and painful state processes; since 1996, a tendency to stabilize economic processes emerged. Therefore, from this study, the authors excluded 1991–1995 years that do not reflect the steady trends of economic activity;

2014 was marked for Ukraine by the loss of state control over the part of country’s territory (the Crimea and a part of Donbas) that reflected in statistical indicators of economic activity of the country and made it impossible to obtain comparable results for this study. Therefore, 2014–2016 years were excluded from the calculations because they did not provide comparability of indicators for all countries investigated.

Using the stochastic frontier function approach, the authors constructed and tested econometric models, which reflect various factors influencing on energy efficiency level (expressed by GDP per 1 kilogram of oil) and dynamics of per capita energy consumption for the selected countries.

To determine the set of factors included in the econometric models, the authors took into account the main determinants from research results, described in Literature review section. On this basis, the indicators of GDP per capita, oil and gas prices, value added of the industrial sector as a proxy for structural changes factor, technological export as a proxy for innovations driver, CO₂ emissions per capita and gross fixed capital formation as a proxy for investment were included in the set of factors influencing on energy efficiency. The authors do not consider population because this factor does not play a significant role for energy efficiency processes as well as for energy consumption in the post-communist countries.

In the empirical analysis, gross fixed capital formation indicator is used. According to the World Bank,⁴⁰ the indicator includes investment in fixed assets (machinery, equipment, etc.) as well as land improvements (fences, ditches, drains and so on); plant, machinery and equipment purchases and the construction of roads, railways and the like, including schools, offices, hospitals, private residential dwellings and commercial and industrial buildings. The indicator of gross fixed capital formation should have a positive correlation with GDP performance since all above-mentioned investments are included in GDP. In addition, having such a broad data on gross fixed capital formation, it is difficult to state a theoretical relation of the above-mentioned gross fixed capital formation and energy consumption per capita. Thus, there is no theoretical support how the construction of roads, railways, schools, offices, hospitals, etc. would influence the energy consumption per capita. For this reason, to construct factor model on energy consumption per capita, the authors used the same parameters as for the previous energy efficiency model, excluding factor of the gross fixed capital formation. The authors also excluded the factor of CO₂ emissions per capita from the last model because of direct correlations between this factor and the dependent variable, since the more energy is produced or consumed the more CO₂ emissions volumes are generated. Excluding of above-mentioned factors from the considered models is consistent with the results presented by other researchers.^18,24,36,39

The authors included a dummy for countries, which are subject to the European energy policy to both econometric models. This dummy would serve as institutional proxy to take into consideration the heterogeneity of 11 post-communist countries of the Eastern Europe. The European energy policy dummy is zero for all the non-European Union countries, and unity for the ‘new’ European Union members starting the year of accession.

In order to test the effect of free market economy influence, the EBRD data on ‘governance and enterprise restructuring’ indicator is used. Progress in the EBRD approach is measured against the standards of industrialized market economies. The measurement scale for the indicators ranges from 1 to 4+, where 1 represents little or no change from a rigid centrally planned economy and 4+ represents the standards of industrialized market economy. Due to the fact that some of the variables included in panel regression can be trending, the authors include time year dummies to address the issue.

Given the discussion above, the authors can construct a regression model to estimate the influence of different factors on energy efficiency for a panel of 11 countries, based on the World Bank and the EBRD data sets,^40,41 as follows

E F_{t} = E (Y_{t}, P_{t}, G P_{t}, I V A_{t}, C O_{2 t}, T E_{t}, F C_{i}, G E R_{t}, E U_E P_{t}, t_{t})

(1)

where

EF_t is energy efficiency (GDP per 1 kilogram of oil);

Y_t is GDP per capita (in constant prices);

P_t is the real price of energy in terms of oil prices;

GP_t is the real price of energy in terms of gas prices;

IVA_t is the value added of the industrial sector (in constant prices);

CO_2t is CO₂ emissions per capita (metric tons);

TE_t is the amount of technological export;

FC_t is gross fixed capital formation (in constant prices);

GER_t is the institutional dummy (ranges against the standards of industrialized market economies from 1 to 4+);

EU_EP_t is the institutional dummy (1 for countries subjected to European energy policy, 0 – otherwise);

t_t is the annual dummy (1996–2013).

In order to estimate these relations empirically, the authors need to transform all variables into logarithms in order to work with elasticizers. Adopting the stochastic frontier function for energy efficiency of a national economy, the resulting log-log functional form of equation (1) can be estimated as follows

e f_{t} = β_{0} y_{t} + β_{1} p_{t} + β_{2} g p_{t} + β_{3} i_{t} + β_{4} c o_{2 t} + β_{5} t e_{t} + β_{6} f c_{t} + β_{7} g e r_{t} + β_{8} e u_e p_{t} + β_{9} t_{t} + u_{t}

(2)

where

ef_t is the natural logarithm of energy efficiency (GDP/kg of oil, EF_t);

y_t is the natural logarithm of GDP per capita (Y_t);

p_t is the natural logarithm of the real price of energy in terms of oil prices (P_t);

gp_t is the natural logarithm of the real price of energy in terms of gas prices (GP_t);

i_t is the natural logarithm of the value added of the industrial sector (IVA_t);

co_2t is the natural logarithm of CO₂ emissions per capita (CO_2t);

te_t is the natural logarithm of amount of technological export (TE_t);

fc_t is the natural logarithm of gross fixed capital (FC_t);

ger_t is the institutional dummy (GER_t);

eu_ep_t is the institutional dummy (EU_EP_t);

β₀,…, β₉ are regression coefficients of the model;

u_it is an error term.

Another important issue to be discussed is per capita energy consumption in the post-communist countries. The authors used the following regression to estimate the influence of different factors on energy consumption per capita for a panel of 11 countries, based on the World Bank and EBRD data.^40,41

E_{t} = E (Y_{t}, P_{t}, G P_{t}, I V A_{t}, T E_{t}, G E R_{t}, E U_E P_{t}, t_{t})

(3)

where

E_t is aggregate energy consumption per capita (kg of oil equivalent).

In order to estimate the above-mentioned relations empirically, the authors transform all variables into logarithms and work with elasticizers. Adopting the stochastic frontier function for energy consumption of national economy, the resulting log-log functional form of equation (3) can be estimated as follows

e_{t} = β_{0} y_{t} + β_{1} p_{t} + β_{2} g p_{t} + β_{3} i_{t} + β_{4} t e_{t} + β_{5} g e r_{t} + β_{6} e u_e p_{t} + β_{7} t_{t} + u_{t}

(4)

where

e_t is the natural logarithm of aggregate energy consumption per capita (E_t).

Research results and discussion

Modelling energy efficiency

According to the results presented in Table 1, it is seen that GDP per capita is one of the most significant factors of energy efficiency increasing for the post-communist countries. An increment of GDP per capita by 1% does increase energy efficiency by 0.53%, which means that the richer the society, the higher level of efficiency it can reach. This result can be explained by the fact that richer societies have higher rates of saving (investment) compared to the consumption rate growth. It creates the accumulation of surplus funds in the economies that can be invested in energy-efficient projects. In addition, richer countries have a different structure of the economy and can afford themselves to develop high-technological industries, which are more energy efficient on average. Moreover, an increase in GDP per capita is also associated with the development of service sector that also creates GDP. These conclusions are consistent with the results of other studies.^8,10,12,15

Table 1.

The regression analysis of energy efficiency (GDP per 1 kg of oil) for the panel of 11 countries.

ef_t	Coefficient	SE	z	P > \|z\|	95% Confidence interval
y_t	.5280585	.0288032	18.33	0.000	.4716053, .5845117
p_t	−.4005176	1.476453	−0.27	0.786	−3.294313, 2.493278
gp_t	.9443461	1.880199	0.50	0.615	−2.740776, 4.629469
i_t	−.2752879	.0571556	−4.82	0.000	−.3873108, −.1632651
co_2t	−.5805169	.0412213	−14.08	0.000	−.6613093, −.4997246
te_t	−.0259844	.0240671	−1.08	0.280	−.0731551, .0211863
fc_t	.3373337	.0615208	5.48	0.000	.2167552, .4579123
ger_t	.0129805	.0278211	0.47	0.641	−.0415477, .0675088
eu_ep_t	.1118674	.0550645	2.03	0.042	.003943, .2197918
у1996	−.0355612	.1828648	−0.19	0.846	−.3939696, .3228473
у1997	−.0390779	.1461695	−0.27	0.789	−.3255647, .247409
Other time year dummies y1998–y2012
_cons	−3.243717	2.306536	−1.41	0.160	−7.764445, 1.277011
sigma_u			0
sigma_e			.07367607
rho			0 (fraction of variance due to u_i)

Random-effects GLS regression; Group variable: id, R-sq: within = 0.9597, between = 0.8770, overall = 0.9238; corr(u_i, X) = 0 (assumed); Number of obs = 197, Number of groups = 11, Obs per group: min = 13, avg = 17.9, max = 19; Wald chi squared (25) = 2072.89; P > chi squared = 0.0000.

Source: authors’ calculations based on the World Bank and the EBRD data, estimated with Stata 14.0.

The influence of gas and oil prices on energy efficiency dynamics is not significant according to results of the research. This situation can be explained by the fact that some post-communist countries during the studying period had long-term import contracts on gas supply with fixed gas prices; therefore, the dynamics of world gas prices and related oil prices did not have a significant impact on the countries’ economies.

The next significant factor influencing energy efficiency is CO₂ emissions per capita. Unlike GDP per capita, it is characterized by the reverse effect on GDP per 1 kg of oil. Thus, an increment of CO₂ emissions per capita by 1% does decrease energy efficiency by 0.58%, which means that the more CO₂ emissions we produce through using more energy, the lower level of efficiency we can reach. This result is logical because CO₂ emissions is the direct consequence of burning fossil fuels in growing scale, and, therefore, the increment of CO₂ emissions per capita means that we use energy less efficiently. This conclusion is confirmed by other studies.^24,36,39

As of the industry value added level, it is also seen from the Table 1 that increment of GDP share created in the industrial sphere leads to decrease in energy efficiency level of an economy. The last statement is logical because industrial production always requires more resources, including energy, than service sector. Therefore, the energy efficiency level of industrial production on average is lower compared to the service sphere. Such a conclusion is consistent with results of other studies.^11,12,15 An increase in industry value added levels for the post-communist countries by 1% decreases energy efficiency by 0.28%.

The factor of gross fixed capital has a positive impact on energy efficiency. Its increase by 1% increases energy efficiency of the post-communist countries by 0.34%. Like GDP per capita growth, gross fixed capital increment provides the necessary conditions for implementing energy-efficient projects through improving the technical and material base of energy-efficient changes. This correlation is confirmed by other papers.^18,24

The results of the model state that the technological export factor is not significant for energy efficiency processes and its changes do not cause visible improvements in energy efficiency of the post-communist economies. The EBRD indicator ‘governance and enterprise restructuring’ appeared to be insignificant while the statistical significance of the European Union energy policy dummy supports the idea that the European Union accession improves energy efficiency indicators.

All explanatory factors such as GDP per capita, industry value added, CO₂ emissions per capita, gross fixed capital formation and the European Union energy policy dummy are appeared to be inelastic. The result obtained from modelling can be explained by the fact that the majority of the considered post-communist countries have inherited a heavy industrial complex since they were centrally planned economies. Thus, the heavy industry in many of the post-communist countries received intensive development that was fixed in high levels of industry value added.

The availability of cheap energy resources made low energy efficiency as a starting point for all post-communist countries. Since 1991, the post-communist countries got an opportunity to develop their economy independently, and all of them have undergone serious transformations. However, the degree of transition from planned economy to the market system was not the same in different post-communist countries, as well as economic achievements were different. From Table 1, it is seen that institutional changes towards the European Union membership influenced positively on energy efficiency growth in the post-communist countries.

Modelling energy consumption

Based on the results presented in Table 2, the authors conclude that GDP per capita is the most significant factor of increasing per capita energy consumption in the post-communist countries on average. An increase in GDP per capita by 1% does increase per capita energy consumption by 0.21%. Therefore, the authors can affirm that richer the society becomes the more energy it consumes. This result can be explained by the fact that richer people have more opportunities to satisfy their needs, including larger apartments, longer travelling distances, etc. All these issues promote to higher energy consumption rates. According to Table 2, the impact of oil and gas prices on changes of energy consumption is not statistically significant.

Table 2.

The regression analysis of aggregate energy consumption per capita for the panel of 11 countries.

ef_t	Coefficient	SE	z	P > \|z\|	95% Confidence interval
y_t	.2116623	.0467703	4.53	0.000	.1199942, .3033304
p_t	.4101139	.6190412	0.66	0.508	−.8031845, 1.623412
gp_t	−.57373	.7884374	−0.73	0.467	−2.119039, .9715788
i_t	.0789444	.0243137	3.25	0.001	.0312903, .1265984
te_t	−.0306009	.0134936	−2.27	0.023	−.0570478, −.004154
ger_t	−.0358261	.0302456	−1.18	0.236	−.0951063, .0234541
eu_ep_t	.0278148	.0266562	1.04	0.297	−.0244304, .0800601
у1996	.0026513	.0768415	0.03	0.972	−.1479553, .153258
у1997	−.0131509	.061315	0.21	0.830	−.133326, .1070243
Other time year dummies y1998–y2012
_cons	4.355621	1.111651	3.92	0.000	2.176824, 6.534417
sigma_u			.09075438
sigma_e			.05341881
rho			.74268818 (fraction of variance due to u_i)

Random-effects GLS regression, Group variable: id, R-sq: within = 0.5212, between = 0.1828, overall = 0.1850; corr(u_i, X) = 0 (assumed); Number of obs = 197, Number of groups = 11, Obs per group: min = 13, avg = 17.9, max = 19; Wald chi squared (23) = 137.48; P > chi squared = 0.0000.

Source: authors’ calculations based on the World Bank and EBRD data, estimated with Stata 14.0.

As of the industry value added level, it is also seen from Table 2 that the larger share of GDP is created in the industrial sphere, the more per capita energy consumption it requires. The last results are very logical, since the industrial sector requires more resources than the service sector. An increase in industry value added levels for the post-communist countries by 1% increases energy consumption per capita by 0.08%.

A negative correlation was found for high technological export influence on energy consumption. According to the obtained results, increase in high technological export by 1% reduces energy consumption per capita by 0.03%. This can be explained by the fact that economy’s reconstruction towards increasing the share of high technologies and service sector leads to energy consumption reduction per capita.

Both institutional dummies (the EBRD indicator ‘governance and enterprise restructuring’ and the European Union energy policy dummy) appeared to be insignificant. The last means that mentioned institutional changes are not important factors to determine the per capita energy consumption.

Also, like in the previous table, all explanatory factors such as per capita GDP, industry value added and technological export appeared to be in accordance with theory and inelastic.

Conclusions

In this paper, the authors investigated the influence of different factors on energy efficiency and energy consumption for the panel of 11 post-communist countries in the Eastern Europe during 1996–2013. The stochastic frontier function approach, as well as comparative analysis, was used to examine long-run dynamic relations. The major findings are as follows:

1. The research results on energy efficiency relations show that economic growth is the essential factor for energy efficiency increase. The next important driver is CO₂ emissions per capita. The significant factor is a fixed capital, which provides a material and technical base for energy efficient projects’ implementation as well as the structural factor, aimed at restructuring the economy and reducing the share of industry in it. At the last place, there is a factor of the European Union energy policy, which shows that the European Union accession improves national energy efficiency indicators. This factors’ distribution is quite logical because the implementation of structural and institutional changes in the economy require significant investment that can be available only in conditions of economic recovery. Therefore, GDP growth is the key to enhancing the positive impact of other factors and implementing energy efficient projects.

The authors found that all factors excluding the structural factor (value added of the industrial sector) and CO₂ emissions per capita are factors of a positive influence. The structural factor and CO₂ emissions per capita are factors with a reverse effect, i.e. their positive change causes negative changes in energy efficiency level.

2. For the post-communist countries, the authors proved that GDP growth is a key factor increasing both energy efficiency and energy consumption. In the context of per capita energy consumption growth, the next but less important factor are structural changes determined by industry share in the national economy; the third one is a factor of innovation, development and implementation of high technologies. Given the specifics of the post-communist countries, the price factor (dynamics of world prices for basic energy resources) does not play a significant role in changing energy consumption and increasing energy efficiency. Institutional transformations (governance and enterprise restructuring and the European Union energy policy) appear to be unimportant for energy consumption dynamics.

As for the first three factors, their impact on per capita energy consumption is seen quite logical. Energy consumption increase depends on the income increase (which is measured by per capita GDP), economy’s structure (the larger industry share compared to the share of service sector indicates less energy efficiency and growing energy consumption) and intensity of innovations’ implementation (technological exports’ reduction increases energy consumption).

3. Given the research results, energy efficiency policy in the post-communist countries should be aimed at providing further economic growth to enhance the positive impact of other factors and implementing energy efficient projects. It is expedient to improve policies in the energy sector followed the European Union patterns, which should include increasing prices for non-renewable energy resources and decreasing consumer prices for renewable energy and to introduce preferential taxation and additional funding for implementation of green energy efficient technologies at public and private enterprises. The economic policy should be directed to active reforming of the national economy towards the growth of services sector’s share and introducing innovative energy efficient technologies in all spheres of the national economy with special focus on technologies that reduce CO₂ emissions. Further research could examine the efficiency of identified policy instruments and correct the tools’ set for certain post-communist countries with regard to the national specifics.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was prepared under the framework of the joint Ukrainian–Lithuanian research project ‘Development of institutional and economic basis for sustainable development and “green” economy at regional level’ (No. 0116U007179) and was funded by a grant (No. TAP LU-4–2016) from the Research Council of Lithuania.

Lina Sineviciene has been working as a Lecturer at the Department of Finance, School of Economics and Business, Kaunas University of Technology, Lithuania. She got the Doctoral degree in Social Sciences (Economics) from Kaunas University of Technology in 2013. Lina Sineviciene has published more than 30 scientific papers, she is a laureate of scholarships provided by the Lithuanian Academy of Sciences (2017-2018). She is a leader of 1 and a contributor of 3 scientific and research projects, including international ones. Her research interests are: capital investment, fiscal policy, fiscal policy interaction with private investment, sustainable development.

Iryna Sotnyk has been working as Professor and Deputy Head of Department of Economics and Business-Administration at Sumy State University, Ukraine. In 2002 she got the scientific degree of PhD in Economics. In 2010 she got the scientific degree of Doctor of Sciences (Economics). In 2004 she was awarded by academic rank of Associate Professor; in 2012 ? by academic rank of Professor of Department Economics and Business Administration. Iryna Sotnyk is a laureate of the Cabinet of Ministers of Ukraine Prize (2004) and scholarships of the Cabinet of Ministers (2011-2012) and the Verkhovna Rada of Ukraine (2012-2013). Iryna Sotnyk has published more than 250 scientific and 35 educational papers. She is a leader of 8 and a contributor of more than 25 scientific and research projects, including international ones. The sphere of her scientific interests includes economics of energy and resource saving, environmental economics, sustainable development.

Oleksandr Kubatko has been working as Associate Professor of Economics and Business- Administration Department at Sumy State University, Ukraine. In 2010 he got the scientific degree of PhD in Economics. In 2015 he was awarded by academic rank of Associate Professor. Oleksandr Kubatko has published more than 50 scientific papers, including 5 papers in international peer-reviewed journals. He is a leader of 2 and a contributor of more than 10 scientific and research projects, including international ones. The sphere of his scientific interests includes environmental economics, sustainable development, and health economics.

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