Renewable Energy Consumption-Growth Nexus in European Countries: A Sectoral Approach

Abstract

The renewable energy consumption plays a significant role in achieving sustainable development, but a sectoral approach is necessary to design the better recommendations for each sector. In this context, the main objective of this paper is to assess the impact of the use of this type of energy on economic growth in 23 European Union (EU) member states in the period 1990–2020. Besides overall renewable energy consumption, different utilisations of this energy are considered: in industry, transport, in commercial and public services, and for residential purposes. The methodological background is built around panel data models that start from a Cobb–Douglas function. The renewable energy consumption is considered an important factor that should generate economic growth. The panel data approach based on causality analysis and Augmented Mean Group and Common Correlated Effects Mean Group estimators suggests that renewable energy use in industry does not determine economic growth, but economic development is a cause for more utilisation of this energy in industry. In addition, more renewable energy consumption in transport enhances economic growth. A high level of economic development can promote the consumption of renewable energies in industrial sectors. In this way, industrial companies can allocate more financial funds to research in the field of renewable energies and can afford to adopt renewable energy sources. Investment in biofuels can contribute to achieving sustainable transport in the EU.

Keywords

renewable energy economic growth panel data models panel causality

Introduction

EU energy policy has very specific objectives. These include diversifying energy sources and promoting renewables in the energy mix; developing a fully integrated European energy market; promoting energy efficiency; supporting the reduction of greenhouse gas emissions; funding research and innovation in low-carbon technologies; and supporting public–private partnerships in the field of low-carbon energy. The ultimate goal is to achieve carbon neutrality by 2050 and is mainly related to the promotion of low-carbon energy sources (renewable energies) in the EU. Renewable energy has some significant advantages: decrease of pollution and dependence on fossil fuel production, fight against resource depletion, reduction of energy poverty (Adom et al., 2021; Rezaei & Ghofranfarid, 2018), reduction of fossil fuel import needs and dependence on this type of exports for many countries (Gökgöz & Güvercin, 2018).

The EU strategy in the energy field is very ambitious and the progress achieved on this path is significant due to massive investments in this type of energy. The European Central Bank finances investments in the field of renewable energy and there are EU funds earmarked for innovation in technological processes, pilot plants and smart grids (Simionescu et al., 2020). The European Investment Bank, on behalf of the European Commission, provides technical assistance to private or public actors to support investments in energy efficiency or renewable energy in buildings or transport.

The European Union set its ambitious targets for renewable energy use at a level of 20% by 2020, 32% by 2030 and to reach full carbon neutrality by 2050. The EU achieved and even exceeded its ambitious 2020 target, with a 22.1% share of renewable energy consumption in the energy mix, but this target was achieved in the context of the pandemic, when total energy consumption decreased. This increase was supported by a significant increase in the share of renewables in electricity generation (37.5% in 2020 due to low operating costs in the wind and hydropower sectors, or even in the solar energy sector, the latter sector recording the highest growth in recent years), although the share of renewables in transport and buildings (heating and cooling) also increased, but at a slower pace. In the electricity sector, renewables will overtake fossil fuels for the first time in 2020 (EEA, 2022).

Among the EU countries with the best renewable energy performance in 2020 are the Netherlands, Luxembourg, Sweden, Spain, Portugal and Cyprus. Except for France, all EU countries have achieved their 2020 renewable energy targets. Iceland relies heavily on its geothermal energy. Norway follows and relies on its hydropower. Albania and Latvia are betting on hydro, wind and biofuels. The Scandinavian countries (Sweden, Finland and Denmark) are committed to biofuels and lead the EU countries in this field. Austria, Portugal, Croatia, Lithuania and Estonia also lead the group of European countries. On the other hand, Luxembourg, Belgium, Hungary, Poland and Malta occupy the last positions among the EU countries according to their shares of renewables in total energy consumption. The EU has the largest maritime area in the world. Among the main consumers of renewable energy, the UK and Germany rank first, and China is the first worldwide. Globally, demand for renewables increased by 3% in 2020, while for all other energy sources a decline is observed (IEA, 2021). The highest growth is also attributed to electricity generated by renewable energy sources. The EU also reached its target of 10% renewables in transport in 2020, based mainly on biofuels. To reach the 14% target in 2030, the use of biogas must be expanded immediately in the transport sector. In this sector, only the northern European countries (Sweden, Norway and Finland) reached the 10% target in 2020 and far exceeded it, but also Iceland and the Netherlands reached this target. The rest of the European countries did not reach the 10% target in the transport sector. Sweden has the highest share of renewables in transport and applies a carbon tax on fuels, but also exemptions and reductions for the use of biofuels. Northern European countries implement policies to support electric mobility and show a significant supply of renewable electricity (Sweden, Finland, Norway).

Although the share of renewables in electricity used in transportation increased, it still represents a low percentage of total renewables used in transportation. The transport sector is one of the sectors that uses the least renewable energy, while energy demand in this sector is growing faster than in any other. Globally, China, India and the Association of Southeast Asian Nations (ASEAN) account for half of the growth in biofuel production, with another significant share coming from Latin America, mainly Brazil. The transport sector accounted for almost 25% of global energy-related greenhouse gas (GHG) emissions in 2019 and this share could increase to 60% by 2050 if appropriate measures are not taken. Transport accounts for one third of total energy consumption worldwide (mainly due to road transport), but only 3.7% of the total energy used in this sector comes from renewable sources. European targets for renewable energy use in 2030 in the sectors are 14% in transport, 28% in industry, 37% in buildings and 50% in power generation. Renewable energy generation has become cheaper and more affordable and more efficient. Like transport, the industrial sector accounts for one-third of total energy consumption in the EU, while renewables account for 18% of total industrial energy demand. Sweden, Finland and Germany are the largest consumers of renewable energy in the industrial sector, with half of the total demand for renewables across the EU. The Baltic States and the Scandinavian countries rank first in terms of their share of renewables used in the biomass-based industrial sector. Poland, France and Spain are also among the top countries in this area. Most EU countries use biomass as renewable energy in this sector, while Germany, Italy and the United Kingdom use mainly renewable electricity. The target for this sector in terms of renewable energy use is set at 28% by 2030 in the EU area, especially through the development of renewable electricity use in the industrial sector. Renewable energy use in EU households accounted for 18.2% in 2019 and 20.1% in 2020. For buildings, renewable energy use accounts for 22% of total energy demand in the EU, based mainly on biomass and renewable electricity. Austria and Cyprus lead the way in terms of the use of solar water heating. France and Finland use geothermal heating. Heat pumps that convert heating applications into electricity are widely used in France, Sweden and Germany. The urbanisation process favours the use of renewable energies for heating and cooling. The target for renewable energy use in buildings has been set at 32% by 2030, especially through the use of renewable electricity and solar water heaters in most EU countries, or through the use of bioenergy where energy demand is high (Central European and Baltic States) (IRENA, 2018).

There are many studies that investigated the relationship between renewable energy consumption and economic growth for EU countries (Kasperowicz et al., 2020; Marinaș et al., 2018; Menegaki, 2011; Okyay et al., 2014; Soava et al., 2018; Saad & Taleb, 2018; Ucan et al., 2014). Most of those studies produced for the EU panel or for a part of the EU member states found a positive relationship between renewable energy consumption and economic growth. Only a few researchers found mixed results or no relationship. Some previous studies investigated the relationship between total energy consumption in transport and economic growth in EU countries and showed a close relationship (Rokicki et al., 2021) and emphasised the need for sustainable transport for sustainable development. Tutak and Brodny (2022), in a recent study, investigated renewable energy consumption in economic sectors and households in the EU-27. They found that renewable energy increased 3-fold during the period 2000–2019, with the largest increase occurring in the old EU Member States and in the transport sector. The study has also shown a positive relationship between renewable energy consumption and economic growth and a negative relationship between renewable energy consumption and GHG emissions or conventional energy consumption for all EU countries.

Therefore, it is necessary to carry out specific research to study the relationship between renewable energy and economic growth in the EU area in economic sectors. In this way, European and national authorities can design appropriate policies in the energy field with the aim of promoting renewable energies in those sectors that support sustainable economic growth. We have investigated this relationship for 23 EU countries during 1990–2020, based on the AMG and CCEMG estimators.

Literature Review

The importance of renewable energies in the energy mix is growing worldwide. Among the advantages of using renewable energies are that they are inexhaustible resources, have a low environmental impact and have the capacity to develop and promote growth in the areas where they are implemented, both in terms of GDP and employment (Alexandri et al., 2016).

The concern for advancing the development of the renewable energy research field is evident in the growing literature that demonstrates the potential of renewable energy use worldwide and its relationship with the economic conditions of countries. And while the nexus between economic growth and energy use in the context of various countries has been the subject of much empirical work, there is far less in the area of renewable energy.

The debate on whether or not energy consumption contributes to economic growth has direct implications for the formulation of policy strategies. A large body of research has been devoted to examining the relationship between energy consumption, whether renewable, non-renewable or general, and economic growth. This association can be grouped into the following hypotheses:

• Growth hypothesis: occurs when energy has a positive unidirectional effect on economic growth (Kasperowicz et al., 2020; Soava et al., 2018). Energy is a complementary component of production, like capital and labour. Conservative energy policies can have a detrimental effect on economic growth, while expansionary energy policies can produce an increase in economic growth.

• Feedback hypothesis: occurs when there is a positive bidirectional association between energy and economic growth (Zafar et al., 2019). Expansionary energy policies cause an increase in economic growth while conservative energy policies can have a detrimental effect on growth.

• Neutrality hypothesis: when there is no relationship between energy use and economic growth, with restrictive policies being applied (Ozcan and Ozturk, 2019).

• Conservative hypothesis: establishes unidirectional causality from economic growth to energy consumption (Alam & Murad, 2020; Saad & Taleb, 2018). Economic growth drives energy consumption, so restrictive energy policies are appropriate.

The research has been applied to many countries in the world for different data periods and with different methodological approaches. The results show that there is no consensus on the existence of a relationship between both magnitudes and neither on the direction of causality between them, in case there is a link. Table 1 shows the recent literature on the relationship between renewable energy consumption and economic growth. These papers analyse individual countries as time series or panel groups of countries. Some focus only on analyzing the connection with renewable energy in general (Bildirici & Kayıkçı, 2022; Neagu et al., 2021) and others on exploring the nexus by distinguishing between the effect of energy consumption on economic growth is separated by renewable and non-renewable energy (Kahia et al., 2017). The most commonly used methods include Autoregressive Distributed Lag (ARDL), Vector Autoregression (VAR), Granger causality or co-integration and Vector Error Correction (VEC).

Table 1.

Recent Literature on the Relationship Between Renewable Energy Consumption and Economic Growth.

Author	Period	Region	Methodology	Supported hypothesis	Impact
Bildirici and Kayıkçı (2022)	1976–2019	G20 countries	Panel Fourier bootstrapping ARDL model	Conservative	Positive
Iqbal et al. (2022)	2000–2018	BRICS	ARDL, pool mean group (PMG), mean group (MG) and the Dumitrescu–Hurlin panel causality tests	Feedback	Positive
Wang et al. (2022)	1997–2015	OECD countries	Kao panel co-integration test	Growth	Positive
Mounir and El-houjjaji (2022)	1980–2020	G7 countries	Toda and Yamamoto and Lemmens et al. tests and Hatemi-J’s asymmetric causality approach.	Growth (Germany) Conservative (US, Canada) Neutrality (France, UK, Italy and Japan)	Positive
Akram et al. (2021)	1990–2014	BRICS	Fixed effect panel quantile regression	Feedback	Negative
El-Karimi (2021)	1980–2019	Germany, UK and France	Toda and Yamamoto time causality test and Lemmens et al.	Neutrality (UK and France) Feedback (Germany)	Positive
Li and Leung (2021)	1985–2018	7 European countries	Econometric modelling techniques	Neutrality
Neagu et al. (2021)	1995–2015	Central and Eastern European countries	Multivariate panel data	Feedback	Positive
Alam and Murad (2020)	1970–2012	25 OECD	Panel co-integration	Conservative	Positive long-term; negative short-term
Kasperowicz et al. (2020)	1995–2016	29 European countries	Panel VEC	Growth	Positive
Aydin (2019)	1980–2015	26 OECD	Panel causality	Feedback	Positive
Can and Korkmaz (2019)	1990–2016	Bulgaria	ARDL bounds test	Feedback	Positive
Ozcan and Ozturk (2019)	1990–2016	17 emerging countries	Bootstrap panel causality	Neutrality
Zafar et al. (2019)	1990–2015	APEC countries	FMOLS	Growth	Positive
Cai et al. (2018)	1965–2015	G-7 countries	ARDL	Growth	Positive
Marinaș et al. (2018)	1990–2014	10 European Union	ARDL	Mixed
Saad and Taleb (2018)	1990–2014	12 EU countries	Panel VEC	Conservative short run, Feedback long run	Positive
Soava et al. (2018)	1995–2015	28 EU countries	Co-integration	Growth	Positive
Tugcu and Topcu (2018)	1980–2014	G7 countries	Asymmetric causality; NARDL model	Mixed
Amri (2017)	1990–2012	72 countries	Dynamic simultaneous equation	Feedback	Positive
Destek and Aslan (2017)	1980–2012	17 Emerging Economies	Bootstrap panel causality	Mixed
Kahia et al. (2017)	1980–2012	MENA countries	Panel VEC	Feedback	Positive
Alper and Oguz (2016)	1990–2009	New EU Member countries	Asymmetric causality	Mixed
Destek (2016)	1971–2011	Newly industrialised countries	Asymmetric causality	Mixed
Inglesi-Lotz (2016)	1990–2010	OECD countries	Panel co-integration	Growth	Positive
Shahbaz et al. (2016)	1991–2015	BRICS	Co-integration and VEC	Feedback	Positive
Bilgili and Ozturk (2015)	1980–2009	G7 countries	Panel co-integration POLS, DOLS	Growth	Positive
Bozkurt and Destek (2015)	1980–2012	4 OECD	Toda–Yamamoto causality	Growth	Positive
Chang et al. (2015)	1990–2011	G7 countries	Panel Granger causality	Mixed
Halkos and Tzeremes (2014)	1990–2011	36 countries	Non-parametric techniques	Growth	Positive
Kula (2014)	1980–2008	19 OECD	Countries Panel co-integration	Conservative	Positive
Okyay et al. (2014)	1990–2011	15 European countries	Panel co-integration, panel causality test	Growth	Positive
Salim et al. (2014)	1980–2011	OECD countries	Panel VEC	Conservative	Positive
Ucan et al. (2014)	1990–2011	15 EU countries	Panel co-integration	Feedback	Positive
Magnani and Vaona (2013)	1997–2007	Italy	Panel VEC	Growth	Positive
Apergis and Payne (2012)	1990–2007	80 countries	Panel VEC	Feedback	Positive
Tugcu et al. (2012)	1980–2009	G7 countries	Autoregressive Distributed Lag	Feedback	Positive
Apergis and Payne (2011a)	1990–2007	16 emerging market countries	Panel VEC	Conservative, Feedback	Positive
Menegaki (2011)	1997–2007	27 European countries	Panel VEC	Neutrality
Apergis and Payne (2010a)	1985–2005	20 OECD	Panel VEC	Feedback	Positive
Apergis et al. (2010)	1984–2007	19 countries	Panel co-integration and VEC	Feedback	Positive

Most studies have found causal relationships in at least one direction, and the hypothesis of no causal relationship, that is, neutrality, has had the least empirical evidence. The impact of the relationship between these variables can be positive (an increase, for example, in energy consumption favours economic growth) or negative (Akram et al., 2021; Alam & Murad, 2020).

Methodology

The methodological background is built around panel data models that start from an extended Cobb–Douglas function. The renewable energy consumption is considered an important factor that should generate economic growth. The data series used in this study are described in Table 2.

Table 2.

Data Description.

Variable	Notation for variable in logarithm	Unit of measurement	Definition	Source of data
Gross domestic product	GDP	Constant 2015 US $	Value of goods and services made in one country in a certain period	World Development Indicators
Gross fixed capital formation	K	Constant 2015 US $	Acquisition of produced assets and assets for own utilisation minus disposals
Employment to population ratio	L	%	Proportion of the population in a country that is employed
Exports	EXP	Constant 2015 US $	Value of goods and services provided by a country to the rest of the states
Foreign direct investment	FDI	Constant 2015 US $	Net inflows
Renewable energy consumption	REC	Ktoe	Consumption of energy based on renewable resources	International Energy Agency (IEA, 2021)
Renewable energy consumption in industry	IND	Ktoe	Consumption of energy in industry based on renewable resources
Renewable energy consumption in transport	TRANSP	Ktoe	Consumption of energy in transport based on renewable resources
Residential renewable energy consumption	RES	Ktoe	Consumption of renewable energy by households, excluding fuels for transport
Renewable energy consumption for commercial and public services	COM	Ktoe	Consumption of renewable energy in commercial and public services

Source: Own construction.

An extended Cobb–Douglas production function is proposed by augmented it with renewable energy consumption:

\begin{array}{c} {G D P}_{i t} = F (K_{i t}, L_{i t}, {EXP}_{i t,} {F D I}_{i t} {, R E C}_{i t}) \end{array}

(1)

GDP - the Gross Domestic Product in constant prices

K - Gross Fixed Capital Formation

L - Employment to population ratio

EXP - export of goods and services

FDI - foreign direct investment

REC - renewable energy consumption

i - index for country t - index for year

The variables refer to more EU countries (23 member states) for which data series are available in the case of renewable energy consumption: Austria, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Ireland, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain and Sweden. The panel data refer to the period 1990–2020.

REC is replaced by other indicators corresponding to renewable energy consumption in industry (IND), transport (TRANSP), by households (RES) and in commercial and public services (COM).

The log linear specification is used and model (1) could be rewritten as

\begin{array}{c} {\ln (G D P)}_{i t} = α_{i t} + α_{K} {\ln (K)}_{i t} + α_{L} {\ln (L)}_{i t} + α_{EXP} {\ln (EXP)}_{i t} + α_{N E C} {\ln (F D I)}_{i t} + α_{R E C} {\ln (R E C)}_{i t} {+ u}_{i t} \end{array}

(2)

α_K, α_L, α_EXP, α_FDI and α_REC indicate elasticity of gross fixed capital formation, employment to population ratio, exports, FDI and renewable energy consumption.

$u_{i t}$ - error

The robustness check is conducted by considering renewable energy consumption in industry, transport, residential purpose and in commercial and public services.

The methodology consists in preliminary tests before estimation and proper estimations and causality analysis. Preliminary tests refer to cross-sectional dependence, slope heterogeneity, unit root and co-integration.

Preliminary Tests

The cross-sectional dependence between countries is due to network connections, common shocks like a global crisis and issues related to correct specification of the model. It is necessary to control the cross-sectional dependence to obtain consistent and unbiased estimators (Pesaran, 2015).

The null hypothesis sets up null covariance between errors which means cross-sectional independence: $c o v (u_{i t}, u_{j t}) = 0$ , if the following regression is considered:

{G D P}_{i t} = α_{i} + β_{i} X_{i t} + u_{i t}

I - country index, t - time index, $X_{i t}$ - explanatory variables (k x 1 vector).

The CD statistic is calculated as

C D = \sqrt{\frac{2 T}{N (N - 1)}} \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} \frac{(T - k) {\hat{ρ}}_{i j}^{2} - E [(T - k) {\hat{ρ}}_{i j}^{2}]}{v a r [(T - k) {\hat{σ}}_{i j}^{2}]}

T - period length, N - number of countries, $\hat{ρ_{i j}}$ - estimated coefficient of pair-wise correlation.

Slope heterogeneity might be controlled to provide reliable estimation. The biased-adjusted dispersion is computed to check for slope homogeneity ( ${\bar{∆}}_{a d j}$ ):

{\bar{∆}}_{a d j} = \sqrt{N} ∙ \frac{N^{- 1} \tilde{S} - E ({\bar{z}}_{i t})}{\sqrt{v a r ({\bar{z}}_{i t})}}

\tilde{S} = \sum_{i = 1}^{N} {({\hat{β}}_{i} - {\tilde{β}}_{W F E})}^{'} \frac{X_{i}^{'} I_{t} X_{i}}{{\tilde{σ}}_{i}^{2}} ({\hat{β}}_{i} - {\tilde{β}}_{W F E})

E ({\bar{z}}_{i t}) = k

v a r ({\bar{z}}_{i t}) = \frac{2 k (T - k - 1)}{T + 1}

{\tilde{β}}_{W F E}

- weighted fixed effect pooled estimator,

{\hat{β}}_{i}

- estima

t o r

σ_{i}^{2}

estimated dispersion,

I_{t}

- unit matrix.

The cross-sectionally augmented Dickey–Fuller (CADF test) is used since it is robust to cross-sectional dependence. The statistic of this test is based on the next equations:

∆ Y_{i, t} = α_{i} + β_{i} Y_{i, t - 1} + γ_{i} {\bar{Y}}_{t - 1} + δ_{i} ∆ {\bar{Y}}_{i, t} + e_{i t}

{\bar{Y}}_{t - 1} = \frac{1}{N} \sum_{i = 1}^{N} Y_{i, t - 1}

∆ {\bar{Y}}_{i, t} = \frac{1}{N} \sum_{i = 1}^{N} ∆ Y_{i, t}

For non-stationary data in level, Westerlund test is applied to check for co-integration. The null hypothesis of this test states the lack of co-integration:

∆ Y_{i, t} = δ_{i}^{'} d_{t} + ρ_{i} (Y_{i, t - 1} - β_{i}^{'} X_{i, t - 1}) + \sum_{j = 1}^{K} φ_{i j} Y_{i, t - j} + \sum_{j = 0}^{K} φ_{i j} X_{i, t - j} + u_{i, t}

ρ_{i}

- speed of adjustment to equilibrium.

The group mean statistics are employed to check co-integration for at least one cross-sectional unit:

G_{t} = \frac{1}{N} \sum_{i = 1}^{N} \frac{ρ_{i}}{s e (\hat{ρ_{i}})}

G_{a} = \frac{1}{N} \sum_{i = 1}^{N} \frac{T ρ_{i}}{ρ_{i}^{'} (1)}

The panel statistics are employed to check if entire panel is co-integrated:

P_{t} = \frac{\hat{ρ_{i}}}{s e (\hat{ρ_{i}})}

P_{a} = T \hat{ρ}

Panel Estimations and Causality

Under cross-sectional dependence, slope heterogeneity and co-integration, Augmented Mean Group estimator (AMG) and CCEMG estimator (Common Correlated Effects Mean Group) could be constructed.

The CCEMG estimator captures unobserved common effects with heterogenous factor loadings ( $f_{t}$ ):

{G D P}_{i t} = α_{i} + β_{i} X_{i t} + γ_{i} {\bar{G D P}}_{i t} + δ_{i} {\bar{X}}_{i t} + c_{i} f_{t} + ε_{i t}

α_{i}

β_{i}

- country-specific intercept and slope,

γ_{i}

δ_{i}

c_{i}

- parameters,

X_{i t}

- explanatory variables,

ε_{i t}

- innovation,

f_{t}

- unobserved common factor.

Common dynamic effect coefficient is used to control for unobservable common factors in the case of AMG estimators.

∆ Y_{i t} = α_{i} + β_{i} ∆ X_{i t} + \sum_{t = 1}^{T} θ_{t} D_{t} + φ_{i} f_{t} + ε_{i t}

A M G = \frac{1}{N} \sum_{i = 1}^{N} \tilde{β i}

$\tilde{β_{i}}$ - estimates, D - dummy variable for time.

The Juodis et al. (2021) test (JKS test) is employed to check for causality in unbalanced panels which is suitable for models with homogenous/heterogenous parameters. Dumitrescu–Hurlin test could not be applied in this case, since the panels are unbalanced. The issue of Nickell bias is solved by using Half-Panel Jackknife method. If a linear dynamic panel model is employed, the null hypothesis states that $x_{i, t}$ does not Granger cause $y_{i, t}$ . ( $β_{p, i} = 0$ , for any i and p):

y_{i, t} = ϕ_{0, i} + \sum_{p = 1}^{P} ϕ_{p, i} y_{i, t - p} + \sum_{p = 1}^{P} β_{p, i} x_{i, t - p} + ε_{i t}

ϕ_{p, i}

- heterogeneous autoregressive coefficients,

β_{p, i}

- heterogeneous feedback coefficients,

ε_{i, t}

- shocks, i=1,2,…,T; t=1,2,…,N; p=1,2,..,P

The previous model could be rewritten as

y_{i, t} = z_{i, t}^{'} ϕ_{i} + x_{i, t}^{'} β_{i} + ε_{i, t}

β_{i, t} = {(β_{1, i}, \dots, β_{p, i})}^{'}

ϕ_{i} = {(ϕ_{0, i}, \dots, ϕ_{p, i})}^{'}

x_{i, t} = {(x_{i, t - 1}, \dots, x_{i, t - p})}^{'}

z_{i, t} = {(1, y_{i, t - 1}, \dots, y_{i, t - p})}^{'}

This is equivalent with

y_{i} = Z_{i} ϕ_{i} + X_{i} β_{i} + ε_{i}

y_{i} = {(y_{i, 1}, \dots, y_{i, T})}^{'}

Z_{i} = {(z_{i, 1}, \dots, z_{i, T})}^{'}

X_{i} = {(x_{i, 1}, \dots, x_{i, T})}^{'}

ε_{i} = {(ε_{i, 1}, \dots, ε_{i, T})}^{'}

Under the null hypothesis

β_{i} = β = 0

. The POLS estimator of

β

is computed as

\hat{β} = {(\sum_{i = 1}^{N} X_{i}^{'} M_{Z_{i}} X_{i})}^{- 1} (\sum_{i = 1}^{N} X_{i}^{'} M_{Z_{i}} y_{i})

M_{Z_{i}} = I_{T} - Z_{i} {(Z_{i}^{'} Z_{i})}^{- 1} Z_{i}^{'}

When $T, N \to \infty, \frac{N}{T} \to κ^{2} \in [0; \infty)$ , $\sqrt{N T} (\hat{β} - β_{0}) \to J^{- 1} N (- κ B, V)$

J = {p l i m}_{N, T \to \infty} {(N T)}^{- 1} \sum_{i = 1}^{N} X_{i}^{'} M_{Z_{i}} X_{i}

B - bias when T and N present the same order, V - variance-covariance matrix.

The bias elimination is ensured by the half-panel jackknife estimator:

\tilde{β} = \hat{β} + (\hat{β} - \frac{1}{2} ({\hat{β}}_{1 / 2} + {\hat{β}}_{2 / 1})) = \hat{β} + T^{- 1} \hat{B}

When $T, N \to \infty, \frac{N}{T} \to κ^{2} \in [0; \infty)$ :

{\hat{W}}_{H P J} = N T {\tilde{β}}^{'} {({\hat{J}}^{- 1} \hat{V} {\hat{J}}^{- 1})}^{- 1} \tilde{β} \to χ^{2} (P)

\hat{J} = {(N T)}^{- 1} \sum_{i = 1}^{N} X_{i}^{'} M_{Z_{i}} X_{i}

\hat{V} = \frac{1}{N (T - 1 - P) - P} \sum_{i = 1}^{N} X_{i}^{'} M_{Z_{i}} {\hat{ε}}_{i} {\hat{ε}}_{i}^{'} M_{Z_{i}} X_{i}

for heteroskedastic errors in cross-section.

$\hat{V} = {\hat{σ}}^{2} \hat{J}$ for homoscedastic errors, where ${\hat{σ}}^{2} = \frac{1}{N (T - 1 - P) - P} \sum_{i = 1}^{N} {(y_{i} - X_{i} \hat{β})}^{'} M_{Z_{i}} (y_{i} - X_{i} \hat{β})$

Results and Discussion

Before conducting the panel data analysis, the descriptive statistics are reported in Table 3 for the values in logarithm. The data are transformed by applying the natural logarithm to make interpretations of the results in terms of elasticities. The highest economic growth was registered by Germany in 2019, while Estonia reached the minimum value in 1990. Besides the economic performance, Germany used the most energy from renewable sources in 2013, while Luxembourg had the lowest performance in terms of REC. Sweden registered the highest REC in industry in 1997, Germany was leader of REC in transport in 2007 and of REC in commercial and public services in 2010, while France consumed maximum renewable energy in households in 1991.

Table 3.

Descriptive Statistics (Logarithmic Values).

Variable	Mean	Standard deviation	Minimum	Maximum
GDP	26.12983	1.409263	23.04565	28.91195
K	24.54131	1.44319	20.98563	27.34866
L	3.964082	0.1053749	3.630641	4.264636
FDI	22.40097	1.910817	14.50929	27.32178
EXP	25.26967	1.321475	22.08167	28.19064
REC	4.031615	1.341779	−0.4385019	6.579485
IND	2.620012	1.556235	−2.385956	5.22292
TRANSP	1.516773	2.029366	−5.808476	5.066367
RES	3.214218	1.713241	−4.710309	6.009341
COM	0.4273536	1.781328	−6.906756	4.545848

Source: Own calculations in Stata 15.

The cross-sectional dependence is determined by the social, political and economic connections between countries in the sample as EU member states. This hypothesis is checked using Pesaran’s CD. On the other hand, heterogeneity is due to specific national evolutions that made counties to design own targets in terms of renewable sources consumption. The slope heterogeneity test of Pesaran and Yamagata (2008) is employed in this case. The results in Table 4 suggest strong evidence of cross-sectional dependence and heterogeneity.

Table 4.

The Results of Cross-Sectional Dependence and Slope Heterogeneity Tests.

Variable	Pesaran CD test	${\bar{∆}}_{a d j}$
GDP	74.96* (<0.01)	19.46* (<0.01)
K	56.15* (<0.01)	20.02* (<0.01)
L	16.80* (<0.01)	20.13* (<0.01)
FDI	36.07* (<0.01)	21.18* (<0.01)
EXP	80.00* (<0.01)	8.99* (<0.01)
REC	59.23* (<0.01)	10.34* (<0.01)
IND	34.03* (<0.01)	9.88* (<0.01)
TRANSP	42.48* (<0.01)	15.11* (<0.01)
RES	30.40* (<0.01)	9.43* (<0.01)
COM	21.58* (<0.01)	8.88* (<0.01)

Source: Own calculations in Stata 15; p-values in brackets; * indicates significance at 1% level.

Next, the presence of unit root is checked using the second-generation tests that are robust to cross-sectional dependence and heterogeneity. Moreover, the unbalanced panel suggest that CADF test should be applied in this case and the results are shown in Table 4. The equation associated to CADF test is augmented by one and two lags, because this test is sensitive lags number.

The results in Table 5 indicate that the data for the all of the variables are stationary in the first difference at 5% significance level. Therefore, the co-integration relationship could be checked for data in level.

Table 5.

The Results of CADF Test.

Variable (in logarithm)	Data series in level (constant and trend)		Data series in the first difference (constant)
Variable (in logarithm)	Augmented by one lag	Augmented by two lags	Augmented by one lag	Augmented by two lags
GDP	−3.766* (<0.01)	9.321 (0.999)	4.556* (<0.01)	3.237* (<0.01)
K	−5.966* (<0.01)	9.045 (0.999)	−6.342* (<0.01)	4.003* (<0.01)
L	−2.073** (0.019)	0.427 (0.665)	−5.068* (<0.01)	−4.136* (<0.01)
FDI	6.211 (0.999)	7.740 (0.999)	2.637* (0.004)	3.885* (<0.01)
EXP	1.129 (0.871)	7.975 (0.999)	6.556* (<0.01)	3.984* (<0.01)
REC	−4.819* (0.001)	−1.857** (0.032)	−10.439* (<0.01)	−6.433* (<0.01)
IND	−3.604* (0.001)	1.434 (0.924)	−14.269* (<0.01	3.065* (<0.01)
TRANSP	−5.546* (<0.01)	9.296 (0.999)	−6.119* (<0.01	−3.677 (0.001)
RES	−1.538*** (0.062)	4.664 (0.999)	−7.762* (<0.01)	−5.128* (<0.01)
COM	8.691 (0.999)	10.008 (0.999)	3.778* (<0.01)	4.623* (<0.01)

Source: Own calculations in Stata 15; p-values are written in brackets; *, **, *** show significance at 1%, 5%, 10% level, respectively.

The data series are integrated of order 1 at 1% significance level. Under heterogeneity and cross-sectional dependence, the co-integration is checked using Westerlund test. More connections are checked between GDP, K, L, EXP, FDI and one of the following variables: REC, RES, COM, TRANSP, IND (see Table 6).

Table 6.

The Results of Westerlund Test.

Statistics	GDP, K, L, EXP, FDI and:
	REC		IND		TRANSP		RES		COM
	values	p-values	values	p-values	values	p-values	values	p-values	values	p-values
Gt	−2.4477*	0.0072	−1.6214***	0.0525	−1.6224	0.052	−1.9319**	0.0267	−2.161**	0.0153
Ga	−1.611***	0.052	−1.6332	0.05	−1.355	0.089	−1.618	0.052	−1.803	0.051
Pt	−1.5674***	0.0585	−1.3749***	0.0846	−1.3533	0.088	−1.3425***	0.0897	−1.3077***	0.0955
Pa	−1.799***	0.051	−1.802	0.051	−1.4022	0.078	−1.3356	0.09	−1.3202	0.089

Note: *, **, *** show significance at 1%, 5%, 10% level, respectively.

Source: own calculations in Stata 15.

According to Table 6, there is a co-integration relationship between GDP, K, L, EXP, FDI and each of the following variables related to renewable energy consumption: REC, RES, COM, TRANSP, IND at 10% significance level.

Given these results, long-run parameters are built using Augmented Mean Group (AMG) estimators. Robustness analysis is conducted using CCEMG (Common Correlated Effects Mean Group) estimator as alternative method. However, alternative methods could be represented by DOLS and FMOLS estimators, but these estimators could determine inconsistent and biased results in the presence of cross-sectional dependence and slope heterogeneity (Adeneye et al., 2021). These limitations are overcome by CCEMG estimator.

According to Table 7, in most of the cases, K, L and EXP had a positive and significant impact on growth, while the influence of FDI is not significant. REC, TRANSP, RES and COM had a positive and significant impact on economic growth, while renewable energy consumption in industry had not a relevant influence on GDP.

Table 7.

The Results of AMG Estimators (1990–2020).

Variable	Coefficients
K	0.217239* (<0.001)	0.2220857* (<0.001)	0.1528179* (<0.001)	0.2411914* (<0.001)	0.174707* (<0.001)
L	0.1748827* (<0.009)	0.1279935 (0.219)	0.3519247* (<0.001)	0.1508282* (0.023)	0.2091257** (<0.043)
EXP	0.2758392* (<0.001)	0.2867706* (<0.001)	0.2721129* (<0.001)	0.2840796* (<0.001)	0.3068368* (<0.001)
FDI	−0.0007628 (0.739)	−0.0014316 (0.599)	0.0002724 (0.915)	−0.0007809 (0.73)	−0.0015134 (0.564)
REC	0.0626888* (<0.001)	––	––	––	––
IND	––	0.0112423 (0.466)	––	––	––
TRANSP	––	––	0.0061538** (0.02)	––	––
RES	––	––	––	0.0548506** (0.015)	––
COM	––	––	––	––	0.0146793*** (0.079)
Constant	12.87443* (<0.001)	12.97585* (<0.001)	13.91684* (<0.001)	12.21266* (<0.001)	13.32048* (<0.001)

Source: Own calculations in Stata 15; p-values in brackets; *, **, *** indicates significance at 1%, 5%, 10% level, respectively.

The causal analysis is also important to observe the sense of the relationship between economic growth and variables related to renewable energy consumption. The Juodis et al. (2021) (JKS) causality test is applied in this case and the results are reported in Table 8.

Table 8.

The Results of JKS Test.

Null hypothesis	Wald statistic	p-value	Half-panel jackknife estimator
Null hypothesis	Wald statistic	p-value	Coefficient	p-value
$∆$ GDP $↛ ∆$ REC	0.44740939	0.5036	−0.1047673	0.504
$∆$ GDP $↛ ∆$ IND	4.1925515	0.0406	−0.7440552	0.041
$∆$ GDP $↛ ∆$ TRANSP	1.2172587	0.2699	1.076322	0.270
$∆$ GDP $↛ ∆$ RES	0.00223459	0.9623	−0.0152652	0.962
$∆$ GDP $↛ ∆$ COM	0.16920876	0.6808	0.2766333	0.681
$∆$ REC $↛ ∆$ GDP	0.00001901	0.9965	0.0000428	0.997
$∆$ IND $↛ ∆$ GDP	0.02636985	0.871	0.0006972	0.871
$∆$ TRANSP $↛ ∆$ GDP	5.2743191	0.0216	−0.006478	0.022
$∆$ RES $↛ ∆$ GDP	0.45279855	0.501	0.0032072	0.501
$∆$ COM $↛ ∆$ GDP	0.01084218	0.9171	0.0002468	0.917

Source: Own calculations in Stata 15.

The causality analysis in Table 8 suggests that economic growth is a Granger cause for renewable energy consumption in industry and renewable energy consumption in transport is Granger cause for economic growth at 5% significance level. These results indicate that the economic development is essential to support the use of renewable energy sources in industry, while more renewable energy use in transport could contribute to economic growth.

For robustness, the models are built in Table 9 using CCEMG estimator. In this case, the CCEMG estimators confirm the positive and significant impact of K and EXP on growth and the lack of significance for FDI. On the other hand, only REC and RES had a positive and significance effect on economic growth.

Table 9.

The Results of CCEMG Estimators (1990–2020).

Variable	Coefficients
K	0.2345648* (<0.001)	0.2462671* (<0.001)	0.1585054* (0.006)	0.2347432* (<0.001)	0.2008962* (<0.001)
L	0.0684406 (0.94)	0.0994756 (0.143)	0.5208102* (<0.001)	0.0721967 (0.296)	0.1271845 (0.126)
EXP	0.2303238* (<0.001)	0.2058126* (<0.001)	0.2842637* (<0.001)	0.2320614* (<0.001)	0.2061632* (<0.001)
FDI	0.0005338 (0.811)	−0.0011582 (0.574)	0.0015377 (0.698)	−0.0002844 (0.894)	2.37e-06 (0.999)
REC	0.0321891*** (0.087)	––	––	––	––
IND	––	0.0032607 (0.707)	––	––	––
TRANSP	––	––	−0.0081019 (0.182)	––	––
RES	––	––	––	0.0464995** (0.036)	––
COM	––	––	––	––	0.0156829 (0.1)
Cross-section averaged GDP	0.1684018 (0.023)	0.0926041 (0.052)	0.2910892* (0.001)	0.1275728** (0.023)	0.0113369 (0.79)
Cross-section averaged K	−0.2162002* (<0.001)	−0.1983417* (<0.001)	−0.2342928* (0.008)	−0.195341* (<0.001)	−0.1347901* (0.002)
Cross-section averaged L	0.2617075*** (0.056)	0.2217917*** (0.072)	0.417058* (0.006)	0.2487623** (0.06)	0.3341175** (0.047)
Cross-section averaged EXP	0.0456834 (0.144)	0.0887956* (0.002)	−0.1029433 (0.118)	0.0794491** (0.018)	0.1430834* (<0.001)
Cross-section averaged FDI	0.0062578*** (0.097)	0.0058691*** (0.085)	0.0120813 (0.14)	0.0085358*** (0.054)	0.004398 (0.125)
Cross-section averaged REC	0.0335634*** (0.086)	––	––	––	––
Cross-section averaged IND	––	0.0736614* (<0.001)	––	––	––
Cross-section averaged TRANSP	––	––	0.0034578 (0.582)	––	––
Cross-section averaged RES	––	––	––	0.0256422** (0.029)	––
Cross-section averaged COM	––	––	––	––	0.0185234 (0.125)
Constant	12.53638* (<0.001)	13.42849* (<0.001)	11.6749* (<0.001)	12.15787* (<0.001)	13.30502* (<0.001)

Source: Own calculations in Stata 15; p-values in brackets; *, **, *** indicates significance at 1%, 5%, 10% level, respectively.

The overall renewable energy consumption contributes to economic growth while the situation on sources of utilisation provides a different picture. The results based on AMG and CCEMG are different, but the renewable energy use in residential purposes positively impacts economic growth, while capital formation and exports are important drivers of growth.

According to the achieved results, renewable energy in industry is not a factor of economic growth. Considering the JKS causality test, economic growth can predict renewable energy consumption in industry, so there is a unidirectional causality between those two variables, not a bidirectional one. Regarding the renewable energy consumption in transportation, AMG estimator shows its positive impact on the economic growth. The causality test also demonstrates a unidirectional causality running from RE in transport sector to economic growth. On the other hand, CCEMG estimations doesn’t validate this positive relation between RE in transport and economic growth, but the share of RE in transport in EU is the lowest among the investigated economic sectors. The RE use in residential area proved to have a significant and positive impact on economic growth according to both estimators. The capital accumulation and exports are also significant factors that supports economic growth in the EU countries.

The positive relation between capital accumulation and trade was demonstrated for a panel of selected European countries by Meyer and Shera (2017) or by Cristescu and Tilvar (2019).

The positive impact of the overall renewable consumption on economic growth was also demonstrated by many other previous studies elaborated for the EU panel (Kasperowicz et al., 2020; Okyay et al., 2014; Soava et al., 2018; Saad & Taleb, 2018; Tutak & Brodny, 2022; Ucan et al., 2014). However, the JKS causality test doesn’t prove any causality between total RE consumption and economic growth, but these results are in line with the study of Soava et al. (2018) who analysed the causality for each EU country and found no-causality between these variables for many EU countries.

Rokicki et al. (2021) demonstrated in his study a positive relation between RE in transport sector and economic growth for EU area, although the share of RE in transport is the lowest among the investigated sectors of EU and it should increase by using electricity and biofuels in this sector.

Energy needs are very important for households functioning, and thus, they are related to the sustainable development goals elaborated for EU area. Moreover, the increased needs of household sector for energy demands significant measures adopted by the authorities in order to alleviate the pressure upon the environment. The EU energy policy in the residential area refers to subsides granted to households for solar panel installation or tax reductions for households that act environmental-friendly (Matuszewska-Janica et al., 2021). In most of the EU area, the electricity and gas prices for households are regulated and national authorities can influence the energy price for households (Rademaekers et al., 2018). So, energy policy is different in each European country, but its main feature is represented by the support granted to households for their ecological behaviour. This can explain the large shares of the renewable energy use in the residential sector in EU area, the largest shares being displayed by Baltic countries, Balkan countries (Romania, Bulgaria, Slovenia, Croatia, Greece), Scandinavian countries (Sweden and Finland) and France, Germany and Italy (Matuszewska-Janica et al., 2021; Piekut, 2021).

The empirical findings present policy implications for the analysed economies generating efficient energy-use strategy in reducing pollution. The use of cleaner production processes could generate new green jobs and could enhance the import of substitutes for pollutant fossil fuel goods. In this context, the clean energy resources can contribute to the achievement of economic growth ensuring the environmental protection in the 23 EU member states. From this point of view, this efficient energy-use strategy contributes to the achievement of the objectives on climate change previously stated in the 2015 Paris agreement.

Conclusions

This study has investigated the relationship between renewable energy consumption in total and in sectors/households and economic growth for 23 EU countries during 1990–2020, using gross fixed capital formation, employment, exports and FDI as control variables. We tested for cross-sectional dependence using Pesaran’s cross-sectional dependence test and Persaran and Yamagata’s slope heterogeneity test, and the cross-sectional dependence and heterogeneity hypotheses were validated. We then tested for co-integration using the Westerlund test, which is applied in cases of cross-sectional dependence and data heterogeneity. The test showed that the variables are co-integrated in the long run. We applied the AMG estimator to test the relationship between the exogenous variables and economic growth. We have shown that all exogenous variables are significant in achieving economic growth in the EU area, except FDI and renewable energy consumption in the industrial sector. We have investigated causality using the JKS test and found unidirectional causality running from economic growth to renewable energy consumption in the industrial sector. We have also shown a unidirectional causality between renewable energy consumption in transportation and economic growth. For robustness we have applied the CCEMG estimator. The results of the CCEMG estimator found that exports, gross fixed capital formation, total renewable energy consumption and renewable energy consumption in residential sectors are significant factors positively influencing economic growth, while the other variables are not significant for economic growth.

Based on the results obtained, we can conclude that globalisation expressed as foreign trade, investments, total renewable energy consumption and renewable energy consumption in the residential sector can promote long-term sustainable economic growth in the EU area. European and national authorities should support investments in the field of renewable energy by granting tax facilities and financing to renewable energy producers or households promoting environmentally friendly behaviour. They should support research and innovation in low-carbon technologies, public–private partnerships in the field of low-carbon energy, pilot plants and smart grids, solar panels. They should also stimulate the use of renewable energy sources by making everyone aware of the benefits of using RE. This public awareness, together with reducing the costs of RE use, can help promote RE sources in households and in all economic sectors.

Authorities should also reduce the exemptions or facilities granted to the use of fossil fuels. In this way, dependence on fossil fuels will decrease and the process of resource depletion will slow down. In addition, the burden on the environment in terms of carbon emissions will decrease significantly. Investments in the field of renewable energies can also generate jobs and support employment, economic growth and the well-being of the population, all of which are objectives of the sustainable development strategy drawn up at EU level. According to the results obtained, it is absolutely necessary to include the domestic sector in the process of transition from non-renewable to renewable energy sources, as this sector generates a large part of the total energy demand. By supporting the financing of the RE sector, the costs associated with the use of RE sources will decrease and households and other economic sectors will not rely primarily on primary solid biofuels, which are cheaper sources of RE. If Eastern Europe uses mainly primary solid biofuels, Western Europe relies on solar, thermal or biogas. In any case, renewable energy sources used by households have started to diversify in all EU countries.

The importance of energy consumption in achieving economic growth, especially through industrial development, cannot be denied. The elasticity of energy consumption to economic growth is higher in developing countries than in developed countries. In the context of the current increase in pollution and global warming, the use of renewable energy to decrease dependence on fossil fuels has become crucial for all economies of the world. The results of the causality test showed that there is a unidirectional causality running from economic growth to renewable energy consumption in industry. This shows that a high level of economic development can promote renewable energy consumption in industrial sectors. Thus, industrial companies can allocate more financial funds to research in the field of renewable energy and can afford to adopt renewable energy sources. Moreover, in the context of strong economic growth, the authorities can release significant public funds to support public–private partnerships in the field of renewable energies, to grant tax facilities or guarantees to investors producing or implementing renewable energies.

The causality test also demonstrated a unidirectional causality running from renewable energy consumption in transportation and economic growth. Of course, the role of the transportation sector in achieving high rates of economic growth is well known. But in the context of the strict environmental regulations applied in EU countries, there is a constant concern about the increase of energy demand in the transport sector that may cause high carbon emissions, taking into account the high contribution of this sector to overall pollution. Therefore, there is a need to adopt renewable energy sources in this sector. The implementation of solutions based on electricity from renewable sources in this sector can achieve the goal of reducing carbon emissions. In addition, investments in biofuels can greatly contribute to achieving the goal of sustainable transport that can lead to sustainable economic growth in the EU area. Greater recognition of these solutions among all stakeholders for sustainable economic growth can encourage these developments towards new forms of transport. Sustainable transport can support long-term sustainable economic development and can support new investments in the production and implementation of renewable energy sources in industry. All this will support the goal of carbon neutrality in the EU area by 2050.

Besides the valuable results for policy purposes, this study has some limitations. For example, the research does not take into account the differences between old and new EU member states and is limited to only 23 EU countries. Therefore, in a future study it is necessary to compare old and new member states to design different policies that have the same objective in terms of RE consumption to achieve economic growth.

The present study is limited to a panel data approach, but a cross-country analysis could be conducted to test its robustness. Further research could be based on a time series approach for each country at the sectoral level.

Since household RE consumption represents a very important factor for achieving sustainable economic growth in the EU area, a direction for future research could be represented by a deeper investigation of households in terms of RE consumption, according to their main demographic, social, economic or cultural characteristics, because the household sector is very diversified.

It could also be of great interest to expand the panel of European countries according to the data available for households and economic sectors or to elaborate a cluster analysis of European countries according to RE consumption for each individual sector. In this way, the main similarities and differences between countries in terms of RE consumption in each sector could be highlighted.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Javier Cifuentes-Faura

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