University of Groningen
Faculty of Economics and Business
DD MSc in International Economics and Business (IE&B) with Corvinus
University of Budapest
Master Thesis
Beyond Energy Transition. Assessing factors influencing renewable energy
in Europe
Author: Jacopo Busolin
Student number: 3500365
E-mail address: J.Busolin@student.rug.nl
Supervisor: Catrinus Jepma
Co-assessor: Balázs Felsmann
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Abstract
In recent years the global energy system has moved to a new historical turning point. A new paradigm, so-called “Energy Transition”, is now re-shaping the economies toward a more sustainable and less polluting path. In this context, European Union, thanks also to many different reasons (including favorable geographical location, developed state of the economy and well-established aptitude toward environmental cause), has played a pivotal role. Digging deeper, evidence of renewable energy performances highlight that substantial heterogeneity exists among countries. As such, this study aims to disentangle the nature of these underlying differences. I employ fixed effect estimator for 28 countries over the period 2000-2015. To account for internal heterogeneity, I further slice up the macro region into four sub-spatial categories. The results suggest that geographical conditions, economic factors and policy instruments all have an influential role in renewable energy supply in Europe. They show nonetheless different and opposed trends for the various groups: while geographical conditions exhibit a positive regular relationship with the countries involved, economic and policy factors reveal alternative findings between one region and another. In turn, this has important consequences for the research question, giving evidence on how various countries/regions respond differently to the same inputs.
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Table of contents
Tables and Figures ... 4
List of abbreviations ... 4
1. Introduction ... 5
2. Literature Review and Hypothesis Tests ... 7
2.1 Literature review ... 7
2.2 Hypothesis test ... 10
3. Data and Methods ... 13
3.1 Data specification ... 13
3.2 Methodology ... 18
4. Empirical Results, Robustness check and Discussion ... 20
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Tables and Figures
Figure 1. Source: OECD ... 6
Table 1. Variable definition ... 17
Table 2. Regression output ... 22
Table 3. Regression output (robust) ... 25
List of abbreviations
CO2: Carbon Dioxide ... 9EEA: Environmental Energy Agency ... 14
GHG: Greenhouse Gas ... 8
GW: GigaWatt ... 16
IRENA: International Renewable Energy Agency ... 5
kWh: kiloWatt hour ... 15
OECD: Organization for Economic Co-operation and Development... 6
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1. Introduction
In the last few decades, global energy demand has kept increasing at steady pace and predictions forecast this trend to grow even faster in the years ahead. During the same period of time, temperatures have also risen profusely. The result of this series of events has been translated into phenomena such as climate change and environmental turmoil (IRENA 2018). Aware of these mounting problems, individuals and nations have started to take actions across various fields. In the energy sector, which system contribute only in Europe for more than a quarter of total emissions (Eurostat data1), the solution has been defined "Energy Transition". With this term, individuals commonly refer to the complete transformation of the current fossil fuel-based energy system into a more sustainable one, in which renewable energy sources, energy efficiency and energy storage technologies dominate (Squoridis & Csala 2014). The goal of this shift is clearly to reduce environmental impact while at the same time reconvert the economic system (and all the activities that goes with it) under a more efficient and sustainable paradigm. Along these lines, important steps have been taken by the European Union. While the first binding commitment followed the climate agreements at the end of the 1990's2, real comprehensive targets for the EU members were set in 2007 with the "2020 climate and energy package". In this document, countries agreed upon a series of European-wide targets together with individual goals to be reached by 2020 in the area of pollution, renewable energy and energy efficiency (European Commission 2007). From that point, in the space of 10 years significant improvements can be observed. Nevertheless, especially in terms of individual performances on renewable energy, substantial differences remain between members (Figure 1). In consequence of these developments, I believe that understanding the reasons that lie behind can be of fruitful meaning. Identifying the nature of such trend might have important implications for the present debate. In the academic literature, there is an intense number of research focused on the relationship between energy transition and the European Union (Meyer 2003; Moreno et al. 2012; Bölük and Mert 2014) But very few of these studies tried to disentangle the causes influencing the renewable path inside the EU. In general, they have been more defined on particular aspects of this broad category.
1 http://ec.europa.eu/eurostat/web/environment/air-emissions-inventories/database.
6 Figure 1. Source: OECD
The papers that went closer to the cited goal are series of studies conducted by Marques et al. (2010; 2011). In these studies, the authors tried to analyze the motivations driving energy transition in Europe. Despite the good intent, in their research the analysis suffer from three shortcomings: first, the authors did always narrow down on the same set of reasons. Second, they devoted abundant space on the negative effect that fossil fuels have on renewables. Third, they do not take into account the important role that energy policies can have on the development of the renewable sector in Europe. As a result, in this study I move forward the discussion on the motivations driving energy transition in Europe. The attempt is nonetheless to go ahead of it: I want indeed not only to extend the analysis on different factors and to include the participation of energy policies but also to understand how these factors differ within the broader EU group. Consequently, in this study I will slice up the countries into five separate regions: EU, North-West, South, East and North-East. The intent in this case, beside the analysis of the European Union as a whole, is to answer a threefold set of questions: 1) to what extent the groups considered resemble the more general experience of European Union? 2) To what extent the groups are close to each other? 3) To what extent they actually outdistance from the others? In order to answers these questions, in this study I will adopt panel data regression model on a sample of 28 countries over a period of sixteen years (from 2000 to 2015). As a dependent variable, I will use the share of renewable energy sources to the total primary energy supply of each country in each year. To measure the factors influencing the renewable outcome I will make use of a number of variables belonging to three different categories: geographical conditions, economic factors and policy instruments. Ultimately, the results reveal various
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Share of renewable energy in European Union, 2005 - 2015
(% of total primary energy supply)
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interesting evidences: first, the results suggest that geographical conditions have a significant role in positively explaining the performances of the countries. At the same time, the findings show how economic factors contribute in both promoting and discouraging the use of renewable energy by member states (depending on the specific variable and region considered). Finally, the result for the policy instruments put their role into a different perspective. On the one hand, it suggests that is the combination of policies that play a role in the diffusion of renewable energy at European level. On the other hand, it also shows that individual policies can have either a positive effect on the renewable energy supply or actually a negative impact to the countries adopting them. This last finding in particular is of particular relevance for policymakers since it suggests a more attentive examination of the role that these policies have to stimulate renewable energy across countries.
The remainder of this paper is developed as follows: Section 2 discusses the relevant literature used for this study and it presents a set of apposite hypotheses. In section 3, I describe in more detail the data, process of collection and the methodology applied to carry out the empirical analysis. Section 4 presents the result of both tests and robustness check and it follows a discussion of the findings. Finally, in Section 5 I sum up the analysis with a conclusion and the possible limitations of this research.
2. Literature Review and Hypothesis Tests
In this section, I will first examine the relevant literature used for this study and summarize the main takeaways. Afterward, I will introduce some important dynamics that in my view deserve attention and I will link them with the proper hypothesis tests.
2.1 Literature review
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At the macro-level, the success of the Union toward a transformed energy system can be included within the framework of an overall mutating (and more conscientious) environment. Specifically, two set of factors have played an important role: First, increased commitment toward climate change and raised awareness of the role of the energy sector to the global pollution. Second, the process of reforms of the energy market in the direction of a more liberalized and less monopolized system. On the one hand, thanks also to the international efforts concerning human contribution to climate change, European members agreed to set binding target against GHG emissions (Meyer 2003). On the other hand, first steps were taken to remove those barriers that not only constrained energy sector from more uniformity of prices and services but also artificially supported market dominant position within countries; the final objective was (and currently is) the creation of a unified single energy market (Heddenhausen 2007). The goal of such initiatives was twofold: increasing competition of the energy market among various members and at the same time stimulating positive reactions to the challenge for a more sustainable path. The combination of the two effects considered, supported by a new range of policies and regulations at the country level, brought new entrants to the market and changed the nature of the market.
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sources have on GHG emissions. Interestingly, the authors found that RES technologies reduce by around half CO2 per capita emissions respect to fossil fuels ones. As also mentioned in the paper, this has important consequences with respect to the environmental targets set by European Union and its member states (Bölük and Mert 2014).
What appears evident from the above cases is that a more coherent analysis, which measures the impact of various factors on the regional production of electricity generated by RES, is still on an early stage. Some noted cases exist in the literature, although they apply mostly to US context (Bird et al. 2005; Menz and Vachon 2006; Carley 2009). In the case of European Union, an exemption to this is given by the works made by Marques, Fuinhas and Manso (2010; 2011). The authors are the only ones who proposed an in-depth analysis of the renewable energies drivers in Europe. In all their works they used panel data approach to discover which factors are more or less relevant in explaining the “contribution of renewable energy as percentage of total primary energy supply” (Marques, Fuinhas and Manso 2010; p. 6878). Considering the time frame 1990-2006, the authors took into account the contribution of many factors, ranging from socio-political-institutional to more energy-related ones. Their work demonstrated that some factors, such as the importance of fossil fuels to electricity generation, energy import dependency and CO2 emissions, always have a statistically significant effect on the contribution of RES to energy supply. Other factors instead (for example fossil fuel prices) are found to be less significant.
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important implications for those governments that intend to foster adoption of RES in their country (Kilinc-Ata 2016).
As for this study, the aim of this paper goes exactly in this direction: by filling the gap to the existing literature and by adding up new unexplored approach, the present study intends to contribute in three ways. First, by focusing specifically on the European Union, this research takes into account the importance of the energy policy and uses it to understand its effectiveness in relation to the European context3. Second, in order to have the most satisfactory evidence for the all set of countries, this paper will focus on period 2000-2015. In this way, it will be possible not only to include the most recent data available but also to take into account the fact that many countries (and relative policies) became EU members around the early stage of the new century. Last but not least, by considering the heterogeneous combination of members being part of the European Union, this study will divide the region into different groups so that it will be possible to analyze to what extent such groups differ both from each other and from the EU as a whole. In this way, it will be possible to take into account the internal diversity without compromising the consistency of the results.
2.2 Hypothesis test
As said, the aim of this research is to explore the underlying forces that push forward (or backward) the supply of RES within the context of European Union. For this reason, it is interest of this paper to provide adequate measures that extend on many dimensions of interests. In the following, I will carry out a set of testable hypothesis and I will try to explain the rationale of how these variables can influence the result:
- Income effect. The interplay between income and renewable energy usage has been extensively analyzed (Carley 2009; Menegaki 2011). This is even more the case in the context of EU. It is undeniable indeed that one of the factors that explain the achievements of EU toward energy transition is a relatively high-income level. The reason is simply that renewable energies have historically been an expensive source of energy so that “higher income countries are relatively capable of sustaining the costs of
3The study made by Kilinc-Ata (2016) doesn’t distinguish between US and EU, which may partially bias the
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RE technologies” (Kilinc-Ata 2016, p. 86). As such, I expect that the higher the level of GDP per capita, the greater the adoption of RES technologies.
- Density of the population. To my knowledge, there is no study that tried (or is trying) to consider the impact of population density on the supply of RES. Nevertheless, especially in a densely populated region like Europe, this factor might be of particular importance for the deployment of RES technologies. After all, it is not novel the fact that, from a social point of view, community and local interventions have played a consistent role on the efforts to promote the use of RES (van der Horst 2007). Especially for large and obtrusive technologies such as on-shore wind, spatial-related dynamics can matter considerably (Wüstenhagen 2007). As such, I expect that the more densely populated is the country/region, the lesser renewable energy will be produced.
- Pollution effect. Clearly, one of the main arguments (if not the most important) why today society is trying to push systematically the use of RES as part of the energy mix is due to the excessive pollution of incumbent technologies. Given the broad range of noxious gases that are produced worldwide, a fruitful exercise would be to possibly aggregate into a single indicator. In the present analysis, in order to better capture the spillover effects related to the environment, I will include GHG per capita. As such, I expect that the higher the level of GHG emissions per capita inside a country, the stronger the incentive to push in the direction of RES.
- Dependence on electricity. As a matter of fact, countries differ in the energy source mix as well as in their use of it (depending on the different needs). Moreover, for the majority of countries, the electricity generated in Europe is supplied largely either by fossil fuel technologies or nuclear power. Since neither of the mentioned alternatives is considered renewable (or sustainable) by the European institutions, it is plausible to assume that a large use of electricity inside a country will tend to move members toward more sustainable sources of electricity production. As such, I expect that the higher the level of the electricity consumed per capita, the stronger the incentive to adopt RES in the electricity mix.
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of external dependency. As such, I expect that the higher the import dependency is, the larger the incentive to invest in RES.
- Fossil fuel price. Historically, producing electricity through renewables has been relatively expensive. Fossil fuels, on the other hand, have provided for many years a stable and cheaper alternative. Such dichotomy becomes nonetheless less evident when the price of the latter increase. In this case, indeed, the gap between the two is reduced and the incentive to switch to RES is higher (Chang et al. 2009). To the extent that also renewable energy does not increase in price (which does not seems to be the case in recent years, Moreno et al. 2012), the logical outcome would result on a substitution effect. As such, I expect that the higher the fossil fuel price, the greater the use of alternative RES.
- Biomass4 available. For many countries natural endowments play a pivotal role in the
production of RES. Whether for centuries biomass has been the cornerstone which provided energy in Europe, in more recent years technological development allowed EU countries to exploit the power of biomass in a more efficient and sustainable way (McKendry 2002). It is also one of the very few renewable sources that contribute for multiple purposes other than electricity (for example heating). Since natural endowment is extremely complicated to reduce to a single comprehensive variable (or even simply to calculate) and since in some EU countries it represents a fundamental component, it is plausible to assume that more biomass can be correlated to a larger capital of resources. As such, I expect that the higher the biomass available within a country, the higher the supply of RES.
- Energy storage technology. Thanks to the increased importance of RES technologies, in Europe there has been a renovated interest for energy storage facilities. After all, one of the main critics given to RES is that they are an intermittent source of energy (Evans et al. 2012). For this reason, such technologies constitute an important boost for a wider use RES. Currently, Europe is the region that has the greatest access in the world in terms of power capacity. Within this list, “Pumped Hydro Storage” (PHS) is by far the most important contributor5 and its use has proved to increase the stability of the grid
4 For a good definition of biomass see McKendry (2002): “Biomass is a term for all organic material that stems
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(Evans et al. 2012). Because of this, as a proxy of energy storage technology, I will use overall PHS capacity by different member states. As such, I expect that the larger the capacity of a country/region in term of energy storage technology, the higher the incentive to use RES technologies.
- Energy policy. With respect to the adoption of RES by different countries, one of the most important forces that explain the success in Europe is due to energy policies. Particularly in recent years, deployment of RES has been less market-driven and more policy-driven. Within the set of policies, the widest and probably most effective strategy adopted by the countries to support deployment is the financial incentive (either quantity-based and/or price-based). Given the relatively high cost of RES compared to traditional technologies, such scheme has been designed to reduce the cost-benefit markup and to make them more attractive. For this reason, in order to evaluate the contribution of the energy policy, I will include four different policy variables adopted by the EU members to support the use of RES. As such, I expect that presence of one or more policy schemes will contribute to a higher production of energy from RES.
3. Data and Methods
In this section, I will describe in more detail the set of variables and motivate them individually. After that, I will introduce the methodology that has been chosen to conduct the present analysis and the rationale behind it.
3.1 Data specification
For this empirical research, annual data are collected for 28 European Union countries over the period 2000-2015. The choice of the selected period depends upon three distinct reasons: First, it allows the analysis to make use of the most recent available data. Second, the period considered coincides (or shortly follows) with the entrance of several members inside the EU6. Third, in order to provide more coherent and significant results, the intention of this analysis was to have a dataset that was as balanced as possible; for some countries, indeed, previous data were unbalanced or even missing. As per the variables, first of all the dependent
available: Electro-chemical, Electro-mechanical, Hydrogen Storage and Thermal storage. For further information visits the US Department of Energy (DOE) website: http://www.energystorageexchange.org/
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variable Y is denoted as PRES and it defines the share of energy supply by RES to the total primary energy supply by each country in each year. The choice of this measure follows the studies made in the literature (see for instance Marques et al. 2010) and reflects the twofold idea that 1) target of EU institutions and national policies is to foster the contribution of RES supply by the countries, and that 2) for many countries in Europe the share of energy supply by RES for heating and transportation represent a relatively significant part of the energy mix (EEA 2017). For this variable data are collected by the OECD website.
With respect to the other explanatory variables, data are gathered from many different sources and are presented as follows:
The income effect on RES is denoted as GDP and it includes the level of GDP per capita by each country in each year. The use of this variable to measure the income effect is prevalent in the literature and represents the common point shared by most of the studies reviewed for the current research (Carley 2009; Marques et al. 2010; 2011; Zhao et al. 2013). Data are measured in thousands of Euro at the 2010 exchange rates and are collected by the Eurostat website.
The density of the population is denoted as PDENSITY and it defines the amount of people per square kilometer of land area by each country in each year. Despite reviewed studies do not show interest, I consider such variable a not secondary barrier to the distribution of RES within a certain territory. Data are measured in “midyear population divided by land area in square kilometers“(World Bank) and are collected by the data bank of the World Bank website.
The impact of pollution on RES is denoted as GHG and it describes the amount of GHG per capita released into the atmosphere by each country in each year. Similarly to GDP per capita, also for this variable it is found certain common ground in the reviewed literature (Marques et al. 2010; Kilinc-Ata 2016). As expression of the environmental pressure affecting EU institutions and countries, this variable is meant to describe that process by which “ the need to reduce carbon emissions and efforts to fight global warming force countries/states turn to RE sources” (Kilinc-Ata 2016). Data are measured in tons of CO2 equivalents and are collected by the Eurostat website.
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rely on alternative sources of generation to meet their needs. The consumption data are measured in kWh and are collected through the OECD statistics website.
Dependence on import is denoted as IMDEP and it defines ”the percentage of net import in gross inland consumption and bunker” (Eurostat website). The use of such variable is not new in the literature and it can be seen as representative to the effect that energy security play on various countries in Europe (Marques et al. 2010). Given the scarce amount of resources Europe Union has available, the potential of renewable energies to reduce dependence and at the same time meet growing needs is high. As a result, use of this variable can effectively be relevant to understand the extent to which countries and individuals consider the security of supply fundamental. Data are measured in percentage level and are collected by the Eurostat website.
The impact of fossil fuel price on RES is denoted as FFP and it includes the average annual value of oil at the Brent spot crude price. The choice of oil crude price as a proxy for the fossil fuel category reflects not only its relative centrality on the global energy consumption mix but also its traditional influence on fossil fuel markets. Historically indeed, the nominal price of major fossil fuel sources tended to be significantly correlated to the oil price in the long-run (Shafiee & Topal 2010). With respect to its role on RES, reviewed literature commonly agreed on the importance of this variable and suggests that price substitutability effect can be an important dimension to take into account (Chang et al. 2009, Marques et al. 2010, Kilinc-Ata 2016). Data are measured in Euro per barrel and are collected by the Bloomberg Terminal data service.
The effect of biomass on the share of RES is denoted as BIOAV and it defines the quantity of biomass energy available for final consumption by each country in each year. As population density, also this variable is novel to the empirical research reviewed. The use of this variable is intended to count for the natural endowments of the different European countries. In fact, despite often overlooked, this is an important factor that might contribute largely in explaining heterogeneous country/region performances. For this reason, it is relevant to examine to what extent this factor differ. Data are measured in thousand TOE and are collected by the Eurostat website.
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to the intermittency problem generated by the use of some renewable technologies. Data are measured in GW and are collected by the US Department of Energy website.
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retrieved by several sources: IEA Policies and Measures database, Council for European Energy Regulators (CEER) website and in Jenner et al. (2013). The final sample consists of 12 explanatory variables and can be observed in Table 1. For sake of understanding, I divided the variables into 3 different categories: Geographical conditions, Economic factors and Policy
instruments. The choice reflects the different importance of the variable considered. While
geographical conditions represent those factors that are mostly dependent on natural characteristics of countries (therefore with limited influence from human behavior), economic factors and policy measures are both affected by conditions that are mutating by their very nature.
Table 1. Variable definition
Variable Definition Source
Dependent variable
PRES RES supply (% of total primary energy supply) World Bank
Geographical conditions
PDENSITY People per sq. km of land area World Bank
BIOAV Biomass available for final consumption (Toe) Eurostat
ESTORAGES Pumped hydro storages (GW capacity) US Department of Energy (DOE)
Economic factors
GDP GDP per capita (thous. EUR, 2010 exchange rate) Eurostat
GHG Greenhouse gas per capita (CO2 equivalents/cap) Eurostat
FFP Oil Brent spot crude price (EUR/barrel) Bloomberg Terminal
IMDEP Import dependency ratio (% of net imports in gross
inland consumption and bunkers) Eurostat
ELPC Electricity consumption per capita (kWh/cap) OECD
Policy instruments
FT Feed-in tariff (binary) IEA, CEER, Jenner et al.
(2013)
QUOTA Quota system (binary) IEA, CEER, Jenner et al. (2013)
TENDER Tender system (binary) IEA, CEER, Jenner et al. (2013)
18 3.2 Methodology
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𝑃𝑅𝐸𝑆𝑖𝑡 = 𝛼𝑖+ 𝛽2𝑃𝐷𝐸𝑁𝑆𝐼𝑇𝑌𝑖𝑡+ 𝛽2𝐵𝐼𝑂𝐴𝑉𝑖𝑡+ 𝛽3𝐸𝑆𝑇𝑂𝑅𝐴𝐺𝐸𝑆𝑖𝑡+ 𝛽4𝐺𝐷𝑃𝑖𝑡+ 𝛽5𝐺𝐻𝐺𝑖𝑡 + 𝛽6𝐹𝐹𝑃𝑖𝑡+ 𝛽7𝐼𝑀𝐷𝐸𝑃𝑖𝑡+ 𝛽8𝐸𝐿𝑃𝐶𝑖𝑡+ 𝛽9𝑃𝑂𝐿𝐼𝐶𝐼𝐸𝑆𝑖𝑡+ 𝑒𝑖𝑡
where PRESit is the dependent variable representing RES share by country i at year t, αi is the intercept capturing the individual effect, β is the slope coefficient which is assumed to be constant for each country, POLICIESit describes the policy scheme adopted (either feed-in
tariff, quota and tender system, investment grants or a combination of them) by country i at year t, and eit is the error term.
Once estimated and obtained the first results of the regression, this research will extend the analysis of the factors that shape RES supply in European Union by dividing the countries into different sub-regional groups. The aim of this exercise is clearly to test the diversity as well as the affinity between the groups that form such heterogeneous combination. In particular, with this practice I want to explore the following three related questions:
1. To what extent individual regions either share the same conditions or outdistance from the benchmarking case of the EU as a whole?
2. To what extent various regions are integrated with each other (not only upon the same objective but also especially in terms of concrete results)?
3. To what extent these regions systemically differ due to geographical, economic and political reasons?
The sense of this test nurture from the twofold reason that first, at the current state, such attempt is missing from the interested literature; second, the realization that the goal of energy transition pass through the identification of the patterns that facilitate further progresses in the targeted direction. If indeed the institutions at both EU and national level want to reduce carbon emissions and at the same time achieve energy objectives (such as security, efficiency and sustainability) it is primary important to identify when regional approach collude with national priorities or when they move along the same lines (Stang et al. 2017).
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is Group 2 and it includes Portugal, Spain, Italy, Malta, Greece, and Cyprus. Possibly defined as the southern block, this region has a multi-colored energy mix, is relatively similar from cultural and political point of view and it shares historically a quite low level of energy resource endowments. While heterogeneous, this group has the right economical and geographical incentives to foster the level of RES and indeed some of the countries have already made good progress in recent years. The third is Group 3 and it includes Croatia, Slovakia, Czech Republic, Hungary, Poland, Slovenia Romania and Bulgaria. Forming the center and eastern side of Europe, this group has a related historical and political background, it shares an overall high level of energy import dependency (primarily from Russia) and it has been for the most part similar in terms of policy response to transition challenge (Stang et al. 2017). The last is Group
4 and it includes Denmark, Sweden, Finland, Latvia, Lithuania and Estonia. Situated in the
north-east region, this group shares medium-to-high performances in terms of RES supply, it has a relatively good amount of natural resource endowments and it has proved noticeable regional energy cooperation (Stang et al. 2017).
4. Empirical Results, Robustness check and Discussion
In this section, I will first introduce the estimated results of the equation, I will continue with a series of robustness checks and finally I will conclude with a general discussion of the main findings.
4.1 Empirical results
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To start with, the results suggest that in almost all the cases geographical conditions play a determinant role inside the European Union. With respect to the population density within a certain territory, we can see that not only at the EU level but also both in Southern and Eastern Europe such condition is detrimental to the diffusion of addition RES technologies. On the contrary, it is possible to notice that availability of large quantity of biomass inside the EU is a key motivation supporting the adoption of RES; despite the heterogeneity of territory and landscape, all the countries in Europe seem to make good use of their natural resource endowment to support a greater mixture of cleaner technologies. Similarly, important support by natural resources can be found in the energy storage technologies; the combination of favorable geographical conditions and availability of water sources provide many regions in EU of hydro storage technologies, which in turn appear to help significantly further adoption of renewable in a wider sense. Indeed, the use of such technology demonstrated of being an efficient support to balance demand and supply of electricity where other renewable technologies (for instance wind) are present in the system (Yekini Suberu et al. 2014).
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be due to geopolitical reasons since it can help those countries to reduce the historical energy dependence from near Russia (Stang et al. 2017).
Table 2. Regression output
(1) (2) (3) (4) (5)
Variables EU Group 1 Group 2 Group 3 Group 4
Geographical conditions PDENSITY -0.000590*** 0.000134 -0.000212* -0.00337*** -0.00233 (0.000117) (0.000185) (0.000123) (0.000727) (0.00240) BIOAV 0.00577*** 0.00403*** 0.00429*** 0.0205*** 0.0631*** (0.00125) (0.00113) (0.00113) (0.00367) (0.0114) ESTORAGE 0.00965*** 0.0221*** 0.00707* 0.0616* -0.00108 (0.00371) (0.00348) (0.00365) (0.0336) (0.00731) Economic factors GDP 0.00174*** 0.00190*** 0.000736 0.00165 0.000165 (0.000610) (0.000480) (0.00164) (0.00132) (0.00229) GHG -0.0123*** -0.00474*** -0.0185*** -0.0167*** -0.0171** (0.00116) (0.00101) (0.00204) (0.00216) (0.00430) FFP 0.000456*** 0.000148** 4.64e-05 0.000165** 0.000321
(6.19e-05) (6.35e-05) (7.80e-05) (6.95e-05) (0.000251)
IMDEP 0.00414 -0.0256* -0.285*** -0.0919*** 0.0902** (0.0121) (0.0137) (0.0419) (0.0241) (0.0244) ELPC 0.00551* 0.00176 0.0123** -0.00886 0.0280** (0.00299) (0.00290) (0.00510) (0.00650) (0.0103) Policy instruments FT 0.00287 0.00676* 0.00633 0.0108*** 6.73e-05 (0.00313) (0.00372) (0.00401) (0.00382) (0.00541) QUOTA1 0.00382 0.00678 - -0.00544 0.0356* (0.00623) (0.00568) (0.00632) (0.0164) TENDER2 -0.000578 0.00418 -0.0155** - -0.0445*** (0.00499) (0.00380) (0.00703) (0.00358) INV 0.00766* 0.00451 0.00780 -0.00777* 0.0361** (0.00416) (0.00386) (0.00584) (0.00457) (0.0104) Constant 0.211*** -0.0529 0.448*** 0.563*** 0.0697 (0.0278) (0.0504) (0.0473) (0.0901) (0.188) Observations 448 128 112 112 96 R-squared 0.664 0.869 0.898 0.911 0.846 Number of countries 28 8 6 8 6
Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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On the other hand, in all the other cases, this can be explained by the fact that in these regions countries have better access to cheaper fossil fuel energy sources. With respect to the dependence of electricity, it is possible to notice a positive and significant effect on the energy mix decisions. Particularly in the South and North-East, its contribution revealed of being an important incentive for the use of RES.
Finally, the estimates of the policy variables suggest uncertain and unexpected findings regarding their contribution to the energy transition trajectory. As described here, the implementation of supporting schemes seem to be less effective than other explanatory variables previously discussed. First, the results indicate that feed-in tariff, which is the most widely used instruments in Europe, appears statistically ineffective in explaining the promotion of RES at the EU level. On the other hand, it shows to have a positive and statistically significant effect on the North-West and Eastern regions of Europe. In terms of quota-based policies, the analysis shows mostly insignificant results; a notable exception is North-East region where it seems to actually exist a positive and significant relationship. Regarding tender system, the results do not show any significant effect at the EU level but evidence a negative relationship with the adoption of RES in the Southern and North-East region. In part with surprise, this result goes hand in hand with the literature claiming the poor effectiveness of tender procedure (Haas et al. 2011). It has nonetheless important consequences for policy-makers in Europe as it shed new lights on the downside of specific policies. Lastly, the analysis shows opposed results in terms of the relationship between investment grants and RES. On the one hand, indeed, the results reveal a positive effect of investment grants at both EU and North-East level. In contrast, this seems to be statistically negative to the adoption of RES in Eastern Europe. This may be explained by the fact that such form of support scheme considers an initial investment effort by the energy producers. In the case of Eastern Europe, this could be counterproductive due to the lower income level that makes harder to bear investment costs.
4.2 Robustness check
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identically distributed. Therefore this test aims to check whether the variance of the error term is effectively underestimated (which can compromise the significance of the coefficients). The second test performed is the Wooldridge test for autocorrelation in panel data. As suggested by the definition, in this case I test the presence of autocorrelation among variables over time. It is indeed plausible to assume for instance that changes in RES supply could be dependent not only on present values but also on past values of my explanatory variables. The third test diagnosed is the Pesaran's test of cross-sectional independence. By this mean, I test the likelihood that variables evidence cross-sectional dependence. As also explained in De Hoyos & Sarafidis (2006) this can result by the presence of common factors, such as economic and financial integration of countries, “which implies strong interdependencies between cross-sectional units” (De Hoyos & Sarafidis 2006, p.482). The results of the tests can be observed in the appendix section (B-D). In all the three cases the null hypothesis is rejected, confirming that data are characterized by heteroskedasticity, autocorrelation and cross-sectional dependency. In order to solve this problem, I will re-estimate the fixed-effects regression using Driscoll-Kraay standard errors. The use of this method, following the relevant literature, “produces heteroscedasticity-consistent standard errors that are robust to very general forms of spatial and temporal dependence” (Hoechle 2007, p. 282). In Table 3 I present the results of the estimation. The conclusion reveals three different patterns: in some cases estimated coefficients robustly survived after the regression, in some cases they lost their strength and became statistically insignificant and in other cases new coefficients acquire significance.
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previous results two coefficients became statistically significant. On the one hand, outcome suggests a positive relationship between quota-based policy and the North-West part of Europe. At the same time, results also show that investment grants seem to play a consistent role in the diffusion of RES in Southern Europe.
Table 3. Regression output (robust)
(1) (2) (3) (4) (5)
Variables EU Group 1 Group 2 Group 3 Group 4
Geographical conditions PDENSITY -0.000590*** 0.000134 -0.000212* -0.00337*** -0.00233 (9.38e-05) (0.000112) (9.76e-05) (0.000877) (0.00135) BIOAV 0.00577*** 0.00403*** 0.00429*** 0.0205*** 0.0631*** (0.00123) (0.00108) (0.000953) (0.00288) (0.00573) ESTORAGE 0.00965*** 0.0221*** 0.00707*** 0.0616** -0.00108 (0.00186) (0.00160) (0.00167) (0.0185) (0.00483) Economic factors GDP 0.00174** 0.00190*** 0.000736 0.00165 0.000165 (0.000771) (0.000176) (0.00113) (0.00123) (0.00356) GHG -0.0123*** -0.00474*** -0.0185*** -0.0167*** -0.0171*** (0.00324) (0.00105) (0.00122) (0.00289) (0.00326) FFP 0.000456*** 0.000148* 4.64e-05 0.000165 0.000321 (0.000156) (7.50e-05) (5.12e-05) (0.000126) (0.000262) IMDEP 0.00414 -0.0256 -0.285*** -0.0919*** 0.0902*** (0.0125) (0.0144) (0.0439) (0.0207) (0.0163) ELPC 0.00551 0.00176 0.0123** -0.00886 0.0280*** (0.00585) (0.00376) (0.00345) (0.00772) (0.00682) Policy instruments FT 0.00287 0.00676** 0.00633 0.0108*** 6.73e-05 (0.00356) (0.00235) (0.00459) (0.00227) (0.00505) QUOTA1 0.00382 0.00678*** - -0.00544 0.0356* (0.00604) (0.00182) (0.00807) (0.0168) TENDER2 -0.000578 0.00418 -0.0155*** - -0.0445*** (0.00956) (0.00226) (0.00403) (0.00926) INV 0.00766** 0.00451 0.00780* -0.00777*** 0.0361** (0.00285) (0.00485) (0.00370) (0.00196) (0.0114) Constant 0.211*** -0.0529 0.448*** 0.563*** 0.0697 (0.0324) (0.0397) (0.0526) (0.101) (0.134) Observations 448 128 112 112 96 R-squared 0.664 0.869 0.898 0.911 0.846 Number of countries 28 8 6 8 6
Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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Given the marginal role of various support schemes to statistically explain the relationship between the energy policies adopted and the European Union, I decided to perform an additional test by compounding the different policies into a single variable. Defined as POLICY, the new variable takes the value of 1 if any of the four policies is into force and 0 otherwise. The results are provided in the appendix section (E). As it can be observed, the new policy variable results positively and robustly significant at the 5 percent level. This has important consequences for two reasons: on the one hand, it suggests that the whole gives effectively a better interpretation than the single parts individually taken. On the other hand, it downsizes the importance of the individual policies to explain how these instruments influence RES supply at the EU level.
4.3 Discussion
In consideration of the results obtained, there are several important remarks we can point out. First, geographical conditions turn out to be essential elements explaining the relationship between member states and their corresponding share of renewables. In both the tests performed, population density, availability of biomass and pumped hydro storage capacity appeared statistically determinants factors influencing the use of RES. In particular, the results concerning the availability of biomass suggest a certain common ground between various countries that is irrespective of their effective capacity and of the geographical region in which they are located. In fact, this is important for at least two reasons: First, the use of biomass as modern energy source plays a pivotal role in helping European countries meet their renewable energy requirements. Second, the potential of biomass energy derived from natural resources put country-specific performances into perspective.
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At the same time, the robust negative impact of import dependency is also surprising and it suggests that in many countries security of supply plays a bigger role than pursuing more sustainable alternatives.
Lastly, the analysis carried out set new reference points about the role of policy instruments in promoting renewable energy in Europe. Despite the limitations of the present study in meaningfully taking into account the contribution of such variables, the tests conducted reveal some important discoveries. First of all, results indicate that individual energy policies are relatively ineffective in explaining their contribution at the EU level. With the only exception of investments grants, the policy instruments analyzed showed to be statistically insignificant. This might suggest that is the combination of different policies that overall can have an impact in promoting renewable energies at the EU level. Second, the examples of tender system and investment grants tell how some policies schemes can not only be ineffective but actually compromise the efforts of some countries to promote more RES. This result has important implications for policy-makers as it suggests a more careful consideration of policy instruments to stimulate energy support in Europe. Third, the present study contributes in demystifying part of the prominent role given to the policy support schemes by showing how their role, although important, might actually be less relevant than expected.
5. Conclusion
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To start with, estimations suggest that geographical conditions play a sizeable role in the adoption of renewable energy in Europe. All the variables in question, namely population density, biomass available and pumped hydro storage capacity, evidence significant (and robust) effect for the renewable supply not only at the EU level but also across various regions.
Next, the results also show that economic factors have in many cases an influential role in renewable energy supply in Europe. On the one hand, the effect of income, pollution and fossil fuel price have a robustly significant effect at the EU level; with respect to the different regions, the results appear to be robust for most of the previously significant coefficients7. On the other hand, the dependence of import and on electricity are both found to be significant across regions (in most cases robust) but not at the EU level.
Finally, the results of the policy instruments show different and opposed trends for the various groups. At the EU level, estimates regarding individual policies show that only investment grants play a robustly significant role in the diffusion of renewable energy. Vice versa, when considering the effect of the various policies altogether, coefficient appear positively (and robustly) significant. In the case of North-West region, the analysis suggests that both feed-in tariff and quota system have a positive and robust impact on the renewable energy supply. In the case of Southern Europe, the result reveals a positive effect of investment grants on the renewable supply but also a negative and robust effect of tender system. The opposite can be said in the case of the Eastern Europe, where investment grants play a (robust) negative role but feed-in tariff seem to robustly encourage additional renewable supply. Lastly, in the case of North-East region, findings reveal that while both quota system and investment grants have a positive and robust effect, also in this case tender system seems to negatively and robustly influence the renewable energy supply.
Overall, the present study aimed to shed new lights on the determinants that contribute in pushing either forward or backward the energy transition in European Union. Nevertheless, despite some positive insight this analysis provided, there a number of limitations that must be considered. First of all, in this paper I do not take into account of the heterogeneous effect of the policy support schemes inside the countries. Due to lack of quantitative data, the
29
effectiveness (i.e. the intensity) of the different policies is tacitly represented by the use of dummy variables. Whether properly considered, the impact of different policies could have provided different results. Secondly, in this paper I extend the analysis only to a defined number of factors. Far from being all-embracing, there are certainly other variables which are part of the categories chosen (geographic, economic, policies) that could be equally important in explaining the effectiveness of the results. Similarly, in this paper I also focus the research on a limited set of categories. Given the vastness of the subject selected for this study, other categories (for instance social factors or political factors) could have likewise being chosen. Finally, the purpose of this study was to circumscribe the analysis on the European Union. As such, in order to offer a more comprehensive image of EU context, other neighboring (and not) countries have voluntarily left outside of this study. Including them, it could have contributed to showing a different scenario than the one here depicted.
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Appendix
Appendix A. Summary Statistics
Variable Name Obs Mean SD Min Max
PRES 448 0.11 0.09 0 .4223 PDENSITY 448 171.17 236.06 16.99402 1349.606 BIOAV 448 2.40 2.77 0 13.2227 ESTORAGE 448 1.47 2.08 0 8.12 GDP 448 23.62 15.61 3 84.4 GHG 448 10.64 4.25 4.4 30.7 FFP 448 51.86 20.40 25.55052 87.06487 IMDEP 448 0.55 0.29 -.4982775 1.041403 ELPC 382 6.48 3.46 1.9877 17.2159 FT 448 0.68 0.47 0 1 QUOTA 448 0.19 0.39 0 1 TENDER 448 0.05 0.22 0 1 INV 448 0.17 0.38 0 1
Note: For ease of interpretation, variables GDP, ELPC and BIOAV have been adjusted. The value represented are in thousands.
Appendix B. Modified Wald test
Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (28) = 1218.83 Prob>chi2 = 0.0000
Appendix C. Wooldridge test
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Appendix D. Pesaran test
Pesaran's test of cross sectional independence = 25.936, Pr = 0.0000 Average absolute value of the off-diagonal elements = 0.468
Appendix E. Regression output (robust with policy) Variables EU Geographical conditions PDENSITY -0.000568*** (0.000103) BIOAV 0.00579*** (0.00112) ESTORAGE 0.0118*** (0.00109) Economic factors GDP 0.00171** (0.000785) GHG -0.0124*** (0.00316) FFP 0.000423*** (0.000150) IMDEP 0.00457 (0.0116) ELPC 0.00319 (0.00489) Policy instruments POLICY 0.0129** (0.00506) Constant 0.214*** (0.0332) Observations 448 R-squared 0.670 Number of groups 28
