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ERASMUS UNIVERSITY ROTTERDAM
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Erasmus School of Economics
Master Thesis
A renewable energy diffusion research
Jasper Veel
315627
Professor Philipp Koellinger
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Rotterdam, 25-07-2011
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Summary
Big differences between countries are observed in terms of the market share of renewable energy
in the total energy supply. A panel data research is performed in order the indentify the factors
that affect the market share of renewable energy. Previous literature indicates possible effects of
oil prices, R&D input and output factors, share of renewable energy and a number of
geographical and demographic differences between countries. This research concludes that
differences between countries lead to differences in the diffusion of renewable energy and the
data research finds evidence for relations described in previous literature. This outcome
corresponds with the probit or rank model of diffusion which states that different entities have
different characteristics and perceptions towards innovations that affect the diffusion process.
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Keywords: Renewable energy; innovation diffusion; panel data
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Table of Contents
1.
LIST OF FIGURES ...................................................................................................................................... 4
2.
LIST OF TABLES ........................................................................................................................................ 5
3.
MOTIVATION ............................................................................................................................................. 6
4.
INTRODUCTION ........................................................................................................................................ 8
5.
FACTS & FIGURES .................................................................................................................................. 11
6.
LITERATURE REVIEW ........................................................................................................................... 16
6.1 Innovation diffusion ....................................................................................................................................... 16
6.2 Exhaustible resources .................................................................................................................................... 21
6.3 Policies .......................................................................................................................................................... 22
6.4 Oil price......................................................................................................................................................... 24
6.5 Research & Development .............................................................................................................................. 27
6.6 Nuclear energy .............................................................................................................................................. 29
7.
GEOGRAPHICAL INDEX ........................................................................................................................ 31
8.
HYPOTHESES ........................................................................................................................................... 36
9.
DATA .......................................................................................................................................................... 37
Data description .................................................................................................................................................. 37
10.
STATISTICAL MODEL ....................................................................................................................... 40
11.
RESULTS ............................................................................................................................................... 45
11.1 Fixed effects vs. Random effects .................................................................................................................. 45
11.2 Model specification tests ............................................................................................................................. 46
11.3 R&D input vs. output ................................................................................................................................... 46
11.4 Regression results........................................................................................................................................ 47
12.
CONCLUSION....................................................................................................................................... 50
13.
LIMITATIONS AND DIRECTIONS FOR FURTHER RESEARCH .................................................. 54
14.
REFERENCES ....................................................................................................................................... 57
14.1 Papers and literature ................................................................................................................................... 57
14.2 Internet ........................................................................................................................................................ 60
APPENDICES ........................................................................................................................................ 62
Appendix A: List of dummies ............................................................................................................................... 62
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15.
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1. List of Figures
Figure 1: Schematic overview of the energy system
Figure 2: World total primary energy supply in Mtoe
Figure 3: OECD 30 total primary energy supply in Mtoe.
Figure 4: China’s total primary energy supply in Mtoe
Figure 5: Iceland’s total primary energy supply in Ktoe
Figure 6a: Patents granted versus R&D budget
Figure 6b: Patents granted versus R&D budget for the energy sector
: List of Figures
Figure 7: Histogram of the % of renewables in the TPES.
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2. List of Tables
Table 1: Summary statistics
: List of Tables
Table 2: Regression outcomes for the random effects and fixed effects regression
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3. Motivation
This thesis is my final work in the light of my Master program in Entrepreneurship, Strategy and
Organisation at the Erasmus University Rotterdam
My goal for the thesis project was to find a topic which both includes economic theories and is a
current field of importance in research and politics. The Master course in innovation economics
took my attention during the academic year. This thesis combines theories in the field of
innovation economics with a social relevant topic. The thesis is about renewable energy sources.
The world has a limited amount of fossil fuels available, which will be gone in the next century if
we continue to use them at this pace. The alternative for the use of the fossil fuels lies in a
number of renewable energy sources available. The natural energy resources available, like wind
and solar energy, can be used to generate electricity.
The need for renewable energy to be implemented makes it an up-to-date topic. There is attention
for this topic in both politics and research. There can be thought about of a wide variety of events
that happen nowadays that can be related to renewable energy.
The recent nuclear meltdown in Fukushima led to the world wide perception that nuclear energy
is not an alternative for fossil and renewable energy. The generation of nuclear energy comes
with risk, this is especially not suitable in dense populated areas and areas exposed to earthquakes
on a regular basis. As an answer to the problems in Fukushima, Germany decided to be nuclear
free by 2022. The limited amount of fossil fuels present on earth and the dangers coming with
nuclear energy paves the way for renewable energy resources.
Improved technologies in the field of renewable energies led to higher energy conversion rates.
The state of the technology makes it possible for countries to strive towards a 100% renewable
: Motivation
energy future.
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The current research in the field of renewable energy can be divided in two ways. The first way is
the research in the technology. This research has the goal to come up with the efficient
conversion technologies to turn renewable energy resources into electricity.
The second kind of research is the non-technological research. This kind of research also includes
the economic research in the field of renewable energy. The economic research done in this field
will be discussed in detail in the literature review.
This thesis will include both literature and data research. It is the final work in my Master
: Motivation
program and should include a range of techniques and skills taught in the Master program.
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4. Introduction
The prime part of the world’s primary energy supply is generated with the use of fossil fuels. Oil,
natural gas and coal do account for more than 80% of the world’s total primary energy supply.
(Kelly & Turgul, 2009) (International Energy administration, 2006) The fossil fuel reserves are
limited and the use of them comes with negative externalities. The use of fossil fuels does come
with the emission of carbon dioxide, which is the main driver of global heating. The emissions
coming from our energy lead to changes in the atmospheric composition. (Karl & Trenberth,
2003) (Houghton et. al. 2001) The carbon dioxide characteristic of fossil fuels leads to the
interest to develop and adopt alternative energy sources. These alternative energy resources that
serve as a substitute for fossil fuels should be free of emissions.
There is a wide variety of renewable energy technologies available to turn the earth’s available
natural resources into electricity. These technologies do have common characteristics. Their cost
structure could be described with high fixed cost and low or non variable costs. A big initial
investment should be made but no fuel costs are necessary. The only variable costs are for
maintenance and operation. The total output of a renewable energy plant is determined by the
final output. (Heal, 2009)
The current coal and oil power plants require both an initial investment and fuel costs. There has
to be paid for oil and coal for the period the power plant runs. The price of the energy in this case
is determined by both the fuel costs and the investments made. This price could differ due to
price differences in the fuel. In the case of a renewable energy plant with no or low variable costs
there is basically free energy for the operating period after the initial investment is made.
The renewable energy resources can be divided in different categories. Wind, solar, hydro,
geothermal, tidal, biofuels and waste-to-energy are among them. All these types of energy
sources are present around the world. Some energy sources can however not be exploited
supply of wind and sun. Each country or place in the world has different sources which are
: Introduction
throughout the world. Wind and solar energy for example do require constant and abundant
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available to be exploited. Solar energy is most present in desert areas, tidal and wind energy is
present among coastlines and geothermal energy does require magma close to the earth’s surface.
In order to make renewable energy sources beneficial over fossil fuels, they have to be economic
viable. The renewable energy sources have to be able to compete with the fossil fuels in terms of
price. A number of different things could eventually lead to a situation where renewable energy
sources could compete with fossil fuels. Technological changes make renewable energy
techniques more efficient, generating a higher output. Increasing oil prices increase the prices of
fossil fuels making them more expensive compared to renewable energy. Policies put in place by
governments could both increase the price of fossil fuels or decrease the price of renewable
energy techniques.
The market share of renewable energy in the total energy supply increased in recent years. This
market share is expressed in percentage of the total energy supply. This research will try to
indentify the factors that affect the growth of this market share. Different factors could in one
way or another affect the market share. The literature review part will discuss previous literature
in the field of renewable energy, with the main goal to identify the factors that positively or
negatively affect the market share of renewable energy.
The contribution of this research is to get a broad understanding of the process which led to an
increase in market penetration of renewable energy. A broad understanding could enable us to
create policies in order to increase the market share and could be used to forecast the future
developments in renewable energy. New in this research compared to previous research done is
the introduction of a geographical measure. Different country characteristics in terms of
geography and demography give them the option to exploit different renewable energy resources.
Countries with a coastline have a potential for wind and tidal energy where countries with deserts
and/or abundance of sun do have a big potential for solar energy.
This thesis is build up out of several parts. The final purpose is to be able to answer the
: Introduction
hypotheses set in chapter 8.
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Chapter 5 is a facts & figures chapter. This chapter is included to get familiar with the energy
system and the corresponding terminology. The facts & figures does provide an insight in the
current amounts of renewable energy exploited worldwide.
Chapter 6 is the literature review. This section will discuss the previous literature in the field of
renewable energy. The main target is to identify the factors that positively or negatively influence
the adoption of renewable energy sources.
Chapter 7 will focus on the geographical index introduced in this research. Different country
characteristics are discussed that could influence the potential for renewable energy exploitation.
The final goal is to be able to introduce these characteristics into the statistical model.
Chapter 8 is about the hypotheses. The literature review and the geographical index does lead to a
number of hypotheses which will be tested.
Chapter 9 is about the data. The different factors discussed in the literature part are taken from
different data sources for a number of OECD countries for a time span of 38 years. Summary
statistics show the variation in the dataset.
Chapter 10 is about the statistical method used. It is necessary to indentify a statistical model
which fits the data and enables us to answer the hypotheses set in chapter 8.
Chapter 11 is the results part. The results of the regression analyses will be discussed together
with their statistical and economical significance.
Chapter 12 is the conclusion. The hypotheses set in chapter 8 will be answered. The conclusion
will provide insight in the diffusion process in the OECD countries included in this research
: Introduction
Chapter 12 is about the research limitations and the directions of further research.
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5. Facts & Figures
The purpose of this thesis is to get a broad understanding of the diffusion process of renewable
energy. The starting point of this research is a description of the energy system.
The energy system is divided into different energy supplies. Figure 1 gives a schematic view of
the energy system.
Figure 1: Schematic overview of the energy system. Source: Hoogwijk (2004). Based on (Beer,de
1998)
contains energy stored as fossil-fuels, kinetic energy from wind and tidal movements and thermal
energy coming from the sun.
: Facts & Figures
The primary energy supply is the amount of energy available on earth. This energy supply
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The primary energy supply on the world can be made useful with the use of conversion
technologies. The end product of this conversion is electricity or heat, the secondary energy.
Electricity can be stored and transported and can finally be made useful. (Hoogwijk, 2004)
The next term of interest for this research is renewability. What exactly is renewable energy?
Sorensen (2000) states that energy is renewable if the rate of extraction is lower than the rate at
which new energy is supplied. Coal and oil are not replaced at the same rate as it is extracted
making them non-renewable. Solar and wind energy are continuously present on earth and are
unlimited in terms of practice. Solar and wind energy cannot be extracted at a faster rate than it is
provided, making them renewable according to Sorensen his definition. The earth’s surface does
receive a yearly solar energy flux of 3 x1024 J per year. The proven reserves of oil, gas uranium
and coal do represent only 2.5 x 1022 J in 1977. The earth receives the equivalent of all the
nonrenewable energy reserves in solar energy per week. Covering 0.1% of the earth’s surface
with 10% conversion rate solar panels would provide us with the world’s yearly demand for
energy, which is 3 x 1020 J. (Bolton & Hall, 1979)
Figure 2 does represent the world’s total primary energy supply expressed in million tons of oil
equivalent (Mtoe). The total primary energy supply increased throughout the period. The total
share of oil and coal did increase, despite innovations in renewable energy. The different colors
do represent the different types of energy sources. It can be seen that oil, coal and gas are the
most important source of energy throughout the period of 1972 till 2008. Geothermal, solar and
hydro energy do represent a minor share of the total energy supply.
: Facts & Figures
Figure 2: World total primary energy supply in Mtoe
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Source: www.iea.org/stats
Figure 3 Is the same graph as figure 2 but not for the world but the 30 OECD countries,
excluding the 4 countries that entered the OECD in 2010. It can be seen that the total primary
energy supply stagnated in 2003 and even decreased during the last year.
The shares of oil and coal do remain constant during the indicated time period. Nuclear energy
became of a bigger importance during the time period indicated. The shares of renewable energy
do represent a minor share during the time period represented. The booming economies of China
and India are not represented in this graph. These economies do account for a large share of the
worldwide increased demand for energy.
Figure 4 does represent the total primary energy supply of China. It can be seen that the
This increase was mainly generated with fossil fuels.
If figure 2 and 3 are compared it can be seen that the 30 OECD countries do account for almost
half of the world’s total primary energy supply.
: Facts & Figures
economic growth in recent years came with a major increase in the total primary energy supply.
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Figure 3: OECD 30 total primary energy supply in Mtoe.
Source: www.iea.org/stats
: Facts & Figures
Figure 4: China’s total primary energy supply in Mtoe
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Source: www.iea.org/stats
Figure 5 does represent the total primary energy supply of Iceland. It can be seen that the total
energy supply increased 5 times in the indicated time period. This is however done with only
renewable energy. Iceland is an one of the few examples of a country that generates the biggest
share of their energy supply renewable.
Figure 5: Iceland’s total primary energy supply in Ktoe
Source: www.iea.org/stats
The different graphs of the world, the OECD countries and the specific examples of China and
Iceland do indicate the overall difference around the world in the market penetration of
renewable energy. These variations between countries could have different origins. The purpose
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of this thesis is to get an understanding of the diffusion process of renewable energy. This
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common understanding could help us to explain why some countries are able to produce most of
their energy renewable where others do fail to do so.
The answer I will try to answer in this thesis is: “What factors affect the market penetration of
renewable energy?”
This research question will be answered with the use of a couple hypotheses. These hypotheses
will be set after the literature review. This literature review will explain the theory behind
innovation diffusion and will indicate the factors that could positively or negatively affect the
market penetration, hypotheses will be set accordingly.
6. Literature Review
There is a wide variety of literature available on the topic of renewable energy. This literature
focuses on different parts of the whole spectrum of renewable energy. This literature review is
divided in different parts. Every part will discuss a specific topic within the field of renewable
energy. In the light of this research, investigating the general concept of renewable energy market
penetration, previous findings from different fields of study will be combined. Each topic
discussed in the literature review corresponds to an independent variable used in the research.
The possible effect of an independent variable on the market penetration of renewable energy
will be discussed.
The specific case of renewable energy is an example of innovation diffusion. It is a technique
which can under certain circumstances be an alternative for the existing energy sources. A
description of general diffusion models is included. The literature review will end with a general
conclusion about which factors do have a positive or negative influence on the market share of
case of renewable energy is made in order to evaluate differences.
6.1 Innovation diffusion
: Literature Review
renewable energy. A comparison between the general innovation diffusion model and the specific
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The dependent variable in my research is the market share of renewable energy. Renewable
energy is an innovation in the field of energy. It is a substitute for the existing techniques, like the
use of oil, to generate energy. An innovation has in general some advantages over existing
techniques. These advantages included no emissions and non-exhausting in the case of renewable
energy. An innovation can diffuse within the market replacing an existing technique. This process
is called innovation diffusion. A description of this concept will be given, in order to be able to
relate it with the special case of renewable energy.
The diffusion process does exist out of four different factors: the innovation, the communication
channels, time and the social system. The innovation itself is an idea or an product that is new to
the social system. This idea or product is brought under attention within the social system with
the use of the communication channels. The time factor does indicate the speed of adoption
within the social system. This social system could be the society as a whole, a sector or
organizations. (Rogers, 2003)
The diffusion of a single product or technique does in general follow a S-curve. Adoption is
slowly in the introduction phase. A increasing number of adopters does increase the speed of
adoption after the introduction phase. The final part of the diffusion process is characterized by a
decrease in adoption speed. The actual form of the S-curve does depend on a number of
characteristics of the product or idea. A fast diffusion leads to a steep S-curve where a slow
diffusion leads to a flat graph. (Mahajan & Peterson, 1985) (Rogers, 2003)
Rogers describes diffusion as the communication process of an idea or innovation. If the
innovation is an object or a product, diffusion is seen as the actual purchase or use of the
innovation. An innovative idea could be adopted within a society without an actual purchase.
(Rogers, 2003)
theories rely on different assumptions made towards adopters.
The first one is the epidemic theory. This theory states that the most important factor for
innovation is information. The information is brought into the social network by the adopters.
The innovation spreads out due to the contact between adopters and non-adopters. The adopters
: Literature Review
There are four different theories that are used to describe the innovation diffusion process. These
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basically infect the non-adopters. (Stoneman 1983; Karshenas & Stoneman 1993; Jaffe et al.
2002)
The second model is the probit or rank model. This model states that all potential users do have a
different value they acquire when adopting the innovation. The adoption of an innovation is in
general costly. Due to the fact that the technique does get cheaper when there are more users,
more and more will adopt as time passes. The value of a new technique could however also be
explained in non-monetary terms. A renewable energy technique does not come with the
emission of carbon dioxide, which has value for society as a whole. (Karshenas & Stoneman
1993; Jaffe et al. 2002)
The third model is the stock model. This model takes into account that every potential adopter
has the same characteristics. This model is used to describe the introduction of new production
techniques introduced by firms. It is beneficial for a firm to expand the output when the new
technique is introduced. This process is in equilibrium throughout the diffusion process. Both the
users of the old and the new technique can only charge the equilibrium price. The firms adopting
the new technology are decreasing the profits of those firms who did not yet adopted the new
technology. The trade-off between the profits and the price of the innovation during the diffusion
process determines which firms adopt and a diffusion curve is generated. (Karshenas &
Stoneman 1993; Stoneman 2002)
The fourth model of the diffusion process is the order model. This model states that there is a
difference in the returns for high-order and low-order adopters. The early adopters can require
excess returns which cannot be obtained by the late adopters. The trade-off between the price of
the innovation and the possible excess returns leads to the diffusion path. The excess returns
obtained by early adopters could by about geographical sites or the acquisition of skilled labor.
The diffusion process does not only take economic factors into account. Christopher Freeman
introduced the “innovative system” in 1987. (Freeman, 1987). This innovative system takes a
variety of factors into account which could positively or negatively affect the diffusion process.
The system of renewable energy diffusion is seen as a “technological system”. Each system for a
specific renewable energy technique is different and the ability to diffuse does depend on a
: Literature Review
(Karshenas & Stoneman 1993; Stoneman 2002)
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number of factors. The technological system describes the competition between two different
techniques that can be used to perform a certain function. In this case there is competition
between renewable energy and the incumbent energy technology. (Jacobsson & Johnson, 2000)
(Carlsson and Stankiewicz, 1991)
Jacobsson & Johnson (2000) conclude that the technological system is suitable to describe the
process of renewable energy diffusion. The technological system consist out of three different
elements. The actors in the system combined with their competence. These actors should be
technological, political or financially able to develop and diffuse new technologies. The networks
in the technological system do provide ways to spread information and technology. The
institutions in the technological system come in the form of legislation, education and culture.
These institutions do provide incentives and network connections for a technology to diffuse.
(Jacobsson & Johnson, 2000) The lack of decent competences, networks and institutions could
harm the diffusion process. Jacobsson & Johnson (2000) do give examples of failures in each of
the three categories that harm the diffusion process: poorly articulated demand, failure of the
increasing returns process, weak connectivity between actors favouring the “new” technology
and legislation in favour of the incumbent technology. The lack of complementary goods and
infrastructure harms the diffusion of renewable energy. Renewable energy has to be exploited
regional due to problems with energy storage. In order to use renewable energy for transport it is
necessary to replace our current car park with electrical cars. These cars do need charge points
and are still relatively expensive. These two examples do indicate the importance of
complementary goods and infrastructure in the special case of renewable energy. The lack of this
infrastructure and complementary goods could increase the price of renewable energy.
The innovation diffusion theories do have some characteristics in common. The firms or
individuals do have the option to choose between the different techniques or products. The
different diffusion models. The rate of diffusion depends on the different characteristics of the
new technology and the system it diffuses in. The new and incumbent technique do compete in
terms of costs and performance. The factors that affect the diffusion rate are however not limited
to cost and performance alone..
: Literature Review
reasons why they would choose for the incumbent or new technique do however differ across the
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The existing diffusion theories are applicable for the case where there is a single standardized
technology which is introduced in the market. In the case of renewable energy there is a number
of different techniques. The total package of renewable energy technologies is likely to behave
different from the diffusion processes described above. Different characteristics within the
technological system could lead to different diffusion rates within certain renewable techniques
or countries.
At this very moment there is a clear difference between countries in the share of renewable
energy and the efforts done to increase this market share. The different countries do have
different perceptions towards renewable energy. Some countries do have cheaper options to
exploit renewable energy, which makes the new technique more competitive in an earlier stage. It
is likely that more and more countries will adopt renewable if it becomes more cost competitive
to incumbent energy techniques. This process does have characteristics in common with the rank
or probit diffusion model. Different countries do have different values and different (cost)
characteristics towards renewable energy.
Countries do clearly have different values and characteristics towards renewable energy. The
information about the renewable energy diffusion is widely available. Techniques get better if
more countries invest in renewable techniques. The information spreading about renewable
energy is likely to occur when some countries do use renewable energy and when they share their
information and experiences with other countries. This spreading of information between users
and non-users could be described by the epidemic diffusion model.
The order model states that early adopters do benefit from the option to acquire geographical
interesting sites and skilled labour. Late adopters do not obtain these excess returns. It is however
the case that late adopters do benefit from superior techniques in the case of renewable energy. A
renewable energy plant, like a wind park, is an investment for a long time span. A increase in
way through a long time span. Countries do have an option to wait, assuming that technologies
and performances are better in the future. Countries do however not compete in terms of
renewable energy like companies would do. Countries could not acquire location advantages
when they are an early adopter.
: Literature Review
output or a decrease in investment and maintenance costs does have a financial impact for all the
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The stock model of innovation is not likely to take place in the special case of renewable energy
diffusion. The stock model does rely on the assumption of equal characteristics of potential
adopters and the assumption that only the equilibrium price can be charged. Potential adopters do
however have different perceptions towards renewable energy and higher prices can be charged
for renewable energy. The purpose of this thesis is to indentify differences between countries that
affect the diffusion process, this does not stroke with the assumption of equal characteristics.
The S-curve is at this moment not applicable in the case of renewable energy. A slow diffusion is
seen worldwide. Only if an exponential growth of the renewable energy market share is observed
in the coming years, it will turn out to be an S-curve. In that case the current state of the diffusion
process is in the flat first part of the graph. A slow adoption leads to a flat S-curve, which could
be the case in the coming years. A S-curve of an innovative consumer product is usually steep,
the adoption is fast because a consumer product has a small replacement time span. A power
plant is usually build for a period of a couple decennia, adoption is in general slow.
6.2 Exhaustible resources
Hotellings (1931) wrote a standard work about the economics of exhaustible resources. He
argued that the exhaustible resources in the world were exploited at a too rapid rate. The price of
these exhaustible resources was to low in his opinion, leading to wastefully production and the
consumption of these resources. The price path of non-renewable resources should be adjusted to
the discount rate. This adjustment is necessary to maximize the economic value of the resources.
A fast extraction at low prices makes the total economic value small. This work is nowadays seen
as a standard work and the starting point of economic studies in the field of the replacement of
Nordhaus et al. (1973) describes energy as a necessary input for an industrialized economy like
the United States. If the finite resources are gone the standard of life will fall back to the
prehistoric days. Energy is a necessary input in a lot of processes and the energy sources used at
that time are non-renewable. In 1973 there is no significant use of the renewable energy
technologies already available at that time, like sun and geothermal energy.
: Literature Review
fossil fuels.
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The current development of the sustainable energy sector is basically done in three ways. Energy
savings on the demand side, efficiency improvements on the production side and by the
replacement of fossil fuels by various sources of renewable energy. (Lund, 2007) The effort put
in place to reduce the demand for energy are done in order to decrease or keep the total energy
supplied constant. The improvements made in the process of energy production are done for both
fossil and renewable energy sources. My research is focusing on the replacement of fossil fuels
by renewable energy.
An ideal world would have a energy system which is completely consisting out of renewable
energy, if the goal is to In order to get to such a world the current energy systems have to be
converted into renewable energy systems. Turner (1999) gives an example of the United States.
The United States consume half of the total world energy consumed. Using a 10% efficiency
solar-to-electricity panel an area of 25,921 km² is sufficient to produce enough energy for the
United States, this area is equivalent to 0,2 percent of the total land surface of the country. A
combination of different renewable energy sources would even make this area smaller. Turner
argues that a combination of different renewable energy sources would require only a small area
of land, compared to the states as a whole, to generate this energy. If the world’s largest
consumer is able to, the rest of the world will also be able to produce all their consumed energy
by renewable sources. It is possible with the current technologies to produce all our energy out of
renewable sources. (Turner, 1999) (Bolton & Hall, 1979) Improved future technologies will
reduce the land area necessary to produce this energy due to the fact that they would increase the
conversion rate.
6.3 Policies
An important field of research within the field of renewable energy is the search for the best
to promote the use of renewable energies. Different policies can be put in place to promote the
use of renewable energy. It is of interest to see which policy is most effective Policies come in
different forms, both monetary and non-monetary.
: Literature Review
policy in order to increase the share of renewable energy. International treaties force governments
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Countries have set ambitious targets to reduce carbon dioxide emissions and to replace fossil fuel
technologies by renewable energy sources. The European union set the target to provide 22% of
their electricity production with renewable resources in 2010 (IEA, 2003; Fischer2007).
The available literature does focus on the question which type of policy is most effective in order
to increase the market share of renewable energy. Policies could provide an incentive and
direction for the invention, innovation and diffusion of renewable energy technologies.
Jaffe et al. (2002) study policies which try to motivate individual firms to adopt pollution
decreasing technologies. They divide the environmental policies in two different categories. The
first group of policies is market-based. This group contains policies which contain subsidies,
pollution permits and pollution charges that encourage firms to undertake actions in order to
reduce pollution or emissions. The policies provide monetary incentives that align their interests
with the social interests.
The second group of policies is the command-and-control category. The policies set in this group
do not contain monetary incentives like subsidies or pollution charges. The command-and-control
policies set uniform technology standards for all firms. These policies do force firms to adapt
technologies that reduce pollution. ( Jaffe et al. 2002)
The command-and-control policies do have some disadvantages. These policies do set common
standards for all firms. The policies force firms to adopt a certain amount of technology in order
to reduce pollution. These technologies could however not be effective within every single firm.
The market-based policies provide a good incentive to adopt a certain technology, because it pays
the firms. Firms do receive subsidies and they do not have to pay emission taxes, the firms could
both decrease their expenses and decrease their taxes. The policies in this group are monetary,
making the adoption of new technologies economical effective. The empirical findings are
Fischer& Newell (2007) investigate the effect of different environmental policies which tend to
reduce carbon dioxide emissions and promote innovation and diffusion of renewable energy. The
research makes a distinction between 6 different policies in the electricity sector that focus on the
reduction of greenhouse gas emissions. The performance of the six different policies is evaluated
: Literature Review
generally in line with this finding. (Jaffe et al. 2002)
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according to their goals. These goals do include: emissions reduction, production of renewable
energy, R&D and economic surplus.
The writers do distinguish six different policies:
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Emissions price, which gives an monetary incentive to reduce emissions.
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Tax on fossil-fueled energy, this favors renewable energy and makes fossil-fuel based
technologies less attractive.
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Tradable emissions performance standard, sets an overall emission standard.
-
Portfolio standards, these policies do require a certain share of energy coming from
renewable energy.
-
Production subsidy for renewable energy, this improves the competitiveness of
renewable energy in comparison with fossil-fuel techniques.
-
Subsidies for R&D investment, promotes investments made in R&D with the final
goal to develop more efficient renewable energy techniques as outcome.
The researchers conclude that the emission price policy is the most effective. It does give an
incentive for both producers and consumers at the same time. It gives an incentive towards fossilfuel energy producers to reduce their emission intensity. Renewable energy producers are
encouraged to expand production. Consumers get the incentive to move towards renewable
energy producers, because there is a price advantage. The emission price is an example of a
market-based policy. A portfolio of a number of policies is however more efficient than a single
policy.
The other five policies do provide different combinations of the incentives given in the emission
price policy. The performance of these policies depends on this combination of incentives and on
the relative strength of the incentive. (Fischer & Newell, 2008)
resources. Emission prices are expected to have a positive effect on the market penetration of
renewable energy.
6.4 Oil price
: Literature Review
Emission prices influence the price difference between emitting and non-emitting energy
2
4
In order to become the leading source of energy renewables have to be cost competitive with
fossil fuels. An increase in the oil price could lead to a competitive advantage for renewable
energy resources, which will lead to more innovation and more market share.
Previous literature concluded that high oil prices have a significant positive influence on the
innovations made in the field of energy-efficiency. Popp (2002) investigated the relation between
oil prices and patent applications for energy saving technologies. The development of energy
saving technologies comes from the demand-pull theory of innovation. The innovations made in
the field of energy efficiency are more valuable in periods with high oil prices. The value of the
innovations is increased due to higher possible savings in terms of money or due to an increased
demand for energy saving technologies in times of high oil prices. The technology-push theory
of innovation is not applicable in this case. The technology push theory of innovation is not based
on a specific market need. (Popp, 2002)
The study includes both current oil prices and lagged prices. The effect between oil prices and
innovation, measured in terms of patent applications, is highest in the case of current oil prices. A
possible explanation for this quick adoption is the fact that patent applications are made early in
the invention process, due to the fact that these applications are cheap and easy to obtain.
Popp finds a difference in effect between the demand and the supply technology. A possible
explanation for this is the maturity of the demand technologies. These technologies were already
available before the oil crisis of 1979 and were put in place afterwards. These technologies
became economical viable due to the higher oil prices. (Popp, 2002)
Oil prices could also be treated as an exogenous factor. If cost reductions in renewable energy
occur, this will lead to a transfer from the use of fossil fuels to an alternative source of energy.
demand decreases due to prices reductions in renewable energy, the prices of fossil fuels will
decrease. The lowering of the price of the substitute has a double effect. It leads to a decrease in
the price of fossil fuels. This decrease in price leads to an increase in demand. The final effect is
determined by the relative strength of both effects. In the case of this research it is the innovation
: Literature Review
The current prices of fossil fuels are determined by their current and future demand. If the
2
5
that influences the oil price. The oil price itself does not have an influence on the market
penetration of renewable energy. (Hoel, 2009)
The literature that relates fuel prices with renewable energy focuses also on the relation with
energy-efficiency techniques. These techniques decrease the demand for energy in a specific
case. More fuel-efficient cars and better home insulation are common examples of such
techniques. Jaffe & Stavins (1995) did a research to relate the energy prices with the adoption of
home insulation in the US. They found a significant positive effect between the energy prices and
the adoption of home insulation. They also examined the effect of the adoption cost changes. The
effect of the adoption cost changes on the adoption of home insulation was three times higher
than the effect of energy prices. This finding is a confirmation of the theory that investment costs
are more important than the long term operating expenses.
Renewable energy technologies do have high fixed costs and none or low variable costs. This
cost structure could be a factor which affects the adoption of renewable energy techniques in
negative manner. The high fixed price for the renewable energy could harm it’s adoption speed,
even if energy prices in the long run are equal to those produced by a fossil-fueled plant.
(RREEFF, 2009)
The worldwide oil price is different from the price end consumers have to pay for their oil related
energy products. Taxes are in general a major share of the prices charged. These taxes differ a lot
between countries, due to different policies put in place to decrease the share of fossil fuels in the
energy production. The price of a liter diesel in PPP US$ is included in this research. This diesel
price is depending on more factors than just the worldwide oil price. The correlation between the
diesel price and the oil price is low, 0.13351. This indicates that the diesel price is more
depending on other factors than the worldwide oil price. The diesel prices are expressed in PPP
US$. The diesel price variable gives an indication of the fossil fuel tax effects and is expressed
income on energy. Countries that charge taxes on diesel are likely to do so on the fossil fuels used
for electricity production. A tax regime with the purpose to decrease the use of fossil fuels will
affect both diesel, gasoline and coal prices. Gasoline prices are not included in this research.
1
The sources of this data will be discussed in the data chapter
: Literature Review
relative to the GDP. If these diesel prices increase, end users have to pay a larger share of their
2
6
Within the time span studied in this research there was a change from leaded to unleaded
gasoline. Due to this there is no complete dataset which has leaded or unleaded gasoline prices
throughout the complete time span of this thesis research (1971-2008). The low correlation
between the oil price and the diesel prices enables us to use them at the same time within the
statistical model. The diesel prices in PPP are relative to the GDP. A high diesel price indicates
that end users spend a larger share of their income on diesel.
6.5 Research & Development
Research & Development is an important driver in the renewable energy market. The R&D
generates innovative output which can improve the efficiency of renewable conversion
techniques. The improved efficiency will lead to cost decreases which makes the renewable
energy sources more cost competitive.
Margolis & Kammen (1999) state that R&D funding is closely linked with innovative output. A
high R&D intensity in the energy sector is necessary to develop renewable energy systems.
Developing countries do not have the possibility to invest in energy related R&D. These
countries do rely on the innovative output from the “rich” countries.
The IEA did a R&D intensity survey among 17 member countries in 1980 and 1995 in order to
investigate the trends in energy R&D intensity. In 1995 98% of all energy R&D is carried out by
ten member countries only. The R&D budgets in these countries did however decrease
dramatically since 1980. The R&D budgets did fall in all energy sectors. The nuclear, fossil-fuel
and renewable energy sector suffered from R&D budget cutbacks.
Margols & Kammen (1999) related R&D fundings with patents grants in the US. They performed
this research for the R&D sector as a whole and the energy sector specifically. Figure 6a
funding and patents granted for the energy sector. There is again a high correlation between
funding and patents granted. The decreased R&D funding in the energy sector led to a decrease in
the patents granted.(Margols & Kammen, 1999)
: Literature Review
indicates a high correlation between R&D funds and patents granted. Figure 6b relates R&D
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7
R&D budgets are a proxy for the innovation. Input in innovation processes do however not
always lead to innovations. Public R&D budgets are just one input in the innovation process.
Private investments are not taken into account.
R&D budgets are expected to have a positive influence on the market penetration of renewable
: Literature Review
energy.
2
8
Figure 6a and 6 b: Patents granted versus R&D budgets
Source: Margolis & Kammen (1999)
The R&D input variable in this research is the total R&D expenditure as a percentage of the
GDP. This input variables includes both private and public expenditures. Due to the introduction
of the private expenditures in this variable as well, it is slightly different from the R&D funding
variable in figure 6a and 6b.
R&D budgets are an input variable in the R&D process. Research is a process with insecure
outputs. The input is not directly linked with the output in terms of new technology.
The R&D output in this research is measured in terms of the patent applications per capita. The
amount of patent applications gives an indication of the ability of a country to innovate. It should
however be taken into account that not all innovations are patented. It does however give a proxy
for the innovative output.
6.6 Nuclear energy
Nuclear energy is an alternative for fossil fuels and for renewable energy. Nuclear energy is
supported as an alternative for the use of fossil fuels. (Ayari et al., 2009) Nuclear energy is a
technique which does not cause carbon dioxide emissions. A energy policy which is driven by the
need for emission free energy resources, in terms of carbon dioxide, could lead to the
introduction of nuclear energy plants. Nuclear energy plants have another negative externality
compared to fossil fuel power plants; they create nuclear waste. This waste is dangerous and a
Nuclear energy does need a fuel in the presence of uranium. Nuclear energy is not a nonexhausting type of energy sources and due to this not renewable. The amount of uranium present
on earth could however provide us energy for a very long time span, compared to the
conventional fossil fuels.
: Literature Review
major issue of nuclear power is the question how to deal with this waste.
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9
Nuclear energy comes with a number of negative externalities, mainly in the form of safety
issues. Nuclear waste is dangerous for a long period of time, it should be stored in special
bunkers. The safety issues are the main disadvantage of nuclear energy. The recent earthquake in
Japan showed us the potential dangers of the use and exploitation of nuclear energy.
The effect the market share of nuclear energy has on the amount of renewable used is depending
on the political perception towards nuclear energy. If nuclear energy is seen as comparable to
renewable energy it will have a negative effect on the market penetration of renewable energy
sources. If nuclear energy is not seen as a renewable and sustainable solution it will not block the
development and adoption of renewable energy.
The share of nuclear energy is measured in terms of percentage of electricity generated. This is
slightly different from the measurement of renewable energy market penetration which is a
percentage of the total primary energy supply.
The share of nuclear energy in the total amount of electricity generated is expected to have a
: Literature Review
significant negative effect on the market penetration of renewable energy.
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0
7. Geographical index
The main contribution of this research in the field of renewable energy literature is to introduce a
extensive list of control variables which includes their specific country characteristics in
geographical and demographical terms, together with their possibilities to exploit renewable
energy.
The ability to exploit renewable energy is expressed in terms of the country specific potential.
The world energy council report distinguishes four different types of renewable energy potential.
The first category is the geographical potential, which is the theoretical energy flux which can be
extracted from an specific area. This flux consist out of all energy resources present within the
specific area. The second category is the technical potential, which takes the energy losses into
account which occur during the conversion process. This technical potential does increase when a
better technology to convert energy becomes available. The third category is the economic
potential, which is the technical potential of an alternative energy sources in terms of production.
The final category is the implementation potential, which indicates the total amount of secondary
energy implemented. This category also takes non-economic factors into account. Possible
factors the think of are political, institutional and ecological. (Hoogwijk, 2004; de Vries et al.
2006).
Hoogwijk (2004) did a comprehensive study of the potential of the renewable energy sources on
a global and a regional scale. The study focuses on the electricity production from solar, wind
and biomass. The study includes a grid technology which takes 50km2 areas into account. The
however far behind the scope of this research.
A decision system is developed to assess the economical and financial potential of the
exploitation of renewable energy. This GIS (Geographical Information System) takes
geographical and social factors into account that do effect the exploitation potential for renewable
: Geographical index
energy potentials are determined with individual land cost functions per grid. This method is
3
1
energy. This GIS is a energy management tool which can be used for each energy sources
separately and can take an extensive list of local land characteristics in account.
The GIS system is based on a sequence of steps which lead from the theoretical to the economical
potential. Each step comes with a number of restrictions. The final step is based on the analysis of
alternative investments and the analysis of financial indicators. (Voivontas et al., 1997)
The restrictions set to the exploitation of renewable energy come in different forms. There are
restrictions for safety, to minimize visual impact and due to legal constraints. These restrictions
could be different for every single type of renewable energy and differ from place to place.
Visual impact is a bigger concern in urban than in rural areas. The GIS system does rely on local
data en local restrictions to assess the economical potential of renewable energy, like the grid
study performed by Hoogwijk (2004).
A energy management system that uses local data is not suitable to explain the country
differences in the exploitation of renewable energy. In order to indentify country differences in
the exploitation of renewable energy an alternative is used. A list of control variables which
control for country specific characteristics is included. These variables focus on different
geographical and demographic factors that could influence the economical potential and diffusion
of renewable energy. It should be taken into account that some of these factors have a different
affect on different types of renewable energy sources. Mountains for example are necessary for
the exploitation of hydro energy, where the accessibility of mountains could have a negative
effect on the exploitation of wind energy. The complete list of control variables is discussed,
together with the possible effect it will have on the diffusion of renewable energy.
Population density
Renewable energy techniques for wind and solar energy require a lot of space and it is impossible
to exploit these in urban areas. The population density also give an indication of the land costs.
The land is usually more expensive in dense populated countries or regions. High costs of land
lead to higher exploitation costs of renewable energy. This variable is a time-series variable. The
variations over time are however small.
: Geographical index
The population density gives an idea about the available space to exploit renewable resources.
3
2
Sea border
The sea border dummy indicates whether or not a specific country has a sea border. This dummy
is one of the dummies introduced to describe to geographical characteristics of a country. A sea
border is necessary to be able to exploit tidal energy resources. A sea border also gives an
opportunity to exploit wind parks off-shore.
Deserts
The desert dummy indicates whether or not a country hosts a desert. The desert dummy is another
variable introduced to describe the geographical characteristics. A desert is a suitable area to
exploit solar energy. Deserts experience a lot of sunshine and are usually not densely populated.
There are just a few countries in our dataset that have a desert. A significant effect of the desert
dummy should lead to more case specific research concerning these countries.
Mountains
The mountain dummy indicates whether or not a country hosts a mountain or a mountain range.
Mountains with over a 1000 meters are taken into account. Mountains are required to exploit
hydro power energy. There are just a few countries in our dataset that do not have mountains over
a 1000 meter within their territory. A significant effect of the mountain dummy should lead to
more case specific research concerning the effect of mountains present.
Oil
The oil dummy indicates whether or not a country has proven oil reserves. Countries with a world
share over 0.5% are taken into account. The existence of oil could be an incentive not to exploit
renewable energy resources. Fossil fuels are present within their own borders and these reserves
other countries. (The world factbook,2010)
Gas
The gas dummy indicates whether or not a country has proven oil reserves. Countries with a
world share over 0.5% are taken into account. The existence of gas reserves could be an incentive
not to exploit renewable energy resources. Fossil fuels are present within their own borders and
: Geographical index
can be exploited for their own use. These countries do not dependent on the import of oil from
3
3
these reserves can be exploited for their own use. These countries do not dependent on the import
of gas from other countries. (The world factbook,2010)
Coal
The coal dummy indicates whether or not a country has proven oil reserves. Countries with a
world share over 0.5% are taken into account. The existence of coal reserves could be an
incentive not to exploit renewable energy resources. Fossil fuels are present within their own
borders and these reserves can be exploited for their own use. These countries do not dependent
on the import of coal from other countries. (The world factbook,2010)
Latitude
The latitude variable indicates the distance from a country to the equator. The distance is taken
from the closest point and is expressed in latitude degrees. The latitude of the closest point of a
country gives an overall indication of the climate zone a country is in. This latitude is however a
proxy for the climate zone. Climate zones do not depend on the distance to the equator alone.
Countries close to the equator do experience more sun, rain and high temperatures. These
characteristics could lead to less energy consumption and certain climate characteristics could
make a climate zone more or less suitable to exploit different types of renewable energy.
Energy consumption
The energy consumption variable indicates the energy consumption per capita. The energy
consumption differs a lot across countries. A higher consumption per capita does indicate a
higher dependence on energy. This high dependency could lead to a another perception towards
fossil-fuel and renewable energy resources. The per capita energy consumption is a time series
Emission tax
The emission tax dummy indicates whether or not a country put emission taxes into place. The
literature review did already show that an emission price policy is an effective manner to increase
the diffusion process of renewable energy. There are four countries in the dataset that had carbon
taxes in place since 1990. These countries are Denmark, Finland, Norway and Sweden. These
: Geographical index
variable.
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4
countries do have a dummy variable with the value 1. There are more and more countries that
introduce emission taxes right now, together with a uniform European Union trading scheme. The
effect of these measures will be seen in the coming years and are due to this not included in the
dummy, because they will not have an effect in the period before 2008. (Prasad, 2008)
Appendix A gives the list with the dummy variables and the distance to the equator per country.
The population density and energy use variables will be treated in the data chapter, because they
: Geographical index
area time series variables.
3
5
8. Hypotheses
The purpose of this research is to get a broad understanding of the diffusion process of renewable
energy in the energy system.
The research question of this research is: “What factors affect the market penetration of
renewable energy?”
The literature review gives an overview of the literature available in the field of renewable
energy. The previous literature gives an overview of factors that influence the market penetration
of renewable energy. The literature review leads to the following sub-hypotheses which will be
tested.
Oil price
The oil price does have a significant effect on the market penetration of renewable energy.
The diesel price does have a significant effect on the market penetration of renewable energy.
Nuclear Energy
The share of nuclear energy in the electricity production does have a significant effect on the
market penetration of renewable energy.
Research & Development
The R&D expenditures as percentage of the GDP does have a significant effect on the market
penetration of renewable energy.
The amount of patent applications per capita does have a significant effect on the market
: Hypotheses
penetration of renewable energy.
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6
9. Data
Data description
The data used in this research is obtained from both the World Development Indicator Index from
the World bank2, the OECD site3 and the site of the Illinois Oil and Gas association.4
The panel data includes 29 OECD countries from the 1971-2008 period. The countries included
in the dataset are: Australia (1), Austria (2), Belgium (3), Canada (4), Czech Republic (5),
Denmark (6), Finland (7), France (8), Germany (9), Greece (10), Hungary (11), Iceland (12),
Ireland (13), Italy (14), Japan (15), South Korea (16), Luxembourg (17), Netherlands (18), New
Zealand (19), Norway (20), Poland (21), Portugal (22), Slovakia (23), Spain (24), Sweden (25),
Switzerland (26), Turkey (27), United Kingdom (28) and the United States (29). The dataset
contains 29 countries and a 38 year time span, this relates to a total of 1102 (=29 x 38)
observations per variable in the case of no missing data.
This research is about the OECD countries. The OECD exists out of 34 different countries
nowadays. A total of five countries is left out of the dataset. Chile, Estonia, Israel and Slovenia
entered the OECD in 2010. Due to this there is a lot of data missing for these countries. Mexico is
also left out of the dataset, they entered the OECD in 1994 but there is also a lot of missing data.
The dependent variable in the research is the defined as: the contribution of renewable to energy
supply, as a percentage of the primary energy supply.5 The dependent variable indicates the share
of renewable energy in the total amount of energy consumed. This data is obtained from the
OECD site. The share of renewables differs a lot over time and between the different countries
present. The table 1 with summary statistics shows 0.1% as the lowest share of renewable energy
present, where the highest share of renewable is equal to 82.4%. The mean in this dataset is 12.1
%. There should however be taken into account that this is an average over all countries. The
total share of renewable in the primary supply of these 29 countries could differ due to big
http://data.worldbank.org/data-catalog/world-development-indicators
http://www.oecd.org/document/0,3746,en_2649_201185_46462759_1_1_1_1,00.html
4
http://www.ioga.com/
5
OECD Factbook 2010: Economic, Environmental and Social Statistics - ISBN 92-64-08356-1 - © OECD 2010
3
: Data
2
3
7
differences in the primary supply of energy between countries. The dependent variable in this
dataset contains 1091 observations, which corresponds to 11 missing values.
The dataset contains a number of different independent variables. The independent variables
discussed in the literature review are the ones that will be tested in this research: share of nuclear
energy in the electricity production, oil prices, R&D budgets as a percentage of the GDP, number
of patent applications per 100000 inhabitants and diesel prices in PPP US$. GDP per capita in
constant 2000 US$, energy use per capita in KWH and population density are included as control
variables. All of these independent variables are obtained from the World Bank Development
Indicators Index. Table 1 with the summary statistics shows the number of observations per
variable. The R&D variable includes a lot of missing data. 315 observations compared to the
maximum number of 1102 indicates a share of missing data close to 70 %. The percentages of
GDP spent on R&D varies across the countries. The standard deviation is however small
compared to the mean, indicating that most observations do exist round the mean.
There is also a big variation in the distribution of the independent variables in the dataset. The
biggest power consumption per capita (Iceland;2008) is 100 times higher than the least
consuming country in a specific year. (Turkey;1971)
The GDP differs between just over 2000 $ per capita for Turkey in 1971 and 56625 $ per capita
for Luxembourg in 2007. The most important issue in this case is that almost all of the countries
in our dataset are seen as high income countries. Being a high income country gives a country the
option to make a decision for renewable energy, they have to financial resources required to. Poor
countries do not have such a option, they just try to obtain energy with the few financial
resources they have. The GDP is expressed in constant US dollars with 2000 as a reference year.
In this case all the GDP per capita observations are inflation corrected.
The number of patent applications per 100000 inhabitants vary a lot. The lowest number of patent
applications observed is 0.16 per 100000 inhabitants, were the highest amount of patent
The oil price is obtained from the site of the Illinois Oil and Gas association and is inflation
adjusted. There should be noted that these oil prices are yearly averages. These oil prices are, of
: Data
applications per 100000 inhabitants is just over 300.
3
8
course, the same for all countries. The data indicates the sharp increase in the oil prices in 1979
and in 2008..
The share of nuclear energy sources in the total electricity produced does vary a lot across
countries. Some countries do not have nuclear power plants where other countries do rely on
nuclear energy a lot.
The dataset runs from 1971 to 2008. The years are transformed into time dummies. T is defined
as 1 in 1971 and is constructed as following: T = year -1970. T2 is the square of this time
variable.
Table 1: Summary statistics
% renewable
energy
electric
power
consumption
in kwh
Observations
Mean
Standard Deviation
Minimum
Maximum
1091
12.1
15.3
0.1
82.4
1102
7206
5490
237
500067
GDP per
capita 2000
US$
1013
18190
9652
2137
56625
R&D
expenditures
in % of GDP
315
1.7
0.9
0.37
4.17
986
28.65
43.35
0.16
302.83
1102
43.35
21.36
15.93
99.11
1064
13.9
19.15
0
79.07
1102
122.19
108.77
1.68
487.13
Patent
applications
per 100000
inhabitants
Yearly
average oil
prices in $
inflation
adjusted
% of
electricity
produced
coming from
nuclear
sources
Population
density
: Data
Variable
3
9
Diesel price
per liter in
PPP US$
717
0.82
0.46
0.12
3.21
10.Statistical model
In order to investigate the relationship between the independent variable and the explanatory
variables it is necessary to use a regression model that fits our data. The characteristics of the
dependent variable and the data distribution determine the model to be used. The values of the
dependent variable, the share of renewable energy in the TPES, are expressed in percentages. The
values run between 0 and 100 and do not take negative values.
A technique commonly applied in the case of a continuous positive variable with a limited range
is the class of models called censored regression models. The censored regression models can be
placed into two different categories. The first category includes a quantitative variable which is
top coded. This top coding means that part of the data is not reported as their absolute value but
that it is higher than a certain threshold. A common example in this category is a regression with
wealth as the dependent factor. This wealth is recorded as values up to a certain threshold.
Wealth levels above this threshold are recorded as being above the threshold only.
The second category of the censored models has the characteristics of an optimizing problem.
The regressions in this category do describe an economic agent which chooses a level of the
dependent variable. Common examples in this category are the expenses on R&D for a single
firm and contributions to a savings account. A possible option for the economic agent is to
besides the corner solution of y=0. (Woolridge, 2002)
Tobin (1958) developed a model to regress data with a limited dependent variable. The Tobit
model is build on the assumption that regression data could include a lot of observations that are
zero or at least close to the minimum level. Tobit developed his model while regressing
: Statistical model
choose for y=0. This y=0 is the corner solution outcome. Y could take random positive values
4
0
household income on the expenditures on durable goods. He observed that a significant number
of households did not spend money on durable goods at all. Due to the fact that a significant
number of observations is close to zero it does not hold the linearity assumption. In the case
where the linearity assumption does not hold is not appropriate to use the least squares
regression. (Amemiya, 1984)
Figure 7 is the histogram of the dependent variable. This histogram shows the skewness of the
data. A major share of the data points is observed between 0 and 20. There is however no corner
solution, none of the renewable energy shares is zero. The data is also not top coded. The shares
of renewable energy are reported as absolute values. It is not desired to top code the data, the
high market shares of renewable energy are of special interest. Which independent variables led
to this high market shares. The fact that the data does not fit the characteristics of the tobit model
could lead to misspecification of the estimates.
: Statistical model
Figure 7: Histogram of the % of renewables in the TPES.
4
1
The purpose of this research is to identify the differences between countries that lead to a
differences in market share. Is there a significant effect of one or more of the independent
variables? It is not the purpose to estimate the value of an observed effect. Due to the fact that the
existence of a significant effect and not the estimators are of interest a least squares regression
could be appropriate. An least square regression is not a perfect fit to the data, because it can
predict negative values of the dependent variable. The least squares regression has however a
major advantage over the use of the tobit model. It allows us to run both a fixed and random
effects regression. A Hausman test can be executed in order to evaluate the difference between
the fixed and random effects model. A regression model includes individual specific effects. The
random effects model treats these individual effects as random. The fixed effects model states
that this individual effects are correlated with the regressors. This allows a limited form of
endogeneity. The specific fixed characteristics of an entity has an influence on the regression
estimates. The entity observed in this research are the countries. The fixed effects model takes
into account that specific country characteristics influence the regression outcome. The
geographical and demographic control variables are dropped in the case of the fixed effects
model because they are time invariant and are fixed characteristics of a country. The use of the
random effects model enables us to include the time invariant control dummies. Due to the fact
that only the significance of the effect and not the actual value is of interest the least squares
regression is used instead of the tobit model.
A dependent variable with some outliers at the right side does give rise to the question whether or
not to remove them from the dataset. The research in the field of renewable energy does however
focus on the question why some countries do have a high market share of renewable energy. The
countries with high shares of renewable energy are of high interest in this case, it is of interest
why these countries are able to have a high share of renewable energy compared to others. Due to
A common technique within the least square regression is to transform the dependent variable
into a log variable. The major advantage of this transformation is that the estimates can be treated
as elasticities. The estimates are however not of interest in this case, the dependent variable is not
log transformed.
: Statistical model
the relevance of these outliers they will not be removed from the dataset.
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2
The variable of R&D expenditures includes a lot of missing data, only having 315 out of the
maximum number of 1102 observations. The output variable of R&D, patent applications per
100000 inhabitants, has not a lot of missing data. Two different least squares regressions will be
executed. One with the output variable of R&D and one with the input variable of R&D.
The OLS models estimated are the following:
: Statistical model
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑛𝑠𝑖𝑡𝑦
𝑜𝑖𝑙 𝑝𝑟𝑖𝑐𝑒
𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑝𝑜𝑤𝑒𝑟 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
% 𝑜𝑓 𝑛𝑢𝑐𝑙𝑒𝑎𝑟 𝑒𝑛𝑒𝑟𝑔𝑦
𝑝𝑎𝑡𝑒𝑛𝑡 𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑠
𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎
𝑠𝑒𝑎 𝑏𝑜𝑟𝑑𝑒𝑟
𝑚𝑜𝑢𝑛𝑡𝑎𝑖𝑛𝑠
Share of renewable energy = 𝛼𝑖 +
β +𝜀𝑖𝑡
𝑑𝑒𝑠𝑒𝑟𝑡𝑠
𝑜𝑖𝑙
𝑔𝑎𝑠
𝑐𝑜𝑎𝑙
𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑒𝑞𝑢𝑎𝑡𝑜𝑟
𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑡𝑎𝑥
𝑙𝑖𝑡𝑒𝑟 𝑑𝑖𝑒𝑠𝑒𝑙 𝑖𝑛 𝑈𝑆$
𝑡
𝑡2
4
3
𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑑𝑒𝑛𝑠𝑖𝑡𝑦
𝑜𝑖𝑙 𝑝𝑟𝑖𝑐𝑒
𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑝𝑜𝑤𝑒𝑟 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
% 𝑜𝑓 𝑛𝑢𝑐𝑙𝑒𝑎𝑟 𝑒𝑛𝑒𝑟𝑔𝑦
𝑅&𝐷 𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑠
𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎
𝑠𝑒𝑎 𝑏𝑜𝑟𝑑𝑒𝑟
𝑚𝑜𝑢𝑛𝑡𝑎𝑖𝑛𝑠
Share of renewable energy = 𝛼𝑖 +
β +𝜀𝑖𝑡
𝑑𝑒𝑠𝑒𝑟𝑡𝑠
𝑜𝑖𝑙
𝑔𝑎𝑠
𝑐𝑜𝑎𝑙
𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑒𝑞𝑢𝑎𝑡𝑜𝑟
𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑡𝑎𝑥
𝑙𝑖𝑡𝑒𝑟 𝑑𝑖𝑒𝑠𝑒𝑙 𝑖𝑛 𝑈𝑆$
𝑡
𝑡2
The 𝛼𝑖 are the individual specific effects. These individual effects could be random and fixed.
Some of the regressors are constant over time, they will be omitted in the regression output of the
fixed effects model. The β is the regressor estimate. The 𝜀𝑖𝑡 is the idiosyncratic regression term.
The share of nuclear energy, R&D expenditures in %, Patent applications, diesel and oil prices
are the variables tested in the hypotheses. GDP, population density and distance to the equator are
control variables having continuous values. The sea border, mountain, deserts, oil, gas, coal,
emission taxes are dummy variables. These dummies do take the value 1 if it is present and 0
: Statistical model
otherwise.
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4
11.Results
11.1 Fixed effects vs. Random effects
Both the fixed effects and random effects model are executed. The Hausman test enables us to
compare these two different models. The Hausman test has under the null hypothesis that the
individual effects are random. The alternative hypothesis states that the estimators under the
random effects model are inconsistent. The Hausman test compares the coefficient estimates
under the random and fixed effects model and evaluates the differences. The null hypothesis is
not rejected if the difference between the estimators is small.
The Hausman test is executed for the two different regression models, the one with the output and
the one with the input variable of R&D.
The first model specification, with R&D expenditures as a percentage of the GDP included, gives
an overall chi statistic with p-value 0.5478. The H0 is not rejected is this case. The random
effects model can be used.
The second model specification, with the number of patent applications per 100000 inhabitants
included, gives an overall chi statistic with p-value 0.1089. The H0 is not rejected in this case.
The random effects model can be used.
The outcome of both the Hausman tests enables us to use the random effects model. These
random effects models do have a big advantage in the light of this research. The use of the
random effects model enables us to test the effect of the control variables which are constant over
time. This control variables are omitted in the case of the fixed effects model.
Table 2 shows the regression output. Four different model specifications are presented. Model (1)
is a random effects model with the R&D input variable. Model (2) is a fixed effects model with
the R&D input variable. Model (3) is a random effects model with the R&D input variable.
missing data can be seen in the number of observations.
: Results
Model (4) is a fixed effects model with the R&D input variable. The clear difference in terms of
4
5
11.2 Model specification tests
The least square regression model relies on the assumptions of a homoskedasticity distribution
and no presence of serial correlation. Homoskedasticity is defined as the errors having equal
variances. The Wald test for presence of heteroskedasticity is executed. The found p-value for the
test statistic is 0.0000. The H0 of homoskedasticity has to be rejected. There is heteroskedasticity
present within the dataset. The presence of heteroskedasticty leads to biases in the test results..
In timeserie studies, like this one, observations have to be independent and therefore
uncorrelated. In the case where the observations are correlated there is presence of serial
correlation. The Woolridge test for autocorrelation in panel data gives a F test statistic with pvalue 0.4900. There is no evidence for serial correlation between the variables in the panel data.
The presence of heteroskedasticty has some implications for test results. The test results have to
be treated carefully. The regressions could be controlled with the Robust function in Stata. This
function takes heteroskedasticity into account. The use of this Robust function leads to no
significant effect of one of the variables tested in both model specifications. Due to this a nonrobust regression is executed. The outcomes of this regression have to be treated with caution
11.3 R&D input vs. output
The two different model specifications do differ in terms of the R&D variable included. Two
different models are executed due to the fact that the expenditures on R&D as a percentage of the
GDP has a lot of missing data. The missing data would lead to a model with a low number of
observations in comparison to the total number of observations possible.
The regression output table indicates this difference in observations. Having 291 observations in
the model with the input variable and having 641 observations in case of the output variable. The
: Results
outcomes in terms of significant effects are different.
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6
The maximum number of observations is 1102. A number of variables in the dataset have some
missing data. The regressions only have a minor share of the total number of observations due to
the adding up of the missing values.
11.4 Regression results
The two model specifications show some differences and some similarities in terms of outcome.
The first model specification indicates a significance effect of the R&D expenditures, the third
model specification does not indicate an significant effect of the number of patent applications.
The first model specification indicates a significant effect of three out of the four variables tested
in the hypotheses. A significant negative effect negative effect is found of the diesel prices on the
share of renewable energy. This is opposite from the expected effect. It is expected that higher
fossil fuel taxes and higher oil prices would lead to an increased share of renewable energy. This
positive and significant effect is found for the inflation adjusted oil prices. A significant positive
effect is found of the R&D expenditures. An increase in R&D expenditures would lead to an
increased market share of renewable energy.
No significant effect is found of the share of nuclear energy.
The third model specification does give an significant effect for one two of the four variables
tested in the hypotheses. The share of nuclear energy tends to have a significant negative impact
on the market share of renewable energy. This negative effect was expected, a possible
explanation for this relationship is discussed in the literature review. A positive and significant
effect is found for the oil prices. No evidence is found for a significant effect of patent
applications and the diesel prices.
The regression outcomes show some similarities in the found effects of the control variables. The
population density tends to have a negative effect on the market share of renewable. The market
The gas and coal reserve dummies do have a significant effect on the market share of renewable
energy. The effects of these dummies are however not the same. Countries with gas within their
borders are more likely to exploit renewable energy. Countries with big coal reserves are less
: Results
share of renewables is significantly smaller in densely populated countries.
4
7
likely to exploit renewable energy, their renewable energy share are significantly smaller.
Emission taxes have a significant positive effect on the market share of renewable energy. There
are only three countries in the dataset with emission taxes. This small groups of countries have a
major impact on the relationship between the dummy and the share of renewable energy. This
: Results
significant effect is only found in the third model specification.
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Table 2: Regression table random effects and fixed effects
share of electricity production
from nuclear energy
Diesel price per liter in PPP
US$
Patents per 100000
inhabitants
(1)
(2)
-0.004
0.001
(0.031)
(0.04)
2.63*** 2.51***
(0.74)
(0.750)
(3)
(4)
-0.022**
(0.011)
-0.022*
(0.011)
0.06
(0.43)
-0.01
(0.008)
0.60
(0.43)
-0.011
(0.009)
Oil price
0.04**
(0.02)
0.037*
(0.02)
0.01**
(0.006)
0.014**
(0.006)
GDP/1000
-0.005
(0.08)
0.026
(0.088)
0.029
(0.031)
0.029
(0.032)
Electricity consumption/1000
-0.21
(0.26)
-0.35
(0.30)
-0.02
(0.10)
-0.047
(0.10)
-0.04**
(0.02)
-0.04
(0.04)
-0.019*
(0.01)
-0.016
(0.01)
Population density
Distance to the equator
0.011
(0.26)
-0.20
(0.23)
Emission tax
10.76
(7.33)
17.08***
(5.64)
Sea border
-2.82
(4.56)
-3.57
(3.84)
Mountain
7.13
(5.62)
9.25
(4.45)
Deserts
-5.17
(6.60)
-6.55
(5.61)
Oil
-10.41
(10.44)
-4.59
(8.88)
Gas
12.20**
(5.95)
9.32*
(4.94)
Coal
-8.46*
(4.73)
-8.51**
(4.08)
Expenditures on R&D
1.86*** 2.05***
(0.58)
(0.63)
t
-0.40
(0.88)
-0.42
(0.88)
0.24**
(0.09)
0.24**
t2
0.008
(0.01)
0.009
(0.014)
-0.003
(0.002)
-0.003
(0.002)
0.67
291
0.19
291
0.59
641
0.18
641
R2
Observations
Significance levels ***,p<0.01;**, p<0.05;*,p<0.1
: Results
Variable
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12. Conclusion
The negative externalities coming with the use of fossil fuels and the limited amount available on
earth led to the interest to use renewable energy resources. These energy resources do not come
with carbon dioxide emission and are available in practical unlimited amounts, wind and solar
energy for example are available in abundance.
Renewable energy is a substitute for the use of fossil fuels. Their final output, energy, is the
same. Renewable energy is more expensive than the incumbent energy resources and has a
different financial structure, having high fixed costs and no fuel costs. The research done in the
field of renewable energy has led to renewable energy being more competitive towards
incumbent resources.
There is a big difference between countries in their ability to exploit and use renewable energy.
Some countries like Norway and Iceland are almost completely dependening on renewable
energy. While other countries like the Netherlands produce only a marginal share of their total
energy with the use of these renewable energy resources.
The purpose of this thesis is to identify the factors that positively or negatively affect the
diffusion of renewable energy and to get a broad understanding of this diffusion process.
Previous literature stated that the oil price, share of nuclear energy in the electricity production
and R&D can influence the diffusion process. High oil prices make renewable energy more cost
competitive. Diesel prices are included in the model as well. The price an energy producer has to
pay is depending on both taxes and oil price. The diesel price differs from the oil price in terms of
fossil fuel taxes and is expressed relative to the GDP. Nuclear energy is seen as an alternative to
renewable energy, a higher share of nuclear energy production could lead to less investments and
interests for renewable energy. R&D efforts and expenditures lead to improved conversion
Countries have different geographical and demographic characteristics that make them more or
less suitable to exploit renewable energy. Previous studies used grid studies with individual cost
functions to indentify the economical potential of renewable energy in certain region. This thesis
: Conclusion
techniques for renewable energy, making them more cost competitive.
5
0
uses a list of control variables which controls for a number of country characteristics which
could influence the ability to use and exploit renewable energy.
Five hypotheses are set according to the factors discussed in the previous literature. The effect of
oil prices, diesel prices, nuclear energy share and in- and output measures of R&D are tested.
The study relies on both the epidemic and probit model of diffusion. These diffusion models state
that entities do have different characteristics and values that make them diffuse later or earlier in
the process. The research done is this thesis has the purpose to indentify the differences in
characteristics that lead to diffusion differences between countries.
The dependent variable in this research is the share of renewable energy expressed in
percentages. This variable takes values between 0 and 100. A limited range of the dependent
variable could lead to the use of a censored model, the tobit model. The dependent variable has a
major share of the observations between 0 and 20%. There is however no corner solution were
y=0. None of the observations does have an actual value of 0. The tobit model doesn’t give a
good fit in this case. Another drawback of the tobit model is that it is not possible to run a fixed
and random effects regression. The least squares regression enables us to run both a fixed and
random regression. The Hausman test indicated that the random effects regression is as good as
the fixed effects model. The geographical and demographic factors included in the model are
constant over time. The predictive effect of these variables is as good as a fixed effect estimator.
The least squares regression cannot be used to evaluate the value of the estimators. The model is
misspecified and can only be use to evaluate the existence of a significant effect of one of the
variables on the share of renewable energy. There is heteroskedasticity present within the dataset.
It is necessary to be careful with the conclusions. The misspecification of the model could lead to
biased outcomes
The models are executed with the input and output variable of R&D separately. There is a lot of
The model specification were the R&D expenditures are taken into account indicates a significant
effect of the diesel prices and the R&D expenditures on the share of renewable energy. An higher
share of investments in R&D leads to a bigger share of renewable energy. The significant effect
: Conclusion
missing data in the R&D expenditures data.
5
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of the diesel prices does have a negative sign. The diesel price in PPP US$ dollars takes taxes and
is expressed as energy expenses relative to GDP into account. A possible explanation for the
negative sign could be that countries where energy is relatively expensive compared to their GDP
per capita, consumers do not want to pay extra for renewable energy. It is more likely that
countries where energy is relatively cheap consumers are willing to pay extra for renewable
energy. Diesel prices and oil prices do have a low correlation indicating that diesel prices do rely
on more than just the oil price. A positive significant effect is found for the oil prices in both the
model specifications. A positive effect was expected, because it makes fossil fuels less price
competitive compared to renewable energy. No significant effect is found for the share of nuclear
energy.
The second model specification includes the number of patent applications per 100000
inhabitants. There is a significant effect found of the share of nuclear energy on the share of
renewable energy. This effect was expected and supported by the literature. No significant effect
is found for the diesel prices and the number of patent applications.
The list of control variables has a similar outcome throughout the two model specifications.
Population density has a negative effect on renewable energy. The exploitation of renewable
energy requires a lot of land and is more easy in less densely populated countries. There is a
significant effect of both the coal and gas reserves within a country. Countries with gas reserves
are more likely to exploit renewable energy, where countries with coal reserves are less likely to.
The model which includes the patent applications also indicates a significant effect of mountains
and emission taxes. The second model specification has a significantly more data points. The
difference in data points could be an explanation for the differences in outcome.
The hypotheses set are only partly supported.
Evidence is found for a significant effect of the R&D expenditures and the share of nuclear
energy. No effect is found for the number of patent applications. The fact that not all inventions
also the case that countries do not compete in renewable energy techniques. Inventions made
somewhere can be exploited in another countries. This lack of competition could lead to the fact
that patents do not have a significant effect.
: Conclusion
are patented could be an explanation why no significant effect of patent application is found. It is
5
2
A significant effect is found for the diesel prices in one model specification, a negative effect is
found. A positive significant effect of the oil prices is found in both the model specifications.
Higher oil prices make renewable energy sources more cost competitive, leading to higher shares
of renewable energy.
The introduction of the geographical control variables added value to the models and does
indicate that individual characteristics do affect the market share of renewable energy.
It seems that the diffusion process of renewable energy is slow and is depending on different
country specific characteristics. This leads to a flat S-curve and the diffusion process can be
described in terms of the epidemic and probit or rank model of diffusion. There is clearly a
difference in diffusion due to country specific differences. More case specific research is
necessary in order to identify the effect of individual geographical, demographic and eventually
: Conclusion
institutional differences between countries.
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13. Limitations and directions for further research
The research done provides us with a common understanding of the diffusion of renewable
energy. It does however not provide a consistent outcome in terms of specific variables that
significantly affect the diffusion. Previous literature stated the significant effect of oil prices,
share of nuclear energy and R&D variables. The dataset used does give us some support for the
effects described in previous literature.
The major shortcoming of this research is the statistical model. Both the tobit and least squares
models do not fit the data perfectly. The presence of heteroskedasticity leads to the fact that the
model is probably misspecified which can lead to biased estimators. The overall conclusion is
that individual country effects do have an influence on the diffusion of renewable energy. Some
hypotheses set are supported by the data. These outcomes have to be handled with care due to
model specification problems.
The research is an overall research which includes a number of variables described in previous
literature and a list of control variables which capture a share of country specific geographical
and demographic characteristics. The hypothyzed variables and the list of control variables are
however not complete. There could be thought of a number of other factors that could influence
the diffusion process of renewable energy. Only geographical and demographic control variables
characteristics.
The variable of R&D expenditures has a lot of missing data, only 315 out of the possible 1102
data points are present. A full sample of this variable would enable us to perform a better
research concerning the effect of these research spending on the diffusion process. The difference
in data availability between the input and output variable of R&D made it impossible to test the
effect of the R&D on the same level. The use of less observations does indicate a loss in years
studied. A dataset which has more observations on R&D expenditures could make it possible to
compare the two different model specifications used in this thesis.
The least squares models do not perfectly fit the characteristics of the dependent variable used. In
order to add more value to this research a better regression technique should be introduced or
: Limitations and directions for further research
are introduced. The regression results could also be controlled for political and institutional
5
4
developed. A regression technique that fits the data can also be used to evaluate the value of the
found effects. The current least squares models only allow us to perform a fixed and random
effects regression to investigate the existence of a significant effect.
No distinction is made between different renewable energy techniques in this study. Solar energy
does require other country characteristics than hydro power does. A study which relates specific
renewable energy techniques could give us an understanding of which energy techniques are
usable in which countries. The geographical control variables introduced in this research could
have contradictory effects on different renewable energy techniques. The existence of deserts
would increase a countries potential for solar energy but could negatively affect the ability to
exploit hydro power.
A significant effect of high oil prices is found. It could however also be the case that the volatility
of the oil price is an incentive to invest in alternative energy resources. Volatility is indicated by
heavy upward and downward fluctuations of the price, making it virtually impossible to predict
future prices. The effect of the oil price volatility could be tested in future research. A energy
survey performed by KPMG expects that this volatility would lead to increased renewable energy
investments. (KPMG)
Previous grid studies used individual region specific cost functions in order to evaluate regional
overall study done in this thesis. These grid studies make it possible to evaluate strengths of
found effects.
The main outcome of this study is that the diffusion process is slow and that individual
characteristics do have a high impact on the market diffusion of renewable energy.
Some countries in the dataset do have high shares of renewable energy, where others do have a
marginal share. Case specific studies could give us insight in these special cases where a high
share of renewable energy is present. Some countries like Norway and Iceland should be studied
in detail, it is of interest to understand their ability to exploit a large share of renewable energy.
The countries who are able to have a (almost) completely renewable energy system should serve
as examples for countries who are not able to do so.
: Limitations and directions for further research
differences in economical potential. These grid studies or performed in more detail than the
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5
A significant effect is found of several geographical dummies. The creation of this dummies is
however arbitrary. Mountains over a 1000 meter are taken into account and fossil fuels reserves
are taken into account if they are bigger than 0.5% of the total world production. The use of other
definitions for these dummies could lead to other outcomes. In order to understand the real effect
of the dummies found a case study is necessary. Countries with gas reserves can be studied in
order to see whether these countries have higher share of renewable energy than countries with
big coal reserves. This thesis indicates that these country differences do lead to different market
shares but does not give a description of the actual process why this is the case.
Both the world oil price and the diesel price in PPP US$ are included in the dataset. The price for
fossil fuels is different for end users due to taxes and is different relative to the GDP. In order to
evaluate the effect of the oil price on the share of renewable energy a more detailed dataset is
necessary. A dataset which includes detailed data about fossil fuels prices and country specific
taxes could add more value to this research.
At this very moment there is big interest for renewable energy in the media and nuclear energy is
criticized due to the nuclear disaster in Fukushima. This attention for renewable energy together
with better performing energy conversion techniques can lead to increase in diffusion speed. A
study comparable to this one could be done in 10 years to see whether some affects are found
: Limitations and directions for further research
which are not found throughout the time span studied in this research.
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14. References
14.1 Papers and literature
Amemiya, Takeshi. Tobit Models: A Survey. Journal of Econometrics 24 (1984) 3-61
Ayari, N., Blazsek, S. and Mendi, P. (2009) Renewable energy innovations in Europe: a dynamic
panel data approach, Universidad de Navarra, Working Paper No. 11/09.
Beer, J. de, Potential for industrial energy-efficiency improvement in the long term, Utrecht
University (1998), pp: 277
Bolton J.R. & Hall D.O. Photochemical conversion and storage of solar energy. Annual. Reviews
Energy. 1979. 4:353-401
Cameron. Colin.A. & Trivedi. Pravin. K. Microeconometrics Using Stata. A State Press
Publication StataCorp LP. College Station, Texas. 2009
Carlsson, B., Stankiewicz, R., On the nature, function and composition of technological systems.
Journal of Evolutionary Economics (1991) (2), 93-118.
Drukker. David M. Testing for serial correlation in linear panel-data models. The Stata
journal.(2003) 3. Number 2. pp. 168-177
Fischer. Carolyn, Newell. G. Richard. Environmental and technology policies for climate
Freeman, C. 1987. Technology Policy and Economic Performance: Lessons from Japan,
London, Frances Pinter
: References
mitigation. Journal of Environmental Economics and Management 55 (2008) 142–162
5
7
Heal. Geoffrey. The economics of renewable energy. NBER (National Bureau of economic
research) working paper 15081 (2009)
Hoel, Michael. Bush Meets Hotelling: Effects of Improved Renewable Energy Technology on
Greenhouse Gas Emissions (January 28, 2009). CESifo Working Paper Series No. 2492; FEEM
Working Paper No. 1.2009.
Hoogwijk, M., 2004. On the global and regional potential of renewable energy sources. Utrecht
University /http://www.library.uu.nl/digiarchief/dip/diss/2004-0309-123617/full.pdf
Houghton J.T. et. al. Climate Change 2001: The Scientific Basis. Cambridge Univ. Press,
Cambridge 2001
Hotelling. Harold. The Economics of Exhaustible Resources. The journal of political economy.
Vol. 39. No. 2 April 1931 137-75.
International Energy Agency (IEA), Renewables Information, IEA, Paris, 2003.
Jacobsson. Staffan. & Johnson. Anna. The diffusion of renewable energy technology: an
analytical framework and key issues for research. Energy Policy 28 (2000) 625-640
Jaffe. B. Adam & Stavins. G. Richard. Dynamic Incentives of Environmental Regulations: The
Effects of Alternative Policy Instruments on Technology Diffusion, Journal of
Environmental Economics & Management 29 (1995) 43-63.
Jaffe. B. Adam, Newell. G. Richard, Stavins. N. Robert. Environmental policy and technological
change. Environmental and resource economics. 22 (2002). 41-69
Karl. T.R & Trenberth. K.E. Modern global climate change. Science 5 December 2003:
Karshenas. Massoud & Stoneman L. Paul. Rank, Stock, Order, and Epidemic Effects in the
Diffusion of New Process Technologies: An Empirical Model. The RAND Journal of Economics
Vol. 24, No. 4 (Winter, 1993), pp. 503-528
: References
Vol. 302 no. 5651 pp. 1719-1723
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Kelly R. Cowan & Turgul Daim. Comparative Technological road mapping for renewable
energy. Technology in Society 31 (2009) 333-341
Lund Hendrik. Renewable energy strategies for sustainable development. Energy 32 (2007) 912919
Mahajan. Vijay & Peterson. A. Robert. Models for innovation diffusion. 1985 Sage Publications
p6-8
Margolis. Robert M. & Kammen. Daniel M. Underinvestment: The energy technology and R&D
policy challenge. Science Vol. 285, No. 5428 (Jul. 30, 1999), pp. 690-692
Nordhaus D. William, Houthakker Hendrik, Solow Robert . The Allocation of Energy. Brookings
Papers on Economic Activity, Vol. 3 (1973), pp. 529-576
Popp, D. Lessons from patents: using patents to measure technological change in environmental
models, NBER (National Bureau of economic research) Working Paper No. 9978. (2003)
Rogers. E.M. Diffusion of innovations. 5th edition. 2003 Simon & Schuster limited.
Sørensen, B., 2000, Renewable Energy. 2nd edition, Academic Press
Stoneman, P. Renewables in global energy supply. International Energy Administration; 2006. p.
4. (1983), The Economic Analysis of Technological Change. Oxford: Oxford University Press.
Stoneman, P. 2002. The Economics of Technological Diffusion. Blackwell
The world factbook 2010
Turner, A. John. A realizable renewable energy future. Science Vol 285 (1999) 687-689
: References
Publishers, Oxford. Chapter 2-3, 12-54.
5
9
Voivaontas. D. , Assimacopoulos. D. Mourelatos. A. Corominas. J. Evaluation of renewable
energy potential using a GIS decision support system. Renewable Energy Vol 13 No. (1998) 3
333-344
Vries, de J. M. Bert, Vuuren, van P. Detlef, Hoogwijk, M. Monique. Renewable energy sources:
Their global potential for the first-half of the 21st century at a global level: An integrated
approach. Energy policy 35 (2007) 2590-2610
Woolridge, Jeffrey M. Econometric analysis of cross section and panel data. The MIT Press.
Cambridge, Massachusetts. London, England. 2002
World Energy Council (WEC), 1994. New Renewable Energy Resources—
A Guide to the Future. Kogan Page Limited, London.
14.2 Internet
IEA statistics. www.iea.org/stats
IEA. Clean energy progress report (2011).
http://www.iea.org/papers/2011/CEM_Progress_Report.pdf (02-06-2011)
Illinois Oil and Gas association. www.ioga.com (08-05-2011)
KPMG survey. Pr newswire. http://www.prnewswire.com/news-releases/majority-of-energyexecs-see-us-oil-over-121-per-barrel-this-year-shale-expected-to-have-transformative-impactkpmg-survey-121698908.html (27-06-2011)
OECD statistics. www.oecd.org
Prasad. Monica. On carbon, tax and don’t spend. The new York times. March 25. 2008.
RREEFF Research September 2009.
http://www.dbadvisors.com/content/_media/1175_InfrastructureInvestmentsInRenewableEnergy.
pdf (12-06-2011)
: References
http://www.nytimes.com/2008/03/25/opinion/25prasad.html (03-06-2011)
6
0
U.S Energy Information Administration. http://www.eia.doe.gov/energyexplained (18-05-2011)
: References
World databank. databank.worldbank.org
6
1
15. Appendices
Appendix A: List of dummies
sea
border
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
1
0
1
1
0
1
1
1
1
1
0
1
1
1
1
1
0
1
1
1
1
1
1
1
1
0
1
1
1
mountains
1
1
0
1
1
0
1
1
1
1
1
1
1
1
1
1
0
0
1
1
1
1
1
1
1
1
1
1
1
oil
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
gas
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
0
0
0
1
coal
1
0
0
1
1
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
emsion
tax
distance from
equator
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
13
46
49
45
48
55
60
43
46
36
46
64
51
36
30
35
49
51
33
58
49
36
48
36
55
45
36
50
25
note:gas only includes countrues with a world share of 0.5% or more
note:oil only includes countries with a world share of 0.5% or more
note:coal only includes countries with a world share of 0.5% or more
: Appendices
Australia
deserts
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
1
6
2