Paper to be presented at the DRUID 2012

on

June 19 to June 21

at

CBS, Copenhagen, Denmark,

AN INVESTIGATION INTO THE DETERMINANTS OF DIFFUSION OF WIND

POWERKonstantinos Delaportas

UCL - University College LondonSSEES - School of Slavonic and East European Studies

konstantinos.delaportas@uclmail.net

AbstractMy research seeks to investigate the factors that influence the diffusion of wind power in a total of 130 countries over thetime period 1990-2009. The paper treats electricity from wind power as an ecoinnovation, and tries to add to theliterature that examines the barriers of diffusion of ecoinnovations, by drawing upon the theory of diffusion ofinnovations. In particular, it tries to unify a ?neoclassical economic approach? with a sociological perspective, and thenuses hazard models to examine the factors that can explain differences in the speed of diffusion of wind power acrosscountries. To model the aforementioned theoretical framework, the paper uses hazard models in an attempt to identify thereasons why a certain event has occurred within a given period of time. In this paper two major questions are examined:first, what are the factors explaining whether a country has integrated wind energy into its electricity generation system,and second, what factors can explain the speed of diffusion of wind technologies into a country. The results mainlyconfirmed the neoclassical economic approach, suggesting the various elements of the market as the most importantdeterminants of profitability these included the profitability of adopters, of suppliers, the availability of wind, as well as thecarbon and market lock in. Moreover, the existence of a change agent in the form of a green party and the country?strade with the pioneers of wind energy were also significant determinants, while all other institutional determinants werefound insignificant.

Jelcodes:O33,-

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AN INVESTIGATION INTO THE DETERMINANTS OF DIFFUSION

OF WIND POWER

INTRODUCTION

The aim of this paper is to examine the determinants of first adoption of wind energy.

Although the issue of innovation and the issue of invention has been the focus of a plethora

of research works, the issue of adoption of an innovation has not received the appropriate

attention. This paper aims to deal with this issue by investigating the factors that can explain a country’s decision to adopt wind turbines in order to generate electricity.

The structure of the paper is the following: section two investigates some of the main

literature on diffusion of innovation, and examines the particularities of the energy sector

with respect to diffusion. Section three gives an overview of the data and the methodology

that is used to examine adoption, section four presents and discusses the results, while section five concludes.

LITERATURE REVIEW

The determinants of innovation and its diffusion, although yet not clearly understood, have

been the focus of a wide deal of research; nevertheless, relatively less emphasis has been

placed on the determinants of first adoption of the innovation. In other words, I expect to be

a different set of factors that influence the decision of an agent to substitute an existing

technology. These factors vary according to the extent that the agent decides to substitute the existing technology.

The rest of this section summarizes two key theoretical approaches in the diffusion of

innovation literature, continues with examining the particularities of innovation diffusion in the energy sector.

To understand what factors influence the diffusion of innovation, first we need to

understand what is the mechanism of diffusion. Then according to each stage, the various

determinants will be proposed.

DIFFUSION PROCESS

Burt (1973) summarizes the literature on the diffusion process by arguing that the process

begins when an agent becomes aware of the innovation and ends by the individual’s decision to adopt or reject it.

The literature suggests the existence of three stages between the moment of information on

the existence of the innovation and the decision towards its adoption. In particular, Burt

(1973) argues that “this process of adoption can be broken into three successive stages. The

process is initiated by the potential adopter becoming aware of an innovation’s availability.

Following awareness of the innovation, the potential adopter proceeds to gather information

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and/or advice relevant to the innovation from both personal and formal sources. During the

course of this communication activity, the potential adopter reaches a psychological decision

regarding adoption of the innovation. This second phase of adoption can thus be termed a “decision period”. The final phase of the adoption is the process behavioral adoption.”

In this research I call these three stages as the information period, the decision-shaping

period and the decision-making period. Schematically this process can be illustrated in the following diagram:

DETERMINANTS OF DIFFUSION

NEOCLASSICAL ECONOMICS PERSPECTIVE

Neoclassical economists believe that the only determinant of diffusion of an innovation is

related to its cost; i.e. that technology choices respond to changes in the price; the cheaper

the product, or the higher the expected profitability the higher should the rate of its

adoption be. However, Griliches’ (1957) research on hybrid corn illustrated that only part of

the increased adoption rate of hybrid corn in the US can be attributed to profitability.

However, he argued that if economic profits were clear-cut, then this facilitated the diffusion process.

Although investigation in the topic of hybrid seed might not sound an attractive research

subject, this research is of particular interest because of the importance of hybrid seeds to

the farmers at that stage. Seeds are the main input in the production process of farmers,

which was at that time the major source of income. Thus, when we investigate the diffusion

rates of certain technologies we need to take into account the importance of these technologies into the agents’ economic activities.

Griliches, analyses the diffusion process by suggesting three categories of factors that

examine diffusion at each of the stage. One group of factors can explain the decision to

adopt, another explains the speed of diffusion once the adoption decision has been made,

and the last one group examines the “ceiling”, i.e. the maximum share of the market that the innovation can capture (Griliches 1957, pp. 505-506).

Griliches argues that the decision to adopt is first and foremost dependent upon the

availability of the innovation in the region in question” (Griliches 1957, p. 507); the

availability is a proxy for the supply of the innovation; the producers’ decision to supply

depends on the perceived profitability of the regions from the suppliers of the innovation. In

turn, this depends on the size of the market, the marketing costs, the cost of innovation in

that area, and the expected rate of acceptance by the consumers. What he concluded was

that the expected pay-off of the suppliers was the major factor influencing their decision to introduce the product to the market.

When attempting to examine the diffusion factors, i.e. the reasons that lead consumers to

purchase/adopt the innovation, Griliches argues that the expected profitability of the new

innovation to the agents is the principle reason for adoption, since the higher the stimulus

the faster is the probability of adoption (Griliches 1957, p. 516). His empirical findings

Information period Decision-shaping period Decision-making period

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suggested that profitability can explain a substantial variation (around 50-60%) in the

differences in the diffusion speeds across regions. It is obvious that this approach assumes

that all farmers have a similar understanding of the benefits of the innovation, partly

implying a perfect functioning of the market, a standard approach of the neoclassical

economists. However, how realistic is it that all agents understand fully and can predict the

monetary benefits of adopting an innovation? It is undeniable that one of the main reasons

for adopting an innovation is the potential benefits, but what determines their perception of

these benefits? What factors shape their beliefs? Moreover, aren’t there some institutional

factors – e.g. adopting because of the competitor adopted – that could help explaining their

decisions? The issue of the importance of profitability is an interesting one particularly when

examining the RETS, where micro agents (investors) might be solely motivated by the

profitability, but the profitability is dependent on the price, which is set by government. So it

could be quite interesting to examine the interplay. This is a way of artificially increasing the

benefits of an innovation in order to attract new agents is captured by the literature on incentives1.

On the issue of the importance of profitability, Rogers summarizes the literature on

agriculture that illustrates that profitability is important, but not the sole important factor in

the diffusion process (Rogers 1988, p. 215).

ROGERS DIFFUSION FRAMEWORK

There are 2 ways to understand the determinants of diffusion. The first way looks at the

characteristics of an agent that decides whether to adopt an innovation. The second way

looks at the innovation and what are its characteristics that make it attractive to be adopted.

Roger’s approach follows the latter, and suggests that 49 to 87 percent of the variance in the

rate of diffusion can be predicted based on an innovation’s 5 characteristics: Relative

advantage, Compatibility, Complexity, Trialability, Observability (Rogers 1988). The

remaining variation can be attributed by a wide spectrum of factors such as “the type of

innovation-decision, the nature of communication channels diffusing the innovation at

various stages in the innovation-decision process, the nature of the social system, and the

extent of change agents' promotion efforts in diffusing the innovation” (Rogers 1988, p. 232).

The following table is a graphical representation of his diffusion framework:

1 See Rogers pp. 217-223

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Relative advantage is defined as “the degree to which an innovation is perceived as being

better than the idea it supersedes” (Rogers 1988, p. 213). This is the most straightforward

measurable diffusion parameter, and can be expressed in either monetary or social

advantages, depending on the nature of the innovation. The higher the innovation’s relative

advantage, the higher its rate of diffusion. He then identifies two categories that reflect this

characteristic: profitability, and status. Rogers argues that status, a trait especially related to

highly visible innovations, is important at the beginning of the diffusion process, but its

importance decreases with time as more and more people adopt it and its status begins to decline.

Compatibility is “the degree to which an innovation is perceived as consistent with the

existing values, past experiences, and needs of potential adopters” (Rogers 1988, p. 223).

The higher the compatibility of the innovation with the already established practices, the

lower is the uncertainty and thus the higher the potential for diffusion. Moreover, this is an

important criterion as the previously introduced ideas constitute the basis of comparison.

The compatibility criteria relate to the sociocultural values and beliefs, the previously

introduced ideas, and the client needs for innovations. Rogers suggests that it is positively

related to the rate of diffusion, but that the statistical evidence does not illustrate it as a

major diffusion determinant (Rogers 1988, p. 226). This criterion can be further analysed by looking into its various characteristics such as its name, and its market positioning,

In an attempt to reap the full benefits of compatibility, it might be tempting to introduce an

innovation with a high degree of similarity with existing practices; however, such an action

entails two crucial risks. Firstly, there is some kind of trade-off between compatibility and

relative advantage. The more similar the innovation is to the one it is replacing, maybe the

lower is its potential to improve existing practices, and thus the lower its perceived

advantage. Secondly, there is always the issue of innovation negativism, whereby a failure of

an incremental innovation may hinder the further stages of an innovation process (Rogers

1988). If you decide to gradually diffuse an innovation and thus split its adoption process

into various steps, you might lower the chances of each step being rejected as it is less

radical than the established practices, but at the same time you increase the number of stages and thus lowering the probability of adopting the eventual innovation.

232Diffusion of Innovations

Observability

Observability is the degree to which the results of an innovation arevisible to others. The results of some ideas are easily observed andcommunicated to others, whereas some innovations are difficult todescribe to others. We suggest Generalization 6-5: The observabilityof an innovation, as perceived by members of a social system, ispositively related to its rate of adoption.

Most of the innovations studied in diffusion research aretechnological ideas. A technology is a design for instrumental actionthat reduces the uncertainty in the cause-effect relationships involvedin achieving a desired outcome. A technology has two components:(1) a hardware aspect that consists of the tool that embodies, thetechnology as material or physical objects, and (2) a software aspectthat consists of the information base for the tool. An example, cited inChapter 1, is computer hardware (the equipment) and software (thecomputer programs). Usually the software component of atechnological innovation is not so apparent to observation, so innova-tions in which the software aspect is dominant possess less observabil-ity, and usually have relatively slower rates of adoption.

Explaining Rate of Adoption

Rate of adoption is the relative speed with which an innovation isadopted by members of a social system. It is generally measured as thenumber of individuals who adopt a new idea in a specified period. Sorate of adoption is a numerical indicant of the steepness of the adop-tion curve for an innovation.

We showed previously in this chapter that one important type ofvariable in explaining the rate of adoption of an innovation is itsperceived attributes. Table 6-1 indicated that 49 to 87 percent of thevariance in rate of adoption is explained by the five attributes (relativeadvantage, compatibility, complexity, trialability, and observability).In addition to these perceived attributes of an innovation, such othervariables as (1) the type of innovation-decision, (2) the nature of com-munication channels diffusing the innovation at various stages in theinnovation-decision process, (3) the nature of the social system, and(4) the extent of change agents' promotion efforts in diffusing the in-novation, affect an innovation's rate of adoption (Figure 6-1).

Attributes of Innovations and Their Rate of Adoption 233

III. Communication Channels (e.g., mass

media or interpersonal)

IV. Nature of the Social System

(e.g., its norms, degree of interconnectedness, etc

V. Extent of Change Agents' Promotion Efforts

Figure 6-1. A paradigm of variables determining the rate of adoption of

innovations.

The type of innovation-decision is related to an innovation's rateof adoption. We generally expect that innovations requiring anindividual-optional innovation-decision will be adopted more rapidlythan when an innovation is adopted by an organization (Chapter 10).The more persons involved in making an innovation-decision, theslower the rate of adoption. If so, one route to speeding the rate ofadoption is to attempt to alter, the unit of decision so that fewer in-dividuals are involved. For instance, it has been found in the UnitedStates that when the decision to adopt fluoridation of municipal watersupplies is made by a mayor or city manager, the rate of adoption isquicker than when the decision is made collectively by a public

referendum.The communication channels used to diffuse an innovation also

may have an influence on the innovation's rate of adoption (Figure6-1). For example, if interpersonal channels must be used to createawareness-knowledge, as frequently occurs among later adopters, therate of adoption will be slowed.

The relationship between communication channels and rate ofadoption are even more complicated than Figure 6-1 suggests. The at-tributes of the innovation and the communication channels probably

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Rogers defines complexity as “the degree to which an innovation is perceived as relatively

difficult to understand and use” (Rogers 1988, p. 230), and argues that its negatively related

to the rate of adoption, an argument that has yet to find rigid empirical support. In my

opinion, this is very user specific criterion; for example, you cannot expect a recent

mathematics graduate to have the same ease in understanding on how a new computer software operates as a historian emeritus.

Trialability is “the degree to which an innovation may be experimented with on a limited

basis” (Rogers 1988, p. 231); the higher its degree of trialability, the lower the uncertainty

that surrounds the innovation and thus the likelier its adoption. Moreover, the importance

of this characteristic decreases with the number of adopters, and its assumed to be more

important at the early adoption stages. But this assumes that riskiness is negatively

associated to adoption. Is that always the case??

Observability is “degree to which the results of an innovation are visible to others” (Rogers

1988, p. 232). He argues that the more visible a technology is to members of a social group, the higher its rate of diffusion.

The type of innovation decision suggests that emphasis needs to be paid on the number of

agents involved in the adoption-decision making. The more agents involved the slower is the

adoption process, suggesting that an adoption decision by an organization is taken with

more of a difficulty than the decision of a single agent. The communication channels and the

way they operate in a given social system have a definite role in the rate of diffusion; yet,

Rogers does not specify the nature of this interaction as well as its impact on the diffusion

rate. Lastly, the efforts of the change agent have a certain influence on the diffusion rate, but its importance again depends on the diffusion rate and is a topic still under-researched.

Rogers recognizes the dynamic nature of all the above determinants, and suggests the

diffusion effect, which is defined as “the cumulatively increasing degree of influence upon

an individual to adopt or reject an innovation, resulting from the activation of peer networks

about an innovation in a social system” (Rogers 1988, p. 232). In other words, there is a

domino effect of adoption implying that the diffusion rate accelerates with its number of

adopters. This effect relates to the availability of information on the innovation as well as its

communication systems, and suggests that there is a minimum level of information

necessary for an agent to adopt an innovation. But is this level of information the same to all

agents, or it varies? Maybe more risk loving agents may require less information than risk-loving individuals.

PARTICULARITIES OF INNOVATIONS IN THE ENERGY SECTOR

Innovations in the energy sector are in many ways particular in their analysis, so some

authors have gone as far as providing a new concept that of eco-innovation. An eco-

innovation can be viewed as “The production, assimilation or exploitation of a product,

production process, service or management or business methods that is novel to the

organization (developing or adopting it) and which results, throughout its life cycle, in a

reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives” (Oltra 2008, p.2).

Oltra (2008) and Popp (2010, p. 2) argue that ecological innovations are similar to all other

innovation types, in the sense of that their analysis depends on factors which are difficult to

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evaluate. But most importantly, the main particularity related to innovation in this sector is

the fact that there is a double externality problem, where the existing market failures of

the innovation process, are accentuated in the environmental markets, because of the

failure of the market to price pollution, and thus the producers have no incentive to reduce

its production in the absence of policy/regulations.

Yet some authors argue that the negative externalities of this market failure are somewhat

alleviated if reductions in pollution are viewed as inefficiencies, and thus firms have

incentives to improve their efficiency and thus reduce their negative environmental impact.

Moreover, they argue that because of their positive environmental impact, these

innovations are always socially desirable, and thus justifying the need for state involvement

to establish equilibrium in the market. However, there are some RETs, which despite their

evident environmental benefits, fail to gather unanimous public support. A typical example

is the case of wind turbines, whose installation faces significant opposition by various social groups despite their clear environmental benefits, for reasons related to appearance.2

Moreover, RETs have various physical/technical differences when compared to the

traditional energy generating facilities that need to be taken into account. Conventional

electricity generating facilities such as coal/oil fired and nuclear require extensive and

expensive physical facilities. On the contrary, some RETs are small scale and of a modular

nature, allowing for factory-based automatic production, and much less on-site

construction. Therefore, renewable energy technologies are more similar to mass-

production technologies than to conventional power plants (Neij 1997, p. 1100). This

suggests that RETs do not share all the particularities of existing analyses of the energy

sector. At the same time, RETs because of they have lower energy densities than traditional

energy producing means, these installations occupy larger physical space and are thus more

likely to create influence a larger number of stakeholders (Wüstenhagen et al 2007, p. 2684).

These differences have illustrated the importance of examining other stakeholders who were earlier considered of minor importance when investigating the diffusion process.

Another issue that needs to be taken into consideration when analyzing the renewable

energy innovation has to do with the complicated infrastructures to which energy

technologies are bounded to (Wüstenhagen 2007, p. 2685). This technological

complicatedness makes the introduction of any radical innovation more difficult than the

diffusion of other “independent products”. Moreover, although their introduction is small

scale their investment and siting decision still affects a multitude of other stakeholders, and

consequently becomes a political rather than a pure economical decision (Wüstenhagen

2007, p. 2686). Therefore, their diffusion analysis simply on an economic basis might not be ideal.

RETs, and wind technologies in particular, belong to a sector that has some distinct

characteristics when compared to other mainstream industries, something that needs to be

accounted for when investigating it. In particular, Jacobsson (2000) recognizes 3

particularities. The first one is related to the size of the market, which is enormous and thus

the amount of time needed for any substantial transformation to take place is quite

extensive. The second one relates to the subsidization of the incumbent/traditional energy

sources, either in the form of R&D incentives or the non-reflection of the environmental

2 There is also some evidence that wind turbines have a negative impact on birds and their surroundings.

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costs in the prices of energy produced from them. Moreover, the energy sector, because of

its strategic importance for the government and is thus always under strict monitoring and

regulation. Thus, any analysis of major technological change in this sector needs to

incorporate the role of the government as a major stakeholder. Lastly, Jacobsson, but also

other authors like Nakicenovic (Nakicenovic 2002) and Unruh (2000) underline that the

resistance of the established players in any kind of change is quite significant. This resistance

gives rise to dangers of potential technological lock-in to fossil fuel technologies, a

development which could lead to a reversal of the world economy’s trend towards decarbonization.

THE DIFFUSION PROCESS OF RETS

Grubb (1990) constitutes one of the earliest attempts to examine the reasons for which RETs

had failed to achieve a deep entry into the energy sector, despite the common agreement

on their medium and long term advantages. He argues that RETs constitute a ‘‘Cinderella

option’’ implying that their use and potential is neglected by current policy makers (those in

the 1980s) and proposes four potential explanations for this neglect. The first concerns a

lack of data on the actual energy value of renewable resources, which leads to uncertainty

and underinvestment. The second is related to the excessive conservatism exhibited by

international organizations and governments based on the pessimist intellectual legacy of

the 1970s, with these studies offering pessimistic projections on technologies and costs,

thus discouraging government support funds to flow in the renewable sectors. Thirdly, there

seems to be a lack of the necessary informative institutions that would efficiently

disseminate the necessary renewables information to the policy community and allow it to

create measures promoting the innovation. Lastly, he suggests a lack of vision i.e. skepticism and adverse attitude of policy makers towards potential of renewable technologies.

Studies supporting the importance of regulation for diffusion of environmental technologies

have also been the topic for most empirical diffusion research. The main rationale stems

from the double externality problem, and claims that if the introduction of these

technologies does not bring efficiency gains or in other words provides sufficient profitability

to justify its adoption by the investors, then policy is required to promote diffusion. For

example, Popp argues that even if the all the advantages of RETs are taken into

consideration, these benefits are largely external to the individual producer; the producer

will have an incentive to adopt more costly clean technologies only if they provide additional

costs savings, thus justifying incentives for government intervention (Popp et al 2010, p. 24).

Other research comes from Gray and Shadbegian (1998), who find a strong relationship

between regulation and diffusion. Their research was focused on the paper and pulp

industry, and aimed at examining the factors that influence the technology adoption choice of the plants. Similarly,

Moreover, Kerr and Newell (2003) investigate the diffusion of lead reduction technologies in

US refineries. They develop and test a model that suggests that diffusion will take place by

firms gradually as the cost falls and thus the benefits increase, while simultaneously

regulatory stringency increases the value of adoption; firms with lower benefits or higher

costs will adopt more slowly. The importance of regulations is such that the authors argue is

the main explanatory variable behind the diffusion of lead reducing technologies comes from increased regulatory stringency.

However, not all types of regulation have an immediate effect on the adoption of

ecoinnovations. Snyder et al. (2003) examines the diffusion of membrane-cell technology in

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the chlorine manufacturing industry, and argues that direct regulations to chlorine

manufacturing facilities did not have a significant impact on the diffusion of new technology.

Rather indirect measures to end-products influenced consumer demand, which in turn

influenced the decision of producers to adopt new, cleaner technologies. Others (Popp

2006) have proposed optimal policy mixes, but the extent of this optimality is very much country specific, something not sufficiently taken into account in these papers.

Linked to the issue of regulations is the strength, effect and mechanism of political economy

that permeates the system, as it affects demand for regulation (Lovely, & Popp 2008).

Moreover, Lovely and Popp (2008) suggest that openness facilitates technology adoption

and diffusion, and also that developing countries adapt regulations in earlier stages of economic development than the developed countries.

Micro-level analysis finds that the more sophisticated a plant, the easier/smoother/faster

the diffusion of the new technology is (Kerr, & Newell 2003). A similar argument could me

made for diffusion of RETs, and test the hypothesis that the more sophisticated and

technological advance a country is the faster is the diffusion of a new technology. Or if we

want to narrow the hypothesis even more, we could argue that the more technologically

advanced and diversified the energy sector of a country, the faster the diffusion of RETs. To

support this claim, we could look back in the literature of diffusion and match it with a

theory.

The principal-agent problem can also be used to explain why some environmental friendly

technologies can face difficulties with their adoption. The main argument is that if the

person deciding for the adoption of the new technology is not the one that reaps the

rewards then the process of diffusion is impeded. The typical example is that of the adoption

of energy savings technologies for a rented house. The owner has no incentive to invest in

more expensive energy efficient measures, if these costs are not passed on to the tenant. At

the same time the tenant could benefit by paying lower utility bills, but these costs might

take longer to be paid back, and assuming he is interested in a short-let, he might choose an

alternative home with a lower rent (Popp et al 2009). This conflict of interest might lead to

what is known as the energy paradox, whereby although some environmental technologies

provide cost savings to their adopters, they are still not diffused (Jaffe, & Stavins 1994;

Newell et al 2004). As a corollary, policy aimed at improving the financial benefits of these technologies is ineffective (Popp et al 2010, p. 24).

Another explanation for this energy paradox comes from Shama (1983), who observed the

low speed of diffusion of energy conservation technologies, which bring positive economic

costs to adopters. He argues that if this phenomenon is examined purely from the

economics and engineering perspective, then it seems as an irrational and paradoxical

behavior of consumers. However, if the behavioral aspect is included in the analysis, then

this behavior can be considered as perfectly rational. In more details, he uses Rogers’

diffusion framework to examine this energy paradox, and concludes by providing various

policy recommendations.

For the adoption of an innovation it is necessary to examine the preferences of the agents

involved in the process. Such an attempt is made by Masini and Menichetti (2010) who

investigate the decision making process behind RET investments. They use behavioural

finance and build a model that examines the structural and behavioral characteristics of

investors as factors determining diffusion. The main shortcoming of this paper is the fact

that it treats RETs as one, and does not distinguish between different types of technologies

9

and the investors’ attitudes towards them, and also its exclusive focus on the European market.

Another factor that has most likely an impact on the diffusion of RETs is prices. Some earlier

research on ecoinnovations has illustrated energy prices as significant determinants of

technology adoption (Boyd, & Karlson 1993). Thus, it could be argued that prices are also

significant for the diffusion of RETs. The only paper that investigated this phenomenon

explicitly was Rehfeld’s (2007), who stressed the importance of getting the prices economically right, in order to achieve maximum diffusion.

Similarly, literature suggests the importance of factor costs; firms with higher factor costs

will tend to adopt a cost cutting technology faster than those with lower costs. Fisher-

Vanden et al. (2006) use a panel of 22,000 Chinese large and medium enterprises and

attempt to determine the factors that explain China’s improvements in energy efficiency

over the period 1995-2001. Their findings suggest that energy efficiency improvements

stemming from the diffusion of cleaner technology are mainly attributed to rising energy

prices. This finding which underplays the eminent role of regulation of all previous studies

might help to shed light on the particularities of transition/emerging economies, since this is

one of the very few studies that examine diffusion in such economic environment.

Therefore, a significant determinant of technological diffusion is its impact on the firm’s

profitability.

Neij (1997) uses experience curves to study the diffusion of wind and solar, and argues that

the most important factor for their diffusion is how fast their prices (measured as the cost of

generating electricity) will fall when compared to traditional electricity producing factories.

She argues that the potential for cost reduction is higher for RETs than for traditional

technologies; her research also illustrates the importance of R&D for decreases in the total

cost, and only if these two facts are combined one could expect successful diffusion of RETs.

Similarly, Nakicenovic (2002) illustrates the importance of learning by doing in the diffusion

of new technologies, and makes a case study of the decarbonization of energy. He proposes

a theoretical model that shows that although it is more costly to invest in clean technologies

now, the learning effects from the diffusion of these technologies will allow for a prompt payoff of the investments and thus facilitate even more the diffusion.

General literature on diffusion stipulates a positive relationship between firm size and

adoption propensity (Karshenas, & Stoneman 1993; Geroski 2000; Levin et al 1987; Saloner,

& Shepard 1992). The number of studies supporting a negative relationship is very limited,

with Oster and her study of steel firms (Oster 1982). The lower the number of adopters, the

higher the speed of diffusion as less agents will have to accept the new innovation. Hence, it

could be argued that more oligopolistic/higher concentrated industries might accept the

innovation more easily. Moreover, the larger the size of a firm the easier and less costly and risky it is to adopt new technologies, as it is more diversified.

In the energy sector, Rose and Joskow (1988) investigate the impact of size and ownership

on technology diffusion in the US electricity market in a sample of 144 electric utilities over

the 1950 through 1980 period. Their results indicate that diffusion is positively related to

firm size, but at the same time argue for the existence of a maximum optimal size after

which further size increases leads to lower diffusion. Moreover, their results underline the

importance of ownership structure for diffusion, with investor/privately-owned companies

faster adopters than foreign one, and find weak support for the influence fuels prices as a

diffusion determinant. Kerr and Newell’s research in petroleum refineries also support this

10

finding in the energy sector, with larger and more sophisticated refineries more likely to

adopt new technologies (Kerr, & Newell 2003). In this case however the size was a proxy for

sophistication, which was in turn thought of as positively related to diffusion. This finding is

noteworthy because it could be argued that the more sophisticated the plant, the more

difficult it would be to introduce radical changes, i.e. the more locked-in it would be to established technologies. But at the same time, empirical evidence suggested the opposite.

Looking at the cross-country adoption of RETs, the argument of size could be translated into

a proxy for the size of the country, or more appropriately the wealth and/or entrepreneurial

climate and/or the innovativeness of the country. The more wealthy and entrepreneurial

and innovative the country’s mentality is, the more likely it is to take risks and adopt new

technologies. Similarly, it could be argued that countries with more

concentrated/monopolistic energy sectors, the diffusion could be higher. However, this

hypothesis might not be validated, as it might seem that concentrated energy sectors are

state dominated, and thus less prone to innovation. But again, it is different to talk about

innovation and different about its diffusion. In state-owned companies it might be easier for governments to impose the regulations, and thus in this case diffuse an innovation.

Lanjouw and Mody (1996) construct a patent data set from 1972 to 1986 for the US, Japan

and Germany in order to study the creation and diffusion of environmental technologies.

Moreover, they examine international technology transfer from these 3 industrialized

economies to 14 lower and middle income countries. This period is important because it was

a period of a period of rapidly increasing public awareness and concern about environmental

damage, similar to today’s situation. Environmental concerns came to the forefront in the

early 1970s, triggered in some in- stances by specific accidents, as in Japan. They found that

innovation is related to pollution abatement expenditures, which are in turn related to the

level and stringency of environmental regulations. To study diffusion they investigate trade

in environmental technology and domestic patenting by foreigners in the sphere of

environmental technology, and find that most patents in developing countries comes from

technologies sited for developed countries rather than technologies adapted for developing

countries, suggesting the importance of technology transfer from developed to developing countries.

Dechezleprêtre et al. ( 2010) looks at patents and the diffusion of RETs in emerging markets,

a total of 76 countries, including emerging markets. They find that R&D and innovations

mainly focused on industrialized countries, particularly Japan, Germany and the USA.

However, some 16% of total patents comes from emerging markets, especially China, Russia

and South Korea. On the issue of international diffusion of these technologies, the evidence

suggests that there is not much transfer activity across countries; most of the transfer takes

place among developed countries, but there seems to be no evidence indicating diffusion

among emerging markets. Thus, what is an important area of research for diffusion is how

this technology flows across international boarders, esp. to emerging economies that are now developing at extremely high rates and are thus the prime polluters.

Lovely and Popp (2008) focus on the adoption of environmental regulation as the first step

in the international diffusion of environmental technologies (Popp et al 2010, p. 28). In

particular, they examine how the existing technological stock, mostly originating in

developed countries, induces regulation in developing countries. They focus their research

on the adoption of coal-fired power plants to adopt pollution control regulations in a

mixture of 45 developed and developing countries.

11

One of the most interesting arguments of the paper is that when we investigate diffusion of

clean technology, the starting point should be the introduction of the regulation rather than

the introduction of the technology itself. Thus, they argue that the first step for

understanding international diffusion of clean technologies is the understanding of the

determinants of regulation.

Popp (2004) investigates the innovation and diffusion process of air pollution control

equipment; he focuses on USA, Japan, and Germany and uses patent data to examine how

technological innovations in the field of air pollution occur and diffuse between these three

countries. His findings justify the hypothesis that stricter regulation induces innovation, but

the interesting point of this paper is his attempt to examine the diffusion of these

technologies across borders. In more details, he measures innovation in terms of patents

and views the diffusion as taking place in two ways: either as the direct adoption of a

technology from a different country, or as an input in the creation of a new technology (i.e. a

knowledge spillover). If, for example, a patent of an innovation created in Japan is cited by a

patent made in the USA, then this suggests the existence of a knowledge spillover from

Japan to the USA. The patent evidence suggests that technology transfer is more indirect,

and takes mostly the form of knowledge spillovers rather than direct adoption of foreign

innovations.

However, I believe that the main problem of this paper is the selection of countries. By

selecting only these 3 countries they make the implicit assumption that this technology is

pioneered and exists only in these 3 countries. As a non-expert in the field of this

technology, I cannot claim that this assumption is not valid. However, it would be surprising

if no other OECD country developed any similar technology within this time span, something which if true can cause considerable omitted variable bias in the analysis.

12

METHODOLOGY

Modeling the time to technology diffusion/adoption leads naturally to the use of statistical

methods developed for analyzing duration data; these methods are commonly known as

duration or hazard models. Hazard models are focused on the occurrence of a particular

event, usually known as failure, which occurs after a certain period of time has passed. The interest is not solely whether or not the event will occur, but also the timing of the event.

The hazard rate is the probability of the country failing in the time interval of our analysis t,

given that it has survived up until time t. Examples of failures are the failure of a component

in a machine, the death of a patient, or the movement into unemployment of a worker. In

this analysis, although counterintuitive, failure denotes the fact that a country has started to exploit its wind resources in order to generate electricity.

The survivor function is the probability that no event has occurred before time t. The

cumulative distribution function serves as the complement to the survivor function and illustrates what is the probability of an event occurring before time t.

A common issue in survival analysis is the issue of censoring. Censoring occurs, or better, an observation is censored if it does not fail within the time period of the analysis.

There are three main approaches that could be used to model time to an event: non-

parametric, semi-parametric, and parametric. The non-parametric method lets the data to

“talk by themselves”, i.e. does not attempt to estimate either the baseline hazards or the

coefficients. Semi-parametric models leave the baseline hazard unspecified and rather focus

on calculating estimates for the coefficients by simply using the ranks of time. This method is

also called the Cox-proportional hazards model. Nevertheless, proportional hazard models

require that the impact of any individual covariate on the hazard rate is the same for all

values of t. Lastly, parametric models complement semi-parametric analysis by assuming

that the baseline hazard follows a certain distribution and attempting to model/determine it. This paper focuses on non-parametric and semi-parametric methods.

HAZARD MODELS IN DIFFUSION STUDIES

Framing the theoretical issue of technology adoption in the concept of hazard models, leads

us to investigate what factors determine the conditional probability of technology adoption

in time t given that the technology has not already been adopted by that time. Within the

economics literature, hazard models have been used to analyzing labor economics issues,

such as unemployment spells, but they have to a more limited extent used to issues related to technology adoption (Kiefer 1988).

ΗAZARD ΜODELS IN ECONOMICS AND ENVIRONMENTAL ECONOMICS

Kiefer (1988) performed an investigation in the potential use of hazard models or duration

studies in the science of economics, although he was not the first to use them. Among other

potential usages, he recommends their utilization in the field of technology studies, and in

particular, when one examines the time to adoption of new technologies (Kiefer 1988).

Hazard models are not widely used in environmental economics, but there have been some

attempts to utilize them, mainly in order to investigate the impact of regulations on the

diffusion of some environmental innovations.

13

Snyder et al (2003) used hazard models in order to investigate the impact of regulation on

the use of chlorine. They used these econometric models because their interest was on the

timing of the introduction of the innovation rather than other factors such as causality,

which is better investigated under different econometric techniques. They examined

diffusion at the firm level, and in particular, the decision to adopt a new more friendly

technique for chlorine manufacturing. Their focus is the USA over a 30-year period, and their

findings suggest that regulation has no impact on the decision of firms to retrofit the innovation, but rather influences solely the decision of firms to exit the industry.

Lovely and Popp (2008) firstly construct a general equilibrium model and form their

hypothesis on the impact of regulation in their fictional economy, and then they use hazard

models to test the diffusion of environmental regulation across countries. Kerr and Newell

(2003) use hazard models to investigate how lead reduction technologies were diffused in US petroleum refineries in the period 1971-1995.

THEORETICAL MODEL

Cox models are useful for our analysis because the scope of this paper is not to determine

whether or not the probability of failure change over time, but which factors influence the probability of failure. According to this model, the hazard function h(t) is equal to

or

where h(t) is the rate at which the country introduces electricity from wind at time t, given

that they have not by time t-1, h0 is the baseline hazard (which remains unspecified in this

model) when the values of all covariates are equal to zero, and X is a vector of all covariates

and their corresponding parameter vector β that the model assumes to have an impact on h(t).

The baseline hazard (h0) is assumed to be common across all agents of our analysis, and to

vary only with time and not with any other variable including the covariates. Moreover, “it is

based on the assumption that all hazard functions across the different levels of variables in

the model are proportional to a baseline hazard function” (Somers, & Birnbaum 1999).

For the Cox model, the baseline hazard remains unspecified. This presents the analysis with

some problems, such as some loss in the efficiency of the estimators, because some

information may be left out. However, this efficiency loss is generally small and can disappear completely in asymptotic results (Moeller, & Molina 2003).

However, there have been various models which attempt to determine the shape of h0.

These models are known as fully parametric, with the simplest being the exponential which

assumes that h0 is constant over time and equal to γ. Other common functional

specifications of h0 are the Weibull, the Gompertz, the loglogistic, etc. Their main difference

from the exponential is that they assume the value of h0 to vary with time. The Weibull for

example assumes that the baseline hazard is a monotonic with respect to time, while the

lognormal assumes that the hazard increases and then decreases. Nevertheless, these methods are not going to be used in this paper.

14

DATA

The two primary databases used in this research are the World Bank Development

Indicators, and the IEA database on Renewables. The IEA Renewables database has

information on renewable electricity production and for 134 countries for the period 1990-

2010. The WDI have a similar amount of countries and a wider range of indicators, and a

larger time span, but we are limited by the dependent variable that comes from the IEA

database. Some of the data like the FIT indicator or the green party were collected from a variety of web resources, as there is not any freely available database.

The following table illustrates the key summary statistics of our dataset:

15

RESULTS AND INTERPRETATIONS

The assumption is technology improves over time, and by improvement a decrease in the

cost/increase in the benefits is implied. Thus, it could be assumed that the risk of failure (i.e. the hazard) increases over time.

NON-PARAMETRIC ANALYSIS

The above two graphs illustrate the Kaplan-Meier failure and survival estimates. The Kapla-

Meier failure plot illustrates the probability of a subject failing at time t given that it has

survived up to time t. In this case, the probability that a country starts using wind energy in

year 10 (i.e. year 2000) is somewhere between 15-25%3. Similarly, by looking at the survival

estimate, it can be inferred that the probability of not adopting wind by year 10 is somewhere between 75-85%.

However, this analysis implicitly assumes that time is the sole determinant of adoption, an

assumption which clearly paints a distorted picture of reality. Time in itself does not

determine anything; rather, as time passes other variables change, which in turn have a

causal relationship to the probability of adoption of wind. To capture some of this factors, semi-parametric analysis is used, the results of which are presented in the following section.

SEMI-PARAMETRIC ANALYSIS

The advantage of Cox models is that they do not restrict the baseline hazard into some

predetermined shape, thus allowing for a more flexible specification of the model. In particular, the baseline hazard can be determined after specifying the model.

A total of 240 models were tested, from which roughly 50% succeeded in the various specification tests; the indicators that were tested are tabulated below:

3 The shaded area represents the pointwise confidence bands of the Kaplan-meier function, which is the solid blue.

16

Starting with the level of emissions, the results suggest that it is positively related to the

hazard rate, i.e. the higher the level of CO2 per GDP the higher is the probability of adoption

of wind. The explanations for this result are multiple, but the most probable argues that the

higher the Co2 per GDP, the less clean the country’s electricity production is, and the higher the pressure that is placed to the country by agents to clean its production.

The actual impact of these agents is captured in the model, and in particular by the

statistically significant hazard rate of the green party dummy. The existence of a green party

in the country increases the probability of adoption of wind by 15.68%. Nevertheless, EU

membership or the signing of the Kyoto protocol have proven statistically insignificant

factors in determining the probability of adoption.

An argument could be made that the change agent per se does not have an impact in the

country’s decision to adopt wind, unless the country is open and democratic. Therefore, the

polity2 indicator has been used. The indicator was used in the model in two ways. The first

was as a stand-alone indicator, and the other one was as an interaction with the dummy

variable for green party. In the first case, the aim was to capture the effect of democratic

openness on the probability of adoption; nevertheless, the indicator was statistically

insignificant, implying that the probability of first adoption of wind does not depend on the

level of the country’s democracy. In the second formulation, the aim was to moderate the

impact of green party; in other words, it was assumed that the impact of green party will

vary with the country’s freedom. The more open the country the stronger the impact of

green party on the society, and thus the higher the probability of adoption. Nonetheless,

this was not confirmed by the data, and the explanation might lie on the phenomenon

under observation. In other words, the aim of this model was to examine the determinants

of the speed of first adoption, rather than the determinants of full diffusion. Thus, even if

there is not an open democratic environment, just the existence of one green party might

reflect some kind of environmental awareness in the country, which could be enough to

incentivize the country to adopt at least one wind turbine. To allow for wind technology to

spread to the country however, the strength of the change agent (green party in this case) must be significant, and thus the interaction variable could become statistically significant.

Another important determinant from literature is the size of the market; the larger the size

of the market the larger is the potential for profit for the supplier of the innovation, and thus

the larger his effort is in attempting to promote that technology in the market. In this case,

we assumed that the market is the electricity market, and we proxied that by the amount of

17

electricity that is consumed per capita. Although the absolute size of the market could have

been used, we assumed that the important factor for first diffusion would not be the actual

size of the market, but how energy rich was that country per each citizen. For example, even

though Russia consumes almost 25 times the amount of electricity consumed by Denmark,

we should expect that the supplier of the technology would see that the profit potential in

Russia is 25 times that of Denmark. However, if we compare the electricity consumption per

capita, then we see that the two countries are almost identical, a finding that yields a much

more realistic depiction of reality. Consequently, our model confirmed that the higher the amount of electricity per capita, the larger is the probability of adopting wind.

On a similar note on profitability, apart from the profitability of the supplier of the

innovation another at least equally important factor suggested by the diffusion literature is

the profitability of the buyer of the innovation. In this case, the profitability was captured by

three factors: the price of the wind turbine, the level of Feed-in Tariffs, and the price of

crude oil. The price of the wind turbine and crude oil were not statistically significant, while

the level of FIT was. The first finding is somewhat surprising, but the explanation probably

lies in the quality of data available. The only available data was on Danish wind turbines

from 1989 to 2001. Then these figures were converted to local currencies, and we assumed

that these are the prices each country faced when deciding whether to purchase the turbine

or not. However, this is an unrealistic depiction of reality, since a significant amount of costs for wind turbines are the transport costs, which vary significantly with distance.

As far as FITs are concerned, it is a fact that wind technologies are significantly more

expensive than conventional fossil fuel technologies. Therefore, it is a widely accepted fact

that for their adoption and diffusion there is need for government policy. Although there are

various ways of government intervening and promoting wind energy, one of the most

effective is based on the use of market instruments, and in particular the use of FIT. This

general finding is also confirmed in this paper, which argues that the implementation of FIT in the country increases the probability of adoption of wind by 17%.

Continuing the investigation into the profitability aspects of adoption, the gap however

between wind and conventional fossil fuel technologies decreases with time, mainly because

technology improves, but also because the price of fossil fuel increases. To capture the

dynamics of this changing gap, the price of wind turbines was used as it was previously

mentioned, and the assumption made was that as the technology improves, the cost of the

turbines will go down. Apart from cost, the efficiency of turbines should also have been

captured, but no satisfactory data were found. Moreover, to capture the effect of changing

of fossil fuel prices, the price of crude oil was used, since gas and coal are a bit harder to

capture. Nonetheless, the price of oil also seemed insignificant, maybe because the world price was used at local currencies rather than the local price converted into US dollars.

To capture the observability criterion, we tried three different indicators. One was distance

from Denmark, which is arguably the pioneer in wind energy; we argue that the closer a

country to the innovator, the higher is the probability that it will adopt wind. However,

physical distance is not enough; for example a country might be neighboring to another, but

they might have no trade relationships. Thus, the amount of trade with Denmark was also

tested. Neither of the two indicators was found to be statistically significant on their own, so

a third alternative was tested, which was the interaction of the two terms. Distance was

then moderated by trade intensity, and the results suggest that for two countries with the

18

same distance from Denmark, the one with the highest level of trade has the higher probability of adoption, thus confirming Roger’s observability criterion.

A prominent issue in technology diffusion studies is the issue of technological lock-in. The

stronger this lock-in, the harder is for the economy/sector to move to an alternative

technological configuration. In the context of wind adoption, two types of lock-in were

identified, a technological and a market lock-in. The technological refers to how much the

economy’s electricity sector is based on traditional fossil fuel technologies. This was

captured the indicator on energy use as a % of GDP. The more energy inefficient the country

is, the more we assume it is based on old technologies, and thus the higher the resistance of

incumbent energy players to change. This negative relationship between energy intensity and probability of adoption was confirmed by our model.

Similarly, the other element of resistance to change was the market lock-in. An economy or

sector is locked-in a particular technology if it is making an accounting profit by continuing

to use that technology. In the case of the energy sector, an economy might be finding

profitable to use conventional fossil fuel technologies and these may account for a

significant amount of its GDP, thus preventing it from shifting to alternative renewable

technologies such as wind. Nevertheless, energy markets suffer from externalities, as many of the costs of fossil fuel technologies are not captured by the market price.

This phenomenon of market lock-in was explicitly captured by the indicator of “Natural

Resource rents as a % of GDP”, which is a sum of oil rents, natural gas rents, coal rents (hard

and soft), mineral rents, and forest rents. Ideally, we should use an indicator which focuses

solely on oil, natural gas, and coal rents, but we expect a country with high reserves of oil,

gas and coal is likely to have mineral and forest reserves; thus, this indicator should capture

similar effects. The coefficient of this indicator was statistically significant, and its impact

was as expected, i.e the higher the country’s market lock-in, the lower the probability of adoption of wind.

Another issue, which is particular to the energy sector is the issue of energy dependence.

The vast majority of countries have inadequate fossil fuel reserves to cover all their energy

needs, so their energy supply depends on imports from other countries. This poses

considerable risks, which have been widely investigated from a wide range of social

sciences: from geopolitics and international relations, to energy economics and political

economy. Moreover, the importance of this factor is also evident by the extent to which this

subject has dominated the agendas of almost all developed energy-importing countries,

particularly those of the EU.

A way that a country can decrease its energy dependency is either by decreasing its energy

needs and/or increasing its domestic energy production. Renewable energy, and wind in

particular contributes to the increasing of domestic production, assuming that the country

has adequate wind resources. This argument was supported by the model which found a

positive relationship between “Imports of Energy as a % of total energy” and the probability of adoption.

Another determinant in diffusion and innovation studies is the economy’s technological

capability. There is a very wide and diverse literature on the ways to measure technological

capability, but two of the most conventional, and widely available indicators are the

economy’s GDP per capita, and the years of schooling. In this case however, neither of these

19

two were found to be significant, a finding that maybe has to do with the process that we examine, which is simply the first adoption rather than full diffusion.

Lastly, the amount of wind resources in the country has exerts a small but positive influence

on the probability of adoption. This indicator classifies a country’s land area into 10 different

groups according to the full load wind hours. In other words, it measures how many hours a

wind turbine could work at full capacity. Clearly, the larger the amount of land at the higher

full load wind hour group, the higher the amount of wind. The results suggest that the

higher the amount of land area the higher is the probability of adoption. This is a widely

expected result, since the more of the resource the country has, the more attractive the technology is, assuming that it will be more profitable.

20

CONCLUSION

The scope of this paper was the identification of the factors that determine the first

adoption of wind power by a country. Despite the fact that a great deal of research has been

written on the determinants of innovation and diffusion of renewable technologies, much less emphasis has been placed on the determinants of first adoption.

This paper aims to cover this gap by building on the theoretical framework provided by two

major schools of thoughts: that of neoclassical economics and that of sociology. Griliches

and Rogers were chosen as the best representatives of these two schools, and then their

theoretical approaches were combined with the particularities of the energy sector to build

a theoretical model that can explain the determinants of first adoption of wind energy in countries around the world.

Hazard models were then used to determine which factors can explain first adoption, and

the data sources were taken from a multiplicity of international sources. The results mainly

confirmed the neoclassical economic approach, suggesting the various elements of the

market as the most important determinants of profitability these included the profitability

of adopters, of suppliers, the availability of wind, as well as the carbon and market lock in.

Moreover, the existence of a change agent in the form of a green party and the country’s

trade with the pioneers of wind energy were also significant determinants, while all other institutional determinants were found insignificant.

21

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