ThesisBWVolger1734717

On the Role of Geography and Business Models

in Social Niche Up-Scaling The Development of Wind Energy Cooperatives in the

Netherlands (1986 – 2014)

Student name: B. W. Volger

Student number: 1734717

Date: 24 April 2015

Document: Master Thesis Earth Sciences and

Economics

Track: Energy

Course number: AM_1150

First supervisor: Dr. E. Vasileiadou

Second supervisor: Dr. M. Waterloo

Date: April 30, 2015

On the Role of Geography and Business

Models in Social Niche Up-Scaling The Development of Wind Energy Cooperatives in the

Netherlands (1986 – 2014)2014)

Student name: B. W. Volger

Student number: 1734717

Date: 24 April 2015

Document: Master Thesis Earth Sciences and

Economics

Track: Energy

Course number: AM_1150

First supervisor: Dr. E. Vasileiadou

Second supervisor: Dr. M. Waterloo

Date: April 30, 2015

ii

Acknowledgements

I would like to express my sincere gratitude to my supervisor Dr. Eleftheria Vasileiadou. Her

thoughtful comments on this research and her enthusiasm for scientific research in general have

been an inspiration to me. I would like to thank my parents, Bob and Marianne, for believing in me,

at times when I did not believe in myself and my abilities, to accomplish what has been a difficult

journey; the road to graduation. My two brothers Berry and Boyd for making me want to get the best

out of myself. Roxanne van den Bosch for showing me that it takes hard work and long nights

without any sleep to finalize a Master’s thesis. I would like to express a special word of thanks to my

grandmother Wilhelmina (Wil) van Heusden† without her dedication to her grandchildren my

graduation would not have been possible. Lastly, I would like to thank the eight wind energy

cooperatives that were involved in this research for the time and efforts they have devoted to

providing me with all the information that was needed to complete my research.

iv

Summary

The transition to a sustainable energy system is a defining challenge of the current generation. Wind

energy is expected to play an important role in this sustainable future. At the same time, the Dutch

government has great difficulty achieving targets set for the implementation of new capacity,

because current policies suffer from a lack of social embedding and the resulting public resistance.

Empirical research has shown that local participation can increase the acceptance of new wind

energy projects and why wind energy cooperatives (WECs), wherein citizens i.e. members collectively

own and operate one or more wind turbine(s), could provide a business form to successfully

implement wind energy in the Netherlands and contribute to the sustainable energy transition. WECs

may be seen as specific business models for the exploitation of wind turbines, but there is great

variation in the degree to which they appear in the Dutch energy landscape e.g. in the amount of

members and production capacity they have. Which factors have contributed to the growth of WECs

in the Netherlands over the last 30 years? The prevailing framework for studying sustainable

transitions, the multilevel perspective (MLP), has difficulty addressing questions concerning

geographical unevenness. Therefore, in this research the analytical framework is supplemented by a

conceptual notion from economic geography; proximity, and insights from business model theory.

The development of WECs in the Netherlands between 1986 and 2014 took place within three

distinct time periods; an emergence phase (1986-1996), characterized by monopoly conditions, a

consolidation phase (1997-2012) and the entry of a new business model (2013-2014), both in a

competitive electricity market. During the research period, the socio-technical conditions in which

the WECs had to realize membership and production capacity growth became increasingly complex.

Two different business models can be distinguished, the first, which signaled the emergence of the

socio-technical system, or niche, was introduced by the Organization for Renewable Energy

(Organisatie voor Duurzame Energy, or ODE, in Dutch), and propagates the exploitation of wind

turbines in local communities. The second business model was introduced by Windcentrale and has

no connection with any specific region in the Netherlands. WECs that use the former model, except

Windvogel, rely on factors in geographical proximity to their founding location to expand their

production capacity, whereas WECs that use the latter business model do not have this dependence

on local conditions. Expansion to other regions decreases the local dependence, a strategy first put

to use by Windvogel, and can thereby contribute to growth. A second important business model

development has been the professionalization a number of WECs. These WECs developed from

idealistic initiatives that relied on volunteers and active members into organizations with paid

employees that have greater abilities to cope with the more demanding circumstances. The

professionalization coincides with a simplified role of members in the organization, wherein paid

employees are now responsible for managing the growth of the WEC.

The main conclusions of this research are that, with respect to their production capacity and

members, WECs have developed organizationally, and, with respect to their founding locations,

WECs have expanded their activities geographically. The factors that have contributed to the growth

of WECs in the Netherlands over the last 30 years are their geographical location, geographical

expansion and the hiring of paid staff members, which enabled them to increase both members and

production capacity.

vi

Nederlandse samenvatting

De transitie naar een duurzame energievoorziening is een van de belangrijkste uitdagingen van de

huidige generatie. Windenergie zal naar verwachting een belangrijke rol spelen in deze duurzame

toekomst. Op hetzelfde moment heeft de Nederlandse overheid grote moeite met het bereiken van

haar doelstellingen voor tot de installatie van nieuwe windturbines, omdat het huidige beleid te

lijden heeft onder een gebrek aan maatschappelijke inbedding en maatschappelijke weerstand.

Empirisch onderzoek heeft aangetoond dat de lokale participatie het draagvlak voor nieuwe

windenergieprojecten kan vergroten en daarom zouden windenergie coöperaties (WECs), waarin

burgers als leden gezamenlijk een of meer windturbine(s) bezitten en exploiteren, als bedrijfsvorm

een bijdrage kunnen leveren aan het succesvol implementeren van windenergie in Nederland en

zodoende aan de duurzame energietransitie. WECs kunnen worden gezien als een specifiek

bedrijsmodel voor de exploitatie van windturbines, maar er bestaat een grote mate van variatie

waarin WECs zich voordoen in het Nederlandse energielandschap, bijvoorbeeld met betrekking tot

de hoeveelheid leden en production capaciteit die ze hebben. Welke factoren hebben bijgedragen

aan de groei van WECs in Nederland de afgelopen 30 jaar? Het meest gebruikte raamwerk voor het

bestuderen van duurzame transities, het multilevel perspective (MLP), heeft moeite met het

verklaren van geografische oneffenheden. Daarom is in dit onderzoek het analytisch kader aangevuld

met een conceptueel begrip vanuit de economische geografie; nabijheid of proximiteit, en met

inzichten uit de theorie voor bedrijfsmodellen.

De ontwikkeling van WECs in Nederland tussen 1986 en 2014 hebben plaatsgevonden binnen drie

verschillende perioden; een opkomst fase (1986-1996), gekenmerkt door monopolie

omstandigheden, een consolidatiefase (1997-2012) en de introductie van een nieuw bedrijfsmodel

(2013-2014), beide in een competitatieve elektriciteitsmarkt. Gedurende de onderzoeksperiode zijn

de sociaal-technische omstandigheden waarin de WECs hun lidmaatschaps en

productiecapaciteitsgroei moesten realiseren steeds complexer geworden. Twee verschillende

bedrijfsmodellen kunnen worden onderscheiden, de eerste, die de opkomst van de sociaal-technisch

systeem, of niche, kenmerkt werd geïntroduceerd door de Organisatie voor Duurzame Energie (ODE),

en het propageert de exploitatie van windturbines in de lokale gemeenschappen. De tweede

business model werd geïntroduceerd door Windcentrale en houdt geen verband met een specifieke

regio binnen Nederland. WECs die het eerste model gebruiken, behalve Windvogel, zijn afhankelijk

van de factoren in de geografische nabijheid van hun locatie om hun productiecapaciteit uit te

breiden. Integenstelling hiervan zijn WECs die gebruikmaken van het tweede bedrijfsmodel niet

afhankelijkheid van de lokale omstandigheden. Uitbreiding naar andere regio's kan de lokale

afhankelijkheid verlagen, een strategie die voor het eerst werd beproeft door Windvogel, daarmee

bijdrage aan groei. Een tweede belangrijke ontwikkeling is de professionalisering van een aantal

WECs. Deze WECs hebben zich ontwikkeld van idealistische initiatieven die vertrouwnde op

vrijwilligers en actieve leden binnen hun organisaties naar organisaties met betaalde medewerkers

die beter in staat zijn om om te gaan de veeleisende omstandigheden. De professionalisering valt

samen met vereenvoudigde rol die leden hebben gekregen in de organisatie, waar betaalde

medewerkers nu verantwoordelijk zijn voor het realiseren van de groei van de WEC, een taak

waaraan betaalde medewerkers meer tijd kunnen besteden dan vrijwilligers.

vii

De belangrijkste conclusies van dit onderzoek zijn dat, met betrekking tot hun productiecapaciteit en

leden, WECs zich organisatorisch hebben ontwikkeld, en dat, met betrekking tot hun locaties, WECs

hun activiteiten ook geografisch hebben uitgebreid. De factoren die een bijdrage hebben geleverd

aan de groei van WECs in Nederland de afgelopen 30 jaar zijn hun geografische locaties, geografische

expansie en het in dienst nemen van betaalde medewerkers, wat hen in staat heeft gesteld te

groeien in zowel ledenaantallen en productiecapaciteit.

viii

Contents

Acknowledgements ..................................................................................................................................ii

Summary ................................................................................................................................................. iv

Nederlandse samenvatting ..................................................................................................................... vi

List of Figures and Tables ......................................................................................................................... x

1. Introduction ......................................................................................................................................... 1

1.1 Introduction ................................................................................................................................... 1

1.2 Research aim and research question ............................................................................................ 2

1.3 Outline of the thesis ...................................................................................................................... 3

2. Theory .................................................................................................................................................. 5

2.1 Multi-level perspective on sustainable transitions ....................................................................... 5

2.2 Role of geography in transitions ................................................................................................... 7

2.3 Wind energy cooperatives as business models ............................................................................. 9

3. Methodology ..................................................................................................................................... 13

3.1 Data collection ............................................................................................................................. 13

3.2 Geographical Information Systems ............................................................................................. 14

3.3 Multivariate regression analysis .................................................................................................. 15

4. Development of WECs in the Netherlands ........................................................................................ 17

4.1 Quantitative development .......................................................................................................... 17

4.1.1 Emergence phase: 1986 - 1996 ............................................................................................ 19

4.1.2 Consolidation phase: 1997 - 2012 ........................................................................................ 21

4.1.3 New business model: 2013 - 2014 ....................................................................................... 22

4.2 Geographical development ......................................................................................................... 23

4.2.1 Emergence phase: 1986 - 1996 ............................................................................................ 24

4.2.2 Consolidation phase: 1997 - 2012 ........................................................................................ 26

4.1.3 New business model: 2013 - 2014 ....................................................................................... 27

5. Factors determining the growth of WECs ......................................................................................... 31

5.1 Factors determining production capacity growth ....................................................................... 31

5.1.1 Descriptive statistics ............................................................................................................. 31

5.1.2 Model results ........................................................................................................................ 32

5.2 Factors determining the number of members ............................................................................ 33

5.2.1 Descriptive statistics ............................................................................................................. 33

5.2.2 Model results ........................................................................................................................ 35

ix

6. Discussion and conclusion ................................................................................................................. 37

Bibliography ........................................................................................................................................... 41

Appendices ............................................................................................................................................ 49

Appendix 1 List of active WECs in the Netherlands .......................................................................... 49

Appendix 2 List of interviewees ........................................................................................................ 51

Appendix 3 Interview protocol .......................................................................................................... 52

Appendix 4 Correlation table ............................................................................................................ 53

x

List of Figures and Tables

Figure 1 Multi-level perspective on transitions ....................................................................................... 6

Figure 2 Relationship between local projects and an emerging global community with shared rules .. 7

Figure 3 Distribution of WECs based on founding year and size .......................................................... 13

Figure 4 WECs, members and installed production capacity in the Netherlands 1986-2014 .............. 17

Figure 5 Development of production capacity shares per WEC 1986-2014 ......................................... 18

Figure 6 Development of membership shares per WEC 1986-2014 ..................................................... 19

Figure 7 Spatial distribution of quantitative development of WECs 1996 ............................................ 25



Table 1 Input variables multivariate regression analysis ...................................................................... 16

Table 2 Descriptive statistics on explanatory variables for production capacity growth ..................... 32

Table 3 Model results production capacity growth .............................................................................. 33

Table 4 Descriptive statistics on explanatory variables for membership growth ................................. 34

Table 5 Model results member growth ................................................................................................. 35

1

1. Introduction

1.1 Introduction

Completing the transition from a fossil fuel based energy system towards a system based on

renewable energy technologies is a defining challenge of this generation, as continued greenhouse

gas emissions are very likely to lead to increased risk of societal impacts from climate change related

events (IPCC, 2012). Currently, wind energy is one of the renewable energy technologies that are

expected to play a major role in completing this task (IEA, 2014; Daniëls & Kruitwagen, 2010).

However, in most countries, the implementation of wind energy has suffered from a systematic

neglect of the social embedding of the technology. Especially in the Netherlands, local protest groups

oppose to, and halt, wind energy projects, because they see wind turbines as “a disturbance to the

natural landscape, wild life, and as noisy and ugly objects.” (Verbong, Geels, & Raven, 2008, p. 560).

Empirical studies in Denmark and Germany have shown that getting people socially and economically

involved in wind energy projects through local ownership increases acceptance of wind energy

projects (Christensen & Lund, 1998; Krohn & Damborg, 1999; Musall & Kuik, 2011). To this end, wind

energy cooperatives (or WECs) could provide a way to successfully implement wind energy in the

Netherlands and contribute to the sustainable energy transition.

A WEC consists of members that collectively procure wind turbine technology to achieve shared

goals. Currently, there are twenty-three WECs active in the Netherlands, of which the eldest was

founded in 1986 (Van Loenen, 2003). In total the WECs have 24,000 members and owned 60

megawatt1 (MW) of production capacity, but individual projects differ to a great extent in

membership numbers and installed production capacity (Elzenga & Schwencke, 2014). But why are

some WECs more successful in their quantitative expansion than others? Which factors determine

their success and overall growth? Answering these questions can help us understand the

circumstances under which renewable energy technologies can start to influence the current energy

regime and contribute to the sustainable energy transition and may subsequently help speed-up the

transition (Geels & Schot, 2007; Geels, 2011). We can understand a sustainable energy transition as

“large scale transformations within society or important subsystems during which the structure of

the societal system fundamentally changes.” (Verbong & Loorbach, 2012, p. 6)

In transitions research, the multi-level perspective (MLP) is one of the most influential frameworks of

explaining how such transitions come about (Raven, Schot, & Berkhout, 2012), namely through the

alignment of processes within, and between, its three constituent levels; niche, regime and

landscape (Geels & Schot, 2010). Strategic Niche Management (SNM) provides an approach to

governing the alignment processes within an emerging, global, innovative community (Geels &

Raven, 2006) by building shared expectations between actors through interactive learning in

expanding social networks (Geels, 2011). This dynamic process should eventually result in an

increasing application of the socio-technical concept under study i.e. up-scaling (Coenen, Raven, &

Verbong, 2010). By conceptualizing WECs as such socio-technical niches I aim to understand the

processes that lead to their growth and the factors that determine their up-scaling.

1In 2013 WEC in the Netherlands owned 58.34 MW in production capacity, which constituted 2.35% of the total land-based wind turbine capacity in the Netherlands in that year (CBS, 2014)

2

Even though the MLP framework has been extremly influential, it has had difficulty in addressing

questions related to geographical uneveness (Coenen, Benneworth, & Truffer, 2012), because the

framework lacks an explicity notion of geography. Therefore, recently, efforts have been made to

supplement transitions research with insights from economic geography (Coenen, Raven, & Verbong,

2010; Coenen, Benneworth, & Truffer, 2012; Raven, Schot, & Berkhout, 2012). In this research the

geographical dimension of the transition process is made explicit through the concept of proximity

(Boschma, 2005). Additionally, the concept of business models is used to conceptually assign

economic maneuverability to WECs and to show how they strategize, cooperate and adapt in a

dynamic economic and socio-technical environment.

1.2 Research aim and research question

The main aim of this thesis is to find out what has driven the growth of WECs in the Netherlands over

the time period 1986-2014 and to distil from this factors that contribute to niche up scaling.

The research question I will answer in this thesis is:

“Which factors have contributed to the growth of WECs over the last 30 years in the Netherlands?”

The sub-questions that will be answered in order to answer the main research question are:

1. How have WECs in the Netherlands developed the last 30 years with respect to their

production capacity and members?

2. How have WECs in the Netherlands developed the last 30 years with respect to their

geographical locations?

3. Which factors determine the growth of WECs in the Netherlands?

The research question will be answered through a combination of qualitative and quantitative

methodologies. To answer question 1, a series of interviews were conducted. Data from the

interviews is supplemented with document analysis like newsletters, annual (financial) reports and

websites of WECs. To address question 2, I make use of Geographical Information System software

(ArcGIS 10.0) to make a visual representation of the, spatial distribution of WECs and the local (bio-)

physical conditions at their locations and I use build-in tools of the program to collect data for my

data set. For question 3, I use the multivariate regression method on quantitative indicators of WECs,

with a dataset constructed for this reason.

WECs can function as vehicles for societal change (Huijben & Verbong, 2013). They have the ability to

implement wind energy technology in a way that incorporates the social embeddedness that has

been lacking from national governmental policies (Verbong, Geels, & Raven, 2008). This is needed

because, wind power has become a controversial renewable energy technology (Verbong & Geels,

2007), but it is also a technology that is expected to make an important contribution to meeting CO2

emission reductions goals (Daniëls & Kruitwagen, 2010). By better understanding how it is that WECs

grow, the WECs can get better support in their further diffusion (Geels & Schot, 2007). However, it

can also highlight limitations of the contribution of WECs to the energy transition in the Netherlands,

which is, after all, one of the most densely populated areas in the world (The World Bank, 2014).

3

1.3 Outline of the thesis

In the next section, chapter 2, an overview is given of the relevant literature on transitions, economic

geography and on business models. Chapter 3 comprises of the methodology section, followed by

the first analysis section; chapter 4. In chapter 4 the results of the case analysis are presented. It

forms the qualitative section of this research and addresses sub-questions 1 (section 4.1) and 2

(section 4.2). The goal of chapter 5 is to find factors that determine the growth of WECs in members

and production capacity (sub-question 3) and it comprises of the quantitative section of this

research. Here the results of a multivariate regression analysis are presented and analyzed. Inputs in

the two statistical models include relevant variables that were uncovered in chapter 4. Chapter 5 is

followed by a discussion and conclusion section, chapter 6, in which a reflection is made on the

implications of the results in this research for transition studies, methodological and practical

implications, possible limitations and further research.

5

2. Theory

2.1 Multi-level perspective on sustainable transitions

The research is positioned in the broader sustainability transition literature, drawing from the multi-

level perspective (MLP) on transitions (see Figure 1). The MLP framework has been widely used in

transitions research in general (see Geels & Schot, 2010) and for the analysis of the sustainable

energy transition more specifically (Verbong & Geels, 2007). The MLP consists of three analytical

levels: i) the socio-technical landscape ii) the socio-technical regime iii) and the niche level. MLP has

two conceptual dimensions; a structural dimension indicating the degree of structuration of activities

(vertical axis), increasing from bottom (niche) to the top (landscape). Structures contain rules and

institutions that coordinate and guide the behavior of actors, giving direction and stability to learning

processes from which it is hard to deviate, resulting in a lock-in in a socio-technical regime (Geels &

Schot, 2010). The temporal dimension (horizontal axis), indicates the length of the processes taking

place at the three levels from relatively short-term processes at the niche level to long-term

dynamics at the landscape level (Raven, Schot, & Berkhout, 2012).

The core notion of MLP is that: “transitions happen through interactioning processes at the three

levels.” (Geels & Schot, 2007, p. 400) At the landscape level long-term processes condition the

activity of actors (Rip & Kemp, 1998), it forms a broad and exogenous environment, which is, in the

short-term, “beyond the direct influence of regime and niche actors.” (Geels & Schot, 2010, p. 23)

Changes at the landscape level can put pressure on the regime level forcing it to adapt (Geels &

Schot, 2007). Pressures can be the result of e.g. increasing concerns about climate change or the

privatization of the electricity market (Verbong & Geels, 2010). Landscape pressures that are exerted

on the regime can result in tensions, creating windows-of-opportunity for new socio-technical

configurations to up-scale, as regime actors disagree about rules to accommodate them (Geels,

2011). The regime consists of an interdependent network of actors such as users, policy-makers and

firms embedded in a semi-coherent set of structural rules (Giddens, 1984) reproduced in institutions,

or global rules, that act as “historical accretions of past practices and understandings” (Barley &

Tolbert, 1997, p. 99) and as “carriers of history” (David, 1994) leading to technological trajectories

(Kemp, Schot, & Hoogma, 1998).

Socio-technical innovations emerge in protected spaces called niches. New socio-technical concepts

are able to contribute to the transition if they are sufficiently developed (Geels & Schot, 2007) and

when they are able to link up with on-going socio-technical dynamics (Rip & te Kulve, 2008). At the

niche level, activities are unstructured and actors in local practices have high levels of interpretative

freedom (Bijker, 1995). Key internal processes for niche development are second-order learning,

construction of social networks and the articulation of expectations (Kemp, Schot, & Hoogma, 1998).

Radical innovations require the formation of heterogeneous social networks (Kemp, Schot, &

Hoogma, 1998) that provide resources and protection against the selection environment, carry

expectations and enable learning across actors and locations (Coenen, Raven, & Verbong, 2010).

Second-order learning in niche experiments serves to learn “about user preferences, cultural and

symbolic meaning, industry and production networks, regulations and government policy and

societal and environmental effects of the new technology.” (Coenen, Raven, & Verbong, 2010, p.

299)

6

Figure 1 Multi-level perspective on transitions (from Geels, 2011)

Expectations are “a set of cognitive rules that are oriented to the future and related to actions that

give direction to search and development activities.” (Geels & Raven, 2006, p. 375) Cognitive rules

may be seen as existing cognitive structures where actors (unconsciously) draw from “to interpret

situations and challenges.” (Geels & Schot, 2010, p. 49) Articulating expectations helps mobilize

resources and enroll more actors into the support network by providing promises about future

benefits (Kemp, Schot, & Hoogma, 1998). Initially cognitive rules guiding projects “are diffuse, broad

and unstable.” (Geels & Schot, 2010, p. 86) Knowledge that is gathered in local projects and shared

between practices starts an external learning process (Raven, 2005). Aggregation and generalization

of local lessons by intermediary organizations (Geels & Deuten, 2006) leads to the selection of best

practices and structuration of cognitive rules, activities and alignment of expectations in a global

community (Geels & Raven, 2006). The local-global model is shown in Figure 2.

Expectations can be adjusted by new actors as they reinterpret lessons from preceding activities,

thereby revealing latent opportunities (Chesbrough, 2010; Geels, 2011), which, in turn, can help

mobilize new, and more global, actors (Geels & Raven, 2006; Seyfang, Hielscher, Hargreaves,

Martiskainen, & Smith, 2014) by envisioning a future that is better aligned with socio-technical

developments (Rip & te Kulve, 2008). Up-scaling may then be referred to as “increasing the scale,

scope and intensity of niche experiments by building a constituency behind a new technology, setting

in motion interactive learning processes and institutional coordination and adaptation, which help to

create the necessary conditions for its successful diffusion and development.” (Coenen, Raven, &

Verbong, 2010, p. 296).

7

Figure 2 Relationship between local projects and an emerging global community with shared rules (from Coenen et al., 2010)

Thus in this research I study WECs as socio-technical niches developed in the electricity regime and,

based on previous work in MLP, I would expect that their up-scaling depends on factors such as

heterogeneous learning, construction of social networks and the alignment of expectations among

different actors (internal) and projects (external). However, according to Coenen et al. (2012)

transition studies in general, and MLP in particular, should put more emphasis on spatial variety as a

result of the occurrence of “[a] ’natural’ variety in institutional conditions, networks, actor networks

and resources across space.” (p. 976) What matters for niche development, next to the factors

specified above, are specificities of place, uneven endowments and access to resources, possible

advantages associated with local geography. This is problematic for the MLP framework, since it has

no explicit notion of geography (see Figure 1; Raven, Schot, & Berkhout, 2012). In the next section I

review literature on economic geography and the related concept of proximity in order to make the

analysis of the development of WECs in the Netherlands geographically sensitive.

2.2 Role of geography in transitions

There is an ongoing endeavor to supplement research on transition with insights from economic

geography (Coenen, Raven, & Verbong, 2010; Coenen, Benneworth, & Truffer, 2012; Raven, Schot, &

Berkhout, 2012). Focal point in the debate is the nature of space itself (Yeung, 2005); either space is

relative and emergent, or space is absolute, defined as a territory with spatial boundaries (Raven,

Schot, & Berkhout, 2012). The first perspective considers space for innovations to emerge out of

interactions between actors who are “creating and reconfiguring networks and power within them,

causing knowledge, resources, technologies and innovations to flow” (Raven, Schot, & Berkhout,

2012, p. 70). No causal power is assigned to territorial factors, because “networks are not inherently

bound by geography” (Boschma, 2005, p. 69). Malmberg and Maskell (2006) argue that a distinction

must be made between inputs that are suscentible to become fluid, like natural and financial

resource endowments, and resources that are less prone to flow across geographic boundaries,

including the institutional set-up, which, in line with the second perspective, can provide a relatively

durable comparative advantage to projects (Raven, Schot, & Berkhout, 2012). I use the absolute

notion of space in this research because there exists, at least for the majority, a clear territorial

connection between WECs and their locations (Van Loenen, 2003), indicating that territorial

boundaries are relevant.

8

Boschma (2005) argues that geographical proximity, the absolute distance between actors in a

network, tends to reinforce other forms of relational promixity2 important to niche development by

facilitating face-to-face interaction and the creation of shared experiences (Storper & Venables,

2004). Increasing levels of relational proximity can weaken the necessity of colocation (Boschma,

2005). The extent to which the niche development process leads to a decreasing need to be within

geographical proximity to certain actors in a network remains unclear. For example, the institutional

set-up as captured in the concept of institutional thickness, the comparative ability of governance

bodies to work together locally (Amin & Thrift, 1995), explains why regions differ in their ability to

support innovative activities (Coenen, Benneworth, & Truffer, 2012). Over time local governments

can create experiences with local cooperation, which can result in “a common sense of purpose,

shared expectations or vision around a widely held agenda for regional development.” (Coenen,

Benneworth, & Truffer, 2012, p. 974). Therefore, a co-creation of local benefits between WECs and

local goverments could provide the durable means for growth identified by Malmberg and Maskell

(2006).

Using five notions of proximity of Boschma (2005), Coenen et al. (2010, pp. 297-298) explore how

proximity can affect the growth of local niche projects. Geographical proximity fosters social

proximity, which is conductive for building social networks, and refers to build-up of mutual trust

from shared experiences and past cooperation. Trust between actors is needed before they can start

commiting resources. Organizational proximity can play a complementary role where mutual trust is

insufficient, by exercising control during the emergence of innovative projects. It refers to the extent

to which relationships are shared in a formal and organizational arrangment, where activities of

actors can be controlled, coordinated and structured. Articulation of shared expectations is requires

social and cognitive proximity. Cognitive proximity relates to an overlap in knowledge and

competences amongst organizations. Building shared expectation in a network increases cognitive

proximity, and can eventually lead to an increase in institutional proximity; the extent to which

actors share similarities in the contextual norms and values on the regime level.

Proximity can also act in a constraining way on niche development (Boschma, 2005; Coenen, Raven,

& Verbong, 2010). Short geographical distances can bring actors together, but a secluded

geographical territory can also put restrictions on the access to resources. Furthermore, too much

relational proximity can hamper second-order learning and induce lock-in (Geels & Schot, 2010).

Cognitive proximity, for example, is needed for actors to share knowledge in a meaningfull way, but

too much cognitive proximity can lead to recycling of prevelant ideas, guiding principles and problem

solving strategies, which makes projects less adaptive when faced with changing (market) conditions

and challenges (Geels & Raven, 2006; Geels & Schot, 2010). Therefore, I will identify the impact of

the geographical locations of WECs with respect to the concept of proximity, and I will explore to

what extent different concepts of proximity may influence the growth of WECs. In addition, it is

interesting to study how WECs incorporate proximity (or distance) in their business model (Geels,

2011), a concept that is addressed in the next section.

2 Relational proximity indicates the relative distance between actors, and is a function of interaction: frequent

interactions can build stronger networks of actors that can support more distant relationships. Actors “define and create spaces with their own institutional arrangements, power relations, governance institutions and dynamics, which offer ‘proximity’ between actors.” (Coenen, Benneworth, & Truffer, 2012, p. 969)

9

2.3 Wind energy cooperatives as business models

The starting point of this research is that WECs offer specific business models for wind turbines to

operate. We can think of a business model as containing the instructions for combining physical and

social technology under a strategy (Beinhocker, 2007)3: “the rational of how to create, deliver and

capture value” (Osterwalder & Pigneur, 2010, p. 14). Physical technologies refer to “methods and

designs for transmitting matter, energy and information from one state to another in pursuit of a

goal.” (Beinhocker, 2007, p. 244) Social technologies refer to “methods and designs for organizing

people in pursuit of a goal.” (Beinhocker, 2007, p. 262) The socio-technical context that the BMs are

embedded in is a vital part of understanding up-scaling of a niche, as shown by Jolly et al. (2012) who

analyze the up-scaling of individual solar-PV BMs in India. Similar to radically new technologies, local

business models experiments can provide lessons on their desirability (Chesbrough, 2010). Johnson

and Suskewicz (2009) argue that business models coevolve with four elements: “an enabling

technology, an innovative business model, a market-adoption strategy, and a favorable government

policy.” (Johnson & Suskewicz, 2009, p. 3) In order for innovative BM to up-scale, coordination of all

the four elements is needed (Huijben & Verbong, 2013).

For this research I am interested in the extent to which the business model of WECs, so the way they

organize the social and physical technology, plays a role in their up-scaling. A WEC may be seen as a

collection of social entrepreneurs that need to coordinate their activities in order to achieve shared

goals (Jolly, Raven, & Romijn, 2012). Social entrepreneurs combine “a social goal with a business

mentality.” (Witkamp, Raven, & Royakkers, 2011, p. 667) According to Dóci et al. (2015) social

entrepreneurs, in order to up-scale, have to “create the necessary physical and social infrastructure”

(p. 88) like user and producer networks and institutional arrangements “to legitimate, regulate and

standardize new practices” (Jolly, Raven, & Romijn, 2012, p. 202). Individual projects usually do not

possess the necessary resources and competences to establish this infrastructure, therefore

cooperation between multiple projects may be needed for the up-scaling (Jolly, Raven, & Romijn,

2012). WECs in the Netherlands with overlapping goals can thus be expected to organize themselves,

under a coherent strategy in their attempt to grow, giving rise to a distinct business model (Huijben

& Verbong, 2013).

A (wind energy) cooperative is a legal business form, akin to an association that engages in

commitments with, and for the benefit of its members. In contrast to a regular association the

cooperative is allowed to redistribute the profits across its members. A formal definition is provided

by the Dutch Civil Code:

“A cooperative is an association established by notarial deed as a cooperative. It

must be clear from the statutes that the objective of the cooperative is to provide in

certain material needs of its members through agreements, other than insurance,

concluded with them in the business that for their benefit practices or is being

practiced.” (Van Loenen, 2003, p. 7)

3 Beinhocker (2007) uses the term business plan instead of business model. The term has been altered to bring it more in sync with the relevant business model literature.

10

A cooperative has to have a registration at the Chamber of Commerce and subsequently has a

registration number (KvK-nummer in Dutch), and either has the legal title Cooperative with Excluded

Liability (shortly EL, or UA in Dutch, for Uitgesloten Aansprakelijkheid) or Cooperative with Limited

Liability (abbreviation LL, or BA in Dutch, for Beperkte Aansprakelijkheid), which protects members

from financial liability arising from (financial) commitments that the cooperative engages in. In case

of wind energy cooperatives the main activity is the collective procurement and/or operation of one

or more wind turbine(s), or the demonstrable ambition to do so in the near future, for the benefits of

the members, which are mostly related to the promotion of the use of renewable energy

technologies (Elzenga & Schwencke, 2014).

The first WECs in the Netherlands were founded in 1986 with help from the Organization for

Renewable Energy (Organisatie voor Duurzame Energy, or ODE, in Dutch) (Van Loenen, 2003;

Agterbosch, 2006). ODE stimulated locally active environmental protection groups financially and

organizationally to establish wind energy cooperatives after a Danish model, where WECs first

arrived on the scene in 1980 (Verbong, 2001; Van Loenen, 2003). In Denmark local communities

collectively own and operate wind turbines and use the benefits for local purposes. Over a period

ranging from 1986 until 1992, in this vein, fifteen WECs were established in Dutch coastal areas (Van

Loenen, 2003; Agterbosch, 2006) with a low degree of urbanization (Oteman, Wiering, & Helderman,

2014). It took until 2009 for new projects to initiate; between 2009 and 2014 a total of twelve new

WECs were founded. Remarkably, eight of these were set up by a new ‘organization of

organizations’: Windcentrale, a commercial company that facilitates the purchase of wind turbines by

aspiring shareholders through crowdfunding. The company started in the highly urbanized

municipality Amsterdam.

Currently, WECs in the Netherlands differ greatly in size and organization (an overview of the WECs

can be found in appendix 1); WECs like Deltawind and Zeeuwind have more than 1,500 members and

close to 20 MW in production capacity (Elzenga & Schwencke, 2014) and have become professional

organizations with full-time employees (Van Loenen, 2003; Agterbosch, 2006). At the other end of

the spectrum there are WECs that have fewer than 200 members, own less than 1 MW of wind

turbine capacity and fully rely on volunteers for their daily operation (Elzenga & Schwencke, 2014).

Another salient difference between the WECs can be found in their work areas. Most cooperatives

are bound to the location where they were founded e.g. ZEK was founded in Zaanstad and aims to

promote renewable energy in the Zaanstreek (an industrial area consisting of a collection of

municipalities in the north-west of the Netherlands connected via the river Zaan). However, there

are also WECs with a national scope such as Windvogel (Van Loenen, 2003; Elzenga & Schwencke,

2014) and the cooperatives founded by the Windcentrale.

As the discussion above has shown, even though WECs follow a specific business model, in practice

there is variation in the degree to which they appear in the Dutch energy landscape, with respect to

e.g. production capacity, number of members and their geographical location. Jolly et al. (2012)

analyze the up-scaling of local projects over a number of business model dimensions including the

organizational, functional and geographical dimension. Organizational up-scaling concerns the

growth of the organization. Development in the functional dimension entails the increase in the

number and types of activities that are undertaken by an initiative. Expansion to new geographical

area is captured by the geographical dimension.

11

Oteman, Wiering and Helderman (2014) argue that the up-scaling of local energy projects is

conditioned by the (bio-) physical conditions at the location where they start. Relevant conditions

include natural resource endowments such as wind conditions, but also urbanization levels, because

“urbanized regions will be less suitable for large-scale plans as physical space is limited, contested

and expensive.” (Oteman, Wiering, & Helderman, 2014, p. 3). Moreover, renewable energy projects

are more likely to start in rural areas, because it can create jobs in economically subordinate regions.

Urban residents, by contrast, have a preference for projects with low spatial impact, contrary to wind

turbines, because they assign a high value to the quality of their local environmental (Bergmann,

Colombo, & Hanley, 2008).

The extent to which adjustments made in the organizational, functional and geographical dimension

of the WECs have had an impact on the quantitative dimension (Jolly, Raven, & Romijn, 2012) is the

subject of chapter 5. In addition, (bio-) physical conditions at the municipal and provincial level are

included in the statistical analysis to test their influence on the growth of WECs in the Netherlands.

The five categories are operationalized in sixteen variables that are presented in Table 1.

13

3. Methodology

3.1 Data collection

The data for this research was collected through various sources; first of all through a series of semi-

structured interviews with representatives from eight different WECs (list of participants can also be

found in Appendix 2). The group of WECs that was founded between 1986 and 1992 (𝑛 = 11) has

had time to develop and some initiative did so to a relatively large extent. A second group entered

from 2009 and onwards (𝑛 = 12), these WECs have had relatively little time to expand their member

base and most initiatives are still in the process of installing their first wind turbine. However, a

number of initiatives in this group have grown to a size that surpasses most of the WECs in the first

group. Figure 3 shows this distribution graphically. The figure shows on the horizontal axis the

number of standard deviations from the average year of founding (�̅� = 1995.94). On the vertical

axis it indicates the standard deviations from the average amount of production capacity (�̅� =

4145.12) per case.

Figure 3 Distribution of WECs based on founding year and production capacity

Zeeuwind

WWC

Meerwind

Eendragt

Windvogel

ZEK

NDSM Energie

Windcentrale

Kennemerwind

CWW

Deltawind

UWindWDE

Onze Energie

ZuidenwindWP Nijmegen

-1

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Selected cases

Other cases

14

In this way Figure 3 can be divided into four quadrants; first, the top-left (high age and above average

performance), second is top-right (low age and above average performance), third in the bottom-left

corner (high age and below average performance) and the fourth in the bottom-right (low age and

below average performance). Potential interviewees were identified based on a balanced sample

from the four quadrants, which allows a comparison between cases over age and performance in

order to find factors for up-scaling. This follows the selection of interviewees based on a design of

increasing variation on the dependent variables and context (Weiss, 1995). Identified participants

were send an email and asked if they were prepared to participate in an interview for the research.

Individuals that were willing to cooperate were included in the research. In total eight interviews

were conducted, from quadrant one Meerwind, Windvogel and Zeeuwind were included. Quadrant

two only contains WECs from Windcentrale4; therefore Windcentrale was the only participant from

that section. WECs from the third quadrant include Eendragt, WWC and ZEK. In quadrant four, eight

out of twelve WECs belong to Windcentrale. In addition to including Windcentrale, the

representation of the fourth quadrant was supplemented by NDSM Energie. The interviews took

place in June and July of 2014 and they lasted approximately one hour. The interviews were recorded

and transcribed verbatim (see Appendix 3 for the interview protocol).

Data collection was supplemented by document analysis and academic resources such as van Loenen

(2003) and Elzenga and Schwencke (2014). Van Loenen (2003) provides an overview of the

development of WECs in the Netherlands from 1986 until 2002, which includes, amongst other

things, annual member numbers and production capacity. Elzenga & Schwencke (2014) give an

overview of cooperatives and their members and production capacity in February 2014. This was

supplemented by information identified in the annual reports of the cooperatives and their strategic

documentation to construct an overview of WEC development over time. For all case, data for 2014

is used as input for the multivariate regression analysis. Statistics on population density of

municipalities and provinces were retrieved via the Dutch Central Statistical Office (or CBS in Dutch).

Coordinates of the locations of the WECs and their wind turbine locations were obtained using

Google Maps. The coordinates were subsequently added into a Geographical Information System

(ArcGIS) file, which allowed the measurement of the distance between the founding places and

production capacity.

3.2 Geographical Information Systems

Geographical Information Systems (GIS) can facilitate scientific analysis with the description and

explanation of patterns and processes at geographic scales (Longley et al., 2005). ESRI’s ArcGIS allows

the visualization of the patterns and processes. The collected data from the interviews and

miscellaneous resources was combined with the spatial data from Google Maps i.e. x and y

coordinates, added to an ArcGIS file and made into a Point Events layer. The layer is geo-referenced

with the World Geodetic System 1984 (WGS 1984) coordinate reference system, which is a reference

system based on a model for the ellipsoid of the Earth and is generally used to display Global

Positioning Systems (GPS) locations (NIMA, 2000). In order to make the layer functional, the Point

Events layer is turned into a shape-file and projected onto the Rijksdriehoek coordinate system using

4 WECs founded by the Windcentrale are the WECs Blauwe Reiger, Bonte Hen, Grote Geert, Jonge Held, Ranke Zwaan, Rode Hert, Trouwe Wachter and Witte Juffer, which are included separately in the regression analysis.

15

Data Management tools included in ArcGIS 10.0. Background data on provinces and municipalities is

included through ArcGIS Online. The end result of this process is a geographical representation of the

spatial distribution of WEC development over time. The number of observations varies per period,

depending on the entry in, and exit out of, the population of cooperatives. Three distinct time frames

are included: 1986-1996, with 𝑁 = 13 observations, 1997-2012, with 𝑁 = 15 observations and

2013-2014 with 𝑁 = 23 active WECs. For these years, the number of members and the production

capacity per WEC, together with their corresponding locations, are projected on a map of the

Netherlands.

3.3 Multivariate regression analysis

The collected data is divided into four quantitative variable groups based on the analysis of up-

scaling performance of individual business models by Jolly et al. (2012): a) variables related to

quantitative up-scaling of the WECs; b) organizational variables related to the size of the organization

of the different cooperatives; c) functional variables related to the means of production activities and

d) variables related to the geographical expansion of productive activities per WEC. From Oteman et

al. (2014) the variable category (bio-)physical is included to account for the locational level of

urbanization and natural resource endowments (see Table 1). The variables are used as input in the

statistical software package SPSS 21.0.

SPSS 21.0 permits the performance of a stepwise linear regression method, which allows the

regression of multiple variables and simultaneously removing the variables that are insignificant. This

entails a succession of regression runs, removing the weakest correlated variable with each run,

leaving, at the end, the variables that best explain the distribution of observations (Arbuckle, 2012).

Multivariate regression models are designed to estimate the effect on dependent variable (𝑌𝑖) of

changing an independent variable e.g. (𝑋1𝑖) while holding the other independent variables (𝑋𝑛𝑖)

constant (Stock & Watson, 2007). Accordingly, the linear regression model that is used in the analysis

is:

𝑌𝑖 = 𝛽0 + 𝛽1𝑋1𝑖 + 𝛽2𝑋2𝑖 + ⋯ + 𝛽𝑝𝑋𝑝𝑖 + 𝜀𝑖, 𝑖 = 1 , … , 𝑛

Where subscript 𝑖 indicates the 𝑖𝑡ℎ of 𝑛 observations, 𝛽𝑛 represents the coefficients, or slope of the

independent variables, that are estimated and 𝜀𝑖 is the error term.

The objective of this analysis is to find how the different business model elements contribute to

quantitative up-scaling of WECs. Therefore the number of members registered to a WEC and amount

of production capacity owned by a cooperative are included as the dependent variables in the

regression analysis. The variables derived from the other three groups are included as independent

variables (𝑁 = 23). Input data comes from the WECs’ documents and websites and the eight

interviews with the included cooperatives. The article by Elzenga and Schwencke (2014) was

consulted for information on the number of members and production capacity of WECs that were

not included in the interviews. Furthermore, Dr. E. Vasileiadou provided additional information

concerning membership levels of eight WECs based on her own research.

16

Table 1 Input variables multivariate regression analysis

Up-scaling dimension

Primary indicators Input variables Unit of measurement

Quantitative Expansion in number of members and amount of production capacity

Number of members Count number

Production capacity Kilowatt (kW)

Organizational Organizational expansion related to managerial and financial capacity

Number of paid employees Count number

Height registration fee for new members Euro's

Height annual contribution Yes/No [0-1]

Interest rate members Percentage

Functional Expansion in the number of related activities besides the production of wind energy

Number of activities5 besides the production of wind power (including the production of electricity using micro-turbines)

Count number

Geographical

Geographical expansion from the location of founding

Location of founding Degrees longitude (x-coordinate)

Degrees latitude (y-coordinate)

Distance location of founding to production location Kilometers (km)

Number of different municipalities with production capacity owned Count number

Number of different provinces with production capacity owned Count number

(Bio-)physical Natural factor endowments and urbanization Wind speed Meters per second (m/s)

Population density municipality of founding Inhabitants per square kilometer (inh./km2)

Population density province of founding Urbanization level

Inhabitants per square kilometer (inh./km2) High-Low [1-5]

5 Activity classes are based on Boon and Dieperink (2014). The authors use five business model categories, amongst other characteristics, to distinguish between local renewable energy organizations, namely: i) collective procurement of energy; ii) collective procurement of technology; iii) education and facilitation; iv) delivery of energy; v) collective generation of electricity. Categories (iii) and (v) are not included in the analysis, because, in the case of (iii) it was beyond the scope of this research to construct the necessary conceptual boundaries in order to rightly quantify this category, and (v) is dropped because this is already taken into account, indrectly, in the variable Production capacity, since WECs that do not have any production capacity do not have the means for the collective generation of electricity under the definition used in this research. WECs that do own production capacity per defenition collectively produce electricity. The two categories are replaced by the category Other forms of renewable electricity production. Collective solar power production falls under this category for example, as well as the collective electricity production using micro wind turbines (ranging between 0.4 and 2.5 kW, see Peacock, Jenkins, Ahadzi, Berry and Turan (2008)). Per case it was counted in how many of the categories the WECs were active, in the case of the added category Other forms of renewable electricity production this sums over the number of technologies used in collective production, which provides the total number of activities score.

17

4. Development of WECs in the Netherlands

4.1 Quantitative development

After the emergence of the first WECs in the Netherland in the late 1980s, the number of initiatives

has increased to twenty-three in 2014. In 2014, the cooperatives have about 24,000 members and

own 66,315 kW of installed wind turbine capacity. Figure 4 shows the development of the number of

WECs in the Netherland, the amount of people that are registered as members of a cooperative and

the production capacity owned by WECs from 1986 until 2014. Figure 4 shows roughly three periods

with different growth rates: 1986-1996, 1997-2012 and 2013-2014. These time periods overlap with

electricity market conditions as distinguished by Agterbosch (2006), but they deviate to an extent

because this research has a different emphasis; Agterbosch (2006) argue that the difference in

production capacity growth of WECs is determined by a dichotomy in organizational

profesionalization in response to changing institutional and social conditions (pp. 121-145). I will

broaden this focus to a wider set of dimensions wherein WECs can adapt. Furthermore, this research

spans a wider time period, beyond 2004, and therefore I make a slightly different temporal

distinction than Agterbosch (2006)6.

Figure 4 WECs, members and installed production capacity in the Netherlands 1986-2014

6 Agterbosh (2006) distinguishes three phases of changing conditions for WECs: the “monopoly phase” (1989-1995), a transitional period, “interbellum”, lasting two years (1996-1997), followed by an electricity market phase characterized as a “free market” 1998 until 2004 (pp. 121 – 139).

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WECs

Members (x 1,000)

Production Capacity (MW)

Emergence phase: 1986-1996

Consolidation phase: 1997-2012

New Business model: 2013-2014

18

Figure 4 shows an initial rapid expansion in total member numbers and production capacity; the

amount of members grows from approximately 180 in 1986 to around 4,400 in 1996 and production

capacity increased to a total installed capacity of 11.1 MW. This period represents the initial stage of

niche development, and will be referred to as the “emergence” phase wherein new projects entered

the scene and started to form a new socio-technical configuration. After 1996 the figure indicates an

increased growth rate in both quantities. Membership numbers nearly double from 4,400 to

approximately 8,700 members in 2012, while the production capacity owned by WEC increases from

11.1 MW to 49.1 MW. This period will be referred to as the “consolidation” phase in the niche

development. WECs that were founded before 1996 either grew further, at differing rates, or

stopped, during the second phase, but all the growth was realized by projects that started during the

emergence phase. Then, in the last period, there is a rapid growth in member numbers and

production capacity. The last period is characterized by the introduction of new WECs with a new

business model.

The start of the niche development took place in an electricity market that was characterized by

monopoly conditions (Agterbosch, 2006). In 1989 the Electricity Law set the stage for the

deregulation of the Dutch electricity market in 1998, and introduced the electricity distribution

companies (or EDCs, see Verbong and Geels, 2007, p. 1029) that bought the electricity production

from the first cooperatively owned wind turbines (Agterbosch, 2006; Verbong & Geels, 2007). After

1998, the monopoly power of EDCs was broken by the opening of the electricity whole-sale market

to electricity retailers (Agterbosch, 2006; Verbong & Geels, 2007), the economic conditions for the

production of wind energy improved and a more competitive environment arose (Agterbosch, 2006;

IEA, 2013). Figure 5 shows the changes the shares WEC have in the total production capacity during

the research period. Starting from 1996, Zeeuwind and Deltawind have had a disproportionately

large share in the total production capacity; a combined share that peaked in 2004 at 87%. After

2004, this share started to decrease, inter alia, in favor of Windvogel. Since 2013, Windcentrale has

founded eight WECs that have been quick to take a relatively large share of the total installed

capacity; together they realized more than 75% of the growth in installed capacity since 2013. Figure 5 Development of production capacity shares per WEC 1986-2014

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Deltawind

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ZEK

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Delft

19

Figure 6 shows the individual shares of WECs in the total member numbers between 1986 and 2014.

The figure indicates that members are more evenly distributed tan production capacity across the

initiatives. Windvogel can be seen to account for a relatively large share of the total members in the

data set, the largest by 2011, but, what stands out in the figure is that the rapid production capacity

growth attributed to the Windcentrale has been accompanied by a rapid growth in memberships.

Together the eight WEC had almost 14,000 members in 2014, which constitutes a share of

approximately 57% of the total. In the next three sub-paragraphs of this section I look at how the

growth in production capacity and memberships can be related to adjustments in organizational,

functional and geographical business model dimensions. Hereby, I look specifically at the obstacles

and barriers that individual WECs faced within the three time frames and how they have tried to

overcome them in their pursuit to growth.

Figure 6 Development of membership shares per WEC 1986-2014

4.1.1 Emergence phase: 1986 - 1996

ODE played an important role in the emergence of WECs in the Netherlands. The organization

stimulated the founding of WECs. ODE wanted to challenge the reigning centralized powers in the

Dutch electricity system and provide an alternative to nuclear power favored by the national

governments (Verbong, 2001; Agterbosch, 2006). To accomplish this ODE approach locally active

Environmental movement groups: “Eendragt was founded from a group active in the Environmental

movement […] ODE stimulated this group to start a WEC.” (Interviewee 8) The organization of the

first cooperatives was based on a successful model from Denmark “promoted and explained by

employees of ODE.” (Van Loenen, 2003, p. 15) Local communities could earn money from the

participation in wind turbines by selling locally produced electricity (Verbong, 2001). Therefore, right

from the start, there has been a territorial connection between WECs and their economic activities.

The territorial connection was further reinforced by an agreement made between the WECs in

collaboration with ODE; WECs agreed not to install production capacity in territory of other WECs

(Van Loenen, 2003), which was meant to avoid mutual competition (Agterbosch, 2006).

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Windvogel

Zeeuwind

Deltawind

Meerwind

Kennemerwind

CWW

WP Nijmegen

Onze Energie

ZEK

UWind

WWC

Eendragt

WDE

Zuidenwind

NDSM

Kennemerland

Alkmaarse WC

Delft

20

Initial growth came from existing social structures: members of the local environmental movements

that founded the cooperative also provided initial funding for the establishment of the organization

(Agterbosch, 2006) and the financial means to install the first wind turbines: “a small group of

members from the [regional environmental federation in Zeeland] pooled up money and made an

effort to install wind turbine.” (Interviewee 5) In return the members received a return on their

investment and a vote in the General Members Meeting (or ALV in Dutch), the principle decision-

making body of the organization (Van Loenen, 2003). Returns were initially low or absent, because

electricity produced by wind turbines was relatively expensive (SFI, 2009) and the energy distribution

companies paid low electricity tariffs7 (Agterbosch, 2006). WECs that were able to construct strong

links with the local and regional energy distribution companies (LEDCs and REDCs) got relatively high

feedback tariffs such as Deltawind and Zeeuwind8 (Agterbosch, 2006). Zeeuwind indicated that “the

biggest obstacle was getting permission to supply power to the grid. The law dictated that the power

company had a monopoly and no one else was allowed to supply power. We needed to negotiate an

amount [of electricity] and a price with the energy company.” (Interviewee 5) The first wind turbines

were mainly installed in the municipalities where the WECs were founded. All WECs that were

interviewed and that were active during the initial period indicated that they had little problem

finding locations for their wind turbines; ZEK indicated that they “even got our fees for free from the

municipality.” (Interviewee 6)

ODE provided a platform for WECs: “every month, someone from our organization went to ODE to

exchange experiences.” (Interviewee 6) ODE redistributed this knowledge through its journal,

currently named WindNieuws9. Windvogel indicated that “from the beginning ODE has been a

coordinator of cooperative developments, of which WindNieuws is a lasting result.” (Interviewee 1)

Members of ODE supplied each other with loans as well; Deltawind provided a loan for ZEK to

finance a part of their first turbines (Interviewee 6) CWW financed part its first production capacity

by a loan from Frisse Wind (Mars, 2003). However, wind turbine technology continuously grew in size

and, accordingly, their capacity also increased over this period (IEA, 2013), which made turbines

more capital intensive. Therefore, for the installation of their first three wind turbines between 1992

and 1994 CWW was also partially dependent on a mortgage granted by the local Rabobank (Mars,

2003). This not only illustrates the importance of social structures, but also the influence of the

evolution of wind turbine technology on the function of members in WECs; already at the beginning

of the 1990s, wind turbines had grown to a scale that made them capital intensive to a degree that

made it difficult to be funded by local members alone. Furthermore, the WECs paid their members

above market interest rates at the end of the first stage (Agterbosch, 2006), members became a

relatively expensive financial source; WWC indicated that they arranged for a (partial) mortgage to

finance the realization of two turbines in 1995 “because a loan at ASN was less expensive than a loan

from our members.” (Interviewee 7)

7 Members were prepared to accept low financial returns because their motivations were idealistic rather than financial, which is also reflected by the voluntary basis on which they operated, an exception is Zeeuwind that hired its first paid staff member in 1989 (Agterbosch, 2006). 8 Another WEC that received favourable tariffs was Kennermerwind. The WEC was also allowed to use a former wind turbine test-site formerly owned by Provincial Electricity Company Noord-Holland (PEN, later changed into NUON, now part of Vattenfall). 9 The journal changed names a number of time of the course of its existence, see Verbong (2001) for a historic overview.

21

4.1.2 Consolidation phase: 1997 - 2012

During the second period, competition became an important element in the development of WECs in

the Netherlands. The deregulation meant that the territorial connection between WECs and the

energy distribution companies weakened, but it also meant that cooperatives were free to choose

from a larger set of electricity retailers that offered better tariffs for their electricity (Agterbosch,

2006). At the same time, continuing improvements in wind turbine technology (IEA, 2013) and

subsidy schemes on green electricity improved the economic conditions for wind energy. This led to

increasing demand for turbine locations by market parties other than WECs (Agterbosch, 2006).

Negative publicity concerning wind turbines (Verbong & Geels, 2007) made local and regional

administrations more reluctant to allow the installation of new wind turbine projects putting

restrictions on the supply of available locations (Breukers, 2006). This made the opportunities for

production capacity growth scarcer, while demand grew. Additionally, increased procedural

requirements to start new projects made installing a new wind turbine a complex process

(Agterbosch, 2006; Elzenga & Schwencke, 2014)

Cooperatives with paid staff members were better equipped to grow in this increasingly complex and

competitive environment; organizational functions that were performed by active members during

the initial phase on a voluntary basis were now being done by professionals (Elzenga & Schwencke,

2014). Zeeuwind indicated that:

“[…] are dealing with a lot of competition, because the locations are

becoming increasingly scarce. Currently, energy companies are our

competitors […] but also farmers, because they realize that they can make

good money with the production of wind energy.” (Interviewee 5)

But this was not always the case; Windvogel did not hire any staff until 2013 (Interviewee 1), and

managed to install the third largest share of the total production capacity at the end of the second

time-period. Windvogel invested in production capacity beyond the area where it started in, in

contrast to the other ODE related WECs (Agterbosch, 2006). Although the largest share of their

production capacity came from the installation of a 600 kW wind turbine in 2000, which was added

to the production capacity of the WECs first turbine (80 kW), the remaining capacity of their total 840

kW came from mergers with two local wind turbine associations around 200210, one of these

locations was scaled-up in 2005 from 80 kW to 2 MW. Windvogel also adopted the members of the

associations, (partly) explaining their membership growth11 (Agterbosch, 2006). Windvogel indicated

that an important reason for them to expand to locations beyond their municipality was that “local

regulations made it difficult to install new turbines.” (Interviewee 1) This illustrates that a potential

strategy after the first period was to look outside the area of founding for opportunities to grow. The

case further shows that the complex conditions under which WECs have to achieve production

capacity growth can be circumvented by purchasing existing wind turbines, which, in addition, makes

production capacity growth less financially demanding (Interviewee 1).

10 Haagse Windmolenvereniging (80 kW) and Windvereniging De Amstelmolen (80 kW), Schoonstroom, Zuid-Holland Wind and Frisse Wind owned no production capacity (Windvogel, 2002; Van Loenen, 2003). 11 Windvogel indicated that they realized substantial membership growth by allowing (new) members to profit from a discount on solar-PV cells through a collective procurement program (Interviewee 1; Windvogel, 2014).

22

4.1.3 New business model: 2013 - 2014

The third stage starts with the founding of the first of eight WECs under the umbrella of the private

company Windcentrale in 2013. Windcentrale facilitates the acquisition of existing wind turbines and

divides them into shares (or “winddelen”), which are then sold to members online through

crowdfunding. Individuals are then placed into cooperatives. The shares entitle the members to a

part of the yearly production12 of their cooperatively owned wind turbine over a period of fifteen

years (Windcentrale, 2014). Members pay a purchasing price per share, but thereafter members

don’t pay a unit price per electricity consumed. Electricity produced by the designated wind turbine

is supplied to the wind-shareholders through energy retailer GreenChoice. This business model is

different from the model introduced by ODE, valid for both the first and second phase, where WECs

mainly received revenues from selling their electricity production to retailers. Members are paid an

interest over a loan they supplied to the cooperative13. Instead, members of WECs that were

founded by Windcentrale receive no interest over their investment, but speculate on the future

increase of the consumer price of electricity14 (Windcentrale, 2014).

Windcentrale introduces a new business model, but also incorporates elements of previous one.

Windcentrale acts as the management board of the eight WECs (Windcentrale, 2013) with a paid

staff of eight employees (Interviewee 4). Therefore, like Zeeuwind, Windcentrale operates in a

professional way. Comparable to Windvogel, Windcentrale buys existing production capacity without

being restricted to a specific territory, but has adopted a national scope for the growth of its

production capacity. However, unlike all other ODE-related WECs, Windcentrale removed the

territorial connection between members and wind turbines from its business model. Once

Windcentrale has found a party that is willing to sell a wind turbine, everybody in the Netherlands is

able to purchase a share, irrespective of their location. Restrictions on the location of potential

members are also absent at Zeeuwind, but members would be contributing to the goals set by the

WEC that are regional; namely: “a completely sustainable energy supply in Zeeland by 2050.”

(Zeeuwind, 2014) Funding from members is the only financial input with which Windcentrale buys

wind turbines and therefore a large member base is needed. The disconnection of members and

production capacity has expanded the potential to find new members.

Parallel to this geographical development, the function of members in the organizations of the WECs

is increasingly simplified. Windcentrale, and the other professionalized WECs, have replaced the

central organizational role of (active) members with paid employees. A further simplification was

made with the development of members as consumers, which was coupled with the reinstatement

of their role as principle financiers. These steps make it easier for a more general public to join the

niche concept, because it decreases the relative distance between the active member and the non-

active consumer, the latter being more in line with current user-practices and norms for electricity

consumption, which is important in the up-scaling of renewable energy technologies (Verbong &

Geels, 2010). Therefore, switching from a conventional electricity supplier to a model based on

12 Typically around 500 kilowatt hours (kWh) per share at a price between €200 and €500 (Windcentrale, 2014) 13 Part of the revenues is often allocated to local organizations such as a bird asylum (Eendragt) or used to support sustainable energy related education programs at local schools (ZEK). 14 The company Windcentrale earns a commission fee per wind share sold as well as an annual fee per wind share for the management of a cooperative (Windcentrale, 2015).

23

consumers that own their own production capacity has become easier and available to a broader set

of people, instead of the culture of “idealistic autonomy” (Verbong, 2001) from the first two periods.

The increase in potential for member growth is reflected in the large amount of members placed in

the eight WECs founded by Windcentrale, which, at the same time, is needed to be able to buy the

costly wind turbines.

During the first and second period the WECs acquired the competences for the operation of a WEC:

“we can assemble a wind turbine ourselves, because we have the knowledge to do this and we have

proved this for ourselves and others.” (Interviewee 1) Interviewee 1 indicated that this knowledge

came from within the organization and that “everybody has their own specialty about which they

know something.” (Interviewee 1) Meerwind indicated that they did the project development of two

new wind turbines in 2012 on their own. In order to do this, the chairman formed a building

committee from specialized members (Interviewee 2). In contrast, NDSM Energie has appointed

Renewable Energy Factory (REF), a consultancy firm, as their “wind advisor who does the subsidy

application, helps with the financial close and the negotiations with the wind turbine manufacturer,

and builds the business case.” (Interviewee 3) The interviewee also indicated that this is expertise

that they do not (yet) have at their disposal (Interviewee 3), whereas WECs that were established

during the initial period explicitly indicated that they are aware of this possibility for outsourcing

some of their activities to external parties, such as REF, but that this is expensive and unnecessary

since they have this expertise in their organization (Interviewee 2, 6, 8).

Results in this section indicate that the socio-technical conditions in which WECs had to realize

membership and production capacity growth became increasingly complex. Two different business

models can be distinguished, the first, from the initial period, propagates the exploitation of wind

turbines in local communities. The second has no connection with any specific region in the

Netherlands. WECs that use the former model, except Windvogel, rely on geographical proximity to

their founding location to expand production capacity, whereas, WECs that use the latter model, do

not have this dependence on local conditions. The professionalization of WECs coincides with the

simplified role of members in the organization, wherein employees are now responsible for

managing growth to which they can devote more time. Next, I look at how the development of WEC

is reflected in spatial patterns and how growth can be related to local conditions.

4.2 Geographical development

The previous section showed the impact of socio-technical dynamics on the growth of memberships

and production capacity of WECs. The spatial distribution of WECs over time is expected to be

affected by these dynamics as well. Wind conditions affect the feasibility of new projects, therefore,

with the initially low economic performance of wind turbines; I expect the first initiatives to be

founded at locations with relatively good wind conditions. Later, as the technologic and economic

conditions for wind energy improve, locations with increasingly less available wind resources can be

occupied. The degree of urbanization at the founding location of a WEC is expected to give an

indication of how contested the possible space for local expansion is i.e. wind placement competing

with other land-use functions like housing, agriculture, businesses etcetera (De Groot, 2006). These

local (bio-) physical circumstances condition the emergence and growth of WECs (Oteman, Wiering,

& Helderman, 2014) and form the starting point for the analysis of the geographical development of

24

WECs in the Netherlands. The maps in this section indicate the statutory founding places of active

WECs in a particular year. Maps on the left display the amount of members registered with the WECs

and on the right the maps show the production capacity owned by the WECs. Maps on the left side

also display population density figures of 2013 for the Dutch municipalities (CBS, 201315), the

population density data is supplemented by data on urbanization levels16 (CBS, 2013). On right the

average yearly wind speeds measured over the period 1971-2000 (KNMI, 201517).

4.2.1 Emergence phase: 1986 - 1996

ODE stimulated the founding of the first WECs coastal regions first, because at these locations the

feasibility of the first projects would be highest (Agterbosch, 2006). Figure 7 shows the spatial

distribution and the production capacity and membership quantities of the WECs for 1996. The maps

indicate that WECs are located in areas with varying, but generally good wind conditions, and

relatively low population densities, although the degree of urbanization varies; Eendragt,

Kennemerwind and ZEK started in municipalities which are strongly urbanized, while CWW,

Deltawind and WDE were founded in hardly urbanized municipalities. The rest of the WECs started in

moderately urbanized areas. These local (bio-) physical conditions do not seem to be the main

factors driving their establishment; rather, their location of founding is related to the pre-existence

of social cohesion in the form of environmental groups (Agterbosch, 2006). Furthermore, although

the performance of wind turbine technology improved during the 1990’s, which meant that turbines

“could also run economically more in land” (Van Loenen, 2003, p. 17), after 1992 no new WECs were

established. Van Loenen (2003) mentions that “ODE lacked the organizational and financial resources

to support the founding of new initiatives in other provinces” (p. 17), indicating that the coordination

and structuration of activities by ODE played a decisive role during the emergence of the niche. Wind

turbines that were installed during this period were located in close proximity to the founding places

of the WECs; on average within 10 kilometers distance, ZEK also highlights their symbolic value, as

they indicated that: “our wind turbine is really our totem pole.” (Interviewee 6)

Deltawind owned more than 40% of the total production capacity in 1996, which amounts to 4.58

MW. According to Figure 7 the WEC is located in a relatively windy region of the Netherlands.

Deltawind is also located in a sparsely populated municipality, Goeree-Overflakkee, with a population

density of 184 inhabitants per km2 and a hardly urbanized character. Therefore, Deltawind would

have had ample room for their growth, and, even more importantly at this stage, access to relatively

abundant natural resources. However, these local (bio-) physical conditions are to a large extent

comparable to the local conditions for CWW. This WEC was founded in the same year, has an

average annual wind speed, identical to Deltawind, of 5.25 meter per second, the municipality where

15 Figures from 2013 are comparable to population density patterns from previous years (see CBS, 2015) 16 CBS (2015) ranks the urbanization of municipalities from 1 - 5, from highly urbanized to not urbanized, based the amount of addresses per km2: 1 = highly urbanized (≥ 2,500 Addr./km2), 2 = strongly urbanized (1,500 – 2,500 Addr./km2), 3=moderately urbanized (1,000 – 1,500 Addr./km2), 4=hardly urbanized (500 – 1,000 Addr./km2) , 5=not urbanized (< 500 Addr./km2). 17 Figures from this period are comparable to previous years, because wind speeds depend to a large extent on the roughness of the surface which is low for flat surfaces such as open water (Stepek & Wijnant, 2011) and therefore wind speeds remain highest in areas surrounded by open water. The wind maps only illustrate this notion, but do not show any local variation. Local variations (Stepek & Wijnant, 2011) are used for the regression analysis in chapter 5 (see KNMI, 2015).

25

it was founded has a comparable population density (328 inh./km2)18 and the same urbanization

level, but WWC has by 1996 installed six times less production capacity than Deltawind. CWW

encountered obstruction from provincial administration of Noord-Holland that rejected the WECs

initial plans for a 70 kW turbine, because of disturbance to the landscape (Mars, 2003). Additionally,

Deltawind was able to profit from their relationship with the local energy distribution company,

which gave them more financial leeway to start new projects (Agterbosch, 2006). This example

shows the relative importance of relational proximity over local (bio-) physical conditions for the

growth of WECs.

Figure 7 Spatial distribution of quantitative development of WECs 1996

Developments in wind turbine technology also meant that relying on local members as a financial

resource could impose a restriction production capacity growth, but WECs started to adapt their

visions on external financiers and turned to banks for capital (Agterbosch, 2006). CWW was the first

WEC that financed the installation of wind turbine capacity through a mortgage indicating that “to

acquire this money by recruiting new members would be a long way to go and could mean a delay

[of the installation] for years” (Mars, 2003). WWC indicated that they choose ASN Bank as an

external resource to partly finance the realization of two wind turbines in 1995, because it is a “green

bank.” (Interviewee 7) Similar motivations were given by other WECs; interviewees 1, 7 and 8 have

indicated that they exclusively loan money from either ASN Bank or Triodos Bank, which are

nationally operating banks but have shared sustainability goals19, which made the step to these

nationally operating financial institutions smaller. At the same time, Triodos has had experience with

financing wind energy projects and could thus better anticipate the risks “demanded less equity” and

other banks “imposed stricter conditions for a mortgage.” (Interviewee 2)

18 This figure is almost twice as high as the population density in Goeree-Overflakkee, but this has been caused by the merger of Broek in Waterland in 1991, the initial municipality where CWW started (145 inh./km2 in 1986), into the municipality Waterland where CWW is now located. 19 See (ASN, 2015) and (Triodos, 2015).

26

4.2.2 Consolidation phase: 1997 - 2012

Socio-technical dynamics made it increasingly difficult for WECs to install new capacity (Agterbosch,

2006). According to Windvogel “the most important [step in in the process of installing a new wind

turbine] is acquiring the permit to build.” (Interviewee 1) Windvogel also indicated that they

experienced hardly any problems around the installation of their first wind turbine, but that the

problems came with the increasing size and impact of wind turbines “and the government that

started to regulate.” (Interviewee 1) At the end of the second period, Windvogel owned six wind

turbines in five different municipalities in four different provinces (Windvogel, 2014). In comparison,

the production capacity of Zeeuwind is distributed over eight municipalities, but all in Zeeland

(Zeeuwind, 2015). Zeeuwind stated that “all thirteen municipalities in Zeeland are members of the

cooperative” (Interviewee 5), which indicates that they endorse the vision of the WEC. Furthermore,

Zeeuwind has established partnerships with municipalities (Borsele and Sluis) and the province for

the experimentation with other forms of renewable energy technologies (Zeeuwind, 2015), further

adding to the interaction between local government bodies and the WEC.

A similar sense of common purpose can be found in the case of Deltawind. The national government

has made an inter-provincial agreement (or IPO) that divides and makes room for 6,000 MW of wind

turbine capacity on land by 2020 (Rijksoverheid, 2014). Province Zuid-Holland has to realize 735.5

MW of this production capacity (Zuid-Holland, 2014), an additional 466.5 MW compared to 2013

(CBS, 2014). Goeree-Overflakkee has been designated as a suitable area for the large-scale

development of wind energy (≥100 MW) by the Ministry of Infrastructure and the Environment (MIE,

2014) and has to find room for the implementation of 200 - 300 MW of wind turbine capacity on the

island. The municipality attaches great importance to local participation in realizing this ambition, a

means to increase acceptance amongst local stakeholders (Goeree-Overflakkee, 2013). Local

participation is at the locus of the business model of the WECs that were established during the

emergence phase. Zeeuwind stated that “We have the experience with creating support through

communication, compensation and participation.” (Interviewee 5) Goeree-Overflakkee has signed an

agreement with the Windgroep for the participation in the development of wind projects on the

island (Goeree-Overflakkee, 2015), a cooperative of local wind energy initiators including amongst

others, local farmers and Eneco, but coordinated by Deltawind (van Rixoort, 2013).

Three new cooperatives were founded during the second time period, but none of them has installed

any production capacity as of December 2014. Onze Energie and NDSM Energie focus on increasing

the renewable energy production in the northern district of Amsterdam, and work together to realize

new wind turbines. NDSM Energie has indicated that they have “acquired the exclusive right from the

municipality to four locations in the industrial area surrounding the former shipyard of the NDSM,

but building plans are obstructed by Provincial regulations.” (Interviewee 3) Since December 2012

the Deputy States of the province Noord-Holland restricted the issuing of permits for new wind

energy projects, because of “visual pollution, quality of the living environment and the cultural

history of the landscape.” (HaarlemsDagblad, 2012) The measure entails that for every new wind

turbine that is implemented two need to be removed, with a minimum of six (Echo, 2014), meaning

that a WEC should have at least twelve turbines, but of the WECs based in Noord-Holland only

Kennemerwind is able to meet that amount. Eendragt, Meerwind, WWC and ZEK have indicated that

their plans for production capacity expansion were hampered by the provincial regulations.

27


The case of Windvogel illustrates that merging with projects in other regions can help with realizing

growth by avoiding the opposition of local policies. The territorial connection between the members

and their wind turbines is still valid for Windvogel, since the participation of local citizens still forms

the locus of the business model of Windvogel (see Windvogel, 2014), but the place of founding has

lost most of its siginificance20. Other WECs that were interviewed indicated that they were not

prepared to participate in projects outside their work areas (Interviewees 2, 3, 5, 6, 7), despite the

unwillingness of the local or provincial governing bodies to support the local cooperative ownership

of wind turbines. Meerwind indicated that do not want to participate in the installation of production

capacity outside their region (municipality Haarlemmermeer) because they “are a local wind energy

cooperative and want to use the benefits locally; the revenues go to the members as well as to

sponsoring local associations.” (Interviewee 2) Conversely, Deltawind and Zeeuwind have the

capabilities and organizational capacity to contribute to the major challenge that has been bestowed

upon local governments to implement national CO2 mitigation targets.

4.1.3 New business model: 2013 - 2014

WECs that were founded by Windcentrale in 2013 and 2014 are all located in the Amsterdam (see

Figure 7). The eight cooperatives founded by Windcentrale owned eight wind turbines by 2014. The

turbines are located in three different municipalities and three different provinces. Furthermore, the

production locations are at a large distance from the place of founding; the first two wind turbines

that were purchased for cooperatives Grote Geert and Jonge Held are located at almost 173

kilometer distance from Amsterdam. The founders of Windcentrale have a background in the

business environment, a contrast with the idealistic environmental movement of the WECs that were

20 An image that is amplified by the fact that Windvogel moved its office from Gouda to Utrecht (Windvogel, 2012), which is in another province.

28

established during the first period. WEC that use wind energy in a local context initially look for

locations that are nearby. NDSM Energie for example wants to profit from wind energy production

locally and wants to install production capacity close by, but this also means that they have to deal

with local restrictions. Windcentrale by contrast, does not have to bother with processes at the policy

level to add more production capacity. According to Windcentrale “we are not very much involved in

the preliminary process: finding a suitable location, the permit process. Until now this has been a

part that we have left to the companies that are specialized in this process.” (Interviewee 4)


WECs that were established by Windcentrale share no connection with ODE, or with its successor

RESCoopNL21. Windcentrale indicated that they have had no support from other cooperatives: “the

cooperative model we have is really one-of-a-kind, so it is difficult to ask others for help when they

do not have the experience either.” (Interviewee 4) The purchasing of existing wind turbines relieves

Windcentrale from the dependence on local and regional authorities, but shifts the focus towards to

alignment with electricity consumers through the internet. Online telecommunication can decouple

the specifically local and help to approach more potential members on a wider scale. This also allows

Windcentrale to act quickly when they have found a party that is willing to sell production capacity:

the company then buys the turbine and sells the available shares. For the WEC Het Rode Hert, for

example, were sold within thirteen hours (Windcentrale, 2014).

21 RESCoopNL is the spin-off of the wind energy section of ODE and aims to actively involving citizens-based (wind) energy associations and cooperatives i.e. citizens in exploiting sustainable resources in the Netherlands (RESCoopNL, 2014). The organization started in 2013 (RESCoopNL, Founding of RESCoopNL, 2013), is a cooperative of wind energy cooperatives and has the same function as ODE has had in the past (Interviewee 5): it supports starting WECs by facilitating knowledge exchange and networking with experienced WECs e.g. by organizing workshops (RESCoopNL, 2014).

29

The results in this section indicate that there are spatial variations in the size of WECs. In the case of

WECs that use the local business model, production capacity growth is associated with the relational

proximity to local parties that have control over constraining resources for their quantitative

development. Firstly, during the emergence phase, increased relational proximity to local and

regional energy distributors (LEDCs and REDCs) resulted in higher financial returns on electricity

production. Secondly, after the emergence phase, through the build-up of shared expectations

between WECs and municipal and provincial governments for the future development of local and

regional wind energy capacity. This indicates that geographical proximity can lead to institutional

proximity and that it is this process that creates the local conditions that lead to growth, but in the

absence of these conditions geographical proximity can work as a constraint. Geographical expansion

is then a potential strategy to grow, although the territorially-based model is holding back most of

the WECs that were founded during the emergence phase of the niche. Windcentrale creates

relational proximity with national users and consumers of electricity, and (potential) members,

mainly through virtual interaction. In the next chapter I will look at which business model

developments determined the growth of WECs in a statistically significant way.

31

5. Factors determining the growth of WECs

Chapter 4 showed that adjustments in the business model have contributed to the growth of the

memberships and production capacity of WECs. In this chapter two statistical models are used to test

the influence of changes in the business model dimensions on the membership and production

capacity growth of WECs. The first multiple regression model includes the production capacity as the

dependent variable and the second model uses the amount of members in an equivalent form.

Results that are presented in the next paragraphs are the outcomes of the final model

configurations, but a preceeding step has been the generation of a bivariate table including the

correlation, or the absence of it, between all the variables included in the datat set (see Appendix 4).

The selection of the independent variables is based on the correlations with dependent variables,

which are then entered into the model. Cases are excluded pairwise in order to cope with missing

data entries (IBM, 2014).

5.1 Factors determining production capacity growth

5.1.1 Descriptive statistics

Production capacity entries in the data set include two outlying cases: Deltawind and Zeeuwind. In

order to handle this, a new variable is created; logProdCapacity, which is the logarithmic

transformation of the production capacity data. Figure 10 shows the distribution of the dependent

variable for this analysis. Fifteen of the cases fall within one standard deviation from the mean. Five

cases22 are without any production capacity as of December 2014 and are at two standard deviations

below the mean. Deltawind and Zeeuwind are each between two and three standard deviations

larger than the mean.

22 Uwind has a wind turbine project in which the WEC is actively involved, but it is in 100% ownership of Eneco. Therefore the entry for this case equals zero.

32

The descriptive statistics of the explanatory variables that are included in the model are shown in

Table 2. Explanatory variables included in the model are the number members per WEC (Members),

the number of paid employees that work for a WEC (Staff), the number of different municipalities

and provinces in which a WEC owns production capacity (variable Municipalities and Provinces) and

the east-west distribution of the locations where the WECs have been established (X-coordinates).

Three of the five explanatory variables included in the model are related to the geographical

dimension of WEC development (Municipalities, Provinces and X-coordinate), while the remaining

two independent variables relate to the quantitative (Members) and organizational dimension

(Staff).

Table 2 Descriptive statistics on explanatory variables for production capacity growth

Std.

Deviation Up-scaling dimension

N Min Max Mean

Members

23 49.00 3276.00 1047.65 926.29 Quantitative

Staff

22 0 8 3.64 3.85 Organizational

Number of municipalities 23 0 8 1.48 1.75 Geographical

Number of provinces 23 0 4 1.00 .798 Geographical

X-coordinates 23 3.890401 5.864273 4.901032 .405451 Geographical

Except for the number of paid employees, where data on WindPower Nijmegen is missing, the data

set is complete for all cases (𝑁 = 23). On average, WECs in the Netherlands had 1,048 members. The

largest WEC in the data set in terms of memberships is Windvogel with a total of 3,276 members.

Most of the WEC have no paid staff, whereas Windcentrale has eight employees. The territorial

connection of the WECs is reflected by the average spread over different municipalities and

provinces; the mean number of the former is one-and-a-half and the latter not higher than one.

Zeeuwind owns and operates all its wind turbines in the same province, but does so in eight different

municipalities. Windvogel is active in the maximum amount of provinces: four.

5.1.2 Model results

Production capacity growth is best explained by the model including the latitude coordinates (X-

coordinates) of the WECs and the membership numbers. The model explains approximately sixty-two

percent (𝑅2 = 0.623) of the variation of the dependent variable. Of this sixty-two percent, the x-

coordinates explain forty-seven percent and the amount of members fifteen percent. Both

independent variables are statistically significant; X-coordinates beyond the 1% significance level and

the explanatory variable Members is statistically significant at the 5% level. The coefficient for the

variable X-coordinate is negative (𝛽𝑋−𝑐𝑜𝑜𝑟𝑑𝑖𝑛𝑎𝑡𝑒 = −1.813), indicating that being located in the

western parts of the Netherlands positively affects the production capacity growth of WECs and

moving to the east, while holding the amount of members constant, the level of production capacity

decreases. The coefficient of the Members variable is positive (𝛽𝑀𝑒𝑚𝑏𝑒𝑟𝑠 = 0.001), indicating that,

while controlling for latitude; the growth in production capacity is positively influenced by growth in

members.

33

Table 3 Model results production capacity growth

Dependent variable: Log(Production capacity)

Independent variables

Coefficients (β)

X-coordinate

-1.813** Stand. error

(0.565)

R2-change

0.471

Members

0.001* Stand. error

(0.0002)

R2-change

0.151

Constant

10.696** Stand. error

(2.898)

Summary Statistics

SER

0.948

R2

0.623

N

23

*, ** Indicate that the individual coefficient is statistically significant at the 5% and 1% significance level respectively, using a two-sided test.

The results from this model show that there are significant benefits for WECs associated with their

spatial location when it comes to production capacity growth. These benefits are not related to any

specific (bio-) physical conditions that were controlled for in this analysis: the average annual wind

speeds share no statistically significant relationship with the dependent variable, which is also true

for all of the statistics related to urbanization i.e. population density at the municipality and province

level and the level of urbanization. Instead, the results confirm the found in the previous chapter; the

growth of production capacity is determined by the locational conditions associated with institutions

at the municipality and provincial level. The results also show that increasing membership levels can

provide a means for production capacity growth. How the growth in memberships can be explained

is the subject of the next section.

5.2 Factors determining the number of members 5.2.1 Descriptive statistics

The data entries for the amount of members that are registered with a WEC do not include outliers,

but the data has a more irregular distribution than the entries for the production capacity levels.

Windvogel has a member count of more than two standard deviations large than the mean value of

1,048 members. Five entries are between one and two standard deviations, of which three are below

the mean value (NDSM Energie, WDE and Zuidenwind). The rest of the cases are within one standard

deviation from the average number of members.

34

Staff is included in this model as well, which has one missing entry. The variable MaxDistance to

origin measures the distance from the wind turbine location that is farthest away from the founding

location of a WEC, to the location of founding. Here, the number of observations is equal to 19

because four of the cases have no measurable distance to production locations, because they still

have to start their first project. The production location that is farthest away is owned by WECs Grote

Geert and Jonge Held, WECs that were established at 173 kilometer from their wind turbine

locations. Registration fee accounts for the monetary sum that must be paid before a person can

acquire a full membership to a WEC. The highest registration fee had23 to be paid at Grote Geert and

Jonge Held. Six WECs do not require a financial payment from aspiring members. Of the five variables

included in the model two are related to the organizational dimension of the business model,

another two are related to the geographical dimension and one relates to the quantitative

dimension.

Table 4 Descriptive statistics on explanatory variables for membership growth

Std.

Deviation Up-scaling dimension

N Minimum Maximum Mean

Staff

22 0 8 3.64 3.85 Organizational

Registration fee 23 0 351 105.74 112.44 Organizational

Number of provinces 23 0 4 1 0.80 Geographical

MaxDistance to origin 19 0.74 173.09 42.94 50.43 Geographical

Log(ProdCapacity) 23 0 4.28 2.52 1.47 Quantitative

23 For these WECs the registration fee corresponds to the price of one share in the turbine that is owned by the WEC. It is no longer possible to join these WECs, or any of the other WECs that were established by Windcentrale.

35

5.2.2 Model results

Membership growth is best explained by a model that includes the independent variables

Maxdistance to origin, Number of provinces and Staff. This model explains more than 93 per cent

(𝑅2 = 0.935) of the variation in membership numbers in the data set. Maxdistance to origin explains

most of the variation in the model; almost 58%. Number of provinces has an r-squared of 0.285 and

therefore explains around 29 percent. The rest of the variation is explained by the number of paid

employees that are working at a WEC (10%). All variables are statistically significant at the 1%

significance level and have positive coefficients. Maxdistance origin (with 𝛽 = 8.747) indicates that

increasing the distance between the founding place of a WEC and its production capacity locations

increases the number of members. The positive correlation with Number of provinces (𝛽 = 616.822)

indicates that locating production capacity in an increasing number of different provinces increases

membership. Lastly, hiring paid employees and increasing the number of staff leads to an increase in

membership levels (𝛽𝑆𝑡𝑎𝑓𝑓 = 95.800).

Table 5 Model results member growth

Dependent variable: Members

Independent variables

Coefficients (β) Maxdistance origin

8.747**

Stand. error

(1.518)

R2-change

0.548

Number of provinces 616.822**

Stand. error

(76.800)

R2-change

0.285

Staff

95.800**

Stand. error

(19.885)

R2-change

0.101

Constant

-293.084**

Stand. error

(113.294)

Summary Statistics

SER

259.625

R2

0.935

N

23

** Indicates that the individual coefficient is statistically significant at the 1% significance level using a two-sided test.

36

The results from the second model show that there are significant benefits for membership growth

for WECs associated with their geographical expansion, the expansion of production activities away

from their original spatial location. This shows again that space is important, but that it works in the

opposite direction; it is not the spatial location itself that determines growth, rather it is the

movement away from it. Spatial expansion is captured in two ways by the model. Firstly, through the

absolute increase in distance i.e. as measured in kilometers and secondly by an increase in

administrative or regulatory distance: the expansion to new provinces, which brings with it a

different regulatory environment both at the municipality and the provincial level. A common

attribute is that geographical expansion can provide new opportunities for growth when they are

unavailable at the location of founding. The positive and significant correlation between the amount

of members and the number of employees confirms that hiring a paid staff, taking away

responsibilities from (active) members and making their role in the organization of a WEC less

demanding, results in membership growth.

Putting the results from this chapter together, they show that geography forms the most important

dimension for the growth of WECs; three of the five significant relationships found in the two

regression models are associated with the geography. The results also show that there is a dichotomy

in its explanatory direction; the first results indicate that the institutional conditions associated to the

spatial location, and therefore with the geographical proximity to these conditions (Boschma, 2005),

are beneficial and a significant contributing factor to the production capacity growth of WECs. The

second model shows that also contribution to the growth of WECs is the movement away from the

original spatial location. This indicates that when a WEC is started at a location were the institutional

set-up is favorable; there is no need to expand geographically in order to growth. However when the

conditions are unfavorable a strategy can be to look beyond the restricted territorial area for

opportunities to expand quantitatively, since geographical proximity in these case restricts the access

to the spatial resources needed for growth (Boschma, 2005), but, as shown in the previous chapter,

this is a strategy that only Windvogel and Windcentrale are willing to pursue.

Another divide that is shown by the results is between the physical, or technological, and the social

side of a business model. One vital resource for the expansion of production capacity is the access to

available spaces for the construction of new turbines. According to Malmberg and Maskell (2006)

local institutions are resources that are least sensitive to the globalization process and “through

cumulative causation the […] institutional set-ups are reproduced generating stable patterns of […]

territorial differentiation.” (Coenen, Raven, & Verbong, 2010, p. 297) The local institutions are only

relevant when it comes to the installation of new capacity, which explains why WECs were only able

to increase their production capacity in different administrative regions by buying existing

installations. Resources that are susceptible to begin to flow through networks are, amongst others,

financial resources (Malmberg & Maskell, 2006) and it is financial resources that the members

provide. This confirms the statement made by Boschma (2005) that social networks, and the

resources that flow across it (Geels, 2011), are not bound to geography. It is this adjustment that

constitutes the fundamental change in strategy that was made by Windcentrale.

37

6. Discussion and conclusion

The development of WECs in the Netherlands over the last thirty years has taken place within three

distinct time periods. During the research period, the socio-technical conditions in which the WECs

had to realize membership and production capacity growth became increasingly complex. Over the

last thirty years a number of WECs have developed from idealistic initiatives that rely on volunteers,

and exploiting a limited amount of wind turbines, into professional organizations that have

considerable quantities of production capacity and members. The professionalization coincides with

simplified role of members in the organization, wherein employees are now responsible for

managing the growth of the WEC. WECs also partly developed from local organizations, with distinct

geographical regions and locations where they realized most of their growth, towards nationally

operating organizations without a strict territorial predisposition, which has resulted in a changing

geographical distribution of activities. Therefore, it can be concluded that, with respect to their

production capacity and members, WECs have developed organizationally, and that, with respect to

their founding locations, WECs have expanded geographically.

The growth of WECs in the Netherlands is to a large extent determined by local conditions at the

geographical locations. The colocation of a WEC with favorable conditions provides a statistically

significant basis for production capacity growth. The favorable conditions are co-created between

WECs and regime parties that have local or regional control over resources, first with EDCs and at

later stages with municipal and provincial governments. The latter have to execute national wind

energy policies, while wind power is a form of renewable energy that has encountered increasing

public resistance. WECs have the knowledge to increase support for wind energy projects, but only

the professional organizations had developed the capabilities to install wind turbines on the

necessary scale. Geographical expansion is a statically significant strategy in case local conditions are

disadvantageous, albeit through increasing memberships. Members provide all the capital for

production capacity growth in the new business model, whereas (part of) this significance has been

lost in the previous business model. This leads to the conclusion that the factors that contributed to

the growth of WECs in the Netherlands over the last 30 years are the geographical location,

geographical expansion and the hiring of paid staff members.

Coenen et al. (2010) argue that adding a spatial context to SNM “will force it to address the question

how and why experiments are performing differently in different geographical settings” (p. 296)

“Niches do not emerge out of nowhere.” (Raven, Schot, & Berkhout, 2012, p. 71) The results in this

research show that pre-existing social structures conditioned the emergence of WECs; social

proximity had already been established in local environmental movements. A second contributing

factor was the input of ODE that presented a business model for the operation of wind turbines to

the actor groups: local communities could profit from locally produced electricity and use the

benefits to better their local environment. This confirms the hypothesis of Boon and Dieperink (2014)

that the promise of potential local benefits from the use of renewable energy technologies increases

the probability that a local renewable energy project will be started. After the organization stopped

supporting the founding of new projects in 1992, even though technological improvement meant

that they became more economically feasible, the founding of new WECs ceased (Van Loenen, 2003),

implying an overriding importance of organizational proximity in creating the necessary conditions

for the emergence of the niche.

38

The business model for the local development of wind turbines relies on factors in geographical

proximity to their founding location. Relational proximity to regime groups is promoted by this

geographical proximity and gives rise to institutional proximity. In this research it has been shown

that it is the difference in the build-up of institutional thickness that determines the geographical

unevenness and that it constitutes an important factor for up-scaling. This result confirms the

hypothesis of Coenen et al. (2012) that “a spatially variegated institutional landscape gives rise to

some regions […] forging ahead in terms of sustainability transition processes while others lag

behind.” (p. 975) Strategy adjustments have been beneficial for growth by avoiding the build-up of

institutional thickness, but it required the entrance of an outside organization to overcome the

significance of local institutions in the process of up-scaling, which indicates that the cognitive

proximity amongst the actors was too high in the local business model to allow sufficient second-

order learning to take place. A switch occurred from institutional proximity to policy-makers towards

the users of electricity, supporting the claim of Dóci et al (2015) that a transition does not occur

everywhere in the regime simultaneously but in different interlinked sub-regime structures, by

creating what may be called “virtual proximity” through extensive use of internet.

To address the lack of geographical sensitivity in the MLP framework (Coenen, Benneworth, &

Truffer, 2012), the analytical framework in this research was supplemented by the notion of

proximity (Boschma, 2005). This has given insight into how transitions come about and how niches

can start to influence regime structures, both of which present a challenge for the local-global model

(Geels & Raven, 2006; Coenen, Raven, & Verbong, 2010). Therefore, I would recommend that greater

efforts should be made to further elaborate MLP with an absolute notion of through proximity,

because it seems that the current research is moving into the direction of the relative view on space

(Raven, Schot, & Berkhout, 2012). Including theoretical insights from business model studies in the

MLP framework can compensate for the lack of agency (Smith, Stirling, & Berkhout, 2005) by making

explicit how entrepreneurs combine resources (Geels & Schot, 2010) and innovative institutions at

the micro level (Coenen, Raven, & Verbong, 2010). In general the results in this research imply that

there should be more attention for heterogeneity in the MLP framework, at the regime (Smith,

Stirling, & Berkhout, 2005) as well as at the niche level, since the assumption of a single learning

trajectory makes it problematic to account for the fast growth rate of an initiative that takes no part

in the ongoing learning process.

Methodologically the results imply that transition research can benefit from Geographical

Information Systems, or GIS, and in the case of this research ArcGIS. The tools that are included in

this platform allow researchers to visualize and explore socio-spatial patterns and relevant

geographical quantities. Furthermore, the software package that was used during this research

contains a broad range of analytical tools, of which this research has only scratched the surface, that

could be of added value for a second-generation MLP (Raven, Schot, & Berkhout, 2012). The multi-

methodological approach of this research shows the continuing relevance in transition studies to

cross disciplinary boundaries (Perez, 2010). However, the ontological nature of the MLP framework

in process theory makes appropriateness of methodological tools based on variance theory like

multivariate regression analysis questionable (Geels & Schot, 2010, pp. 91-101). The results in this

research imply that linear cause-and-effect approaches can have a place in transition research, albeit

in combination with qualitative research and a narrative explanation of processes over time.

Uncovered patterns can then be tested statistically.

39

The results have practical implications for policymakers and managers of developing (sustainable)

businesses. The results in this research indicate that governing transitions, that is all activities of

political and administrative actors to guide, steer, control or manage the (transition) of societies

(Kooiman, 1993), is about creating shared visions for the future through “the process of interaction

and decision-making among the actors involved in a collective problem that lead to the creation,

reinforcement, or reproduction of social norms and institutions.” (Hufty, 2011, p. 405) Policymakers

should play the role of enabler by interacting in a socio-political dimension instead of exercising top-

down control. In the case of the management of sustainable businesses it is important to put

emphasis on maintaining a sufficiently level of diversity within the organization to support a

pluralistic set of strategies to handle possible future challenges (Beinhocker, 2007) and in the

network of the organization to assure ample second-order learning (Boschma, 2005). Furthermore, it

should be clear to managers that if they want to grow their sustainable businesses beyond a certain

size they have to hire paid personnel.

One limitation of this research relates to the data in the dataset that was used in the regression

analyses. Firstly, the distribution of the variable data does not meet the assumption for the method

of ordinary least squares (OLS) that the errors are normally distributed (Stock & Watson, 2007).

Secondly, the number of observations included in the data set is limited. However, to my knowledge,

the number of cases that was included in the dataset is exhaustive, and therefore represents the

entire population of initiatives that match the object of enquiry in the Netherlands. Furthermore, I

believe that the data has practical value, even though they do not meet the requirements of

maximum likelihood, which is confirmed by the qualitative analysis of the development of WECs.

Triangulation of results, using interviews, GIS and statistical analyses, is the main strength of this

study. A second limitation concerns the tentative nature of the conclusions. Transitions are long-term

processes that are hard to capture by cause-and effect relations. The degree of development of the

niche, and therefore the extent to which the factors uncovered in this research explain up-scaling, is

uncertain at this stage. Dóci et al. (2015) have put forward three criteria to assess the development

of a social niche, so futher research could include such an assessment.

Further empirical research using proximity is required, for instance by extending it to other

renewable energy technologies such as the implementation of solar-PV panels. This technology has a

lower spatial impact that wind turbine, but it would nonetheless be interesting to look the

development of spatial patterns in response to adjustments in regulations that have a specifically

geographic impact such as the postal-code arrangement (Postcoderoos-regeling in Dutch;

WijKrijgenKippen, 2014). At the interface of social and organizational proximity and their role in

explaining the emergence of socio-technical concepts there is a second opportunity for further

research. Coenen et al. (2010) point to the lack of attention in SNM literature for the compensatory

nature of the two forms of proximity and call for empirical research on the trade-off between trust-

building and organizational control. Environmental groups that were active during the first time

period (1986-1996) could provide interesting (historical) study objects for this purpose. This research

has also shown the impact of technological developments in a wider societal context i.e. the internet

on the opportunities for new business models (Teece, 2010). Therefore, a future research avenue can

be in the direction of on ongoing socio-technical trajectories and how they may enable sustainable

business model development.

41

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49

Appendices

Appendix 1 List of active WECs in the Netherlands

Founding

year

Production Capacity Founding place

Name Legal form Status Members X-coordinate Y-coordinate Municipality Work area Province

WWC 1986 Cooperative EL Active 125.00 565.00 Grootebroek 5.222713 52.699039 Stede Broec West-Friesland Noord-Holland

Zeeuwind 1987 Cooperative EL Active 1730.00 19115.00 Goes 3.890401 51.503636 Goes Zeeland Zeeland

Kennemerwind 1988 Cooperative EL Active 800.00 2905.00 Alkmaar 4.737354 52.632057 Alkmaar Kennemerland Noord-Holland

CWW 1989 Cooperative EL Active 500.00 4600.00 Monnickendam 5.034018 52.459705 Waterland Waterland Noord-Holland

Deltawind 1989 Cooperative EL Active 1600.00 15005.00 Oude-Tonge 4.221273 51.692519 Goeree-Overflakkee Goeree-Overflakkee Zuid-Holland

Meerwind 1989 Cooperative EL Active 1051.00 4850.00 Hoofddorp 4.680935 52.302520 Haarlemmermeer Haarlemmermeer Noord-Holland

Eendragt 1989 Cooperative EL Active 123.00 1369.50 Den Helder 4.763321 52.955850 Den Helder Northern Noord-Holland Noord-Holland

UWind 1989 Cooperative EL Active 130.00 0.00 Houten 5.148675 52.025063 Houten Houten Utrecht

WDE 1990 Cooperative EL Active 97.00 80.00 Wilnis 4.879688 52.198321 De Ronde Venen Ronde Venen Utrecht

Windvogel 1991 Cooperative LL Active 3276.00 4605.00 Reeuwijk 4.723480 52.047325 Bodegraven-Reeuwijk The Netherlands Zuid-Holland

ZEK 1991 Cooperative EL Active 150.00 80.00 Zaandam 4.826741 52.445637 Zaanstad Zaanstreek Noord-Holland

Onze Energie 2009 Cooperative EL Active 220.00 0.00 Amsterdam 4.961981 52.384385 Amsterdam Northern Amsterdam Noord-Holland

Zuidenwind 2011 Cooperative EL Active 70.00 0.00 Thorn 5.841018 51.163437 Thorn The Netherlands Noord-Holland

NDSM 2012 Cooperative EL Active 49.00 0.00 Amsterdam 4.893577 52.400984 Amsterdam Northern Amsterdam Noord-Holland

Grote Geert 2013 Cooperative EL Active 2477.50 2300.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Jonge Held 2013 Cooperative EL Active 2538.50 2300.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Windpower Nijmegen 2013 Cooperative EL Active 347.00 0.00 Nijmegen 5.864273 51.838895 Nijmegen Nijmegen Gelderland

Ranke Zwaan 2014 Cooperative EL Active 1541.00 2000.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Rode Hert 2014 Cooperative EL Active 1662.00 2000.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Witte Juffer 2014 Cooperative EL Active 1430.25 2000.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Blauwe Reiger 2014 Cooperative EL Active 1383.50 850.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Bonte Hen 2014 Cooperative EL Active 1394.75 850.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

Trouwe Wachter 2014 Cooperative EL Active 1400.50 850.00 Amsterdam 4.879286 52.371098 Amsterdam The Netherlands Noord-Holland

50

Population density

municipality

Population density

province

Average annual wind

speed

Maximum distance to

origin

Average distance to origin

Registration

fee

Related markets

Different

municipalities Different provinces Name Staff Contribution Interest Urbanization

WWC 0.00 0.00 1.00 7.00 0.00 1465.00 3.00 1022.00 2.00 1.00 4.25 9.74 5.64

Zeeuwind 6.00 110.00 0.00 5.50 5.00 399.00 3.00 214.00 8.00 1.00 3.75 30.58 14.80

Kennemerwind 1.00 0.00 0.00 4.00 0.00 3230.00 2.00 1022.00 2.00 1.00 3.75 23.91 14.97

CWW 0.00 0.00 1.00 7.50 0.00 328.00 4.00 1022.00 1.00 1.00 5.25 4.64 4.64

Deltawind 7.00 50.00 0.00 5.46 2.00 184.00 4.00 1269.00 1.00 1.00 5.25 8.67 4.69

Meerwind 0.00 0.00 1.00 5.00 1.00 807.00 3.00 1022.00 1.00 1.00 4.25 8.43 7.61

Eendragt 0.00 50.00 1.00 7.00 2.00 1259.00 2.00 1022.00 2.00 1.00 5.25 10.31 4.41

UWind 0.00 0.00 0.00 4.50 2.00 874.00 3.00 900.00 1.00 1.00 3.75 0.74 0.74

WDE 0.00 0.00 0.00 . 2.00 425.00 4.00 900.00 2.00 1.00 4.75 3.80 3.80

Windvogel 2.00 50.00 0.00 4.00 3.00 434.00 3.00 1269.00 5.00 4.00 4.75 66.09 38.43

ZEK 0.00 50.00 1.00 . 3.00 2025.00 2.00 1022.00 1.00 1.00 3.25 8.01 8.01

Onze Energie 0.00 50.00 0.00 0.00 2.00 4822.00 1.00 1022.00 0.00 0.00 3.75 . .

Zuidenwind 0.00 50.00 0.00 0.00 1.00 524.00 5.00 522.00 0.00 0.00 4.75 . .

NDSM 0.00 50.00 1.00 0.00 0.00 4822.00 1.00 1022.00 0.00 0.00 3.75 . .

Grote Geert 8.00 351.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 173.09 173.09

Jonge Held 8.00 351.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 173.09 173.09

Windpower Nijmegen . 25.00 0.00 0.00 0.00 3103.00 1.00 406.00 0.00 0.00 3.25 . .

Ranke Zwaan 8.00 200.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 53.04 53.04

Rode Hert 8.00 200.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 53.04 53.04

Witte Juffer 8.00 200.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 53.04 53.04

Blauwe Reiger 8.00 215.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 45.19 45.19

Bonte Hen 8.00 215.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 45.19 45.19

Trouwe Wachter 8.00 215.00 0.00 0.00 1.00 4822.00 1.00 1022.00 1.00 1.00 3.25 45.19 45.19

51

Appendix 2 List of interviewees

Interviewee number Organization Interview date

Interviewee 1

Windvogel 18/06/2014

Interviewee 2

Meerwind 25/06/2014

Interviewee 3

NDSM Energie 26/06/2014

Interviewee 4

Windcentrale 2/7/2014

Interviewee 5

Zeeuwind 3/7/2014

Interviewee 6

ZEK 10/7/2014

Interviewee 7

WWC 18/7/2014

Interviewee 8

Eendragt 22/07/2014

52

Appendix 3 Interview protocol

General questions:

1. What was the intention to start this organization?

2. Does your organization have any paid employees?

3. To what extent does your organization have the intent to grow?

4. What were, during the founding of you organization, with respect to the installation of wind turbines?

5. Is your organization, besides the production of wind power, involved in the production of other forms of renewable energy?

Learning:

6. Your organization is a member of [insert platform]. Can you tell me how this platform contributes to your organization?

7. Are there any other platforms that are important for you organization?

8. Does your organization currently support another WEC?

9. Does your organization currently receive support from another WEC?

10. In what sense has the contact of your organization with other WECs changed since it was founded?

Network:

11. How is the decision made to install a new wind turbine or replace an existing one?

12. Can you describe for me the process from the decision to install a new turbine until its completion?

13. What is currently the average loan that the members have supplied to your organization?

14. Who are the end-users of the electricity that your organization produces?

15. Are there currently any other organization that support your organization in any way?

53

Appendix 4 Correlation table

Correlations

Founding year Members Production capacities Turbines Staff Registration fee Interest rate

Number related markets

Number of municipalities

Founding year Pearson Correlation 1 .233 -.412 -.525* .622** .706** -.959** -.375 -.492*

Sig. (2-tailed) .284 .051 .010 .002 .000 .000 .078 .017

N 23 23 23 23 22 23 21 23 23

Members Pearson Correlation .233 1 .381 .260 .704** .660** -.200 .236 .396

Sig. (2-tailed) .284 .073 .231 .000 .001 .384 .277 .062

N 23 23 23 23 22 23 21 23 23

Production capacities Pearson Correlation -.412 .381 1 .907** .245 -.042 .457* .569** .681**

Sig. (2-tailed) .051 .073 .000 .272 .849 .037 .005 .000

N 23 23 23 23 22 23 21 23 23

Turbines Pearson Correlation -.525* .260 .907** 1 .103 -.175 .500* .531** .765**

Sig. (2-tailed) .010 .231 .000 .649 .425 .021 .009 .000

N 23 23 23 23 22 23 21 23 23

Staff Pearson Correlation .622** .704** .245 .103 1 .863** -.538* .013 .052

Sig. (2-tailed) .002 .000 .272 .649 .000 .014 .953 .820

N 22 22 22 22 22 22 20 22 22

Registration fee Pearson Correlation .706** .660** -.042 -.175 .863** 1 -.629** -.044 -.073

Sig. (2-tailed) .000 .001 .849 .425 .000 .002 .840 .740

54

N 23 23 23 23 22 23 21 23 23

Interest rate Pearson Correlation -.959** -.200 .457* .500* -.538* -.629** 1 .239 .452*

Sig. (2-tailed) .000 .384 .037 .021 .014 .002 .297 .040

N 21 21 21 21 20 21 21 21 21

Number related markets Pearson Correlation -.375 .236 .569** .531** .013 -.044 .239 1 .721**

Sig. (2-tailed) .078 .277 .005 .009 .953 .840 .297 .000

N 23 23 23 23 22 23 21 23 23

Number of municipalities Pearson Correlation -.492* .396 .681** .765** .052 -.073 .452* .721** 1

Sig. (2-tailed) .017 .062 .000 .000 .820 .740 .040 .000

N 23 23 23 23 22 23 21 23 23

Number of provinces Pearson Correlation -.378 .568** .166 .183 .037 -.013 .304 .383 .552**

Sig. (2-tailed) .075 .005 .449 .404 .869 .954 .180 .072 .006

N 23 23 23 23 22 23 21 23 23

Number of locations Pearson Correlation -.593** .331 .770** .850** -.006 -.174 .576** .670** .962**

Sig. (2-tailed) .003 .123 .000 .000 .978 .426 .006 .000 .000

N 23 23 23 23 22 23 21 23 23

x-coordinates Pearson Correlation .374 -.431* -.742** -.705** -.322 -.133 -.343 -.595** -.622**

Sig. (2-tailed) .079 .040 .000 .000 .144 .546 .128 .003 .002

N 23 23 23 23 22 23 21 23 23

y-coordinates Pearson Correlation -.033 -.090 -.444* -.340 -.064 .117 .120 -.365 -.219

Sig. (2-tailed) .881 .685 .034 .113 .777 .595 .604 .087 .316

55

N 23 23 23 23 22 23 21 23 23

Annual windspeeds Pearson Correlation -.652** -.228 .283 .238 -.563** -.614** .750** .117 .168

Sig. (2-tailed) .001 .295 .190 .275 .006 .002 .000 .595 .444

N 23 23 23 23 22 23 21 23 23

PopdensMunFoundYear Pearson Correlation .857** .250 -.412 -.415* .613** .730** -.823** -.391 -.410

Sig. (2-tailed) .000 .250 .051 .049 .002 .000 .000 .065 .052

N 23 23 23 23 22 23 21 23 23

PopDensMunicipality2013 Pearson Correlation .840** .235 -.423* -.418* .588** .709** -.817** -.397 -.421*

Sig. (2-tailed) .000 .281 .044 .047 .004 .000 .000 .061 .045

N 23 23 23 23 22 23 21 23 23

Urbanization levels Pearson Correlation -.677** -.254 .345 .328 -.540** -.620** .657** .235 .252

Sig. (2-tailed) .000 .243 .107 .127 .010 .002 .001 .280 .245

N 23 23 23 23 22 23 21 23 23

PopdensProvFoundYear Pearson Correlation .234 .359 -.280 -.359 .294 .347 -.182 -.301 -.380

Sig. (2-tailed) .283 .093 .195 .092 .185 .105 .429 .163 .073

N 23 23 23 23 22 23 21 23 23

PopdensProvince2013 Pearson Correlation -.045 .258 -.226 -.271 .069 .120 .069 -.240 -.305

Sig. (2-tailed) .839 .235 .301 .210 .760 .586 .766 .270 .157

N 23 23 23 23 22 23 21 23 23

MaxDistOrigin Pearson Correlation .632** .740** -.097 -.190 .601** .859** -.630** -.118 -.073

Sig. (2-tailed) .004 .000 .692 .436 .007 .000 .007 .630 .767

56

N 19 19 19 19 19 19 17 19 19

AvDistOrigin Pearson Correlation .682** .669** -.159 -.268 .619** .886** -.668** -.191 -.186

Sig. (2-tailed) .001 .002 .515 .267 .005 .000 .003 .434 .446

N 19 19 19 19 19 19 17 19 19

LogProdCapacity Pearson Correlation -.252 .647** .569** .512* .515* .332 .367 .193 .496*

Sig. (2-tailed) .246 .001 .005 .013 .014 .121 .101 .378 .016

N 23 23 23 23 22 23 21 23 23

57

Correlations

Number of provinces

Number of locations x-coordinates y-coordinates

Annual windspeeds PopDensMunicipality2013 Urbanization levels

Founding year Pearson Correlation -.378 -.593** .374 -.033 -.652** .840** -.677**

Sig. (2-tailed) .075 .003 .079 .881 .001 .000 .000

N 23 23 23 23 23 23 23

Members Pearson Correlation .568** .331 -.431* -.090 -.228 .235 -.254

Sig. (2-tailed) .005 .123 .040 .685 .295 .281 .243

N 23 23 23 23 23 23 23

Production capacities Pearson Correlation .166 .770** -.742** -.444* .283 -.423* .345

Sig. (2-tailed) .449 .000 .000 .034 .190 .044 .107

N 23 23 23 23 23 23 23

Turbines Pearson Correlation .183 .850** -.705** -.340 .238 -.418* .328

Sig. (2-tailed) .404 .000 .000 .113 .275 .047 .127

N 23 23 23 23 23 23 23

Staff Pearson Correlation .037 -.006 -.322 -.064 -.563** .588** -.540**

Sig. (2-tailed) .869 .978 .144 .777 .006 .004 .010

N 22 22 22 22 22 22 22

Registration fee Pearson Correlation -.013 -.174 -.133 .117 -.614** .709** -.620**

Sig. (2-tailed) .954 .426 .546 .595 .002 .000 .002

N 23 23 23 23 23 23 23

Interest rate Pearson Correlation .304 .576** -.343 .120 .750** -.817** .657**

58

Sig. (2-tailed) .180 .006 .128 .604 .000 .000 .001

N 21 21 21 21 21 21 21

Number related markets Pearson Correlation .383 .670** -.595** -.365 .117 -.397 .235

Sig. (2-tailed) .072 .000 .003 .087 .595 .061 .280

N 23 23 23 23 23 23 23

Number of municipalities Pearson Correlation .552** .962** -.622** -.219 .168 -.421* .252

Sig. (2-tailed) .006 .000 .002 .316 .444 .045 .245

N 23 23 23 23 23 23 23

Number of provinces Pearson Correlation 1 .465* -.353 .080 .268 -.328 .221

Sig. (2-tailed) .025 .099 .716 .217 .127 .311

N 23 23 23 23 23 23 23

Number of locations Pearson Correlation .465* 1 -.659** -.225 .261 -.505* .331

Sig. (2-tailed) .025 .001 .302 .228 .014 .123

N 23 23 23 23 23 23 23

x-coordinates Pearson Correlation -.353 -.659** 1 -.094 -.089 .111 .000

Sig. (2-tailed) .099 .001 .669 .686 .614 .999

N 23 23 23 23 23 23 23

y-coordinates Pearson Correlation .080 -.225 -.094 1 -.143 .396 -.499*

Sig. (2-tailed) .716 .302 .669 .514 .062 .015

N 23 23 23 23 23 23 23

Annual windspeeds Pearson Correlation .268 .261 -.089 -.143 1 -.799** .806**

59

Sig. (2-tailed) .217 .228 .686 .514 .000 .000

N 23 23 23 23 23 23 23

PopdensMunFoundYear Pearson Correlation -.319 -.497* .110 .383 -.783** .998** -.911**

Sig. (2-tailed) .137 .016 .616 .071 .000 .000 .000

N 23 23 23 23 23 23 23

PopDensMunicipality2013 Pearson Correlation -.328 -.505* .111 .396 -.799** 1 -.915**

Sig. (2-tailed) .127 .014 .614 .062 .000 .000

N 23 23 23 23 23 23 23

Urbanization levels Pearson Correlation .221 .331 .000 -.499* .806** -.915** 1

Sig. (2-tailed) .311 .123 .999 .015 .000 .000

N 23 23 23 23 23 23 23

PopdensProvFoundYear Pearson Correlation .297 -.384 -.178 .569** -.059 .438* -.361

Sig. (2-tailed) .169 .070 .417 .005 .788 .037 .090

N 23 23 23 23 23 23 23

PopdensProvince2013 Pearson Correlation .405 -.280 -.223 .589** .139 .195 -.159

Sig. (2-tailed) .055 .196 .306 .003 .526 .373 .470

N 23 23 23 23 23 23 23

MaxDistOrigin Pearson Correlation .047 -.171 .065 .031 -.477* .604** -.585**

Sig. (2-tailed) .849 .485 .791 .899 .039 .006 .009

N 19 19 19 19 19 19 19

AvDistOrigin Pearson Correlation -.059 -.278 .129 .078 -.518* .656** -.619**

60

Sig. (2-tailed) .812 .250 .599 .751 .023 .002 .005

N 19 19 19 19 19 19 19

LogProdCapacity Pearson Correlation .501* .533** -.687** .229 .115 -.078 .021

Sig. (2-tailed) .015 .009 .000 .294 .603 .723 .923

N 23 23 23 23 23 23 23

61

Correlations

PopdensProvFoundYear PopdensProvince2013 MaxDistOrigin AvDistOrigin LogProdCapacity

Founding year Pearson Correlation .234 -.045 .632** .682** -.252

Sig. (2-tailed) .283 .839 .004 .001 .246

N 23 23 19 19 23

Members Pearson Correlation .359 .258 .740** .669** .647**

Sig. (2-tailed) .093 .235 .000 .002 .001

N 23 23 19 19 23

Production capacities Pearson Correlation -.280 -.226 -.097 -.159 .569**

Sig. (2-tailed) .195 .301 .692 .515 .005

N 23 23 19 19 23

Turbines Pearson Correlation -.359 -.271 -.190 -.268 .512*

Sig. (2-tailed) .092 .210 .436 .267 .013

N 23 23 19 19 23

Staff Pearson Correlation .294 .069 .601** .619** .515*

Sig. (2-tailed) .185 .760 .007 .005 .014

N 22 22 19 19 22

Registration fee Pearson Correlation .347 .120 .859** .886** .332

Sig. (2-tailed) .105 .586 .000 .000 .121

N 23 23 19 19 23

Interest rate Pearson Correlation -.182 .069 -.630** -.668** .367

62

Sig. (2-tailed) .429 .766 .007 .003 .101

N 21 21 17 17 21

Number related markets Pearson Correlation -.301 -.240 -.118 -.191 .193

Sig. (2-tailed) .163 .270 .630 .434 .378

N 23 23 19 19 23

Number of municipalities Pearson Correlation -.380 -.305 -.073 -.186 .496*

Sig. (2-tailed) .073 .157 .767 .446 .016

N 23 23 19 19 23

Number of provinces Pearson Correlation .297 .405 .047 -.059 .501*

Sig. (2-tailed) .169 .055 .849 .812 .015

N 23 23 19 19 23

Number of locations Pearson Correlation -.384 -.280 -.171 -.278 .533**

Sig. (2-tailed) .070 .196 .485 .250 .009

N 23 23 19 19 23

x-coordinates Pearson Correlation -.178 -.223 .065 .129 -.687**

Sig. (2-tailed) .417 .306 .791 .599 .000

N 23 23 19 19 23

y-coordinates Pearson Correlation .569** .589** .031 .078 .229

Sig. (2-tailed) .005 .003 .899 .751 .294

N 23 23 19 19 23

Annual windspeeds Pearson Correlation -.059 .139 -.477* -.518* .115

63

Sig. (2-tailed) .788 .526 .039 .023 .603

N 23 23 19 19 23

PopdensMunFoundYear Pearson Correlation .431* .183 .613** .664** -.062

Sig. (2-tailed) .040 .404 .005 .002 .779

N 23 23 19 19 23

PopDensMunicipality2013 Pearson Correlation .438* .195 .604** .656** -.078

Sig. (2-tailed) .037 .373 .006 .002 .723

N 23 23 19 19 23

Urbanization levels Pearson Correlation -.361 -.159 -.585** -.619** .021

Sig. (2-tailed) .090 .470 .009 .005 .923

N 23 23 19 19 23

PopdensProvFoundYear Pearson Correlation 1 .957** .314 .338 .288

Sig. (2-tailed) .000 .191 .157 .183

N 23 23 19 19 23

PopdensProvince2013 Pearson Correlation .957** 1 .090 .104 .313

Sig. (2-tailed) .000 .713 .671 .146

N 23 23 19 19 23

MaxDistOrigin Pearson Correlation .314 .090 1 .990** .247

Sig. (2-tailed) .191 .713 .000 .307

N 19 19 19 19 19

AvDistOrigin Pearson Correlation .338 .104 .990** 1 .194

64

Sig. (2-tailed) .157 .671 .000 .426

N 19 19 19 19 19

LogProdCapacity Pearson Correlation .288 .313 .247 .194 1

Sig. (2-tailed) .183 .146 .307 .426

N 23 23 19 19 23

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

ThesisBWVolger1734717

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