"First come, first served" or "the more, the merrier"?
Organizational dynamics of citizen-led solar initiatives
and the presence of photovoltaic installations in
Germany

Lukas Mueller, Timothy Peter Marcroft, Constantin von Beck, Jan Pedro

Zeiss, Valeria Jana Schwanitz, August Wierling, Lars Holstenkamp

► To cite this version:

Lukas Mueller, Timothy Peter Marcroft, Constantin von Beck, Jan Pedro Zeiss, Valeria Jana Schwanitz, et al.. "First come, first served" or "the more, the merrier"? Organizational dynamics of citizen-led solar initiatives and the presence of photovoltaic installations in Germany. Journal of Cleaner Production, 2024, 449, pp.141861. 10.1016/j.jclepro.2024.141861. hal-05123966

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# "First come, first served" or "the more, the merrier"? Organizational dynamics of citizen-led solar initiatives and the presence of photovoltaic installations in Germany

Lukas Mueller<sup>a, b, *</sup>, Timothy Peter Marcroft<sup>b</sup>, Constantin von Beck<sup>b</sup>, Jan Pedro Zeiss<sup>b</sup>, Valeria Jana Schwanitz<sup>b</sup>, August Wierling<sup>b</sup>, Lars Holstenkamp<sup>c</sup>

## ARTICLE INFO

Handling Editor: Kathleen Aviso

## ABSTRACT

This paper examines the emergence of 1,072 solar initiatives (268 associations and 804 cooperatives operating photovoltaic systems) from 2006 to 2018 in Germany. The study draws on organizational ecology theory to identify factors that influence the likelihood of establishing a solar initiative by investigating the impacts of two opposing effects: legitimation effects and competition effects. We show that the density of medium-scale photovoltaic installations in a district negatively affects the likelihood of establishing a solar initiative, suggesting that competitive dynamics prevail between different market entrants, dampening the emergence of solar initiatives. By contrast, we find that the founding of a solar initiative in a district has a positive effect on the probability of founding other solar initiatives in the same district, confirming legitimacy effects. Further, we show that solar initiatives are more likely to be founded in rural areas, especially in densifying rural areas.

## 1. Introduction

The transition to renewable energy is a growing global phenomenon, with citizens playing a pivotal role in advocating for and driving sustainable energy solutions. Throughout Europe, individuals and communities are taking action to promote and support renewable energy projects, from individuals installing residential photovoltaic systems to groups collectively starting citizen-led organizations. The recently compiled ENBP database covering collective action in the energy transition in 30 European countries finds over two million people involved in 10,540 citizen-led initiatives engaging in 22,830 projects representing between 6.2 and 11.3 billion euros of investment in 7.2–9.9 GW of renewable energy capacity (Schwanitz et al., 2023).

Germany, in particular, has seen a significant rise in citizen-led initiatives, with numerous organizations engaging especially in solar photovoltaic technology. As of 2020, there are 5,015 identified citizen initiatives representing nearly 400,000 members, typically organized under one of two legal forms: cooperatives (eingetragene Genossenschaften, eG) or associations (eingetragene Vereine, e.V.) (Wierling

et al., 2023). Cooperatives are commercial societies founded to promote the interests of their members, who manage the organization democratically and are in principle aligned with the International Cooperative Alliance's seven cooperative principles (ICA, 1995), i.e., voluntary and open membership, democratic member control, member economic participation, autonomy and independence, education, training, and information, cooperation among cooperatives, and concern for the community. Associations are similar in that they are organized democratically and exist to represent their members' interests but are disallowed from engaging in commercial business operations and are thus generally engaged in activities to benefit their members, as with sports clubs or professional associations, or society more broadly, such as with charitable associations. A significant proportion of these initiatives invest in and operate photovoltaic installations, as these are comparatively simple and granular technologies with a low barrier to adoption (Wilson et al., 2020). The emergence of both citizen energy initiatives (frequently in the form of energy cooperatives) and residential photovoltaic adoption in Germany have been subject to empirical study before, and our analysis is closely aligned with the studies conducted by

<footnote><sup>a</sup> Department of Business Administration, Economics and Law, Technical University of Darmstadt, 64289, Darmstadt, Germany</footnote>

<footnote><sup>b</sup> Department of Environmental Sciences, Western Norway University of Applied Sciences, Sogndal, 113-8656, Norway</footnote>

<footnote><sup>c</sup> Institute of Sustainability Governance, Leuphana University Lueneburg, 21335, Lueneburg, Germany</footnote>

<footnote>* Corresponding author. Department of Business Administration, Economics and Law, Technical University of Darmstadt, 64289, Darmstadt, Germany. E-mail address: lukas.mueller2@tu-darmstadt.de (L. Mueller).</footnote>
Punt et al. (2022) and Liu and Guenther (2022). While these studies primarily investigate sociological factors and institutional relatedness, this paper focuses on technological relatedness and peer effects as potential determinants in the emergence of such organizations.

Studies examining the drivers of residential photovoltaic adoption show that environmental attitudes have little influence, whereas socio-economic factors and neighborhood (peer) effects emerge as more important determinants in Germany (Schulte et al., 2022; Alipour et al., 2020; Karakaya and Sriwannawit, 2015; Kastner and Stern, 2015). These findings are supported by Schaffer and Brun (2015) and Rode and Weber (2016), who emphasize the importance of imitation and neighborhood effects for adopting photovoltaics at the local level. Specifically, Rode and Weber (2016) note that “observing a photovoltaic system at work and talking about it with a trusted person can increase the likelihood of a [residential] PV installation”; or, as in our case, the establishment of a citizen-led initiative that operates photovoltaic installations, organized as either a cooperative or an association. For brevity, we refer to them hereafter as solar initiatives. We hypothesize that with enough role models and thus sufficient technical and financial experience among the population, citizens will gain confidence in the business model and invest their own money. As the administrative structure of feed-in tariffs in Germany does not differentiate between a household installing solar panels on their rooftop or a citizen initiative doing so on a municipal building, the people making up the initiative may thus have relevant previous experience from their own individual solar projects. Peer effects have been widely documented in previous residential photovoltaic literature, particularly passive peer effects, i.e., observing neighbors’ photovoltaic installations (Palm, 2017). Drawing from these insights, this study intends to answer the following research question:

*Which peer effects dominate the founding rate of solar initiatives in Germany?*

For this purpose, we aim to test two hypotheses, as well as their antitheses. First, we test whether there is a positive or negative relationship between the existence of solar initiatives in a district and the emergence of new solar initiatives, commonly referred to as institutional relatedness (Punt et al., 2022). Second, we test whether there is a positive or negative relationship between the density of medium-scale solar photovoltaic installations and the emergence of solar initiatives in the same district, a proxy for technological relatedness (see, e.g., Neffke et al., 2011; Rigby, 2013).

Through the lens of organizational ecology theory, we investigate statistical relationships involving (I) the density of existing solar initiatives and (II) the density of medium-scale photovoltaic systems and the probability of establishing a solar initiative in a given district. We define medium-scale photovoltaic systems as rooftop systems with a capacity of 20-100kWp. We document both a significant positive effect of the density of existing solar initiatives, which constitute legitimacy effects, on the formation rate of new solar initiatives and competition effects as the density of such initiatives increases, consistent with organizational ecology theory. The effects persist on the local as well as the national level. However, the density of existing medium-scale photovoltaic systems significantly negatively affects the founding rate of solar initiatives in the same district. The effect is robust to the inclusion of state-fixed effects and the exclusion of associations. That is, solar initiatives emerge where fewer medium-scale photovoltaic systems exist.

Our contribution to literature is twofold. First, we analyze the emergence of solar initiatives, considering the possible competitive dynamics within the overall population of organizational species operating medium-scale photovoltaic systems. Second, to the best of our knowledge, this is the first study to examine the emergence of citizen-led (solar) initiatives in Germany empirically, including the two common legal forms, associations and cooperatives. In this way, we enrich the burgeoning literature that has focused thus far primarily on cooperatives.

The remainder of the paper is structured as follows. Section 2 provides background literature and hypotheses. Section 3 describes the data and methodology. Section 4 presents the results. Section 5 discusses and concludes.

## 2. Literature

Organizational ecology theory offers a valuable framework for investigating the emergence of solar initiatives within the broader population of organizations involved in photovoltaic system operations (e.g., Punt et al., 2021; Liu and Guenther, 2022). Applying Hannan and Freeman (1977) definition, populations are collections of organizations sharing similar forms and characteristics within a specific setting. A population of organizations can be subdivided into different organizational species or organizational forms (see Bertoni et al., 2017; Ivery, 2007, respectively). These subsets are characterized by their unique set of competencies and configurations, which create a competitive edge in the occupied niche and guarantee that they can outcompete other actors (Hannan and Freeman, 1977). Hence, we may posit that solar initiatives and other organizations, such as for-profit companies operating medium-scale photovoltaic systems, represent different organizational species and are likely to occupy distinct niches. Private for-profit investors, i.e., companies, usually seek to maximize the economic return on their investments, which is why their niche may prioritize investments in areas with favorable geophysical conditions for the operation of photovoltaic plants.

Conversely, citizen-led initiatives and cooperatives are known to engage in economic activities without focusing on economic gains as their sole or primary goal (e.g., Yildiz et al., 2015). The emergence of different organizational species in their respective niches is partly governed by the countervailing interaction between legitimacy and competition effects as functions of market actor density. Our study investigates both the legitimation and competition effects within the respective species (Hypothesis H1) and within the overall population of all species operating photovoltaic systems, e.g., private investors, corporates, and individual citizens (Hypothesis H2).

### 2.1. Legitimation effect & relatedness

According to organizational ecological theory, the legitimacy of emerging industries is a key factor for the success of organizations and can be grouped into the categories of cognitive and socio-political legitimation (Aldrich and Fiol, 1994). Cognitive legitimacy refers to the knowledge about the industry, organizational forms, and business models among actors, whereas socio-political legitimation can be achieved through support by public leaders and compliance with laws and norms. Aldrich and Fiol (1994) relate a higher probability of replicating functioning organizational models to increased cognitive legitimacy. In the context of this investigation, we focus primarily on cognitive legitimacy effects. However, Punt et al. (2022) conclude that the most important dimensions of legitimation differ in spatial scale. On a local level, the socio-political legitimacy effects are interpreted as guiding, underlining the importance of support from local public actors such as municipalities, whereas on the national level, cognitive legitimacy fosters cooperative development (Punt et al., 2022).

Closely related to the cognitive legitimacy concept is the principle of relatedness, which builds on the same notion of familiarity to explain the emergence of industries (see Hidalgo et al., 2018). The principle can be divided into two branches, namely technological and institutional relatedness. Technological relatedness can be described as the shared technological knowledge base between organizations. There is an important distinction here between a basic technical understanding of a concept necessary for actors to learn from one another and specific knowledge for day-to-day operations, which is not a prerequisite for technological relatedness (Petruzzelli, 2011). Institutional relatedness,
on the other hand, is defined as “the degree of informal embeddedness” (Peng et al., 2005) within the landscape of dominant institutions, which can provide resources and legitimacy to related organizations (Peng et al., 2005). Punt et al. (2022) explore the effects of “institutional relatedness” through the lens of organizational ecology theory and argue that renewable energy cooperatives can leverage organizational knowledge and legitimacy acquired by cooperatives in other industries in the same district. Drawing from a sample of 993 German energy cooperatives founded between 2006 and 2016, the study finds that the presence of cooperatives in other sectors is positively related to the founding rate of energy cooperatives. The results are supported by the survey-based findings of Fischer et al. (2021), who observe that the lack of awareness of energy cooperatives among non-members is a strong limiting factor for expanding civic engagement, which points to the potential of information campaigns.

Ahlemeyer et al. (2022) review the emergence of solar initiatives from the perspective of technological relatedness. They detail the success factors for energy cooperatives based on a literature review and interviews, finding that both committed individuals with sufficient technical skills and the existence of low-complexity business models are key.

Moving from citizen energy initiatives to the residential sector, several meta-analyses have examined the factors that motivate individuals to install residential rooftop photovoltaic systems (Schulte et al., 2022; Alipour et al., 2020; Karakaya and Sriwannawit, 2015; Kastner and Stern, 2015) and underline the importance of legitimation effects.

Karakaya and Sriwannawit (2015) and Kastner and Stern (2015) report that subjective investment-related knowledge and energy-related investment decisions are positively associated with adoption, while environmental attitudes and technical affinity tend to be less important factors. Schulte et al. (2022) find that positive personal benefits are a strong motivator for adoption. Similarly, Alipour et al. (2020) identify financial incentives, knowledge about photovoltaic systems, media, and neighbors as important determinants of adoption. In addition, Schaffer and Brun (2015) find that socioeconomic factors are the most important determinants of residential photovoltaic adoption in Germany. Neighborhood effects also play an important role, whereas they confirm that households' ecological attitudes have little influence.

Rode and Weber (2016) investigate the spatiotemporal diffusion of residential photovoltaic systems in Germany and find that imitative adoption behavior is highly localized and an important determinant of diffusion. The authors argue that eye-catching and attention-grabbing start-up installations could trigger a diffusion process at the local level.

Investigating peer effects for Swedish photovoltaic adoption qualitatively, Palm (2017) differentiates between active and passive peer effects. Active peer effects are direct interactions between peers in existing social networks (e.g., colleagues, relatives) helping to overcome doubts about technological complexity in a trustful environment. The study concludes that these active peer interactions are more important than passive effects, such as simply noticing photovoltaic installations in the neighborhood without active interaction with the owner. Both active and passive peer effects can be seen as vectors for the diffusion of legitimation effects at the local level.

Based on the assumption that existing photovoltaic projects tend to increase the cognitive legitimacy of solar initiatives and the argument of technological relatedness, we formulate our first hypotheses as follows:

**H1a.** The emergence of solar initiatives in a district is positively autocorrelated with the density of existing solar initiatives in the same district.

**H2a.** The emergence of solar initiatives in a district is positively related to the density of medium-scale photovoltaic plants in the same district.

## 2.2. Competition effects

In economics, competition is the necessary result of scarce resources. In the case of citizen engagement in photovoltaics, scarcity can be experienced, e.g., in search of members or in the availability of suitable rooftop spaces for installations (see Wierling et al., 2022). The competition over spatial resources may not be limited to other citizen initiatives but could include households and commercial market actors. Interestingly, Punt et al. (2022) report a negative correlation between solar irradiance in a given region and the density of energy cooperatives, arguing that initiatives are left with those sites that are not economically viable for other market participants but which the initiatives can pursue as their objectives are not limited to profit but include values such as contributing to the ecological transition and local development (Bauwens, 2016). Solar initiatives may thus occupy niches that are not otherwise occupied by other organizational species, which implies lower levels of competition for spatial resources. However, other studies report competitive effects between different species that dampen the emergence of new organizations. Liu and Guenther (2022) analyzed an early sample of 253 energy cooperatives founded between 2003 and 2010. They find that the complex interplay of legitimacy and competition governs the rise of alternative actors. Furthermore, their results suggest that energy cooperatives are more likely to be formed where incumbent utilities have higher average age and greater size diversity, as both the institutional inertia that comes with age and the competition-damping effects of incumbent size diversity make them less able to respond to new and shifting market demands for renewable energy.

Based on the argument of limited resources and spatial competition, we postulate our second set of hypotheses as the antithesis of our initial hypotheses:

**H1b.** The emergence of solar initiatives in a district is negatively autocorrelated with the density of existing solar initiatives in the same district.

**H2b.** The emergence of solar initiatives in a district is negatively related to the density of medium-scale photovoltaic plants in the same district.

# 3. Data and methodology

## 3.1. ENBP data

Data on citizen-led initiatives is obtained from the ENBP database (Wierling et al., 2023). The database includes 5,015 German citizen-led organizations engaging in the energy transition. We focus on those organizations that operate solar photovoltaic systems for electricity generation, founded between 2006 and 2018. Restricting the sample to organizations founded within the defined observation period yields 1, 390 initiatives. Filtering for initiatives that operate solar photovoltaic systems restricts the sample to 1,078 initiatives. Four and two observations were excluded due to missing founding dates and address information, respectively. Hence, we count 1,072 solar initiatives in our final sample, with 268 associations and 804 cooperatives. To put the numbers in perspective, the sample of Punt et al. (2022) includes 993 German energy cooperatives founded between 2006 and 2016, of which 184 have no clear specification of the energy type. The remaining cooperatives focus on wind power, solar PV, and bio-energy. Yildiz et al. (2015) list 635 cooperatives generating energy as of yearend 2013. Liu and Guenther (2022) examine 253 energy cooperatives founded between 2003 and 2010.

To obtain the founding rate of solar initiatives, we group all entity observations at the NUTS-3 level and derive the quarterly number of foundations for each district. Table 1 displays the final number of spells in our sample. Approximately 95% of all 20,904 district-quarter observations are zero; that is, no foundation is recorded in that district for the respective quarter. Furthermore, approximately 91% of the remaining
<caption>**Table 1**</caption>

This table presents data on the quarterly count and percentage shares of solar initiatives foundations.

<table><thead><tr><th>Foundations of SI per quarter.</th><th>0</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><th>Frequency</th><td>19,923</td><td>896</td><td>79</td><td>5</td><td>0</td><td>1</td></tr><tr><th>% of total spells</th><td>95.31%</td><td>4.29%</td><td>0.38%</td><td>0.02%</td><td>0.00%</td><td>0.00%</td></tr><tr><th>% of total spells (non-zero)</th><td></td><td>91.34%</td><td>8.05%</td><td>0.51%</td><td>0.00%</td><td>0.10%</td></tr><tr><th>Total spells</th><td>20,904</td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>thereof non-zero</th><td>981</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

non-zero observations exhibit one foundation, whereas only 8.05% reveal two foundations per district and quarter. Hence, the information loss from constructing a binary dummy variable to conduct logistic regression is negligible.

## 3.2. Empirical strategy

Our empirical analysis follows a two-step procedure. We begin our analysis by examining the spatial autocorrelation of both medium-scale photovoltaic systems and solar initiatives using Moran's I. Moran's I is a statistic used to measure spatial autocorrelation, that is, the similarity of values for a variable among observations that are geographically close to one another. A positive value of Moran's I indicates that similar values tend to be found near one another, while a negative value indicates that dissimilar values tend to be found near one another (Fornahl and Brenner, 2009). The range of possible values is limited to [-1, 1]. Formally, Moran's I can be expressed as follows:

$$I = \frac{N * \sum_{i=1}^{N} \sum_{j=1}^{N} [w_{ij} * (y_i - \bar{y}) * (y_j - \bar{y})]}{\sum_{i=1}^{N} \sum_{j=1}^{N} [w_{ij} * \sum_{i=1}^{N} (y_i - \bar{y})^2]}$$

where N denotes the number of districts in Germany, wij are the elements of a matrix of spatial weights with zeroes on the diagonal and yi denotes the variable of interest; the number of medium-size photovoltaic systems or the number of solar initiatives per district i.

After assessing the spatial autocorrelation of both variables in isolation, we turn to the main econometric model analyzing the emergence of solar initiatives in Germany, grounded on organizational ecology theory following, e.g., Punt et al. (2022), Liu and Guenther (2022), Hannan and Freeman (1977), and Bigelow et al. (1997).

The formal expression of the empirical model reads as follows:

$$\lambda_{it} = \exp \left( a_0 + \beta_1 n_{it} + \beta_2 n_{it}^2 + \beta_3 (N_t - n_{it}) + \beta_4 (N_t - n_{it})^2 + \gamma' \mathbf{x} + \delta_k + \varepsilon_{it} \right)$$

where λit denotes the binary dummy variable for the founding rate in district i at year t, nit denotes the density of solar initiatives in the respective district i at year t, Nt denotes the density of solar initiatives at the national level, and x denotes a vector of additional explanatory variables conditional on the respective model. NUTS-1 (state) fixed effects are denoted by δk and εit denotes the remaining error term. Since Germany is divided into 16 federal states (NUTS-1 level) we use dummy variables (fixed-effects) for the individual federal states to examine whether the results are driven by differences between the federal states or whether the effects remain evident when analyzing within-state effects. Descriptive statistics and correlations are provided in Table 2.

<caption>**Table 2**</caption>

This table presents descriptive statistics, variable descriptions and scales, and a correlation matrix.

<table><caption>Panel A: Descriptive Statistics</caption><thead><tr><th>Variable</th><th>Mean</th><th>Median</th><th>SD</th><th>Description</th></tr></thead><tbody><tr><td>n</td><td>1.50</td><td>1.00</td><td>2.28</td><td>Cumulative sum of founded solar initiatives (NUTS-3 (district) level)</td></tr><tr><td>n<sup>2</sup></td><td>7.43</td><td>1.00</td><td>25.25</td><td>Squared term</td></tr><tr><td>N</td><td>599.48</td><td>664.50</td><td>383.93</td><td>Cumulative sum of founded solar initiatives (national level)</td></tr><tr><td>N<sup>2</sup></td><td>5,067.71</td><td>4,416.71</td><td>4,383.96</td><td>Squared term (10<sup>-2</sup>)</td></tr><tr><td>nPV</td><td>0.44</td><td>0.19</td><td>0.67</td><td>Cumulative sum of registered medium-scale photovoltaic systems (20-100kWp; NUTS-3 (district) level) (10<sup>-3</sup>)</td></tr><tr><td>nPV<sup>2</sup></td><td>0.65</td><td>0.03</td><td>2.08</td><td>Squared term (10<sup>-3</sup>)</td></tr><tr><td>CF Solar</td><td>12.80</td><td>12.90</td><td>0.57</td><td>Average capacity factor solar power plants in %</td></tr><tr><td>East</td><td>0.19</td><td>0.00</td><td>0.39</td><td>Binary dummy indicating NUTS-1 districts (states) of former Eastern Germany (w/o Berlin)</td></tr><tr><td>Green</td><td>0.08</td><td>0.08</td><td>0.03</td><td>Share of votes for the Green party</td></tr><tr><td>Pop dens</td><td>0.52</td><td>0.20</td><td>0.68</td><td>Population density in thousands per square kilometer</td></tr><tr><td>GDP</td><td>3.30</td><td>2.94</td><td>1.50</td><td>GDP per capita in ten-thousands (10<sup>-4</sup>)</td></tr><tr><td>Area</td><td>0.89</td><td>0.80</td><td>0.72</td><td>Area size in thousand square kilometers</td></tr><tr><td>SST-1</td><td>0.39</td><td>0.00</td><td>0.49</td><td>Binary dummy indicating metropolitan areas</td></tr><tr><td>SST-2</td><td>0.10</td><td>0.00</td><td>0.30</td><td>Binary dummy indicating urban areas</td></tr><tr><td>SST-3</td><td>0.25</td><td>0.00</td><td>0.43</td><td>Binary dummy indicating densified rural areas</td></tr><tr><td>SST-4</td><td>0.27</td><td>0.00</td><td>0.44</td><td>Binary dummy indicating sparsely populated rural areas</td></tr></tbody></table><table><caption>Panel B: Correlation Matrix</caption><thead><tr><th>Variable</th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th><th>16</th></tr></thead><tbody><tr><td>n</td><td>1</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>n<sup>2</sup></td><td>2</td><td>0.87</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N</td><td>3</td><td>0.42</td><td>0.23</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N<sup>2</sup></td><td>4</td><td>0.41</td><td>0.23</td><td>0.98</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>nPV</td><td>5</td><td>0.57</td><td>0.43</td><td>0.44</td><td>0.43</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>nPV<sup>2</sup></td><td>6</td><td>0.45</td><td>0.40</td><td>0.26</td><td>0.26</td><td>0.91</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CF Solar</td><td>7</td><td>0.01</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.14</td><td>0.08</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>East</td><td>8</td><td>-0.15</td><td>-0.11</td><td>0.00</td><td>0.00</td><td>-0.20</td><td>-0.14</td><td>-0.18</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Green</td><td>9</td><td>0.13</td><td>0.08</td><td>0.00</td><td>0.00</td><td>-0.05</td><td>-0.05</td><td>0.19</td><td>-0.51</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pop dens</td><td>10</td><td>-0.07</td><td>-0.05</td><td>0.01</td><td>0.01</td><td>-0.26</td><td>-0.16</td><td>-0.05</td><td>-0.17</td><td>0.48</td><td>1.00</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GDP</td><td>11</td><td>0.07</td><td>0.04</td><td>0.22</td><td>0.22</td><td>0.02</td><td>0.03</td><td>0.21</td><td>-0.27</td><td>0.46</td><td>0.50</td><td>1.00</td><td></td><td></td><td></td><td></td></tr><tr><td>Area</td><td>12</td><td>0.20</td><td>0.17</td><td>0.00</td><td>0.00</td><td>0.29</td><td>0.22</td><td>-0.22</td><td>0.35</td><td>-0.39</td><td>-0.53</td><td>-0.40</td><td>1.00</td><td></td><td></td><td></td></tr><tr><td>SST-1</td><td>13</td><td>-0.13</td><td>-0.11</td><td>0.00</td><td>0.00</td><td>-0.31</td><td>-0.21</td><td>0.00</td><td>-0.12</td><td>0.42</td><td>0.70</td><td>0.49</td><td>-0.64</td><td>1.00</td><td></td><td></td></tr><tr><td>SST-2</td><td>14</td><td>0.07</td><td>0.04</td><td>0.00</td><td>0.00</td><td>0.02</td><td>-0.02</td><td>0.14</td><td>-0.12</td><td>0.04</td><td>-0.15</td><td>-0.11</td><td>0.00</td><td>-0.27</td><td>1.00</td><td></td></tr><tr><td>SST-3</td><td>15</td><td>0.07</td><td>0.07</td><td>0.00</td><td>0.00</td><td>0.22</td><td>0.15</td><td>0.14</td><td>-0.10</td><td>-0.11</td><td>-0.31</td><td>-0.18</td><td>0.17</td><td>-0.45</td><td>-0.19</td><td>1.00</td></tr><tr><td>SST-4</td><td>16</td><td>0.03</td><td>0.02</td><td>0.00</td><td>0.00</td><td>0.11</td><td>0.10</td><td>-0.23</td><td>0.31</td><td>-0.38</td><td>-0.37</td><td>-0.29</td><td>0.54</td><td>-0.48</td><td>-0.20</td><td>-0.34</td><td>1.00</td></tr></tbody></table>
### 3.3. Variable of interest

To test our hypotheses on legitimacy or competitive effects of the density of photovoltaic systems on the emergence of solar initiatives, we collect data on photovoltaic systems from Marktstammdatenregister (2021) and derive the aggregated number of plants installed per quarter at the NUTS-3 level (district level, German: Kreise).

We derive data from 1,602,044 unique photovoltaic systems with zip code information installed from January 1, 2005, to December 31, 2018. We follow the classification of Fraunhofer ISE (Wirth, 2021), adapted by Wierling et al. (2021), deriving the following categories:

* 1–10 kWp: small-scale residential rooftop systems

* 10–100 kWp: medium-scale rooftop systems on commercial and public buildings

* >100–500 kWp: large industrial rooftops

* >500 kWp: field systems

In this study, we focus on medium-scale photovoltaic systems, as the operation of these represents a typical business model for energy cooperatives (Schwanitz et al., 2023a; Wierling et al., 2021, 2022). Specifically, Schwanitz et al. (2023a) note that of all 16,069 production units registered in the ENBP database, photovoltaic systems account for the vast majority (82%). The average (median) capacity of photovoltaic installations is 177 kWp (29 kWp). The majority of systems are thus small- and medium-scale photovoltaic systems, yet the average capacity is skewed by a few large field systems. Similar numbers are reported in Wierling et al. (2021).

Restricting the sample to units with a maximum capacity of 100 kWp yields 1,563,446 units. To put the numbers in perspective: The ENBP database comprises 8,098 units operated by solar initiatives in Germany and installed over the observation period, thereof 7,215 exhibiting a capacity of less than or equal to 100 kWp. Accordingly, units of solar initiatives in our sample account for 0.46% of the total number of installed small- and medium-scale photovoltaic systems in Germany. Schaffer and Brun (2015) note that the majority (71%) of installed photovoltaic systems in Germany are (residential) small-scale photovoltaic systems (up to 16kWp). As the inclusion of residential systems potentially induces noise, we impose a lower threshold of 20 kWp to focus on non-residential units in the main analysis. We analyze the results for alternative thresholds in the respective subsection. We lag the founding rate of photovoltaic plants by one year (four quarters) to avoid reverse causality.

### 3.4. Control variables

Following previous studies, we control for several idiosyncrasies that may influence the establishment of solar initiatives, such as socioeconomic variables and regional geophysical potential. The latter has been implemented in different approaches so far. In contrast to Punt et al. (2022) and Liu and Guenther (2022), which included regional yield potential as a control variable using solar irradiance or sunshine duration, respectively, we use a refined approach that more accurately reflects the effective regional yield potential. We employ the average capacity factors, thereby not only accounting for solar radiation and sunshine duration but also for geophysical and topographic effects. We source data on capacity factors of photovoltaic plants covering hourly records from 2015 to 2021 from Renewables.ninjas (Pfenninger and Staffell, 2016). We thereby follow the recommendation of Moraes et al. (2018), who conclude that the data quality significantly improved from 2015 due to transparency portals and globally increasing electricity capacities. te Heesen et al. (2019) note that at least five years of data must be available to draw conclusions on the effective yield and performance of photovoltaic systems. Accordingly, we calculate the cross-sectional capacity factors for each NUTS-2 region averaged over time. A validation study of the data from Renewables.ninja was

performed by Muñoz Ortiz et al. (2021) for the Nordic countries. Calculated yield output in the Renewable.ninja dataset shows an average annual correlation of 0.844 to measured yield output and a maximum difference in capacity factors of 2.5%. We are therefore confident that the average cross-sectional capacity factors are a more robust representation of districts' effective yield potential than solar irradiation or sunshine hours. Our sample exhibits an average capacity factor for solar power plants in Germany of 12,80%, which may be expressed as a specific yield (equal to the annual number of full load hours) of 1,121.28 kWh/kWp (calculated as 0,128*8760h). Wirth (2021) notes that the expected long-term specific yield over the period 1998–2018 amounts to 1,060kWh/kWp, which translates into a capacity factor of 12.10%. Between 2015 and 2021, we saw several years with exceptionally high solar irradiation levels; the capacity factors thus seem plausible.

Similar to Punt et al. (2022) and Liu and Guenther (2022), we control for the environmental attitude of a district proxied by the share of green votes. Previous studies show that environmental attitudes, in particular, post-materialist concerns (Grant, 2018), are among the strongest determinants of Green Party voting in Germany (Schumacher, 2014) and indeed for the support of parties with green platforms more widely in Europe and around the world (Papp, 2022). We source these data from Schraff et al. (2022).

We further obtain socioeconomic control variables at the NUTS-3 level from Eurostat (Eurostat, 2022), namely, the land area of districts, population, and gross domestic product per capita, similar to Punt et al. (2022). Additionally, we derive data on settlement structure typology from the German Federal Institute for Research on Building, Urban Affairs, and Spatial Development (BBSR, 2022). The BBSR has developed four settlement structure types for Europe-wide comparative analyses of urban and rural regions at the NUTS-3 level, thereby accounting for deficits of classification systems such as Eurostat's urban-rural classification. The settlement structure types are categorized as metropolitan (SST1), urban (SST2), densified rural (SST3), and sparsely populated rural (SST4) areas. The classification of the BBSR settlement structure typology appropriately reflects metropolitan areas but also emphasizes the importance of medium-sized cities and their juxtaposition with large city regions. We provide descriptive sample statistics and a correlation matrix in Table 2.

At this point, we focus on descriptive differences in variables between the different settlement structure types. For this, we provide statistics in Table 3. Panel A shows the percentage distribution of variables by settlement structure type. It can be seen that 51.43% of the population in Germany live in metropolitan areas, while 20.10% and 18.70% live in densified and sparsely populated rural areas, respectively. Most photovoltaic installations in Germany are situated in the

<caption>**Table 3**</caption>

This table presents the percental distribution of variables per SST in Panel A and median values of variables per SST in Panel B. As a readable example, 27.07% of all solar initiatives in our sample are located in SST-1 (metropolitan) areas. The median capacity factor of photovoltaic plants in SST-1 areas is 12.94%.

<table><caption>Panel A: Share per SST in %</caption><thead><tr><th>SST</th><th>N (SI)</th><th>N (PV)</th><th>Population</th><th>Area</th></tr></thead><tbody><tr><td>1</td><td>27.06</td><td>16.66</td><td>51.44</td><td>13.13</td></tr><tr><td>2</td><td>13.67</td><td>11.27</td><td>9.76</td><td>10.63</td></tr><tr><td>3</td><td>30.00</td><td>38.41</td><td>20.10</td><td>30.37</td></tr><tr><td>4</td><td>29.27</td><td>33.66</td><td>18.70</td><td>45.88</td></tr></tbody></table>

<table><caption>Panel B: Median values per SST</caption><thead><tr><th>SST</th><th>CF in %</th><th>Green Votes in %</th><th>GDP</th></tr></thead><tbody><tr><td>1</td><td>12.94</td><td>9.60</td><td>42,500</td></tr><tr><td>2</td><td>13.09</td><td>8.10</td><td>32,000</td></tr><tr><td>3</td><td>13.04</td><td>7.40</td><td>32,900</td></tr><tr><td>4</td><td>12.55</td><td>5.50</td><td>30,200</td></tr></tbody></table>
latter areas, with 38.40% and 33.66%, respectively. Similarly, most solar initiatives were established in these areas, 30.00% and 29.27 %, respectively.

Panel B reports the median statistics of control variables. The results are similar if the mean values are taken. The median GDP is substantially lower in sparsely populated rural areas and largest in metropolitan areas. Interestingly, the median GDP of densified rural areas exceeds that of urban areas. The capacity factors seem substantially lower in sparsely populated rural areas compared to the rest of the sample. This may help explain the findings observed in Punt et al. (2022), i.e., a negative relation between the emergence of solar initiatives and solar irradiance in a district. Additionally, a strong and gradual decline in the share of the vote for the Greens can be observed with increasing rurality.

4. Results

The following chapter presents the results of this study. We begin
with a spatial visualization of the distribution of solar initiatives and
medium-scale PV installations. Thereafter, we present the spatial auto-
correlation results using Moran's I. Finally, we present the results of our
regression models. We present results for similar models based on our
sample of solar initiatives, testing hypotheses H1a and H1b. We then test
our hypotheses H2a and H2b and analyze the relationship between the
prevalence of medium-scale photovoltaic installations and the emer-
gence of solar initiatives.

4.1. Spatial analysis

Fig. 1 shows the cumulative sum of both variables at the end of the sample period (year-end 2018), aggregated at the district level.

At first glance, both figures appear fairly similar. Interestingly, in-
stallations of medium-scale photovoltaic systems seem clustered in
southern districts of Germany, especially Bavaria and Baden-
Wuerttemberg, similar to the findings of Schaffer and Brun (2015).
Moreover, these areas show the highest levels of specific yield in Ger-
many (te Heesen et al., 2019). There is also a dense cluster of
medium-scale photovoltaic systems in North Rhine-Westphalia. At the
same time, we observe a lower density of medium-scale photovoltaic
installations in the new states (i.e., former East Germany) as well as in
Rhineland-Palatinate and Saarland. Similar trends can also be observed
for solar initiatives, however far less pronounced. Statistical evidence of
the spatial distribution is provided in Fig. 2.

The analysis of quarterly Moran's I rejects the null hypothesis that

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Quarterly Moran's I of medium-scale photovoltaic systems (blue squares) and solar initiatives (red crosses) per NUTS-3 district. Bars indicate the respective significance levels. Note that all observations of medium-scale photovoltaic systems are highly significant. Accordingly, the respective P-values are not visible in the graph. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

medium-scale photovoltaic systems are randomly distributed across all districts. We find high and significant levels of spatial autocorrelation for medium-scale photovoltaic systems in all quarters. The spatial autocorrelation of solar initiatives became substantial and statistically significant after the end of 2008. Comparing the results to Punt et al. (2022), this is about the time when German energy cooperatives began to flourish. However, the overall level of spatial-autocorrelation is lower compared to medium-scale photovoltaic systems. This result implies that solar initiatives seem to emerge where other solar initiatives already exist, supporting the arguments of institutional and technological relatedness.

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Spatial distribution of photovoltaic installations and solar initiatives at the end of 2018. The cumulative sum of each variable is aggregated at the NUTS-3 level.</figcaption></figure>
<table><caption>**Table 5**
This table shows the results of the extended logistic regression models. The models 7–9 are based on the full sample whereas the models 10–12 are based on the subsample restricted to cooperatives. Standard errors are clustered at the NUTS-3 (district) level, denoted in parentheses next to the respective coefficients. Statistical significance is denoted as: \*p < 0.05, \*\*p < 0.01, and \*\*\*p < 0.001.</caption><thead><tr><th></th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th></tr></thead><tbody><tr><td>nPV</td><td>-0.3912</td><td>(0.2270)</td><td>-0.6192**</td><td>-0.5999</td><td>-0.6288*</td><td>-0.8481**</td></tr><tr><td>nPV<sup>2</sup></td><td>0.0561</td><td>(0.0611)</td><td>(0.0592)</td><td>(0.2207)</td><td>(0.3254)</td><td>(0.3095)</td></tr><tr><td>Pop. Dens.</td><td>-0.0833</td><td>(0.1107)</td><td>0.0938</td><td>(0.0577)</td><td>0.1205</td><td>0.1664</td></tr><tr><td>GDP</td><td>0.0004</td><td>(0.0367)</td><td>0.0452</td><td>(0.0579)</td><td>0.1288</td><td>0.0146</td></tr><tr><td>Area</td><td>0.1617**</td><td>(0.0577)</td><td>-0.0026</td><td>(0.0387)</td><td>-0.0664</td><td>-0.0304</td></tr><tr><td>n</td><td>0.8421***</td><td>(0.0805)</td><td>0.2829**</td><td>(0.0788)</td><td>0.1663*</td><td>0.3318***</td></tr><tr><td>n<sup>2</sup></td><td>-0.0414***</td><td>(0.0089)</td><td>0.8958***</td><td>(0.1072)</td><td>1.0772***</td><td>1.1291***</td></tr><tr><td>N</td><td>0.0045***</td><td>(0.0005)</td><td>-0.0416***</td><td>(0.0077)</td><td>-0.0639***</td><td>-0.067***</td></tr><tr><td>N<sup>2</sup></td><td>-0.0006***</td><td>(0.0000)</td><td>0.0045***</td><td>(0.0006)</td><td>0.0087***</td><td>0.0008</td></tr><tr><td>N<sup>2</sup></td><td>-0.1009</td><td>(0.1059)</td><td>-0.0006***</td><td>(0.0000)</td><td>-0.0014***</td><td>-0.0014***</td></tr><tr><td>East</td><td>0.1623*</td><td>(0.0709)</td><td>-0.0644</td><td>(0.1570)</td><td>-0.2943*</td><td>-0.0004</td></tr><tr><td>CF Solar</td><td>0.2690</td><td>(1.4000)</td><td>0.1439*</td><td>(0.1408)</td><td>0.2327**</td><td>-0.0004</td></tr><tr><td>Green</td><td></td><td></td><td>0.9868</td><td>(1.348)</td><td>-1.3809</td><td>-0.0004</td></tr><tr><td>SST-2</td><td></td><td></td><td>0.0174</td><td>(0.1286)</td><td></td><td>-0.0004</td></tr><tr><td>SST-3</td><td></td><td></td><td>0.2815**</td><td>(0.0907)</td><td>-0.2658*</td><td>-0.0004</td></tr><tr><td>SST-4</td><td></td><td></td><td>0.1713</td><td>(0.1175)</td><td>0.1979</td><td>-0.0004</td></tr><tr><td>Intercept</td><td>-6.2768***</td><td>(0.8741)</td><td>-6.2422***</td><td>-2.5422</td><td>-7.4634***</td><td>-7.4778***</td></tr><tr><td>NUTS-1 Dummies</td><td>No</td><td></td><td>No</td><td>(2.1438)</td><td>No</td><td>Yes</td></tr><tr><td>loglik</td><td>-3.2563</td><td></td><td>-3.2529</td><td>Yes</td><td>-2.4954</td><td>-2.4923</td></tr><tr><td>AIC</td><td>6.5396</td><td></td><td>6.5378</td><td></td><td>5.0169</td><td>5.0166</td></tr><tr><td>Observations</td><td>20,904</td><td></td><td>20,904</td><td></td><td>20,904</td><td>20,904</td></tr></tbody></table>

rooftop systems, which are usually sized at just under 100 kWp. In this subsection, we investigate whether the founding rate of solar initiatives depends on the number of installations of a certain size present. To this end, we re-estimate Model 7 with minor modifications. First, we standardize all numeric non-binary variables. This allows us to compare the relative importance of a variable at the cost of a less intuitive interpretation of the coefficients. Second, we increase the lower threshold for inclusion of photovoltaic systems value iteratively by 5kWp. We do not impose an upper threshold. The standardized coefficients are plotted in Fig. 3.

The results further support the hypothesis of spatial competition. We find that the founding rate of solar initiatives does not depend significantly on either the number of residential photovoltaic systems (≤10kWp) or the number of large-scale photovoltaic systems (>=100kWp). The coefficient is largest when a threshold of 35kWp is imposed, which is close to the typical capacity of photovoltaic systems installed and operated by solar initiatives (29kWp, see Schwanitz et al. (2023a)). As a final check, we set the lower and upper threshold to 0kWp and 10kWp, respectively, to check if the founding rate of solar initiatives depends significantly on the number of residential photovoltaic systems present. The obtained standardized coefficient of -0.0788 (standard error = 0.2020) is both economically and statistically insignificant. Overall, this exercise reveals that the lack of medium-sized photovoltaic systems emerges as a key determinant of the emergence of solar initiatives, while we find no evidence that the number of residential photovoltaic systems is a decisive factor.

## 5. Discussion and conclusion

This study examined the emergence of citizen-led solar initiatives in Germany from 2006 to 2018, utilizing organizational ecology methodology and spatial analyses using quarterly foundations at the NUTS-3 level. The sample comprises 804 cooperatives and 268 associations

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Standardized regression coefficient of the nPV. This diagram shows the regression coefficients for logistic regressions as shown in Model 8 of Table 5. The lower threshold value regarding the capacity of the photovoltaic systems to be included is iteratively increased by 5kWp. An upper threshold is not imposed. Note that all numerical, non-binary variables have been standardized to a mean of zero and a standard deviation of 1. Triangles denote statistical significance at the 5 % level and dots at the 1 % level.</figcaption></figure>
that operate medium-scale photovoltaic systems in Germany. Consistent with organizational theory and the concept of institutional relatedness, we find legitimacy effects emanating from existing solar initiatives. The spatial autocorrelation of initiatives identified using Moran's I supports Hypothesis H1a beyond the district level. However, the density of medium photovoltaic installations has a negative impact on the likelihood of establishing a solar initiative, thereby supporting Hypothesis H2b. The results do not support the idea of technological relatedness at the district level, possibly due to the dominance of competitive dynamics among actors and organizations operating medium-scale photovoltaic systems.

Our study therefore provides no evidence in favor of the technological relatedness argument. Rather, we find that solar initiatives emerge, where the installation rate of a certain type of photovoltaic systems lags behind, namely medium-scale photovoltaic systems. The effect is robust to alternative thresholds, but we find no evidence that the number of residential photovoltaic installations is related to the probability of foundation. These results indicate that the neighborhood and peer effects reported in the literature on residential photovoltaic systems do not drive the emergence of solar initiatives. Rather, it appears that spatial constraints in terms of available roof space are a major limiting factor for citizen engagement. This is in line with some anecdotal evidence that the authors gained during their work with citizen-led initiatives. Especially in Switzerland, the lack of available rooftops rather than the source of funding has often been claimed to be the major bottleneck in developing new projects. The survey results of Schmid et al. (2020) provide a possible rationale. The authors note that energy cooperatives often collaborate with municipalities, whereby the municipality provides the energy cooperatives with roof space for the installation of photovoltaic systems. This argumentation also supports the finding that solar initiatives are more likely to be established in rather rural areas, as larger municipalities (i.e., urban areas) often have the expertise and resources to install and operate photovoltaic systems themselves and thus generate and retain the associated revenues (Schmid et al., 2020). Alternatively, it may be that some significant proportion of citizens who engage in these initiatives are motivated by the absence rather than the presence of visible signs of the energy transition. Many studies suggest that “environmental values” or “normative motivations” play a significant role in determining the level of engagement in citizen energy initiatives, implying that for these people, there is not as much renewable energy being produced in their area as there should be (Seyfang et al., 2013; Dóci and Vasileiadou, 2015; Bauwens, 2016; Fischer et al., 2021; Radtke et al., 2022).

So, it is possible that neither “first come, first serve” nor “the more, the merrier” are appropriate analogies, but rather some form of “let’s do it ourselves” activism. This thought is consistent with citizens acting out of a sense of need and urgency rather than simply copying what others have already done. Especially if the successful adoption of medium photovoltaic systems is driven by local policies and information campaigns, it is interesting to note that these campaigns do not seem to encourage the emergence of further citizen-led solar initiatives, once a certain density has been reached. In any case, our results provide avenues for further research.

Although we are able to replicate the findings of previous studies partially, we show that these results are sensitive to legal forms. Interestingly, we find no evidence of a significant difference in the emergence of solar initiatives between former Eastern and former Western districts. However, we do find evidence when we restrict the sample to cooperatives and do not control for the type of settlement structure, which is consistent with previous literature (Punt et al., 2022; Liu and Guenther, 2022). The result implies that generalizing results from studying cooperatives as a form of citizen-led organizations must be done with caution. It would seem that, potentially for historical reasons, the name ‘cooperative’ is what presents a challenge in the new states rather than the act of citizens cooperating in service of the energy transition. A detailed discussion of this matter is outside the scope of this

paper. However, for an in-depth review of citizen action in former Eastern Bloc countries, the interested reader is referred to Schwanitz et al. (2022), who report a generally disabling environment for collective renewable energy investments in Poland due to the socialist heritage resulting in fewer collective than individual prosumer actions.

Our study, however, has certain limitations. First, it is based on quantitative analysis and may have missed important qualitative factors that could affect the emergence of citizens' initiatives. However, an in-depth examination of the motivations or experiences of individuals involved in solar initiatives, which could unearth important factors in understanding where and why these initiatives emerge, is outside the scope of this paper. In addition, the study did not examine the role of policy or institutional factors (e.g., government policies or funding programs) in encouraging or discouraging the emergence of solar initiatives.

Because the energy transition in general, and civic engagement in particular, is a rapidly evolving field in which new business models regularly emerge in response to regulatory and market changes, the fact that our period of analysis only extends to 2018 means that future studies will be needed to assess more recent trends. However, our study period covers the heyday of solar initiatives, and extending the period further to 2020 would result in a negligible increase in findings (21) (see also Schwanitz et al. (2023b)). This may change, though, with a new wave of solar initiatives.

Further research is also needed to understand the emergence and impact of a broader range of citizens' initiatives engaged in the energy transition and cleaner energy production, as this contributes to a holistic comprehension of the full landscape of energy policy and the different types of actors involved (Berka and Creamer, 2018; Bielig et al., 2022). Citizen-led initiatives often emerge as grassroots movements in response to local energy challenges and opportunities, with both their actions and organizations taking diverse forms (Bauwens et al., 2022). As such, they embody a wider range of perspectives, motivations, and interests than organizations whose primary activity is the production and sale of renewable energy, as with most of the cooperatives and associations in our sample. Market-oriented actors are often structured around a more specific set of objectives, the operation of a financially viable business, than community-based associations (Bauwens, 2016). Moreover, other citizen-led initiatives may be less formalized and more agile than associations and cooperatives with commercial activities, allowing them to experiment with novel forms of governance, actions, and financing that are better suited to their local context. Finally, these other citizen-led initiatives may also be more inclusive and diverse than cooperatives and similar organizations, as they may draw on a wider range of social networks and engage with a broader range of stakeholders in their efforts to promote the energy transition. Therefore, focusing exclusively on cooperatives may overlook and neglect the idiosyncratic characteristics and dynamics of citizen-led organizations shaping the energy transition. Researchers can augment the findings with an analysis of initiatives that participate in the transition without operating their own facilities and focus on consultation, education, and awareness raising (Wierling et al., 2022).

Moreover, this study focuses on a specific type of clean energy production in isolation, namely solar energy. Our study thus leaves room for analyzing the role of technological relatedness regarding different types of production, e.g. wind power, biomass, and district heating. Some previous literature has argued that technological complexity does not necessarily influence technological relatedness negatively (see Juhász et al., 2021). Citizen-led energy initiatives and the variety of their business models and activities can provide an ideal research object to test and validate theoretical frameworks accounting for spillover effects and path dependencies in production research (e.g. Chatzistamoulou et al., 2022), especially outside the realm of traditional corporate organizations. Furthermore, while this is the first study to link the prevalence of photovoltaic systems to the emergence of solar initiatives, our research opens avenues for future analyses. What are the drivers of
medium-scale photovoltaic adoption, especially medium-scale photo-
voltaic systems? Does the local adoption rate significantly change after
the successful establishment of solar initiatives? We leave those ques-
tions to future research.

Eventually, our work offers clear implications and recommendations for policymakers. Firstly, our findings emphasize the implications of previous studies, i.e. the importance of institutional relatedness. We reiterate the need for policymakers to foster civic awareness and understanding of organizational and entrepreneurial knowledge and the legal structures and requirements to translate their ambition to engage into action. One potentially fruitful approach for maximizing the benefits of institutional relatedness would be policies to support the creation and promote the activities of networks and umbrella organizations. While additional subsidies towards solar initiatives may be impossible under European regulations if they already benefit from state-supported feed-in tariffs, financial support for network organizations should be possible and effective. Secondly, our results suggest that there may be untapped potential for civic engagement in sparsely populated rural areas where real estate for developing larger-scale photovoltaic installations may be more available (assuming that grid congestion is not an issue). While these rural areas will present their own challenges for the organizations of citizen initiatives, providing technical and administrative assistance as well as funding outside training and interventions to bring in expertise perhaps not present in the area could unlock some of the opportunities for local (sustainable) economic development. Indeed, rural development is the primary goal of a number of similar initiatives throughout Europe (see Schwanitz et al., 2023a for examples) and the best-practices from these successful experiments could be transposed to areas of Germany seeking to both revitalize the countryside and participate in the energy transition. Finally, our findings related to the difference in organizational forms between former East and West Germany underline the need for policymakers to remain flexible when crafting policies to support civic engagement in the energy transition; a model that works in one place may not work in another for cultural, historic, or institutional reasons. Piloting this support with regard to desired impacts and outcomes, rather than specific legal forms, will be key.

CRediT authorship contribution statement

**Lukas Mueller:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Timothy Peter Marcroft:** Writing – review & editing, Writing – original draft, Validation, Data curation. **Constantin von Beck:** Writing – review & editing, Validation, Data curation. **Jan Pedro Zeiss:** Writing – review & editing, Validation, Data curation. **Valeria Jana Schwanitz:** Writing – review & editing, Funding acquisition, Conceptualization. **August Wierling:** Writing – review & editing, Funding acquisition, Conceptualization. **Lars Holstenkamp:** Writing – review & editing, Conceptualization.

Declaration of competing interest

None of the authors declares competing interests.

Data availability

The data used is freely available from the sources provided in the
text.

Acknowledgements

This paper is supported by the European Union's Horizon 2020
research and innovation programme under grant agreements No 837722
and No. 883823 (project COMETS - Collective action models for energy

transition and social innovation, project EERadata - Towards a FAIR and
open data ecosystem in the low-carbon energy research community,
resp.). The authors collectively thank all researchers who compiled the
German subset of the ENBP dataset, namely, Heather Argandeh Paudler,
Constantin von Beck, Lukas Mueller, August Wierling, Simon Dufner,
and Jan Pedro Zeiss.

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