Green Entrepreneurship: Should Legislators Invest in the Formation of Sustainable Hubs?

Abstract

This study explores which local factors affect the creation of sustainable or green startups in a geographical area. The analysis aims to help regional legislators get a more nuanced view of regional economic and sustainable policymaking and to promote a transition toward a greener economy. Building on knowledge spillover theory, results from 4301 companies across Europe show that the driving factors for the emergence of green startups go beyond funding opportunities. Knowledge exchange and industry networks are equally if not more important in attracting green entrepreneurs. Results also reveal that green startups are more likely than non-green startups to change their location. Of those who change their location, green founders prefer large cities and have a negative inclination to establish their companies in small cities. Companies in the sustainable information technology (IT) industry are less likely to change their location, whereas green manufacturing companies are more likely to change. In summation, results indicate that the location choices and resulting evolution of clusters for green startups are based on a number of variables, including local knowledge stock and spillovers, company density, availability of educated talent, and industry affiliations.

1. Introduction

With the ongoing climate change, researchers and policymakers are keen to find more effective ways to slow or reverse global warming. Most nations agree that corporate practices are an essential part of the cause of environmental hazards and that fundamental change is required to mitigate adverse environmental and socio-economic consequences [1,2,3]. In this debate on combating climate change, entrepreneurship is often cited as a panacea for current societal challenges. In fact, startups are often considered the spark needed to fuel innovation. In light of the rising demand for sustainability, many researchers propose that green entrepreneurship has the potential to shape the future of economic and social welfare and, ultimately, plays a crucial role in providing a cure for transitioning towards a greener society. However, there is still a considerable amount of ambiguity about the role of green entrepreneurship and how it may unfold [4,5].

As will be shown throughout this paper, entrepreneurship—and green entrepreneurship in particular—is vastly different from established corporate operations. Sustainable businesses are often faced with greater challenges than purely economically oriented companies [1,6]. For instance, while regular businesses are devoted to making a profit, achieving growth, or reserving assets [7], green enterprises typically aim to increase social welfare in addition to achieving economic objectives. The dichotomy of reducing environmental impact while staying economically viable raises the demand for external funding. In fact, green companies often face extensive technological development in order to achieve innovation, which prolongs a potential market entry and increases financial dependencies. Green firms also show a higher need for knowledge and talent than regular companies, and it is often expected that these individuals not only exhibit technical expertise or business acumen but also share ideological views with the company [8].

As a result of these challenges, sustainable ventures are often considered to having higher entry barriers, lowering the number of new firm entries, and slowing sustainable progress. In an attempt to encourage innovation, policymakers frequently provide financial incentives either via subsidies on products [9] or by offering funding to potential founders [10]. While these initiatives have demonstrated some success, this paper argues that funding alone is not the only factor encouraging green entrepreneurship. Specifically, it is argued that sustainable firms are particularly dependent on their founding environment. Surprisingly, despite the potential for being a key accelerator in achieving the long-term change that entrepreneurship is often presented with, the relationship between startups and their entrepreneurial environment, particularly the factors that go beyond financial support, remains nascent.

Recently, some theoretical attempts have been made to explore why sustainable startups locate in a specific area [11,12]. Researchers argue that green firms have a high demand for knowledge and, thus, locate in regions with a better access to academic institutions. While these studies provide initial evidence of the importance of knowledge stock in sustainable entrepreneurship, most of these insights are hypothetical and more prescriptive than descriptive [4]. It is yet to be thoroughly answered why green hubs are formed, what factors motivate entrepreneurs to move, and how to potentially leverage insights into effective policymaking [13,14,15].

To address these questions, it is crucial to examine cross-cultural migration patterns using actual data. Unfortunately, most studies on this subject either focus on a singular economy (such as Italy in the case of Giudici et al. [12]), which limits the generalizability and prevents evaluating the influence of national or bilateral policies [11], or are theoretical or hypothetical scenario-based studies [16,17,18]. As a result, Giudici et al. [12] propose that scholars should “compare and contrast the creation of cleantech startups in the diverse European countries”.

In response to this call, the current study explores the local factors that affect the creation of sustainable or green startups in a geographical area. A sample of 4301 companies was extracted primarily from the startup database ’Crunchbase’ as well as the statistical office of the European Union ’Eurostat’. The novel dataset includes location choices and founder demographics from 21 European countries as well as a wide range of control variables. Contrary to most studies on green entrepreneurship, the wealth of the dataset further allows for examining industry-specific location choices and interactions with founders’ career histories.

This paper combines existing research on how local factors influence startup development [19,20,21,22,23,24] with the emergent sustainability research. To achieve this, the present study reconciles research on spillover theory and assumes that the proximity to universities and the density of existing firms play a crucial role in hub formation. Specifically, this study first attempts to identify prominent geographical influencing factors that distinguish green from non-green firms. Several local factors are considered: the availability of scientific and technical knowledge, the number of incumbent firms and startups as a potential indicator of sector-specific clustering, and access to both talent and funding as the main drivers attracting and agglomerating sustainable startups. Once these distinctions are made, rural areas are considered in particular. Building on migration patterns of founders—namely whether they deliberately chose rural or urban areas to establish their companies—as well as industry effects within the sustainability sector, the likelihood and hazard of green firm creation in a geographically rural area are identified.

The findings show that regional access to scientific and innovative knowledge alongside access to funding and talent positively predict the number of green entrepreneurs in a given region. However, when comparing rural with urban areas, it was found that not all predictors have the same effect. While most green startups prefer to locate in urban regions, rural areas are not necessarily disadvantaged. Funding and startup networks are important predictors of urban founders. Rural areas attract sustainable entrepreneurs when the technological output is high and the region displays a strong network of universities and incumbent firms. This study also found that migration patterns of green founders depend on the business sector or industry in which the company operates. While sustainable IT startups favor larger cities, despite a higher competition for talent and funding, sustainable manufacturing startups prefer to establish their companies in rural areas with arguably lower support structures for green ventures. This paper concludes with a discussion on how policymakers can encourage this form of entrepreneurship in their local regions.

2. Theory

When analyzing the characteristics of entrepreneurship and its emergence, it is natural to start examining where such companies are founded. Startups and their regions are interrelated, as such that economic growth and innovation can be fostered by a region’s legislature and, in turn, a thriving startup culture can lead to regional development and increase economic welfare [25,26,27]. A considerable amount of research has investigated the impact of entrepreneurship on a region [28,29,30], the relationship between academia and startups through knowledge spillovers [31,32], as well as the formation of industry or technology clusters [33,34,35] and spatial determinants of entrepreneurship [36,37]. However, the relationship between green entrepreneurship and a region is yet to be examined thoroughly.

2.1. Knowledge Spillover Theory

Startups often exhibit a high demand for technical and scientific knowledge. According to the spillover theory of entrepreneurship [26,38], this knowledge likely spills over from research institutions and enterprises in a region, favorably influences the establishment of startups in that area, and fosters the development of innovative products and services [12,20].

Several scholars empirically illustrate the importance of university knowledge in promoting local innovation and entrepreneurship [31,39]. Due to regular face-to-face exchanges between researchers and practitioners, the effects of university knowledge spillover are often argued to be highly localized [12,32,39]. Bonaccorsi and colleagues [20] show that the content of university knowledge spillover is strongly related to the universities’ scientific expertise, and Bonnet et al. [40] found a close relation between entrepreneurial tendency and education among undergraduate engineering students. The regional availability of universities focusing on traditional sciences, such as physics or chemistry, and technical majors, such as electronics or engineering, appears to be positively related to technology-based entrepreneurship in that area [12]. Consequently, spillovers are specifically relevant for technology startups.

2.2. Spatial Agglomeration and Hub Formation Theory

As a prerequisite of specialized technological clusters, spillover theory provides the foundation for hub formation theory. Introduced by Marshall in the late 1800s, spatial concentration and agglomeration externalities have been identified as important components of innovation and economic progress [41,42,43]. Early interpretations of the theory explore the proclivity of information to disseminate, or spill over, to create regional benefits and strengthen industrial growth. Building on this, spatial economics research explains the choice of a firm location mostly through optimization behavior, which entails either reducing production costs (including workers and capital) or maximizing potential profits [44]. Since the introduction of the new economic geography (NEG) in the 1990s and the growing literature on agglomeration economies [45,46,47], the importance of spatial concentration on the location choice of economic activities and the establishment of new businesses has been widely explored [48,49,50,51]. To date, it is commonly believed that entrepreneurial activity is not fairly spread in geographical areas [52,53] and that spatial distribution of entrepreneurial activity is dependent on a range of local characteristics, including the availability of funding, access to knowledge and talent, industry networks, and public policies [28,32,39,54].

The benefits of being close to other businesses and the resulting formation of sector-specific hubs have been highlighted by a growing segment of theoretical and empirical entrepreneurial research [31,36,55]. This research builds on the idea that small-business innovation may be modeled as a function of the inventive activity and generation of new knowledge of incumbent enterprises and organizations [31]. This clustering of akin businesses plays a central role in the formation of new technology-based enterprises since startups rely on access to knowledge and innovative ideas through spatially localized knowledge-spillover processes [56,57,58,59].

Despite the importance of localized spillovers, two opposite theories on the location of such hubs have been introduced. One theory posits that migration to technological clusters is often found in metropolitan areas with a higher population, such as San Francisco, New York, or London [60]. In Germany, for instance, entrepreneurs are found to migrate to conurbations with a higher total number of other entrepreneurs, namely Berlin, Hamburg, and Munich [61]. Urban regions can have several advantages, including having better access to funding, such as venture capitalists [62], closer connections to universities [63], culturally and ethnically diverse environments [64], and greater availability of talent [65,66,67]. All of these factors are expected to favor the likelihood for a start-up culture to emerge [68].

Another perspective in the formation of hubs is put forward by the local embeddedness theory. It is proposed that entrepreneurs launch their businesses close to where they live or work [69,70,71]. Thus, despite being associated with a lesser thriving startup culture, several studies suggest that smaller cities can equally attract startups. In Germany, cities such as Essen, Duisburg, or Leipzig are demonstrating strong growth in the number of businesses and jobs in the technology sector [72,73,74,75]. Part of the reason for the rise of new technological entrepreneurship centers are the rising expenses in metropolitan areas [76]; this is the case for capital cities such as London or Berlin with exceeding rent prices and higher wages. Alternatively, research proposes a local bias in entrepreneurship, whereby founders prefer establishing their businesses in a familiar environment, either their home region or an area in which they have deep roots. Accordingly, founders facilitate their social and professional connections in their region [77] and take advantage of having better access to local resources, such as funding, talent, customers, and suppliers [78]. Researchers also suggest that home regions lead to higher satisfaction and success of founders due to social ties and existing networks in their region [79,80]. Lastly, digital technologies nowadays allow startups to outsource certain tasks (e.g., cloud-based computing [81]), giving them less dependency on local talent and greater flexibility in selecting a location [82].

Together, these findings pose a puzzle: Are green entrepreneurs better off establishing their firms in their local regions or should they follow trends of migrating to urban areas? Understanding the importance of local entry barriers and examining why certain locations can attract and retain green entrepreneurs is crucial for explaining regional variation in entrepreneurial activity.

2.3. The Formation of Green Hubs

As will be shown in the course of this paper, different regions have different representations of sustainable businesses. Some initial research [12,15] has identified various geographical concentrations focusing on environmental technologies. Corradini [55], for instance, demonstrates that the number of green startups is not evenly spread, emphasizing the relevance of local features. As a form of sustainable entrepreneurship, green entrepreneurship is the process of developing and distributing marketable products or services with a focus on environmental preservation, programs, and processes [83]. The geographical clustering appears to be particularly relevant in the establishment of environmental enterprises and the sustainability sector.

One potential explanation for this difference is that sustainable startups are often associated with complex or novel technologies, resulting in high demand and fluid access to knowledge [84,85,86]. Proximity to academic institutions as well as access to a broad range of talent and support organizations, therefore, provide an ideal breeding ground for green enterprises. As a result, green clusters are likely formed where these conditions are met.

Another hypothesis derived from previous literature builds on the difference between tangible and intangible location factors. Different regions have different abilities to recognize and respond to technology opportunities [87,88,89,90]. Previous research has found that even after controlling for industry structure, labor qualification, and economic well-being, a significant portion of the attraction of new businesses between regions remains unexplained, highlighting the potential importance of intangible factors [12,91,92].

These characteristics can likely define localized associative capacities and learning processes [93,94,95]. In other words, green startups are more likely to search for informal or intangible (value-driven) location characteristics rather than purely economic factors. In fact, sustainable businesses generally employ individuals with high environmental identity and social intelligence [96]. These components describe the collection of informal relational resources that enable idea sharing and recombination, which in turn result in more effective information transmission and increased capacities to take advantage of technical opportunities revealed by knowledge spillovers. This might lead to a regional environment with greater technical dynamism and more technological entrants.

2.4. Research Hypotheses

In essence, spillover theory suggests that startups locate in areas where they can maintain close ties to research institutes and incumbent companies to leverage existing knowledge for their technological developments. Building on this, startups are likely to agglomerate into hubs with better overall access to that knowledge. Due to a range of challenges unique to green companies, this study argues that entrepreneurs in this realm have an increased demand for external support and are more likely to be embedded in their respective regions. As a result, the following hypotheses can be derived:

2.4.1. Funding

Compared to conventional startups, sustainable technology companies are often associated with ample research and development, prototyping, patenting, and manufacturing, as well as commercialization expenses [97]. In fact, green entrepreneurs are faced with a two-fold challenge of catering to both environmental and technological externalities [98,99]. Accordingly, green startups are associated with substantial financial risks that may reduce overall investment opportunities. This is underlined by below-average investment returns in the sustainability sector [100].

Moreover, sustainable technology providers compete against established organizations (e.g., conventional power production or fossil fuels) that are well-oiled and well-connected, allowing them to potentially offer compelling pricing at the expense of hidden societal costs [101]. Investors or traditional financial institutions, such as banks, are also more likely to be inexperienced with innovative technologies, prompting them to provide investments at higher rates, again affecting sustainable business models [102,103]. Lastly, access to funding, markets, and government protection are all dependent, in part, on the amount of cognitive or socio-political legitimacy a green startup achieves [104]. Legitimacy theory builds on the idea that entrepreneurs’ perceptions of institutions and organizations can have a significant influence on their decisions and actions. Founders must learn to identify and behave in accordance with societal norms to obtain legitimacy and ensure the development and survival of their businesses [105,106]. Green startups are particularly challenged to signal their value to stakeholders. As part of the legitimization process of new businesses, green organizational identity, driven by the assumption of prospects, must match the expectations of a wide range of audiences in addition to entrepreneurs’ values and views [107]. As a result, pro-environmental behavior by businesses might be viewed as a bid for legitimacy.

To cope with these challenges, it is expected that external funding is an essential factor in determining green entrepreneurs’ location choices. Specifically, it is assumed that green startups locate in areas with higher funding availability.

Hypothesis 1 (H1).

There is a positive relationship between the number of green startups and access to funding in a given region.

2.4.2. Scientific Knowledge

Given that green startups can be considered a subgroup of technology startups [108] and are often associated with high innovation and technological output, and building on spillover theory, it is expected that the regional availability of university knowledge positively relates to green entrepreneurship in a given region. Since green companies have a higher demand for technical expertise than average startups, they are expected to locate in areas with a higher potential for knowledge spillover.

Hypothesis 2 (H2).

There is a positive relationship between the number of green startups and number of universities in a given region.

2.4.3. Human Knowledge

One aspect linked to knowledge spillover is access to talent. This does not necessarily mean university graduates but talent on all levels. Simply put, innovation requires talent. Sustainable technologies generally demand a critical mass of knowledge, multidisciplinary skills, and interpersonal and organizational management resources that are complicated, cumulative, and, most importantly, expensive or difficult to obtain [109].

Moreover, Parrish [110] found that many entrepreneurs establish green businesses because they care about the environment and wish to improve society. As shown by Speckemeier and Tsivrikos [8], employees can be equally sensitive toward the company’s environmental identity. As a result, searching for suitable talent and finding a match may be more challenging since employees need to bring the required technical or managerial skills and should also exhibit a high person-organization fit with the company’s values to integrate their expertise, work attitudes, and professional conduct effectively. In this regard, significant efforts must be made to mobilize and train employees, acquire know-how, and educate employees about new green technologies and services, as well as their commercialization [111].

Hypothesis 3 (H3).

There is a positive relationship between the number of green startups and number of educated talent in a given region.

2.4.4. Marketable Knowledge

While the number of universities and educated talent can provide an implicit measurement of a region’s knowledge stock, researchers in the realm of spillover theory frequently cite the number of patents as a way to measure innovation output and marketable knowledge in a certain region [112,113,114]. Colombelli [21], for instance, demonstrates that the number of patent applications filed by local businesses correlates with the local technological output. Building on this, it is anticipated that the amount of technical knowledge available on the market, reflected in the number of patent registrations, will have a favorable impact on forming green firms on a regional level.

Hypothesis 4 (H4).

There is a positive relationship between the number of green startups and number of patents in a given region.

2.4.5. Industry Knowledge

While traditional approaches favor location choices with lower competition in order to increase market share [115], in today’s globalized economy, more companies are actively seeking industry collaboration to achieve a mutual added value [116,117]. Building on spatial concentration theory, it is postulated that green startups favor regions with a high firm density over sparsely populated areas to create additional positive spillover effects from established companies.

Specifically, it is expected that green startups choose their locations based on the number of both established firms as well as other startups. The rationale for this thesis is threefold: Firstly, one key business model that green companies adopt is to address clients who aim to reduce the costs of their operations, minimize environmental hazards and carbon footprint, and adhere to relevant laws, regulations, and other environmental standards. This highlights the importance of customer proximity as demands and regulations can change from one region to another. Thus, being close to a higher number and broader range of potential customers may stimulate business development and sales. Secondly, a goal of new firms is to be acquired by a larger firm, again supporting the idea that proximity to a heterogeneous group of established companies is preferred over sparsely populated regions [46]. Lastly, being close to other startups helps green startups to leverage existing infrastructure.

Hypothesis 5 (H5).

There is a positive relationship between the number of green startups and number of incumbent firms in a given region.

Hypothesis 6 (H6).

There is a positive relationship between the number of green startups and number of non-green startups in a given region.

3. Method

3.1. Data Sources and Variable Construction

A novel dataset containing a broad range of company information was obtained via different online databases. The first source is the online directory of startups Crunchbase. The database allows for the mining of company statistics, founding date, founding members, industry sectors, financing rounds, and employee biographies used in recent prior work [63,118]. The second set of data was collected from the global online talent database Experteer. Contrary to LinkedIn, the database is designed explicitly for high-ranking individuals and entrepreneurs, consists of 1.3 million individuals globally, and primarily focuses on talent in Europe rather than North America. Economic and macro data were collected via the statistical database of the European Commission Eurostat.

To collect a balanced sample of companies across regions and to account for Europe’s geographic fragmentation, a multilayered random sampling method was employed. First, from the sample of 1.3 million individuals in the talent database, only current founders (i.e., people whose job title includes the keyword “founder”) were selected; synonyms such as “entrepreneur” and translation of the keywords in other languages were used to expand the data-mining process. Additionally, only individuals who founded their companies in Europe were included, resulting in a total of 51,000 entries. Self-employed individuals, freelancers, and part-time entrepreneurs were excluded.

To address the primary research objective of finding differences between green and non-green companies, the next step filtered the sample based on their sustainability practices and business model. The goal was to select about 50% of the sample based on their focus on sustainable products or services. To determine which companies can be categorized as green, the pre-defined industry categories were used. Although the information is primarily user-generated, marketing and research specialists from the databases review them regularly. In addition, each company’s website was carefully reviewed to see if its primary goods, services, and technology could be classified as green. This classification was based on current definitions of green enterprises provided by the Bureau of Labor Statistics in the United States [119]. The bureau’s definitions give a clear, comprehensive, and quantifiable description of green activities, making the classification simple and unambiguous, for example, (a) “businesses that produce goods or provide services that benefit the environment or conserve natural resources” or (b) “businesses that use more environment-friendly production processes or use fewer natural resources than their peers” [92]. The second part of the sample served as a control group and was collected randomly without any industry filter being used.

The final dataset contains a total of 4301 companies from 21 European countries. For several reasons, the sample focuses on companies operating in Europe. Firstly, countries in Europe are frequently ranked among the highest in sustainable development and progress towards achieving all 17 SDGs. In fact, in the 2021 ranking of the SDG performance of all 193 UN member states, 28 of the top 30 countries are from Europe [120]. Moreover, European countries are often described as having a high quality of entrepreneurship and extent and depth of supporting an entrepreneurial ecosystem [121]. Lastly, based on the Environmental Performance Index published by the World Economic Forum, European countries dominate the top 10 when it comes to environmental spending [122]. The high prevailing sustainable attitude of its citizens and companies can ideally serve as a role model in developing an explanatory and regulatory framework for other regions. Examining the effectiveness of such policies in Europe constitutes an ideal empirical context in which green entrepreneurship is studied. The countries included in this study are Austria, Belgium, Bulgaria, Czech Republic, Denmark, England, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Norway, Poland, Portugal, Romania, Spain, Sweden, and Switzerland. Due to the smaller population and size of the sub-sample, some countries have been merged; e.g., Denmark, Sweden, Norway, and Finland have been labeled as Scandinavia; see

Table A2. Descriptive statistics (continuous variables).

Variable	Coding	N	Min	Max	M	SD	p50
Age	measured in years	4301	18	92	44.88	10.05	45
Education	ranges from 1 = vocational collage to 4 = PhD	4301	1	4	2.32	0.95	3
Competition	number of startups in the same region	4301	0	678	162.85	225.26	19
Universities	number of universities in the same region	4301	0	25	14.54	7.90	2
Funding	investment into startups in a given region in the same region	4301	3.25	65.49	50.12	13.62	48
Distance	between previous and founding location	4301	0	18,488	773.56	2269.22	62

for a full list. Industries in this sense include all secondary industries beyond being a green or non-green firm.

3.1.1. Dependent Variable

The main dependent variable of interest is dummy-coded and indicates whether a company is considered green based on the aforementioned criteria (

Table 1. Description of variables.

Variable	Description
Main dependent variable
Green startups	Number of environmental startups in the region (source: Experteer and Crunchbase)
Independent variables
University index	Students in higher education per city divided by population of the city (year: 2020, source: Eurostat)
Education index	Adults aged 25–64 with a university degree per city divided by population of city (year: 2020, source: Eurostat)
Employment	Number of employed individuals devided by economically active population in the city (year: 2020, source: Eurostat)
Company density	Number of companies in the city (year: 2020, source: Eurostat)
Startup density	Percentage of startups in the city (year: 2020, source: Eurostat)
Patents	Number of patent applications per 1000 inhabitants in the city (year: 2020, source: Eurostat)
Additional variables
Location change	Dummy variable that equals 1 if the founder has changed the location (+50 km) when establishing the company and zero otherwise
Changed to small	Dummy variable that equals 1 if the founder has changed the location to a small city (population <50,000) when establishing the company and zero otherwise
Changed to large	Dummy variable that equals 1 if the founder has changed the location to a large city (population >250,000) when establishing the company and zero otherwise
Changed country	Dummy variable that equals 1 if the founder has changed the country when establishing the company and zero otherwise
Distance (log)	The distance in km between the previous and founding location (log-transformed)
Control variables
Gdp per capita	Gross Domestic Product per capita of the region (year: 2020; source: Eurostat)
Population density	Population per square kilometer in the region (year: 2020; source: Eurostat)
Country	Country of residence of the founder
Industry	Industry affiliation, beyond being a green or non-green company
Founder gender	Male, female, or unidentified
Founder age	Measured in years
Founder education	Ranges from 1 = vocational collage to 4 = PhD
Founding year	Indicates the year in which the company was established (source: Experteer)

). As

Table A1. Descriptive statistics (dichotomous variables).

Variable	Coding	N	%
Male	male	3267	75.9
	female	1025	24.1
	total	4301	100
Green founder	founded green company	1965	45.7
	founded non-green company	2336	54.3
	total	4301	100
Metropolis	founded in small city	2373	55.2
	founded in large city	1928	44.8
	total	4301	100
Location change	founded in same location	2689	62.5
	changed location	1612	37.5
	total	4301	100
Changed to small	no change to small city	3551	82.6
	changed to found in small city	750	17.4
	total	4301	100
Changed to large	no change to large city	3439	80.0
	changed to found in large city	862	20.0
	total	4301	100
Changed country	founded in same country	3743	87.0
	changed to found in another country	558	13.0
	total	4301	100
Country	Germany	1645	38.2
	United Kingdom	868	20.2
	France	606	14.1
	Spain	256	6.0
	Scandinavia	208	4.8
	BeNeLux	205	4.8
	Switzerland	151	3.5
	other European countries	362	8.4
	total	4301	100
Industry	government and education	40	0.9
	financial services	762	17.7
	manufacturing	717	16.7
	consulting	1177	27.4
	media and telecommunications	281	6.5
	pharma and life science	92	2.1
	research	236	5.5
	information technology	389	9.0
	clothing and retail	206	4.8
	tourism	134	3.1
	others	267	6.2
	total	4301	100

Note: BeNeLux includes the countries: Belgium, Netherlands, and Luxembourg. Scandinavia includes the countries: Denmark, Norway, Finland, and Sweden. United Kingdom includes the countries: England and Ireland. Other European countries include: Austria, Bulgaria, Czech Republic, Greece, Italy, Poland, Portugal, and Romania.

shows, 45.7% of the companies in the sample fit in this category. The remaining 54.3% of companies serve as a control group and take a value of zero.

3.1.2. Independent Variables

Building on the hypotheses outlined above, six predictor variables are included in the analyses. All variables were constructed from the raw data provided by Eurostat. Specifically, the database has a wide range of statistics and indices. What makes this dataset unique is that it measures these statistics on the city level, allowing to draw data from a total of 1100 cities across Europe [123].

The first variable of interest, funding, was adapted from the 2020 European survey on the amount of funding that enterprises received for R&D or other innovation activities. The variable has been log-transformed to account for the large difference between regions.

The second predictor, universities, is used to measure the potential for knowledge spillover effects from academic institutions. The variable is measured using the number of academic institutions, including vocational colleges, universities, and business schools, as listed by Eurostat and is divided by the region’s population. Using the university quota as a relative value rather than the total amount of universities for each region allows to control for effects that arise merely due to larger populations, for instance, in metropolises.

On the contrary, the third variable, education, takes an absolute value that counts the number of individuals on the job market with at least a bachelor’s degree in 2020. This variable aims to quantify the educated talent on the market.

To measure the level of technical output the number of patents registered in a given region is further included in the analyses as indicated by Eurostat in 2020. Previous studies have commonly utilized the number of authorized patents as a proxy for innovation when measuring spillover effects [124,125,126].

The final two predictor variables measure company density on two levels. Firstly, the number of incumbent firms per capita is measured using companies that are older than five years in a given region per 1000 citizens living in the region as of 2020. The Eurostat database defines several milestones for the survival of a startup and the categorization of an incumbent firm. Taking the longest estimate, after five years, a company is no longer considered a startup. The result is a quota that aims to operationalize firm density and industry networks in a region. This measure is commonly used in entrepreneurial literature [127,128,129]. The variable, startups, counts enterprise birth rates in each region between 2018 and 2020.

3.1.3. Location Factors

To account for a potential regional bias and to determine migration patterns, a range of location variables based on the location choices made by founders are further constructed.

Location change indicates whether a founder has changed the location (>50 km) between the previous employment and founding location.

Change to small or large city are two dichotomous variables that indicate whether a founder has changed to either a small or large city upon the inception of the company. The city size is measured using the total number of citizens living in a city in 2020. As defined by the Cities in Europe Report [130], a small city in Europe is characterized by having a population of <50,000 citizens and a large city is defined as having a population of >250,000.

Changed country indicates whether the founder changed the country upon inception.

Distance (log) calculates the distance, in logged km, between the previous employment or university and the founding location.

3.1.4. Control Variables

Several control variables that might impact green entrepreneurship were further taken into account. Green entrepreneurial behavior is found to be influenced by a firm’s age and country [131,132]; hence this study controlled for founding year and country, both dummy-coded [91,133]. Personal characteristics can also impact the likelihood of becoming a green entrepreneur. Thus, demographic control variables were included in the analyses, namely age, gender, and education. As shown in Table A1, the founders included in this analysis are 45 years old on average, 75.9% are male, and 70% have at least an undergraduate degree. International venturing also varies across industries [134]. Thus, the analyses further controlled for industry affiliations. Industries were categorized regardless of the green or non-green label. For instance, a company can belong to the IT sector and also be categorized as either green or non-green. There are no green-only companies in the dataset. Lastly, following Giudici et al. [12], the regression models control for population, GDP per capita, and employment rate at the country level, all of which were collected from the Eurostat database.

4. Results

The research hypotheses are tested in a two-stage process. In the first stage, a series of analyses of variance (ANOVA) is conducted to assess whether a significant difference between green and non-green companies can be found. The results show that the percentage of green companies is significantly higher in areas with more funding, a higher number of universities per capita, higher educational level of the workforce, higher number of registered patents, as well as a higher number of incumbent companies and startups (F(1, 4299) = 299.947, p < 0.001, F(1, 4299) = 49.679, p < 0.001, F(1, 4299) = 157.404, p < 0.001, F(1, 4299) = 222.662, p < 0.001, F(1, 4299) = 1876.189, p < 0.001, F(1, 4299) = 995.003, p < 0.001, respectively). These results provide preliminary support for the hypotheses. To understand the magnitude of the effect, examine interaction effects, and account for possible confounding variables, several ordinary least square (OLS) regressions are conducted in the following part.

Table 2. Location factors for green versus non-green startups.

DV: Green Firm	Models
	OLS-1	OLS-2	OLS-3	OLS-4	OLS-5
Gdp per capita	0.049 *** (0.001)	0.070 *** (0.001)	0.086 *** (0.001)	0.029 *** (0.001)	0.017 (0.000)
Population	0.215 *** (0.001)	0.232 *** (0.001)	0.223 *** (0.001)	0.027 (0.001)	0.022 (0.001)
Employment	0.067 *** (0.653)	0.063 *** (0.658)	0.087 *** (0.685)	0.084 *** (0.635)	0.064 ** (0.677)
Funding	0.088 *** (0.001)				0.087 *** (0.000)
University density		0.063 *** (0.001)			0.097 *** (0.000)
Education		0.059 *** (0.001)			0.142 *** (0.001)
Patents			0.061 *** (0.001)		0.048 ** (0.000)
Incumbent firms				0.237 *** (0.001)	0.231 *** (0.001)
Startups				0.028 ** (0.001)	0.128 *** (0.001)
Demographics	Yes	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes	Yes
Founding year FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Obs.	0.131	0.138	0.128	0.178	0.198
R-sq.	4301	4301	4301	4301	4301

Note: All models include green startup as the main dependent variable. The dummy-coded variable takes a value of one if the startup produces a green product or service and zero otherwise. Before entering the main variables of interest, the models first enter country, industry, and founding year fixed effects as well as founder demographics gender, age, and education as control variables. Model (OLS-1) further enters the the number of green startups in a given region. The second model (OLS-2) enters the number of universities in a given region. Model (OLS-3) indicates the population density in a given region. Model (OLS-4) indicates how much green investment is provided in a given region. The final model enters all of the above mentioned variables. Each observation corresponds to a given startup(i). All regressions use OLS. Significance noted as: p-value < 0.05 = **, p-value < 0.01 = ***.

shows the results of the OLS regression estimates. The dependent variable in each model is green firm; a dummy-coded variable takes a value of “1” if the company is considered green and zero otherwise. Models OLS 1–4 enter the primary variables of interest to address the hypotheses, and the final model includes all variables of interest. Each model further adds regional and economic control variables, namely GDP per capita, population, and employment rate, as well as the founder demographics of gender, age, and education, as control variables. To account for trends that may be specific to the industry affiliation, the country in which the founder is located, and the year in which the company was founded, industry, country, and founding year fixed effects ($f_{Industry}$, $f_{Country}$, and $f_{Foundingyear}$) are also added.

The first variable of interest examines whether sustainable startups indicate a higher demand in funding compared to conventional firms. Model OLS-1 reveals that funding positively predicts the number of green firms ($\beta$ = 0.088, SE = 0.001, p < 0.001). Based on the log odds ratio, green startups are 8% more likely to locate in areas with more funding available than non-green startups. Thus, a higher amount of capital available in a given area has a favorable impact on the number of green businesses that are created there [135]. This finding complements research hypothesizing that green startups exert a higher dependency on external funding [136].

The second model examines the importance of university density to find support for potential scientific and technical knowledge spillover effects. The first coefficient is positive and significant, thus supporting H2. In regional contexts, the presence of universities facilitates the formation of green startups. In other words, the more knowledge stock is available in local settings, the more green startups there will be. This confirms Lans and colleagues’ [137] theorizing that higher education and green innovation may complement one another and provide the academic foundation for the establishment of sustainable startups.

The second variable entered in model 2 is education. Beyond counting the percentage of academic institutions in the region as a proxy for fresh knowledge, this predictor aims to detect the relevance of education in the workforce (i.e., amount of educated talent quantified by the individuals with at least a college degree) for the job market. Results show that the coefficient is strong in magnitude and positive at the 1% significance level. Notably, the education coefficient ($\beta$ = 0.059, SE = 0.001, p < 0.010) is smaller than the university coefficient ($\beta$ = 0.063, SE = 0.001, p < 0.001) in the individual model. However, when including all variables of interest in the final model (OLS-5), the magnitude and the significance of both predictors increase significantly. Interestingly, the education coefficient ($\beta$ = 0.130, SE = 0.142, p < 0.001) becomes even stronger in magnitude than the university coefficient ($\beta$ = 0.097, SE = 0.001, p < 0.001). In conclusion, strong support is found for Hypotheses H2 and H3.

These results support previous research that has acknowledged the roles of universities in knowledge spillover for sustainable [138] and green companies [15]. Thus, results indicate a potential interdependency between available talent and location choices. It can be concluded that green firms choose locations with more talent, and in return, more talent will be made available in areas with higher demand. The results on education are in line with current market research indicating that green enterprises have become increasingly attractive to younger and highly educated individuals as a result of rising awareness of climate change [139,140,141].

A second way of measuring the importance of technological spillover effects on sustainable startups is illustrated in model OLS-3. The number of patents registered in a region also positively predicts the creation of green firms. This suggests that the number of green startups in a given location positively relates to the local pool of technical knowledge measured in patents (H4).

For variables related to industry spillovers, results provide support for the hypothesis concerning the positive effect of the local density of existing organizations on green entrepreneurship (H5). The local presence of established firms is a relevant determinant for the creation of green startups. In all estimates, the company-density variable is positive and significant at the 1% level. Interestingly, startup density is found to be a positive but small predictor of green entrepreneurship ($\beta$ = 0.028, SE = 0.007, p < 0.05) in model OLS-4 and a strong positive predictor ($\beta$ = 0.128, SE = 0.001, p < 0.001) in model OLS-5. Thus, results provide evidence for the importance of startups in attracting green firms in a given geographical area H6. These findings indicate that green companies are more likely to emerge in areas with a diverse industrial network. Spillover effects and industry networks can impact entrepreneurial outcomes, thereby supporting the spatial agglomeration and hub formation theory. Furthermore, existing startup hubs or areas with a higher quota of other entrepreneurs positively affect the location choice of green entrepreneurs.

Lastly, in most models, the three control variables entered first positively predict the formation of green startups. Compared to conventional ventures, the number of sustainable firms appears to be higher in regions with higher GDP, higher population density, and lower unemployment. This indicates that green entrepreneurship is more likely to emerge in wealthier regions. However, it is notable that when entering all predictors, the employment rate becomes considerably lower in magnitude and population and GDP become non-significant. These results indicate that macro-economic indices may help getting a rudimental understanding of the location choices of green entrepreneurs yet confound with factors that may be the actual cause for entrepreneurial behavior. For instance, the positive and significant effect of being located in a highly populated region may imply that proximity to large urban agglomerates significantly increases the chances of creating a green startup [12]. Given that population becomes insignificant in OLS-4, the model in which the company and startup density predictors were entered, indicates that the density of companies, particularly startups, plays a more substantial role than population density.

Together, these results provide suggestive evidence of a robust relationship between location factors and green startups. The proximity to and the number of universities in the region as an indicator of a potential scientific knowledge spillover depicts a positive and statistically significant effect on green startups. This is supported by the connection between the number of patents registered on the likelihood of a green technological entry. Access to funding and educated talent further present a favorable location factor for green startups. Lastly, the positive and significant effect of both startups and established companies indicates that a diverse and dense industry network incentivizes green entrepreneurs to set up their firms in such an environment.

4.1. Location Choices among Green Entrepreneurs

Reflecting on the results, it would be reasonable to infer that more green startups are located in metropolitan or urban regions as these regions are typically associated with more funding, universities, and talent available. However, does this mean that rural legislators are inherently inferior in attracting green entrepreneurs?

Taking funding, for example, a number of studies have proposed that green companies are located in areas where there is more funding available, whether this is at a national [15] or regional [92] level, which emphasizes the role of financial incentives in location choices. A follow-up question that emerges from the first regression analysis is whether funding is the panacea for stimulating green entrepreneurship, as it is often assumed by legislative bodies and, more importantly, what location factors motivate sustainable founders to forgo better funding opportunities in a certain region and instead establish the company in a rural geographical area. To provide more nuanced insights on the role funding plays in firm creation, the following analysis examines the distinct location patterns and illustrates the conditions under which green startups potentially forego funding opportunities for other location benefits.

Table 3. Location choices among sustainable founders.

	Models
	OLS-1 Location Change	OLS-2 Changed to Small City	OLS-3 Changed to Metropolis	OLS-4 Changed Country	OLS-5 Distance (log)
Green startup	0.045 *** (0.015)	−0.156 *** (0.012)	0.202 *** (0.012)	0.085 *** (0.011)	−0.078 *** (0.075)
Demographics	Yes	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes	Yes
Founding year FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Obs.	4301	4301	4301	4301	4301
R-sq.	0.163	0.141	0.108	0.040	0.036

Note: Model (OLS-1) examines whether a person has changed the location (of >50 km) to set up the company. The outcome in model (OLS-2) is a dummy-coded variable that equals one if the founder has moved to a small city to establish the company and zero otherwise. Model (OLS-3) indicates whether a founder has moved to a metropolis to establish the new company. Model (OLS-4) indicates whether the founder has change the country to found the company. The outcome in model (OLS-5) measures the actual distance in km between the previous and the current company location. The last model is also dummy-coded equalling one if the person has moved back to the home city to establish the company and zero otherwise. Country, founding year, and industry fixed effects as well as founder demographics are added. Standard errors are clustered at the country level and are represented in parentheses. All regressions use OLS. Significance noted as: p-value < 0.01 = ***.

examines the likelihood that founders of green startups change their location upon establishing a company and, if so, what location they choose. Each observation corresponds to a given startup${}_i$. The dependent variables $Y_i$ represent the location choices. The first model examines the dummy-coded variable location change, indicating whether a founder has changed the location (>50 km) upon inception. Models OLS-2 and OLS-3 further split the group and examine whether those who did change chose a small or large city to found in. Having firms from over 20 countries also allows for investigating who changed the country upon inception, measured in OLS-4. To account for the close regional proximity of countries in Europe compared to countries with larger geographical areas, such as China or the US, another perspective is added to location choices by examining the distance (in km) between the previous and current employment locations in model 5. Given the skewed distribution of distances, this variable is log-transformed. All models enter green firm as the primary predictor, examining whether green startups significantly differ in their location choices compared to regular startups. All models include the same control variables and fixed effects as used in Table 2.

Overall, founders of green startups are about 13% more likely to change their location upon inception compared to non-green entrepreneurs ($\beta$ = 0.045, SE = 0.015, p < 0.001, model 1). Of the 2336 non-green founders, 729 (31.21%) decided to change their location as opposed to 883 (44.94%) of the 1965 green founders. Among those who change, the coefficient for changes to large cities is strong in magnitude, positive, and significant at the 1% level. This indicates that green founders are more attracted to metropolises than non-green founders. Changes to small cities are equally significant, yet the coefficient is negative, indicating that green founders are less likely to establish their firms in a small city. Among the green founders who changed their location, 611 (69.20%) chose a large and 272 (30.80%) chose a small city. Thus, large cities are the preferred founding location for green startups. Green entrepreneurs are also more likely to change the country upon inception but tend to move smaller distances.

These results support the argument put forward by Tien et al. [142], who indicate that rural areas are at a disadvantage when it comes to attracting sustainable entrepreneurs. In contrast, the overall flexibility to change the location indicates that green startups are less bound to a particular region and potentially make their location choices deliberately, which is a good indicator for rural legislators. Thus, it is interesting to examine what factors potentially motivate or discourage a founder from setting up a new firm in a rural area.

The third regression analysis includes all predictors introduced in Table 2 in an attempt to identify rural location choices and derive potential policy implications. The dependent variables are the location choices, similar to Table 3. Contrary to previous models that focus on the distinction between green and non-green firms,

Table 4. Location choices among sustainable founders.

Filter: Green Only	Models
	OLS-1 Location Change	OLS-2 Changed to Small City	OLS-3 Changed to Metropolis	OLS-4 Changed Country	OLS-5 Distance (log)
Funding	0.123 *** (0.001)	−0.021 (0.001)	0.148 *** (0.001)	0.053 * (0.002)	0.086 *** (0.001)
Universities	0.078 *** (0.001)	0.113 *** (0.001)	−0.001 (0.001)	0.022 (0.001)	0.075 *** (0.004)
Education	−0.020 (0.000)	0.021 (0.001)	−0.037 (0.000)	−0.021 (0.000)	0.003 (0.000)
Patents per capita	0.337 *** (0.000)	0.078 ** (0.000)	0.305 *** (0.000)	0.234 *** (0.000)	0.331 *** (0.000)
Firm density	0.112 *** (0.001)	0.141 *** (0.001)	0.016 (0.001)	0.120 *** (0.001)	0.123 *** (0.001)
Startup density	0.032 (0.001)	−0.037 (0.001)	0.062 * (0.001)	−0.044 (0.001)	−0.001 (0.000)
Demographics	Yes	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes	Yes
Founding year FE	Yes	Yes	Yes	Yes	Yes
Industry FE	Yes	Yes	Yes	Yes	Yes
Obs.	1965	1965	1965	1965	1965
R-sq.	0.140	0.079	0.082	0.047	0.093

Note: Each observation corresponds to a given founder(i). Only green founders are included in the analysis. Refer to Table A1 and Table A2 for a detailed overview of the variables included in the models. Founder demographics (gender, age, and education) as well as country, founding year, and industry fixed effects are included in all models. Standard errors are clustered at the country level and are represented in parentheses. All regressions use OLS. Significance noted as: p-value < 0.10 = *, p-value < 0.05 = **, p-value < 0.01 = ***.

focuses only on green startups. All coefficients are entered in the same model to control multicollinearity among the predictors. The same control variables, demographics, and fixed effects are entered.

Model OLS-1 examines the founder’s willingness to change the founding location. Among green founders, higher funding, university density, patents registered, and the number of companies per capita all positively predict location changes. This again supports the notion that green founders are not indifferent to their chosen location and that several factors can influence migration patterns.

Of those who changed their location, the number of universities, the technological output (i.e., patents), and the incumbent firm density appear to be significant and positive predictors of stimulating changes to rural areas (OLS-2). Notably, neither startup density nor funding positively predicts changes to small cities, potentially indicating the futility of such location factors. Instead, a rural area attracts green founders with high technological output and a strong network of universities and incumbent firms.

Interestingly, metropolises reveal a somewhat different outcome. Green entrepreneurs are stimulated to set up their companies in urban areas when funding, patent output, and startup density are high. Increased funding may only be relevant for urban areas as operational costs are generally higher in larger cities, thus, incentivizing founders to move when financial support is given. Additionally, higher startup density may highlight the importance of startup hubs and communities, often found in larger cities, to motivate green founders to choose an urban environment.

Separating small and large city founders reveals several important nuances in the migration patterns of green entrepreneurs, which policymakers can consider when designing a legislative strategy to attract founders. In summary, technological output, measured by the number of patents registered in a region, is a strong positive predictor of attracting green founders regardless of the city or country size. While patents may be considered a proxy for the technological advancement of a region rather than a direct factor considered by entrepreneurs, it supports the assumption that green firms are keen to settle down where innovation is thriving.

Additionally, rural areas are not at a disadvantage per se. In fact, a significant number of green founders chose small cities over metropolitan areas, potentially based on an extensive university infrastructure and company network. This result sheds light on the fact that some green founders might value quality over quantity. More specifically, while urban regions often offer better funding opportunities, more universities, and more educated talent, the results of OLS-2 imply that some rural areas may be specialized in a particular technology or sector, which constitutes an attractive location factor.

Lastly, while funding can incentivize a location change, it only attracts urban founders. Since this coefficient is non-significant rather than negative for rural founders, funding can be considered a non-essential factor. This result can have two interpretations: either rural areas are less attractive, despite having higher funding opportunities, or green entrepreneurs potentially forego better funding opportunities in urban areas and instead emphasize academic and professional networks when deciding to found in rural areas.

4.2. Location Choices Based on Industry Affiliations

The last section of the analysis focuses on location choices across industries. Four location choices are entered as dependent variables. The primary predictors are green firm and industry affiliation. Two distinct industries were chosen to determine potential sector-specific preferences: IT and manufacturing. The rationale for choosing these two industries lies in the nature of their business model and the required resources to establish their business. When establishing a new company, most industries share a similar set of tangible and intangible resource requirements—for instance, comparing a sustainable restaurant versus a sustainable fashion retailer. Both companies need to acquire startup capital, pay for a storefront, and hire talent [60]. However, the two industries chosen in this study can be found on two opposite sides of the spectrum of the required resources. Specifically, IT companies are typically characterized by having little to no inception costs [143]. Most resources needed to operate the business, such as servers, can be outsourced, and talent can be hired gradually. In contrast, manufacturing companies often need to purchase a number of machines and typically need more space for production. Consequently, these two industries were chosen to detect differences between two opposite industries. The same set of controls and fixed effects were used. Due to the industry focus of this analysis, industry fixed effects ($f_{Industry}$) are excluded from the following models.

Table 5. Location choices by industry.

Model	Location Change		Changed to Small City		Changed to Metropolis		Changed Country
	OLS-1a	OLS-1b	OLS-2a	OLS-2b	OLS-3a	OLS-3b	OLS-4a	OLS-4b
Green firm	0.075 *** (0.015)	0.062 *** (0.016)	−0.143 *** (0.013)	−0.128 *** (0.013)	0.226 *** (0.013)	0.197 *** (0.013)	0.099 *** (0.011)	0.102 *** (0.012)
IT	−0.131 *** (0.032)		−0.178 *** (0.026)		−0.003 (0.028)		−0.093 *** (0.024)
Green firm × IT	−0.048 *** (0.041)		0.014 (0.033)		0.071 *** (0.035)		−0.013 (0.030)
Manufacturing		0.104 *** (0.027)		0.139 *** (0.022)		−0.005 (0.023)		0.070 *** (0.020)
Green firm × Manufacturing		0.048 ** (0.039)		−0.085 *** (0.032)		0.022 (0.033)		−0.050 ** (0.029)
Demographics	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Country FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Founding year FE	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	4301	4301	4301	4301	4301	4301	4301	4301
R-sq.	0.156	0.136	0.091	0.080	0.098	0.093	0.035	0.028

Note: The same location outcomes as those described in Table 3. Green firm is an indicator that equals one if the firm produces sustainable products or services and zero otherwise. The variable IT is dummy-coded and indicates whether firm is affiliated to the IT sector. Manufacturing indicates whether firm is affiliated to the manufacturing industry. The same set of controls and fixed effects are used, excluding industry fixed effects. Standard errors are clustered at the country level and are represented in parentheses. All regressions use OLS. Significance noted as: p-value < 0.05 = **, p-value < 0.01 = ***.

joins ranks with previous findings showing that green founders are more likely to change their location. Moreover, green founders again show a higher tendency to move to large cities and a lower tendency to settle in smaller cities compared to other founders. Founders working in the IT sector are more likely to found locally (i.e., not change their location upon inception), ($\beta$ = −0.131, SE = 0.043, p < 0.001), compared to other industries. Furthermore, IT founders are less likely to change to small cities. Interestingly, results indicate a negative interaction between being green and working in the IT sector on changing the location ($\beta$ = −0.048, SE = 0.041, p < 0.001). Working in the sustainable IT industry makes founders stay in their current location, thus, reducing the tendency of green founders to change their location. Descriptive statistics indicate that 29.44% of green founders in the IT industry are willing to change, as opposed to 25.35% of non-green founders in the IT industry. A positive interaction on changes to large cities indicates that the preference of green founders to choose larger cities can be amplified when these founders are also working in the IT industry.

Founders in the second industry of interest, manufacturing, are more likely to change their location ($\beta$ = 0.104, SE = 0.027, p < 0.001). The positive interaction between being a green founder and working in the manufacturing industry underlines their similarity, indicating that sustainable manufacturing founders are even more likely to change their location than green founders in other industries. Notably, the negative interaction between being green and working in the manufacturing industry on changes to small cities ($\beta$ = −0.085, SE = 0.032, p < 0.001) shows that the location preferences of green founders can be significantly influenced by industry affiliation. In this case, green founders are more likely to change to smaller cities when working in the manufacturing industry.

Taken together, the results reveal several important insights into the location choices of sustainable entrepreneurs. While green founders generally prefer to change their location, those working in the IT industry prefer to stay local. In contrast, sustainable manufacturing founders are even more likely to change than average green entrepreneurs. Moreover, green founders’ low preference for rural areas can be reversed when founders work in the manufacturing industry and reinforced when working in the IT sector.

There are several potential explanations for these findings. Firstly, IT startups often have lower founding costs than companies in the manufacturing industry [76]. For instance, internet companies usually have much lower infrastructure demands such as machines and can deploy resources remotely [81]. As a result, IT founders are more flexible in choosing a location and can literally set up the company from the garage or home office. Secondly, technology startups are often founded during the time a founder is at university [63], resulting in a lower percentage of location changes. For manufacturing firms, however, higher change rates may be explained by the fact that production generally takes place in areas that cater to the industrial output, for example, by providing low-cost industrial spaces, lower average salaries for workers, and a specialized pool of skilled but not necessarily highly educated workers [144]. The likelihood of having ideal production conditions in the founder’s current region is relatively low, potentially explaining the higher change rate. Thus, IT entrepreneurs have more freedom in their location choice for new ventures than those depending on tangible resources.

In summary, sector-specific orientation in green entrepreneurship is not universal. Results show significant interaction effects between two fundamentally different industries: green manufacturing and green IT.

5. Discussion

Today, it is unclear what role regional clusters play in stimulating sustainable entrepreneurship. Several researchers [3,15,145,146] highlight the increasing demand for research on the interactions between green ventures, industries, governments, universities, and non-governmental organizations in order to guide them to work together more effectively, resolve pressing environmental issues in the long term, and develop necessary technologies in a competitive way. However, in the current literature, the relation between sustainable development and entrepreneurship is mostly prescriptive rather than descriptive and exceedingly optimistic [4]. As a result, it is unclear to what extent entrepreneurs can contribute to the transition toward a greener economy, how they are motivated and incentivized, whether there are structural barriers to sustainable ventures capturing economic rents, and whether sustainability-oriented entrepreneurs differ from traditional entrepreneurs. More studies are needed to gain a better understanding of the impact of public policy and how it might influence the occurrence of sustainable entrepreneurship [4].

This study emphasizes the importance of both academic and industrial spillovers in attracting sustainable entrepreneurs. It was hypothesized that besides funding, green firms indicate a higher demand for industry collaborations, exchange with research institutes, and integration into existing startup communities. As a result, spillover effects are expected to go beyond scientific and academic knowledge originating from universities and equally spill over from incumbent firms close to the green venture. This research provides an important nuance to rural green entrepreneurship and the development of sustainable technological clusters through the lens of spillover theory.

Results show that most green startups prefer to locate in urban regions. However, rural areas are not at a disadvantage per se. In fact, green founders value different location factors depending on whether they choose a small or large city to set up their business. A high number of universities motivate a location change and specifically encourage green founders to move to a rural area. Building on the previously discussed hub formation, it is probable that green founders are specifically searching for technological or sector-specific clusters to which they are willing to move despite their general preference for urban areas. Across Europe, specialized universities and research institutes are often found in non-metropolitan areas. For instance, the Fraunhofer organization, one of the primary industry research institutes in Germany, has offices across 83 locations, and only eight of them are located in larger cities [147].

Moreover, while funding and startup networks are the important predictors of urban founders, rural areas can equally attract sustainable entrepreneurs when the technological output is high and the region has a strong network of universities and incumbent firms. This presents an opportunity for rural legislators to determine their focus in designing legislative agendas when the firm network, universities, and output are high. As a result, the formation of high-tech or sector-specific hubs gains importance due to this result.

Lastly, the migration tendencies of green founders can either be enforced or reversed depending on the industry subsector. The preference for larger cities is even higher when green founders work in the IT industry. However, entrepreneurs working in green manufacturing are more likely to establish the firm in rural areas, contrary to the general aversion to such locations.

5.1. Theoretical Implications

This study adds to the theoretical literature in several ways. Firstly, the current findings highlight the importance of knowledge spillover theory. As green enterprises can be considered a subset of technology-based startups [108], results extend our understanding of spillover theory by showing that the availability of university knowledge in close proximity has a positive impact on the number of green startups in a given region. In a nutshell, sustainable startups have an above-average demand for technological expertise, funding, and talent. Areas with a tight network of academic institutions and industry experts increase the likelihood that sustainable firms settle down and eventually form a hub.

Results also support the recombinant knowledge hypothesis [148], which states that information is derived through the availability of a variety of distinct types of regional knowledge. Congruent with previous studies [83,149], the results of this study deliver strong evidence that knowledge is multi-dimensional. To date, this study is among the first that holistically extend knowledge spillover theory to sustainable entrepreneurship. This is particularly important for advocates of spillover theory as green startups are not only a critical research domain but, as shown throughout this paper, face several unique challenges compared to conventional startups. Understanding how different facets of knowledge spillover, namely academic knowledge, marketable knowledge, and industry knowledge, favor the formation and migration of green entrepreneurs, thus, provides important extensions to our current understanding of how knowledge networks are formed and transferred.

Furthermore, this research adds to research in spatial economics that explains that location preferences are made by optimization behavior that involves either minimizing the costs of moving inputs and outputs or minimizing the production costs (including wages and capital), which results in maximizing potential profit [44]. Thus, it can be concluded that the decision of where to create a startup is influenced by a location’s ability to reduce costs, generate growth signals, and acquire key resources, among other factors. This research provides an essential contribution to this notion as founders in different industries vary in their location preferences. Access to knowledge, innovative technology, and high-quality talent—typically found at a higher rate in metropolitan regions—is central in choosing a founding location for IT entrepreneurs. Nonetheless, manufacturing startups are often characterized by high operating costs, making cost reduction a key location factor.

Lastly, this paper advances our knowledge on aggregated metropolitan economic indicators as well as individual-level factors in examining individual location choices [60,76,150] and is among the first to transfer the idea of green entrepreneurship to the local level across a range of different industries and different European countries.

5.2. Practical Implications

Policymakers and society as a whole are becoming increasingly concerned about the mounting environmental ramifications [151]. Green startups have the potential to contribute to long-term technical advancement and to lead the transition toward a green economy, which protects the environment and, more broadly, fosters a more equitable society [152]. Thus, understanding what factors foster the establishment of pro-environmental firms is critical to practitioners and policymakers.

Sustainable startups, as well as sustainable firms in general, exhibit significant differences compared to conventional companies. As a result, traditional policy approaches may not suffice when aiming to attract green entrepreneurs, and especially rural areas often struggle to provide favorable infrastructure to attract those firms. The primary goal of this paper is to outline the challenges faced by sustainable ventures to raise awareness for a reconsideration of policy priorities, both in the EU and abroad, and provide educated implications for legislative development.

In revealing what local factors foster green entrepreneurship, this paper addresses and integrates two research issues that to date have received limited attention: How green startups are created and what factors encourage the formation of sustainability clusters. Thus, this study offers important insights for understanding the paths of transition to the green economy.

One of the most important levers that policymakers can employ to stimulate local development is increasing local networks [21]. As knowledge spillovers from universities enhance the creation of innovative green startups in regional contexts, policymakers are advised to strengthen academic networks and offer opportunities for founders to interact with researchers. An important contribution found in this study shows that spillover effects can go beyond scientific and academic knowledge originating from universities and can equally spill over from incumbent firms close to the green venture. Therefore, this study provides practical implications that can aid policymakers in determining how and under what situations established organizations in a region can be leveraged to foster strategic collaborations with green startups.

This research can also assist supranational governing bodies. The European Union, which is in the center of this study, allocates funding to different regions to advance innovation and entrepreneurship and promote the shift toward a greener society. Critically, results indicate that external funding is not the only factor in attracting green entrepreneurs. In fact, sustainable founders in rural areas are not attracted by funding and instead value science–industry connections. Rather than making funding available directly to founders, legislators in these less-populated areas can, for instance, use grants to enhance the knowledge exchange between academic institutions and green firms by organizing local events, such as innovation summits, or by inviting more incumbent organizations to scientific conferences.

Moreover, the perception of the sustainability industry may be more complex than often assumed [153]. This study shows that green founders can follow opposite migration patterns depending on their industry affiliation. For example, the higher willingness of sustainable manufacturing founders to move can prompt policymakers to look beyond regional borders and even potentially attract founders from other countries. Marketing efforts and regional branding should be conducted on a national rather than provincial level. Additionally, since founders in the IT industry tend to establish their companies locally, the transition from university to the industry and the barriers upon registration should be made as effortless as possible; policymakers can consider establishing entrepreneurial courses for technical PhD students to close the gap between scientific research and industry entrants. This can also prevent talent from leaving a particular region and contribute to forming a technology hub.

5.3. Limitations and Future Research

This paper offers important insights into how complementing policy initiatives at the regional levels might assist the creation of new European laws. However, additional studies are needed to assess the efficacy of the European policy to stimulate the development of green startups and determine if the new policies will be sustained over time. This study focuses on European countries due to their high environmental performance and entrepreneurial culture. This is intended to catalyze insights and derive a managerial road map for other regions and legislators and was inspired by previous research using a similar approach. For instance, a related study conducted by Ionescu et al. [154] examined innovation and green entrepreneurship policies in Europe to derive potential implications for regions with lower economic performance. However, it would be fruitful to apply the findings in regions with lower environmental or entrepreneurial advocacy. Specifically, this study invites researchers from other continents, such as Africa or Asia, to build on the experimental framework and extend results cross-culturally.

While the breath of data allows for covering a wide range of demographics and regional differences, it also constrains the inclusion of specific policy measures since not all regions publish government spending and funding data made available specifically to sustainability startups. Detailed information concerning the environmental policies issued at the regional and provincial levels would be beneficial for future research. In addition, this study only considered the industry affiliation to characterize green startups. Future studies should consider adding performance measures to examine which locations provide an ideal breeding ground for the success of green ventures as well as their environmental outcome [155].

Lastly, this research introduces a foundation for additional case studies or detailed investigations into the relationships between green startups and universities or research institutions. By linking spillover theory with green entrepreneurship on a theoretical level and providing evidence on the importance of universities for the establishment of green firms, future research might consider taking a deeper look at the form and extent of their relationship (e.g., by assessing joint research projects and collaboration contracts). Thus, future studies could take the results and design case studies or qualitative interviews to verify the notions derived in this research.

6. Conclusions

Entrepreneurship is frequently considered unsustainable [137], diminishing its capacity to contribute to and provide answers for long-term regional development. Combining the two theories helps researchers and policymakers consider green entrepreneurship a fruitful yet complicated source of business opportunity. Finding solutions for rural areas’ development requires an understanding of the motivation of green entrepreneurs and environmental factors that can benefit a firm success.

Research from European regions reveals that local knowledge spillovers can explain the agglomeration of green startups but does not provide a panacea for all green startups. The fact that founders’ career histories and industry affiliation determine, at least in part, the location chosen for the firm inception, specifically in some cases away from typical hubs, shows the versatility of founder decisions.