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Inter‑organisational Sustainability Cooperation Among
European Regions andtheRole ofSmart Specialisation
MirkoKruse1
Received: 5 June 2022 / Accepted: 11 January 2024
© The Author(s) 2024
Abstract
Innovation represents one of the most crucial levers for regional prosperity and
sectoral renewal. Additionally, it is applied to address challenges such as a sustain-
able transition and the battle against climate change. Since innovation is the result
of cooperation between different actors with different backgrounds, the topic is
increasingly studied from a systemic perspective. Here, not only internal coopera-
tion but also cross-border connections between regions become important. While
smart specialisation, a European policy for innovation and cohesion, highlights the
role of interregional cooperation, practical manifestations and research on this aspect
have remained limited so far. This article addresses this gap by discussing the rel-
evance of interregional cooperation for knowledge creation and presents empirical
evidence on cooperation between organisations in different European regions in the
field of environmental sustainability. The underlying dataset was constructed from
Horizon 2020 (H2020) research projects with Northern Germany as an exemplary
set of regions chosen as the core of a social network analysis (SNA). The findings
reveal that involvement in interregional projects is concentrated particularly in urban
regions and correlates with GDP and population density. On the other hand, also
organisations in regions with different structural characteristics are involved in inter-
regional cooperation, and H2020 managed to introduce new cooperation patterns.
Finally, the empirical data do not adequately match the regional smart specialisation
strategies (S3) which raises questions on updating smart specialisation as a policy.
Keywords Smart specialisation· Innovation policy· Europe· Interregional
cooperation· Horizon 2020· Social network analysis
JEL Codes R11· O30· O19· Q55
* Mirko Kruse
kruse_mirko@web.de
1 Faculty ofBusiness Studies andEconomics, University ofBremen, Bremen, Germany
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Introduction
The economy in market-based societies is subject to constant structural change.
Here, innovation and knowledge creation are key factors for companies, sectors,
regions, and countries to successfully adapt to technological change (Landabaso,
1997). This recognition is even more true nowadays considering the multitude
of severe events calling for adaptations of production processes, consumption
patterns, value chains, or regulatory frameworks. Among these events are the
COVID-19 pandemic, geopolitical tensions, the emergence of disruptive tech-
nologies, or the increasing urgency for a sustainable transition of the economy in
accordance with planetary boundaries (Gong etal., 2022). Successfully managing
said transition will require exploiting innovative capacity at all levels to develop
new solutions and create new technological pathways. Innovation here functions
as an instrument to tackle grand challenges including, but not exclusively, the sus-
tainable transition of the economy (Fagerberg & Hutschenreiter, 2019; Losacker
etal., 2021). Thereby, the distribution of innovative activity in space is not ran-
domly distributed but tends to be spatially concentrated. As a consequence, the
geography of innovation receives increasing attention (Coenen & Morgan, 2020).
In Europe, the European Commission has introduced the European Green Deal,
a package of ambitious targets, specific policies, incentives, and directives, to
achieve several objectives: overcome the pandemic-related recession and increase
resilience against further crises, as well as the battle against climate change and
the aspiration to become climate neutral (European Commission, 2021). The cen-
tral levers to address these objectives are research and development (R&D) and
innovation. Accordingly, the concept of smart specialisation, one of the key strat-
egies of European innovation policy, comes into the spotlight again (Doranova
etal., 2012; European Commission, 2020a). This approach was inspired by theo-
ries of regional innovation systems and the exploitation of place-based potential
and has seen a remarkable career in the last decade following its implementation
(Doranova etal., 2012; Van den Heiligenberg etal., 2017; Giustolisi etal., 2022).
The concept has provoked academic criticism primarily because its origins are
both political and theoretical, creating a certain level of fuzziness. As the concept
now is increasingly discussed again in the context of the Green Deal and the sus-
tainable transition of European regions, several questions must be answered, and
shortcomings are to be addressed. One of the most severe shortcomings of smart
specialisation so far is its outward-orientation, meaning the relevance of exter-
nal cooperation and knowledge flows between regions. While the positive effects
of knowledge transfer and mutual learning have been demonstrated empirically
and smart specialisation conceptually strives to facilitate interregional coopera-
tion (e.g. Guastella & Van Oort, 2015; Mitze & Strotebeck, 2018; Balland etal.,
2019), practical implementation and empirical analyses have remained limited.
Thereby, deepening interregional cooperation is also crucial for the political
goal of a gradual European integration and might become even more important as
the current phase of globalisation appears to come to an end and internal coopera-
tion increases in importance (Brodzicki, 2017; Gong etal., 2022). The fragmented
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Journal of the Knowledge Economy
nature of the European research system has been identified as a major weakness
preventing Europe from exploiting its full potential and catching up with more
unified competitors such as the United States (European Commission, 2017). To
exploit the full potential of European cooperation, which is also required to suc-
cessfully address the grand challenge of climate change, existing policies such as
smart specialisation will have to change as well. The paper at hand aims to con-
tribute to this discussion by providing empirical evidence on interregional cooper-
ation in Europe in the field of environmental sustainability. Thereby, a novel data-
set to quantify cooperation is constructed analysing cooperative patterns between
organisations in different European NUTS2 regions. As regions are no actors in a
narrower sense, organisations within these regions are used as a proxy. While the
majority of previous studies in this particular field rely on qualitative studies (e.g.
Fellnhofer, 2017), further empirical tools such as social network analyses and sta-
tistical methods are applied to provide a thorough overview and allow for deeper
insights. To do so, the remaining of this paper is structured as follows: the “Smart
Specialisation, Sustainability, and Interregionality” section introduces the policy
of smart specialisation in the context of European innovation policy in general
and discusses its recent relevance in the context of sustainability. In the following,
interregional cooperation and its embeddedness in innovation system studies are
outlined and discussed with regard to smart specialisation. Afterwards, the “Inter-
regional Scientific Collaboration in Europe” section presents the data and methods
used for the analysis before the findings are presented. The paper closes with a
concluding outlook in the “Conclusion” section.
Smart Specialisation, Sustainability, andInterregionality
The Idea ofSmart Specialisation
Smart specialisation represents one of the central strategies of European innovation
and cohesion policy. The theoretic foundation of the concept is to be found in lit-
erature on regional innovation systems (RIS). This approach emphasises the cru-
cial role of the regional level and geographical proximity between regional innova-
tion actors for the generation of new knowledge and innovation (Trippl, 2008). The
RIS concept was developed in the 1990s and builds upon the foundations of pre-
ceding theories such as national innovation systems (NIS), transition studies, inno-
vative milieu, or industrial districts (McCann & Ortega-Argilés, 2015; Tödtling &
Trippl, 2018; Rakas & Hain, 2019). Thereby, the rationale of smart specialisation
as a policy goes back to the identification of, one the one hand, a manifesting pro-
ductivity gap between Europe and other economic areas such as the USA, and, on
the other hand, internal development gaps within Europe, particularly in the process
of the Eastern enlargement (Janik etal., 2020). At the same time, it was discussed
how to increase the efficiency of European cohesion and innovation policies as it
showed that previous attempts had resulted in fragmentation and inefficient over-
laps (Larosse etal., 2020; McCann & Soete, 2020). Previously, regional funding
was invested thinly across several sectors without resulting in significant impact on
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Journal of the Knowledge Economy
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innovation capability and structural renewal as a result (Gianelle, Kyriakou etal.,
2020). Smart specialisation came into play as the result of merging the two streams
of discussion on interregional inequality and updating European cohesion policy
(Foray etal., 2011; Kruse, 2023).
Content-wise, the pivotal idea of smart specialisation is place-based, meaning
that the idea of a “one-size-fits-all” solution in terms of innovation policy is rejected.
Instead, it is argued that each region needed to find its own niche and develop its
own strategy to innovation instead of trying to emulate experiences from apparently
successful regions (Gianelle, Kyriakou etal., 2020). As regions are unique in their
economic and social structure, a successful strategy for one region might be a
dead-end for others (Di Cataldo etal., 2020). Thereby, smart specialisation should
motivate regions to prioritise and focus their resources on those innovative sectors
which they are specialised in, and which offer the highest probability of performing
well in the future (Rusu, 2013; Foray, 2014; Mora et al., 2019). By doing so,
comparative advantages are to be built and potential agglomeration benefits can
be realised (Gianelle, Kyriakou et al., 2020). Thereby, the choice of priorities
should recognise the structural renewal of existing specialisations by focusing on
complementing industrial and technological activities (Foray etal.; 2009; Vezzani
etal., 2017; Balland etal., 2019). The selection of said investment priorities should
not come from top-down planning but emerge from a process of entrepreneurial
discovery, meaning the explorative involvement of regional experts from different
backgrounds (Foray, 2013; Foray & Goenaga, 2013; McCann & Soete, 2020).
After its establishment, smart specialisation witnessed a remarkable career in
European policy, being promoted as a fundamental pillar of cohesion policy in 2014
and as an ex ante conditionality for territories to be eligible for European funding
(European Union, 2013; Janik etal., 2020; Di Cataldo etal., 2020). By now, most
regions in Europe have applied the smart specialisation concept by developing indi-
vidual smart specialisation strategies (S3), and the variety and quantity of research
have increased remarkably (McCann & Soete, 2020). However, recent studies imply
that smart specialisation is only partially implemented in regions and persistence
remains to change established processes on a regional level (e.g. Gianelle, Guzzo
etal., 2020; Larosse etal., 2020; D’Adda etal., 2021). Moreover, the fast success
story of smart specialisation made the concept an example of “policy running ahead
of theory” (Foray etal., 2011: 1), and several shortcomings have been outlined in
recent years. One aspect of criticism refers to the term “specialisation” which often
leads to the misunderstanding of interpreting smart specialisation as a modern kind
of Porter-inspired cluster policy, whereby the concept aims towards diversified spe-
cialisation (Asheim et al., 2016). Further criticism revolves around the questions
which regions do benefit. When smart specialisation was established, it was pro-
moted as a measure to support less-developed regions while it later became clear
that those regions benefit to a smaller degree as they lack the institutional capacity
to implement the concept and conduct the process. Nevertheless, the basic idea of
smart specialisation is widely received to be positive, underlining the place-sensitive
approach, the focus on knowledge and innovation, and the involvement of regional
actors in entrepreneurial discovery (Hassink & Gong, 2019; Foray, 2019).
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Journal of the Knowledge Economy
Smart Specialisation andEnvironmental Sustainability
The partial implementation in practice and ongoing clarifications in theory underline
that smart specialisation is far from being a completed concept. As the programming
period 2014–2020 recently terminated, the discussion on how to update cohesion
policy and smart specialisation for 2021–2027 has been extensive and remains
ongoing. It is agreed that the update process should involve a critical evaluation of
the past as well as a discussion which targets to address with smart specialisation
(Tuffs etal., 2020a). In this regard, the primary task of smart specialisation has been
to support innovation in regions helping them to shape structural change (Gianelle,
Kyriakou etal., 2020). Recently, the discussion accelerated again to apply regional
innovation strategies in order to foster green growth and support certain challenges
such as renewable energy or eco-innovation (Foray etal., 2012; Esparza-Masana,
2021). While support in this challenge is required in every region, particularly
less-developed regions which have been suffering from regional decline and are
frequently specialised in non-green technologies that are likely to suffer from
structural change, might benefit (Pîrvu etal., 2019; Provenzano etal., 2020).
The idea to deploy innovation policy to address certain targets is not new but
aligns with earlier strategies such as Europe 2020 which called for not only growth
in itself but smart, inclusive, and sustainable growth (McCann & Soete, 2020). This
aspiration has recently been taken up by the idea of mission-oriented innovation
policy as a new paradigm that regards innovation as an instrument to address larger
societal missions. As previous missions have focused on topics such as defence,
one of the most recent and pressing challenges to be addressed is climate change
(Mazzucato, 2018a; Mazzucato etal., 2019). In this context, it is discussed whether
smart specialisation might play a role for the implementation of the European Green
Deal by integrating the targets of the Sustainable Development Goals (SDGs) and
structural renewal in regional innovation strategies (Montresor & Quatraro, 2018;
Gifford & McKelvey, 2019; Larosse et al., 2020; Nakicenovic et al., 2021). The
discussion goes so far as considering renaming smart specialisation strategies (S3)
into smart specialisation strategies for sustainability (S4). This need for reinterpreta-
tion, redesign, and reintegration of smart specialisation is also officially recognised
by the European Commission (McCann & Soete, 2020; Nakicenovic etal., 2021).
Although sustainability and smart specialisation have already been intertwined over
time, the idea of including additional dimensions rather than strengthening the core
idea first has also provoked criticism (Benner, 2020; Kruse, 2023).
However, research on how smart specialisation could contribute to sustainable
development at regional level is still limited but increases gradually. At the same
time, the attention towards environmental innovation and sustainability is also grow-
ing in related fields such as regional studies and economic geography (e.g. Truffer &
Coenen, 2011; Markard etal., 2012; Gibbs & O’Neill, 2017; Montresor & Quatraro,
2018; Losacker etal., 2021). In the context of smart specialisation and sustainabil-
ity, existing research has been focusing on the opportunities for regional innovation
offered by circular economy approaches (Hristozov & Chobanov, 2020), renewable
energy (Steen etal., 2018), or structural change in old industrial areas (Prause etal.,
2019) with certain regions as examples (Polido etal., 2019).
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Interregional Cooperation inEurope
Interregional collaboration concepts are based on the recognition of a crucial role
of regions for innovation. This assumption is backed by economic geography and
extensive research analysing the concentration of economic activity in time and
space (Audretsch & Feldman, 2004; Guastelle & van Oort, 2015; Hidalgo etal.,
2018). Accordingly, regions exhibit a critical mass of economic actors interacting
in a regional innovation system allowing for a free flow of knowledge and the emer-
gence of innovation. Since spillovers do not easily travel across space, spatial con-
centration of innovative activity is the result. This effect is likely to be self-enforcing
represented in the fact that most of the growth in Europe in the last decade has been
concentrated in cities (Asheim etal., 2018; McCann & Soete, 2020; Pinheiro etal.,
2022). Therefore, regions are also discussed as ideal starting points in the context of
sustainable transition (Potts, 2010; Montresor & Quatraro, 2018).
However, regions do not act in isolation, and positive effects do not only arise from
intra-regional cooperation but also from inter-regional cooperation with other regions.
Such external cooperation contributes to innovativeness, particularly in less-developed
regions, shapes regional development and diversification, allows for the exploitation of
synergies, and prevents regional lock-in effects through the promotion of diversification
(e.g. Benneworth etal., 2014; De Noni etal., 2017; Santoalha, 2018; Mikhaylov etal.,
2018; Schulz, 2019). Particularly in a globalised learning economy, the external aspect
of cooperation should therefore not be left out of consideration. This is even more true
as the recent framing of innovation policy with a stronger focus on transformative
change also highlights the relevance of interregional cooperation (McCann & Ortega-
Argilés, 2016; Schot & Steinmueller, 2018; Giustolisi etal., 2022). Grand challenges,
such as a sustainable economic transition, require different perspectives and diverse
knowledge to be addressed and lay beyond the scope of individual regions or even
countries (Attolico & Scorza, 2016; van den Heiligenberg etal., 2017; Angelis, 2021).
Empirically, it is suggested that knowledge spillovers depend on distance and different
kinds of proximity—among others geographical, relational, functional, institutional,
cognitive, social, or technological proximity—between regions (Lundquist & Trippl,
2009; Boschma & Frenken, 2010; Basile etal., 2012).
Accordingly, innovation systems, focusing on the role of interaction between dif-
ferent actors, stretch across borders. Concepts of global innovation systems (GIS),
national innovation systems (NIS), or technological innovation systems (TIS) have
adopted a cross-border approach from early on (Carlsson, 2006; Shapiro etal., 2010;
Binz & Truffer, 2017). For instance, Chesnais (1992) demonstrated how the opera-
tions of multinational enterprises influence the structure of NIS. Regional innova-
tion systems (RIS) have for a long time been analysed in isolation rather than in
cooperative cross-border settings (Gosens etal., 2014; Li etal., 2022). Stepwise,
the approach has been broadened leading to the establishment of the concept of
cross-border regional innovation systems (CBRIS). Conceptually, CBRIS incor-
porate informational exchange and knowledge diffusion across borders and can be
understood as the most advanced form of integration between regions towards an
integrated innovation space (Lundquist & Trippl, 2009, 2011; Asheim etal., 2011;
Pietrobelli & Rabellotti, 2011; Korhonen etal., 2021). Interregional cooperation
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Journal of the Knowledge Economy
across borders can also relate to a worldwide level, associated with foreign direct
investment (FDI), or global value chain (GVC) concepts (Audretsch & Feldman,
2004; Asheim & Herstad, 2005; Boschma, 2021). However, cross-border coopera-
tion is a more common topic in the literature, referring to the high level of proximity
between neighbouring regions (Lepik & Krigul, 2014; Scott, 2015).
In Europe, research on cross-border cooperation is long established as it can be
understood as an aspect of European integration (De Sousa, 2012; Del Bianco & Andevy,
2015). The process of transnational and interregional cooperation in Europe increased in
the nineteenth century and took off after World War 2 resulting from a political will
for integration (Van der Vleuten & Kaijser, 2005; Scott, 2015). This understanding
was facilitated by agreements such as the Maastricht Treaty and institutionalised
in cross-border cooperation agreements, or the establishment of “euroregions” and
“macroregions” as testbeds for practical transregional and transnational cooperation
(Lina & Bedrule-Grigoruta, 2009; Hudec & Urbancikova, 2010; Studzieniecki, 2016;
Noferini et al., 2020). Moreover, an additional incentive to E cooperation across
regions is the prospect to fully exploit the potential of the European internal market
by overcoming its fragmentation. The establishment of a European research area
with coordinated and integrated interregional research activities has been promoted
as a vision in this regard (Frenken et al., 2007; European Commission, 2020b;
Rakhmatullin etal., 2020). Interregional projects such as INTERREG or HORIZON
represent an institutionalisation of this aspiration (Cassi etal., 2008; Martin-Uceda &
Vicente Rufí, 2021; European Commission, 2022). Also, European instruments such
as smart specialisation cannot be separated from the idea of interregional cooperation.
However, since smart specialisation has emerged from RIS studies, the limitations
described above apply equally and the almost exclusive focus of smart specialisation
on endogenous knowledge flows is among the most common criticisms mentioned in
academic research and policy documents (Tuffs etal., 2020b; Woolford etal., 2021).
Until now, the majority of smart specialisation strategies (S3) do not include or
facilitate interregional cooperation despite an “outward-looking” orientation being
named as a constituting element of the approach from the very beginning (Foray
etal., 2012). This aspired outward orientation was backed by the fact that structural
change and regional innovativeness both benefit from cooperation, external connect-
edness, and knowledge exchange with regions facing similar challenges. Moreover,
the resources and knowledge that a region needs for its development might not be
available at home but outside the region. Different regional characteristics therefore
allow for different perspectives and solutions, as smart specialisation highlights with
its focus on finding the niche and regional competitive advantage for future speciali-
sation (McCann etal., 2015; Mariussen etal., 2019; Foray, 2018). Also, the cohe-
sion aspect of smart specialisation is addressed by extra-regional collaboration since
particularly less-developed and technologically lagging regions often lack the inter-
nal capabilities and networks that they require for a catch-up process (Radosevic &
Ciampi Stancova, 2015; Barzotto etal., 2019; Ghinoi etal., 2020). The same holds
for the focus on grand challenges such as climate change which require the coopera-
tion of different regions. In this regard, Castellani etal. (2022) found indications of a
positive influence of different forms of FDI on regional specialisation in green tech-
nologies, indicating a positive influence of cooperation for a green transition. Most
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Journal of the Knowledge Economy
1 3
likely, an exclusive focus on European regions might not suffice, but an improved
European research cooperation appears to be a necessary foundation for a success-
ful implementation of the Green Deal targets (Woolford etal., 2021; Tuffs etal.,
2020a). Instead, also cooperation with non-EU regions considering certain chal-
lenges might come into play (Uyarra etal., 2014).
However, not only implementation but also research on interregional coop-
eration and smart specialisation has remained limited so far (Radosevic & Ciampi
Stancova, 2015; Balland & Boschma, 2021; Weidenfeld etal., 2021). Apart from
policy papers and qualitative studies, for instance, by Muller etal. (2017), authors
like Gianelle etal. (2014), Girejko etal. (2019), and Kruse and Wedemeier (2021)
present methodologies to identify common priorities between regions as a foun-
dation for common smart specialisation strategies (S3). However, these papers do
not empirically test the efficiency of cooperation and confine to offering a theo-
retical toolkit for policymakers to assess the potential of cooperation with other
regions. Other, more qualitatively oriented, papers presented by Sörvik etal. (2016)
or Mueller-Using etal. (2020) place an emphasis on the factors that motivate or
prevent regions from cooperation. As a result of these shortcomings, transnational
collaboration and strengthening the outward orientation of smart specialisation are
among the demands when it comes to updating cohesion policy and smart speciali-
sation (Esparza-Masana, 2021; Woolford etal., 2021). This also includes strength-
ening the already-existing interregional partnership platforms on smart specialisa-
tion and SDGs which the European Commission has been working on since 2015
and previous approaches to interregional collaboration such as the Vanguard Initia-
tive (Rakhmatullin etal., 2020; Smart Specialisation Platform, 2022a). Moreover,
the Interregional Innovation Investment (I3) instrument represents an additional
European attempt to promote interregional investment particularly in areas relevant
for transformation. The future interconnection with smart specialisation and other
instruments, however, is still under development (Tuffs etal., 2020b).
Interregional Scientific Collaboration inEurope
Materials andMethods
The most common approach in academic research to quantify and map interregional
knowledge flows is the application of patent statistics and co-patenting analyses
involving different regions. With a focus on Europe, Greunz (2005), Sebestyén
and Varga (2013), Guastella and Van Oort (2015), Montresor and Quatraro (2018),
Santoalha (2018), Barzotto etal. (2019), Balland and Boschma (2021), and Li etal.
(2022) apply patent-based analyses. Moreover, von Proff and Brenner (2011) deploy
this approach for German regions, and Yang et al. (2019) and Dosso and Lebert
(2020) do the same for co-patenting on a worldwide level. Co-patenting data are
also used in China, e.g. by Ye and Xu (2021), to construct inter-city cooperation
networks, by Cao etal. (2021) to map the technological field of energy saving, or by
Sun and Cao (2015). However, it has extensively been discussed in the literature that
patent data come with several limitations. One of the most striking ones is that not
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Journal of the Knowledge Economy
all kinds of research necessarily lead to patents as not all inventions are patentable
or patented (Grilliches, 1998). Moreover, patenting activity differs significantly
across scientific disciplines and technologies (Hoekman etal., 2008). This leads to
a regional bias with less-developed regions being structurally neglected in patent-
based analyses (Kakderi etal., 2020). Therefore, other measures of interregional
cooperation are suggested and applied, e.g. co-publications (Hoekman etal., 2008;
Acosta etal., 2011), foreign direct investments (FDI), or monetary flows (Makkonen
et al., 2016; Todeva & Rakhmatullin, 2016). Interregional trade data flows in
Europe are assessed by Gianelle etal. (2014) or Basile etal. (2016), each based on
data from PBL Netherlands Environmental Assessment Agency. Wall and van der
Knaap (2011) construct a dataset of multinational companies and their ownership
linkages with international subsidiaries, while Mitze and Strotebeck (2018) deploy
a commercial industry directory to assess research collaborations in the German
biotechnology industry. Less common are qualitative approaches in interregional
analyses. Here, interview-based studies are presented by Miörner et al. (2018)
and Uyarra et al. (2018), while cooperation networks in cross-border regions are
qualitatively analysed by Fratczak-Müller and Mielczarek-Zelmo (2020).
To empirically assess interregional cooperation at a regional level, particularly in
the field of environmental sustainability, an appropriate dataset is required. For the
European case, this task is challenging for two reasons: on the one hand, the Euro-
pean statistics department Eurostat does not provide regional trade data which would
make a good indicator of interregional involvement and interregional networks. On
the other hand, sustainability is a cross-cutting topic which cannot be assigned to
traditional sector classifications such as the NACE classification. The majority of
the previously described analytical approaches falls short of the task of construct-
ing a regional level database on sustainability cooperation. Instead, it was decided
to use the CORDIS (Community Research and Development Information Service)
database for this task. The CORDIS database contains information on research pro-
jects funded by the EU under the HORIZON and FP7 programmes. Although there
are other funding schemes such as INTERREG which particularly focus on interre-
gional cooperation, these data are less accessible and not compatible with CORDIS
and have been dropped for these reasons. Here, it needs to be remarked that coop-
eration between organisations is tracked rather than cooperation between regions as
such. The geographical location of these organisations in different NUTS2 regions,
however, allows to apply inter-organisational cooperation as a proxy for cooperative
patterns between regions although the analytical level is different, and organisations
rarely address policies or strategies as regions do as a motivation.
One of the advantages of CORDIS in comparison to other approaches is that the
project data can be transformed to a quantitative form and filtered thematically. For
this paper, the projects funded under the Horizon 2020 research and innovation pro-
gramme were analysed (last update 21.01.2022). Horizon 2020 was running from
2014 to 2020 with a budget of about €80 billion to fund multi-national research
and innovation projects in Europe dealing with societal challenges (Mazzucato,
2018b; Giarelis & Karacapilidis, 2021). These programmes have a scientific focus
and include diverse organisations from different regions primarily from Europe but
also from beyond (Boezeman & de Coninck, 2018). The CORDIS database lists
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Journal of the Knowledge Economy
1 3
qualitative information about projects, their focus and results, as well as about
participating organisations, their type, location, and role. Since the programming
period recently ended and Horizon 2020 was replaced by Horizon Europe, the list
can be assumed to provide a complete overview (CORDIS, 2022). However, it
has to be noted that Horizon 2020 primarily addressed technology-oriented pro-
ject partners. While Interreg might have provided a more general picture, analysing
Horizon data inevitable involves a technology bias.
To produce a subset of those projects related to environmental sustainability for
later analysis, a two-step approach was applied: (1) projects were selected on the
basis of funding calls related to environmental sustainability (see Annex 1), and (2)
the project list was filtered for the key terms “green” (1622 projects), “sustainab*”
(1538 projects), and “environment” (9179 projects) in their title or abstract. Finally,
both lists were merged, doublings eliminated, and each project abstract qualitatively
checked to exclude projects not fitting the desired criteria of environmental sustain-
ability. This allowed to reduce the set of 23,378 projects involving 172,730 organisa-
tions funded by Horizon 2020 to 9777 projects and 39,519 organisations. The post-
codes associated with the organisations involved in the projects were then used to
link each region to the respective NUTS3 and NUTS2 region. After all, 72 organisa-
tions could not be linked to a NUTS region due to missing information. Moreover,
each project was attributed to a textual topic to allow for further differentiation. Five
hundred seventeen projects were related to “bioeconomy”, 114 projects to “blue
economy”, 451 to “circular economy”, 410 to “climate research”, 85 to “sustainable
construction”, 1429 to “renewable energy”, 555 to “sustainable mobility”, and 307
to “sustainable technology” (see Annex 2). The numbers give an impression of the
internal focus of environmental sustainability projects in Horizon 2020.
To gather information about interaction between organisations and regions within
the dataset, a social network analysis (SNA) was conducted. SNAs are receiving
increasing attention particularly in economic geography and regional innovation
studies as they allow for an empirical analysis of inter-organisational interaction
as well as knowledge flows inside a network (Tel Wal & Boschma, 2009; Stuck
etal., 2015). Studying the relationship between actors promises to reveal additional
information compared to studying the actors independently. Moreover, SNAs are
regarded as an appropriate tool to analyse cross-regional and interregional innova-
tion systems (Cooke, 2001; Stuck etal., 2015). Common analytical aspects of SNAs
involve the identification of the role of actors in a network and their relationship
among each other as well as the identification of hubs, communities, or authori-
ties via quantitative graph analysis (Wasserman & Faust, 1994; Bandyopadhyay
etal., 2010; Alamsyah etal., 2013; Tabassum etal., 2018). SNAs can be constructed
on different kinds of data that involve various regions (Cidell, 2020; Ghinoi etal.,
2021). Also, CORDIS data have previously been used for SNA, for instance, by
Ertan (2016), based on project data from the 7th Framework Programme, the prede-
cessor of Horizon 2020, or by Bralić (2018), Doussineau etal. (2020), and Morisson
etal. (2020) each based on Horizon 2020 data.
In this paper, the full dataset of cooperation is additionally broken down to a
regional subset covering Northern Germany (involving the NUTS2 regions DE50,
Bremen; DE60, Hamburg; DE80, Mecklenburg-Vorpommern; DE91, Braunschweig;
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Journal of the Knowledge Economy
DE92, Hannover; DE93, Lüneburg; DE94, Weser-Ems; DEF0, Schleswig-Holstein).
Constructing a subset was motivated by the fact that the full dataset would be too
large to analyse individual connections so that a focus had to be applied. Thereby,
Northern Germany qualified itself through the diverse nature of regions including
large cities (Hamburg, Bremen) on the one hand and more rural regions (Lüneburg,
Weser-Ems) on the other hand. Also, the region has been analysed in the context of
sustainable transition, matching the focus of this paper (Hassink etal., 2021; Kruse
& Wedemeier, 2022). This regional subset complements Morisson etal. (2020) who
conducted a network analysis based on the Italian region of Calabria. Constructing
a network with the eight Northern German NUTS2 regions as the core and with-
out modelling connections among the partner regions with each other results in an
SNA with 9179 edges and 357 unique combinations of the eight regions cooperating
with each other and other regions around the world. Calculations were conducted
using the R Studio programme (version 4.2.0) including the igraph and sna packages
(Csardi & Nepusz, 2006; Butts, 2020). Graphical illustrations were prepared using
the Gephi programme (version 0.9.5 202205022109).
Results
In a descriptive way, Figs. 1 and2 illustrate the absolute number of organisations
involved in projects on environmental sustainability in European regions. Thereby,
Fig. 1 Organisations involved in interregional H2020 Sustainability Projects, NUTS2 level, 2022. Source:
CORDIS (2022), own depiction
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it was decided to abstain from a differentiation subject to certain years as the ana-
lysed projects have different durations. Moreover, project funding received was not
included as a weight since it did not match with further analytical steps of SNA.
Instead, the number of organisations was accumulated per region as a measure of
the strength of interregional involvement in sustainability research (for the list of
regions and number of identified organisations on NUTS2 level, see Annex 3). The
geographical mapping follows the NUTS classification (“nomenclature of territorial
units for statistics”) provided by Eurostat (2021). As can be seen, the distribution is
not even, but organisations involved in interregional projects are highly concentrated
in certain regions. Generally, there is no clear West-East or North-South picture as
the intensive of cooperation is highly shaped by individual hotspots (see Fig. 2).
While these hotspots tend to be capital regions or highly urbanised areas, they are
found in all parts of Europe. A dominance of Western or Northern Europe, as found
in other European studies (e.g. Kruse etal., 2022), is not observable here. However,
in Eastern Europe, many regions have not been involved in projects on environmen-
tal sustainability so far which represents a potential still to be tapped.
To empirically test whether certain structural characteristics of regions influence
the number of organisations involved in interregional research, a Pearson’s product-
moment correlation was calculated and tested using NUTS2-level data (see Table1).
The tested variables included GDP per capita at current market prices (GDP) and
Fig. 2 Organisations involved in interregional H2020 Sustainability Projects, NUTS3 level, 2022. Source:
CORDIS (2022), own depiction
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Table 1 Pearson Correlation and Test Results
Sources: Eurostat (2023a, b, c; d; e, f, g, h), own calculations
GDP GVA Age Density GERD WASTEEMP WASTEGEN Cooling
Correlation coefficient 0.4162655 0.7825878 −0.1283997 0.3618056 0.3716083 0.6668699 0.4090069 0.07141354
t-test statistic 7.0924 19.475 −1.9932 5.9747 5.3851 12.172 4.9507 1.0858
Degrees of freedom 240 240 237 237 181 185 122 230
p value 1.47E-11 < 2.2e-16 4.74E-02 8.39E-09 2.23E-07 < 2.2E-16 2.40E-06 2.79E-01
Confidence Interval
(95%) [0.3062361,
0.5153195]
[0.6283639,
0.8270705]
[−0.251201853,
-0.001529623]
[0.2462146,
0.4672486]
[0.2394596,
0.4902390]
[0.5787589,
0.7395908]
[0.2507749,
0.5459523]
[−.05791786,
0.19838746]
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gross value added (GVA) which allow for a quantification of the development stage
of the regional economy. The indicators for median age of the population (AGE) and
population density (DENSITY) describe regional structures, while gross domes-
tic expenditure on R&D (GERD) refers to the relevance attributed to research in
regions. Moreover, indicators were analysed that can function as a proxy for envi-
ronmental aspects. Since the availability of environmental data for Europe is lim-
ited, particularly at regional level, these data can only be an approximation. An indi-
cator was included measuring the employment in waste collection, treatment, and
disposal activities as well as materials recovery (WASTEEMP) as well as an indica-
tor measuring the amount of municipal waste in tonnes (WAGEGEN). The latter
data come from a pilot project and therefore are only available for 2013, while all
other data refer to 2019 as the base year. The generation of waste gives an idea of
the public awareness towards environmental affairs. Finally, an index was included
measuring the need for additional cooling of buildings as an indicator of regional
climate change impact (COOLING). (Eurostat, 2023a, b, c, d, e, f, g). Naturally,
testing data of a single year does not yield a sufficient number of observations to
provide empirical significance. However, the correlation test helps to interpret and
classify the results.
The p value of the results implies correlations of different strengths between the
engagement of regions in environmental sustainability research projects and the
tested variables. For the interpretation of results, the effect strength suggested by
Cohen (1988) is applied. Based on this assumption, GDP and GVA underline that
the involvement in interregional projects is affected by economic strength. Inter-
estingly, the spending on R&D (GERD) is only moderately correlated allowing to
conclude that research projects are also initiated in regions which are still in the
process of transformation towards a knowledge economy. Also, the population den-
sity is only moderately correlated as well as the median age of the population with
a weak negative correlation. These results suggest that highly urbanised regions are
more equipped to get involved in interregional cooperation, but an urban structure
does not represent a definite requirement. Finally, the environmental indicators are
moderately (WASTEGEN) and highly correlated (WASTEEMP). This can be seen
as an indication that the involvement in interregional sustainability projects does
indeed reflect regional environmental awareness to a certain degree and the involve-
ment can be interpreted also as a measure of regional sustainability relevance. On
the other hand, the regional impact of climate change (COOLING) does not signifi-
cantly influence whether regions get involved in related research projects.
Considering that smart specialisation is promoted as a tool to support regional
structural change and sustainable transition, the question arises whether the empir-
ical results of certain regions being strongly involved in interregional projects on
environmental sustainability match the smart specialisation strategies (S3) formu-
lated by the regions. To test this assumption, the Eye@RIS3 database, containing
information about priorities in S3 of European NUTS2 regions, which is the level
S3 are implemented at, was filtered for those regions listing domains related to envi-
ronmental sustainability in their strategy (Smart Specialisation Platform, 2022b).
Regarding the domains, only the scientific domains were analysed since cooperation
data relate to Horizon 2020 representing a framework of research and innovation
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projects (for the filter criteria, see Annex 4. The list of regions is accessible in
Annex 3). Of 371 NUTS2 regions, 232 did list a scientific specialisation in sus-
tainability, while 139 did not. Regarding the involvement in interregional projects,
the analysed NUTS2 regions on average were involved in 101 projects. Of the 100
regions that scored above average in interregional cooperation projects on environ-
mental sustainability, 23 did not list sustainability as a scientific focus. On the other
hand, seven of the 55 regions not involved in any project listed environmental sus-
tainability as a scientific priority in their S3. Assuming that smart specialisation (1)
aims to promote economic specialisations such as environmental sustainability and
(2) aims to promote interregional cooperation, it seems remarkable that the lists of
regions involved in interregional sustainability projects and regions that have fixed
outward-orientation and sustainability in their S3 are not congruent.
Regarding the constructed network of Northern German NUTS2 regions, the
social network is shown in Fig.3. Those regions that Northern Germany frequently
cooperates with are shown in the middle of the network with coloured edges as an
additional weight indicating the intensity of cooperation. The NUTS codes reveal
that cooperation in interregional projects on environmental sustainability focuses
primarily on other regions in Germany as well as Austria, Belgium, Denmark, Fin-
land, France, Italy, the Netherlands, Poland, Spain, Sweden, Switzerland, and the
UK. It appears to be of no coincidence that, apart from Luxemburg, all neighbouring
countries to Germany are among the most important cooperation partners. The full
cooperation network is provided in Annex 5. An additional perspective is provided
in Fig.4 which illustrates the intensity of cooperation between Northern Germany
Fig. 3 Weighted network of Northern German Regions in H2020Sustainability projects, 2022. Source:
CORDIS (2022)
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and European regions. Here, it is revealed that neighbouring regions tend to cooper-
ate with Northern German regions. This supports the assumption of (geographical
and cultural) proximity as a facilitating factor for cooperation. However, geographi-
cal proximity is not a limiting factor for cooperation, as strong cooperative ties are
observable with regions in all parts of Europe including non-EU countries such
as Turkey or the UK. This picture can partly be explained by the nature to receive
funding. Nevertheless, Fig.4 allows to state that environmental cooperation is not
geographically limited in Europe and the Horizon funding scheme appears to have
succeeded in connecting researchers from regions which would not have cooperated
assuming the traditional proximity hypothesis.
An Additional empirical analysis of the network has been conducted by measuring
different kinds of centrality, namely, closeness, betweenness, degree, and eigenvec-
tor centrality. These measures give an indication on the overall position of a node
and the theoretical time it would take to reach other nodes (closeness centrality), the
extent at which a node lies between other nodes in the network and the percentage of
shortest paths passing through the node (betweenness centrality), the number of links
incident upon a node (degree centrality), and the relative score of each node meas-
uring how well a well-connected node is connected to other well-connected nodes
(Tabassum etal., 2018). Table2 lists the top-20 regions for each measure of central-
ity and the respective value. Not surprisingly, the Northern German regions score the
highest which is due to the design of the network putting said regions in the centre
Fig. 4 Interregional cooperation of Northern Germany in H2020 Sustainability Projects, NUTS2 level,
2022. Source: CORDIS (2022), own depiction
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Journal of the Knowledge Economy
of it. However, the regions beyond Northern Germany, which play an important role
within the cooperation network, are similar to those in the centre of Fig.3.
Discussion andLimitations
The descriptive findings show differentiated geographical patterns when it comes to
the involvement of European regions in interregional research projects dealing with
environmental sustainability. At NUTS2 level, a light distinction between Western
and Eastern Europe becomes visible (see Fig.1). Thereby, Eastern European NUTS2
regions in their majority are in fact involved in interregional projects rather than
being not involved at all, but to a considerably smaller degree than other regions.
The picture becomes clearer when looking at the NUTS3 regions (see Fig.2). Here,
it can be seen that interregional activity is highly concentrated in particular regions
which are also to be found in Eastern or Southern Europe which often are regarded
as less-developed areas in regional studies. Hoekman etal. (2008) describe these
patterns as “elite structures”. These regions with particularly strong interregionality
Table 2 Centrality measures of the network of Northern German regions in H2020Sustainability projects,
2022
Source: own calculations
Rank Closeness centrality Betweenness centrality Degree centrality Eigenvector
centrality
1 DE60 0.002283 DE60 16985.7835 DE60 1982 DE60 1.0000
2 DE50 0.002262 DE50 15367.3087 DE50 1927 DE50 0.9490
3 DE91 0.002183 DEF0 9211.3129 DEF0 1320 DEF0 0.6742
4 DEF0 0.002141 DE91 9000.5362 DE91 1206 FR10 0.5932
5 DE94 0.002066 DE94 6180.0952 DE94 1018 DE91 0.5809
6 DE92 0.002024 DE92 4329.4028 DE92 869 DE94 0.5076
7 DE80 0.001942 DE80 3311.3653 DE80 574 BE10 0.4247
8 DE93 0.001842 DE93 1646.5477 DE93 425 DE92 0.4124
9 CH02 0.001420 FR10 1.5533 FR10 306 ES30 0.3990
10 CH04 0.001420 ES51 1.0067 BE10 222 NL33 0.3864
11 DE21 0.001420 NL33 0.9708 ES30 209 ITI4 0.3433
12 DEA2 0.001420 BE10 0.9097 NL33 206 DE21 0.3340
13 ES51 0.001420 DK01 0.8054 ITI4 176 ES51 0.3246
14 FR10 0.001420 ES30 0.7047 ES51 174 DK01 0.2982
15 NO08 0.001420 ITI4 0.5825 DE21 171 DE80 0.2750
16 UKJ1 0.001420 EL30 0.3955 DK01 153 EL30 0.2596
17 EL30 0.001420 FI1B 0.3883 EL30 137 FI1B 0.2497
18 ITI4 0.001420 UKI3 0.3416 FI1B 132 DEA2 0.2461
19 NL31 0.001420 DE21 0.3236 DEA2 129 UKI3 0.2294
20 PT17 0.001420 NO08 0.2876 UKI3 115 NO08 0.2208
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scores are particularly urban, and most NUTS3 concentration patterns refer to capi-
tal or major city agglomerations. The conducted correlation analysis confirms that a
connection between regional factors such as GDP or economic structure and inter-
regional orientation can be assumed (see Table1). More rural areas, for instance,
in Eastern Europe but also in large parts of Germany, are not active in interregional
cooperation. This finding partly contradicts Santoalha (2018) identifying regions in
Benelux, Germany, and Central and Eastern Europe to be relatively strong in inter-
regional collaboration. However, this contradiction might be due to the focus of the
particular dataset in this paper on environmental sustainability as Horizon projects
are research-oriented and high-tech research tends to be spatially concentrated to a
high degree. Moreover, the dataset cannot provide an answer to the question whether
certain groups of regions do not deal with environmental sustainability at all or
whether they simply do not engage in high-level research and interregional collabo-
ration. This is further amplified by the fact that organisations rather than regions
themselves were analysed. As sustainability is hardly measurable using individual
indicators, the findings need to be complemented by additional research applying
different datasets to paint a more complete picture.
Thereby, the observed concentration patterns align with related literature on
regional innovation. Spatial clusters of knowledge-intensive regions are regularly
identified and attributed to urban advantages, density, and clusters of innovation
actors from the triple helix (Van den Heiligenberg etal., 2017). Particularly com-
plex economic activities and scientific research tend to concentrate in larger cit-
ies and metropolitan areas (Acosta etal., 2011; Balland etal., 2018; Tödtling &
Trippl, 2005). From a cohesion perspective, these findings are alarming: smart spe-
cialisation and innovation policy in Europe focus on bridging existing regional dis-
parities by empowering less developed regions. The evidence that particularly those
regions that would benefit most from interregional knowledge exchange are the least
involved was expectable but is not desirable from a policy perspective (Camagni
& Capello, 2013; McCann & Ortega-Argilés, 2015; Corradini, 2019). Moreover,
the future topic of a sustainable transition, which is also particularly relevant for
less-developed regions as they tend to be more vulnerable due to an old-industrial
economic structure and fewer green specialisations, again reveals structures to the
disadvantage of less-developed regions. Existing policy instruments apparently have
not managed to overcome the persistent dichotomy which is likely to reproduce
since research generally also translates into economic hard facts in the long run.
However, the picture might become more differentiated when other, less competi-
tive, collaborative programmes such as Interreg, as opposed to Horizon 2020 data in
this paper, are considered, as suggested by Woolford etal. (2021).
Regarding the fit between scientific specialisation mentioned in official S3
and actual performance as measured by involvement in research projects, both
spheres do not fully match. The analysis has shown that a group of regions which
are quite active in interregional projects on environmental sustainability do not
mention this as a strength in their S3, while, on the other hand, some regions offi-
cially announce a specialisation which is not backed by statistical analysis. Here,
it needs to be remarked that organisations rarely address policies or strategies
such as smart specialisation strives to do. As organisations are used as a proxy
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for interregional cooperation, they must not necessarily have an impact on smart
specialisation strategies. In this context, a different methodological approach
was chosen by D’Adda et al. (2018) asking the same question for technological
domains in Italian regions. Also here, the findings imply that S3 and real-life per-
formance are characterised by a certain level of divergence. The same finding is
mentioned by Sörvik and Kleibrink (2015) as well as Deegan etal. (2021) imply-
ing that European smart specialisation and European science policy need to be
better aligned and the preparation of S3 requires a stronger statistical foundation.
The second analytical step of this paper, the construction of a cooperation net-
work of Northern German regions, also confirms previous studies. It is generally
assumed that knowledge spillovers tend to focus on close regions whereby different
measures of proximity such as geography, similar languages, culture, and policies
are relevant (Greunz, 2005; Basile etal., 2012; Dosso & Lebert, 2020). Our analysis
shows that Northern German regions cooperate with all parts of Europe and also
several countries beyond Europe (see Annex 5). Although strong cooperative ties
are observed with regions in direct proximity, the Horizon programme has success-
fully contributed to the establishment of scientific cooperation with regions which
would otherwise not have cooperated following the proximity hypothesis. This can
be interpreted as a step towards the establishment of a European research area as
HORIZON allows to bridge some of the major obstacles, namely, that research-
ers cooperate based on geographical proximity and tend to cooperate with similar
organisations in similar regions (Frenken etal., 2007). Moreover, in light of grand
challenges, such as the fight against climate change, external cooperation is strongly
advised (Uyarra etal., 2014). Northern Germany matches this suggestion, and the
analysis blends in with other papers assigning the region an important role for a sus-
tainable transition (e.g. Hassink etal., 2021; Kruse & Wedemeier, 2022).
Conclusion
Innovation has been identified as one of the key levers for regional prosperity and
sectoral renewal. Accordingly, innovation in Europe is not only discussed in terms
of cohesion and bridging interregional disparity but also as a means to contribute
to a sustainable transition facilitated by the EU Green Deal. In this context, coop-
eration and knowledge exchange have led to the recognition that innovation is to be
studied from a network perspective, institutionalised in systematic theories such as
regional innovation systems (RIS). These also form the theoretic foundation of smart
specialisation, the European policy approach to support innovation and regional
positioning. Cooperation, mutual learning, and knowledge exchange are thereby evi-
dently important factors for regional economic prosperity, new path development
and diversification (Mariussen etal., 2016). Despite smart specialisation highlight-
ing the relevance of interregional cooperation since the time the concept was devel-
oped about a decade ago, practical implementation and empirical research in this
regard have remained limited. The paper at hand addresses this issue by discussing
how smart specialisation might contribute to the grand challenge of a sustainable
transition in Europe and which role interregional cooperation can play in this regard.
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Moreover, the current state of research on interregional cooperation in Europe is
presented showing that the previous studies predominantly rely on patent data for
empirical analyses. To broaden the picture and overcome the limitations of pat-
ent data, such as a technological and regional bias, data on Horizon 2020 (H2020)
research projects in Europe were analysed and a database of interregional activity
related to environmental sustainability was constructed.
The findings reveal that organisational involvement in interregional European
projects is highly concentrated in urban and capital regions. A correlation analy-
sis confirms that regional characteristics such as GDP or population density posi-
tively influence a region’s involvement in interregional research projects on environ-
mental sustainability. This aspect is alarming from a policy perspective as existing
divergency patterns are reproduced this way instead of being bridged. Particularly
an urban-rural separation is likely to keep manifesting when today’s research trans-
lates into economic strength in the future. Moreover, this development contradicts
the aspiration of smart specialisation to use innovation policy for the achievement of
regional convergence. Also, it was shown that smart specialisation strategies (S3) do
not adequately match practical specialisations when it comes to interregional activ-
ity. Since other studies suggest the same implication of S3 not reflecting economic
reality, this raises questions for an update of smart specialisation which should pay
more attention to statistical analyses prior to the strategy formulation process. To
receive further insights into the internal network structure of the database, a social
network analysis (SNA) was conducted, placing the Northern German NUTS2
regions in the centre. This analysis proved that cooperation appears to be positively
influenced by geographical and cultural proximity, but cooperation is also observ-
able with regions that are neither geographically nor culturally proximate. It can be
assumed that the aspiration of Horizon 2020, to promote interregional cooperation
and facilitate knowledge flows between regions, has been successful to the point
where cooperation networks are established that would not have emerged without
European research funding. This is particularly relevant in the field of environ-
mental sustainability research considering the increasing need to adapt to the UN
SDGs and to overcome previous limitations of a fragmented European research area
(Kattel & Mazzucato, 2018; Mazzucato & Penna, 2020). Generally, the analyses in
this paper confirm that innovation cooperation on environmental sustainability in
Europe is established but further measures are required to address certain shortcom-
ings such as regional convergence.
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Annex 1. Horizon 2020 Calls Related toEnvironmental Sustainability
Section Work programme Time Call Topic
Excellent science
Future and Emerging
Technologies 2018–2020 FET proactive topics in the EIC Enhanced Pilot (2019-2020)
FETPROACT-EIC-08-2020
Industrial leadership
Leadership in enabling and
industrial technologies 2014–2020 Factories of the Future
FoF 3 – 2014
Energy-efficient Buildings
EeB 5 – 2015
EeB 6 – 2015
EeB 7 – 2015
EE 2 – 2015
Sustainable Process Industries
SPIRE 2-2014
SPIRE 4-2014
SPIRE 6-2015
SPIRE 7-2015
LCE 2-2014/2015
LCE 3-2014/2015
EE 18-2014/2015
Waste 1-2014
Societal challenges
Food Security, Sustainable
Agriculture and Forestry,
Marine, Maritime and
Inland Water Research
and the Bioeconomy
2014–2015 Call for Sustainable Food Security
SFS-x-20xx
Call for Blue Growth: Unlocking the Potential of Seas and Oceans
BG-x-20xx
Call for an Innovative, Sustainable and Inclusive Bioeconomy
ISIB-x-20xx
2016–2017 Call Sustainable Food Security - Resilient and Resource-
Efficient Value Chains
SFS-xx-20xx
Call Blue Growth - Demonstrating an Ocean of Opportunities
BG-xx-20xx
Call Rural Renaissance - Fostering Innovation and Business
Opportunities
RUR-07-2016
Call Bio-based Innovation for Sustainable Goods and Services -
Supporting the Development of a European Bioeconomy
BB-xx-20xx
2018–2020 Call Sustainable Food Security
SFS-xx-20xx
LC-SFS-19 bis 25 - 20xx
Call Blue Growth
BG-xx-20xx
Call Food and Natural Resources
FNR-xx-20xx
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Section Work programme Time Call Topic
Secure, Clean and
Efficient Energy 2014–2015 Call Energy Efficiency
EE x - 20xx
Call Competitive LOW-CARBON Energy
LCE - x - 20xx
Call Smart Cities and Communities
SSC - x - 20xx
Call SMEs and Fast Track to Innovation for Energy
SIE x - 20xx
2016–2017 Energy Efficiency Call 2016-2017
EE-xx-20xx
Call Competitive Low-Carbon Energy
LCE - x - 20xx
Smart, Green
and Integrated
Transport
2014–2015 Call Mobility for Growth
MG.x.x-20xx
Call Green Vehicles
GV.x.20xx
2016–2017 Call 2016-2017 Mobility for Growth
MG-x.x-20xx
Call 2016-2017 Green Vehicles
GV-xx-20xx
2018–2020 Call 2018-2020 Mobility for Growth
LC-MG-x-x-20xx
MG-BG-xx-20xx
Call Building a Low-Carbon, Climate Resilient Future: Green
Vehicles
LC-GV-xx-20xx
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Section Work programme Time Call Topic
Climate Action,
Environment, Resource
Efficiency and Raw
Materials
2014–2015 Call Waste: A Resource to Recycle, Reuse and Recover Raw
Materials
WASTE-x-20xx
Call Water Innovation: Boosting its value for Europe
WATER-x-20xx
Call Growing a Low Carbon, Resource Efficient Economy with
a Sustainable Supply of Raw Materials
SC5-x-20xx
2016–2017 Call Greening the Economy
SC5-xx-20xx
2018–2020 Call Building a Low-Carbon, Climate Resilient Future: Climate
Action in Support of the Paris Agreement
LC-CLA-xx-20xx
Call Greening the Economy in Line with the Sustainable
Development Goals (SDGs)
CE-SC5-xx-20xx
Secure societies - Protecting
freedom and security of
Europe and its citizens
2014–2015 Call Disaster-Resilience: Safeguarding and Securing Society,
Including Adapting to Climate Change
DRS-9 bis 11 - 20xx
Europe in a changing
world
2018–2020 TRANSFORMATIONS-03-2018-2019
TRANSFORMATIONS-06-2018
Focus areas
2018–2020 Societal Challenge 3 Secure, Clean and Efficient Energy
SC3 - x - 20xx
Societal Challenge 4 Smart, Green and Integrated Transport
SC4 - x - 20xx
Societal Challenge 2 Food Security, Sustainable Agriculture
and Forestry, Marine, Maritime and Inland Water Research
and the Bioeconomy
SC2 - x - 20xx
LEIT – NMBP
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Annex 2. Thematic Priorities inH2020 Projects onEnvironmental
Sustainability, NUTS2 Level. Source: CORDIS (2022), Own Depiction
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Annex 3. Number ofOrganisations Involved inH2020 Projects
onEnvironmental Sustainability andScientific S3 Priorities, NUTS2
Level, Source: CORDIS (2022)
NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
BE10 Région de Bruxelles-Capitale/Brussels Hoofdstedelijk
Gewest
1246 0 1
BE21 Prov. Antwerpen 290 0 1
BE22 Prov. Limburg (BE) 50 0 1
BE23 Prov. Oost-Vlaanderen 270 0 1
BE24 Prov. Vlaams-Brabant 300 0 1
BE25 Prov. West-Vlaanderen 72 0 1
BE31 Prov. Brabant wallon 47 0 0
BE32 Prov. Hainaut 44 0 0
BE33 Prov. Liège 56 0 0
BE34 Prov. Luxembourg (BE) 4 0 0
BE35 Prov. Namur 20 0 0
BG31 Severozapaden 2 0 0
BG32 Severen tsentralen 4 0 0
BG33 Severoiztochen 16 0 0
BG34 Yugoiztochen 7 0 0
BG41 Yugozapaden 191 0 0
BG42 Yuzhen tsentralen 21 0 0
CZ01 Praha 180 1 0
CZ02 Strední Cechy 10 1 0
CZ03 Jihozápad 24 1 0
CZ04 Severozápad 16 1 0
CZ05 Severovýchod 16 1 0
CZ06 Jihovýchod 72 1 0
CZ07 Strední Morava 16 1 1
CZ08 Moravskoslezsko 13 1 0
DK01 Hovedstaden 527 1 1
DK02 Sjælland 35 1 1
DK03 Syddanmark 98 1 1
DK04 Midtjylland 202 1 1
DK05 Nordjylland 128 1 1
DE11 Stuttgart 279 1 1
DE12 Karlsruhe 179 1 1
DE13 Freiburg 121 1 1
DE14 Tübingen 48 1 1
DE21 Oberbayern 717 1 1
DE22 Niederbayern 18 1 1
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
DE23 Oberpfalz 23 1 1
DE24 Oberfranken 18 1 1
DE25 Mittelfranken 37 1 1
DE26 Unterfranken 33 1 1
DE27 Schwaben 43 1 1
DE30 Berlin 412 1 1
DE40 Brandenburg 132 1 1
DE50 Bremen 153 1 1
DE60 Hamburg 187 1 1
DE71 Darmstadt 197 1 1
DE72 Gießen 13 1 1
DE73 Kassel 36 1 1
DE80 Mecklenburg-Vorpommern 59 1 1
DE91 Braunschweig 99 1 1
DE92 Hannover 63 1 1
DE93 Lüneburg 27 1 1
DE94 Weser-Ems 86 1 1
DEA1 Düsseldorf 239 1 1
DEA2 Köln 619 1 1
DEA3 Münster 46 1 1
DEA4 Detmold 25 1 1
DEA5 Arnsberg 73 1 1
DEB1 Koblenz 10 1 1
DEB2 Trier 5 1 1
DEB3 Rheinhessen-Pfalz 45 1 1
DEC0 Saarland 15 1 1
DED2 Dresden 87 1 0
DED4 Chemnitz 52 1 0
DED5 Leipzig 87 1 0
DEE0 Sachsen-Anhalt 55 1 1
DEF0 Schleswig-Holstein 96 1 1
DEG0 Thüringen 39 1 1
EE00 Eesti 217 1 1
IE04 Northern and Western 70 0 0
IE05 Southern 196 0 0
IE06 Eastern and Midland 311 0 0
EL30 Attiki 800 1 1
EL41 Voreio Aigaio 17 1 0
EL42 Notio Aigaio 14 1 1
EL43 Kriti 72 1 1
EL51 Anatoliki Makedonia, Thraki 13 1 1
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
EL52 Kentriki Makedonia 284 1 1
EL53 Dytiki Makedonia 16 1 1
EL54 Ipeiros 9 1 1
EL61 Thessalia 27 1 1
EL62 Ionia Nisia 3 1 1
EL63 Dytiki Ellada 45 1 1
EL64 Sterea Ellada 21 1 1
EL65 Peloponnisos 6 1 1
ES11 Galicia 167 0 0
ES12 Principado de Asturias 54 0 1
ES13 Cantabria 38 0 1
ES21 País Vasco 673 0 1
ES22 Comunidad Foral de Navarra 156 0 1
ES23 La Rioja 36 0 0
ES24 Aragón 195 0 1
ES30 Comunidad de Madrid 1111 0 1
ES41 Castilla y León 178 0 1
ES42 Castilla-la Mancha 35 0 1
ES43 Extremadura 35 0 1
ES51 Cataluña 1008 0 1
ES52 Comunitat Valenciana 382 0 1
ES53 Illes Balears 29 0 1
ES61 Andalucía 315 0 1
ES62 Región de Murcia 81 0 1
ES63 Ciudad de Ceuta 0 0 0
ES64 Ciudad de Melilla 0 0 0
ES70 Canarias 83 0 1
FR10 Île de France 1900 0 1
FRB0 Centre - Val de Loire 68 0 0
FRC1 Bourgogne 65 0 0
FRC2 Franche-Comté 0 0 1
FRD1 Basse-Normandie 25 0 1
FRD2 Haute-Normandie 16 0 1
FRE1 Nord-Pas-de-Calais 56 0 1
FRE2 Picardie 42 0 1
FRF1 Alsace 53 0 1
FRF2 Champagne-Ardenne 14 0 1
FRF3 Lorraine 26 0 1
FRG0 Pays-de-la-Loire 81 0 1
FRH0 Bretagne 122 0 1
FRI1 Aquitaine 106 0 1
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
FRI2 Limousin 8 0 1
FRI3 Poitou-Charentes 34 0 1
FRJ1 Languedoc-Roussillon 75 0 1
FRJ2 Midi-Pyrénées 201 0 0
FRK1 Auvergne 23 0 1
FRK2 Rhône-Alpes 270 0 1
FRL0 Provence-Alpes-Côte d’Azur 195 0 1
FRM0 Corse 2 0 1
FRY1 Guadeloupe 4 0 1
FRY2 Martinique 3 0 1
FRY3 Guyane 1 0 1
FRY4 La Réunion 1 0 1
FRY5 Mayotte 0 0 0
HR02 Panonska Hrvatska 8 0 0
HR03 Jadranska Hrvatska 65 1 0
HR05 Grad Zagreb 140 1 0
HR06 Sjeverna Hrvatska 18 1 0
ITC1 Piemonte 456 0 1
ITC2 Valle d’Aosta/Vallée d’Aoste 6 0 1
ITC3 Liguria 233 0 1
ITC4 Lombardia 691 0 1
ITF1 Abruzzo 29 0 1
ITF2 Molise 3 0 0
ITF3 Campania 161 0 1
ITF4 Puglia 123 0 1
ITF5 Basilicata 14 0 1
ITF6 Calabria 23 0 1
ITG1 Sicilia 47 0 1
ITG2 Sardegna 29 0 1
ITH1 Provincia Autonoma di Bolzano/Bozen 47 0 1
ITH2 Provincia Autonoma di Trento 76 0 1
ITH3 Veneto 232 0 1
ITH4 Friuli-Venezia Giulia 87 0 1
ITH5 Emilia-Romagna 403 0 1
ITI1 Toscana 297 0 0
ITI2 Umbria 43 0 0
ITI3 Marche 69 0 1
ITI4 Lazio 870 0 1
CY00 Kypros 217 1 0
LV00 Latvija 142 1 1
LT01 Sostines regionas 70 1 0
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Journal of the Knowledge Economy
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
LT02 Vidurio ir vakaru Lietuvos regionas 49 1 0
LU00 Luxembourg 104 1 1
HU11 Budapest 216 0 0
HU12 Pest 35 0 0
HU21 Közép-Dunántúl 22 0 0
HU22 Nyugat-Dunántúl 18 0 0
HU23 Dél-Dunántúl 10 0 0
HU31 Észak-Magyarország 14 0 0
HU32 Észak-Alföld 6 0 0
HU33 Dél-Alföld 23 0 0
MT00 Malta 59 1 1
NL11 Groningen 102 0 1
NL12 Friesland (NL) 20 0 1
NL13 Drenthe 22 0 1
NL21 Overijssel 110 0 1
NL22 Gelderland 387 0 1
NL23 Flevoland 17 0 1
NL31 Utrecht 247 0 1
NL32 Noord-Holland 417 0 1
NL33 Zuid-Holland 879 0 1
NL34 Zeeland 13 0 1
NL41 Noord-Brabant 277 0 1
NL42 Limburg (NL) 83 0 1
AT11 Burgenland (AT) 14 1 1
AT12 Niederösterreich 110 1 1
AT13 Wien 562 1 0
AT21 Kärnten 22 1 1
AT22 Steiermark 291 1 1
AT31 Oberösterreich 95 1 0
AT32 Salzburg 20 1 0
AT33 Tirol 34 1 1
AT34 Vorarlberg 15 1 1
PL21 Malopolskie 62 1 1
PL22 Slaskie 51 1 1
PL41 Wielkopolskie 57 1 1
PL42 Zachodniopomorskie 20 1 1
PL43 Lubuskie 1 1 1
PL51 Dolnoslaskie 34 1 1
PL52 Opolskie 3 1 1
PL61 Kujawsko-Pomorskie 5 1 1
PL62 Warminsko-Mazurskie 11 1 0
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
PL63 Pomorskie 56 1 1
PL71 Lódzkie 38 1 1
PL72 Swietokrzyskie 4 1 1
PL81 Lubelskie 20 1 1
PL82 Podkarpackie 4 1 1
PL84 Podlaskie 0 1 1
PL91 Warszawski stoleczny 236 1 1
PL92 Mazowiecki regionalny 5 1 1
PT11 Norte 260 1 0
PT15 Algarve 30 1 1
PT16 Centro (PT) 129 1 1
PT17 Área Metropolitana de Lisboa 472 1 1
PT18 Alentejo 56 1 1
PT20 Região Autónoma dos Açores (PT) 27 1 1
PT30 Região Autónoma da Madeira (PT) 22 1 1
RO11 Nord-Vest 49 1 1
RO12 Centru 51 1 1
RO21 Nord-Est 24 1 1
RO22 Sud-Est 37 1 1
RO31 Sud - Muntenia 13 1 1
RO32 Bucuresti - Ilfov 254 1 0
RO41 Sud-Vest Oltenia 10 1 1
RO42 Vest 10 1 1
SI03 Vzhodna Slovenija 85 1 0
SI04 Zahodna Slovenija 330 1 0
SK01 Bratislavský kraj 84 0 0
SK02 Západné Slovensko 28 0 0
SK03 Stredné Slovensko 22 0 0
SK04 Východné Slovensko 12 0 0
FI19 Länsi-Suomi 103 0 1
FI1B Helsinki-Uusimaa 564 0 1
FI1C Etelä-Suomi 104 0 1
FI1D Pohjois- ja Itä-Suomi 141 0 1
FI20 Åland 1 0 0
SE11 Stockholm 343 0 1
SE12 Östra Mellansverige 234 0 1
SE21 Småland med öarna 30 0 1
SE22 Sydsverige 128 0 1
SE23 Västsverige 351 0 1
SE31 Norra Mellansverige 32 0 1
SE32 Mellersta Norrland 16 0 1
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
SE33 Övre Norrland 103 0 1
UKC1 Tees Valley and Durham 23 0 0
UKC2 Northumberland and Tyne and Wear 65 0 0
UKD1 Cumbria 19 0 0
UKD3 Greater Manchester 92 0 1
UKD4 Lancashire 4 0 0
UKD6 Cheshire 28 0 0
UKD7 Merseyside 9 0 0
UKE1 East Yorkshire and Northern Lincolnshire 18 0 0
UKE2 North Yorkshire 34 0 0
UKE3 South Yorkshire 2 0 0
UKE4 West Yorkshire 59 0 0
UKF1 Derbyshire and Nottinghamshire 69 0 0
UKF2 Leicestershire, Rutland and Northamptonshire 57 0 1
UKF3 Lincolnshire 5 0 0
UKG1 Herefordshire, Worcestershire and Warwickshire 122 0 0
UKG2 Shropshire and Staffordshire 22 0 0
UKG3 West Midlands 148 0 0
UKH1 East Anglia 165 0 0
UKH2 Bedfordshire and Hertfordshire 69 0 0
UKH3 Essex 65 0 0
UKI3 Inner London - West 421 0 0
UKI4 Inner London - East 127 0 0
UKI5 Outer London - East and North East 66 0 0
UKI6 Outer London - South 5 0 0
UKI7 Outer London - West and North West 67 0 0
UKJ1 Berkshire, Buckinghamshire and Oxfordshire 215 0 0
UKJ2 Surrey, East and West Sussex 101 0 0
UKJ3 Hampshire and Isle of Wight 101 0 0
UKJ4 Kent 15 0 1
UKK1 Gloucestershire, Wiltshire and Bristol/Bath area 185 0 0
UKK2 Dorset and Somerset 20 0 0
UKK3 Cornwall and Isles of Scilly 8 0 1
UKK4 Devon 118 0 0
UKL1 West Wales and The Valleys 73 0 1
UKL2 East Wales 44 0 1
UKM5 North Eastern Scotland 37 0 1
UKM6 Highlands and Islands 43 0 1
UKM7 Eastern Scotland 167 0 1
UKM8 West Central Scotland 6 0 1
UKM9 Southern Scotland 4 0 1
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Journal of the Knowledge Economy
NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
UKN0 Northern Ireland (UK) 58 0 1
IS00 Ísland 131 0 0
LI00 Liechtenstein 0 0 0
NO02 Innlandet 14 0 0
NO06 Trøndelag 267 0 0
NO07 Nord-Norge 76 0 1
NO08 Oslo og Akershus (statistical region 2016) 356 0 1
NO09 Agder og Rogaland (statistical region 2016) 66 0 1
NO0A Vestlandet (statistical region 2016) 222 0 1
NO0B Jan Mayen og Svalbard 0 0 0
CH01 Région lémanique 218 0 0
CH02 Espace Mittelland 160 0 0
CH03 Nordwestschweiz 104 0 0
CH04 Zürich 230 0 0
CH05 Ostschweiz 42 0 0
CH06 Zentralschweiz 24 0 0
CH07 Ticino 31 0 0
ME00 Crna Gora 0 1 1
MK00 Severna Makedonija 42 0 0
AL01 Ver i 0 1 0
AL02 Qender 0 1 0
AL03 Jug 0 1 0
RS11 Beogradski region 92 1 0
RS12 Region Vojvodine 49 1 0
RS21 Region Sumadije i Zapadne Srbije 5 1 0
RS22 Region Juzne i Istocne Srbije 4 1 0
TR10 Istanbul 125 0 0
TR21 Tekirdag, Edirne, Kirklareli 0 0 0
TR22 Balikesir, Çanakkale 3 0 0
TR31 Izmir 36 0 0
TR32 Aydin, Denizli, Mugla 5 0 0
TR33 Manisa, Afyonkarahisar, Kütahya, Usak 1 0 0
TR41 Bursa, Eskisehir, Bilecik 9 0 0
TR42 Kocaeli, Sakarya, Düzce, Bolu, Yalova 19 0 0
TR51 Ankara 103 0 0
TR52 Konya, Karaman 3 0 1
TR61 Antalya, Isparta, Burdur 4 0 0
TR62 Adana, Mersin 4 0 0
TR63 Hatay, Kahramanmaras, Osmaniye 1 0 0
TR71 Kirikkale, Aksaray, Nigde, Nevsehir, Kirsehir 1 0 0
TR72 Kayseri, Sivas, Yozgat 4 0 0
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NUTS2 Region Interregional
projects Scientific
priorities
NUTS1 NUTS2
TR81 Zonguldak, Karabük, Bartin 0 0 0
TR82 Kastamonu, Çankiri, Sinop 1 0 0
TR83 Samsun, Tokat, Çorum, Amasya 0 0 0
TR90 Trabzon, Ordu, Giresun, Rize, Artvin, Gümüshane 2 0 0
TRA1 Erzurum, Erzincan, Bayburt 1 0 0
TRA2 Agri, Kars, Igdir, Ardahan 0 0 0
TRB1 Malatya, Elazig, Bingöl, Tunceli 0 0 0
TRB2 Van, Mus, Bitlis, Hakkari 1 0 0
TRC1 Gaziantep, Adiyaman, Kilis 2 0 0
TRC2 Sanliurfa, Diyarbakir 0 0 0
TRC3 Mardin, Batman, Sirnak, Siirt 0 0 0
Source: CORDIS (2022); Smart Specialisation Platform (2022a, b)
Annex 4. Scientific S3 Domains Related toEnvironmental
Sustainability
Scientific domain Scientific subdomain
01—Exploration and exploitation of the earth 01.01—Atmosphere
01.02—Climate and meteorological research
01.07—Sea and oceans
02—Environment (All subdomains)
04—Transport, telecommunication, and other
infrastructure
04.26—Protection against harmful events in town and
country planning
05—Energy (All Subdomains)
08—Agriculture 08.72—Agriculture forestry impact on the environment
Source: Smart Specialisation Platform (2022a, b).
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Annex 5. Network ofNorthern German Regions
inSustainability‑related H2020 Projects. Source: CORDIS (2022)
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1 3
Funding Open Access funding enabled and organized by Projekt DEAL.
Data Availability The author confirms that the data supporting the findings of this study are available within
the article and its supplementary materials. Additional data and calculations are available on request.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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