Inter-organisational Sustainability Cooperation Among European Regions and the Role of Smart Specialisation

Kruse, Mirko Article — Published Version Inter-organisational Sustainability Cooperation Among European Regions and the Role of Smart Specialisation Journal of the Knowledge Economy Provided in Cooperation with: Springer Nature Suggested Citation: Kruse, Mirko (2024) : Inter-organisational Sustainability Cooperation Among European Regions and the Role of Smart Specialisation, Journal of the Knowledge Economy, ISSN 1868-7873, Springer US, New York, NY, Vol. 15, Iss. 4, pp. 16735-16779, https://doi.org/10.1007/s13132-024-01760-z This Version is available at: https://hdl.handle.net/10419/319173 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) Journal of the Knowledge Economy (2024) 15:16735–16779 https://doi.org/10.1007/s13132-024-01760-z 1 3 Inter‑organisational Sustainability Cooperation Among European Regions andtheRole ofSmart Specialisation MirkoKruse1 Received: 5 June 2022 / Accepted: 11 January 2024 / Published online: 5 February 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 sustainable 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 cooperation 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 relevance 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 interregional 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 [email protected] 1 Faculty ofBusiness Studies andEconomics, University ofBremen, Bremen, Germany 16736 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 technologies, or the increasing urgency for a sustainable transition of the economy in accordance with planetary boundaries (Gong etal., 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 sustainable transition of the economy (Fagerberg & Hutschenreiter, 2019; Losacker etal., 2021). Thereby, the distribution of innovative activity in space is not randomly 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 central levers to address these objectives are research and development (R&D) and innovation. Accordingly, the concept of smart specialisation, one of the key strategies of European innovation policy, comes into the spotlight again (Doranova etal., 2012; European Commission, 2020a). This approach was inspired by theories 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 etal., 2012; Van den Heiligenberg etal., 2017; Giustolisi etal., 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 sustainable 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 external 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 cooperation (e.g. Guastella & Van Oort, 2015; Mitze & Strotebeck, 2018; Balland etal., 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 cooperation increases in importance (Brodzicki, 2017; Gong etal., 2022). The fragmented 16737 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 successfully 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 contribute to this discussion by providing empirical evidence on interregional cooperation in Europe in the field of environmental sustainability. Thereby, a novel dataset 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 statistical 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 “Interregional 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, andInterregionality The Idea ofSmart 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 literature on regional innovation systems (RIS). This approach emphasises the crucial role of the regional level and geographical proximity between regional innovation 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 preceding theories such as national innovation systems (NIS), transition studies, innovative 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 productivity 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 etal., 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 overlaps (Larosse etal., 2020; McCann & Soete, 2020). Previously, regional funding was invested thinly across several sectors without resulting in significant impact on 16738 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 innovation capability and structural renewal as a result (Gianelle, Kyriakou etal., 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 etal., 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 etal., 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 etal., 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 etal.; 2009; Vezzani etal., 2017; Balland etal., 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 etal., 2020; Di Cataldo etal., 2020). By now, most regions in Europe have applied the smart specialisation concept by developing individual 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 etal., 2020; Larosse etal., 2020; D’Adda etal., 2021). Moreover, the fast success story of smart specialisation made the concept an example of “policy running ahead of theory” (Foray etal., 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 specialisation (Asheim etal., 2016). Further criticism revolves around the questions which regions do benefit. When smart specialisation was established, it was promoted 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). 16739 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 Smart Specialisation andEnvironmental 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 etal., 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 etal., 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 etal., 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 etal., 2019; Provenzano etal., 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 etal., 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 reinterpretation, redesign, and reintegration of smart specialisation is also officially recognised by the European Commission (McCann & Soete, 2020; Nakicenovic etal., 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 growing in related fields such as regional studies and economic geography (e.g. Truffer & Coenen, 2011; Markard etal., 2012; Gibbs & O’Neill, 2017; Montresor & Quatraro, 2018; Losacker etal., 2021). In the context of smart specialisation and sustainability, existing research has been focusing on the opportunities for regional innovation offered by circular economy approaches (Hristozov & Chobanov, 2020), renewable energy (Steen etal., 2018), or structural change in old industrial areas (Prause etal., 2019) with certain regions as examples (Polido etal., 2019). 16740 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 Interregional Cooperation inEurope 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 etal., 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 emergence of innovation. Since spillovers do not easily travel across space, spatial concentration 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 etal., 2018; McCann & Soete, 2020; Pinheiro etal., 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 etal., 2014; De Noni etal., 2017; Santoalha, 2018; Mikhaylov etal., 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 & OrtegaArgilés, 2016; Schot & Steinmueller, 2018; Giustolisi etal., 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 etal., 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 etal., 2012). Accordingly, innovation systems, focusing on the role of interaction between different 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 etal., 2010; Binz & Truffer, 2017). For instance, Chesnais (1992) demonstrated how the operations of multinational enterprises influence the structure of NIS. Regional innovation systems (RIS) have for a long time been analysed in isolation rather than in cooperative cross-border settings (Gosens etal., 2014; Li etal., 2022). Stepwise, the approach has been broadened leading to the establishment of the concept of cross-border regional innovation systems (CBRIS). Conceptually, CBRIS incorporate 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 etal., 2011; Pietrobelli & Rabellotti, 2011; Korhonen et al., 2021). Interregional cooperation 16741 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 cooperation 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 etal., 2020). Interregional projects such as INTERREG or HORIZON represent an institutionalisation of this aspiration (Cassi etal., 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 etal., 2020b; Woolford etal., 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 etal., 2012). This aspired outward orientation was backed by the fact that structural change and regional innovativeness both benefit from cooperation, external connectedness, 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 specialisation (McCann etal., 2015; Mariussen etal., 2019; Foray, 2018). Also, the cohesion aspect of smart specialisation is addressed by extra-regional collaboration since particularly less-developed and technologically lagging regions often lack the internal capabilities and networks that they require for a catch-up process (Radosevic & Ciampi Stancova, 2015; Barzotto etal., 2019; Ghinoi etal., 2020). The same holds for the focus on grand challenges such as climate change which require the cooperation of different regions. In this regard, Castellani etal. (2022) found indications of a positive influence of different forms of FDI on regional specialisation in green technologies, indicating a positive influence of cooperation for a green transition. Most 16742 Journal of the Knowledge Economy (2024) 15:16735–16779 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 successful implementation of the Green Deal targets (Woolford etal., 2021; Tuffs etal., 2020a). Instead, also cooperation with non-EU regions considering certain challenges might come into play (Uyarra etal., 2014). However, not only implementation but also research on interregional cooperation and smart specialisation has remained limited so far (Radosevic & Ciampi Stancova, 2015; Balland & Boschma, 2021; Weidenfeld etal., 2021). Apart from policy papers and qualitative studies, for instance, by Muller etal. (2017), authors like Gianelle etal. (2014), Girejko etal. (2019), and Kruse and Wedemeier (2021) present methodologies to identify common priorities between regions as a foundation for common smart specialisation strategies (S3). However, these papers do not empirically test the efficiency of cooperation and confine to offering a theoretical toolkit for policymakers to assess the potential of cooperation with other regions. Other, more qualitatively oriented, papers presented by Sörvik etal. (2016) or Mueller-Using etal. (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 specialisation (Esparza-Masana, 2021; Woolford etal., 2021). This also includes strengthening the already-existing interregional partnership platforms on smart specialisation and SDGs which the European Commission has been working on since 2015 and previous approaches to interregional collaboration such as the Vanguard Initiative (Rakhmatullin etal., 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 etal., 2020b). Interregional Scientific Collaboration inEurope Materials andMethods 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 etal. (2019), Balland and Boschma (2021), and Li etal. (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 etal. (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 16749 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 sustainability, 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 environmental 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 sustainability 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, Finland, 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 H2020Sustainability projects, 2022. Source: CORDIS (2022) 16750 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 and European regions. Here, it is revealed that neighbouring regions tend to cooperate with Northern German regions. This supports the assumption of (geographical and cultural) proximity as a facilitating factor for cooperation. However, geographical 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 eigenvector 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 measuring how well a well-connected node is connected to other well-connected nodes (Tabassum etal., 2018). Table2 lists the top-20 regions for each measure of centrality 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 16751 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 andLimitations 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 etal. (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 H2020Sustainability 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 16752 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 scores are particularly urban, and most NUTS3 concentration patterns refer to capital or major city agglomerations. The conducted correlation analysis confirms that a connection between regional factors such as GDP or economic structure and interregional orientation can be assumed (see Table1). 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 interregional 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 collaboration. 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 etal., 2017). Particularly complex economic activities and scientific research tend to concentrate in larger cities and metropolitan areas (Acosta etal., 2011; Balland etal., 2018; Tödtling & Trippl, 2005). From a cohesion perspective, these findings are alarming: smart specialisation and innovation policy in Europe focus on bridging existing regional disparities 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 competitive, collaborative programmes such as Interreg, as opposed to Horizon 2020 data in this paper, are considered, as suggested by Woolford etal. (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 officially 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 16753 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 performance are characterised by a certain level of divergence. The same finding is mentioned by Sörvik and Kleibrink (2015) as well as Deegan etal. (2021) implying 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 network 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 etal., 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 successfully 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 researchers cooperate based on geographical proximity and tend to cooperate with similar organisations in similar regions (Frenken etal., 2007). Moreover, in light of grand challenges, such as the fight against climate change, external cooperation is strongly advised (Uyarra etal., 2014). Northern Germany matches this suggestion, and the analysis blends in with other papers assigning the region an important role for a sustainable transition (e.g. Hassink etal., 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, cooperation 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 evidently important factors for regional economic prosperity, new path development and diversification (Mariussen etal., 2016). Despite smart specialisation highlighting the relevance of interregional cooperation since the time the concept was developed 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. 16754 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 patent 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 analysis confirms that regional characteristics such as GDP or population density positively influence a region’s involvement in interregional research projects on environmental 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 translates 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 activity. 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 observable 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 environmental 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 shortcomings such as regional convergence. 16755 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 Annex 1. Horizon 2020 Calls Related toEnvironmental 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 ResourceEfficient 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 16756 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 16757 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 16758 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 Annex 2. Thematic Priorities inH2020 Projects onEnvironmental Sustainability, NUTS2 Level. Source: CORDIS (2022), Own Depiction 16765 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 16766 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 16767 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 Pohjoisja 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 16768 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 16769 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 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 Veri 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 16770 Journal of the Knowledge Economy (2024) 15:16735–16779 1 3 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 toEnvironmental 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). 16771 1 3 Journal of the Knowledge Economy (2024) 15:16735–16779 Annex 5. Network ofNorthern German Regions inSustainability‑related H2020 Projects. Source: CORDIS (2022) 16772 Journal of the Knowledge Economy (2024) 15:16735–16779 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 Commons 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/. References Acosta, M., Coronado, D., Ferrándiz, E., & y León, M.D. (2011). 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