MAPPING URBAN NETWORKS OF ROMANIAN CITIES IN COHESION PROJECTS 2007-2013: A STATISTICAL ANALYSIS.
For more than twenty years, policy developments occurred in the field of spatial planning, leading to the territorialization of the cohesion policy of the European Union (EU). In more detail, territorialization of a policy depends on its "capacity to promote territorial development and/or territorial cohesion, targeting a specific territorial scale (from urban to European)" (Medeiros, 2016, p. 95). The goal of territorial cohesion "makes explicit that there is a territorial or spatial dimension to the primary social and economic goals of the EU" (Duhr et al., 2007, p. 303). A "key to interpreting [...] territorial cohesion" (Rivolin and Faludi, 2005, p. 198) is the concept of polycentrism, embedded in a multilevel governance system.
Territory became a new dimension of EU policy since its introduction in the European Spatial Development Perspective (European Communities, 1999). The cohesion policy of EU officially introduced this territorial component for the first time in the multiannual financial perspective 2007-2013, adding the strategic goal of territorial cooperation as the third objective of structural funds, along with the first two, convergence and competitiveness. The new objective aimed to enhance three strands of territorial cooperation, financed by INTERREG: cross-border cooperation (CBC), transnational and interregional cooperation.
The assessment of the results of the strands of territorial cooperation proved to be challenging "due to their complexity, to the particular fuzziness of their objectives, and to shortcomings in monitoring systems and data collection" (Barca, 2009, p. 97). While spatial planning was seen "as an experimental field for European governance" (Rivolin and Faludi, 2005, p. 195), it was argued that, as a financial EU instrument, INTERREG is a "top-down stimulus for bottom up processes" (Bohme and Waterhout, 2008, p. 235), offering "a multilevel forum for the exchange of ideas" (Giannakourou, 2012, p. 120) having as hallmark "networking, learning and innovation" (Colomb, 2007, p. 355) and facilitating 'policy transfer' and 'horizontal Europeanization' (Duhr and Nadin, 2007, p. 376). It was also pointed out that "the intensity and effectiveness of territorial cooperation vary greatly, with the greatest impact occurring in regions where integration and cooperation are already well developed (e.g. Baltic Sea, Benelux area)" (Barca, 2009, p. 98).
Situated at the Eastern periphery of Europe, the first years of European integration represented for post-communist Romania a 'funds frenzy', 'pro-cohesion' period (Munteanu and Servillo, 2014, p. 2267), EU funds being seen as catching-up tools. EU influence brought positive changes in terms of "learning and networking effects [...] since the first cross-border cooperation projects" (Munteanu and Servillo, 2014, p. 2267).
This paper aims to be an assessment of the realities: the urban networks that emerged from territorial cooperation projects involving Romanian cities, during the first seven years of EU membership. Who are the networking partners and what influences the intensity of their connections? The general purpose is to discuss the effectiveness of INTERREG in producing polycentric connections between cities, at each of its operational levels: cross-border, transnational and interregional.
The paper is organized as follows: section two provides a theoretical discussion on polycentrism as a part of the EU normative agenda; the third section develops the research questions, our methodology, the dataset we built, the methods we used and also, provides the results. Apart from summarising the methodological and substantive findings of this paper, the final section also addresses limitations and future research questions, as well as emerging policy implications from this analysis.
Polycentrism and the normative goal of a balanced EU territory
At the core of EU Urban Agenda, we find the concept of polycentrism. The Agenda acknowledges the "polycentric structure of Europe" (Urban Agenda for the EU, 2016, p. 4). Even though it is at the core of the EU normative agenda, polycentrism proved to be a "versatile and fuzzy" concept (Burger and Meijers, 2012, p. 1127), despite the attempts of clarification (Kloosterman and Musterd, 2001; Davoudi, 2003).
The European Spatial Development Perspective (ESDP) was promoting the goal of "polycentric" and "balanced spatial development of the European territory" (European Communities, 1999, p.10), as a guiding principle in order to achieve two rather competing goals: to boost the global market competitiveness of Europe and to limit the intra-European regional development disparities. ESDP revealed the existence of "one single globally outstanding, dynamic integration zone" (European Communities, 1999, p. 20), a pentagon composed by the metropolises of London and Paris, defined as global cities, complemented by Milan, Munchen and Hamburg. Outside, there was a large periphery with a few disconnected metropolises, meant to expand furthermore, along with the Eastern enlargement.
According to this polycentric rationale, the EU core area is characterized both by morphological polycentricity--populous cities with big economies and highly developed infrastructure, as well as by functional polycentricity--a sort of space of flows, composed by networks of flows and cooperation between urban areas. Complementing morphological and functional polycentricity was considered a policy goal of European spatial development, leading to the creation of "several dynamic zones" (ESPON, 2005, p. 37) of economic integration, as growth areas outside Western Europe's Pentagon. The European Spatial Planning Observation Network (ESPON), as an applied research programme aimed at supporting the formulation of territorial development policies in Europe, financed several research studies on measuring polycentricity.
ESDP seems to be inspired by seminal theoretical works which considered the concepts of 'globalization' and 'network' as interconnected: in a globalised economy the biggest economic advantages belong to the most connected actors (Sassen, 1991; Castells, 1996; Taylor, 2006). Global or world cities were advocated since the 80s (Friedman and Wolff, 1982; Sassen, 1991). Understood as "a space of flows" (Castells, 1996; Taylor, 2006), a globalized city is supposed to register higher performances, in terms of 'the quality of services' offered to its citizens (Kaufmann et al., 2005).
In the context of globalization, the old paradigm of territories and nations states is replaced with a new one, of "places, flows and networks," thus "rising the relevance of connectivity" (Pain et al., 2016, p. 1139). Urban systems are perceived as "set[s] of interdependent nodes (for example, centers) and the patterns of interaction between these nodes" (Burger and Meijers, 2012, p. 1130). Moreover, the existence of multidirectional flow patterns with no significant orientation towards a particular center was considered a clearer description of functional polycentricity (Burger and Meijers, 2012, p. 1134). Cities' ability to act is fueled by the multilevel perspective of EU governance, as a new type of governance emerged in the process of implementing regional and structural policy reforms (Marks and Hooghe, 2004), which were designed to produce cohesion between the prosperous EU core and the less developed peripheries, along the divide North-South (in the 80s) and West-East (in the 90s).
As a major difference from state-centric governance, multilevel governance includes new legitimacy for action--the preference for networks and the involvement of multiple actors from different levels, whether above the national level (transnational) or below it (subnational), as well as new spaces for action, across the usual administrative borders, modifying the conventional understanding of the spatial peripherality of a location. This research approaches territorial cohesion from a multilevel perspective on the dynamic formation of core and peripheral regions, overlapping at different spatial scales (regional, national, European and global) (Lang et al., 2015).
The challenge of understanding polycentrism, as in the case of core-periphery dynamics, consist in the fact that it is scale-dependent: polycentricity at one level, may be monocentricity at another (Nadin and Duhr, 2005). This was considered especially relevant in the context of the Eastern enlargement (ESPON, 2006). Encouraging polycentric development at the macro-European scale, focused on growth poles and the associating new growth areas to the European core, criticized by the place-based theory (Barca, 2009), could lead to "increasing monocentricity in the developing peripheral regions of the EU" (Hall and Pain, 2006, p. 4).
At the meso-national and interregional level, in Central and Eastern Europe, encouraging the capital cities to be competitive on a European scale could increase already existing internal discrepancies between them and the other cities. Intranational cohesion gaps have widened in all post-communist countries (Barca, 2009, p. 81; Maier 2012, p. 143). Finally, at the intraregional micro-level, developing a polycentric metropolis, could lead to a more a monocentric, less sustainable region (Kloosterman and Musterd, 2001).
Previous research showed that territorial cooperation increased participants' awareness and knowledge (Stein, 2010, p. 11; Colomb, 2007, p. 362) on endogenous development (Barca, 2012), or territorial capital (Stein, 2010), but also on European strategic concepts like policentricity, hence "increasing the way in which project participants view their neighbours and how regions perceive their own position in the wider European picture" (Colomb, 2007, p. 363). Within this context, this article represents a case study in which we analyze the networks of participants in territorial cooperation projects involving the Romanian cities, during the first years of European integration. We look at the map of urban networks in projects using the lens of polycentrism, at different operating scales of INTERREG.
Romanian city-nodes and the intensity of their connections in territorial cooperation projects
This paper focuses on city connections in urban networks by accessing territorial cooperation projects under the INTERREG framework, during the financial framework 2007-2013. We chose this period due to the closed present status of the multiannual financial perspective and also, due to the fact that its debut coincided with the beginning of Romania's EU membership, hence its full eligibility for cohesion policy. We aimed to identify the most prominent territorial cooperation city-nodes and then calculate the intensity of their connections.
Measuring connections in projects will respond to our research questions: (1) What Romanian cities are the most prominent territorial cooperation nodes in INTERREG projects?; (2) What is the intensity of their connections with other cities?; (3) At what scale of INTERREG are the most important nodes mainly operating? As an empirically based exploration of the effectiveness of INTERREG in producing polycentric connections, in a multi-scalar perspective, our research provides interpretations regarding types of city-nodes, types of connections and types of projects and makes assumptions on the polycentric character of territorial cooperation developed by Romanian cities by implementing INTERREG.
Our research starts from assessing the connections of 41 Romanian cities (all the county capitals plus Bucharest) which we considered to be nodes of networking in territorial cooperation projects, between 2007 and 2013. We used the EUROSTAT database regarding accessed projects. For an extensive approach, we have included in our study all types of INTERREG programmes: cross-border, transnational and networking (ESPON, INTERREG IV C, URBACT II), as well as cross-border programmes with regions coming from accession countries or which are part of the European Neighborhood Policy and also macro-strategies (European Union Strategy for the Danube Region). Our initial database consisted in 913 projects involving the 41 Romanian cities. We have grouped the cities in eight regions, according to their NUTS 2 membership: West, North-West, Center, North-East, South-East, South-Muntenia, South-West Oltenia and Bucharest-Ilfov.
Our goal was obtained in two steps: starting with calculating, using network-statistical methods, the prominence of the nodes in terms of number of projects and budgets, we continued by selecting the most prominent node in each of the eight development regions of Romania, and then we calculated the intensity of connections developed by each node with other cities. Our paper approaches two levels on analysis: the urban network of the most prominent territorial cooperation Romanian nodes, and a zoomed perspective into the connections of each of the eight territorial cooperation nodes. Conclusions were drawn regarding the hierarchy of Romanian city-nodes in territorial cooperation projects, their most intense connections, the types of projects and the scale involved; reflections regarding polycentric connections of the Romanian territorial cooperation city-nodes were added too. In our paper, we have used descriptive statistics as well as elements of graph theory.
Determinants of the prominence of territorial cooperation city-nodes
In order to assess the prominence of city-nodes, we identified two variables: projects and budgets. Aiming to create a mathematical formula for computing the prominence of city-nodes, we used a database consisting of the number of projects and the total budgets from projects for each of the 41 cities. We decided to give equal importance to these two variables. Moreover, we have divided both databases for each of the analyzed city according to two types of roles that a city might have played: lead of a project, versus partner in a project. In order to decide between the importance of being a lead of a project, compared to the role of being a partner in a project and to assign a weighted importance of these two sub-variables in our formula, we studied two correlations between our sets of data: number of projects where a city i is lead (NPLi) versus number of projects where a city i is partner (NPPi) (Figure 1) and total budgets from projects where city i is lead (BLi) versus total budgets from projects where city i is partner (BPi) (Figure 2).
Due to the significant correlations between NPLi and NPPi with correlation coefficient r=0.890, as well as between BLi and BPi, with correlation coefficient r=0.894, we decided the give equal weight to all our variables in our measure of the prominence of a city-node (PNi), described by the following equation:
PNi = NPLi + NPPi + BLi/mBL + BPi/mBP (1)
mBL is the mean value of the budgets of all 41 cities in projects where they are Lead; mBP is the mean value of the budgets of all 41 cities in projects where they are Partner. The results obtained after applying Eq. 1 on the data provided for each of the 41 cities are illustrated in Figure 3. It is interesting to see that some of the results are counterintuitive: in North-West region, Oradea is a more prominent city-node than Cluj-Napoca, the most important city of the region in morphological terms (size of population and economy); in the Center, Alba Iulia is a more prominent node than populous and wealthier Brasov, or Sibiu. In South-West Oltenia, Giurgiu is the most prominent node, even though other cities in the region are twice or three times more populated (Pitesti and Ploiesti). We might be tempted to consider that connections in projects are not related to size, still, the most prominent city-node is the country capital city. For a more insightful argumentation, we further assess the connections of the most prominent city-node in each of the eight Romanian NUTS 2 regions (Figure 3), by developing the indicator of intensity of connections.
Determinants of the intensity of connections of territorial cooperation city-nodes
We used as a variable the contacts between cities in cohesion projects and we aimed to create a mathematical formula to assess these contacts. We decided to analyze all the cities taken two by two and we defined three other variables, corresponding to the types of possible contacts between cities, in projects: Lead-Partner (LP), Partner-Lead (PL) and Partner-Partner (PP). Then we built a database for all the 41 Romanian cities and their contacts, taken two by two, divided into LP, PL and PP. In order to assess the weighted importance of the three types of contacts in building our formula, we studied correlations between the number of contacts through projects where Romanian city i is partner to the city lead of the proiect j (LPij) and the number of contacts through projects where Romanian city i is lead and the partner city is j (PLij) (Figure 4), then between LPij and the number of contacts through projects where a third city is lead and the Romanian city i is partner to another city j, other than the project lead (PPij), and finally, between PLij and PPij data. Finding that the only significant correlation exists between LPij and PLij, with the correlation coefficient r=0.934, we decided to give them equal importance in our equation, and to downgrade the importance of PP data.
Further, we have developed the following Equation of the intensity of connections (IC) of Romanian city-nodes:
ICij = LPij + PLij + 0.5 x PPij (2)
By applying Eq. 2, be reached at the following hierarchy of the most intense connections of the eight most prominent Romanian city-nodes:
Table 1. The hierarchy of the most intense connections of Romanian city-nodes in territorial cooperation projects, 2007-2013 Rank NUTS2 region City-Node Connected Intensity city/Country of Connection 1 North-West Oradea Debrecen/ Hungary 91.5 2 West Timisoara Szeged/ Hungary 66.5 3 Bucharest Bucharest Sofia/ Bulgaria 35.0 4 North-East Iasi Chisinau/ Rep. 22.5 Moldova 5 South-Muntenia Giurgiu Ruse/ Bulgaria 12.5 6 South-East Constanta Varna/ Bulgaria 11.0 7 South-West Craiova Vidin/ Bulgaria 6.0 Oltenia 8 Centre Alba Iulia Ljubljana/ Slovenia 2.0 Source: Authors' own calculations
The next step consisted in building a valued symmetrical matrix representing the intensity of connections between 16 cities: the eight most prominent city-nodes from Romania and their most intense connections (which are all foreign). Using the tools of graph theory applied in network sciences, we described our matrix as a network composed by 16 nodes and 57 edges, where edges represent the existence of contacts between city-nodes and also their intensity, computed using Eq. 2. Then, we calculated for our network two indicators of centrality: the weighted degree and eigenvector centrality (Newman, 2004; Bonacich, 1987). This allowed us to draw conclusions regarding the connections of Romanian cities among themselves, as well as with other intensely connected cities.
The network is graphically represented in Figure 5, based on the matrix of intensity of connections. This visualization allows us to observe the differences between the intensity of connections, represented by the thickness of edges. The most intense connections of the eight most prominent Romanian city-nodes involve external partners, mostly cross-border cooperation (CBC). The prevalence of CBC (compared to transnational cooperation), also characterizes the North-West European core, where it represents a "strong political commitment' (Duhr and Nadin, 2007, p. 384), hence a totally different intensity of connections. Several explanations were offered: transnational issues are less visible, they have a long-term nature and also, cooperation is supported and it is more important where there are preexisting historical or other type of links. Visible outcomes, short-term results and preexisting links are, at the same time, main incentives for CBC.
The intensity of CBC is higher at the Western border of Romania compared to the Eastern or Southern border. This seems to follow the East-West polarization pattern which characterizes development in Central and Eastern Countries (REPUS, 2007). We also see the emergence of several triads of intense connections: Bucharest-Sofia-Ljubljana as a club of capital cities, and also Bucharest-Chisinau-Varna and Varna-Constanta-Chisinau.
At the Western border of Romania, despite the relative proximity of the pairs of intense connections Timisoara-Szeged and Oradea-Debrecen, there aren't complementary intense connections between Timisoara and Oradea, or between Szeged and Debrecen. Intra-national connections, as well as "intra-national cohesion among regions draws less attention in post-communist countries" (Maier, 2012, p. 143). Among these 16 cities, the city with the biggest weighted degree within the network is Debrecen. The weighted degree hierarchy is represented in Figure 6. Debrecen is followed by its partner Oradea, then Bucharest, followed by the other two intensely connected cities, Szeged and Timisoara. Among the Romanian cities, Alba Iulia has the smallest weighted degree, having the fewest number of intense connections.
Looking at centrality with a different metric, Figure 7 shows that the biggest eigenvector centrality belongs to Bucharest, reflecting the fact that it is connected to all the other 15 city-nodes. It is followed by two other capital cities. Among the Romanian cities, at some distance, Constanta and Timisoara have similar centrality scores, followed by the group Oradea-Craiova-Alba Iulia-Iasi.
Three Romanian cities, Bucharest, Constanta and Timisoara have an above average eigenvector centrality in city networks created whitin cooperation projects. Bucharest is the only city connected to all the other seven Romanian cities. We see therefore an unbalanced distribution of connections between Romanian cities, monopolized by the capital city. Moreover, among these seven Romanian cities, the only nodes which are connected are Timisoara-Oradea, and Constanta-Craiova, but the intensities of their connections are low. As the Romanian city-nodes are actually not being connected among them, Figure 5 shows an atypical core-periphery structure, with regards to city connections in cohesion projects: an internal periphery of missing connections, surrounded by cross-border connected regions.
Even though Oradea registered a high weighted degree within our network, its low eigenvector centrality score is expressing the concentration of its connections with a limited number of other cities with high weighted degree centrality scores. Whether this concentration of connections is associated or not with geographical criteria, is a further subject of discussion to be addressed later in our study.
Another indicator computed for our network is graph density (Schaeffer, 2007), with a score of 0.475 out of a maximum of 1. This refers to the actual connections between city-nodes to the potential connections between them. The score of our network expresses the existence of unconnected city-nodes. They are, by excellence, the ones with low centrality scores and, in addition, we already saw that the intensity of connections between Romanian cities is low.
What are the implications of graph density for network polycentricity? In a seminal contribution on functional polycentricity Green (2007) spoke about network density as the degree to which centers in a region are functionally connected. Still, research shows that network density and functional polycentrism, as balanced distribution of linkages between nodes, should be disentangled (Burger and Meijers, 2012, p. 1134) as we can find high/low network density mixed with un/balanced connectivity of nodes. In our case, we found a low graph density and unbalanced connections of city-nodes, dominated by the capital city.
Zooming into the most intense connections of the most prominent Romanian city-nodes
In this section we will make further interpretations regarding the types of city-nodes and of their connections. We extracted from EUROSTAT data regarding the types of projects and compiled them for each city in a graphic presentation. Also, we zoomed into the most intense connections (IC) of each of the eight most prominent Romanian nodes (computed with Eq 2) and we created maps for each of them, using our database of the intensity of connections between cities.
Zooming in: Timisoara
What assumptions can we make on what type of city-node Timisoara is, considering the types of projects it is involved in and the proximity of its most intense connections? By analyzing the types of projects Timisoara was involved in (Figure 8), we see that 80 percent of its projects are CBC, between Hungary and Romania.
Also, when we look at the Top 10 intense connections of Timisoara (Figure 9), we discover mostly Hungarian cities situated in the geographical proximity: Szeged (IC=66.5), Debrecen (10=5.0), Arad (IC=3.5) Morahalom (IC=3.0), Szentes (IC=2.0), Bekescsaba (IC=2.0), Nyiregyhaza (IC=2.0). The exceptions are represented by Bucharest (IC=2.0) and Venice (IC=4.0; Timisoara and Venice are twin cities) and Sevilla (IC=2.5).
Given the big difference in terms of intensity of connections between Timisoara-Szeged and all other Timisoara's connections, we see an unbalanced distribution of its most intense connections. Zooming in on the connection between Szeged and Timisoara, in order to analyze the number and direction of contacts between the two cities, we counted 44 lead projects (LP), 21 partner-lead projects (PL) and three partner-partner projects (PP); this can be translated into an asymmetric relation, in which Szeged initiated more than twice as many contacts (44) in projects led by it and having Timisoara as a partner, than Timisoara did (21). Both cities are part of the Dunare-Koros-Mures-Tisza (DKMT) Euroregion, one of the oldest in this area, established at Szeged in 1997. Being less efficient then the smaller Bihar-Hajdu-Bihor Euroregion (Debrecen-Oradea) it was assumed that in the case of the Romanian-Hungarian border scale matters in CBC (Baranyi, 2006, p. 158). Timisoara can be considered a regional cross-border node.
Zooming in: Oradea
An even bigger predominance of CBC HU-RO projects (Figure 10) exist, representing 96 percent of all Oradea's projects.
Looking at the Top 10 intense connections of Oradea, distributed among 11 cities (Figure 11), we discover mostly Hungarian cities, situated in the geographical proximity: Debrecen (IC=91.5), Szeged (IC=9.5) Nyiregyhaza (IC=4,5), Gyula (IC=3.0), Bekescsaba (IC=3.0), Biharkeresztes (IC=3.0), Hajd (IC=2.0), Berettyoujfalu (IC=2.0), Szarvas (IC=2.0) and Korosszegapati (IC=2.0).
Due to the significant discrepancy between the intensity of connection between Oradea and Debrecen and all the other connections, we observe an unbalanced distribution of Oradea's connections. Moreover, analyzing the number of contacts between Oradea and Debrecen, and their direction: LP-44, PL-45, PP-5, we observed the reciprocity character of the connection between the two cities, both initiating a similar number of contacts in projects, inviting also the other city. As a conclusion, Oradea can be seen as a regional cross-border node.
Among these CBC projects, some of them treat "transnational issues" (Duhr and Nadin 2007, p. 381), such as waste management, in an "axial cooperation" rationale (Bohme et al., 2003, p. 56; see also Colomb, 2007, p. 359), facilitated by the cross-border flow of five rivers.
The transnational projects treat instead 'common issues' (Duhr and Nadin, 2007, p. 381), such as innovation in public transport, sharing best practices with partner cities. Common issues are not place-specific and the benefits of transnational cooperation consist in bringing innovative and effective solutions, shared within the resulting 'virtual networks' (Bohme et al., 2003, p. 57; see also Colomb, 2007, p. 359). The Euroregion Bihar-Hajdu-Bihor, created in 1998 at Debrecen, builds upon the experience of PHARE CB program (launched in 1996) and re-integrates a historical region (Baranyi 2006, p. 158).
Zooming in: Alba lulia
Alba Iulia represents a distinct case compared to the previous analyzed cities. Not being eligible for CBC projects, the total number of projects is small (11): mostly interregional (56 percent of projects; see Figure 12), while the rest of the projects being equally divided between transnational South-East Europe (SEE), and urban cooperation projects (URBACT II).
As one of the most Europeanized Romanian cities (Tursie, 2016, p. 224), Alba Iulia's Top 10 most intense connections (Figure 13) are distributed in several countries (Hungary, Slovenia, Germany, France, Spain and Greece), but their intensity is low: Burgos (IC=1.5), Sofia (IC=1.5), Pecs (IC=1.0), Szombathely (IC=1.0), Strasbourg (IC=1.0), Potsdam (IC=1.0), Berlin (IC=1.0), Barcelona (IC=1.0), Rome (IC=1.0), Trikala (IC=1.0), Kavala (IC=1.0) resulting a low intensity balanced distribution of contacts.
Moreover, after we analyzed the most intense connection of Alba lulia (Ljubljana IC 2), the number and directions of their contacts: LP-1, PL-0, PP-2, we characterized the relation between the two cities by asymmetry as well by passivity from Alba Iulia's part, due to the fact that it didn't initiate any contacts with Ljubljana in projects led by it. Also, Alba Iulia didn't lead any project in 2007-2013, but still, it was a more prominent connectivity node than all the other cities in this NUTS 2 Centre region.
The two transnational projects involving Alba Iulia treated common issues: developing the competitiveness of small and medium-size enterprises in SEE in the wood sector; territorial agendas for small and medium-size SEE cities. Given the typology of Alba Iulia's projects, we might consider it a transnational node, but nevertheless, a small one.
Zooming in: Iasi
Moving to the Eastern border, 77 percent of Iasi's projects are CBC Romania-Ukraine-Moldova, with countries which are part of the European Neighborhood Policy (ENP) (Figure 14).
Analyzing Top 10 of Iasi's most intense connections (Figure 15), we mostly find these to be with cities in Moldova and Ukraine: Chisinau (IC=22.5), Ungheni (IC=10.0), Odessa (IC=6.5), Balti (IC=5.5), Soroca (IC=5.0), Falesti (IC=5.0), Hincesti (IC=4.5), Reni (IC=4.0), Chernivtsi (IC=3.5) and an exception represented by Paris (IC=3.5) with whom Iasi is partner in three ESPON research projects, as 'virtual network' (Bohme et al., 2003, p. 57).
The distribution of values of Iasi's Top intense connections is more balanced compared to corresponding value distributions at the Western border of Romania. This can be related to the lower intensity of inter-city connections between EU and non-EU countries.
Regarding the number and direction of contacts between Iasi and its most intense connection, Chisinau: LP-6, PL-13, PP-7, they reflect an asymmetric relation, where Iasi initiated and led more than twice as many contacts (13) as Chisinau (6). At a different scale of intensity of connections, Iasi-Chisinau is the reversed case of Szeged-Timisoara, showing that border cities coming from older EU Member States (MS) (Szeged), being socialized with using EU funds, are more active in initiating cooperation projects than cities coming from newer MS (Timisoara) and that cities coming from EU MS (Iasi) are more active in initiating contacts than cities coming from non-EU MS, even if they are capital cities (Chisinau). Our assumptions confirm similar conclusions that "the older the cooperation, the stronger are the bounds between two border areas" (Medeiros, 2011, p. 147). Given the types of projects Iasi is involved in, and the proximity of its partners, we can consider Iasi a regional cross-border node.
Zooming in: Constanta
Constanta is an atypical city compared to the previous ones, given its geographical position, as a sea port: 52 percent of its projects were financed by an instrument available for CBC between ENP countries from the Black Sea Basin (Figure 16). Also, 12 percent of its projects are transnational (SEE).
Constanta's Top 10 most intense connections (Figure 17) involve Black Sea Basin countries: Moldova, Ukraine, Bulgaria, Greece, Armenia, Georgia, Turkey and Romania. The values of these most intense connections are more closely distributed: Varna (IC=11.0), Chisinau (IC=9.0), Dobrich (IC=7.5), Istanbul (IC=7.0), Odessa (IC=6.5), Thessaloniki (IC=5.5), Tbilisi (IC=4.5), Yerevan (IC=4.0), Trabzon (IC=3.5), Bucharest (IC=3.0), Sofia (IC=3.0) and Silistra (IC=3.0). Also, if we look at the most intense connection, Constanta-Varna, and count the number and direction of their contacts: LP-5, PL-5, PP-2, we see a reciprocity relation, where both cities are initiating the same amount of contacts in projects where they play the lead role.
At a different scale of intensity of connections, the pair Constanta-Varna is similar to Debrecen-Oradea, showing that the intensity of CBC at Bulgarian-Romanian border is lower than the one at the Hungarian-Romanian border. Once again, the explanation might be related to the fact that Hungary is an older EU MS compared to Romania and Bulgaria, so it had more time to gain know-how in cohesion projects. From all these considerations, we can say than Constanta is a transnational Black Sea Basin node, even though it is a rather small node.
Zooming in: Giurgiu
Having 81 percent of its projects composed by CBC Bulgaria-Romania (Figure 18), Giurgiu exploited its natural advange as a Danube port city, in South-East Europe projects (14 percent). Giurgiu cooperated most intensily with its neighbor, the Bulgarian Danube port city of Ruse, IC=12.5 (Figure 19).
Most of its other connections have low intensity (IC below 3.5), but they are distributed in several countries (Romania, Bulgaria, Serbia, Croatia, Hungary, Austria and Slovakia) following the European path of the Danube river. This explains why Giurgiu is surprisingly connected to capital cities such as Wien (IC=3.5), Bratislava (IC=1.5), Budapest (IC=1.5), Belgrade (IC=1.0) and Bucharest (IC=2.0) or to other Danube river cities, such as Dunaujvaros (IC=1.5), Vukovar (IC=1.0) or Galati (IC=1.0). Still, most of its connections are distributed in a limited geographical cross-border Bulgaria-Romania area: Borovo (IC=1.0), Veliko Tarnovo (IC=1.0), Slivo Pole (IC=1.0), Razgrad (IC=1.0), Fundulea (IC=1.0), Voluntari (IC=1.0).
Moreover, looking at the number and directions of contacts between Giurgiu and Ruse, the values: LP-7, PL-3, PP-5, indicate a relational asymmetry, where Ruse initiated more contacts (7) in projects led by it, than Giurgiu did (3). It is important to note that some CBC projects, and all the three transnational projects approached transnational issues: water quality, waste management and harbour infrastructure development. Giurgiu can be considered a small regional cross-border node, having transnational opportunities in the Danube macro-region.
Zooming in: Craiova
Craiova's CBC Bulgaria-Romania projects constituted 87 percent of total number of projects (Figure 20). The most intense connection is Vidin (IC=6.0) and the values of the other Top 10 most intense connections of Craiova are closely distributed among cities from Bulgaria and Romania.
The intensities of these connections are generally low (Figure 21): Montana (IC=4.0), Pleven (IC=4.0), Sofia (IC=3.5), Ruse (IC=3.5), Calafat (IC=3.0), Bucharest (IC=2.5), Vratsa (IC=2.5), Veliko Tarnovo (IC=2.5) and Constanta (IC=2.5).
Looking at the relation Craiova-Vidin, the number and direction of their contacts: LP1, PL-4, PP-2, reflect an asymmetry: Craiova initiated more contacts (4) in projects led by it than Vidin did (1). At the Romanian-Bulgarian border we found similar asymmetric relations between the two pairs Giurgiu-Ruse and Craiova-Vidin, but with different lead predilection, on the Bulgarian (Ruse) or on the Romanian (Craiova) side of the border. As a conclusion, Craiova can be considered a small regional cross-border node.
Zooming in: Bucharest
As capital city, Bucharest represents a clearly different type of city-node. It is the most prominent node of Romania, in terms of number of projects and amount of money from projects (Eq.1). Slightly more than half of its projects have a transnational character (South-East Europe). This type of projects involves more partners, distributed in a wider geographical area.
Only 16 percent of Bucharest's projects have a CBC character exploiting the geographical proximity of the Romanian-Bulgarian border, followed by interregional projects (13 percent) (Figure 22).
Visualizing the distribution of the Top 10 most intense connections (Figure 23), we observe a balanced distribution of connections with other capital cities from the proximity, from Bulgaria, Hungary, Austria, Serbia, Slovenia, Albania and Greece. The most intense connection is with Sofia (IC=35.0), followed by Budapest (IC=28.0), Wien (IC=23.0), Belgrade (IC=20.0), Ljubljana (IC=15.5), Ruse (IC=15.0), Venice (IC=12.5), Bratislava (IC=11.0), Tirana (IC=11.0), Athens (IC=10.5) and Thessaloniki (IC=10.5). While ESDP encouraged the creation of growth areas able to complement the Pentagon, the intense connections of Bucharest seem to be more a "regional 'cluster'" (Maier, 2012, p. 140), as intense connections to the core European capital cities were not registered. This state of affairs is to be verified against the critique that the preference to polycentric networks of city regional nodes, produces corridors of 'in-between spaces' and 'internal peripheries' (Herrschel, 2009).
The number and directions of contacts between Bucharest and Sofia: LP-2, PL-11, PP-44, reveal the asymmetrical relation between the two cities, Bucharest being the initiator of more contacts (11) in projects which it led, than Sofia did (2). However, as a distinct particularity compared to all other Romanian cities (except for the Alba Iulia-Ljubljana relation, but at a much lower scale), Bucharest and Sofia have significant more contacts (44) as partners in projects led by a third party, than direct contacts as lead-partner or partner-lead; this expresses the higher opportunities offered to them, as capital cities coming from Eastern Europe, to partner up in transnational projects. In conclusion, Bucharest, the capital city of Romania, is a transnational node. As a detail, most of the projects led by Bucharest are of CBC type (Romania-Bulgaria), while among the only two South-East European projects led, just one approaches a transnational issue (risk assessment for the Danube flood plains).
General image of the intense connections of Romanian city-nodes
Putting together the zoomed in data from the eight analyzed cities, we obtained a general image of the network of most intense connections of the Romanian city-nodes (Figure 24).
The map illustrates once again, this time in a comparative manner, the assumptions made while discussing each zoom in. Looking at the most intense connections of the Romanian cities, we observed that they are based upon geographical proximity (most of the Romanian territorial cooperation city-nodes are regional cross-border nodes) and also, based upon connection between similar nodes, in terms of second-order cities (Timisoara-Szeged, Oradea-Debrecen and Craiova-Vidin).
In general, we observed the higher intensity of connections at the Western border of Romania, compared to the Southern or Eastern border. Also, at the Western border we observed the polarizing character of two couples of connected cities: Timisoara-Szeged and Oradea-Debrecen, while at the Eastern and Southern border the connections were less intense, but more balanced.
The reciprocity of contacts among cities was available in only two cases, which were very distinct in terms of intensity of connection: Oradea-Debrecen (higher intensity) versus Constanta-Varna (lower intensity). Asymmetric relations were registered in all other cases, and we could observe a West-East flow of the initiative of cooperation, between cities from older/newer EU MS (Szeged-Timisoara) or cities from EU MS/Neighborhood policy countries (Iasi-Chisinau). We also found the existence of most intense connections without close geographical proximity in the case of capital cities Bucharest-Sofia, as beneficiaries of transnational cooperation.
Bucharest is the most prominent Romanian city-node and the only transnational prominent Romanian node. We also found that non-similar nodes can be united by truly transnational issues (Danube Basin and Black Sea Basin), such was the case of second order nodes connected remotely to capital cities (Alba Iulia-Ljubljana, Giurgiu-Wien and Constanta-Istanbul) and these are the 'meaningful territories for cooperation' (Duhr and Nadin, 2007, p. 389).
Conclusions: emerging policy implications
Identifying the most connected Romanian city-nodes in territorial cooperation projects and their most intense connections, the attributes of the city-nodes and of their connections on each of the operating scales of INTERREG, allowed us to make remarks with potential policy value for cohesion policy.
The analysis we have done has provided empirical evidence that, in some cases, converges with our prior intuition (Bucharest is the most prominent node of territorial cooperation) but in other cases it contradicts our intuition (we found out, surprisingly, that Oradea is a more prominent node than Cluj-Napoca, Giurgiu is a more prominent node than Ploiesti or Pitesti, Alba Iulia is a more prominent node than Brasov or Sibiu). In other words, even though we intuitively associated size (population, economy) with prominence in territorial cooperation projects, our data show that they are not necessarily connected. The key of understanding these results is the multi-scalar perspective.
Transnational INTERREG cooperation projects, aiming to produce meaningful cooperation between cities, favor capital cities, in their endeavor to be competitive on a macro- European scale. The only Romanian city for whom transnational cooperation represents the biggest share of its portfolio is Bucharest, while other cities have a small share of transnational projects, as partners in projects led by others. Still, Bucharest does not play in the 'big league' of European cities in INTERREG projects, as its most intense connections form a rather regional cluster, peripheral to the Pentagon core. Giurgiu and Alba Iulia are territorial cooperation nodes which managed to enter the network of transnational projects, even though are irrelevant in size.
City leaders in charge of smaller cities can transform their cities into hubs of territorial cooperation. In this context, it may be important for them to have a set of incentives and strategies to network with other cities and to identify meaningful transnational cooperation issues, in order to tap into the European opportunities that a given city can benefit from.
At the national/ interregional INTERREG level, Bucharest shows a clear centrality in the Romanian urban network, while the other nodes seem rather disconnected among themselves in territorial cooperation projects. The core-periphery relation can be seen at this level as having the capital city as well as the border areas at the core of intense connections (with a decreasing intensity from West to East), and an internal periphery of missing connections.
At the cross-border level of INTERREG we found the most intense connections, expressing the prevalence of CBC in territorial cooperation projects. In terms of intense connections, border regions are no longer the peripheries. Furthermore, the large volume of projects between Timisoara-Szeged and Oradea-Debrecen creates in their regions a polarized, monocentric CBC.
Oradea is a more prominent territorial cooperation node than the wealthier and more populous Cluj-Napoca, having more incentives for networking and attracting EU funds due to its border status. Our research described INTERREG network embeddedness as a form of inter-city integration, without proposing a performance assessment. Recent literature assessed the effect of institutional, functional and cultural indicators of integration on the performance of urban regions, defining performance as "the extent to which urbanization economies have developed, proxied by the presence of metropolitan functions" (Meijers et al., 2018, p. 1).
Prominence in territorial cooperation projects was not verified, in our study, in correlation with the emergence or the performance of polycentric urban regions in Romania, and this is a limit of our approach. Being limited to assessing networking between cities in the INTERREG framework, as an instrument for European integration, this approach is not able to show true agglomeration effects (Meeteren van et al., 2016) and solid network linkages, but rather it just illustrates a sort of incipient Europeanization of cities in the EU's periphery. More in-depth investigation on learning in territorial cooperation projects (Colomb, 2007) would also be a future research path.
Baranyi, B. (2006). Euroregions across the Eastern Borders of Hungary: a Question of Scale? In: J. W. Scott (ed.) EU enlargement, Region Building and Shifting Borders of Exclusion and Inclusion, Ashgate, pp. 149-161.
Barca, F. (2009). An Agenda for a Reformed Cohesion Policy. A Place-Based Approach to Meeting European Union Challenges and Expectations, Independent Report prepared at the request of Danuta Hubner, Commissioner for Regional Policy.
Barca, F., McCann, P. and Rodriguez-Pose, A. (2012). The Case for Regional Development Intervention: Place-Based versus Place-Neutral Approaches, Journal of Regional Sciences, 51(1), pp. 134-152.
Bohme, K. and Waterhout, B. (2008). The Europeanization of Planning. In: A. Faludi (ed.) European Spatial Research and Planning, Cambridge: Lincoln Institute of Land Policy, pp. 225-248.
Bohme, K., Josserand, F., Haraldsson, I., Bachtler, P. and Polverari, L. (2003). Trans-national Nordic-Scottish Co-operation: Lessons for Policy and Practice, Nordregio Working Paper, Stockholm, Nordic Research Institute Nordregio.
Bonacich, P. (1987). Power and Centrality: A Family of Measures, American Journal of Sociology 92: 1170-1182.
Burger, M. and Meijers, E. (2012). Form Follows Function? Linking Morphological and Functional Polycentricity, Urban Studies, 49(5), pp. 1127-1149.
Castells, M. (1996). The rise of network society, Cambridge: MA/Oxford Blackwell Publishers.
Colomb, C. (2007). The Added Value of Transnational Cooperation: Towards a New Framework for Evaluating Learning and Policy Change, Planning, Practice and Research, 22(3), pp. 347-372.
Davoudi, S. (2003) Polycentricity in European Spatial Planning: from an Analytical Tool to a Normative Agenda?, European Planning Studies, 11, pp. 979-999.
Duhr, S. and Nadin, S. (2007). Europeanization through Transnational Territorial Cooperation? The Case of InTeRREG IIIB North-West Europe, Planning, Practice and Research, 22(3), pp. 373-394.
Duhr, S., Stead, D. and Zonneveld, W. (2007). The Europeanization of Spatial Planning through Territorial Cooperation, Planning, Practice and Research, 22(3), pp. 291-307.
ESPON (2005). ESPON 1.1.1. Potentials for polycentric development in Europe, ESPON Monitoring Committee, Luxembourg.
ESPON (2006). ESPON 1.1.3. Enlargement of the European Union and the wider European Perspective as regards its Polycentric Spatial Structure, ESPON Monitoring Committee, Luxemburg.
European Communities (1999). ESDP. European Spatial Development Perspective. Towards Balanced and Sustainable Development of the Territory of the European Union, Luxembourg: Office for Official Publications of the European Communities.
Friedman J. and Wolff G. (1982). World City Formation: An Agenda for Research and Action, International Journal of Urban and Regional Research, 6, pp. 309-344.
Gephi (2017), Gephi 0.9.2., open source software program, available from: https://gephi.org.
Giannakourou, G. (2012). The Europeanization of National Planning: Explaining the Causes and the Potentials of Change? Planning, Practice and Research, 27(1), pp. 117-135.
Green, N. (2007). Functional Polycentricity: a Formal Definition in Terms of Social Network Analysis, Urban Studies, 44, pp. 2077-2103.
Hall, P. and Pain, K. (eds.) (2006). The Polycentric Metropolis. Learning from Megacity Region in Europe, London: Earthscan.
Herrschel, T. (2009). City Regions, Policentricity and the Construction of Peripheralities through Governance, Urban Research and Practice, 2(3), pp. 240-250.
Hooghe, L. and Marks, G. (2004). Contrasting Visions of Multi-level Governance, in Bache, I., Flinders, M.(eds.) Multi-level Governance, Oxford: Oxford University Press, pp. 17-30.
Kaufmann, D., Leautier, F. and Mastruzzi, M. (2005). Governance and the City: An Empirical Exploration into Global Determinants of Urban Performance, World Bank Policy Research Working Paper, p. 3712.
Kloosterman, R. and Musterd, S. (2001). The Polycentric Urban Region: Towards a Research Agenda, Urban Studies, 38(4), pp. 623-633.
Lang, T., Henn, S., Sgibnev, W. and Ehrlich, K. (eds.) (2015). Understanding Geographies of Polarization and Peripheralization. Perspectives from Central and Eastern Europe and Beyond, Palgrave Macmillan.
Maier, K. (2012). Europeanization and Changing Planning In East-Central Europe: An Easterner View, Planning, Practice and Research, 27(1), pp. 137-154.
Meijers, E., Hoogerbrugge, M. and Cardoso, R. (2018). Beyond Polycentricity: Does Stronger Integration between Cities in Polycentric Urban Regions Improve Performance, Tijdschrift voor Economische en Sociale Geografie, 109(1), pp. 1-21.
Medeiros, E. (2011). (Re)defining the Euroregion Concept, European Planning Studies, 19(1), pp. 141-158.
Medeiros, E. (2016). Is there a Rise of the Territorial Dimension in the Cohesion Policy? Finisterra, LI, 103, pp. 89-112.
Meeteren van, M., Poorthuis, A., Derudder, B. and Witlox, F. (2016). Pacifying Babel's Tower: A Scientometric Analysis of Polycentricity in Urban Research, Urban Studies, 53(6), pp. 1278-1298.
Munteanu, M. and Servillo, L. (2014). Romanian Spatial Planning System: Post-Communist Dynamics of Change and Europeanization Processes, European Planning Studies, 22(11), pp. 2248-2267.
Nadin, V. and Duhr, S. (2005). Some Help with Euro-planning Jargon. Town and Country Planning, 74(3), p. 82.
Newman, M. E. J. (2004). Analysis of Weighted Networks. Physical Review E, 70(5) 056131, pp. 1-9.
Pain, K., Van Hamme, G., Vinciguerra, S. and David, Q. (2016). Global Networks, Cities and Economic Performance: Observations from an Analysis of Cities in Europe and USA, Urban Studies, 53(6), pp. 1137-1161.
REPUS (2007). Regional Polycentric Urban System--Final Report, INTERREG III B CADSES: Bologna/Budapest.
Rivolin, U.J. and Faludi, A. (2005). The Hidden Face of the European Spatial Planning: Innovations in Governance, European Planning Studies, 13(2), pp. 195-215.
Sassen, S. (1991). The Global City, New York, London, Tokyo (1st edition), Princeton: NJ, Princeton University Press.
Schaeffer, S. E. (2007). Graph clustering, Computer Science Review, 1(1), pp. 27-64.
Stein, A. (2010). Territorial Cohesion in the Context of Interregional and Transnational Cooperation, European Spatial Research and Policy, 17(1), pp. 1-22.
Taylor, P. J. (2006). Cities within Spaces of Flows: Theses for a Materialist Understanding of the External Relations of Cities, in Taylor, P., Derudder, B., Saey, P. et al. (eds.), Cities in Globalization, London: Routledge, pp. 287-297.
Tursie, C. (2016). The Europeanization of Cities--A preliminary Assessment of Romanian Cities Competing for the 2021 European Capital of Culture Title, in Micle, M., Mesaros, C. (eds), Communication Today: An Overview from Online Journalism to Applied Philosophy, Budapest: Trivent Publishing, pp. 211-226.
"Urban Agenda for the European Union. Pact of Amsterdam" (2016). Agreed at the Informal Meeting of EU Ministers Responsible for Urban Matters on 30 May 2016 in Amsterdam, The Netherlands.
Corina Tursie (1), Remus Boata (2)
(1) Corina Tursie (Corresponding Author) is Lecturer at the Department of Politics, West University of Timisoara; she has a PhD in Political Science awarded by SNSPA Bucharest; her areas of competence are EU policies and institutions, with a special research interest in urban development.
(2) Remus Boata is a Scientific Researcher at the Romanian Academy Timisoara Branch, Astronomical Observatory; PhD in Physics, with main research in mathematics and physics applicability in astrophysics; he recently developed a secondary research interest in network science applied to diverse systems.
Caption: Figure 1. Scatter plot of variable NPL vs. variable NPP
Caption: Figure 2. Scatter plot of variable BL vs. variable BP
Caption: Figure 4. Scatter plot of variable LP vs. variable PL
Caption: Figure 5. The map of the intensity of connections between 16 cities (using Gephi)
Caption: Figure 9. The most intense connections of Timisoara in territorial cooperation projects 2007-2013
Caption: Figure 11. The most intense connections of Oradea in territorial cooperation projects 2007-2013
Caption: Figure 13. The most intense connections of Alba lulia in territorial cooperation projects 2007-2013
Caption: Figure 15. The most intense connections of Iasi in territorial cooperation projects 2007-2013
Caption: Figure 17. The most intense connections of Constanta in territorial cooperation projects 2007-2013
Caption: Figure 19. The most intense connections of Giurgiu in territorial cooperation projects 2007-2013
Caption: Figure 21. The most intense connections of Craiova in territorial cooperation projects 2007-2013
Caption: Figure 23. The most intense connections of Bucharest in territorial cooperation projects 2007-2013
Caption: Figure 24. Map of the most intense connections of Romanian city-nodes
Figure 3. Prominence of the 41 Romanian city-nodes (organized by in NUTS 2 membership) Timisoara; 107,472 Arad; 50,367 Deva; 0,000 Resita; 2,141 Cluj; 18,623 Bistrita; 6,565 Baia Mare; 39,943 Satu Mare; 87,206 Oradea; 137,050 Zalau; 1,106 Alba lulia; 9,703 Brasov; 7,612 Miercurea Ciuc; 8,925 Sibiu; 6,591 St. Gheorghe; 2,290 Tg. Mures; 1,155 Suceava; 33,512 Botosani; 11,661 Piatra Neamt; 6,819 Iasi; 74,741 Bacau; 3,587 Vaslui; 12,775 Constanta; 41,413 Braila; 8,685 Buzau; 1,124 Galati; 38,398 Foscani; 0,000 Tulcea; 15,239 Alexandria; 4,353 Calarasi; 10,478 Giurgiu; 28,158 Pitesti; 1,092 Ploiesti; 2,171 Targoviste; 1,053 Slobozia; 0,000 Craiova; 25,338 Dr. Tr. Severin; 16,948 Rm. Valcea; 0,000 Slatina; 5,115 Tg. Jiu; 0,000 Bucuresti; 167,608 Source: Authors' own calculations Note: Table made from pie chart. Figure 6. Weighted degree of 16 nodes: the most prominent eight Romanian city-nodes and their partners in the most intense connections City Weighted degree Debrecen 106,5 Oradea 104,5 Bucharest 97 Szeged 81,5 Timisoara 76,5 Sofia 74 Chishau 50,5 Ljubljana 39,5 Ruse 36,5 Constanta 32 Iasi 29 Varna 27,5 Craiova 18 Giurgiu 15 Visin 12,5 Alba lulia 4,5 Source: Authors' own calculations Note: Table made from bar graph. Figure 7. Eigenvector Centrality of 16 nodes: eight most prominent Romanian city-nodes and their partners in the most intense connections City Eigenvector centrality Bucharest 1 Sofia 0,973 Ljubljana 0,832 Debrecen 0,661 Constanta 0,642 Timisoara 0,604 Varna 0,561 Szeged 0,554 Chishau 0,534 Ruse 0,49 Oradea 0,463 Vidin 0,462 Craiova 0,435 Alba Iulia 0,423 Iasi 0,408 Giungiu 0,24 Source: Authors' own calculations Note: Table made from bar graph. Figure 8. Types of projects of Timisoara, 2007-2013 2007-2013 Hungary-Romania (HU-RU) 80% 2007-2013 Intereg IVC 12% 2007-2013 Romania-Serbia IPA CBC (RO-RS) 4% 2007-2013 South East Europe 3% 2007-2013 URBACT II 1% Source: Authors' own calculations Note: Table made from pie chart. Figure 10. Types of projects--Oradea, 2007-2013 2007-2013 Hungary-Romania (HU-RU) 96% 2007-2013 Intereg IVC 1% 2007-2013 South East Europe 2% 2007-2013 URBACT II 1% Source: Authors' own calculations Note: Table made from pie chart. Figure 12. Types of projects of Alba Iulia, 2007-2013 2007-2013 Interreg IVC 56% 2007-2013 South East Europe 22% 2007-2013 URBACT II 22% Source: Authors' own calculations Note: Table made from pie chart. Figure 14. Types of projects of Iasi, 2007-2013 2007-2013 ESPON 11% 2007-2013 Interreg IVC 6% 2007-2013 Romania-Ukraine-Maldova ENPI CBC 77% 2007-2013 South East Europe 3% 2007-2013 URBACT II 3% Source: Authors' own calculations Note: Table made from pie chart. Figure 16. Types of projects of Constanta, 2007-2013 2007-2013 Black Sea Basin ENPI CBC 52% 2007-2013 ESPON 2% 2007-2013 Romania-Bulgaria (RO-BG) 34% 2007-2013 South East Europe 12% Source: Authors' own calculations Note: Table made from pie chart. Figure 18. Types of projects of Giurgiu, 2007-2013 2007-2013 Interreg IVC 5% 2007-2013 Romania-Bulgaria (RO-BG) 81% 2007-2013 South East Europe 14% Source: Authors' own calculations Note: Table made from pie chart. Figure 20. Types of projects of Craiova, 2007-2013 2007-2013 Romania-Bulgaria (RO-BG) 87% 2007-2013 South East Europe 13% Source: Authors' own calculations Note: Table made from pie chart. Figure 22. Types of projects of Bucharest, 2007-2013 2007-2013 Black Sea Basin ENPI CBC 2% 2007-2013 Central Europe 1% 2007-2013 ESPON 3% 2007-2013 Hungary-Romania (HU-RO) 6% 2007-2013 Hungary-Slovakia-Romania-Ukraine ENPI CBC 1% 2007-2013 Interreg IVC 13% 2007-2013 Romania-Bulgaria (RO-BG) 16% 2007-2013 Romania-Ukraine-Maldova ENPI CBC 6% 2007-2013 South East Europe 51% 2007-2013 URBACT II 1% Source: Authors' own calculations Note: Table made from pie chart.
|Printer friendly Cite/link Email Feedback|
|Author:||Tursie, Corina; Boata, Remus|
|Publication:||Romanian Journal of Political Science|
|Date:||Jun 22, 2018|
|Previous Article:||NETWORK STRUCTURES OF INTERAGENCY COLLABORATION AMONG COUNTERNARCOTICS STAKEHOLDERS IN AFGHANISTAN.|
|Next Article:||THE INSTITUTIONALISATION OF SOCIAL SCIENCES: BRINGING GRADUATES' EMPLOYMENT INTO THE EXPLANATION.|