EUROPEAN JOINT COORDINATION LOCAL WEAK

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4 4 EUROPEAN JOINT COORDINATION LOCAL WEAK COORDINATION

5 Gibbons/Novotny Mode 1 Mode 2 Implications for Urban Policy

6 University University Knowledge Learning Industry Government Industry Government Market

7 Helix Spider We believe a city to be smart when investments in human and social capital and traditional (transport) and modern (ICT) communication infrastructure fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through a participated governance. (Caragliu, Del Bo and Nijkamp, 2011).

8 9 cities (Bremerhaven, Edinburgh, Karlstad, Kristiansand, Lillesand, Groningen, Kortrijk, Osterholz, and Norfolk county) Domains: e-gov and ICTs GDP and income Population and density Employment and Human Capital Infrastructure Business Local Government Tourism and cultural heritage Leisure and recreation Urban Audit Collection of data: direct contact with city officials and statisticians

9 A special issue on the Journal of Urban Technology ( Smart cities ) A book chapter (A.Caragliu, M.Deakin, C.Del Bo, S.Giordano, K.Kourtit, P.Lombardi, P. Nijkamp, An advanced triple-helix network model for Smart Cities performance, IGI Global) A special issue on Innovation

10 Baseline data to used to calculate the Knowledge Economy Indicator for the 9 Smart Cities include: The Economic Incentive and Institutional Regime Education and Human Resources The Innovation System ICTs We then normalized the indicators according to the formula in the next slide

11 1) The actual data (u) is collected from urban datasets 2) Ranks are allocated to cities based on the absolute values (actual data) that describe each and every one of 6 variables (rank u). Cities with the same performance are allocated the same rank. Therefore, the rank equals 1 for a city that performs the best among those in our sample on a particular variable (that is, it has the highest score), the rank equals to 2 for a city that performs second best, and so on 3) The number of cities with higher rank (Nh) is calculated for the whole sample 4) The following formula is used in order to normalize the scores for every city on every variable according to their ranking and in relation to the total number of cities in the sample (Nc) with available data : Normalized (u) = 10*(1-Nh/Nc) 5) The above formula allocates a normalized score from 0 to 10 for each city

12 Knowledge Economy Indicator Smart cities KEI 2 1 0

13 Knowledge University Learning EU27 Smart Cities Government Industry Market

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15 But, if we de-construct the average Smart Cities value and zoom in on each of the nine cities, we obtain markedly different results: Knowledge Economy Indicator Knowledge e-services University 3,0 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5-2,0 i2010 Learning Results are rich and difficult to compare; a more detailed analysis is needed. Intellectual property Bremerhaven Edinburgh Karlstad Kristiansand Lillesand Groningen Government Industry Kortrijk Osterholz ICT-related employment Market RTD Norfolk EU27 SCRAN

16 Knowledge Economy Indicator Knowledge University 2,5 2,0 1,5 1,0 0,5 i2010 Learning Bremerhaven EU27 0,0-0,5 e-services -1,0-1,5 Intellectual property Government Industry ICT-related employment RTD Market

17 Knowledge Economy Indicator Knowledge e-services University 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0 i2010 Edinburgh Learning EU27 Intellectual property Government Industry ICT-related employment RTD Market

18 Knowledge Economy Indicator University 2,0 1,5 i2010 Knowledge 1,0 0,5 Learning Karlstad 0,0-0,5 EU27 e-services -1,0 Intellectual property Government Industry ICT-related employment RTD Market

19 Knowledge Economy Indicator Knowledge e-services University 1,0 0,8 0,6 0,4 0,2 0,0-0,2-0,4-0,6-0,8 i2010 Kristiansand EU27 Learning Intellectual property Government Industry ICT-related employment RTD Market

20 Knowledge Economy Indicator Knowledge e-services University 3,0 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5 i2010 Lillesand Learning EU27 Intellectual property Government Industry ICT-related employment RTD Market

21 Knowledge Economy Indicator Knowledge e-services University 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5-2,0 i2010 Groningen EU27 Learning Intellectual property Government Industry ICT-related employment RTD Market

22 Knowledge Economy Indicator Knowledge University 2,0 1,0 0,0 i2010 Learning Kortrijk EU27-1,0 e-services -2,0 Intellectual property Government Industry ICT-related employment RTD Market

23 Knowledge Economy Indicator Knowledge e-services University 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5 i2010 Osterholz-Scharmbeck EU27 Learning Intellectual property Government Industry ICT-related employment RTD Market

24 Knowledge Economy Indicator Knowledge e-services University 2,5 2,0 1,5 1,0 0,5 0,0-0,5-1,0-1,5-2,0 i2010 Norfolk EU27 Learning Intellectual property Government Industry ICT-related employment RTD Market

25 Indicators for the New Triple Helix University Learning Industry Market Variable Measure Notes University (% people aged enrolled in tertiary education) Learning ( labour force with ISCED 5 and 6 education) Industry (Number of companies per 1,000 pop.) Market (Per capita GDP) Government Knowledge e-services ICT-related employment Government (% labour force in government sector-l to Q: Public administration and community services; activities of households; extra-territorial organizations ) Knowledge (Patent applications to the USPTO per 1,000 inh.) Per capita number of administrative forms available for download from official web site Business R&D expenditure Business R&D expenditures (2006) Intellectual property Knowledge Economy Indicator i2020 Number of local units manufacturing ICT For the EU, % of GDP produced by the products over total active companies ICT industry Source: NUTS1/2 data from the Regional Innovation Scoreboard 2009 Number of patent applications to the Co-patenting between industry and USPTO shared by at least one company universities and one university since Average World Bank KEI score Municipal scores calculated by the Edimburgh team. AM_page5.asp

26 References 1. Caragliu, A; Del Bo, C. & Nijkamp, P (2011). Smart cities in Europe, Journal of Urban Technology, forthcoming 2. A. Caragliu, M. Deakin, C. Del Bo, S. Giordano, K. Kourtit, P. Lombardi, P. Nijkamp (2011). An advanced Triple-Helix network model for smart cities performance, in O. Yalciner Ercoskun (ed.), Green and ecological technologies for urban planning: creating smart cities, Hershey (PA): IGI Global

27 Performance: ratio between input and output DEA: comparative analysis

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30 Great variety in smart cities Relevance of multiple helix Meaning of performance analysis Message: reinforce strong points and address weak points

31 Editors: Karima Kourtit & Peter Nijkamp No. 4, 2011 Published by Taylor & Francis (UK)

32 design a spatiallyintegrated and balanced urban land use strategy that is compatible with ecological sustainability need for conflict management and pro-active inclusions strategies for less privileged groups in urban areas turn mass population movement towards urban agglomerations into new opportunities manage sustainable accessibility and mobility of urban systems through new logistic and infrastructur al concepts design of fit-for-purpose institutional mechanisms and structures in a multilayer dynamic system of urban areas develop an effective policy to ensure that the benefits of agglomeration advantages are higher than their social costs develop effective measures for eco-friendly and climateneutral metropolitan areas develop a balanced national (or supra-national) strategy for emerging connected city systems satisfy the socioeconomic demand of an increasingly large share of urban population for high-quality urban amenities manage production and investments to the benefit of sustainable economic development of urban areas

33 Improvement transport systems & infrastructure New information technology Climate change Demographic transformation Increased globalisation Rising urbanization in Europe Regional, national and international competition push cities Cities are in competition in a way that is similar to competition between companies and products 33 33

34 Competition among cities is like riding a bicycle: if you don t pedal, you ll fall off. However, globalization is making us increasingly uniform, so we must construct and promote our difference in order to continue existing Mirón, Urban Land Institute

35 The Special Issue of Journal Innovation on Smart Cities in the Innovation Age : Provides a unique forum for discussing worldwide urban challenges and developments Addresses in particular the feasibility of smart cities concepts by presenting a series of applied studies on the success conditions and implications of smart city strategies and ideas The papers on all aspects of European urban developments contribute to the improvement of social science knowledge and to the setting of a policy-focused European research agenda

36 Table of Contents 1. Smartness and European Urban Performance: Assessing the Local Impacts of Smart Urban Attributes by Andrea Caragliu and Chiara Del Bo 2. Intelligent Cities as Smart Providers: CoPs as Organizations for Developing Integrated Models of egovernment Services by Mark Deakin 3. Modelling the Smart Cities Performances by Patrizia Lombardi, Silvia Giordano, Hend Farouh and Wael Yousef 4. Is Innovation in Cities a Matter of Knowledge Intensive Services? An Empirical Investigation by Roberta Capello, Andrea Caragliu and Camilla Lenzi 5. Smart Networked Cities? by Emmanouil Tranos and Drew Gertner 6. Open Innovation Among University Spin-off Firms: What is in it for Them, and What Can Cities Do? by Marina van Geenhuizen 7. Bright Stars in the Urban Galaxy The Efficiency of Ethnic Entrepreneurs in the Urban Economy by Mediha Sahin, Alina Todiras, Peter Nijkamp and Soushi Suzuki 8. Smart Cities in Perspective A Comparative European Study by Means of Self-Organizing Maps by Karima Kourtit, Peter Nijkamp and Daniel Arribas

37 1. Smartness and European Urban Performance: Assessing the Local Impacts of Smart Urban Attributes by Andrea Caragliu and Chiara Del Bo: Provides a comparative benchmark analysis of the growth performance of various smart cites in Europe Points in the direction of the critical importance of space specific characteristics in shaping the economic benefits of smart urban qualities, providing a justification for place-based public policies that account for local characteristics Identifies different clusters with respect to the impacts of smartness on urban performance and wealth, highlighting the need for geographically-differentiated policy actions. 2. Intelligent Cities as Smart Providers: CoPs as Organizations for Developing Integrated Models of egovernment Services by Mark Deakin Analyses the learning aspects of smart cities Interprets intelligent cities as facilitators and communities of practice for designing and implementing e-government services Identifies how the growing interest in intelligent cities has led universities to explore the opportunities communities of practice (CoPs) offer to industry in order to become smart providers of online services

38 3. Modelling the Smart Cities Performances by Patrizia Lombardi, Silvia Giordano, Hend Farouh and Wael Yousef Addresses the assessment and modelling of the performance of smart cities is an intriguing research challenge Proposes a novel research agenda for the development of a testing exercise with the participation of main city stakeholders, offering a reflexive learning opportunity for cities to measure what options exist to improve their performances 4. Is Innovation in Cities a Matter of Knowledge Intensive Services? An Empirical Investigation by Roberta Capello, Andrea Caragliu and Camilla Lenzi Raises the question whether a high innovation degree in cities is related to the local presence of knowledge-intensive services Argues that the linkage between the presence of cities in the region and their innovative performance is mediated by the urban industrial structure Argues that a positive correlation is likely to exist between the presence of large cities in a region and its innovative performance. Such a relationship could also depend on the presence of knowledgeintensive services, rather than on advanced manufacturing activities

39 5. Smart Networked Cities? by Emmanouil Tranos and Drew Gertner Argues that cities are part of a broad national or global network, both physical and virtual Investigates conceptually and empirically the issue of smart networked cities Argues that the local policy agenda and more specifically smart city initiatives should be informed about and address the structure of the transnational urban network, as this can affect the efficiency of such local policies 6. Open Innovation Among University Spin-off Firms: What is in it for Them, and What Can Cities Do? by Marina van Geenhuizen Argues that smart cities are most likely well equipped with an advanced knowledge infrastructure which may induce important benefits Offers a new perspective on the open innovation potential provided by university spin-off firms Examines a particular category of high-tech firms, university spin-offs, and highlights resources that are missing and the level of openness in learning networks to gain these resources Argues that the vitality of modern cities is nowadays strongly influenced by cultural diversity

40 7. Bright Stars in the Urban Galaxy The Efficiency of Ethnic Entrepreneurs in the Urban Economy by Mediha Sahin, Alina Todiras, Peter Nijkamp and Soushi Suzuki Argues that the new urban entrepreneurs usually coined ethnic entrepreneurs play a prominent role Presents findings on the efficiency profiles of ethnic entrepreneurs in Dutch cities. Argues that the se entrepreneurs appear to move increasingly to high-skilled segments of urban business life, offering a boost to the local economy. 8. Smart Cities in Perspective A Comparative European Study by Means of Self-Organizing Maps by Karima Kourtit, Peter Nijkamp and Daniel Arribas Presents a study on the relative differences among smart cities by analysing a multi-dimensional set of urban attributes related to smart cities Employs an analytical tool set which is based on self-organising mapping analysis Points the idea that some cities (actually most of them) have 'converged', that is, they have become more similar over the observation period,while others have become a bit of outliers in positions where they were not found before

41 This special issue offers new horizons on the innovation and knowledge drivers, the functioning and the positioning of smart cities There is a need for a conceptual clarity of smart cities, that is evidence-based and appropriate for empirical measurement and comparison For strategic policy support, an evidence-based monitoring and benchmarking system for smart cities has to be designed (urban compass) It is also evident that strategic urban policy should exploit the knowledge-intensive and creative potential of smart cities: knowledge creation, access and use are critical parameters for the future of our cities

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