EUROPEAN JOINT4    COORDINATION321    LOCAL WEAK     COORDINATION
 Gibbons/Novotny   Mode 1   Mode 2 Implications   for Urban Policy
University                            University                                     Knowledge                Learning    ...
Helix           SpiderWe believe a city to be smart wheninvestments in human and social capitaland traditional (transport)...
   9 cities (Bremerhaven, Edinburgh, Karlstad, Kristiansand,    Lillesand, Groningen, Kortrijk, Osterholz, and Norfolk   ...
A special issue on the Journal of Urban Technology (“Smart cities”)A  book chapter (A.Caragliu, M.Deakin, C.Del Bo, S.Gi...
 Baseline datato used to calculate the Knowledge Economy Indicator for the 9 Smart Cities include:  • The Economic Incent...
1) The actual data (u) is collected from urban datasets2)   Ranks are allocated to cities based on the absolute values (ac...
10 9 8 7 6 5 4     Knowledge Economy Indicator 3   Smart cities KEI 2 1 0
University             60.0             50.0             40.0 Knowledge   30.0           Learning             20.0        ...
   But, if we de-construct the average Smart Cities value    and zoom in on each of the nine cities, we obtain    markedl...
University   Knowledge Economy       2,5                                                                 Bremerhaven      ...
University   Knowledge Economy      2,5                          2,0         i2010                         Edinburgh      ...
University   Knowledge Economy       2,0                                      i2010       Indicator           1,5         ...
University                          1,0                                      Kristiansand   Knowledge Economy             ...
University   Knowledge Economy      3,0                                    Lillesand                          2,5         ...
University                             Groningen   Knowledge Economy      2,5                          2,0          i2010 ...
University                              2,0Knowledge Economy Indicator                 i2010                             K...
University   Knowledge Economy      2,5                                 Osterholz-Scharmbeck                          2,0 ...
Norfolk                          University   Knowledge Economy      2,5                          2,0          i2010      ...
Indicators for the New Triple Helix             Variable                    Measure                                       ...
References1.   Caragliu, A; Del Bo, C. & Nijkamp, P (2011).     “Smart cities in Europe”, Journal of Urban     Technology,...
 Performance: ratio   between input and output DEA: comparative     analysis
Great variety in smart citiesRelevance of multiple helixMeaning of performance analysis               Message: reinforc...
   Editors:    Karima Kourtit &    Peter Nijkamp   No. 4, 2011   Published by Taylor &    Francis (UK)
manage                                  develop a        design a spatially-                       sustainable develop an ...
    Improvement transport systems & infrastructure    New information technology    Climate change    Demographic tran...
“Competition among cities is like riding a bicycle: if you don’t pedal, you’ll fall off”.   However, globalization is maki...
The Special Issue of Journal Innovation on ‘Smart Cities in the  Innovation Age’:   Provides a unique forum for discussin...
Table of Contents1.   Smartness and European Urban Performance: Assessing the Local Impacts of     Smart Urban Attributes ...
1.   Smartness and European Urban Performance: Assessing the Local Impacts     of Smart Urban Attributes by Andrea Caragli...
3.   Modelling the Smart Cities Performances by Patrizia Lombardi,     Silvia Giordano, Hend Farouh and Wael Yousef      ...
5.   Smart Networked Cities? by Emmanouil Tranos and Drew Gertner           Argues that cities are part of a broad nation...
7.   Bright Stars in the Urban Galaxy – The Efficiency of Ethnic Entrepreneurs     in the Urban Economy by Mediha Sahin, A...
   This special issue offers new horizons on the innovation and    knowledge drivers, the functioning and the positioning...
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities
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Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities

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Peter Nijkamp - Performance of Smart Cities
Peter reviews the development of innovation models, the emergence of a 'multiple helix' of forces/actors shaping Smart Cities, the development of a model of the knowledge economy, and analysis of the 9 municipal partners in the Smart Cities project using this model.
The special issue of Innovation, the European Journal of Social Science Research on 'Smart Cities in the Innovation Age' is also reviewed.

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Transcript of "Creating Smarter Cities 2011 - 13 - Peter Nijkamp - Performance of Smart Cities"

  1. 1. EUROPEAN JOINT4 COORDINATION321 LOCAL WEAK COORDINATION
  2. 2.  Gibbons/Novotny Mode 1 Mode 2 Implications for Urban Policy
  3. 3. University University Knowledge Learning Industry GovernmentIndustry Government Market
  4. 4. Helix SpiderWe believe a city to be smart wheninvestments in human and social capitaland traditional (transport) and modern(ICT) communication infrastructure fuelsustainable economic growth and a highquality of life, with a wise management ofnatural resources, through a participatedgovernance. (Caragliu, Del Bo and Nijkamp,2011).
  5. 5.  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
  6. 6. 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
  7. 7.  Baseline datato 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
  8. 8. 1) The actual data (u) is collected from urban datasets2) 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 on3) The number of cities with higher rank (Nh) is calculated for the whole sample4) 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
  9. 9. 10 9 8 7 6 5 4 Knowledge Economy Indicator 3 Smart cities KEI 2 1 0
  10. 10. University 60.0 50.0 40.0 Knowledge 30.0 Learning 20.0 10.0 EU27 0.0 Smart CitiesGovernment Industry Market
  11. 11.  But, if we de-construct the average Smart Cities value and zoom in on each of the nine cities, we obtain markedly different results: Results are rich and difficult to compare; a more detailed analysis is needed. University Knowledge Economy 3,0 Bremerhaven 2,5 i2010 Indicator 2,0 Edinburgh 1,5 1,0 Karlstad Knowledge 0,5 Learning 0,0 Kristiansand -0,5 -1,0 -1,5 Lillesand e-services -2,0 Intellectual property Groningen Kortrijk Government Industry Osterholz ICT-related Norfolk RTD employment EU27 Market SCRAN
  12. 12. University Knowledge Economy 2,5 Bremerhaven 2,0 i2010 Indicator 1,5 EU27 1,0 Knowledge 0,5 Learning 0,0 -0,5 -1,0 e-services -1,5 Intellectual property Government IndustryICT-related employment RTD Market
  13. 13. University Knowledge Economy 2,5 2,0 i2010 Edinburgh Indicator 1,5 Knowledge 1,0 Learning 0,5 EU27 0,0 -0,5 e-services -1,0 Intellectual property Government IndustryICT-related employment RTD Market
  14. 14. University Knowledge Economy 2,0 i2010 Indicator 1,5 1,0 Karlstad Knowledge 0,5 Learning 0,0 -0,5 EU27 e-services -1,0 Intellectual property Government IndustryICT-related employment RTD Market
  15. 15. University 1,0 Kristiansand Knowledge Economy 0,8 i2010 Indicator 0,6 0,4 EU27 Knowledge 0,2 Learning 0,0 -0,2 -0,4 -0,6 e-services -0,8 Intellectual property Government IndustryICT-related employment RTD Market
  16. 16. University Knowledge Economy 3,0 Lillesand 2,5 i2010 Indicator 2,0 1,5 Knowledge 1,0 Learning EU27 0,5 0,0 -0,5 -1,0 e-services -1,5 Intellectual property Government IndustryICT-related employment RTD Market
  17. 17. University Groningen Knowledge Economy 2,5 2,0 i2010 Indicator 1,5 EU27 1,0 Knowledge 0,5 Learning 0,0 -0,5 -1,0 -1,5 e-services -2,0 Intellectual property Government IndustryICT-related employment RTD Market
  18. 18. University 2,0Knowledge Economy Indicator i2010 Kortrijk 1,0 Knowledge Learning EU27 0,0 -1,0 e-services -2,0 Intellectual property Government Industry ICT-related employment RTD Market
  19. 19. University Knowledge Economy 2,5 Osterholz-Scharmbeck 2,0 i2010 Indicator 1,5 EU27 1,0 Knowledge 0,5 Learning 0,0 -0,5 -1,0 e-services -1,5 Intellectual property Government IndustryICT-related employment RTD Market
  20. 20. Norfolk University Knowledge Economy 2,5 2,0 i2010 Indicator EU27 1,5 1,0 Knowledge 0,5 Learning 0,0 -0,5 -1,0 -1,5 e-services -2,0 Intellectual property Government IndustryICT-related employment RTD Market
  21. 21. Indicators for the New Triple Helix Variable Measure Notes University (% people aged 20-24 enrolledUniversity in tertiary education) Learning ( labour force with ISCED 5 andLearning 6 education) Industry (Number of companies perIndustry 1,000 pop.)Market Market (Per capita GDP) Government (% labour force in government sector-L to Q: PublicGovernment administration and community services; activities of households; extra-territorial organizations ) Knowledge (Patent applications to theKnowledge USPTO per 1,000 inh.) Per capita number of administrativee-services forms available for download from official web site Number of local units manufacturing ICT For the EU, % of GDP produced by theICT-related employment products over total active companies ICT industry Source: NUTS1/2 data from the RegionalBusiness R&D expenditure Business R&D expenditures (2006) Innovation Scoreboard 2009 Number of patent applications to the Co-patenting between industry andIntellectual property USPTO shared by at least one company universities and one university since 1977. http://info.worldbank.org/etools/kam2/KKnowledge Economy Indicator Average World Bank KEI score AM_page5.asp Municipal scores calculated by thei2020 Edimburgh team.
  22. 22. References1. Caragliu, A; Del Bo, C. & Nijkamp, P (2011). “Smart cities in Europe”, Journal of Urban Technology, forthcoming2. 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
  23. 23.  Performance: ratio between input and output DEA: comparative analysis
  24. 24. Great variety in smart citiesRelevance of multiple helixMeaning of performance analysis Message: reinforce strong points and address weak points
  25. 25.  Editors: Karima Kourtit & Peter Nijkamp No. 4, 2011 Published by Taylor & Francis (UK)
  26. 26. manage develop a design a spatially- sustainable develop an effective balanced manage turn mass integrated and accessibility policy to ensure that national (or production and population the benefits of supra-national) balanced urban and mobility investments to movement agglomeration strategy for land use strategy towards urban of urban the benefit of that is compatible agglomerations systems advantages are emerging higher than their connected city sustainable with ecological through new economic into new social costs systems sustainability opportunities logistic and development infrastructur satisfy the socio- of urban areas al concepts develop economic demand need for conflict effectivemanagement and pro-active of an increasingly design of fit-for-purpose measures for large share ofinclusions strategies for less institutional mechanisms eco-friendly urban populationprivileged groups in urban and climate- and structures in a multi- for high-quality areas neutral layer dynamic system of urban amenities urban areas metropolitan areas
  27. 27.  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 products33 33
  28. 28. “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 mustconstruct and promote our difference in order to continue existing” Mirón, Urban Land Institute
  29. 29. 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
  30. 30. Table of Contents1. Smartness and European Urban Performance: Assessing the Local Impacts of Smart Urban Attributes by Andrea Caragliu and Chiara Del Bo2. Intelligent Cities as Smart Providers: CoPs as Organizations for Developing Integrated Models of eGovernment Services by Mark Deakin3. Modelling the Smart Cities Performances by Patrizia Lombardi, Silvia Giordano, Hend Farouh and Wael Yousef4. Is Innovation in Cities a Matter of Knowledge Intensive Services? An Empirical Investigation by Roberta Capello, Andrea Caragliu and Camilla Lenzi5. Smart Networked Cities? by Emmanouil Tranos and Drew Gertner6. Open Innovation Among University Spin-off Firms: What is in it for Them, and What Can Cities Do? by Marina van Geenhuizen7. Bright Stars in the Urban Galaxy – The Efficiency of Ethnic Entrepreneurs in the Urban Economy by Mediha Sahin, Alina Todiras, Peter Nijkamp and Soushi Suzuki8. Smart Cities in Perspective − A Comparative European Study by Means of Self-Organizing Maps by Karima Kourtit, Peter Nijkamp and Daniel Arribas
  31. 31. 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
  32. 32. 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 performances4. 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 knowledge- intensive services, rather than on advanced manufacturing activities
  33. 33. 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 policies6. 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
  34. 34. 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
  35. 35.  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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