A Smart City is characterized by a clever combination of investments in – and a clever use of – resources (in particular, human, social, creative, infrastructural, technological and business capital) that fuel sustainable economic growth and produce a high quality of life, under conditions of a wise management of natural resources and a broadly supported governance system (see Caragliu et al., 2011, 2012). A series of contributions on the attributes and success factors of Smart Cities can be found in a forthcoming issue of the journal Innovation (2011). A prerequisite for Smart Cities is the existence of and access to a strong local knowledge base. Such a knowledge base should have a broad base, in which both frontier research and standard research are performed in a balanced combination, while ensuring a sound mix of blue sky research and applicability. Thus, all quadrants of the socalled Pasteur quadrant are to be developed from a balanced perspective in a Smart City (see Figure 1). The smart combination of all four elements in the Pasteur quadrant in an urban context is, however, not yet sufficient to bring cities at a competitive edge in a global network economy. Knowledge has to be produced, but it should also be disseminated, accessed, absorbed and utilized by all stakeholders in the urban arena (Nijkamp and Kourtit, 2011). In the past decades we have witnessed a drastic transformation of ‘ivory tower’ research towards a linear transmission model from knowledge producers (mainly universities) to knowledge consumers (mainly industries and governments), later on followed by interactive science communication models, science valorization and commercialization initiatives, and recently more (pro-)active science marketing programs. In a more general context, we observe also a transition from Mode 1 to Mode 2 in the Gibbons/Novotny terminology (Gibbons et al., 1994; Nowotny et al., 2001), with increasing emphasis on open innovation systems ranging from national to regional or local ones. An important visual and analytical tool to map out the above mentioned knowledge force field is offered by the so-called triple helix model (Erkowicz and Leijdesdorff, 1997). Clearly, the triple helix model is only a stylized representation of a complex knowledge fabric. It has recently been generalized towards a multiple helix model (Caragliu et al., 2012), which is mapped out in Figure 2. A main question is of course whether sufficient data are available to represent in a comparative sense the smartness (in terms of input or resource indicators) or the socio-economic achievement (in terms of urban output or performance indicators). This calls for applied comparative case study research. For various indicators (e.g., GDP, population, employment, human capital, infrastructure, business, cultural heritage, urban amenities) a wealth of information is available on European cities. For others indicators such as e-Government, ICT quality, social capital, public participation, leisure patterns, segregation, it is much more demanding to acquire relevant information. Of course, Eurostat data, Urban Audit data, EVS data or Figure 1 | The Pasteur quadrant +
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