Dealing with smartness at local level: experiments and lessons learned

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Tuesday, November 29, 2016

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The smart city issue is becoming central in the social and political debate. From the measurement point of view, in the related literature, however, there are mainly papers focusing on specific and heterogeneous local projects. Moreover, the outcomes of these papers are mostly rankings based on peculiar and often non standardised methodologies.

In this framework, the present paper carries out a research aimed at i) defining properly the con- cept of smart city ii) analysing and comparing the existing methodologies to measure a multidi- mensional concept as smartness at local level is, iii) deriving policy implications in order to improve citizens’ quality of life through the smart city instrument.

A review of the existing theoretical and empirical literature stresses the steady evolution of the meaning of the smart city concept at local level, integrating technological and digital innovation with territorial and social aspects. Starting from a pilot database derived from other related stud- ies, a principal component analysis is run in order to verify whether some methodological inno- vations can produce improvements in the measurement of Italian Cities smartness with regard to previous experiences. More specifically, findings suggest that cluster analysis is one of the possi- bilities for investigating a measurement system of smartness in a more robust way. Through this alternative approach to rankings, best practice examples of other cities can be interpreted with re- gard to their specific profiles, thus making it easier to adopt them in a more effective way, con- tributing to overcoming the mere distinction between best and worst realities. According to the preliminary conclusions it is also possible to imagine a dynamic framework in which smart cities could transit from a cluster to another as a consequence of policy effectiveness at local level.

The possibility to implement this kind of innovative analysis of smartness at local level is obvi- ously strictly linked to the availability of “smart” data such as administrative or big data, so as to converge towards a measurement system including specific local aspects.