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KPIs to Drive Smart City Assessment

KPIs to Drive Smart City Assessment

Arianna Fonsati
Copyright: © 2021 |Pages: 24
ISBN13: 9781799870913|ISBN10: 179987091X|ISBN13 Softcover: 9781799870920|EISBN13: 9781799870937
DOI: 10.4018/978-1-7998-7091-3.ch009
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MLA

Fonsati, Arianna. "KPIs to Drive Smart City Assessment." Handbook of Research on Developing Smart Cities Based on Digital Twins, edited by Matteo Del Giudice and Anna Osello, IGI Global, 2021, pp. 172-195. https://doi.org/10.4018/978-1-7998-7091-3.ch009

APA

Fonsati, A. (2021). KPIs to Drive Smart City Assessment. In M. Del Giudice & A. Osello (Eds.), Handbook of Research on Developing Smart Cities Based on Digital Twins (pp. 172-195). IGI Global. https://doi.org/10.4018/978-1-7998-7091-3.ch009

Chicago

Fonsati, Arianna. "KPIs to Drive Smart City Assessment." In Handbook of Research on Developing Smart Cities Based on Digital Twins, edited by Matteo Del Giudice and Anna Osello, 172-195. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7091-3.ch009

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Abstract

Developing evaluation methods using key performance indicators (KPIs) to drive smart cities assessment represents a very interesting topic that have been deeply analysed over time. The aim of the chapter is to present several different approaches towards the assessment of cities' performance, discussing advantages and disadvantages of such methods that could be integrated in order to get more farsighted results in terms of decision-making strategies. For this reason, the chapter firstly analyses the different context in which KPIs are applied, defining the fields of application and describing the characteristics of such indicators. Then, two examples of European projects using indicators for evaluation purposes are studied in detail, highlighting the different level of application of indicators, used for assessments at city level in the first project (INDICATE) and at a district/building level in the second one (DIMMER). At the end, the use of KPIs as metrics of evaluation to develop multi-criteria decision approaches (MCDA) is discussed by introducing several examples.

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