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Modelling the smart city performancePatrizia Lombardi

a, Silvia Giordano

b, Hend Farouh

c& Wael

Yousefd

a

Politecnico di Torino, Department of Housing and Cities , Turin ,ItalybSITI Innovation Research Centre of Politecnico di Torino , Turin ,

ItalycHousing and Building National Research Center (HBRC) , Giza ,

EgyptdDepartment of Urban Planning , Al-Azhar University , Cairo ,

Egypt

Published online: 19 Apr 2012.

To cite this article:Patrizia Lombardi , Silvia Giordano , Hend Farouh & Wael Yousef (2012)

Modelling the smart city performance, Innovation: The European Journal of Social Science

Research, 25:2, 137-149, DOI: 10.1080/13511610.2012.660325

To link to this article: http://dx.doi.org/10.1080/13511610.2012.660325

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Modelling the smart city performance

Patrizia Lombardia*, Silvia Giordanob, Hend Farouhc and Wael Yousefd

aPolitecnico di Torino, Department of Housing and Cities, Turin, Italy; bSITI InnovationResearch Centre of Politecnico di Torino, Turin, Italy; cHousing and Building National ResearchCenter (HBRC), Giza, Egypt; dDepartment of Urban Planning, Al-Azhar University, Cairo,Egypt

(Received 13 October 2010; final version received 10 October 2011)

This paper aims to offer a profound analysis of the interrelations between smart

city components connecting the cornerstones of the triple helix. The triple helixmodel has emerged as a reference framework for the analysis of knowledge-basedinnovation systems, and relates the multiple and reciprocal relationships betweenthe three main agencies in the process of knowledge creation and capitalization:university, industry and government. This analysis of the triple helix will beaugmented using the Analytic Network Process to model, cluster and beginmeasuring the performance of smart cities. The model obtained allows interac-tions and feedbacks within and between clusters, providing a process to deriveratio scales priorities from elements. This offers a more truthful and realisticrepresentation for supporting policy-making. The application of this model is stillto be developed, but a full list of indicators, available at urban level, has beenidentified and selected from literature review.

Keywords: Analytic Network Process; smart city components; triple helixapproach

Introduction

The application of information and communications technology (ICT) in the context

of future cities is often indicated by the notion of smart city. This concept has been

quite fashionable in the policy arena in recent years. Compared with the concept of

digital city or intelligent city (Lombardi et al. 2009), the main focus is not limited

to the role of ICT infrastructure but is mainly on the role of human capital/edu-

cation, social and relational capital, and environmental issues. These are consideredimportant drivers of urban growth.

In order to explore the concept of a smart city, a revised triple helix model

has been recently proposed by Lombardi et al. (2012) focusing on the production

of knowledge by universities and government and the production of innovations

that are patented by industry and universities as an index of intellectual capital

(Etzkowitz 2008, Caragliu et al. 2009, Deakin 2010). This model presupposes that

the three helices operate in a complex urban environment, where market demand,

governance, civic involvement and citizens characteristics, along with cultural and

social capital endowments, shape the relationships between the traditional helices of

university, industry and government.

*Corresponding author. Email: [email protected]

InnovationThe European Journal of Social Science Research

Vol. 25, No. 2, June 2012, 137149

ISSN 1351-1610 print/ISSN 1469-8412 online

# 2012 ICCR Foundation

http://dx.doi.org/10.1080/13511610.2012.660325

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The results of the above study has shown the analysis to baseline the develop-

ment of smart cities in terms of their dual roles as generators of intellectual capital,

creators of wealth and regulators of standards (university, industry, civil society

and government), as well as supporting the social learning and knowledge-transfer

abilities that are needed to meet the requirements of their regional innovation

systems.

Although this analysis has been a useful start for understanding the main

governance component of smart cities, it does not consider the other recognized

aspects related to ecological sustainability. In addition, it does not recognize the

number of relationships and feedbacks between categories that are dependent upon

the interconnected and systemic nature of the aspects involved.

This paper proposes a different model that involves the civil society as one of

the main key actors, alongside universities, industry and government (Etzkowitz

and Zhou 2006). This new framework is used for classifying smart city performance

indicators and for structuring an ANP (Analytic Network Process) exercise (Saaty

2005) with the aim of investigating the relations between smart cities components,actors and strategies to which they are moving. This exercise was conducted within

a focus group, involving a number of experts in different disciplines.

Smart city components and performance indicators

The concept of the smart city has been quite fashionable in the policy arena in

recent years, with the aim of drawing a distinction from the terms digital cityand

intelligent city. Its main focus is still on the role of ICT infrastructure, but much

research has also been carried out on the role of human capital/education, social

and relational capital, and environmental interests as important drivers of urban

growth. (Komninos 2002, Shapiro 2008, Deakin 2010).

Although there is no agreement on the exact definition of a smart city, a number

of main dimensions of a smart city have been identified through a literature review

(Giffingeret al. 2007, Van Soom 2009, Fusco Girardet al. 2009) and include: smart

economy; smart mobility; smart environment; smart people; smart living; and smart

governance.

These six dimensions connect with traditional regional and neoclassical theories

of urban growth and development. In particular, the dimensions are based on theories

of regional competitiveness, transport and ICT economics, natural resources, human

and social capital, quality of life, and participation of citizens in the governance

of cities.

Although the term smart city is not very widely used yet in spatial planning

literature or urban studies, it is still possible to identify various aspects of it as a basis

for further elaboration. However, it should be noted that in the literature the term

is not used in a holistic way; rather it is used with reference to various aspects that

range from a smart city as an IT-district to a smart city as regarding the education

(or smartness) of its inhabitants.

In association with economy, smart city is used to describe a city with a smart

industry. This implies especially industries in the fields of ICT as well as other

industries employing ICT in their production processes. The name smart city isalso used for business parks or districts comprising companies within this field

(Giffingeret al. 2007, Fusco Girard et al. 2009, Caragliuet al. 2009).

138 P. Lombardiet al.


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The term is also used in relation to the education of a city s inhabitants. A smart

city therefore has smart inhabitants in terms of their educational grade. In addition,

the term is referred to the relation between the city government administration and its

citizens. Good governance or smart governance is often referred to as the use of new

channels of communication for the citizens, e.g. e-governance or e-democracy

(Florida 2002, Benner 2003, Torres et al. 2005, Lombardi et al. 2009).

The term smart city is furthermore used to discuss the use of modern tech-

nology in everyday urban life. This includes not only ICT but also, and especially,

modern transport technologies. Logistics as well as new transport systems are

smart systems that improve urban traffic and inhabitants mobility. Moreover,

various other aspects referring to life in a city are mentioned in connection to the

term smart city, like security/safety, green, efficient and sustainable energy (Benner

2003, Komninos 2007, Giffinger et al. 2007, Caragliu et al. 2009).

To sum up, there are several fields of activity that are described in the literature

in relation to the term smart city: industry, education, participation, technical

infrastructure and various soft factors (Giffinger et al. 2007).

The triple helix model has recently emerged as a reference framework for the

analysis of knowledge-based innovation systems, and relates the multiple and

reciprocal relationships between the three main agencies in the process of knowledge

creation and capitalization: universities, industry and government (see for a recent

overview Etzkowitz 2008, Deakin 2010, Lombardiet al. 2012). In the context of the

present analysis, this paper will focus on this model as a starting point for the

assessment of the performance of smart cities. In order to link the evaluation of smart

city components and the three main helices of the model, a modified triple helix

framework is proposed that adds another unifying factor to the analysis, namely civil

society (Etzkowitz and Zhou 2006).

The advanced model presupposes that the four helices operate in a complex

urban environment, where civic involvement along with cultural and social capital

endowments shape the relationships between the traditional helices of university,

industry and government. The interplay between these actors and forces determines

the success of a city in moving on a smart development path.

This framework can be operationalized by focusing on the measurement of the

four helices and linking these to the main dimensions of a smart city (smart economy,

smart mobility, smart environment, smart people, smart living and smart govern-

ance). The result of this exercise is the development of a novel framework for

classifying smart city performance indicators, as shown in Table 1.As one can see, both the main components/activities and the main actors/helices

of a smart city are represented. The identified clusters are: smart governance(related

to participation);smart human capital(related to people); smart environment(related

to natural resources);smart living(related to the quality of life); and smart economy

(related to competitiveness).

The sources of data for Table 1 were both a literature review, including EU

projects reports and the Urban Audit dataset, and indicators selected from statistics

of the European Commission, the European Green City Index, TISSUE, Trends

and Indicators for Monitoring the EU Thematic Strategy on Sustainable Develop-

ment of Urban Environment and the smart cities ranking of European medium-sizedcities. This includes more than 60 indicators classified in the five clusters. These

60 indicators were selected based on a questionnaire and two focus groups

Innovation The European Journal of Social Science Research 139


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Table 1. Smart city components, triple helix and performance indicators.

Revised triple

helix

Clusters

Smart Governance Smart Economy

Smart Human Capital

Indicators Smart Liv

University No. of universities and

research centers in the city

Public expenditure on

R&D percentage of

GDP per head of city

population

Percentage of population

aged 1564 with

secondary-level education

living in Urban Audit

Percentage of

professors and

researchers inv

international p

and exchange

No. of courses entirely

downloadable from the

internet/total no. courses

Public expenditure on

education percentage

of GDP per head of city

population

Percentage of population

aged 1564 with higher

education living in Urban

Audit

Number of gra

international m

per year

Number of research

grants funded by

international projects

Percentage of inhabitants

working in education and

in research & development

sector

Percentage of

accessible cour

people with dis

(PWD)

Governement e-Government on-line

availability (percentage of

the 20 basic services that

are fully available online)

GDP per head of city

population

Voter turnout in national

and EU parliamentary

elections

Proportion of t

in for recreatio

sports and leisu

Debt of municipal

authority per resident

Share of female city

representatives

Green space (m

which the publ

access, per capi

Percentage of households

with computers

Median or average

disposable annualhousehold income

City representatives per

resident

Number of pub

libraries

Unemployment rate Number of the

and cinemas


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Table 1 (Continued)

Revised triple

helix

Clusters


Smart Human Capital


Percentage of households

with Internet access at

home

Energy intensity of the

economy gross inland

consumption of energy

divided by GDP

Health care

expenditure

percentage of G

capita

Tourist overnig

in registered

accommodatio

year per residen

Civil society e-Government usage by

individuals (percentage

individuals aged 1674

who have used the

Internet, in the last 3

months, for interaction

with public authorities)

Percentage of projects

funded by civil society

Foreign language skills Total book loa

other media pe

resident

Participation in life-long

learning (%)

Museum visits

inhabitant

Individual level of

computer skills

Theater and cin

attendance per

inhabitant


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Table 1 (Continued)

Revised triple

helix

Clusters


Smart Human Capital


Individual level of internet

skills

Industry Number of research grants

funded by companies,

foundations, institutes/No

annual scholarships

Employment rate in:

High Tech and

creative industries

Renewable energy

and energy efficieny

systems

Financial

intermediation and

business activities

Culture and

entertainment industry

Commercial services

Transport and

communication

Hotels and

restaurants

Patent applications per

inhabitant

Number of ent

adopting ISO 1

standards

All companies (total

number)

Employment rate in

knowledge-intensive

sectors

Proportion of p

undertaking in

based training


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Table 1 (Continued)

Revised triple

helix

Clusters


Smart Human Capital


Number of local units

manufacturing High

Tech & ICT products

Companies with

headquarters in the city

quoted on national stockmarket

Components of domestic

material consumption

GDP, Gross domestic product.


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with specialists and professionals to select the most relevant indicators to the

proposed clusters.

Furthermore, the authors of this study have identified the relations between

the smart city components by way of an ANP. The next section illustrates the

development of the ANP model that is used for investigating the interrelations

between smart city components and actors and finally for verifying whether the cities

are smart, and if not, whether they are moving in the right direction.

Assessing the smart citys performance

A city is a complex system. Its complexity is due to the individuals unpredictable

interrelations. As complex systems, cities have unpredictable behaviors and, when

some actions are set up, reactions and feedbacks can be obtained. Cities are places

where different interrelated ecosystems live, and they are also communications

systems (Abler et al. 1970). The relationships can be effected by tangible and

intangible infrastructures and they can produce tangible or intangible networks.Tangible networks are created by transport and telecommunications infrastructures,

which can be named hard infrastructures (Wakelin 1990). The intangible net-

works, as networks of capital (economic and human), are produced by the soft

infrastructures, which comprise education and governance, and by the economic

infrastructures (Wakelin 1990). Because of its network nature, a city should be

described using a more truthful and realistic model representation based on a

network system with the expression of relations between elements. Moreover, the

Analytic Network Process, an advanced version of the Analytic Hierarchy Process,

seems more appropriate for supporting decision-making in this field.

The ANP model consists of clusters (i.e. groups of homogeneous elements ofa decision problem), elements (i.e. nodes of the network), interrelationships between

clusters, and interrelationships between elements. It allows interactions and feedback

within and between clusters and provides a process to derive ratio scales priorities

from the elements (Saaty 2005). Synthetically, the ANP methodology involves the

following main steps (Saaty 2005, Saaty and Vargas 2006).

I. Structuring the decision-making model. This activity involves an identifica-

tion of both of the elements constituting the decision problem and their

relationships. The network model is constituted by various clusters of ele-

ments, and alternatives or options from which to choose. Each element can

have influence and inter-dependence relations: this can be a source, that is,

an origin of a path of influence, or a sink, that is, a destination of paths

of influence. There are two kinds of interdependences: between elements

related to different clusters (outer or external connections) and within the

same cluster (inner or internal relations). The latter relation is identified

as a loop.

II. Developing pairwise comparison of both elements and clusters to establish

relations within the structure. In this step, a series of pairwise comparisons are

made by participants in the decision-making process (usually experts,

managers and citizens representatives) to establish the relative importance

of decision elements with respect to each component of the network. In pair-wise comparisons, a ratio scale of 19 is used (named the fundamental scale

or Saaty scale). The numerical judgments established at each level of the



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network form pair matrixes that are used to derive weighted priority vectors of

elements (Saaty 2001).

III. Achievement of the final priorities. In order to obtain the global priority

vector of the elements, including the alternatives, the mathematical approach

encompasses the use of supermatrices (portioned matrices composed of

submatrices consisting of priority weight vectors of the elements that have

been evaluated). A final supermatrix is obtained at the end of the process,

containing the global priority vector of the elements.

As required by step I, a complex model was developed that involves all of the men-

tioned clusters of a smart city, i.e. Smart Governance (related to participation);

Smart Human Capital (related to people); Smart Environment (related to natural

resources); Smart Living (related to the quality of life); and Smart Economy (related

to competitiveness).

The relationships between indicators (and clusters) have been identified using

a control hierarchy (Saaty 2001) composed of the four axes of the adopted triplehelix, i.e. universities, industry, government and civil society. Figure 1 shows this

control hierarchy. Each axis is organized by a subnetwork consisting of:

the five clusters representing the above-mentioned smart city component/

activities including the relative selected indicators;

a cluster of alternatives made by four policy visions (or prototypes) of smart

cities as derived from the Urban Europe Joint Programme Initiatives

(see report by Nijkamp and Kourtik 2011): Connected City (smart logistic

and sustainable mobility), Entrepreneurial City (economic vitality), Liveable

City (ecological sustainability) and the Pioneer City (social participationand social capital).

As an example, Figure 2 shows the Civil Society subnetwork, where both the

Smart Governance and Smart Economy clusters include only one element,

respectively: E-gov usage by individuals and Percentage of projects funded by

civil society. This nodes organization allows inner connections in the other clus-

ters, as in the Smart Human Capital, where the Foreign language skills influence

Figure 1. The main network.



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other nodes, such as Individual level of computer skills and Individual level

of internet skills.

Bidirectional relationships are identified as follows:

Smart Economy and Smart Environment, where the Percentage of projects

funded by civil societyis the direct cause of the Relationship to percentage of

citizens engaged in environmental and sustainability oriented activities;

Smart Human Capital and Smart Living, where the Participation in life-

long learning is connected by all smart living nodes (Museum visits per

inhabitant, Theater and cinema attendance per inhabitant and Total book

loans and other media per resident).

Additional mono-directional connections can be identified between the otherclusters.

As required in Step II of the ANP methodology, an assessment exercise was

conducted with the aim of developing pairwise comparisons, of both elements

(or nodes) and clusters. Figure 3 shows one of several pair matrixes that were

Figure 2. The Civil Society subnetwork.

Figure 3. Pairwise cluster comparison using Saatys scale.



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composed and used to derive weighted priority vectors of elements (Saaty 2001).

In each pairwise comparisons matrix, a ratio scale of 19 is used. In particular,

Figure 3 shows the cluster comparison matrix for the alternatives.

The achievement of the final priorities of all the elements included in the model

is obtained in Step III. This includes the overall priorities of the alternatives

obtained by synthesizing the priorities of the alternatives from all the subnetworks.

The final results are:

(1) Entrepreneurial City (48%);

(2) Pioneer City (20%);

(3) Liveable City (17%);

(4) Connected City (13%).

The Entrepreneurial City. This image assumes that, in the current and

future global and local competition, Europe can only survive if it is able to

maximize its innovative and creative potential in order to gain access toemerging markets outside Europe; cities are then spearheads of Europes

globalization policy.

The Pioneer City. This image refers to the innovative melting pot

character of urban areas in the future, which will show an unprecedented

cultural diversity and fragmentation of lifestyles in European cities; this

will prompt not only big challenges, but also great opportunities for smart

and creative initiatives in future cities, through which Europe can become

a global pioneer.

The Liveable City.The final image addresses the view that cites are not only

energy consumers (and hence environmental polluters), but may

throughsmart environmental and energy initiatives (e.g. recycling, waste recupera-

tion) act as engines for ecologically benign strategies, so that cities may

become climate-neutral agents in a future space-economy; cities in Europe

are then attractive places to live and work.

The Connected City. The image of a connected city refers to the fact that

in an interlinked (from local to global) world, cities can no longer be

economic islands in themselves (no fortresses), but have to seek their

development opportunities in the development of advanced transporta-

tion infrastructures, smart logistic systems and accessible communication

systems through which cities become nodes or hubs in polycentric net-works (including knowledge and innovation networks).

Conclusions and further steps

This paper has illustrated an on-going study in the field of smart cities evaluation.

The analysis started from a revised notion of triple helix considering that civil society

usually plays a prominent role toward the realization of sustainable development

in cities (Etzkowitz and Zhou 2006).

In order to assess the connections between smart city development and this

institutionalization of the triple helix, an ANP model was developed. This interrelatedmodel was used for investigating the relations between smart city components, actors

and visions, or strategies to which the smart cities are moving.



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The results show that the Entrepreneurial City is the policy vision with higher

priorities in all the sectors considered in the model, i.e. universities, government, industry

and civil society. Some relevant urban planning and policy implications of this vision are:

A high degree of entrepreneurial activities and a constant flow of new firm

creation are prerequisites for finding a new role within the new global eco-

nomic landscape. Innovation and creativeness are thus the necessary ingre-

dients for entrepreneurial cities in Europe.

Special emphasis has to be given to new architectures, building technologies,

intra-urban mobility solutions, public space management, e.g. for lighting

or citizen information management, integrated urban energy planning and

management, and ICT-based solutions that offer various opportunities for

new urban design and management.

New requirements for efficient, effective and reliable infrastructures (such as

energy, ICT, water, waste treatment and management) may occur. Since an

appropriate infrastructure is essential for cities attractiveness for companiesand people alike and therefore their economic development, emphasis has to

be given to the determination of these requirements within the scope of cities

as complex systems.

The ANP not only underlines the complexity of the reference system, but it also

improves the relationships and the inter-connections between all the constituting

elements of the smart cities model. The main innovative features of the model are:

the introduction of the civil society as a crucial stakeholder that empowers the

classical triple helix model composed of university

government

industry; the use of the four aforementioned helices, representing the main stakeholders

operating in a smart urban development, as control criteria for modeling

the decision making proble, rather than implying the traditional benefits

opportunitiescostsrisks control hierarchy (Saaty 2005);

a more truthful and realistic city model representation based on a network

system with the expression of relationships between elements;

the development of the model as well as the assessment exercise is the result

of a participative process, involving expertise on urban planning, sustainable

development evaluation, urban sociology and urban economy;

measurement of asmart city policy vision, considered as an holistic, inter-

related, multistakeholders concept, based on both quantitative indicators and

experts view.

The results obtained from this exercise are interesting but clearly the model requires

further implementation and improvement. The main limitations are as follows:

influence of the focus group in the definition of the indicatorsrelationships

and weights;

high number of indicators and, consequently, high number of pairwise

comparisons, resulting in a large amount of time required to achieve a result;

technical problems still included in the beta version of the software used fordeveloping stages 2 and 3 of the ANP application (available online from www.

superdecisions.com).

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In conclusion, the assessment exercise illustrated in this paper is a pilot study. It still

requires the development of a testing exercise with the participation of main city

stakeholders, offering a reflexive learning opportunity for the cities to measure what

options exist to improve their performance. This will be the future task of the

authors.

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