A Comprehensive Framework for Smart Cities

Giovanni Maccani

, Brian Donnellan

and Markus Helfert

School of Business, National University of Ireland, Maynooth, Ireland

School of Computing, Dublin City University, Dublin, Ireland

Keywords: Smart City, Open Innovation Ecosystems, Governance, ICTs, Urban Development.

Abstract: In this paper Smart City initiatives will be explored from the perspective of the enabler factors required for

such intitiatives to be successful. In detail, we see smart cities made of five collectively exhaustive dimen-

sions, i.e. Technology, Social Infrastructure, People-Private-Public Partnerships, Governance and Manage-

ment, and Smart Information Services. Thus, after a brief introduction of the domain of analysis, the starting

point will be a systematic review of the literature. Then we will describe each perspective explaining why

and how it has to be considered. Finally we will propose some discussions, in particular around the applica-

bility of our framework for embedded assessment and measurement tools (e.g. Balanced Scorecard).

1 INTRODUCTION

Since the Copenhagen summit (2009), there is a

growing perspective that “nations talk, cities act”.

Cities are more and more characterized by “mega-

trends” which are going to crash their balances

(Kanter et al., 2009). Particularly the steady growth

of urbanisation (by 2030 over 60% of global

population will live in cities), demographic change

(by 2030 the over 65 generation will almost double,

from 7% to 13%), and climate change concerns

(cities are now responsible for 80% of the global

GHG emissions) (Mulligan, 2010), require a quick

and radical reaction. Moreover, the economic crisis

and the consequential urgency to undertake

disruptive innovation can provide the impetus to

overcome the resistance to change, turning the

problems into opportunities.

The Smart City concept is emerging as a way to

tackle and solve the problems arising from these

mega-trends. This term is understood as a certain

intellectual ability that addresses several innovative

socio-technical and socio-economic aspects of

growth (Zygiaris, 2012). However, despite

researchers, multinational companies as well as

governments are strongly pushing towards smarter

approaches for cities, it is still missing a common

understanding and an embedded well acknowledged

definition of such initiative (Caragliu et al., 2011).

The discussion in this field revolves around diverse

concepts and issues such as “Digital City”

(Besselaar et al., 2005), “Intelligent City”

(Komninos, 2008), “Creative City” (Hall, 2000),

“Knowledge City” (Dirks, 2009), “Ubiquitous City”

(Lee, 2008), “Smart Communities” (Kanter et al.,

2009), and more. Thus there is a growing need to

reflect on this concept, its construction and

underlying assumptions to enable transparency and

new readings.

So far literature focused on many aspects that

can be understood as parts of the Smart City

initiative (e.g. ICT infrastructure, cities' critical

services, smart economies, and so on), but attempts

to holistically and systematically tackle the Smart

City concept are still almost lacking. Furthermore,

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 (Lombardi

et al., 2011). As a consequence we aim at the

development of a comprehensive framework for

Smart Cities. We believe that through the careful

description of each element that builds up such

initiatives, and the identification of the relationships

between these key components, we can provide a

systematic conceptualization of the strategy and the

ideas arising from the literature. Its development

will allow the construction of measuring

frameworks, enabling an easy-to-read format for a

better understanding by the huge amount of

stakeholders involved, and it will be the fundamental

starting point for supporting software-based

Maccani G., Donnellan B. and Helfert M..

A Comprehensive Framework for Smart Cities.

DOI: 10.5220/0004374400530063

In Proceedings of the 2nd International Conference on Smart Grids and Green IT Systems (SMARTGREENS-2013), pages 53-63

ISBN: 978-989-8565-55-6

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

decision-making processes (for example Analytical

Network Process (Saaty, 2005)). It will be also a tool

to communicate, and overcome resistances to change

by all the actors that are somehow engaged in Smart

Cities development, to move forward towards its

real holistic implementation.

To do that, the first step was a careful review and

analysis of the existing literature. Then within this

paper we will explain which areas we believe to be

the most suitable and completed for systematically

describe Smart Cities initiatives. In other words we

are going to provide mutually exclusive and

collectively exhaustive dimensions that fully

encompass the enabler factors of Smart Cities.

2 LITERATURE REVIEW

Before starting in conceptualizing the dimensions of

a Smart City, we need to deeply understand its core

conceptual elements. The Smart City concept is still

emerging, and the work of defining and categorizing

it is in progress (Boulton et al., 2011). Thus, we

implemented a systematic approach for a review of

the existing literature. The main aim of this analysis

is to provide an embedded definition of Smart City,

from which we will derive the dimensions for our

comprehensive framework, the main focus of this

paper.

This section can be divided in two main parts.

First, we extracted all the definitions available on

journal papers. After finding them, we discovered a

common syntactic structure that can be adapted to

each definition, and that includes six categories that

can be seen as the main abstract areas that have to be

covered in providing a correct definition within this

domain of analysis. Following these areas we

developed a concept matrix, and we mapped into it,

all the elements arising from the definitions.

Secondly, we have also included conference papers

and corporate reports into the study. Particularly, we

considered all the developed frameworks provided

by authors as attempts to define Smart Cities with

comprehensive dimensions. After reading each paper

we mapped again the dimensions in relation to the

areas used for the definitions matrix.

2.1 Definitions

As mentioned in the brief introduction to this

section, we have started analysing all the journal

papers available. At this very first stage, the

requirement for an article to be taken into account

for our review was the presence of the word “Smart

City/Cities” either within the abstract, or the

keywords, or the title of the paper itself. In total we

collected 24 papers, within 7 different journals out

of the 40 considered. Then, we found that only 13

papers were aligned with the aim of our review.

Going ahead, of the 13 papers considered, 11

provided a definition of Smart City (for the text

definitions see Appendix 1). In order to provide a

taxonomy useful for this literature review we first

looked at the syntax of these sentences aiming at

defining a common structure characterized by a

number of main areas in which all the concepts that

arise from these definitions can consistently fit.

Looking at the texts of the definitions we then

classified in a high level of abstraction the

components of such phrases stating that: “The Smart

City is a [Context] that exploits / uses / leverages /

develops an [Infrastructure] with-a-certain /

implementing an [Approach] supported by [Factors]

to enable [processes] to achieve/ improve / enhance /

increase [Goals]”. In other words, every single key

concept that is stated in the definitions provided in

Appendix 1 is related to one of these 6 main areas,

i.e. context, infrastructure, processes, approach,

factors, goals. So, after checking its validity in first

approximation, we've actually decomposed all the

definitions into their key words or notions. Hence,

with all the words/notions available, after some

intuitive and obvious simplifications (e.g. “reduce

urbanisation's impact on the environment” (Helal,

2011), and “ecological performances” (Kourtit et al.,

2012) were both considered together with the goal

“environmental sustainability” (Lombardi et al.,

2011), (Tranos et al., 2012)), we developed a

concept matrix in which each key word/notion is

related to the author and grouped within the category

it belongs to (see Appendix 2). It should be also

noted that replacing randomly a single term within

its area of belonging in the structure we defined

above, the resulting sentence assumes a meaning as

a definition of Smart Cities' domain.

After a first look at the concept matrix (see

Appendix 2), we can immediately infer that the

common understanding of Smart City is an initiative

that exploits technologies to deliver smart

information services aiming at better environmental

performances, increase or add efficiencies, and

improve city's competitiveness or, in other words,

develop the so called Smart Economy (Giffinger et

al., 2007). Another recurring aspect refers to the

human/social capital as a key enabler of Smart

Cities. However, insights about extremely needed

innovative approaches (Schaffers et al., 2012), and

new management and governance principles (Nam

SMARTGREENS2013-2ndInternationalConferenceonSmartGridsandGreenITSystems

et al., 2011b) are still lacking in these definitions.

Probably, most of the definitions provided come

from individual research needs or prospectives

(Abdulrahman et al., 2012). So, we conclude stating

that only informal descriptions are available within

the existing literature, rather than embedded well

acknowledged definitions.

2.2 Frameworks

To enrich our review of the literature we have also

considered all the contributions in terms of

developed frameworks that, through the definition of

homogeneous dimensions, aimed at defining the

Smart City concept. Conference papers and

corporate reports were also taken into account for

this analysis.

As we did for the definitions we first extracted

the useful data from each framework and then we

listed all the dimensions that were chosen by each

author. After that, each dimension was

contextualized, consistently with its content, within

the area that we found to be critical in defining

Smart Cities, i.e. infrastructure, process, approach,

factors and goals (see paragraph 2.1). We did not

take into account the “Context” area as it is not

useful for our scope at this stage. Thus, the aim of

this paragraph is to provide, consistently with the

categorization used for the definitions, an overview

of the dimensions that have been chosen by authors

in attempting at completely define the Smart City

concept.

As a result of our analysis, we found that from

the 18 contributions available, 7 were inspired and

related to two main taxonomies: the “System of

Systems” view developed by IBM (Dirks, 2009),

and the study conducted in (Giffinger et al., 2007).

In detail, IBM sees Smart Cities based on six core

systems, i.e. people, businesses, transports,

communication, water, and energy. “Instrumenting”,

“interconnecting”, and providing them the

“intelligence”, would constitute what they called

“System of systems” (Dirks, 2009), seen as the

unique building block placed at the highest level,

which enables smart services within each of the six

main city's functions. Aligned with this point of

view are the works of (Harrison et al., 2010, 2011),

(Naphade et al., 2011). Then, the second most

quoted framework starts from the definition of six

core systems that are considered as the critical areas

for Smart Cities. These areas are: Smart Economy,

Smart Environment, Smart Living, Smart People,

Smart Governance, and Smart Mobility (Giffinger et

al., 2007). This path to define and conceptualize

Smart Cities was followed also by 3 more works.

The first in chronological order was (Toppeta, 2010);

in his paper each of the the six areas was enriched

with innovative ideas. Then, one year later, the same

approach was used by (Caragliu et al., 2011) and

(Lombardi et al., 2011) that combined those six axes

with the Triple-Helix model and particularly with the

actors involved in this approach (i.e. government,

businesses, and universities (Shinn, 20029).

Now we move ahead with the other contributions

in terms of frameworks published to describe the

Smart City. In (Kourtit et al. 2012) the smart city is

conceptualized as a mix of human capital,

infrastructural capital, social capital, and

entrepreneurial capital. Abdulrahman, categorized

Smart Cities into seven smart systems connected

respectively to cities' critical activities and services.

They are: Smart Grid, Smart Meter, Intelligent

Transportation System, Smart Water, Smart Home,

Smart Food, and Smart Healthcare (Abdulrahman et

al., 2012).

Moving now to the conference papers, (Angoso,

2009) sees a Smart City as a Green City in which the

technology is the main critical success factor as

enabler of the proper urban network, which is able to

connect vehicles, assets, employees, and people

under the same “Smart City 2.0” umbrella.

(Mulligan, 2010)'s vision is similar, given that he

considers the Smart City's goal focused “just” on

environmental sustainability performances. In his

opinion a Smart City is the one which achieves the

proper balance between competitiveness,

environment, and quality of life, with the

government as the key player. He, together with

Siemens Ltd., developed an innovative measure

called “Green City Index” across eight dimensions:

carbon emissions, energy, buildings, transport, waste

and land use, water, air and governance. (Washburn,

2010) defined the Smart City's dimensions in

relation to the services offered. Hence, the categories

that in his opinion build up the Smart City are:

governance, education, healthcare, public safety, real

estate, transportation, and utilities. So, he provided a

technology-led Smart City Blueprint. Going ahead,

(Woetzel et al., 2010) provided a very interesting

tool (i.e. the Urban Sustainability Index) for

assessing the performances of Smart Cities. This

approach is based on a quantitative evaluation across

what they call the critical city's functions, that are:

economic performance, social conditions,

sustainable resource use, finances, and governance.

(Maloney, 2011) instead, went deeper into the

Smart-Sustainable City concept providing a

framework that describes how a strategy should be

AComprehensiveFrameworkforSmartCities

built up by mayors. He considers Smart Cities as

environments in which innovative ICTs are used to

deliver smart services in some application areas (i.e.

shopping, tourism, culture, city marketing, public

and private education and health, road and public

transportation, and entertainment). (Steinert et al.

2011) goes beyond the purely technological point of

view and proposed two pillars (more as requirements

than building blocks): the smart city broadband

network and the smart city public-private approach.

The first refers to technology and particularly

highlights the need of a single integrated network

supported by an innovative policy framework,

defined by city's authorities. Furthermore the second

takes into account the approach that has to be

implemented for the ICT infrastructure management.

Then, in (Nam et al., 2011a) the key factors are

about technologies, humans, and institutions. A few

months later they focused their work on the

innovation component of Smart Cities (Nam et al.,

2011b). Here, the dimensions they defined were:

technology (to serve as a tool for innovation),

organization (to manage innovation) policy (to

create an enabling environment) and the surrounding

context. Moreover, a useful point of view comes

from EU Commission understanding of Smart Cities

in terms of future actions; thus, in (Schaffers et al.,

2012) were introduced many drivers and

components for cities to become smarter that can be

joined together into three homogeneous

perspectives: technology, open innovation ecosystem

(moving from the triple helix collaboration research

concept to a “multiple-helix” one that includes also

citizens) that has to become “user-driven” through

citizens' empowerment, and urban development.

Similarly, (Chourabi et al., 2012)'s aim was to

provide an “integrative framework for a better

understanding of Smart Cities”. It included eight

critical success factors: management and

organization, technology, policy context, people and

communities, governance, economy, built

infrastructure, and natural environment. The firsts

three are considered core factors, and the other as

secondary ones in building a holistic strategy.

Finally, (Zygiaris, 2012) defined layers as

subsequent steps to reach the Smart City vision and

to estimate city's smartness, rather than key

perspectives. The layers (starting from the bottom)

he found are: City (for its readiness), Green City (to

decrease urban carbon footprint), interconnection (to

develop a broadband economy), instrumentation (to

ensure that the “real-time” capability), open

integration (to provide an open integrated space),

application (to apply the previous layers to the real

city life), and innovation (to ensure smart growth).

Finally, as we did for the definitions, we mapped

each dimension with the five critical areas into the

concept matrix (see Appendix 3). From this table we

can immediately see that none of the frameworks

covered all the aspects we identified as critical in

analysing the definitions. Aspects such as

management and governance principles are broadly

explained within these papers, and so, we also have

a clearer overview of the approaches that have to be

taken. However, we can affirm that there is still a

lack of a comprehensive conceptualization that can

be the proper base for an embedded definition, and

subsequent initiatives of standardisation and

integrated measurement tools.

3 SMART CITY DIMENSIONS

Within this third section we present the core of this

document: a conceptualization of the foundation of

Smart Cities. Starting from a careful and structured

literature review (section 2), we developed an

embedded definition of Smart City following the

syntactic structure we used in analysing the existing

literature; so we define Smart City as an urban area

that leverages its technological and social

infrastructure implementing people-private-public

partnerships supported by an innovative governance

in terms of policies, leadership and proper ongoing

management principles, to enable smart information

services, aiming at improving its critical

capabilities. As a consequence we derive the

dimensions for our comprehensive framework, in

terms of mutually exclusive and collectively

exhaustive areas that fully encompass all the enabler

factors of Smart Cities. These dimensions are:

“Technology”, “Social Infrastructure”,

“Governance”, “3P Partnership”, and “Information

Services”. Each key area will be carefully described,

in terms of its content and why did we choose it,

within the next paragraphs.

3.1 Technological Infrastructure

As shown in Appendix 2 and Appendix 3,

technological factors are commonly understood as

one of the most important pillars, and so, nowadays

ICT is an essential part of urban development, and it

is necessary for all Smart Cities (Abdulrahman et al.,

2012). To introduce how the technological

infrastructure should look like we try here to retrace

the path that has led to its creation. We believe that

SMARTGREENS2013-2ndInternationalConferenceonSmartGridsandGreenITSystems

there are three fundamental milestones in building

the technological background for Smart Cities,

named the Spatial Intelligence of Cities (Mitchell,

2007): Ambient Intelligence (Gasson et al., 2007),

Digital City (Besselaar and Koizumi, 2005), and the

Intelligent City (Komninos, 2002).

- Ambient Intelligence (AI): an AI environment

should facilitate human contact, be oriented towards

community and cultural enhancement, help to build

knowledge and skills for work, citizenship and

consumer, and finally be consistent with long term

sustainability (“Greening OF IT” and “Greening BY

IT” (Maccani et al., 2012)). To build up the City's AI

we identified three main requirements. First it is

necessary, and it is a well acknowledged field in the

literature, a broadband infrastructure seen as a smart

combination of telecommunication networks (for

their resiliency, stability, and security) and the web

(for its software-driven programability easily

customizable) (Steinert et al., 2011). Secondly, we

have the Internet of Things (IoT) (Uckelmann et al.,

2011) and the related concept of Semantic web

(Ashton, 1999). An AI-oriented perspective of IoT

would mean leverage the possibility to collect and

analyse the digital traces left by citizens at their

interaction and when they interface with widely

deployed smart objects to structure the knowledge

about human life, environment, and social

behaviours (Guo et al. 2011). As third fundamental

requirement we refer to the “instrumentation step”

proposed by IBM (Dirks, 2009), in which sensors

are placed across the city to enable observation of

urban systems at a “micro-level” (Harrison et al.

2011). Other technologies and ICT-related concepts

that can be seen as the enablers of such environment

are crowd-sourcing (Estellés et al., 2012), RFID,

software agents, affective computing,

nanotechnologies, and biometrics (Gasson et al.

2007).

- Digital City (DC): this second milestone adds to

the previous one the ubiquitous computing

component (Greenfield, 2006). In other words, the

key element here is a wireless infrastructure that if

combined with an AI world would allow the delivery

of services accessible through wireless mobile

devices and enabled by SOA (Service Oriented

Architecture) including web services and mobilised

software application (Tanabe et al., 2002). The key

turning point here is to think about machines that

have to fit the human environment, rather than

forcing humans to enter theirs (York et al., 2004). At

this step then the Smart City is both “instrumented”

and “interconnected” (Dirks, 2009).

- Intelligent City (IC): while the DC is based on

digital communication, the IC provides problem

solving capabilities. Thus, all ICs are DCs, but all

DCs are not intelligent (Komninos, 2008). Here the

digital space becomes a tool that contributes to the

capacity of the community to use collective

intelligence and engineer new solutions to people

needs. From the previous milestone we added two

key components to achieve the development of an

Intelligent City: collective intelligence and

cooperative distributed problem solving.

Concluding, we believe that the achievement of

these three milestones described above, means the

development of the technological part of the Smart

City, the Spatial Intelligence of City.

3.2 Social Infrastructure

Developing human resources and social capital is

recognized, obviously together with technology, as

one of the enabling factor for Smart Cities by a large

portion of literature (Toppeta, 2010). There are four

critical aspects for human factors within Smart

Cities, and they are (Nam et al., 2011a): Learning

City (Plumb et al., 2007), Creative City (Hall, 2000),

Human City (Streitz, 2009), and Knowledge City

(Dirks, 2009) (concept analogous to the Learning

City one). The combination of these concepts is the

foundation of the Smart City's social infrastructure.

In fact, through a Learning City is provided an

extremely needed highly skilled information

workforce (Moser, 2001), aspect related to a higher

education infrastructure and better-educated

individuals. Human's creativity and the development

of their digital skills are crucial requirements to

overcome the gaps identified by (Komninos, 2008),

i.e. to turn innovative digital technologies into

applications. A highly skilled human community is

also a major requirement to achieve the critical mass

for collective intelligence processes (Sestini, 2011a).

So far in real Smart City cases there are mainly

education-related initiatives in this field, and the

particular focus has been on how to use ICTs to

improve the education sector. Potentially ICTs can

increase access from rural areas as well as from

those who cannot be full time students and other

benefits would come from costs reduction and from

the quality of the education itself (Washburn et al.,

2010). Concrete examples may be: provide life-long

e-learning systems, e-books loan, collaborative

design for entrepreneurs, location-based proximity

services (Toppeta, 2010) and so forth.

AComprehensiveFrameworkforSmartCities

3.3 Public-people-Private Partnership

Accordingly to literature, collaboration,

participation, engagement, and partnerships are key

words related to this field (Odendaal, 2003)

(Giffinger et al., 2007). Hence, as the third

dimension of our framework we need a collaboration

model to actually establish technological and social

components as real enablers for Smart Cities. To

promote these approaches the largest part of the

literature refers to a model called “triple-helix”

which focuses in particular on relations between

universities, industry and government at an urban

and regional scale (Shinn, 2002) (also a “multiple-

helix” approach, including market, learning and

knowledge, was defined (Caragliu et al., 2011)). It is

generally understood “as a selection environment

for knowledge creation and innovation, ushering in

place-based strategies to exploit local creativity and

social capital to achieve a new urban vitality, i.e.

growth” (Lombardi et al., 2012). However, the

potential value of co-creation through citizens, and

more generally end-users, involvement is ignored in

this model. As a consequence the collaboration

model that has to be set up has to be between local

governments, research institutes and universities,

citizens and businesses. The main goal here is to

develop what the EU Commission calls an User-

Driven Open Innovation Ecosystem. This

perspective is critical to bridge the gap between

short-term city development priorities (demand pull)

and long-term technological research and

experimentation (technology push) (Schaffers et al.,

2012). Within this perspective a great importance is

given to Living Labs methodologies (Pallot, 2009).

They can be seen as a User-Centred Open

Innovation Ecosystem (Chesbrough, 2003), that

aims at the integration of concurrent research and

innovation processes (Bilgram et al., 2008) within a

“3P” (Public-Private-People) Partnership. A Living

Lab approach involves user communities, solution

developers, research disciplines, local authorities

and policy makers, and investors. So, the value is

co-created and the emergence of breakthrough ideas

is highly stimulated. Thus, it provides the

opportunity to co-create, explore, experiment, and

validate innovative Smart Cities scenarios based on

technology platforms aiming at satisfying real

emerging inhabitant’s needs, and at improving their

quality of life. Using this approach citizens are

brought at the driving seats towards innovation, and

their creativity skills result also improved (Ratti et

al., 2011).

3.4 Governance and Management

A Smart City initiative was also seen by a portion of

literature as the application of intelligence to city

management (Boulton et al., 2011). So far, for a long

time, cities' initiatives were dominated by top-down

approaches (Schaffers et al., 2012). Consistently

with what stated within the Public-Private-People

Partnership perspective the balance between bottom-

up (or grassroots-driven (Sestini, 2011b)) and top-

down strategies must be strengthened. Within the

perspectives seen so far it can be easily inferred that

sharing and integrate the information and the

knowledge is one of the most critical objectives. To

achieve these goals a managerial interoperability

across city's smart services, applications, and

organizations is extremely needed (Nam et al.,

2011b).

What is critical to understand at this stage is who

is going to manage the Smart City initiative. In

(Witters, 2012) is described a study conducted in

existing projects. Particularly, the “key initiation

models” were defined and carefully described , in

terms of who kicked off the projects in such case

studies. The result found in his research was that the

city-authority has to be the unique central

management body.

A critical part of this dimension refers to

leadership. One of the main challenges of local e-

government implementations is a lack of a central

figure to promote progress, integrate decisions, and

foster structural and procedural changes (Almazan et

al., 2009). To overcome this issue, and at the same

time to conceptualize a pivotal role as a unique

decision-maker for a Smart City initiative, we

believe that a Smart City CIO could leverage the

potential of technological and social infrastructure.

The rise in stature of the CIO and CTO within the

city and county arena has grown or evolved, along

with the ever-increasing complexity for managing

communication and information technology. So,

being a "cheerleader" for economic development in

Smart Cities should be a natural progression for

most of CIOs (Schrier, 2012). In fact, the figure of

the CIO would be in the boardroom to understand

the business model of a city, rather just the technical

aspects of an IT department (Parker, 2005). Hence,

the Smart City CIO should be responsible for

defying and managing a vision for these initiatives,

across the infrastructure components and services.

According to (Washburn et al., 2010) CIOs are

(together with technology and social infrastructure)

enabler of the Smart City, providing project

management expertise, best practices for

SMARTGREENS2013-2ndInternationalConferenceonSmartGridsandGreenITSystems

interoperability, and being responsible for staff

training and for redefine the organization. Finally a

Smart City CIO should be part and the main

promoter of People-Private-Public Partnerships

(Shark, 2010). However, the CIO in the private

sector is more flexible than the one in the public

sector, since the “GCIO” (Government CIO)

functions are regulated by laws (Nguyen, 2008).

Concluding, the last critical part of this block

refers to the policy-context. Obviously, every single

case has to deal with its political and historical

background. However, some common priorities can

be defined. First of all, governments should focus on

an open broadband regulatory framework (Steinert

et al., 2011). Laws in this way should enable safe

and secure sharing of data and content seamlessly, in

real time, and wirelessly, between individuals,

governments, companies, and objects (e.g. sensors,

devices, buildings). Secondly, an innovative policy-

context should push towards the actual adoption of

the “3P” Partnership approach. As a consequence, a

big systematic effort from policy makers in this way

is highly needed.

3.5 Smart Information Services

At this point of development we have all what we

need to reach the Smart City mission. As a first

approximation, we can state that the final goal of a

Smart City is to provide smart services (Giffinger et

al., 2007) in order to: improve city's inhabitants'

quality of life (Hall, 2000), decrease city's carbon

footprint (Angoso, 2009), reduce traffic congestions

(Mulligan, 2010) and enable Intelligent

Transportation Systems (Chen-Ritzo et al., 2009),

improve educational (Toppeta, 2010) and healthcare

sectors (Washburn et al., 2010), increase

employment rate (Lombardi et al,. 2012) and define

new business models to attract companies for a

sustainable economic growth (Doobs et al., 2012),

provide efficient and transparent e-government

services (Chourabi et al., 2012), increase food

supply efficiency and effectiveness (Abdulrahman et

al., 2012), increase water (Venkatesen, 2010) and

energy supply efficiency (Stancic, 2009), provide

advanced waste management practices (Maloney,

2011), and improve public safety (Witters, 2012). All

these services are enabled by the four perspectives

previously described in this document.

In particular we believe we can conceptualize the

main objective of this area in just one simple

sentence: make sense of data. There is a huge

amount of data hidden in cities (Hill, 2008), and our

lives, the decisions we make, and the actions we take

can be much better through data availability. We live

in a “data-driven life” (Wolf, 2010). A very useful

example can be the implementation of an integrated

collective awareness platform (De Liddo et al.,

2010) at a city's scale. Its application would be

focused on participation, sharing, exchange, crowd-

sourcing, and open data to achieve bottom-up social

innovation, through models and simulations based

on real-world data and multidisciplinary

understanding of the complex socio-technical

interrelations (Sestini, 2011a). This approach would

enable more informed decisions, a better

management of resources, capacities and

relationships in a transparent way. Moreover within

the previous dimensions we have already provided

solutions to what are generally understood by

literature as main barriers to its adoption (i.e. data

overload, how to achieve the critical mass, balance

bottom-up and top-own initiatives etc. (Sestini,

2011b)). In fact, for instance with the proper

“digital-skilled” population and a People-Private-

Public partnership reach the critical mass is not a big

challenge anymore. Other barriers, such as data

overload, can be overcome with innovative

technological solutions (e.g. Big Data (Schawn,

2011)).

Concluding, in section 3 we provided five

dimensions that, consistently with the working

definitions and the frameworks found in the existing

literature, describe holistically the Smart City

concept. In the next section we will present some

conclusions on the results achieved with this

analysis.

4 CONCLUSIONS

Concluding, the discussion here has been focused on

Smart Cities initiatives, and particularly on a

systematic approach to tackle this topic holistically.

We defined five dimensions as the five main areas

that encompass all the enabler factors of a Smart

City: Technology, Social Infrastructure, People-

Private-Public Partnership, Governance and

Management, and Smart Information Services.

Within the introduction section we mentioned how

this conceptualization will be useful for researchers,

governments and companies, in relation to

innovative measurement frameworks, further

processes standardizations. We want also to

underline how these dimensions represent not only a

framework that takes into account all aspects of

Smart Cities, but also, combining these perspectives,

we can have insights on how the strategy for develop

AComprehensiveFrameworkforSmartCities

such initiative should be built up. In detail these

areas allow us to describe, in first approximation, a

Smart City strategy in just one sentence. So, with the

proper technological and social infrastructure,

through the right organization and the appropriate

management, partnerships between governments,

businesses, and people can be enabled, and they are

needed to overcome the existing gaps for the actual

delivery of smart services to the city's community,

and so achieve the Smart City mission. Keeping this

sentence in mind, let's then consider how this

framework can be the starting point of a (extremely

needed (Lombardi et al., 2012)) systematic

embedded framework for measuring Smart City

performances, communicate and create awareness

among stakeholders, and manage the Smart City

strategy. Considering the Balanced Scorecard (BSC)

(Kaplan and Norton, 1992) as a model to assess and

manage a strategy (the most widely used among

companies, public sector organizations (Aslani,

2009), and IT functions (Van Grembergen et al.,

2005)), the taxonomy we have proposed here can be

considered as a big step forward in its development.

In fact, the BSC model foresees the balanced

assessment of perspectives characterized by

homogeneous content that enclose within them all

the components of a strategy. Moreover, these

perspectives have to provide an outline of the

strategic path that leads to the achievement of the

mission, in terms of cause-effect relationships

(otherwise this would lead to what Kaplan and

Norton called “a simple KPI Scorecard” (Kaplan and

Norton, 1996)). Then, our approach can be definitely

considered as an optimal starting point (and maybe a

little bit more) for the development of an embedded

measurement framework, as the BSC is. In fact,

within this document, we demonstrated through a

careful review of the literature (see section 2) that

the five dimensions take into account all the enabler

factors of Smart Cities, and furthermore they

identify a strategic direction in terms of cause-effect

relationships (see the sentence above in this

paragraph). What is needed now is to systematically

define the SMART (Specific Measurable Achievable

Realistic Timely) goals for each perspective (our

dimensions' description provides already some

suggestions), and the subsequent critical success

factors (CSF) for each goal. Finally, these CSFs are

helpful to find the proper set of performance

indicators in order to measure their level of

achievement.

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APPENDIX

1: Definition Table

SMARTGREENS2013-2ndInternationalConferenceonSmartGridsandGreenITSystems

2: Definition Concept Matrix

3: Frameworks Concept Matrix

AComprehensiveFrameworkforSmartCities