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The Model of Smart Cities in Theory and in Practice
Rajiv Singh Irungbam
irungbamrajivsingh@yahoo.com
Abstract
Today, the term “smart” is commonly
implied in various day-to-day commodities
as an attractive feature, such as smart car
for
mobility,
smart
phones
for
communication, smart TV for entertainment
or smartcard for multiple services. On a
grand scale, the term has even caught
attention in the paradigm of urban planning
policy and practices.
Globally, many approaches are undertaking
to upgrade cities as “smart”. However,
despite gaining momentum in equating the
term “smart” with cities, there is no “one”
universally accepted model of smart cities
till date. Given this limitation, this paper
reviews the theoretical construct of smart
cities from the infinite and multi-dimensional
definitions of smart cities to underscore its
fundamental concept and implication. It is
observed that smart cities as a concept has
evolved from a narrow perspective of
integrating innovative technology towards
building intelligent infrastructure, into a
more intricate urban system that calls for
social and institutional participation.
This descriptive paper seeks to acknowledge
the fundamental principle of smart cities as
an urban model by acknowledging what is
commonly implied in professional circles,
ranking system, assessment tools and
implementation procedures. It is done so
through an archival method referring to
published literature, journals, reports,
official websites, case studies, assessment
guidelines, initiatives, and practices on
smart cities as primary sources of
information.
Finally, the paper identifies similarities in
global patterns for practicing the new urban
development model and observes that
entitling a city as “smart” remains
unscrupulous due to the limitation of a “onesize-fits all” model of smart cities and limits
to a city-marketing strategy.
Keywords: Urban Model, Smart cities,
Technology, Concept and Practices
Introduction
It is eight years now, since the worlds’ urban
population was accounted to supersede rural
population (United Nations 2008). In the
midst of urbanisation comes scarcity of
resources, economic austerity, and several
other challenges occurring simultaneously.
While cities are rapidly growing physically
and demographically on the one hand, there
are trends of aging and shrinking
instantaneously on the other hand.
Demographic growth and rapid urbanisation
are explicitly associated with cities of
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developing countries, however, economic
austerity leading to declining population,
higher emigration changing social structures
and aging societies are some challenges of
existing developed cities. Due to the urban
turmoil, the quality of life in cities is
undoubtedly diminishing. The quest to cope
with such rapid urban changes is for many
cities a challenge. As a city changes and
expands the financial means to sustain
growth without compromising the quality of
life is still a challenge. Cities not only need
provision
of
additional
physical
infrastructure, but also simultaneously seek a
new efficient and effective urban planning
policy and practice.
As a result, new tools for transforming cities
have evolved and many urban theorists have
devised alternative means to tackle urban
problems. Such exemplary urban models
could be seen in the form of ‘New
Urbanism’ (Congress for the New Urbanism
1999), ‘Intelligent City’ or ‘Digital City’
(Mitchell 1996; Mitchell 2000), ‘Knowledge
City’ (Carrillo 2006), ‘Eco-City’ (Register
2006), and ‘Creative City’ (Florida 2002;
Landry 2008) among others. Apart from that
IBM (global giant technology company)
kick started a “Smarter Cities”1 programme
as part of the Smarter Planet initiative during
the period of world’s economic austerities in
2008, to help improve cities (Harrison &
Donnelly 2011; Kehoe et al. 2011; Paroutis
et al. 2013). The IBM initiative firmly
believes that ‘technology has a vital role to
play in dealing with many of the current
issues cities grapple with’ (Kehoe et al.
2011, pp.1). Hence, the IBM smart cities
initiative2 was conceived with the aid of
digital
technology
within
city’s
infrastructure.
And in February 2009, Cisco (another giant
technology company) initiated a global
communities through “Smart + Connected
Communities (S+CC)” as a platform to
transform physical communities to become
digitally connected through networks of
fibre optic to enable information sharing,
and engage on ‘economic, social and
environmental sustainability’ (otherwise
referred as the three key principles of
sustainable development) (Chakrabarti 2011,
pp.1).
However, the usage of the prefix “smart” in
urban reform could be traced back to “smart
growth” Agenda 21 undertaken during the
1992 UN Conference on Environment and
Development held in Rio de Janeiro.
Similarly, in 1997 the World Foundation
conducted a Global Forum for “Smart
Communities” at the International Centre for
Communication in San Diego. The objective
of the communities was ‘…to set away from
2
“IBM defines a smarter city as one that makes
optimal use of all the interconnected information
available today to better understand and control its
operations and optimize the use of limited resources”
(Kehoe et al. 2011).
1
Their classic example is the installation of Rio
Operation Centre in Rio de Janeiro after several
incidents of flood affecting the city. Available at:
http://www03.ibm.com/press/us/en/pressrelease/33303.wss
[Accessed June 2, 2015].
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the conventional model of building towns
and cities along railroads, waterways, or
interstate highways, but rather to build along
information
highways’
(smartcommunities.org
n.d.,
pp.1).
Therefore, the aim of the smart growth
through smart communities was to support
city development through ‘collaboration and
cooperation’ by taking advantage of the
possibilities enabled by information
technology (ibid). Not much difference from
the World Foundation of smart communities,
the World Teleport Association created a
special interest group in 2001 called the
“Intelligent Community Forum (ICF)” for
similar kind of cross-cities-collaboration,
and later in 2007, a “Smart2l” community
was created as part of their special intelligent
community to promote smart practices (Nam
& Pardo 2011).
Although the rhetoric prefix “smart” before
cities is arguable, the fundament to
necessitate urban improvement with added
technology remains less contended. In this
regard, Townsend (2013) like many other
urban scholars draws the role of technology3
for the progress of cities. In his recent book
titled Smart Cities: big data, civic hackers
and the quest for a new utopia, he states that
the creation of papyrus during the ancient
Egyptian civilization, the construction of
printing press during 15th century. Or the
development of steam engine during the late
Oxford dictionary defines ‘technology’ as “the
application of scientific knowledge for practical
purposes, especially in industry, for example in
designing new machines”
3
18th century and the invention of electricity
and telegraph during 19th century all fuelled
the advancement of cities (ibid). Likewise, in
essence, smart cities concept called for
integrating innovative advanced technology
into our urban infrastructure, with the
perception
that
technologies
were
historically evolved to enhance cities.
Undoubtedly, countless progress was made
over the last and current century in the field
of digital technology and fields such as
computers, mobile phones, tablets and
internet saw growth. These products today
are not only advanced but they have become
more accessible and affordable today.
According to the report by Kleiner Perkins
Caufield Byers (KPCB) on Internet Trends
in 2008, half of the urban population are
already using mobile phones and internet to
communicate (Meeker 2012). The figure is
only increasing with the growing market of
smartphones and tablets (Meeker 2014). The
availability of such ubiquitous technology in
our urban environment is considered as a
viable scope for introducing a new urban
model such as “smart cities”.
However, as Harrison & Donnelly (2011,
pp.4) pointed out,‘... the core motivation for
global interest to adopt smart cities concept
was grounded in the cities desire for
economic development’. This means smart
cities practices were orientated purely
towards technological market as alternate
means of economic growth. Therefore, many
critics have opposed the model of smart
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cities.
For example, Hollands (2008,
pp.314) argue that the smart cities model
reflect
as
a
‘high-tech
urban
entrepreneurialism’ negating to consider
some of the already evolved concepts and
models concerning ‘technological and
creative city’. Hollands (ibid, pp.313) also
accused that the term “smart cities” are used
by the urban managerial rather as a ‘selfcongratulatory’
label
to
retain
competitiveness in the global placemarketing. Furthermore Nam & Pardo
(2011) acknowledged that the discussion on
smart cities has been made without solid
conceptualization of other urban challenges
and factors.
Therefore, the current understanding of
“smartness” truly in a city is uncertain, both
in theories and in practice. Given this
context, the aim and objective of this paper
is to seek a comprehensive understanding of
smart cities as a new model of urban
development,
perceived
from
both
theoretical and practical implication in the
global context. To conduct the descriptive
assessment, the paper gathered “smart cities”
definition through archival method; referring
to several literatures, journals, reports,
official websites, case studies, assessment
guidelines,
initiatives,
and
practices
currently accessible on smart cities. first of
all, for theoretical construct, the paper
discusses the various multi-dimensional
definitions, and tabulate following thee
similar categorisation conceptualised by
Nam & Pardo (2011). Secondly, for practical
implication, the paper assimilates the
components and factors of smart cities with
an overview on various assessment tools and
applied frameworks. Finally, the paper will
present observations on the pattern of smart
cities practices adopted internationally.
Through this paper, the author attempts to
highlight the extent to which smart cities
have progressed as a viable urban model and
what are the limitations still disguising the
model.
In
Theory:
Towards
defining “smart cities”
Until now, there is no standardised definition
of “smart cities”, but there are different
perceptions which are ambiguously used in
the international dialogue. Often used as
counter-argument for not establishing a
universally viable definition is the fact that
cities are varied, with inexplicably different
needs and urban challenges. Despite cities
being culturally, climatically, economically,
ethnically and geographically different, the
common objectives of most smart city
concepts align. With raising economic
competitiveness, necessitate efficiency in
infrastructural
services,
reduce
environmental impact and enhanced the
urban quality of life being always a common
denominator.
Collected from various sources, there are
approximately 24 varying definitions on
smart cities proposed since the new
millennium. Hall (2000) among the first to
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project the vision on smart cities clearly
suggest utilizing every means of technology
on all the physical components of our urban
environment such as road, bridges, building,
energy, water and waste to secure cities
future. While Giffinger et al. (2007, pp.11)
refer to the notion of using technology on
infrastructure
as
the
‘search
and
identification of intelligent solutions’ and
Washburn et al. (2010, pp.2) identifies it as
the application of ‘smart computing
technologies’.
In the earlier represented model of the smart
cities, technological applications such as
data analytics, programming, ICT, smart grid
and remote sensors were deployed as the
core functional components. Many of the
other initiatives undertaken by global techgiant companies such as IBM Smarter Cities
(Kehoe et al. 2011) and Cisco Smart +
Connected Communities (Menon 2015)
vouches the model with the same spirit.
However, the initial model of smart cities
faced many criticisms due to its narrow
perspective on viewing the actual
functioning of cities. Others suggest, such
technological services are purely provided
by global technologies firm, and reasons’ to
why they draw attention on enticing the
application of such complex systems
(Paroutis et al. 2013). Meaning that the main
motivation and objectives for undertaking
such large-scale transformation initiatives
were heavily relied on business growth
under the context of delivering quality
services (Baron, 2012, p.34).
Furthermore, it is strongly opposed that
technology and ICT are not to be taken as
the only integral part for effective
functioning of smart cities, but rather it
should be seen just as one entity for
enabling (Eger 2009, pp.47-53). Seeking
other factors that could potentially play a
role, Nam & Pardo (2011) compared similar
conceptual variants of smart city such as
digital city, wired city, ubiquitous city,
human city, knowledge city, smart
community etc., and concluded that many of
these models constitutes other dimensions
such as people and institution. Similarly
Meijer & Rodríguez Bolívar (2013)
promotes a ‘socio-techno synergy’ through
an amalgamation of smart people and smart
collaboration besides smart technology.
By using a recent compilation of various
definitions on smart cities, similar to that of
Albino et al. (2015, pp.4-6), and using of the
three key characteristics or dimensions
identified by Nam & Pardo (2011), a
comprehensive definitions on smart cities is
tabulated and illustrated in Table 1.
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Table 1: Evolution of Smart Cities definition and identification of key dimension.
Source: Adapted from (Albino et al. 2015, pp.4-6) with author’s addition.
Source /
Timeline
Hall
(2000)
Giffinger et al.
(2007)
Source/
Timeline
Eger
(2009)
Washburn et
al. (2010)
Chen
(2010)
Harrison et al.
(2010)
Caragliu et al.
(2011)
Komninos
(2011)
Thite (2011)
Nam and
Definition
A city that monitors and integrates conditions of all of its critical
infrastructures, including roads, bridges, tunnels, rails, subways, airports,
seaports, communications, water, power, even major buildings, can better
optimize its resources, plan its preventive maintenance activities, and
monitor security aspects while maximizing services to its citizens.
Smart city generally refers to the search and identification of intelligent
solutions, which allow modern cities to enhance the quality of the services
provided to citizens.
Definition
Smart community – a community that makes a conscious decision to
deploy technology aggressively as a catalyst to solve its social and
business needs – will undoubtedly focus on building its high-speed
broadband infrastructures, but the real opportunity is in rebuilding and
renewing a sense of place, and in the process a sense of civic pride.
Smart city is the use of Smart Computing technologies to make the critical
infrastructure components and services of a city—which include city
administration, education, healthcare, public safety, real estate,
transportation, and utilities—more intelligent, interconnected, and
efficient.
Smart cities will take advantage of communications and sensor capabilities
sewn into the cities’ infrastructures to optimize electrical, transportation,
and other logistical operations supporting daily life, thereby improving the
quality of life for everyone.
A city connecting the physical infrastructure, the IT infrastructure, the
social infrastructure, and the business infrastructure to leverage the
collective intelligence of the city
A city is smart when investments in human and social capital and
traditional (transport) and modern (ICT) communication infrastructure fuel
sustainable economic growth and a high quality of life, with a wise
management of natural resources, through participatory governance.
(Smart) cities as territories with high capacity for learning and innovation,
which is built-in the creativity of their population, their institutions of
knowledge creation, and their digital infrastructure for communication and
knowledge management.
Creative or smart city experiments [....] aimed at nurturing a creative
economy through investment in quality of life, which in turn attracts
knowledge workers to live there and work in smart cities. The nexus of
competitive advantage has [...] shifted to those regions that can generate,
retain, and attract the best talent.
A smart city infuses information into its physical infrastructure to improve
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T
Dimension
S
I
●
●
Dimension
T
S
I
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
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conveniences, facilitate mobility, add efficiencies, conserve energy,
improve the quality of air and water, identify problems and fix them
quickly, recover rapidly from disasters, collect data to make better
decisions, deploy resources effectively, and share data to enable
collaboration across entities and domains.
A smart city is based on intelligent exchanges of information that flow
between its many different subsystems. This flow of information is
analysed and translated into citizen and commercial services. The city will
●
act on this information flow to make its wider ecosystem more resourceefficient and sustainable. The information exchange is based on a smart
governance operating framework designed to make cities sustainable.
T - Technological dimension
S - Social dimension
I – Institutional dimension
Pardo (2011)
Gartner
(2011)
Source/
Timeline
Thuzar
(2011)
Cretu
(2012)
Guan
(2012)
Bakıcı et al.
(2012)
Barrionuevo et
al.
(2012)
Kourtit and
Nijkamp
(2012)
Definition
Smart cities of the future will need sustainable urban development policies
where all residents, including the poor, can live well and the attraction of
the towns and cities is preserved. [...] Smart cities are cities that have a
high quality of life; those that pursue sustainable economic development
through investments in human and social capital, and traditional and
modern communications infrastructure (transport and information
communication technology); and manage natural resources through
participatory policies. Smart cities should also be sustainable, converging
economic, social, and environmental goals.
Two main streams of research ideas: 1) smart cities should do everything
related to governance and economy using new thinking paradigms and 2)
smart cities are all about networks of sensors, smart devices, real-time data,
and ICT integration in every aspect of human life.
A smart city, according to ICLEI, is a city that is prepared to provide
conditions for a healthy and happy community under the challenging
conditions that global, environmental, economic and social trends may
bring.
Smart city as a high-tech intensive and advanced city that connects people,
information and city elements using new technologies in order to create a
sustainable, greener city, competitive and innovative commerce, and an
increased life quality.
Being a smart city means using all available technology and resources in
an intelligent and coordinated manner to develop urban centres that are at
once integrated, habitable, and sustainable.
Smart cities are the result of knowledge-intensive and creative strategies
aiming at enhancing the socio-economic, ecological, logistic and
competitive performance of cities. Such smart cities are based on a
promising mix of human capital (e.g., skilled labor force), infrastructural
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T
●
●
●
Dimension
S
I
●
●
●
●
●
●
●
●
●
●
●
●
●
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capital (e.g., high-tech communication facilities), social capital (e.g.,
intense and open network linkages) and entrepreneurial capital (e.g.,
creative and risk-taking business activities)
Smart cities have high productivity as they have a relatively high share of
Kourtit et al.
highly educated people, knowledge-intensive jobs, output-oriented
(2012)
planning systems, creative activities and sustainability-oriented initiatives.
Smart city [refers to] a local entity - a district, city, region or small country
●
-which takes a holistic approach to employ[ing] information technologies
IDA (2012)
with real-time analysis that encourages sustainable economic development.
A community of average technology size, interconnected, sustainable,
Lazaroiu and
●
comfortable, attractive and secure.
Roscia (2012)
The application of information and communications technology (ICT) with
Lombardi et
●
their effects on human capital/education, social and relational capital, and
al. (2012)
often indicated is environmental issues by the notion of smart city.
T - Technological dimension
S - Social dimension
I – Institutional dimension
Source/
Timeline
●
●
●
Dimension
Definition
T
A smart city is understood as a certain intellectual ability that addresses
several innovative socio-technical and socio-economic aspects of growth.
These aspects lead to smart city conceptions as “green” referring to urban
infrastructure for environment protection and reduction of CO2 emission,
“interconnected” related to revolution of broadband economy, “intelligent”
Zygiaris
●
declaring the capacity to produce added value information from the
(2013)
processing of city’s real-time data from sensors and activators, whereas the
terms “innovating”, “knowledge” cities interchangeably refer to the city’s
ability to raise innovation based on knowledgeable and creative human
capital.
Smart cities are defined as places where information technology is
Townsend
●
combined with infrastructure, architecture, everyday objects, and even our
(2013)
bodies to address social, economic, and environmental problems.
Smart cities initiatives try to improve urban performance by using data,
information and information technologies (IT) to provide more efficient
Marsal●
services to citizens, to monitor and optimize existing infrastructure, to
Llacuna et al.
increase
collaboration
among
different
economic
actors,
and
to
encourage
(2014)
innovative business models in both the private and public sectors.
T - Technological dimension
S - Social dimension
I – Institutional dimension
Upon reviewing the varied definitions, it is
observed that the notion of smart cities have
progressed from merely a sophisticated
●
S
I
●
●
●
technological input into a more conducive
one. The later definitions aligning more on
an anthropocentric approach suggest that
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cities aiming for economic growth and
gaining higher quality of life must put
investment
in
human
and
social
development,
besides
techno-orientedinfrastructures. By all means, making a wise
management of natural resources and
including participatory governance are
considered as mandatory factors for
perceiving smart cities (Caragliu et al. 2011,
pp.65), and other suggest that even our
bodies and everyday object should
contribute to the fight against social,
economic and environmental problems
(Townsend 2013, pp.15).
Despite noteworthy contributions to the field
of smart city research, Kim & Steenkamp
(2013, pp.368) fear the focus of the
contemporary smart city models is limited –
arguing that it lacks a ‘holistic and integrated
approach and fail to sufficiently address
significant contemporary urban challenges
facing many cities and neglect human
factors’. In addition to the earlier discussion,
Cretu (2012, pp.59) suggested that smart
cities should give its prime focus on
governance and economy with forward
thinking and later utilise the networks of
sensors, smart devices, real-time data and
ICT to integrate in every urban scenario. In
other words, smart cities also means a
combination of different collaborative
sectors constituting of both public and
private entities enabled by ICT infrastructure
to facilitate the exchange of information and
delivery of high and resource efficient
services to its citizens (Ponting, 2013, pp.5).
Therefore, from the above observations, the
dimension of smart cities constitutes not
only that of technology, but social and
institutional as well – meaning that inclusive
human
resource
management,
and
transparent governance play a crucial role in
delivering such forward looking urban
model. Governance acting as a good
moderator between the role of citizens and
high-tech infrastructural services provider
becomes unarguably the puissance for any
smart urban transformation. To this date, the
model of smart cities has theoretically made
progress with some consideration on social
and institutional integration. This approach
calls for a more humanistic integration with
technology and collaborative participation
addressing the need for smarter democracy
(Meijer and Rodríguez Bolívar, 2013, pp.27).
Dimensions of Smart cities
As discussed above the trends of definitions
delineate the dimensions of the smart cities
as technological, social, and institutional.
Based on these dimensions, different urban
stakeholders approach the concept of smart
cities. As Santis et al. (2014) observed,
corporate enterprises are mainly oriented to
technology, network infrastructures and ICT
as they provide the services or products,
while academicians mainly focus on
defining an conducive theoretical framework
to address social and environmental
challenges, and institutional bodies limit
itself to organisational frameworks and
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governance. These dimensions collectively
form the overall notion of smart cities with
some ostensible interrelation.
Technological Dimension
The notion of “smartness” of a city often
overlaps with that of being “intelligent”. It is
only recently that “smart” is taken more as a
market friendly language than it former
elitist term “intelligent” (Albino et al. 2015,
pp.5).
In the works of Mitchell (1996; 2000), the
wake of the digital revolution has potentially
raised the intelligence of the cities.
Similarly, the core aspect of technological
oriented smart cities consist of raising up the
intelligence with wide range of electronic
and digital technologies, by creating a cyber,
digital, wired, information, or knowledgebased city (Albino et al. 2015, pp.10). It
revolves around the availability of urban
data with the increasing use of ubiquitous
technology, known in the technical jargon as
‘big data’. Accessing those data or in other
words ‘data mining’ and utilising it through
urban analytic and computing to better
understand and respond to our urban system,
is seen in this facet as an opportunity for
Smart(er) Cities deployment (Kitchin, 2014,
pp.11). For IBM, this means ‘making the
invisible visible’ by following up closely on
citizens choices and actions to detect
patterns of behaviour or its anomalies
(Harrison & Donnelly 2011, pp.8).
The technological frontier of cities are
driven by the advancement of web
technologies, what (Schaffers, Ratti and
Komninos, 2012, pp.3) refer as the three
waves. First being the World Wide Web
(www.) initiated in the 90s along with the
development of the internet. Second, being
the increase in communications bandwidth,
commonly understood as the ‘dotcom’
boom. And third, as the transformation of
broadband to embedded systems and
wireless networks (Wi-Fi) giving rise to
what is understood as ‘the Internet of
Things’ or ‘Internet of Everything’ (IoT)
(ibid; Haubensak 2011; Coe et al. 2001).
Additionally, the recent ‘smartphone’ market
explosion adds a fourth wave, since
ubiquitous computing are accessible to a
larger group inexpensively, increasing the
flow of information (Townsend 2013).
For technology giant company such as IBM,
Cisco Systems, Siemens AG, General
Electric, Accenture, Microsoft, HP, Google
etc. the technological dimension is the key
component to their conceptual framework of
smart cities (Albino et al. 2015; Townsend
2013). According to them, cities targeting to
attain “smart” must put investments in
making a city’s core system smarter, which
in the long run is believed to create
economic growth through cost savings and
increased efficiencies (Dirks et al. 2010,
pp.2). And, worldwide research is currently
focusing on development of new technology
as there is a potential market and business
opportunities in both developed and
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developing countries (Liu & Peng 2014;
cited in Albino et al. 2015, pp.5), “this is a
huge, huge opportunity”, says Peter Löscher,
CEO of Siemens (cited in Fisher 2011, pp.1).
This is the reason why global technology
giant enterprises are seen racing for position
around the smart cities project (Townsend
2013). And cities around the world devour to
the idea of increasing connectivity to
maintain competitiveness in the neo-liberal
economy (Graham 2002). This is often
where criticism emphasising on business-led
smart cities as urban development model
arises (Harvey 1989; Hollands 2008).
Apart from that privacy and data security are
among the most pressing challenges, facing
Internet of Things-driven smart cities –
implying that technology is also known for
its unpredictability or ability to crash often
referred as the “blue screen of death” in
technical reference or “brittle and buggy” in
the words of Townsend (2013).
Social Dimension
As argued by many urban scholars, digital
connectivity and being intelligent alone do
not steer the smart city wheels, but
creativity, knowledge, skills and the
abundance of talent pool in a proximate
cluster drives the city towards economic
growth (Caragliu et al. 2011; Hollands 2008;
Glaeser 2012). Winters (2011, pp.2-3)
observation on smart cities delineates that
cities containing flagship state universities,
higher level of graduates and skilled
workforce play an important role in the
growth of smart cities – an observation
similar to Landry's (2008) urban thesis on
“Creative City”.
Economic growth and activities in smart
cities are largely associated with the
presence of superior talent that has the
aptitude and capacity for incubating
innovation (Dirks et al. 2010). Similarly,
Kaunas University of Technology’s project
on ‘smart development of social systems’
points towards ‘intelligence, learning,
digitality,
innovativeness,
knowledge
management, sustainability, networking and
agility’ (Sinkiene et al. 2014, pp.938).
Therefore, talent capture or “brain gain” is
becoming an increasingly valued resource to
enhance technological innovation (OECD
2008, pp.165). In a nutshell, a smart city is
associated with smart people (Nam & Pardo
2011). The smart people are symbiotic for
their ‘affinity to lifelong learning, social and
ethnic plurality, flexibility, creativity,
cosmopolitanism or open-mindedness, and
participation in public life’ (ibid, pp.287).
In this aspect, the work of Florida (2002),
particularly in the Northern America and
Landry (2008) in Europe highly influence
urban policy maker for acknowledging the
forces and nature of “Creative Class”. Many
strategies in creative social frontier focuses
on fostering thriving urban environment with
high-end quality housing, robust ICT,
recreation, and entertainment (culture, art,
and digital media) around working clusters.
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But, such development falls into criticism
for being elites and socially exclusive
(Graham 2002; March & Ribera-Fumaz
2014). Others argues that smartness is
beyond
merely
intelligence
and
knowledgeableness, it is rather a social
construction comprising of one’s cultural
capital, social capital and innate intelligence
(Sinkiene et al. 2014, pp.934). Hence, a
progressive smart city requires the input and
contribution of various group of people and
not alone by the adoption of ‘hi-tech
infrastructure’
and
‘self-promotional
websites’ (Hollands 2008, pp.316). Also,
smartness of a city reflects to the smartness
of how the people in their community is
integrated (Navarrete et al. 2011, pp.1). Such
outlook envisions to provide an opportunity
for citizens participation and influence over
local decision making (Coe et al. 2001).
On one hand, community participation is an
important role, not only as a civic right but
rather as a social responsibility to contribute
to the collective wellbeing of the city. It is
also significant as citizens would be the end
user for any technological diffusion
(Mongeau 2012). On the other hand, the
availability of ubiquitous computing
provides a new platform for social
entrepreneurialism (Townsend 2013, pp.282320). Given the right tuning through
education, awareness and leadership,
Townsend believes they have the potential
for contributing to social and economic
growth (ibid.). Therefore, it becomes crucial
for public institutions to facilitate an
environment where technology and citizens
are systematically synchronised.
Institutional Dimension
As observed, Caragliu et al. (2011)
definition on smart cities is used as the
starting point for many of the dialogues that
followed on institutional dimension. As it is
described, all actions on the smart city
ranging from social investment to ICTenabled infrastructures investment should
channel
through
a
framework
of
“participatory governance” (ibid, pp.65).
Institution here is referred to all the
pertaining stakeholders such as government,
enterprises,
academic
institution,
organisation, and local body to actively
involve in the designing processes of smart
cities.
The concern for collaboration and open
information among institutions is influence
from the success story of a multi-national
corporate working model. Drawing from
both success and failure business model of
Silicon Valley in California and Route 128
in Boston, Saxenian (1994) concluded that
collaboration and sharing of information
amid competition were a crucial factor for
one region’s (city’s) prosperity ahead of the
other. Over the last decade, this know-how
has given the public sector a new mode of
organisational and policy implication
through shared information, transparency,
openness and collaboration (Ferro et al.
2013, pp.137). Since the 90s, forum such as
smart
communities
and
networked
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communities were initiated using ICT
infrastructure to allow such exchange (Nam
& Pardo 2011). Through the ICT-mediated
governance, otherwise known as egovernance, the decision making and
implementation process is envisioned to
bring larger coverage and transparency
(Albino et al. 2015). For truly effective local
governance, today and in the future, it will
be crucial that ‘government and politicians
not only govern effectively, efficiently and
economically but to engage citizens in open
and participative information sharing and
decision-making’ (Coe et al. 2001, pp.92).
However, pointed out, there lie many
challenges in adopting a new model of
institutionalisation in the construction of
smart cities. The critical questions are: How
can the institutions provide opportunities for
both industry and potential grassroots to
innovate? How do they balance the load on
government’s responsibilities while enabling
citizen’s empowerment? And how will they
secure the good use of open data from
misuse? Such questions are raised, because
these institutions will face the shame upon
failure of the functioning of the smart cities
(Townsend's 2013, p.225). To tackle some of
the challenges, an effective use of social
learning is required (Collins et al. 2002,
pp.8), and multi‐stakeholders need to step-up
over the classical private and public
partnership governance for effective
institutionalisation of smart cities. In a
nutshell, institutions are suggested to step
away from being silos, and instead become
coalescence to function effectively.
In Practice: Components of
Smart Cities
The recent shift on the use of the term smart
city in the planning doctrine, from
theoretical to practical, has led to the
delineation of functional components of the
model (Ponting 2013, pp.12). As a result, in
various literatures, policies and practices,
smart cities are built on a model of varying
components based on the needs and
challenges of the city. On one hand, the
focus on certain components is convenient
for cities to disseminate implementation and
operation; on the other hand, they enable
comparative assessment with other cities.
Reviewing it is necessary to give a broader
perspective of how the model is to be put in
practice.
Cisco frames the smart cities on four
components with four further assets of each
component. They are classified as; utilities
(power, water and waste), transportation
(rail, road, air and logistics), real estate
(residential, commercial, retail/hotel and
public buildings), and city services
(healthcare, education, fire/police/defence
and municipal services) (Falconer &
Mitchell 2012, pp.6). Other than that a more
comprehensive
and
frequently
acknowledged component of smart cities
was proposed by the Department of Spatial
Planning, Vienna University of Technology.
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They developed six key components or
characteristics to measure the smartness of
70 European medium-sized cities since
20074 (Giffinger et al. 2007). The six
components with the prefix “smart”
constitute of; economy, people, governance,
mobility, environment, and living as
illustrated below in Figure 1. These
components also align with traditional
regional and neoclassical theories of urban
growth and development model (Caragliu et
al. 2011, pp.69).
factors and indicators which are used to
represent the smartness of the cities. Using
some of the adopted factors and indicators as
a reference, the six components can be
discussed in the sub-headings below.
While this model lacks applicability to cities
in developing countries, as it excludes
utilities
parameter
unlike
Cisco’s
framework, it cannot be argued since the
model was based on European cities where
most utilities infrastructure are already inplace. Despite this basic limitation, the
model is an exemplary one as many
alternative models that followed are
fundamentally based on these six
components. For example Fast Company’s
Smart City Wheel5, Forum Pa’s Icityrate6
and Between’s Smart City Index7 (Santis et
al. 2014) are predominantly based on this
model. The only differentiation is that each
component is characterised by different
4
Since 2007, various versions are used with varying
results. Available at: www.smart-cities.eu [Accessed
July 15, 2015].
5
Available
at:
http://www.fastcoexist.com/1680856/the-top-10smartest-european-cities [Accessed July 17, 2015]
6
Available at: http://www.icitylab.it/il-rapportoicityrate/edizione-2012/metodologia/ [Accessed July
17, 2015]
7
Available at: http://www.between.it/ita/pagina-nontrovata.php [Accessed July 17, 2015]
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Figure 1: The Practical Components of Smart Cities.
Source: Author’s own illustration derived from (Giffinger et al. 2007)
Smart Economy (Business Innovation)
Building sustainable economic growth has
been a leading aspect for most smart city
structures. One of the main objective for
IBM’s Smarter City initiative is also guided
towards business growth and development,
for building city economy (Kehoe et al.
2011). However, smart economy as shown in
Table 2, encircles around economic
competitiveness from business innovation,
high
entrepreneurship,
trademarks,
productivity, flexibility of labour market,
international connectivity as well as the
ability to transform the business or industry.
Each of these factors has one to three
indicators that are used to measure the
overall
smart
economy.
Table 2: The Factors and Indicators of Smart Economy.
Source: Author derive from (Giffinger et al. 2007)
Factors
1. Innovative spirit
2.
Entrepreneurship
3.
4.
5.
Economic image & trademarks
Productivity
Flexibility of labour market
6.
International embeddedness
Indicators
R&D expenditure in % of GDP
Employment rate in knowledge-intensive sectors Patent
applications per inhabitant
Self-employment rate
New businesses registered
Importance as decision-making centre (HQ etc.)
GDP per employed person
Unemployment rate
Proportion in part-time employment
Companies with HQ in the city quoted on national stock
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7.
Ability to transform
market
Air transport of passengers
Air transport of freight
Not Specified
Smart People (Social and Human Capital)
The second component of smart city is
mainly to do with social and human capital
as partly discussed in the social dimension of
smart cities. Here, the level of educational
and working qualification, their affinity to
lifelong learning, social and ethnic plurality,
flexibility, creativity, cosmopolitan or openminded spirit and active participation in
public life have an implication on smart
cites as prescribed in Table 3. According to
Smart Cities Council (2014) this particular
group or set of people include elected
officials, city planners, policymakers,
citizens, business leaders, financiers and
public-private partnerships.
Table 3: The Factors and Indicators of Smart People.
Source: Author derive from (Giffinger et al. 2007)
Factors
1. Level of qualification
2.
Affinity to lifelong learning
3.
Social and ethnic plurality
4.
5.
6.
Flexibility
Creativity
Cosmopolitanism/Openmindedness
7.
Participation in public life
Indicators
Importance as knowledge centre (top research centres, top
universities etc.)
Population qualified at levels 5-6 ISCED
Foreign language skills
Book loans per resident
Participation in life-long learning in %
Participation in language courses
Share of foreigners
Share of nationals born abroad
Perception of getting a new job
Share of people working in creative industries
Voters turnout at European elections
Immigration-friendly environment (attitude towards
immigration)
Knowledge about the EU
Voters turnout at city elections
Participation in voluntary work
Smart Governance (Civic Participation)
Being “silos” or decentralised form of
organisation is the biggest challenges for
most cities reducing efficiencies. Currently,
there is a shift towards a cohesive, co-
ordinated and integrated form of governance
model
through
online
participation
contributing to increased awareness,
efficiency, effectiveness and transparency in
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government service delivery (PwC 2014,
pp.17). From this point, the third component
is about civic participation in decisionmaking, accessibility of public and social
services, level of transparency in governance
and political strategies & perspectives as
listed in Table 4. In order to strengthen
policies, it is ideal for city administration to
collaborate with citizens and other
stakeholders collectively (Bartenberger &
Grubmüller-régent 2014, pp.17). This
component is widely embedded in many
cities today through e-governance, capacity
building, and inter-operative system.
Table 4: The Factors and Indicators of Smart Governance.
Source: Author derive from (Giffinger et al. 2007)
Factors
1. Participation in decision-making
2.
Public and social services
3.
Transparent governance
4.
Political strategies &
perspectives
Indicators
City representatives per resident
Political activity of inhabitants
Importance of politics for inhabitants
Share of female city representatives
Expenditure of the municipal per resident in PPS
Share of children in day care
Satisfaction with quality of schools
Satisfaction with transparency of bureaucracy
Satisfaction with fight against corruption
Not Specified
Smart Mobility (Sustainable Transportation)
The fourth component is about building an
efficient mobility through sustainable
transportation network. The factor here
constitutes; accessibility to quality local
public
transportation,
(inter)-national
(accessibility to, air, land, and water)
transportations, which are sustainable,
modern and safe. Mobility here also means
the availability of information and
communication infrastructure as drawn in
Table 5.
Table 5: The Factors and Indicators of Smart Mobility.
Source: Author derive from (Giffinger et al. 2007)
Factors
1. Local accessibility
2.
3.
4.
(Inter-) national accessibility
Availability of ICTinfrastructure
Sustainable, innovative and safe
transport systems
Indicators
Public transport network per inhabitant
Satisfaction with access to public transport
Satisfaction with quality of public transport
International accessibility
Computers in households
Broadband internet access in households
Green mobility share (non-motorized individual traffic)
Traffic safety
Use of economical cars
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Smart Environment (Natural Resources)
Smart environment, is considered by its
attractiveness of natural conditions (such as
climate, share of open green space etc.),
pollution,
efforts
on
environmental
protections as well as sustainable
management of resources in terms of energy,
water etc. as shown in Table 6. Similarly, the
smart city model should promote an ideal
balance and interaction among built
environment and green environment in the
city (Kim & Steenkamp 2013). However,
these factors are in less incorporation in
many practices of the smart cities.
Table 6: The Factors and Indicators of Smart Environment.
Source: Author derive from (Giffinger et al. 2007)
Factors
Attractiveness of natural
conditions
Pollution
Environmental protection
Sustainable resource
management
Indicators
Sunshine hours
Green space share
Summer smog (Ozone)
Particulate matter
Fatal chronic lower respiratory diseases per inhabitant
Individual efforts on protecting nature
Opinion on nature protection
Efficient use of water (use per GDP)
Efficient use of electricity (use per GDP)
Smart Living (Urban Quality of Life)
Last but not the least, smart living is defined
by the enhanced urban quality of life
(UQOL). It is often measured by; quality of
housing, education, cultural facilities,
healthiness and safety, as well as creating a
unified and attractive atmosphere for tourism
and eliminates urban poverty as depicted in
Table 7. It may be stressed here, that the
growth in the quality of living is also
correlated to the overall growth of the above
components. This component reflects the
end result of smart cities practice, since all
the actions taken in the other domain have
the objective of raising the quality of life
(Shapiro 2006).
Table 7: The Factors and Indicators of Smart Living.
Source: Author derive from (Giffinger et al. 2007)
Factors
1. Cultural facilities
Indicators
Cinema attendance per inhabitant
Museums visits per inhabitant
Theatre attendance per inhabitant
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2.
Health conditions
3.
Individual safety
4.
Housing quality
5.
Education facilities
6.
Touristic attractiveness
7.
Social cohesion
Life expectancy
Hospital beds per inhabitant
Doctors per inhabitant
Satisfaction with quality of health system
Crime rate
Death rate by assault
Satisfaction with personal safety
Share of housing fulfilling minimal standards Average living
area per inhabitant
Satisfaction with personal housing situation
Students per inhabitant
Satisfaction with access to educational system Satisfaction
with quality of educational system
Importance as tourist location
(overnights, sights)
Overnights per year per resident
Perception on personal risk of poverty
Poverty rate
The above six components constitute the
general implications of smart cities in
practice, while the factors and indicators
illustrated in each of the tables forms a
guiding criteria for observing and evaluating
it. Nevertheless other assessment framework
and tools acknowledge smart cities’
practices with more varying indicators.
Overview
on
Smart
Cities
Assessment
Many institutions ranging from multigovernmental
institutions,
business
consultancies, research foundations, and
media channels at national, regional, and
global levels have established guidelines,
benchmarking, or assessment tools to guide
the practicing of smart cities. Through these
mediums, several dialogues on the factors of
identifying smart cities have been carried
out.
In Europe, Lombardi et al. (2012) adopted a
performance framework for smart cities,
under five categories with 60 indicators,
similar to the six components of Giffinger et
al. (2007) referred above. A more simplified
method is proposed by Lazaroiu & Roscia
(2012) based on ambiguous logic using 18
indicators for start-up cities. Similarly,
Caragliu et al. (2011) assessment on smart
cities in Europe is based on six indicators
such as per capita GDP in PPS, employment
in the entertainment industry, multimodal
accessibility, length of public transport
network, e- governance, and human capital,
using data sets of Urban Audit from 2003
until 2006.
Recently, Carli et al. (2013) also proposed a
conceptual framework, away from the
conventional one, to analyse and compare
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measurement systems for smart cities. They
proposed the measurement indicators based
on two categories as; objective and
subjective data. Objective data are grounded
on city’s physical infrastructure (e.g., public
transport network capillarity), urban assets
(e.g., green space shares) and conditions of
the general context (e.g., air quality); while
subjective data uses citizen’s satisfaction and
well-being. Indicators are measured using
both traditional tools and new indicators
from real-time data of physical infrastructure
(such as smart grid, smart meter) and social
infrastructure (such as social networking)
(Carli et al. 2013, pp.1289-90).
In the States, the Natural Resources Defence
Council developed a Smarter Cities ranking
system
with a strong preference on
environmental related measures (Albino et
al. 2015). The cities were recognized as
“smarter” for their investment in green
power as well as energy efficiency and
natural conservation policy (Skye 2010).
While global media like Forbes, produced a
list of
world’s smartest cities with
contribution from scientist Joel Kotkin
(Kotkin 2009). The ranking relied on a city’s
built form like compactness and efficiency
and favourability for business and economic
growth (Albino et al. 2015).
In global context, Shanghai Academy of
Social Sciences (2014) conducted a broad
evaluation and ranking of Smart Global
Cities,
with
support
from
PricewaterhouseCoopers and the British
Economist
Intelligence
Unit.
Their
assessment uses three components as smart
infrastructure (internet, physical, and
economic space), smart economy (digital
creativity, content originality) and smart
governance (service and management), with
a total of 14 indicators. However, the
evaluation gave a very narrower perspective
on city’s smartness.
Apart from that an international coalition
was formed to assist cities in adopting Smart
Cities8, through its “Readiness Guide”
(Smart Cities Council 2014). The
instrumental guide book consists of a
framework, capturing the relationship
between a city’s responsibilities and its
enablers, in terms of technologies. The
framework defined eight universal aspects of
city’s responsibilities which are; built
environment, energy, telecommunications,
transportation, water and wastewater, health
and human services, public safety, and
payment and finance. In addition, the
smartness is channelled through its seven
technology
enablers
categorised
as;
instrumentation and control, connectivity,
interoperability, security and privacy, data
management, computing resources, and
analytics (ibid, pp.22-24). This framework
was developed for cities to assess themselves
rather than to compare between cities. It acts
as a hand-held guideline for cities in
8
Smart city here is defined as those that uses
information and communication technology (ICT) to
enhance cities liveability, work-ability and
sustainability (Smart Cities Council 2014) .
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planning,
decision
implementation.
making
and
The pitfalls for most of these assessments
were concluded in a recent study conducted
by Jones Lang LaSalle, titled as the
“Business of Cities” (Moonen & Clark
2013). The report assembled 150 widest
possible collections of global city indexes,
benchmarks, and comparative rankings. The
study pointed out that the potential problems
with most assessment tool are; the data
quality, geographic bias, boundary coverage,
originality, and non-up-to-date information
(ibid, p.4). The study also concluded that
many of the ranking are used for indicative
purpose only, and cannot be represented as
the actual position of the cities (ibid, p.4).
Other scholar also argues that many studies
do not follow existing modelling when
introducing their benchmarking methods,
and the public view for the final ranking
without focusing on the methodological
aspects (Anthopoulos et al. 2015, pp.526;
Giffinger et al. 2007, pp.14). The reasoning
for the shallow assessment is often asserted
by the very limitation of a comprehensible
definition and data comparability. That
allows many of the benchmarking tools to be
used for gaining global acknowledgement
through the dissemination of best practices
and projects (Santis et al. 2014). As a result,
we find many cities globally holding a smart
city placard, what Hollands (2008) referred
it as the “self-declaratory” smart cities.
“Smart” Practicing Cities
“Smart” practicing cities are simultaneously
emerging in numbers since the last decade.
There is a trend globally for adopting this
new urban model, by both internationally
renowned cities to unfamiliar ones. Cities
like San Diego, San Francisco, Brisbane and
Amsterdam were among the frontrunner,
while other cities like Southampton,
Manchester, Vancouver and Montreal
followed the practice on smart cities
(Allwinkle & Cruickshank 2011).
However, given the inconsistent use of the
model, it is difficult to identify the actual
figure of smart cities universally practiced.
According to the report published in 2011 by
ABI Research, a New York market research
company, 102 Smart cities have been
estimated worldwide. Out of which; 38 falls
in Europe, 35 in North America and 21 in
Asia-Pacific and the remaining 8 are in Latin
America, Africa and Middle East
(Hatzelhoffer et al. 2012).
Unlike the benchmarking and assessment
conducted on smart cities as discussed
above, awarding and crediting of cities as
“smart” through global alliance or
competitions are common traits for smart
practices. In this context, two examples of
cities credited as smart cities are illustrated
in table 8, with a cumulative list of cities
classified alphabetically and by regions.
Since 2007, the Intelligent Community
Forum (ICF) annually announces their
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accomplished “Smart2l Communities”9 from
many allies’ cities around the globe. The
selection is based on five success criteria of
their intelligent community (i.e., broadband
connectivity, knowledge workforce, digital
inclusion, innovation, and marketing and
advocacy)
(Nam
&
Pardo
2011).
Additionally, IBM’s initiative on Smarter
Cities has congratulated 126 cities globally
since 2010 until last year. The Smarter Cities
Challenge is centred on their eight “smart”
themes
(as
administration,
citizen
engagement,
economic
development,
education & workforce, environment, public
safety, social services, transportation. and
urban planning). The “smart” tag is
bestowed upon cities that adhere to one or
more of the eight given themes (IBM 2015).
The observations made on the two listings
suggest that the parameters for “smart”
selection vary, limiting only few cities to
qualify in both the accreditations. It also
explains that most practices are mainly
project based and they do not represent the
overall smartness of the city, although few
have considered taking initiatives on a
holistic approach. In terms of distribution,
“smart” practicing cities are not surprisingly
in abundance, in Northern America. They
stand out among others because most of the
initial initiatives on smart cities (such as
Smart Growth), were materialised in this
region (see also Townsend 2013).
9
Available at:
https://www.intelligentcommunity.org/index.php?sub
menu=Awards&src=gendocs&ref=Smart21&category
=Events&link=Smart21 [Accessed July 22, 2015]
Nevertheless, the model is appropriated into
cities of other continents, and particularly
Asian and European cities acclaimed the
concept on a precedential scale.
In Europe alone, enormous initiations are
dominantly carried out in countries like
Germany, Netherlands, Spain, and the
United Kingdom. Meanwhile, European
smart cities lead runner are considered to be
Amsterdam, Barcelona and London (Ponting
2013). Because of the geographical
proximity, there urban innovations on smart
cities are transferred as business model to
other cities in Europe including Asia. For
example, the city of Barcelona is known for
its land zoning in the 22@ district of
Poblenou (an old rundown industrial district)
to construct a self-sufficient city (March &
Ribera-Fumaz 2014). With learning from the
successive business clustering model of
Silicon Valley, Barcelona transformed the
land-use of former industrial district (known
as 22a in the city planning nomenclature)
into a district for ICT and other innovative
business
incubator
as
22@.
The
technological
implication
includes
centralized heating and cooling, pneumatic
waste collection system and high-speed
wireless broadband connectivity (ibid,
pp.10).
Apart from that in 2006, Germany through
Deutsche Telekom hosted a T-City contest
so as to find a partnering city to promote the
use of ICT in urban infrastructure. As a
result, the city of Friedrichshafen at Lake
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Constance won the contest, and the city was
awarded with the high-end state-of-the-art
broadband technology. Over the course of
five years, many initiatives and projects have
been implemented there to encourage and
innovate the city in smart practice
(Hatzelhoffer et al. 2012).
Europe
Africa
Asia
Regions
Table 8: List of Smart Practicing Cities.
Source: Author addition to (Nam & Pardo 2011)
ICF (Smart21 Communities)10
(2007 - 2015)
IBM (Smarter Cities Challenge)11
(2010 - 2015)
Astana (Kazakhstan); Bangalore (India);
Chongqing
(China);
Changhua
County
(Taiwan); Doha (Qatar); Gangnam District,
Seoul (Korea); Hong Kong; Hsinchu City
(Taiwan);
HwaSeongDongTan
(Korea);
Hyderabad (India); Ichikawa (Japan); Jaipur,
Rajasthan (India); Jia Ding (China); Kabul
(Afghanistan); Mitaka (Japan); New Taipei
City; Taichung City (Taiwan); Shanghai
(China); Seoul (Korea); Singapore; Shiojiri City
(Japan); Suwon (Korea); Taoyuan County;
Taitung County (Taiwan); Tel Aviv (Israel);
Tianjin (China); Yokosuka (Japan)
Ahmedabad (India); Allahabad (India); Cebu
(Phillipines); Chengdu (China); Chennai (India);
Cheongju (Korea); Chiang Mai (Thailand);
Chonburi (Thailand); Dà Nãng (Vietnam); Date
(Japan); Delhi (India); Foshan (China); Ho Chi
Minh City (Vietnam); Huizhou (China); Ishinomaki
(Japan); Jakarta (Indonesia); Jeju (South Korea);
Jinan (China); Jurong Lake District (Singapore);
Khon Kaen (Thailand); Kyoto (Japan); Makati City
(Philippines); Nanjing (China); Negeri Sembilan
(Malaysia); New Taipei City (Taiwan); Pingtung
County (Taiwan); Pune (India); Sapporo (Japan);
Sendai (Japan); Surat (India); Taichung (Taiwan);
Tainan (Taiwan); Vizag (India); Xuzhou (China)
Abuja (Nigeria); Accra (Ghana); Cape Town (South
Africa); Durban (South Africa); Johannesburg
(South Africa); Lagos (Nigeria); Mombasa County
(Kenya); Nairobi (Kenya); Rabat (Morocco);
Sekondi-Takoradi (Ghana);Tshwane (South Africa)
Amsterdam (Netherlands); Athens (Greece); Belfast
(Northern Ireland); Birmingham, (UK); Brussels
Capital Region (Belgium); Bucharest (Romania);
Copenhagen, Denmark); Dortmund (Germany);
Dublin, Ireland); Eindhoven (Netherlands); Faro
(Portugal); Glasgow (United Kingdom); Helsinki
(Finland); Katowice (Poland); Lodz (Poland); Lodz
(Poland); Siracusa (Italy); Stavanger (Norway);
Vilnius (Lithuania)
Cape Town (South Africa); Nairobi County
(Kenya); Nelson Mandela Bay (South Africa)
Barcelona
(Spain);
Besançon
(France);
Birmingham (UK); Castelo de Vide (Portugal);
Dundee,
Scotland
(UK);
Eindhoven
(Netherlands); Frankfurt (Germany); Glasgow,
Scotland (UK); Hammarby Sjostad (Sweden);
Heraklion
(Greece);
Issy-les-Moulineaux
(France); Karlskrona (Sweden); Malta (Malta);
Manchester (UK); Oulu (Finland); Reykjavík
(Iceland); Sopron (Hungary); Stockholm
(Sweden); Sunderland (UK); Tallinn (Estonia);
Tirana (Albania); Trikala (Greece)
10
https://www.intelligentcommunity.org/index.php?submenu=Awards&src=gendocs&ref=Smart21&category=Event
s&link=Smart21 [Accessed July 22, 2015]
11
http://smartercitieschallenge.org/smarter-cities.html [Accessed July 22, 2015]
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Oceania
Middle/
South
North America
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US: Albany (New York); Arlington County
(Virginia); Ashland (Oregon); Aurora (Illinois);
Bettendorf
(Iowa);
Bristol
(Virginia);
Chattanooga (Tennessee); Cleveland and
Columbus Region (Ohio); Austin, Corpus
Christi (Texas); Dakota County (Minnesota);
Danville (Virginia); Dublin (Ohio); Dubuque
(Iowa); Florida High Tech Corridor; LaGrange
(Georgia); Mitchell (South Dakota); Northeast
Ohio; Loma Linda (California); Philadelphia
(Pennsylvania); Riverside (California); San
Francisco; Spokane (Washington); Walla Walla
Valley (Washington); Westchester County
(New York); Winston-Salem (Carolina);
Canada:
Burlington
(Ontario);
Calgary
(Alberta); Edmonton (Alberta); Fredericton and
Saint John (New Brunswick); Kenora and
Kingston (Ontario); Moncton (New Brunswick);
Montreal metropolitan area (Quebec); Ottawa
(Ontario); Parkland County (Alberta); Quebec
City (Quebec); Sherbrooke (Quebec); Stratford
(Ontario); Surrey (British Columbia); Toronto
(Ontario); Vancouver (British Columbia);
Waterloo (Ontario); Western Valley (Nova
Scotia); Windsor-Essex (Ontario); Winnipeg
(Manitoba)
Barceloneta (Puerto Rico); Curitiba, Paraná
(Brazil); Piral (Brazil); Porto Alegre (Brazil);
Durango and Tuxtla Gutiérrez (Mexico); Rio de
Janeiro (Brazil)
Atlanta (United States); Austin, (United States);
Baltimore (United States); Baton Rouge (United
States); Birmingham (United States); Boston
(United States); Boulder (United States); Buffalo
(United States); Burlington (United States);
Chicago (United States); Dallas (United States);
Denver (United States); Detroit (United States);
Durham (United States); Edmonton (Canada);
Fresno (United States); Houston (United States);
Jacksonville (United States); Knoxville (United
States); Louisville (United States); Mecklenburg
County (United States); Memphis (United States);
Milwaukee (United States); New Orleans (United
States); Newark (United States); Omaha (United
States); Ottawa (Canada); Philadelphia (United
States); Pittsburgh (United States); Providence,
United States); Quebec City, Canada); Reno,
United States); Richmond, United States);
Rochester (united States); St. Louis (United States);
Suffolk County (United States); Surrey (Canada);
Syracuse (United States); Tucson (United States);
Waterloo (Canada)
Australia: Ballarat; Coffs Harbour (New South
Wales); Gold Coast City; lpswich and Sunshine
Coast, Queensland; Prospect, South Australia
(Australia); State of Victoria; Whittlesea,
Victoria (Australia); New Zealand: Whanganui
Ballarat (Australia); Christchurch (New Zealand);
Geraldton (Australia); Gold Coast (Australia);
Melbourne
(Australia);
Perth
(Australia);
Townsville (Australia)
In Asian region, countries like China, Hong
Kong, India, Japan, Singapore, South Korea,
and Taiwan among others, are promoting
economic growth through smart city
programs (Albino et al. 2015; Datta 2015;
Tok et al. 2014; Townsend 2013). New
development like Singapore’s IT2000 plan,
Antofagasta (Chile); Curitiba (Brazil); Guadalajara
(Mexico); Medellin (Colombia); Porto Alegre
(Brazil); Rio de Janeiro (Brazil); Rosario
(Argentina); San Isidro (Peru); Santiago (Chile);
Toluca, Mexico); Trujillo, Peru); Valparaiso (Chile)
E-Taoyuan and U-Taoyuan in Taiwan are
some examples of smart implementation
model undertaken (ibid). In China alone, six
provinces and 51 cities have included smart
cities in their government planning agenda
(Liu & Peng 2014, pp.72). And according to
the statistics of the Chinese Smart Cities
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Forum, 36 new cities are under new
development (ibid). Although this has to do
with China’s rapid urbanisation, the high
level of inclination towards the new urban
model is intriguing (Appleyard et al. 2007).
Similar figure goes out to India with its
mission in 2015 to implement 100 Smart
cities within the span of five years (Ministry
of Urban Development 2015).
Unlike smart cities of the global North
which are retrofitted to existing cities, brandnew smart cities are also built from ground
zero as Greenfield project, such as Songdo
(South Korea), Masdar City (Abu Dhabi) or
Lusail (Qatar) etc. (Lee et al. 2013; Tok et
al. 2014).
Songdo in Korea, known for its largest
Greenfield smart city initiative, was planned
to house 75,000 inhabitants with an original
estimated cost of $35 billion (Albino et al.
2015, pp.14). Songdo’s model deploys a
unique economic growth mechanism known
as Special Economic Zone (SEZ). The
development zone strategized on lower taxes
and less regulation, ‘inspired by those
created in Shenzhen and Shanghai in the
1980s by premier Deng Xiaoping which
kick-started China’s economic rise’ (John
Kasarda and Greg Lindsay, Aerotropolis,
2011; cited in Townsend 2013). The plan
includes installing telephonic chip in every
built unit so users can transmit information
from various devices, where the information
produced will be analysed in a central
command centre (Shwayri 2013; Halpern et
al. 2013).
Similarly, Masdar City in the gulf region
initiated as an eco-city in 2006 by the
Masdar Corporation for a population of
90,000 (40,000 residents and 50,000 daily
commuters), has directed its development
towards smart cities (Tok et al. 2014,
pp.136). Built with a budget of US $22
billion, the development is also based on a
free economic zone model. According to
their planning and marketing strategy, the
development relies of three aspects;
Economic – related to real estate
development,
intellectual
property
ownership,
and
human
capital;
Environmental – through usage of renewable
energy, green buildings and intelligent
transportation; and Social – as benefits
generated from living in the city (ibid).
Likewise, many of the upcoming projects on
smart cities in Asia also follow a similar
trend of Greenfield project which are
exclusively focused on SEZ, and does not
cater to their existing cities.
As March & Ribera-Fumaz (2014) have
argued, it is relevant to re-enquire, How the
smart cities concept and parameters of the
global North fits into the urban requirements
for the cities in the global South, and how
could one weigh the concept from their
different practices? For instance, the above
list implies a rhetoric impression that Asia
constitutes a high share of smart practicing
cities, while many of these cities still lack in
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delivering basic services to a large sum of its
citizens.
Hence, more debate on smart practices has
sprung out of the Greenfields smart cities.
Adam Greenfield argues in Against the
Smart City (2013) that ‘corporate-designed
cities such as Songdo (Korea), Masdar City
(UAE), or PlanIT Valley (Portugal) eschew
actual knowledge about how cities function
and represent “empty” spaces that disregard
the value of complexity, unplanned scenarios
and the mixed uses of urban spaces’ (as cited
in Albino et al. 2015, pp.6). Similarly,
Hollands (2008; 2015) urdermines such
innitiations as nothing less than a liberal
market orriented “urban enterpreneurialism”,
where it is easier for giant IT companies to
augment “off-the-shelf” products. While
other fears that such model constitutes of
neoliberal components like privatisation,
growth-oriented policy, open markets,
deregulation, maximisation of profits and
efficency (Ponting 2013, pp.42).
The major issues with many of the smart
cities is whether or not and in which case,
smart cities meets the requirement of cities.
Such cities not only require large investment
in technical aspect but also convolution in
goverance. Many of the rhetoric claims are
yet to deliver the essential urban quality of
life. Ethical questions on privacy and
security of data acquired from citizens are
part of the threat smart cities has to
acknowledge. Many of these factors bring
skitisim and undermines the concept of
smart cities and their practices.
Conclusion
From the above discussion on smart cities,
both in theory and in practice, we could
make the following inferences.
In theory, the model of Smart cities is a
derivative of other pre-existed urban concept
such as intelligent city, creative city etc.
Technological implication has been the core
factor to earlier initiatives on Smart cities,
but as argued by other scholars, it is no
longer the only pre consideration. Today, the
concept takes into account several urban
aspects, and it has a wider spectrum.
The concept implies increasing efficiency in
infrastructures, business attractiveness and
economic
growth,
increasing
social
inclusiveness, transparency in governance,
and enhancing urban quality of life in
overall. However, there are equal challenges
and threats to tackle along such as
technological glitches, privacy and security,
level of civic engagement, and balance on
institutional responsibilities.
Given the varied definitions and multidimensional facets of the term, the “onesize-fits-all” model of Smart cities is not
applicable. Although, the global urban
challenges are inexplicably different, the
limitation to form a universally viable
framework gives rooms for misuse of the
term rhetorically. This means that, different
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actor project the “pros” of smart cities from
their limited perspective and failed to
acknowledge the “cons” that underlies in
other spectrum.
In practice, the model of smart cities is
channelled through best practices and
crediting system. While, the components and
frameworks on smart cities are developed
merely as a mediator in assisting the
practices and not every city necessarily
applies all the components to address
smartness. This implies that smart city
practice in many sense are not conducive.
Institutional and social exclusiveness is seen
as an impediment in many practices, apart
from false claiming already argued by
(Hollands 2008; Hollands 2015; Greenfield
2013). Since, the Smart cities practices
associates with a high neoliberal trend
(Ponting 2013, pp.42), there are market
uncertainties – meaning that urban
attractiveness and economic growth are not
always secured. At the same time,
institutionalising the practice follows a
hierarchical top-down approach, lacking a
prolific social participation. Other concern is
on selective accommodations through an
area based development – meaning that the
outcomes inclines towards being gentrified
posing social insecurity, which has been one
of the distresses in urban discourse of other
similar model.
In order to become a viable urban
development or redevelopment model, smart
cities would have to secure such ambiguities
both in theory and in practice. Different
stakeholders of the city need to collate and
draw varied perspective into a unified
framework that takes into account of various
urban needs and challenges of both well
established and newly developing cities. The
future ranking and assessment should
reconsider based on the unified framework
to test the practices of smart cities.
Moreover, the motif for business growth
should be rebalanced with tackling issues on
social and environmental challenges. Last
but not the least, smart cities should reach
out to be “cities for all”, a quest carried out
in many other progressive urban model.
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