THE MODEL OF SMART CITIES IN THEORY AND IN PRACTICE

RAJIV SINGH IRUNGBAM

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 “one-size-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 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 initiative[2] 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 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 technology[3] 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 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 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 ‘self-congratulatory’ label to retain competitiveness in the global place-marketing. 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 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 tech-giant 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.
TimelineDefinitionDimensionTSIEger
(2009)
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.●● Washburn et al. (2010)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.●  Chen
(2010)
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.  Harrison et al. (2010)A city connecting the physical infrastructure, the IT infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city●● Caragliu et al. (2011)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.●●●
Komninos (2011)

(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.●●●
Thite (2011)

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. ●●
Nam and Pardo (2011)

A smart city infuses information into its physical infrastructure to improve 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.●●●Gartner
(2011)
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 resource- efficient 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
Source/
TimelineDefinitionDimensionTSIThuzar
(2011)
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.●●●Cretu
(2012)
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.● ●Guan
(2012)
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. ●●
Bakıcı et al. (2012)

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.●  Barrionuevo et al.
(2012)
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.●●●Kourtit and Nijkamp (2012)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 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)●●●Kourtit et al. (2012)Smart cities have high productivity as they have a relatively high share of highly educated people, knowledge-intensive jobs, output-oriented planning systems, creative activities and sustainability-oriented initiatives. ●●IDA (2012)Smart city [refers to] a local entity - a district, city, region or small country -which takes a holistic approach to employ[ing] information technologies with real-time analysis that encourages sustainable economic development.● ●Lazaroiu and Roscia (2012)A community of average technology size, interconnected, sustainable, comfortable, attractive and secure.●  Lombardi et al. (2012)The application of information and communications technology (ICT) with their effects on human capital/education, social and relational capital, and often indicated is environmental issues by the notion of smart city.●●

T - Technological dimension          S - Social dimension          I – Institutional dimension
Source/
Timeline
DefinitionDimension
TSI
Zygiaris
(2013)
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” declaring the capacity to produce added value information from the 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.
Townsend
(2013)
Smart cities are defined as places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems.
Marsal-Llacuna et al. (2014)Smart cities initiatives try to improve urban performance by using data, information and information technologies (IT) to provide more efficient services to citizens, to monitor and optimize existing infrastructure, to increase collaboration among different economic actors, and to encourage 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 technological input into a more conducive one. The later definitions aligning more on an anthropocentric approach suggest that cities aiming for economic growth and gaining higher quality of life must put investment in human and social development, besides techno-oriented-infrastructures. 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.2-7).

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 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 knowledge-based 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 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.
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.282-320). 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 ICT-enabled 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 communities were initiated using ICT infrastructure to allow such exchange (Nam & Pardo 2011). Through the ICT-mediated governance, otherwise known as e-governance, 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. They developed six key components or characteristics to measure the smartness of 70 European medium-sized cities since 2007[4] (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).
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 in-place. 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 Wheel[5], Forum Pa’s Icityrate[6] and Between’s Smart City Index[7] (Santis et al. 2014) are predominantly based on this model. The only differentiation is that each component is characterised by different 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.
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)
FactorsIndicators
1.       Innovative spirit§  R&D expenditure in % of GDP
§  Employment rate in knowledge-intensive sectors Patent applications per inhabitant
2.       Entrepreneurship§  Self-employment rate
§  New businesses registered
3.       Economic image & trademarks§  Importance as decision-making centre (HQ etc.)
4.       Productivity§  GDP per employed person
5.       Flexibility of labour market§  Unemployment rate
§  Proportion in part-time employment
6.       International embeddedness§  Companies with HQ in the city quoted on national stock market
§  Air transport of passengers
§  Air transport of freight
7.       Ability to transform§  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 open-minded 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)
FactorsIndicators
1.       Level of qualification·         Importance as knowledge centre (top research centres, top universities etc.)
·         Population qualified at levels 5-6 ISCED
·         Foreign language skills
2.       Affinity to lifelong learning·         Book loans per resident
·         Participation in life-long learning in %
·         Participation in language courses
3.       Social and ethnic plurality·         Share of foreigners
·         Share of nationals born abroad
4.       Flexibility·         Perception of getting a new job
5.       Creativity·         Share of people working in creative industries
6.       Cosmopolitanism/Open- mindedness·         Voters turnout at European elections
·         Immigration-friendly environment (attitude towards immigration)
·         Knowledge about the EU
7.       Participation in public life·         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 government service delivery (PwC 2014, pp.17). From this point, the third component is about civic participation in decision-making, 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)
FactorsIndicators
1.       Participation in decision-making·         City representatives per resident
·         Political activity of inhabitants
·         Importance of politics for inhabitants
·         Share of female city representatives
2.       Public and social services·         Expenditure of the municipal per resident in PPS
·         Share of children in day care
·         Satisfaction with quality of schools
3.       Transparent governance·         Satisfaction with transparency of bureaucracy
·         Satisfaction with fight against corruption
4.       Political strategies & perspectives·         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)
FactorsIndicators
1.       Local accessibility·         Public transport network per inhabitant
·         Satisfaction with access to public transport
·         Satisfaction with quality of public transport
2.       (Inter-) national accessibility·         International accessibility
3.       Availability of ICT-infrastructure·         Computers in households
·         Broadband internet access in households
4.       Sustainable, innovative and safe transport systems·         Green mobility share (non-motorized individual traffic)
·         Traffic safety
·         Use of economical cars

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)
FactorsIndicators
§  Attractiveness of natural conditions·         Sunshine hours
·         Green space share
§  Pollution·         Summer smog (Ozone)
·         Particulate matter
·         Fatal chronic lower respiratory diseases per inhabitant
§  Environmental protection·         Individual efforts on protecting nature
·         Opinion on nature protection
§  Sustainable resource management·         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)
FactorsIndicators
1.       Cultural facilities·         Cinema attendance per inhabitant
·         Museums visits per inhabitant
·         Theatre attendance per inhabitant
2.       Health conditions·         Life expectancy
·         Hospital beds per inhabitant
·         Doctors per inhabitant
·         Satisfaction with quality of health system
3.       Individual safety·         Crime rate
·         Death rate by assault
·         Satisfaction with personal safety
4.       Housing quality·         Share of housing fulfilling minimal standards Average living area per inhabitant
·         Satisfaction with personal housing situation
5.       Education facilities·         Students per inhabitant
·         Satisfaction with access to educational system Satisfaction with quality of educational system
6.       Touristic attractiveness·         Importance as tourist location
(overnights, sights)
·         Overnights per year per resident7.       Social cohesion
·         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 multi-governmental 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 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 Cities[8], 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 planning, decision making and implementation.
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 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). 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 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).
Table 8: List of Smart Practicing Cities.
Source: Author addition to (Nam & Pardo 2011)
RegionsICF  (Smart21 Communities)[10]
(2007 - 2015)
IBM (Smarter Cities Challenge)[11]
(2010 - 2015)
AsiaAstana (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)
AfricaCape Town (South Africa); Nairobi County (Kenya); Nelson Mandela Bay (South Africa)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)
EuropeBarcelona (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)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)
North AmericaUS: 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)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)
Middle/
South
Barceloneta (Puerto Rico); Curitiba, Paraná (Brazil); Piral (Brazil); Porto Alegre (Brazil); Durango and Tuxtla Gutiérrez (Mexico); Rio de Janeiro (Brazil)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)
OceaniaAustralia: 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: WhanganuiBallarat (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, 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 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, brand-new 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 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 multi-dimensional facets of the term, the “one-size-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 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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[1] “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).
[2] 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://www-03.ibm.com/press/us/en/pressrelease/33303.wss [Accessed June 2, 2015].
[3] Oxford dictionary defines ‘technology’ as “the application of scientific knowledge for practical purposes, especially in industry, for example in designing new machines”
[4]  Since 2007, various versions are used with varying results. Available at: www.smart-cities.eu [Accessed July 15, 2015].
[7]  Available at: http://www.between.it/ita/pagina-non-trovata.php [Accessed July 17, 2015]
[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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