A Unified Smart City Model (USCM) for smart city
Conceptualization and Benchmarking
Business School, TEI of Thessaly
41110 Larissa, Greece
Faculty of Technology, Policy & Management,
Delft University of Technology,
Professor, Brunel Business School, Brunel University London Uxbridge, United Kingdom
Smart cities have attracted an extensive and emerging interest from both science and industry with an increasing number of
international examples emerging from all over the world. However, despite the significant role that smart cities can play to
deal with recent urban challenges, the concept has been being criticized for not being able to realize its potential and for being
a vendor hype. This paper reviews different conceptualization, benchmarks and evaluations of the smart city concept. Eight
different classes of smart city conceptualization models have been discovered, which structure the unified conceptualization
model and concern smart city facilities (i.e., energy, water, IoT etc.), services (i.e., health, education etc.), governance,
planning and management, architecture, data and people. Benchmarking though is still ambiguous and different perspectives
are followed by the researchers that measure -and recently monitor- various factors, which somehow exceed typical
technological or urban characteristics. This can be attributed to the broadness of the smart city concept. This paper sheds light
to parameters that can be measured and controlled in an attempt to improve smart city potential and leaves space for
corresponding future research. More specifically, smart city progress, local capacity, vulnerabilities for resilience and policy
impact are only some of the variants that scholars pay attention to measure and control.
Smart city; e-government; measurement; benchmarking; modeling; frameworks; architectures.
Smart cities have been research for over a decade and there are many ways of looking at Smart Cities. Recently Smart Cities
are viewed as ecosystems which are generally defined as communities of interacting organisms and their environments, and
are typically described as complex networks formed because of resource interdependencies (Gretzel et al., 2015). Similarly,
an ecosystem can be seen as “an interdependent social system of actors, organizations, material infrastructures, and symbolic
resources” (Maheshwari and Janssen, 2014). Ecosystems, like other kinds of systems, are comprised of elements,
interconnections and a function/purpose, but are special types of systems in that their elements are intelligent, autonomous,
adaptive agents that often form communities and also because of the way they adapt to elements being added or removed.
According to this definition, four critical elements exist in ecosystems: (1) interaction/engagement; (2) balance; (3) loosely
coupled actors with shared goals; and, (4) self-organization (Gretzel et al., 2015). This term has been adopted by businesses,
where an “ecosystem” describes the relationships between economic entities (i.e., producers, distributors, intermediaries,
consumers etc.). Moreover, information and communication technologies (ICT) industry uses the term of digital ecosystems,
which are focused on interactions among technological agents (devices, databases, programs, etc.) and respective information
flows and form the infrastructure for digital business ecosystems.
Smart cities have been realized as intelligent digital ecosystems installed in the urban space (Neirotti et al., 2014; Piro et al.,
2014; Desouza and Flanery, 2013; Wey and Hsu, 2014; Lee et al., 2014; Giffinger et al., 2007; Churabi et al., 2012).
However, smart cities have not been limited to ICT and they shifted to ‘smart people’ and their corresponding creativity.
From this point of view, they are focused on enhancing urban life regarding six dimensions: people, government, economy,
mobility, environment and living (Giffinger et al., 2007). Angelidou (2014) approached smart city using a civil engineering
and urban architecture lens and classified smart cities as new versus existing cities, and corresponding smart city projects to
“soft” versus “hard” implementations. More than 150 smart city cases can be observed around the world, which can be
classified in (a) from-scratch city cases; (b) hard ICT infrastructure focused cases; and (c) soft ICT infrastructures in the urban
space (Anthopoulos et al., 2016). Since there is no clear smart city approach yet, there have been several attempts by
international organizations to standardize smart city solutions, such as for smart water, energy, transportation, buildings etc.
Recently, scholars have started criticizing the use of smart city concept and potential (see for example Söderström et al., 2014,
Nam and Pardo, 2011; Brown, 2014). Some scholars argue that smart city is mostly the outcome of vendors’ marketing
campaigns (Söderström et al., 2014), others say that smart cities reflect little more than usual urban innovations (Nam and
Pardo, 2011), while Brown (2014) criticizes the whole concept of smart city by questioning their effectiveness. Moreover,
many scholars argue about technological adjectives to the “city”. For instance, Allwinkle and Cruickshank (2011) argue about
the “self-congratulating” efforts that city leaders follow when they claim to be “smart” and in this regard they differentiate
“smart city” (the city that holds the computational power to perform tasks) from “intelligent city” (the city that utilizes the
results from the application of innovation within the urban space). Churabi et al. (2012) compare the alternative technological
adjectives to the smart city, while Anthopoulos and Fitsilis (2013) define a roadmapping tool for smart city technological
To shed light on the smart cities concepts, various models for understanding and conceptualizing smart cities have been
developed, which aim to define their scope, objectives and architectures. Also benchmarking methods for comparing smart
city initiatives with each other have been developed. The aim of this paper is to analyze the existing smart city
conceptualization modeling and benchmarking methods. Such a presentation is of extreme interest to the smart city domain,
due to the continuous public spending in this domain, for which no agreed framework has been defined to evaluate the
achievements regarding the initially grounded expectations.
The remainder of this paper is structured as follows: section 2 provides an overview of the research approach, followed by an
analysis of existing smart city modeling and benchmarking approaches and concluding with a brief discussion on the most
appropriate to apply for the purposes of this paper. The following section discusses findings, while section 5 contains some
conclusions and future thoughts.
2. RESEARCH APPROACH
To attain the objective literature was reviews using the following sources: international standards organizations for smart city
documents; and SCOPUS, with searches only in journals that publish smart city articles (Anthopoulos, 2015), with the
combination of terms “smart city”, “model” and “assessment or evaluation or benchmarking”. Article search was performed
within the period of 1997 (appearance of smart city concepts in literature) to early 2016. More than 4,800 articles were
returned from this crawl, where screening was used to leave out irrelevant publications like editorial, measurements on
individual smart solutions (i.e., smart water; smart transportation etc.) as well as articles discussing issues mostly focused on
city growth (like “urban growth assessment”) or modeling and benchmarking in general. Screening examined citations that
leave space for further exploration resulted in 48 publications as shown in Table 1.
“smart city” &
Albino and Dangelico (2015); Anthopoulos (2015);
Anthopoulos and Fitsilis (2013); Bakici et al. (2013); Baron
(2012); Batty et al. (2012); Bellini et al. (2014); Calvillo et
al. (2016); Caragliu et al. (2011); da Cruz and Marques
(2014); De Marco et al. (2015); Desouza and Flanery
(2013); Duarte et al. (2014); Edvinsson (2016); Fan et al.
(2016); Fei. (2012); Giffinger et al. (2007); Glebova et al.
(2014); Hancke et al. (2013); Kii et al. (2014); Kourtit et al.
(2014); Hollands (2008); ISO (2014); ITU (2014); Lazaroiu
and Roscia (2012); Liu et al. (2014); Lee et al. (2013); Lee
et al. (2014); Leydesdorff and Deakin (2011); Lombardi et
al. (2012); Malek (2010); Marsal-Llacuna et al. (2015); Mori
and Christodoulou (2012); Naphade et al. (2011); Neirotti et
al. (2014); Pires et al. (2014); Shapiro (2006); Shwayri
(2013); Singhal et al. (2013); Söderström et al. (2014);
Strategic Energy Technologies Information System (SETIS)
(2012); Thite (2011); Tsolakis and Anthopoulos (2015);
United Nations (2014); UN Habitat (2014); Winters (2011);
Yovanof and Hazapis (2009); Zygiaris (2012)
3.1 Smart city conceptualization models
Many scholars and several organizations try to conceptualize smart city and understand its synthesis with alternative models
(Table 1). This conceptualization has been performed from different perspectives and in this respect, some aggregation is
necessary. The first class of models that comes up from this aggregation addresses smart city architecture and corresponding
component definition. In this respect, Anthopoulos (2015) compared eight (8) models and concluded to a seven-axe modeling
tool, which confirms the previously mentioned 6 dimensions of smart city (Giffinger et al., 2007) and extends them with
coherency in terms of social equity and engagement, while Neirotti et al. (2014) extend it with the incorporation of smart
building. Glebova et al. (2014) conceptualize smart city with 5 key elements (intellectual transport system, public security,
energy consumption management and control, environmental protection and ICT) and they define indexes to measure ICT
component in Russian cities. Hollands (2008) discusses about smart city structure in terms of instrumentation (based on data
collection), interconnection (enable data flow) and smart (utilize data to improve urban living). Hancke et al. (2013) define
the sensing areas in smart city and develop a corresponding architecture. IBM (Söderström et al., 2014) uses a nine pillar
system and an equation that combines instrumentation, interconnection and intelligence. Naphade et al. (2011) suggested an
alternative smart city model that consists of alternative 7 key elements: government services, transportation, energy and water,
healthcare, education, public safety and other core ICT systems. Yovanof and Hazapis (2009) define an architectural
framework for smart service delivery, which consists of infrastructure, service and policy. Finally, Zygiaris (2012) introduced
a smart city reference model, which consists of six layers (innovation, applications, integration, instrumentation,
interconnection, environment and city), each consisting of several components and entities for smart city formulation.
The second class of models analyzes smart city with a focus to its governance. From this point of view, Albino and Dangelico
(2015) compare alternative smart city definitions, theories and approaches and summarize them to four dimensions
(networked infrastructute; urban growth; social inclusion and environment). On the other hand, Baron (2012) uses a 3-level
model to conceptualize intelligence for urban resilience. The International Standards Organization (ISO, 2014b) uses a table
to define city characteristics, where smartness is pursued. The International Telecommunications Union (ITU, 2014a) in its
attempt to define smart city conceptualized it with 4 core themes and 4 attributes. Lee et al. (2014) presented a framework,
which focuses on the integration technological and institutional perspectives in attempting to understand the process of
building a smart city and consists of 6 ”stylized facts”: urban openness; service innovation; partnership formation; urban
proactiveness; infrastructure integration; and governance. Additionally, Leydesdorff and Deakin (2011) argue that community
concerns a key component of the city innovation system. Their approach utilizes the triple-helix model, which studies
networks of university, industry and government and generates knowledge and innovation under a disciplined manner. Among
their findings, the application of the triple-helix model in two cities (Montreal and Edinburgh) shows that cultural
development within a city is not a spontaneous product of market economics, but a product of the policies which need to be
carefully constructed by a governing authority. In general, cities can be considered as densities in networks among three
relevant dynamics: the intellectual capital of universities, the industry of wealth creation, and their participation in the
democratic government which forms the rule of law in civil society. Similarly, Lombardi et al. (2012) focus on the triple helix
model again, which they extend with a civil society indicator group, in order to measure smart city components performance.
Lombardi et al. (2012) adopted Giffinger et al. (2007) model, but they exclude smart mobility from their model. Furthermore,
Liu et al. (2014) defined a value chain assessment model, which was inspired by Porter (1985) business value chain analysis
(primary activities: inbound logistics, operations, outbound logistics, marketing and sales, and service; supportive activities:
firm infrastructure, human resources, technology development and procurement). This value chain structure was aligned to
Giffinger et al. (2007) and Naphade et al. (2011) smart city models. According to Liu et al. (2014), 33 elements concern smart
city primary activities, while another 27 address the supportive activities. Finally, United Nations Habitat (United Nations,
2014) defined five dimensions for city prosperity, which have been adopted by standardization bodies in their benchmarking
systems. International Standards Organization (2014) proposed a standard for city services and quality of life, as a means to
measure smart city sustainable development.
The third model class defines tools for smart city management. In this respect, Lee et al. (2013) utilized Technology
Roadmapping in an attempt to predict technology development in smart city. More specifically, they applied Quality Function
Development (QFD) and defined interconnections between services and devices, and between devices and technologies in
smart city. Technology roadmapping was capitalized by Anthopoulos and Fitsilis (2013) too, as a means to define patterns for
smart city technological evolution and they showed that cities evolved from one technological form to another, while eco-city
is the most preferred among the others.
The next class emphasizes data, where Batty et al. (2012) adopted IBM (Söderström et al., 2014) and Giffinger et al. (2007)
approach and defined a framework for smart city programme definition, which consists of three components: data analysis,
infrastructure and management. Bellini et al. (2014) defined a knowledge model (ontology) for smart city big and open data
harvesting and analysis, named KM4City, which consists of major smart city key elements – data sources: (public)
administration; street-guide (with regard to street facilities); point-of interest (services and activities of interest); local public
transport; sensors (ambient, weather, traffic flow, pollution etc.); temporal (time intervals that associate a timeline to the
events); and metadata (associated with the datasets and their status conditions). Similarly, Edvinsson (2016) seeks for
knowledge production sources and network and considers the city as a knowledge tool.
The next two classes try to design smart city with an emphasis on facilies -i.e., energy (Calvillo et al., 2016), water, buildings
etc.- and services -i.e., health (Fan et al., 2016), education, tourism, safety etc.- respectively, while the seventh class
prioritizes people in smart city in terms of employment growth source definition (Shapiro, 2006) and human capital
attractiveness (Thite, 2011). The last eighth class addresses environment and more specifically new modes of ecological urban
living and corresponding socio-political relations (Shwayri, 2013). In this same regard, Tsolakis and Anthopoulos (2015)
capitalized system dynamics in an attempt to determine the structure of an eco-city and define a model that can support
decision makers in optimal planning and future predictions. Their model contains 5 interconnected components/subsystems:
(i) population, (ii) housing, (iii) business, (iv) energy and (v) environmental pollution. Simulations with the use of this model
with date from the city of Tianjin generated arguments with regard to eco-city’s efficiency to succeed in urban sustainability.
The overview of the models show approaches emerge progressively since 2014, when standardization is being performed to
deal with the heterogeneity of the smart city concept. Some of the models have hardly any overlapping factors, whereas most
models capture a large number of aspects. The broadness of these aspects results to the unclarity of the concept. Yet there are
6 dimensions that are part of most models; people, government, economy, mobility, environment and living and can be seen
as the most generic conceptual approach to smart city, which generate corresponding architectures. Nevertheless,
conceptualization becomes more complicated when scholars try to focus on particular urban issues like energy and health and
questions whether such focused models respect the more generic ones.
Table 1. Smart city conceptualization models
Smart city dimensions
Resource, Transportation, Urban infrastructure, Living,
Government, Economy, Coherency
Giffinger et al. (2007)
Smart city components
Smart Economy, Smart Governance, Smart People, Smart
Mobility, Smart Living, Smart Environment
Glebova et al. (2014)
Smart city conceptual
Intellectual transport system, public security, energy
consumption management and control, environmental
protection and ICT
Hancke et al. (2013)
Sensor areas in smart city
Smart Infrastructure, Smart Surveillance, Smart Electricity
and Water distribution, Smart Buildings, Smart Healthcare,
Smart Services and Smart Transportation
Smart City Model
Instrumented (based on data collection)
Interconnected (enable data flow)
Smart (utilize data to improve urban living)
IBM (Söderström et al.,
Nine Pillar Models
Smarter City Equation
Planning and Management Services
Instrumentation (the transformation of urban phenomena
into data) + Interconnection (of data) + Intelligence
(brought by software)
Naphade et al. (2011)
Smart city model
Government services, transportation, energy and water,
healthcare, education, public safety and other core ICT
Neirotti et al. (2014)
Smart City domains
Natural resources and energy, Transport and mobility,
Living, Government, Economy and people
Yovanof and Hazapis
Digital City Architectural
Framework for Smart
Infrastructure (communications); Mobilized Services
(capability to mobilize data, applications and users); Policy
(legal framework to foster innovation)
Smart City reference model
Multi-tier smart city model with several components and
Albino and Dangelico
Smart City Dimensions
- city’s networked infrastructure that enables political
efficiency and social and cultural development
- emphasis on business-led urban development and
creative activities for the promotion of urban growth
- social inclusion of various urban residents and social
capital in urban development
- the natural environment as a strategic component for the
Three level-model for city
intelligence for resilience
First level of city smartness: led by example
Second level of city smartness: govern the private urban
Third level of city smartness: integrated approach
A table of city characteristics
where smartness is applied
City History and Characteristics
City Subsystems (actors, activities, facilities and buildings,
hard infrastructure, soft infrastructure, technical systems, city
Attributes and Core themes
Attributes: sustainability; quality of life; urban aspects;
intelligence or smartness
Core themes: society; economy; environment; governance
Lee et al. (2014)
Framework for smart city
Urban Openness, Service Innovation, Partnerships
Formation, Urban Proactiveness, Smart city infrastructure
integration, Smart city governance
Leydesdorff and Deakin
Triple-Helix Model of Smart
Networks of universities, industry and government
Liu et al. (2014)
Smart city value chain
Primary Activities: smart inbound logistics; smart
operations; smart outbound logistics; smart marketing; smart
Supportive Activities: smart government; smart
infrastructure; smart procurement; smart technology
Lombardi et al. (2012)
Triple helix model for smart
city analysis and
A table with rows: University, Government, Civil Society,
and columns: smart governance, smart economy, smart
people, living, environment
United Nations Habitat
(United Nations, 2014)
Dimensions of City
Productivity and the Prosperity of Cities,
Urban Infrastructure: Bedrock of Prosperity,
Quality of Life and Urban Prosperity,
Equity and the Prosperity of Cities, Environmental
Sustainability and the Prosperity of Cities
Planning and Management
Anthopoulos and Fitsilis
for Smart City development
Patterns for smart city technological evolution
Lee et al. (2013)
for Smart City development
Interconnections between services and devices, and between
devices and technologies
Data and knowledge
Batty et al. (2012)
Structure of FuturICTs smart
Data Analysis and Modelling: Mobility and Transport
Behavior; Urban Land Use Transport; Urban Market
Transactions; Urban Supply Chains
Infrastructure: Sensing & Networks, New Social Media;
Management: Decision Support and Participation; City
Bellini et al. (2014)
Knowledge Model for Smart
City data (KM4City
Administration; street-guide; point-of interest; local public
transport; sensors; temporal; and metadata
City as a knowledge tool
Knowledge key driver definition and interrelation discovery
(ICT and multimedia; University; Society and
Entrepreneurship; Knowledge Cafes/Cathedrals; Diversity;
Calvillo et al. (2016)
Smart City Energy
Interventions and Energy
System Design Model
Energy interventions areas: Generation, Storage,
Infrastructure, Facilities and Transport
Energy System Design Model:
(i) System Input (resources, costs, geolocation, energy
prices, regulation, demand)
(ii) System Output (capacity, total production, costs,
environmental benefits, viability)
Fan et al. (2016)
Smart health organization
Multi-tier architecture for smart health service production in
Neoclassical city growth
Employment growth sources: productivity, quality of life
Urban factors for human
Magnets (a healthy and well-educated workforce, clean
environment, vibrant business climate, and a solid social and
cultural infrastructure) and glue (city infrastructure, flexible
City as a range of ubiquitous services (including u-health, u-
education, u-transport and u-government)
Tsolakis and Anthopoulos
Eco-city System Dynamics
A system of 5 interconnected components/subsystems: (i)
population, (ii) housing, (iii) business, (iv) energy and (v)
3.2 Smart city benchmarking
Smart city benchmarking should have the purpose to compare them with each other based on various constructs and factors.
However, existing literature regarding smart city benchmarking returns different types of measurement, which evaluate
alternative city factors. Table 2 provides the results of this analysis and benchmarking methods have been aggregated again in
an attempt to clarify what and how it is being measured. More specifically, five (5) classes have been extracted, two of which
measure smart city performance, while the next two assess either city performance or urban sustainability and resilience -
hence the results are used for smart city estimations-. A final class evaluate policy making with estimations on their expected
The first class concerns smart city progress measurement, basically in terms of the six dimensions that have been
conceptualized earlier. In this regard, Albino and Dangelico (2015) compared various smart city benchmarking indexes
(Lombardi et al., 2012); Lazaroiu and Roscia, 2012); Giffinger et al., 2007); Global Power City Index that is based on
various stakeholders’ perceptions and it was created by the Japanese Institute for Urban Strategies; the Smarter Cities
Ranking introduced by the Natural Resources Defense Council that measures environmental-related criteria; Forbes smart city
ranking regarding urban economic performance) and concluded to a 72 measurement model. Caragliu et al. (2011) analyzed
data from the Urban Audit dataset produced by the European Statistical Office (Eurostat), with regard to European smart
cities. From the 250 available indicators in this dataset, which are measured across several domains in cities (demography;
social aspects; economic aspects; civic involvement; training and education; environment; travel and transport; information
society; culture and recreation) they focused on 6 of them (Per Capita Gross Domestic Product (GDP) in Purchasing Power
Standards (PPS); Employment in the Entertainment (Creative) Industry; Multimodal Accessibility; Length of Public Transport
Network; e-Government; and Human Capital). In their paper they performed several statistical analysis methods and they
discovered a positive association between urban wealth and the presence of a vast number of creative professionals; a high
score in a multimodal accessibility indicator; the quality of urban transportation networks; the diffusion of ICTs (most
noticeably in the e-government industry); and, finally, the quality of human capital. Some studies (Duarte et al., 2014;
Glebova et al., 2014) focus on ICT and define corresponding assessment frameworks (connectivity, accessibility and
communicability). With regard to the 6 dimensions of smart city, (Lazaroiu and Roscia, 2012) defined a model with
corresponding indices in an attempt to assess urban intelligence or in other words, how “good” or “bad” a city is in achieving
its smartness (Vanolo, 2014) or its level of progress (Fei, 2012). Moreover, in their attempt to develop their smart city
roadmapping framework, Lee et al. (2013) defined a set of indexes that can measure smart city components: service
performance, corresponding devices for service access and technology. Indexes regarding smart service assessment concerns
service measurement, service anticipation, space type, infrastructure components and formal type and were grouped in sub
categories, while they were calculated with time scales. On the other hand, device assessment concerns their importance,
performance level (maturity, use and productivity) and anticipation. Finally, technology was classified in 5 categories
(sensing, processing, network, interface and security) and is being evaluated with regard to its importance, performance level
(applications availability, future evolution potential, maturity, substitute existence at national level, most advanced nation in
this technology) and anticipation. Finally, Lombardi et al. (2012) identify several quantitative indexes in their model
presented earlier (Table 1) and they follow the analytic network process (ANP) in their attempt not only to measure specific
city elements, but also to identify and measure the relationships between model’s components.
The second class, addresses real-time smart city monitoring. In this respect, Malek (2010) studied the suitability of the
Informative Global Community Development Index (IGC), for monitoring the Smart Cities initiative. IGC refers to a creative
and innovative community which can develop its own technology. In his work he assumed that the process of developing an
intelligent city has to maximize community’s interest in terms of ICT, but his findings from Subang Jaya smart city in
Malaysia did not justify this claim. Similarly, Marsal-Llacuna et al. (2015) performed a study on urban monitoring
contribution to smart city measurement. Their work compared indicators that address city’s sustainability and livability or
sustainable and livable cities respectively. Corresponding groups of indicators are opposite with the first group measuring
urban environment and local economy with long term data and data from big cities, while the second group measuring quality
of life with real time conditions with data even from mid-sized cities. Moreover, their work accounted standardization efforts
(i.e., ISO Global City Indicators for City Services and Quality of Life) and suggest a Smart City monitoring synthetic indices
Smart City real-time monitoring index.
The next class, measures city capacity in various terms, ranging from size and global city performance (Kourtit et al., 2014),
to its potential or good urban governance (UN Habitat, 2014) and urban competitiveness (Singhal et al., 2013). Kourtit et al.
(2012) wanted to measure the innovation potential of smart cities and in this respect, they performed a principal component
analysis (PCA) in European cities. Their study identified the most relevant variables with the highest loading factors, in
regard to advanced business and socio-cultural attractiveness (ADBA), presence of a broad (public and private) labour force
and public facilities (PBLFPF) and presence and use of sophisticated e-services (PUSS) of smart cities. De Marco et al.
(2015) propose several safety measurement indicators, which provide decision makers with a significant tool to develop
corresponding policies. Their study developed a three-level index named global safety indicator, which is analyzed in road
safety and personal safety (second level). Road safety uses parameters that measure corresponding mobility threats (surface
quality, traffic, construction sites, accidents and parking spaces) and personal threats (noise, distress and rallies and events).
Similarly, Winters (2011) defined a benchmarking model that measures city population growth. More specifically he defines
variables and formulas to calculate inhabitants’ input and output flows and to measure agglomeration changes within the
urban ecosystem. His study showed that in-migration occurs for education purposes and it is mainly based on people from the
same state, while many of the immigrants select to remain within the smart city, which results to corresponding population
The fourth class emphasizes on sustainability -both economic and environmental- (Pires et al., 2014; Mori and Christodoulou,
2012; ITU, 2015), local government effectiveness (da Cruz and Marques, 2014) and resilience (Desouza and Flanery, 2013).
Such a measurement is not a simple process and involves alternative values, while the adoption of a synthesized index, a
composite index or a single indicator should be avoided. It is appropriate to compare environmental, economic and social
aspects respectively among cities at least, because the aspects are complex complement or trade-off relationships and because
a composite index often implies weak measurement (Mori and Christodoulou, 2012). Moreover, the European Initiative on
Smart Cities or more specifically the Strategic Energy Technologies Information System (SETIS) focused on smart energy
and defined a set of key performance indicators that are able to measure carbon emission reduction in Europe: For energy
networks, these include: meeting 50% of heat and cooling demand from renewable energy sources (RES); launching at least
20 exemplars by 2015 for “smart grids” coupled with “smart building” equipment, and measuring energy consumption with
The final class, addresses policy making in cities, which can be also evaluated with regard to its potential impact (Kii et al.,
2014) even with a focus on particular decisions (i.e., energy consuming impact (Gouveia et al., 2016)). Beyond the above
scientific studies, several market analyses can be located that evaluate city performance from alternative perspectives. For
instance, with regard to city attractiveness for investments, top four factors concern easy access to markets, customers and
clients (instead to the availability of quality staff); quality of telecommunications; transport links with other cities and
internationally; and current local economic climate (Cushman and Wakefield, 2009).
Given the broadness of this field it is not surprisingly that there are many benchmarking approaches developed. In a similar
vein to the modelling overview, the benchmarking comparisons also show the diversity of dimensions that are taken into
account. The benchmarks look sometimes at completely different aspects, which hampers comparison. This makes it hard or
even impossible to compare the benchmarking outcomes with each other. In one benchmark, a city might be doing well,
whereas the same city might be performing lower in another benchmark. In general, it appears that scholars do not follow
exiting modeling when they introduce their benchmarking methods.
Table 2. Smart city benchmarking tools
Smart city progress
Albino and Dangelico
72 Smart City Indexes’ set
60 indexes from Lobardi et al. (2012) and 12 indexes from
Lazaroiu and Roscia (2012)
Caragliu et al. (2011)
6 Smart City Indicators (data
analysis from Urban Audit
Per Capita Gross Domestic Product (GDP) in Purchasing
Power Standards (PPS)
Employment in the Entertainment Industry
Length of Public Transport Network
Level of smart city progress
3 level Analytical Hierarchy Process:
Level 1: Target level (the smarter development level);
Intermediate Level 2: development level of
informatization; innovation capability; comprehensive
Indicator Level 3: informatization development; ICT;
proportion of employment in ICT; R&D expenditure;
proportion of employment in R&D; new product
development; industrial solid waste; products from waste
Duarte et al. (2014)
Digital City Assessment
Glebova et al. (2014)
Indexes for ICT element
New urban technologies; ICT in education; ICT in public
health care; e-government
Lazaroiu and Roscia (2012)
Model for computing “the
smart city” indices
Economy, Mobility, Environment, People, Living,
Lee et al. (2013)
Smart Service Assessment
Smart Device Assessment
Service Assessment: service measurement, service
anticipation, space type, infrastructure components and
Device Assessment: importance, level and anticipation
Technology Assessment: importance, level and
Liu et al. (2014)
Smart city value chain
(SCVC) assessment model
Indexes for Primary and Supportive Activities
Lombardi et al. (2012)
Triple helix model for smart
city analysis and performance
A table with rows: University, Government, Civil Society,
and columns: smart governance, smart economy, smart
people, living, environment
Smart city monitoring
Smart city monitoring index
Informative Global Community Development
Marsal-Llacuna et al. (2015)
Smart city monitoring indexes
Smart City monitoring synthetic indices
Smart City real-time monitoring index
Kourtit et al. (2012)
City Innovation Potential
A set of quantitative indexes that measure local economic
and business environment, labor market, city infrastructure,
governmental economic performance, tourism and cultural
heritage and leisure.
Kourtit et al. (2014)
Global City Performance
Economy, Research and Development, Cultural
Interaction, Livability, Environment, Accessibility
De Marco et al. (2015)
Safety measurement indexes
Global safety indicator (mobility safety and personal safety
Singhal et al. (2013)
Physical Environment, Social Capital, Finance,
Development, Investment, User Potential
UN Habitat (2014)
Good Urban Governance
Effectiveness, Equity, Participation, Accountability,
Formulas for smart city growth
Migration rate measurement (in and out) and
corresponding source definition
Sustainability and resilience
Desouza and Flanery (2013)
Resilience City Evaluation and
Resources and Processes (Physical)
People, Institutions, Activities (Social)
da Cruz and Marques (2014)
Sustainable Local Government
Social, Economic, Environmental and Government criteria
Sustainable development of
Indicators for city
services and quality of life
Economy, Education, Energy, Environment, Finance, Fire
and Emergency Response, Governance, Health,
Recreation, Safety, Shelter, Solid Waste,
Telecommunication and Innovation, Transportation, Urban
Planning, Waste water, water and sanitation
Smart Sustainable City Key
Performance Indicators (KPIs)
Environmental Sustainability, Productivity, Quality of Life,
Equity and Social Inclusion, Infrastructure development
Mori and Christodoulou
City Sustainability Indexes
Various indexes that measure environmental, social and
economic performance (Ecological Footprint (EF),
Environmental Sustainability Index (ESI), Dashboard of
Sustainability (DS), Welfare Index, Genuine Progress
Indicator (GPI), Index of Sustainable Economic Welfare,
City Development Index, emergy/exergy, Human
Development Index (HDI), Environmental Vulnerability
Index (EVI), Environmental Policy Index (EPI), Living
Planet Index (LPI), Environmentally-adjusted Domestic
Product (EDP), Genuine Saving (GS))
Pires et al. (2014)
21 ECOXXI Indicators, grouped in the following sectors:
Sustainable, Development Education, Marine and
Coastal Environment Institutions,
Nature Conservation and Biodiversity, Forest Planning,
Air, Water, Waste, Energy, Transport, Noise, Agriculture,
Smart energy efficiency’s
50% of heat and cooling demand produced from RES
20 corresponding exemplars’ launch by 2015
Kii et al. (2014)
model for policy evaluation in
Parameter groups: employed household; unemployed
household; shopping place substitution; firm; developer;
landowner; road link cost function
Gouveia et al. (2016)
concept for an Integrative
Energy City Plannin
Residential sector: buildings and businesses
Transport sector: private and public fleet
Other energy consuming sectors: water/sewage/waste
systems, public lighting, daily activities (i.e., schools etc.)
4. UNIFIED SMART CITY MODEL
In recent years, there have been many approaches to conceptualize and benchmark smart cities. Conceptualization is a
necessary process for smart city definition, which has been recently completed by standardization bodies. Our literature
findings show that the ISO (2014), the ITU (2014), the UK Standards (BSI, 2014) and the US National Institute of Standards
and Technology (NIST, 2014) are in the process or they have defined smart city: innovation -not necessarily but mainly based
on the ICT-, which aims to enhance urban life in terms of people, economy, government, mobility, living and environment.
Indeed, standardization respects the 6 dimensions (people, governance, mobility, economy, environment and living) that are
recognized for enhancement and are agreed by all scholars, even with small variations. Furthermore, existing standards mainly
focus on urban sustainability and resilience, which demonstrate that smart city efforts are, or will be placed mainly on these
directions, while scholars go deeper and try to conceptualize smart city in terms that serve particular sectors (i.e., energy,
health etc.), without necessary to respect existing modeling.
On the other hand, researchers have tried to evaluate smart city from different lenses: smart city progress; city performance
and competitiveness; sustainability and resilience; and even policy making impact. Only Lazaroiou and Roscia (2012)
benchmarking framework respects the 6 smart city dimensions, which leave space for the further improvement of smart city
measurement. Furthermore, only 3 works (Fei, 2012; Duarte et al., 2014; Glebova et al., 2014) try to evaluate ICT and smart
solutions directly, which again leave a space for corresponding optimization.
Fig. 1: the unified smart city conceptual model (USCM)
Finally, the above analysis returns an initial taxonomy of smart city modeling and benchmarking. More specifically, 8 classes
of conceptual models have been discovered, that address smart city architecture, governance, planning and management,
data and knowledge, energy, health, people and environment viewpoints. These 8 classes compose a unified smart city model
(UFCM), which is depicted on (Fig. 1) and summarize existing smart city conceptualization approaches. Additionally, 6
classes of benchmarking tools have been identified and address smart city progress, smart city monitoring, city capacity, city
sustainability and resilience, and policy impact. Both these classes could evolve further when there’s a need to approach
specific domains, but they should respect the six smart city dimensions.
This paper reviewed existing smart city conceptualization and benchmarking methods and synthesized them into a unified
smart city model. A systematic overview of the main smart city structure was performed and 6 common components have
been discovered to be agreed by scholars. The overview confirmed the diversity of factors taken into account and different
views that can be taken for smart city understanding, which can become complicated when they go deeper to serve particular
sectors (i.e., energy and health). To this end, the paper focused on models and assessment frameworks, which are either still
being developed by prestigious organizations or are being tested by scholars and an initial taxonomy of conceptual modeling
and benchmarking has been extracted.
The smart city field has come to a uniform definition, which deals with innovation (not necessarily but mainly ICT-based) in
the urban space that aims to enhance the 6 city dimensions (people, economy, government, mobility, living and environment).
This is a very broad definition to cover the many and variety of initiatives in this field, but it can be respected when further
analysis is necessary. As such smart cities are an umbrella term for all sorts of innovations in the urban environment.
Moreover, standards –such as the ones introduced by (ISO, 2014; ITU, 2014; BSI, 2014; NIST, 2014)- are under
development or review for smart cities and corresponding solution definitions are being delivered, which illustrate that
vendors and organizations with commercial vested interest may aim to dominate this evolving market. With regard to smart
city assessment, scholars mainly evaluate the impact of innovation on urban performance, the urban capacity itself, rather than
the direct smart solution or the entire smart city system’s performance. Instead, some monitoring frameworks have been
introduced. Both these findings show that the smart city domain is still embryonic and promises important future results for
governments, academia and industry. As future research, we recommend the draft of sector-driven conceptual models that
respect the 6 smart city dimensions, together with benchmarking models that again respect these dimensions but, which
measure smart city performance.
Small parts of this paper have been presented in AW4City 2015 workshop, in conjunction with the 24th World Wide Web
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