Eco-cities an Integrated System Dynamics Framework and a Concise Research Taxonomy

8/18/2019 Eco-cities an Integrated System Dynamics Framework and a Concise Research Taxonomy

1/14

Sustainable Cities and Society 17 (2015) 1–14

Contents lists available at ScienceDirect

Sustainable Cities and Society

j ournal homepage: www.elsevier .com/ locate /scs

Eco-cities: An integrated system dynamics framework and a conciseresearch taxonomy

Naoum Tsolakis a,∗, Leonidas Anthopoulos b

a Department of Mechanical Engineering, Aristotle University of Thessaloniki, 541 24, Greeceb Business School, TEI of Thessaly, 41110, Greece

a r t i c l e i n f o

Article history:

Available online 14 March 2015

Keywords:

Eco-cities

Sustainable development

System dynamics

a b s t r a c t

Urbanism is an emerging phenomenon and international predictions demonstrate that more than half of

the increasing world population will live in cities by 2050. Interdisciplinary studies try to develop smartand eco-friendly solutions in order to enhance urban living. This paper validates that the eco-city urban

modeling emerges radically and concerns an ideal paradigm for sustainable urban development. Aftercomparing alternative methods for eco-city modeling, this paper attempts to assess the sustainability of

the eco-city with the System Dynamics (SD) simulation-based technique. A holistic SD methodologicalframework is proposed, as a means to assist decision-makers, local governments and managers designing

and adopting effective policies for monitoring and assessing the sustainable performance of eco-cities.This framework is formed and tested with two case studies respectively and useful findings about the

efficiency of the eco-city modeling are generated.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

Today, more than 3.5 billion people live in urban areas, while

the projections of the United Nations reveal that by 2050 morethan half of theurban population will live in cities (United Nations,2012). This urbanization trend results to intensive energy andnatural resource demands in modern cities (Agudelo-Vera, Mels,

Keesman, & Rijnaarts, 2011). However, cities are considered thecenters of innovation and can advance clean energy systems,sustainable transportation, waste management and spatial devel-opment strategies to reduce greenhouse gases (Khare, Beckman,

& Crouse, 2011). In this context, scientific urban design and man-agement are necessary to promote urban sustainable developmentin order to secure the appropriate agglomeration conditions forwell-being (Cao & Li, 2011; Chen, Acey, & Lara, 2015; Gaitani et al.,

2014; Ruza, Kim, Leung, Kam, & Ng, 2015) and mitigate the eco-logical (global) footprint of cities (Monstadt, 2009; UN-HABITAT,2008).

On this ground, the development of green, ecological cities has

been introduced as a means to support sustainable urban develop-ment within a social, economic, environmental and demographiccontext. The ecological city or eco-city concept was introduced

∗ Corresponding author. Tel.: +30 2310 995896; fax: +30 2310 996018.

E-mail addresses:[email protected] (N. Tsolakis), [email protected]

(L. Anthopoulos).

by Urban Ecology, a nonprofit organization that was founded in

1975 by the philosopher Richard Register (Roseland, 1997). Hewas the first to coin the term “eco-city”, a city that ensures

the well-being of its citizens via a holistic urban planning andmanagement approach with the aim of eliminating waste andemissions.

Nevertheless, an eco-city is a synthesis of different complex

subsystems that need to be adjusted and coordinated in order todeliver the desired outcomes. As such, many researchers studyonly “myopically” the eco-city system, while research problemsarise due to the lack of analytical tools that can capture the com-

plex and dynamic nexus of the involved urban ecological factorsand systems, and assess their development both in terms of envi-ronmental impact and business growth. Furthermore, literaturelacks in robustanalytical tools that canperform monitoring, bench-

marking and assessment of the impact of corresponding planningpolicies (Lizarralde, Chmutina, Bosher, & Dainty, 2015; Murakamiet al., 2011; Price et al., 2013; Zhou & Williams, 2013).

This paper addresses the complex structure of the eco-city in an

attempt to answer the following research questions (RQs):

(a) RQ1: how can an eco-city be modeled in order to support deci-

sion makers in appropriate planning and future predictions?(b) RQ2: what is the structure of an eco-city with the application

of System Dynamics (SD) modeling technique?(c) RQ3: how effective is eco-city modeling with SD techniques?

http://dx.doi.org/10.1016/j.scs.2015.03.002

2210-6707/© 2015 Elsevier Ltd. All rights reserved.

http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.scs.2015.03.002http://www.sciencedirect.com/science/journal/22106707http://www.elsevier.com/locate/scsmailto:[email protected]:[email protected]://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.scs.2015.03.002http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.scs.2015.03.002mailto:[email protected]:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.scs.2015.03.002&domain=pdfhttp://www.elsevier.com/locate/scshttp://www.sciencedirect.com/science/journal/22106707http://localhost/var/www/apps/conversion/tmp/scratch_4/dx.doi.org/10.1016/j.scs.2015.03.002


2/14

2 N. Tsolakis,L. Anthopoulos / Sustainable Cities and Society 17 (2015) 1–14

All the above research questions are critical to be answered,

since the eco-city appears to be adopted as the ideal urbanismapproach across the globe (Anthopoulos & Fitsilis, 2013) withoutthe suitability of this approach for effective urban sustainabilitybeen validated yet. More specifically, the answer to RQ1 will com-

parealternative eco-city modeling toolsand justify the applicabilityof SD as the most appropriate eco-city simulation tool. The answerto RQ2 will demonstrate the complex eco-city system with the useof SD with data from a real case study. Finally, in an attempt to

answer RQ3 the SD model – defined with the answer to RQ2 – istestedwith simulations anddata from a secondprestigious eco-citycase study andreturns importantfindings regardingboth theappli-cability of the proposed SD model and the effectiveness of eco-city

paradigm for urban development.This paper follows a multi-method approach to answer all

these questions. More specifically, literature findings are utilized toanswer RQ1. Both literature findings and thecase study of Hsinchu

Science Park in Taiwan are utilized foranswering RQ2. Finally, sim-ulation scenarios with data from a second case study from TianjinEco-City in China, return useful findings, which give answer to RQ3and generate future research potentials.

The remainder of this paper is structured as follows: Section 2performs a literature review on eco-cities and the corresponding

modeling tools. The objective of Section 2 is two-fold: (i) to capturethecritical factors andsubsystems that have beenidentifiedas vital

to an eco-city structure in order to consider them in the proposedmodeling framework, and (ii) to document the actual added-valueof this work compared to the existing modeling approaches thathave been developed thus far in previous studies. Then, Section 3

composes a conceptual SD modeling framework, which is consid-ered to be effective to analyze the complex eco-city system. Theapplication of the proposed framework is tested with simulationscenariosand data from a prestigious eco-city case study,and inter-

esting insights are provided. Finally, Section 5 discusses findingsand highlights future research potentials.

2. Eco-cities: literature reviewandcritical taxonomy

2.1. Background

Regarding the ecological cities, the existing literature is ratherlimited and mainly refers to real case studies of: (i) reformingattempts of existing urban settings toward the eco-city conceptualparadigm, and (ii) new construction projects for establishing eco-

cities, mainly in China where the urbanization and environmentalproblems aremoreprominent. Tothis end,the Chinese governmentlaunched a demonstrationprogram of 5 low-carbon pilot provincesand 8 pilot cities in 2010, in its attempt to promote low-carbon

urban development (Khanna, Fridley, & Hon, 2014).Eco-cities, a stepbefore the utopiaof a “gaia-level city”,are con-

sidered the epitome of an environmental sustainable city (EcocityBuilders, 2011). The first recorded publication that created a

momentum regarding the issue of eco-cities is the “Eco-city Berke-ley”(Register, 1987). In the aforementioned publication, the authordiscusses innovative solutions to ecologically improve the city of

Berkeley in California. Recently, according to a survey conductedby Joss, Tomozeiu, and Cowley (2011), the number of eco-citiesglobally is reported to be 174 while the corresponding amount in2009 was only 79. Recently, only in South Korea alone (Yigitcanlar

& Lee, 2014), 64 corresponding eco-city projects were under devel-opment across the country in 2013. South Korea has capitalizedeco-city and developed an ideal prototype in New Songdo, Seoul,which is expected to position the country possibly at the forefront

of new eco-city development, potentially producing a model for

export (Shwayri, 2013).

The fundingschemes to support eco-city initiatives have gained

great interest and this is further implied by the capital focus on aplethora of related constructions projects. Remarkably, the WorldBank, motivated by the challenges confronted by modern cities,launched the “World Bank Urban and Local Government Strat-

egy” (The World Bank, 2000). The respective published strategicroadmap clearly states the four main axes of the Bank’s urbansupport scheme to achieve greater impact, which are namely: (i)Formulating national urban strategies, (ii) Supporting city devel-

opment strategies, (iii) Scaling up services for the poor, includingupgrading low-income urban neighborhoods, and (iv) Expand-ing assistance for capacity building. Indicatively, the World Banksupports the Sino-Singapore Tianjin Eco-City and published an

extensive technical report regarding the key issues and the relatedchallenges that may arise during the development of an eco-city(The World Bank, 2009). Following, the above strategy declaration,the World Bank realized the economic implications of ecological

cities and launched the “Eco2 Cities” initiative. In the respectivereport the Bank claims the significance and practicability of theapplication of both qualitative and quantitative indicators in eco-cities with regard to the assessment of the value creation and the

derived financial benefits (The World Bank, 2010). The Bank alsooffers a framework that can be tailored to the specific case of each

city as to secure environmental and economic sustainability.Except for the publications supported by international orga-

nizations, there has been a great academic interest on the issueof ecological cities. Specifically, Li, Zhang, and Li (2011) leveragea plethora of indicators to evaluate the construction progress of Chinese cities toward the eco-city paradigm and provide evidence

that Central and Eastern Chinese cities are more advanced thanthose located at the Western side of the country. Furthermore, Wu,

Jiang, and Yang (2012), motivated by the prevailing concern overthe rapid urbanization in China and the increased interest over

eco-city projects, recommend the incorporation of carbon footprintinto the least-cost planning of eco-city schemes. Through the anal-ysis of the Tianjin eco-city and the Ningbo-Cixi Wetland Centercases, the authors conclude that carbon costs should be taken in

account when financially evaluating alternative ecological inter-ventions within an eco-city project. Their main aim is to assistdecision-makers in achieving optimal outcomes with referto finan-cial, social and environmental dimensions throughout the lifespan

of an eco-city project.

2.2. Critical planning components

Designing, constructing, and managing urbansettings involves acomplex and integrated decision-making process. This is even moreaccentuated when urban planning deals,for example, with flagshipeco-city projects that have to balance social, environmental and

economic ramifications (Caprotti,2014;Thornbush,Golubchikov,&Bouzarovski,2013). In general, eco-city design andplanningshould

tacklecriticalplanning constituentsrelated to specific components,such as sustainable urban growth dynamics in terms of population

and economic growth accompanied by the appropriate life-styleand education development efforts; urban transportation; green-house gas (GHG) emissions and solid waste management; and

energy consumption, in terms of efficiency and clean energy provi-sion against local demands (Khanna et al., 2014), as seen in Fig. 1. Inthe following sub-sections, we discuss each one of the componentsinvolved along with the related research works.

2.2.1. Sustainable urban growth dynamics

The issue of urban sustainability and growth has attracted theintenseacademic interest by researchers over that last two decades

(Fung & Kennedy, 2005; Gülen & Berköz, 1996; Ho, Wang, & Lu,

2002; Lehmann, 2011; Nijkamp & Perrels, 1994; van Bueren & ten


3/14

N. Tsolakis, L. Anthopoulos / Sustainable Cities andSociety17 (2015) 1–14 3

Fig. 1. System Dynamics causal loop diagram.

Heuvelhof, 2005). Indicatively, Banister, Watson, and Wood (1997)elaborated empirical evidence from the UK and the Netherlandsand they concluded that significant relationships between energy

consumption in transportation and the physical characteristics of the urban arrangements, like the city size and the population den-sity, exist. Furthermore, Rotmans, van Asselt, and Vellinga (2000),highlight the necessity to develop an integrated city planning tool

that can capture the nexus of the environmental, socio-culturaland economic factors that shape the development of creative and

sustainablecities (Khannaet al., 2014; Ratiu,2013). Under thiscon-text, Evans and Marvin (2006) rely upon their prior experience in

relevant research programs in the UK and emphasize on the neces-sity to adopt radical interdisciplinary approaches when tacklingthe issue of sustainable cities. In addition, Lizarralde et al. (2015)motivated by the built environment in the UK highlight the impor-

tance of policy-making agendas policies toward enhancing urbanplanning sustainability and resilience. At a greater extent, Kennedyand Hoornweg (2012) endorse the international initiatives towardurban sustainability and support the application of a comprehen-

sive “urban metabolism” framework to assess the environmentalimpact of cities. “Urban metabolism” is defined as “the sum total

of the technical and socio-economic processes that occur in cities,

resulting in growth, production of energy, and elimination of waste”

(Kennedy, Cuddihy, & Engel-Yan, 2007). To that end, they stressthe need for gathering data regarding material and energy flowsin metropolitan areas in order to allow continuous environmentalmonitoring and promote necessary ecological policy interventions.

Finally, to support the continuous efforts for the benchmarkingof eco-cities Zhou, He, Williams, and Fridley (2015) propose anindicator-based tool for evaluating urban sustainability perfor-mance along 33 factors, while Ciumasu (2013) applies Dynamic

Decision Trees for thecaseof the city of Iasi, Central Europe, for theidentification and prioritization of potential risks to support thedevelopment of urban sustainability.

At a states’ level, Canada has introduced the “Green Municipali-

ties” concept in order to promotegreen infrastructure and land useplanning (Li,Zhang,etal.,2011;Li,Li,etal.,2011). Through research

andpractice in major Canadian cities, the authors intend to provide

enlighteningimplications for the respective Chinese cases. Further-more, Barredo, Demicheli, Lavalle, Kasanko, and McCormick (2004)employed the Cellular Automata (CA) modeling tool to capture

the growth dynamics of the city of Lagos in Nigeria. They verifiedthe tool’s effectiveness and they stress the necessity to formulatehypotheses of events when simulating urban expansion and sus-tainability. Moreover, Vancheri, Giordano, Andrey, and Albeverio

(2008) and Vancheri, Giordano, Andrey, and Albeverio (2008) sim-ilarly developed a modeling framework for the analysis of urban

dynamics through leveraging CA tools and they applied it in asmall region of Switzerland. Additionally, Bojórquez-Tapia et al.

(2011) proposed a mixed GIS and Multi-Criteria Decision Analysistool for tackling decision-making problems over the urban basinsof Mexico and Upper Lerma. The authors in their study synthe-size the majority of drivers that yield environmental pollution, and

through a weighting system they present a spatial pattern to assistthe dynamic process of environmental policy-making in the region.

2.2.2. Urban transportation

Transportation is a major indirect cause of the global warm-ing phenomenon (Harvey, 2013) and thus the reduction of caruse is a key planning component for designing sustainable urban

settlements (Choguill, 2008). To that end, Maleki and Zain (2011)

investigated 155 neighborhoods of the municipal of Subang Jaya,Kuala Lumpur, Malaysia with regard to the distance of residencesfrom facilities and daily necessary services in the region. The

authors conclude that density, land diversity (non-residential landuse) and employment influence the distance to facilities, promotewalkability and foster the reduced use of cars and fossil fuels.

2.2.3. Greenhouse gas emissions

Notably, while cities occupy less than 1% of the earth surface,

they emitabout 80%of theglobal GHG emissions(Feng et al.,2014).Therefore, the development of emissions-free cities is the utopicvision of any urban planner. However, Premalatha, Tauseef, Abbasi,and Abbasi (2013) discuss the eco-city paradigms of the Dongtan

City in China and the Masdar City in Abu-Dhabi and conclude that

it is not possible to reduce urban carbon emissions provided that


4/14


even ‘eco-city prototypes’ continue to employ energy and materi-

als in as wasteful manner. Additionally, Sun, Yuan, Zhang, Wang,and Wang (2014) investigated the carbon emissions in the YellowRiver Delta High-efficient Eco-economic Zone from 2005 to 2011for 6 sectors and confirm the necessity to implement targeted poli-

cies for limiting GHG emissions in urbansocieties. Additionally, Caoand Li (2011) highlight the prominent role of low-carbon eco-cityplanning toward sustainable development through presenting thestrategic planning framework of the Tianjin eco-city in China.

2.2.4. Solid waste management

The cardinal aspect of urban sustainable development to attractthe scientific interest is the management of solid waste in cities

(Asase, Yanful, Mensah, Stanford, & Amponsah, 2009; Davoudi &Evans, 2005). Suggestively, Kaseva and Gupta (1996), through theirstudy over the scavengers in the city of Dar es Salaam, Tanzania,very early identified the pivotal role of recycling and waste recov-

eryas an effectiveurban sustainable practice forthe environmentaldomain. They further stressed that refuse management comprisesan alternative, viable proposition toward energy generation andunemployment. Following that, Baud, Grafakos, Hordijk, and Post

(2001) tackled the issue of solid waste management toward urbansustainability context. Their work is based on a review of a nine-

point indicator system on three developing countries and theyconclude that public-private alliances in urban, solid waste man-

agementare required in order to achieve sustainable development.Also, Zaman and Lehmann (2011) identified waste as the mostimportant challenge in designing sustainable cities. To this effect,they reviewed the waste streams of the cities of Adelaide and

Stockholm, and they concluded that integrated waste managementstrategies along with the appropriate technologies, tools and sys-tems can assist in transforming metropolitan areas into zero wastecities. Oakleyand Jimenez(2012)providea more focused, quantita-

tive studyregardingthe sustainable management of waste forsmallcities based on a case in Villanueva, Honduras. Moreover, Sutharand Sajwan (2014) elaborate the Rapid Impact Assessment Matrixto evaluate the suitability of municipalwaste disposal sites in cities

in the city of Dehradun, India, in terms of ecological, social, cul-tural and economic impact. In addition, Ramaswami, Chavez, andChertow (2012) propose the integration of Material Flow Analysisand Greenhouse Gas Accounting methodologies to local communi-

ties to better comprehend urban sustainability dynamics.

2.2.5. Energy consumption

Within this framework, Reiter and Marique (2012) developed a

hybrid Geographic Information System/statistical analysis tool toassess the energy consumption in the city of Liège, Belgium.

The authors conclude that energy consumption in buildingsgreatly overwhelms the energy consumption in the transporta-

tion sector, and they support building renovations as a necessaryenvironmental policy. From a social perspective, Dagoumas (2014)

presents synergies and conflicts between economic development,employment and energy poverty for the London region over theperiod 2000–2100 with the aim to foster sustainable and equalityoutcomes in urban settings.

2.3. Eco-city modeling

Previous sections used literature findings and identified theprimary eco-city components, without concluding on their inter-

relations. In order to define the appropriate eco-city modeling,various existing approaches to models and frameworks werecompared. Table 1 presents a synopsis of the matching eco-citykey components with the existing studies. This taxonomy con-

firms the previously identified eco-city components, while it can

assist greatly in drafting the agenda for future research through

summarizing the existing research and identifying the literature

gaps and overlaps. In general, despite the plethora of studies thatexamine the various eco-city components individually, a lack of holistic approaches for the design and management of eco-cities isidentified.

Except the above approaches, the eco-city can be approachedas part of the emerging smart city context. As it was mentionedbefore, the eco-city appears to be the preferred smart city formeven for cases like Amsterdam, which started as a broadband city

and emerged to an eco-city (Anthopoulos & Fitsilis, 2013). In thiscontext, smart city modeling can demonstrate eco-city synthe-sis too. Table 2 illustrates corresponding models and frameworks,which consider smart sustainable city from different lens. Smart

city modeling is mostly quantitative and analyzes the urban con-text in a set of components, which are measured with variousindicators. Many of these models (Anthopoulos, forthcoming; daCruz & Marques, 2014; International Telecommunications Union

(ITU), 2014; Lazaroiu & Roscia, 2012; Neirotti, De Marco, Cagliano,Mangano, & Scorrano,2014) contain componentssimilar to the pre-viously presented ones,but they allconcern quantitative indicators’sets.

Among the above modeling techniques, SD is broadly applied(Table 1), while it has been applied recently to measure single

components in eco-cities too, such as transportation (Haghshenas,Vaziri, & Gholamialam, 2014). SD is a simulation methodological

tool which is appropriate to analyze, comprehend and managethe development of complex feedback systems through causalloop and stock and flow diagrams (see Figs. 1 and 2 in Tsolakis& Anthopoulos, 2015). SD approach was first developed by the

MIT Sloan School of Management Professor Jay W. Forrester in1956. Originally, the tool was used by Forrester (1961) to examinethe instable employment environment in General Electric, whileover the years SD was employed to conduct simulations of diverse

scientific and engineering systems (Forrester, 1969). Accordingto Roberts (1978), the feedback control characteristics of the SDapproach render it to be the appropriate tool for decision-makingat the strategic level regarding a wide range of problems, from

managerial and socioeconomic to organizational ones.Therefore, from a SD perspective, sustainable urban develop-

ment has only recently become subject of scientific research. Theinitial implementation of the SD approach upon urban develop-

ment was introduced by Forrester in 1974. The elaboration of SDmodeling allows the consideration, in the urban planning models,of the interdependencies among social, ecological, economic andothersubsystemsthat further assist in the assessmentof alternative

policies for urban sustainability and predict possible divergenceswith regard to initial planning (Pretorius, Pretorius, & Benade,2014). However, eco-cities have only been infrequently addressedin literature through theSD approach(Haghshenas etal.,2014; Han,

Hayashi, Cao, & Imura, 2009), and more importantly the literaturedocuments the lack of comprehensive SD models regarding eco-

cities. Contrarily, the available SD modeling approaches only referto independent subsystems or moduli of eco-cities thus ignoring

the complex nexus of interrelations between the various subsys-tems (Struben & Sterman, 2008).

Chen, Ho, and Jan (2006) assess different policies for the air

purification in the city of Taipei. Their SD scenario analyses revealthat in order to achieve sustainable environmental developmentand air purification in urban areas, a comprehensive approach andthorough evaluation of different intervention policies is required.

In addition, Wang, Lu, and Peng (2008) leveraged the SD approachto examine the economic, social, and environmental sustainabilityof the transportation system in Dalian Central City in China. Theiranalysis confirmed the necessity to support public transportation

and innovation of carbon-free technologies in vehicles. Also, Han

et al. (2009) propose a rather integrated SD and CA model to gain


5/14


Table 1

Critical taxonomy of the existing research.

SD Ql. Qn. References

Sustainable urban

growth dynamics

Guan et al. (2011), Hanet al.(2009), Xu and Coors (2012)

Ecocity Builders (2011), Evans and Marvin (2006), Kennedy and Hoornweg(2012), Lehmann

(2011), Lizarralde et al. (2015), Ratiu (2013), Rotmans et al. (2000), TheWorld Bank (2009,

2010), van Buerenand tenHeuvelhof (2005)

Barredo et al. (2004), Bojórquez-Tapia et al. (2011), Ciumasu (2013), Fungand Kennedy (2005),

Gülen and Berköz (1996), Hoet al., 2002, Li, Zhang, et al. (2011), Vancheri, Giordano, Andrey,

and Albeverio (2008), Vancheri, Giordano, Andrey, and Albeverio (2008) Zhou et al. (2015)

Urban

transportation

Chen et al.(2006), Struben and Sterman (2008), Trappey et al. (2012), Wang et al.(2008),

Haghshenas et al. (2014)

Choguill (2008), Maleki and Zain (2011), TheWorld Bank (2009)

Banister et al. (1997), Khanna et al.(2014)

GHG emissions Chen et al.(2006), Feng et al.(2012), Trappey et al.(2012);Haghshenaset al. (2014)

Cao andLi (2011), Kennedy and Hoornweg (2012), Li, Zhang, et al. (2011), Premalatha et al.

(2013), Ramaswami et al. (2012), TheWorld Bank (2009)

Fung and Kennedy (2005), Sun et al.(2014), Wuet al. (2012), Khannaet al. (2014)

Solid waste

management

Guan et al. (2011), Zhang et al. (2008)

Asase et al.(2009); Baud et al. (2001), Davoudi and Evans (2005), Kaseva and Gupta (1996),

Zaman and Lehmann (2011)

Oakley and Jimenez (2012), Suthar and Sajwan (2014), Khannaet al.(2014)

Energy

consumption Feng et al.(2012);Haghshenaset al.(2014)

Kennedy and Hoornweg (2012), Ramaswami et al. (2012), TheWorld Bank (2009)

Banister et al. (1997), Dagoumas (2014), Nijkamp and Perrels (1994), Reiter and Marique(2012), Khannaet al.(2014)

Symbols: SD for System Dynamics, Ql. for Qualitative, Qn. for Quantitative.

Table 2

Smart city modeling techniques.

Model Model structure References

Nine PillarModeland Smarter

City Equation

Planning and Management Services IBM Institute for Business

Value (2009)Infrastructure Services

Human Services

Instrumentation (the transformation of urban phenomena into

data) + Interconnection (of data) + Intelligence(brought by

software)

Smart Sustainable City Key

Performance Indicators

Environmental Sustainability, Productivity, Quality of Life,

Equity and Social Inclusion, Infrastructure development

International Telecommunications

Union (ITU) (2014)Smart city dimensions Resource, Transportation, Urban infrastructure, Living,

Government, Economy, Coherency

Anthopoulos (forthcoming)

ISO 37120

Sustainable development of

Communities Indicators for city

services andquality 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

International Standards Organization

(ISO) (2014)

Dimensions o f C ity P rosperity Productivity a nd t he P rosperity o f C ities, U rban I nfrastructure:

Bedrock of Prosperity, Quality of Life andUrban Prosperity,

Equity and the Prosperity of Cities, Environmental

Sustainability and the Prosperity of Cities

UN-HABITAT (2014)

Smart City domains Natural resources and energy, Transport and mobility,

Buildings, Living, Government, Economy and people

Neirotti et al. (2014)

Local Sustainable Development

Indicators

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, Tourism

Pires, Fidelis, and Ramos (2014)

Global City Performance Measurement

Indexes

Economy, Research and Development, Cultural Interaction,

Livability, Environment, Accessibility

Kourtit, Macharis, and Nijkamp (2014)

Resilience City Evaluation and

Implementation Framework

City components: Resources and Processes (Physical)

People, Institutions, Activities (Social)

Desouza and Flanery (2013)

Sustainable Local Government

Scorecard

Social, Economic, Environmental and Government criteria da Cruz andMarques(2014)

Competitiveness p arameters Physical E nvironment, S ocial Capital, F inance, Development,Investment, User Potential

Singhal, McGreal, and Berry (2013)

Digital City Assessment Framework Connectivity, Accessibility, and Communicability Duarte, de Carvalho Figueiredo, Leite,

and Rezende (2014)

Model for computing “the smart city”

indices

Economy, Mobility, Environment, People, Living, Governance Lazaroiu and Roscia (2012)

Framework for smart city analysis Urban Openness, Service Innovation, Partnerships Formation,

Urban Proactive ness, Smart city infrastructure integration,

Smart city governance

Lee, Hancock, andHun (2013)


6/14


a greater insight on the environmental impact of urban growth.

Specifically, they examine the case of Shanghai in China and theyincorporate in theirmodel the macroscopic factors that affecturbanexpansion such as GDP, migration, income per capita and popula-tion. Their projections indicate the spatial expansion of Shanghai

by 1475km2 until 2020.Recently, Guan, Gao, Su,Li, andHokao(2011)developed a syner-

getic model combining SD and GIS capabilities to evaluate differentscenarios for the development of the Chongqing city in China

against natural resources’ depletion and environmental degra-dation. They incorporated the economy-resource-environments(ERE) system approach and the composite sustainable develop-ment index developed by Krajncand Glaviˇ c (2005). The researchers

confirmed that the investments in environmental protection fostersustainable development. In addition, Zhang, Zhang, Chen, Chen,and Zhao (2008) developed a comprehensive SD model to cap-ture the water management strategy in the Tianjin city and further

examined four different scenarios to assess their impact towardwater pollution. Furthermore, Trappey et al. (2012) developed a SDmodel to capture and evaluate the green transportation policy onthe Penghu Low Carbon Island in the Taiwan Strait. The authors

evaluate the effectiveness of recommended green transportationpolicies through analysing different scenarios. They concluded that

merely tackling the transportation sector cannot lead to the goal of carbon emissions’ reduction. On the contrary, a holistic approach

has to be adopted including the expansion of renewable energysources, the utilization of energy-saving appliances, etc.

Feng, Chen, andZhang(2012)developed an integratedSD modelto capture the energy consumption and CO2 emissions in Beijing,

China. Their projections reveal that the transport and service sec-tors will graduallyreplace the industrial sectorand will becomethemain sources of the city’s pollution. Therefore, the potential urbanpollution mitigation strategies have to focus on these two sec-

tors. Finally, Xu and Coors (2012) f ollowed an integrated approachthrough combining SD, GIS and 3D visualization techniques toquantify and explain the correlation and interaction among thesustainability indicators for the residential region of Plieningen in

Stuttgart, Germany.

3. A systemdynamicsmodeling framework

In this section, a first-effort modeling approach for planning

and managing eco-cities is developed through utilizing the theoryof System Dynamics, which has a proven track record for tack-ling strategic decision-making problems (Forrester, 1961, 1969;Roberts, 1978; Sterman, 2000; Sterman, 2001), considering also

the key insights obtained from the conducted literature reviewof Section 2. More specifically, in terms of water footprint, carbonfootprint and solid waste.

In thiscontext, a SD modeling frameworkis proposed that could

be employed by a decision-maker/regulator as a strategic decision-making tool for investigating the impact of several policies tailoredalong the five aforementioned subsystems on the sustainabilityperformance of particular eco-cities. The primary objective of the

decision-maker would be to determine theportfolioof policyinter-ventions that would foster sustainable growth. On a secondarylevel, the model would provide estimates of the city growth inthe long-term in order to assist the strategic urban planning in

emerging economies.

3.1. System under study

In an effortto holisticallycapture the constituents of an eco-city

based on the literature review and provide an integrated method-

ological approach, we identified a plethora of parameters and

Fig. 2. Eco-city management levels [Adapted by Iakovou, Vlachos, Achillas, and

Anastasiadis, 2014].

variables. A summary of the developed model parameters includesthe following:

• seventy six (76) arrays and scalars,• nine (9) levels,• forty eight (48) auxiliaries,• nineteen (19) constants (control variables),• eighty six (86) links,• seventeen (17) flows,• nine (9) static objects, and• one (1) dynamic object.

3.2. Model description

The proposed eco-city model management levels have been

embeddedin thedevelopedSD model,as it is illustrated in theasso-ciated causal loop diagram (Fig. 2). As presented in Fig. 2, the sumof each individual subsystem’s operations represents the overallsustainability performance of the perceived eco-city.

To begin with, the housing sector and the population den-sity of the conceptual city are positively related to the populationincrease. In addition, the solid waste subsystem is further loadedas the population increases. In our system we do not calculate

refuse based on the housing units but rather as volume of producedwaste per capita. Moreover, the industrial subsystem contributesto the energy consumption and to the water pollution thought therespective reinforcing loop via the business subsystem. Also, the

workforce is divided between the industry and the services’ sec-tor. Additionally, the services’ sector also consumes electric energy

andis incorporated in theoverall business systemof thecity. Waterconsumption is made by both the city residences and the business

sector in its production processes. Similar logic underlines also thesubsystem of the environmentalemissions. Finally, we assumethatthe population and the business sector consume electrical energy

through the relative reinforcing loops.The causal loop diagram dictates that the government policy

can create favorable conditions that in the long run can lead tourban sustainable development and enhanced financial benefits

for potential residents. On the other hand, inadequate governmentpolicy restricts the city’s sustainable growth potential. It is impor-tant to clarify that by inadequate government policy we do notrefer to extensive bureaucratic regulations but rather, we assume

that a comprehensive policy framework can provide the necessary

stimuli for the successful development of eco-cities.


7/14


3.2.1. “Population” subsystem

First of all, in the developed SD model exists the “Popula-tion” subsystem (see Fig. 3 in Tsolakis & Anthopoulos, 2015) thatdescribes the population on the conceptual eco-city. The stockvariable describes the accumulated population each year. The

population increases through the population increase flow. Thepopulation increase was derived based on the Malthusian growthmodel, an exponential growth that is considered as the first prin-ciple of population dynamics in the field of population (Malthus,

1798). The model describes the net global population increase andwe adopted it in our research due to the lack of any particular datathrough Eq. (1):

N t +1 −N t = N 0 × expat × (expa − 1) (1)

In Eq. (1), N denotes the population at time t and a is a coeffi-cient equal to 0.016888. We also consider a floating population thatwill temporarily live in the city. The parameters and the variables

associated to the “Population” subsystem are analytically insertedin the accompanying Data in Brief article (see Table 1 in Tsolakis &Anthopoulos, 2015).

3.2.2. “Housing” subsystemIn the housing subsystem (see Fig. 4 in Tsolakis & Anthopoulos,

2015) we tried to capture the growth in the number of dwellingsrelated to the total population growth. In order to be more realistic,we included the number of unoccupied houses as the constructionactivity aggravates the environment (Cheng,Chiang, & Tang, 2006).

The parameters and the variables associated to the “Housing” sub-system are analytically inserted in the accompanying Data in Brief article (see Table 2 in Tsolakis & Anthopoulos, 2015).

3.2.3. “Business” subsystem

In an effort to capture the economic activity in the city, andfollowing the spirit of Ho and Wang (2005), we tried to capturethe overall business value created in the city through the devel-oped entrepreneurial activity (see Fig. 5 in Tsolakis & Anthopoulos,

2015). Thus, we included both an industrial subsystem (see Fig.6 in Tsolakis & Anthopoulos, 2015) that will be developed in thesuburbs of the city, and a services’ sector subsystem (see Fig. 7 inTsolakis & Anthopoulos, 2015). Further, we assumed that the fail-

ure rate in the services’ sector is higher than the respective ratein the industrial sector. The parameters and the variables associ-ated to the “Business” subsystem and the related “Industry” and“Services” subsystems are analytically inserted in the accompany-

ing Data in Brief article (see Tables 3–5 in Tsolakis & Anthopoulos,2015 respectively).

3.2.4. “Energy consumption” subsystem

In our model we also included an “Energy Consumption” sub-system (see Fig. 8 in Tsolakis & Anthopoulos, 2015) as it is clearlyreported that energy consumption accounts for a large propor-tion of the carbon emissions (Golove & Schipper, 1997; Matthews,Hendrickson, & Weber, 2008). For the purpose of this study, we

leveraged the existing data regarding the Tianjin city, a promi-nent eco-city paradigm. First of all, the annual energyconsumptionper capita in Tianjin was 2790kWh (Department of Industry &Transport Statistics, 2002Department of Industry and Transport

Statistics, 2000–2002). Additionally, the electricity use per GDP inindustry is projected to be 1.2 kWh per US dollar, whereas in theservices’industry it is 0.8kWh per USdollar(USAID Asia,2011). Theparameters and the variables associated to the “Energy Consump-

tion” subsystem are analytically inserted in the accompanying Data

in Brief article (see Table 6 in Tsolakis & Anthopoulos, 2015).

3.2.5. “Environmental pollution” subsystem

The environmental pollution subsystem that captures the totalimpact on the environment is the core of the eco-city subsystems.We further identify the water pollution subsystem (see Fig. 9 inTsolakis & Anthopoulos, 2015), the GHG emissions subsystem (see

Fig. 10 in Tsolakis & Anthopoulos, 2015), and the solid waste sub-system (see Fig. 11 in Tsolakis & Anthopoulos, 2015).

3.2.5.1. “Environmental pollution: water” subsystem. Another vital

source of environmental pollution is the amount of sewage gener-ated in a city as water resources are considered an integral partof an urban socio-economic-environmental system as there is astrong correlation between residential water use and living stan-

dards (Zhang & Brown, 2005). According to the study of Zhang et al.(2008), in Tianjin city the water consumption per capita is esti-mated tobe 160 m3/year. The study also documents that thewaterpollution index ranges between 0.8 and 1.3. The parameters and

the variables associated to the “Environmental Pollution: Water”subsystem are analytically inserted in the accompanying Data inBrief article (see Table 7 in Tsolakis & Anthopoulos, 2015).

3.2.5.2. “Environmental pollution: emissions” subsystem. Regardingthe GHG emissions, in our modeling approach we decided to

include only the carbon dioxide (CO2) as this is considered themajor contributor to global warming contrary to other greenhouse

gases such as NO x, CH4, and SO2 (Pugsley, Berruti, and Chakma,1994). According to Geng, Peng, and Tian (2011), in 2007 in Tian-

jin the CO2 emissions were about 20.82 tons/capita. Furthermore,for the estimation of the total carbon dioxide emissions from the

business sector, we followed the study of Price et al. (2013) whoreport a volume of 1.40kg CO2 generated perUS dollar. The param-eters and the variables associated to the “Environmental Pollution:Emissions” subsystem are analytically inserted in the accompany-

ing Data in Brief article (see Table 8 in Tsolakis & Anthopoulos,2015).

3.2.5.3. “Environmental pollution: solid waste” subsystem. Besides

the water and air pollution, a major challenge for an eco-city of the future is the management of the municipal solid waste. Studiesupon the Tianjin city, where environmental monitoring is con-sidered a vital part of the eco-city transformation effort, report

the direct relation of integrated solid waste management to theregional economy (Geng, Zhu, & Haight, 2007) and the potentialto reduce GHG emissions through effective solid waste manage-ment policies (Wang, He, Yan, Ma, & Han, 2012). In this study we

simply consider that in the city of Tianjin the rate of the generatedsolid waste per day is about 4500tons(Zhao, van derVoet, Zhang,&Huppes, 2009). The parameters and the variables associated to the“Environmental Pollution: Solid Waste” subsystem are analytically

inserted in the accompanying Data in Brief article (see Table 9 inTsolakis & Anthopoulos, 2015).

3.3. Model validation

The developed system dynamics model was tested regarding itsvalidity and reliability, and it was further verified against its struc-

ture and functionality according to the tests described by Sterman(2000). To that end, we initially tested thestructural validity of ourmodel in terms of dimensional consistency. Based on real data, weexamined the preliminary numerical results of the model variables

and we ascertained their rationality. Additionally, we comparedthe trend in the urban environmental pollution with the relativepollution patterns in other “eco-city prototypes”.

Following that, according to Toka, Iakovou, Vlachos, Tsolakis,

and Grigoriadou (2014), we run extreme-condition tests to inves-

tigate the realistic behavior of our model, even under extreme


8/14


Fig. 3. Total business value (policy scenario #1).

Fig. 4. Solid waste (policy scenario #1).

Fig. 5. Water pollution (policy scenario #1).


9/14


Fig. 6. CO2 emissions (policy scenario #1).

policy-making decisions. For example we set model coefficients tozero and no pollution was generated. In addition, we conducted

sensitivityanalyses and we interchangeably examined positive andnegativevaluesetsof theeffect ofeachexternalfactor.To this effect,we observed corresponding system responses regarding the totalgenerated pollution at the eco-city level.

Furthermore, we elaborated integration error test methods. In

ourcase, we used the Euler integration method as the Runge–Kuttamethod is not suggested for systems that include random distur-bances (Sterman, 2000). Also, the shortest time constant in outmodel is set to 1 year. As the standard SD practice suggests that

the integrating time step (DT) cannot exceed the ¼ of the shortesttime constant, we examined an initial DT of ¼ years and 1/8 yearsand the model did not yield any diverse results. Finally, the modelcode was developed through using the simulation programming

language Powersim 2.5c (Powersim, 1996).

4. An illustrative conceptual case study: policies under

study – simulation results anddiscussion

In this section, we demonstrate the applicability of the pro-posed SD modeling framework through an illustrative case studyof a conceptual eco-city, following the modeling process describedby Sterman (2000), while employing updated real data and

realistic quantitativeestimates associated with the Tianjin Eco-Cityin China. Furthermore, the structure of our model is based on the

framework provided by Ho and Wang (2005) who tackle the issueof the sustainable development forthe case of Hsinchu Science Parkin Taiwan.

The applied SD model investigates the performance of an eco-city along the three pillars of sustainability through capturing

economic, social and environmental aspects over a time horizonof 50 years. To that end, our conceptual model first captures andassesses the endogenous structure of an eco-city system. More-over, this study focuses on the environmental impact of applied

interventionary policies.Our simulation analyses reveal that the environmental impact

of an eco-city is highly correlated to the population of the city.Simulation results reveal that after 50 years, a newly constructed

eco-city exhibits signs of great increase in population growth and

environmental pollutionthus creating greatconcerns aboutthe rai-son d’être of such urban projects. Detailed simulation results arepresented in the subsections that follow.

4.1. Policy scenario #1: research anddevelopment intensification

Despite the goal of creating cities that have minimal environ-mental impact, it is vital to secure the economic prosperity of the

Fig. 7. Energy consumption (policy scenario #1).


10/14


Fig. 8. Total business value (policy scenario #2).

citizens. Therefore, the first dimension that needs to be consid-

ered is the investments in Research and Development (R&D) thatcan drive economic growth. Besides, there is a clear trend of smartcities – cities with established sophisticated information technol-

ogy infrastructure to promote urban competitiveness – towardtransforming into green cities (Kim, Shin, Choe, Seibert, & Walz,2012; Yamagata & Seya, 2013). This is further highlighted by ourliterature review findings which reveal that smart city paradigms

make a transient shift toward the model of green cities. In addi-tion, labor productivity is pivotal regarding the overall value of thebusiness sector in the city.

As regard the first scenario, we assume that the institutional

setting dictates the increase in the R&D expenditure ration from0% to 7% and simultaneously an increase in the labor productivityfrom 50% to 80%. This strategy significantly affects the monetary

output of the city and reveals that economic prosperity aggravatesenvironmental pollution.Naturally, the increase in the R&D investments and labor pro-

ductivity enhance the total value of the overall business sector inthe conceptual city under study. The simulation results demon-

strate that the R&D and productivity focus results in an annualgrowth rate of 8.66% in the overall business value (see Fig. 3).Notably, the annual business value increase rate follows a negative

exponential trend which is characteristic for linear systems with a

zero desirable state (Sterman, 2000).Notably, abrupt increase is observed in the generated urban

solid waste that follows a somehow linear growth pattern with an

increase rate of 2.97% (see Fig. 4). Furthermore, the water pollutionfollows the same growth pattern as the total business value withan average growth rate of 8.76% (see Fig. 5).

In addition,the CO2 emissionspresent the most interesting evo-

lution trend, which is due to the regulations that we have insertedin the system. We can observe that policy interventions take placewhenever there is recorded a sharp increase in the GHG emissions(see Fig. 6). The intensification of research and production nor-

mally increase the CO2 emissions but they have a lower effect incontrast to the water pollution. The observed annual increase ratein gas emissions is 5.34%. Finally, the energy consumption follows

the same growth pattern as the total business value and he waterpollution but at an annual rate of 7.38% (see Fig. 7).

4.2. Policy scenario #2: environmental management strategy

application

In the ideal case, a city is desirable to resemble the eco-

city concept and have a zero environmental impact. To that end,

Fig. 9. Solid waste (policy scenario #2).


11/14


Fig. 10. Water pollution (policy scenario #2).

Fig. 11. CO2 emissions (policy scenario #2).

Fig. 12. Energy consumption (policy scenario #2).


12/14


several environmental pollution control policies and regulations

are implemented. Under this context, in our model we set somequantitative key performance indicators and we assume that incase they are preceded by the performance of thecity (for examplethe CO2 emissions surpass a certain threshold) then a strict envi-

ronmental strategy is implemented to keep the system within thepollution boundaries.Naturally, we take into consideration that theimplementation of such strategies is gradually enforced. The strate-gies that we have outlined, in line with the KPIs for the planning

and development of the Tianjin eco-city (Tianjin Eco-city, 2012),are the following: (i) water supply of the city to be 50% providedby non-traditional sources such as desalination and recycled water,(ii) proportion of renewable energy utilized in our conceptual eco-

city will be at least 20%, (iii) recycling of solid wasteat 60%, and (iv)CO2 emission cap of 100,000,000 kg.

Thesecondscenariostresses therole of theenvironmentalinsti-tutional regulations. First of all, there is no change in the total

business value that follows a normal norm with an annual growthrate of 8.66% (see Fig. 8).

An obvious difference is reported regarding the volume of solidwaste. The applied measures result in an annual decline in the

yielded refuse of 9.26% (see Fig. 9). The slight oscillations presentin the corresponding curve of Scenario 2 impose the effort to keep

the system in equilibrium.The most remarkable trend is that observed in the water pol-

lution field. The implementation of the environmental measuresappear to have a positive influence after a decade, whereas afterthirty the water pollution reaches the same levels regardless of theapplied policies and institutional interventions. The annual growth

decline in water pollution is 5.77% (see Fig. 10).The greatest effect of policy interventions seems to be in the

CO2 emissions. The implementation of even slight measures leadsto major decline in the GHGs (see Fig. 11). The achieved annual

reduction rate is simulated to be 79.51%. Finally, the investigatedpolicies do not appear to have any significant impact as the annualreduction rate is calculated 3.47% (see Fig. 12).

5. Summary and conclusions

This paper addresses the problem of the eco-city paradigm

assessment with a multi-method approach. It grounded threeresearch questions and applied alternative methodologies in anattempt to answer them. More specifically, RQ1 concerned theidentification of the most appropriate eco-city modeling toolsand it

was answered with literature review. Specially, literature findingsregarding eco-city and smart eco-city modeling illustrated that SDis the most appropriate corresponding tool. Moreover, literaturereview and findings from the case study of Hsinchu Science Park

(Taiwan) gave answer to RQ2 and demonstrated the conceptualeco-city structure, consisting on five components/sub-systems of

an eco-city: (i) population, (ii) housing, (iii) business, (iv) energy,and (v) environmental pollution.

In an attempt to answerthe third research question of this paper(RQ3) regardingthe effectiveness of SD modeling and theidentifiedconceptual framework to assess an eco-city, simulation scenarios

were executed with the utilization of data from the Tianjin Eco-City case study. More specifically, authors investigated the effectof policy intervention strategies with regard to the economic out-put and the environmental pollution in terms of water pollution,

CO2 emissions and solid waste generation. Simulation outcomesindicate that investments upon eco-city initiatives result in theabrupt population increase and increased value of the overall busi-ness sector comprising of both heavy industry and services’ sector.

Naturally, the environmental pollution increases accordingly. On

the other hand, policy interventions with regard to environmental

protection only achieve in limiting the increasing pollution rate.

The only environmental aspect that interventions seem effective isthe reduction of CO2 emissions. A major observation is that after30 years the population increases at a grade that an eco-city can-not be realized. The most prominent policy intervention is to set

a limit on the population that an envisaged eco-city can accom-modate as human activities appear to have a major environmentalpollution impact. These findings give answer to RQ3 and validatetheeffectiveness of theintroduced conceptual model to evaluate an

eco-city,while theygenerate arguments regarding the efficiency of the eco-city to meet planning expectationsfor urban sustainability.To this end, eco-city effectiveness must be explored further.

Nevertheless, this paperhas some limitations.More specifically,

data from a single case study were utilized under the simulationscenarios. Although Tianjin concerns a representative case, futureresearch will incorporate data from more eco-cities in order tovalidate or update this model accordingly.

Except from the above limitation, the introduced conceptualmodel is an effort to capture the complex nexus of an eco-city, butitssubsystems were notanalyzedin a detailed manner, as each sub-system can be considered an individual research field. For example

authors assumed that the populationand business subsystems con-sume only electric energy, whilst excluding other forms of energy

sources. Additionally, the effect of other key subsystems such asthe transport sector and any risk management challenges were not

accounted.Moreover, for the purposes of this conceptual models, firms

were considered either to belong to the services’ of the heavyindustry sector, whereas a more detailed model should cluster

the business environment according to production sectors (i.e.car manufacturers, textile industry, communication services, etc.).Additionally, a flat research and developmentratio that is uniformlyapplied to the industrial and the service’s sectors was considered.

Furthermore,there are no considerations aboutthe agricultural andthefood industry in thecity as an eco-city should be self-sustained(Waggoner, 2006), while, educational institutions and researchcenterswere not consideredeither. Financialelements likeinflation

rates and operational costs or any political/institutional instabilityfactors were not accounted.

Finally, other crucial aspects, such as tax incentives, funding(seed money, venture capitals) that have not been examined in

the past and could yield insightful managerial insights and realisticpolicy-making recommendations will also be considered.

References

Agudelo-Vera, C. M., Mels,A. R.,Keesman, K. J.,& Rijnaarts, H. H.M. (2011). Resourcemanagement as a key factor for sustainable urban planning. Journal of Environ-mental Management , 92(10), 2295–2303.

Anthopoulos, L. (2015). Understanding the smart city domain: A literature review.In M. P. Bolivar(Ed.), NewYork:Public administration and informationtechnologyseries (Vol.3) Transforming citygovernments for successfulsmart cities. NewYork:Springer Science+ Business Media (forthcoming).

Anthopoulos, L., & Fitsilis, P. (2013). Using classification and roadmapping tech-niques for smart city viability’s realization. Electronic Journal of e-Government ,11(1), 326–336.

Asase,M., Yanful, E. K.,Mensah,M., Stanford,J., & Amponsah, S. (2009). Comparisonof municipal solid waste management systems in Canada and Ghana: A casestudy of the cities of London, Ontario, and Kumasi, Ghana.WasteManagement , 29(10), 2779–2786.

Banister, D.,Watson,S., & Wood,C. (1997). Sustainablecities: Transport, energy,andurban form. Environment andPlanning B: Planning andDesign, 24(1), 125–143.

Barredo,J., Demicheli, L., Lavalle,C., Kasanko,M., & McCormick, N. (2004). Modellingfuture urban scenarios in developing countries: An application case study inLagos, Nigeria.Environment and Planning B: Planning andDesign, 31(1), 65–84.

Baud, I.,Grafakos, S.,Hordijk,M., & Post, J. (2001).Qualityof lifeand alliancesin solidwaste management. Cities, 18(1), 3–12.

Bojórquez-Tapia,L. A.,Luna-González, L.,Cruz-Bello,G. M.,Gómez-Priego, P., Juárez-Marusich, L., & Rosas-Pérez, I. (2011). Regional environmental assessment formultiagency policy making: Implementing an environmental ontology through

GIS-MCDA. Environment andPlanning B: Planning andDesign, 38(3), 539–563.

http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0040http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0035http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0030http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0025http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0020http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0015http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0005


13/14

http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0360http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0355http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0350http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0345http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0340http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0335http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0330http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0325http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0320http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0315http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0310http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0305http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0300http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0300http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0300http://refhub.elsevier.com/S2210-6707(15)00022-0/sbref0300http://refhub.elsevier.co

Eco-cities an Integrated System Dynamics Framework and a Concise Research Taxonomy

Documents

Transcript of Eco-cities an Integrated System Dynamics Framework and a Concise Research Taxonomy