Presentation at CeDEM Asia 2014

1. Evaluating Green Smart City’s Sustainability with an
Integrated System Dynamics Model
Dr. Leonidas Anthopoulos, Associate Professor
TEI of Thessaly, Greece

2. Smart Green Cities
 Eco-city: (1975) city well-being is established with
holistic planning and management for waste and
emission control
 Smart cities: (1990s) urban innovation that deal with
city challenges (i.e., living, leisure, traffic etc.)
 Green cities: smart city approach, where ICT and
innovation are utilized for sustainable development
 Energy consumption management
 Emission control
 Solid waste management
 Resource management etc.
2 Green City Complexity

3. The problem
 Urbanism increment (6.3 billion by 2050, over 50% in
cities,) complicates city well being, resource and
waste control
 Eco-city: a synthesis of complex subsystems, which
is difficult to be evaluated
 The question: how can a green city be evaluated
regarding its sustainability within a complicated
nexus of social, economic and cultural factors?
3 Green City Sustainability and SD

4. Literature analysis
 Initiatives: urban transformation to eco-cities; eco-cities from scratch;
World-bank Sino-Singapore and Tianjin eco-cities and eco2-cities.
 Smart city cases mostly evolve to eco-cities
 Literature findings: taxonomy of existing research
SD Ql. Qn.
Urban Growth Dynamics &
Sustainability
X
X
X
Transportation X
X
X
Emissions (Green House Gas – GHG) X
X
X
Solid Waste X
X
X
Energy X
X
X
 Symbols: SD for System Dynamics, Ql. for Qualitative, Qn. for Quantitative
4 Green City Sustainability and SD

5. System Dynamics (SD)
 a simulation tool
 appropriate to analyze
and understand the
development and the
behavior of complex
systems over time
 SD modeling
elements:
 Feedbacks (loops):
capture system’s
behavior
 Stock structures:
memory
 Flow structures:
dynamics’ flow between
stocks
 Delays: time intervals
between desired and
actual system’s state
5 Green City Sustainability and SD
Layer
Variable
Constant
Normal
Flow
Information
Flow
Delay

6. Methodology
 SD is used to illustrate eco-city components and
interrelations
 SD analysis in 3 stages:
 Eco-city model conceptualization
 Hypothesis and simulation formulation for SD validation
 Simulation scenario execution
 Case studies and simulation:
 Model formulation: sustainable development of the
Hsinchu Science Park in Taiwan
 Data for simulation: Tianjin Eco-City in China
 50-year prediction period
6 Green City Sustainability and SD

7. Causal loop diagram
7 Green City Sustainability and SD

8. Stock and flow diagram
 Simulation software Powersim 2.5c
 76 arrays and scalars; 9 levels; 48 auxiliaries; 19
constants (control variables); 86 links; 17 flows; 9
static objects, and 1 dynamic object
 Population subsystem:
 stock variable that calculates the annual population
change
 net global population: Nt+1−Nt=N0×eat*(ea−1), where N is
the population at time t and a is a coefficient equal to
0.016888 and e equals to 2.71828
 Temporary floating population living in the city was also
accounted
8 Green City Sustainability and SD

9. Population subsystem
 dt concerns date change: values of ¼ years and ⅛ years were tested
Total_Population=+dt*(Floating_Population)+dt*(Population_Increase)–dt*(Population_Decrease)
Total_Population = 1000
Population_Increase=
IF(Population_Density<Population_Density_Cap,Total_Population*(EXP(0.016888*TIME))*(EXP(0.0
16888)-1),0)
Population_Decrease = DELAYINF(Floating_Population,3)
Floating_Population = INT(Total_Population*Floating_Population_Modulus)
Land_Area = 85
Population_Density = Total_Population/Land_Area
Annual_Population = INT(Total_Population)
Population_Density_Cap = 65
Net_Population_Increase = Floating_Population+Population_Increase
Workforce = Total_Population*0.53
Floating_Population_Modulus = 0.05
9 Green City Sustainability and SD

10. Population Subsystem
10

11. Other Subsystems
 Housing subsystem: calculates household growth (number of
unoccupied houses was accounted)
 The economic activity subsystem calculates the overall
business value (both an industrial subsystem and a service
sector subsystem)
 Energy Consumption subsystem illustrates corresponding
carbon emissions. Tianjin data:
 Consumption 2790 kWh
 Electrical power use per GDP in industry is projected to be 1.2 kWh
per US dollar
 services’ industry it is 0.8 kWh per US dollar
 Environmental pollution subsystem:
 Water: 160 m3/year / capita consumption 0,8-1,3 water pollution
index
 Emissions: 20.82 ton/capita CO2 and 1,4Kgr/US dollar from
business
11 Green City Sustainability and SD
 solid waste: 4500 tons

12. The entire green city SD
12 Green City Sustainability and SD

13. Scenario 1 - Research and Development
Intensity
 R&D expenditure in an eco-city increases from 0% to
7% and labor productivity from 50% to 80%
respectively
 The simulation results:
 annual business growth rate of 8.66%
 2,97% solid waste production increase
 8.76% water pollution growth
 5.34% annual increase of CO2 emission (lower than
water)
 7.38% energy consumption growth
13 Green City Sustainability and SD

14. Scenario 2 - Environmental Management
Strategy
 Several eco-friendly policies and regulations are considered
 Tianjin data:
 water supply of the city to be 50% provided by non-traditional
sources such as desalination and recycled water;
 proportion of renewable energy will be at least 20%;
 solid waste recycling at 60%; and
 CO2 emission cap of 100,000 tons
 Simulation results:
 no effect to business value, which increases 8.66% annually
 9,26% annual solid waste decrease by
 5,77% annual water pollution decrease:
 Pollution stops after a decade
 Pollution reaches the same level after 30 years
 79,51% of annual CO2 emission reduction
 3,47% annual energy consumption reduction (not very important
impact)
14 Green City Sustainability and SD

15. Thank You
謝謝
15 Green City Sustainability and SD