Urban Energy Metabolism using Ecological Network Analysis

Urban Energy Metabolism using Ecological Network Analysis: Case study of four Chinese

cities

Brian D. Fath Biology Department, Towson University, Maryland, USA

Dynamic Systems Program, IIASA, Austria

Yan Zhang, Zhifeng Yang, Shengsheng Li

School of Environment, Beijing Normal University, China

Outline

1)Thermodynamics and sustainability2)Complex Systems Cycle3)Cities as Complex Systems4)Ecological Network Analysis5)Case study of four Chinese cities

Urban Metabolism and networks

1. Thermodynamics and sustainability

Natural and human systems build and maintain order by taking in high quality energy, using it, and passing degraded energy outside the system boundary.

Our society is dependent on the energy flows that support it AND having a sink for the waste.

System(human or

natural)

High qualityEnergy Input

Low qualityEnergy output (heat)


Simplified Ecosystem


Simplified Urban System


2) Complex Systems Cycle

Exergy

stored

Connectedness

Exploitation – pioneer stage

Conservation – mature stage

Release –creativedestruction

Reorganization


Develop-

mental

potential

Connectedness

Developmental potential declines during the successional cycle


Ecosystem dynamics as a guide for socio-ecological design and development

• Increase in structure• Increase in network connectivity• Increase in information• Increase in cycling of energy and material• Increase in energy capture, use, and dissipation

• Decrease in net productivity• Decrease in Production/Respiration• Decrease in Production/Biomass


3. Cities as complex systems

“Urban planning is a problem of handling organized complexity”

Jane Jacobs, 1961

Many interacting parts, fine grained, local interactions, emergent properties.



Chicago Pop. growth

1850 - 30,0001870 - 300,000 1890 - 1,000,000 1920 - 2,000,000

the rebuilding that began almost immediately spurred Chicago's development into one of the most populous and economically important American and international cities

Fire 1871

Chicago as a CAS

http://upload.wikimedia.org/wikipedia/commons/e/e8/Chicago-fire1.jpg

http://upload.wikimedia.org/wikipedia/commons/6/6e/Chicago-fire2.jpeg

Other Socio-ecological examples:

• San Francisco Earthquake 1906• Hurricane Katrina 2005• Oil shock - Suburban Sprawl 21st

Century

Path Analysis -enumerates number of

pathways in a network

Flow Analysis (gij = fij/Tj) – identifies flow intensities

along indirect pathways

4. Ecological Network Analysis

Storage Analysis (cij = fij/xj) – identifies storage intensities along indirect

pathways

Utility Analysis (dij = (fij-fji)/Ti) – identifies utility intensities along indirect

pathways


x1

x2x3 f32

f13

z1

y3 y2

f21

f31

y1

A

0 0 1

1 0 0

1 1 0

F

f

f

f f

0 0

0 0

0

13

21

31 32

z

z

1

0

0

y

y

y

y

1

2

3

x

x

x

x

1

2

3

Input Output Storage

Internal flows

Connections


Network Analysis

System of interacting components

Quantify direct and indirect effects

Identify system-level relations

Depo sited

De tritus

x2 = 1 00 0.0 0

Filter

Feed e rs

x 1 = 2 00 0.0 0

M eio fau na

x4 = 2 4.1 21 40

M icro biota

x3 = 2 .41 21

Depo sit

Feed e rs

x5 = 1 6.2 74 0

Preda tors

x6 = 6 9.2 36 7

z1 = 4 1.4 69 7

y4 = 3 .57 94

y5 = 0 .43 03

y6 = 0 .35 94

y3 = 5 .76 00

y2 = 6 .17 59

y1 = 2 5.1 64 6

f26

= 0 .32 62f

21 = 1 5.7 91 5

f61

= 0 .51 35

f25 = 1 .90 76

f32

= 8 .17 21

f65 = 0 .17 21

f52 = 0 .64 31

f42 = 7 .27 45f54 = 0 .66 09

f24 = 4 .24 03

f53

= 1 .20 60

f43

= 1 .20 60

Directed,

weighted

flows of

conservative material (energy)

Internal Flows: F=(fij)Boundary Input Z=(zj)Boundary Output Y=(yi)Total Throughflow: Ti=Storages: X=(xi)

f zij jj


Network Indirect Effects

Flow: N = G0 + G + G2 + G3 + G4 + …

Storage Q = P0 + P + P2 + P3 + P4 + …

Utility: U = D0 + D + D2 + D3 + D4 + …

integral = initial + direct + indirect

input

Flow: N = (I – G)–1

Storage: Q = (I – P)–1

Utility: U = (I – D)–1

gij=fij/Tj, pij=(fij/xj)Δt dij=(fij–fji)/Ti,

D epo sited

D e tritus

x2 = 1 00 0.0 0

Filter

Feed e rs

x 1 = 2 00 0.0 0

M eio fau na

x4 = 2 4.1 21 40

M icro biota

x3 = 2 .41 21

D epo sit

Feed e rs

x5 = 1 6.2 74 0

Preda tors

x6 = 6 9.2 36 7

z1 = 4 1.4 69 7

y4 = 3 .57 94

y5 = 0 .43 03

y6 = 0 .35 94

y3 = 5 .76 00

y2 = 6 .17 59

y1 = 2 5.1 64 6

f26

= 0 .32 62f

21 = 1 5.7 91 5

f61

= 0 .51 35

f25 = 1 .90 76

f32

= 8 .17 21

f65 = 0 .17 21

f52 = 0 .64 31

f42 = 7 .27 45f54 = 0 .66 09

f24 = 4 .24 03

f53

= 1 .20 60

f43

= 1 .20 60


Identify Relations

Direct and integral

relations

Depo sited

De tritus

x2 = 1 00 0.0 0

Filter

Feed e rs

x 1 = 2 00 0.0 0

M eio fau na

x4 = 2 4.1 21 40

M icro biota

x3 = 2 .41 21

Depo sit

Feed e rs

x5 = 1 6.2 74 0

Preda tors

x6 = 6 9.2 36 7

z1 = 4 1.4 69 7

y4 = 3 .57 94

y5 = 0 .43 03

y6 = 0 .35 94

y3 = 5 .76 00

y2 = 6 .17 59

y1 = 2 5.1 64 6

f26

= 0 .32 62f

21 = 1 5.7 91 5

f61

= 0 .51 35

f25 = 1 .90 76

f32

= 8 .17 21

f65 = 0 .17 21

f52 = 0 .64 31

f42 = 7 .27 45f54 = 0 .66 09

f24 = 4 .24 03

f53

= 1 .20 60

f43

= 1 .20 60

sD

0 0 0 0

0

0 0 0

0 0 0

0 0

0 0 0

sU

predation

neutralism


Assess mutualism

sD

0 0 0 0

0

0 0 0

0 0 0

0 0

0 0 0

sU

Direct utility Integral utility

Zero sum More positive signs

( )( ) ,

( )

S FJ F

S F

( ) max( ( ( )) 0)

( ) ( min( ( ( )) 0))

iji j

iji j

S F sign u F

S F sign u F

Network Mutualism occurs when J(F)>1:

more positive relations than negative ones

Oyste

r Exam

ple

10

10)( FJ

11

25)( FJ


Find Utility regime

Systems with multiple values for J(F)

Each J(F) value represents a possible regime for system

Find regime that maximizes J(F)

sU3

sU2

sU1

J F( )1

7

2 J F( )

3

5

4J F( )

2

6

3

x2

x3

x1

sU

?

?

Example:


5. Urban Metabolism:

Case study of Four Chinese Cities


Zhang et al. 2010. Ecol. Model. 1865-1879.

Beijing

Chongqing

Shanghai

Tianjin

Energy

transformation

sector

Energy

exploitation

sector

I ndustrial

sector

P rimary energySeco nd ar y ene rg y

Input

Output

Input

LossLoss

Loss

By pr odu c t r eso ur ce re co ve ry

B yp ro du ct resou rce recove ry

L iving

sector

P rimary energy

Primary energy

Secon da ry en e rgy

By pr od uc t r eso ur ce r ecov e ry

B yproduct

resource

recovery

Loss

Byp roduct r

es ource

recove ry

Input

Output

Input

Output

Output

Conceptual model of urban energy metabolic processes


L iving sector

i=4

I ndustrial

sector

i=3

E nergy

exploitation

sector

i= 1

y1

f41

f21 f 31

z1

f 25

z2 y2

z4

z 3

f32

f35

f54

Energy

transformation

sector

i= 2

R ecovery

i=5

f42 f 52

f53

Ecological network of urban energy metabolism


Beijing (FB) Shanghai (FS)1 2 3 4 5 1 2 3 4 5

1 0 0 0 0 0 1 0 0 0 0 0

2 0.087 0 0 0 0 2 0.093 0 0 0 1.0363 0 1.929 0 0 0 3 0.009 2.946 0 0 0.0084 0 0.080 0 0 0 4 0 0.143 0 0 0

5 0 0 0 0 0 5 0 0.004 1.032 0.008 0

Tianjin (FT) Chongqing (FC)1 2 3 4 5 1 2 3 4 5

1 0 0 0 0 0 1 0 0 0 0 0

2 0.517 0 0 0 0.080 2 1.425 0 0 0 0.0823 0.024 1.045 0 0 0.119 3 1.514 0.627 0 0 0.3464 0 0.175 0 0 0 4 0.106 0 0 0 0

5 0 0 0.199 0 0 5 0 0.001 0.427 0 0

Direct flows among sectors (units: 107 t standard coal eq.)

0.070

0.553

0.272 0.105

-0.4 -0.2 0.0 0.2 0.4

1

2

3+4

5

0.008

0.047

0.602

0.308

0.035

-0.4 -0.2 0.0 0.2 0.4

1

2

3+4

5

0.013

0.208

0.521

0.236 0.023

-0.4 -0.2 0.0 0.2 0.4

1

2

3+4

5

0.739

0.046

0.139

0.042

0.034

-0.4 -0.2 0.0 0.2 0.4

1

2

3+4

5

Beijing Shanghai

Tianjin

Chongqing

Ecological structure of the urban energy metabolic system.

Sectors: 1 energy exploitation; 2 energy transformation; 3 industrial;

4 household; 5 recovery.


Beijing sgn(UB) Shanghai sgn(US)1 2 3 4 5 1 2 3 4 5

1 + – + + 0 1 + – + + –

2 + + – – 0 2 + + – – +

3 + + + – 0 3 + + + – –

4 + + – + 0 4 + + – + +

5 0 0 0 0 0 5 – – + + +

Tianjin sgn(UT) Chongqing sgn(UC)1 2 3 4 5 1 2 3 4 5

1 + – + + – 1 + – – – –

2 + + – – + 2 + + – – +

3 + + + – – 3 + + + – –

4 + + – + + 4 + – – + –

5 – – + + + 5 – – + + +

Integral relations for urban metabolic systems

Sectors: 1 energy exploitation; 2 energy transformation; 3 industrial;

4 households; 5 recovery


2.25

11)(

BUJ

92.013

12)(

CUJ

78.19

16)(

TUJ

78.19

16)(

SUJ

Conclusions

Cities dependent on energy resources

Urban trophic structure mostly inverted

Additional energy recovery systems needed

3 of 4 cities showed mutualistic metabolic relations

Households and industry always in competition for energy


THANK YOU FOR YOUR ATTENTION

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Urban Energy Metabolism using Ecological Network Analysis