The structure, evolution and sustainability of urban socio-economic system

Ecological Informatics 10 (2012) 2–9

Contents lists available at SciVerse ScienceDirect

Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf

The structure, evolution and sustainability of urban socio-economic system G.Y. Liu, Z.F. Yang ⁎, M.R. Su, B. Chen State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China

a r t i c l e

i n f o

Article history: Received 22 April 2011 Received in revised form 13 August 2011 Accepted 11 October 2011 Available online 19 October 2011 Keywords: Ecological network analysis Extended exergy analysis Structure Utility Urban ecological economic system

a b s t r a c t Analyzing the structure and functioning of the urban system revealed ways to optimize its structure by adjusting the relationships among compartments, thereby demonstrating how ecological network analysis can be used in urban system research. Based on the account of the extended exergy utilization in the sector of urban socio-economic system, which is considered as the composition of extraction (Ex), conversion (Co), agriculture (Ag), industry (In), transportation (Tr), tertiary (Te) and households (Do) sectors, an urban ecological network model is constructed to gain insights into the sustainable urban development process. Taking Beijing city as a model case, the network accounting and related ecological evaluation of a practical urban economy are carried out in this study in the light of flux, utility and structure analysis. The results showed Beijing had made some progress in moving manufacturing economy into service economy during the 10 years. The increased output of the domestic sector (labor services) relieves the energy demand being placed on agricultural, industrial, transportation and tertiary sectors. The transportation sector was becoming a controlling factor to fluctuate the other sectors and the cross-regional trade is beginning to overshadow the crucial role of local manufacturing, which is due partly to the restructuring of the Beijing's commodity markets since the 1990s. The major implication of our work is that the exergy-based network analysis can be refined to become an integrative tool for evaluation, policy-making and regulation for ecosystem management concerning structure and efficiency at urban levels. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Following the ecological metaphor, urban socio-economic system represents a type of agglomeration economy (Ashton, 2008), which will benefit from knowledge of how ecosystems function under exploited conditions (Patten, 2010). The sustainable urban ecosystem would be expected to evolve based on the maintenance of healthy natural ecosystem functions, economic opportunities and desirable interactions among urban sectors with each other and the environment. In order to achieve these goals, a location-specific understanding of socio-economic subsector's interactions is necessary (Fath, 2006; Fath et al., 2008; Zhang et al., 2010a,b). Since the mid-1980s, a lot of experts and scholars have carried out thorough and meticulous researches into the urban socio-economic operation. However, the main urban study fields have been focused on the fluxes of single elements and on ‘nutrition’ with respect to that corresponding element, while the analysis of sector's interactions, system structure and evolution are seldom mentioned (Fischer-Kowalski, 1998; Huang et al., 2006). Urban structure focuses on how patterns in social relationships shape organizations and affect city itself and its subsectors' evolution and sustainability within those organizations (Scott, 2004). Urban ecological network analysis can be used to examine the ordered

⁎ Corresponding author. Tel.:+86 10 58807951; fax: + 86 10 58800397. E-mail address: [email protected] (Z.F. Yang). 1574-9541/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2011.10.001

arrangements of relations that are contingent upon exchange among members of urban systems (Wellman and Berkowitz, 1988). Ecological Network Analysis (ENA) is an environmental application of input–output (I–O) analysis, which was pioneered by Leontief (1941) and since then has also been applied to study energy flows in the economic system (Costanza, 1980; Costanza and Herendeen, 1984). According to Rowley (1997), the purpose of network analysis is to examine relational systems to determine how the nature of relationship structures impacts behavior, and three key questions should be answered preliminarily for a range of purposes, including: (a) What are the boundaries of the network under study? (b) What type(s) of value data should be collected for assessing the strength of the ties? (c) Are the exchange ties between network partners reciprocal? Several researchers are vigorously examining the structure and functioning of urban socio-economic system and exploring the consequences of various ecosystem change scenarios based on recombination and conceptualizations of the urban nodes (Borrett et al., 2007; Christensen and Pauly, 1993; Fath, 2007; Fath and Halnes, 2007; Fath and Patten, 1999; Patten, 1991; Wulff et al., 1989), requantification of the exchange flux (Chen and Qi, 2007; Ertesvåg, 2001, 2005; Scheffer et al., 2000; Wall, 1990; Zhang and Chen, 2010), and sector symbiosis linkages (Fath and Killian, 2007; Shevtsov et al., 2009; Tollner et al., 2009; Ulanowicz, 2004; Zhang et al., 2010a,b). However, researchers always have focused on documenting individual cases by using different conceptualizations of the systems, terminology and methods, which reduces the possibilities of generalizing and comparing

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

findings across them. Preliminary work is still needed to characterize urban ecosystems systematically — their structure, functions and mechanisms through which different types of changes happen. This paper tries to address the need to characterize urban socioeconomic system in a systematic way by developing a framework that integrates ecological, economic and organizational theories and methodologies that could be applied to cases of wide time scales. A single case is analyzed to illustrate how such a framework and methodologies can be applied to provide deeper insight into how regional ecosystems are structured, function and develop, and provided the implications of these attributes for system sustainability. The main questions answered through this research are: what are the organizational structures among actors at various levels in urban systems? How do these structures change over time? What is the role of sectoral relations in these organizational structures, particularly with regard to observed sectoral symbiosis linkages? As a continuation of our earlier efforts of unified analysis based on extended exergy for quantifying the flux of exchange in Beijing society (Liu et al., 2011a) and the ecological network model for the urban metabolic system (Liu et al., 2011b; Zhang et al., 2010a,b), the present work provides an ecological network determination of sectoral linkages, utility relations and structural characteristics on the urban ecological economic system, with emphasis on a joint application of the extended exergy synthesis and ecological network analysis methods for regional comparison. 2. Methodology

3

system compartments have been chosen following Wall's and Sciubba's approach. The analysis is also built on the earlier work of Chen and Qi (2007) and Zhang and Chen (2010) for the societal exergy utilization of Chinese society. A typical systems diagram can be shown in Fig. 1. In this study, the entire Chinese society system is broken down into the following sectors: Ex-sector: extraction, including mining and quarrying, oil refining and processing, and the inflow of energy carriers from the external environment; Co-sector: conversion, comprising heat and power plants; Ag-sector: including harvest, forestry, fishery, and food processing; In-sector: manufacturing industry except food industry and oil refineries; Tr-sector: transportation services; Te-sector: tertiary, including construction and other services; Do-sector: domestic sector, households. The domestic sector has changed from a final consumption to an important labor exergy inflow transferring to other sectors. In this study, capital flows would not be accounted into extended exergy in order to avoid double-counting because one part in capital flows is the relatively equivalent value of resource, commodity and labor exergy and the other part in capital flows used in banks and governments would not be accounted due to the lack of reliable data. Thus the boundary of the model is confined to the whole city without regard to the bank system and government.

2.1. The urban ecological network model 2.2. Extended exergy accounting of inter-sectional flows In urban systems, sectors consume the materials (goods and services) produced by others, and can be modeled as a food web with trophic structure (Hardy and Graedel, 2002). At the urban level, the

To quantify the network, the flows of the chosen exergy into and out of each compartment should be determined. The input fluxes

Fig. 1. Systems diagram for urban ecological 8-node network model.

4

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

include imports from abroad (resources such as primary and secondary fuels, electricity, ores, products, etc.) and free local resources (natural resources such as agricultural products, livestock, wood, etc.), which are combined in the pathway crossing the system boundary and in the system respectively. Resource exergy includes general resource exergy and water resource exergy. The exergy consumed relies on the reference environment. Szargut (1985, 1988, 1989) proposed the method of the standard chemical exergy accounting, who chose the atmosphere, ocean and the earth's crust as the reference environment. The natural resources are traditionally described as energy resources and material resources. Wall (1977, 1987) introduced the concept of exergy, which is a unified measure of matter, energy and information, into resource accounting. Exergy for a given system is defined as the maximal amount of work that can be extracted from the system in the process of reaching equilibrium with its local environment, chosen to have a direct bearing on the behavior of the system with respect to the time and length scales, depending on the observer's objectives and knowledge (Chen et al., 2008; Szargut, 1971, 1980, 1985, 2004), that is,
 tot tot Ex ¼ T 0 Seq −S

ð1Þ

tot and S tot are the where T0 is the temperature of the environment, Seq entropies in thermodynamic equilibrium and at the given deviation from equilibrium, respectively. Of the total system Ex acts as a combination of the given system and the local environment. The chemical exergy of general resource can be expressed as the following formula (Wall, 1977, 1987):

Ex ¼ ∑ ni ðμ i −μ i0 Þ þ RT 0 ∑ ni ln I

i

ci ci0

ð2Þ

where T0 is the temperature of the environment; n is mol; μi is the chemical potential for the material (substance) i in relation to its standard state; μi0 is the chemical potential for the material in the environment in relation to its standard state; c is concentration i in its present state and ci0 is the chemical concentration of substance i in its environmental state. With the formula above, exergy especially chemical exergy can be accounted. The detailed standard chemical exergy results of substances and elements can be referred to Szargut (2005). An ecosystem network is described by the direct flow matrix, F, which includes all flows between n-compartments inside a system but excludes relations between compartments and the environment. We denote flows between compartments via fi,j (oriented from columns to rows such that fi,j is a flow from j to i). Then due to additivity of exergy, the exergy balance model for each node can be expressed as: IEa;i þ Ne;i þ

j≠i X

f ij −OEa;i −

j

j≠i X

f ji −ELoss;i ¼ 0;

ð3Þ

j

where IEa, i/OEa, i is the imports and exports from/to abroad, Ne, i is the free local resources into the i-th node, fij/fji is the input and output from/to other sectors, and ELoss, i is the exergy loss of the i-th node.

In the general balancing procedure we employ a balancing in which a matrix comprises elements for the exchanges between nodes, local input, imports, exports and loss. 2.3. Extended exergy-based urban ecological network indices To illustrate different perspectives on the conventional interpretation of urban ecological economic attributes, we may define several indices with reference to those original ecological network parameters (Cohen, 1993; Fath, 2004; Fath and Halnes, 2007; Patten, 1985; Patten, 2010). The methods and indices derived from ENA are listed in Table 1. 2.3.1. Flux analysis n P

The throughflow into each compartment is given by

Tþ i ¼

f ij þ IEa;i þ Ne;i , and analogously, the throughflow out of each comn P f ji þ OEa;i þ ELoss;i . At the steady-state partment is given by T − i ¼ j¼1

j¼1

compartmental inflows and outflows are equal and we get the total n P T i , and I is the idensystem throughflow (TST) value TST ¼ IT ⋅T⋅I ¼ i¼1

tity matrix. As the total use of economic investment, it indicates the total size of a regional consumption. External dependence degree (EDD), which indicates whether a sector has a strong dependence degree of the import, is defined as n P

IEa;i EDD ¼ i¼1 : TST

ð4Þ

The higher the value of EDD, the greater the dependence degree of the corresponding sector. Renewability index (RI) is defined as n P

N e;i RI ¼ i¼1 : TST

ð5Þ

This is the ratio of local resources use to TST. In the long run, only economic patterns with higher renewability index are sustainable. Yield index (YI), which indicates the whole yield of the system, is defined as n P

OEa;i YI ¼ i¼1 : TST

ð6Þ

2.3.2. Utility analysis The methodology for network utility analysis is described in following literatures (Fath, 2007; Fath and Patten, 1999). In this method, a direct utility matrix is constructed and used to analyze the functions within the network (Fath and Borrett, 2006). From the urban network structure that we derived, we analyzed the mutual relationships between elements of the network. In the network utility which gives us a direct net flow matrix D = D(F) based on the steady net flows

Table 1 Main methods and indices of ENA methodology. Analytic method

Contents

Indices

Flux analysis

Calculate the flow parameters of material and energy of each compartment within the ecosystem. Represent interconnection patterns between compartments in digraph or adjacency matrix. Analyze the direct and indirect relationships between components and the mutualism they perform of the concerned ecosystems.

TST, EDD, RI, YI, etc.

Structural analysis Utility analysis

Pathways numbers, pathway length. Integrated mutualism index, synergism rate.

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

between compartments normalized by the compartmental throughf −f flow such that dij ¼ ij T i ji ðj; i ¼ 1; …nÞ. In addition to the direct relations proceed directly between two compartments. The integral utility matrix U, which describes the contribution of all direct and indirect relations, is found by summing all powers of D: UðFÞ ¼

∞ X

−1

m

DðFÞ ¼ ðI−DðFÞÞ

:

ð7Þ

m¼0

The result is useful because the signs of U provide the integral relations between network nodes (which may vary from the direct relations) and the overall sign comparisons classify the network according to the degree of mutualism (see Fig. 2). If (su21, su12) = (+, −), compartment 2 exploits compartment 1. If (su21, su12) = (−, +), compartment 2 was exploited by compartment 1. If (su21, su12) = (−, −), then compartment 2 competes with compartment 1, leading to negative impacts for both compartments. By contrast, if (su21, su12) = (+, +), the relationship between the two compartments represents mutualism, in which both compartments benefit from their interaction. If (su21, su12) = (0, 0), the relationship between the two compartments is neutral. We calculate a utility function J(F) as a share of positive relations in sgn(U). JðFÞ ¼

Sþ ðFÞ ; S− ðFÞ

ð8Þ

where S+(F) is the number of all positive relations, and S−(F) is the number of all negative relations in matrix U(F). Thus, J(F) characterizes the ratio between the number of all positive and that of all negative relations. We consider J(F) as a goal function related to network mutualism in the energy system. When J(F) > 1, mutualism occurs, which indicates that the system overall has more positive relations than negative ones. 2.3.3. Structure analysis Couples in the prolonged group reported increased companionship to a lesser extent and remained more active with the network. Node couple degree (CDij) is defined as: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ! u m k k! m pk ⋅nk u X pij ⋅nij X ji ji t CDij ¼ ⋅ k k k¼1 k¼1 rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

 ¼ PLTij ⋅K−1 ⋅nij ⋅ PLTji ⋅K−1 ⋅nji ; 0

ð9Þ

1

n B X C
 2 B C wij CDij C= n −n ; CD ¼ 2⋅B @ A

i; j ¼ 1 i≠j; i > j

ð10Þ

5

T

w ¼ ðw1 ; w2 ; ⋯; wn Þ !T ! n n n n X n X X X X γk1 ; γk2 ; ⋯; γkn = γ ki ; ¼ k¼1

where wi ¼

k¼1 n P k¼1

k¼1

ð11Þ

i¼1 k¼1

γk;i measures the contribution that i-th node makes to

the whole network system. 2.4. Case study and data sources Historical and contemporary information about the Beijing's economic and ecological conditions was obtained from published literature (Yearbook). Quantitative economic census data were retrieved from the Beijing Census Bureau which provided annual accounts of establishments by sector. Natural resource data, such as land use and water supply, were obtained from the Beijing Environmental Protection Agency and Beijing Forestry Bureau. 3. Results Examination of the various aspects of the Beijing ecosystem includes a discussion of Beijing's exergy flux, exergy utilization efficiency and urban system structure, which together lend integrative insight to the urban network system. The extended exergy of seven sectors are calculated in earlier reference (Liu et al., 2011a,b). The contribution of resource use and the flow values can be listed, as shown in Fig. 2 3.1. The evolution of exergy conversion process from 1996 to 2006 Fig.3 depicts the exergy conversion process in Beijing from 1996 to 2006, in which all the data available are from the statistical yearbook of the government. The resource exergy flows go from left to right, that is, from the resource base to the consumer, and next to the outside, in which the width of the flow is determined by its exergy content. To simplify the conversion figure, only the major exergy flows are presented (>1.5 E + 10 MJ) and the inflows of the diagram are ranked by the basic resource classification (Wall, 1987) and extended inputs including capital, labor and services (Sciubba, 2001). The whole exergy flow conversion process is denoted by block diagram (Wall, 1977, 1987) and Domestic sector acts as an output source of labor. The resources base is listed on the left side of the conversion diagram whereas the resources demand in the society appears on the right side, determining the level of the conversion chain of the resources. In 1996, linkages between sectors were weak (see Fig. 3a). The maximum exergy input to Beijing is imported energy. Most of the energy and capital gave direct impetus to industrial sector, which was second

Fig. 2. Systems diagram for ecological network analysis.

6

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

(a) Extended exergy analysis of seven sectors in 1996

(b) Extended exergy analysis of seven sectors in 2006

Fig. 3. Extended exergy analysis of seven sectors in (a) 1996 and (b) 2006.

largest dominated by local labor. In-sector also had links with firms in the trade sector to sell their goods locally and provided the largest economic output. Meanwhile, Tertiary sector was fundamentally taking shape and absorbing the largest laborers in 1996. But the economic output is small. The fluxes of the agricultural and transportation sectors only make up a very small proportion. The supply of the basic foods to the urban areas depended on locally grown products.

By 2006 (see Fig. 3b), exergy linkages had been much stronger between sectors, and imports and exports had grown significantly. The total exergy nearly doubled and local resources input fell sharply. The size of agricultural sector had remained relatively constant, though it started to rely on imported food. Transportation sector became a new investment focus which was dominated by local labor and capital. There was strong presence of tertiary sector which jumped to the most exergy

7

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

and it remains to be seen how the composition of the urban sectors and the relationship among them will change during the period of perturbation.

Table 2 Node couple degree of urban network. Flux

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

TST(PJ)

EDD

RI

YI

6522.99 6441.79 6182.44 6460.11 6769.28 6871.94 6912.99 7669.80 8016.74 10565.26 11883.84

33.22% 32.41% 31.11% 31.63% 32.41% 30.87% 29.84% 31.24% 32.76% 35.78% 38.57%

5.40% 5.46% 5.62% 4.39% 3.68% 4.09% 4.24% 4.07% 4.10% 2.95% 1.92%

18.76% 18.02% 16.89% 17.50% 17.87% 16.58% 16.27% 16.50% 17.78% 19.82% 23.68%

3.2. Analysis of the ecological network indicators for Beijing 3.2.1. Flux analysis Four indicators describing the exergy throughflow and utilization of Beijing are calculated in Table 2. The total system exergy throughflow (TST) in Beijing nearly doubled from 6522.99 PJ (in 1996) to 11883.84 PJ (in 2006), in which Te-sector took up the largest proportion each year except in 1997 (see Table 3). The percentages of exergy throughout in Te-sectors, which acted as the leading forces, rose alternatively with those of In-sector. However, the external dependence degree of Beijing is high (EDD of the whole city reduced firstly and increases afterward with the increase of energy consumption). In 2006, EDD was 38.57% and RI is 1.92%, revealing that Beijing strongly depends on the import of energy. In addition, the immigrant outside the province is an important extended exergy input in Beijing (increasing from 6.3 PJ). This is due to the fact that Beijing, as an immigration city, starts highly developing labor intensive service industry and absorbs foreign capitals which make a special industry strategy given its actual circumstances.

Table 3 The total system exergy throughflow of each sectors (Unit: PJ).

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Ex

Co

Ag

In

Tr

Te

Do

1297.32 1249.08 1192.89 1259.20 1313.34 1364.14 1332.26 1454.75 1405.10 1866.81 2180.03

425.50 417.76 404.38 419.43 458.79 437.06 468.28 496.99 560.33 667.02 694.75

243.83 228.37 227.17 222.49 236.77 247.64 232.07 298.70 364.34 471.66 398.44

1462.04 1499.13 1364.90 1340.69 1333.43 1238.95 1277.60 1259.53 1237.60 1548.48 1641.16

199.15 203.97 216.54 215.30 218.47 235.36 247.92 286.39 292.54 468.02 633.01

1493.46 1481.61 1470.06 1630.53 1764.16 1827.21 1855.50 2191.88 2435.34 3270.09 3804.15

1401.69 1361.87 1306.50 1372.48 1444.31 1521.59 1499.36 1681.56 1721.49 2273.18 2532.30

3.2.2. Utility analysis Based on the flow matrix F, the indirect normalized flow matrix U and sgn (U) matrix were computed using network utility analysis among 7 compartments. Sgn (U) is presented in Table 4. From 1996 to 2006, the ecological relationships between the extraction and agricultural sectors changed from mutualism to an exploitation relationship, with (su31, su13) changing from (+, +) to (−, +), which represents that extraction changed from mutualism with the agricultural sector to consuming energy from extraction to agriculture production during this period. The results agree with the fact that Beijing had transformed the traditional primary agricultural production to economic agricultural production, such as vegetables and flowers, for enhancing the profit of agro-products and increasing farmers' incomes. The relationships between the conversion and domestic sectors changed from

consuming sector and offered the largest capital output. The region's shift to a service-based economy can be characterized as the postindustrial phase in urban ecosystem development. It is logical that Beijing has had achieved some success of symbiosis initiatives in moving manufacturing economy into service economy during the 10 years. Thus, the strategies adopted with Beijing's development results the inter-sector exergy linkages and structure shift to the previous periods,

Table 4 The Beijing's integral utility matrices, U, for the ecological economical system and the corresponding sgn matrices. U1996=

Ex

Co

Ag

In

Tr

Te

Do

Ex Co Ag In Tr Te Do

0.8036 0.5267 0.0443 0.2148 0.2208 0.0686 − 0.0381

− 0.1922 0.8679 0.0067 − 0.0161 − 0.0441 − 0.0010 − 0.0060

0.0011 − 0.0092 0.9884 − 0.0174 − 0.0200 0.0058 − 0.0395

− 0.1820 − 0.2512 − 0.0571 0.8620 − 0.0393 0.0218 − 0.2701

− 0.0220 − 0.0150 − 0.0079 − 0.0322 0.9780 − 0.0078 − 0.0373

0.0088 − 0.0178 − 0.1453 − 0.1505 − 0.1120 0.9439 − 0.1649

− 0.1163 − 0.1226 0.2004 0.1818 0.2772 0.2087 0.8663

U2000=

Ex

Co

Ag

In

Tr

Te

Do

Ex Co Ag In Tr Te Do

0.7524 0.4636 0.0579 0.2936 0.2800 0.0745 − 0.0485

− 0.1913 0.8743 0.0053 − 0.0339 − 0.0591 0.0056 0.0015

− 0.0005 − 0.0098 0.9872 − 0.0164 − 0.0212 0.0028 − 0.0414

− 0.2485 − 0.2786 − 0.0622 0.8348 − 0.0775 0.0100 − 0.2118

− 0.0313 − 0.0200 − 0.0118 − 0.0403 0.9693 − 0.0088 −0.0431

0.0239 − 0.0378 − 0.1574 − 0.1565 − 0.1333 0.9395 − 0.1977

− 0.1259 − 0.1454 0.2187 0.1362 0.2746 0.1991 0.8796

U2006=

Ex

Co

Ag

In

Tr

Te

Do

Ex Co Ag In Tr Te Do

0.8094 0.4238 0.0310 0.3058 0.2360 0.0724 − 0.0314

− 0.1619 0.9070 0.0007 − 0.0137 − 0.0388 0.0063 − 0.0040

− 0.0018 − 0.0057 0.9922 − 0.0082 − 0.0124 0.0004 − 0.0335

− 0.1919 − 0.2197 − 0.0463 0.8835 − 0.0625 0.0010 − 0.1430

− 0.0541 − 0.0321 − 0.0126 − 0.0496 0.9656 − 0.0123 − 0.0604

− 0.0051 − 0.0776 − 0.1336 − 0.1841 − 0.1256 0.9312 − 0.2565

− 0.1226 − 0.1266 0.1795 0.1293 0.2123 0.1892 0.8895

2 6 Ex 6 6 Co 6   6 Ag Sgn UBeijing;1996 ¼ 6 6 In 6 6 Tr 6 4 Te Do 2 6 Ex 6 6 Co 6   6 Ag Sgn UBeijing;2000 ¼ 6 6 6 In 6 Tr 6 4 Te Do 2 6 Ex 6 6 Co 6   6 Ag Sgn UBeijing;2006 ¼ 6 6 In 6 6 Tr 6 4 Te Do

Ex þ þ þ þ þ þ −

Co − þ þ − − − −

Ag þ − þ − − þ −

In − − − þ − þ −

Tr − − − − þ − −

Te þ − − − − þ −

3 Do − 7 7 − 7 7 þ 7 7 þ 7 7 þ 7 7 þ 5 þ

Ex þ þ þ þ þ þ −

Co − þ þ − − þ þ

Ag − − þ − − þ −

In − − − þ − þ −

Tr − − − − þ − −

Te þ − − − − þ −

3 Do − 7 7 − 7 7 þ 7 7 þ 7 7 þ 7 7 þ 5 þ

Ex þ þ þ þ þ þ −

Co − þ þ − − þ −

Ag − − þ − − þ −

In − − − þ − þ −

Tr − − − − þ − −

Te − − − − − þ −

3 Do − 7 7 − 7 7 þ 7 7 þ 7 7 þ 7 7 þ 5 þ

8

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9

competition to an exploitation relationship and back again, with (su72, su27) changing from (−, −) to (−, +), and then to (−, −), which indicates that Beijing's domestic sectors changed from competition with the conversion sector to diversion of energy from conversion sector, and then back to competition with conversion sector. The results represented that along with population increase, domestic sector faced the pressure of consumer demands for energy. The ecological relationship between the extraction and tertiary sectors changed from mutualism to an exploitation relationship, with (su61, su16) changing from (+, +) to (−, +), which represents that during this period extraction changed from mutualism with the tertiary sector to consuming energy from extraction to tertiary production, such as goods and services. The relationships between the conversion sector and the tertiary sector were (su62, su26) = (−, −) initially, and then (−, +), which represents the change from an exploitation relationship to mutualism. The changes in the relationship between the agricultural and domestic sectors were (su54, su45) = (−, −) initially, and then (−, +), which represents the change from competition to an exploitation relationship. This indicates that during this period, Beijing's tertiary sector changed from competition with the conversion sector to diversion of energy from the conversion sector. Except for the ecological relationships mentioned above, the other 17 pairs of ecological relationships remained constant during the study period. The ecological relationships between the Co- and Ex-, In- and Ex-, Tr- and Ex-, Do- and Ex-, Ag- and Co-, In- and Co-, Trand Co-, Te- and Co-, Do- and Co-, In- and Co-, Tr- and Ag-, Te- and Ag-, Tr- and In-, Te- and In-, Te- and Tr-, were all exploitation ones (= (−, +)), which indicates the pressure of consumer demands for energy. Meanwhile, the ecological relationships between the Doand Ex-, In- and Co-, Tr- and Co-, Do- and Co-, In- and Ag-, Tr- and Ag-, Te- and Tr-, were all competition relationships, with (−, −), which reflects competition among these sectors for utilization of the local resources. The ecological relationships between the Do- and Ag-, Do- and In-, Do- and Tr-, Do- and Te-, were all mutualism, with (+, +). The results reflect the mutualism between the domestic sectors and other sectors: the increased output of the domestic sector (labor services) relieves the energy demand being placed on agricultural, industrial, transportation and tertiary sectors. Based on the signs in the integral utility matrices J(F), there are 21 positive signs and 29 negative signs in 1996, 22 positive signs and 28 negative signs in 2000, and 20 positive signs and 30 negative signs in 2006. Although the mutualism indices fluctuated year by year, all the values of J(F) were less than 1, which indicated that the system overall has more positive relations than negative ones.

Table 5 Node couple degree of urban network.

3.2.3. Structure analysis The couple degree (CD) indicator measures the structural trade intensity of the system. All the values of the CD are non-zero, meaning all the sectors based on current division method lie on a huge social cyclic route. Some node couple degrees, such as Tr- and Do-, Coand Tr-, Ag- and Tr-, Te- and Tr-, have experienced a gradual increase, which reveals that the transportation sector was becoming a controlling factor to fluctuate the other sectors (Table 5). Meanwhile, the CDs of other interactions, including In- and Ex-, Do- and Ex-, Agand In-, In- and Te-, In- and Do-, decreased during the ten years. The results show that cross-regional trade is beginning to shift the crucial role of local manufacturing production, which is due partly to the restructuring of the Beijing's commodity markets since the 1990s.

Acknowledgment

4. Conclusions To provide an effective avenue to evaluate the capacity of the urban socio-economic system, we incorporate the network analysis framework into this study to integrate indicators of flux, utility and structure. Based on the account of the extended exergy utilization,

Co

Ag

In

Te

Tr

Do

Beijing1996 Ex – Co 0.17 Ag 0.18 In 0.22 Te 0.17 Tr 0.25 Do 0.33

Ex

– – 0.02 0.06 0.01 0.10 0.17

– – – 0.07 0.02 0.10 0.18

– – – – 0.06 0.14 0.22

– – – – – 0.09 0.17

– – – – – – 0.25

– – – – – – –

Beijing2000 Ex – Co 0.18 Ag 0.18 In 0.20 Te 0.17 Tr 0.28 Do 0.33

– – 0.02 0.04 0.01 0.12 0.17

– – – 0.05 0.02 0.12 0.18

– – – – 0.04 0.14 0.20

– – – – – 0.11 0.17

– – – – – – 0.27

– – – – – – –

Beijing2006 Ex – Co 0.17 Ag 0.17 In 0.18 Te 0.16 Tr 0.28 Do 0.31

– – 0.03 0.04 0.02 0.14 0.17

– – – 0.04 0.02 0.14 0.17

– – – – 0.03 0.15 0.18

– – – – – 0.14 0.17

– – – – – – 0.29

– – – – – – –

which is considered as the composition of extraction, conversion, agriculture, industry, transportation, tertiary and households sectors, an urban ecological network model is constructed to gain insights into the sustainable urban development process. The case of Beijing was used to demonstrate how this framework could be applied. This study is relevant for understanding the historical development of such systems. Although it is a small regional economy, economic geographical characteristics were observed during the transition of this region from a social sectoral cluster. The level of cooperation that spurred the development of the cluster arose through a symbiosis initiative, thus the “follow the materials” approach of industrial ecology provided insight, which may have been otherwise overlooked. The integrated framework enabled identification of the system's interaction and structure as well as resulting and potential problems associated with the sectors. While the case study had a unique history of development, the framework can be used to insightfully and holistically study other self-organizing urban systems.

This work is supported by the National Natural Science Foundation of China (Grant no. 40871056), the Fundamental Research Funds for the Central Universities and the National Ministry of Science and Technology (Grant No. 2007BAC28B03). References Ashton, J.R., 2008. The new public health. International Encyclopedia of Public Health 2008, 521–528. Borrett, S.R., Fath, B.D., Patten, B.C., 2007. Functional integration of ecological networks through pathway proliferation. Journal of Theoretical Biology 245 (1), 98–111. Chen, G.Q., Qi, Z.H., 2007. Systems account of societal exergy utilization: China, 2003. Ecological Modelling 208, 102–118. Chen, G.Q., Jiang, M.M., Yang, Z.F., Chen, B., Ji, X., Zhou, J.B., 2008. Exergetic assessment for ecological economic system: Chinese agriculture. Ecological Modelling 4, 14–24. Christensen, V., Pauly, D., 1993. Trophic models of aquatic ecosystems. ICLARM Conference Proceedings 26, 390. Cohen, J.E., 1993. Improving food webs. Ecology 74 (1), 252–258. Costanza, R., 1980. Embodied energy and economic valuation. Science 210, 1219–1224. Costanza, R., Herendeen, R.A., 1984. Embodied energy and economic value in the United States economy: 1963, 1967, and 1972. Resources and Energy 6, 129–164. Ertesvåg, I.S., 2001. Energy, exergy and extended exergy analysis of the Norwegian system. Journal of Power & Energy 215, 62–141.

G.Y. Liu et al. / Ecological Informatics 10 (2012) 2–9 Ertesvåg, I.S., 2005. Energy, exergy, and extended-exergy analysis of the Norwegian society 2000. Energy 30, 649–675. Fath, B.D., Patten, B.C., 1999. Review of the foundations of network environ analysis. Ecosystems 2, 167–179. Fath, B.D., 2004. Distributed control in ecological networks. Ecological Modelling 179, 235–245. Fath, B.D., 2006. Sustainable ecosystem patterns. Modelling Socio-Natural Systems Symposium, Stockholm, Sweden, 25 October. Fath, B.D., 2007. Community-level relations and network mutualism. Ecological Modelling 208, 56–67. Fath, B.D., Borrett, S.R., 2006. A MATLAB function for Network Environ Analysis. Environmental Modelling and Software 375–405. Fath, B.D., Halnes, G., 2007. Cyclic energy pathways in ecological food webs. Ecological Modelling 208, 17–24. Fath, B.D., Killian, M.C., 2007. The relevance of ecological pyramids in community assemblages. Ecological Modelling 208 (2–4), 286–294. Fath, B.D., Rovenskaya, E., Lobanova, G., 2008. Network analysis of energy transportation systems: a case study of Eurasian natural gas pipeline model. Proceedings: Advances in Energy Studies Workshop. Graz, Austria, June 30–July 2. Fischer-Kowalski, M., 1998. Society's metabolism: the intellectual history of materials flow analysis part I 1860–1970. Journal of Industrial Ecology 2 (1), 61–78. Hardy, C., Graedel, T.E., 2002. Industrial ecosystems as food webs. Journal of Industrial Ecology 6 (1), 29–38. Huang, S.L., Lee, C.L., Chen, C.W., 2006. Socioeconomic metabolism in Taiwan: energy synthesis versus material flow analysis. Resources, Conservation and Recycling 48 (2), 166–196. Leontief, W., 1941. The structure of American economy, 1919–1929. Cambridge, (mors): Harvard University Press, (Second Ed. 1951, New York, Oxford University Press). Liu, G.Y., Yang, Z.F., Chen, B., Su, M.R., Zhang, Y., Zhang, L.X., Jiang, M.M., 2011a. Extended exergy analysis of urban socioeconomic system: a case study of Beijing, 1996–2006. International Journal of Exergy 9 (2), 168–191. Liu, G.Y., Yang, Z.F., Chen, B., Zhang, Y., 2011b. Ecological network determination of sectoral linkages, utility relations and structural characteristics on urban ecological economic system. Ecological Modelling 222 (15), 2825–2834. Patten, B.C., 1985. Energy cycling in the ecosystem. Ecological Modelling 28, 1–71. Patten, B.C., 1991. Network ecology: Indirect determination of the life-environment relationship in ecosystems. In: Higashi, M., Burns, T.P. (Eds.), Theoretical Studies of Ecosystems. Cambridge University Press, Cambridge. Patten, B.C., 2010. Natural ecosystem design and control imperatives for sustainable ecosystem services. Ecological Complexity 7 (3), 282–291. Rowley, T.J., 1997. Moving beyond dyadic ties: a network of stakeholder influences. Academy of Management Review 22 (4), 887–910.

9

Scheffer, M., Brock, W., Westley, F., 2000. Socioeconomic mechanisms preventing optimum use of ecosystem services: an interdisciplinary theoretical analysis. Ecosystems 3, 451–471. Sciubba, E., 2001. Beyond thermoeconomics? The concept of extended-exergy accounting and its application to the analysis and design of thermal systems. Exergy, An International Journal 1, 68–84. Scott, D.S., 2004. Template for sustainability. International Journal of Hydrogen Energy 29 (1), 17–22. Shevtsov, J., Kazanci, C., Patten, B.C., 2009. Dynamic environ analysis of compartmental systems: a computational approach. Ecological Modelling 220 (22), 3219–3224. Szargut, J., 1971. Thermal economy in Metallurgy. Slask, Katowice. Szargut, J., 1980. International progress in second law analysis. Energy 5 (8–9), 709–718. Szargut, J., 1985. Letter to the editor. Applied Energy 21 (4), 315–316. Szargut, J., 1988. Exergy losses in the chains of technological processes. Bulletin of the Polish Academy of Sciences, Technical Sciences 36 (7–8), 513–521. Szargut, J., 1989. Chemical exergies of the elements. Applied Energy 32 (4), 269–286. Szargut, J., 2004. Optimization of the design parameters aiming at the minimization of the depletion of non-renewable resources. Energy 29, 2161–2169. Szargut, J., 2005. Exergy method. WitPress, Southampton, Boston. Tollner, E.W., Kazanci, C., Schramski, J.R., Patten, B.C., 2009. Control system approaches to ecological systems analysis: invariants and frequency response. Ecological Modelling 220 (22), 3233–3240. Ulanowicz, R.E., 2004. Quantitative methods for ecological network analysis. Computational Biology and Chemistry 28, 321–339. Wall, G., 1977. Exergy — a useful concept within resource accounting. Research Report no.42–77. Institute of Theoretical Physics, Goteborg, Sweden. Wall, G., 1987. Exergy conversion in the Swedish society. Resources and Energy 9, 55–73. Wall, G., 1990. Exergy conversion in the Japanese society. Energy 15 (5), 435–444. Wellman, B., Berkowitz, S.D., 1988. Social structures: A network approach. University of Cambridge, Cambridge. Network analysis in marine ecology: methods and applications. In: Wulff, F., Field, J.G., Mann, K.H. (Eds.), Coastal and Estuarine Studies, 32, p. 284. Zhang, B., Chen, G.Q., 2010. Physical sustainability assessment for the China society: exergy-based systems account for resources use and environmental emissions. Renewable and Sustainable Energy Reviews 14 (6), 1527–1545. Zhang, Y., Yang, Z.F., Fath, B.D., 2010a. Ecological network analysis of an urban water metabolic system: model development, and a case study for Beijing. Science of the Total Environment 408 (20), 4702–4711. Zhang, Y., Yang, Z.F., Fath, B.D., Li, S.S., 2010b. Ecological network analysis of an urban energy metabolic system: model development, and a case study of four Chinese cities. Ecological Modelling 221 (16), 1865–1879.