at SciVerse ScienceDirect

Habitat International 36 (2012) 462e470

Contents lists available

Habitat International

journal homepage: www.elsevier .com/locate/habitat int

Comprehensive carrying capacity of the urban agglomeration in the Yangtze RiverDelta, China

Huimin Liu*

Institute of City Construction & Disaster Management, School of Economics and Management, Tongji University, 1239 Siping Road, Tongji University, Shanghai 200092, China

Keywords:Urban agglomerationComprehensive carrying capacityTime-series global factor analysisSpatial differentiationCluster

* Tel.: þ86 21 65983943.E-mail address: kellyhuizi@yahoo.com.cn.

0197-3975/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.habitatint.2012.05.003

a b s t r a c t

Urban agglomeration (UA) is a complex artificial system. Carrying capacity reflects the environmentalcapability to support human activity. From the perspective of resource supply and demand, the paperselects 12 representative indicators to evaluate the carrying capacity of land, water, transportation andenvironment. 16 cities of the UA in the Yangtze River Delta, China, are selected as data samples.Time-series global factor analysis is employed to extract the principal factors of the index of 2000 and2008. The results show that the comprehensive carrying capacity of the UA tends to benign developmentas a whole except for Shanghai. Carrying capacities of land and water have become the two criticalfactors to restrict economic and social development. Based on the hierarchical cluster analysis, the valuesdifferentiate the UA into significant gradients. The coefficient of variation shows that the spatialdifferentiation is conspicuous and expanding. The paper also proposes some policies for the governmentand planners to successfully design and implement the UA.

� 2012 Elsevier Ltd. All rights reserved.

Introduction

Urban agglomeration (UA) with the core of metropolis isa modern spatial pattern. It has become the principal geographicunit for countries to participate in the global competition and theinternational division of labor. The development of UA hasprofound impacts on the country’s competitiveness and it alsoshows great significance for sustainable and stable economicdevelopment of the country (Allen, 2001). As the most importantspatial development form, UA will promote mega-cities to becomethe new focus for research. It will correspondingly shift theresearches of the city system from the scale structure to the spaceand functional structure. China is currently in the rapid develop-ment period of urbanization (Zheng et al., 2009), and the increasingspeed of the urbanization level rises up to 1.4% annually (Chen &Hao, 2005). UA has also promoted the national industrializationinto an accelerated process. However, whilst pursuing the devel-opment speed of urbanization, they neglect the improvement ofurbanization development quality that should be placed at theforemost position. There is a contradiction between the speed andthe quality. Therefore, UA has become the highly concentratedregion of various eco-environmental problems. First, land pollu-tion, water quality degradation, resource and energy shortages,

All rights reserved.

traffic congestion and other similar factors have brought consid-erable burdens and pressures upon human living standards.Further, the problems would damage the functions of ecologicalservices. Finally, they would hinder the sustainable ecosystemdevelopment and threaten regional even to the national security(Wang, Zhou, & Hu, 2000). The development of UA should be onbehalf of the whole of economy, society and ecology. How tocoordinate the relationship between environmental deteriorationand economic development while ensuring the consistent devel-opment of UA is becoming an emerging scientific subject, whichshould be focused on. Therefore, the studies on the carryingcapacity of UA are drawing more broad attention.

Urban ecosystem is especially distinguished from otherecosystem types. Many dynamic factors and their complex inter-actions affect ecosystem health and human health (Tzoulas et al.,2007). The need has been well recognized to maintain andimprove the urban eco-environment because of its functions ofsupport, provision, regulation, and culture for continued socialdevelopment (MillenniumEcosystemAssessment, 2005). A numberof efforts have been made to establish assessment indicators of therelationship between environmental condition and economicdevelopment. There are some relatively well-known methods suchas the performance assessment of healthy cities (Takano, Nakamura,&Watanabe, 2002), the sub-systemmodel (Guo, Yang, &Mao, 2002)and the Driving ForceePressureeStateeExposureeEffecteActionmodel (Spiegel et al., 2001). However, scholars in different regions

H. Liu / Habitat International 36 (2012) 462e470 463

have difficulties at various levels in acquiring index data. Therefore,there are many differences in the aspect of index selection and itsfocus point. Though there aremanykinds of computationalmethodsto adopt, there is no recognized evaluation model in China.

When carrying capacity is first proposed in the field of ecology,it represents the largest number of a certain kind of living thingscarried by a regional ecosystem (Long & Jiang, 2003; Odum, 1972).Ecologists generally consider the carrying capacity as themaximumvalue of resources provided by a region on the premise of noreduction in the ability to support offspring (Chung, 1988). More-over, planners usually define carrying capacity as the ability ofaccommodating population growth or physical developmentwithout considerable degradation and apparent damage(Schneider, Godschalk, & Axler, 1978). Carrying capacity is theability of a natural or an artificial system to support the variousdemand, which refers to the systematic inherent limits. Oncebeyond it, instability, degradation, or irreversible damagewill occursubsequently (Godschalk & Parker, 1975). As a social conceptfocusing on humans, carrying capacity can also be described as aneconomy scale that the natural system of a region can sustain(Seoul Development Institute, 1999). With the increase of pop-ulation, economic growth and technological advancement, thehuman consumption of resources keeps expanding. The phenom-enons of the over-use of resources will occur. Non-harmoniousrelationships among resources, environments, population andeconomy will arise to hamper sustainable development (Zhu et al.,2010). Carrying capacity not only belongs to the ecology, but alsocan be applied to discuss the problems of economics, eco-economies, geography, environmental science or other sciences(Downs, Gates, & Murray, 2008; Retzer & Reudenbach, 2005).Studies on urban carrying capacity have been conducted in twomain fields. One is focusing on the single carrying capacity ofa scarce resource such as land, water (Feng & Huang, 2008) andother critical mineral resource. The other is concentrating on thecomprehensive carrying capacity closely in related to the economicand social activities of humans, for example, regional carryingcapacity and population carrying capacity, etc. Researchers paymore attention to the determinative physical factors in theassessing process of the urban carrying capacity (Table 1), espe-cially focusing on seven primary factors. Energy and green areas arethe key factors of environmental and ecological carrying capacity.Roads, subway systems, water supply, sewage treatment, and wastetreatment are the determinative factors of urban facilities carryingcapacity (Oh et al., 2005).

As UA is a complex system, studying its sustainability must takea city as both an individuality and an agglomeration’s componentinto consideration (Egger, 2006). Studies either on the single factor

Table 1Determinative components of urban carrying capacity.

Components Definition

Environmental & ecological The degree of human activity that environmentsand ecosystems within an area can supportwithout causing serious degradation or damageon maintenance of quality of life.

Urban facilities The degree of human activity that facilities andservices within an area can support withoutcausing serious degradation or damage onmaintenance of quality of life.

Public perception The amount of activity or degree of changethat can appear before recognizing the visualor psychological quality of environmentdifferently than previously perceived.

Institutional The administrative/financial condition of acity that can maintain the optimal scale ofurban development toward public goals.

carrying capacity or on the comprehensive carrying capacityrequire all-around and accurate data. Assessment approach shouldbe applied for data comparison. At present, most assessmentstudies on the comprehensive carrying capacity of UA concentrateon the respective cities in the space and the static comparisons inthe time. Spatial differentiation analysis and dynamic comparisonare rarely applied. This paper defines the concept of the carryingcapacity of UA as a system that includes the single carryingcapacities of four resources (land, water, transportation and envi-ronment) and the comprehensive carrying capacity. Firstly, basedon the equalization between resource supply and demand, thepaper will establish the assessment index of 16 cities of the UA inthe Yangtze River Delta (YRD), China. Secondly, it will utilize theapproach of time-series global factor analysis to evaluate the singlecarrying capacities of land, water, transportation and environmentfrom 2000 to 2008. Thirdly, it will calculate the comprehensivecarrying capacity by principal component extraction. Finally, byhierarchical clustering analysis and coefficient of variation, it willbe available to discuss the dynamic rule and spatial differentiationof the UA.

Urban agglomeration of the Yangtze River Delta, China

In China, rapid industrialization has caused some seriousproblems, for example the depletion of natural resources, degra-dation of major ecosystems, and pollution. Because of the naturalresources’ homogeneity utilization, environmental problems of theUAs in China are predominantly represented as the pressure rein-forcement and the pollution superposition. 1) The development ofUA has increased the intensity of urban land use and seriouslydamaged the balance of the green space system. Under the strongintervention of human activities in the urbanization process, thefunctions of soil continue to transform, evolve and disappear. Theperformance includes the level of soil fertility decline, soil envi-ronment and quality deterioration. 2) UAs are basically in the sameriver basin district. The rapid expansion of the urban and the lack ofperfect coordination mechanism make the rivers as major water-shed pollutant rivers. As a result, the situation of quality-inducedwater shortage is formed. 3) Unreasonable space layout of UA hasgiven rise to the cross-regional superposition, mixture, diffusionand plume effect of air pollution.

For its superior natural environment as well as the economicand cultural atmosphere, the YRD has been a pioneer region ofChina’s urbanization. The UA of the YRD is the most urbanized areawith the highest population density in China. The YRD covers anarea of 110,800 km2, accounting for 1.1% of China’s land area. In2008, however, the Gross Domestic Product (GDP) of the YRDaccounted for 17.5% of the whole nation’s GDP, 4.3 trillion yuan. Thecenter of YRD is around Shanghai with Nanjing, Hangzhou, Ningbo,Suzhou, andWuxi as the five sub-centers. Besides, it includes otherfive cities of Jiangsu province (Yangzhou, Nantong, Changzhou,Zhenjiang and Taizhou) and five cities of Zhejiang province (Jiaxing,Huzhou, Shaoxing, Zhoushan and Taizhou). It takes Huhanghighway, Huning highway and multiple high-speed railways as theties to form awhole. The number of the permanent residents is over100 million.

The pressures of resource carrying capacity and populationdensity are originally tremendous. The ecological environment ofthe UA in YRD has paid a great cost for the regional rapid urbani-zation. It has become the new vulnerable eco-environmental zonein China. On one hand, water pollution, acid rain, soil pollution, andsolid waste accumulation have become prominent pollutionproblems. All the YRD sections of the Beijing Hangzhou GrandCanal, Lake Tai, the downstream section of the Yangtze River, andthe Qiantang River suffer from various degrees of pollution, among

H. Liu / Habitat International 36 (2012) 462e470464

which Lake Tai does the most. Consequently, quality-induced watershortage occurs. Atmospheric pollution, especially SO2 pollution, isalso very serious in the YRD. Acid rain has happened in 14 of the 16central cities. Soil pollution contains not only heavy-metal pollu-tion, but organic pollution from industrial development in theregion. Ecological degradation is also serious. In recent years,industrial and domestic waste has increased enormously. Thewastewater and exhaust emissions of industry have accounted forabout 9.5% and 5.3% of the nation respectively (Gu et al., 2011).Over-extraction of groundwater has caused a large area of landsubsidence and water pollution. Land degradation, loss of biodi-versity, and all other ecological damage have brought seriousthreats to human survival and sustainable development. Cross-border have caused conflicts between regions constantly.Resource shortage of energy, water and land has become the mainproblem plaguing the economic development of the UA in the YRD.Transportation pressure and environmental governing capabilityhave directly impacted the development efficiency of the UA.

Time-series global factor analysis approach

Indicators selection and data source

Referring to the previous academic achievements, the paper hasestablished a comprehensive carrying capacity index of UA in theYRD. Based on the scientific, operability, hierarchy, dynamic andcompleteness, the index has selected 12 indicators from thestandpoints of resources’ supply and demand. The indicators ofsupply mean the urban environmental supporting capacity forhuman activities, and the indicators of demand show the behaviorsaffecting capacity to the environment, which aims at two direc-tions. One is the shortage of land andwater resources, reflecting thedeveloping motivation problems. The other refers to the trans-portation pressure and environmental governing capability,showing the development efficiency problems. As a result, thecomprehensive carrying capacity of the UA can be evaluated (Fig.1).

Supply

(C1)

Demand

(C2)

Supply

(C3)

Demand

(C4)

Comprehensive Carrying Capacity Inde

Land (B1) Water (B2)

reP

-at

ipa

Cec

ruos

eR

reta

WD(

5 )

reP

-at

ipa

Cno

itp

musn

oC

reta

WD(

6 )

tliu

B-

saer

Apu

’no

isne

miD

D(1 )

reP

-at

ipa

Cde

tavit

luC

foae

rA

dnal

mra

FD(

2 )

reP

-at

ipa

Ccu

rtsn

ocfo

aer

Ano

itD(

3 )

PD

Gna

uY

000,

01fo

tne

meri

uqe

Rdn

aL

D(4 )

Fig. 1. Comprehensive carrying capacity

Statements of indicators are below.

D1: Built-up Areas’ Dimension (km2)

Built-up Area refers to the urbanized area of a city.City of China is not the geographically urbanized area, but the

administrative organizational unit. Therefore, the state nationalBureau of Statistics usually uses the Built-up Area (D1) to report theurbanized areas’ dimension. D1 includes the sections of the requi-sitioned land area and the non-agricultural construction area. Oneis the dense urban area, and the other is the land for constructionscattered in suburban areas, closely with the city, completed withbasic municipal public facilities (such as airports, sewage-treatment plants, communication systems). D1 does generally notinclude the large area of farmlands and lands unsuitable forconstruction sites in the urban areas.

D2: Per-Capita Area of Cultivated Farmland (hm2)

D2 ¼ Arable Land Area/the Number of Total Population.

D3: Per-Capita Area of Construction (km2)

“Land for construction use” refers to the lands to put up build-ings and structures. It includes the lands for urban and ruralhousing, the lands for industrial andmining using, and the lands forpublic facilities, water conservancy facilities, communications,tourism and military installations.

D3 ¼ Land Area for construction/the number of TotalPopulation.

D4: Land Requirement of 10,000 Yuan GDP (km2)

It is a fixed indicator to illustrate the demanded city area when10,000 Yuan gross domestic product (GDP) is output.

D4 ¼ City Area � 10,000/Total GDP.

Supply

(C5)

Demand

(C6)

Supply

(C7)

Demand

(C8)

x of Urban Agglomeration (A)

Transportation (B3) Environment (B4)

reP

-at

ipa

Cae

rA

dao

Rna

brU

D(7 )

snos

reP

000,

01re

Pse

suB

fore

bmu

ND(

8 )

reP

-ae

rA

dnal

neer

Gat

ipa

CD(

9 )

tliu

B-

eta

Reg

arev

oC

yren

eer

Gsa

erA

puD(

10 ) degr

ahcs

iD

reta

Wet

saWl

airt

sudn

Ifo

emu

loV

D(11 )

dnI

foe

mulo

VO

Slai

rtsu

2no

issi

mE

D(12 )

index of the UA in the YRD, China.

Table 2KMO measure and Bartlett’s Test.

KaisereMeyereOlkin measure of sampling adequacy 0.729Bartlett’s test of sphericity Approx. Chi-square 303.179

Df. 66Sig. 0.000

H. Liu / Habitat International 36 (2012) 462e470 465

D5: Per-Capita Water Resource (m3)

D5 ¼ Gross Amount of Water Resource/the Number of TotalPopulation

D6: Per-Capita Water Consumption (m3)

D6 ¼ Total Amount of water-supply/the number of TotalPopulation

D7: Per-Capita Urban Road Area (m3)

D7 ¼ Total urban Road area/the number of Total Population

D8: Number of Buses Per 10,000 Persons (thousand)

D8 ¼ Total Amount of Buses � 10,000/the number of TotalPopulation

D9: Per-Capita Greenland Area (m3)

D9 ¼ Total Greenland Area/the Number of Total Population.

D10: Built-up Areas Greenery Coverage Rate (%)

It means the percentage of gardens and green area of thebuilt-up area, which measures the major indicator of the city’sgreen coverage.

D11: Volume of Industrial Wastewater Discharged (t)

It refers to the industrial wastewater volume discharged toexternal through total discharge outlets of enterprises.

D12: Volume of Industrial SO2 Emission (t)

It means the SO2 emission volume of total industries.Table A of appendix shows the indicator values of D1 to D12. Data

are mainly from <China City Statistical Yearbook> (2001, 2009),<Annual Statistical Report of China City Construction, 2000>,<Water Resources Bulletin in Zhejiang Province, China>(2001,2008) and <Water Resources Bulletin in Jiangsu Province,China>(2001, 2008).

Time-series global factor analysis modeling

After the comprehensive carrying capacity index of the UA isestablished, the evaluation should be done. As a widely usedevaluation method, Principal Component Analysis (PCA) hasoutstanding features of comprehensiveness and objectivity. PCAcan achieve the simplification of cross-sectional data and the staticassessment of the sample variables in the particular years.However, the comprehensive carrying capacity of urban isa prominently dynamic and poly-dimensional system, with thetime characteristics of the development. In the conditions of natureoperation and human intervention, it presents respective featuresby resource consumption, pollution emission, ecological restora-tion and environmental administration, etc. The variables withdifferent characteristics form a planar data series, and thenconstitute the data cartridge, which is termed of multi-dimensionaltime series. If PCA is done to each planar data series of the urbancomprehensive carrying capacity in the time series, the simplifi-cation results will be completely distinct. Then, the uniformity,integration and comparison of the conclusions are difficult toensure. Time-series Global Factor Analysis Approach (TGFAA) can

transform the dynamic multi-dimensional data to the unifiedhyperplane. By the conversion, assembly and reordering of thedata, the principal components of different time series have thesame compositions. As a result, the systematic characteristics canbe reflected. Specific to the urban comprehensive carrying capacity,TGFAA can obtain the simplified subspaces, extract the valuableinformations, and reveal the dynamic evolutions.

The key steps of TGFAA of the urban comprehensive carryingcapacity assessment are as following.

Multi-dimensional time series

It supposes n cities as the evaluation samples of the urbancomprehensive carrying capacity. Each sample has p indicatorvariables. The matrix of Rn�p has been set up. The span of time issupposed as t. Then, K of the multi-dimensional time series isconstructed.

K ¼ �xt˛Rn�p; t ¼ 1;2;.; T

�

Taking x1, x2,., xp as the index of variables at themoment of t, Xt

will be indicated as the follows.

26664

xt11 xt12 . xt1pxt21 xt22 . xt2p« « «

xtn1 xtn1 . xtnp

37775 ¼

2664et1et2«etn

3775t ¼ 1;2;.; T

For the global factor analysis, the multi-dimensional time seriesof the comprehensive carrying capacity of the UA has beenestablished.

Normalization of the evaluation indicators

Evaluation indicators of the urban carrying capacity mostlyadopt different dimensions of which values usually differ greatly.So, the sample data must be dealt with normalization to eliminatethe dimensions’ impact. In this paper, the comprehensive carryingcapacity index divides the indicators into two types. This kind ofindicators (D1, D2, D5, D7, D9, D10) is on behalf of the carryingcapacity improving with their values increasing, which is positive.The indicators (D3, D4, D6, D8, D11, D12) represent the carryingcapacity deteriorating with their values decreasing, which isnegative. The paper utilizes the sample range normalizationapproach to process two kinds of indicators.

For the positive indicators:

x*ij ¼�xij �min

�xj����

max�xj��min

�xj��

ði ¼ 1;2;.; tn; j ¼ 1;2;.; pÞFor the negative indicators:

x*ij ¼�max

�xj�� xij

���max

�xj��min

�xj��

ði ¼ 1;2;.; tn; j ¼ 1;2;.; pÞIn the above formulas, max(xj) is the maximum value of xj and

min(xj) is the minimum value of xj. After normalization, the multi-dimensional time series are transformed to X*. A correlation

Table 3Total variance explained of time-series global principal factors.

Component Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings

Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %

1 5.707 47.557 47.557 5.707 47.557 47.557 4.250 35.416 35.4162 2.175 18.124 65.681 2.175 18.124 65.681 3.378 28.149 63.5653 1.469 12.240 77.921 1.469 12.240 77.921 1.442 12.018 75.5834 0.678 5.646 83.567 0.678 5.646 83.567 0.958 7.984 83.5675 0.614 5.115 88.6816 0.371 3.088 91.7697 0.358 2.984 94.7538 0.230 1.920 96.6749 0.174 1.454 98.12710 0.125 1.040 99.16811 0.076 0.629 99.79712 0.024 0.203 100.000

H. Liu / Habitat International 36 (2012) 462e470466

coefficient matrix of X*should be achieved to judge whether theindicators are appropriate for PCA or not.

Covariance matrix V of X* calculation

Global principal factors’ extraction of covariance matrix VThis step mainly calculatesm eigenvalues of l1, l2,., lm (m < p)

and their corresponding eigenvectors of u1, u2, ., um.Orthogonal transformation is employed to extract the compo-

nents to be the global principal factors when their cumulativevariance contribution exceeds 75%.

Factors rotationTo ensure the global principal factors more typical and easily

interpreted, it introduces a Max Variance method to rotate factors.Global principal factors of Y1, Y2, ., Ym can be expressed as the

series below.

Y1 ¼ u11x1 þ u21x2 þ.þ um1xmY2 ¼ u12x1 þ u22x2 þ.þ um2xm/Ym ¼ u1mx1 þ u2mx2 þ.þ ummxm

Comprehensive evaluation function establishment

F ¼ ðl1Y1 þ l2Y2 þ.þ lmYm Þ=ðl1 þ l2 þ.þ lmÞ¼ b1X1 þ b2X2 þ.þ bmXm

F means the scoring functions of the urban comprehensivecarrying capacity.

bi ¼ ðl1ui1 þ l2ui2 þ.þ lmuimÞ=ðl1 þ l2 þ.þ lmÞ

Respective resource factor carrying capacity achievement

Wi ¼ jbi j=ðjb1j þ jb2j þ.þ jbmjÞ

Table 4Valuable weights of the comprehensive carrying capacity.

D1 D2 D3 D4 D5 D6

0.0672 0.0795 0.0764 0.0470 0.0968 0.0853

The above equation means the weight of value Xi. Then, therespective carrying capacity of land, water, transportation andenvironment can be achieved.

Assessment process of the comprehensive carrying capacity

First of all, sample range normalization is done to the positiveand negative indicators. Based on the covariance matrix V, it isnecessary to use the methods to determine the data afternormalization feasible for TGFAA.

(1) From Table 2, KMO > 0.7 means variables appropriate to factoranalysis and Bartlett’s Test of sphericity is the null hypothesis. Itillustrates the variables correlative to match the factor analysisrequirements.

(2) Orthogonal transformation is employed to obtain cumulativevariance contributions. It extracts the top three factors as theglobal principal factors because their cumulative variancecontribution has overtaken 75% (Table 3).

(3) The Max variance method is applied to rotate factors toconstruct the factor matrix. The scores of the principal factorscan be respectively expressed by the functions below.

F1 ¼ �0:240ZX1 þ 0:146ZX2 þ 0:216ZX3 þ 0:008ZX4

þ 0:105ZX5 þ 0:252ZX6 þ 0:091ZX7 þ 0:085ZX8

þ 0:040ZX9 þ 0:171ZX10 þ 0:213ZX11 þ 0:094ZX12

F2 ¼ �0:058ZX1 � 0:003ZX2 þ 0:076ZX3 þ 0:189ZX4

þ 0:037ZX5 þ 0:111ZX6 þ 0:391ZX7 � 0:229ZX8

þ 0:308ZX9 þ 0:158ZX10 � 0:049ZX11 þ 0:161ZX12

F3 ¼ �0:016ZX1 � 0:420ZX2 � 0:124ZX3 þ 0:043ZX4

þ 0:693ZX5 þ 0:065ZX6 � 0:068ZX7 � 0:008ZX8

� 0:049ZX9 þ 0:095ZX10 þ 0:176ZX11 þ 0:027ZX12

F1 is on behalf of the indicators of D1, D3, D6, D11, D12, whichshow the resource demand. F2 represents the indicators of D4, D7,

D7 D8 D9 D10 D11 D12

0.1111 0.0751 0.0783 0.1064 0.0855 0.0915

Table 5The single and the comprehensive carrying capacity of the UA in the YRD (2000, 2008).

Carrying Capacity Index, 2008 Carrying Capacity Index, 2000

Urban Land Water Transportation Environment Comprehensive Land Water Transportation Environment Comprehensive

Shanghai �0.0202 0.0000 0.0111 0.1391 0.1300 0.0412 0.0113 0.0144 0.1188 0.1857Nanjing 0.0684 0.0304 0.0492 0.2781 0.4260 0.1373 0.0026 �0.0294 0.1623 0.2728Wuxi 0.1118 0.0617 0.0878 0.2332 0.4944 0.1375 0.0687 �0.0450 0.2115 0.3727Changzhou 0.1408 0.0622 0.0135 0.2301 0.4467 0.1628 0.0739 �0.0452 0.2177 0.4092Suzhou 0.1084 0.0680 0.0887 0.1799 0.4449 0.1510 0.0749 �0.0622 0.1429 0.3066Nantong 0.1789 0.0812 0.0225 0.2509 0.5335 0.1523 0.0851 �0.0746 0.2115 0.3742Yangzhou 0.1754 0.0817 0.0047 0.2538 0.5157 0.1548 0.0819 �0.0683 0.2268 0.3953Zhenjiang 0.1546 0.0705 0.0369 0.2762 0.5382 0.1628 0.0724 �0.0569 0.2324 0.4108Taizhou (Jiangsu) 0.1740 0.0855 0.0086 0.2454 0.5135 0.1474 0.0864 �0.0741 0.2032 0.3629Hangzhou 0.0840 0.0802 0.0462 0.1640 0.3745 0.0895 0.0886 �0.0547 0.1687 0.2922Ningbo 0.1117 0.0764 0.0290 0.2177 0.4348 0.1267 0.0900 �0.0588 0.2044 0.3622Jiaxing 0.1744 0.0891 0.0192 0.2432 0.5259 0.1657 0.0783 �0.0678 0.2043 0.3806Huzhou 0.1515 0.0986 0.0240 0.2690 0.5432 0.1193 0.0904 �0.0639 0.2058 0.3516Shaoxing 0.1343 0.0940 0.0617 0.2576 0.5477 0.1252 0.0993 �0.0720 0.2565 0.4090Zhoushan 0.0955 0.1707 �0.0163 0.2715 0.5214 0.0680 0.0818 �0.0484 0.2509 0.3523Taizhou (Zhejiang) 0.1088 0.0935 0.0031 0.2712 0.4766 0.0923 0.1053 �0.0652 0.1756 0.3079

H. Liu / Habitat International 36 (2012) 462e470 467

D8, D9, D10, reflecting the resource utilization intensity. F3 meansthe indicators of D2, D5, showing the resource supply ability.

(4) According to the weights of the valuables (Table 4), the singlecarrying capacity of land, water, transportation, environment ofthe UA in the YRD in 2000 and 2008 can be separately calcu-lated. Then, the comprehensive carrying capacity will be ach-ieved (Table 5).

Spatio-temporal differentiation analysis

Hierarchical cluster analysis

For the further study on the carrying capacity, HierarchicalCluster Analysis (HCA) is used to analyze the classification charac-teristics of the 16 cities of the UA. A cluster tree diagram generatedfrom the average linkage between groups can differentiate the UAinto three ranges. The results significantly show the spatial differ-ences of the urban comprehensive carrying capacities (Fig. 2). In2000, the lowest level is Shanghai. The middle level is other fourcities (Nanjing, Suzhou, Hangzhou and Taizhou (Zhejiang)). The restbelongs to the highest level. In 2008, the first level is still Shanghai.The second level consists of Changzhou, Suzhou, Nanjing, Ningboand Hangzhou. The rest is the third level.

Spatial gradient analysis

According to the cluster results, the scores of the comprehensivecarrying capacity differentiate into three spatial gradients. Scores of0e0.2 is a low gradient; scores of 0.2e0.5 is a middle gradient;

Fig. 2. HCA tree diagram of the comprehensive carrying capa

scores of 0.5 and above is a high gradient. Hereby, the spatio-temporal distribution of carrying capacity from 2000 to 2008 hasbeen formed.

The left of Fig. 3 shows the spatial gradients of the compre-hensive carrying capacity of the UA in 2000. Shanghai is in the lowgradient; all rests are in the middle gradient. From the right ofFig. 3, Shanghai is still in the low gradient of the UA in 2008. Citiesof Nanjing, Wuxi, Changzhou, Suzhou, Hangzhou, Ningbo andTaizhou (Zhejiang) are in the middle gradient. Other eight cities ofNantong, Yangzhou, Zhenjiang, Taizhou (Jiangsu), Jiaxing, Huzhou,Shaoxing and Zhoushan are in the high gradient. It can beconcluded that the comprehensive carrying capacity of the UA hasobtained overall improvement. At the same time, the obviousspatial differentiation is an objective reality. The differentiationdegrees maintain the same trend with the urban economicdevelopment.

Spatial variation analysis

By the coefficient of variation (C.V), the paper analyzes thereasons caused the spatial differentiation of the comprehensivecarrying capacity from 2000 to 2008. Statistically, C.V termed of thestandard deviation coefficient, is used to measure the variabledifference degree. Usually, C.V is compared by the ratio of standarddeviation and the average.

C.V of comprehensive carrying capacity of the UA in 2000 is17.44% and 22.08% in 2008 (Fig. 4). It illustrates the spatial differ-entiation features of the carrying capacity is more obvious. From2000 to 2008, the uneven distribution tendency of the land andwater carrying capacity has become gradually evident. The locationof Shanghai is advantageous in the UA of the YRD, China. At the

city of UA in the YRD, China (left is 2000, right is 2008).

Fig. 3. Spatio-temporal differentiation of the comprehensive carrying capacity of the UA in the YRD, China (left is 2000, right is 2008).

Fig. 4. Coefficient variation of the comprehensive carrying capacity of the UA in theYRD, China.

H. Liu / Habitat International 36 (2012) 462e470468

same time, supply and demand contradiction of land and otherresources in Shanghai is highlighted. The conflict can be observedfrom its lower resource carrying capacity and the expanding gapwith other cities. Other major cities integrated with Shanghai, suchas Hangzhou, Nanjing, Ningbo, Suzhou, Wuxi and Changzhou,constitute the core area of the UA. Attributed to the greatimprovement of the transportation carrying capacity, the trafficintegration of the UA has promoted the core area to gather a massof population and industries. However, it results in the reduction ofresource abundance and the increase of resource consumption.Therefore, in contrast to the other cities of the UA, the carryingcapacities of the core area have been lower. Especially, the negativecorrelation between the carrying capacity and the urban economicdevelopment is obvious. It shows the strong dependency on thetrend of population and resource gathering to the metropolis.

Conclusions and discussion

UA is an important urban development form. It is also a focus onthe performance of the regional economic force in the space. Theurbanization degree is a principal measure of the UA developmentstandards. Nevertheless, blindly improve the urbanization level andignore the protection of eco-environment will lead to the unsus-tainable development. The result is the reduction of the UA in theleading role in the regional development. Based on the assessmentresults of the UA, dynamic changes and the pattern of spatialdifferentiation has achieved. The regional differences of thecomprehensive capacity are significant. According to the researchresults, the conclusions below can be drawn.

The comprehensive carrying capacity of the UA tends to thesound development as a whole. Comparing the results of 2008 to2000, all cities have obtained the value improvements except forShanghai. A majority of the urban comprehensive carrying

capacities increased 0.12 degree except for Hangzhou, Ningbo andChangzhou. The carrying capacity of the UA has processed typicalfraction characteristics. In 2008, the indicator value is from 0.1300to 0.5477, showing the distinctly spatial differentiation. Among allthe cities, the carrying capacity of Shanghai has been the lowest(smaller than 0.2). Ones of Nanjing, Wuxi, Suzhou, Changzhou,Hangzhou and Ningbo are from 0.3 to 0.5. The values of other citiesare over 0.5. Reduction in the carrying capacity has been associatedwith rapid economic growth. To a great extent, the economicdevelopment of the UA is dependent on the model of resourceconsuming and scale pulling. As environmental strategies, the YRDneeds to adopt policies to enhance the carrying capacity in thelong-run. With the concentration of various industries in the delta,improvement of energy efficiency and usage of renewable energy inthose industries are important.

The carrying capacities of water and land have become thecritical factors to restrict economic and social development of theUA. During the period from 2000 to 2008, more than half of thecities have an obvious drop in the water carrying capacity. Thecause can be mainly originated from the conflict between thewater-supply reduction and the per-capita water demand increase.The imbalance of supply and demand has also influenced thedecline of the land carrying capacity. To continue pushing proactivepolicies and actions, China will emphasize saving energy whileimproving energy efficiency, developing new green energy,focusing on energy balance of supply and demand. Meanwhile, theUA of the YRD must set up the environmental monitoring system,disaster forecasting and alarm system. It is necessary to superviseand inspect the regional ecological crisis and the actions ofbreaking law and regulation. To implement the policies and actions,on February 1, 2011, five provinces and eight cities are selected todemonstrate the low-carbon development plan in the twelfth Five-Year Socioeconomic Plan. Hangzhou is the city in the YRD that isselected.

The UA of the YRD is located in the same basin of the YangtzeRiver valley and Taihu Lake valley. The eco-environmental preser-vation and restoration are a huge systematic project. The economicdevelopment status of each city is unbalanced. The developed citiessuch as Shanghai, Suzhou, Hangzhou and Ningbo face the higherecological pressure. They generally have the urgent need toimprove the environment instead of the simple economic growth.However, the developing cities else still pay more attention to therapid increase of economy. Different purposes lead to the orientedcontradiction of the policy and strategy of the development. It is in

H. Liu / Habitat International 36 (2012) 462e470 469

dire need of close coordination and cooperation to break thedepartments’ limit to establish the regional system. The urbanassociation proposed by the 16 cities of the UA in the YRD is in theimplementation process. Asmanagement policies, the coordinationcommittee of eco-environmental protection will be set up to dealwith inter-valley pollution prevention and renovation. It will alsohave the right to construct major cross-regional infrastructure. Aseconomic strategies, Shanghai shall take the lead to push forwardthe ecological compensation mechanisms. The mechanisms willensure to control the upper reach and relieve the downstreampressure.

The UA of the YRD is the densest area in China. It accommodatesa huge number of population and industries, urgently demands theresources of energy, land and water. Resource shortage has becomean obstacle to the development. Meanwhile, the environmentalpollution problem has been going to be a hard nut to crack. As a frailartificial ecological zone, it seriously threatens the human survivaland the economic and social development. As migration strategies,the UA should appropriately guide the population and industries tosmall and medium cities of higher carrying capacity. It is also

Table AIndicator values of D1 to D12 from original statistical data in 2008 and 2000.

Urban D1 D2 D3 D4 D5

2008a Shanghai 886 0.22 175 0.46 289Nanjing 592 0.58 96 1.74 377Wuxi 208 0.45 42 1.08 324Changzhou 121 0.67 34 1.99 455Suzhou 318 0.55 50 1.27 522Nantong 69 0.92 2 3.19 306Yangzhou 75 1.00 18 4.22 383Zhenjiang 98 0.83 36 2.73 465Taizhou (Jiangsu) 59 0.95 14 4.16 308Hangzhou 367 0.40 49 3.47 2278Ningbo 242 0.55 49 2.48 1260Jiaxing 77 0.94 23 2.16 808Huzhou 72 0.84 27 5.62 1812Shaoxing 90 0.57 20 3.71 1294Zhoushan 50 0.26 52 2.94 7399Taizhou (Zhejiang) 115 0.38 24 4.79 1180

2000a Shanghai 550 0.32 110 1.39 315Nanjing 201 0.83 30 6.46 391Wuxi 102 0.61 17 3.87 309Changzhou 68 0.97 20 7.28 471Suzhou 86 0.78 15 5.51 374Nantong 60 0.92 9 10.86 381Yangzhou 48 1.05 11 14.06 452Zhenjiang 57 1.02 24 8.50 463Taizhou (Jiangsu) 37 0.95 6 14.29 421Hangzhou 177 0.46 22 12.00 1696Ningbo 69 0.60 16 7.97 1604Jiaxing 43 0.94 12 7.24 324Huzhou 54 0.77 20 15.39 988Shaoxing 32 0.59 5 10.66 1517Zhoushan 50 0.27 48 12.63 738Taizhou (Zhejiang) 69 0.41 11 13.94 1926

necessary to speed up the integration process to promote a coor-dinated regional development.

The study is limited in somewhat. It only evaluates the carryingcapacity of several resources such as land, water, transportationand environment. It doesn’t take the soft factors of science, tech-nology, culture and humanity into account. It only selects someindicators and data of 2000, 2008 to do the time-series global factoranalysis. By the collection of long time data, the perfect evaluationsystem will be established in further research.

Acknowledgements

This work was supported by grant No. (71003074) from NSFC(the National Natural Science Foundation of China); grant No.(B310) Key Disciplinary Fields of Shanghai.

Appendix A

D6 D7 D8 D9 D10 D11 D12

251 6.62 12.54 25.92 40.62 44,120 298,000168 16.27 10.7 141 46.13 37,712 137,57778 21.98 13.09 66.33 43 43,278 96,06481 11.54 7.92 28.64 41.95 32,601 66,65668 23.49 11.72 52.37 41.95 58,094 176,99022 13.4 7.92 35.99 41.8 16,174 72,15323 10.81 6.78 25.09 43.45 9640 83,00958 15.52 8.83 59.3 42.19 9280 62,0689 14.87 3.38 26.3 40.2 15,680 58,576

101 10.04 16.57 30.57 38.6 75,585 91,98372 9.12 13.8 37.34 37.45 17,266 134,30219 10.47 10.26 44.36 41.83 16,235 111,73930 15.3 6.27 27.57 44.63 10,706 46,31623 17.16 12.48 52.85 44.42 28,461 61,49042 6.81 6.4 25.51 40.26 1848 24,85121 13.79 3.31 32.81 42.57 5328 48,378

220 6.16 13.74 9.25 20.90 72,446 37,700248 4.01 6.49 20.40 41.00 65,022 141,07257 4.81 2.29 8.88 35.50 17,893 81,75949 2.54 4.60 10.26 31.70 14,728 39,04242 1.68 1.82 5.01 31.10 34,648 190,23614 0.46 0.41 2.88 33.00 11,146 71,31325 0.96 1.25 3.88 35.90 8907 60,57653 2.41 2.22 10.36 36.10 7580 61,03011 0.66 0.31 1.94 25.30 11,313 20,72954 1.90 3.22 10.44 34.40 49,545 95,22947 1.42 2.83 4.49 33.30 6617.7 116556.431 1.43 0.88 3.03 21.70 4389 955922 1.75 1.37 4.72 22.00 3964 12,38217 0.47 0.96 2.80 37.80 2565 526537 2.76 3.68 24.51 31.40 782 656216 2.25 0.58 2.98 11.10 2461 5470

H. Liu / Habitat International 36 (2012) 462e470470

References

Allen, J. S. (2001). Global city-regions: Trends, theory, policy. Oxford: OxfordUniversity Press.

Chen, B. C., & Hao, S. Y. (2005). Research on the impact of natural resources onurbanization level of China. Journal of Natural Resources, 20(3), 394e399, (inChinese).

Chung, S. (1988). A conceptual model for regional environmental planning centeredon carrying capacity measures. Korean Journal of Regional Science, 4(2),117e128.

Downs, J. A., Gates, R. J., & Murray, A. T. (2008). Estimating carrying capacity forsandhill cranes using habitat suitability and spatial optimization models.Ecological Modelling, 214, 284e292.

Egger, S. (2006). Determining a sustainable city model. Environmental Modeling &Software, 21, 1235e1246.

Feng, L. H., & Huang, C. F. (2008). A risk assessment model of water shortage basedon information diffusion technology and its application in analyzing carryingcapacity of water resources. Water Resource Management, 22, 621e633.

Godschalk, D. R., & Parker, F. H. (1975). Carrying capacity: a key to environmentalplanning. Journal of Soil and Water Conservation, 30, 160e165.

Gu, C. L., Hu, L. Q., Zhang, X. M., Wang, X. D., & Guo, J. (2011). Climate change andurbanization in the Yangtze River Delta. Habitat International, 35, 544e552.

Guo, X. R., Yang, J. R., & Mao, X. Q. (2002). Primary studies on urban ecosystemhealth assessment. China Environmental Science, 22(6), 525e529.

Long, T., & Jiang, W. (2003). Advances in water resources and water environmentalcarrying capacity. Advances in Water Science, 14(2), 249e253.

Millennium Ecosystem Assessment. (2005). Ecosystems and human well-being:Biodiversity synthesis. Washington DC: World Resources Institute.

Odum, E. P. (1972). Fundamentals of ecology. Philadelphia: Saunders.

Oh, K., Jeong, Y., Lee, D., Lee, W., & Choi, H. (2005). Determining developmentdensity using the urban carrying capacity assessment system. Landscape andUrban Planning, 73, 1e15.

Retzer, V., & Reudenbach, C. (2005). Modelling the carrying capacity andcoexistence of pika and livestock in the mountain steppe of the South Gobi.Mongolia. Ecological Modelling, 189, 89e104.

Schneider, D. M., Godschalk, D. R., & Axler, N. (1978). The carrying capacity concept asa planning tool. Planning Advisory Service Report 338. Chicago: AmericanPlanning Association.

Seoul Development Institute. (1999). A study on the environmental capacityassessment of Seoul (I). Seoul Korea: Seoul Development Institute.

Spiegel, J. M., Bonet, M., Yassi, A., Molina, E., Concepcion, M., & Mast, P. (2001).Developing ecosystem health indicators in Centro Habana: a community-basedapproach. Ecosystem Health, 7(1), 15e26.

Takano, T., Nakamura, K., & Watanabe, M. (2002). Urban residentialenvironments and senior citizens’ longevity in megacity areas: the importanceof walkable green spaces. Journal of Epidemiology and Community Health, 56,913e918.

Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaozmierczak, A., Niemela, J., et al.(2007). Promoting ecosystem and human health in urban areas using greeninfrastructure: a literature review. Landscape and Urban Planning, 81(3),167e178.

Wang, R. S., Zhou, Q. X., & Hu, D. (2000). Urban ecological regulation. Beijing:Meteorology Press. (in Chinese).

Zheng, Y. T., Chen, T., Cai, J. M., & Liu, S. G. (2009). Regional concentration andregion-based urban transition: China’s mega-urban region formation in the1990s. Urban Geography, 30(3), 312e333.

Zhu, Y. H., Drake, S., Lü, H. S., & Xia, J. (2010). Analysis of temporal and spatialdifferences in eco-environmental carrying capacity related to waterin the Haihe River Basins, China. Water Resources Management, 24, 1089e1105.