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Reg Environ Change (2010) 10:55–63

DOI 10.1007/s10113-009-0091-y

ORIGINAL ARTICLE

Spatial distribution of the environmental resource consumption

in the Heihe River Basin of Northwestern China

Cuifang Wu Æ Zhongmin Xu

Received: 24 September 2008 / Accepted: 9 May 2009 / Published online: 26 May 2009

Ó Springer-Verlag 2009

Abstract Sustainable development needs to consider the

inequality between economic development and resource

consumption and keep it within a rational range. Therefore,

quantitatively measuring this inequality is an important and

hot issue. Using the ecological footprint as the index of

environmental resource consumption, the spatial inequality

of this parameter and the reasons for the inequality were

analyzed using the Gini coefficient and the Theil index in

the Heihe River Basin of northwestern China. The spatial

inequality of resource consumption was clearly high.

Decomposing the Theil index into urban and rural areas

showed that spatial inequality was mainly derived from the

inter-regional inequality between urban and rural areas

(this accounted for 63.89% of the total inequality). When

the Theil index was decomposed into different sections

along the Heihe River Basin (e.g., the upper, middle, and

lower reaches), the spatial inequality was mainly derived

from the internal inequality within the middle reach (this

accounted for 80.95%). Finally, the regression analysis of

the Gini coefficients of the ecological footprint, resident

income and population revealed that the ecological footprint

inequality increased with rising income and population

inequality, although the latter had a lower effect than

the former.

Keywords Environmental resource Spatial distribution

Ecological footprint Gini coefficient Theil index

Regression analysis

C. Wu (&) Z. Xu

State Key Laboratory of Frozen Soil Engineering,

CAREERI, CAS, 730000 Lanzhou Gansu, China

e-mail: wucf05@gmail.com

Introduction

Sustainable development needs to consider the inequality

between economic development and resource consumption

and keep it within a rational range. Although

inequalities in income and expenditure are relatively well

researched, comparatively little attention has been paid, to

date, to inequalities in resource consumption. This is

clearly a shortcoming when it comes to developing

informed policies for sustainable consumption and social

justice. It has also been argued for a long time that

widespread inequalities represent a significant challenge

to sustainable development (WCED 1987). The emerging

debate around environmental justice (Agyeman and Evans

2004) has been influential in drawing attention to the fact

that the poorer people suffer deprivations not just in terms

of the lower incomes but also in terms of access to

resources and the environmental quality. In short, it is

now recognized that the differential access to environmental

goods and services has a detrimental effect on the

quality of life experienced by deprived communities and

socially excluded groups and can reinforce deprivation

(DEFRA 2005).

In essence, there is a need to reduce resource consumption

requirements of households while ensuring that

resource inequalities are minimized. In spite of these recognitions,

only relatively limited attempts have been made

so far to understand the distribution of access to environmental

resources or to measure environmental inequalities

among the different social groups (Lucas et al. 2004;

Styme and Jackson 2000; Papathanasopoulou 2005; Papathanasopoulou

and Jackson 2005). This paper extended the

distributional analysis beyond disparities in incomes and

expenditures in order to investigate inequalities in levels of

resource consumption. In particular, the paper presented a

123


56 C. Wu, Z. Xu

model for analyzing the household resource consumption

inequalities (Papathanasopoulou and Jackson 2009).

Traditionally, research concerning distribution has

focused on the distribution of income and wealth. However,

researchers in the field of ecological economics have

expanded the discussion of distribution to include nonmonetary

measures of well-being. For example, White

(2007) used the Gini coefficient and Atkinson index to

examine how the human demand on bioproductive lands, as

measured by the ecological footprint, was distributed across

the globe. Measuring the distribution of natural resource use

will be necessary to achieve an economy that is sustainable,

just, and efficient. Styme and Jackson (2000) also used both

the Gini coefficient and the Atkinson index in their study of

how national sustainable welfare measures, such as the

Index of Sustainable Economic Welfare (ISEW), might take

income inequality into account. Chen et al. (2007) used

standard measures of inequality (the Gini coefficient and the

Theil index) to measure the total inequality of the tourism

economy in Jiangsu Province in China and decomposed the

whole province differences into three regions: Southern

Jiangsu, Central Jiangsu and Northern Jiangsu. Lu and Xu

(2005) applied the two-stage nested Theil decomposition

method to conduct an empirical examination of the regional

disparities in China. This paper decomposed the overall

regional inequality into three components: between regions,

between provinces, and within province.

While the distribution of income was clearly important,

some researchers have begun to address how the use of scarce

source and sink capacities were distributed. Azar et al. (1996)

proposed a framework for sustainability indicators which

included indicators for a just distribution of resources rather

than income. They recommended an indicator for intragenerational

justice that compared the amount of a resource

used per capita for a region to the amount of the resource used

per capita for the world. Such an indicator implied that

equality in resource use was desirable. Aubauer (2006)

argued that while a reduction in throughput was necessary to

achieve sustainability, such a reduction should be accomplished

in as equitable a manner as possible. He suggested

that a sustainable level of throughput could best be achieved

by the distribution of tradable resource certificates that would

entitle the holder to a specified share of throughput. Aubauer

argued in favor of an equal distribution of resource use by

recommending that every person should receive the same

amount of resource certificates; any other distribution would

allow some to use a greater share of resources than others,

which would reduce the natural life chances of others.

Majority of the above investigations on distribution of

resource consumption was conducted in a global or country

level. Little attention was paid to inequalities in a regional

dimension within a country, especially in a river basin

dimension. In the Heihe River Basin of northwestern China,

123

there is an amazing conflict between ecological environment

and economic development. It is a better place to

study the environmental equality in a river basin dimension.

The Heihe River Basin is the second largest inland river

basin in the arid region of northwest China (Fig. 1). It lies

to the west of the Shiyang River Basin and to the east of the

Shule River Basin. Administratively, the basin includes

part of Qilian County in Qinghai Province, some counties

and cities in Gansu Province, and part of Ejina Banner in

Alxa League of Inner Mongolia. The Heihe River Basin

has experienced rapid socioeconomic development and

increased population density since 50 years ago. However,

the extensive exploitation of water and land resources in

the upper and middle parts of the basin has led to a sharp

decrease in water resources in the lower reach of the Heihe

River. This has resulted in severe deterioration of the ecoenvironment

in the Ejina Banner Oasis, which is located in

the downstream sections of the basin (LIGG 1999).

Fig. 1 Study area and location of the Heihe River Basin in Northwest

China: a Northwest China, b study area


Spatial distribution of the environmental resource consumption 57

Table 1 The distribution of water resources, population and economy

in the Heihe River Basin (Fang 2002)

Section

of the

basin

Geographic differentiation in the basin is evident. From

the south to the north lie three major geomorphologic units:

the southern Qilian Mountains, the middle Hexi Corridor,

and the northern Alxa High-plain (Li et al. 2001). The

landscape structure and spatial changes of the Heihe River

Basin are manifested mainly by the desert-oasis-river

landscape pattern (Cheng et al. 1999). There are great

differences in the supporting capacity of the natural environment,

resource allocation and the economic development

level from the upper reach to the lower reach along

the Heihe River (Table 1). These inequalities and the

severe division of benefits are the main rootstock of the low

economic efficiency, the worsening of the ecological

environment, and the occurrence of economic and political

problems in the basin. Thus, analysis of the spatial distribution

of the environment resource use is an urgent issue in

the Heihe River Basin.

This paper examined the spatial distribution of current

environment resource consumption by utilizing the method

developed to measure the distribution of income. Using the

ecological footprint obtained from the detailed social survey

conducted in the Heihe River Basin in 2005, estimates

of the Gini coefficient and the Theil index showed how

spatial inequality in the total footprint could be explained

by the inequality of footprints in sub-regions. The basic

reasons for the existence of the spatial inequality were

discussed and explained based on the results of regression

analyses of the human influencing factors (e.g., population

and affluence). These results not only had important significance

for local sustainable development and policy

making, but also provided theory and reference for sustainable

development of other river basins across the world.

Methods

Total water

resources

(%)

Average water

resources demanded

for many years(%)

Population

(%)

GDP

(%)

The upper

reaches

47.85 1.87 1.80 2.26

The middle

reaches

31.90 67.09 97.30 96.49

The lower

reaches

20.25 31.04 0.84 1.25

Sum total 100.0 100.0 100.0 100.0

To understand how environmental resource consumption

is distributed, a broad measure of natural resource consumption

is needed. For this study, the ecological footprint

was used to measure resource consumption. The Gini

coefficient and Theil index were used to measure the

degree of inequality in the distribution of resource

consumption.

Methods of measuring the ecological footprint

The ecological footprint is a concept that has become

popular as a means of describing human demands on the

environment. An ecological footprint measures resource

use by estimating the amount of bioproductive land that is

necessary to support a given level of consumption (Wackernagel

and Rees 1996). Many researchers have proposed

and developed methods of calculating the ecological

footprint for different objectives based on the method

proposed by Wackernagel and Rees [e.g., the componentbased

approach (CBA) (Simmons and Chambers 1998;

Simmons et al. 2000; Wackernagel et al. 2005) and input–

output analysis (Hubacek and Giljum 2003; Wiedmann

et al. 2006)]. Among them, the CBA offers better accuracy

and utility. In this paper, the CBA was used to calculate the

ecological footprint in the Heihe River Basin. It incorporates

the basic life cycle data for energy demand, material

consumption (food, water, and manufactured goods), waste

production, and the area of land one occupies. The ecological

footprint analysis in the Heihe River Basin in 2004

was limited to five major components due to the limited

data and time. They included food, housing, commodities,

transportation, and public service. In the future, a more

comprehensive analysis should be conducted, but the current

calculations reflected significant ecological impacts of

household consumption in the Heihe River Basin.

The Gini coefficient and Theil index

The inequality measurement method is very important in

investigating ecological footprints. Many methods have

been used to measure the degree of inequality in a distribution

(Cowell 1995; Sen and Foster 1997). This paper

utilized two measures, which were the Gini coefficient and

the Theil index, to analyze the spatial inequality of

resource consumption.

The Gini coefficient is based on the Lorenz curve, a

cumulative frequency curve that compares the distribution

of a specific variable (e.g., income) with the uniform distribution

that represents equality. A diagonal line represents

this equality distribution. The greater the deviation of

the Lorenz curve is from this line, the greater the inequality

is (Shi et al. 2003).

The Gini coefficient is widely used to signify inequalities

in economy and ecology (Ruitenbeek 1996; Beaugrand

and Edwards 2001; Fernandez et al. 2005; Jacobson et al.

2005; Bosi and Seegmuller 2006). It quantifies the area

123


58 C. Wu, Z. Xu

between the Lorenz curve and the line of perfect equality

expressed as a fraction of the area under the y = x line. It

ranges from 0, when all units are equal, to a theoretical

maximum of 1 in an infinite population in which all units

but one yield 0 (Weiner and Solbrig 1984; Sadrasa and

Bongiovanni 2004). The Gini coefficient G is calculated as:

G ¼ 2 X

n

n

iyi

i¼1

n þ 1

n

ð1Þ

where G is the Gini coefficient; yi is the ecological footprint

weight of the ith group in the total ecological footprint

(strictly follow the ascending order y1 \ y2 \ y3 \

\ yn); and n is the number of groups.

The Theil index was derived from Shannon’s measure of

information entropy and was introduced by inequality

accounting (Theil 1967; Shorrocks 1980; Tsui 1993;

Schwarze 1996; Akita 2003; Lu and Xu 2005; Chen et al.

2007). Its formula is:

I ¼ 1 X

n

n

log

i¼1

l

ð2Þ

li where li is the mean ecological footprint of the ith group

and l is the total mean ecological footprint. Under the

grouping calculating circumstance, the total inequality is

equal to the sum of inter-group inequality and intra-group

inequality:

I ¼ IW þ IB ¼ X piIiþ X pi log pi

ð3Þ

where I W is the inter-group inequality; I B the intra-group

inequality; pi the weight of the population; qi the weight of

the ecological footprint; and Ii is the Theil index of the ith

intra-group inequality.

Data

Data source

The ecological footprints were calculated according to the

data of household consumption, which were obtained from

the detailed social survey in the Heihe River Basin in 2005

conducted by the Cold and Arid Regions Environmental

and Engineering Research Institute (CAREERI), Chinese

Academy of Science. The survey was carried out face-toface

and covered the whole Heihe River Basin, except for

Qilian. Because there were different consumption types for

the dwellers in the urban and rural areas, questionnaires

were divided into two types: urban and rural questionnaires.

According to random sampling method, at the

confidence of 95%, sampling size was designed to be

2,500. A total of 2,500 questionnaires were dispensed and

2,372 questionnaires were received. The statistical results

123

qi

about the conditions of informants’ basic social economy

were as follows: There were 696 urban residents, 1,676

rural residents, 1,257 men, and 1,082 women. There were

493, 975, 472, 251, 125, and 23 people with an education

level of elementary school, junior high school, senior

higher school (including technical secondary school),

junior college, undergraduate, and graduate, respectively.

Groupings depending on household income from 1 to

5,000, 5,001 to 10,000, 10,001 to 20,000 and above 20,001

Yuan RMB contained 589, 768, 616, and 366 people,

respectively.

Calculating the ecological footprint

Household consumption was divided into food, housing,

transportation, commodities, and public service (Table 2).

Based on the data from the Heihe River Basin social survey

and equivalence and yield factors from the Redefining

Progress’ household ecological footprint calculator (Wackernagel

et al. 2003), the Heihe River Basin’s ecological

footprints were calculated (Table 3).

Table 3 showed that the per capita ecological footprint

in urban areas was higher than that in rural areas. In Minle,

the per capita ecological footprint in urban area was

2.36 ghm 2 higher than that in rural area; In Ejina, the per

capita ecological footprint in urban area was 4.59 ghm 2

higher than that in rural area. Among ten towns, the per

capita ecological footprint was the smallest in Minle, and

the biggest in Ejina; among ten counties, the per capita

ecological footprint was the smallest in Gaotai, and the

biggest in Ejina.

Except for the three counties of Suzhou, Jiayuguan and

Ejina, the total ecological footprints in urban areas were

lower than those in rural areas in other counties. In urban or

Table 2 The components of household consumption in the Heihe

River Basin

Consumption Components

Food Flour, rice, cereal, grease, pork, beef, mutton,

chicken, egg, fish, vegetable, fruit, sugar, vegetable

oil, wine, drink, milk, eating out

Housing Payout, building area, coal, firewood, coal gas, liquid

petroleum gas, electricity

Transportation Bus, railroad, car, taxi, motorcycle, airplane

Goods Cotton, wool, synthetic, cigarette, drug,, medical

treatment instrument, Porcelain, glass, leather,

plastic products, wooden furniture, plastic and

metal furniture, major appliances, small

appliances, computers and other electronic

equipment, car parts for repair

Services Postal services, hotel, water, sewer, garbage service,

household insurance, medical insurance and

services, telephone, entertainment, education


Spatial distribution of the environmental resource consumption 59

Table 3 The calculation results

of ecological footprint in urban

and rural areas in the Heihe

River Basin

rural areas, the internal inequality of per capita ecological

footprint was low, but the inequality of the total ecological

footprint, in both urban and rural areas, was much higher

and amazing.

Spatial distribution of environmental resource

consumption

Intra-county distribution

Urban areas Ecological footprint

per capita

(ghm 2 /capita)

The Gini coefficient and Theil index were calculated in the

Heihe River Basin using Eqs. 1 and 2. The results are listed

in Table 4.

In the Heihe River Basin, the Gini coefficient of the

ecological footprint was 0.44 and the Theil index was 0.18.

These values indicated that the total ecological footprint

exhibited great inequality. In sub-regions, mainly including

ten counties, the parameter values showed great imbalance.

The degree of inequality of the ecological footprint was

greatest in Ganzhou and Minle, where both Gini

Table 4 The inequalities of resource consumption in counties in the

Heihe River Basin

Ecological

footprint

(910 4 ghm 2 )

Rural

areas

Ecological footprint

per capita

(ghm 2 /capita)

Ganzhou 4.80 82.73 Ganzhou 2.77 89.63

Linze 4.79 10.54 Linze 2.42 30.27

Gaotai 4.93 11.83 Gaotai 2.09 27.98

Shandan 5.38 24.32 Shandan 2.25 33.45

Minle 4.54 12.98 Minle 2.18 44.95

Sunan 5.15 5.61 Sunan 2.90 7.22

Jinta 5.06 16.04 Jinta 2.57 28.26

Suzhou 4.58 64.55 Suzhou 2.53 56.09

Jiayuguan 4.79 76.33 Jiayuguan 2.62 5.29

Ejina 7.52 8.43 Ejina 2.93 1.40

Gini coefficient Theil index

Ecological

footprint

(910 4 ghm 2 )

coefficients were 0.46, and Theil indexes being 0.19. The

degree of inequality of ecological footprint was the lowest

in Ejina with a Gini coefficient of 0.11, and a Theil index

of 0.05. The values of the other places fell between

Ganzhou and Ejina. The spatial inequality of the ecological

footprint in the Heihe River Basin was very high because

of the inequality of the level of economic development,

population size, and consumption patterns.

Using the contribution of the ecological footprints of ten

counties to the total ecological footprint, a cumulative

frequency curve of ecological footprints in the Heihe River

Basin was mapped (Fig. 2). The inequality of the ecological

footprint among counties was clear, and there was a

gentle slope on the curve before Inflexion A (Sunan). At

Inflexion A, the cumulative frequency of ecological footprints

was 1.8%. The curve exhibited a remarkable rising

trend after Inflexion A. At Inflexion B (Minle), the

cumulative frequency of the ecological footprints was

35.6%. After Inflexion B, the curve rose more sharply. The

ecological footprints in both Ejina and Sunan were the

smallest in the Heihe River Basin, which signified that

Heihe drainage basin 0.44 0.18

Ganzhou 0.46 0.19

Linze 0.28 0.11

Gaotai 0.22 0.09

Shandan 0.39 0.16

Minle 0.46 0.19

Sunan 0.13 0.05

Jinta 0.34 0.14

Suzhou 0.43 0.18

Jiyuguan 0.41 0.17

Ejina 0.11 0.05 Fig. 2 Cumulative frequency curve of ecological footprint in the

Heihe River Basin

123


60 C. Wu, Z. Xu

resource consumptions in these areas were the smallest.

The ecological footprints in Ganzhou, Suzhou, and Jiayuguan

were the largest, indicating that resource consumptions

in these areas were the greatest; the cumulative

frequency of the ecological footprints in the three areas

exceeded half of the total cumulative frequency, accounting

for 64.5%.

The intra-structure of spatial distribution

According to the method of spatial inequality decomposition,

sample data can be decomposed into urban and rural

areas or different sections along the Heihe River Basin

(e.g., the upper, middle, and lower reaches). Thus, the total

inequality of region could be divided into two parts: intraregion

and inter-region inequalities. Tables 5 and 6 showed

the results of this decomposition.

The inequality in the Theil index between urban and

rural areas was 0.12, and the contribution rate accounted

for 63.89% of the total inequality in the ecological footprint.

This value was much higher than that of the internal

inequality within urban or rural areas. The Theil index

within urban area was 0.11, which was a little lower than

that between urban and rural areas. However, it was much

higher than that within rural area (0.05). The contribution

of the inequality within urban area accounted for 19.44%

of the total inequality, which was close to the contribution

within rural area (16.67%). Thus, the inequality observed

in the Heihe River Basin resulted mostly from the

inequality between urban and rural areas. The inequality in

resource consumption within urban area was higher than

that within rural area, but the contribution rate of the

inequality within urban area to the total inequality was

almost equal to that within rural area. Inequalities in economic

development between urban and rural areas constituted

the main reason for the observed difference. But the

inequality in population size between urban and rural areas

also played a role. Although the internal inequality in

resource consumption within urban area was higher than

that within rural area, the contribution to the Theil index

within urban area was nearly equal to that within rural area

due to the smaller number of people living in urban area.

Table 6 showed that the total inequality was mostly

derived from the internal inequality within the upper,

Table 5 The results of decomposing the Theil index by urban and

rural areas

Total

inequality

Between urban

and rural areas

Within

urban

areas

Within

rural

areas

Theil index 0.18 0.12 0.11 0.05

Contribution rate (%) 100 63.89 19.44 16.67

123

Table 6 The results of decomposing the Theil index by the upper,

the middle and lower reaches

Theil

index

Between

reaches

contribution

rate (%)

The upper

reaches

contribution

rate (%)

middle, and lower reaches in the Heihe River Basin. Their

summed contribution exceeded 90%. The contribution

within the middle reach reached 80.95%, but the contribution

among reaches was only 9.30%. The main reason

for these results was that both richer and poorer counties

were centralized in the middle reach of the river, with a

large inequality in economic development between them

and an uneven population distribution. Counties in the

upper and lower reaches had a lower level of economic

development with the smaller inequality among them, the

smaller population, and an even population distribution.

Thus, the spatial inequality in the ecological footprint in

Heihe River Basin was mainly caused by the internal

inequality within its middle reach.

According to the results of decomposing the Theil index

by urban and rural areas, the inequality in the ecological

footprint in the Heihe River Basin will decrease by 36.11%

if the internal differences within both urban and rural areas

are eliminated. And it will decrease by 63.89% if the interregional

inequality between urban and rural areas is eliminated.

Additionally, the inequality in the Heihe River

Basin will decrease by only 9.3% if the inter-regional

difference among the upper, middle, and lower reaches is

eliminated according to the results of decomposing the

Theil index by upper, middle, and lower reaches. However,

it will decrease by 80.95% if the inequality within the

middle reach is eliminated. So, lowering inequalities

between rural and urban areas or within the middle reach

should be particularly considered if the inequality in ecological

footprint in Heihe River Basin wants to be reduced.

Reasons for the spatial inequality

The middle

reaches

contribution

rate (%)

0.18 9.30 2.83 80.95 6.92

The lower

reaches

contribution

rate (%)

The IPAT identity is a widely recognized formula for

analyzing the effects of human activities on the environment

(Stern et al. 1992; Harrison and Pearce 2000). IPAT

emerged from the Ehrlich-Holdren/Commoner debate in the

early 1970s about the principal driving forces of anthropogenic

environmental impacts. IPAT specifies that environmental

impacts are the multiplicative product of three

key driving forces: population, affluence (per capita consumption

or production) and technology (impact per unit of

consumption or production). IPAT is a parsimonious


Spatial distribution of the environmental resource consumption 61

specification of key driving forces behind environmental

change and, further, it identifies precisely the relationship

between those driving forces and impacts.

Many studies have shown that the mainly humanistic

factors influencing the environment included the population,

affluence, and technology (DeHart and Soule 2000;

Roca 2002; York et al. 2003; Xu et al. 2005). Because it

was difficult to measure technology quantitatively, the

impacts of population and affluence on the ecological

footprint inequality were only considered in this study.

Income represented affluence in the following regression

analysis.

To quantitatively reflect the impact of population and

income on ecological footprint inequality, the following

regression model was established:

Y ¼ a þ bX1 þ cX2 þ dlogðX3Þþf logðX4Þþe ð4Þ

where Y is the Gini coefficient of the ecological footprint;

X1 signifies the Gini coefficient of resident income; X2 is

the Gini coefficient of the population; and X3 and X4

represent population and per capita income, respectively.

The regression model yielded the following:

Y ¼ 0:07 þ 0:50X1 þ 0:37X2 ðR 2 ¼ 0:94Þ

Goodness of fit with this equation is 94%, which shows

that the Gini coefficients of population and resident income

can explain 94% of the inequality in resource consumption.

Moreover, the various coefficients are not zero remarkably

at a level of 0.05, which shows that the equation is a good

fit. The analysis results show that income and population

inequality have proportional effects on ecological footprint

inequality, but population inequality affects the ecological

footprint inequality to a relatively lower degree than

income inequality. Because the population size and per

capita income have little effect on the regression coefficient

of the Gini coefficient of ecological footprint, they are

excluded from this model.

Conclusion and discussion

Main conclusions

Some preliminary conclusions could be made according to

the analyses and discussions described above:

1. From the spatial distribution perspective, the inequality

of the ecological footprint was quite high in the

Heihe River Basin. Policy makers should pay more

attention to it and improve it.

2. Decomposing the spatial structure into urban and rural

areas indicated that the inequality in the Heihe River

Basin was mostly derived from inequality between

urban and rural areas. Decomposing the spatial structure

into the different sections along the Heihe River

Basin (e.g., the upper, middle, and lower reaches)

showed that the spatial inequality of the ecological

footprint was mostly derived from the internal

inequality within the middle reach in the Heihe River

Basin.

3. Regression analysis of the inequality of resource

consumption revealed that income and population

inequalities had proportional effects on ecological

footprint inequality, but population inequality less

affected ecological footprint inequality than income

inequality.

Discussion

The results of the above analyses highlighted several

important findings. Concerning the method of equality

assessment, the Theil index was used for the spatial

decomposition of inequality. It had some advantages in

comparison to Gini coefficient (Yitzhaki and Lerman

1991). The Theil index was a decomposable index of

inequality within the spatial context. It explained the total

amount of inequality in a distribution by the extent of

inequality found within groups and between them. The

decomposition of the Gini coefficient only identified the

inequality within groups, but it did not identify the

inequality between different groups because there was a

percentage which was not decomposed (residual error)

(Lambert 1993). The method proposed in this paper could

be widely used to study the distribution of environmental

inequality in the other similar areas.

From available literatures, the inequality of total ecological

footprint was divided into four components: energy,

food, forest, and built-up land. Inequality in the distribution

of each of the ecological footprint components was

examined and used to explain inequality in the distribution

of total ecological footprint (White 2007). However, this

paper introduced the Theil index for subgroup decomposition

of inequality to the spatial context. These research

results would provide a theoretical basis to payments for

environmental services between urban and rural areas or

different sections along the Heihe River basin.

How can human welfare be advanced without pressure

on the environment? The ImPACTS equation of sustainable

evaluation offered some suggestions, such as reducing

the population, elevating technical efficiency, improving

living and consumption patterns, establishing better social

production and consumption systems, and conserving

resources (Xu et al. 2005). The relationship between consumption

patterns and ecological footprints needs further

study. Due to the difficulty in obtaining some necessary

123


62 C. Wu, Z. Xu

data, this paper only analyzed the spatial inequality of the

ecological footprint in the Heihe River Basin. Analyses

using time-series data need to be conducted in the future,

especially in combination with spatial analyses. Such an

approach will allow the justice of the environment resource

to be better investigated from temporal and spatial

dimensions.

Acknowledgments This work was funded by the National Natural

Science Foundation of China (No. 40671076) and the Innovation

Project of the Chinese Academy of Science (No. KZCX2-XB2-04-

04). We would like to thank the members of the ecological economic

team from CAREERI and the Water Source Self-control Restraint

Institute who took part in the social investigation work in the Heihe

River Basin in 2005 and helped to collect the data presented in this

paper. Generous assistance from the editors and reviewers are greatly

appreciated and thus acknowledged.

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