Download Human-environment interactions in China: Evidence of land

Research in Human Ecology
Human-environment interactions in China:
Evidence of land-use change in Beijing-Tianjin-Hebei
Metropolitan Region
Yuheng Li1
Institute of Geographic Sciences and Natural Resources Research
Chinese Academy of Sciences, Beijing
Qian Zhang
School of Forestry & Environmental Studies
Yale University, Connecticut
Abstract
This paper aims to investigate human-environment interactions in the Chinese context by studying land-use
change in terms of agglomeration of human activities. The
research is based on theories showing that resource flows agglomerate differently in the spatial dimension. The paper creates the urban-rural linkage index and decomposes the study
area into three parts: urban, peri-urban and periphery areas.
Research findings show that urban areas tend to experience
faster arable land and built land change than the peri-urban
and periphery areas. The findings also indicate that in the
Chinese context of fast urbanization and economic growth,
resource flows like people, capital, goods and information
tend to agglomerate in the urban areas, followed by the small
towns and medium cities, as well as the rural peripheries.
Keywords: Beijing-Tianjin-Hebei Metropolitan Region,
human-environment interactions, land-use change, urbanrural resource flows
Introduction
Human-environment interactions have been an important research topic since the 1940s. In the sphere of human
ecology, adaptation, ecosystems, and systems approach are
particularly relevant to understanding the study of human-environment interactions (Galvin, 2006). Basically, these concepts explain how people live in the environment and how
people change the environment to meet their needs and
wants. The idea of involving humans as a natural component
of ecosystems resulted in the need to understand how humans
interact with the natural environment. Human history in the
past centuries has shown how we use nature to meet our
needs, and how the environment is affected by human activi-
26
ties. This is particularly evident in East Asian regions where
rapid urbanization is often coupled with rapid industrialization and economic development. China, for instance, saw a
decrease of 8.3 million square hectares of arable land from
1996 to 2008 as its urbanization rate rose from 30.5% to
45.7% (Ministry of Land and Resources of the People’s Republic of China, 2009). Due to mass population influx, cities
like Beijing, Shanghai, and Zhengzhou are in a state of approaching water crisis while the water supply of Guangzhou
and Chongqing is in an alarming state (Xu, Yu, & Wu, 2007).
Seemingly, human activities in East Asian regions like China
have over-influenced the natural environment, and induced
an imbalance of man-nature. Bai and Imura (2000) also point
out that the diversity and complexity of urban environmental
problems in East Asia have made it more difficult for municipalities to effectively tackle this situation.
Basically, a city is an ecosystem, but one with complex
social, economic, and natural parts (Ma & Wang, 1984). It
can also be considered an artificial ecosystem dominated by
human activities, sustained by natural life support systems,
and vitalized by ecological processes (Wang & Ouyang,
2003). Nevertheless, urban development has been largely destructive (McHarg, 1969). McHarg points out that “we build
where we should farm, cut forests where we should grow
them, and design forms where we should follow nature’s
morphologies.” Thus, his argument has left us a question:
How should the work of humans remain connected with nature, as they design and build their physical environment?
Generally, the conversion of land from its natural state to
human uses is the most permanent and often irreversible effect of human interaction with the natural environment (Jolly
& Torrey, 1993). Land use and land cover change (LUCC)
studies mainly include modeling the spatial and temporal patterns of land conversion, and analyzing the causes and consequences of land change (Burgi, Hersperger, & Schneeberg-
Human Ecology Review, Vol. 20, No. 1, 2013
© Society for Human Ecology
Li and Zhang
er, 2004; Irwin & Geoghegan, 2001; Long, Heilig, Li, &
Zhang, 2007; Seto & Kaufmann, 2003). However, current
studies do not clearly interpret the relationship between
LUCC and human activities. One reason is that the mobility
and agglomeration of human activities differ from the countryside to the cities. For instance, urban areas are usually the
places of major population and fast economic growth, which
contrasts to the sparsely distributed populations in rural
areas. Consequently, such different agglomeration of human
activities leads to different LUCC in different places. However, this phenomenon has not been clearly examined. This
paper aims to investigate human-environment interactions in
the Chinese context by studying land-use change in terms of
the agglomeration of human activities.
Human activities and land-use change:
Theoretical basis and research framework
Generally, land-use change is tightly related to humanenvironment interactions, comprising the dynamic relations
between human activities and the physical environment.
Land-use morphology, which is the overall pattern of actual
land cover in a certain area at a given time, varies with socioeconomic development. According to Grainger (1995),
land-use transition indicates the changes in land-use morphology over time, and corresponds to a particular socioeconomic development phase. Mitsuda and Ito (2011) also point
out that it is socioeconomic factors which influence land-use
change among different land-use types, although natural factors such as potential productivity and topographic relief help
determine the land-use types.
Basically, an urban ecosystem is driven by physical
forces; for example, solar energy, and social forces such as
money, power, and spirit. These forces interact with each
other and formulate the ecological forces driving the functional flow of material, energy, people, capital, and information (Wang, 1988). In a broader context, urban and rural areas
link to each other in terms of mutual resource flows (Li,
2012). According to Potter, Binns, Elliott, and Smith (2004),
urban-rural resource flows are initiated in an attempt to make
use of each other’s differences or complementarities. These
mutual resource flows can be understood in terms of the central place theory: the central place supplies various levels of
services to the surrounding areas, which provide food and
other resources to the central place (Christaller, 1933). This
division was mainly attributed to the spatial agglomeration
of agricultural and industrial activities. According to
von Thünen’s (1826) model of agricultural land use and
Weber’s (1909) industrial location theory, the location of
agricultural activities in rural areas and industrial activities in
urban areas depends on both costs and potential profits. In
Human Ecology Review, Vol. 20, No. 1, 2013
new economic geography, high-profit resource agglomeration was mainly induced in places of interrelated industry
concentrations, reliable infrastructure and high accessibility
to the market (Fujita, Krugman, & Venables, 1999; Krugman,
1991). Glaeser (2008) argued that the major benefit of resource agglomeration was reduced cost, labor pooling and
knowledge spillovers. Such agglomeration has seen continuous rural-urban migration and economic activities concentrating to the cities, where infrastructure expansion takes
place, inducing arable land conversion at the peri-urban
areas.
Recently, small towns and medium cities have been recognized as playing an important role in urban-rural linkages,
given the strong linkages with their rural hinterlands (Baker
& Claeson, 1990). Situated between urban and rural areas,
small towns and medium cities act as attractive destinations
for rural migrants, offering non-farm employment and access
to education and medical services. The role of small towns
and medium cities has also been recognized when out-migration and industry transfers from downtown to suburbs occurs
due to congestion problems in urban areas (Li, 2013). Krugman and Elizondo (1996) point out that resource relocation to
small towns and medium cities is attributed to the “centrifugal” forces that tend to break the agglomerations in urban
areas. The centrifugal forces include pure external diseconomies like congestion and pollution, urban land price increases, transportation cost increases, and the preferences of
moving away from highly competitive urban areas to less
competitive rural locations (Tabuchi, 1998). In Indonesia,
McGee (1991) found places of mixed agricultural and nonagricultural activities that are generally located in villages
and cities respectively. Then, he used the concept of
“desakota” (desa means village while kota means city in Indonesian) to describe the symbiosis of urban and rural areas
which resulted from the transformation into a dispersed metropolis.
The spatial distribution of human activities, based on the
above analysis, can be divided into three categories: nonagricultural activities agglomerating in urban areas; rural peripheries of major agricultural economies; and small towns
and medium cities (urban-rural interface) of mixed agricultural and non-agricultural activities. This paper hypothesizes
that urban areas are agglomerating major human activities
due to tight urban-rural linkages, while the agglomeration of
human activities is sparse in rural peripheries. The agglomeration of human activities in small towns and medium cities
(the peri-urban areas) stays at a medium level. Correspondingly, land-use change in the three types of human settlements will also be different. Thus, the arable land and builtup land change could reflect the specific agglomeration of
human activities in the spatial dimension.
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Li and Zhang
Study area and methodology
Study area
Beijing-Tianjin-Hebei Metropolitan Region (Jing-Jin-Ji
region for short; Jing, Jin and Ji are the abbreviations for Beijing, Tianjin and Hebei) is located in eastern China, and includes Beijing, Tianjin (directly governed cities under the jurisdiction of central government), and eight prefecture-level
cities in Hebei Province2 (Figure 1). Each of these 10 urban
administrations consist of certain counties (county-level
cities) and urban districts.
In the post-reform era, Jing-Jin-Ji region experienced
rapid socioeconomic change. Urbanization of this region increased from 28.2% in 1990 to 46.5% in 2005 while the per
capita gross domestic product (GDP) increased from 5,785 Y
=
per person to 22,252 Y
= per person in the same period (National Bureau of Statistics of China, 1991, 2006). The socioeconomic development also caused fast land-use change.
The built-up land in this region increased from 11,917.1 km2
in the late 1980s to 14,442.7 km2 by 2000 with a growth of
21.2%, while the arable land decreased from 86,342.6 km2 to
84,090.2 km2 in the same period (Liu et al., 2005a).
Creation of urban-rural linkage index
The previous section discussed the strong connection between urban-rural linkages (resource flows and agglomeration) and land-use change. This section creates the urbanrural linkage index and decomposes counties and urban districts of Jing-Jin-Ji region into three classifications. A number of studies have introduced and described urban-rural linkages, although there is no single, clear definition of the concept (Ginsburg, 1990; Zhou 1991). Lin (2001) identified the
spatial forms of urban-rural linkages in the Pearl River delta
of China by assessing this concept in terms of several variables. The results presented differences of urban-rural linkages in central cities, peri-urban zones and periphery zones.
Figure 1. The cities and counties in Jing-Jin-Ji region
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Li and Zhang
However, selected variables (population, employment, and
land-use intensity) are not adequate to show urban-rural linkages which include flows of people, capital, materials, information and technology. Furthermore, Lin’s (2001) study used
data from 1991 and did not show the evolution of urban-rural
linkages. Basically, the consequences of the flows and agglomeration of human activities will eventually cause demographic and economic changes in both urban and rural areas.
Due to data availability, we selected six variables to measure
demographic and economic changes in the counties and
urban districts of Jing-Jin-Ji region (Table 1).
were extracted by principal component analysis in the period
1990-2000, accounting for 81.6% of the variance (Table 2).
The first component, which explains 63.3% of the variance,
is the most important component among the variables. This
component includes five variables and excludes the increase
of rural household per capita net income, which belongs to
the second component. Three components were extracted in
the period 2000-2005, accounting for 71.3% of the variance
(Table 3). The three components explain 36.1%, 18.4% and
16.8% respectively.
Table 1. Six variables for assessing urban-rural linkages
Table 2. Rotated component matrix of the analysis period 19902000
Category
Variables
Variables
Demography
Increase of percentage of urban population to the total
population
Increase of percentage of non-agricultural employees to the
total employees
Economy
Increase of non-agricultural production
Increase of rural household per capita net income
Increase of per capita GDP
Increase of rural electricity consumption
GDP = gross domestic product
Considering possible correlation of the variables and inconsistences of the variables’ units, we used principal component analysis to transform these correlated variables into a
smaller number of uncorrelated variables. In the first step, we
calculate the urban-rural linkage index scores of all the counties and urban districts of Jing-Jin-Ji region. Second, we decompose the counties and urban districts through cluster
analysis in terms of the index scores.
Remote sensing data was used to detect and monitor
land-use change in Jing-Jin-Ji region in the years 1990, 2000,
and 2005. This data is from the Chinese National Land Cover
Database and developed by the Chinese Academy of Sciences (Liu et al., 2005a, 2005b). To simplify the study, a hierarchical classification system of 25 land cover classes was
aggregated to arable land, urban and rural built land, and others. In this database, Jing-Jin-Ji region was divided into 109
counties and 10 urban districts. Thus, the economic and demographic data for all the units in this region also follows
this division. The economic and demographic data is for the
calendar years 1991, 2001, and 2006 from the Hebei Economic Yearbook, Beijing Statistical Yearbook, and Tianjin
Statistical Yearbook.
Decomposition of Jing-Jin-Ji region
According to the conventional rule of extracting components which have eigenvalues greater than 1, two components
Human Ecology Review, Vol. 20, No. 1, 2013
Components
Increase of percentage of urban population to
the total population
Increase of percentage of non-agricultural
employees to the total employees
Increase of non-agricultural production
Increase of rural household per capita net income
Increase of per capita GDP
Increase of rural electricity consumption
Initial eigenvalues
% of variance
Factor 1
Factor 2
0.954
-0.196
0.594
0.965
0.157
0.594
0.816
3.800
63.341
-0.195
0.185
0.865
0.452
0.182
1.096
18.269
GDP = gross domestic product
Table 3. Rotated component matrix of the analysis period 20002005
Variables
Components
Factor 1
Increase of percentage of urban population
to the total population
0.182
Increase of percentage of non-agricultural
employees to the total employees
0.735
Increase of non-agricultural production
0.545
Increase of rural household per capita
0.664
net income
Increase of per capita GDP
0.617
Increase of rural electricity consumption
0.688
2.164
Initial eigenvalues
36.072
% of variance
Factor 2
Factor 3
0.537
0.778
-0.328
0.485
0.187
-0.476
0.456
-0.476
-0.189
1.101
18.358
-0.066
0.293
-0.228
1.009
16.816
GDP = gross domestic product
The sub-models for the components in 1990-2000 and
2000-2005 were made according to the component coefficients.
In the period 1990-2000:
F1=0.3047X1+0.3047X2+0.4950X3+0.4894X4+0.0805X5+0.4186X6 (1)
F2=0.4318X1–0.1863X2–0.1767X3+0.1872X4+0.8262X5+0.1738X6 (2)
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Li and Zhang
In the period 2000-2005:
F1=0.0841X1+0.3396X2+0.2518X3+0.3068X4+0.2851X5+0.3179X6 (3)
F2=0.4877X1–0.2979X2+0.4405X3+0.4142X4–0.4323X5–0.1717X6 (4)
F3=0.7711X1+0.1853X2–0.4718X3–0.0654X4+0.2904X5–0.2260X6 (5)
The component scores of counties and urban districts of
Jing-Jin-Ji region in these two periods were computed using
the above formulas. The scores are uncorrelated and represent the six variables. These scores are used as the base for
cluster analysis through which group memberships are assigned to all the units. The paper uses PASW Statistics 18 and
conducts K-means cluster analysis for each urban administration (Beijing, Tianjin and eight cities in Hebei Province).
The analysis reveals a natural break that decomposes all the
counties and urban districts in each of the 10 urban administrations into three classifications (Figure 2).
In the period 1990-2000, the first decomposition, which
is named “urban areas”, consists of the urban districts of Beijing, Tianjin, Shi Jiazhuang, Chengde, Zhang Jiakou and
Baoding as well as their close counties. The second decomposition is named “peri-urban areas” and includes counties
that mainly surround the first classification. Decomposition
three covers the countryside and the distant counties in each
urban administration and is called “periphery areas.” Basically, the decompositions of urban areas, peri-urban areas and
periphery areas are not administrative divisions. Instead, they
show the agglomeration of human activities in the counties or
urban districts of each urban administration.
In the period 2000-2005, the urban areas include urban
districts of Beijing, Tianjin, Shi Jiazhuang, Qin Huangdao,
Chengde and Zhang Jiakou as well as some counties that are
located between Baoding and Shi Jiazhuang. The peri-urban
areas consist of counties surrounding the first decomposition,
while counties in the northern and eastern parts of Jing-Jin-Ji
region belong to the periphery areas. Comparison between
decompositions in the two periods shows that the location of
urban areas has shifted from widely distributed in each urban
administration to concentrating in the places between Beijing
and Shi Jiazhuang. Correspondingly, the location of the periurban areas has shifted mainly from the northeast of Jing-JinJi region to the southwest, covering areas between Beijing
and Shi Jiazhuang (Figure 2).
Generally, changes in the three decompositions identified by the cluster analysis also indicate socioeconomic
Figure 2. Land-use classifications in Jing-Jin-Ji region, 1990-2000 and 2000-2005
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Li and Zhang
Table 4. Demographic and economic features in the three decompositions in Jing-Jin-Ji region, 1990-2000 and 2000-2005
1990-2000
Urban
areas
Peri-urban
areas
Periphery
areas
Number of units
Total population in 2000 (million)
Annual population increase (%)
Per capita GDP in 2000 (Y
= /person)
Annual per capita GDP increase (%)
28
29.5
2.1
19,715
7.6
37
18
0.7
9,563
8.7
54
23.4
0.6
7,509
9.7
2000-2005
Urban
areas
Peri-urban
areas
Periphery
areas
Number of units
Total population in 2005 (million)
Annual population increase (%)
Per capita GDP in 2005 (Y
= /person)
Annual per capita GDP increase (%)
24
30.7
1.2
30,307
9.1
55
24.2
0.7
13,557
7.2
40
19.8
1.2
17,045
17.8
GDP = gross domestic product
changes during the research period. As shown in Table 4, the
number of units in the peri-urban areas increased greatly (3755) while those in the urban (28-24) and periphery areas (5440) dropped. From 2000 to 2005, population in the three decompositions increased in the urban and peri-urban areas
(4.1% and 34.4%, respectively) and decreased in the periphery areas (-15.6%). This suggests that fast population growth
has contributed to the development of the peri-urban areas.
The increase of per capita GDP of the three decompositions
in the research period is 53.7%, 41.8% and 127%. This indicates that fast economic growth has helped the periphery
areas to develop into peri-urban areas.
Arable and built-up land change in
Jing-Jin-Ji region
Generally, arable land in Jing-Jin-Ji region in the period
1990-2000 decreased by 2,178.5 km2, an annual decrease of
0.3%, while built land increased by 1,721.5 km2, an annual
increase of 1.3%. In the period 2000-2005, arable land in this
region decreased by 496.3 km2, an annual decrease of 0.1%,
while built land increased by 1,494.7 km2, an annual increase
of 2%. The spatial distribution of land-use types in Jing-JinJi region in 1990, 2000, and 2005 is shown in Figure 3. In
general, the increase of built-up land took place mainly in the
urban districts of Beijing, Tianjin, Shi Jiazhuang, and Tangshan as well as their surrounding areas in the period 19902000, while in the period 2000-2005 the increase of built-up
land primarily occurred in the areas between Beijing and Shi
Jiazhuang. The northern and western parts of this region,
which are mainly mountainous, display little land-use change
in the research period.
To examine the differences of land-use change in the
three decompositions, we designate R to denote the relative
change of a certain land type in a period. R is expressed as:
R = (|Nb-Na|*Ma) / (|Mb-Ma|*Na)
(6)
In this formula, Na and Nb are the size of a certain land
type in a certain area at the beginning and the end of the study
period; Ma and Mb are the size of a certain land type in the
whole region at the beginning and the end of the study period.
Figure 3. Land-use change in Jing-Jin-Ji region, 1990, 2000, and 2005
Note: Urban & Rural Area indicates the built-up land in urban and rural areas.
Human Ecology Review, Vol. 20, No. 1, 2013
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Li and Zhang
tion in the period 2000-2005 increased compared with that in
the period 1990-2000. This also suggests that socioeconomic
development since 2000 has accelerated the rate of change of
both arable land and built-up land in Jing-Jin-Ji region.
This section also further analyzes the influence of socioeconomic factors on land-use change in the three decompositions of Jing-Jin-Ji region in the two research periods.
(1) Demographic factors. Initial population density (D90
and D20) of each county or urban district and its increase
(∆D) in the two periods were selected to show the influence
of population change on land-use change.
(2) Economic factors. Per capita GDP in 1990 and 2000
(Pgdp90 and Pgdp20) and its increase in the two periods
(∆Pgdp) were chosen to show the influence of economic
level; we also selected the ratio of non-agricultural industrial
employment to the total employment in 1990 and 2000 (Na90
and Na20) and its increase in the two periods (∆Na) to show
the influence of economic structure on land-use change.
L90, L20, and L05 are the land size (arable land and builtup land) in 1990, 2000, and 2005.
Table 5. Average relative change of arable land and built-up land
in Jing-Jin-Ji region, 1990-2000 and 2000-2005
1990-2000
Urban areas
Peri-urban areas
Periphery areas
Arable land
Built-up land
Change
amount
(km2)
Average
relative
change (%)
Change
amount
(km2)
Average
relative
change (%)
-824.2
-651.6
-702.7
2.1
1
0.6
881.7
583.3
256.5
1.5
1.3
0.4
-157.9
-328
-326.1
2.2
1.6
1.5
755.1
388.9
350.7
1.8
1.4
0.7
2000-2005
Urban areas
Peri-urban areas
Periphery areas
Table 5 presents the results of the average relative land
change of each decomposition in the period 1990-2000 and
2000-2005. The figures clearly show the differences of landuse change among the three decompositions in these two periods. In the period 1990-2000, both the arable land and builtup land in the urban areas have the highest rates of change,
followed by the peri-urban areas and the periphery areas. The
average relative change of arable land and built-up land follows the same sequence in the three decompositions in the
period 2000-2005. This indicates that dramatic land-use
change takes place mainly in the urban areas and their surrounds, while periphery areas have comparative slow landuse change. The land-use rate of change in each decomposi-
The regression model can be written:
lnL20-lnL90=α+β(D90,∆D)+θ(Pgdp90,∆Pgdp,Na90,∆Na)+ε
(7)
lnL05-lnL20=α+β(D20,∆D)+θ(Pgdp20,∆Pgdp,Na20,∆Na)+ε
(8)
The research uses PASW Statistics 18.0 to test the influence of these variables on land-use change in the counties
and urban districts of Jing-Jin-Ji region in the three classifi-
Table 6. Regression results of factors driving land-use change, 1990-2000
D90
∆D
Jing-Jin-Ji
-
-0.266
(-2.524)*
Urban areas
-
Classification
∆Pgdp
Na90
∆Na
R2
-
-0.236
(-2.511)*
-
-0.031
(-0.333)
0.378
-0.174
(-0.999)
-
-0.695
(-2.816)**
-
0.290
(1.182)
0.589
-0.452
(-3.064)**
-
-0.266
(-1.784)*
-
0.175
(1.229)
0.591
-0.179
(-1.301)
0.412
Pgdp90
Arable land
Peri-urban areas
Periphery areas
-
0.050
(0.333)
-0.411
(-2.779)**
0.143
(1.037)
-
Built-up land
Jing-Jin-Ji
-
0.110
(1.174)
-
0.204
(2.075)*
-
-0.003
(-0.029)
0.254
Urban areas
-
0.162
(0.901)
-
0.68
(2.668)*
-
-0.329
(-1.301)
0.552
Peri-urban areas
-
0.544
(3.711)**
-
0.139
(0.940)
-
-0.075
(-0.527)
0.597
Periphery areas
-
-0.004
(-0.025)
0.093
(0.629)
-
0.056
(0.383)
0.210
0.151
(0.955)
Figures in parentheses are associated t values; *= sig. < 0.1, **= sig. < 0.01
32
Human Ecology Review, Vol. 20, No. 1, 2013
Li and Zhang
Table 7. Regression results of factors driving land-use change, 2000-2005
Classification
D20
∆D
Pgdp20
∆Pgdp
Na20
∆Na
R2
-0.155
(-1.876)*
-
0.123
(1.514)
0.527
0.248
(0.670)
0.618
0.051
(0.466)
0.709
-0.387
(-2.106)*
0.478
0.149
(1.714)*
0.411
-0.187
(-0.661)
0.801
-0.089
(-0.865)
0.743
0.307
(1.614)
0.416
Arable land
Jing-Jin-Ji
Urban areas
Peri-urban areas
Periphery areas
0.248
(3.046)**
-0.005
(-0.048)
-
-0.486
(-6.018)**
-
-0.401
(-2.086)*
0.260
(0.818)
-0.272
(-1.807)
-0.592
(-5.714)**
-0.422
(-3.504)**
-0.017
(-0.160)
-0.132
(-0.872)
-
0.053
(0.333)
-0.263
(-3.033)**
-
-0.054
(-0.615)
-0.044
(-0.110)
-0.506
(-2.877)**
Built-up land
Jing-Jin-Ji
Urban areas
Peri-urban areas
Periphery areas
0.325
(3.715)**
-0.022
(-0.209)
-
-0.155
(-1.056)
0.961
(3.961)**
-0.212
(-1.110)
0.593
(6.023)**
0.470
(4.104)**
0.103
(1.005)
0.142
(0.906)
-
-0.060
(-0.362)
-0.327
(-1.061)
0.442
(2.427)*
Figures in parentheses are associated t values; *= sig. < 0.1, **= sig. < 0.01
cations in the periods 1990-2000 and 2000-2005. Before the
regression, a correlation analysis was made to test for autocorrelation among the variables. The analysis showed that
initial population, initial per capita GDP, and the ratio of nonagricultural industrial employment to the total employment in
1990 and 2000 were strongly correlated to other variables in
most classifications. Thus, these variables were excluded
from the regression analysis in those classifications.
According to the regression results, the driving factors
for land-use change in the three decompositions in these two
periods are different (Tables 6 and 7). Factors contribute to
the built-up land increase but arable land decreases in these
decompositions. Generally, the land-use change in the region
and three decompositions in the first period are mainly attributed to the increase value of the factors. However, factors
of both the initial value and increase value show significance
in the second period. This indicates that factors’ influence on
the land-use change in the period 2000-2005 is based mainly
on the growth of factors in the period 1990-2000. This finding also interprets the fact that the rate of land-use change in
the period 2000-2005 is higher than in the period 1990-2000.
In the period 1990-2000, land-use change in the urban
areas is attributed to the increase of per capita GDP, while
population increase was more significant in the peri-urban
areas. However, in the period 2000-2005, population increase
and initial per capita GDP were significant in both the urban
areas and peri-urban areas. This difference indicates that the
driving factors to land-use change in the urban and peri-urban
Human Ecology Review, Vol. 20, No. 1, 2013
areas have shifted from economic growth to population
growth in the research period. Specifically, the high economic level in 2000 after 10 years of growth helped attract more
immigrants to the urban and peri-urban areas in the period
2000-2005. The factors of significance to land-use change in
the periphery areas have changed from the initial economic
level in 1990 to the development of non-agricultural industries in the whole research period. This implies that development of non-agricultural industries in the traditionally agriculture-dominated economy of the periphery areas has contributed to land-use change in these areas.
Concluding remarks
By examining land-use change in Jing-Jin-Ji region, this
paper provides evidence of how humans affect the physical
environment in the Chinese context. In general, places of
high agglomeration of human activities have faster land-use
change than those in the peri-urban areas and rural peripheries. The findings imply that in the context of fast urbanization and economic growth, resource flows like people, capital, goods, and information tend to agglomerate in the urban
areas in China, followed by the small towns and medium
cities, as well as the rural peripheries.
Unlike most studies which analyze land-use change
without taking human activities into consideration, this paper
brought these two parts into the same research framework
and revealed human influence on the physical environment in
33
Li and Zhang
the spatial dimension. Upon the achievement of potential harmony of man-nature, McHarg (1969) emphasized that people
should design with nature, and there needs to be human cooperation and a concern for the natural environment and ecology when dealing with urban design. What this paper has
added is that the achievement of man-nature harmony should
consider the agglomeration of human activities. Specific
land-use planning and environmental management should be
made in the urban, peri-urban and rural peripheries so as to
coordinate the agglomeration of human activities among
them.
Acknowledgments
The writing of this paper was supported by two grants from the National Natural Science Foundation of China (41130748 and 41301190).
The paper was presented at the 2011 Association of American Geographers
annual congress, Seattle, Washington, USA, 12-16 April. The authors appreciate the audience’s comments.
The authors also thank Professor Yansui Liu, Chinese Academy of
Sciences, Beijing for valuable advice in improving and revising the paper.
Endnotes
1.
2.
Direct all correspondence to Yuheng Li, 11A Datun Road, Chaoyang
District, Beijing 100101, China. Tel: 86 10 64889034, email:
[email protected]
Hebei Province has 11 prefecture-level cities, but three cities in the
south—Hengshui, Xingtai and Handan—are not included in this
analysis.
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