Download Research on Economy Sustainable Development of Mining Cities of China

Research on Economy Sustainable Development of Mining Cities
of China1
BI Puyun 1 XU Kejian 2
1 School of the Earth Sciences and Resources China University of Geosciences (Beijing)Beijing,
P.R.China 100083
2 School of Humanities and Economic Management, China University of Geosciences (Beijing)Beijing,
P.R.China 100083
[email protected]
，
Abstract: The sustainable development of the mining cities’ economy is an important part of healthy
economy in China; this paper appraises the economy sustainable development of China’s mining cities
by constructing the index system of economy sustainable development and using the method of factor
analysis. At last this paper suggests that the mining cities should promote economy benefit of enterprise
and develop the foreign-oriented economy, but not enhance the investment in fixed assets.
Key words: mining city; sustainable development; factor analysis
1 Introduction
The mining cities supply a lot of energy resources and mineral resources which includes more than 90
percent of coal and oil and more than 80 percent of ironstone according to statistics for the development
of china and make important contribution to the national economy. But the resources of mining cities
have reduced sharply for these years and the economic structure is not appropriate, the economy
sustainable development of mining cities is faced with threats. The paper attempts to evaluate the typical
mining cities’ economy sustainable development.
2 The index system of economy sustainable development of Mining city
2.1 the construction of index system
To study the economy sustainable development of mining city in china, we firstly should study the
economy scale of the mining city. Economy scale includes: The average per capita GDP and the growth
rate of GDP; investment[1], consumption, export are the power of the regional economic development,
which are embodied concretely in the investment in fixed assets, retail sales of consumer goods, the
degree of dependence on foreign trade; the economic efficiency is the material base of economy
sustainable development. We may choose gross labor productivity of industry, ratio of total assets to
industrial output value, ratio of profits to industrial cost, per capital income tax of enterprise as index to
analyze. This paper puts forward that the index system of economy sustainable development of mining
city is composed of three levels, the first level is the target, the second level is the index, the third level
is the essential factor which includes 3 indexes and 9 essential factors, as shown in table 2-1:
，
Table 1
target
Economy
sustainable
development
1
Index
Economic
scale
Economic
force
Index system of economy sustainable development
factor
per capital GDP Yuan
（
）
Growth rate of GDP（%）
Investment in fixed asset（Hundred million Yuan）
per capital retail sales of consumer goods（Yuan）
label
X1
X2
X3
X4
The paper is supported by the young teachers found projects of School of Humanities and Economic
Management China University of Geosciences founded
，
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（ ）
Gross Labor Productivity of industry(Yuan /person、year)
the degree of dependence on foreign trade %
Economic
efficiency
Ratio of total assets to industrial output value (%)
Ratio of profits to industrial cost %
X9
X6
X7
（ ）
）
X8
per capital income tax of enterprise(Yuan
X5
2.2 data gathering
According to the mining life cycle[2] of mineral resources and the type of mineral resources of mining
cities, the paper has selected Shuozhou city, Jincheng city, Yangquan city, Linfen city in Shanxi
Province, Jiaozuo city, Pingdingshan city in Henan Province, Panzhihua city in Sichuan Province, Fuxin
city, Panjin city, Liaoyuan city in Liaoning Province, Jixi city in Heilongjiang Province, Chenzhou city
in Hunan Province, Kelamayi city in Xinjiang Uygur Autonomous Region as the typical mining city.
The paper is to analyze the sustainable development ability of these cities. The statistical data comes
from each mining city’s statistical yearbook. As is shown in the table below:
Table2
cities
per
capital
GDP
Growth
rate of
GDP
Investment
in fixed
asset
the statistic of mining cities
per capital
retail sales
of
consumer
goods
per capital
income
tax of
enterprise
Gross Labor
Productivity
of industry
Ratio of
total assets
to
industrial
output
value
Ratio of
profits to
industrial
cost
Panzhihua
25539
14.7
148.72
412.8
2516.5
130399
8.74
4. 99
Shuozhou
15370
16.23
152. 94
4143.7
1645.5
184928
12.68
7.13
Yangquan
18665
9. 8
134.4
4559. 67
2805. 07
71443
8. 27
5.78
Jixi
12379
13
88.4
3275.68
539. 83
32397
12.7
3. 3
Pingdingshan
13580
15.8
365. 3
3401.25
2359.48
87173
16.5
8. 6
Chenzzhou
12517
8. 3
248. 8
4413.64
1785. 67
125410
Karamay
96006
9
285. 06
7730.24
128479. 6
390739
39.9
37.4
Jincheng
16490
11.8
215. 5
3955. 28
3533. 24
103439
12.02
14.66
Fuxin
8227
11.1
104.1
1676. 68
500
39256
5.12
1. 33
Jiaozuo
20583
17.2
491. 33
4393.79
5638.41
112748
33.83
12.91
Panjin
39316
6.1
270. 2
7215. 58
15074
247858
22.63
20.52
Liaoyuan
13918
21.1
201. 88
4145. 01
610. 39
78118
9. 9
1. 85
Linfen
14242
13.3
234. 2
3718. 47
9470.87
127592
17.34
6.14
3 The evaluation of economy sustainable development of typical Mining city in
China
3.1 Introduction of evaluation method
Factor analysis[3] needs to pick up the fewer unrelated abstract indexes (common factors) from the
original data. Every original index can be shown by linear combination of the common factors which
has the corresponding contribution rate to the degree of original index. It is thought that the common
factors will respond to the original index information when the accumulative contribution rate achieves
75 percent.
、
、
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3.2 Whether the data suits factor analysis
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
.779
Bartlett's Test of
Sphericity
Approx. Chi-Square
87.657
df
28
Sig.
.000
We can see the computation of KMO is 0.779 which is above 0.7 from the chart, so the effect of factor
analysis for the data is better. In Bartlett’s test of sphericity, we can conclude that the hypothesis of data
independence does not exist, so the test of applicability of factor analysis takes effect.
3.3the computation of variables’ communalities
Communalities
X1
X2
X3
X4
X5
X6
X7
X8
Initial
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
Extraction
.920
.697
.890
.690
.844
.872
.939
.962
Extraction Method: Principal Component Analysis.
，
We can find that the communalities of all the variables which has been shown in the chart are large it
shows that more information has been reserved when the variables space convert into the factors
space so the effect of factor analysis is obvious.
，
3.4 Total variance explained
From the chart we can find that the accumulative contribution rate of first two factors has achieved
85.180 percent. We pick up two common factors because the eigenvalues of the first two factors are
5.432, 1.382 partly which are above 1.0.
Total Variance Explained
Initial Eigenvalues
Total
1
2
3
4
5
6
7
8
5.432
1.382
.603
.320
.147
.066
.031
.020
% of
Variance
67.906
17.275
7.539
3.998
1.833
.819
.385
.245
Cumulative
%
67.906
85.180
92.719
96.717
98.551
99.370
99.755
100.000
Extraction Sums of Squared
Loadings
%
of
Cumulative
Total
Variance
%
5.432
67.906
67.906
1.382
17.275
85.180
Extraction Method: Principal Component Analysis.
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Rotation Sums of Squared
Loadings
%
of
Cumulative
Total
Variance
%
5.167
64.591
64.591
1.647
20.589
85.180
3.5 Calculation of component matrix
a
Component Matrix
Component
1
X8
X1
X6
X5
X7
X4
X3
X2
2
.980
.945
.928
.907
.878
.831
.449
-.481
-.029
-.164
-.104
-.146
.411
.009
.829
.682
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
According to the chart, we can get the factor model among the variables. We can find that the first main
factor is decided by x1,x4,x5,x6,x7,x8, their components on the main factor are
0.945,0.831,0.907,0.928 0.878 and 0.980;the second main factor is decided by x2 x3, their
components on the main factor are 0.829, 0.628. We can think that the first main factor which is called
benefit factor reflects economic benefit, at the same the second main factor which is called dynamic
factor reflects economic growth which is decided by the investment in fixed assets.
、
、
、
3.6 Evaluation of the economy sustainable development of mining city in china
We can compute the factors’ scores to evaluate the condition of the mining cities by the method of
regression, the component score coefficient matrix has been shown as follows:
Component Score Coefficient Matrix
Component
1
X1
X2
X3
X4
X5
X6
X7
X8
2
.199
-.212
-.074
.146
.189
.184
.080
.180
-.070
.455
.601
.046
-.060
-.029
.329
.026
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Component Scores.
We can describe the two common factors as the linear form of the eight indexes according to the
component score coefficient matrix. The function has been got as follows:
F1=0.199X1-0.212X2-0.074X3+0.146X4+0.189X5+0.184X6+0.080X7+0.180X8
F2=-0.070X1+0.455X2+0.601X3+0.046X4-0.060X5-0.029X6+0.329X7+0.026X8
The integrated statistic measure can be computed according to the contribution rate of variance of every
variable. The formula is as follows:
F=0.75186F1+0.24814F2
According to the formula, we can compute to get the ability of sustainable development of typical
mining cities.
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Table3 Rank of typical mining cities of China
Cities
Panzhihua
Fac-1
-0.50797
Fac-2
-0.55326
Integrated score
-0.51921
Shuozhou
-0.23914
-0.13268
-0.21272
8
Yangquan
-0.09423
-1.05511
-0.33266
9
Jixi
-0.47133
-0.79994
-0.55287
12
Pingdingshan
-0.54044
1.0524
-0.14519
7
Chenzhou
.
.
0
4
Kelamayi
2.76351
0.23898
2.137073
1
Jincheng
-0.06435
-0.29262
-0.12099
5
Fuxin
-0.61348
-1.19721
-0.75833
13
Jiaozuo
-0.29107
2.39634
0.375784
3
Panjin
1.13387
-0.35619
0.764127
2
Liaoyuan
-0.85042
0.60036
-0.49042
10
Linfen
-0.22495
0.09893
-0.14458
6
Rank
11
we can find: (1) Kelamayi city and Jiaozuo city have the better economic benefit than other cities; the
economic growth potential of Kelamayi city and Jiaozuo city and Jixi city are larger than other cities ;( 2)
Kelamayi city , Jiaozuo city and Panjin city have the best ability of economic sustainable development.
Fuxin city and Jjixi city have the worst ability of the economic sustainable development.(3)the
economic benefit of all the mining cities is too bad, but the economic growth potential is better which is
forced by investment in fixed assets.(4)the economic benefit has strong relation with the cities’
economic sustainable development ability.
4 The suggestion on Chinese mining cities’ economy sustainable development
According to the analysis
(1) The mining cities’ economy sustainable development is decided by economic benefit and economic
growth force. The economic benefit has strong relation with the economy sustainable development
ability. So the mining cities’ economy sustainable development must focus on increasing the economic
benefit by technical advancement not enlarging the fixed assets investment. The enterprise of mining
cities must adopt the modern management technique to increase the labor productivity and improve
employee labor skill to reduce the labor expense to get lager profit share.
(2)We can find that it is the investment in fixed assets not the consumption and foreign trade has strong
force to the mining cities’ economic growth, so as the other cities, Chinese mining cities must pay
attention to the power of consumption at the same time the development of export oriented economy is
the advisable way of mining cities’ economy sustainable development.
，
，
5Conclusion
The economy sustainable development of mining cities depends on economy growth but not economy
benefit. The economy growth of mining cities depends on investment in fixed assets and the
consumption and export has little effect to economy growth. So we think the economy sustainable
development of mining cities needs to focus on promoting the economy benefit and developing
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foreign-oriented economy.
References
[1] Gao hongye,Western Economics,(2003),p105-106
[2] lv guoping, mining city:regulate life cycle and poromote sustainable development, China Geology &
Mining Econonic(2002)
[3] Hao liren, SPSS practical statistical analysis, (2003), p304-306
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