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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 , 1157 ( ) 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. 、 、 1158 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. 1159 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. 1160 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 1161 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 1162