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ИССЛЕДОВАНИЕ ЗАВИСИМОСТИ МЕЖДУ РЕГИОНАЛЬНЫМ ЭКОНОМИЧЕСКИМ РОСТОМ И ЗАГРЯЗНЕНИЕМ ОКРУЖАЮЩЕЙ СРЕДЫ НА ОСНОВЕ НЕЧЕТКИХ МОДЕЛЕЙ THE RESEARCH ON THE RELATIONSHIP BETWEEN ECONOMIC DEVELOPMENT AND ENVIRONMENT POLLUTION IN HUBEI PROVINCE BASED ON GRAY MODEL
Луо Цзуань / Luo Juan Уханьский текстильный университет (КНР) А.Н. Ильченко / A. Ilchencko Ивановский государственный химико-технологический университет
Оценка скоординированного развития экономики и окружающей среды есть высшая цель в обеспечении качества жизни в развивающихся странах. В этой статье сделана попытка использования теории нечеткого прогнозирования для изучения взаимоотношений между экономическим ростом и экологическим загрязнением на примере промышленного региона Китая за период с 2003 по 2012 г.г. Приводятся результаты нечеткого моделирования и прогноз развития ситуации в регионе на последующие пять лет. Полученные результаты помогут обоснованию управляющих решений в региональной политике улучшения качества жизни.
Ключевые слова: экономическое развитие, загрязнение окружающей среды, нечеткие модели, региональная экономика, промышленный регион Китая.
1. Introduction.
The contradiction between economy and environment became one of the major problems in our society today. During the process of industrial production emissions of waste lead to atmosphere, water and soil; pollution directly reduce the human living environment quality; industry rapid development need more energy what lead to overexploitation of the natural environment; conversely, environmental effects of industrial development severely restricts the further development of industrial economy. These results are serious in the process of China's regional industrial economic development. Hu-bei province, which is one of big industrial and agricultural provinces in China, made great economic development in recent years. The total of province's GDP is 2.22505 trillion Yuan and GDP per person is about 38 thousand Yuan in 2012. But it is under pressure to environment pollution at the same time. Total volume of industrial waste water discharged, waste gas discharged, industrial solid wastes
are respectively about 916 million tons, 1.9512 trillion standard cubic meters and 76 million tons [1]. Because of iron, steel and chemical industry are the mainly characteristics of the industrial structure of Hubei province, industrial pollution is serious. It is the main source of environmental pollution in Hubei Province; the wastewater, gas waste and solid waste produced by traditional industry cause more serious damage on the environment. For example, Hubei's air quality is poor in recent years; the main reason is the large amount of pollutant discharge. An industry source makes the greatest contribution in pollutants. With the development of the rural economy, agricultural pollution problems became increasingly prominent; for example, pesticides and fertilizers seep into lakes and underground water with rainfall; untreated animal manure and sewage became a new pollution source; a lot of straw direct combustion causes serious air pollution.
Evaluation of the economic and environmental coordinated development is a hot
issue in the field of sustainable development in developing countries. There are many valuation models [2]: Input-output model [3, 4], EKC econometric model [5, 6], Gray model [7, 8, 9], the coordination of the development and coordination of comprehensive evaluation model [10, 11]. Currently the economic and environmental development dynamic evaluation need to be resolved: the judgment of coordination state standard and the prediction of future trends. In 1982 Deng Julong the Professor of Huazhong University of Science and Technology of China proposed Gray Model theory; this method can be used for forecasting, decision-making, evaluation, planning and control, system analysis and modeling in social, economic and scientific technology fields, particularly, it has a unique effect on short time series, less statistical data analysis and incomplete information system. It caused attention of many scholars at home and abroad and is widely used in recent 20 years [12].
In this article we take Hubei Province as an example and use Gray Model to forecast the situation of economic development and environmental pollution for the next five years. We also analyze the relationship between environmental pollution and economic development by the gray correlation analysis. Then we can provide the basis for the decision-making of regional sustainable development.
2. Introductions to Gray Model.
Gray Model is a method of studying of uncertainty system with partial information known and partial information unknown. Environment system which is as a part of the natural ecology has changing characteristics of ecological lag and unpredictable natural factors, and there are very complex relationships between factors. Single factor, single target and linear analysis method is incapable of action. The state variables of the system in discrete form are the main research objects in gray forecasting model. It is not a method of processing the data generated in stochastic process by statistical laws or prior treatment but to find the sequence rules through the
process of organizing original data series (also called number generation). The random quantity can be regarded as a gray value. We can obtain a series of regulatory sequences, namely it has monotonic property. In this article we use summation operator to get forecasting data. Next we will give the detailed steps of gray model [13, 14, 15].
2.1. Establishment of Gray Model. X(0) = (x(0)(1),x(0)(2),---x(0)(n)) is a nonnegative known sequence, X(1) = (x(1)(1),x(1)(2),---x(1)(n))is once accumulated generating sequence, and there
x
(i)
(k) = 2 x (0)(0, k = 1,2,
•n.
We can create a time series variable parameters of Gray Model:
— + ax(1) = b dt
There are 1 order and 1 variable, so we call it GM (1,1). There a is development of coefficient, b is called gray control number, and they are constant. After calculating of a and b value, we put them into the Gray Model and get the estimation
^(k +1) = (x(0)(1) - -)e-ak + -.
a a
Then we can get the original sequence:
€0) (k+1)=x1 (k+1) - xP (k) (k=0,1,2-, n)
£0)(k)(k = 1,2, - - n) is the fitted values of original data sequence, €0)(k) (k > n) is the predictive value of the original sequence. 2.2. Gray Model Test. Gray Model test generally includes residual test and posterior-variance test.
(1) Residual test: Absolute error:
A(0)(i) =|x (0)(i) - £0)(i )|, i = 1,2,-, n Relative error: A(0)(i)
O (i) = 100%, i = 1,2,-, n
x (0)(i)
(2) Posterior-variance test:
Standard deviations of primitive sequence:
S =
1 N 1 -Vrr(0)
N -
-X [ x (0)(k) - X )]2 ;
1 K =1
— 1 N
( X = N X x (0)(k );)
k=1
Standard deviations of absolute error sequence:
S 2 =.
1
N -1 K=
X [A(0) (k) - A(0) ]2 ;
- 1 N
(A(0) = 1X A(k );)
N K=1
S
Then we get posterior-variance ratio C = ,
and small error probability P = | A(0) (i) - A^ |< 0.6745^}. The table 1 is the posterior-variance test standard.
Table 1
Posterior-variance test st andard
P >0.95 >0.80 >0.70 < 0.70
C <0.35 <0.50 <0.65 > 0.65
grades good qualified basic qualified not qualified
If the residual test, posterior variance test passed, we can use this model to forecast the Index. Otherwise, we need residual error correction.
3. Prediction on economic development and environmental pollution.
In order to study the relationship between economy and environment, it is necessary to select representative indexes of reflect the situation between economy and environment. In this article we select economic indi-
cators the Hubei province from 2003 to 2012 [1]: GDP (X1), total investment in fixed assets (X2), the proportion of the tertiary industry (X3); environmental indicators: industrial waste water discharged (Y1), gas waste discharged (Y2), industrial solid wastes produced (Y3). We establish the Gray Model based on the above data and predict the index data from 2013 to 2017.
Each index model is built according to the theory of gray prediction.
GDP (X1): jS€(1)(k +1) = 29260.45e°'17k -24503.70 Total investment in fixed assets (X2): (k +1) = 8806.13e024k - 6922.54 Proportion of the tertiary industry (X3): €X)(k +1) = -3063.66e-001k + 3105.76 Industrial waste water discharged (Y1): jS€(1) (k +1) = 25273880e0004k - 25179182.51 Waste gas discharged (Y2): ^(k +1) = 58067.68e012k -51360.68 Industrial solid wastes produced (Y3): ^(k +1) = 29325.4e010k -26203.40
Table 2
Results of index test
Index Average relative Small error C Grade
error probability
X1 0.05 1 0.11 good
X2 0.05 1 0.05 good
X3 0.01 1 0.19 good
Y1 0.03 0.8 0.64 basic qualified
Y2 0.11 0.9 0.28 good
Y3 0.03 1 0.09 good
We see every index test passed, and Then we use the above models to forecast inmodel fitting effect is better from table 2. dex data from 2013 to 2017.
Economic development and environmental po
Index 2013 2014 2015 2016 2017
X1 24871.38 29553.05 35405.14 41549.84 49450.16
X2 20690.90 26301.30 33432.97 42498.42 54021.98
X3 36.65 36.15 35.67 35.18 34.71
Y1 95858.84 96210.59 96563.64 96917.99 97273.63
Y2 22852.96 25835.14 29206.49 33017.77 37326.41
Y3 8924.941 9947.868 11088.04 12358.89 13775.39
lution volume prediction
Table 3
4. Gray Relationship Analysis.
Gray relationship analysis is a method to analyze the relationship between different things in gray system.
The concept of correlation coefficient (all the data below need to be standardized) is follow:
rJk) =
nun min
i Jt
+,£?тах max i t
0*0 - x, (k)
(&) - Xj (к) I +p max max
(i = l,2;~,m,k = l,2, — ,n)
where ri (k) is the correlation coefficient from X to x0 at time k, p is the distinguish coefficient, generally is between 0 and 1, and usually p = 0.5. Then we get correlation degree
from xi to x0 :
& = [r(1) + r (2) + ••• + r (n)]/n. If the correlation is more close to 1, the
degree of correlation is greater. According to the experience, when the correlation of two factors is greater than 0.6, the correlation is significant.
We use the incidence degree coefficient formula for calculating correlation coefficient and correlation degree, and get the table 4, table 5 and table 6.
Table 4
Gray correlat ion degree of economic indicators and water po lution
Index 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 gray correlation degree
X1 0.45 0.51 0.38 0.37 0.38 0.54 0.5 0.88 0.35 0.85 0.521
X2 0.45 0.5 0.37 0.36 0.36 0.49 0.49 0.9 0.36 0.85 0.513
X3 0.52 0.67 0.69 0.7 0.65 0.62 0.52 0.47 1 0.33 0.617
Table 5
Gray correlation degree of economic indicators and gas waste
Index 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 gray correlation degree
X1 0.33 0.38 0.4 0.46 0.48 0.6 0.66 0.84 0.37 0.38 0.49
X2 0.33 0.37 0.39 0.45 0.45 0.54 0.65 0.83 0.38 0.38 0.48
X3 0.98 0.77 0.73 0.95 0.92 0.69 0.71 0.57 0.98 0.69 0.80
Table 6
Gray ^ correlation degree of economic indicators and industrial solid ^ wastes
Index 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 gray correlation degree
X1 0.33 0.35 0.39 0.46 0.56 0.7 0.96 0.51 0.37 0.33 0.50
X2 0.98 0.35 0.38 0.45 0.52 0.62 0.97 0.51 0.38 0.33 0.55
X3 0.98 0.67 0.69 0.95 0.82 0.82 0.87 0.95 0.97 0.98 0.87
From the a
bove tab
e we can see that the
proportion of tertiary industry impact on the
environment pollution is relatively large. The proportion of tertiary industry reflects the economic growth rate, so this shows that the effect of the industrial structure and economic growth on the environment pollution is relatively large, especially on air pollution and solid waste pollution.
5. Conclusions.
Over the past decade the economic development of Hubei Province is in rapid growth phase, gray forecasting economic development through the next five years shows that the economic development of Hubei Province will continue to grow rapidly, but have also been seen in worsening environmental pollution, which is due to improve the level of industrialization in Hubei Province and the decrease tertiary industry tends in recent years. This "high-growth, high pollution" of economic growth in the long run is not feasible, is not consistent with the economic environment and the scientific concept of development coordinated strategy for sustainable development. Pollution reduction task is very heavy in Hu-bei Province, we should adjust the economic structure, increase investment in environmental protection, strengthen the monitoring of pollution sources to avoid environmental pollution levels increased along with economic development.
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Рукопись поступила в редакцию 01.07.2014.
THE RESEARCH ON THE RELATIONSHIP BETWEEN ECONOMIC DEVELOPMENT AND ENVIRONMENT POLLUTION IN HUBEI PROVINCE BASED ON GRAY MODEL
Luo Juan, Angelina N. Ilchenko Evaluation of the economic and environmental coordinated development is a hot issue in the field of sustainable development in developing countries. In this article we take the case of Hubei Province as an example and use gray forecast theory to research the relationship between economic development and environmental pollution from the period of 2003 to 2012. We get the result of Gray Model and forecast the situation of economic development and environmental pollution for the next five years. We also analyze the relationship between environmental pollution and economic development by the gray correlation analysis. Then we can provide the basis for the decision-making of regional sustainable development.
Key words: economic development, environment pollution, Gray Model, Hubei province, China industrial area.
