The relationship between income and NSP in zhejiang province: Is ...

Available online at www.sciencedirect.com

Energy Procedia 5 (2011) 1737–1741

IACEED2010

The relationship between income and NSP in zhejiang province: Is there an environmental Kuznets curve? Wang shuang-yinga,b*, Lu wen-conga a b

School of Management, Zhejiang University, Hangzhou 310058, P.R.China Financial School, Jiangxi Normal University, Nanchang 330027,P.R.China

Abstract In this study, using methods of emission coefficient, we measured the total pollutant emissions of livestock manure and rural life, which are important non-point source pollution source in agriculture, in zhejiang province from 1990 to 2009. We investigated the relationship between rural net income and rural environmental quality of zhejiang province with the Environmental Kuznets Curve (EKC) model. The results shows that there is no significant relationship between rural pollution and per capita net income of rural residents in zhejiang province, which means it doesn’t well agree with EKC curve. Other factors may have a great influence on relationship of them.

© 2011 Published by Elsevier Ltd. Open access under CC BY-NC-ND license. Selection and peer-review under responsibility of RIUDS Keywords: NSP, rural net income, EKC

1. Introduction Ev idence suggests that some pollutants follow an inverse-U-shaped pattern relative to countries’ incomes.[1] Due to its similarity to the t ime -series pattern of income inequality described by Ku znets (1955)[2], who hypothesized that income inequality rises firstly and then falls as economic develop ment proceeds, the environmental pattern has been called an ‘environ mental Ku znets curve’ (EKC). Shafik and Bandyopadhyay (1992)[3] and Selden and Song (1994) [4] presented evidence that some pollutants have historically followed an inverted U-curve with respect to income . EPA survey in 2003 showed that agricultural non-point source pollution is the la rgest source of the of rivers and lakes’ pollution in U.S., which causes about 40% of the water quality in rivers and lakes failed. 60% of the water environ ment pollution originated fro m agricultural sources. But in recent years, Ch ina

* Corresponding author. Tel. 0571-88203727,13656656529. E-mail address: [email protected], [email protected].

1876–6102 © 2011 Published by Elsevier Ltd. Open access under CC BY-NC-ND license. doi:10.1016/j.egypro.2011.03.296

1738

Wang shuang-ying and Lu wen-cong / Energy Procedia 5 (2011) 1737–1741

saw cyanobacteria booming in Taihu lake, Chaohu lake and so on. [5] More than 50% of the parts are fro m the non-point source pollution (i.e., the upper reaches of agriculture, domestic sewage and aquaculture). So far, 30% to 50% of the surface water on the world were suffered fro m non-point source pollution, among wh ich the agricultural non-point source pollution contributed to the most [6]. In recent ten years, the Chinese government has promoted “increase” as the core of the agricultural develop ment strategy vigorously, proposing a “high-yield, h igh-quality, high-efficiency” agricu ltural development policy. A large nu mber of modern agricu ltural inputs, such as fertilizers, pesticides, plastic sheeting, were widely used. Meanwhile, large-scale livestock breeding industry levels and total production is also rising, all of which are having a negative impact on the environment and environmental problems have gradually appeared and showed some signs of deteriorat ion. Randomness, uncertainty, large temporal and spatial differences is the characteristics of agricultural non-point source pollution [7]. As one of the richest provinces in China, zhejiang province has always been “a land of abundance”. Meantime, zhejiang province is a high agriculture polluted area with high speed. This art icle will test the emp irical evidence for an inverted U-curve hypothesis using data of zhejiang province on agricu lture non point source pollutants and income, and will reveal the relationship between income and agricultural non-point source pollution. 2. NSP and Review of literature of EKC Non-point source is a term applied to water pollution wh ich does not emanate from any “discernible, confined and discrete conveyance”. The principal pollutant fro m non -point sources is sediment fro m erosion caused by agricultural activit ies. Non-point source pollution is defined as water pollution, by the leaching and erosion of rainfall runoff, caused by pollutants fro m at mosphere, surface and underground entering water bodies, such as rivers, lakes, reservoirs and o ceans .[8] Agriculture non-point source pollution (A G-NPS) is defined as that in agricultural production activities, through surface runoff and underground seepage, nutrients like nit rogen and phosphorus, organic and inorganic pollutants like pesticides and heavy metals, soil particles and other sediments cause environmental pollution, especially water (Liu ji-hu i,2007)[9] What is the source of agriculture water pollution is been looking for by many researcher. More and more human activities are lead to pollution. Existing literature on the relationship between environmental quality and develop ment mainly focuses on EKC approach, according to which environ mental damage starts to decrease as the country prospers. It is hypothesized that the environment—income relationship might be similar to that suggested by Kuznets for income inequality in relation to economic development, namely of an inverted U shape. The earliest work of Grossman and Kruger (1991) describes a systematic explanation for the relation between environment and inco me [10]. Selden et al. (1999) b reak the technique effect further down to energy intensity, energy mix and other techniques effect [11]. Furthermore, Stern (2004) defined input mix effect, indicating the substitution of environmentally less harmful inputs for more harmfu l inputs. Heerink et al. (2001) elaborate that the extent to which these effects dominate relies on the incentives faced by economic actors and policy makers [12]. Traditional emp irical criterion of EKC includes an indicato r of environmental degradation as the dependent variable and levels and squares of real per cap ita GDP as independent variables. Because of quantitative problems encountered in measuring environmental quality, different environmental variables have been used in empirical studies. Emp irical studies of EKC using cross -section, panel data or time series estimation techniques with different environmental variables reach controversial results about the existence of EKC. Harbaugh et al. (2002) assert that the evidence for EKC is less robust than previously claimed [13]. They claim that the existence and location of turning points for different pollutants are very sensitive to the samples used. Several factors are effective in calculation of the turning points.

1739


In Vincent (1997) [14]’s study, he states that the relationship between one air and five water pollution indicators and inco me doesn’t agree with the EKC fo rm for Malaysian states. Focacci (2003) [15] and Focacci (2005)[16] investigate the relationship between per capita inco me and CO2 intensity for some countries between 1960 and 1997, such as US, Brazil and Ch ina, India and conclude that EKC hypothesis is not valid for these countries as well. Perman and Stern (2003) [17] discuss the relationship between GDP and sulfur emissions for 1960–1990 among 74 countries by cointegration analysis. Findings do not support the EKC hypothesis, and they conclude that EKC for sulfur emissions is a problematic concept. 3. Model and data 3.1 models The model used in paper is represented by˖ (1) E0 E1inc E2inc 2 H where loa is pollution load˗inc is the median household income ; H is a randomly distributed error term, and E0 is the intercept.

loa

Environmental indicator in this article is selected from agricultural production and rural living. We choose three main indicators which include animal livestock manure and rural living sewage. Livestock manure and rural living sewage include total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD), whose emissions are calculated according to the following formula:

Q(TN ) i uE(TN ) i

(2)

PTP Q(TP) i uE(TP) i

(3)

PTN PCOD

Q(COD ) i uE(COD ) i

In above formula,

(4)

PTN , PTP , PCOD represent the amount of total nitrogen emissions (TN),

phosphorus (TP) emissions and chemical oxygen demand (COD) emissions in turn;

Q(TN ) i , Q(TP ) i , Q(COD ) i represent the amount of population or livestock i; E(TN ) i , E(TP ) i , E(COD ) i represent emission coefficient of TN, TP and COD. This article introduces a term, agricultural non-point source pollution load, which greatly improves scientific and comparability of the pollution sources evaluation. Formula about pollution load is:

Pij

Cij Ci 0

u

Qij

M ij

(5)

Ci 0 In above formula, Pij is pollution load (cubic meters) of pollutants i in pollution sources j ; Cij is the emissions concentrations of pollutants i in pollution sources j;

Ci 0 is the evaluation criteria of pollutants i,

in this paper, ċ water quality standard in GB3838-2002 of Surface Water Quality Standards is adopted, in which TN is1mg / L ,TP is 0.2 mg / L ,COD is 20 mg / L. Qij is the amount of medium emissions of pollutants i in pollution sources j, 3.2 data

M ij is the amount of loss pollutants i in pollution sources j (tons).

1740


All data are fro m 2001-2010 ǉ Zhejiang Statistical Yearbook Ǌ and some related literature or document. According to statistical standards and related literature ( Yang shu-jing e.t.2009)[18], the difference of human waste emissions in people's lives are not significant, and summary of d ischarge coefficients shown in Table. T able 1. Livestock manure pollutant emissions based on the literature (kg/head· year) pollutant

cow dung

cow urine

pig dung

pig urine

sheep dung

average excretion(kg/d)

30.00

18.00

3.75

4.66

2.00

N(kg/head· a)

8.61

1.46

1.36

0.34

0.45

P2O5(kg/head· a)

31.90

29.20

2.34

2.17

2.28

COD(kg/head· a)

226.30

21.90

20.70

5.91

4.40

T able 2 . Loss rate of manure pollutants into water bodies

T able 3. Domestic pollution discharge coefficients

(%) pollutant

cow dung

pig dung

(kg/person· year) sheep dung

pollutant

Rural sewage

Human waste

TP

5.50

0.17

5.20

TP

0.56

3.06

TN

5.68

1.085

5.30

TN

0.16

0.524

COD

6.16

2.955

5.50

COD

5.99

19.8

T able 4 overall emissions of manure pollution from 1900 to 2009 in zhejiang province year

pollution load per unit arable land (m³/ha)

per capita annual net income (yuan)

year

pollution load per unit arable land (m³/ha)

per capita annual net income (yuan)

1900

50758.14

1099

2000

59673.04

4254

1991

12531.59

1211

2001

64987.03

4582

1992

52432.25

1359

2002

68060.71

4940

1993

56326.56

1746

2003

72668.97

5431

1994

57799.52

2222

2004

73336.59

6096

1995

54612.18

2966

2005

71751.61

6660

1996

53731.26

3463

2006

78076.2

7335

1997

54198.09

3684

2007

78735.46

8265

1998

54300.81

3815

2008

79594

9258

1999

54099.02

3948

2009

73692.39

10007

4. Estimates results and conclusion In this paper, EVIEW S 5.0 is used to estimating parameter of EKC curve model by OLS method, two processes are estimated respectively. As figure below showing ˈwhen quadratic coefficient is 0ˈ it shows that constant through the t test, significance probability is less than 0.01ˈsimu ltaneously ˈ F=30.02078>F0.01(1, 19)=8.18, which passed equation test of significance. When the quadratic coefficient is not zero, the coefficient of the model passes t test to more than 5% significant level. In equation test of significance, F = 17.82838> F0.01 (1, 19) = 8.18, the model is significant at the 1%


significance level; R2 =0.639173, it indicates that rural per cap ita net income of variables can’t strongly explain the amount of pollution load on per unit arable land. Overall, there is no significant relationship between rural pollution in zhejiang province and per cap ita net income of rural residents, which means it doesn’t well agree with EKC curve. It can be seen of quadratic that there are close to inverted "U" to some extent fro m the negative coefficient. Maybe there are other factors, such as financial support for farming, and rural water -saving consciousness, raise the level of urbanization, having a greater impact to them. T able 5 results Variable C

LOA

E0 E1 INC H

LOA

E0 E1INC E2 INC 2 H

40138.39˄9.172578˅*

30042.84˄4.06242˅*

4.533173˄5.479122˅*

9.671803˄3.018131˅*

INC

——

-0.000487˄-1.654613˅

Adjusted R-squared

0.604338

0.639173

D.W

2.001951

2.290406

F-statistic

30.02078

17.82838

INC 2

* Significance at 99%

References [1] Grossman, G., Krueger, A., Economic growth and the environment. Quarterly Journal of Economics;1995;110 (2):353–377. [2] Kuznets, S., Economic growth and income inequality. American Economic Review.1955.45 (1), 1–28. [3] Shafik, N. and S. Bandyopadhyay, Economic Growth and Environmental Quality, Time Series and Cross-Country Evidence.;,World Bank Working Papers, Washington1992,p904-9119. [4] Selden T . M. and D. Song, Environmental Quality and Development: Is There a Kuznets Curve for Air Pollution Emissions, Journal of Environmental Economics and Management XXVII, 1994,p.147–162. [5] http://env.people.com.cn/GB/8220/84923/index.html/2010-11-22. [6] Huang hong,Zou changwei,e.t. Review on China's non-point source pollution. Ecological Environment, 2004, 3(2): 255-257. [7] Chen wen-ying e.t.. Environmental impact and control of agricultural non-point source pollution. Northern Environmental, 2005, (2): 43-45. [8] Li huai-en e.t. Mathematical model of non-point source pollution,1996.7:P23. [9] Liu ji-hui e.t. Agricultural Non-point Source Pollution Study, Water Resources and Water Engineering, 2007, 18(1): 29~32 [10] Grossman, G.M., Kruger, A.B., Environmental impacts of the North American free trade agreement. NBER Working Paper 1991,p3914. [11] Selden, T .M., Forrest, A.S., Lochart, J.E, Analyzing the reductions in US air pollution emissions: 1970–1990. Land Economics 1999. 75 (1), 1–21. [12] Heerink, N., Mulatu, A., Bulte, E., Income inequality and the environment: aggregation bias in environmental Kuznets curves. Ecological Economics 2001. 38(3), 359–367. [13] Harbaugh, W.T., Levinson, A., Wilson, D.W., Reexamining the empirical evidence for an environmental Kuznets curve. T he Review of Economics and Statistics, 2002.84 (3), 541–551 [14] Vincent, J.R., Testing for environmental Kuznets curves within a developing country. Environment and Development Economics 2 (4), 1997.417–431. [15] Focacci, A., Empirical evidence in the analysis of the environmental and energy policies of a series of industrialised nations, during the period 1960–1997, using widely employed macroeconomic indicators. Energy Policy,2003. 31 (4), 333–352. [16] Focacci, A., Empirical analysis of the environmental and energy policies in developing countries using widely employed macroeconomic indicators: the cases of Brazil, China and India. Energy Policy, 2005.33 (4), 543–554. [17] Perman, R., Stern, D.I., Evidence from panel unit root and cointegration tests that the environmental Kuznets curve does not exist. T he Australian Journal of Agricultural and Resource Economics ,2003. 47 (3), 325–347. [18] Yang shu-jing e.t, Ningxia irrigation of agricultural non-point source pollution load method. Agricultural Sciences in China. 2009, 42(11): 3947~3955.

1741