Abstract
In the present study we have separately investigated and compared the impact of the value added of industry sector on Environmental Performance Index in selected developed and developing countries. The economic literature has studied the impact of economic development on environment thus far. In other words, most of the conducted economic researches in environmental economy have been striving to figure out a significant correlation between environment and economic development. Being innovative, the present study, however, has specifically dealt with the impact of the value added of industry and agriculture sector on Environmental Performance Index. Using maximum amount of data available from 2006 to 2010 for 61 developing countries and implementing panel data method, we achieved similar results for industry and agriculture sector. Simply put, the impact of value added of industry and agriculture sector on environment performance index in developing countries was significant and positive.
Keywords: Environmental Performance Index, value added of industry sector, value added of agriculture sector, data panel method.
Introduction
Economy has always been a dynamic system consisting of ongoing processes of extraction, production, and consumption. Ending in consumption, this process always produces waste which returns to the environment (air, soil, or water).This increasingly accumulating body of wastage and its untimely spread in the environment results in biological changes in the environment and even may annihilate it in such a way that it damages health and hygiene in human beings or negatively impacts the man s welfare and comfort.

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Environmental problems have been investigated from different aspects. This trend commenced in 1960sand has been mainly concentrated on industrial spoilage due to the increasing growth of industrial economies. There has been an array of business and environment meetings in different parts of the world so as to protest to disastrous environmental circumstances stemming from increasing development in business (Holinger, 2008).
Scientists believe that the higher levels of economicactivities (production or consumption) require more energy and raw material which in turn leads to a larger amount of wastage. An increasing amount of natural supplies, wastage accumulation, and pollutant centering destroys the environment. In fact, despite the sharp rise in income level, human welfare will be deteriorating. Moreover, destruction of natural resources endangers economic activities. So in order to preserve environment and even economic activities we need to stop economic growth so that we head toward abiding economy (Panayotou, 2000).
On the other end of this continuum are people who believe economic growth is the shortest way possible to better the environment. Put another way, they view growth as a prerequisite to environment improvement.In his study named economic growth and income inequality, Kuzents (1955) introduces Kuzents environment curve. He holds that in the route to economic development therelation between percapita and income inequality is like an inverse U .According to this curve, when economic development initiate, income inequality rises with the increases in percapita. However, after increasing to a certain level or turning point, income inequality starts to gradually fall. Owing to some evidence indicating an inverse U shaped link between different indices of environment annihilation andpercapita, just like the one observed between percapita and income inequality in original Kuzents curve, they introduced this curve into environment studies. The result was Kuzents environmental curve indicating an inverse U shaped link between economic growth and pollution indices (environment quality).
There are a few communalities between this paper and the investigations done thus far. We will discuss some of it in what follows.
Grossman and Kruger(1991) carried the first study on economic development and the environment to evaluate the effects of free trade in north America on the environment and to investigate the relation between pollution and economic development. They made use of GDP per capita, time trend, and pollution indices like the spread of dioxide sulfur and suspended Particulate Matter (SPM) in the air. The results indicated a reversed U- shaped link between GDP per capita and the amount of the spread of dioxide sulfur. Dijkgraaf and Vollebergh (2001) investigated the effects of economic growth on the amount of CO2 in the air in Organization for Economic Cooperation and Development (OECD) countries between the years 1960- 1997 and assessed the original Kuzents curve. Theyfound that these countries are settled. The results revealed the amount of CO2 per capita to decrease for each income per capita unit. The threshold for income per capita was 15704 dollars. Frankel and Rose(2005) conducted research on the influence of business on environment in a specified amount of GDP per capita.They concluded that business leads to more production which in turn results in more pollution. They also discovered some external factors at work here. As a consequence, they add, to the original variable -gross product per capita –some more variables like ratio of exports and imports to national gross product ( how much the economy is open), the level of democracy , and population density in the regression model. The results revealed that business can conspicuously impactpollution. It also confirmed Kuzents environmental curveas well. Using species diversity and income per capita,Zibaee and Sheikh Zein-Al-din(2009), in “biological diversity and economic development”, investigate the link between biological diversity and economic development for 121 countries, especially developing ones, for the year 2002. They made use of cross sectional method. They also studied the impact of agriculture section added value, free trade index, trade rate, and population, the amount of the land dedicated to agriculture, protected areas, and finally climate on environment diversity. According to the results, economic development, agriculture section added value, free trade index, and trade rate have a negative and significant effect on environmental diversity. Population was reported to have an insignificant influence and protected area was revealed to have a positive and significant impact upon environmental diversity. It was also revealed that Kuzents environmental curve holds true for developed countries but not for developing ones.
Mukherjee, Sacchidananda and Chakraborty(2010) studied the relation between environment, human development,and economic growth. Based on the regression results, there is a significant nonlinear relation between environment performance and the level of income in countries. According to this relationship environmental performance increases with income in the initial stages; however it starts to fall as it comes to the higher levels of income. In other words, there is a reversed U-shaped relation between the countries income level and environmental performance index.
In “environment performance index and economic development: evidence from developing countries”, JafariSamimi et al.(2011) examined the relation between environmental performance index and economic growth in some selected developing countriesfrom2006 to 2008. They used least squares method in cross sectional data and discovered a positive and significant relation between the two variables.
In their article “environment performance index and economic development: evidence from Organization of the Islamic Conference (OIC) countries”, JafariSamimi et al.(2011) scrutinized the relation between environment performance index and economic growth in these countries between 2006 – 2008. The findings specified positive and significant relation between the two factors in countries under study. In addition, they categorized the countries into three classes based on the level of revenue and found a stronger relation in those with higher levels of income.
In ” environment consistency and economic development: evidence from developing countries”, JafariSamimi et al.(2011) used panel data method to investigate the link between environment consistency index and economic development in some specified developing countries from 2001 to 2005 .The results confirmed the reverse Kuzents curve.
Environmental Performance Index(EPI) is a very important synthetic index which strives to achieve environmental efficiency and evaluates the current position of the forming elements of the index. It also specifies the way to achieve already set aims in each country. Moreover, it provides a beneficial and efficient means to assist policy makers in regard with nuclear environment. Twoforemost objectives can be considered for this index: protecting environment such as reducing environmental pressures on human health and improving natural habitats as well as managingnatural supplies. There are 25 indices to evaluate these two factors in regardwith thesix following fields: environment well-being, air quality, water resources quality, environmental and habitat diversity, the quality of productive natural assets, and consistent energy. This index ranges from 0 to 100. Zero is the worst situation and a hundred is regarded as the best.
Questions and hypotheses
This paper intends to separately probe theinfluence of industry and agriculture on environment performance index in developing countries between the years 2006 – 2010.Since production does have wastage which enters the environment in one way or another, we hypothesize it to be negatively related to environment. On the other hand, owing to pesticide utilization damaging the environment as well as deforestation for the sake of cultivation and similar causes, agricultural undertakings can also jeopardize the environment. Therefore, the following are the most fundamental questions we try to address in this paper:
is there a negative and significant relation between industry and EPI, and also agriculture and EPI?
The following reasons have been hypothesized to address the question posed above:

There is a negative and significant relationship between added value in industry and EPI in selected developing countries in years 2006 -2010.
There is a negative and significant relationship between added value in agriculture and EPI in selected developing countries in years 2006 -2010.

Evaluation and specification of the model
Presenting the model and the variables
The model used to investigate relationship between added value in industry and EPI is as follows:

In which
EPI is environment performance index for country i in year t
Indus is added value in industry for country i in year t
DI is the democracy index for country i in year t
CPI is corruption perception index for country i in year t
HDI is human development index for country i in year t
AndFDI is foreign direct investment for country i in year t
in regard with agriculture we turn to the following model:

The variables used in this model are exactly the same as the aforementioned ones except for the fact that added value in industry is substituted by added value in agriculture.
Model evaluation
This model takes advantage of syntheticdataas well as Chow Test, Lagrange Multiplier(LM) Test and Hausman Test to evaluate agriculture and industry model. The findings endorsed Fix Effects(FE) method and discarded Random Effects(RE) one.
Chow Test and Lagrange Multiplier in both sections reject evaluating the pattern usingpoolingdata. Put another way, itis impossible to use the least squares method to evaluate the pattern. Then we used Hausman Test and statistic to confirm Fix Effects(FE). Stata11 software was used to do the tests and Eviews7 was put in practice to appraise the models. You can see the results in two separate tables(1) and (2).
Table 1: Chow, Lagrange Multiplier and Hausman Tests for Industry Sector

Result

P-value

Test-Statistic

Test

FE

0.0111

1.65

Chow

RE

0.0000

75.66

LM

RE

0.0000

30.05

Hausman

Table 2: Chow, Lagrange Multiplier and Hausman Tests for Agriculture Sector

Result

P-value

Test-Statistic

Test

FE

0.0300

1.51

Chow

RE

0.0103

13.11

LM

RE

0.0024

18.45

Hausman

Evaluating the model for value added in industry
Table 3: the results for evaluating the model for Industry Sector

Depended Variable: Environmental Performance Index(EPI)

Prob.

t-Statistic

Coefficient

Independent Variable

0.0027

3.0648

0.7093

Value Added of Industry Sector

0.3082

-1.0234

-2.1561

Democracy Index

0.0002

3.8841

5.1999

Corruption Perception Index

0.0065

2.7714

11.1236

Human Development Index

0.0000

4.9962

0.5881

Foreign Direct Investment

27.3300

F

0.0000

P-value

0.9382

R2

0.9038

R2 Adjusted

According to table 3, F-test designates regression to be significant. Based on this table the coefficient for value added of industry sector is positive and statistically significant. Therefore, we have every reason to discard the proposition(indicating a negative relationship between value added of industry sector and EPI).
However, based on table 3, DI is negative but statistically insignificant. As a result it cannot be a criterion to decide or compare. On the other hand, CPI is positive and statistically significant. Simply put, there is positive association between CPI and EPI. HDI is also positive and statistically significant signifying a positive relationship between HDI and EPI. FDI is positive and statistically significant as well. In other words, there is positive link between FDI and EPI as well.
Evaluating the model for agriculture section
Table 4: the results for evaluating the model for Agriculture Sector

Depended Variable: Environmental Performance Index(EPI)

Prob.

t-Statistic

Coefficient

Independent Variable

0.0092

2.6473

0.1947

Value Added of Agriculture Sector

0.0006

-3.5290

-3.6825

Democracy Index

0.0015

3.2529

5.3086

Corruption Perception Index

0.0000

4.9071

16.4906

Human Development Index

0.0001

4.1576

0.5240

Foreign Direct Investment

21.91

F

0.0000

P-value

0.9241

R2

0.8819

R2 Adjusted

Based on what we have in table 4, F-test illustrates totalregression to be significant and value added in industry is positive and statistically significant. Therefore, the second hypothesis can be rejected as well- indicating a negative connection between value added in industry and EPI in developing countries.
According to table 4, DI is negative and statistically significant showing a negative relationship between DI and EPI. CPI is positive and statistically significant only to indicate a positive connection between CPI and EPI. Also, HDI is also positive and statistically significant demonstrating a positive relationship between HDI and EPI. Finally, FDI is positive and statistically significant as well. In other words, there is positive link between FDI and EPI.
Conclusion
In this paper we explored the effect of value added in economy on EPI in 61 selected developing countries between 2006 and 2010. The results indicate a positive relation between added value in industry as well as agriculture and EPI in the given time. So it can be concluded that economy, across time, is evolving in a way so as to become more environment friendly. There have been restrictions and limitations on activities impairing the environment in many countries in our time. And those activities perilous to the environment are harshly penalized, too. To name a few, deforestation for cultivation purposes, pesticide exploitation, and industrial undertakings increasing the level of greenhouse gas emission and any other activities damaging the environment are to be severely castigated.
References

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