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范文-Environment Studies

Environment Studies | 环境研究

The Association between the Economic Growth and the Air Pollution:

A Case Study of China

I Introduction

The environmental damages due to the human activities have become a significant concern. The survival and development of humans are highly dependent on the natural resources and the ecological systems that are provided by the planet. The debate between economic activities and the environmental quality has been standing for a long time. A great many of the literature concentrate on this topic. Economists gathered large sample data about the environment of different countries, finding that the environmental quality initially deteriorated and the then improved as the levels of income and consumption increase. The correlation between the economic growth and the quality of the environment is still controversial.

Environmental pollution mainly occurs in the process of the industrialization and the urbanization. Because of the detriments of the environmental pollution (such as air pollution), humans are increasingly realizing the importance of the environmental quality. China, as one of the most fast-growing countries in terms of the economic development, has not done well in the protection of the environment. The growth of economy is across the country, but the growth of cities in China is at the expense of the environmental pollution (e.g. air pollution). The growth of China has shocked the rest of world, but the pressure on environment due to the rapid growth is enormous.

. Thus, the main goal and the motivation of this paper are to provide empirical evidence on this topic. Moreover, this paper attempts to examine the Environmental Kuznets Curves (EKC) hypothesis. A wide variety of empirical studies on the relationship between prosperity and the environmental quality leads to heterogeneous outputs. The results from one country may vary from another one. It is not sure that whether the EKC is applicable for every country, especially for the emerging countries such as China which presents a different growth pattern. In short, the research on China in this topic will contribute to the existing EKC researches. More specifically, the research questions of this paper are presented as the following:

· What is the association between the economic growth and the air pollution in China?

· Whether EKC depicts the relationship in China?

In addition, this research pays the close attention to the period of 2001 -2010 in which China demonstrated tremendous growth. The structure of this paper is shown as the following. Chapter 1 introduces the motivation and the research questions of the paper; Chapter 2 presents the background information about China; Chapter 3 presents the literature review about the theme of this research; Chapter 4 describes the methodology of the paper, including the data selection and the model specification; Chapter 5 presents the results from data analysis; Chapter 6 is the conclusion.

II Background

2.1 Chinese Economic Growth

During the past decade, the average annual GDP of China is over 10% due to the effective economic policies. The overall increase in the national economic significantly improves the living standard of Chinese people.  Along with the development of the economy, the infrastructure is also developed. The industrial structure of China has been experiencing changes as well, from the emphasis of manufacturing to the services and information industries. In response to the most recent financial crisis, China adopted a bundle of measures to promote the domestic demand and contribute to the recovery of the world’s sluggish economy. The following table gives a more direct depiction of the economic development of China.

Table 1 Trend of the Proportions of Three Industries in terms of GDP

Year

Agriculture

Industry

Service

per capital GDP (Yuan)

2001

14.30%

45.20%

40.50%

8622

2002

13.70%

44.80%

41.50%

9398

2003

12.80%

46.00%

41.20%

10542

2004

13.40%

46.20%

40.40%

12336

2005

12.10%

47.40%

40.50%

14185

2006

11.10%

47.90%

40.90%

16500

2007

10.80%

47.30%

41.90%

20169

2008

10.70%

47.40%

41.80%

23708

2009

10.30%

46.20%

43.40%

25608

2010

10.10%

46.80%

43.10%

29992

Source: China Statistic Year Book 2006-2010

It can be seen from Table 1 that the proportion of agriculture in the total GDP of China decreased over time while the contribution of service industry to the GDP increased over the period. The contribution of industry sector to the GDP of China increased at first and then showed a slight decline and remained stable later. This indicates the shift of industrial structure in China. In addition, the GDP per capital in China presented the overall upward trend from 2006 to 2010, indicating the significant achievement of China in the economic fields.

3.2 Air Pollution

Along with the development of economy in China, environmental quality has been improved than before. But the environmental pollution has become serious in the major cities of China. The dispatch between the economic growth and the environmental quality becomes obvious since the government of China has not paid enough attention for the environmental problems. More specifically, the airborne pollution in China comes from the emission of coal combustion and the particulates. In China, the air quality monitoring is conducted in main counties and cities, including the monitoring of sulfur dioxide, particulate matters and Nitrogen Dioxide. Let’s take the acid rain for an example. On the basis of the China Environmental Statistic Book (2012), there are more than half of the 494 cities in China that experienced the acid rain. In addition, more than thirty percentages of the cities reported that the frequency of the acid rain was more than 25% and about 11% of the cities reported that the frequency of the acid rain was more than 75%. In addition, in the past years, China has done limited efforts to protect the environment.

III Literature Review

3.1 Empirical Studies on EKC

Many studies identify the correlation between the growth of income and the environmental quality. More specifically, when the environmental quality deteriorates at the initial process as the economy rises, but the pollution in the environment eventually declines with the continuous increase in the prosperity (Grossman and Krueger, 1995; Hilton and Levinson, 1998; Shafik, 1994). This implicates that the deterioration in the developing countries could be temporary and probably due to the less advanced technology in production and the in the environmental protection. This implication comes from two aspects: on the one hand, along with the increase in the income, people are becoming more and more care the quality of the environment where they live in; on the other hand, as the development in economy, the change in the industrial structure from the focus on manufacturing industry to the services and information industries also contribute to the recovery of the environmental quality.

The study of Harbaugh et al. (2002) is an expansion of the study of Grossman and Krueger (1995). In their study, they point out that the selection of the sample countries will affect the relationship depicted in EKC. More specifically, they find that the choice of data and model production would affect the results of EKC and thus, they recommend that the future research on this topic should be narrowed into countries with similar characteristics rather than to achieve a universal outcome towards the relationship between the economic growth and the environmental quality (the environmental pollution).

The central of the EKC is the association between the income level and the environmental quality. But the study of Torras and Boyce (1998) find that the income inequality has the similar effect in the equality of environment. In their study, they replaced the quadratic function with cubic function. By doing so, the downturn in pollution followed by later upturn is allowed. Their study implies that the high income would have the opposite effect on the environmental quality when the level of income reached a certain level. The advancement in technology may explain the downturn in pollution followed by later upturn.

But there is a problem in the study of Torras and Boyce (1998): the ambiguity between the income inequality which is measured by the Gini coefficients and the environmental quality. The study of Boyce (1994) identify the adverse effect of income inequality on the environmental quality while the study of Scruggs (1998) point out that the equality hypothesis of Boyce (1994)’s study is questionable and suggests that the income inequality is not correlated with the environmental pollution.  Thus, with this kind of consideration, the income inequality is not taken as an explanatory variable in the present study.

Moreover, the study of Borghesi (1999) points out that the stage of the development in the economy will also affect the use of the natural resources and thus shows the influence on the EKC. More specifically, Borghesi (1999) argues that countries engage in the significant exploitation of the natural resources at their initial development phase. Thus, the pressure on the environment is enormous and the quality of environment is exposed to the negative effect of the exploitation of the natural resources. Along with the development, the increase in the costs of the use of natural resources results in a reduction of the exploitation and accelerates the shift towards the industries environmentally friendly.

In addition, the study of Yandle et al. (2004) also put efforts in the explanation of the shape of EKC. The explanation under their study is very interest. They argue that the pursuit of environmental quality is a luxury good as the income goes up. They point out that “the income elasticity of demand for environmental resources varies with the level of income”. It is apparent that the income elasticity in the early development phase is much lower than that in the latter development stage. After a certain level, the demand of high quality environment for people is higher than that of the demand for higher income. The increasing demand for high quality environment reduce the deterioration of the environment and hence the improvement in the environment.

The shape of EKC varies in the empirical studies. In contrast to the traditional bell shape, Peters and Murray (2006) identify a “L” shaped EKC. More specifically, their study collected the data of air quality and Gross National Income (GNI) for Asian cities and the proxies of quality include PM-10, CO, SO2, and NO2. Their study may be indicative that the “pre-pollution” stage is not appropriately applicable to the countries in Asia. The presence of SO2 decreased at GNI of $ 520 US dollars per capita but the presence of NO had no significant impact on the EKC. The PM-10 and CO both decreased when the GNI reached $ 500 US dollars per capita. This study may imply that Asian countries might not follow the typical EKC shape.

3.2 Empirical Studies in China

The study of Ma and Li (2006) use the data from 1986 to 2003 to analyze the EKC of industrial wastes, including the water, gas and solid wastes. Their study reached a conclusion that the pollution of environment in China will not automatically decrease when the economic growth of China goes up over time period when they applied the time series method. The conclusion of their study is associated with the conclusion that is achieved by Zhang et al. (1999), which argues that the relationship between the economic growth and the environmental pollution only shows the very weak EKC characteristics. This indicates that the examination about whether the relationship between the economic growth and the environmental pollution in China follows the EKC through using the updated data.

Peng and Bao (2006), on the basis of the panel data on 30 provinces in mainland China during the period of 1996-2002, investigate the correlation between the environmental quality and the income per capita. In their study, they find that the correlation between the environmental quality and the income per capita is highly sensitive for the choice of the pollution indicators and the functional forms. In addition, their model takes social factors into consideration. They suggest that the change in population density and the trade openness and environmental policies and laws all affect the EKC. Fu (2008) also reaches the same suggestion that the change in industrial structure will affect the characteristics of EKC.

3.3 Summary

On the basis of the literature review, the correlation between the economic growth and the environmental pollution might not follow the pattern assumed by EKC. Thus, this paper has two main tasks: first of all, it will examine whether the EKC model exist in China and secondly identify where the pollution begins to decline in China. More importantly, this paper utilized more recent data (the period of 2006-2010) and the data set is more correlated with the economic growth in China. Since 2006 when China joined the World Trade Organization, the country has experienced tremendous growth. The urbanization and the industrialization develop into a new level and the income of Chinese people also experience tremendous increase. It will be able to test the correlation between the economic growth and the environmental pollution in China.

IV Methodology

4.1 Data

4.1.1 Variables

The dependent variables in the present study are Particulate Matters (PM), Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2) in milligram/cu.m. The explanatory variable is per capita income. Moreover, the cubic functional form is adopted in this research which is similar to the study of Grossman and Krueger (1995) and the study of Shafik(1994). The cubic functional form of per capita income allows the presence of U –shaped relationship as well as the second turning point.

4.2.2 Data Set

  The three air pollution variables: Particulate Matters (PM), Sulphur Dioxide (SO) and Nitrogen Dioxide (NO) are retrieved from the China Statistics Yearbook from 2006 to 2010. So does the data about the per capita income in the same period. The data include pollution data and income data of 31 provinces and municipalities in China. Thus, all of these data are location-specific.

4.2 Model Specification

The model presented in this section is the simplified model that is used by Torras and Boyce (1998) but the present research does not include the inequality variables in the model at all because of the ambiguity between the income inequality which is measured by the Gini coefficients and the environmental quality. The model is applied to 31 provinces and municipalities. The theoretical foundation of this model has been discussed in the literature review.

This simplified model does not take the effect of trade, technology advancement, the regional difference and so forth into consideration. The model for this research is presented as the following:

POLit = α + β1 *Yit + β2* Yit^ 2+ β3 *Y it^3 +ξit

Where POL refers to the three pollution variables, Y = per capita income, i is province signal (i= 1, 2, 3, 4, 5…N) and β1, β2, and β3 are coefficients.

Thus, the three models can be written as:

PML = α + β1 *Yit + β2* Yit^ 2+ β3 *Y it^3 +ξit (Equation 1)

SO= α + β1 *Yit + β2* Yit^ 2+ β3 *Y it^3 +ξit (Equation 2)

 

NO= α + β1 *Yit + β2* Yit^ 2+ β3 *Y it^3 +ξit (Equation 3)

All three equations are estimated under ordinary least squares (OLS). The panel data analysis and the fixed model will be utilized in the model in order to control the province specific variables.  In addition, the STATA 12 will be used to conduct the data analysis.

V Results

5.1 Descriptive Statistics

Table 1 summarizes the descriptive statistics of dependent variables as well as independent variables. The total number of observations is 155. The average value of per capita GDP in China from 2006 to 2010 is 25670.01 Yuan with the range from 5787 Yuan to 76074 Yuan. The mean of the PM is 0.099426, and the mean of SO2 is 0.047324 and the mean of NO2 is 0.041155.

Table 1 Descriptive Statistics

 

 

 

 

 

 

Variable

Obs

Mean

Std. Dev.

Min

Max

 

 

 

 

 

 

percapitagdp

155

25670.01

15529.85

5787

76074

pm

155

0.099426

0.026848

0.038

0.192

so

155

0.047342

0.019207

0.005

0.113

no

155

0.041155

0.012995

0.012

0.068

 

In addition, the data set is panel data. Thus, this research also described the data in regions. The Table 1 b presents the summary of descriptive statistics of variables by province.

Table 1 b Descriptive Statistics of Variables by Province

 

region

percap~p

pm

so

no

  Beijing

62791.6

0.135

0.0402

0.0582

  Tianjin

56301.8

0.0986

0.06

0.0434

  Hebei

22614.8

 

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