Janice Koh ENVIRONMENTAL SCIENCE- PEAK 2ND YEAR

Environmental Kuznets Curve Using Total Nitrogen

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Janice Koh - 380006E

Professor Kei Yoshimura, Kazuo Oki & Keigo Noda

Earth System Science 2

Spring Semester 2015

July 26, 2015

Environmental Kuznets Curve Using Total

Nitrogen – Report

Keywords: EKC, total nitrogen, GDP, GDP per capita, HDI, Happinesss

Contents:

Introduction ………………..……………………………………. Pg 2

Background ………..………………………………………….… Pg 2

Previous study ………………………………………………….. Pg 3

Purpose ………………………………………………………...... Pg 4

Methodology …………………………………………………….. Pg 4

Discussion and Results ………………………………………… Pg 6

Conclusion ………………………………………………………. Pg 13

References ……………………………………………………… Pg 15

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1. Introduction

The research question of this paper is to investigate if an EKC or a U-shaped curve

exists if the x-axis, which is the environmental pollutant is made to be the total nitrogen

of a country, and the y-axis, which are ways of measuring the country’s development

be made to be GDP, GDP per capita, HDI, and Happiness.

The total nitrogen of a country is calculated by taking the country’s total

consumption of nitrogen fertilizer and dividing it by the total area of arable land and

permanent crops area. ‘GDP, which is the Gross Domestic Product is the monetary value

of all the finished goods and services produced within a country's borders in a specific

time period.’1 It is a good measure of the country’s economic health. GDP per capita is

also another indicator, which is obtained by simply taking the GDP divided by the

country’s population. HDI, which is the Human Development Index, is a measure of the

country’s level of social and economic development. It uses the criteria such as the life

expectancy at birth, mean years of schooling, expected years of schooling, and gross

national income per capita.2 Happiness, which is defined by the survey used by Gallup,

is a measure of how people live their lives. The survey is conducted by asking a series

of questions on a persons positive and negative experiences. An example of such

question is ‘How well-rested are you this week?’3

From the results of the EKC, we will be able to determine the priorities of the

country, estimate the turning point, and spot trends and similarities which exists in

developing and developed countries.

2. Background

The Environmental Kuznets Curve (EKC) is a hypothetical inverted-U-shaped curve

between environmental quality and economic development. In general, as income per

capita increases, the environmental pollutant emitted will in the beginning increase,

and after reaching a threshold level, the environmental pollutant emitted will

decrease.4 The EKC can be used to indicate the country’s economic and environmental

development, especially the stage in which the country is in, and also reveals which

form of development is more highly prioritized.

The EKC is divided into two stages: the first stage, where rapid pollution occurs, and

the later stage, where the decline of pollution follows. In the first stage, environmental

1 [Gross Domestic Product (GDP)] 2 [Human Development Index] 3 [Clifton] 4 [Stern, The Environmental Kuznets Curve]

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degradation occurs, as rapid growth depletes natural resources and increases the

emissions of pollutants, putting pressure on to the environment. Also, there is a lack of

interests and financial support into the improvement of environmental conditions from

businesses and the government, as people are more interested into the increasing of

the amount of material output and into the growth of business and jobs.5 In addition,

due to the poor economic condition of the country, investment into costly

environmentally-friendly technologies can simply not be performed.

Soon, the degradation of the environment will reach a turning point, and after which

the improvement of environmental conditions ensues with the increase of income per

capita. A possible reason for this change is for the occurrence of an increase in

awareness, and concern into the importance of the improvement of environmental

conditions by the public, businesses, and the government.6 As people begin to start

valuing the environment, regulatory institutions becomes more effective. As the

economic conditions improve more financial support becomes available for

environmental-friendly technologies to be implemented and later developed. In

essence, the EKC curve greatly depends heavily on what people value, if it is for a

healthy economy or for a healthy environment.

It is to note that not all environmental factors could be applied to EKC, for example,

environmental factors such as biodiversity and land-use change are not applicable, as

they are not reversible, hence, it cannot be applied to the EKC. 7 Also, another

limitation of the EKC is that it is difficult to measure environmental quality and to

obtain actual data on environmental quality.

3. Previous study

A previous study in January 2005 conducted by the Louisiana State University by

researchers Paudel, Krishna P., Hector Zapata, and Dwi Susanto, focused on finding if

an EKC relationship exists when the environmental factors are nitrogen, phosphorous,

and dissolved oxygen. In their paper titled "An Empirical Test of Environmental Kuznets

Curve for Water Pollution," 53 watershed sites in Louisiana were studied, and data from

the years 1985 to 1999 were used. The study employed statistical methods and was able

to derive a U-shaped curve for the environmental factor nitrogen, with a turning point

of approximately $12,972.

5 [Stern, Common , Barbier, Economic Growth and Environmental Degradation: ] 6 [Dinda, Environmental Kuznets Curve Hypothesis: A Survey] 7 [Stern, The Rise and Fall of the Environmental Kuznets Curve]

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4. Purpose

The reasons that nitrogen was chosen as an environmental factor is that it is one of

the limiting factors for phytoplankton bloom in a body of water that leads to the onset

of eutrophication occurring in the body of water. The prevention of eutrophication

allows for the quality of water, and the biodiversity of the lake ecosystem in a body of

water to be maintained.

There are several point sources of nitrogen pollution, such as from the usage of

excess fertilizers, animal manure, soil erosion, storm water, wastewater, fossil fuels,

detergents, lightning, and bacteria fixation. It is hard to determine the concentration

of nitrogen that gets run off into a body of water, as there are many point sources of

pollution. Hence, the total amount of fertilizers used by a country in agriculture is

utilize in the calculation of total nitrogen for the EKC in this research paper. It is also

assumed that when the total amount of fertilizers are reduced, the quality of water

correspondingly improves, which might not be the case in reality. This is based on the

assumption that the presence of less fertilizers corresponds equally to less excess

nitrogen run-off.

The factors GDP, and GDP per capita were chosen because it is a good measure of

the country’s economic output, while HDI gives a good indicator of the country’s level

of social and economic development, and Happiness reveals the contentment level of

the people in the country.

5. Methodology

The methodology used is in this paper is to make graphs, where the values for the

x-axis are GDP, HDI, and Happiness, and the values for the y-axis is Total Nitrogen. For

these graphs, seventeen countries were used and they are the United States, United

Kingdom, Canada, Switzerland, Denmark, Spain, Norway, Ecuador, Columbia, China,

Japan, Indonesia, Malaysia, Singapore, Afghanistan, Bangladesh, and Togo.

Another graph was also made where the units were changed from GDP to GDP per

capita for the x-axis. Also a change of units for total nitrogen was done, in which in one

graph the total kilograms was kg/ha, and in the other only kg was used. The x-axis GDP

per capita remains the same when the units of total nitrogen was changed, which allows

us to discern for any changes when the units for total nitrogen were modified. It is

important to note that different countries from the previous graph were used when the

units are changed. In this graph twelve countries were used, and they are as follows:

United States, United Kingdom, Canada, China, Japan, Republic of Korea, Malaysia,

Indonesia, Brazil, Bangladesh, Afghanistan, and Togo.

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The data obtained for Total Nitrogen in both cases was obtained from Knoema,

which obtained the data from the Food and Agriculture Organization of the United

Nations FAOSTAT/Resource STAT, under the category of Total Fertilizers Consumption,

and the available years of data are from 2002 to 2012.8 Same for GDP, and GDP per

capita the data was also obtained from Knoema, which obtained the data from The

World Bank Data Source. 9 For HDI, the data was obtained from United Nations

Development Programme – Human Development Reports. For the case of Happiness,

only one year was available, and the data was taken from the 2012 Gallup survey. Data

from the years 2002 to 2012 for GDP, GDP per capita, and HDI were available, however,

not so for Happiness.

An additional graph was also made where the y-axis is the amount of nitrogen

present within Kasumigaura Lake and the Arakawa River, measured in the units of

milligrams over liters. The x-axis used will be the GDP of Tokyo and Saitama

respectively in million yen. The years used for Arakawa River is from 2001 to 2009, and

the years used for Lake Kasumigaura is from 2001 to 2012.

In summary:

1st graph

X-axis: GDP, HDI, and Happiness Y-axis is Total Nitrogen

Countries used: United States, United Kingdom, Canada, Switzerland, Denmark,

Spain, Norway, Ecuador, Columbia, China, Japan, Indonesia, Malaysia, Singapore,

Afghanistan, Bangladesh, and Togo.

2nd graph

X-axis: GDP per capita Y-axis is Total Nitrogen (kg/ha) and only (kg)

Countries used: United States, United Kingdom, Canada, China, Japan, Republic of

Korea, Malaysia, Indonesia, Brazil, Bangladesh, Afghanistan, and Togo.

3rd graph

X-axis: GDP of Tokyo and Saitama (in million yen) Y-axis is Total Nitrogen (mg/L)

Place of interest : Lake Kasumigaura and Arakawa River

8 [World Development Indicators ] 9 Ibid.

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6. Discussion and Results

Graph of GDP (constant 2005 US$ in billions) vs Total Nitrogen (kg/ha)

FIGURE 1: GDP (CONSTANT 2005 US$ IN BILLIONS) VS TOTAL NITROGEN (KG/HA) –

(FILE: GDP AND HAPPINESS)

FIGURE 2: CORRELATION COEFFICIENT TABLE OF GDP VS TOTAL NITROGEN

As seen from the graph above, generally, as GDP increases the consumption of

fertilizers also increases. Only five out of seventeen countries had a negative

correlation coefficient, and also to note that these countries United Kingdom, Japan,

Singapore, Spain, and Norway are all already developed countries. In addition, a line is

drawn near the 100 GDP log-scale (constant 2005 US$ in billions), where a turning point

in the EKC curve seems to exist.

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Graph of HDI vs Total Nitrogen(kg/ha)

FIGURE 3: HDI VS TOTAL NITROGEN – (FILE: HDI)

FIGURE 4: CORRELATION COEFFICIENT TABLE OF HDI VS TOTAL NITROGEN

It can be drawn from the graph above, that in general, as HDI increases the

consumption of fertilizers also increases. In addition, from the table we can see that

most of the countries which have a positive correlation coefficient, are developing

countries, while already developed countries have a negative or weak correlation

coefficient. Also, a line, where the supposed turning point exist, is drawn near the 0.8

HDI mark.

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Graph of Happiness vs Total Nitrogen(kg/ha)

FIGURE 5: HAPPINESS (GALLUP) VS TOTAL NITROGEN (KG/HA) – (FILE: GDP AND HAPPINESS)

The graph above depicts, an unclear relationship between happiness and total

nitrogen. As only one year of data is used, it is hard to determine if any or no

relationship exists. More data from more years of survey results on happiness by Gallup

is needed for a conclusion on the relationship between happiness and total nitrogen to

be made.

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Graph of GDP per capita vs Total Nitrogen(kg/ha)

FIGURE 6: GDP PER CAPITA (CURRENT US$) VS TOTAL NITROGEN (KG/HA)

– (FILE: GDP PER CAPITA VS TOTAL NITROGEN)

FIGURE 7: CORRELATION COEFFICIENT TABLE OF GDP PER CAPITA (CURENT US$) VS TOTAL

NITROGEN (KG/HA)

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Similar with the graph of GDP (constant 2005 US$ in billions) vs Total Nitrogen

(kg/ha), GDP per capita generally increase, as the consumption of fertilizers also

increase. Only three out of the twelve countries had a negative correlation coefficient,

and also to note that these countries United Kingdom, Japan, South Korea are

developed countries. In addition, a line, in which a turning point in the EKC curve seems

to exist, is drawn near the 10,000 GDP per capita log-scale (current US$), which is

different from the line drawn at 100 GDP log-scale for the graph of GDP vs Total

Nitrogen.

Graph of GDP per capita vs Total Nitrogen(kg)

FIGURE 8: GDP PER CAPITA (CURENT US$) VS TOTAL NITROGEN (KG)

– (FILE: GDP PER CAPITA VS TOTAL NITROGEN IN KILOGRAMS)

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FIGURE 9: CORRELATION COEFFICIENT TABLE OF GDP PER CAPITA (CURENT US$) VS TOTAL

NITROGEN (KG)

The results of this graph is similar to that of the previous graph. The only

difference from the two graphs is that the country United States shows a negative

correlation coefficient when the units for total nitrogen are in kilograms, and a positive

correlation coefficient when the units for total nitrogen are in kilograms over hectares.

The reason for this change is that over the years 2002 to 2012, there has been a

decreased in the area for arable land in the United States.

Graph of EKC of Arakawa River and Lake Kasumigaura

FIGURE 10: EKC OF ARAKAWA RIVER, TOKYO GDP - (FILE: ARAKAWA AND KASUMIGAURA)

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FIGURE 11: EKC OF ARAKAWA RIVER, SAITAMA GDP - (FILE: ARAKAWA AND KASUMIGAURA)

FIGURE 12: EKC OF KASUMIGAURA LAKE, TOKYO GDP - (FILE: ARAKAWA AND KASUMIGAURA)

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For the Arakawa River, there appears to be a negative correlation between GDP

and total nitrogen (mg/L), when both the GDP of Tokyo and Saitama were used,

whereas for Lake Kasumigaura a positive correlation is seen. The negative correlation

of the Arakawa River, however, does not mean that the conditions of the lake has

improved. An increase in total nitrogen of the Arakawa River can be seen in the years

of 2008 to 2009. The same increase in total nitrogen is also found in the Kasumigaura

River. The reason for the similarity between the three graphs are because that the GDP

used in the three graphs are almost the same. In addition, the coefficient correlation

of all three graphs are relatively small, with the highest value of 0.22 for Lake

Kasumigaura, and the lowest value of -0.05 for Arakawa River vs GDP of Saitama.

7. Conclusion

For the graphs of total nitrogen and GDP or GDP per capita, generally, as GDP or

GDP per capita increases the consumption of fertilizers also increase. However, it is

important to bear in mind that each country has its own turning point. Due to the

availability of only ten years of data for the total nitrogen of each country, it is difficult

to find a turning point, as the countries are either in the 1st stage of increased

environmental pollution, or already in the end stage where environmental conditions

improves. In most cases, a clear turning point of the EKC curve for the environmental

factor of total nitrogen for some countries is not seen. In addition, there is a difference

in the vertical line, which is drawn to indicate the turning point of the EKC, value for

GDP and GDP per capita. For GDP the vertical line value is approximately a hundred

times smaller than the value of that of GDP per capita. However, the units for the two

graphs are different. For GDP the unit was in USD at 2005, and as for GDP per capita

the units used was in current USD. The usage of different units could be one of the

reasons behind the differences in the vertical line value for both graphs.

As for the graph of total nitrogen and HDI, a positive correlation can be seen for

developing countries, whereas a negative or weak correlation is seen in developed

countries. The vertical line for HDI lies around the value of 0.8 for HDI. For the graph

of total nitrogen and happiness, and unclear relationship is seen. As only one year 2012

is used, it was hard to identify any trends or relationships. More data on the Gallup

survey for happiness is needed, over the years for the graph of total nitrogen and

happiness.

Changing the units of total nitrogen from kg/ha to kg, produce almost similar results.

In addition, the vertical line drawn in both graphs has almost the same value. Among

the countries investigated, only one country which is the United States changed from

having a positive correlation coefficient when the units of kg/ha were used to a (Paudel,

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Zapata and Susanto) (Stern, The Rise and Fall of the Environmental Kuznets Curve)

(Dinda, Environmental Kuznets Curve Hypothesis: A Survey) (Dinda, A theoretical basis

for the environmental Kuznets curve) (Stern, Economic Growth and Environmental

Degradation: The Environmental Kuznets Curve and Sustainable Development)negative

correlation coefficient when the units of kg are used.

A study on the Arakawa River and Lake Kasuimigaura, was undertaken in order to

simulate the same results as that of the study conducted in 2005 in Lousiana. The

correlation coefficient for all three graphs are very low. Only the graph of the EKC of

Arakawa River and GDP of Saitama had a negative correlation coefficient. As mention

earlier, having a negative correlation coefficient does not necessarily means that the

conditions on the lake are improving. Since the three graphs, employ almost the same

GDP, some similar trends among the three graphs can be seen.

In conclusion, the EKC curve can be used as an interesting method or a useful tool

to measure the country’s development and the amount of its environmental pollution.

From the graphs produced we can identify the stages of the EKC that a country is in,

and we can compare the performance of one country with that of another. Perhaps, we

could use the results from the EKC to make future decisions regarding the reduction of

environmental pollution. By the comparison of one country’s better performance at

being able to reach development while maintaining a low amount of environmental

pollution, we could pressure another country to take steps, which will not affect the

country’s development, to reduce the amount of environmental pollution in that

country. Hopefully, from the results of the EKC, people will be more aware and

interested of the environmental problems that impends our environment, society, and

future.

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