Urbanization and Impacts

7/29/2019 Urbanization and Impacts

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A Cross-Country Analysis of the Impacts of Urbanization

on the Society, Environment and Economy

Deepnath Majumder

Abstract

The contemporary world is an urban world. This is evident from the expansion of urban areas and the

extension of urban influences across much of the habitable surface of the planet. The 20 th century has

seen rapid urbanization of the world, with the global urban proportion rising from 13% in 1900 to

46.7% in 2000. This increase in the worlds urban population is going to continue in the 21st century.

So, urbanization is a predominant phenomenon in todays world. Urbanization has several positive

and negative aspects. The positive impacts include improvements in economy, better health and

education facilities, etc. The negative impacts include deforestation, air and water pollution, growth

of slums, high energy use, etc. So, is urbanization good or bad for a country? This study is an attempt

to find an answer to this question.

1. IntroductionThis study aims at establishing the impacts of urbanization on the society, environment and economy

of a country. A quantitative approach ( econometric analysis of bi-variate regression models ) has

been followed for this purpose. The study proceeds as follows.

2. Defining Urbanization

The definition of urban varies from country to country, and, with periodic reclassification, can also

vary within one country over time, making direct comparisons difficult. An urban area can be defined

by one or more of the following: administrative criteria or political boundaries ( e.g., area within the

jurisdiction of a municipality or town committee ), a threshold population size ( where the minimum

for an urban settlement is typically in the region of 2,000 people, although this varies globally

between 200 and 50,000 ), population density, economic function ( e.g., where a significant majority

of the population is not primarily engaged in agriculture, or where there is surplus employment ) or

the presence of urban characteristics ( e.g., paved streets, electric lighting, sewerage ). In 2010, 3.5

billion people lived in areas classified as urban. Urbanization is the proportion of a country that is

urban ( UNICEF ). For this study, we are representing urbanization by the variable Urban Populationas Percentage of the Total Population ( UPOP ). This gives the percentage of total population living

in the urban areas. This variable gives an idea of the extent of urbanization of a country and it is the

independent variable for our study.


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3. Global Context of UrbanizationThe twentieth century witnessed the rapid urbanization of the worlds population. The global

proportion of urban population increased from a mere 13 per cent in 1900 to 29 per cent in 1950 and,

the United Nations estimates that between 1950 and 2025, the number of urban dwellers will increase

nearly sevenfold, from 738 million to 5.1 billion.

However, the distribution of the worlds urban population varies across regions.

In case of the less developed regions, the level of urbanization is much lower than in case of the more

developed regions, and the large share of the population still dwell in rural areas. However,

urbanization tends to rise as the level of development increases and since socio-economic

development is expected to continue in all countries, the levels of urbanization are generally

projected to rise in the future. Thus, by 2030 the less developed regions are expected to have 56 per

cent of their population living in urban areas, nearly triple the proportion they had in 1950 (18 per

cent). As for the more developed regions, the majority of the population ( 74% in 2005 ) already

dwell in urban areas and it is expected that by 2030, the urban proportion in these regions is expected

to rise to 81%. At present, Asia has the largest number of urban dwellers amongst all the continents,

and Oceania having the least. The region-wise break-up of the worlds urban dwellers across theyears is given in the following table:

Table 1

Total and Percentage Urban Populations by Major Areas for Selected Periods, ( 1950-2030 )

Major

Area

1950 1975 2000 2005 2030

Population

in millions

Percentage

of world

urban

population

Population

in millions

Percentage

of world

urban

population

Population

in millions

Percentage

of world

urban

population

Population

in millions

Percentage

of world

urban

population

Population

in millions

Percentage

of world

urban

population

Africa 33 4.51 105 6.93 294 10.34 347 11.02 742 15.11

Asia 234 31.97 575 37.95 1363 47.93 1553 49.30 2637 53.68

Europe 277 37.84 443 29.24 522 18.35 526 16.70 546 11.12

Latin

America

and the

Carribean

70 9.56 197 13.00 394 13.85 434 13.78 609 12.40

Northern

America

110 15.03 180 11.88 249 8.75 267 8.47 347 7.06

Oceania 8 1.09 15 1.00 22 0.78 23 0.73 31 0.63

Total 732 100.00 1515 100 2844 100 3150 100 4912 100


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4. DataThe data for the World Development Indicators have been collected from the World Bank DataBank

and the statistical data of the Food and Agriculture Organization of the United Nations ( FAO ) for

the years 1990, 1995, 2000, 2005 and 2010. In cases, where the data is not available for these 5

years, the figures have been computed with the help of figures from around these years using

mathematical methods of averages and interpolations.

The principle followed for choosing the countries for the sample is wide representation from all

the different income groups and regions. However, uniformity in number representing income group

or region cannot be maintained as the major objective is to obtain uniform sample for the five years

under study and to have data for all the variables for all the samples. Ultimately, the sample consists

of 90 countries.

These countries may be classified as follows, according to the World Bank classification( 2005 ):

Low Income Group. Benin, Cambodia, Cameroon, Congo,Rep., Cte d'Ivoire, Ethiopia, Ghana,

India, Kenya, Mongolia, Mozambique, Nepal, Nicaragua, Nigeria, Pakistan, Senegal, Sudan,

Tanzania, Togo, Vietnam, Zambia, ZimbabweLower Middle Income Group. Algeria, Angola, Azerbaijan, Belarus, Brazil, Bulgaria, China,

Colombia, Cuba, Dominican Republic, Ecuador, Egypt,Arab Rep., El Salvador, Guatemala,

Honduras, Indonesia, Iran,Islamic Rep., Iraq, Jamaica, Morocco, Peru, Philippines, Romania, Sri

Lanka, Syrian Arab Republic, Thailand, Tunisia, Ukraine

Upper Middle Income Group. Argentina, Botswana, Chile, Costa Rica, Czech Republic, Gabon,

Hungary, Malaysia, Mauritius, Mexico, Oman, Russian Federation, South Africa, Trinidad and

Tobago, Turkey, Uruguay

High Income ( Non OECD ) Group. Cyprus, Israel, Malta, Qatar, Singapore, United Arab

Emirates

High Income ( OECD ) Group. Australia, Austria, Belgium, Canada, Denmark, Finland, France,

Germany, Greece, Ireland, Japan, Korea,Rep., Luxembourg, Netherlands, Portugal, Spain, United

Kingdom, United States

5. Effects of UrbanizationUrbanization brings with it several consequences both adverse and beneficial. These have impacts

on the society, the environment and the economy. Increasingly, urban growth is influenced by

continued global economic integration. The globalization process has had a very uneven impact

across the world, with both positive and negative impacts on cities. The positive include rising

prosperity, the enduring importance of urban cores, and increased democracy, while the negative

consist of sharpening imbalances, increased social disorder and destruction of ecological balance.

Cities are the great engines of economic growth. In the last century, the majority of growth

in economic activities in all parts of the world has been in urban centres. Economic globalization has

resulted in changes to employment structures, with fewer workers employed in agriculture and

manufacturing, and more employed in services. Today, around 65% of the world's economically

active population work in industry and services, mostly in urban areas.

Also, urbanization brings with it certain improvements in educational facilities. Children

living in cities receive high quality primary and secondary education, and also higher education as


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the best schools, colleges and universities of a country are generally located in the cities only.

Besides, the percentage of people receiving education also increases, leading to an increase in the

literacy rates.

Urbanization has significant impact on the environment. In a city, due to less vegetation

and exposed soil, the majority of the Suns energy gets absorbed by asphalt and urban structures

composed of a high percentage of non-reflective and water-resistant construction materials. There isless evaporative cooling, and as a result, the city records higher temperatures compared to the

surrounding rural areas. Thus urban heat islands are formed, which are a major cause of concern.

Air pollution is another serious problem. In cities, the air is very much polluted and

causes various types of illness and air borne diseases, especially among elderly people and children.

Most of the ambient air pollution in urban areas is caused by harmful gases and smoke released from

factories, automobiles, etc. Large scale deforestation is also one of the negative aspects of

urbanization. Deforestation is considered to be positively related with urban growth rates. As the

cities gradually expand, they require more land, leading to cutting down trees, destruction of forests,

etc. Also, in the last few decades, land use practices have become so intensive and predominant that

we can see their impacts in forms of uncontrolled development, deteriorating environmental quality,loss of prime agricultural lands, destruction of wetlands, and loss of fish and wildlife habitats

everywhere on the earth.

Health challenges are an important cause of concern in cities. Problems may arise from

issues related to water, environment, violence and injury, non-communicable diseases

(cardiovascular diseases, cancers, diabetes and chronic respiratory diseases), unhealthy diets and

physical inactivity, harmful use of alcohol, etc. However, there are positive aspects as well. With

increase in levels of urbanization, people get access to better healthcare and facilities. So,

urbanization has both positive and negative health impacts. In many cases, the net effect on national

health is unclear.

So, urbanization has both positive and negative aspects. Hence, the question whetherurbanization is good or bad for a country is a matter of debate. The present study attempts to throw

some light on this.

6. Choice of Suitable IndicatorsThe major objective of the study is to find out the impacts of urbanization on the society and the

environment. For this purpose, 11 World Development Indicators from 5 categories

( education, environment, health, labor and social protection, economic policy and debt ) have been

chosen. The variables chosen are Percentage of population with access to improved sanitation

facilities ( ACSF ), Percentage of population with access to improved water source ( ACWS ), Totalannual freshwater withdrawals( in billion cu.m) ( ANFRW ), Carbon dioxide emissions( metric tons

per capita ) ( CARBON ), Employment to population ratio( % ), ages 15 years and above ( EPR ),

Energy use( kg of oil equivalent per capita ) ( ENU ), Forest area ( sq.km ) ( FRST ), Infant mortality

rate per 1000 live births ( IMRT ), Life expectancy at birth ( years ) ( LIFE ), Per capita GDP in

terms of current US $ ( PCGDP ), Primary school pupil teacher ratio ( PSPTR ).


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These variables may be categorized as follows:

Education. Primary school pupil teacher ratio

Environment. Carbon dioxide emissions( metric tons per capita ), Energy use( kg of oil equivalent

per capita), Forest area ( sq.km ), Total annual freshwater withdrawals( in billion cu.m)

Health. Life expectancy at birth ( years ), Infant mortality rate per 1000 live births, Percentage of

population with access to improved sanitation facilities, Percentage of population with access toimproved water source

Labor and Social Protection. Employment to population ratio( % ), ages 15 years and above

Economic policy and debt. Per capita GDP in terms of current US $

All the variables chosen here have some positive or negative relationships with

urbanization. The rationale behind the choice of each variable is discussed below.

ACSF. Access to improved sanitation facilities refers to people with at least adequate access

to excreta disposal facilities that can effectively prevent human, animal, and insect contact with

excreta. Improved facilities range from protected pit latrines to flush toilets ( World Bank ). This is a

clear health indicator and has a strong effect on living conditions. Generally, developed countries

have very good sanitation facilities, whereas for the developing countries, sanitation problems persist

as the facilities are often underdeveloped.

ACWS. Access to an improved water sourcerefers to people with access to at least 20 liters of water

a person a day from an improved source, such as piped water into a dwelling, public tap, tubewell,

protected dug well, and rainwater collection, within 1 kilometer of the dwelling

( World Bank ). Similar to ACSF, it is also a health indicator. Water can affect human health on

many levels, such as disease-causing agents ( pathogens ) or pollutants in water, insufficient amounts

of fresh water per person, etc. So, proper source of water is highly essential. Normally efficient water

supply is positively linked with higher per capita income and therefore to urban areas.

ANFRW.Annual freshwater withdrawalsare total water withdrawals, not counting evaporation

losses from storage basins. Withdrawals also include water from desalination plants in countries

where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources

where extraction from nonrenewable aquifers or desalination plants is considerable or where water

reuse is significant ( World Bank ). Urbanization is positively related to both industrialization and

modernization. As a country becomes modernized, its water demands for both domestic and

industrial purposes greatly increases, which is the case for most of the developed countries. This

simultaneously leads to increase in withdrawal of freshwater from various sources.

CARBON.Carbon dioxide emissions account for the largest share of greenhouse gases, which are

associated with global warming. Thus, CO2 emissions are a serious environmental concern. These

emissions can occur from industries, transport, commercial and public services, etc. All these factors

are positively associated with modernization and industrialization, which in turn are closely linked to

urbanization.

EPR. Employment to population ratiois the proportion of a countrys population that is employed.

People ages 15 and older are generally considered the working age population (World Bank ).


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Employment and economic opportunities are key factors driving urbanization, attracting people from

rural to urban areas.The employment to population ratio indicates how efficiently an economy

provides jobs for people who want to work. A high ratio means that a large

proportion of the population is employed. But a lower employment to population ratio can be seen as

a positive sign, especially for young people, if it is caused by an increase in their education.

ENU. Energy userefers to the use of primary energy before transformation to other end-use fuels,

which is equal to indigenous production plus imports and stock changes, minus exports

and fuels supplied to ships and aircraft engaged in international transport ( World Bank ). Energy use

is closely related to modernization, growth in the modern sectors like industry, motorized transport,

etc. Energy use has been growing rapidly in low and middle-income economies, but high-income

economies still use almost five times as much energy on a per capita basis.

FRST. Forest area is land spanning more than 0.5 hectare with trees higher than 5 meters and a

canopy cover of more than 10 percent or with trees able to reach these thresholds in situ. It excludes

land that is predominantly under agricultural or urban land use ( World Bank ). One of the majordrawbacks of urbanization is the large scale deforestation. Deforestation is contributor to global

warming and is often cited as a major cause of enhanced greenhouse effect. It also destroys the

ecological balance.

IMRT. Infant mortality rate is the number of infants dying before reaching one year of age, per

1,000 live births ( World Bank ). It is a very important health indicator. It reflects the nutrition and

sanitary conditions of a country. It also indicates the degree of existence of contagious diseases in a

country, as infants are more susceptible to these problems. Infant mortality rate is high where people

live in conditions, under which basic health needs are not met, whereas it is low in places having

proper health facilities.

LIFE.Life expectancy at birth is the number of years a newborn infant would live if prevailing

patterns of mortality at the time of its birth were to stay the same throughout its life ( World Bank ).

This indicator directly reflects the levels of health, nutrition, and income and thus, indirectly, links

employment and shelter. A low figure indicates that there is a sizeable percentage of the population

facing poor living conditions and there is a lack of proper health facilities in the country.

PCGDP. Gross domestic product ( GDP ) is the market value of all officially recognized final goods

and services produced within a country in a given period of time. Per Capita GDP ( PCGDP ) is anindicator of a country's standard of living and a standard measure of its economic growth.

PSPTR. Primary school pupilteacher ratio is the number of pupils enrolled in primary school

divided by the number of primary school teachers (regardless of their teaching assignment) (

World Bank ). It is a very important education indicator. It gives an indication of the quality of


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educational care available in the primary level of education. The higher the number of pupils per

teacher, the lower the quantity and quality of personal care available for each pupil.

7. MethodologyThe main purpose of this study is to check the effect of urbanization on the economy, society and

environment of a country. For this purpose, bivariate regression analysis has been carried out taking

urban population as percentage of the total population ( in each case ) as the independent variable and

the World Development Indicators as the dependent variables. The equation chosen is a simple linear

equation of the form: Y= A + BX, where Y denotes the dependent variable ( World Development

Indicators ), and X denotes the independent variable ( Urban population as percentage of the

population ).

8. FindingsThe results of the bivariate regression analysis are given as follows in tables 2-12.

Table 2

Regression Results between ACSF & UPOP

Year Constant Coefficient R-Square R-Bar-Square F-Statistics

1990 9.460(t ratio-1.630)

1.059*

(t-ratio-10.853)0.572 0.568 117.785*

1995 11.833

(t value-1.940)

1.015*

(t-value-10.121)

0.538 0.533 102.439*

2000 15.731*(t-value-2.440)

0.957*(t-value-9.289)

0.495 0.489 86.276*

2005 20.114*(t-value-2.950)

0.892*(t-value-8.403)

0.445 0.439 70.607*

2010 23.718*(t-value-3.335)

0.834*(t-value-7.726)

0.404 0.397 59.690*

* marked values indicate that the values are significant considering 5% level of significance


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Table 3

Regression Results between ACWS & UPOP


1990 48.654*(t ratio-12.391)

0.603*(t-ratio-9.135)

0.487 0.481 83.4438*

1995 51.804*(t value-13.325)

0.560*(t-value-8.806)

0.468 0.462 77.552*

2000 56.192*(t-value-15.098)

0.505*(t-value-8.490)

0.450 0.444 72.080*

2005 60.530*

(t-value-16.632)

0.450*

(t-value-7.947)

0.418 0.411 63.159*

2010 64.685*(t-value-17.622)

0.394*(t-value-7.078)

0.363 0.356 50.093*


Table 4

Regression Results between ANFRW & UPOP


1990 53.513*(t ratio-2.137)

-0.382(t-ratio-(-0.907))

0.009 -0.002 0.823

1995 55.982*(t value-2.119)

-0.407(t-value-(-0.941))

0.010 -0.001 0.886

2000 61.589*(t-value-2.130)

-0.455(t-value-(-0.984))

0.011 0.000 0.969

2005 64.976*(t-value-2.1)

-0.473(t-value-(-0.982))

0.011 0.000 0.965

2010 71.793*(t-value-2.050)

-0.539(t-value-(-1.014))

0.012 0.000 1.028



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Table 5

Regression Results between CARBON & UPOP


1990 -3.046*(t ratio-(-2.011))

0.152*(t-ratio-5.988)

0.289 0.281 35.852*

1995 -1.985(t value-(-0.888))

0.133*(t-value-3.629)

0.130 0.120 13.168*

2000 -1.639(t-value-(-0.692))

0.124*(t-value-3.279)

0.109 0.099 10.751*

2005 -0.995(t-value-(-0.394))

0.114*(t-value-2.905)

0.087 0.077 8.436*

2010 0.809(t-value-0.381)

0.073*(t-value-2.254)

0.055 0.044 5.081*


Table 6

Regression Results between ENU & UPOP


1990 -781.371(t ratio-(-1.288))

52.559*(t-ratio-5.156)

0.232 0.223 26.584*

1995 -831.987(t value-(-1.235))

51.899*(t-value-4.708)

0.201 0.192 22.165*

2000 -516.709(t-value-(-0.668))

47.080*(t-value-3.809)

0.142 0.132 14.505*

2005 -93.756(t-value-(-0.101))

42.033*(t-value-2.895)

0.087 0.077 8.382*

2010 79.506(t-value-0.093)

37.410*(t-value-2.896)

0.087 0.077 8.389*



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Table 7

Regression Results between EPR & UPOP


1990 67.949*(t ratio-24.392)

-0.198*(t-ratio-(-4.221))

0.168 0.159 17.816*

1995 69.221*(t value-24.357)

-0.209*(t-value-(-4.490))

0.186 0.177 20.161*

2000 69.338*(t-value-23.755)

-0.199*(t-value-(-4.256))

0.171 0.161 18.116*

2005 70.981*(t-value-23.109)

-0.211*(t-value-(-4.403))

0.181 0.171 19.388*

2010 71.316*(t-value-22.101)

-0.201*(t-value-(-4.110))

0.161 0.152 16.892*


Table 8

Regression Results between FRST & UPOP


1990 31303.055(t ratio-0.099)

6481.002(t-ratio-1.215)

0.016 0.005 1.476

1995 -364.661(t value-(-0.001)

6764.473(t-value-1.257)

0.018 0.006 1.580

2000 -39528.038(t-value-(-0.117)

7186.458(t-value-1.331)

0.020 0.009 1.771

2005 -37853.380(t-value-(-0.109))

6916.424(t-value-1.279)

0.018 0.007 1.636

2010 -49585.529(t-value-(-0.139))

6876.028(t-value-1.267)

0.018 0.007 1.605



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Table 9

Regression Results between IMRT & UPOP


1990 100.917*(t ratio-13.645)

-1.056*(t-ratio-(-8.498)

0.451 0.444 72.214*

1995 95.079*(t value-12.939)(p-value-0.000)

-1.001*(t-value-(-8.321))

0.440 0.434 69.240*

2000 85.593*(t-value-12.214)

-0.893*(t-value-(-7.972))

0.419 0.413 63.550*

2005 73.215*(t-value-11.065)

-0.748*(t-value-(-7.261)

0.375 0.368 52.715*

2010 62.050*(t-value-9.946)

-0.620*(t-value-(-6.545))

0.327 0.320 42.833*


Table 10

Regression Results between LIFE & UPOP


1990 51.030*(t ratio-27.607)

0.280*(t-ratio-9.008)

0.480 0.474 81.152*

1995 50.968*(t value-24.715)

0.286*(t-value-8.473)

0.449 0.443 71.791*

2000 50.909*(t-value-21.746)

0.294*(t-value-7.867)

0.413 0.406 61.893*

2005 52.535*(t-value-20.954)

0.279*(t-value-7.156)

0.368 0.361 51.211*

2010 54.866*(t-value-22.225)

0.259*(t-value-6.903)

0.351 0.344 47.646*



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Table 11

Regression Results between PCGDP & UPOP


1990 -5179.340*(t ratio-(-2.739))

196.648*(t-ratio-6.188)

0.303 0.295 38.295*

1995 -7731.320*(t value-(-2.964))

262.737*(t-value-6.155)

0.301 0.293 37.878*

2000 -7522.682*(t-value-(-2.782))

255.838*(t-value-5.919)

0.285 0.277 35.031*

2005 -10035.778*(t-value-(-2.308))

351.114*(t-value-5.187)

0.234 0.225 26.903*

2010 -12622.938*(t-value-(-2.739))

433.558*(t-value-5.383)

0.248 0.239 28.979*


Table 12

Regression Results between PSPTR & UPOP


1990 42.604*(t ratio-15.820)

-0.259*(t-ratio-(-5.722))

0.271 0.263 32.741*

1995 44.509*(t value-14.760)

-0.292*(t-value-(-5.926))

0.285 0.277 35.112*

2000 47.543*(t-value-14.645)

-0.337*(t-value-(-6.501))

0.324 0.317 42.266*

2005 48.522*(t-value-13.767)

-0.369*(t-value-(-6.717))

0.339 0.331 45.116*

2010 46.465*(t-value-14.090)

-0.354*(t-value-(-7.079))

0.363 0.356 50.119*



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9. InferenceFrom the regression results, the following inferences may be drawn:

a) In table 2, for the regression between ACSF and UPOP, it can be seen that for all the five years,the values of the constant and the coefficient are positive and significant, except for the values of

the constant for the years 1990 and 1995. The values of the F-Statistics are also significant for all

the years. The values of R-Square are reasonable, considering the fact that urbanization is not the

only factor that has effect on access to sanitation facilities.

Conclusion. It can be concluded that the relationship between ACSF and UPOP is directly

proportional, i.e. as UPOP increases, ACSF increases and when UPOP decreases, ACSF

decreases. Hence, when a country becomes more urbanized, a greater percentage of its people get

access to improved sanitation facilities.

b) In table 3, for the regression between ACWS and UPOP, it can be seen that for all the five years,the values of the constant and the coefficient are positive and significant. The values of the F-

Statistics are also significant for all the years. The R-Square values are also reasonable,

considering the fact that there are a number of factors that may influence access to improved

water sources, and urbanization is just one of them.

Conclusion. It can be concluded that the relationship between ACWS and UPOP is a directly

proportional one, i.e. as UPOP increases, ACWS increases and when UPOP decreases, ACSF

decreases. Hence, as a country becomes more urbanized, a greater percentage of its people get

access to improved water sources.

c)

In table 4, for the regression between ANFRW and UPOP, it can be seen that for all the years, thevalues of the constant are positive and significant, whereas the values of both the coefficient and

the F-Statistics are insignificant. The values of the coefficients are also negative. The R-Square

values are also extremely low, around 1%, which means that only 1% of the variation in annual

freshwater withdrawals is explained by urbanization.

Conclusion. The results for the regression between ANFRW and UPOP are very inconclusive.

Rather, it can be said that there does not exist much of a relationship between annual freshwater

withdrawals of a country and the variations in its percentage of urban population.

d) In table 5, for the regression between CARBON and UPOP, it can be seen the values of theconstant are negative ( except for 2010 ) and insignificant ( except for 1990 ). On the other hand,

the values of the coefficient are positive and significant. The values of the F-Statistics are also

significant. The R-Square values are also quite low, which indicates that a very small percentage

of the variation in carbon dioxide emissions is explained by urbanization.

Conclusion. The results for the regression CARBON and UPOP are quite inconclusive.


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e) In table 6, for the regression between ENU and UPOP, it can be seen that the values of theconstant are negative ( except for 2010 ) and insignificant. However, the values of both the

coefficient and the F-Statistics are positive and significant. The R-Square values are low, which

indicates that the variation in energy use of a country is not much explained by urbanization.

Conclusion.No definite conclusion can be drawn from these results.

f) In table 7, for the regression between EPR and UPOP, it can be seen that the values of theconstant are positive for all the years, while the values of the coefficient are negative. However,

both the values of the constant and the coefficient are significant. The values of the F-Statistics

are also significant, but the R-Square values are low.

Conclusion. It may be concluded that as the value of UPOP increases, the value of EPR

decreases, though very slowly. Thus, as a countrys percentage of urban population increases, the

level of employment in the country slowly decreases.

g)

In table 8, for the regression between FRST and UPOP, the values of the constant are negative( except for 1990 ) and insignificant. The values of the coefficient are positive and insignificant.

The F-Statistics values are also insignificant. The R-Square values are very low.

Conclusion. The results for the regression between FRST and UPOP are very inconclusive.

Rather, it can be said that there does not exist much of a relationship between forest area of a

country and the variations in its percentage of urban population.

h) In table 9, for the regression between IMRT and UPOP, the values of the constant are positiveand significant for all the years. However, the values of the coefficient are negative and

significant. The values of the F-Statistics are also significant. The R-Square values arereasonable, considering the fact that urbanization is just one of the many factors that has influence

on the infant mortality rate in a country.

Conclusion. It can be concluded that as the percentage of urban population of a country

increases, the value of the infant mortality rate of the country gradually decreases.

i) In table 10, for the regression between LIFE and UPOP, the values of both the constant and thecoefficient are positive and significant for all the five years. The values of the F-Statistics are also

significant. The R-Square values are reasonable, considering that life expectancy at birth depends

on a large number of factors, besides urbanization.

Conclusion. It can be concluded that the relationship between LIFE and UPOP is directly

proportional, i.e. as UPOP increases, LIFE increases and when UPOP decreases, LIFE decreases.

Hence, when a country becomes more urbanized, the life expectancy of its people increases.


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j) In table 11, for the regression between PCGDP and UPOP, the values of the constant are negativeand significant for all the variables. The values of the constant are positive and significant. The F-

Statistics values are also significant. The R-Square values are a bit low.

Conclusion. It can be concluded that PCGDP increases with increase in the value of UPOP, i.e.

as a country becomes more urbanized, the per capita Gross Domestic Product of the countrygradually increases.

k) In table 12, for the regression between PSPTR and UPOP, the values of the constant are positiveand significant for all the years. However, the values of the coefficient are negative and

significant. The F-Statistics values are also significant. The R-Square values are reasonable,

considering that the primary school pupil teacher ratio is determined by a number of factors and

not only urbanization.

Conclusion. As the value of UPOP increases, the value of PSPTR gradually decreases. Thus, as

the percentage of urban population in a country gradually increases, its primary school pupil

teacher ratio decreases, i.e. lesser number of pupils come under the guidance of a single teacher.

10.Summary and Concluding RemarksThis paper tries to establish a relationship between urbanization and certain human

development indicators from different categories, i.e. health, education, environment, labor and

social protection, economic policy and debt. From the results of the bi-variate analysis, it may be

stated that with the increase in the level of urbanization of a country, the health conditions of a

country improves, i.e. more people get access to improved water and sanitation facilities, theinfant mortality rate decreases and the life expectancy of people increases. Thus, urbanization has

a positive impact on the health conditions of people in a country. On the other hand, no definite

conclusions can be drawn for the environment indicators. So, the relationship impact of

urbanization on the environment is somewhat uncertain. The education indicator, i.e. primary

school pupil teacher ratio decreases with increase in urbanization, thereby indicating

improvement in education facilities of a country. So, it may be concluded that urbanization has a

positive impact on the level of education and education facilities in a country. Similarly, it can be

seen that urbanization has a positive impact on the economy of a country, which is indicated by

the increase in the countrys per capita GDP with increase in its percentage of urban population.

As far as employment is concerned, the results indicate a decrease in the percentage of

employment in a country with the increase in the percentage of urban population, thereby

indicating a negative impact of urbanization on employment.

So, finally it may be concluded that urbanization has more positive impacts than

negative ones. It has positive impacts on the health conditions, level of education and the

economy of the country, whereas it has a negative impact on its level of employment. The impact

of urbanization on environment is, however, uncertain.


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11.ReferencesBairoch, P. & Goertz.G ( 1986 ). Factors of Urbanisation in the Nineteenth Century DevelopedCountries: A Descriptive and Econometric Analysis. Retrieved July 4, 2013, from

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Deforestation. Retrieved June 28, 2013,from http://en.wikipedia.org/wiki/Deforestation.

Dickey, J.W. & Watts, T.M.Analytic Techniques in Urban and Regional Planning. McGraw-HillBook Company.

Dogan, M. & Kasarda, J.D., eds. ( 1988 ). A World of Giant Cities. New Delhi: SagePublications.

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Cohen, C.K., Horlacher, D.E. & MacKellar, F.L. ( 2009, December 10 ).Is Urbanization Goodfor a Nations Health?. Retrieved July 5, 2013 from http://epc2010.princeton.edu/papers/100012

Jahan, Dr.K.K., ( 2012, October ). Pressure of Population on Urbanisation Activities in India -Some Evidences. Retrieved June 27, 2013, fromhttp://theglobaljournals.com/gra/file.php?val=MTgw.

Kundu, A. ( 2009, June ). Urbanisation and Migration: An Analysis of Trends, Patternsand Policies in Asia. Retrieved June 29, 2013, fromhttp://hdr.undp.org/en/reports/global/hdr2009/papers/HDRP_2009_16.pdf.

Norstrm, A. ( 2010, February 9 ). Urbanization drives deforestation. Sustainable DevelopmentUpdate. Retrieved July 3, 2013, from http://www.sdupdate.org/component/content/article/10-news/21-urbanisation-drives-deforestation-for.

Pacione, M. ( 2009 ). Urban Geography A Global Perspective. Abingdon: Routledge.

Peng, X., Chen,X. & Cheng,Y. Urbanization and its Consequences.Encyclopedia of Life SupportSystems ( EOLSS ). Retrieved June 29, 2013, from http://www.eolss.net/Sample-Chapters/C04/E6-147-18.pdf.

Thomas, S. ( 2008, January ). Urbanization as a driver of change. The Arup Journal. RetrievedJune 30, 2013, fromhttp://www.sustainabilityforhealth.org/system/documents/334/original/F1581776-19BB-316E-40DE3EAA1ED8B831.pdf?1263555597.

UNDP.Human Development Reports, different issues.


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Urbanization. Retrieved July 4, 2013, fromhttp://water.tkk.fi/wr/tutkimus/glob/publications/Haapala/pdf-files/URBANIZATION.pdf.

Urbanization. Retrieved June 30, 2013,from http://en.wikipedia.org/wiki/Urbanization.

Urbanization-Impacts. Retrieved June 29, 2013, from

http://wgbis.ces.iisc.ernet.in/energy/lake2006/programme/proceedings/Presentations/Lake%202006%20-%20Presentations/28%20Dec%202006/Session%20IV/Abijith.pdf.

Urbanization and Environment. Retrieved June 29, 2013, fromhttp://water.tkk.fi/wr/tutkimus/glob/publications/Haapala/pdf-files/URBANIZATION%20AND%20ENVIRONMENT.pdf .

Urban Heat Islands ( UHIs ). Retrieved July 7, 2013, from http://www.urbanheatislands.com/.

World Bank. World Development Reports, different issues.

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2012/World Development Indicators. Retrieved June 30, 2013, fromhttp://data.worldbank.org/sites/default/files/wdi-2012-ebook.pdf


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APPENDIX A

Cross Country Data for the World Development Indicators for the year 1990

TABLE A.1

Data for UPOP, ACSF, ACWS & ANFRW

Country Name UPOP ACSF ACWS ANFRW

Algeria 52.085 88 94 4.1

Angola 37.144 29 42 0.48

Argentina 86.984 90 94 28.237

Australia 85.4 100 100 21.644

Austria 65.765 100 100 3.8722

Azerbaijan 53.749 57 70 15.926

Belarus 65.981 93 100 4.338

Belgium 96.377 100 100 8.2764

Benin 34.485 5 57 0.11

Botswana 41.933 38 93 0.1038

Brazil 73.922 68 89 40.989

Bulgaria 66.377 99 100 7.494

Cambodia 15.546 9 31 2.184

Cameroon 39.657 48 49 0.3984

Canada 76.582 100 100 42.2

Chile 83.271 84 90 20.29

China 26.442 24 67 492.44

Colombia 68.276 67 89 3.7412

Congo, Rep. 54.324 20 70 0.04

Costa Rica 50.683 93 93 10.1008

Cote d'Ivoire 39.345 20 76 0.9436

Cuba 73.364 80 82 1.9294

Cyprus 66.776 100 100 0.2156

Czech Republic 75.22 100 100 3.536

Denmark 84.843 100 100 1.132

Dominican Republic 55.226 73 88 15.1346

Ecuador 55.09 69 72 19.426

Egypt, Arab Rep. 43.478 72 93 45.668

El Salvador 49.233 75 74 0.729

Ethiopia 12.621 3 14 5.558

Finland 79.367 100 100 3.0082

France 74.056 100 100 36.57

Gabon 69.143 36 79 0.06

Germany 73.118 100 100 53.67

Ghana 36.441 7 53 0.3

Greece 58.843 97 96 6.4164

Guatemala 41.117 62 81 2.933

Honduras 40.46 50 76 1.194

Hungary 65.838 100 96 6.351

India 25.547 18 69 498.96

Indonesia 30.584 32 70 74.34

Iran, Islamic Rep. 56.33 79 90 73.62


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Iraq 69.706 67 81 42.088

Ireland 56.906 99 100 0.79

Israel 90.359 100 100 1.8424

Jamaica 49.444 80 93 1.40862

Japan 77.339 100 100 90.76

Kenya 16.748 25 44 2.049Korea, Rep. 73.844 100 90 25.47

Luxembourg 80.947 100 100 0.0622

Malaysia 49.794 84 88 11.1668

Malta 90.381 100 100 0.0392

Mauritius 43.9 89 99 0.5385333

Mexico 71.419 64 85 56

Mongolia 57.033 50 54 0.428

Morocco 48.391 53 97 11.024

Mozambique 21.096 11 73 0.605

Nepal 8.854 10 36 10.008

Netherlands 68.684 100 76 8.7364

Nicaragua 52.337 43 100 1.288Nigeria 35.282 37 47 3.63

Oman 66.102 82 80 1.223

Pakistan 30.576 27 85 155.25333

Peru 68.901 54 75 18.98

Philippines 48.59 57 85 67.595

Portugal 47.915 92 96 8.463

Qatar 92.786 100 100 0.2724

Romania 53.217 71 75 20.446

Russian Federation 73.394 74 93 71.65

Senegal 38.9 38 61 1.36

Singapore 100 99 100 0.19

South Africa 52.037 71 83 13.474Spain 75.351 100 100 40.48

Sri Lanka 17.199 70 67 9.769

Sudan 28.61 27 65 14.94

Syrian Arab Republic 48.931 85 86 10.5485

Tanzania 18.884 7 55 5.184

Thailand 29.424 84 86 57.31

Togo 28.589 13 49 0.091

Trinidad and Tobago 8.534 93 88 0.38856

Tunisia 57.946 74 81 2.765

Turkey 59.203 84 85 31.6

Ukraine 66.757 95 97 26

United Arab Emirates 79.051 97 100 1.5442667

United Kingdom 78.14 100 100 11.842

United States 75.3 100 99 473.54

Uruguay 88.973 94 96 0.65

Vietnam 20.255 37 57 45.27

Zambia 39.407 46 49 1.7634

Zimbabwe 28.988 41 79 1.22


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TABLE A.2

Data for CARBON, ENU, EPR & FRST

Country Name CARBON ENU EPR FRST

Algeria 3.118500 877.1799 34.40000 16670

Angola 0.428612 569.243 66.10000 609760

Argentina 3.449912 1411.466 56 347930

Australia 16.83737 5052.769 58.09999 1545000

Austria 7.909182 3235.539 53.49999 37760

Azerbaijan 10.26875 3652.533 56.10000 9360

Belarus 11.14860 4470.353 59.59999 77800

Belgium 10.88248 4844.185 45.19999 6770

Benin 0.149810 348.0551 72 57610

Botswana 1.575697 912.3281 52.30000 137180

Brazil 1.395835 937.0612 55.59999 5748390

Bulgaria 8.690224 3276.160 45 33270

Cambodia 0.047319 229.2447 79.49999 129440

Cameroon 0.142696 408.9092 63.29999 243160

Canada 16.19504 7503.957 60.80000 3101340

Chile 2.589012 1062.294 47.79999 152630

China 2.167703 768.2612 74.59999 1571406

Colombia 1.726851 729.5235 45.10000 625190

Congo, Rep. 0.497344 324.6939 62.19999 227263Costa Rica 0.962661 660.3373 54.90000 25640

Cote d'Ivoire 0.463145 429.0122 63.79999 102220

Cuba 3.154146 1673.461 53.09999 20580

Cyprus 6.069711 1774.998 52.69999 1611

Czech Republic 14.90012 4797.088 60.59999 26290

Denmark 9.676543 3377.480 62.00000 4450

Dominican Republic 1.330273 569.9509 52.3999 19720

Ecuador 1.640763 586.8281 56.7999 138170

Egypt, Arab Rep. 1.336016 568.8145 43.9999 440

El Salvador 0.490964 462.9259 57.2999 3770Ethiopia 0.062440 307.5791 75.3999 167350

Finland 10.37716 5691.571 64 218890

France 6.847263 3841.909 50.4000 145370

Gabon 5.214077 1271.635 52.0000 220000

Germany 12.18959 4420.645 57.3000 107410

Ghana 0.265728 357.6613 64.3999 74480

Greece 7.160051 2110.934 45.2999 32990


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Guatemala 0.569993 494.9107 61.9000 47480

Honduras 0.530252 486.6892 55.1999 81360

Hungary 6.072083 2772.046 48 18010

India 0.790327 362.4954 58.5 639390

Indonesia 0.811332 534.9866 63 1185450

Iran, Islamic Rep. 3.847869 1263.639 40.2000 110750

Iraq 2.888580 1083.237 31.8999 8040

Ireland 8.937987 2842.244 45.1999 4650

Israel 7.196290 2462.523 46.4 1320

Jamaica 3.332520 1166.361 62.7999 3446

Japan 8.860766 3556.223 62.3 249500

Kenya 0.248353 455.2827 67.5999 37080

Korea, Rep. 5.760412 2171.428 58.8000 63700

Luxembourg 26.21686 8931.858 50.7000 860

Malaysia 3.108033 1183.458 60.2 223760Malta 6.150148 1961.642 48 3

Mauritius 1.381911 453.2388 53.3 388

Mexico 3.729434 1452.943 56.2000 702910

Mongolia 4.580921 1558.187 49.6000 125360

Morocco 0.950003 280.0992 45.6999 50490

Mozambique 0.073897 437.1445 77.7 433780

Nepal 0.033247 303.3985 83.4 48170

Netherlands 10.97732 4393.203 51.1999 3450

Nicaragua 0.618464 507.8318 55.4999 45140

Nigeria 0.465141 723.5345 53.3 172340Oman 6.087276 2258.014 50.69999 20

Pakistan 0.613042 381.4923 47.1 25270

Peru 0.976207 448.8828 52.8 701560

Philippines 0.677662 464.3312 58.99999 65700

Portugal 4.226710 1676.744 60.70000 33270

Qatar 24.85579 13019.72 78 0

Romania 6.846948 2681.796 56.5 63710

Russian Federation 16.61089 5928.792 55.39999 8089500

Senegal 0.439539 232.8770 68.2 93482

Singapore 15.40573 3779.059 64.40000 23South Africa 9.474819 2583.979 35.79999 92410

Spain 5.633525 2318.760 43.70000 138184

Sri Lanka 0.221765 324.1952 48.59999 23500

Sudan 0.209826 401.1972 48.29999 763814

Syrian Arab Republic 3.038844 849.1730 44.69999 3720

Tanzania 0.093117 381.9988 78.79999 414950

Thailand 1.679164 734.9293 78.40000 195490


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Togo 0.211085 344.6288 70.50000 6850

Trinidad and Tobago 13.95307 4925.870 44.00000 2407

Tunisia 1.626999 606.5643 40.79999 6430

Turkey 2.694517 974.6125 53.89999 96800

Ukraine 15.98123 4851.538 58.69999 92740

United Arab Emirates 28.75586 11292.48 70.40000 2450

United Kingdom 9.960568 3597.006 59 26110

United States 19.54698 7671.554 60.89999 2963350

Uruguay 1.284402 724.0478 52.80000 9200

Vietnam 0.324280 270.6291 77.90000 93630

Zambia 0.311179 686.9152 64.90000 528000

Zimbabwe 1.480922 888.0586 69.10000 221640

TABLE A.3

Data for IMRT, LIFE, PCGDP & PSPTR

Country Name IMRT LIFE PCGDP PSPTR

Algeria 53.5 67.07178 2452.454 27.7871

Angola 143.7 41.14141 992.7568 36.5724

Argentina 24.4 71.49829 4330.324 18.6829

Australia 7.5 76.99463 18250.95 16.6142

Austria 7.90000 75.53 21458.23 10.8594

Azerbaijan 75.4 64.74636 1237.324 18.5056

Belarus 13.9 70.83658 1704.740 21.1998

Belgium 8.5 76.05195 20349.62 13.0214

Benin 106.8 48.64853 410.6241 30.5464

Botswana 41.3 64.01717 2742.126 31.6565

Brazil 48.8 66.34073 3086.877 36.2781

Bulgaria 18.5 71.64146 2377.335 18.9195

Cambodia 85.1 55.39729 190.6680 34.9536

Cameroon 89.9 53.25309 915.5031 51.484

Canada 6.8 77.37707 20968.04 15.6856

Chile 15.7 73.60356 2393.035 43.2258

China 38.7 69.45973 314.4306 22.3188

Colombia 27.8 68.30553 1212.957 29.9195

Congo, Rep. 75.1 56.24656 1171.561 64.4250

Costa Rica 14.5 75.747 2411.361 31.8808

Cote d'Ivoire 104 52.64414 862.4447 36.2891

Cuba 10.6 74.45980 2709.985 12.319

Cyprus 9.5 76.50470 9641.575 21.3777

Czech Republic 12.6 71.38390 3786.858 24.4874


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Denmark 7.2 74.80536 2345.010 11.2778

Dominican Republic 45.2 67.70419 983.1832 38.5273

Ecuador 40.8 68.85687 1009.295 30.4077

Egypt, Arab Rep. 62.9 62.01282 758.7602 25.1444

El Salvador 46.5 65.99085 900.2531 36.9393

Ethiopia 118.2 47.04539 249.9847 40.6755

Finland 5.6 74.81317 27852.29 15.5905

France 7.3 76.6 21349.66 20.3776

Gabon 68.5 61.28912 6406.901 46.8171

Germany 7 75.31612 21583.84 17.8433

Ghana 76.2 56.84302 398.0944 27.0935

Greece 11.5 76.93902 9190.343 19.6466

Guatemala 55.5 62.28668 857.3364 32.3994

Honduras 42.9 66.33939 623.5840 34.8230

Hungary 16.7 69.31561 3186.444 10.5184India 81 58.35285 373.7851 39.6236

Indonesia 54.1 62.10385 620.7161 23.2714

Iran, Islamic Rep. 47.1 61.91046 2114.703 31.3807

Iraq 36.7 67.53419 441.8030 26.3497

Ireland 7.5 74.74131 13604.44 27.2285

Israel 9.6 76.60731 11264.01 13.9688

Jamaica 28.4 70.64563 1921.426 33.64659

Japan 4.6 78.83682 25123.63 21.15489

Kenya 63.5 59.33995 365.6030 39.12452

Korea, Rep. 6.4 71.29487 6153.094 36.2812Luxembourg 7 74.85329 33177.05 12.94894

Malaysia 14.8 70.06536 2417.773 20.37696

Malta 9.7 75.17578 7192.394 21.471

Mauritius 20.7 69.40487 2506.184 21.12971

Mexico 38.1 70.79109 3116.123 31.06703

Mongolia 76.1 60.53826 1167.947 29.8018

Morocco 63.9 64.146 1032.750 24.9825

Mozambique 150.9 43.17626 185.4355 54.53836

Nepal 93.5 53.95673 190.1130 39.1592

Netherlands 6.8 76.87804 19721.82 17.36358Nicaragua 50.4 64.15802 244.9660 33.27105

Nigeria 126.6 45.63734 291.8695 40.99619

Oman 35.8 70.63295 6255.193 27.54436

Pakistan 94.6 60.76719 357.7320 41.07124

Peru 53.6 65.54951 1212.530 29.0841

Philippines 40.2 65.15939 719.0094 32.6671

Portugal 11.4 73.96585 7779.435 14.8570


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Qatar 16.5 74.12878 15537.46 12.3004

Romania 30.6 69.74122 1650.693 22.6010

Russian Federation 23 68.90243 3485.112 22.7562

Senegal 68.8 53.24834 789.4202 57.5870

Singapore 6.1 75.58219 11845.40 25.7777

South Africa 48.2 61.54961 3182.214 23.1354

Spain 9.3 76.83756 13409.58 23.4829

Sri Lanka 24.2 69.67868 472.08646 30.7965

Sudan 77.3 52.53065 468.35393 34.28006

Syrian Arab Republic 29.6 71.06475 998.7429 26.12085

Tanzania 96.7 50.59714 171.9141 34.889

Thailand 28.8 72.47092 1495.358 20.2664

Togo 85.2 52.99197 444.2568 55.6386

Trinidad and Tobago 32.1 68.94478 4169.556 25.7821

Tunisia 40.3 70.30731 1507.231 29.6881Turkey 59.8 63.06046 2783.586 30.4803

Ukraine 16.6 70.13658 1569.73 22.4587

United Arab Emirates 18.9 72.14353 28032.88 18.0800

United Kingdom 7.8 75.88048 17687.66 19.6798

United States 9.4 75.21463 23037.94 15.2775

Uruguay 20.2 72.48468 2990.825 21.8876

Vietnam 36.1 65.47836 98.03186 34.1883

Zambia 114.2 47.48063 418.3663 44.0122

Zimbabwe 52.5 60.52892 839.0149 35.7780


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APPENDIX B


TABLE B.1



Algeria 55.997 90 93 4.5

Angola 43.988 34 43 0.48

Argentina 88.654 91 95 28.482

Australia 86.106 100 100 22.034

Austria 65.8 100 100 3.7092

Azerbaijan 52.206 57 71 13.486

Belarus 67.926 93 100 4.338

Belgium 96.777 100 100 7.8054

Benin 36.758 7 62 0.11

Botswana 48.981 46 94 0.13124

Brazil 77.61 71 91 50.904

Bulgaria 67.782 100 100 7.494

Cambodia 17.311 10 34 2.184

Cameroon 42.573 48 57 0.4

Canada 77.675 100 100 42.2

Chile 84.372 88 92 20.29

China 30.961 34 74 515.24

Colombia 70.516 70 90 7.4532

Congo, Rep. 56.413 20 70 0.04

Costa Rica 55.789 94 94 7.0088

Cote d'Ivoire 41.21 21 77 1.1792

Cuba 74.277 83 86 4.2734

Cyprus 68.038 100 100 0.217

Czech Republic 74.643 99 100 2.791

Denmark 84.979 100 100 1.132


Ecuador 57.766 76 79 17.686

Egypt, Arab Rep. 42.814 79 94 55.098

El Salvador 53.967 79 78 0.729

Ethiopia 13.827 4 20 5.558

Finland 80.963 100 100 2.4904

France 74.912 100 100 33.28

Gabon 75.359 36 84 0.06

Germany 73.286 100 100 47.62

Ghana 40.139 9 62 0.3

Greece 59.287 97 98 7.03

Guatemala 43.11 67 84 2.933

Honduras 42.941 57 79 1.194

Hungary 65.209 100 97 6.351

India 26.607 21 75 500

Indonesia 35.554 38 74 74.34

Iran, Islamic Rep. 60.236 82 91 80.32


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Iraq 68.78 67 80 49.76

Ireland 57.92 99 100 0.79

Israel 90.866 100 100 1.804

Jamaica 50.615 79 93 1.06532

Japan 78.016 100 100 90.002

Kenya 18.263 27 48 2.049Korea, Rep. 78.239 100 90 25.47

Luxembourg 82.893 100 100 0.0575

Malaysia 55.688 88 92 8.5498

Malta 90.946 100 100 0.0512

Mauritius 43.284 89 99 0.5954

Mexico 73.368 70 88 56

Mongolia 56.805 50 56 0.428

Morocco 51.692 59 98 11.172

Mozambique 26.221 12 76 0.605

Nepal 10.895 15 38 9.943

Netherlands 72.811 100 80 7.984


Oman 71.669 86 81 1.223

Pakistan 31.836 32 87 155.6

Peru 70.951 59 78 18.98

Philippines 48.29 61 87 70.92

Portugal 51.109 95 97 8.463

Qatar 94.998 100 100 0.2814

Romania 53.769 72 80 15.64


Senegal 39.62 41 63 1.36

Singapore 100 99 100 0.19


Sri Lanka 16.44 76 73 9.769

Sudan 32.232 27 63 16.88


Tanzania 20.543 8 55 5.184

Thailand 30.276 88 89 57.31

Togo 30.705 13 52 0.091


Tunisia 61.474 78 86 2.9442

Turkey 62.123 85 89 31.6

Ukraine 66.951 95 97 26


United Kingdom 78.353 100 100 12.092

United States 77.25 100 99 465.8

Uruguay 90.542 94 96 0.65

Vietnam 22.187 46 67 45.27

Zambia 37.104 47 51 1.7224

Zimbabwe 31.732 41 79 1.22


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TABLE B.2



Algeria 3.347034 852.0332 31.9 16230

Angola 0.909698 528.6130 67.30000 603520

Argentina 3.515906 1550.329 47.20000 333270

Australia 17.01161 5121.669 58.29999 1547100

Austria 7.766497 3368.098 56.70000 38070

Azerbaijan 4.356501 1659.706 57.29999 9360

Belarus 6.093680 2427.666 55 80265

Belgium 11.14518 5304.051 45.59999 7586

Benin 0.234900 326.8419 72 54110

Botswana 2.222442 943.0374 58.09999 131265

Brazil 1.596233 995.4832 65 5603910

Bulgaria 6.900326 2752.945 46.70000 33510Cambodia 0.128698 253.9708 80.09999 122450

Cameroon 0.313029 394.3166 65.09999 232160

Canada 15.66363 7861.733 58.29999 3101340

Chile 2.946443 1272.910 51.5 155485

China 2.755755 868.3059 74.90000 1670705

Colombia 1.635261 757.0964 50.20000 620140

Congo, Rep. 0.572988 283.6444 63 226413

Costa Rica 1.402774 676.8805 56.79999 24700

Cote d'Ivoire 0.485945 382.0128 63.90000 102750

Cuba 2.353773 1013.968 50.20000 22465Cyprus 6.683063 2040.748 57.79999 1663.6

Czech Republic 12.10893 4017.612 59 26330

Denmark 10.53635 3705.397 60.79999 4655


Ecuador 2.003811 629.1440 60.29999 128290

Egypt, Arab Rep. 1.542337 568.2624 41.5 515

El Salvador 0.921003 585.8337 56.79999 3545

Ethiopia 0.037735 287.5706 74.59999 144095

Finland 10.34239 5661.903 51.5 221740

France 6.603753 3973.845 48.40000 149450Gabon 3.54786 1244.669 51.20000 220000

Germany 10.59958 4119.871 53.5 109085

Ghana 0.319302 380.7331 65.59999 67710

Greece 7.446147 2132.552 46.40000 34500

Guatemala 0.715410 531.7755 61.79999 44780

Honduras 0.695895 506.5359 58.90000 72640

Hungary 5.814179 2505.137 44.40000 18540


28/52


India 0.953924 398.4347 58.20000 646645

Indonesia 1.128087 656.0538 63.5 1089770

Iran, Islamic Rep. 4.577343 1693.918 38 110750

Iraq 3.726580 1652.619 32.40000 8110

Ireland 9.109477 2946.812 47.20000 5500

Israel 9.780871 2798.570 50.20000 1425

Jamaica 3.912452 1292.460 61.5 3427.5

Japan 9.438419 3956.198 61.5 249130

Kenya 0.275435 439.5342 64.59999 36450

Korea, Rep. 8.311069 3210.160 60.40000 63290

Luxembourg 20.43379 7711.006 48.90000 864

Malaysia 5.845911 1635.157 60.40000 219835

Malta 5.741551 1914.532 46.59999 3

Mauritius 1.630203 586.4324 53.90000 387.5Mexico 3.555099 1406.742 55.70000 685210

Mongolia 3.436225 1172.670 53.90000 121265

Morocco 1.127936 319.1059 47.40000 50330

Mozambique 0.069733 394.4103 78.30000 422830

Nepal 0.094243 310.8007 83.19999 43585

Netherlands 11.40496 4576.470 54.29999 3525

Nicaragua 0.599431 504.2186 53.79999 41640

Nigeria 0.317386 704.8133 53.09999 151855

Oman 7.595163 2731.220 55.29999 20

Pakistan 0.663417 421.5838 46.5 23215Peru 1.002350 460.4488 61.5 696845

Philippines 0.876622 484.3087 61 68435

Portugal 5.176747 2015.020 54.70000 33735

Qatar 61.48102 15758.59 78.30000 0

Romania 5.603084 2053.156 57.79999 63685


Senegal 0.417564 222.8231 68.40000 91232

Singapore 13.36642 5337.158 62.5 23

South Africa 9.035236 2647.773 42 92410

Spain 6.137360 2559.005 38.90000 154031Sri Lanka 0.319669 328.0365 48.70000 22160

Sudan 0.152684 397.4342 46.90000 734362


Tanzania 0.118666 368.0821 77.90000 394785

Thailand 3.042095 1038.120 72.90000 192765

Togo 0.233367 383.3702 72.30000 5855



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Tunisia 1.756639 647.9847 40.79999 7400

Turkey 2.921469 1045.536 50.40000 99130

Ukraine 8.657036 3180.071 57 93920


United Kingdom 9.729973 3727.464 56 27020

United States 19.67105 7763.365 62 2982650

Uruguay 1.424298 797.9140 57.0999 11660

Vietnam 0.403293 303.9805 77.5999 105440

Zambia 0.243385 653.4026 65.5 519670

Zimbabwe 1.29486 842.2248 69.9000 205290

TABLE B.3



Algeria 45.8 68.46587 1476.200 27.27589

Angola 131 42.05092 416.31483 34.46713

Argentina 20.8 72.62363 7402.975 17.24465

Australia 5.7 77.82926 20395.927 18.14897

Austria 5.6 76.71561 30014.273 11.68606

Azerbaijan 68.2 64.57582 397.19812 19.47262

Belarus 13.8 68.46097 1370.672 19.76087

Belgium 6.4 76.84073 28067.97 12.11

Benin 96.8 50.94126 383.9272 49.89695

Botswana 45.4 59.19780 3010.382 25.49106

Brazil 41 68.34063 4751.066 30.53759

Bulgaria 19.2 71.05341 1554.721 17.33218

Cambodia 86.3 56.06526 308.0968 45.02911

Cameroon 87.6 51.99190 626.4717 46.29539

Canada 5.8 77.97756 20117.1 16.22553

Chile 11.4 75.04146 4951.567 36.97981

China 36.3 70.42473 604.2280 22.85133

Colombia 24.3 69.43668 2537.690 24.8125

Congo, Rep. 72 54.60939 774.3254 70.98047

Costa Rica 13 76.80880 3379.254 30.60893

Cote d'Ivoire 99.8 51.05136 749.4717 44.64832

Cuba 8.1 75.20675 2791.414 13.57722

Cyprus 8 77.26231 14212.05 18.39116


30/52


Czech Republic 8.6 73.07487 5595.630 20.18596

Denmark 5.3 75.21268 3139.356 9.9358


Ecuador 33.4 71.252 1774.840 26.35151

Egypt, Arab Rep. 47.9 65.59346 969.3127 25.09622

El Salvador 37.3 68.91380 1657.042 40.23256

Ethiopia 103 49.31363 135.3879 32.7529

Finland 4.3 76.40951 25609.13 16.34906

France 5.4 77.75122 26396.61 18.76642

Gabon 63.8 61.09761 4560.583 52.45657

Germany 5.3 76.42195 30887.86 17.45831

Ghana 69.7 57.99292 380.3275 29.98269

Greece 9 77.58536 12273.60 16.09251

Guatemala 45.3 64.98639 1463.397 33.63184

Honduras 35.6 68.607 701.5217 34.78818Hungary 12.3 69.79170 4411.033 11.39403

India 72.9 59.83156 380.0984 39.62369

Indonesia 44.9 63.97770 1013.699 22.66127

Iran, Islamic Rep. 39 68.15446 1519.977 31.91303

Iraq 35.9 71.23470 451.5466 20.56550

Ireland 6.1 75.57085 18575.62 23.3174

Israel 7.1 77.45122 17310.09 12.56790

Jamaica 24.9 70.26346 2344.141 33.63987

Japan 4.1 79.53634 42522.06 18.54335

Kenya 71 56.14922 329.8480 39.12452Korea, Rep. 5.3 73.39439 11467.81 33.17301

Luxembourg 5.2 76.51219 50592.52 14.71496

Malaysia 11.4 71.16343 4287.112 19.93694

Malta 7.8 77.14390 9717.501 19.57063

Mauritius 19.3 70.32585 3599.551 23.9235

Mexico 31.1 72.73763 3107.073 28.70808

Mongolia 61.5 61.26156 629.6975 23.71569

Morocco 53.5 66.61331 1213.278 28.25967

Mozambique 137.5 45.41095 141.0109 57.59626

Nepal 76.3 57.46863 203.8032 39.48273Netherlands 5.6 77.40463 27101.95 18.09796

Nicaragua 41.4 67.30419 892.9122 38.0089

Nigeria 125.3 45.11570 255.5006 37.20776

Oman 26 73.04302 6183.911 26.06586

Pakistan 86 62.04573 476.1494 37.0356

Peru 40.7 67.98478 2252.642 28.24828

Philippines 34.4 66.01853 1070.239 33.60951


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Portugal 7.6 75.31219 11619.32 12.34

Qatar 13.1 75.28273 16231.31 8.90654

Romania 27.3 69.45609 1563.949 21.56186

Russian Federation 21.2 65.22122 2669.946 19.34735

Senegal 70.7 54.36307 582.9425 59.73722

Singapore 4.1 76.39512 22922.11 25.26535

South Africa 47.5 59.88741 3862.809 36.2809

Spain 6.9 77.98073 15150.95 17.83949

Sri Lanka 19.9 69.22885 718.4438 27.82225

Sudan 71.6 54.29097 458.8534 34.17943


Tanzania 93.2 49.57375 180.5683 36.83172

Thailand 19.3 72.26117 2816.732 20.06

Togo 81.8 54.05009 320.4957 54.86158

Trinidad and Tobago 29.9 68.99709 4224.696 26.27049Tunisia 31.6 71.35365 2012.936 25.22658

Turkey 42.9 66.07309 2879.248 27.74516

Ukraine 16.3 67.11707 935.9681 20.14132

United Arab Emirates 14.2 73.43485 27993.43 16.99968

United Kingdom 6.2 76.83658 19943.77 18.67

United States 7.9 75.62195 27559.16 16.17298

Uruguay 17.6 73.47626 5986.739 20.08104

Vietnam 30.8 69.43168 288.0202 34.79875

Zambia 107.9 43.51526 389.8943 39.09751

Zimbabwe 58.4 53.09839 608.5971 39.11001


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APPENDIX C


TABLE C.1



Algeria 60.787 92 89 5.4966

Angola 48.987 42 46 0.5763

Argentina 90.125 91 96 30.974

Australia 87.165 100 100 22.424

Austria 65.8 100 100 3.6518

Azerbaijan 51.386 62 74 11.046

Belarus 69.973 93 100 4.338

Belgium 97.118 100 100 7.569

Benin 38.333 9 66 0.122

Botswana 53.219 52 95 0.17376

Brazil 81.192 74 94 57.528

Bulgaria 68.899 100 100 6.6768

Cambodia 18.586 17 44 2.184

Cameroon 45.542 49 64 0.73564

Canada 79.478 100 100 44.462

Chile 85.946 92 94 20.29

China 35.877 44 80 525.4

Colombia 72.075 73 91 11.1652

Congo, Rep. 58.695 20 70 0.0436

Costa Rica 59.038 95 95 3.9168

Cote d'Ivoire 43.541 22 77 1.3382

Cuba 75.6 86 90 6.6174

Cyprus 68.648 100 100 0.2011

Czech Republic 73.988 98 100 2.046

Denmark 85.1 100 100 0.86332


Ecuador 60.299 83 86 15.946

Egypt, Arab Rep. 42.797 86 96 64.528

El Salvador 58.912 83 82 1.1172

Ethiopia 14.739 9 29 5.558

Finland 82.183 100 100 2.3964

France 76.895 100 100 31.762

Gabon 80.108 36 85 0.102

Germany 73.067 100 100 41.57

Ghana 43.954 10 71 0.7092

Greece 59.736 98 99 8.767

Guatemala 45.127 71 87 2.933

Honduras 45.458 64 82 1.194

Hungary 64.575 100 99 6.0444

India 27.667 25 81 566.24

Indonesia 42.002 44 78 97.716

Iran, Islamic Rep. 64.042 90 93 87.02


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Iraq 67.83 69 80 61.36

Ireland 59.146 99 100 0.79

Israel 91.203 100 100 1.8202

Jamaica 51.814 80 93 0.72202

Japan 78.649 100 100 89.652Kenya 19.892 28 52 2.049

Korea, Rep. 79.621 100 93 25.47

Luxembourg 83.752 100 100 0.05872

Malaysia 61.977 92 97 7.503

Malta 92.368 100 100 0.05474

Mauritius 42.67 89 99 0.612

Mexico 74.722 75 90 65.96

Mongolia 57.133 49 65 0.428

Morocco 53.335 64 98 12.07

Mozambique 29.098 14 78 0.68852

Nepal 13.431 20 42 9.878

Netherlands 76.799 100 83 8.5618Nicaragua 54.737 48 100 1.288

Nigeria 42.351 34 53 7.638

Oman 71.569 90 83 1.3052

Pakistan 33.138 37 89 165.8

Peru 73.042 63 81 19.196

Philippines 47.99 65 89 74.245

Portugal 54.399 98 99 8.463

Qatar 96.311 100 100 0.2903

Romania 53.004 72 84 10.366


Senegal 40.345 45 66 1.8766

Singapore 100 100 100 0.19South Africa 56.891 75 86 12.66

Spain 76.262 100 100 35.854

Sri Lanka 15.709 82 80 11.7136

Sudan 32.495 27 62 29.404


Tanzania 22.309 9 54 5.184

Thailand 31.143 94 92 57.31

Togo 32.907 13 55 0.1378


Tunisia 63.432 81 90 2.8528

Turkey 64.741 87 93 37.84

Ukraine 67.145 95 97 33.488


United Kingdom 78.651 100 100 14.202

United States 79.089 100 99 471.24

Uruguay 91.325 96 98 2.456

Vietnam 24.374 56 77 45.27

Zambia 34.802 47 54 1.7264

Zimbabwe 33.758 40 80 3.011


34/52

TABLE C.2



Algeria 2.879789 884.9472 30.79999 15790

Angola 0.685141 538.4749 66.69999 597280

Argentina 3.820041 1650.493 49.20000 318610

Australia 17.20903 5644.541 59.40000 1549200

Austria 7.950479 3564.577 56 38380

Azerbaijan 3.666271 1420.288 59.29999 9360

Belarus 5.344180 2467.192 52.5 82730

Belgium 11.28725 5707.434 48.70000 6673

Benin 0.248112 304.1926 71.59999 50610

Botswana 2.432255 1044.621 57.59999 125350

Brazil 1.880367 1074.373 61.70000 5459430


Cameroon 0.218921 402.4460 64.5 221160

Canada 17.37045 8171.664 60.90000 3101340

Chile 3.806399 1632.591 49.59999 158340

China 2.696862 936.6746 73.5 1770005

Colombia 1.456686 649.1698 56 615090

Congo, Rep. 0.334450 259.6402 64 225563

Costa Rica 1.396932 754.8298 56.5 23760

Cote d'Ivoire 0.409566 406.1202 63.40000 103280

Cuba 2.344977 1157.995 51.09999 24350Cyprus 7.261740 2265.444 57.79999 1716.1

Czech Republic 12.13441 3990.673 55.09999 26370

Denmark 8.850879 3489.698 62.90000 4860


Ecuador 1.696411 650.8111 61.70000 118410

Egypt, Arab Rep. 2.089127 601.0266 42.20000 590

El Salvador 0.966704 667.9783 56.20000 3320

Ethiopia 0.08891 284.2286 75.30000 137050

Finland 10.07321 6226.394 55.70000 224590

France 6.001543 4135.012 49.59999 153530Gabon 0.851980 1184.191 50.20000 220000

Germany 10.12146 4094.124 53.70000 110760

Ghana 0.328136 403.6537 66.90000 60940

Greece 8.391708 2480.999 46.70000 36010

Guatemala 0.882395 627.3068 62.20000 42080

Honduras 0.809102 480.8098 63.40000 63920

Hungary 5.606995 2448.233 46 19070


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India 1.125975 433.8315 56.90000 653900

Indonesia 1.234417 726.9529 63.29999 994090

Iran, Islamic Rep. 5.703851 1882.135 38.5 110750

Iraq 2.979613 1066.748 32.90000 8180

Ireland 10.83281 3608.370 56.5 6350

Israel 9.968362 2901.714 49.40000 1530

Jamaica 3.985086 1478.474 57.70000 3409

Japan 9.612933 4090.515 59.40000 248760

Kenya 0.333334 439.1633 61.5 35820

Korea, Rep. 9.520954 4002.751 58.29999 62880

Luxembourg 18.88551 7617.914 52.20000 868

Malaysia 5.406947 2011.964 61.5 215910

Malta 5.413532 1771.322 47 3

Mauritius 2.332671 719.6260 54.70000 387

Mexico 3.816725 1451.820 58.70000 667510Mongolia 3.112899 993.8702 56.5 117170

Morocco 1.177536 355.5721 46.09999 50170

Mozambique 0.074143 394.0889 79.90000 411880

Nepal 0.132549 332.2751 84.09999 39000

Netherlands 10.38355 4597.828 61.20000 3600

Nicaragua 0.741537 536.1814 56.20000 38140

Nigeria 0.640168 732.4486 52.40000 131370

Oman 9.966914 3569.938 50.29999 20

Pakistan 0.736560 439.2840 47.20000 21160

Peru 1.171482 472.5744 65 692130Philippines 0.875175 515.7356 57.90000 71170

Portugal 6.157546 2412.804 58.5 34200

Qatar 58.76934 17619.96 75.59999 0

Romania 4.009474 1613.200 60.09999 63660


Senegal 0.414308 252.2475 68.40000 88982

Singapore 11.82336 4647.038 62.09999 23

South Africa 8.377511 2483.263 38.29999 92410

Spain 7.31274 3029.303 45.5 169878

Sri Lanka 0.531947 435.9003 52.40000 20820Sudan 0.161856 390.2062 47.70000 704910


Tanzania 0.077890 393.3803 76.80000 374620

Thailand 3.191342 1144.554 70.80000 190040

Togo 0.283047 440.3325 73.80000 4860

Trinidad and Tobago 18.97275 8260.303 54.09999 2336

Tunisia 2.083213 763.9368 40.09999 8370


36/52


Turkey 3.397059 1199.917 46.59999 101460

Ukraine 6.523008 2720.694 51 95100


United Kingdom 9.231429 3785.476 58.40000 27930

United States 20.24879 8056.819 63.59999 3001950

Uruguay 1.607511 936.6955 57.29999 14120

Vietnam 0.684549 370.1625 76.59999 117250

Zambia 0.178289 612.0676 69.80000 511340

Zimbabwe 1.110112 790.3185 70.30000 188940

TABLE C.3



Algeria 38.8 70.02417 1794.405 28.3962

Angola 118.7 45.20492 298.4058 42.3921

Argentina 18.1 73.71797 7695.594 19.3425

Australia 5.1 79.23414 21708.03 17.8758

Austria 4.6 78.02682 23974.18 13.39

Azerbaijan 56.7 66.75722 655.0974 18.6857

Belarus 10.9 68.91219 1273.049 18.2068

Belgium 4.7 77.72195 22697.01 11.9828

Benin 86.6 52.56534 361.9501 52.6495Botswana 50.5 50.76548 3204.115 26.7229

Brazil 31.2 70.13826 3696.146 24.7969

Bulgaria 17.7 71.66341 1579.348 16.8298

Cambodia 76.4 57.46414 293.5684 50.1228

Cameroon 86.3 50.07126 592.372 57.2916

Canada 5.3 79.23658 23559.50 17.4159

Chile 9.1 76.82173 5144.589 32.2268

China 28.8 71.24170 949.1780 22.8560

Colombia 21.1 71.00097 2511.974 26.4524

Congo, Rep. 69.5 54.14585 1026.831 60.4805Costa Rica 11 77.80407 4068.821 24.9407

Cote d'Ivoire 94.9 50.16087 628.2281 44.9842

Cuba 6.6 76.36580 2752.552 11.4999

Cyprus 5.4 77.95692 13421.65 17.7250

Czech Republic 5.6 74.96829 5724.837 16.8854

Denmark 4.6 76.59268 4657.019 10.1221



37/52


Ecuador 28.1 73.39204 1291.341 23.2513

Egypt, Arab Rep. 35.6 69.08265 1475.844 22.9810

El Salvador 28 69.70161 2211.022 43.5257

Ethiopia 86 51.71012 123.6809 67.3391

Finland 3.5 77.46585 23529.53 16.6872

France 4.4 78.95853 21774.99 18.7464

Gabon 60.3 59.69095 4102.624 46.6306

Germany 4.4 77.92682 22945.70 15.2833

Ghana 63.8 58.38224 259.9906 33.7852

Greece 6.8 77.88780 11396.23 13.4149

Guatemala 37.2 67.74417 1716.685 32.5606

Honduras 28.8 70.38202 1142.709 34.0589

Hungary 9.5 71.24634 4542.720 10.7092

India 64.2 61.61387 450.4151 39.9999

Indonesia 37.6 65.64636 773.3109 22.4461Iran, Islamic Rep. 35.3 69.73965 1550.090 26.1486

Iraq 34.4 70.74348 1063.481 21.3902

Ireland 5.8 76.54073 25629.65 20.9389

Israel 5.6 78.95365 19859.30 13.5707

Jamaica 21.5 70.47229 3479.056 33.6331

Japan 3.3 81.07609 37291.70 20.6906

Kenya 70 52.29963 406.5230 34.4369

Korea, Rep. 4.9 75.85548 11346.66 32.1207

Luxembourg 3.9 77.87317 46453.24 12.0789

Malaysia 9.1 72.14248 4005.5563 19.5577Malta 6.5 78.2 10377.03 19.1402

Mauritius 16.4 71.66341 3861.038 26.1176

Mexico 24.1 74.27424 5816.614 27.1579

Mongolia 48.6 63.12134 471.4733 32.5843

Morocco 44.2 68.67514 1271.8111 28.76272

Mozambique 116 47.23785 236.80964 63.98903

Nepal 61.8 61.52163 225.16868 38.03822

Netherlands 5.1 77.98780 24179.731 16.8

Nicaragua 34.1 69.64573 1006.6273 35.66299

Nigeria 112.5 46.27231 371.76808 42.9015Oman 17.7 74.11029 8774.9338 25.08144

Pakistan 75.9 63.16195 511.7025 32.99996

Peru 29.8 70.47626 2060.576 28.74385

Philippines 29.4 66.79514 1048.070 35.31923

Portugal 5.5 76.31463 11470.89 13.23902

Qatar 10.5 76.30319 30052.76 13.1214

Romania 23.2 71.16341 1650.968 18.5625


38/52


Russian Federation 17.8 65.34146 1775.141 17.58649

Senegal 67 55.73924 492.2862 50.91758

Singapore 2.9 78.05122 23814.55 23.58854

South Africa 52.3 54.77631 3019.946 33.46174

Spain 5.5 78.96585 14413.78 14.54434

Sri Lanka 16.4 70.98429 854.9267 26.58198

Sudan 66.6 56.97409 358.5292 25.69616


Tanzania 77.9 50.37329 307.9861 41.35754

Thailand 15.9 72.51522 1943.237 20.79357

Togo 78.7 54.80948 270.0008 37.45983


Tunisia 24.7 72.6 2245.335 23.21045

Turkey 28.4 69.44687 4189.478 30.5

Ukraine 15.9 67.86341 635.7089 19.88406United Arab Emirates 10.6 74.61590 34395.14 16.14708

United Kingdom 5.6 77.74146 25057.61 18.66476

United States 7.1 76.63658 35081.92 15.00811

Uruguay 14.8 74.88682 6914.362 20.75667

Vietnam 26.2 71.94551 401.5478 29.52151

Zambia 91 41.92987 318.9268 57.81632

Zimbabwe 62.8 44.61797 534.7911 39.05300


39/52

APPENDIX D


TABLE D.1



Algeria 66.689 94 85 6.161

Angola 53.943 51 48 0.6405

Argentina 91.382 90 96 32.57

Australia 88.182 100 100 22.58

Austria 66.534 100 100 3.657

Azerbaijan 52.389 74 77 11.354

Belarus 72.354 93 100 4.338

Belgium 97.292 100 100 6.7444

Benin 41.003 11 70 0.13

Botswana 57.32 57 96 0.194

Brazil 82.834 76 96 58.562

Bulgaria 70.179 100 100 6.2274

Cambodia 19.174 24 54 2.184

Cameroon 48.541 48 71 0.9594

Canada 80.122 100 100 45.97

Chile 87.623 96 96 11.34

China 42.522 55 87 525.4

Colombia 73.581 75 92 12.65

Congo, Rep. 60.988 19 71 0.046

Costa Rica 61.706 95 96 2.68

Cote d'Ivoire 46.836 23 79 1.409

Cuba 75.6 89 92 7.555

Cyprus 69.444 100 100 0.2074

Czech Republic 73.698 98 100 1.7186

Denmark 85.856 100 100 0.61568


Ecuador 63.607 90 92 15.25

Egypt, Arab Rep. 43.027 93 98 68.3

El Salvador 61.648 85 86 1.376

Ethiopia 15.7 14 37 5.558

Finland 82.905 100 100 1.8884

France 81.555 100 100 32.056

Gabon 83.494 33 86 0.13

Germany 73.355 100 100 35.04

Ghana 47.694 12 79 0.982

Greece 60.316 98 100 9.6526

Guatemala 47.172 75 91 2.933

Honduras 48.649 72 85 1.194

Hungary 66.365 100 100 5.69

India 29.235 30 86 610.4

Indonesia 45.937 50 80 113.3

Iran, Islamic Rep. 67.558 97 94 91.86


40/52


Iraq 67.042 71 80 66

Ireland 60.477 99 100 0.79

Israel 91.518 100 100 1.9048

Jamaica 52.027 80 93 0.5847

Japan 85.978 100 100 90.04

Kenya 21.675 30 55 2.4606Korea, Rep. 81.345 100 96 25.47

Luxembourg 83.928 100 100 0.0602

Malaysia 67.575 96 100 10.9272

Malta 93.645 100 100 0.0539

Mauritius 42.18 89 99 0.679

Mexico 76.308 80 93 76.41

Mongolia 62.494 50 75 0.428

Morocco 54.97 68 98 12.61

Mozambique 29.999 16 80 0.7442

Nepal 15.114 26 45 9.813

Netherlands 80.172 100 86 10.4548


Oman 71.907 95 86 1.3366

Pakistan 34.481 43 90 172.6

Peru 75.034 67 83 19.34

Philippines 48.033 70 90 77.57

Portugal 57.575 100 99 8.463

Qatar 97.449 100 100 0.38396

Romania 52.762 73 89 8.6454


Senegal 41.121 49 68 2.221

Singapore 100 100 100 0.19


Sri Lanka 15.127 88 86 12.95

Sudan 32.76 26 60 37.14


Tanzania 24.192 10 54 5.184

Thailand 32.241 96 94 57.31

Togo 35.185 13 58 0.169


Tunisia 65.128 85 94 2.85

Turkey 66.841 89 97 40.86

Ukraine 67.79 95 98 38.48


United Kingdom 79.007 100 100 14.03

United States 80.731 100 99 476.4

Uruguay 91.925 99 100 3.66

Vietnam 27.281 66 86 67.326

Zambia 36.612 48 58 1.74

Zimbabwe 35.86 40 80 4.205


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TABLE D.2



Algeria 3.257305 984.0914 36.59999 15360

Angola 1.161767 568.5691 65.69999 591040

Argentina 4.023907 1731.413 55.59999 305990

Australia 18.00129 5600.235 61.20000 1539200

Austria 9.055824 4103.304 55.90000 38620

Azerbaijan 4.091801 1649.877 58.5 9360

Belarus 6.112425 2781.034 50.90000 84360

Belgium 10.35679 5600.427 48.90000 6726

Benin 0.314159 327.5248 71.59999 48110

Botswana 2.459429 1027.916 60.29999 119430

Brazil 1.867383 1157.274 64 5304940


Cameroon 0.210574 397.7164 66.59999 210160

Canada 17.42608 8424.041 62.20000 3101340

Chile 3.760414 1739.198 50.79999 160430

China 4.441150 1342.446 72.19999 1930439

Colombia 1.416002 629.2792 59.20000 610040

Congo, Rep. 0.456665 306.4850 65.30000 224713

Costa Rica 1.644843 897.1864 58.29999 24910

Cote d'Ivoire 0.434237 534.6092 63.90000 104050

Cuba 2.314063 955.9132 52 26970Cyprus 7.266083 2150.063 60 1728.5

Czech Republic 11.79542 4390.544 54.70000 26470

Denmark 8.690753 3485.343 62.7000 5340


Ecuador 2.1822175 818.3037 65.1999 108530

Egypt, Arab Rep. 2.3535486 845.3632 42.7000 670

El Salvador 1.0666732 747.8861 55.5 3090

Ethiopia 0.0739188 287.1469 80.0999 130000

Finland 10.408749 6529.458 55.7999 221570

France 6.2021447 4283.718 51.2000 157140Gabon 1.5195243 1352.704 49.9000 220000

Germany 9.8169306 4101.617 51.9000 110760

Ghana 0.3214585 380.2490 67.5 55170

Greece 8.886492 2724.028 48.20000 37520

Guatemala 0.988466 617.6686 63 39380

Honduras 1.107683 580.9928 58.70000 57920

Hungary 5.74167 2734.504 46.5 19830


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India 1.237784 473.0317 58.20000 677090

Indonesia 1.504562 794.1433 59.90000 978570

Iran, Islamic Rep. 6.979772 2467.833 41.5 110750

Iraq 4.090265 973.4757 33.29999 8250

Ireland 10.43089 3479.163 59.5 6950

Israel 8.544039 2669.083 50.29999 1550

Jamaica 4.016488 1419.070 59.29999 3392

Japan 9.690473 4073.946 57.79999 249350

Kenya 0.240419 450.4675 58.70000 35220

Korea, Rep. 9.616486 4366.123 59.20000 62550

Luxembourg 24.82464 9433.495 52.40000 868

Malaysia 6.993930 2433.178 59.79999 208900

Malta 6.683171 2181.127 45.59999 3

Mauritius 2.743053 866.2758 53.20000 349

Mexico 4.085557 1597.933 58.40000 655780Mongolia 3.457778 1030.492 54.40000 113080

Morocco 1.505652 430.0139 46.20000 50810

Mozambique 0.087746 408.7334 80.09999 400790

Nepal 0.118819 334.7129 82.40000 36360

Netherlands 10.55327 4829.935 61.5 3650

Nicaragua 0.796360 613.1895 58.09999 34640

Nigeria 0.744108 761.7430 51.29999 110890

Oman 12.52314 4433.810 49.90000 20

Pakistan 0.861266 476.0369 48.59999 19020

Peru 1.357755 495.1523 63.70000 687420Philippines 0.808745 453.0399 59.79999 73910

Portugal 6.190790 2508.874 57.40000 34370

Qatar 63.19318 20473.80 80.19999 0

Romania 4.389341 1786.640 51.09999 63910


Senegal 0.538991 256.7071 68.69999 86732

Singapore 11.59122 5264.322 61.70000 23

South Africa 8.392574 2716.048 39.09999 92410

Spain 8.144637 3269.024 51.40000 172932

Sri Lanka 0.592686 458.1967 50.59999 19330Sudan 0.283734 393.9179 48.29999 702200


Tanzania 0.144674 442.1304 79 354450

Thailand 4.125496 1486.778 72.19999 188980

Togo 0.247493 438.5606 75 3860

Trinidad and Tobago 21.72791 12755.49 60 2300

Tunisia 2.273547 829.0382 39.59999 9240


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Turkey 3.483379 1238.261 42.20000 107400

Ukraine 7.197277 3033.366 53.59999 95750


United Kingdom 8.999521 3692.301 59 28450

United States 19.71596 7846.804 61.79999 3021080

Uruguay 1.747129 894.5568 57.59999 15200

Vietnam 1.254043 503.1385 75.80000 130770

Zambia 0.197068 629.2148 67.19999 503010

Zimbabwe 0.857045 771.8885 83 172590

TABLE D.3



Algeria 32.3 71.64614 3111.703 25.43721

Angola 107.8 48.53843 1856.959 43.98931

Argentina 15.4 74.74146 4735.983 16.65034

Australia 4.7 80.84146 33944.98 17.87626

Austria 4 79.33170 37067.32 12.40884

Azerbaijan 47.9 68.92273 1578.367 13.44831

Belarus 7.1 68.85122 3126.367 16.03553

Belgium 4 78.98048 36011.46 11.4904

Benin 77.5 53.81373 570.8874 46.8289Botswana 28.5 50.44656 5467.659 25.55037

Brazil 22 71.52953 4743.266 21.04365

Bulgaria 13.8 72.56097 3733.263 16.22654

Cambodia 55.6 59.97939 471.1219 53.21143

Cameroon 83.7 49.41241 944.9874 47.8128

Canada 5.2 80.29268 35087.89 18.20311

Chile 7.9 78.27497 7631.348 26.05133

China 20.3 72.17131 1731.125 19.18562

Colombia 18.2 72.27978 3404.234 28.34839

Congo, Rep. 66.6 55.06485 1722.812 68.79602Costa Rica 9.2 78.56424 4632.856 21.34285

Cote d'Ivoire 88.5 51.57556 908.0232 44.85819

Cuba 5.3 77.68319 3789.166 10.30837

Cyprus 3.9 78.57395 22430.60 17.72706

Czech Republic 4.4 75.92439 12705.62 16.24551

Denmark 4.1 77.84390 5246.513 11.9



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Egypt, Arab Rep. 26.2 71.52868 1208.650 25.6326

El Salvador 19.9 70.69880 2825.181 43.23934

Ethiopia 68.2 55.15665 165.4322 64.17900

Finland 3 78.81707 37318.79 15.53424

France 3.8 80.11463 33819.13 18.55493

Gabon 55.9 60.00558 6321.991 33.16288

Germany 3.9 78.93170 33542.78 14.12233

Ghana 57.9 61.03831 495.9325 32.81364

Greece 4.8 79.23902 21620.71 11.06286

Guatemala 30.6 69.64785 2139.726 31.05577

Honduras 23.4 71.43885 1412.138 30.41686

Hungary 7.2 72.64878 10936.94 10.44291

India 55.8 63.36724 731.7400 39.07012

Indonesia 31.4 67.07336 1257.653 20.41336Iran, Islamic Rep. 27.8 71.33309 2753.612 21.74286

Iraq 32.8 68.46363 1134.736 19.33844

Ireland 4.4 78.47951 48866.39 17.86512

Israel 4.4 80.15122 19330.02 13.01884

Jamaica 18.8 71.47941 4178.908 27.67837

Japan 2.8 81.92512 35781.23 18.89505

Kenya 59.3 53.00956 526.1299 44.75823

Korea, Rep. 4.5 78.43268 17550.85 27.93094

Luxembourg 3.1 79.43170 80925.22 11.32837

Malaysia 7.3 72.98829 5499.293 16.89527Malta 5.7 79.50487 14809.92 11.51

Documents

Urbanization and Impacts