ECOLOGICAL FOOTPRINT, ECONOMIC GROWTH AND ECOLOGICAL ...prr.hec.gov.pk/jspui/bitstream/123456789/9282/1/... · 2.2 The concept of ecological footprint and its methodological issues

ECOLOGICAL FOOTPRINT, ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY

by

Hazrat Yousaf

PhD Scholar in Economics

Reg. # 01/PhD/PIDE/2011

A Dissertation Submitted in Partial Fulfilment of the

Requirement for the Degree of Doctor of Philosophy in Economics

Department of Economics Pakistan Institute of Development Economics

Islamabad, Pakistan 2011-2016

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's is to certify that the research work presented in this thesis, entitled: “Ecological Footprint,,

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i-conomic Growth and Ecological Efficiencv” was conducted by Mr. Hazrat Yousaf under the

'f’FuperV1s1on of Dr. Anwar Hussa1n and Dr. Samlna Khalll. No part of thls the31s has been

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11bmitted anywhere else for any other degree. This thesis is submitted in partial fulfillment ofe requirements for the degree of Doctor of Philosophy in Economics from Pakistan Institute1'Development Economics, Islamabad.

“%’-Student Name: 'Mr. Hazrat Yousaf Signature: mOl/PhD/PIDE/2011

:ipxamination Committee: h)a) External Examiner: Dr. Aneel Salman Signature:k' I WHOD/AssistantProfessor

Department ofManagement ScienceCOMSATS UniversityIslamabad

b) Internal Examiner: Dr. Rehana Siddiqui Signature:Head, Department of Environmental EconomicsPIDE, Islamabad

(\lder $19.?

if Supervisor: Dr. Anwar Hussain Signature: r \ 931.,Assistant Professor

f? PIDE, Islamabad

”Co-Supervisor: Dr. Samina Khalil Signature;gkfll"!

Director, Applied Economics Research Centre. University ofKarachi

Dr. Attiya Y. Javid Signature: fig:Professor/Head, Department ofEconomics \lPIDE, Islamabad

ii

Dedicated to

My Family

A Source of Inspiration throughout My Educational Career

iii

Acknowledgement

All glories to Allah Almighty, the Omniscient and the Omnipotent and His Benedictions may

be upon His Holy Prophet (Peace be upon him)-A saviour of mankind from darkness of

ignorance, a symbol to be and to do right. My deepest thank to Allah Almighty Who enable to

accomplish this task successfully with great devotions.

The acknowledgment would be inadequate, unless I express my deepest gratitude and

greatest appreciation to my worthy supervisors; Dr. Anwar Hussain, Assistant Professor, Pakistan

Institute of Development Economics (PIDE), Islamabad for his supervision and support. His wide

knowledge and comprehensible way of judgment have been of great importance for me. His broad

analysis and precise assessment enhanced not only the quality of this dissertation, but also complete

understanding of my thesis. I am thankful to him and Dr. Samina Khalil, Director Applied

Economic Research Centre (AERC), University of Karachi for their valuable guidance and

encouraging suggestions during the whole process.

I am immensely grateful to the Vice Chancellor of PIDE, Prof. Dr. Asad Zaman and our

faculty members; Dr. Musleh-ud-Din, Dr. Rehana Siddiqui, Dr. Ejaz Ghani, Dr. Attiya Yasmin

Javed (HoD of Economic Department), Dr. Eatzaz Ahmad, Dr. Karim Khan and Dr. Waseem

Shahid Malik and other teaching faculty who taught and guided me throughout my PhD program

at PIDE.

My special thanks go to Dr. Mathis Wackernagel, President Global Footprint Network USA,

for providing the dataset on ecological footprint. Dr. Wackernagel updated me with the recent

development in the area of ecological footprint. Besides this, the detailed comments and

constructive suggestions were given by two foreign referees; Professor Jeff Gow, Professor of

Economics in School of Commerce University of Southern Queensland Toowoomba QLD 4350,

Australia and Professor Simone D’Alessandro Associate Professor Department of Economics

and Management, University of Pisa, Italy and external examiner Dr. Aneel Salman, HoD

Department of Management Science, COMSATS University, Islamabad improved the quality of

the dissertation. I am indebted to their valuable conclusions and constructive comments. A

special credit goes to the two internal examiners in PIDE; Dr. Rehana Saddiqui and Dr.

Muhammad Nasir at the stages of proposal defence and before thesis submission. I am also

thankful to Dr. Syed Manzoor Ahmed, Dean Faculty of SSM&IT in Lasbela University of

Agriculture, Water & Marine Sciences (LUAWMS) and Mr. Tufail Hakeem secretary of

Pakistan Journal of Applied Economics, University of Karachi for their English proof reading.

I would like to express my deepest gratitude to all my family members and relatives. Special

thank is also due to my well-wishers, colleagues and friends, especially Dr. Kahild Khan, Head

of Economics Department in LUAWMS, Naveed Hayat, Fazal Hadi, Muhsin Ali, Ikramullah,

Ahsan Abbas, Muhammad Umar and administrative staffs (Muhammad Saleem and Saba-ul-

Haq) of Economics Department at PIDE for their precious cooperation.

Hazrat Yousaf

iv

Table of Contents

Table of Contents i

List of Tables vii

List of Figures x

List of Abbreviations xi

Abstract xii

Page#

Chapter One Introduction 1

1.1 The background 1

1.2 Significance of the Study 14

1.3 Objectives 15

1.4 Hypotheses 15

1.5 Organization of the Study 16

Chapter Two

Literature Review

17

2.1 Introduction 17

2.2 The concept of ecological footprint and its methodological issues of

estimation

17

2.3 Ecological footprint and economic growth 20

2.4 Ecological footprint and ecological efficiency 23

2.5 Environment and energy consumption 24

2.6 Ecological footprint and trade 28

2.7 Ecological footprint and working hours 30

2.8 Growth and energy consumption 32

2.9 Contribution of the study 34

Chapter Three The Theoretical Background 36

3.1 Introduction 36

3.2 The theoretical perspective of Neo-Malthusian: economic growth and

environment

37

v

3.3 The theoretical perspective of Neoclassical economists: economic

growth and environment

40

3.4 Ecological modernization perspective 42

3.5 World system and treadmill production perspectives 44

3.6 Export dependence perspective 45

Chapter Four Data and Methodology 46

4.1 Introduction 46

4.2 Data 46

4.3 Methodology 51

4.3.1 Atkinson Index of ecological footprint inequality based on environment

intensity and per capita income

55

4.3.2 Empirical specification of various influencing factors of ecological

footprint and its components

57

4.3.3 The expected theoretical linkages between dependent and

independent variables

62

4.4 Analytical tools 65

4.4.1 The computation of ecological efficiency 65

4.4.2 The computation of ecological efficiency index 65

4.4.3 The computation of environmental impact intensity 66

4.4.4 The computation of Atkinson index of equality 68

4.4.5 The Econometric modelling 68

4.4.5.1 The Fixed effect model 68

4.4.5.2 The Random effect model 72

4.4.5.3 The Hausman test 74

vi

Chapter Five Trends in the Ecological Footprint, Economic Growth

and Ecological Efficiency

75

5.1 Introduction 75

5.2 Trend of ecological footprints, resources consumption and socio-

economic variables

75

5.3 Trend of ecological footprints, economic growth and ecological efficiency 88

5.4 Analysis of ecological efficiency index, maximum and mean level of

ecological efficiency

90

Chapter Six Ecological Footprint, Environmental Impact Intensity and

Income Inequality

98

6.1 Introduction 98

6.2 Ecological footprint, environmental impact intensity and

income inequality of high income countries

98


income inequality of middle income countries

106

6.4 Discussion 114

Chapter Seven The Driving Forces of Total Ecological Footprint and

its Components

115

7.1 Introduction 115

7.2 The driving forces of total ecological footprint and its component in high-

middle income countries

115

7.3 The driving forces of total ecological footprint and its component of high

income countries

124

7.4 The driving forces of total ecological footprint and its component of

middle income countries

137

7.4 Discussion 148

vii

List of Tables Page#

Table 1.1: Averages per capita income, population and ecological footprint,

emissions and Biocapacity, 2003-2011

7

Table 1.2: Averages of export, import, agriculture, manufacturing,

services and urban population, 2003-2011

8

Table 1.3: Averages of export, import, agriculture, manufacturing,

services and urban population, 2003-2011

9

Table 1.4: Averages of services, built-up footprint and Biocapacity, 2003-2011

9

Table 5.1: Trend of Ecological Footprints and Its Components

(Global ha/person 2003-2011) of High Income Countries

76

Table 5.2: Total Ecological Footprints vs Biocapacity of High Income Countries

78

Table 5.3: Trend of Resources Consumption, 2003-11 of High Income Countries

79

Table 5.4: Trend of GDP, Population, Urbanization and Hours Works, 2003-11 of

High Income Countries

80

Trend 5.5: Trend of Export, Agriculture, Manufacturing and Services; 2003-11 of


80

Table 5.6: Trend in Ecological Footprints and Its Components

(Global ha/person, 2003-2011) of Middle Income Countries

82

Table 5.7: Total Ecological Footprints vs Biocapacity of Middle Income Countries 84

Chapter Eight Conclusion and Recommendations

156

8.1 Introduction 156

8.2 Summary of the study 156

8.3 Policy recommendations 161

8.4 Limitations and direction for future research 163

APPENDIX A 164

APPENDIX B 169

APPENDIX C 178

APPENDIX D 181

APPENDIX E 192

REFERENCES 193

viii

Table 5.8: Trend of Resources Consumption, 2003-11 of Middle Income Countries

85

Table 5.9: Trend of GDP, Population, Urbanization and Hours Works; 2003-11 of

Middle Income Countries

86

Table 5.10: Trend of Export, Agriculture, Manufacturing and Services; 2003-11 of


87

Table 5.11: Ecological Footprints, Economic Growth and

Ecological Efficiency; 2003-2011of High Income Countries

88

Table 5.12: Ecological Footprints, Economic Growth and

Ecological Efficiency; 2003-2011 of Middle Income Countries

89

Table 5.13: The gap between efficiency in resources utilization and

maximum level of ecological efficiency of High Income Countries; 2003-11

94

Table 5.14: The gap between efficiency in resources utilization and

maximum level of ecological efficiency of Middle Income Countries; 2003-11

96

Table 6.1: Atkinson Index of Equality: Total Footprint, Per Capita income,

And Environmental intensity, 2003-11 of High Income Countries

99

Table 6.2: Atkinson Index of Equality: Crop land Footprint, Per Capita income,

and Environmental intensity, 2003-11 of High Income Countries

100

Table 6.3: Atkinson Index of Equality: Grazing Footprint, Per Capita income,

and Environmental intensity: 2003-11 of High Income Countries

101

Table 6.4: Atkinson Index of Equality: Forest Footprint, Per Capita income,


102

Table 6.5: Atkinson Index of Equality: CO2 Footprint, Per Capita income,


103

Table 6.6: Atkinson Index of Equality: Fish Footprint, Per Capita income,


104

Table 6.7: Atkinson Index of Equality: Built Up Footprint, Per Capita income,


106

Table 6.8: Atkinson Index of Equality: Total Footprint, Per Capita income, 107

ix

and Environmental intensity, 2003-11 of Middle Income Countries

Table 6.9: Atkinson Index of Equality: Crop land Footprint, Per Capita income,


108

Table 6.10: Atkinson Index of Equality: Grazing Footprint, Per Capita income,

and Environmental intensity: 2003-11 of Middle Income Countries

109

Table 6.11: Atkinson Index of Equality: Forest Footprint, Per Capita income,


110

Table 6.12: Atkinson Index of Equality: CO2 Footprint, Per Capita income,


111

Table 6.13: Atkinson Index of Equality: Fish Footprint, Per Capita income,


112

Table 6.14: Atkinson Index of Equality: Built Up Footprint, Per Capita income,


113

Table 7.1: The Driving Forces of Total Ecological Footprint:

High-Middle Income Countries (Random effect model)

117

Table 7.2: The Driving Forces of Components of Ecological Footprint:

High-Middle Income Countries (Random and Fixed effect models)

120

Table 7.3: The Driving Forces of Components of Ecological Footprint:

High-Middle Income Countries (Random and Fixed effect models)

123


High Income Countries (Random effect model)

127

Table 7.5: The Driving Forces of The Components of Ecological Footprint:

High Income Countries (Random and Fixed effect models)

131


High Income Countries (Random and Fixed effect models)

135


Middle Income Countries (Random effect model)

139


Middle Income Countries (Random and Fixed effect models)

143


Middle Income Countries (Random effect model)

146

x

List of Figures

Page#

Fig. 1.1: Time trend of humanity’s ecological demand

10

Fig. 3.1: A circular flow of factors of production, environment and economy

36

Fig. 3.2: A graphical illustration of Ehrlich’s model

39

Fig. 3.3: Per capita consumption and its effect on the environment

40

Fig. 3.4: The environmental Kuznets curve 41

Fig. 3.5: The EMT channel of modernization regarding declining in

environmental Damage/ ecological sustainability

43

Fig.5.1: Percentage Share of Components of Ecological Footprints of


76

Fig.5.2 : Percentage Share of Agriculture, Manufacturing & Services Intensity of


81

Fig.5.3: Percentage Share of Components of Ecological Footprints of


83

Fig. 5.4: Ecological Efficiency Index of High and Middle Income Countries: 2005-11

94

Fig. 5.5: Percentage share of components of total ecological footprint in


95

xi

List of Abbreviations

EF Ecological Footprint

EE Ecological Efficiency

EEI Ecological Efficiency Index

EKC Environment Kuznets Curve

FAO Food Agriculture Organization

GDP Gross Domestic Product

GFN Global Footprint Network

RI Resource Intensity

STIRPAT Stochastic Impact by Regression on Population, Affluence and Technology

UN United Nations

UNDESA United Nations Department of Economic and Social Affairs

UNIDO United Nations Industrial Development Organization

NUDP United Nations Development Program

UNEP United Nations Environment Program

WDI World Development Indicator

WWF World Wildlife Fund

xii

ABSTRACT

The ecological footprint is one of the important environmental impact indicator of

humanity’s demand for crop, forest, fishing grounds, grazing and built-up land as well as for the

area of land required to assimilate CO2 emissions and waste generated by human activities. This

indicator describes resource budget and environmental degradation of globe, a region, a nation

or a city in a given year. This study examined trends of ecological footprint, economic growth

and ecological efficiency of middle and high income countries. It also estimated the gap between

a country’s efficiency in resource utilization and maximum ecological efficiency of total

footprints and its components. Besides, inequality in the distribution of income, environmental

impact intensity (or ecological efficiency) and ecological footprint for the group of middle and

high income countries is also estimated. The study used the panel dataset for the period 2003-

2011 that covered 35 High and 77 Middle income countries. The data on the Ecological footprint

was obtained from Global Footprint Network. The Stochastic Impact by Regression on

Population, Affluence and Technology (STIRPAT) model was used as an analytical tool to

examine the effect of various driving forces on total ecological footprint, cropland, forest, fishing

grounds, grazing land, CO2 footprint and built-up land footprint. The Atkinson Index was used

as an analytical tool to examine inequality between High and Middle income countries in

distribution of income, footprints and environmental impact intensity. The findings revealed that

the high income countries used more ecological resources than their biocapacity as compared to

middle income countries. The ecological footprint, GDP per capita, ecological efficiency, fossil

fuel consumption, and level of urbanization and service intensity of high income countries are

larger than middle income countries. While population density, annual working hours, and

manufacturing and services intensity of high income countries are lower than middle income

countries. Similarly, the sampled countries have more potential in cropland, forest and grazing

land activities, followed by CO2 footprint, fishing grounds and built-up land footprint for

achieving maximum level of ecological efficiency.

The regression analysis of combined panel supports the environmental Kuznets Hypothesis

in case of total ecological footprint and its components. The separate panel model regression

analysis of high income countries supports the hypothesis in case of total ecological footprint,

fishery, and grazing and built-up land footprint. The results of middle income countries of total

ecological footprint, cropland, CO2 footprint and grazing land footprint support the hypothesis

that decoupling of economic growth accelerates environmental sustainability. The major driving

forces that contribute to increase in total ecological footprint are economic growth, population,

xiii

level of urbanization, fossil fuel consumption, export intensity and income inequality. Similarly,

a rise in economic growth, population, export and manufacturing intensity, working hours, coal,

oil and gas consumption increases CO2 footprint of the sample countries. However, further level

of economic development and education improve environmental quality by reducing cropland,

fishing grounds and forest footprint. The comparison of resource distribution through Atkinson

Index shows that high income countries have larger equality in footprint and environmental

impact intensity than middle income countries in case of grazing land, forest, fishing grounds

and built-up land.

It is suggested that both high and middle income countries should control ecological

overshooting. Investment in education is instrumental in reducing the ecological footprint. Rural

areas should be developed through creating job opportunities, agro-based business activities and

small scale industries which will reduce pressure on built-up land footprint. Production and use

of renewable energy alternatives such as wind, solar system and micro hydro power plants can

lessen the CO2 footprint and also leads toward environmental sustainability. The high and middle

income countries should prioritize the utilization efficiency of cropland, forest and grazing land.

The high income countries should reduce their footprint associated with forest, CO2, fishing

grounds and built-up land, because its average environmental impact intensity is greater than

their biocapacity. The middle income countries should reduce cropland and grazing land

footprint due to their larger mean environmental impact intensity than high income countries.

ECOLOGICAL FOOTPRINT, ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY

by

Hazrat Yousaf

PhD Scholar in Economics

Reg. # 01/PhD/PIDE/2011

Supervisor

Dr.Anwar Hussain

Assistant Professor

Pakistan Institute of Development Economics, Islamabad

Co-Supervisor

Prof. Dr. Samina Khalil

Director, Applied Economics Research Centre

University of Karachi

A Dissertation Submitted in Partial Fulfilment of the

Requirement for the Degree of Doctor of Philosophy in Economics

Department of Economics Pakistan Institute of Development Economics

Islamabad, Pakistan 2011-2016

1

CHAPTER ONE

INTRODUCTION

1.1 The Background

In the last forty years, developed and developing/emerging economies have experienced

high economic growth, urbanization and per capita consumption of goods and services (UNDP,

2006; UNEP, 2007; Anders and John, 2009; GFN, 2014). The ecologists and environmentalists

have opine that these changes have increased environmental disaster (Goudie, 1981; Haberl,

2006; Nelson et al., 2006). In the past century, the population of the world has reached 7 billion,

whereas humanity’s resource consumption and residual emissions are faster than earth’s

regenerating capacity (Erb et al., 2007; Hoekstra, 2009; GFN, 2014). Extraction of natural

resources has reached to 45% in the last 25 years at global level (Turner, 2008; Krausmann et

al., 2009; Giljum et al., 2011; Behrens et al., 2007).

In case of emerging economies 559 million people live in cities of China, followed by India

with 329 million. Developed nations such as the United State of America has the largest urban

population which consist of 246 million (GFN, 2012). The highest per capita income and

transition from agriculture to industrialization generated more resource consumption, and

residual emissions (Foley et al., 2005; Haberl, 2006; Hertwich and Peters, 2009; Behrens et

al., 2007).

The increased carbon emissions i.e. more than 60% from the energy consumption to

facilitate the rapid economic growth has attracted an important concern for the environmental

sustainability between high and middle income countries (Adewuyi and Awodumi, 2017). The

increasing trend in CO2 emissions and energy consumption has accelerated climate change and

food security issues in different parts of the world. Similarly, modernity and market

liberalization have changed consumption of goods and services. However, different regions of

the world have different environmental impacts on the globe, due to differences in energy

2

consolidation, consumption of material goods and services, CO2 emissions, urbanization and

economic growth (Ahmed and Azam, 2016). Thus, the literature of development and

environmental economics examined the nexus between the material resource consumption,

economic growth, modernization and energy consumption. Since, the rapid economic

development and low CO2 emissions is the highest priority of the high income countries and

most part of their policies are concerned with the sustainable development. On the other hand,

middle-income countries are trying to achieve high economic growth by utilizing their material

resources and energy (GFN, 2016a; Zaman and Abd-el Moemen, 2017). The environmental

scientists argue that global warming, climate change and fossil fuel consumption are factors of

acceleration of CO2 emissions. Energy related CO2 emissions has increased by 19 percent and

will reach 25-90 percent in 2030 (Chen et al., 2016). The variation in the pattern of rainfall,

melting of snow and ice, raising the sea level, variation in the temperature of air and ocean,

worsening the wild life and agriculture productivity are mainly due to global warming and

climate change. Under these scenarios, the economists and environmentalists have turned their

attention from simple economic development into environmentally friendly economic

development in the last few decades. They argue that to decouple the economic growth, indeed

requires the environmental stability and environmental protection. The relationship between

economic development and environmental sustainability is complementary for sustainable

development (GFN, 2016a; Salahuddin et al., 2016).

The global climate change of the 21st century is one of the most important challenge facing

humans. Governments worldwide are trying to reduce the CO2 emissions because the

environmental sustainability has been worsened by CO2 emissions in the past two decades

(IPCC, 2014; Iwata and Okada, 2014). The humans’ activity in the form of energy consumption

for the years 2005 to 2013 reached to 60% (NBSC, 2016). The CO2 emissions of energy

consumption is 90% (IEA, 2015). According to Kyoto Protocol, developed countries and

developing countries are responsible for reducing CO2 emissions. The developed countries

3

provide financial assistance or environmentally friendly technology for developing countries

in this regard. The financial assistance is based on the Certified Emissions Reductions (CER)

by the developing countries (IEA, 2015).

Besides, human activity in the form of crop consumption, forest, grazing land, fisheries

and urbanization is more than regenerating capacity of the sphere since 1960s. The world’s

ecological footprint1 per capita in year 1961 was 2.27 gha2 per capita and reached to 3.01gha

in the year 2013. The biocapacity3 per capita in corresponding years was 3.12 gha and 1.73gha.

The ecological deficit in the year 2013 of Asia was 1.4 gha per capita. It requires 1.3 earths for

the regeneration of resource as consumed by Asia (GFN, 2016a). The CO2 emissions of this

region has increased significantly in the past two decades. In this region, the major CO2 emitter

is China where its share in the CO2 emissions of the world in 2013 is 28% (IEA, 2015). The

international community has asked China to reduce CO2 emissions and therefore China has

planned to reduce its CO2 emissions by 40-45 percent in year 2020 by reducing energy

consumption by 15 percent (GFN, 2016a; He et al., 2017).

The scenario of urban growth in high and middle income countries produces various issues,

for example greater demand for energy consumption and greater demand for material goods

and services. As urban population increased by more than 250 percent and it increased energy

consumption by 50 % (Al-mulali et al., 2013; Al-Mulali et al., 2015). The unsystematic and

unbalanced pattern of the urban development particularly in developing and emerging

countries, produce negative externality. More than 50 percent CO2 emissions are being emitted

by these regions (Behera and Dash, 2016). The effect of urbanization on economic

1 Ecological footprint shows impact of humans activities on environment, expressed in term of area of land

required to support humanity consumption in form of cropland, forestry, fishing grounds, grazing and built-up

land as well as the area of land to absorb the CO2 emission(GFN, 2014). 2 gha stands for global hectare and it is the unit measure of ecological footprint and biocapacity. One hectare is

approximately equal to 2.47 acres(Jorgenson and Burns, 2007; GFN, 2016a). 3 Biocapacity indicates the available productive area required to generate resources as well as to absorb

wastes(GFN, 2014).

4

development, energy consumption and CO2 emissions have been investigated by researchers.

However, its effect on the consumption material resource still requires the research work. In

this regard, the current study has utilized ecological footprint as material resources

consumption indicator to investigate the effects of driving forces. The increasing trend of

urbanization in middle income countries would lead to increase world’s urban population by

65 percent in 2050. Middle income countries are in the phase of industrialization and

urbanization, which led to more energy consumption. To meet the growing energy demand,

coal has become the first choice for rich resources and low cost benefits. However, coal

consumption is the primary source of CO2 emissions (He et al., 2017; Ouyang and Lin, 2017).

The major challenge to high-middle income countries is a sustainable development process.

The sustainable development is a rapid economic development process under durable

environment. In sustainable development; numerous works have investigated the impact of

economic development, energy consumption on the environment using the CO2 emissions as

environmental indicator. However, the CO2 emissions captured only a small part of

environmental damage due to the anthropogenic activity in the form of energy consumption,

cropland, fisheries, grazing land, and forestry and built-up lands (GFN, 2016a; Uddin et al.,

2017).

In the year 2014, global production of fish was 93.4 million tons, in which marine and

inland fisheries were 81.5 million tones and 11.9 million tons respectively. The major

contributors to this production are China, Indonesia, the United State and the Russian

Federation. The 87 percent of fish and fishery products are used directly for human

consumption and are used for non-food activities. The fish and fishery production provides a

significant share in the international trade of countries. As China is the main producer and

exporter of fish and fishery products. It is expected that the fish and fishery trade will increase

in the coming years due to climate change and food security. It will further deplete the fisheries

and will accelerate the footprint (FAO, 2016).

5

Due to increasing trend of population and more material resource consumption for luxury

life style and expansion in economic development have threatened the earth’s biocapacity. The

impact of human activities on environment and resource supply measured as biocapacity are

used as indicators for environmental sustainability (Khan & Hussain, 2017 ; Rashid et al.,

2018). The impact of human activities on environment is measured in term of ecological

footprint. It was developed primarily by (Wackernagel and Rees, 1996). It quantifies the

amount of area of land requires to support humanity’s demand for resource consumption and

assimilating residuals of a given population (Jorgenson and Burns, 2007; Knight et al., 2013).

The components of overall ecological footprint consist of cropland, forest, grazing land,

fisheries and built-up land footprint. The cropland, forest and fisheries footprints quantify the

production all crops, forest, fish and seafood products that a country uses. The grazing and

built-up area of footprints measure the area required for grazing of livestock, housing,

transportation, industry and hydroelectric power. It is an environmental impact indicator that

is related to ecological footprint and planet’s biocapacity (Monfreda et al., 2004). The unit

measure of footprint is global hectares (gha). The estimation and calculation of ecological

footprint is based on two main factors (Khan & Hussain, 2017 ; Rashid et al., 2018). Firstly, it

includes and keep record of crops, forest, fisheries, grazing and urban activities and energy

use. Secondly, these resources are converted into area of land for the impact of human activities

on environment.

In the following part of the study, we highlighted some descriptive statistics of developed

and developing countries to strengthen the significance of the study. Table 1.1 indicate that

Australia is the lowest populated country followed by the UK, where China and India are

relatively higher populated countries. However, per capita income of developed countries for

example Japan, UK, USA and Australia is much higher than developing countries and is

obtained through resource consumption and have deficit in their biocapacity. The results also

6

provide a clear message that in near future, particularly high and emerging economies would

increase imbalances in consumption of resources and disrupt the well-being of other regions

of the world. Thus, it was necessary to analyze the trend of urbanization, terms of trade,

husbandry, manufacturing and service activities of these nations.

7

Table 1.1

Averages per capita income, population and ecological

footprint, emissions and Biocapacity 2003-2011

Countries

Per

Capita

Income

in US$

Population

Million

EF CF BC

Biocapacity

Deficit or

Reserve

Number

of

Earths

required

China 3014 1327 2.49 1.5 0.93 -1.56 1.45

India 987 1204 0.91 0.4 0.5 -0.46 0.53

Pakistan 895 165 0.7 0.2 0.4 -0.3 0.40

Japan 37806 128 3.8 2.5 0.69 -3.11 2.21

UK 39848 62 4.15 2.3 1.37 -2.78 2.41

USA 45766 305 6.76 4.5 3.65 -3.11 3.93

Australia 40662 22 8.32 3.6 16.06 7.74 4.84

EF: Ecological Footprint, CF: Carbon Footprint BC: Biocapacity

Source: Global Footprint Network, www.footprint network.org and World Bank Data set

The major contributor to GDP in developed countries is the service sector while agriculture

and manufacturing sectors are the major contributor of GDP in developing nations. However,

the share of exports and imports as a percent of GDP of developed and developing nations.

The figure also shows that trade, population and services sector, the pressure on the resource

consumption of developed nations is more stressful than the developing nations. The trend

comparison of high-middle income countries in population, urbanization, income, coal, oil, gas

and other socioeconomic factors during 2003-11 are depicted in Appendix A.

8

Table 1.2

Averages of export, import, agriculture, manufacturing,

Services and urban population 2003-2011

Countries

Exports

% of

GD

Imports

% of

GDP

Population

in the

largest

city % of

urban

population

Agricultural

land % of

land area TOT

Manufacturing

% of GDP

Services

% of

GDP

China 28.21 24.34 3.04 54.86 6.65 39.64 43.83

India 21.30 25.10 5.71 60.51 13.80 16.05 52.38

Pakistan 13.80 19.33 22.50 46.59 7.55 14.89 53.89

Japan 15.08 14.98 32.12 12.70 5.77 19.26 71.51

UK 27.52 29.94 18.97 71.49 18.46 10.98 77.27

USA 11.82 15.97 7.451 34.78 6.25 12.94 77.71

Australia 19.86 21.13 22.70 54.69 8.61 9.43 69.82

TOT: Term of Trade

Source: World Bank Dataset

It has been projected that the population of the world will be 9.1 billion in 2050, as 34

percent higher than the current population (in 2015 of 6.5 billion). The urbanization will be 70

percent and income will be multiples than from year 2015. However, there will be more

resources required to meet the demand of the increasing population. In 2013, the biocapacity

of the world was 1.73 gha and the ecological footprint was 2.84 gha per capita, represents the

ecological deficit. Thus, there is an ecological deficit of 1.11 gha (FAO, 2016; GFN, 2016a).

The per capita crop footprint of USA and Australia is smaller than their biocapacity and

have biocapacity surplus of 0.4 and 2.2 gha per capita as shown in Table 1.3. The other nations

used their land for agricultural activities and have ecological overshooting. However, increase

in income, population and biocapacity deficit are the key influencing factors of the cropland

footprint in high-middle income countries.

9

Table 1.3

Averages of agriculture, crop footprint and Biocapacity: 2003-2011

Countries

Agricultural land

% of land area

Crop

footprint BC

Biocapacity Deficit

or Reserve

Number of Earths

required

China 54.86 0.51 0.30 -0.21 0.91

India 60.51 0.3 0.20 -0.10 0.53

Pakistan 46.59 0.29 0.28 -0.01 0.51

Japan 12.70 0.51 0.16 -0.35 0.91

UK 71.49 0.84 0.66 -0.18 1.50

USA 44.78 1.10 1.50 0.40 1.96

Australia 54.69 3.00 5.20 2.20 5.35

BC: Biocapacity

Source: Global Footprint Network, www.footprint network.org and World Bank Data set

The average built-up footprint in India, Pakistan and Japan is higher than their biocapacity.

It means that the demand of built-up land for urban population and services will increase in

these countries. It will further increase their environmental degradation as suggested by

biocapacity deficit. The trend comparison of ecological overshoot of high-middle income

countries during 2003-11 are depicted in Appendix B.

Table 1.4

Averages of services, built-up footprint and Biocapacity 2003-2011

Countries

Services %

of GD

Built-up

footprint BC

Biocapacity

Deficit or Reserve

Number of

Earths required

China 43.83 0.11 0.12 0.01 0.19

India 52.38 0.90 0.50 -0.40 1.60

Pakistan 53.89 0.70 0.40 -0.30 1.25

Japan 71.51 0.89 0.85 -0.04 1.58

UK 77.27 0.19 0.19 0 0.33

USA 77.71 0.10 0.20 0.10 0.17

Australia 69.82 0.07 0.08 0.01 0.12

BC: Biocapacity

Source: Global Footprint Network, www.footprint network.organd World Bank Data set

Besides, most of the developed and developing countries are in environmental deficit.

Humanity demand for material resources is more than the earth’s biocapacity. In year 2011,

global ecological footprint was 2.6 gha per capita, exceeding the available biocapcity of 1.7

gha per capita by 53%. The ecological footprint is one of the key indicators for environmental

10

sustainability, because it converts human activities into demand on earth’s regenerative

capacity (Kitzes et al., 2009; Galli et al., 2012). It describes the scenario of a nation’s footprint

by comparing it with biocapacity over time (Galli et al., 2012). The trend of ecological

footprint, its components and biocapacity of high-middle income countries depicted in

Appendix B, shows variation in demand for crop, fisheries, grazing land, forest, built-up land

and CO2 footprint because of differences in their consumption pattern and lifestyle activities

(Kitzes et al., 2009; GFN, 2014; Felix et al., 2016).

Similarly, during 2003-11, the burden of human activity during 2003-11 was greater than

the earth’s biocapacity, as depicted in Appendix B. However, the ecological footprint of high

income countries is far greater than the footprint of middle income countries. Figure 1.1 shows

human demand and comparing the same with earth’s ecological capacity for over the last 40

years. One vertical unit the figure corresponds to the entire regenerative capacity of the earth

in a given year. Human demand exceeds nature’s total supply from the 1970s onwards,

overshooting it by 53% in 2010.

Figure 1.1

Time trend of humanity’s ecological demand

11

The ecological footprint of high and middle income countries since 1960s has shown

inequality because the ecological footprint of high income countries has substantially increased

from 3.62 to 6.39 gha i.e. 76% average increased in its ecological footprint (Galli et al. (2012)).

The ecological footprint of middle income countries has increased from 1.84 gha in 1960 to

2.20 gha in 2005 i.e. 20% average increase in its ecological footprint which showed 40%

inequality between high and middle income countries in demand for ecological footprint (Galli

et al., 2012).

Likewise, they also presented inequality in CO2 and cropland footprint between high and

middle income countries. The CO2 footprint grew from 31% in 1965 to 63% in 2005 and

reduced cropland footprint from 37% to 18% in the same period of high income countries. The

CO2 footprint of middle income countries increased by 15% and cropland land reduced by 31

% during 1965-2005. The inequality between these nations in case of CO2 and cropland

footprint was 17% and 21% respectively, because of transformation from agricultural to

industrial societies and different impact of resources use inequality on its biocapacity supply

(Haberl, 2006; White, 2007; Galli et al., 2012). The inequality comparison of total ecological

footprint with cropland, forest, CO2 and built-up land footprint of 140 countries showed that

CO2 and forest footprint has larger inequality than total ecological footprint. The cropland and

built-up land footprint showed lower inequality than total ecological footprint in period 2003

(White, 2007). The Gini Coefficients of cropland and built-up land footprint were 0.27 and

0.39 respectively, lower than total footprint of 0.45 while the Gini Coefficients of CO2 and

cropland footprint were respectively 0.67 and 0.56, showing 65% and 34 % share for

inequality (White, 2007). The application of inequality through Gini Coefficient showed that

reduction in energy use by the nations that largely depend on energy would not just lead to

increase environmental sustainability but it also reduces inequality particularly the CO2 and

forest footprints. The application of inequality through Atkinson index linked total ecological

footprint with per capita income and environmental impact intensity (White, 2007).

12

The predefined condition for our society and economic development is a healthy planet.

Because, for the consumption of material goods and services indeed requires a clean and

sustainable environment. However, the increasing demand for resources has led to increase

pressure on environment. That is why it is more important to understand whether the humans

are under the Earth’s ecological capacity or not. As it requires 1.5 years for the production and

replenish of resources that are consumed in a single year by human. It implies that humanity

demand leads to generate ecological overshoot in resource consumption where ecological

footprint exceeded the earth’s biocapacity. The major contributor to ecological footprint is CO2

footprint. Its major sources are the fossil fuel consumption, urbanization, luxury life style and

industrialization (Khan & Hussain, 2017 ; Rashid et al., 2018). The CO2 footprint is three

times larger in year 2012 than it was in year 1961. It is argued that the ecological deficit

nations can operate their economic activities with reference to utilizing their own ecological

stock; importing resources from other nations and exploiting common environment by

releasing emissions from the fossil fuel consumption into the atmosphere (Xie et al., 2015).

The ecological overshoot is the result of depleted fisheries, deforestation, and biodiversity

loss and climate changes. As in the last four decades, more than 50 percent of vertebrate and

wildlife declined (Wang et al., 2012; GFN, 2016b; Sim and Park, 2016). It shows that the risk

to the earth’s ecosystem has increased.

These dynamics help us to link the resource consumption with its influencing factors,

because the environmental sustainability and sustainable development are become the targeted

goals by world-wide. This study has addressed issues related to resource usage, economic

growth and ecological efficiency in middle and high income countries. More specifically, this

study responded to questions: what is the trend in resource consumption in middle and high

income countries. Does high economic growth lead to depletion of resources? Does more

resource consumption lead to lower economic wellbeing: GDP? What components of

ecological footprint are used inefficiently? What are the effects of different factors on the use

13

of resources? What is the maximum level of resource consumption to achieve ecological

efficiency? What can be the policy options in light of our results for middle and high income

countries? Is inequality in per capita income, environmental impact intensity and total

ecological footprint and its components in high and middle income countries similar?

14

1.2 Significance of the Study

The significance of this study comes from the fact that the tension between economic

growth and environmental sustainability has become more debatable issue in the world. Over

the past two decades, CO2 emissions of fossil fuel, the consumption of crops, forestry, fish and

fishery products, grazing and built-up lands of high and middle income countries is more than

the earth’s biocapacity. It is due to the increasing trend in economic development, population,

urbanization and industrialization in countries of high-middle income. Therefore, this study

provides a significant contribution in the existence literature by focusing on three main

research questions. Firstly, it focuses on understanding the relationship among ecological

footprint, economic growth and ecological efficiency. Secondly, it tries to compare income

and environmental inequality between high-middle income countries4. Thirdly, it identifies the

driving forces that increase the ecological footprint. The findings will be more policy oriented

for formulating appropriate policies for environmental and sustainable development. This

study will provide a base line for further research in the area environmental economics.

4 This study performed analysis for high and middle income countries and excluded Lower income countries

due to unavailability of data on total ecological footprint, cropland footprint, forest, grazing land footprint,

fishing grounds, CO2 footprint and built-up land footprint for Lower income countries

15

1.3 Objectives

This study aims to meet the following objectives:

1. To examine trends of ecological footprint, economic growth and ecological efficiency

of middle and high income countries.

2. To estimate the ratio between a country’s efficiency in resource utilization and

maximum ecological efficiency of total footprints and its components.

3. To estimate inequality in distribution of income, environmental impact intensity or

ecological efficiency and ecological footprint for the group of middle and high income

countries.

4. To empirically test the impact of various driving forces on total ecological footprint,

cropland, forest, fishing grounds, grazing land, CO2 footprint and built-up land

footprint for High and Middle income countries.

1.4 Hypotheses

The following hypotheses are tested:

1. Fossil fuels coal, oil and natural gas, trade openness and share of manufacturing

goods lead to a higher carbon footprint.

2. Increase in urbanization, economic growth and population would lead to higher

ecological footprint.

3. The share of agriculture items in total export and domestic consumption of

agriculture goods would lead to vast utilization and thus higher footprint.

4. Consumption of animal products and livestock would have a tendency to

depletion of grazing land footprint.

5. Higher employment level, more working hours and services-manufacturing

intensity would lead to significant stress on the built-up footprint.

16

6. Higher expenditure on education would lower the depletion of crop and forest

land footprint.

7. Trend of footprints, economic growth and ecological efficiency in middle and

high income countries is similar.

1.5 Organization of the Study

This dissertation is divided into eight chapters. The background, significance, objectives

and hypotheses of the study have been given in chapter one. Chapter two covered empirical

literature, including the relationship between ecological footprints and economic growth;

ecological footprints and ecological efficiency; growth and ecological efficiency; ecological

footprints and its methodological issues for calculation. The theoretical foundations covering

theoretical perspective of neo-Malthusian economic growth and the environment; perspective

of neo-classical economists; ecological modernization; world system and treadmill production

and export dependence perspectives are discussed in chapter three. Chapter four focused on

the data and methodology, covering ecological efficiency index, environmental impact

intensity, Atkinson index of inequality, empirical specification of various influencing factors

of ecological footprints alongwith developing econometric models. Chapter five discussed

trend analysis of ecological footprints, economic growth and ecological efficiency alongwith

comparing total ecological footprint with its biocapacity and gap between maximum and mean

level of ecological efficiency. Chapter Six presented inequality in per capita income,

environmental impact intensity and ecological footprint through Atkinson index. Chapter

seven analyzed the impact of various driving forces of total ecological footprint, cropland

footprint, grazing land, forest, fishing grounds, CO2 footprint and built-up land footprint

between high and middle income countries. Chapter eight covered summary of the study, major

findings, policy implications and limitations/direction for future research.

17

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter covered empirical literature review classified into nine sections. The section

2.1 covered the introduction of the chapter. Section 2.2 focused on ecological footprint and its

methodological issues of estimation. Section 2.3 explained the relationship between ecological

footprints and economic growth, section 2.4 explained ecological footprints and ecological

efficiency, section 2.5 focused on the relationship between environment and energy

consumption, section 2.6 covered ecological footprint and trade, section 2.7 covered ecological

footprint and work hours and section 2.8 covered growth and energy consumption. Section 2.9

focused on the contribution of the study.

2.2 The concept of ecological footprints and its

methodological issues of estimation

The concept of ecological footprint is primarily developed by Wackernagel and Rees

(1996). It measures the area of land required to produce crop, forest, sea and river fishes,

grazing activities, built-up land for infrastructures and to assimilate CO2 emissions and waste

generated by a region, a nation or society in a given year. This is resource consumption

accounting tool by comparing ecological footprint and biocapacity. The biocapacity deficit is

occurred in case of total ecological footprint greater than biocapacity and reverse will be with

bicapacity larger than total ecological footprint. The unit of measurement of ecological

footprint and biocapacity is global hectares per capita (White, 2007; Daniel et al., 2008; Galli

et al., 2012; Uddin et al., 2017). The ecological footprint used for the analysis of inequality,

as an indicator for environmental sustainability, traced out the relationship between ecological

footprint and human development index for different nations of the world (Alessandro et al.,

2012; Galli et al., 2012; Uddin et al., 2017).

18

The estimation of ecological footprint which is based on the composition of cropland,

forest, grazing land, fisheries, built-up and CO2 footprint, however, has several methodological

issues based on the criticisms elaborated by the researchers for example Ayres (2000); Moffatt

(2000); White (2007); Daniel et al. (2008); Fiala (2008); Galli et al. (2012); Wim and Luc

(2014); Galli et al. (2016). They argued that the area of land required to support humanity

demand and to assimilate CO2 emissions and waste generated by human activities based on

various data sources like production statistics, World Food Organization, World Bank,

direction of trade statistics and other trade accounts of each nations and different energy

consumption statistics. This means the calculation of ecological footprint combines different

sources of data in one indicator. However, a single indicator may receive overestimation of

resources or double counting (Wim and Luc, 2014; Galli et al., 2016).

The second methodological issue arises in form of excluding the area of deserts and icecaps

land when calculating ecological footprint which leads to create biasness. There are many

examples where indigenous population live for livestock and mining activities in desert, arid

and semi-arid land. The third methodological issue is related to the fields forest, cropland,

grazing and fisheries areas where its footprint is hypothetical than actual. The accurate

estimation of its ecological footprint are hard. In particular, the various productive concepts of

land are not considered in case of ecological footprint estimates. For example, land used for

transport purposes, urbanization and mining is larger than the land used for grazing, forestry

and crop activities. Therefore, instead of accurate productivity, the earth’s average productivity

is not appropriate for estimating ecological footprint.

The fourth methodological issue is related to CO2 emissions. The ecological footprint

account only includes CO2 emissions generated from energy use. This does not include

emissions generated from other greenhouse gases and other sources like industrial process,

waste and nuclear power plant. However, Ivan and Anna (2005); Galli et al. (2012); Galli et

al. (2016) argued that the methodology of ecological footprint estimation proposed by

19

Wackernagel and Rees (1996) can be critiqued on the basis of 1) the difficulty in the surface

of area to the resources come from the sea; 2) assign average global productivities to the

components of ecological footprint instead of considering the land condition; 3) the constant

technology assumption for resources extraction; and 4) the biasness in case of CO2 emissions

only included from fossil fuel consumption.

Li et al. (2010) however, argued that the ecological footprint and biocapacity estimated by

the Global Footprint Network for globe, regions, nations and cities at a point in time as

annually. It underestimates the dynamic perspective of future resources. They further argued

that the ecological footprint account does not take into account the role of increasing future

consumption and change in technology. Because it constantly depends on the assumption of

constant technology. As for example it is stated that it would require 5 earths if everyone follow

the consumption pattern of America. It has not included that the importance of technological

change in technology and resource efficiency. Similarly, it found that the increasing

consumption pattern of developing countries could follow developed countries and before such

growth in consumption the technological change would be expected to occur. The account of

ecological footprint doesn’t answer such changes. Regarding built-up land footprint, it is

assumed that it occurs mostly in productive land. However, Valada (2010) argued that it is

expected particularly in case of Middle East and Asia where urbanization take place in arid

non-productive land. It, therefore creates biasness in ecological footprint account.

There are many other methodological issues in the ecological footprint estimation for

example yield productivity of products, arid and non-arid land, conversion of resources to

global hectare, the issue of equivalence factor of cropland, forest, grazing land and built-up

land. However, Galli et al. (2013); Galli et al. (2016) argued that the methodology issues in

ecological footprint estimation ignore the role of non-renewable fossil energy stock depletion

and monocultures in present increase of agriculture productivity.

20

Similarly, the choice of production and society’s lifestyles affects both the distribution and

use of the resources. The ecological footprint account ignored such possibilities on ecological

footprint demand, which promotes biasness in policy implementation (White, 2007; Daniel et

al., 2008; Galli et al., 2012). However, every measure has some merits and demerits. The

ecological footprint is not without methodological issues but it is still well suited for analysis

of various issues. For example how resources are distributed between different regions of the

world, comparison between biocapacity and ecological footprint by White (2007). The current

study estimates the trend of ecological footprint, economic growth and ecological efficiency

for high-middle income countries by using panel dataset for period 2003-2011 because

previous studies addressed hardly the trend analysis between these regions.

2.3 Ecological footprint and economic growth

In the field of environmental economics, researchers in the past few decades, especially

examining the relationship between economic development and ecological footprint. They

found positive, negative and even insignificant impact of economic development on footprint.

The studies like Dietz et al. (2003); Jorgenson and Burns (2007); Turner (2008); Jill et al.

(2009); Clark and Jorgenson (2011); Knight et al. (2013); Al-Mulali et al. (2015); Asici and

Acar (2016); Uddin et al. (2017) found the positive relationship between growth and footprint.

Jorgenson and Burns (2007) found that level of urbanization and service based activities

are major factors for a positive association between growth and footprint. These factors

increase demand for the crop, forest, animals and sea products as well as in industrial

production. It adds more consumption based environmental impact. However, urbanization is

the more responsible factor for environmental degradation. It increases energy usage and CO2

emissions (Cole, 2004; Cole and Neumayar, 2004; Jill et al., 2009; Jorgenson et al., 2010;

Kaneko and Poumanyvong, 2010; Al-mulali et al., 2013; Behera and Dash, 2016; He et al.,

2017). Jill et al. (2009) found that increasing use of energy in different sectors is responsible

21

reason for positive relations. Because, it produces CO2 emissions to environment in large

volume. Reduction of energy consumption causes to cut 50% CO2 footprint and yields non-

significant effect of growth on ecological footprints. Andrew and Brett (2011) added that the

military expenses and level of urbanization are responsible factors in case of positive nexus of

economic growth and ecological footprints. They over use the global environmental space,

demand for the production of armaments industries and built-up land.

Studies like Anders and John (2009); Knight et al. (2013) came to know that longer work

hours and employment to population ratio in business, agriculture, industries and mining leads

to generate a positive and significant association of economic growth with total ecological and

CO2 footprints. Some studies further added that the export factor is a factor which enhances

environmental degradation with economic growth. Because, export-related activities utilize

crop, forest, grazing, forest and therefore CO2 emissions occur (Jorgenson and Burns, 2007;

Jorgenson, 2009). However, the studies likeMostafa (2010); Yong et al. (2013); A. Usama et

al. (2014) found a negative association between footprint and growth. Mostafa (2010) on the

ground of inadequate distribution of income, argued that it increases environmental

degradation, generating negative association between growth and environmental impact

indicator i.e. total ecological footprint. Yong et al. (2013) found that due to trade openness,

capital inflow and different environmental policies contribute to bring variation in resource

usage and further economic growth, thus the negative association arises between growth and

ecological footprint. A. Usama et al. (2014) found negative effect of growth on footprint and

such result arises due to well-developed financial sectors and improvement in environmental

quality through environmental friendly technology. It reduces environmental degradation and

enhances economic growth. However, that does not mean that technology change only causes

negative association as Marco et al. (2008) pointed out that foreign direct investment through

dirty production process is responsible factor in this respect. Because clean environmental

22

technology reduces environmental degradation on one side, while foreign direct investment

increases both resource consumption and economic growth on the other hand.

The studies, likeKaneko and Poumanyvong (2010); Liddle and Lung (2010); Perry (2014)

pointed out that urbanization and urban density contributes to improve urban utilities which

lower energy usage and environmental degradation. The main focus of these studies based on

the cross sectional and panel dataset for total ecological footprint. They focused hardly on the

impact of various driving forces of the total ecological footprint and its components. As Uddin

et al. (2017) examined the impact of real income, financial development and trade openness

on the total ecological footprint, using the 27 highest emitting countries panel dataset for the

period of 1991-2012. They argued on the basis of empirical findings that the acceleration in

financial development and trade openness impact negatively on the total ecological footprint

while the growth in real income increases the footprint. They argued that real income growth

depends on the exploitation of domestic natural resource and eco-services. Therefore, an

increase in real income leads to increase the material resource and consequently increases the

ecological footprint. Asici and Acar (2016) argued that the environmental Kuznets curve

hypothesis validates for the relationship between income and ecological footprint of

production. However, when countries become rich, they export the ecological cost of their

consumption to poorer economies. Al-Mulali et al. (2015) argued that the validity of the

environmental Kuznets curve hypothesis depends on the status of the technologies in the

economic system. Technologies that promote energy efficiency, energy saving and renewable

energy support the hypothesis. Toth and Szigeti (2016) argued that the channel through which

the ecological footprint accelerated is over-consumption of material goods and services. It

accelerates the fossil fuel consumption and consequently emits the greater CO2 in atmosphere.

The growth in ecological footprint increased after industrial revolution. They further argued

that the environmental degradation is not population, but consumption pattern in developed

23

countries. The studies of Galli et al. (2016) are also supported the arguments. However,

Lanouar (2017); Mrabet and Alsamara (2017) on the empirical findings argued that the nexus

between ecological footprint and economic growth holds the U-shaped behavior. Since the

inclusion of trade openness, urbanization, electricity consumption and financial development

are worsening to ecological footprint and CO2 footprint. The results show that the improvement

in energy efficiency will decouple the nexus between ecological footprint and economic

growth. Therefore, the current study empirically estimates the association between total

ecological footprint, cropland, and forest, fishing grounds, CO2 and built-up land footprint with

economic growth for high-middle income countries by using panel dataset.

2.4 Ecological footprint and ecological efficiency

An ecological footprint is an environmental impact indicator of resource consumption,

where, ecological efficiency is the ability to produce economic output by using less resource

inputs and waste output. The various factors that influence the relationship between resource

consumption and ecological efficiency/environmental impact intensity. Dietz et al. (2003)

found that increase in affluence leads to increase ecological efficiency, but it doesn’t lead to

explain that environmental sustainability has been achieved. Because, a greater increase in

income leads to increase resource demand and consequently increase environmental damage

alongwith efficiency in resource utilization. Anders (2009) on the basis of longer work hours

increase economic output, the resource consumption and environmental impact intensity also

to grow. While, good management skills lower footprints and increase ecological efficiency

even in longer working hours. However, some studies York (2006, 2010) found that

improvement in technology increases ecological efficiency as well as consumption of

resources. York et al. (2009) found that increase in ecological efficiency is associated with an

increase in total ecological footprints and the availability of cheaper technology. This increases

the extraction of resources and environmental degradation. Due to limitation of panel dataset,

24

the main focus of these studies was on cross sectional dataset which led to generate biasness

because the economic structure varies from region to region and hence lead to different demand

for ecological footprint and ecological efficiency. Lu and Chen (2017) on the basis of the

empirical findings suggest that the ecological footprint can be more stable and ecological

efficiency can be improved in case of the steady consumption pattern and environmental

mitigation policy. Szigeti et al. (2017) examined the relationship among ecological footprint,

income and ecological efficiency for the year 2009. On the basis of empirical findings, it is

argued that many countries have increased the income and reduced the ecological footprint and

consequently improve the ecological efficiency. Ninety percent countries started to move in

the direction of sustainable development. Lanouar (2017)argued that the driving forces behind

the environmental improvement in the long-run are the economic development, urbanization

and life expectancy. They used the ecological footprint as a proxy for environmental

degradation. The current study, however, empirically estimated the trend of ecological

footprint, economic growth and ecological efficiency between high and middle income

countries through panel dataset analysis.

2.5 Environment and energy consumption

Researchers have conducted different studies in developed and developing countries

regarding the nexus of the economic growth and environment, through the EKC hypothesis.

The increasing trend of climate change, environmental degradation and resource use have

attracted the social scientists, for example Soytas et al. (2007); Ang (2008); Jalil and Mahmud

(2009); Jalil and Feridun (2011); Ilhan and Ali (2013); Gulden and Mehmet (2014); A. Usama

et al. (2014); Apergis and Ozturk (2015); Boluk and Mert (2015); Jebli and Youssef (2015);

Tutulmaz (2015); Li and Zhao (2016); Charfeddine and Mrabet (2017); Lanouar (2017) to

investigate the relationship between environmental quality and energy consumption. A. Usama

et al. (2014) estimate the impact of energy consumption on ecological footprint. They found

25

the positive association between energy consumption and ecological footprint. They argued

that due to the strong financial development, on which one side promotes more foreign direct

investment and on the other hand, trade openness and urbanization lead to increasing the

economic growth. The net effect of these activities increased demand for energy consumption

and the level of ecological footprint. This argument was also supported byJalil and Mahmud

(2009); Jalil and Feridun (2011); Ilhan and Ali (2013). They used CO2 as an environmental

impact indicator. However, Claessens and Feijen (2007); Tamazian et al. (2009) found the

negative association between environment and energy consumption. They argued that energy

efficiency and enterprise performance is increased by financial development and therefore

energy is negatively affecting CO2 emissions. Zhang and Cheng (2009) argued that the

technological changes increase the energy efficiency and reducing the CO2 emissions. Their

empirical estimates suggest that energy consumption is negatively related to CO2 emissions

for China’s economy. The literature of Pachauri and Jiang (2008); Dodman (2009); Liu (2009);

Lin and Liu (2010); Liu (2012); Zhu and Peng (2012) argued that due to increase in public

infrastructure efficiency such as public transportation, reduces energy consumption and CO2

emissions. However, the other side literature argued that because of low energy efficiency, low

level of energy saving policies and an absence of environmental awareness are generating

positive association between energy consumption and environmental degradation. Shi-Chun et

al. (2016) explored how urbanization generates a positive association between energy

consumption and CO2 emissions. It is found that due to huge pressure of public infrastructure

energy consumption is positively related to CO2 emissions and environmental degradation.

However, the study like Gulden and Mehmet (2014) tested the theory of disaggregation of

energy consumption into fossil and renewable energy consumption on environment used CO2

emissions as a proxy for environmental degradation. They argued that the relationship between

CO2 emissions and energy consumption takes the form of an invert-U shape. The findings did

26

not support the EKC hypothesis because of mix energy consumption technology rather than

renewable energy technology and lack of strong regulatory policy for the reduction of CO2

emissions. Most of them did not support the EKC hypothesis when using total ecological

footprint as an environmental impact indicator and focused on cross-sectional dataset. Lanouar

(2017) argued that growth in energy consumption increases the environmental degradation

particularly in the oil-exporting countries. Charfeddine and Mrabet (2017) argued that the

nexus between environment and economic development holds the environmental Kuznets

curve hypothesis due to the electricity consumption and financial development are negatively

affecting the environment. The author used the CO2 footprint as a proxy for environmental

degradation. Boluk and Mert (2015); Li and Zhao (2016) argued that due to the trade openness,

the impact of energy consumption on the CO2 emissions is positive. As economy is more open

in term of trade, acceleration in transportation and industrial activities increase the demand of

the fossil fuel. It consequently increases CO2 emissions. They further added that, besides, fossil

fuel, promote energy efficiency and near location of export industrial zones minimize the

environmental degradation. This result is also supported by the findings of Jebli et al. (2016)

in case of the OECD countries. They argued that to compete with the environmental

degradation, the efficient strategies are more trade and more use of renewable energy

consumption. However, Bilgili et al. (2016) investigated the impact of economic development

and energy consumption on the environment, using the CO2 emissions as a proxy for the

environmental degradation. They argued that the energy consumption from renewable sources

and improvement in energy efficiency are the responsible factors behind the negative impact

of energy consumption on the environment. The implication of renewable energy consumption

in different sectors of an economy decreases the impact of economic development on the CO2

emissions.

27

Boluk and Mert (2015) investigated the impact of renewable energy and economic growth

on the CO2 emissions. They argued that the implication of energy consumption from renewable

sources effects CO2 emissions negatively. However, further economic development based on

the use of renewable energy consumption increases CO2 emissions. Therefore, the net effect

in a country case is ambiguous. Kang et al. (2016) argued that the urbanization and the coal

combustion are the most important elements in which CO2 emissions have increased. The

urbanization increases demand for material goods and services while the coal consumption due

to its lower price and abundant volume increases the environmental degradation. Therefore,

the expansion of urbanization and trade has become the harmful determinants to the

environment in case of some high-middle income countries. In order to limit CO2 emissions,

energy needs renewable energy and efficiency. This is supported by the findings of Ahmed

and Azam (2016); Ahmad et al. (2017) that in order to achieve higher economic growth and

minimize the environmental degradation requires the replacement of environmentally friendly

technology. However, the environmental awareness and environmental regulations can reduce

the degradation of the environment and mitigate the climate changes (Ozokcu and Ozdemir,

2017).

Ali et al. (2016); Ali et al. (2017) claim that foreign direct investment via transformation

of dirty technology to middle income countries from high income countries increase the CO2

emissions instead of reduction in it. During the period of 1971-2012, they investigated the

environmental Kuznets curve hypothesis in Malaysia context. The reduction in environmental

degradation is not only from the reduction in output but the increase in environmental

regulations activities (Apergis et al., 2017). The current study empirically estimated the

relationship between energy consumption with total ecological footprint and CO2 footprint as

environmental impact indicators for high and middle income countries separately.

28

2.6 Ecological footprint and trade

With the current increasing trend of environmental degradation and the nexus between

trade and environment, much attention was given to the field of environmental economics

where like Jorgenson and Rice (2005); Rice (2007); Anders and John (2009); Jorgenson and

Clark (2011) conducted the impact of trade on the environment by using different indicators

as environmental degradation. While some previous studies, like Tobey (1990); Low and

Yeates (1992); Beghin and Poitier (1995); Jaffe et al. (1995); Strutt and Anderson (1999);

Dean (2002); Copeland and Taylor (2003); Hossain (2012); Ilhan and Ali (2013); Shahbaz et

al. (2013a); A. Usama et al. (2014); Summaiya et al. (2015); Farhani et al. (2016); Halicioglu

and Ketenci (2016); Rudolph and Figge (2017) investigated the environmental impact on trade

pattern in case of developed and developing countries and found different outcomes.

The studies, like Beghin and Poitier (1995); Strutt and Anderson (1999); Mohammad et al.

(2012); Rubaiya (2012); Summaiya et al. (2015) argued that trade liberalization leads to

enhance environmental degradation while the findings of Dean (2002); Cole and Elliott (2003);

Cole (2004); Derek (2008); Eunho et al. (2010) argued that environmental degradation is

negatively associated with trade. However, from the last few decades the environmental

sociologist conducted the ecological footprint-trade nexus after the introduction of ecological

footprint as consumption based environmental impact indicator. They tested the ecologically

unequal exchange hypothesis that due to favorable term of trade and disproportion flow of raw

material and natural resources consumption from less developed to more developed countries

leads to increase the environmental degradation in the form of greater use of the total ecological

footprint. To test the hypothesis, Jorgenson and Rice (2005); Rice (2007) used weighted index

of vertical flow of export from less developed to developed countries and found a negative

association between export and ecological footprint.

29

Similarly, Jorgenson and Rice (2005); Jorgenson (2009), empirically tested the export

dependency perspective, using the value of export as a percentage of GDP and found a negative

impact on the total ecological footprint. However, these findings support the ecologically

unequal exchange and export dependency perspectives that export is negatively related to the

ecological footprints. The studies, like Anders and John (2009); A. Usama et al. (2014) found

a positive association between ecological footprint and export because of inclusion of other

determinants like working hours per employee, military expenditure, service intensity, energy

consumption and urbanization of environmental degradation. However, Hossain (2012) argued

that the energy consumption and trade openness have unidirectional effect on CO2 emissions.

Ilhan and Ali (2013) examined the causal relationship between financial development, trade,

economic growth and carbon emissions. The findings suggested that as a share of trade in GDP

increases, it increases the demand of energy use and consequently increases the CO2 emissions.

Shahbaz et al. (2013a) argued that trade openness improves the environmental quality due to

the application of environmentally friendly technology. They further argued that the

combination of higher degree of financial system development and trade openness through

technique effect can increase environmental quality. Shahbaz et al. (2013a); Shahbaz et al.

(2013b); Boutabba (2014) argued that the financial development and economic growth

increase the trade openness and therefore increases the environmental degradation particularly

in case of developing countries. The developing countries are mainly depended on application

of dirty, polluting technology in their industrial sectors. Farhani et al. (2014) investigated the

dynamic relationship between the environment, economic growth, energy consumption and

trade openness. There is bi-directional relationship between the variable and the unidirectional

relationship exists between economic development and energy consumption to the CO2

emissions. Halicioglu and Ketenci (2016) investigated the nexus between environment and

trade openness.

30

The empirical findings support the environmental Kuznets curve and displacement

hypotheses. The higher environmental polluting countries are trying to reduce environmental

degradation by exporting the energy intensive goods while the transition countries for the

acceleration of economic growth are exporting the energy intensive products. Thus, the

environmental degradation is displaced from one region to transition region. Rudolph and

Figge (2017) examined the environmental consequences of globalization, by using the

ecological footprint as a consumption-based environmental impact indicator. They used the

economic globalization, social and political globalization indices of globalization. Economic

and political globalization has increased ecological footprint, they added. It is because of

increasing demand for energy and material goods and services. Social globalization improves

environmental sustainability due to the better coordination among different society groups.

The newly industrialized countries are trying to accelerate their trade openness and therefore

greater demand for energy consumption. All these lead to increase the environmental

degradation. All these studies limited to the nexus between trade and total ecological footprint

and hardly focused on the nexus between trade, total ecological footprint and its components.

The current study, however, empirically estimated the impact of trade on total ecological

footprint and CO2 footprint by using a panel data set for high and middle income countries

separately.

2.7 Ecological footprint and work hours

The scholars investigated the association of working hours with varieties of issues like

employment, income inequality, and time preference, consumption of goods and services and

well-being. The economists, particularly, have drawn many conclusions by taking into account

the influences of other factors, for example wages, length of time, location, urbanization,

education in case of association between environment and working hours (Knight et al., 2013).

Bowles and Park (2005); Oh et al. (2012) linked working hours with income inequality,

31

individual income and preferences where they argued that working hours is negatively

associated with income inequality while positively related to individual income and

expenditure that leads to enhance well-being. There are also studies ofReynolds (2004);

Galinsky et al. (2005); Otterbach (2010) who investigated the relationship between work time

preference and actual working time. Similarly, Alesina et al. (2005); Pouwels et al. (2008)

explored the association between working hours and well-being where they argued that longer

working hours lead to lower happiness in the case of Europe and America because of reduction

of free time for other activities. However, many Scholars are currently investigating the

association between working hours and environment, particularly by using ecological footprint

and CO2 emissions as environmental impact indicators (Knight et al., 2013). David and Mark

(2006) explored how the working hours are positively associated with energy consumption and

environment degradation, where they argued under controlling employment to population

ratio, labor productivity and population that, increase in working hours for production lead to

more demand for energy consumption and consequently increases environmental degradation.

The studies of Joliet (2005); Robinson (2006); Lajeunesse (2009); Coote et al. (2010)

argued that the reduction in working hours, meaning reduction of total paid up labor class leads

to reduce consumption of goods and services and then consequently reduce the environmental

degradation. Studies like Anders and John (2009); Knight et al. (2013) explored how working

hours are positively related to ecological footprint, CO2 footprint and CO2 emissions by

relaxing the restrictions of employment to population ratio, labor productivity, where they

argued that increase in working hours increase environmental degradation because of the

increasing demand for employment and labor productivity which further increases production

and demand for energy consumption and working hours. However, due to favorable term of

trade and export of inputs to more developed countries from less developed countries leads to

more environmental degradation even in case of lower working hours Anders and John (2009).

32

However, Anders and John (2009) further argued that the impact of working hours is

positively related to environmental degradation both developed and developing countries

because of larger extraction and consumption of total ecological and CO2 footprint. Juan and

Jordi (2013) compared the impact of two scenarios 1) by keeping current work hours constant

2) and reducing work hours on environment, where it found that second scenario significantly

lower the growth rate of income and energy consumption and consequently reducing CO2

emissions. Kopidou et al. (2017) investigated the common trend and drivers of CO2 emissions

and employment before and after the start of economic crisis, 2000-2007 and 2007-2011,

respectively. They argued that the driving forces behind the environment and the employment

are the economic growth and resource intensity. The higher the economic growth and resource

intensity, the higher the CO2 emissions and employment. These studies based on cross-

sectional dataset while working hours for high and middle income countries showed

differences over time. They have hardly estimated the impact of working hours on ecological

footprints for high and middle income countries separately. Therefore the current study filled

such gap by using panel dataset.

2.8 Growth and energy consumption

The debate over the energy consumption in the process of economic development was

obtained to focus the attention of last few decades to the present time, where the literature of

Chontanawat et al. (2006); Lee (2006); Soytas and Sari (2006); Climent and Pardo (2007); Lee

and Chang (2007); Mahadevan and Asafu (2007); Soytas et al. (2007); Chiou-Wei et al.

(2008); Huang et al. (2008); Jia-Hai et al. (2008); Lee and Chang (2008); Narayan and Smyth

(2008); Bowden and Payne (2009); Nicholas and James (2009); Payne (2009a); Costantini and

Martini (2010); Lee and Lee (2010); Payne (2010b); Ansgar et al. (2011); Ahdi et al. (2013);

33

Ertugrul et al. (2014); Bernard (2015); John et al. (2016) obtained different association

between energy consumption and growth.

However, it is argued that the growth-energy consumption follows the growth,

conservation, neutrality and feedback hypotheses, where the use of energy leads to increase

economic growth as argued by the supporters of growth hypothesis. The conservation

hypothesis argues that the implementation of conservation policies for the reduction of energy

consumption and waste don’t lead to adverse effect on economic growth due to other factors

like political instability, lack of proper infrastructure and particularly mismanagement of

resources lead to increase inefficiency. The neutrality hypothesis argues that the impact of

energy consumption on economic growth is insignificant both in short and long run while the

feedback hypothesis has the views that unidirectional causality from energy consumption to

economic growth is presented in short run while the bidirectional causality is presented in long

run between energy consumption and economic growth because the policies for the energy

efficiency does not lead to adverse impact on economic growth (Squalli, 2007; Ansgar et al.,

2011; Nasir and Rehman, 2011; Apergis and Ozturk, 2015; Ahmad et al., 2017).

34

2.9 Contribution of the study

From the last few decades overexploitation of fisheries, crops, grazing land, livestock and

pollution to fresh water by industries and urbanization increased the ecological footprints and

consequently declined vertebrates population by 58% and marine fishes by 36% of the globe

(GFN, 2014,2016). It implies that the earth is in the age of ecological overshooting where earth

footprint is 54 % greater than its biocapacity. Majority of high income countries and emerging

economies for example China and India have six times larger per capita footprint than the globe

biocapacity of 1.7 gha due to increase use of fossil fuels and energy intensive goods and

services (GFN, 2016a). This implies that high and middle income countries have put

disproportionate pressure on nature while low income countries are trying to meet its basic

needs. Similarly the world’s ecological footprint reached to 1.7% in 2011 and china’s footprint

was 2.5 gha in the same year larger than its biocapacity of 0.9 gha (GFN, 2015). The high

economic growth of China achieved through more consumption of natural resources where its

total ecological footprint reached to 3.9 billion hectares in 2011 while it was 2.6 billion hectares

in 1997, increased by 51% (Wei et al., 2015). Similarly high income countries externalized

their environmental degradation and consequently suppressed well-being and quality of life of

low income countries. The ecological footprint of high and middle income countries was 5 and

2.6 global hectares per person and their biocapacity was respectively 3 and 2.3 global hectares

per person followed by 2 and 0.3 global hectares per person deficit (GFN, 2014, 2015). All

this shows that if everyone follows the consumption pattern and lifestyle of high income

countries, it would require 3 earths and 1.5 earths when follows the lifestyle and consumption

pattern of middle income countries (GFN, 2015). In future the imbalances between demand

and supply of resources as depicted in Appendix B pronounced in different regions of the

world, and rising population, income, urbanization, fossil fuel consumption could lead to

increase demand for cropland, fisheries, grazing, forest and CO2 footprints while climate

change and resource scarcity would disrupt the biocapacity of the globe.

35

The present study empirically contributed from three aspects in the literature of ecological

economics. Firstly, as previous studies have hardly differentiated trend in ecological footprints,

economic growth and ecological efficiency, while in this study, we estimated trend, ecological

efficiency index and the gap between maximum and mean level of ecological efficiency in

resource use, which provided policy guidance regarding environmental degradation to people,

planners, researchers and students who are engaged in environmental field. Secondly, the

existing literature hardly estimated inequality in total ecological footprints and its components,

while in this study; we estimated inequality in the distribution of income, footprints and

environmental impact intensity and highlighted the role of income and environmental intensity

in the computation of the total ecological footprint and its component. Thirdly, the previous

literature of social scientists, environmentalists, sociologists and even ecologists tested

Environmental Kuznets Curve hypothesis and argued that changes in economic growth,

population, political and other socioeconomic factors of different regions could lead to taking

variation in resource use and consequently take environmental variation of the globe.

Based on such argument the current study hasn’t only estimated the EKC5 relationship but

also estimated the ecological overshooting and empirically tested the impact of various driving

forces of the total ecological footprint and its component. Thus the relevance of the current

study is that it provided a guide line to academicians, NGOs, urban planners and governments

regarding inequality, trend of biocapacity, ecological overshooting, socioeconomic variables

and the impact of various driving forces of the total ecological footprint and its component in

case of high and middle income countries.

5 Environmental Kuznets Curve describes environment-growth nexus, where environmental degradation at the

initial stage of economic development increases up to a point, reaches a peak, and then declines with further

economic development (Knight et al., 2013).

36

CHAPTER THREE

THE THEORETICAL BACKGROUND

3.1 Introduction

The expansion of the modern world system that resulted in rapid technological growth has

tightened the impact of factors including population, economic growth, urbanization, market

expansion, industrialization on resource consumption and environmental degradation came

forward from natural resource consumption. Natural resources are generally grouped into two

major categories: renewable and non-renewable natural resources. Renewable resources are

those resources that are capable of regenerating themselves within a relatively short period,

provided the environment in which they are nurtured is not unduly disturbed, such as plants,

fish, forests, soil, solar radiation, wind, tides, and so on (GFN, 2014,2016). Non-renewable

resources are resources that either exist in fixed supply or are renewable only on a geological

time scale, whose regenerative capacity can be assumed to be zero for all practical human

purposes. These resources include metallic minerals like iron, aluminum, copper, and uranium;

and non-metallic mineral like fossil fuels, clay, sand, salt, and phosphates. Human economy

depends on the natural environment for factor of production; assimilate waste and consumption

of amenities as depicted in Fig.3.1:

Figure 3.1

A circular flow of factors of production, environment and economy

37

The next section elaborated the theoretical approach to identify the channels through which

various factors are influencing the ecological footprint/environment of globe and an empirical

literature was followed by chapter 4 of the study.

3.2 The theoretical perspective of Neo-Malthusian:

economic growth and environment

The Malthusian theory has been focusing on the relationship between population,

environment and natural resources (Ahmed, 2004). Malthus predicted severe food shortages

as the population grows geometrically and food grows arithmetically. Malthus focus was only

on food and population, but with the passage of time the Malthusian theory has confronted

with several refinements over time. The neo-Malthusians due to criticisms raised by both

economists and ecologists on Malthusian theory developed their conceptual model

incorporating population, resources, and technology along with human institutions for

environmental sustainability. The theory of neo-Malthusian argued that increased human

activities would lead to increasing stress on the functioning of the environment and in so doing

ultimately lead to environmental degradation. This outcome could arise either from generating

too much waste into the environment or exploiting resource consumption, such as overfishing,

large-scale deforestation, grazing and urbanization. If these outcomes are not controlling then,

it will eventually place bounds on the growth of human activity. In order to incorporate the

fundamental positions of neo-Malthusian regarding the key determinants of environmental

degradation, the (Commoner et al., 1971; Ehrlich and Holdren, 1971) is introduced as:

𝐼 = 𝑃 ∗ 𝐹 − − − − − (3.1)

Here I is the total environmental effect or damage, measured in some standard units. It can

be expressed in a variety of ways, such as overfishing; deforestation and the amount of waste

discharged into the environment yearly. The variable P is population and it is assumed that

more masses lead to more environmental damage i.e.𝜕𝐼 𝜕𝑃⁄ > 0.

38

The variable F is index that measured the per capita environmental impact and can be

expressed

𝐹 =𝐼

𝑃− − − − − (3.2)

The index F is per capita ecological footprint and this is a very important variable and

provides interesting insights when it is discussed in combination with other variables, such as

per capita consumption or income, and the technology by which inputs and outputs are

processed (Ahmed, 2004).

The equation (3.1) exactly states that total environmental impact equal population

multiplied by average impact that each person has on the environment. The full insight of this

equation can be obtained when examining various effects of other variables on the ecological

footprint of an average person. Therefore per capita ecological footprint has expressed by

Ahmed (2004) as:

𝐹 = 𝑓 (𝑃, 𝑦, 𝑔) − − − − − (3.3)

Where y is per capita income GDP of a nation at an aggregate level is expressed as

𝑦 =𝑌

𝑃− − − − − (3.4)

The general assumption regarding variable y in eq. (3.3) states that holding other factor

constant increase in per capita income leads to not just consumption of goods and services but

also to ecological footprint i.e.𝜕𝑓 𝜕𝑃⁄ > 0.

Ehrlich and Holdren (1971) argued that environmental impact increases for two reasons.

First, the size of the population will increase. Second, the impact on the ecological footprint of

more people will also increase. The argument is that if another factor held constant, the

successive addition of people would need increasing use of resource consumption, such as

forest, water, grazing land, energy and other renewable and non-renewable resources. Thus,

by adding more population the per capita impact in term of ecological footprint and

39

environmental degradation would increase successively. Ehrlich and his followers would

contend that rising human population is the predominant factor in accelerating pollution and

other resource problems. According to this model, the impact of population growth on the

environment has primary and secondary impacts in the same direction – suggesting that the

negative impact of population growth is far greater than what it may appear to be when only

factors associated with the primary impact are considered (Fig 3.2).

Figure 3.2

A graphical illustration of Ehrlich’s model.

The channel through which the relationship between change in per capita income (GDP/P)

and ecological footprint is also explained by Commoner et al. (1971); Ehrlich and Holdren

(1971). If other things held constant, an increase in per capita income would change, the

consumption of goods and services. It would increase ecological footprint and consequently

increase environmental damage i.e.𝜕𝑐 𝜕𝑦⁄ ,𝜕𝑓

𝜕𝑐⁄ > 0. Increase in per capita consumption has an

effect on the environment that is independent of population increases as depicted in Figure 3.3.

40

Figure 3.3

Per capita consumption and its effect on the environment.

In the Ehrlich–Commoner model the effect of other factors like technology is captured by

the variable g (Equation 3.3). The effect of technology on ecological footprint is positive due

to inappropriate applications of modern technologies in the extraction, production and

consumption sectors of the economy. This is because technological choices are often made

purely on the basis of profitability considerations rather than environmental sustainability.

In general, the Ehrlich–Commoner model suggest that neo-Malthusians would tend to

claim that the steady increases in population and per capita consumption and the proliferation

of products that are harmful to the environment are the three major factors contributing to

continued global environmental degradation.

3.3 The theoretical perspective of neoclassical economists:

economic growth and environment

The neoclassical economists Grossman and Krueger (1995) and other including the World

Bank (1992) pointed out the role of economic growth in generating pollution/CO2 emissions

through three possible outcomes. The first was an increase in scale of current production, the

second regarding a change in composition of current production and the third consists of a shift

in production techniques. The first factor naturally leads to more pollution in the face of

41

economic growth those results from free trade. The second has ambiguous effects in any

particular country, but could not result in a reduction in pollution everywhere. This leads to

the possibility of pollution. Only the third factor points to the possibility of lower pollution

levels being associated with economic growth (Anders and John, 2009).

Neoclassical theory suggests that the relationship between economic growth and

environmental degradation follow an inverted U-shaped pattern similar to that found by

economist Kuznets (1955) for income inequality. The idea of an Environmental Kuznets Curve

(EKC suggests that as countries modernize, they first pass through a dirty stage marked by

growing ecological impacts; however, they eventually gain the capacity to solve environmental

problems generated by the development process. As countries grow more affluent, increases

in both demand for better environmental conditions and supply of resources that can be devoted

to solving environmental problems (Trainer, 1990; Komen et al., 1997; Anders and John, 2009;

Aslanidis and Iranzo, 2009).

Figure 3.4

The environmental Kuznets curve

42

The vertical axis measures increasing levels of environmental damage. Part A of the figure

3.5 suggests that after a country attains a certain level of per capita income I0, increased income

is associated with lower environmental damage or higher environmental quality. Part B of the

figure suggests that the positive association between income growth and higher environmental

quality does not hold indefinitely. Beyond income level I1, increase in income would lead to

increasing deterioration of the environment.

3.4 Ecological modernization perspective

Over the last two centuries the effect of modernization in form of industrialization,

economic growth, urbanization, trade openness and technological advancement on the globe

is unique in the human history. And there are two side's opinions of the consequences of

modernization on the globe. On one side are those who pointed out that modernization is anti-

ecological and incapable to maintain sustainability. Scholars of this side like neo-Marxian and

Human ecological perspective argue that modernization for the system of capitalism will lead

to environmental degradation when the developed nations further move to industrialization,

urbanization, unequal trade relations and market expansion as we observed it in the last two

decades (Bunker, 1984; Rees, 1992; Dietz and Rosa, 1994; Mol, 1997; Mol and Spaargaren,

2000; York and Rosa, 2003; Rosa et al., 2004; York et al., 2004; Jorgenson, 2005; Dietz et al.,

2007; Jorgenson and Burns, 2007).

On the other side the German political sociologist Huber 1970 in the late 1970s and the

work of Mol (1997) pointed out that modernization lead to ecological sustainability at a globe.

They argue that the continuation of modernization in form of industrialization, economic

development, market expansion and urbanization is the best and perhaps the only way to

achieve ecological sustainability. The view of ecological modernization theorists against the

neo-Marxian and Human ecological perspective is that modernizations lead to anti-ecological

43

and have negative consequences on the globe, where the central principal of EMT is a

modernization greatly concern with ecological rationality. Although at the early stage of

economic development it is not possible to obtain solution for ecological problems, but

progress in economic development will try to mitigate the ecological problems by developed

nations. The rise of the service economy, further modernization in existing institutions and

urbanization, expansion of political rights and civil liberties, and state environmentalism are

all expected to help curb environmental impacts (Mol, 1997; Mol and Spaargaren, 2000). The

consumption-based environmental impacts of a nation through ecological modernization will

decrease. The developed nations will try to decouple the relationship between economic

development and environmental degradation through further development and so less-

developed countries will eventually mitigate consumption-based environmental impacts on

globe (Jorgenson and Clark, 2011).

Figure 3.5

The EMT channel of modernization regarding declining in

environmental Damage/ ecological sustainability

44

3.5 World system and treadmill production perspectives

The cross-sectional analyses regarding a nation’s ecological footprint and economic

development are consistently indicated that total as well as per capita ecological footprints are

largely a function of the level of economic development, usually measured as per capita Gross

Domestic Product. But such relationship is not curvilinear as commonly argued by neo-

classical and ecological modernization theorists. It supported the arguments of world-system

theory and Treadmill of production. These run counter to the ecological modernization theory.

The higher is the level of economic development for more profit accumulation, the higher will

be competition in the global marketplace and consumption of natural resources increased as

argued by the world-system theorists.

The treadmill of production theorists argued that usually producers based in developed

countries and the expansion of products are largely depended on resources which are

commonly extracted from less developed countries. The developed countries externalize

environmental impact by extracting resources of less developed countries and produced

commodities are usually transported to and consume by their population. Increase in economic

development further lead to environmental impact through extraction of natural resources and

waste generated by expansion of production. Thus, according to world-systems theory and

treadmill of production theory, developed countries generate consumption based

environmental impacts that are larger than less-developed countries. And treadmill of

production theory asserts that the effects of resource consumption like fossil fuels would lead

to a negative effect on the total ecological footprint and its components. The area of land

required for cropland, forests, fisheries, grazing land and carbon dioxide will become less and

less when the demand for resources in production process becomes larger and larger.

45

3.6 Export dependence perspective

The theory of export dependence is mainly concerned with the negative consequences of

uneven trade relationship for less-developed countries. The theory asserts that the economic

structure of these countries is based on the export of raw materials which lead to make

exporting countries most vulnerable in the world market, allow the developed countries with

whom they trade get favorable term of trade. The export dependence of this form could lead to

ecological consequences in the form of depletion of cropland, grazing land, forest and

emissions of carbon. They argue that raw materials, agriculture goods and produced

commodities are exported to higher consuming countries on higher on the basis of their

international power which leads to uneven ecological exchange. The export intensity is a

negative effect on a nation’s ecological footprint (Jorgenson and Burns, 2007). The export

dependency theorists assert that the effect of export intensity on ecological footprint is

negative.

46

CHAPTER FOUR

DATA AND METHODOLOGY

4.1 Introduction

This chapter explains data sources and methodology to construct ecological efficiency,

environmental impact intensity, Atkinson index of ecological footprints and specification of

appropriate econometric model.

4.2 Data

To achieve objectives of the study, the quarterly data6 from 2003q1-2011q4 are established

for the panel data analysis. The separate and combine panel models of 35 high and 77 middle

income countries7 are established, defining the total ecological footprint and its components as

the dependent variables. Data used in this study are drawn from various sources including

World development indicators, Global footprint network, the conference board and

international energy statistics. The dataset of ecological footprint derived from the GFN is one

of the international organizations. It documents the ecological footprint by dividing the yearly

consumption of cropland, forest, grazing land, fishing grounds, CO2 footprint and built-up land

activities from the production of land expressed in hectares and this ratio is multiplied by the

yield and equivalence factors derived by the GFN. In the second stage all the area of land

required for cropland, forest, grazing land, fishing grounds, CO2 footprint and built-up land

aggregates in form of total ecological footprint global hectares in a given year. At every stage

of computation process of ecological footprint, the double counting is avoided in order to

improve accuracy of environmental impact indicator i.e. the total ecological footprint. This is

6 Due to the limitation of data availability on the dependent variables, we therefore, converted the annual data into

the quarterly data and re-estimate the separate and combine panel of high-middle income countries as suggested

by external examiner. 7 Appendix E consists of the list of high-middle incomes countries.

47

a comprehensive measure because raw input data for the computation of national’s ecological

footprint derived from different sources for example Food and Agriculture Organization (FAO,

International Energy Agency IEA, United Nations Commodity Trade Statistics Database UN

COMTRADE, World Development Indicator Database WDI, The conference board, Central

for Sustainability and the Global Environment SAGE and other Databases (Galli et al., 2012).

The GFN covered 152 countries, different regions and the World for estimation of their

ecological footprint at irregular basis from 1960 to 2012 with two or five year intervals.

The data on working hours is obtained from the conference board. It is a non-profit business

membership and research group organization. It counts approximately 1200 public and Private

Corporation and other organization and 5 regions i.e. Asia, China, Europe, Middle East and

United dataset on working hours, employment and consumer confidence.

The data on fossil fuel, coal, and oil and gas consumption are obtained from the conference

US international energy statistics. In is an international organization collected data on energy

consumption of national level. The data on livestock and fish production are obtained from the

Food and Agriculture Organization (FAO) for different periods. The data on other variables

are obtained from the World Bank dataset.

48

The definition, unit of measurement and data sources regarding different variables of the

study elaborated in the following Tables.

Table 4.1

Dependent Variables

Dependent

Variables

Definition Measurement

Unit

Data

Source

Data Period

Ecological

Footprint

The area of land required to support

humanity demand and to assimilate CO2

emissions as well as waste generated by

human activities

Global Hectares:

gha

Global

Footprint

Network:

GNF,

www.foot

printnetwo

rk.org/

2003q1-2011q4

Carbon

Footprints:

CARF

The area of land required for

assimilation of CO2 emissions and

Waste generated by human activities

Cropland

Footprints:

CROF

The area of land required to produce

crop activities

Fisheries

Footprint :

FISHF

The area of land required for fisheries

activities

Forest land

Footprint :

FORESTF

forest ecological footprint quantifies the

area required to produce the forest

products

Grazing

land

Footprint:

GRAZF

grazing ecological footprint quantifies

the land requirement for grazing

activities

Built-up

land

Footprint :

BUILTF

built-up ecological footprint is a

measure of land requirement to adjust

urbanization activities

http://www.footprintnetwork.org/



49

Table 4.2

Explanatory Variables

Explanatory

Variables


Unit

Data Source Data Period

Population: PoP Total population Million Word Bank

2003q1-2011q4

Economic

Growth : Yg

Per capita Gross Domestic

product

US$ in

Purchasing

Power Parity:

2000 prices

Word Bank

Urbanization :

UR

The percentage of total pop.

living in urban areas, centered by

subtracting the mean of the log

of percentage urban and then

squared to reduce collinearity

with percentage urban

% of total

Pop.

Word Bank

Fossil Fuels : FF Fossil fuels energy consumption

as percentage total

% Word Bank

Export Intensity

:EI

Export as a percentage of Gross

Domestic Product

% Word Bank

Manufacturing

Intensity: MI

Manufacturing as a percentage of

Gross Domestic Product

% Word Bank

Service Intensity:

SI

Service as a percentage of Gross

Domestic Product

% Word Bank

Agriculture

Intensity: AI

Agriculture as a percentage of

Gross Domestic Product

% Word Bank

Term of Trade:

TOT

Export and Import as a

percentage of Gross Domestic

Product

% Word Bank

Coal Consumption of coal Tons IES

Oil Consumption of oil Barrels IES

Gas Consumption of gas Million cubic

feet

IES

50

Continued…

Explanatory

Variables


Unit

Data Source Data Period

Hours of work

:HW

Annual worked hours per

employee

Hrs The

Conference

Board

2003q1-2011q4

Fish intensity:

SF

Share of fish export as a

percentage of total export

% Word Bank

& FAO

Education: EDU Education expenditure as a

percentage of Gross

Domestic Product

% Word Bank

& FAO

Export of

Merchandize

goods: CA

Export of merchandize goods

as a percentage of Gross

Domestic Product

% Word Bank

& FAO

Export of

primary

goods:EP

Export of primary goods as a

percentage of total export

% Word Bank

& FAO

Income

inequality: IE

Gini Coefficient, the

distribution of income within

countries

% Word Bank

& FAO

Employment to

population ratio:

EM

Employment divided by

population

% Word Bank

& FAO

Crop and

livestock

products

Export of crop and livestock

products

Million tons FAO STAT

51

4.3 Methodology

It is concluded from the discussion of the previous chapter that ecological footprint is

affected by different factors like economic growth, population, urbanization, technology and

fossil fuels. However, in this section we built a theoretical framework from the neo-Malthusian

perspective that focused on the relationship between population, footprints and environmental

degradation and therefore the Ehrlich and Holdren (1971) can be written as:

𝐼𝑡 = 𝑃𝑡 ∗ 𝐹𝑡 −−−−− (4.1)

Where I is the total environmental damage or total ecological footprint; P is the population

and assume that increase in population leads to increase ecological footprint i.e.𝜕𝐼 𝜕𝑃⁄ > 0. The

variable F is per capita ecological footprints (Ahmed, 2004; White, 2007).

𝐹𝑡 =𝐼𝑡𝑃𝑡−−−−− (4.2)

The per capita and total ecological footprints vary from countries to countries and from

region to region because of difference in its standard of living, population and other

socioeconomic factors (Ahmed, 2004; Jorgenson and Burns, 2007; Knight et al., 2013).

However, the ecological footprint has expressed by Ahmed (2004); Knight et al. (2013):

𝐹𝑡 = 𝑓 (𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − (4.3)

The ecological footprint is linked with economic output and whether economic output is

achieved in efficient way or not when using resources. In order to obtain ecological efficiency

let assume that economic output is denoted by Y is a function of capital, labor and other

resource use and let ecological footprints is a function of population, affluence, technology and

other factors:

𝑌𝑡 = 𝑓(𝐾𝑡, 𝐿𝑡, 𝑋𝑡) − − − − − − − (4.4)

𝐹𝑡 = ɸ(𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − − − (4.5)

52

Let ecological efficiency through the definition of Qiu (2013); (Wei et al., 2015) can be

expressed as:

𝐸𝐸𝑡 =𝑌𝑡𝐹𝑡 − − − − − − − (4.6)

Where 𝐸𝐸𝑡 is ecological efficiency; 𝑌𝑡 is economic output and 𝐹𝑡 is environmental

affluence (Ecological footprint in year t. Since, economic output is determined by factors of

production and other inputs as captured by vector X (Ahmed, 2004; Anders and John, 2009;

Jalil and Feridun, 2011; Gulden and Mehmet, 2014), while ecological footprint is determined

by population, affluence, technology and other factor as captured by vector Z (Knight et al.,

2013). The ecological efficiency can be expressed as:

𝐸𝐸𝑡 =𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)

ɸ(𝑃𝑡, 𝑌𝑡, 𝑇𝑡, 𝑍𝑡) − − − − − (4.7)

𝐸𝐸𝑡 =𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)

ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)) − − − − − (4.8)

The last equation (4.8) states that economic output is influenced by capital (K), labor (L)

and other factors like energy consumption, foreign direct investment, term of trade, fiscal

decentralization , institutional quality, inequality etc. captured by vector X while the ecological

footprints depends on population, affluence, technology and other factors like export intensity,

inequality, urbanization, fossil fuel, military expenditure, work hours etc. captured by vector

Z (Jorgenson and Burns, 2007; Knight et al., 2013). However, ecological efficiency shows the

ability to produce economic output with less resources use and pollution output expressed by

ecological footprint and varies due to changes in factors compositions (Anders and John,

2009). To estimate the ecological efficiency the procedure of Qiu (2013); Wei et al. (2015)

followed, where they used GDP as an indicator for economic output and total ecological

footprint as consumption based environmental indicator.

53

The estimation of ecological efficiency through the above process reveals that whether the

efficiency of resource utilization increases, decreases or remain constant in the given period.

In other words, it shows trend analysis of ecological efficiency. In this study, we also compared

the ecological efficiency of the current year with previous year ecological efficiency of

resource utilization through the constructed ecological efficiency index. The procedure we

adopted based on Qiu (2013); Wei et al. (2015) methodology and ecological efficiency index

can be expressed as:

𝐸𝐸𝐼𝑡 =𝐸𝐸𝑡𝐸𝐸𝑡−1

− − − − − (4.9)

Where, 𝐸𝐸𝐼 index of ecological efficiency, 𝐸𝐸𝑡 is the ecological efficiency of current year

t and 𝐸𝐸𝑡−1 is previous year ecological efficiency and since we use the ecological footprint as

a resource consumption indicator and income as a value of nation’s product. The ecological

efficiency index can be computed:

𝐸𝐸𝐼𝑡 =𝐹𝑡−1

𝐹𝑡.𝑌𝑡

𝑌𝑡−1 − − − − − (4.10)

𝐸𝐸𝐼𝑡 =ɸ(𝑃𝑡−1, 𝑇𝑡−1, 𝑍𝑡−1, 𝑓(𝐾𝑡−1, 𝐿𝑡−1, 𝑋𝑡−1))

ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡, 𝑋𝑡)).

𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)

𝑓(𝐾𝑡−1, 𝐿𝑡−1, 𝑋𝑡−1) − − − − − (4.11)

The interpretation of equation (4.11) is straightforward because the value of EEI is greater

than one indicates reduction in the amount of energy or resource consumption and the amount

of pollution emitted per unit of economic output in year t is lower than year t-1. If EEI less

than one, meaning that a country’s resources are used recklessly for generating more income

and economic growth. In other words, a country is achieving its economic growth at the cost

of environmental degradation (Anders and John, 2009). However, it is necessary that whether

there exists the gap between country efficiency in resources utilization and its maximum (best

54

performer) across the countries (Qiu, 2013; Wei et al., 2015). And therefore we followed

definition and computation process of Qiu (2013) as:

𝑅𝐼𝑡 =𝑚𝑎𝑥(

𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)

ɸ(𝑃𝑡,𝑇𝑡,𝑍𝑡,𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)))

(𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)

ɸ(𝑃𝑡,𝑇𝑡,𝑍𝑡,𝑓(𝐾𝑡,𝐿𝑡,𝑋𝑡)))

≥ 1 − − − − − (4.12)

The value of 𝑅𝑡 reflects the gap between anefficiency in resources utilization and maximum

ecological efficiency for year t in group of nations because its value equal to one shows

efficiency in resources utilization is maximum (best performer and 𝑅𝐼𝑡greater than one implies

that efficiency in resources utilization is less than maximum ecological efficiency and have

more room of potential to achieve maximum level of efficiency. However, environmental

impact intensity could be estimated whenever computed ecological efficiency because to

obtain maximum level of ecological efficiency (best performer depends on resource

consumption and consequently increase environmental degradation (York et al., 2004; Juan

and Jordi, 2013).

𝑇𝑡 =𝐸𝐹𝑡

𝑌𝑡 − − − − − (4.13)

𝑇𝑡 =ɸ(𝑃𝑡, 𝑇𝑡, 𝑍𝑡 , 𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡))

𝑓(𝐾𝑡, 𝐿𝑡 , 𝑋𝑡)− − − − − (4.14)

Where, T shows the amount of energy or resources consumption and the amount of

pollution omitted per unit of economic output and from the introduction it is clear that because

of inequality in total ecological footprint and its component the different regions of the world

have different ecological overshooting and environmental degradation. Therefore, we also

estimated inequality in distribution of resources use i.e. total ecological footprint and its

components.

55

4.3.1 Atkinson Index of ecological footprint inequality based on

environment intensity and per capita income

In this section we estimated the role of environment intensity and per capita income in

ecological footprint inequality through the methodology of White (2007); Juan and Jordi

(2013) , where they used the Atkinson (1970) index of inequality by incorporating ecological

footprint as:

𝐴𝐹 = 1 −𝐹𝑒𝜇𝐹−−−− − (4.15)

Where AF is the Atkinson index of inequality, Fe is equally distributed of footprint and 𝜇𝐹

is the mean of ecological footprint. The index value of Atkison index is ranging zero to one. If

resources are equally distributed i.e F1=F2=F3=…..=Fn, the AF will be zero and will be one in

case of completely inequality in ecological footprint. According to White (2007); Juan and

Jordi (2013), where the ecological footprint can be defined as:

𝐹𝑖 = 𝑃𝑖 ∗ 𝑦𝑖 ∗𝐹𝑖

𝑌𝑖−−−− − (4.16)

Where Fi is the ecological footprint of country i, Pi is its population, yi is its per capita

income. The per capita ecological footprint can be expressed as:

𝐹𝑖 = 𝑦𝑖 ∗ 𝑤𝑖 −−−− − (4.17)

Where Fi is per capita ecological footprint of country i and wi is the environmental impact

intensity (or ecological efficiency. By using definition of White (2007); Juan and Jordi (2013)

the Atkinson index can be expressed as:

𝐴𝐹 = 1 −∏{𝐹𝑖𝜇𝐹}

1𝑝𝑖

𝑛

𝑖=1

−−− −− (4.18)

56

Where 𝜇𝐹is mean value of per capita ecological footprint and pi is the relative population

of country i. After manipulating and substituting equation (4.17) into equation (4.18), the index

can be expressed as:

1 − 𝐴𝐹 =∏{𝑦𝑖 ∗ 𝑤𝑖𝜇𝐹

}1/𝑝𝑖

𝑛

𝑖=1

− −−−− (4.19)

1 − 𝐴𝐹 = {𝜇𝑦𝜇𝑤

𝜇𝐹}∏{

𝑦𝑖 ∗ 𝑤𝑖𝜇𝑦 ∗ 𝜇𝑤

}

1/𝑝𝑖𝑛

𝑖=1

−−−−− (4.20)


𝜇𝐹}∏{

𝑦𝑖𝜇𝑦}

1𝑝𝑖

𝑛

𝑖=1

∏{𝑤𝑖𝜇𝑤}

1𝑝𝑖

𝑛

𝑖=1

−−−−− (4.21)


𝜇𝐹} (1 − 𝐴𝑦) ∗ (1 − 𝐴𝑤) − − − − − (4.22)

Where µy is mean value of per capita income and µw is mean value of environmental impact

intensity. The Atkinson indices of ecological footprint, income and environmental impact

intensity are shown by AF, Ay and Aw. The value of 1-Ai indicates an Atkinson measure of

equality where perfect equality would be equal to one and complete inequality would be equal

to zero by White (2007). Thus the interpretation of equation (4.22) is straightforward. The

Atkinson index (1-AF) of ecological footprint depends on distribution of income and

environmental impact intensity (ecological efficiency, and the means of these variables. It is

commonly argued that domestic income inequality is inversely related to a nation’s ecological

footprint. The argument is that nations with higher income inequality would have low per

57

capita ecological footprint because they have relative lower income and mainly focus on export

of raw material and agriculture commodities (White, 2007; Jorgenson, 2009; Juan and Jordi,

2013). However, in this study we estimated and compared Atkinson index of footprints,

environment intensity and per capita income of high and middle income countries.

4.3.2 Empirical specification of various influencing factors

of ecological footprint and its components

In this study we used the STIRPAT model developed by Dietz and Rosa (1994) and further

explored by York and Rosa (2003), by Rosa et al. (2004) and by Dietz et al. (2007). The

STIRPAT model is the reformulation of Ehrlich and Holdren (1971) IPAT where population

(P), affluence (A) and technology (T) are the influencing factors of environment (I). The

coefficients associated with influencing factors show elasticity because the model is a

multiplicative function of population, income and technology (York and Rosa, 2003; Anders

and John, 2009; Knight et al., 2013). However, the basic STIRPAT model used in this study

for empirical testing of neo-classical economists and neo-Malthusian view regarding

environmental impacts of economic growth, population and other explanatory variables known

as the Stochastic Regression Impact of Population, Affluence and Technology (STIRPAT)

model and can be expressed as:

𝐸𝐹𝑖 = 𝛼𝑃𝛽𝑖𝑌𝛾𝑖𝑇𝛿𝑖휀𝑖 −− −−− (4.23)

Where EF=Ecological Footprints, P=population, Y=economic growth, T=Technology and

ε =error term. The constant “α” scales the model, whereas β, γ and δ are exponents of P, Y and

T respectively. To find the relationship between ecological footprint and its influencing factors,

the nonlinear form of equation (4.23) can be converted to a linear form after ln transformation,

because it is easier to work with linear equations rather than nonlinear equations (Ahmet and

58

Sevil, 2015). T is incorporated into error term because of no clear agreement on valid

technology indicators. The nonlinear equation of STIRPAT model after this modification

becomes:

𝑙𝑛(𝐸𝐹) = 𝑎 + 𝛽 ln(𝑃) + 𝛾𝑙𝑛(𝑌) + 𝜇𝑖𝑡 −−−−− (4.24)

The coefficients associated with independent variables are elasticities that show a

percentage change in the dependent variable due to one percentage change in the independent

variable by holding the effects of other factors constant. The various influencing factors of

dependent variable could be incorporated in the STIRPAT model (Knight et al., 2013).

However, in environmental social science the influencing factors of ecological footprint used

are population, economic growth and working hours. The effect of work hours on ecological

footprint was further disaggregated into hours of work per employee, labor productivity and

employment to population ratio by Anders and John (2009); Knight et al. (2013) to test

hypothesis that longer working hour is responsible factor for environmental degradation. The

studies like York and Rosa (2003); Cole (2004); Rice (2007) disaggregated population into

urbanization (i.e. proportion population living in urban area) to test the hypotheses regarding

its effect on environment, emissions, footprint and energy consumption where they found a

positive relationship because of the increase in urbanization in form of single family and high

rise building demand for energy use .

However, the studies like Liddle (2004); Fan et al. (2006) found a negative relationship

between urbanization, energy consumption and CO2 emissions in case of high income

countries because of green technology use for urban activities like electrical transportation

system through which CO2 emissions reduced. The number of studies however, decomposed

the term technology into manufacturing and service’s share of GDP and found a positive effect

on energy consumption while a negative relationship between service and environment

59

degradation was observed (Dietz et al., 2003; York and Rosa, 2003; Fan et al., 2006;

Mahadevan and Asafu, 2007; Kaneko and Poumanyvong, 2010). However, Kaneko and

Poumanyvong (2010); Perry (2014) in STIRPAT model decomposed population into

urbanization and found that higher economic activities associated with urbanization and leads

to increase income of urban residents demand more for energy intensive products like

automobile, air conditioning etc. which increases CO2 emissions, and also added that higher

wealthier nations also care of environment and try to use environmental friendly technology

products. In this study we advanced the STIRPAT model in a number of ways as we

disaggregated the dependent variable into total ecological footprint and its components (i.e.

cropland, forest, grazing, and fisheries, built-up land and CO2 footprint). We estimated the

effect of various influencing factors of each component of footprint by decomposing the basic

influencing factors of STIRPAT model and tested the EKC, modernization, world-system and

export dependency hypotheses. Our first dependent variable is the ecological footprint that

represents total area required to produce the fibers and food, sustain energy consumption, and

give space for infrastructure of a given nations/locality. It shows consumption-based pressure

on environment measured in global hectares per person and is widely used indicator in the

environmental social science (Richard et al., 2003; York and Rosa, 2003; Jorgenson, 2005;

Jorgenson and Burns, 2007; Knight et al., 2013). The empirical specification of influencing

factors of total ecological footprints can be expressed as:

𝐸𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡 , 𝑌2𝑔𝑖𝑡 , 𝑃𝑂𝑃𝑖𝑡 , 𝑈𝑅𝑖𝑡 , 𝐹𝐹𝑖𝑡 , 𝐸𝐼𝑖𝑡 , 𝑆𝐼𝑖𝑡,𝑀𝐼𝑖𝑡 , 𝐴𝐼𝑖𝑡 , 𝐼𝐸𝑖𝑡 , 𝜔𝑖𝑡)−−−−− (4.25)

+ - + + + + + - - -

The second dependent variable is energy (Carbon ecological footprint) quantifies the

carbon emissions global hectares per person. The functional relationship between carbon

footprint and its relevant influencing factors is expressed as:

60

𝐶𝐴𝑅𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡 , 𝑌2𝑔𝑖𝑡 , 𝑃𝑂𝑃𝑖𝑡 , 𝑇𝑂𝑇𝑖𝑡 , 𝑈𝑅𝑖𝑡 , 𝐶𝑂𝐴𝐿𝑖𝑡 , 𝑂𝐼𝐿𝑖𝑡 , 𝐺𝐴𝑆𝑖𝑡 ,𝑀𝐼𝑖𝑡, 𝐻𝑊𝑖𝑡 , 휀𝑖𝑡)−−−−(4.26)

+ - + + + + + ? + ?

The third dependent variable is fisheries ecological footprint is the area required to produce

fish and seafood products in order to fulfill the consumption, accelerate the economic

development and increase share of fish export of a nation. The determinants of fisheries

footprint in its functional form are expressed as:

𝐹𝐼𝑆𝐻𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑆𝐹𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝜇𝑖𝑡) − − − − − (4.27)

+ - + ? +

The fourth dependent variable is cropland ecological footprint quantifies the area required

of crop that consumed by a country’s population and to feed animals whose production like

meat, eggs, milk etc are consumed in a year. The functional form of cropland footprint and its

determinants is expressed as:

𝐶𝑅𝑂𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝐴𝐼𝑖𝑡 , 𝐸𝐷𝑈𝑖𝑡, 𝐶𝐴𝑖𝑡, 𝜗𝑖𝑡) − − − − − (4.28)

+ - + + + - +

Our fifth dependent variable is forest ecological footprint quantifies the area required to

produce the forest products include all timber products, pulp, paper and paperboard that

consumed by a country’s population and to feed animals whose production like meat, eggs,

milk etc. are consumed in a year. The functional form of forest footprint and its determinants

is expressed as:

𝐹𝑂𝑅𝐸𝑆𝑇𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝐸𝑃𝑖𝑡 , 𝐸𝐷𝑈𝑖𝑡 , 𝐼𝐸𝑖𝑡, 𝜖𝑖𝑡) − − − − − (4.29)

+ - + + + - -

The sixth dependent variable is grazing ecological footprint that quantifies the land

requirement for grazing of livestock that provide consumption of animal products includes

61

meat, dairy products and wool for a given nation in year. The determinants of grazing footprint

are expressed in the following functional form:

𝐺𝑅𝐴𝑍𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑃𝐿𝑖𝑡, 𝜓𝑖𝑡) − − − − − (4.30)

+ - + + +

Our last dependent variable is built-up ecological footprint is a measure of land requirement

to adjust urbanization, requirement of industrial sectors, services intensity, housing, and

transportation. The various determinants of built-up footprint are expressed in the following

functional form:

𝐵𝑈𝐼𝐿𝑇𝐹𝑖𝑡 = 𝑓(𝑌𝑔𝑖𝑡, 𝑌2𝑔𝑖𝑡, 𝑃𝑂𝑃𝑖𝑡, 𝑈𝑅𝑖𝑡, 𝑀𝐼𝑖𝑡, 𝑆𝐼𝑖𝑡, 𝐸𝑀𝑖𝑡, 𝜉𝑖𝑡) − − − − − (4.31)

+ - + + ? + +

62

4.3.3 The expected theoretical linkages between dependent

and independent variables: EF and economic growth

The expected relationship between economic growth and ecological footprint is positive in

its linear while negative in its square term. If this maintain, it verifies the environmental

Kuznets curve. The environmental impact of population is assumed that more masses lead to

more environmental damage as described by Ehrlich and Holdren (1971).

4.3.3.1 EF and Population

The Ehrlich and Holdren (1971) argued that environmental impact increases for two

reasons. First, the size of population (P) will increase. Second, the impact on ecological

footprint of more people will also increase. The argument is that if other factor held constant,

successive addition of people would need more resource consumption such as forest, water,

grazing land, energy and other renewable and non-renewable resources. Thus by adding more

population the per capita impact in term of ecological footprint and environmental degradation

increase successively. Ehrlich and his followers contend that rising human population is the

predominant factor in accelerating pollution and other resource problems.

4.3.3.2 EF and Urbanization

Impacts of urbanization on the environment are partially and separately discussed in three

relevant theories: ecological modernization, urban environmental transition and compact city

theories. The first theory focuses on impacts at the national level, while the others discuss

impacts at the city level. Ecological modernization theory emphasizes not only economic

modernization but also social and institutional transformations in explaining the effects of

modernization on the environment. In this theory, urbanization is the process of social

transformation regarded as one important indicator of modernization. It is argued that

environmental problems may increase from low to intermediate stages of development.

However, further modernization can minimize such problems, as societies come to realize the

63

importance of environmental sustainability, seeking to decouple environmental impact from

economic growth through technological innovation, urban agglomeration, and the shift toward

knowledge and service based industries (Crenshaw and Jenkins, 1996; Mol, 1997; Anders and

John, 2009; A. Usama et al., 2014).

4.3.3.3 EF and export intensity

Export dependence theory focuses on the negative consequences of uneven trade

relationships, particularly for less-developed countries. The theory asserts that high levels of

export dependence make an exporting country more vulnerable to world-economic market

forces, and allow the developed nations with whom they exchange to obtain favorable terms

of trade. This type of acts creates negative ecological consequences in the form of depletion of

raw materials. Therefore export dependence in the form of export as percentage of GDP would

have negative ecological effect. However, few theories argued that due to financial

development the impact of trade openness on ecological footprint is positive.

4.3.3.4 EF and SI, MI and AI

Some macroeconomic perspectives suggest that shifting from manufacturing, agriculture,

and extractive activities to a more service-based economy offers a potential solution to

reducing the scale and intensity of the environmental impacts of nation-states. By using these

arguments one can test that the service based economies have relatively low ecological

footprint than manufacturing and agriculture economies. But the world-system and unequal

exchange theories claim that the high income nation due to its power can utilize more resources

and even externalize pollution (Jorgenson and Burns, 2007; Knight et al., 2013).

64

4.3.3.5 EF and Domestic income Inequality

Some evidence predicts that domestic income inequality is inversely related to a nation’s

ecological footprint. The argument is that nations with higher income inequality would have

low per capita ecological footprint because they have relative lower income. They mainly

focus on export of raw material, agriculture goods (Jorgenson, 2005; Jorgenson and Burns,

2007).

4.3.3.6 EF and Energy Consumption

Some evidence suggests that energy consumption is positively related to ecological

footprint (Shahbaz et al., 2013a; A. M. Usama et al., 2014). The argument is that the nation

with more strong financial development, trade openness, and urbanization and services based

activities increases the impact of energy consumption on environmental damage. On the basis

of above argument we hypothesized that energy consumption particularly in form of coal, oil

and gas consumption is positively related to ecological footprint.

4.3.3.7 EF and Education

The high literacy rates have positive effect on natural resources consumption. The

argument is that high income nations correspond with high literacy rates increases

opportunities of depletion of resources (Jorgenson et al., 2005).

65

4.4 Analytical tools

This section covered the estimation procedures of ecological efficiency, index of ecological

efficiency, Atkinson index and the econometric modeling for various driving forces of total

ecological footprint and its components in case of high and middle income countries.

4.4.1 The computation of ecological efficiency

The ecological efficiency which is based on the procedure of Qiu (2013); Wei et al. (2015)

estimated as:

𝐸𝐸2003 =∑ 𝐺𝐷𝑃𝑖2003𝑛𝑖=1

∑ 𝐸𝐹𝑖2003𝑛𝑖=1

−− −−− (4.32)

𝐸𝐸2005 =∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1

∑ 𝐸𝐹𝑖2005𝑛𝑖=1

−− −−− (4.33)

𝐸𝐸2007 =∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1

∑ 𝐸𝐹𝑖2007𝑛𝑖=1

− −−−− (4.34)

𝐸𝐸2009 =∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1

∑ 𝐸𝐹𝑖2009𝑛𝑖=1

− −−−− (4.35)

𝐸𝐸2011 =∑ 𝐺𝐷𝑃𝑖2011𝑛𝑖=1

∑ 𝐸𝐹𝑖2011𝑛𝑖=1

− −−−− (4.36)

4.4.2 The Computation of ecological efficiency index

The ecological efficiency index for the period 2005, 2007, 2009 and 2011 with two years

intervals estimated by using the following computation process:

𝐸𝐸𝐼2005 =∑ 𝐺𝐷𝑃𝑖2005𝑛𝑖=1

∑ 𝐺𝐷𝑃𝑖2003𝑛𝑖=1

∗∑ 𝐸𝐹𝑖2003𝑛𝑖=1


−−−−− (4.37)

66

𝐸𝐸𝐼2007 =∑ 𝐺𝐷𝑃𝑖2007𝑛𝑖=1


∗∑ 𝐸𝐹𝑖2005𝑛𝑖=1


−−−−− (4.38)

𝐸𝐸𝐼2009 =∑ 𝐺𝐷𝑃𝑖2009𝑛𝑖=1


∗∑ 𝐸𝐹𝑖2007𝑛𝑖=1


−−−−− (4.39)

𝐸𝐸𝐼2011 =∑ 𝐺𝐷𝑃𝑖2011𝑛𝑖=1


∗∑ 𝐸𝐹𝑖2009𝑛𝑖=1


−−−−− (4.40)

The ecological efficiency index in this way compares efficiency of resource utilization of

current year with previous year efficiency because value of index greater than one implies that

economic output per units of ecological footprint in current year is greater than previous year.

In other words, reduction in the amount of energy or resources consumption and the amount

of pollution emitted per unit of economic output in current year is lower than previous year.

4.4.3 The Computation of environmental impact intensity

The mean environmental impact intensity of total ecological footprints and its components

estimated as:

𝜇𝑇 =∑ 𝑇𝑡2011𝑡=2003

𝑛−−−− − (4.41)

𝜇𝑇𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − − −(4.42)

𝜇𝑇𝐶𝑂2 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝐶𝑂2 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − −− − (4.43)

67

𝜇𝑇𝑐𝑟𝑜𝑝𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑐𝑟𝑜𝑝𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − − − − (4.44)

𝜇𝑇𝑔𝑟𝑎𝑧𝑖𝑛𝑔 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑔𝑟𝑎𝑧𝑖𝑛𝑔 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − − − − (4.45)

𝜇𝑇𝑓𝑜𝑟𝑒𝑠𝑡 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑓𝑜𝑟𝑒𝑠𝑡 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛−−−−− (4.46)

𝜇𝑇𝑓𝑖𝑠ℎ𝑖𝑛𝑔 𝑔𝑟𝑜𝑢𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑓𝑖𝑠ℎ𝑖𝑛𝑔 𝑔𝑟𝑜𝑢𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− −− −(4.47)

𝜇𝑇𝑏𝑢𝑖𝑙𝑡 𝑢𝑝 𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡 =∑ (

𝑏𝑢𝑖𝑙𝑡 𝑢𝑝 𝑙𝑎𝑛𝑑 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − − − − (4.48)

The mean value show per unit environmental intensity of economic output and we

estimated and compared mean environmental intensity of high and middle income countries,

where its environmental intensity indicated different structure. The higher environmental

impact intensity in case of the total ecological footprint and its components leads to explain

higher per unit of environmental impact of economic output because the ecological footprint

is basically the consumption based environmental impact indicator.

68

4.4.4 The Computation of Atkinson index of equality

In this section the Atkinson index of ecological footprint equality estimated by using the

following equation as:


𝜇𝐹} (1 − 𝐴𝑦) ∗ (1 − 𝐴𝑤) − − − −− (4.49)

Where

𝜇𝐹 =∑ (

𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011

𝑡=2003

𝑛− − − − − (4.50)

𝜇𝑤 =∑ (

𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛− − −− − (4.51)

𝜇𝑦 =∑ (

𝐺𝐷𝑃𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011

𝑡=2003

𝑛− −−−− (4.52)

(1 − 𝐴𝑤) =∏

{

(𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡

𝐺𝐷𝑃𝑡⁄ )

𝑖

∑ (𝑡𝑜𝑡𝑎𝑙 𝑒𝑐𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑓𝑜𝑜𝑡𝑝𝑟𝑖𝑛𝑡𝑡

𝐺𝐷𝑃𝑡⁄ )2011

𝑡=2003

𝑛 }

1/𝑝𝑖

−−− −− (4.53)

𝑛

𝑖=1

(1 − 𝐴𝑦) =∏

{

(

𝐺𝐷𝑃𝑡𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )

𝑖

∑ (𝐺𝐷𝑃𝑡

𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑡⁄ )2011

𝑡=2003

𝑛 }

1/𝑝𝑖

𝑛

𝑖=1

−−−−− (4.54)

69

4.4.5 Econometric modelling

In this section we explained the fixed effects models and random effects models which are

the two Panel estimates approaches and set model selection criteria i.e. the Huasman test.

4.4.5.1 Fixed effect model

The fixed effects model focuses on individual specific effect and assuming time effect on

dependent variable is constant. It implies that intercept of fixed effects model changes cross-

sectional but remains constant over time and the slopes are fixed with respect to both cross-

sectional and over time (Hsiao, 2003; Frees, 2004). We therefore denoted cross-sectional

with the index ith range from 1 to n and the time observation denoted with index tth range

from 1 to T. By using these indices and consider 𝑦𝑖𝑡 is the dependent variable of the ith

county at the tth time point which depends on K exogenous variables, zit,1, zit,2,……..,zit,K of a

𝐾 × 1 column vector (Frees, 2004):

𝑍𝑖𝑡 =

(

𝑧𝑖𝑡,1𝑧𝑖𝑡,2....

𝑧𝑖𝑡,𝐾)

And the data for the ith country arranged through the methodology of (Frees, 2004)as:

(

𝑧𝑖1,1 ,𝑧𝑖1,2 ……𝑧𝑖1,𝐾, 𝑦𝑖1𝑧𝑖2,1 ,𝑧𝑖2,2 ……𝑧𝑖2,𝐾, 𝑦𝑖2

.

.

.

.𝑧𝑖𝑇,1 ,𝑧𝑖𝑇,2 ……𝑧𝑖𝑇,𝐾, 𝑦𝑖𝑇)

70

Where z denoted explanatory variables name, i denoted the ith country, the first numeric

value denoted time period and the second one explained independent variable. Similarly y

denoted dependent variable of the ith country and numeric value connected with it explains

time period from 1 to T. The above expression denoted data arrangement of dependent and

independent variables while the fixed effects model can be expressed as:

𝑦𝑖𝑡 = 𝛾𝑖 + 𝛽/𝑍𝑖𝑡 + 휀𝑖𝑡 −−−−− (4.55) , 𝑖 = 1.2……𝑁

, 𝑡 = 1.2…… . 𝑇

Where 𝛽/ is a 1 × 𝐾 vector of constant (𝛽1, 𝛽2,…… . . , 𝛽𝐾) and 𝛾𝑖 is 1 × 1 scalar constant

represents the effect of variables that are individual specific effect and more and less constant

over time. The error term 휀𝑖𝑡 is captured the irregular effect of omitted variables for both

individuals and time periods with mean zero and variance 𝛿2 . The fixed effect model in

vector system can be expressed as:

=

[ 𝑦𝑖1𝑦𝑖2...𝑦𝑖𝑇]

=

[ 𝑒0...0]

𝛾1 +

[ 0𝑒...0]

𝛾2+. . . . . . . . +

[ 00...𝑒]

𝛾𝑁 +

[ 𝑍1𝑍2...𝑍𝑁]

𝛽 +

[ 휀1휀2...휀𝑁]

− − − − − (4.56)

Where

𝑦𝑖𝑇Χ1 =

[ 𝑦𝑖1𝑦𝑖2...𝑦𝑖𝑇]

, 𝑍𝑖𝑇Χ𝐾 =

[ 𝑧𝑖1,1 𝑧𝑖2,1...

𝑧𝑖𝑇,1

𝑧𝑖1,2𝑧𝑖2,2...

𝑧𝑖𝑇,2

… . . 𝑧𝑖1,𝐾… . . 𝑧𝑖2,𝐾

.

.

.… . . 𝑧𝑖𝑇,𝐾]

, 𝑒/ = (1, 1, … ,1),휀/𝑖1Χ𝑇

= (휀𝑖1,… . . , 휀𝑖𝑇)

𝐸 (휀𝐼) = 0 , 𝐸(휀𝑖휀/𝑖) = 𝛿

2𝑖𝐼𝑇 , 𝐸(휀𝑖휀

/𝑗) = 0 𝑖𝑓 𝑖 ≠ 𝑗

The 𝐼𝑇 is used for TXT identity matrix and parameter estimators 𝛾𝑖 𝑎𝑛𝑑 𝛽 would be

obtained by minimizing

71

𝜆 =∑휀/𝑖

𝑁

𝑖=1

휀𝑖 =∑(𝑦𝑖 − 𝑒𝛾𝑖 − 𝑍𝑖𝛽)/(𝑦𝑖 − 𝑒

𝑁

𝑖=1

𝛾𝑖 − 𝑍𝑖𝛽) − − −− − (4.57)

The partial derivatives of equation (4.57 w.r.t parameter estimators (𝛾 𝑎𝑛𝑑𝛽 and keeping

them equal to zero would yield

𝛾�̂̇� = 𝑦�̅̇� − 𝛽/𝑧�̅̇�

�̂� = {∑∑(𝑧𝑖𝑡

𝑇

𝑡=1

𝑁

𝑖=1

− 𝑧�̅�)(𝑧𝑖𝑡 − 𝑧�̅�)/}−1 {∑∑(𝑧𝑖𝑡

𝑇

𝑡=1

𝑁

𝑖=1

− 𝑧�̅�) (𝑦𝑖𝑡 − �̅�𝑖}

Where

𝑦�̅̇� =∑ 𝑦𝑖𝑡𝑇𝑡=1

𝑇⁄ , 𝑧�̅̇� =

∑ 𝑧𝑖𝑡𝑇𝑡=1

𝑇⁄

The parameter estimator is called Least Square Dummy Variables (LSDV because the

parameter estimator 𝛾�̂̇� requires dummy variables that vary cross-sectionally but constant over

time; whereas 𝛽 does not require the slope dummy variables (Hsiao, 2003; Frees, 2004). Thus,

we could rewrite equation (4.54) as:

𝑌𝑖𝑡 = 𝛾1𝐷1𝑖 + 𝛾2𝐷2𝑖 +⋯+ 𝛾𝑁𝐷𝑁𝑖 + 𝛽 𝑍𝑖𝑡 + 휀𝑖𝑡 −−−−− (4.58)

The first individual of dummy variable is taken value 1 in the sample and zero otherwise.

The dummy variable takes the value 1 for the 2nd individual and zero otherwise, and so on. We

have to test the following hypothesis by using F-(Chow test):

𝐻0 = 𝛾1 = 𝛾2 = ⋯ = 𝛾𝑁

𝐹 − 𝑟𝑎𝑡𝑖𝑜 =(𝑒𝑟𝑟𝑜𝑟 𝑆𝑆)𝑟𝑒𝑑𝑢𝑐𝑒𝑑 − 𝑒𝑟𝑟𝑜𝑟 𝑆𝑆

(𝑛 − 1)𝑆2−−−−− (4.59)

Where error sum of square and S2 are obtained by estimating equation (4.59) with (n-1

degree of freedom. The (𝑒𝑟𝑟𝑜𝑟 𝑆𝑆𝑟𝑒𝑑𝑢𝑐𝑒𝑑) is obtained by estimating reduced model with N-

(n+K) degree of freedom (Frees, 2004).

72

4.4.5.2 Random effect model

The alternative model for estimating Panel data is random effects model by introducing the

intercept for each cross-sectional individual have common intercept 𝛼 and 𝛾𝑖 . The common

intercept 𝛼 is the same for all cross-sectional individuals over time; whereas 𝛾𝑖 exhibits

variation cross-sectional and constant over time. These could be expressed as:

𝛽𝑖 = 𝛼 + 𝛾𝑖 −−−− − (4.60)

And the random effects model would be:

𝑌𝑖𝑡 = 𝛼 + 𝛽/𝑋𝑖𝑡 + 𝜇𝑖𝑡 ; 𝜇𝑖𝑡 = 𝛾𝑖 + 휀𝑖𝑡 −−−−− (4.61)

The error term 𝜇𝑖𝑡 includes idiosyncratic term 휀𝑖𝑡 varies along time and cross-section

individuals and 𝛾𝑖 varies cross-sections but constant over time (Frees, 2004). Most of the

previous literature used total ecological footprint and CO2 emissions as environmental impact

indicator. However, in this study we used the methodology of Frees (2004) to test the effect of

various drivers of total ecological footprint and its components and the relationship is written

as :

ln (𝐸𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡 +

𝛽5𝑙𝑛(𝐹𝐹)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐼)𝑖𝑡 + 𝛽7ln (𝑆𝐼)𝑖𝑡 + 𝛽8ln (𝑀𝐼)𝑖𝑡 + 𝛽9ln (𝐴𝐼)𝑖𝑡 + 𝛽10ln (𝐼𝐸)𝑖𝑡 + 𝜔𝑖𝑡 −

−− (4.62)

ln (𝐶𝑂2𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡 +

𝛽5𝑙𝑛(𝐶𝑂𝐴𝐿)𝑖𝑡 + 𝛽6𝑙𝑛(𝑂𝐼𝐿)𝑖𝑡 + 𝛽7ln (𝐺𝐴𝑆)𝑖𝑡 + 𝛽8ln (𝑀𝐼)𝑖𝑡 + 𝛽9ln (𝐻𝑊)𝑖𝑡 + 휀𝑖𝑡 −−−

−(4.63)

ln (𝐹𝐼𝑆𝐻𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡

+ 𝛽5𝑙𝑛(𝑆𝐹)𝑖𝑡 + 𝜇𝑖𝑡 −−−−− (4.64)

73

ln (𝐶𝑅𝑂𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡

+ 𝛽5𝑙𝑛(𝐴𝐼)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐷𝑈)𝑖𝑡 + 𝛽7ln (𝐶𝐴)𝑖𝑡 + 𝜗𝑖𝑡 −−−−− (4.65)

ln (𝐹𝑂𝑅𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡

+ 𝛽5𝑙𝑛(𝐸𝑃)𝑖𝑡 + 𝛽6𝑙𝑛(𝐸𝐷𝑈)𝑖𝑡 + 𝛽7ln (𝐼𝐸)𝑖𝑡 + 𝜖𝑖𝑡 −−−−− (4.66)

ln (𝐺𝑅𝐴𝑍𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1ln (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2ln (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3ln (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡

+ 𝛽5𝑙𝑛(𝑃𝐿)𝑖𝑡 +𝜓𝑖𝑡 −−−−− (4.67)

𝑙og (𝐵𝑈𝐿𝑇𝐹)𝑖𝑡 = 𝛾𝑖 + 𝛽1𝑙og (𝐺𝐷𝑃)𝑖𝑡 + 𝛽2𝑙og (𝐺𝐷𝑃)2𝑖𝑡+ 𝛽3𝑙og (𝑃𝑂𝑃)𝑖𝑡 + 𝛽4𝑙𝑛(𝑈𝑅)𝑖𝑡

+ 𝛽5𝑙𝑛(𝑀𝐼)𝑖𝑡 + 𝛽6𝑙𝑛(𝑆𝐼)𝑖𝑡 + 𝛽7𝑙og (𝐸𝑀)𝑖𝑡 + 𝜉𝑖𝑡 −−−−− (4.68)

The data for the ith country is written as:

(

𝑙𝑛𝐺𝐷𝑃𝑖2003,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2003,2 ……𝑛𝐸𝑀𝑖2003,𝐾,𝑙𝑛𝐸𝐹𝑖2003, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2003

𝑙𝑛𝐺𝐷𝑃𝑖2005,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2005,2 ……𝑛𝐸𝑀𝑖2005,𝐾,𝑙𝑛𝐸𝐹𝑖2005, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2005

. . . . . . . . . . . . . . . . . . . .

𝑙𝑛𝐺𝐷𝑃𝑖2011,1 ,𝑙𝑛𝐺𝐷𝑃2𝑖2011,2 ……𝑛𝐸𝑀𝑖2011,𝐾,𝑙𝑛𝐸𝐹𝑖2011, … . 𝑙𝑛𝐵𝑈𝐿𝑇𝐹𝑖2011)

74

4.4.5.3 The Hausman test

The selection of random and fixed effects approaches is based on the assumption that

whether 𝛾𝑖𝑎𝑛𝑑 𝑋𝑖𝑡 are correlated. To test this assumption (Hausman and Wise, 1978) proposed

test based on the difference between fixed effects and random effects estimates. The significant

value (or larger value of Hausman test goes in favor of FE model. It implies that the estimations

of fixed effect model are consistent when 𝛾𝑖𝑎𝑛𝑑 𝑋𝑖𝑡 are correlated and random effects is

inconsistent.

The procedure of Hausman test is followed by letting 𝛽𝑅�̂� is the vector of estimation of

random effects (excluding the coefficients of time constant variables or aggregate time

variables and 𝛽𝐹�̂� is vector of fixed effects estimations (Frees, 2004). Thus

𝐻 = (𝛽𝐹�̂� − 𝛽𝑅�̂�) [ 𝑣𝑎𝑟(𝛽𝐹�̂�) − 𝑣𝑎𝑟(𝛽𝑅�̂�)]−1(𝛽𝐹�̂� − 𝛽𝑅�̂�) − − − − − (4.69)

The null hypothesis is rejected when the value of H is larger (or statistically significance

and use the fixed effects model; whereas a small value of Hausman test goes in favor of random

effects model.

75

CHAPTER FIVE

TRENDS IN ECOLOGICAL FOOTPRINT,

ECONOMIC GROWTH AND ECOLOGICAL EFFICIENCY

5.1 Introduction

This chapter is organized into four sections. The introduction is being discussed in Section

5.1. In Section 5.2, the trend of ecological footprint and its components, biocapacity, ecological

overshooting, natural resources consumption and socioeconomic factors will be estimated. In

Section 5.3, the trend in ecological footprint, economic growth and ecological efficiency was

discussed. In the section 5.4, the gap between maximum and mean level of ecological

efficiency of total ecological footprint and its components will be estimated.

5.2 Trend of ecological footprint, resources

consumption and socio-economic variables

This section estimate and discuss the trend of ecological footprint and its components,

resource consumption and socioeconomic factors such as per capita GDP, population, level of

urbanization, annual hours worked per employee, export dependency, agriculture,

manufacturing and service intensity. It would also discuss comparison between the total

ecological footprint and its biocapacity and trend of ecological overshooting. Findings of Table

5.1 reveals a decreasing trend of CO2, crop and built-up land footprints from 2005 to 2011,

which implies that area of land required to satisfy humanity demand, urban activities and

assimilation of CO2 emissions are threatened. This is due to rapid increase of energy

consumption while it leads to deteriorating biocapacity of the globe. Findings of this study

shows that forest has larger environmental deterioration than that of grazing and fishing

grounds footprints, in these nations. The decreasing trend of CO2 footprints in period 2005-

76

2011 implies that according to (GFN, 2016a; Li and Zhao, 2016) that demand of CO2 emissions

related resources increased which led to scarce the area of land for assimilation of CO2

emissions - the reported form of CO2 footprint.

Table 5.1

Trend of Ecological Footprint and Its Components

(Global ha/person 2003-2011)


Year Cropland

Footprint

Grazing

land

Footprint

Forest

Footprint

Fishing

Grounds

Footprint

CO2

Footprint

Built-up

land

Footprint

Total

Ecological

Footprint

2003 1.04 0.23 0.70 0.41 3.86 0.25 6.48

2005 1.15 0.28 0.61 0.17 4.04 0.13 6.40

2007 1.02 0.23 0.70 0.26 3.78 0.11 6.10

2009 1.10 0.40 0.50 0.20 3.10 0.10 5.40

2011 1.20 0.20 0.50 0.20 3.00 0.20 5.30

Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org

Figure 5.1 confirms that environmental impact of CO2 is larger than that of other

components of the footprints, due to rapid increases of energy consumption. The share of CO2

footprint reveals that 59 per cent area of land is required for assimilation of CO2 emissions and

wastes generated by these nations, while 37 per cent area of land is required to support the

cropland, forestry, grazing, and fisheries activities. However, only 4 per cent of the area of

land was captured by the built-up footprint during 2003-2011. Yet, the CO2 footprint was the

major environmental degradation’s driver, followed by the cropland and forest footprints.

77

Figure 5.1

Percentage Share of components of Ecological Footprint of High Income Countries

Source: Author’s Computation based on GFN data-set.

Table 5.2 estimate the comparison between total ecological footprint and biocapacity of

high income countries, for the period 2003-2011. The comparison shows that high income

countries have a deficit in resource use because the total ecological footprint is greater than

the biocapacity, during this period. It further implies that these countries have greater demand

for ecological footprint than their biocapacity. Similarly, the ecological overshoot indicates

that these nations do not only consume its entire budget, but also extract its future generation

and other nations’ biocapacity during 2003-2011. However, the findings were consistent with

(GFN, 2014; Jordi et al., 2016), where they argued that demand of high income countries for

planet’s resources and services exceeded than what the earth had regenerated due to higher

standards of living, consumption of resources and goods and services. The question that why

their footprints are more than their biocapacity is further addressed by estimating the trend of

energy consumption (coal, oil and gas and other socioeconomic factors).

16%

11%

4%

6%

4%

59%

Cropland Forest

Grazing land Fishing Grounds

Built-up land CO2 footprint

78

Table 5.2

Total Ecological Footprint vs Biocapacity


Year Total

Ecological

Footprint

Total Biocapacity Biocapacity

(Deficit or

Surplus)

Ecological

Over Shoot

Global ha/per person

2003 6.48 2.50 (3.98 159%

2005 6.40 3.70 (2.70 73%

2007 6.10 3.10 (3.00 97%

2009 5.30 3.20 (2.10 66%

2011 5.40 3.00 (2.40 80%


The results reported in Table 5.3 reveals that high income nations have almost increasing

trends of energy consumption, except in the year 2009. This was due to lower economic

growth, lower demand for export, agriculture and manufacturing items, and a lower share of

the service sector (Asici and Acar, 2016; GFN, 2016a). The results, further shows that variation

in resource consumption affects both the ecological footprints and biocapacity.

79

Table 5.3

Trend of Resources Consumption, 2003-11


Year Coal consumption

(thousand million tons)

Oil consumption

(thousand barrels per day)

Gas consumption

(thousand Billion Cubic Feet)

2003 7.7 14.0 15.5

2005 7.8 14.4 16.0

2007 7.9 14.5 16.6

2009 7.2 13.5 16.5

2011 8.1 14.7 18.2

Source: Author’s Calculation based on international energy statistics data set https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2

The trend of per capita income, population, urbanization and annual hours-worked per

worker during 2003-2011 of high income countries are reported in Table 5.4. The findings

shows that these countries have almost increasing trend in per capita income, population and

urbanization. However, in the year 2009 per capita income had decreased. This was due to

decreasing trend of work-hours and agriculture intensity, and lower share of export and

manufacturing to the national income. On the other hand, increasing trend of population and

urbanization leads to explain more demand for goods and services that leads to exert pressure

on environment by reducing the biocapacity of these nations during 2003-2011.

https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2

80

Table 5.4

Trend of GDP, Population, Urbanization and Hours Works, 2003-11


Year

GDP Per Capita

(in Million$)

Population

(Million)

Urban population

(Millions of total

population)

Annual hours

worked per

worker

2003 2477 1306 1017 1789

2005 2908 1322 1038 1785

2007 3356 1341 1059 1769

2009 3299 1360 1082 1744

2011 3731 1376 1101 1749

Source: http://www.conference-board.org/data/economydatabase/ and World Bank data set

The export, manufacturing and services as percentage of GDP exhibit increasing trend

during 2003 to 2011. More demand for goods and services lead to increase in consumption of

resources (GFN, 2014, 2016b). In year 2009, shares of these sectors decreased because of

lower demand that reduced consumption of resources and pressure on environment. Findings

of this study, also confirm that they have decreasing trend in agriculture sector because of the

scarcity of cropland footprint and conversion of resources to manufacturing and service

sectors.

Table 5.5

Trend of Export, Agriculture, Manufacturing and Services; 2003-11


Year

Export of goods & services

(% of GDP)

Agriculture

(% of GDP)

Manufacturing

(% of GDP)

Services

(% of GDP)

2003 24 2 16 72

2005 26 2 17 73

2007 28 1 18 74

2009 25 1 15 73

2011 30 1 19 75

Source: World Development Indicator Dataset, 2016, http://data.worldbank.org/indicator

http://www.conference-board.org/data/economydatabase/

http://data.worldbank.org/indicator

81

Figure 5.2 explain the share of agriculture, manufacturing and service intensity measured

as percentage to GDP of high income countries. The service intensity accounts to 18 per cent

of GDP followed by manufacturing and agriculture intensity of 17 and 2 per cent, respectively;

which implies that these nations are mainly dependent on service-based activities that demand

for built-up land. The service based activities alongwith scarcity of built-up land would

generate socioeconomic problems that further limit economic growth of high income countries

(Jorgenson and Clark, 2011; Knight et al., 2013). The share of manufacturing in GDP confirms

more demand for resource consumption and generation of CO2 emissions in large volume, and

consequently the area of land required to assimilate CO2 emissions generated by manufacturing

activities becomes scarcer as shown in earlier findings. The share of agriculture implies that,

as percentage of GDP utilization of cropland area, forest, grazing land footprint becomes

lower, because of conversion of resources to service and manufacturing based activities.

Fig. 5.2

Percentage Share of Agriculture, Manufacturing and

Services Intensity of High Income Countries

Source: Author Computation based on WDI dataset, 2016

2%

17%

81%

Agriculture (% of GDP) Manufacturing (% of GDP) Services (% of GDP)

82

Table 5.6 explains the trend of ecological footprints and its components of middle income

countries for the period 2003-2011. The result confirm that environmental impact of crops,

forestry and CO2 emissions is larger than that of other footprints. The argument is supported

by demand for area of land required for prior footprints which is larger than the other footprints.

Similarly, the increasing trend of total ecological footprints also confirms that demand for area

of land required for consumption of goods and services and to assimilate CO2 emissions

generated by these nations have changed by 24 per cent during 2003-2011.

Table 5.6

Trend in Ecological Footprint and Its Components

(Global ha/person 2003-2011)


Year Cropland

Footprint

Grazing

land

Footprint

Forest

Footprint

Fishing

Grounds

Footprint

CO2

Footprint

Built-up

land

Footprint

Total

Ecological

Footprint

2003 0.79 0.17 0.36 0.17 1.19 0.11 3.0

2005 1.06 0.31 0.33 0.11 1.26 0.12 3.2

2007 0.99 0.30 0.44 0.17 1.16 0.14 3.2

2009 1.00 0.30 0.40 0.20 1.20 0.20 3.3

2011 0.98 0.26 0.39 0.13 1.79 0.15 3.7


The share of cropland, forestry and CO2 footprints is almost 84 per cent of total footprints,

confirming that they have a larger environmental impact than the other footprints at the global

level. These nations are in a phase of development, thereby increasing trend of urbanization

further which exerts pressure on consumption of natural resources to meet the demand for

goods and services. In subsequent sections, the ecological footprints and biocapacity of these

countries are compared to assess whether they are in resources surplus or in deficit, during

2003-2011.

83

Fig. 5.3

Percentage Share of Components of Ecological

Footprints of Middle Income Countries

Source: Author Computation based on GFN dataset.

Comparing footprints to biocapacity confirms that they have overshoot in resource

consumption because footprints are more than its biocapacity, which suggests that they

consume the entire budget of resources and some part of its future biocapacity; and therefore,

ecological overshooting of middle income countries during 2005-2011 was 14 per cent of its

biocapacity. It is further estimated that trend of natural resources consumption and the other

socioeconomic factors justify the increasing trend of ecological overshooting.

28%

13%

6%6%4%

43%

Cropland Forest

Grazing land Fishing Grounds

Built-up land CO2 footprint

84

Table 5.7

Total Ecological Footprint vs Biocapacity


Year Total

Ecological

Footprint

Total Biocapacity Biocapacity

(Deficit or

Surplus)

Ecological

Over Shoot

Global ha/per person

2003 2.78 2.84 0.06 -

2005 3.20 3.00 (0.20 07%

2007 3.20 2.90 (0.30 10%

2009 3.30 2.80 (0.50 18%

2011 3.70 3.10 (0.60 19%


The ecological footprint deficit measures resource deficit of a nation in a giving year. More

importantly, such indicators can be associated with sustainable development by governments,

business and NGOs. It would guide them in the direction where they need to go. As the high

and middle income countries accelerate their economic development at the cost of an

increasing trend in ecological footprint deficit as shown by our findings; therefore, they should

try to accelerate their economic development through resource efficiency. It implies that

ecological deficit must be less than biocapacity. In this way sustainable development can be

achieved.

The results reported in Table 5.8 shows that they have increased trend of natural resource

consumption. During 2003-2011, the coal consumption increased by 67 per cent, while oil and

gas consumption increased by 33 per cent and 19 per cent, respectively. The findings confirm

that demand of the area of land required for assimilation of CO2 emissions has increased by 51

per cent and biocapacity deficit reached to 0.4 gha/person, which leads to explain the

environmental pressure through ecological overshooting by 14 per cent.

85

Table 5.8

Trend of Resources Consumption, 2003-11


Year Coal consumption (thousand million tons)

Oil consumption

(thousand barrels per day)

Gas consumption

(thousand Billion Cubic Feet)

2003 4.6 3.6 6.2

2005 5.2 4.0 6.7

2007 5.9 4.2 7.1

2009 6.6 4.4 7.1

2011 7.7 4.8 7.4

Source: Author’s Calculation based on international energy statistics data set https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2

In Table 5.9 the results confirm that per capita income, population and urban population

have an increasing trend. Such increasing trend leads to demand for goods and services for

consumption of natural resources and to built-up land for support of urbanization (A. M.

Usama et al., 2014; Al-Mulali et al., 2015; GFN, 2016a). The growth rates of population and

urbanization are respectively 5 percent and 18 percent, where the cropland, forest, fisheries,

CO2, grazing and built-up land footprints increased by 29 percent during 2003-2011.

https://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=1&pid=1&aid=2

86

Table 5.9

Trend of GDP, Population, Urbanization and Hours Works; 2003-11


Year

GDP Per Capita

(in Million$

Population

(Million Urban population

(Millions of total population

Annual hours

worked per

worker

2003 1241 4586 547 2019

2005 1665 4705 571 2031

2007 2353 4821 596 2024

2009 2725 4938 623 2003

2011 3792 5058 648 2001

Source: http://www.conference-board.org/data/economydatabase/ and World Bank data set

The reported result of Table 5.10 reveals that export, manufacturing and service sectors are

major contributors to the GDP of middle income countries. The trend analysis confirm that

share of these sectors increased, while agriculture’s contribution to GDP have decreased. The

change in export, manufacturing and services are 21, 25 and 12 per cent respectively, which

implies that percentage of GDP increased the manufacturing and services; while agriculture

decreased as percentage of GDP. It further implies that agriculture intensive economies

converted to manufacturing and service intensive economies.

http://www.conference-board.org/data/economydatabase/

87

Table 5.10

Trend of Export, Agriculture, Manufacturing and Services; 2003-11


Year

Export of goods & services

(% of GDP)

Agriculture

(% of GDP)

Manufacturing

(% of GDP)

Services

(% of GDP)

2003 28 13 24 51

2005 31 11 25 52

2007 32 11 27 54

2009 32 10 28 56

2011 34 10 30 57

Source: World Development Indicator Dataset

88

5.3 Trend of ecological footprints, economic growth and

ecological efficiency

This section explains the trend of ecological footprints, economic growth and ecological

efficiency of high and middle income countries for the period of 2003-2011. The ecological

efficiency is constructed to explain as to how much the economic output is yielded by utilizing

the per global hectare area in the form of gross domestic product. Results of Table 5.11

indicate the increasing trend of ecological efficiency and economic growth from 2003-2011

(except for the year 2009) where economic growth was negative. This is due to less utilization

of area of land, resources consumption and lower demand for agriculture, manufacturing and

export of goods and services. In year 2011 economic growth is positive which leads to higher

demand for ecological footprints and resources consumption. The increasing trend of

ecological efficiency and decreasing trend of total ecological footprints confirms that economic

output in terms of gross domestic product is yielded in efficient way because of less utilization

of per global hectare of area.

Table 5.11

Ecological Footprint, Economic Growth and Ecological Efficiency; 2003-2011

Year


Total Ecological Footprint

(gha/per capita)

Economic Growth

(annual %)

Ecological Efficiency

(1000 of income/gha)

2003 6.48 2.21 1.91

2005 6.40 2.86 2.27

2007 6.10 2.88 2.75

2009 5.30 -3.47 2.11

2011 5.40 1.91 2.66

Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Data set

89

The estimated values of ecological footprints are presented in Table 5.12 which shows an

increasing trend during 2003-2011; while economic growth and ecological efficiency

increased in the period of 2003-2007. In year 2009, the reduction of economic growth led to

reduce the ecological efficiency; and because of the increasing economic growth, the

ecological efficiency improved in the year 2011. From the prior results, nations with more per

capita income appear to have larger ecological footprints and efficiency.

Table 5.12

Ecological Footprint, Economic Growth and Ecological Efficiency; 2003-2011

Year


Total Ecological Footprint

(gha/per capita)

Economic Growth

(annual %)


(1000 of income/gha)

2003 2.78 2.8 0.45

2005 3.20 3.5 0.52

2007 3.20 4.2 0.74

2009 3.30 1.7 0.72

2011 3.70 3.0 1.02


90

5.4 Analysis of ecological efficiency index, maximum and

mean level of ecological efficiency

In this section the ecological efficiency index of total footprints and its components using

chain-based approach proposed by Qiu (2013) is estimated. The value of index is greater than

one which confirms that efficiency of resource consumption in production of economic output

improved in the current year than the previous year. The findings presented in Figure 5.4

suggest that ecological efficiency of both the high and middle income countries in the current

year has improved than the previous year. However, in year 2009 they had less ecological

efficiency than the year 2007 which was due to the decreasing economic output. The result

also suggest that ecological efficiency index of high income countries is less than the middle

income countries; while it was found that these countries consumed resources and services at

a faster rate. The ecological efficiency or resource intensity of each country in high and middle

income regions is relative to the best performer (i.e., highest ecological efficiency and the mean

of resource intensity), presented in Appendix-D of the study. The table 2D shows that the

environmental impact intensity of Pakistan in year 2003 was 1.124 gha/GDP and reached to

1.154 gha/GDP in year 2005. This increasing trend is due to; along with expansion of economic

development, the amount of material resources has been increased in Pakistan. However, on

ward the Pakistan’s environmental intensity has been declined and various reasons are

associated with this trend. Firstly, the financial crises leads to mitigate demand of material

resource and increased supply of biocapacity of Pakistan. Secondly, as the share of service

sector in GDP has been increased and reduced CO2 footprint. Thirdly, the forest and grazing

land has accelerated in Pakistan from 4.8% in year 2005 to 5.17% in year 2011 due to

implementation of environmental protection policies. Fourthly, the numbers of power plants

from use of coal have been converted to natural gas. As a result, the CO2 emissions reduced

and consequently increased supply of biocapacity of Pakistan. The implementation of Micro

91

Hydro Power (MHP) plants instead of wood consumption and CNG instead of coal usage for

domestic purposes also reduced forest and CO2 footprint of Pakistan.

The study also evaluated relative ecological efficiency or relative resource intensity8 of each

high income countries as depicted in Table 3D for the period of 2003-2011. The relative

resource intensity for each nation is calculated by dividing ecological efficiency or

environmental impact intensity per unit of gross domestic product (i.e., EF/GDP) on mean

(0.191 gha/GDP) and the best performer in ecological efficiency (environmental impact

intensity=0.077gha/GDP) of the sample countries. Then the rank is assigned to sample

countries based on these two methods where rank one represents the lowest resource intensity

(most eco-efficient country). According to the findings, the rank of Switzerland is one, which

represents that she is the best performer in ecological efficiency. The rank of Estonia among

High income countries is 77, which represents that it is the lowest performer in ecological

efficiency because the environmental impact intensity of per unit of gross domestic product of

Estonia is the largest. The most efficient nation is Switzerland with one-fourth (0.40) of the

cross-sectional mean environmental impact intensity per unit of gross domestic product; while

the least efficient nation is Estonia with more than double (2.55) mean environmental impact

intensity per unit of gross domestic product. It implies that 6.37 fold (2.55/0.40) difference in

ecological efficiency between Switzerland (the most eco-efficient) and Estonia (the least

efficient).

There are various factor behind best ecological efficiency performance of Switzerland.

Firstly, improvement in public transport instead of private transport leads to mitigate

Switzerland CO2 footprint. Secondly, improve share of service sectors (i.e. 71% in 2010) and

less dependency on resource intensity sectors increased Switzerland’s ecological biocapacity.

8 resource intensity is inverse of ecological efficiency. Higher resource intensity implies lower ecological efficiency.

92

Thirdly, Switzerland with collaboration of business sector to have implanting environmental

friendly technology and through reducing agriculture and forest footprint with various

agreement for the protection of local environment particularly improve his ecological

efficiency. As the National Action Plan monitor ecological condition and identify the

deficiency on regular basis. Fourthly, improvement in water footprint through implementation

of water waste treatment plants has been improved its ecological efficiency.

There are various factors behind lowest ecological efficiency performance of Estonia. Firstly,

huge dependency on oil shale based energy production which has been increased Estonia’s

CO2 footprint by 80%. Secondly, inefficient use of natural resources to make material goods

and services. Thirdly, the average age of forest is lessening and lessening because of increasing

trend in built-up land footprint.

Similarly, it is also calculated that ecological efficiency of each middle income countries

is based on mean and the best performer in resource intensity for the period of 2003-2011. The

most efficient nation, according to these two methods is Timor-Leste and the least ecological

efficiency or the largest environmental impact intensity per unit of gross domestic product’s

country is Congo, whose rank is 77. The most efficient nation, Timor-Leste has 5/6 (0.12)

cross-sectional mean environmental impact intensity of per unit of gross domestic product,

while the least efficient nation, Congo has more than threefold (3.53) mean environmental

impact intensity per unit of gross domestic product. It implies 29.41 fold (3.53/0.12) difference

in ecological efficiency between Timor-Leste (the most eco-efficient) and Congo (the least

efficient).

The Timor-Lest has achieved improvement in agriculture ad forest biocapacity with

cooperation of NGOs and rural communities. The country also initiated sustainable energy

power projects for example wind power, solar power and natural gas instead of coal

consumption to meet the power demand with less environmental impact possible.

93

Improvement in grazing land, LPG instead of firewood improve forest biocapacity and thus

ecological efficiency of Timor-Lest.

The lowest ecological efficiency of Congo can be associated with corruption, war and

political instability leads to reduce economic expansion in the sample period. While on other

side, huge exploitation of natural resources has been sluggish Congo’s ecological performance.

It is concluded from the above discussion that there is a greater variability between middle

and high income countries’ ecological efficiency (29.41 fold and 6.375 fold) differences

between the most and least ecological efficiency nations, respectively. Therefore, improvement

in ecological efficiency in total ecological footprint, cropland, grazing land, forest, fishing

grounds, CO2 footprint and built-up land footprint in the sample countries could lead to reduce

the difference between the most and least eco-efficient nations. Thus, we also find the gap

between mean and the maximum level of ecological efficiency which mark the potential in

total ecological footprint and its components.

94

Fig. 5.4

Ecological Efficiency Index of High and Middle Income Countries: 2005-11


In Table 5.13, the estimated gap between the maximum and mean level of ecological

efficiency of high income countries through the computation process is proposed by Qiu

(2013), where 𝑅𝐼𝑖𝑡 is used to reflect the gap between a country’s i efficiency in resources

utilization and maximum ecological efficiency of year t among a group of nations. The value

of RI revealed that fishing grounds footprints has lower difference from its best performer,

followed by built-up land footprint, CO2 and other components of total ecological footprint. It

implies that the cropland, grazing land and forest footprints have more room in order to achieve

its best performer (maximum level), followed by CO2, built-up land and fishing grounds

footprint respectively.

Table 5.13 The Gap between Efficiency in Resources Utilization

and Maximum Level of Ecological Efficiency, 2003-11


Resources

(millionUS$/gha)

Total

Ecological

footprints

Cropland

footprint

Grazing

land

footprint

Forestland

footprint

CO2

footprint

Finishing

ground

land

footprint

Built-up

land

footprint

Max EE 21085 2785 636542 98431 14576 174538 177071

Mean EE 8540 551 103384 24904 1828 47878 41722

𝑅𝐼𝑖𝑡 2.64 15.16 7.97 3.65 4.24

Figure 5.5 focus on more room on components of ecological footprint for achieving its

maximum level of ecological efficiency in percentage. The cropland, grazing land and forest

95

footprints have 49 per cent more room to achieve its maximum level of efficiency, while CO2

has 25 per cent, followed by 13 per cent and 12 per cent built-up land. The fishing ground have

more potential to achieve its maximum level of efficiency in near future, by high income

countries.

Fig. 5.5

Percentage share of components of Total

Ecological Footprints in High Income Countries

Source: Author Computation based on GFN Dataset.

In Table 5.14, there is an estimated gap between the mean and maximum levels of

ecological efficiency at the disaggregate level of middle income countries, for the period of

2003-2011. The value of RI reveal that CO2 footprint has lower difference from its best

performer, followed by built-up land footprint, fishing grounds and other components of the

total ecological footprint. It implies that other components like cropland, grazing land and

forest footprint have more room in order to achieve its best performer (maximum level of

ecological efficiency), followed by fishing grounds, built-up land, fishing ground footprint and

CO2 footprint, respectively.

42%

19%

21%

18%

Cropland,Grazing & Forest footprints Built-up land

Fishing Grounds footprint CO2 footprint

96

Table 5.14

The Gap between Efficiency in Resource Utilization

and Maximum level of Ecological efficiency, 2003-11


Resources

(millionUS$/gha)

Total

Ecological

footprints

Cropland

footprint

Grazing

land

footprint

Forest

land

footprint

CO2

footprint

Finishing

ground

land

footprint

Built-up land

footprint

Max level

Ecological

Efficiency

562 2987 64233 98431 7775 322473 182969

Mean level

Ecological

efficiency

256 1183 22595 24904 2054 73121 44325

𝑅𝐼𝑖𝑡 2.19 9.32 3.78 4.41 4.12


The results of the Figure 5.6 shows that in order to achieve maximum efficiency of crop,

forest and grazing land, footprints have 42 per cent more room to promote its efficiency

followed by fisheries, built-up land and CO2 of 21 per cent, 19 percent and 18 per cent,

respectively, in case of middle income countries. Such discrepancy leads to estimate inequality

and environmental intensity of resources of high and middle income countries.

97

Figure 5.6

Percentage Share of Components of Total Ecological

Footprint in Middle Income Countries

Source: Author Computation based on GFN Dataset.

42%

19%

21%

18%

Cropland,Grazing & Forest footprints Built-up land

Fishing Grounds footprint CO2 footprint

98

CHAPTER SIX

ECOLOGICAL FOOTPRINT, ENVIRONMENTAL

IMPACT INTENSITY AND INCOME INEQUALITY

6.1 Introduction

This chapter is classified into four Sections. Section 6.1 covers the introduction of the

chapter. In Section 6.2 and 6.3, the estimated inequality and per capita mean values of

ecological footprint, income and environmental impact intensity of high and middle income

countries are presented, respectively. Section 6.4 focus on discussion regarding inequality

between High and Middle income countries.

6.2 Ecological footprint, environmental impact intensity

and income inequality of high income countries

In the previous chapter, the estimated trend of ecological footprint, biocapacity, ecological

overshooting, consumption of natural resources and other socioeconomic factors where the

findings reveal that ecological footprint demand and resource consumption and services are

much faster in high income countries, than the middle income countries that lead to generate

environmental degradation at global level. It is supported by Asici and Acar (2016); GFN

(2016a).

In this chapter, the environmental impact intensity, per capita ecological footprint and

income inequality is estimated through the Atkinson Index. The results reported in Table 6.1

reveal that the share of Atkinson index of equality of environment intensity is 31 per cent less

than the equality in per capita income of 45 per cent which leads to produce only 25 per cent

of equality in share of land among high income nations9 while the average total ecological

9 The percentage share from Table 6.1 to Table 6.14 of total ecological footprint, its component, per capita

income and environmental impact intensity obtained divided each Atkinson value on its total share.

99

footprint is 5.03 gha/person and environmental impact intensity is 2.01 footprints per $1000

of income. It implies that there is more variation in resources consumption and demand for

goods and services that leads pressure on the environment. The trend in the environmental

impact intensity of each country for high income countries is presented in Appendix-D.

According to the trend in environmental impact intensity of high income countries depicted in

Table 1D, shows that 57 percent of the sample countries follow mixed trend in their

environmental intensity in 43 percent countries. Similarly, the trend in environmental intensity

of middle income countries is depicted in Table 2D which shows that 60 percent of the sample

countries follow mixed trend in environment intensity and 40 percent middle income countries

follow a declining trend. Thus, suggesting that countries having a mixed trend in their

environmental intensity should follow the policy of the sample countries where their

environmental impact intensity of per unit of gross domestic product declined during the

sample period.

Table 6.1

Atkinson Index of Equality: Total Footprint, per Capita Income, and Environmental

Intensity, 2003-11

Total Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw

(footprint per $1000 income)

0.340

5.03

0.650

$3154

0.450

2.01

a Source: Global Footprint Network (2015, www.footprint network.org.

b Real GDP per capita. Source: World Bank Data

c Author's calculation, based on F /y (Total Footprint per unit of income

100

Table 6.2 shows the mean of cropland footprint, per capita income and environmental

intensity and the Atkinson indices of equality. The estimated values of Atkinson index of

environmental intensity and cropland footprint reveal that they have larger inequality than the

per capita income (because of 1-Ay is close to one in the period of 2003-2011). The share of

Atkinson index of equality of environmental intensity is 30 per cent, followed by cropland

footprint and per capita income of 31 per cent and 39 per cent. It implies that there is a large

variation in the area of land demanded for cropland among high income countries. According

to mean, the area of land required to support cropland footprint is 0.91gha per capita and

environmental intensity is 0.28 footprints per $1000 of income.

Table 6.2

Atkinson Index of Equality: Cropland Footprint, per Capita Income, and Environmental

Intensity, 2003-11

Cropland Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.514

0.910

0.650

$3154

0.510

0.280



c Author’s calculation, based on F /y (Total Footprint per unit of income.

Table 6.3, estimate the Atkinson indices of environment intensity, at per capita income and

the grazing land footprint, alongwith its mean values. According to the Atkinson index, the

101

distribution of per capita income and grazing land footprint exhibit more inequality than the

environmental impact intensity (because 1-Aw is close to one). The environmental impact per

unit of economic output is 40 per cent and distribution of land to support grazing activities is

28 per cent. The Atkinson index of equality of per capita income is 32 per cent less than that

of environmental impact intensity and grazing land footprint exhibits lower environmental

intensity per unit of economic output. The mean grazing land footprint is 0.27 gha per capita

and the environmental impact intensity is 0.086 footprints of per $1000 of income.

Table 6.3

Atkinson Index of Equality: Grazing Footprint, per Capita Income, and

Environmental Intensity: 2003-11

Grazing land Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.582

0.270

0.650

$3154

0.830

0.086



c Author's calculation, based on F /y (Total Footprint per unit of income.

102

Table 6.4 estimate the mean and Atkinson index of environmental intensity of per capita

income and forest footprint. According to the Atkinson index, the distribution of per capita

income and the forest footprint exhibit more inequality than the environmental intensity. The

share of Atkinson index of equality with respect to environmental intensity is 38 per cent

greater than the equality in per capita income and the forest footprint whose equality shares are

32 per cent and 30 per cent, respectively. According to the mean, the area of land required to

support forest activities demanded by these nations was 0.58 gha per capita and its environment

intensity was 0.27 footprints per $1000 of income. These results suggest that high income

countries have large inequality in distribution of forest land footprint and the environmental

intensity.

Table 6.4

Atkinson Index of Equality: Forest Footprint, Per Capita Income, and Environmental

Intensity, 2003-11

Forest Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw

Mean, µw


0.595

0.580

0.650

$3154

0.752

0.270




103

Table 6.5 estimate the Atkinson index and the mean values per capita CO2 footprint and

income and environment intensity. The calculation of the Atkinson index shows the CO2

footprint inequality related to environmental impact intensity and the per capita income.

According to the index, the distribution of per capita Co2 footprint exhibited more inequality

than environmental intensity and the per capita income. It implies that variation in resource

consumption, annual hours worked per employee, manufacturing and service intensities; and

the CO2 footprint are responsible factors for inequality in environmental intensity; as suggested

by the results. During the period 2003-2011 and according to the index, the distribution of

environmental intensity and CO2 footprints exhibit 27 per cent and 19 per cent equality and

the per capita income of equality is 54 per cent. The average environmental impact of CO2 is

0.95 footprints per $1000 of income, and its footprint is 2.98 gha per capita. It implies that

CO2 has a larger environmental impact intensity that requires more land to assimilate CO2

emissions of high income countries.

Table 6.5

Atkinson Index of Equality: CO2 Footprint, per Capita Income,

and Environmental Intensity, 2003-11

CO2 Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.230

2.98

0.650

$3154

0.320

0.950



c Author's calculation, based on F /y (Total Footprint per unit of income

104

Table 6.6 reports the Atkinson index of income, environmental intensity and fishing

grounds footprints, and its average values. The average in environmental intensity shows that

impact per $1000 of income on environment is 0.05 footprints and the rea of land required to

support fishing activities is 0.17 gha per capita. According to the Atkinson index of equality,

distribution of fishing grounds footprints exhibits more inequality than income and

environment intensity (1-Ay close to zero). The share of environmental intensity and income

is 38 per cent, and 37 per cent is larger than that of fishing grounds footprints of 25 percent.

Table 6.6

Atkinson Index of Equality: Fish Footprint, per Capita Income, and Environmental

Intensity, 2003-11

Fisheries Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.445

0.170

0.650

$3154

0.672

0.054




The reported results of Table 6.7 include Atkinson indices and average values of income,

environmental intensity and built-up footprints. The average built-up footprint is 0.14 gha per

capita and the environmental intensity is 0.04 footprints per $1000 of income. Calculation of

the Atkinson index of built-up land footprints exhibits more inequality than income and

environmental intensity (1-AF is close to zero). The shares of per capita income and intensity

105

is 46 per cent, and 33 per cent and is larger than that of the built-up land footprints of 25

percent.

106

Table 6.7

Atkinson Index of Equality: Built-up Footprint,

per Capita Income, and Environmental Intensity, 2003-11

Built-up land Footprinta Per Capita Income Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.308

0.140

0.650

$3154

0.465

0.044





income inequality of middle income countries

In the following sections, the Atkinson indices and mean footprints, income and the

environmental intensity of middle income countries are estimated. The mean environmental

intensity is 0.94 footprints per $1000 income and the area of land required to support human

activities is 2.23 gha/person. According to the Atkinson index of equality, the distribution of

per capita income and ecological footprints exhibits more inequality than environmental

intensity. The share of Atkinson index of environmental intensity is 50 per cent larger than the

ecological footprint and the income share of 26 per cent and 24 per cent , respectively . The

trend in the impact intensity of each country (for middle income countries) is presented in

Appendix-D of the study.

107

Table 6.8

Atkinson Index of Equality: Total Footprint, per Capita income,


Total Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.310

2.23

0.346

$2355

0.650

0.942




Table 6.9 shows the estimated means of cropland footprint, per capita income,

environmental intensity and its Atkinson indices. The mean environmental impact intensity is

0.43 footprints of per $1000 of income and area of land required for cropland footprint are

0.98gha per capita. According to Atkinson index, distribution of per capita income and

cropland land footprint exhibits more inequality than environmental intensity. The distribution

share of environmental intensity, per capita income and cropland land footprints of equality

are respectively 50 percent, 22 percent and 28 percent.

108

Table 6.9

Atkinson Index of Equality: Cropland Footprint, per Capita Income,


Cropland Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.450

0.980

0.346

$2355

0.790

0.425




Table 6.10 shows the estimated means of grazing footprints, per capita income,

environmental intensity and its Atkinson indices. The mean environmental impact intensity is

0.12 footprints/$1000 of income and area of land required for grazing footprint which is 0.28

gha per capita. The estimated value of per capita income and grazing footprint confirms more

inequality than the environmental intensity. The share of grazing footprint, per capita income

and environmental intensity is 29 per cent, 30 percent and 41 per cent of equality.

109

Table 6.10

Atkinson Index of Equality: Grazing Footprint,

per Capita income, and Environmental Intensity, 2003-11

Grazing land Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.351

0.281

0.346

$2355

0.490

0.120




The estimated mean of environmental intensity and forest footprint reported in Table 6.11

are 0.18 footprints/$1000 of income and 0.35 gha/person. According to the Atkinson index

distribution of income, forest and environmental intensity exhibit more inequality. Forest has

30 per cent of equality distribution followed by income and intensity of 30 per cent and 37 per

cent of equality.

110

Table 6.11

Atkinson Index of Equality: Forest Footprint, per Capita Income,


Forest Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.310

0.350

0.346

$2355

0.380

0.150




Table 6.12 reported indices and means of CO2, income and environmental intensity of CO2

footprints. With reference to the mean value, CO2 environmental intensity is 0.37

footprints/$1000 of income and its footprint is 0.87 gha per capita. The estimated value of

Atkinson indices exhibited the inequality in environmental intensity and CO2 footprint is

relatively larger than inequality in per capita income.

111

Table 6.12

Atkinson Index of Equality: CO2 Footprint, per Capita Income,


CO2 Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.350

0.874

0.346

$2355

0.310

0.371




The average estimated value of environmental intensity and forest footprint reported in

Table 6.13 are 0.18 footprints/thousands of income and 0.35gha/person, respectively. The

Atkinson index with reference to environmental intensity and fisheries exhibits 62 per cent and

80 per cent inequality in environmental intensity and fisheries footprints among middle income

countries exist.

112

Table 6.13

Atkinson Index of Equality: Fish Footprint, per Capita Income,


Fisheries Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.204

0.090

0.346

$2355

0.381

0.038




The Atkinson index of built-up land footprint reported in Table 6.14 exhibits that inequality

in per capita income and built-up land footprint is relatively higher than inequality in

environmental intensity. The relatively equal distribution of environmental intensity and per

capita income could lead to minimizing built-up land footprint inequality among the middle

income countries. The average environmental intensity and built-up footprint are 0.021

footprints/thousands of income and 0.065 gha per capita, respectively.

113

Table 6.14

Atkinson Index of Equality: Built-up Footprint, Per Capita Income,


Built-up land Footprinta Per Capita Incomeb Environmental Intensitya,b,c

(1-AF)

Mean, µF

(global ha/person)

(1-Ay)

Mean, µy

(US dollars)

(1-Aw)

Mean, µw


0.281

0.065

0.346

$2355

0.451

0.028




114

6.4 Discussion

From the prior discussion, it is concluded that nations with high income have higher

measures of environmental intensity and therefore have larger ecological footprints. The per

capita income of high and middle income countries are $3154 and $2355, respectively, and

therefore, there is a larger measure of footprints and environmental intensity by high income

countries in the case of CO2, forest, fisheries and built-up land footprint in the period of 2003-

2011. It shows that the demand for area of land required for CO2 footprint, forest, fishing

grounds and built-up land footprint in case of high income countries is greater than the middle

income countries, which lead to greater environmental impact intensity and ecological over

shooting, as described in Chapter Five of this study.

The inequality of environmental intensity is larger than per capita income of high income

countries and therefore relatively, equal distribution of intensity can be contributed to the

distribution of ecological footprints which is more equal than the distribution of income. In

case of middle income countries, relatively equal distribution of income could contribute to

the distribution of footprints that is more equal than the distribution of environmental impact

intensity.

Similarly, inequality in per capita income, total ecological footprint, cropland footprint,

grazing land, forest, fishing grounds and built-up land footprint have comparison between the

high and middle income countries. reveals that there should be more equal distribution in these

footprints for middle income countries which will lead to reduce its environmental impact

intensity, while there should be more equal distribution in CO2 footprints for high income

countries. This will lead to reduce its environmental impact intensity because the CO2 footprint

has larger inequality for high income countries.

115

CHAPTER SEVEN

THE DRIVING FORCES OF TOTAL ECOLOGICAL

FOOTPRINT AND ITS COMPONENTS

7.1 Introduction

This chapter is classified into five Sections. Section 7.1 explore the introduction of the

chapter. Sections 7.2, 7.3 and 7.4 estimate the various driving forces of the total ecological

footprints and its components of high and middle income countries. Section 7.4 covers the

discussion and the findings.

7.2 The driving forces of total ecological footprint and

its component in high-middle income countries

This section estimates and interprets the various driving forces’ impact on total ecological

footprint, cropland footprint, forest, fishing grounds, grazing land, CO2 footprint and built-up

land footprint by using combined panel of high-middle income countries. Table 7.1 presents

the effect of driving forces on total ecological footprint . The findings confirm the existence of

the EKC hypothesis between affluence and total ecological footprint. It implies that as

economic development increases, ecological footprint increases while further level of

economic development decreases the ecological footprint of sample countries. Besides,

population, the level of urbanization, fossil fuel, manufacturing and agricultural intensity

increases the total ecological footprint. The impact of population on ecological footprint is

larger than the other driving forces, followed by the manufacturing and level of urbanization.

The coefficients associated with these driving forces are positive and statistically significant.

The response of one percent increase in population, agriculture intensity and the level of

urbanization on ecological footprint is 0.64 per cent, 0.58 per cent and 0.13 per cent,

respectively. The impact of export intensity and ecological efficiency is negative and

116

statistically significant. It shows that the response of one per cent increase in these factors on

ecological footprint are 0.36 per cent and 2.56 per cent. Thus, the improvement in ecological

efficiency is decoupling the environmental degradation. The findings suggest that proper urban

planning, environmental friendly service, manufacturing and agricultural activities, as well as

low carbon intensity energy use in the sample countries should mitigate the tension between

environmental degradation and sustainable development.

117

Table 7.1

The Driving Forces of Total Ecological Footprint:

High-Middle Income Countries ( Random Effect Model)

Independent Variables Coefficients t-Statistic

ln(GDP) 2.55* 8.22

ln(GDP2) -0.11* -14.23

ln(POP) 0.64* 9.62

ln(UR) 0.13* 3.04

ln(FF) 0.06* 2.42

ln(EI) -0.36* -5.16

ln(SI) 0.31* 3.94

ln(MI) 0.58* 12.18

ln(AI) 0.04** 2.09

ln(IE) -0.02 -0.55

Ecological Efficiency -2.56* -3.10

Constant -5.04* -8.75

R-Squared: 0.89

F-Statistic: 666.33* Huasman test : 0.0000 (0.921)☼

Sample: 2003Q1-2011q4; & Cross-sectional units = 95; periods included=36

Total Panel (Balanced observations) =3420

*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5% supports the RE model

Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset

118

Table 7.2 focus on the effect of various driving forces on cropland footprint, fishing

grounds and forest footprint by using the aggregated dataset of high-middle income countries.

With reference to determinants of cropland footprint, the empirical results support the EKC

hypothesis. Initial level of economic development increases the cropland footprint, and further

level of economic development increases the sample countries’ cropland footprint. The other

driving forces that contribute to increase in the cropland footprint are population, agricultural

intensity and consumption of agriculture items. The coefficients associated with these forces

are positive and statistically significant. It implies that a one per cent increase in population,

agricultural intensity and consumption of agriculture items increase the cropland footprint by

0.77 per cent, 0.25 per cent and 0.04 per cent, respectively. However, the major driving forces

that greatly contribute to increase the cropland footprint are the initial level of economic

development, followed by population and agricultural intensity. The impact of ecological

efficiency is negative and statistically significant. It implies that improvement in ecological

efficiency decreases the cropland footprint. In the light of the findings, it is suggested that

continued process of economic development and efficiency in material resource consumption

reduces the environmental degradation. The findings are consistent with Wiedmann et al.

(2015); Asici and Acar (2016) results of driving forces of crops footprint that increase further

in level of economic development which leads to increased cropland footprints. It also

provided strong support for the hypothesis that footprint increases with initial level of

affluence.

The findings of fisheries footprint confirms the EKC relationship which reveals that initial

stage of economic development leads to increase fishing ground footprints while the economic

development of sample countries leads to reduce their fishing ground footprint, further. The

results indicate a positive relation between fisheries footprints and the consumption of fisheries

products, and a positive and insignificant impact of population and urbanization on fisheries

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footprints. It implies that a one per cent increase in consumption of fisheries products will

increase the fishery footprints by 0.63 per cent. However, the impact of ecological efficiency

on fishery footprints is negative and statistically significant. It shows that improvement in

ecological efficiency decreases the fishery footprints. Therefore, growth in income, mitigation

of the overexploitation of fish and fishery products, and further improvement in ecological

efficiency would reduce the fishery footprints.

Findings of forest footprints confirm the validity of EKC hypothesis, because the effect of

initial level of affluence on forest footprint is positive as it further increase the level of

affluence which reduce the forest footprints. The other driving forces that accelerate the forest

footprint are population, education and export of primary products. It implies that a one per

cent increase in these driving forces will increase the forest footprints by 0.72 per cent, 0.11

per cent and 0.05 per cent, respectively. The findings also indicate a negative and significant

relation among urbanization, ecological efficiency and the forest footprints. The improvement

in ecological efficiency reduces the forest footprint while the level of urbanization reduces the

environmental sustainability, further. The findings suggest that investment in the education

sector, proper planning for urbanization, replacing export of forest substitute items, and further

increase the ecological efficiency which can lead to reduce the forest footprint.

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Table 7.2

The Driving Forces of the Components of Ecological Footprint:

High-Middle Income Countries (Random and Fixed Effect Models)

Cropland footprint Fisheries footprint Forest footprint

Independent Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic

ln(GDP) 2.46* 2.65 0.84* 3.74 2.67* 13.26

ln(GDP2) -0.01* -2.26 -0.04* -4.89 -0.03* -11.48

ln(POP) 0.77* 8.29 0.07 0.44 0.72* 7.31

ln(UR) 0.12 0.30 0.05 0.22 -0.16** -1.80

ln(AI) 0.25* 2.25 - - - -

ln(EDU) 0.10 0.65 - - 0.11* 2.42

ln(CA) 0.04** 1.67 - - - -

ln(SF) - - 0.63* 11.50 - -

ln(EP) - - - - 0.05** 1.84

Ecological Efficiency -1.08* -2.70 -1.23* -2.28 -5.87* -14.53

Constant -7.74* -2.74 2.70** 1.92 4.87* 14.04

R-Squared: 0.85

F-Statistic: 481.23* Huasman test: 0.000 (0.962) ☼

0.69 192.53*

88.71(0.000) ●

0.75 247.60*

09.40(0.210) ☼



*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less

than 5% supports the FE model.

Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset

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Table 7.3 reports the impact of various driving forces of CO2 footprint, grazing land and

built-up land footprints. With reference to the empirical findings of CO2 footprint, the findings

support the hypothesis; because, increase at initial level of economic development increases

the CO2 footprint and the level of economic development, decreases the CO2 footprint further.

The other driving forces that contribute to increase the footprint are population, urbanization,

coal, oil, gas and the manufacturing intensity. However, the major contributor to CO2 footprint

is population, followed by urbanization and manufacturing intensity. It implies that a one per

cent increase in these factors increases the footprint of CO2 emissions by 0.66 per cent, 0.42

per cent and 0.15 per cent, respectively. The possible recommendation in the light of findings

of CO2 footprint is that sample countries should promote the implication of solar and wind

power generation. Thus, investment in renewable resources would reduce the CO2 footprint

that mostly occurred from the fossil fuel consumption (Uddin et al., 2017).

With reference to the grazing land footprint, the findings support the EKC hypothesis. The

initial level of economic development increases the grazing land footprints and the level of

economic development decreases the footprint, further. However, the impact of initial level of

economic development on grazing land footprint is larger than the further level of economic

development. It implies that with one per cent increase in the initial economic development;

grazing land footprint increase further by 0.51 per cent and the level of economic development

decreases the footprint by 0.01 per cent, further. The other driving forces behind the

acceleration of grazing land footprint are population and production of livestock; because, one

percent increase in population and production of livestock, separately increases the grazing

land footprint by 0.15 per cent and 0.02 per cent. The level of urbanization and ecological

efficiency are observed to reduce the grazing land footprint. The coefficient associated with

ecological efficiency is negative and statistically significant. It implies that one percent

increase in ecological efficiency decreases the footprint by 1.90 per cent. Therefore, reductions

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in these factors through different policies can lead to reduce the grazing land footprint and can

achieve the environmental sustainability.

With reference to the built-up land footprint, the empirical findings support the EKC

hypothesis; because the coefficients associated with initial and further levels of economic

development, have expected and statistically significant signs. It implies that initial level of

development increase the built-up land footprint while the further development decreases the

footprint. The population, urbanization and employment forces contribute to increase the built-

up land footprint. However, the impact of urbanization on footprint is greater than population

and employment. It implies that one per cent increase in urbanization, increases the built-up

land footprint by 0.82 per cent and the population and employment impact on footprint are

0.61 per cent and 0.01 per cent, respectively. The service intensity and ecological efficiency

are observed to reduce the built-up land footprint. The acceleration of service intensity and

ecological efficiency would reduce the built-up land footprint. Reduction in population,

urbanization and employment would reduce the built-up land footprint, in the future.

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Table 7.3

The Driving Forces of the Components of Ecological Footprint:

High-Middle Income Countries (Random and Fixed Effect Models)

CO2 footprint Grazing land footprint Built-up land footprint

Independent

Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic

ln(GDP) 1.54* 17.03 0.51* 7.82 0.57* 12.20

ln(GDP2) -0.02* -7.75 -0.01* -3.61 -0.02* -8.50

ln(POP) 0.66* 8.78 0.15* 3.16 0.61* 13.05

ln(UR) 0.42* 3.72 -0.28* -3.21 0.82* 13.35

ln(EI) -0.09* -5.81 - - - -

ln(COAL) 0.05** 1.71 - - - -

ln(OIL) 0.02* 14.26 - - - -

ln(GAS) 0.05* 7.01 - - - -

Ln(MI) 0.19 8.02 - - -0.09* -5.53

ln(HW) 0.002 0.58 - - - -

ln(PL) - - 0.02** 1.94 - -

ln(SI) - - - - -0.18* -6.13

ln(EM) - - - - 0.01* 3.72


Constant -1.13** -1.86 11.81* 8.31 -0.78 -1.15

R-Squared: 0.95

F-Statistic: 215.7* Huasman test: 0.000(0.972)☼

0.96 190.02*

1.31(0.96) ☼

0.93 105.01*

13.31(0.0001) ●



*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less than

5% supports the FE model.

Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank Dataset

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7.3 The driving forces of total ecological footprint

and its component in high income countries

This section estimates and interprets the impact of driving forces of the total ecological

footprint and its components for the high income countries. Table 7.4 incorporate the impact

of various influencing factors on total ecological footprint. The findings support the EKC

hypothesis because the coefficient associated with GDP is positive and GDP2 is negative. It

implies that further level of economic development reduces the total ecological footprint.

However, the initial stage of economic development increase the total ecological footprint

because the coefficient associated with GDP is positive and statistically significant. It implies

that one per cent increase in economic development leads to 1.92 per cent increases in the total

ecological footprint. Similarly, the coefficient associated with population is positive and

statistically significant as it suggests that one per cent increase in population leads to 0.01 per

cent increase in the footprints, citrus paribus. The results are consistent with York et al. (2004);

Jorgenson and Burns (2007); Anders and John (2009); Mostafa (2010); Torras et al. (2011);

Yong et al. (2013); Al-Mulali et al. (2015); Wei et al. (2015).

The contribution of fossil fuel and urbanization to the ecological footprint is positive and

statistically significant. It implies that one per cent increase in fossil fuel leads to contribute

the total ecological footprint which is 0.01 per cent. The impact of urbanization to footprint

support the modernization perspective. As the society becomes more urbanized, it increases

the material resource use. The findings suggest that one per cent increase in the level of

urbanization, increases the ecological footprint by 0.15 per cent. The findings are consistent

with York and Rosa (2003); Jorgenson and Burns (2007); Anders and John (2009); Ali et al.

(2016) and argue that urbanization of high income countries increased to 882 million in 2000

while it was 703 million in 1975. The projected urbanization in these nations will be 1015

million people living in urban cities in 2030 Behera and Dash (2016). Similarly, the report of

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UN (2014) UN (2014) on urbanization, reveals that 56 per cent of the world’s population lived

in urban areas in 2014, which was 30 per cent of 1950. This leads to increase in demand for

urban activities like urban infrastructures.

Besides, the export, manufacturing, agricultural intensity and the ecological efficiency, the

ecological footprint are also reduced. However, the impact of ecological efficiency on footprint

is larger than the other driving forces, followed by export, agricultural and manufacturing

intensity. It implies that one per cent increase in ecological efficiency decreases the footprint

by 4.31 per cent, citrus paribus. In order to curb the ecological footprint, the appropriate policy

is to promote decoupling process, i.e., the increase in material resource-use should be lower

than the increase in affluence. Regarding the resource productivity, the result is consistent with

Wiedmann et al. (2015).

The negative and statistically significant relationship among export, manufacturing,

agriculture intensity and the ecological footprint suggest that high income countries are trying

to increase through using the environmental friendly technology in agriculture, manufacturing

sectors and the export process zones. One per cent increase in above factors would decrease

the ecological footprint by 0.12 per cent, 0.03 per cent and 0.06 per cent, respectively which

also confirms the arguments of treadmill production theory. It implies that because of favorable

term of trade, high income countries extract resources from less developed countries in the

form of forest; cropland; livestock; and agriculture goods. The result are also in line with

Jorgenson and Burns (2007), the World Bank statistics and Xie et al. (2015), where they argued

that the growth in manufacturing intensity in high income countries showed declining trend

during 2000-2011 because of global financial crises and was extremely high de-growth in

manufacturing intensity in 2008-09.

The manufacturing intensity supports the argument of the World-systems theory and the

theory of uneven ecological exchange. They argued that slower rate in natural resource-use

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and consequently the efficient technology practices in the manufacturing process leads to a

slower increase in the ecological footprint.

The effect of population, urbanization and fossil fuel is positive and statistically significant

and confirmed the ecological modernization perspective. They argued that modernization in

the form of further movement in industrialization, urbanization, unequal trade relation, and

market expansion; leads to increase the total ecological footprint.

The negative effect of service intensity on ecological footprint tends to explain that the

share of service sector in high income countries GDP is continuously increasing, and hence,

increase the consumption of environmentally friendly raw materials.

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Table 7.4

The Driving Forces of Total Ecological Footprint:

High Income Countries( Random Effect Model)

Independent

Variables

Coefficients t-Statistic

ln(GDP) 1.92* 10.80

ln(GDP2) -0.01** -1.62

ln(POP) 0.01** 1.68

ln(UR) 0.15* 5.62

ln(FF) 0.01* 3.53

ln(EI) -0.12* -10.9

ln(SI) -0.01** -0.48

ln(MI) -0.03* -2.57

ln(AI) -0.06 -10.37

ln(IE) 0.01 0.47


Constant 8.17* 10.21

R-Squared: 0.89 F-Statistic: 209.1* Huasman test : 0.000 (0.967)☼

Sample: 2003Q1-2011Q4; Cross-sectional units = 30; periods included=36


*& ** indicate 5 percent and 10percent level of significance. ☼ indicates probability that is greater than 5%, which supports the RE model.

Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World

Bank Dataset

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Table 7.5 estimate the determinants of cropland, forest and fisheries footprints. With

reference to the cropland footprint, the findings suggest that driving forces that contribute to

increases the cropland footprint are at initial level of economic development, population,

agricultural intensity, education and consumption of agricultural products. However, the

impact of economic development on cropland footprint is greater, followed by population,

education and agricultural intensity. As, one per cent increase in eco-growth increases the

cropland footprint by 1.57 per cent; and one per cent increase in population, education and

agricultural intensity, increases the cropland footprint by 0.28 per cent, 0.11 per cent and 0.05

per cent, respectively. The results are consistent with the Jorgenson and Burns (2007); Anders

and John (2009); Al-Mulali et al. (2015); Marie and Olivier (2015); Wiedmann et al. (2015),

where they argued that economic development and population are the major driving forces of

the depletion of resources. In addition, the results do not show evidence of inverted U-shape

Environmental Kuznets Curve. The coefficient associated with further level of economic

development is negative but statistically insignificant. It shows that the cropland footprint is

not sensitive with further level of economic development. The result is consistent with (Jill et

al., 2009; Yong et al., 2013). The empirical findings support the negative association between

urbanization, ecological efficiency and cropland footprint. The increase in ecological

efficiency contributes to greater decrease in the cropland footprint. One per cent increase in

ecological efficiency decreases the cropland footprint by 11.30 per cent, ceteris paribus; and

one per cent increase in urbanization decreases 0.61 per cent of the cropland footprint. The

findings support the modernization perspective. As the economy becomes more urbanized, it

reduces the material resources consumption.

Findings of fisheries footprint support the EKC hypothesis. The coefficients associated

with initial and further level of economic development are statistically significant. The initial

level of economic development is greater to contribute and increase the fishery footprints,

because one per cent increase in economic development increases 3.23 per cent of the fishery

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footprint. The further level of economic development reduce fishery footprint by 10 per cent.

The other driving forces that contribute to accelerate the fishery footprint are urbanization and

export of fish and fishery products. The coefficients associated with these factors are positive

and statistically significant. Their corresponding response on fishery footprint is 5.06 per cent

and 0.26 per cent. The improvement in ecological efficiency contribute to reduce the fishery

footprint, because it is negatively affecting it. The coefficient associated with ecological

efficiency shows that one per cent increase in eco-efficiency decreases 10.18 per cent of the

fishery footprint. Thus, the findings suggest that the policy which contribute to increase

economic development and the ecological efficiency would reduce the fishery footprint.

Similarly, the lower dependency on urban activities and fish and fishery products export would

also reduce the fishery footprints into the future.

With reference to the forest footprint, the findings do not support the EKC hypothesis. The

increase in economic development increases the forest footprint. The other driving forces that

contribute to increase in the forest footprint are population and export of primary products. The

response of one per cent increase in these factors contribute to increase the forest footprint by

0.55 per cent and 0.03 per cent, respectively. The contribution of urbanization and ecological

efficiency to forest footprint is negative. The improvement in urban planning activity in light

of friendly environment and the ecological efficiency would reduce the forest footprint. The

findings shows that one per cent increase in ecological efficiency reduces 2.52 per cent of the

forest footprint. the improvement in urbanization reduces the forest footprint by 1.25 per cent.

Thus, the decoupling process, proper utilization of material resources and environment friendly

urban activities, would reduce the forest footprint in the future. The findings also explain that

high income countries are trying to increase the green economies.

The positive effect of affluence on cropland, fisheries and forest footprints also suggests

that because the high income countries are trying to maintain high standard of living, therefore,

130

they try to consume a greater volume of material footprint. The second possible reason to

increase these footprints alongwith affluence increase is due to conversion in choice preference

towards nutrition and wood related material in construction. As explained by GFN (2014);

Perry (2014); Marie and Olivier (2015); Wiedmann et al. (2015) the affluence has increased

material footprints of developed countries, since 1990.

The positive effect of population and urbanization on material footprints suggest that the

demand for the consumption of cropland items and building infrastructure relates to inputs

increase. The positive effect of education on cropland and forest footprints suggest that as

economies mature in term of education, they give less importance to reduce material footprints.

The results are consistent with findings of Jorgenson (2005); Jorgenson and Rice (2005); Hao

et al. (2016) with inclusion of export of primary goods. They argued that income inequality

increases the ecological footprint because of large dependency of export on primary items.

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Table 7.5

The Driving Forces of The Components of Ecological Footprint:

High Income Countries( Random and Fixed Effect Models)


Independent


ln(GDP) 1.57* 2.98 3.23* 2.40 2.40* 5.97

ln(GDP2) -0.01 -0.44 -0.10* -2.02 0.005 0.34

ln(POP) 0.28* 5.85 -0.95 -0.96 0.55* 6.76

ln(UR) -0.61* -2.36 5.06* 3.92 -1.25* -5.55

ln(AI) 0.05** 1.76 - - - -

ln(EDU) 0.11* 4.64 - - 0.05 0.71

ln(CA) 0.02* 2.21 - - - -

ln(SF) - - 0.26* 2.21 - -

Ln(EP) - - - - 0.03 1.27


Constant 13.86* 3.73 -5.28* -0.22 29.88* 12.62

R-Squared: 0.98

F-Statistic: 33.01* Huasman test: 0.0000(0.978)☼

0.96 430*

0.0000(1.000)☼

0.54 122.0*

0.0000(0.980) ☼





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In Table 7.6, determinants of CO2, grazing land and built-up land footprints is empirically

estimated. The results support the U-EKC hypothesis because further level of economic

development increase the CO2 footprint where the GDP is positive. The driving forces like

population, coal, oil, export and manufacturing intensities are positively related to CO2

footprint. One per cent increase in population increases by 0.05 percent CO2 footprint.

Similarly, one per cent increase in the consumption of coal and oil leads to increase CO2

footprint by 0.04 per cent and 0.27 per cent, respectively. Substituting renewable energy for

non-renewable energy would reduce emissions into the future. The export and manufacturing

intensities affect the CO2 footprint, positively, which implies that one percent increase in

driving forces leads to 0.04 per cent and 0.21 per cent increase in CO2 footprint, respectively.

The positive effect of further affluence, population, export, manufacturing, hours’ work

and energy related inputs on CO2 footprint suggest that high income countries have the

experience of a greater increase in CO2 emissions, alongwith consumption of these driving

forces. As economies mature in term of affluence, and economically open in term of export

and manufacturing, they increase work time of employees and the energy consumption. These

factors collectively accelerate the CO2 emissions. The empirical literature further suggest that

high income countries, in addition to inclusion of increase use of energy consumption lead to

assimilate more CO2 emissions. Adding the export and manufacturing intensity does not

change the significance of affluence and energy consumption. The impact of hours work on

CO2 footprint is positive and significant statistically which is consistent with findings of

Anders and John (2009); Hafstead et al. (2015). They argued that the high income countries

reduced the labour hours while achieving greater economic development because of an

increase in labour productivity. This leads to increase the ecological efficiency in high income

countries.

The urbanization fails to increase the CO2 footprint. As economies modernize, they

reduced the CO2 footprint. The expectation of treadmill production perspective is supported by

133

existence of the U-EKC hypothesis and the negative effect of urbanization on CO2 footprint.

The improvement in ecological efficiency reduces the CO2 footprint, because the coefficient

associated with ecological efficiency is negative and statistically significant. It shows that one

percent increase in ecological efficiency increases the CO2 footprint by 10.1 percent.

With reference to the grazing land footprint, the findings support the EKC hypothesis.

Among high-income countries, the initial level of economic development increases the grazing

land footprints and the further level of economic development appears to reduce the grazing

land footprint. In addition, acceleration in urbanization, production of livestock and the

ecological efficiency appear to reduce the grazing land footprint. However, the impact of

ecological efficiency on grazing land footprint is larger than from urbanization and the

production of livestock. It shows that one per cent increase in ecological efficiency decreases

the grazing land footprint by 3.64 per cent. The high income countries are decoupling their

economic development and material resource use. The increase in material resource use is

lower than the increase in income. Furthermore, the growth in population increases the grazing

land footprints, because the coefficient associated with population is positive and statistically

significant. It shows that one per cent increase in it leads to an increase 0.30 per cent of the

grazing land footprint.

Findings of the built-up land footprint support the validity for EKC hypothesis. The

coefficients associated with initial further levels of economic development which are

statistically significant. The built-up land footprint among high income countries increase with

the increase in the initial economic development. Further economic development reduces the

built-up land footprint. However, the impact of initial economic development on built-up

footprint is greater than the further economic development. It implies that one per cent increase

in initial level of economic development increases built-up land footprint by1.63 per cent,

citrus paribus. The other driving forces that contribute to increase the built-up footprint are

population, urbanization and employment. The impact of service intensity and the ecological

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efficiency on built-up land footprint is negative and statistically significant. However,

improvement in the ecological efficiency largely decreases the footprint, because one per cent

increase in the ecological efficiency reduces built-up land footprint by 4.97 per cent. Thus,

decoupling in built-up land footprint increases the ecological efficiency and reducing

environmental degradation. The further level of economic development, the policy of

controlling population and increase in rural activities (instead of urbanization) will increase

the environmental sustainability in the form of reducing built-up land footprint. It is consistent

with Dietz et al. (2003); York and Rosa (2003); York et al. (2004). The results support the role

of ecological efficiency in order to reduce grazing and the built-up land footprints.

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Table 7.6


High Income Countries( Random and Fixed Effect Models)


Independent Variables Coefficients t-Statistic Coefficients t-Statistic Coefficients t-Statistic

ln(GDP) -0.84* -2.40 0.95* 7.78 1.63* 6.67

ln(GDP2) 0.10* 10.11 -0.01 -3.57 -0.05* -4.34

ln(POP) 0.05** 1.79 0.30* 3.61 0.88* 7.86

ln(UR) -0.44 -3.02 -0.45* -2.47 0.87* 3.54

ln(EI) 0.04* 2.01 - - - -

ln(COAL) 0.04* 4.23 - - - -

ln(OIL) 0.27* 8.23 - - - -

ln(GAS) -0.03* -3.10 - - - -

Ln(MI) 0.21* 0.79 - - -0.02 -0.71

ln(HW) 0.40* 3.29 - - - -

ln(PL) - - -0.03 -1.03 - -

ln(SI) - - - - -0.21** -1.96

ln(EM) - - - - 0.16* 2.48

Ecological Efficiency -10.10* -6.92 -3.64* -10.40 -4.97 -9.83

Constant 21.66* 12.22 10.17* 9.61 4.46* 2.78

R-Squared: 0.77

F-Statistic: 332.01* Huasman test: 0.0000(0.970) ☼

0.98 352.2*

39.80(0.000)●

0.62 17.01*

28.18(0.004) ●



*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less

than 5% supports the FE model.


From the above discussion, it is concluded that major driving forces are positively related

to the total ecological footprint and its components are GDP, population and level of

urbanization. The EKC hypothesis confirms that further economic development leads to reduce

the total ecological footprints and its components, except for built-up land footprints. In case

of total ecological footprints, the major driving forces that lead to increase the total ecological

footprints are GDP, population, urbanization, fossil fuel, export and service intensities, and the

136

income inequality. In case of CO2 footprint, the major driving forces that are positively

affecting the CO2 footprint are GDP, population, coal, oil, gas and manufacturing intensity.

However, the support of EKC hypothesis is very weak due to the reason that the decoupling

process in high income countries is relatively slow. It is also supported by findings of

Wiedmann et al. (2015);Ozbugday and Erbas (2015), where the high income countries reduce

the use of resources alongwith increase in economic growth. The non-significance and even

positive sign associated with coefficient of economic development, support the arguments of

World-system and Treadmill production theories.

For more profit accumulation, the high level of economic development and consumption

of natural resources will increase and lead to more competition in the global marketplace, as

argued by the world-system theorists. The treadmill of production theorists argue that usually

the producers-base in high countries and expansion of products, largely depend on resources

which are commonly extracted from low income countries. The high income countries

externalize environmental impact of extracting resources of low income countries and

produced commodities are usually transported to and consume by their population. Increase in

economic development further lead to environmental impact through extraction of natural

resources and waste generated by expansion of production. Thus, according to the world-

systems theory and the treadmill of production theory, high income countries generated

consumption based environmental degradation.

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7.4 The driving forces of total ecological footprint

and its component in middle income countries

This section estimate the driving forces of total ecological footprint and its component for

the middle income countries. According to Table 7.7, the results confirm the EKC hypothesis

because one per cent increase in initial level of economic development leads to 1.26 per cent

increase in total ecological footprint; and further level of economic development leads to 0.01

per cent reduction in the ecological footprint. It also explain that middle income countries are

trying to achieve the decoupling process as indicated by the negative and statistically

significant signs of the quadratic of affluence. Further economic development in middle

income countries leads to reduce the ecological footprint. However, decoupling process is very

small and there are many aspects of small decoupling.

First, the middle income countries are using less environmentally friendly technology

where the further economic development require more material inputs. Second, the export of

these countries is either agricultural or manufacturing-based raw materials. Since, the

negligible practices of environmentally friendly technology, further level of development that

is based on the aforementioned sectors leads to increase in the material footprint. Third, the

middle income countries may be unable to execute the material footprint efficiently while

accelerating the economic development due to the lack of invention in resource productivity

and negligible coordination among various institutions. Lastly, as economies modernizing in

term of economic development, they need natural resources and therefore, increase the

ecological footprint.

The other driving forces that contribute to increase in the footprint are population,

urbanization, fossil fuel, service and manufacturing intensity. The coefficients associated with

these forces are positive and statistically significant. However, the population and fossil fuel

appear to contribute largely to the footprint. The findings are consistent with Jorgenson and

138

Rice (2005); Jill et al. (2009); York et al. (2009); Knight et al. (2013). The positive and

statistically significant effect of economic development, population, fossil fuel and export

intensity on the total ecological footprint confirms the treadmill production perspective. They

argued that as economies matures in term of acceleration of economic development alongwith

increase in population, therefore they, indeed depend on export and demand of goods and

services. These driving forces collectively and continuously withdraw resources from the

environment and also generate waste. However, the effect of population and fossil fuel on

ecological footprint is more pronounce. Increase in population, demands for crops, fisheries,

grazing and urbanization-based activities that consequently accelerate the material footprint.

An increase in fossil fuel leads to generate CO2 emissions. It is the major contributor to total

ecological footprint (GFN, 2016a). The statistically insignificant effect of export and

agricultural intensity on ecological footprint confirms that in the middle income countries the

process of these activities is very low and therefore, it do not significantly alter their material

footprints. The income inequality fails to increase the ecological footprint because its effect on

footprint is statistically insignificant. However, the decupling process reduce the footprint,

because the ecological efficiency is negatively affecting the footprint. It implies that one

percent improvement in ecological efficiency decreases total ecological footprint by 5.83 per

cent.

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Table 7.7

The Driving Forces of Total Ecological Footprints:

Middle Income Countries( Random Effect Model)

Independent Variables

Coefficients t-Statistic

ln(GDP) 1.26* 6.73

ln(GDP2) -0.01* -9.68

ln(POP) 0.19* 5.48

ln(UR) 0.17* 4.40

ln(FF) 0.16* 12.1

ln(EI) 0.007 0.99

ln(SI) 0.04* 2.28

ln(MI) 0.11* 5.70

ln(AI) 0.01 1.17

ln(IE) -0.04 -1.17


Constant 8.16* 7.46

R-Squared: 0.82 F-Statistic: 103.01* Huasman test: 0.0000 (0.981)☼




Source: Author’s Calculation based on Global Footprint Source: Author’s Calculation based on Global Footprint Network, www.footprint network.org and World Bank

Dataset

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Table 7.8, the impact of driving forces of cropland, fisheries and forest land footprints is

estimated. Findings in the case of cropland footprint confirm the EKC hypothesis because the

coefficient associated with GDP2 is negatively affecting the cropland footprint, which implies

that one percent increase in the initial level of economic development increases cropland

footprint by1.42 per cent, while further level of economic development leads to reduce the

cropland footprint. Similarly, the coefficients associated with population, urbanization and

agricultural intensity are positively affecting the cropland footprint. It implies that one per cent

increase in the level of population, urbanization and agriculture intensity increases the cropland

footprint by 0.50, 0.17, and 0.13 per cent respectively. However, the economic development

and population are greater contributors to increase the cropland footprint. There are various

ways through which this result may be justified. Firstly, due to increase in population, demand

for crops’ related items accelerate. Secondly, due to the agriculture based intensity of middle

income countries, increase in economic development leads to increase the cropland footprint.

Thirdly, mostly the middle income countries are largely populated and face the problem of

food security. In order to satisfy the demand of large population and to minimize the deficiency

of food security, they increase the cropland footprint. Lastly, the economic structure in terms

of consumption of material resources does not support the middle income countries, because

they have deficits in cropland footprint. These factors will lead to increase the cropland

footprint.

The negative and statistically significant effect of education and ecological efficiency on

the cropland footprint supports the ecological modernization perspective. As economies

become more urbanized and educated, they try to increase the green economies; therefore, the

net effect of modernization on cropland footprint becomes helpful. The improvement in

resource productivity decreases the cropland footprint.

The positive and statistically significant effect of agricultural intensity on cropland

footprint can be evaluated by the two sides. First, the increasing trend in pesticides and

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fertilizer practices leads to increase in demand for cropland land. Second, due to climate

changes and the increasing trend in population, the middle income countries, are at the stage

of food security problem. They, therefore try to increase the agricultural practices. For this

purpose, some part of reserve biocapacity of cropland has brough t under cultivation and

accelerated the cropland land footprint.

The driving forces that lead to increase fisheries footprints are the further level of economic

development, population, urbanization and fish export. The impact of population on fisheries

footprint is larger than the other driving forces, because one per cent increase in population,

increases further level of economic development, urbanization and fish export by 1.05 per

cent, 0.02 per cent, 0.41 per cent and 0.64 per cent, respectively. However, the findings support

the U-EKC hypothesis because the coefficient associated with GDP2 is positive and GDP is

negative. The driving forces that contribute to decrease the fisheries footprint are at initial level

of economic development and the ecological efficiency. It shows that the response of one per

cent in these factors decreases fisheries footprint by 0.22 per cent, 1.71 per cent and 0.13 per

cent. The results are consistent with Jorgenson and Clark (2011); Knight et al. (2013); Apergis

and Ozturk (2015). Fisheries footprint is one of the most severed issues because of its

unprecedented economic development and population explosion. The effect of economic

activity is the largest positive contributor to accelerating the fisheries footprint, followed by

population and diet structure effects. The combination of fisheries footprint efficiency and

adjustment in the structure of dietary practices are the most effective approach for controlling

fisheries footprint. With a growing population and recurrent problems of food security, the

middle income countries also leads to accelerate the consumption of fishery resources.

The driving forces contribute to accelerate the forest footprint are economic development,

population and the export of primary products. Further level of economic development,

urbanization and the ecological efficiency contribute in reduction in forest footprint. The

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findings support the EKC hypothesis, because the coefficients associated with economic

development and its further level, are statistically significant. The initial level of economic

development increase the forest footprint by 2.41 per cent in case of one per cent increase in

economic development. Among the middle income countries, one percent growth in income

decreases forest footprint by 0.08 per cent. Among the middle income countries, the

acceleration in the export of primary products increases 0.03 per cent of the forest footprint.

Majority of the middle income countries have the increasing trend in population that

contributes to increase in the forest footprint by 0.26 per cent. The improvement in

urbanization and the ecological efficiency decreases the forest footprint by 1.60 per cent and

4.20 per cent, respectively. As the society becomes more urbanized and increase the

decoupling process in resource productivity, it try to increase the forest reserve. The findings

are consistent with Jorgenson and Burns (2007); Jill et al. (2009); Mostafa (2010); Jorgenson

and Clark (2011); Alessandro et al. (2012); Juan and Jordi (2013); Marie and Olivier (2015);

Wei et al. (2015); Wiedmann et al. (2015); Ali et al. (2016); Asici and Acar (2016).

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Table 7.8


Middle Income Countries(Random and Fixed Effect Models)


Independent


ln(GDP) 1.42* 5.11 -0.22** -1.67 2.41* 21.17

ln(GDP2) -0.03* -6.58 0.02* 6.58 -0.08* -11.09

ln(POP) 0.50* 6.53 1.05* 6.51 0.26* 2.24

ln(UR) 0.17** 1.65 0.41** 1.83 -1.66* -10.4

ln(AI) 0.13* 7.05 - - - -

ln(EDU) -0.05* -3.87 - - 0.01 0.70

ln(CA) -0.01* -0.45 - - - -

ln(SF) - - 0.64* 23.91 - -

ln(EP) - - - - 0.03* 4.68


Constant 4.37* 4.13 -6.60** -2.94 9.28* 5.96

R-Squared: 0.97

F-Statistic: 117.3* Huasman test: 43.85(0.000)●

0.98 369.6*

79.77(0.000)●

0.66 106.1*

0.0000(0.980) ☼



*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model. The ● indicates the probability that is less than

5% supports the FE model.


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Table 7.9 reports the driving forces of CO2 footprint and the grazing and built-up land

footprints. With reference to the determinants of CO2 footprint, the factors contributing to its

increase are the initial level of economic development, coal, oil, gas, manufacturing and work

hours. However, the greater contributors to CO2 footprint are the economic development and

oil. The results show that one per cent increase in these factors contribute to increase CO2

footprint by 2.14 per cent and 0.44 per cent respectively. On the other hand, the urbanization

and the ecological efficiency have quite strong implication for the mitigation of CO2 footprint:

one per cent increase in ecological efficiency reduces the CO2 footprint by 7.99 percent, ceteris

paribus. A one percent increase in the urbanization decreases CO2 footprint by 0.04 percent.

The findings support the EKC hypothesis, because coefficient associated with and further level

of economic development is negative and statistically significant. At the initial level of

economic development, CO2 footprint increases alongwith the growth in income’ but however,

at the further level of economic development, the CO2 footprint decreases. Thus, the policy

makers in the middle income countries should have to pursue sustainable policies regarding

decoupling, growth in income and environmental friendly urbanization activities in order to

reap the maximum environmental sustainability.

The empirical estimate of driving forces of grazing land footprint and the result confirm

the EKC hypothesis negative, but the GDP is positive, because of the coefficient associated

with GDP2. It implies that an increase in GDP leads to increase in grazing land footprint and

further economic development (GDP2) leads to reduce grazing land footprint of middle income

countries. However, the factors like population and production of livestock are positively

related to grazing land footprints which imply that one percent increase in these factors leads

to 0.31 per cent and 0.02 per cent increase in grazing land footprint, respectively. The other

driving forces that contribute to reduce the grazing land footprint are the level of urbanization

and the ecological efficiency. However, the effect of ecological efficiency on grazing land

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footprint is more pronounced than urbanization. It shows that one percent increase in

ecological efficiency reduce grazing land footprint by 3.54 per cent. Thus, improvement in

ecological efficiency, urbanization and controlling population and decoupling the resources

productivity would increase the environmental sustainability by reducing the grazing land

footprint.

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Table 7.9


Middle Income Countries( Random Effect Model)


Independent


ln(GDP) 2.14* 11.3 1.01* 5.77 -2.75* -5.41

ln(GDP2) -0.04* -3.98 -0.01* -2.27 0.21* 6.78

ln(POP) 0.08* 7.09 0.31* 2.18 0.51* 17.41

ln(UR) -0.42* -14.02 -0.71* -3.51 0.06* 5.34

ln(EI) -0.009 -0.48 - - - -

ln(COAL) 0.05* 19.33 - - - -

ln(OIL) 0.44* 12.23 - - - -

ln(GAS) 0.06* 13.05 - - - -

ln(MI) 0.08** 1.71 - - -0.96* -7.02

ln(HW) 0.02* 7.27 - - - -

ln(PL) - - 0.02* 3.16 - -

ln(SI) - - - - -1.58* -8.34

ln(EM) - - - - 0.03* 4.43


Constant 5.82** 1.67 10.58* 5.54 7.51* 12.86

R-Squared: 0.96

F-Statistic: 594.3* Huasman test : 0.0000(0.960) ☼

0.98 375.3*

1.75(0.914) ☼

0.71 996.1*

10.70(0.219) ☼



*& ** indicate 5 percent and 10 level of significance. ☼ indicates probability which is greater than 5%, which supports the RE model.


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The association of built-up land footprint with GDP and its square term, population,

urbanization, manufacturing and service intensity; and employment level are empirically

estimated. The findings confirm the U-EKC hypothesis because the further level of economic

development increases the built-up land footprint. The other driving forces that contribute to

increase the built-up footprint are population, urbanization and employment. However, the

effect of population on built-up footprint is more pronounced because, one per cent increase in

population sparks a 0.051 per cent rise in built-up footprint. The employment leads to a small

increase in built-up footprint than the level of urbanization. From the estimation, one per cent

increase in these factors lead to increase in the built-up footprint by 0.06 per cent and 0.03 per

cent, respectively. The service intensity and ecological efficiency gives the negative effect on

by the built-up footprint as does the economic development. However, they are strongly

mitigated the built-up footprint. As, a 1 percent improvement in ecological efficiency and

increase in service intensity leads to reduce the built-up footprint by 1.58 per cent and 3.38 per

cent, respectively. The findings strongly support the decoupling in growth and modernization

perspective in terms of service and manufacturing activities. The manufacturing intensity gives

the same effect on the built-up footprint as the service and ecological efficiency. The response

of one per cent increase in manufacturing intensity reduces built-up footprint by 0.96 per cent,

ceteris paribus.

From the above discussion, it is concluded that driving forces where they have positive

impact on total ecological footprint and its components are economic development and

population. The major driving forces which increase the total ecological footprint are economic

development, population, fossil fuel and export intensity. In case of cropland footprint, the

economic development, population and agriculture intensity are positively affecting the

cropland footprint while the further level of economic development, urbanization and

education are negatively related to cropland footprint. Similarly, the major driving forces that

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lead to increase the fisheries footprint are economic development, population and export of

fish while urbanization is related to fisheries footprint, negatively. In case of forestland

footprint, the driving forces are economic development, population, urbanization, education,

export intensity and income inequality. The economic development, population, urbanization,

export intensity and income inequality are positively, while education is negatively, related to

forestland footprint. Similarly, the driving forces of CO2, grazing and built-up land footprints

have positive effects on CO2 footprint, grazing and built-up land footprints. However, the

findings support the EKC hypothesis in case of total ecological footprint and its components

which shows that initial level of economic development lead to increase ecological footprint

and then, further level of economic development reduce the ecological footprint.

7.5 Discussion

From the above discussion, it is concluded that major driving forces that contribute to

ecological footprint and its components are not only the population and affluence but many

other factors, as suggested in findings of the study.

With reference to the combined panel, findings have several merits for different policy

makers. Firstly, the combined panel (high-middle income countries) should slow the process

of population, fossil fuel, urbanization and different intensities in order to combat global

environmental pressure. These driving forces increase the total footprint and therefore, slowing

the impact of these on ecological footprint could lead to maintain the environmental

sustainability. Secondly, the findings of total ecological footprint suggest that improvement in

ecological efficiency and the promotion of export intensity in light of pro-environment would

improve the environmental sustainability by reducing the ecological footprint. Thirdly, the

major driving forces that contribute to increase the cropland footprint are affluence, population,

export intensity and the consumption of agricultural products. It is suggested that decoupling

in growth in income, promotion of export and agricultural substitute products would accelerate

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the cropland biocapacity. Fourthly, further level of economic development and ecological

efficiency should be promoted, because these lead to mitigate the cropland footprint. Fifth, the

combined panel should slow the consumption of fish and fishery products, because it accelerate

the fishery footprint. The fisher footprint has the potential of decoupling process. Sixth, the

findings of forest footprint suggest that combined panel should reduce the influence of income,

population, education and the export of primary products. The incremental increase in these

factors increases the forest footprint. Lastly, results of the CO2 footprint show that the process

of increasing population, urbanization, coal, and oil and gas consumption should slow down.

It is possible to introduce environmental friendly technology by the sample countries in the

manufacturing, infrastructure and transport sectors.

By comparing the findings of high and middle income countries with reference to total

ecological footprint; the results support the EKC hypothesis. Initial level of economic

development increase the footprint but further level of economic development reduce the total

ecological footprint. However, the decoupling process is greater in middle income countries

than high income countries, as suggested by the ecological efficiency. The improvement in

ecological efficiency has greater impact on ecological footprint in case of middle income

countries. The other driving forces that contribute to increase the footprint are population,

urbanization and fossil fuel. However, they have greater impact on ecological footprint in the

middle income countries. The reason behind this is the increasing trend of population,

industrialization and urbanization process. For example, urbanization in China expanded from

19.8 per cent (in 1979) to 53.7 per cent (in 2013) and CO2 emissions per capita increased from

1.7 tons to 8.3 tons, in the same period. Thus, there was a positive association among these

factors and the ecological footprint (IEA, 2015; Zi et al., 2016). Similarly, the GDP and export

intensity increase the total ecological footprint which may support the Heckscher-Ohlin trade

theory, i.e., without considering the environmental impact of trade, a country should specialize

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in the production of goods which requires the abundant factors. The coefficient associated with

income inequality revealed that increase in inequality will lead to increase ecological footprint

and thus, suggesting that there should be more equal distribution in income which will lead to

reduce the total ecological footprint of these nations. The coefficient associated with service

intensity suggests that increase in service intensity will lead to reduce its ecological footprint

which support the modernization perspective that conversion from agriculture based to service

based economy leads to reduce environmental degradation. Thus, it supports that these nations

should increase the service based activities.

The similar driving forces that contribute to increase the cropland footprint are affluence,

population and agricultural intensity. Since, the high standard living of high income countries

leads to greater effect of income on the footprint than middle income countries. However, the

increasing trend of population and the greater dependency on agriculture activities of middle

income countries have lager influence on the cropland footprint. The greater the ecological

efficiency the lower would be the cropland footprint, because the negative association between

ecological efficiency and the footprint. In addition, the findings also support the EKC

hypothesis which suggests that more focus on controlling population, reducing dependency on

agriculture as well as increasing further the economic development and more investment in

education sector will lead to reduce the cropland footprint of these countries.

Similarly, findings of the fishing grounds concluded that GDP, population and fish export

increase the fisheries footprint in case of high and middle income countries, thus suggesting

that de-growth, population control and minimizing dependency on export of fisheries related

activities will lead to reduce the fishing grounds footprint. The study concluded that

urbanization increase the fisheries footprint in high income countries, though it is insignificant

while it is negatively related to the fisheries footprint in middle income countries and

statistically significant. It leads to suggest that there should be more focus on better urban

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planning which will lead to reduce fishing grounds footprint because of substitute’s availability

for fish in middle income countries. The findings also confirm the EKC relationship of GDP

and GDP2 with fishing grounds footprint.

From the previous discussion, regarding forest footprint for high and middle income

countries it can be concluded that an increase in GDP, population, urbanization, export and

income inequality lead to increase the forest footprint. Education is negatively affecting the

forest footprint and therefore more investment in this sector could lead to reduce the

environmental degradation via reduction of forest footprint of these countries. Similarly,

reduction in urbanization through appropriate rural development policy, as well as decline in

export of forest related products like papers and wood furniture, could also lead to reduce the

forest footprint.

The major driving forces after increasing the CO2 footprint of these countries are affluence,

population, export intensity, consumption on coal, oil and gas, manufacturing intensity and

work hours. Therefore, reduction in these driving forces through different policies, for

example, adaptation of alternative sources for energy consumption like solar, wind and micro-

hydro power systems could lead to reduce the CO2 footprint of the these countries. Similarly,

reduction in working hours through tax on longer working hours could lead to improve the

environmental sustainability via reducing the CO2 footprint; the result are also supported by

the findings of (Anders and John, 2009).

Findings of this study can contribute to the existing literature regarding the grazing land

footprint. The findings show that population, affluence and production of livestock are the key

determinants of these nations’ grazing land footprint and therefore reduction in these forces

will lead to reduce the grazing land footprint. However, the effect of urbanization and

affluence on grazing land footprint in middle income countries is larger than the other driving

forces and therefore, proper urban planning and the de-growth policy would lead to reduce

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these nations’ grazing land footprint. Similarly, the high income countries would lead to reduce

their grazing land footprint by initiating well urban planning. The ecological efficiency is one

of the leading component that contribute to reduce the grazing land footprint. The grazing land

footprint increases and the findings support the EKC hypothesis at the initial level of economic

development,. The further level of economic development reduce the grazing land footprint.

Findings of the built-up land footprint suggested that population; affluence, urbanization,

service intensity and employment level increase the built-up land footprint. However, it is

suggested that proper urban development, creating employment opportunities in rural sector in

the middle income countries could lead to reduce its built-up land footprint. Similarly, further

affluence and urbanization in case of high income countries are the key determinants which

have larger impact on built-up land footprint; therefore, reduction in these sectors would lead

to improve environmental sustainability. The improvement in the ecological efficiency reduces

the built-up land footprint.

Our findings are coherent with Wiedmann et al. (2015) results. They used material

footprint and the domestic material consumption as consumption-based environmental impact

indicators. The indicators are then divided into crops, fodders, ores, construction of materials

and fossil fuel categories. The study investigate the trend over time, resource productivity and

econometric analysis. The trend of material footprint and domestic material consumption over

time indicate that the postindustrial economic structure and the import dependency for final

consumption in the United Kingdom and Japan leads to greater material footprint than the

domestic material consumption. The domestic material consumption of large resource exporter

countries of Australia, Russia and South Africa was greater than their material footprint. The

Brazil, India and China have a similar material footprint and domestic material consumption

over time. They further argued that specialization leads to change the structure of resources

extraction, particularly the domestic material consumption, because its value increased for

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exporting and decline in importing countries. But, specialization have less effect on material

footprint because it reallocates the burden back to the ultimate point of consumption.

The resource productivity in the case of selected developed countries has no decoupling

process. The main reason was greater dependency on construction material. The fast-growing

economies of China and India shows a result of decoupling. The exporting developing nations

of Chile, Brazil and Russia had a decline trend in resource productivity. The univariate

regression analysis based on three explanatory variables indicate the mixed findings of the

impact of GDP, DE and population density forces on material footprint. They argued that

variation in material footprint is mostly explained by variation in GDP and thus increase in

resource productivity is smaller than the increase in GDP. They estimated that GDP leads to

increase the footprints of selected countries which is consistent with results of this study; in

the case of analysis of high and middle income countries. The population density seems to

have a lesser and mixed influence on footprint indicators. An increase in population density

leads to a positive impact on total footprint, crops, and construction materials, as well as on

the domestic material consumption of crops, fodder, construction and fossil fuels. Variation in

domestic material consumption is mostly explained by variation in the domestic extraction and

had less impact by GDP in the domestic material consumption. The GDP increase the

construction component of material footprint, but not by the domestic extraction. It shows that

10 per cent increase in GDP, increase the construction footprint by 9 per cent. In response of

10 per cent increase in domestic extraction, variable leads to increase construction component

of domestic material consumption by 8 per cent. Findings of this study explain that a further

level of economic development leads to increase cropland footprint.

The findings of Wiedmann et al. (2015); Ali et al. (2016); Asici and Acar (2016); Chen et

al. (2016); Farhani et al. (2016); Kang et al. (2016); Li and Zhao (2016); Shi-Chun et al.

(2016); Uddin et al. (2016); Adewuyi and Awodumi (2017); Ahmad et al. (2017); Apergis et

al. (2017); Charfeddine and Mrabet (2017); Fernandez-Amador et al. (2017); Lanouar (2017);

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Mrabet and Alsamara (2017); Szigeti et al. (2017) strongly support the hypothesis of this study

as the initial level of economic development increases the ecological footprint and

environmental degradation. One of the major conclusion from the findings is the improvement

in ecological efficiency and that decoupling in growth in income reduces the ecological

footprint.

Zhu et al. (2016); Zaman and Abd-el Moemen (2017) results support the findings (of this

study) that growth in income reduces the emissions and growth in population increases the

environmental degradation. The results therefore, support the EKC and the IPAT hypotheses.

However, Uddin et al. (2017) Uddin, et al. (2017) argued that real income confirms the positive

significant impact on the ecological footprint in case of highest emitting countries. They further

argued that there is a unidirectional causal impact running from real income to ecological

footprint. As, our findings suggest the growth in initial income increase the ecological footprint

in high-middle income countries. The reports of GFN (2014, 2016a) support the findings of

this study and that high economic development, population, urbanization and fossil fuel

consumption are the major driving forces that contribute to increase the footprint. However,

increase in ecological footprint is lower than the increase in income which leads to reduce the

environmental degradation, because the ecological efficiency is negatively affecting the

ecological footprint in high-middle income countries.

The findings of Ozbugday and Erbas (2015) are supporting the relationship between

ecological efficiency, ecological footprint and components of ecological footprint. They

argued the increase in energy efficiency reduces CO2 emissions. Increasing energy efficiency

in renewable energy usage is increasing energy efficiency. It reduces CO2 emissions. The IEA

(2015) report also supports the effect of ecological efficiency on environment. Increasing

energy efficiency reduces CO2 emissions. However, the results of Zaman and Abd-el Moemen

(2017) indicate that increase in population pressure increases CO2 emissions and supports the

155

IPAT hypothesis. The study of Wang et al. (2017) employed STIRPAT model to examine the

impact of population, income, energy intensity and urbanization on CO2 emissions. They

argued that the major contributors to CO2 emissions are the intensity of population, income

and energy. Impact of urbanization is ambiguous, because its effect on CO2 emissions differs

in different regions of China. However, the results of study are according to the study of Wang

et al. (2017). The driving forces that contribute to increase CO2 footprint are population,

income and fossil fuel consumption. Urbanization increases CO2 footprint in case of combined

panel.

The important conclusion of this study is the importance of ecological efficiency in

environmental sustainability. Separate and combined samples results show that improvement

in ecological efficiency reduces ecological footprint. Therefore, the decoupling process and

growth in income can improve environmental sustainability. The study of Uddin et al. (2017)

examined the relationship among ecological footprint, real income, financial development and

trade openness for a panel of developed and developing countries, for the period 1991-2012.

They argued that real income increases footprint and financial development reduces ecological

footprint. The 27 highest emitting countries are excessively exploiting natural resources and

eco-services. This is according to the results of our study. Improve ecological efficiency,

change the consumption patterns of people of high-middle income countries, controlling

excessive exploiting of fishing, crops, grazing, forest and built-up land will increase

biocapacity and will promote environmental sustainability. These findings are also compatible

with FAO (2016); GFN (2016a, 2016b); Szigeti et al. (2017).

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CHAPTER EIGHT

CONCLUSION AND RECOMMENDATIONS

8.1 Introduction

This chapter is devoted to the brief summary of the study, followed by key findings and

recommendations based on these findings. Besides, the limitations of this study; directions for

future research are also mentioned in this part of the study.

8.2 Summary of the study

Since last few decades, ecological footprint is one of the growing area of interest in the

emerging fields like environmental-sociology, environmental-economics and ecological

economics. It is the consumption based environmental impact indicator which measure the

area of land required for consumption of goods and services demanded by human to assimilate

CO2 emissions and waste generated by human activities. This includes all cropland, forest,

grazing, fishing grounds and built-up land to produce food, timber, fiber and to

accommodate/provide space for the urban activities and for assimilation of CO2 emissions.

This study try to provide answers regarding linkages between ecological footprint, economic

growth, population, urbanization and other socioeconomic factors. The study estimated trends

of ecological footprint, economic growth and ecological efficiency; and the inequality between

income, ecological footprint and environmental impact intensity. The impact of various driving

forces of total ecological footprint and its components for high and middle income countries

also estimated this by using Panel dataset for the period 2003-2011. The key findings of the

study are:

1. The total ecological footprints of high income countries showed a mixed trend during

2003-2011 because of mixed trend in CO2 and other components of total ecological

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footprint. The total ecological footprints of middle income countries increased in same

period due to increase in demand of cropland, grazing land and CO2 footprints. Demand

for area of land to assimilate CO2 emissions generated by high income countries is 59

percent and 43 percent of total ecological footprints of middle income countries during

2003-2011.

2. Comparing total ecological footprints with its biocapacity reveals that mean footprints of

both regions are more than its biocapacity that leads to generate ecological overshooting

of 94 percent and 14 percent in high and middle income countries respectively. The result

suggests that during 2003-2011, high income countries consumed resources and services

at much faster rate than the middle income countries.

3. With reference to coal, oil and gas consumption, the result suggests that both high and

middle income countries have increasing trend of consumption. The consumption of coal,

oil and gas is much larger in high income countries and therefore, demand of ecological

footprints exceeds supply of biocapacity by 94 per cent (ecological overshooting) during

2003-2011.

4. Comparing population and annual hours worked per worker, reveals that middle income

countries have larger population and work hours but it does not lead to explain that people

of middle income countries consume resources and services at much faster rate. Although

on basis of income and urbanization it can conclude that high income countries consume

resources and services at much faster rate and therefore have larger demand of ecological

footprints than the middle income countries.

5. Comparing trend of export, agriculture, manufacturing and service, the intensity reveals

that middle income countries mainly depend on export, agriculture and manufacturing

sectors. While high income countries depend on services. The result suggests that high

income countries try to increase service based activities in order to maintain its

biocapacity more than ecological footprint. Middle income countries, at the stage of

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development, devote their resources into agriculture and manufacturing sectors that will

lead to increase demand of ecological footprint, much faster than supply of biocapacity

in near future.

6. According to trends of ecological footprint, economic growth and ecological efficiency;

the result suggest that because of the increasing trend in GDP, ecological efficiency of

high income countries was more than the middle income countries, during 2003-2011.

7. The Resource Intensity (RI) exhibits the gap between maximum and mean level of

efficiency. The result suggest that the high and middle income countries have

discrepancy in the case of resources utilization because the cropland, forest and grazing

land have 49 percent and 42 percent potential to achieve its maximum level of ecological

efficiency by high and middle income countries respectively. The CO2 footprint by these

regions have 25 percent and 21 percent more room for achieving maximum level of CO2

ecological efficiency. Similarly, the fishing grounds and built-up land of high and

middle income countries have 25 percent and 37 percent potential for achieving its

maximum level of efficiency.

8. The Atkinson index of total ecological footprint, cropland, grazing land, forest, fishing

grounds and built-up land footprint in High income countries was greater than the Middle

income countries; while the Atkinson index of CO2 footprint in Middle income countries

was greater than the High income countries. Similarly, the Atkinson index of

environmental impact intensity of High income countries was larger than the Middle

income countries, in case of grazing land, forest, fishing grounds, built-up land and CO2

footprint. According to the mean environmental impact intensity, the mean forest, CO2,

fishing grounds and built-up land footprint intensity of per unit economic output in High

income countries is greater than the Middle income countries; while cropland and grazing

land footprint environmental impact intensity of per unit of economic output in Middle

income countries are larger than the High income countries.

159

9. The present study also estimate the impact of various driving forces of the total

ecological footprint and its components where results in case of total ecological footprint

suggest that the major driving forces that leads to affect the total ecological footprint are

GDP, population, urbanization, fossil fuel, export and service intensities and income

inequality. In case of CO2 footprint, the major driving forces that are positively affecting

the CO2 footprint are GDP, population, coal, oil, gas and manufacturing intensity.

10. The econometric findings of the middle income countries suggest that major driving

forces which lead to increase in the total ecological footprint are economic development,

population, fossil fuel and export intensity. In case of cropland footprint, the economic

development, population and agriculture intensity are positively affecting the cropland

footprint; while the further level of economic development, urbanization and education

are negatively related to cropland footprint. Similarly, the major driving forces that lead

to increase fisheries footprint are economic development, population and export of fish

while urbanization is negatively related to fisheries footprint. In case of forestland

footprint, the driving forces are economic development, population, urbanization,

education, export intensity and income inequality. The economic development,

population, urbanization, export intensity and income inequality are positively related;

while education is negatively related to forestland footprint. Similarly, the CO2, grazing

and built-up land footprints are positively affected by GDP and population. However,

the findings support the EKC hypothesis except in the case of built-up land footprint for

middle income countries and the CO2 emission footprint, in case of high income

countries which support the validity of U-EKC hypothesis.

In the light of the above findings, it is concluded that high income countries experienced

greater ecological overshooting than the middle income countries, in the period 2003-2011.

They have experienced greater consumption of coal, oil and gas and have service based

160

activities than the middle income countries. They have discrepancy in utilization of resources

and impacts of various driving forces of the total ecological footprint and its components,

alongwith the discrepancy in inequality and mean environmental impact intensity.

161

8.3 Policy recommendations

Based on the findings of this study, it is concluded that our earth is at the edge of finite

resources but the possibilities are not restricted. Therefore following policies options are

recommended:

1. Findings of the study, reveals that total ecological footprint of High and Middle income

countries is greater than its biocapacity which leads to generate 94 per cent and 14

ecological overshooting in High and Middle income countries, respectively. Therefore,

it is suggested that total ecological footprint of these nations should be reduced, at least

its biocapacity level, which would be possible by matching cropland, forest, grazing

land, fishing grounds and built-up land footprint, with its respectively regenerating

capacity, in a given year.

2. The findings suggest that the level of education is positively related to the total

ecological footprints; therefore, greater investment should go to education sector.

3. The findings reveal that urbanization is positively affecting the built-up land footprint

in High and Middle income countries. It is therefore suggested that there should be

proper planning for rural development (for example creating job opportunities, agro-

based business activities and small scale industries) which will reduce the built-up land

footprint.

4. The findings suggest that fossil fuel, particularly coal and oil, is the major driving

forces which largely increase CO2 ecological footprint. Production and use of

renewable energy alternatives like wind, solar system and micro-hydro power plants

can lead toward environmental sustainability.

5. In their environmental agenda, the high and middle income countries should keep the

utilization efficiency of cropland, forest and grazing land at the first priority; because

they have more potential for achieving its maximum level of efficiency. It would be

162

followed by CO2 footprint, built-up land and fishing ground footprints; otherwise, if

they follow the traditional process of economic development described by high

investment, high growth and low benefit, the utilization of resources will not meet their

need and the environment. It will also be difficult to support their rapid development.

6. The findings of inequality suggest the larger inequality in income, environmental

intensity, total ecological footprint and its components which further increase the

consumption of finite resources/biocapacity and increase the environmental

degradation of globe. Therefore, the High income countries should reduce the forest,

CO2, fishing grounds and built-up land footprint because its mean environmental

impact intensity is greater than biocapacity. Middle income countries should reduce

cropland and grazing land footprint due to its larger mean environmental impact

intensity than the High income countries.

7. The major driving forces that lead to increase cropland footprint are population, GDP,

and agriculture intensity and therefore, suggest that de-growth, population control

policy and conversion from agriculture to service based activities will curtail cropland

footprint.

8. The high and middle income countries should consider the energy usages while

formulating environmental policies because coal, oil and gas consumption increases

CO2 footprint.

163

8.4 Limitations and directions for future research

The limitations and directions for future research are:

1. The methods used by this study in the trend analysis, inequality and gap of resources

consumption can easily be applied to a national or local level to provide the availability

of appropriate data.

2. The separate study for each nation would be more appropriate if time series data is

available. Conducting this type of analysis would provide policy guidelines for a

country understanding of how and to what extent its local activities degrade the

biocapacity.

3. Another area for future research may be possible, by analyzing resource use-ecological

deficit nexus and projection of total ecological footprint where time series data is

available.

164

APPENDIX A

Trend between High and Middle Income Countries Resources Consumption and

Socioeconomic factors

Figure 1A

Trend between High and Middle Income Countries: Coal consumption, 2003-11

Source: Author Computation based on World Bank data set, 2003-11

Figure 2A

Trend between High and Middle Income Countries: Oil consumption, 2003-11


0

1

2

3

4

5

6

7

8

9

2001 2003 2005 2007 2009 2011

tho

usa

nd

mil

lio

n t

ons

Year

HIC Coal ConsumptionMIC Coal Consumption

0

2

4

6

8

10

12

14

16

2001 2003 2005 2007 2009 2011

Tho

usa

nd

bar

rels

per

day

Year

HIC Oil ConsumptionMIC Oil Consumption

165

Figure 3A

Trend between High and Middle Income Countries: Gas consumption, 2003-11


Figure 4A

Trend between High and Middle Income Countries: GDP per capita, 2003-11


0

2

4

6

8

10

12

14

16

18

20

2001 2003 2005 2007 2009 2011

Tho

usa

nd

Bil

lio

n C

ub

ic F

eet

Year

HIC Gas ConsumptionMIC Gas Consumption

0

500

1000

1500

2000

2500

3000

3500

4000

2001 2003 2005 2007 2009 2011

in M

illi

on $

Year

HIC GDP per capitaMIC GDP per capita

166

Figure 5A

Trend between High and Middle Income Countries: Population, 2003-11


Figure 6A

Trend between High and Middle Income Countries: Urban Pop., 2003-11


0

1000

2000

3000

4000

5000

6000

2001 2003 2005 2007 2009 2011

in M

illi

on

Year

HIC PopulationMIC Population

0

200

400

600

800

1000

1200

2001 2003 2005 2007 2009 2011

Mil

lio

ns

of

tota

l P

op

.

Year

HIC Urban Pop.

MIC Urban Pop.

167

Figure 7A

Trend between High and Middle Income Countries: Working Hrs per employee, 2003-11


Figure 8A

Trend between High and Middle Income Countries: Export, 2003-11


1700

1750

1800

1850

1900

1950

2000

2050

2001 2003 2005 2007 2009 2011

Per

em

plo

yee

Year

HIC Working Hrs

MIC Working Hrs

0

5

10

15

20

25

30

35

40

2001 2003 2005 2007 2009 2011

% o

f G

DP

Year

HIC ExportMIC Export

168

Figure 9A

Trend between High and Middle Income Countries: Service intensity, 2003-11


Figure 10A

Trend between High and Middle Income Countries: Manufacture intensity, 2003-11


Figure 11A

Trend between High and Middle Income Countries: Agriculture intensity, 2003-11


0

10

20

30

40

50

60

70

80

2001 2003 2005 2007 2009 2011

% o

f G

DP

Year

HIC Service Intensity

MIC Service Intensity

0

5

10

15

20

25

30

35

2001 2003 2005 2007 2009 2011

% o

f G

DP

Year

HIC Manufacture IntensityMIC Manufacture Intensity

0

2

4

6

8

10

12

14

2001 2003 2005 2007 2009 2011

% o

f G

DP

Year

HIC Agr. IntensityMIC Agr. Intensity

169

APPENDIX B

Trend between Ecological Footprint and Biocapacity

Figure 1B

Trend between Ecological footprint and Biocapacity: High Income Countries, 2003-11

Source: Author Computation based on GFN data set, 2003-11

Figure 2B

Trend between Cropland footprint and its Biocapacity: High Income Countries, 2003-11


0

1

2

3

4

5

6

7

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Ecological FootprintBiocapacity

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Cropland Footprint

Cropland Biocapacity

170

Figure 3B

Trend between Grazing land footprint and its Biocapacity: High Income Countries, 2003-11


Figure 4B

Trend between Forest footprint and its Biocapacity: High Income Countries, 2003-11


Figure 5B

Trend between Fishing ground footprint and its Biocapacity: High Income Countries, 2003-

11


0

0.2

0.4

0.6

0.8

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er

cap

ita

Year

Grazing land FootprintGrazing land biocapacity

0

0.2

0.4

0.6

0.8

1

1.2

1.4

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Forest Footprint

Forest biocapacity

0

0.1

0.2

0.3

0.4

0.5

0.6

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er

cap

ita

Year

Fishing Grounds Footprint

Fishing grounds biocapacity

171

Figure 6B

Trend between Built-up land footprint and its Biocapacity: High Income Countries, 2003-11


Figure 7B

Trend between CO2 footprint and Biocapacity: High Income Countries, 2003-11


Figure 8B

Trend between Ecological footprint and Biocapacity: Middle Income Countries, 2003-11


0

0.05

0.1

0.15

0.2

0.25

0.3

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Built-up land Footprint

Built-up land biocapacity

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Co2 FootprintBiocapacity

0

0.5

1

1.5

2

2.5

3

3.5

4

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Ecological Footprint

Biocapacity

172

Figure 9B

Trend between Cropland footprint and its Biocapacity: Middle Income Countries, 2003-11


Figure 10B

Trend between Grazing land footprint and its Biocapacity: Middle Income Countries, 2003-

11


Figure 11B

Trend between Forest footprint and its Biocapacity: Middle Income Countries, 2003-11


0

0.2

0.4

0.6

0.8

1

1.2

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er

cap

ita

Year

Cropland FootprintCropland biocapacity

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Grazing land FootprintGrazing land biocapacity

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Forest FootprintForest biocapacity

173

Figure 12B

Trend between Fishing grounds footprint and its Biocapacity: Middle Income Countries,

2003-11


Figure 13B

Trend between Built-up land footprint and its Biocapacity: High Income Countries 2003-11


Figure 14B

Trend between CO2footprint and Biocapacity: High Income Countries, 2003-11


0

0.05

0.1

0.15

0.2

0.25

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

Fishing Grounds FootprintFishing grounds biocapacity

0

0.05

0.1

0.15

0.2

0.25

2001 2003 2005 2007 2009 2011Glo

bal

hec

tare

s P

er c

apit

a

Year

Built-up land FootprintBuilt-up land biocapacity

0.00

0.50

1.00

1.50

2.00

2.50

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er

cap

ita

Year

Co2 FootprintBiocapacity

174

Figure 15B

Trend in Ecological Overshoot of High and Middle Income Countries 2003-11


Figure 16B

Trend in Ecological Efficiency of High and Middle Income Countries,2003-11

Source: Author Computation based on GFN and World Bank data set, 2003-11

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

2001 2003 2005 2007 2009 2011

in P

erce

nta

ge

Year

HIC Ecological Overshoot

MIC Ecological Overshoot

0

0.5

1

1.5

2

2.5

3

2001 2003 2005 2007 2009 2011

10

00

of

inco

me/

gha

Year

HIC Ecological Efficiency

MIC Ecological Efficiency

175

Figure 17B

Trend between High and Middle Income countries’ Biocapacity, 2003-11

Figure 18B

Trend between High and Middle Income countries’ ecological footprint, 2003-11

Figure 19B

Trend between High and Middle Income countries’ cropland footprint, 2003-11

Figure 20B

Trend between High and Middle Income countries’ grazing footprint, 2003-11

0

0.5

1

1.5

2

2.5

3

3.5

4

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Biocapacity

0

1

2

3

4

5

6

7

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Ecological Footprint

MIC Ecological Footprint

0

0.2

0.4

0.6

0.8

1

1.2

1.4

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Cropland FootprintMIC Cropland Footprint

176

Figure 21B

Trend between High and Middle Income countries’ forest footprint, 2003-11

Figure 22B

Trend between High and Middle Income countries’ fish footprint, 2003-11

Figure 23B

Trend between High and Middle Income countries’ built-up footprint, 2003-11

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Grazing land Footprint

MIC Grazing land Footprint

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Forest FootprintMIC Forest Footprint

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Fish FootprintMIC Fish Footprint

177

Figure 24B

Trend between High and Middle Income countries’ CO2 footprint,

2003-11

0

0.05

0.1

0.15

0.2

0.25

0.3

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Built-up land Footprint

MIC Built-up land Footprint

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

2001 2003 2005 2007 2009 2011

Glo

bal

hec

tare

s P

er c

apit

a

Year

HIC Co2 FootprintMIC Co2 Footprint

178

APPENDIX C

Comparison between High and Middle Income Countries’ Atkinson indices

Figure 1C

Comparison between High and Middle Income Countries’ Atkinson indices:

Ecological footprint, per capita income and Environment Intensity

Figure 2C

Comparison between High and Middle Income Countries Atkinson indices:

Cropland footprint, its environment intensity and per capita income

0

0.2

0.4

0.6

0.8

Total Ecological

footprint

Per Capita Income Environmental

Intensity

Atk

inso

n I

nd

ex o

f E

qual

ity

High Income CountriesMiddle Income Countres

0

0.2

0.4

0.6

0.8

Cropland footprint Per Capita Income Environmental

Intensity of

Cropland footprint

Atk

inso

nIn

dex

of

Eq

ual

ity


179

Figure 3C


Grazing land footprint, its environment intensity and per capita income

Figure 4C


Forest footprint, its environment intensity and per capita income

Figure 5C


Fish land footprint, its environment intensity and per capita income

00.10.20.30.40.50.60.70.80.9

Grazing land

footprint

Per Capita Income Environmental

Intensity of Grazing

land footprint

Atk

inso

n I

nd

ex o

f E

qual

ity


00.10.20.30.40.50.60.70.8

Forest footprint Per Capita Income Environmental

Intensity of Forest

footprintAtk

inso

n I

nd

ex o

f E

qual

ity


0

0.2

0.4

0.6

0.8

Fish footprint Per Capita Income Environmental

Intensity of Fish

footprint

Atk

inso

n I

nd

ex o

f E

qual

ity


Middle Income Countres

180

Figure 6C


Built-up land footprint, its environment intensity and per capita income

Figure 7C


CO2 footprint, its environment intensity and per capita income

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Built-up footprint Per Capita Income Environmental

Intensity of Built-

up footprint

Atk

inso

n I

nd

ex o

f E

qual

ity


0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Co2 footprint Per Capita Income Environmental

Intensity of Co2

footprint

Atk

inso

n I

nd

ex o

f E

qual

ity


Middle Income Countres

181

APPENDIX D

Table 1D

Trend in Environmental Impact Intensity of High Income Countries

Years

Country 2003 2005 2007 2009 2011

Trend

Australia 0.323 0.230 0.167 0.126 0.134

Mixed

Austria 0.147 0.130 0.114 0.109 0.100

Declined

Belgium 0.218 0.139 0.180 0.163 0.121

Mixed

Canada 0.315 0.196 0.158 0.145 0.126

Declined

Cyprus 0.14 0.110 0.12 0.139 0.113

Mixed

Czech Republic 0.495 0.402 0.313 0.239 0.208

Declined

Denmark 0.163 0.165 0.141 0.131 0.070

Declined

Estonia 0.688 0.619 0.475 0.340 0.313

Declined

France 0.177 0.141 0.121 0.118 0.095

Declined

Germany 0.155 0.122 0.122 0.108 0.095

Declined

Greece 0.278 0.262 0.189 0.159 0.151

Declined

Hungary 0.367 0.318 0.216 0.232 0.200

Mixed

Ireland 0.129 0.123 0.103 0.110 0.088

Declined

Israel

0.234 0.235 0.193 0.148 0.141

Declined

Italy 0.140 0.149 0.132 0.119 0.108

Mixed

Japan 0.142 0.137 0.139 0.097 0.082

Mixed

Korea 0.232 0.084 0.211 0.234 0.185

Mixed

Kuwait 0.339 0.249 0.140 0.269 0.186

Mixed

182

(Continued...

Years

Country 2003 2005 2007 2009 2011 Trend

Netherlands 0.136 0.106 0.121 0.114 0.083 Mixed

New Zealand 0.395 0.277 0.151 0.096 0.135 Mixed

Norway 0.158 0.104 0.065 0.034 0.047 Mixed

Poland 0.651 0.496 0.387 0.358 0.276 Declined

Portugal 0.283 0.236 0.196 0.191 0.144

Declined

Qatar 0.10 0.11 0.155 0.159 0.078 Mixed

Saudi Arabia 0.443 0.198 0.322 0.249 0.172 Mixed

Singapore 0.145 0.139 0.136 0.163 0.112 Mixed

Slovakia 0.395 0.283 0.253 0.213 0.208 Declined

Slovenia 0.241 0.245 0.222 0.166 0.181 Mixed

Spain 0.217 0.217 0.166 0.133 0.107 Declined

Sweden 0.182 0.118 0.110 0.117 0.109 Mixed

Switzerland 0.086 0.091 0.079 0.072 0.055 Mixed

Trinidad 0.375 0.173 0.187 0.551 0.326 Mixed

UAE 0.295 0.235 0.249 0.277 0.247 Mixed

UK 0.164 0.133 0.101 0.121 0.101 Mixed

USA 0.244 0.213 0.166 0.149 0.136

Declined

183

Table 2D

Trend in Environmental Impact Intensity of Middle Income Countries

Years

Country 2003 2005 2007 2009 2011 Trend

Albania 0.507 0.823 0.530 0.413 0.453

Mixed

Algeria 0.740 0.537 0.403 0.464 0.299

Mixed

Angola 0.818 0.440 0.295 0.280 0.178

Declined

Argentina 0.910 0.435 0.315 0.227 0.208

Declined

Armenia 0.950 0.886 0.568 0.514 0.541

Mixed

Azerbaijan 1.954 1.369 0.486 0.343 0.259

Declined

Belarus 1.800 1.233 0.803 0.792 0.579

Declined

Bolivia 0.948 1.684 1.466 1.455 1.090

Mixed

Bosnia 1.148 2.794 1.978 1.463 1.115

Mixed

Botswana 0.677 1.267 0.665 0.622 0.655

Mixed

Brazil 0.783 0.498 0.401 0.331 0.219

Declined

Bulgaria 0.876 0.705 0.687 0.417 4.150

Mixed

Cameroon 1.401 1.386 0.975 1.030 0.882

Mixed

Chile 0.628 0.388 0.308 0.225 0.267

Mixed

China 1.204 1.210 0.828 0.579 0.447

Mixed

Colombia 0.597 0.278 0.400 0.369 0.231

Mixed

Congo 5.427 2.546 2.754 2.797 2.170

Mixed

Costa Rica 0.460 0.483 0.446 0.351 0.248

Mixed

Côte d'Ivoire 1.020 0.948 0.937 0.730 0.828

Mixed

184

(Continued…

Years

Country 2003 2005 2007 2009 2011 Trend

Cuba 0.466 0.465 0.357 0.346 0.258

Declined

Dominican 0.643 0.404 0.318 0.263 0.218

Declined

Ecuador 0.632 0.728 0.525 0.305 0.331

Mixed

Egypt 1.301 1.392 0.987 0.681 0.618

Mixed

El Salvador 0.468 0.563 0.605 0.583 0.442

Mixed

Gabon 0.431 0.189 0.164 0.305 0.213

Mixed

Georgia 0.983 0.732 0.785 0.746 0.388

Mixed

Ghana 2.853 2.961 1.594 1.552 1.058

Declined

Guatemala 0.813 0.729 0.717 0.688 0.534

Declined

Honduras 1.091 1.261 1.110 0.860 0.654

Mixed

India 1.373 1.226 0.870 0.800 0.618

Declined

Indonesia 1.061 0.751 0.652 0.575 0.365

Declined

Iran 0.882 0.854 0.571 0.515 0.267

Declined

Iraq 0.175 0.169 0.171 0.190 0.198

Mixed

Jamaica 0.579 0.257 0.400 0.401 0.315

Mixed

Jordan 0.784 0.733 0.679 0.522 0.313

Declined

Kazakhstan 1.733 0.894 0.671 0.600 0.490

Declined

Latvia 0.668 0.462 0.402 0.311 0.405

Mixed

Lebanon 0.481 0.577 0.483 0.417 0.332

Mixed

185

(Continued...

Years

Country 2003 2005 2007 2009 2011 Trend

Lesotho 1.689 1.515 1.315 1.279 0.902

Declined

Libya 0.699 0.525 0.272 0.433 0.395

Mixed

Lithuania 0.558 0.407 0.380 0.346 0.305

Declined

Macedonia 1.339 1.504 1.393 0.766 0.533

Mixed

Malaysia 0.712 0.435 0.672 0.410 0.282

Mixed

Mauritania 2.522 2.745 2.587 2.295 1.481

Mixed

Mauritius 0.325 0.441 0.678 0.678 0.333

Mixed

Mexico 0.378 0.428 0.325 0.445 0.246

Mixed

Moldova 2.521 1.483 1.129 0.918 0.858

Declined

Mongolia 3.988 3.499 3.387 3.261 1.201

Declined

Montenegro 0.936 0.711 0.561 0.499 0.456

Declined

Morocco 0.670 0.585 0.486 0.520 0.492

Mixed

Namibia 0.590 1.035 0.514 0.339 0.406

Mixed

Nicaragua 1.510 1.744 1.154 1.014 0.804

Mixed

Nigeria 2.601 1.669 1.270 1.191 2.018

Mixed

Pakistan 1.124 1.154 0.804 0.792 0.552

Mixed

Panama 0.425 0.685 0.468 0.302 0.289

Mixed

Paraguay 2.135 2.135 1.381 1.231 1.044

Declined

Peru 0.526 0.577 0.426 0.383 0.361

Mixed

186

(Continued...

Years

Country 2003 2005 2007 2009 2011 Trend

Philippines 1.158 0.727 0.772 0.653 0.422

Mixed

Romania 0.907 0.614 0.330 0.292 0.272

Declined

Russia 1.508 0.704 0.484 0.467 0.335

Declined

Serbia 0.847 3.380 2.518 2.258 2.331

Mixed

South Africa 1.419 0.590 0.425 0.447 0.383

Mixed

Sri Lanka 0.264 0.188 0.197 0.203 0.145

Mixed

Swaziland 0.747 0.592 0.928 0.977 0.642

Mixed

Syrian 0.948 0.691 0.566 0.634 0.359

Mixed

Thailand 1.209 1.339 1.140 1.010 0.923

Mixed

Timor-Leste 0.149 0.122 0.110 0.083 0.063

Declined

Tunisia 0.606 0.548 0.498 0.408 0.407

Declined

Turkey 0.431 0.381 0.290 0.280 0.251

Declined

Turkmenistan 2.474 2.262 1.506 0.961 0.730

Declined

Ukraine 3.217 1.473 0.946 0.904 0.787

Declined

Uruguay 1.047 1.045 0.732 0.414 0.289

Declined

Uzbekistan 4.824 3.315 2.099 1.608 1.217

Mixed

Venezuela

0.722

0.517

0.348

0.251

0.243

Declined

Vietnam 1.427 1.803 1.523 1.217 0.881

Mixed

Yemen 1.162 1.117 0.797 0.726 0.616

Declined

Zambia 2.930 1.113 0.827 0.793 0.508

Declined

187

Table 3D

Ranking of High Income Countries according to their Ecological efficiency performance:

2003-2011

Country

Ecological efficiency

relative to best performer

(RI/RI of best performer)


relative to mean

(RI/mean of RI)

Ecological

Efficiency Rank

Switzerland 1.00 0.40 1

Norway 1.06 0.43 2

Ireland 1.44 0.58 3

Netherlands 1.45 0.59 4

Cyprus 1.51 0.61 5

Japan 1.55 0.62 6

Austria 1.56 0.63 7

Germany 1.56 0.63 8

UK 1.61 0.65 9

Sweden 1.65 0.67 10

Italy 1.68 0.68 11

France 1.69 0.68 12

Denmark 1.74 0.70 13

Singapore 1.79 0.72 14

Qatar 1.83 0.74 15

Belgium 2.13 0.86 16

Spain 2.18 0.88 17

USA 2.36 0.95 18

188

(Continued...

Country


relative to best

performer (RI/RI of

best performer)


relative to mean

(RI/mean of RI)


Rank

Canada 2.44 0.99 19

Korea 2.46 0.99 20

Israel 2.47 0.99 21

Australia 2.55 1.03 22

Greece 2.70 1.09 23

Portugal 2.73 1.10 24

New Zealand 2.74 1.10 25

Slovenia 2.74 1.11 26

Kuwait 3.07 1.24 27

Trinidad 3.12 1.26 28

UAE 3.38 1.36 29

Hungary 3.46 1.40 30

Slovakia 3.51 1.42 31

Saudi Arabia 3.59 1.45 32

Czech Republic 4.30 1.73 33

Poland 5.63 2.27 34

Estonia 6.32 2.55 35

189

Table 4D

Ranking of Middle Income Countries according to their Ecological efficiency performance:

2003-2011

Country Ecological efficiency

relative to best

performer (RI/RI of

best performer)

Ecological

efficiency relative

to mean

(RI/mean of RI)


Rank

Timor-Leste 1.00 0.12 1

Iraq 1.71 0.20 2

Sri Lanka 1.88 0.22 3

Gabon 2.46 0.29 4

Turkey 3.10 0.37 5

Chile 3.43 0.41 6

Mexico 3.44 0.41 7

Dominican 3.48 0.41 8

Colombia 3.54 0.42 9

Cuba 3.57 0.42 10

Jamaica 3.68 0.44 11

Costa Rica 3.75 0.45 12

Lithuania 3.77 0.45 13

Angola 3.80 0.45 14

Venezuela 3.93 0.47 15

Argentina 3.95 0.47 16

Panama 4.09 0.49 17

Brazil 4.21 0.50 18

Latvia 4.24 0.51 19

Peru 4.29 0.51 20

Lebanon 4.32 0.51 21

Libya 4.39 0.52 22

Romania 4.56 0.54 23

Algeria 4.61 0.55 24

Mauritius 4.63 0.55 25

Tunisia 4.66 0.55 26

Malaysia 4.74 0.56 27

190

(Continued...

Country Ecological efficiency

relative to best

performer (RI/RI of best

performer)

Ecological

efficiency relative

to mean (RI/mean

of RI)


Rank

Ecuador 4.76 0.57 28

El Salvador 5.02 0.60 29

Albania 5.14 0.61 30

Morocco 5.19 0.62 31

Namibia 5.44 0.65 32

Jordan 5.72 0.68 33

Iran 5.83 0.69 34

Montenegro 5.97 0.71 35

Syrian 6.03 0.72 36

South Africa 6.16 0.73 37

Indonesia 6.42 0.76 38

Armenia 6.53 0.78 39

Guatemala 6.57 0.78 40

Russia 6.60 0.79 41

Uruguay 6.66 0.79 42

Georgia 6.86 0.82 43

Philippines 7.04 0.84 44

Swaziland 7.33 0.87 45

Botswana 7.33 0.87 46

China 8.05 0.96 47

Kazakhstan 8.28 0.99 48

Azerbaijan 8.32 0.99 49

Yemen 8.34 0.99 50

191

(Continued...

Country Ecological efficiency relative

to best performer (RI/RI of

best performer)


relative to mean

(RI/mean of RI)

Ecological

Efficiency Rank

Pakistan 8.35 0.99 51

Côte d'Ivoire 8.42 1.00 52

India 9.22 1.10 53

Honduras 9.39 1.12 54

Egypt 9.39 1.12 55

Belarus 9.82 1.17 56

Macedonia 10.45 1.24 57

Thailand 10.60 1.26 58

Cameroon 10.71 1.28 59

Zambia 11.64 1.39 60

Nicaragua 11.75 1.40 61

Bolivia 12.54 1.49 62

Lesotho 12.64 1.51 63

Bulgaria 12.89 1.54 64

Vietnam 12.93 1.54 65

Moldova 13.03 1.55 66

Ukraine 13.82 1.65 67

Paraguay 14.95 1.78 68

Turkmenistan 14.97 1.78 69

Bosnia 16.03 1.91 70

Nigeria 16.51 1.97 71

Ghana 18.90 2.25 72

Serbia 21.39 2.55 73

Mauritania 21.94 2.61 74

Uzbekistan 24.65 2.94 75

Mongolia 28.94 3.45 76

Congo 29.61 3.53 77

192

APPENDIX E

List of High and Middle Income Countries

Table 1E

List of High Income Countries

Australia France Netherlands Spain

Austria Germany New Zealand Sweden

Bahrain Greece Norway Switzerland

Belgium Hungary Poland Trinidad

Canada Ireland Portugal UAE

Cyprus Israel Qatar UK

Czech Republic Italy Saudi Arabia USA

Denmark Japan Singapore

Estonia Korea Slovakia

Finland Kuwait Slovenia

Table 2E

List of Middle income Countries

Albania Dominican Lithuania Serbia

Algeria Ecuador Macedonia South Africa

Angola Egypt Malaysia Sri Lanka

Argentina El Salvador Mauritania Swaziland

Armenia Gabon Mauritius Syrian

Azerbaijan Georgia Mexico Thailand

Belarus Ghana Moldova Timor-Leste

Bolivia Guatemala Mongolia Tunisia

Bosnia Honduras Montenegro Turkey

Botswana India Morocco Turkmenistan

Brazil Indonesia Namibia Ukraine

Bulgaria Iran Nicaragua Uruguay

Cameroon Iraq Nigeria Uzbekistan

Chile Jamaica Pakistan Venezuela

China Jordan Panama Viet Nam

Colombia Kazakhstan Paraguay Yemen

Congo Latvia Peru Zambia

Costa Rica Lebanon Philippines

Côte d'Ivoire Lesotho Romania

Cuba Libyan Russia

193

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