1

Ecological footprint, CO2 emissions and economic growth in Qatar : Evidence from a Markov

Switching Equilibrium Correction Model

Charfeddine Lanouar1,

Department of Finance and Economics, College of Business and Economics,

Qatar University, Qatar.

Abstract

Reducing the impact of air pollution and global environment degradation on Human health

and the quality of Qatari living is one of the most important pillars of Qatar 2030 vision.

With respect to this vision, we examine the effects of economic growth, energy

consumption, urbanization, openness trade and financial development on environment

quality during the period 1975-2011 in Qatar. Unlike the existing studies, we use the

ecological footprint and the 𝐶𝑂2 emissions as indicators of environment degradation.

Moreover, we use Markov Switching Equilibrium Correction Model with shifts in both the

intercept and the income per capita coefficient. Our finding show strong evidence for

cointegration with Markov shifts. We found, for both pollutants, that the EKC hypothesis

holds for the Qatar economy when accounting for possible shifts. Moreover, we found that

financial development, urbanization and openness trade worse environment. In contrast,

we found that the effect of electricity consumption on 𝐶𝑂2 emissions is positive and

negative for ecological foot print.

Keywords : Environment degradation, EKC, CO2, foot print, economic growth, Cointegration

with Markov shifts.

JEL classification :

1 P.O.Box: 2713-Doha-Qatar. Email : lcharfeddine@qu.edu.qa. Office : (+974) 4403-7764, Fax : (+974) 4403-5081.

2

1. Introduction

“Long live the planet. Live Humanity. Long live life itself.” With this slogan of the COP21 Paris

climate change conference of November-December 2015, there is actually no doubt in the priority

of preserving the planet from the growing warmer of the earth’s atmosphere in the coming years.

The main outcome from this conference, is the large commitment of more than 187 countries

around the world to reducing their greenhouse gas in order to keep the rise in temperature below

the level of 2°C. Human activity and rapid population increases are the most two important factors

largely advanced to explain increases of Greenhouse Gases (GHS). The U.S and China are the

largest emitter in the world with 21 percent and 15 percent respectively in absolute term (World

Resources Institute, 2005). However, focusing on absolute pollutant level will gives a partial

understanding and does not lead to completely understanding the overall reality about environment

degradation. Thus, it is very important to have a deeper investigation in term of relative pollutants

and in its key determinants. This is of great importance and for a particular interest especially when

dealing with a countries characterized by high level of pollutant per capita such as CO2 emissions

per capita or ecological footprint per capita.

This paper investigates the relationship between environment degradation and economic growth

as well as with some others key determinants of environment degradation in Qatar. This paper is

mainly motivated by the controversy effects of the rapid economic development that have

experienced Qatar in the last four decades due to its abounded hydrocarbon resources. This rapid

economic growth has severely affected the environment quality of the country and has increased

the need to preserve and protect its ecosystem including water, air and lands. The current situation

of Qatar in term of environment degradation is very critical. For instance, according to the World

Bank Indicators (WBI, 2011), Qatar is ranked the first in term of 𝐶𝑂2 emissions per capita and the

second in term of ecological footprint per capita according to the Living Planet Report of 2014.

Following the WWF’s Living Planet Report of 2014, “if all people on the planet had the Footprint

of the average resident of Qatar, we would need 4.8 planets”.

Moreover, Qatar 2030 vision has given a high importance to questions related to air pollution,

climate change and their impacts on economic sustainability. For example, three out of the four

pillars of Qatar 2030 vision are directly or indirectly related to preserving the environment2. Air

pollution, global greenhouse gases (GHG), water pollution and water resources degradation are

among the most serious environmental concerns that actually encounter the country. In addition,

the needs for a high level of economic growth accompanied with a rapid increase of urbanization

and international trade have made a strong pressure in the country energy use considered as the

main source of CO2 emissions of the country.

The Qatar situation in term of air pollution is also very worrying, the local air pollution levels in

Qatar has frequently exceeded recommended levels and are more time higher than the international

standards. In fact, compared to the WHO’s standards of the 24-hour and annual averages

concentration of 50 𝜇g/m3and 20 𝜇g/m3 for PM10 the Qatar’s national air quality standards are

far from these values. For instance, the values for PM10 is around 150 𝜇g/m3 for 24 hours average

concentration and around 50 𝜇g/m3 for the annual average concentration (see the world health

2 The four pillars of Qatar 2030 visions are : (1) economic development, (2) social development, (3) human

development, and (4) environmental development.

3

organization, WHO). These high level have increased the likelihood of diseases related to the

respiratory system such as asthma, chronic obstructive pulmonary disease among many others.

Given that situation, several important questions arise for this country.

1. Is a continuous rise of income positively monotonically associated with environmental

degradation proxy? Or is the relationship nonlinear (U-or Inverted U-shaped)?

2. Which are the most important determinants (macroeconomic and financial variables) of

environmental degradation?

3. What it the type of the relationship between environmental degradation proxy and its

determinants? Is the relationship linear or non-linear?

4. Is the environment degradation- economic growth nexus environmental degradation proxy

dependent?

5. Finally, how is the causal relationship between environment degradation proxies and its

key determinants?

The answers to these questions are critical at this stage of economic development of the Qatar

economy and are of particular interest for building and designing the appropriate strategies for

reducing environment degradation.

The first question which is related to the relationship between environment degradation and

economic growth can be answered by examining the type of the relationship between the

environment degradation proxy and income proxy. In the empirical literature, recent studies have

focused on whether the Environmental Kuznets Curve (EKC) hypothesis holds or not. Following

the EKC hypothesis, the relationship between economic growth and environment degradation is

inverted-U-shaped. From the economic perspective, this means that initially economic growth

increases environment degradation and then declines it after a threshold point of income per capita.

More specifically, at initial level of economic growth, an increase in income is linked with an

increase in energy consumption that raises environment degradation. However, after reaching a

critical level of income, the spending on environment protection is increased, and hence

environment degradation tend to decrease.

In the economic theory, three effects-types has been advanced as a channels through which

economic growth impacts environment quality. The first channel is the scale effect which

postulates that as the economy develop more inputs are needed in term of energy consumption,

water, etc… inducing more degradation of the environment quality. The second channel is the

composition effect following which as the economy develop the economic structure changes and

switch from an agriculture based economy to an industrial based economy, and then to a service

based economy. All along these switching processes, the needs of the economy changes and their

impact on the environment changes also. Finally, the last channel corresponds to the technique

effect which has a positive effect on the environment quality and which drives the curve

downward.

Following this relationship, to improve environmental quality the best way is to become rich (see

for instance Beckerman, 1992; and Cole, 1999). From an econometrical or statistical perspectives,

the EKC hypothesis can be tested by estimating the EKC equation which relies the environment

degradation proxy to the real GDP and to a nonlinear term of the real GDP (the squared real GDP).

4

If the EKC hypothesis holds then the real GDP and the squared real GDP have respectively a

positive and negative signs. This EKC hypothesis has been firstly introduced by Kuznets (1955)

when examining the relationship between economic growth and income inequality which shows

that this relationship is inverted U-shaped. Grossman and Krueger (1995) are the first to examine

this relationship between environment degradation and economic growth in their seminal paper

published on the Quarterly Journal of Economics. They found that this relationship is inverted U-

shaped which validates the EKC hypothesis.

However, until now there is no consensus about the true nature of the relation between real GDP

and environment degradation. Evidence for the EKC hypothesis is very mixed. Overall, the results

seem to depend in many factors including the specification, the pollutants and the econometrics

technique used (Shafik and Bandyopadhyay, 1992; and Stern, 1996, 2001). First, empirical studies

show that the results in term of positive and negative relationships as well as in term of magnitude

differ significantly for the same country depend on the specification studied, linear, quadratic or

cubic. Moreover, the inclusion of other factors in the right hand of the regression such as

urbanization, trade openness, financial development and political stability have a significant

impact on the magnitude of the income per capita variables coefficients. Second, the results differ

significantly following the environment degradation proxy used. For instance, Horvath (1997) and

Holtz-Eakin and Selden (1995) suggest that the use of global pollutants leads to continuously rise

the levels of environment degradation or to a high levels of income per capita turning point, see

also Esteve and Tamarit (2011). Third, the results also seem to depend in the econometric

approach employed.

To answer the second question, we augment the basic EKC equation by adding several

macroeconomic and financial variables such as energy use (electricity consumption), urbanization,

openness trade and financial development. So, by determining which factors explain environment

degradation, policymakers, researchers and international institutions can help on recommending

the adequate economic policies that can improve the environment quality and the live standing of

inhabitants.

In the literature, energy use is considered as the second key determinant of environment

degradation. In the empirical literature several proxies of energy use have been employed such as

energy consumption, energy electricity among many others. The relationship between energy

consumption and environment quality is expected to be positive as energy consumption is

supposed to increase the scale of the economy and worse environment. Several empirical studies

have documented that high level of energy electricity leads to a high level environment of

degradation. Others studies suggest that electricity consumption is an efficient energy and it

decreases environment degradation.

The third potential determinant of environment degradation is urbanization. Previous literature is

inconclusive on the relation between urbanization and environment degradation. Urban studies

advance three theories to explain the channels through which urbanization affects environment

degradation : ecological modernization; urban environmental transition; and compact city theories,

see for instance Poumanyvong and Kaneko (2010). Empirically, a positive relationship between

environment degradation and urbanization is found in several studies (see for instance, Cole and

Neumayer, 2004; York, 2007; Liddle and Lung, 2010; Poumanyvong and Kaneko, 2010; Kasman

and Duman, 2015; among others). In the other hand, a negative relationship is found by Fan et al.

5

(2006), Dodman (2009), Sharma (2011), Hossain (2011) among others. For these authors, the

increase of urban population contributes positively to the development of the structures of

modernity via orienting urbanization towards smart cities, new ICT to shorten the time of

transportation and to spread the usage of e-works, the use of clean and renewable energy sources

which improve environment quality, see for instance Ehrhardt-Martinez (1998). In addition to

these two previous groups of studies, many others studies show that this relationship is ambiguous

and depend in many others factors and especially in the economics structures of each country. For

example, Poumanyvong and Kaneko (2010) show based on a study for 99 countries, using the

stochastic impacts regression on population affluence and Technology model over the period

1975-2005, that urbanization decreases the level of energy use in low-income countries, while it

increases energy use in middle and high-income countries. Recently, Sadorsky (2014) found

mixed results about this relationship and shows that the result depends on the estimation

techniques. More recently, Chikaraishi et al. (2014) show that improving urbanization make

countries more environmentally friendly when the country's GDP per capita and the percentage

share of service industries in GDP are sufficiently high.

The fourth potential determinant of environment degradation is openness trade. Many studies have

showed that the impact of openness trade on environmental quality can be decomposed into scale,

technique, and composition effects, see for instance Antweiler et al. (2001). Empirically, mixed

results have been reported in the previous works. Some studies find a negative relationship

between trade openness and environmental degradation (Shahbaz et al.,2013; Hossain, 2011;

Shaabaz et al., 2012; Jayanthakumaran, 2012). On contrary, other studies find evidence of positive

effect on environmental degradation (Suri and Chapman, 1998; Alber et al., 1999; Kasman and

Duman, 2015 and Ozturk and Acaravci, 2013).

Recent studies in the field supports evidence that financial development is also a key determinant

of environment degradation. Most of studies argue that financial development can affect

environment degradation through efficient/inefficient allocation of financial resources. Moreover,

the development of financial markets can affect positively the environment quality by providing

more funds to investments in research and development on modern and efficient technologies

relating to clean energy. Empirically, there is some evidence of a negative effects of financial

development on environment degradation, see for instance Shahbaz et al. (2013b) for the Indonesia

case, Shahbaz et al. (2013a) for the Malaysia case, Charfeddine and Khediri (2016) for the case of

UAE and Al-mulali and Sab (2012) for thirty Sub Saharan African countries. Ozturk and Acaravci

(2013) find no significant effect of financial development on per capita carbon emissions in the

long- run. In contrast to all these previous results, Boutabba (2014) finds that financial

development increases 𝐶𝑂2 emissions for the India case.

Question three concerns the type of the relationship linking environment degradation and economic

growth and the other explanatory variables. Recent studies in the field provide strong evidence that

this relationship is nonlinear (see for instance Charfeddine and Khediri, 2016; and Shahbaz et al. 2014). Charfeddine and Khediri (2016) show that the 𝐶𝑂2 emissions, real GDP per capita, electricity

consumption, financial development and urbanization are cointegrated and have a long run

relationship. However, the authors show using Gregory and Hansen (1996) and Hatemi-J (2008) that

this long run relationship is characterized by the presence of structural breaks. Esteve and Tamarit

(2011a) examine the long run relationship between 𝐶𝑂2 emissions and income using threshold

cointegration for Spain during the period from 1857-2007. They found evidence for nonlinear

6

relationship using Hansen and Seo (2002) approach. In a second paper Esteve and Tamarit (2011b)

show that linear cointegrated regression model with multiple structural breaks describe better the

relationship between 𝐶𝑂2 emissions and income per capita. The authors found evidence for two

breaks (three regimes) and that the estimated coefficient associated to the income-per capita in the

long run relationship (long run-elasticity) is decreasing overtime. The authors conclude that even if

𝐶𝑂2 emissions per capita is monotonically increasing with income, the long run elasticity of income

decreases overtime and is less than one. In this paper, we follow another way in examining the EKC

hypothesis for Qatar. We consider the possibility of non-linear relationship between environment

degradation proxy and real GDP per capita using Markov switching equilibrium correction model.

The nonlinearity is allowed in both variables (U-shaped versus inverted U-shaped) and parameters

(the intercept and the coefficient associated to the real GDP). To the best of our knowledge this

specification have not been yet used before to investigate the Kuznet curve hypothesis.

To answer the fourth question which aims to examine whether the environment degradation-

economic growth nexus is environment degradation proxy dependent, we use two different proxies

of environment degradation namely : (1) 𝐶𝑂2 emissions per capita which is the most widely used

proxy in the empirical literature, and (2) the ecological footprint proxy which is rarely used in

empirical studies. The ecological footprint proxy is a more global proxy of environment

degradation compared to 𝐶𝑂2 emissions. Ecological footprint is a measure of the demand

populations and activities place on the biosphere in a given year, given the prevailing technology

and resource management of that year.

Finally, the answer to the last question concerning the causality direction between all used

variables is critical for recommending the more appropriate economic policy that are able to reduce

environment degradation. For instance, the causality direction between electricity consumption

and economic growth will provide us with which of the following : growth, conservation, neutral

or feedback hypothesis holds for Qatar.

This paper contributes to the empirical literature of the EKC hypothesis in many ways. First, to

our knowledge this paper is the first to consider the case of the Qatar economy as a single country

to test the EKC hypothesis as well as the different directions of causality between variables.

Second, in addition to the 𝐶𝑂2 emissions largely employed in the empirical literature, in this paper

we employ also the ecological footprint as a new proxy of environmental degradation. Third, we

use recent development of cointegration approach with structural breaks which is also rarely used

for the case of EKC hypothesis. As tests of cointegration with shifts in the cointegration vector,

we use the Gregory and Hansen (1996), Hatemi-J (2008) and to investigate the causal relationship

between all variables using standard Granger causality tests. Fourth, to our knowledge this paper

is the first study that uses Markov Switching Equilibrium Correction Model with shifts in both the

intercept and the income per capita coefficient for the long run relationship between environment

degradation and its key determinants.

The remainder of the paper is organized as follows. Section 2 presents the empirical methodology

to follow. Precisely, it presents how the data is collected and the variables are measured, unit roots

tests with structural breaks and cointegration with single and multiple structural breaks techniques.

Section 4 discusses the empirical findings. Finally, section 5 concludes and propose some policies

that help to reduce environment degradation.

7

2. 𝑪𝑶𝟐 emissions and ecological footprint in Qatar

Which environment degradation proxy is more suitable when investigating the EKC hypothesis?

The answer to this question seems to depend in many factors such as the economic structure and

the stage of economic development of the country under study. However, there is some evidence

among researchers that the EKC hypothesis holds more for local and regional pollutants than for

global pollutants. Recent empirical studies have focused more in global pollutant such as CO2

emissions rather than in local pollutants (see for instance Khedir and Charfeddine, 2016 and

references therein).

In this study, we follow the second trend of literature by using two proxies of environment

degradation namely the CO2 emissions and the ecological footprint indicator. While the data

availability motivate the use of the CO2 indicator, the ecological footprint indicator has emerged

in recent years as an important indicator of environment degradation level. In contrast to the CO2

emissions pollutant largely considered in empirical studies due to the CO2 emissions undesired

effects, or to it is adversely effects on the resource usefulness, the ecological foot print indicator

have the advantage of measuring the total humanity's demand on nature at individual, national,

regional or world level. Moreover, compared to the CO2 emissions, the ecological footprint

indicator is a more global environment degradation proxy which account for cropland, forest,

forest, grazing and crop land as well as fishing land and Carbon emissions.

2.1. Qatar Carbon Dioxide Emissions by source

The actual situation of the state of Qatar in term of both environment degradation proxies (CO2

emissions and ecological footprint) are presented in Figures 1 and 2. Figure 1 report the CO2

emissions per capita for Qatar and the five other GCC countries for comparison. The Qatar,

Kuwait, UAE and Bahrain are among the top 10 emitters of CO2 emissions per capita. Moreover,

as previously indicated Qatar is ranked the first in the world in term of CO2 emissions. For

instance, according to the World Bank data (2010), Qatar greenhouse gases emissions is evaluated

at 40.3 tonnes of carbon dioxide per capita being emitted every year. Compared to the world

average, this figure is around 10 times higher than the global world average evaluated at 4.6 tonnes

per capita.

0

20

40

60

80

100

120

Qatar Bahrain Kuwait Oman Saudi Arabia UAE

8

Figure 1 : CO2 emissions per capita evolution

Four sectors contribute to more than 99% of total CO2 emissions in Qatar. The electricity and heat

production and the manufacturing industries and construction sectors are the largest contributors

to CO2 emissions with a share that present approximately 75% to 85%, see Figure 2. The electricity

and heat production sector contributes highly to the CO2 emissions, this sector contributes

approximately between 45% to 63% during the period of our study. The second contributor to the

CO2 emissions is the manufacturing industries and construction sector with a contribution between

20% and 45%. In contrast to the electricity and heat production sector, the manufacturing

industries and construction sector show in the last years a decreasing of CO2 emissions levels

which can be explained by the regulations laws that limit the amount of CO2 emitted by

manufacturing industries. In addition to these two important sectors, in the last ten years, the

transport sector emerges as the second contributor to CO2 emissions with approximately a same

level than the manufacturing industries and construction sector.

Figure 2 : Qatar Co2 emissions by components

2.2. Qatar foot print by source

In term of ecological footprint, the situation does not differ significantly, Qatar in ranked the

second in the word. Moreover, from Figure 3, one can conclude that Carbon dioxide is the bigger

contributor to ecological footprint indicator. This can be largely explained by the rapid increase of

the transportation, industrial and domestic fuel burning as well as the industrial process since

discovering the oil and gas resources at the end of 1970.

0

10

20

30

40

50

60

70

80

90

100in (%)

Years

Electricity and heat production

Manufacturing industries and construction

Residential buildings, commercial and public services

Transport

9

Figure 3 : Qatar ecological footprint by components

3. Empirical methodology

Since the objective of this paper is to investigate the true nature of the relationship between 𝐶𝑂2 emissions and ecological foot print to their macroeconomic and financial variables, we begin in

subsection 2.1. by presenting the data sources and how variables are constructed. Then, in

subsection 2.2., we introduce the Markov Switching Equilibrium Correction Model (MS-ECM),

the Silvestre et al. (2009) unit root tests with structural breaks, the Gregory and Hansen (1996) and

Hatemi-J (2008) tests for cointegration with structural breaks and the Granger causality test.

3.1. Data and variables description

3.1.1. Data

This paper uses macroeconomics and financial data, including CO2 emissions, ecological foot

print, real GDP per capita, energy use, urbanization, financial development and openness trade, to

investigate the EKC hypothesis for the Qatar economy. All the dataset except the ecological foot

print variable are collected from the world Bank’s development indicators (WDI). The ecological

footprint data is obtained from the National Footprint Accounts (NFAs) of the Global Footprint

Network. This variable is employed as second proxy of environment degradation measures.

The data set used in this paper is annual data which covers the period 1975Q1 to 2007Q4 for

variables used for ecological footprint equation and covers the periods 1980Q1 to 2010Q4 for the

CO2 emissions equations variables. To account for possible existence of structural break in the

cointegration vector we convert the annual frequency data to quarterly frequency data using the

0

2

4

6

8

10

12

196

1

196

3

196

5

196

7

196

9

197

1

197

3

197

5

197

7

197

9

198

1

198

3

198

5

198

7

198

9

199

1

199

3

199

5

199

7

199

9

200

1

200

3

200

5

200

7

200

9

201

1

201

3

201

5

Nu

mb

er o

f E

art

hs

dem

an

ded

Carbon

Built_up_Land

Fishing_Ground

Forest_Land

Grazing_Land

Crop_Land

10

quadratic match sum method3. The use of quarterly frequency data instead of annual data increases

the sample size and then allows us to investigate the possibility of long run relationship with

multiple shifts in the cointegration vector (Gregory and Hansen, 1996; Hatemi-J (2008), Arai and

Kurozumi, 2007; and Kejriwal and Perron, 2010).

3.1.2. Variables description

This section is devoted to present all variables used to investigate the environmental Kuznets curve

hypothesis (EKC). We classify these variables into three categories. The first category presents the

two variables used as proxies of environment degradation named the 𝐶𝑂2 emissions and ecological

footprint variables. The second and third categories includes all independent variables supposed

determine the environment degradation variables. The second category includes the

macroeconomics and financial variables and the third category includes the socio-demographic

variables.

a. Environment degradation proxies

i. CO2 emissions, dependent variable, is defined as the carbon dioxide emissions that are

stemming from the burning of fossil fuels and the manufacture of cement. It includes carbon

dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. It is measured

in metric tons per capita.

ii. Ecological footprint, dependent variable, is measured in global hectares. Following the NFAs,

the ecological footprint is a measure of how much area of biologically productive land and water

an individual, population or activity requires to produce all the resources it consumes and to absorb

the waste it generates, using prevailing technology and resource management practices.

b. Macroeconomics and Financial variables

i. Income per capita is measured as the gross domestic product divided by midyear population

(GDP per capita). Data are in constant 2005 U.S. dollars. To investigate the EKC hypothesis we

use the real GDP per capita in level and its squared form. The economic theory suggests that if

the EKC hypothesis holds the expected sign of the real GDP per capita in level will be positive

and negative for its quadratic form.

ii. Electricity consumption is equal to the production of power plants and combined heat and

power plants less transmission, distribution, and transformation losses and own use by heat and

3 In the theoretical literature several methods have been proposed that allow to convert low frequency data (annual or

quarterly) to more high frequency data (quarterly or monthly). In this paper, we use the local quadratic-match sum

method which has many advantages compared to others methods. In particular, this method ) is not sensitive to the

presence of outliers and structural changes in the original series. This is of particular interest for us as our main

objective is to allow of existence of breaks in unit root and cointegration tests. The local quadratic-match sum method

is the by default method used by the Database of Global Economic Indicators (DGEI) from the Federal Reserve Bank

of Dallas. For more details about this method we refer to the work of Grossman [79].

11

power plants. It is measured in KWh per capita. We expect that electricity consumption variable

will be negatively correlated to the environment degradation variable as this variable is friendly

sources of energy.

iii. Openness trade is the percentage of the total value of exports and imports as a share of GDP.

Following Grossman and Krueger (1991) the reduction of trade barriers affects the environment

quality via increasing the economic activity. Then, it is expected that the openness trade variable

will have a positive sign if the production processes is heavy 𝐶𝑂2 emitter. However, environmental

protection laws have a significant impact on reducing 𝐶𝑂2 emissions and other pollutants. In this

paper, as Qatar is an oil exporting country where the share of gas and oil exports is very important

in the total GDP, then we expect that this variable will have a positive sign.

iv. Financial development is measured by the ratio of domestic credit to private sector scaled by

GDP. Indeed, financial development may play a role in explaining environment degradation. For

instance, financial development may help firms in adopting advanced cleaner and environment

friendly technology in the energy sector which in turn improve environment quality. Financial

development may also attract foreign direct investment, which enhance research and development

(R&D) activities that improve economic activities, and hence, influence environmental quality

(Frankel and Romer, 1999). On the other hand, Financial development can has a negative impact

on environment quality by increasing manufacturing activities which in turn leads to an

environment degradation through an increases of industrial pollution.

c. Socio-demographic variables

As socio-demographic variable we use urbanization. This variable is calculated using World Bank

population estimates and urban ratios from the United Nations World Urbanization Prospects. It

is equal to the percentage of country’s population living in urban areas. While this variable is

generally associated with greater level of environment degradation, an increase of this variable

can improve environment quality through economies scale in the provision of sanitation facilities.

Empirically, the existing literature is inconclusive on the impact that urbanization has on

environment degradation. Then, we suggest that, a priori, no sign expectation can be made on the

relationship between this variable and environment degradation.

3.2. Econometric Model

3.2.1. The Markov Switching Equilibrium Correction Model (MS-ECM)

To investigate the EKC hypothesis for the Qatar economy, we propose to use a Markov-Switching

equilibrium correction model with shifts in the intercept and the slope coefficients of independents

variables.

The general form of the proposed model can be written as follow,

𝛥𝑦𝑡 = 𝛼 𝐸𝐶𝑀𝑡−1,𝑠𝑡 +∑ 𝛤𝑗𝛥𝑋𝑡−𝑖𝑟𝑖=1 + ∑ 𝜋𝑗𝛥𝑦𝑡−𝑗

𝑠𝑗=1 + 𝑢𝑡,

Where,

𝐸𝐶𝑀𝑡−1,𝑠𝑡 = 𝛽𝑠𝑡′ 𝑋𝑡−1 − 𝜇𝑠𝑡,

12

Where 𝛼 measures the rapid adjustment towards the long run equilibrium. 𝑋𝑡 is a vector of

independent variables. 𝑦𝑡 is the dependent variable and 𝛥 is the first difference operator. 𝑟 and 𝑠 are the order lags of the independent and dependent variables in the short run relationship.

𝑠𝑡 is a dummy variable which takes values of 0 and 1. It is governed by a first order Markov chain

which is given by,

𝑠𝑡 = {0 with probability 𝑝1 with probability 𝑞

Where 𝑝 = 𝑃[𝑠𝑡 = 𝑖|𝑠𝑡−1 = 𝑖], 𝑞 = 𝑃[𝑠𝑡 = 𝑗|𝑠𝑡−1 = 𝑗] for 𝑖, 𝑗 = 1, 2 and ∑ 𝑝𝑖𝑗 = 12𝑖=1 .

Under these notation the intercept and slopes coefficients can be written as follow,

𝜇𝑠𝑡 = 𝜇1( 1 − 𝑠𝑡) + 𝜇2𝑠𝑡 ,

𝛽𝑠𝑡 = ((𝛽11, 𝛽1

2), (𝛽21, 𝛽2

2),… , (𝛽𝑛1, 𝛽𝑛

2)), where 𝑛 is the number of independent variables. For

example (𝛽11, 𝛽1

2) are the slope coefficients of the real GDP under the first and second regimes

respectively.

3.2.2. Model specification

The general form of the empirical model investigated in this study is given by the following

equation,

𝐸𝐷 = 𝑓 ( 𝑌, 𝑀𝑎𝑐𝑟𝑜, 𝐹𝑖𝑛, Socio)

Where 𝐸𝐷 refers to the environment degradation variable measured in this paper by 𝐶𝑂2 emissions or ecological footprint; 𝑌 refers to the income per capita variable, 𝑀𝑎𝑐𝑟𝑜 refers to

macroeconomic variables that explain environment degradation such as openness trade and

electricity consumption. 𝐹𝑖𝑛 is the financial development variable and finally 𝑆𝑜𝑐𝑖𝑜 represents the

urbanization variable.

In this paper, we propose After taking the logarithm of all variables, the specification is given by,

𝐿𝐸𝐷𝑡 = 𝜇𝑠𝑡 + 𝛽1𝑠𝑡 𝐿𝑅𝐺𝐷𝑃𝑡 + 𝛽2𝐿𝑅𝐺𝐷𝑃𝑡2 + 𝛽4𝐿𝐸𝑡 + 𝛽5𝐿𝐹𝐷𝑡 + 𝛽6𝐿𝑈𝑅𝐵𝑡 + 𝛽6𝐿𝑂𝑃𝐸𝑁𝑡 + 𝜖𝑡

Where 𝐸𝐷 is the 𝐶𝑂2 emissions and ecological footprint (𝐹𝑃) proxies. The empirical methodology

used to investigate the EKC hypothesis is summarized in the following four steps. First, we start

by testing whether the 𝐶𝑂2 emissions, environment degradation, the real GDP, electricity

consumption, urbanization, trade openness and financial development are characterized by the

presence of unit root or not. This step is an important precondition before testing for the existence

of long run relationship between environment degradation proxy and explicative variables. For

this end, we use both standard unit root tests (Dickey and Fuller, 1979; Phillips and Perron, 1988;

and Kwiatkowski et al.; 1992) and unit root tests allowing for multiple structural breaks of Silvestre

et al. (2003)). Second, for cointegration with structural breaks by using the Gregory and Hansen

(1996), Hatemi-J (2008) and Arai and Kurozumi (2007) tests. Third, we estimate the short and

long run relationship using the two steps of Engle and Granger (1987) cointegration approach. In

addition, we check for residuals stability using the CUSUM and CUSUM squared tests. In the last

13

step, we investigate the linear and nonlinear Granger causality direction sense between all

variables.

In the following subsections, we presents the unit roots tests with structural breaks and the testing

procedure for cointegration with structural breaks.

3.3. Unit roots test with structural break

This subsection is largely inspired from Silvestre et al. (2009) paper. It is devoted to describes the

unit root tests with structural breaks recently developed by Silvestre et al. (2009). These unit roots

tests have many advantage compared to the existence literature on testing for unit roots when series

are subject to change in regime. First, Silvestre et al. (2009) propose a large variety of tests

including the M-tests introduced by Stock (1999). Second, the proposed tests allow for multiple

changes under both the null hypothesis of unit root and the alternative hypothesis of stationarity.

Third, the Silvestre et al. (2009) tests use the quasi-GLS detrending method advocated by Elliot

et al. (1996) which permits tests that have local asymptotic power functions close to the local

asymptotic Gaussian power envelope. Silvestre et al. (2009) treat the breaks dates as known and

then they show that the limiting distribution is the same when the breaks dates are unknown.

Silvestre et al. (2009) consider the following model,

𝑦𝑡 = 𝑑𝑡 + 𝑢𝑡

𝑢𝑡 = 𝛼𝑢𝑡 + 𝜈𝑡 𝑡 = 0,… , 𝑇

Where 𝑦𝑡 is a time series, {𝑢𝑡} is an unobserved mean-zero process. 𝑢0 is supposed to be equal to

0. The disturbance 𝜈𝑡 is defined by 𝜈𝑡 = ∑ 𝛾𝑖𝜂𝑡−𝑖∞𝑖=0 with ∑ 𝑖|𝛾𝑖|

∞𝑖=0 < ∞ and {𝜂𝑡} a martingale

difference sequence adapted to the filtration 𝐹𝑡 = 𝜎 − field{𝜂𝑡−𝑖; 𝑖 ≥ 0}. The long and short run

variance are defined as 𝜎2 = 𝜎𝜂2𝛾(1)2 and 𝜎𝜂

2 = lim𝑇→∞

𝑇−1∑ 𝐸(𝜂𝑡2)𝑇

𝑡=1 , respectively.

The deterministic component in (1) is given by,

𝑑𝑡 = 𝑧𝑡′(𝑇0)𝜓0 + 𝑧𝑡

′(𝑇1)𝜓1 +⋯+ 𝑧𝑡′(𝑇𝑚)𝜓𝑚 ≡ 𝑧𝑡

′(𝜆)𝜓

Where

𝑧𝑡′(𝜆) = [𝑧𝑡

′(𝑇𝑗), 𝑧𝑡′(𝑇1), … , 𝑧𝑡

′(𝑇𝑚)] and 𝜓 = (𝜓0′ , 𝜓1

′ , … , 𝜓𝑚′ )′.

The different forms taken by the 𝑧𝑡′(𝑇0) deterministic component and the 𝜓 coefficients define the

three models considered by Silvestre et al. (2009) in this paper.

These three models are defined by,

𝑧𝑡′(𝑇𝑚

0) = {

𝐷𝑈𝑡(𝑇𝑗) Model 0

𝐷𝑇𝑡∗(𝑇𝑗) Model I

(𝐷𝑈𝑡(𝑇𝑗), 𝐷𝑇𝑡∗(𝑇𝑗))

′ Model II

14

Where Model 0 refers to the “crash” or level shift model, Model I refers to the “changing growth”

slope change model and Model II refers to the mixed change model as in Perron (1989)4.

Silvestre et al. (2009) define 𝑧𝑡′(𝑇0) ≡ 𝑧𝑡(0) = (1, 𝑡)

′ and 𝜓0 = (𝜇0, 𝛽0)′ and for 1 ≤ 𝑗 ≤ 𝑚

𝜓𝑗 = 𝜇𝑗 in Model 0, 𝜓𝑗 = 𝛽𝑗 in Model I and 𝜓𝑗 = (𝜇𝑗 , 𝛽𝑗)′ fin Model II. 𝐷𝑈𝑡(𝑇𝑗) = 1 and

𝐷𝑇𝑡∗(𝑇𝑗) = (𝑡 − 𝑇𝑗) for 𝑡 > 𝑇𝑗 and 0 otherwise. 𝑇𝑗 = [𝑇𝜆𝑗] which denotes the j-th break date, with

[. ] defines the integer part, and 𝜆𝑗 ≡ 𝑇𝑗 𝑇⁄ ∈ (0,1) is the break fraction parameter.

To estimate the breaks dates, they use the global minimization of the sum of squared residuals

(SSR) of the GLS-detrended model,

�̂� = arg min𝜆∈⋀(𝜖)

𝑆(𝛼,̅ 𝜆)

Where 𝑆(𝛼,̅ 𝜆) is the minimum of an objective function, see Silvestre et al. (2009) for more

details. �̅� = 1 + 𝑐̅ 𝑇⁄ is a non-centrality parameter. ⋀𝜖 = {𝜆 ∶ |𝜆𝑖+1 − 𝜆𝑖| ≥ 𝜖, 𝜆1 > 𝜖, 𝜆𝑘 > 1 −𝜖} and 𝜖 is a small arbitrary number, in practice the common value of 𝜖 = 0.15. Proposition 2 in

Silvestre et al. (2009) show the rate of convergence is fast enough to guarantee a same limiting

distribution as when the breaks dates are known.

The proposed tests are defined by,

𝑀𝑍𝛼𝐺𝐿𝑆(𝜆) = (𝑇−1�̃�𝑇

2 − 𝑠(𝜆)2)(2𝑇−2∑ �̃�𝑡−12𝑇

𝑡=1 )−1,

𝑀𝑆𝐵𝐺𝐿𝑆(𝜆) = (𝑠(𝜆)2 𝑇−2∑ �̃�𝑡−12𝑇

𝑡=1 )1/2,

𝑀𝑍𝑡𝐺𝐿𝑆(𝜆) = (𝑇−1�̃�𝑇

2 − 𝑠(𝜆)2)(4𝑠(𝜆)2 𝑇−2∑ �̃�𝑡−12𝑇

𝑡=1 )−1/2,

𝑀𝑃𝑇𝐺𝐿𝑆(𝜆) = ( 𝑐̅2𝑇−2∑ �̃�𝑡−1

2𝑇𝑡=1 + (1 − 𝑐̅) 𝑇−1 �̃�𝑇

2 ) 𝑠(𝜆)2⁄ ,

Where �̃�𝑡 = 𝑦𝑡 − �̂�′𝑧𝑡′(𝜆) and �̂�′ are the estimated values of 𝜓. 𝑠(𝜆)2 is an estimate of the spectral

density at frequency zero of 𝜈𝑡.

3.4. Cointegration with structural break

Testing for parameter instability in macroeconomic relationships is an important issue for both

macroeconomic and econometric sides. As noted by many authors, the luck of control for the

presence of structural breaks inside time series can lead to model misspecification problems and

misleading interpretation (Gregory and Hansen, 1996; and Arai and Kurozumi, 2007). In Addition,

the fact that we investigate the EKC hypotheses during a period of 36 years -from 1975 to 2011,

then it is more likely that the cointegration relationships may be subject to structural breaks.

Testing for cointegration with structural breaks is conducted using the Gregory and Hansen (1996)

4 See also Zivot and Andrews (1992), Ng and Perron (1995) and Lee and Strazicich (2003).

15

and Hatemi-J (2008) tests. The Gregory and Hansen (1996) approach is used when only break is

detected in the long run. The Hatemi-J approach is employed to investigate possible cointegration

with two structural breaks. The approach proposed by Hatemi (2008) is an extension of the

Gregory and Hansen (1996) approach to the case of cointegration vector with two breaks.

In what follow, we present these three approaches in more details.

3.4.1. Gregory and Hansen (1996) cointegration with one break

Gregory and Hansen (1996) propose ADF, 𝑍𝛼 and 𝑍𝑡-type tests designed to test the null of no

cointegration against the alternative of cointegration in the presence of level or regime shifts.

Under the general hypothesis of cointegration with structural breaks, the model is given by:

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝜃 𝑡 + 𝛽0′𝑥𝑡 + 𝛽1

′𝐷1𝑡𝑥𝑡 + 𝑢𝑡 , (1)

Where (𝛼0, 𝛼0 + 𝛼1) are the intercept coefficients both for the first and second regimes,

respectively. In the same manner, 𝛽0′ and (𝛽0 + 𝛽1)

′ are the slope coefficients vectors under the

first and second regimes, respectively. 𝜃 is the linear trend coefficient and 𝑢𝑡 is the error term. 𝐷1𝑡 is a dummy variable defined by,

𝐷1𝑡 = {0 if 𝑡 ≤ [𝑛𝜏1]

1 if 𝑡 > [𝑛𝜏1]

In this paper, we limit our investigation to the level shift (C) and regime shift models of Gregory

and Hansen (1996). The level shift with trend (C/T) model is not considered in this paper for the

simple reason that most economic time series can be adequately described by level shift and regime

shift models, see for instance (Perron, 1989; Lee and Strazicich, 2003).

The level shift (C) model as described by Gregory and Hansen (1996) is given by equation (1)

when we restrict 𝜃 = 0 and 𝛽1′ to the zero vector.

Under this assumption the level shit model is given by,

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝛽0′𝑥𝑡 + 𝑢𝑡 , ,

Concerning the regime shift (level shift and slope coefficients change) (C/S) model, it is given by

equation (1) when 𝜃 = 0.

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝛽0′𝑥𝑡 + 𝛽1

′𝐷1𝑡𝑥𝑡 + 𝑢𝑡 ,

Gregory and Hansen (1996) proposed the three tests of cointegration with shifts that are an

extensions of the standard ADF test of Dickey and Fuller and the Phillips two tests statistics Z

and tZ of cointegration. These three test are given by,

𝐴𝐷𝐹∗ = inf𝜏∈𝑇𝐴𝐷𝐹(𝜏),

𝑍𝑡∗ = inf

𝜏∈𝑇𝑍𝑡(𝜏),

𝑍𝛼∗ = inf

𝜏∈𝑇𝑍𝛼(𝜏),

16

These three tests of cointegration tests are calculated for each possible regime shift τ ∈ T, and then

we take the smallest value for each test among all possible breaks points. We refer to Gregory and

Hansen (1996) for more details.

3.4.2. Hatemi-J (2008) cointegration with two breaks

Hatemi-J (2008) extends the Gregory and Hansen (1996) cointegration approach with one

structural break to the case of cointegration with two structural breaks. As in Gregory and Hansen

(1996), Hatemi-J (2008) consider the case where both intercept and the slopes are subject of

structural breaks (two breaks here).

The model considered by Hatem-J (2008) is given by,

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝛼2𝐷2𝑡 + 𝜃 𝑡 + 𝛽0′𝑥𝑡 + 𝛽1

′𝐷1𝑡𝑥𝑡 + 𝛽2′𝐷2𝑡𝑥𝑡 + 𝑢𝑡 ,

𝐷1𝑡 and 𝐷2𝑡 are dummy variable defined by,

𝐷1𝑡 = {0 if 𝑡 ≤ [𝑛𝜏1]

1 if 𝑡 > [𝑛𝜏1] and 𝐷2𝑡 = {

0 if 𝑡 ≤ [𝑛𝜏2]

1 if 𝑡 > [𝑛𝜏2]

Where 𝜏1 and 𝜏2 represent the location of breaks (unknowns). They are supposed to lies in the

interval (0, 1) and that 𝜏2 > 𝜏1. In that case, (𝛼0, 𝛼0 + 𝛼1, 𝛼0 + 𝛼1+𝛼2 ) are the intercept

coefficients both for the first, second and third regimes, respectively. In a same manner, 𝛽0′ ,

(𝛽0 + 𝛽1)′ and (𝛽0 + 𝛽1 + 𝛽2)

′ are the slope coefficients vectors under the first and second

regimes, respectively. 𝜃 is the linear trend coefficient and 𝑢𝑡 is the error term.

For this case of cointegration with two structural breaks, the level shift and regime shift models

are given by,

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝛼2𝐷2𝑡 + 𝛽0′𝑥𝑡 + 𝑢𝑡 ,

and

𝑦𝑡 = 𝛼0 + 𝛼1𝐷1𝑡 + 𝛼2𝐷2𝑡 + 𝛽0′𝑥𝑡 + 𝛽1

′𝐷1𝑡𝑥𝑡 + 𝛽2′𝐷2𝑡𝑥𝑡 + 𝑢𝑡 ,

As in the case of Gregory and Hansen (1996), Hatemi-J (2008) test statistics are the smallest values

of these three tests across all values of 𝜏1 and 𝜏2. Hetemi-J (2008) suppose that 𝜏1 ∈ 𝑇1 =(0.15, 0.70) and 𝜏2 ∈ 𝑇2 = (0.15 + 𝜏1, 0.85)

𝐴𝐷𝐹∗ = inf(𝜏1,𝜏2)∈𝑇

𝐴𝐷𝐹((𝜏1, 𝜏2)),

𝑍𝑡∗ = inf

(𝜏1,𝜏2)∈𝑇𝑍𝑡(𝜏1, 𝜏2),

𝑍𝛼∗ = inf

(𝜏1,𝜏2)∈𝑇𝑍𝛼(𝜏1, 𝜏2),

3.5. VECM Granger causality

Investigating causality direction within the framework of VECM is an important step after

estimating the long run and short run relationship. The non-rejection of cointegration with

17

structural break suggests that at least one causal link exist among the series. To investigate this

issue of causality, we use both standard linear Granger causality as introduced by Engle and

Granger (1987).

After refining the estimation of the long-run relationship, the Granger causality testing procedure

can be done in the following four steps.

a. The first step consists to recuperate the residuals of the long-run dynamics between all

variables. These residuals are called 𝐸𝐶𝑇1𝑡 and 𝐸𝐶𝑇2𝑡 for the CO2 emissions and the ecological

footprint environment degradation proxies.

These 𝐸𝐶𝑇1𝑡 and 𝐸𝐶𝑇2𝑡 residuals are obtained as follows,

𝐸𝐶𝑇1𝑡 = (𝐿𝐸𝐹𝑃𝑡 − �̂�1𝑠𝑡𝐿𝑅𝐺𝐷𝑃𝑡 − �̂�2(𝐿𝑅𝐺𝐷𝑃)𝑡2−�̂�3𝐿𝐸𝐿𝐸𝐶𝑡 − �̂�4𝐿𝐹𝐷𝑡 − �̂�5𝐿𝑈𝑅𝐵𝑡 − 𝜇𝑠𝑡)

And,

𝐸𝐶𝑇1𝑡 = (𝐿𝐶𝑂2𝑡 − �̂�1𝑠𝑡𝐿𝑅𝐺𝐷𝑃𝑡 − �̂�2(𝐿𝑅𝐺𝐷𝑃)𝑡2−�̂�3𝐿𝐸𝐿𝐸𝐶𝑡 − �̂�4𝐿𝐹𝐷𝑡 − �̂�5𝐿𝑈𝑅𝐵𝑡 −

�̂�6𝐿𝑂𝑃𝐸𝑁𝑡 − 𝜇𝑠𝑡)

where, �̂�𝑙, 𝑙 = 1,… ,6 are the parameters estimates of the coefficients of long-run relationship.

b. In the second step, we estimate the short-run relationships for the CO2 emissions and the

ecological footprint given by the two equations below,

(

𝛥𝐿𝐸𝐹𝑃𝑡𝛥𝐿𝑅𝐺𝐷𝑃𝑡𝛥𝐿𝐸𝐿𝐸𝐶𝑡𝛥𝐿𝐹𝐷𝑡𝛥𝐿𝑈𝑅𝐵𝑡 )

=

(

𝜔1𝜔2𝜔3𝜔4𝜔5)

+∑

(

𝑑1,1,𝑗 𝑑2,1,𝑗 𝑑3,1,𝑗 𝑑4,1,𝑗 𝑑5,1,𝑗

𝑑1,2,𝑗 𝑑2,2,𝑗 𝑑3,2,𝑗 𝑑4,2,𝑗 𝑑5,2,𝑗

𝑑1,3,𝑗 𝑑2,3,𝑗 𝑑3,3,𝑗 𝑑4,3,𝑗 𝑑5,3,𝑗

𝑑1,4,𝑗 𝑑2,4,𝑗 𝑑3,4,𝑗 𝑑4,4,𝑗 𝑑5,4,𝑗

𝑑1,5,𝑗 𝑑2,5,𝑗 𝑑3,5,𝑗 𝑑4,5,𝑗 𝑑5,5,𝑗 )

𝑘

𝑗=1

(

𝛥𝐿𝐸𝐹𝑃𝑡−𝑗𝛥𝐿𝑅𝐺𝐷𝑃𝑡−𝑗𝛥𝐿𝐸𝐿𝐸𝐶𝑡−𝑗𝛥𝐿𝐹𝐷𝑡−𝑗𝛥𝐿𝑈𝑅𝐵𝑡−𝑗 )

+

(

𝜋1𝜋2𝜋3𝜋4𝜋5)

𝐸𝐶𝑇2𝑡−1 +

(

𝜗1,𝑡𝜗2,𝑡𝜗3,𝑡𝜗4,𝑡𝜗5,𝑡)

18

And,

(

𝛥𝐿𝐶𝑂2𝑡𝛥𝐿𝑅𝐺𝐷𝑃𝑡𝛥𝐿𝐸𝐿𝐸𝐶𝑡𝛥𝐿𝐹𝐷𝑡𝛥𝐿𝑈𝑅𝐵𝑡𝛥𝐿𝑂𝑃𝐸𝑁𝑡)

=

(

𝜃1𝜃2𝜃3𝜃4𝜃5𝜃6)

+∑

(

𝑐1,1,𝑗 𝑐1,2,𝑗 𝑐1,3,𝑗 𝑐1,4,𝑗 𝑐1,5,𝑗 𝑐1,6,𝑗

𝑐2,1,𝑗 𝑐2,2,𝑗 𝑐2,3,𝑗 𝑐2,4,𝑗 𝑐2,5,𝑗 𝑐2,6,𝑗

𝑐3,1,𝑗 𝑐4,1,𝑗 𝑐5,1,𝑗 𝑐6,1,𝑗

𝑐3,2,𝑗 𝑐4,2,𝑗 𝑐5,2,𝑗 𝑐6,2,𝑗

𝑐3,3,𝑗 𝑐4,3,𝑗 𝑐5,3,𝑗 𝑐6,3,𝑗

𝑐3,4,𝑗 𝑐4,4,𝑗 𝑐5,4,𝑗 𝑐6,4,𝑗

𝑐3,5,𝑗 𝑐4,5,𝑗 𝑐5,5,𝑗 𝑐6,5,𝑗

𝑐3,6,𝑗 𝑐4,6,𝑗 𝑐5,6,𝑗 𝑐6,6,𝑗 )

𝑘

𝑗=1

(

𝛥𝐿𝐶𝑂2𝑡−𝑗𝛥𝐿𝑅𝐺𝐷𝑃𝑡−𝑗𝛥𝐿𝐸𝐿𝐸𝐶𝑡−𝑗𝛥𝐿𝐹𝐷𝑡−𝑗𝛥𝐿𝑈𝑅𝐵𝑡−𝑗𝛥𝐿𝑂𝑃𝐸𝑁𝑡−𝑗)

+

(

𝜆1𝜆2𝜆3𝜆4𝜆5𝜆6)

𝐸𝐶𝑇1𝑡−1 +

(

𝑢1,𝑡𝑢2,𝑡𝑢3,𝑡𝑢4,𝑡𝑢5,𝑡𝑢6,𝑡)

c. Using these two specifications, the long run causality can be examined by testing the

significance of the lagged error correction term using t-test statistic, 𝜋𝑗 = 0 and 𝜆𝑗 = 0 for 𝑗 =

1, . . ,6 .

d. In the other hand, the short run causality is examined by testing the coefficient of the first

differences of the variables. For example, 𝑐1,2,𝑗 ≠ 0 and 𝑑1,2,𝑗 ≠ 0 ∀ 𝑗 shows that the economic

growth (𝛥𝐿𝑅𝐺𝐷𝑃𝑡) Granger cause ecological foot print (𝛥𝐿𝑃𝐹𝑡) and CO2 emissions (𝛥𝐿𝐶𝑂2𝑡), respectively. The feedback that ecological foot print and CO2 emissions Granger cause economic

growth is obtained when 𝑐2,1,𝑗 ≠ 0 and 𝑑2,1,𝑗 ≠ 0 ∀ 𝑗.

4. Empirical results

4.1. Preliminary analysis

Descriptive statistics of the ecological footprint, 𝐶𝑂2 emissions, economic growth, electricity

consumption, financial development and urbanization variables in term of mean, median, standard

deviation, Skewness, Kurtosis and Jarque-Bera statistics are reported in Table 2 below. The results

show that there is significant difference between the mean and the median for the both ecological

foot print and real GDP variables. The normality hypothesis is rejected for all time series.

Table 1 : Descriptive statistics and traditional unit root test

𝑳𝑬𝑭𝑷 𝑳𝑪𝑶𝟐 𝑳𝑹𝑮𝑫𝑷 𝑳𝑬𝑳𝑬𝑪 𝑳𝑭𝑫 𝑳𝑼𝑹𝑩 𝑳𝑶𝑷𝑬𝑵

Mean

0.479 2.458 9.317 8.365 2.128 3.159 1.695

Median 0.581 2.563 8.856 8.327 2.173 3.167 1.703

Std.dev 0.455 0.293 0.952 0.141 0.347 0.032 0.106

Skew. -1.305 -0.695 1.093 0.398 -0.907 -0.311 -0.642

Kurt. 4.256 2.238 2.823 2.034 4.115 1.675 3.208

J-B 46.18 13.00 24.87 8.088 23.42 11.07 9.583

In the other hand, Table 3 reports the empirical results of pair-wise correlation (under the first

matrix diagonal) and their corresponding t-statistics (above the matrix diagonal). Table 3 shows

19

that all correlations coefficients are highly significant except for the pair (𝐿𝐶𝑂2, 𝐿𝐸𝐿𝐸𝐶). This

non significance of the correlation between 𝐶𝑂2 emissions and electricity consumption is not

surprising as the electricity, as source of energy and input of the production process, is known to

be a clean energy source compared to other energy sources such as oil, coal, gas etc… The pair-

wise correlations between real GDP and both environment degradation proxies are positive

suggesting that economic growth degrades environment. Moreover, financial development and

urbanization are positively correlated to ecological foot print. In the other hand, the CO2 emissions

is negatively and positively correlated with financial development and urbanization, respectively.

Table 2 : Pair-wise correlation

𝑳𝑬𝑭𝑷 𝑳𝑪𝑶𝟐 𝑳𝑹𝑮𝑫𝑷 𝑳𝑬𝑳𝑬𝑪 𝑳𝑭𝑫 𝑳𝑼𝑹𝑩 𝑳𝑶𝑷𝑬𝑵

𝑳𝑬𝑭𝑷 1 - 7.809 11.88 12.89 12.30 0.064

𝑳𝑪𝑶𝟐 - 1 3.302 1.564 -4.246 3.648 8.195

𝑳𝑹𝑮𝑫𝑷 0.565 0.278 1 12.93 3.775 15.93 4.803

𝑳𝑬𝑳𝑬𝑪 0.721 0.136 0.750 1 5.378 13.44 7.607

𝑳𝑭𝑫 0.749 -0.349 0.314 0.427 1 8.208 -6.652

𝑳𝑼𝑹𝑩 0.733 0.305 0.813 0.763 0.584 1 2.666

𝑳𝑶𝑷𝑬𝑵 0.006 0.596 0.399 0.567 -0.516 0.235 1

Pair-wise correlation under the first diagonal and their corresponding t-statistics are above the

diagonal

In addition to this preliminary analysis based on descriptive statistics and pair-wise correlation, we

report in Figure 1 the trajectories evolution of the ecological footprint, 𝐶𝑂2emissions, real GDP,

electricity consumption, financial development and urbanization variables. This Figure shows that

all trajectories has an upward trend during all the period of study, except for the 𝐶𝑂2 emissions

series which shows a downward trend until 1990, and then it remains stable with a new downward

at the end of the period.

20

-1.2

-0.8

-0.4

0.0

0.4

0.8

1.2

1975 1980 1985 1990 1995 2000 2005 2010

LFP

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

1975 1980 1985 1990 1995 2000 2005 2010

LCO2

7

8

9

10

11

12

1975 1980 1985 1990 1995 2000 2005 2010

LRGDP

7.4

7.6

7.8

8.0

8.2

8.4

8.6

8.8

1975 1980 1985 1990 1995 2000 2005 2010

LELEC

0.5

1.0

1.5

2.0

2.5

3.0

1975 1980 1985 1990 1995 2000 2005 2010

LFD

3.10

3.12

3.14

3.16

3.18

3.20

3.22

1975 1980 1985 1990 1995 2000 2005 2010

LURB

1.3

1.4

1.5

1.6

1.7

1.8

1.9

1975 1980 1985 1990 1995 2000 2005 2010

LOPEN

Figure 1 : Evolution of the 𝐶𝑂2 emissions, Ecological foot print, real GDP, electricity

consumption, financial development and urbanization variables

21

4.2. Unit roots tests with and without structural breaks

Non-stationarity of time series, e.g. I(1), is an important pre-condition before investigating

possible long run relationship between environment degradation proxy and economic growth,

electricity consumption, financial development and urbanization using cointegration analysis. In

this study, to investigate non-stationarity, we use both traditional unit root tests (Ng-Perron, 1996)

and unit root tests with structural break (Silvestre et al., 2009).

Empirical results are reported in Table 3 below for standard unit root test and Table 5 for unit root

tests with structural breaks. Standard unit root tests results show that the ecological footprint, 𝐶𝑂2 emissions, economic growth and financial development are non-stationary in level. For the

electricity consumption and urbanization time series, we find that these series are stationary in

level. Using first difference transformation, all variables are non-stationary except the 𝐶𝑂2 emissions series. As noted by many researchers, macroeconomics time series are generally subject

to structural breaks and the use of standard unit root test leads to highly reject the true behavior.

Table 3 : Results of Ng and Perron (1996) unit root tests without breaks.

Level First difference

𝑴𝒁𝜶𝑮𝑳𝑺 𝑴𝑺𝑩𝑮𝑳𝑺 𝑴𝒁𝒕

𝑮𝑳𝑺 𝑴𝑷𝑻𝑮𝑳𝑺 𝑴𝒁𝜶

𝑮𝑳𝑺 𝑴𝑺𝑩𝑮𝑳𝑺 𝑴𝒁𝒕𝑮𝑳𝑺 𝑴𝑷𝑻

𝑮𝑳𝑺

𝑳𝑬𝑭𝑷𝒕 0.550 1.301 0.716 102.85 -0.223 0.722 -0.162 30.917

𝑳𝑪𝑶𝟐𝒕 -7.443 0.249 -1.854 3.568 -9.125 a 0.231 a -2.109 a 2.793

𝑳𝑹𝑮𝑫𝑷𝒕 2.175 1.134 2.466 108.80 -1.368 0.510 -0.698 14.762

𝑳𝑬𝑳𝑬𝑪𝒕 -10.71a 0.213a -2.289a 2.386a 0.033 0.994 0.033 55.806

𝑳𝑭𝑫𝒕 -0.217 0.714 -0.155 30.42 -1.956 0.449 -0.880 11.36

𝑳𝑼𝑹𝑩𝒕 -91.63a 0.073a -6.714a 0.374a -3.808 0.362 -1.378 6.433

𝑳𝑶𝑷𝑬𝑵𝒕 -6.613 0.275 -1.817 3.711 -7.640 0.254 -1.943 3.248

The critical values are -8.10, 0.233, -1.98 and 3.17 for the 𝑀𝑍𝛼𝐺𝐿𝑆, 𝑀𝑆𝐵𝐺𝐿𝑆, 𝑀𝑍𝑡

𝐺𝐿𝑆, and

𝑀𝑃𝑇𝐺𝐿𝑆tests respectively.

The null hypothesis of Ng-Perron tests is the non-stationarity.

The null hypothesis is rejected if the statistic is lower than critical values.

To account for structural breaks under both the null and alternative hypothesis when testing for

unit root, we propose to use Silvestre et al. (2009) unit root tests. Empirical results reported in

Table 5 shows strong evidence for the existence of unit root and for structural breaks in the level

series at conventional level. The bottom panel of Table 5 shows that all series are stationary in first

difference which means that all series are I(1) in level. Moreover, our results show that the

structural breaks are in the origin for the rejection of the stationarity hypothesis when using

standard unit root tests.

The first break occurs for all time series at the end of the seventy decade. The second break

correspond to the middle of the eighty decade except for the electricity consumption series

(1990Q4) and financial development (1981Q4). year 1990Q2 for the 𝐶𝑂2 emissions and

urbanization time series, and to 1981Q1 for electricity consumption, 1983Q3 for financial

development, 1986Q4 for real GDP and 1999Q4 for ecological foot print. Finally, the last break

correspond to the years 2004Q2, 1993Q3, 1997Q3, 1985Q4, 1992Q4 and 1995Q4 for the

22

ecological foot print, the CO2 emissions, the real GDP, electricity consumption, financial

development and urbanization respectively.

Overall, Silvestre et al. (1994) unit root tests suggest that the ecological foot print, 𝐶𝑂2 emissions,

economic growth, electricity consumption, financial development and urbanization are I(1) in

level and I(0) in first difference. This allow us to examine the possibility of long run relationship

among investigated variables. This result suggest also that the presence of break in the level of

these time series may be viewed as an indication of possible existence of shift in the cointegration

vector.

23

Table 4 : Results of Silvestre et al. (2009) unit root tests with structural breaks.

𝑴𝒁𝜶𝑮𝑳𝑺(𝝀) 𝑴𝑺𝑩𝑮𝑳𝑺(𝝀) 𝑴𝒁𝒕

𝑮𝑳𝑺(𝝀) 𝑴𝑷𝑻𝑮𝑳𝑺(𝝀) Breaks dates

In level

𝐿𝐸𝐹𝑃𝑡 -12.06 (-46.71) 0.200 (0.103) -2.421 (-4.828) 35.403 (8.99) 1978Q1, 1984Q3, 1990Q4, 1999Q4, 2004Q2

𝐿𝐶𝑂2𝑡 -11.20 (-45.74) 0.208 (0.105) -2.333 (-4.739) 37.339 (9.27) 1982Q3, 1985Q3, 1988Q3, 1992Q4, 1997Q4

𝐿𝑅𝐺𝐷𝑃𝑡 -15.23 (-46.51) 0.181 (0.103) -2.759 (-4.827) 27.648 (8.88) 1981Q1, 1986Q4, 1996Q1, 2000Q4, 2005Q3

𝐿𝐸𝐿𝐸𝐶𝑡 -8.87 (-46.05) 0.235 (0.103) -2.089 (-4.787) 47.55 (9.08) 1978Q1, 1990Q4, 1996Q3, 2000Q4, 2004Q3

𝐿𝐹𝐷𝑡 -15.69 (-46.98) 0.177 (0.103) -2.785 (-4.826) 27.64 (9.12) 1978Q1, 1981Q4, 1986Q4, 1992Q4, 2000Q3

𝐿𝑈𝑅𝐵𝑡 -7.713 (-47.79) 0.234 (0.102) -1.802 (-4.874) 54.88 (9.38) 1979Q2, 1985Q4, 1989Q4,1995Q3,2004Q3

𝐿𝑂𝑃𝐸𝑁𝑡 -16.62 (-46.31) 0.168 (0.103) -2.793 (-4.80) 25.15 (8.88) 1980Q1, 1983Q4, 1987Q3, 1996Q4, 2001Q4

In first difference

𝐿𝐸𝐹𝑃𝑡 -25.93 (-22.25) 0.138 (0.149) -3.593 (-3.301) -4.059 (-3.301) 1979Q4

𝐿𝐶𝑂2𝑡 -43.55 (-32.49) 0.107 (0.123) -4.651 (-3.991) 5.365 (7.295) 1978Q4, 1983Q4, 1986Q4

𝐿𝑅𝐺𝐷𝑃𝑡 -44.97 (-35.22) 0.105 (0.118) -4.74 (-4.171) 6.282 (8.125) 1985Q4, 1989Q4, 1996Q4

𝐿𝐸𝐿𝐸𝐶𝑡 -46.26 (-32.38) 0.104 (0.124) -4.81 (-4.016) 5.034 (7.175) 1978Q4, 1982Q4, 2003Q4

𝐿𝐹𝐷𝑡 -42.864 (-36.52) 0.108 (0.116) -4.627 (-4.255) 7.028 (8.227) 1982Q4, 1990Q4, 1999Q4

𝐿𝑈𝑅𝐵𝑡 -41.25 (-40.92) 0.0110 (0.110) -4.54 (-4.51) 8.923 (8.939) 1982Q4, 1986Q2, 1997Q3, 2004Q1

𝐿𝑂𝑃𝐸𝑁𝑡 -45.31 (-32.95) 0.104 (0.121) -4.731 (-4.018) 5.665 (7.740) 1981Q4, 1984Q4, 1987Q4

Critical values for the 5% level significance are in parenthesis.

24

4.3. Cointegration with Markov shifts results

4.3.1. Testing for cointegration with structural break

The results of testing for cointegration with one and two structural breaks using the 𝐴𝐷𝐹, 𝑍𝛼 and

𝑍𝑡-type tests proposed by Gregory and Hansen (1996) and Hatemi-J (2008) are reported in Table 5.

Panel A corresponds to the results of the 𝐴𝐷𝐹, 𝑍𝛼 and 𝑍𝑡-type tests of Gregory and Hansen (1996)

when the cointegration vector is characterized by the presence of only one structural break and to

the 𝐴𝐷𝐹, 𝑍𝛼 and 𝑍𝑡-type tests of Hatemi-J (2008) when the cointegration vector is subject to two

structural breaks. Empirical results of applying these tests on the level and slope coefficients shifts

model are reported in column 3 of table 5. Critical values for the 𝐴𝐷𝐹, 𝑍𝛼 and 𝑍𝑡 tests are obtained

by simulation and reported in columns 4 and 5 for the 5% and 10% level of significance. These

critical values depend on the number of explicative variables used, see Gregory and Hansen (1996)

and Hatemi-J (2008) for more details.

Table 5 : Results of Gregory and Hansen (1996), and Hatemi-J (2008) tests of cointegration with

structural breaks

Equation Test

statistics

Estimated

value

5%

Critical

Value

10%

Critical

Value

Break

date 1

Break

date 2

Panel A : Gregory and Hansen results

FP

𝐴𝐷𝐹∗ -15.383 -6.800 -6.561 1982Q2 -

𝑍𝑡∗ -15.442 -6.800 -6.561 1982Q2 -

𝑍𝛼∗ -175.00 -88.83 -81.94 1982Q2 -

CO2 𝐴𝐷𝐹∗ -15.258 -7.191 -6.764 1986Q1 -

𝑍𝑡∗ -15.305 -7.191 -6.764 1986Q1 -

𝑍𝛼∗ -164.30 -97.78 -91.58 1986Q1 -

Panel B : Hatemi-J results

FP 𝐴𝐷𝐹∗ -4.755 -8.345 -8.017 1978Q3 2000Q1

𝑍𝑡∗ -18.095 -8.345 -8.017 1980Q1 2000Q2

𝑍𝛼∗ -185.99 -148.63 -142.33 1980Q1 2000Q2

CO2 𝐴𝐷𝐹∗ -11.684 -8.987 -8.702 1991Q4 2000Q3

𝑍𝑡∗ -14.297 -8.987 -8.702 1993Q3 2001Q1

𝑍𝛼∗ -176.89 -167.53 -159.28 1992Q2 2001Q4

The critical values are obtained by simulations, see Gregory and Hansen (1996) and Hatemi-J

(2008).

Our empirical findings show strong evidence for cointegration with structural breaks using both

Gregory and Hansen (1996) and Hatemi-J (2008) tests. For the Gregory and Hansen test, empirical

results show that the null hypothesis of no cointegration is rejected at conventional level

significance and using the three tests statistics which means that the alternative hypothesis of

cointegration with one structural break cannot be rejected. For instance, the estimated values of

the 𝐴𝐷𝐹∗, 𝑍𝑡∗ and 𝑍𝛼

∗ for the 𝐸𝐹𝑃 and 𝐶𝑂2 equations are -15.383, -15.442 and -175.00, and -

15.258, -15.305 and -164.30, respectively. As Table 4 shows, these estimated values are lower

than the critical values of -6.800 and -88.83 for the 𝐸𝐹𝑃 equation and -7.191 and -97.78 for the

25

𝐶𝑂2 equation respectively for the 𝐴𝐷𝐹∗and 𝑍𝑡∗, and 𝑍𝛼

∗ tests. To summary, this first result indicates

that the hypothesis of cointegration with one structural break is highly accepted. Concerning the

break date, the empirical findings show that the break date corresponds to the second quarter 1982

for the footprint equation and to the first quarter 1986 for the CO2 emissions equation.

To overcome the limits of considering only one structural break for the Gregory and Hansen H

(1996) approach, we discuss now the results of the Hatemi-J (2008) test. Panel B of Table 5 shows

that for both equations (FP and 𝐶𝑂2 emissions) the alternative hypothesis of cointegration with

two breaks cannot be rejected by the 𝑍𝑡∗ and 𝑍𝛼

∗ tests. Only for the ADF test that the null hypothesis

of no cointegration cannot be rejected as the estimated statistic value of the ADF test (-4.755) is

higher than the corresponding critical value (-8.345). Concerning the dates of breaks, the Hatemi-

J (2008) procedure finds two breaks, the first one is approximately a common break and

corresponds to the first quarter 2000 for both FP and CO2 equations.

The second break corresponds to the third quarter 1978 for the ADF test and second quarter 1980

for the 𝑍𝑡∗ and 𝑍𝛼

∗ tests for the FP equation. For the 𝐶𝑂2 equation, we found a break that corresponds

to the fourth quarter 1991, third quarter 1993 and second quarter 1992 when using the ADF, 𝑍𝑡∗

and 𝑍𝛼∗ tests.

4.3.2. Estimating the short and long run relationship

After confirming the existence a long run relationship between the environment degradation proxy

and its key determinants, we proceed in the second step to estimate the short and long run equation

by using the two steps Engle-Granger technique. First, we start by estimating the long run

relationship by applying a Markov switching model on time series in level I(1). In this paper, we

consider that only the intercept and the slope coefficient of the real GDP over time5. Second, we

recuperate the residuals of the first step and we use it as an error correction when estimating the

short run equation. Empirical results of the two long run relationships for the FP and CO2 emissions

equations are reported in Table 6 and in Table 7 for the short run relationships.

4.3.3. Long run analysis

The estimated long run relationships allows to determine the long run marginal impact of the

different independent variables such as economic growth, electricity consumption, financial

development, urbanization and trade openness on the FootPrint and 𝐶𝑂2 emissions environment

degradation proxies. Empirical results for the two long run relationships with Markov shifts for

the for the ecological Foot Print and 𝐶𝑂2 emissions dependent variables are reported in Table 6

below.

Table 6 shows that the majority of coefficients are statistically significant at conventional level.

The Likelihood Ratio (LR) bound test proposed by Davies (1977, 1978) show that a two states

Markov switching specification with shifts in both the intercept and slope coefficient of the real

GDP per capita variable is more appropriate than the linear specification. Moreover, we found that

the estimated values of the 𝑝11 and 𝑝22 probabilities are statistically significant at the 1% level

significance. Moreover, these estimated values are close to the unit which means that each regime

5 The use of this specification of long run relationship with only Markov shifts in the intercept and real GDP slope

coefficient is retained as the more appropriate specification after estimation of several long run relationship models

with Markov shifts in the intercept and slopes coefficients of all independent variables.

26

last6 enough of time. In average, the first regime last for 31.765 Quarter for the 𝐶𝑂2emissions

pollutant equation and 68.21 Quarter for FP pollutant. For the second regime, it last in average

approximately about 108.542 Quarter and 63.36 Quarter for the 𝐶𝑂2 emissions and FP pollutants

respectively.

Table 6 : Estimation of the long run relationship with Markov shifts

𝑳𝑪𝑶𝟐𝒕 𝑳𝑬𝑭𝑷𝒕 No shifts Markov shifts No shifts Markov shifts

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat

𝝁𝟏 -14.992 -4.357 1.341 0.938 -21.709 -5.893 -41.66 -33.62

𝝁𝟐 - - -4.401 -3.309 - - -34.54 -27.85

𝑳𝑹𝑮𝑫𝑷𝒕 𝜷𝟏𝟏 -3.864 -3.369 1.766 8.592 3.185 4.793 3.549 21.01

𝜷𝟏𝟐 - - 2.354 11.50 - - 2.803 16.30

𝑳𝑹𝑮𝑫𝑷𝒕𝟐 0.172 3.198 -0.108 -10.59 -0.163 -4.821 -0.170 -19.32

𝑳𝑬𝑳𝑬𝑪𝒕 0.457 1.865 0.517 4.963 0.354 2.326 -0.183 -1.841

𝑳𝑭𝑫𝒕 -0.398 -4.646 -0.208 -4.470 0.517 9.530 -0.229 -4.371

𝑳𝑼𝑹𝑩𝒕 1.206 8.032 0.315 -7.287 0.919 0.768 0.818 21.96

𝑳𝑶𝑷𝑬𝑵𝒕 1.502 5.975 0.189 2.156 - - - -

𝒑𝟏𝟏 - - 0.968 3.707 - - 0.985 3.427

𝒑𝟐𝟐 - - 0.990 4.259 - - 0.984 5.022

Log-Likelihood 46.184 99.97 23.667 91.461

LR-test 107.57 135.59

Expected duration

Regime 1 - 31.765 Q - 68.21Q

Regime 2 - 108.542 Q - 63.36 Q

Figures 2 and 3 below show the evolution of the estimated probabilities smoothing. These figures

can be used to date exactly the breaks dates. For the 𝐶𝑂2 emissions case, the first regime covers

the period from the first Quarter 1980 to the second Quarter 1991 and from the second Quarter

2001 to the fourth Quarter 2010. For the second regime it covers the period from the third Quarter

1991 to the first Quarter 2001. For the Foot Print case, the probabilities smoothing figure show

that the first regime covers the periods from the first Quarter 1975 to the second Quarter 1981 and

from the second Quarter 2005 to the fourth Quarter 2007. For the second regime, it last from the

third Quarter 1981 to the first Quarter of 2005.

6 Under the Markov switching model each regime last in average for 1 (1 − 𝑝𝑖𝑖) ⁄ periods for each regime 𝑖 = 1,2,

see Hamilton (1989) for more details.

27

0.0

0.2

0.4

0.6

0.8

1.0

1980 1985 1990 1995 2000 2005 2010

Regime 1

0.0

0.2

0.4

0.6

0.8

1.0

1980 1985 1990 1995 2000 2005 2010

Regime 2

Figure 2 : Smoothed probabilities for the CO2 emissions equation.

0.0

0.2

0.4

0.6

0.8

1.0

1975 1980 1985 1990 1995 2000 2005

Regime 1

0.0

0.2

0.4

0.6

0.8

1.0

1975 1980 1985 1990 1995 2000 2005

Regime 2

Figure 3 : Smoothed probabilities for the Ecological FootPrint (FP) equation

It is important to remember that after we have estimated different specifications models before

selecting the final retained specification which is characterized by Markov switching in both the

intercept and the slope coefficient of only the real GDP dependent variables. The real GDP

coefficients (𝛽11, 𝛽1

2) are positive and highly significant under both regimes and for both pollutants.

The slope coefficient of on the nonlinear term of real GDP per capita ( squared real GDP per

capita) is negative and significant at the 1% level significance. In more details, the results indicate

that 1 per cent increase in real GDP per capita increases the 𝐶𝑂2 emissions per capita between

1.766 per cent in regime 1 and 2.35 per cent for regime 2. For the FP dependent variable, an

increase by 1 per cent of the real GDP per capita increases FP by 3.549 per cent under regime 1

and 2.803 per cent under regime 2. On the other hand, the negative coefficient of the nonlinear

term of real GDP per capita is equal to -0.107 for the 𝐶𝑂2 emissions pollutant and -0.170 for the

FP pollutant.

As a result, the positive and negative signs coefficients of the real GDP and squared real GDP

respectively under each regime indicate that the EKC hypothesis holds for both pollutants

(𝐶𝑂2emissions and EFP).

28

The impact of electricity consumption on 𝐶𝑂2 emissions and FP is different. For the electricity

consumption, the effect on 𝐶𝑂2 emissions is positive and highly significant, significant at the 1%

level. In contrast, the effect on the ecological footprint (FP) is not significant at conventional level.

For the 𝐶𝑂2emissions, an increase of 1% in electricity consumption increases 𝐶𝑂2 emissions by

0.515 per cent. This results confirm some empirical evidence in the energy use-environment nexus

literature which suggest that electricity consumption will negatively affects CO2 emissions if the

country uses a friendly environment energy source. However, as in Qatar the whole electricity

production is provide by burning gas then it positively CO2 emissions.

The third determining variable of the 𝐶𝑂2 emissions and FP pollutant it concerns the role played

by trade openness. In our empirical findings, we found that it determine only the 𝐶𝑂2emissions7.

The results show that openness trade affects positively and significantly the 𝐶𝑂2 emissions. We

found that 1 per cent increase in the rate of openness trade is associated with an increase of 0.189

per cent of 𝐶𝑂2 emissions. This empirical finding suggest that trade openness destroy

environmental quality.

The fourth determining variable used to explain the 𝐶𝑂2 emissions and EFP evolution is

urbanization. The estimated slope coefficient of urbanization is positive and significant at the 1%

level significance for both pollutants. The empirical findings show that an increase of 1 per cent

of the urbanization level increases the 𝐶𝑂2 emissions and EFP pollutants by 3.154 per cent and

0.818 per cent respectively. This result confirm some empirical studies in the empirical literature

which found a positive relationship between environment degradation and urbanization level, see

for instance Shahbaz et al. (2014) for the case of UAE. This empirical findings can be explained

by the fact that an increase of urbanization lies in increases in energy demand and 𝐶𝑂2emissions.

Turning now to the Financial development variable, the empirical findings show that the

coefficient on domestic credit to private sector scaled by GDP is negative and statistically

significant at 1 per cent level of significance. This results support the idea following that financial

development decreases 𝐶𝑂2 emissions and EFP. Our result indicate that policymakers and

government should enhanced more financial sector in order to better benefit from its positive

impact on the environmental quality. The result indicates that an increase by 1 per cent of domestic

credit to private sector scaled by GDP will decreases 𝐶𝑂2 and FP by 0.208 per cent and 0.229 per

cent respectively.

4.3.4. Short run analysis

The estimation results of the short run relationship is reported in Table 7 below. We discuss the

results on the basis of each pollutants, the 𝐶𝑂2 emissions and the ecological Foot Print. This Table

shows that the first lag of 𝐶𝑂2 emissions, the real income per capita, the electricity consumption,

the financial development, the trade openness and the first lag of the urbanization variables

significantly affect, in the short run, the 𝐶𝑂2 emissions. We found that at the short run and as

expected, the lagged 𝐶𝑂2 emissions have a positive and significant impact on the actual 𝐶𝑂2 emissions. In particular, the results show that the real income per capita is positively and

significantly linked to the 𝐶𝑂2 emissions in the short run. Moreover, we found that electricity

7 Trade openness variable is not a key determinant of the Foot Print environment degradation proxy.

29

consumption, financial development and urbanization affect positively 𝐶𝑂2 emissions in the short

run. In contrast, we found that openness trade affects negatively 𝐶𝑂2 emissions.

For the case of ecological Foot Print, we found that the first lag of the ecological Foot Print is

statistically significant and positively affects the level of current FP. Our empirical findings show

also that the real GDP per capita, financial development, and the first lag of squared real GDP per

capita impact positively the current level of FP. In contrast, we found that the current level of

squared real GDP per capita and the first lag of real GDP per capita affect negatively the current

level of FP.

The coefficient on the ECMt-1 for the two short run relationships is negative and significantly

different from zero. This coefficient which measures the rapid adjustment towards the long run

equilibrium varies between -0.106 and -0.113. The negative and significant sign of this coefficient

indicates the existence of adjustment towards the long run equilibrium. Moreover, the results

indicate that variation in energy emissions are corrected between 10.6% and 11.30% each year.

Table 7 : Estimation of the short run relationship

Variables 𝑳𝑭𝑷𝒕 𝑳𝑪𝑶𝟐𝒕

Coef. t-stat Coef. t-stat

𝑰𝒏𝒕𝒆𝒓𝒄𝒆𝒑𝒕 0.003 0.738 -0.030 -4.118

𝑬𝑪𝑴𝒕−𝟏 -0.106 -3.592 -0.113 -3.698

𝜟𝑳𝑭𝑷𝒕−𝟏 0.644 9.797 - -

𝜟𝑳𝑪𝑶𝟐𝒕−𝟏 0.628 9.796

𝜟𝑳𝑹𝑮𝑫𝑷𝒕 5.562 3.158 0.313 4.299

𝜟𝑳𝑹𝑮𝑫𝑷𝒕𝟐 -0.303 -3.095 -0.030 -1.191

𝜟𝑳𝑭𝑫𝒕 0.106 1.795 0.205 3.921

𝜟𝑳𝑬𝑳𝑬𝑪𝒕 - - 0.448 3.339

𝜟𝑳𝑶𝑷𝑬𝑵𝒕 - - -0.383 -2.928

𝜟𝑳𝑹𝑮𝑫𝑷𝒕−𝟏 -5.327 -2.993 - -

𝜟𝑳𝑹𝑮𝑫𝑷𝒕−𝟏𝟐 0.297 2.975 - -

𝜟𝑳𝑼𝑹𝑩𝒕−𝟏 - - 0.249 3.406

B-G LM(2) test 0.2208 0.273

RESET test (2) 0.6658 0.189

ARCH(3) 0.1866 0.112

Empirical results concerning residual diagnostic analysis reported in the bottom of Table 7 above

show that the two specifications of 𝐶𝑂2 emissions and Foot Print are appropriate and adequate to

describe the evolution of the two pollutants. For instance, the Breusch-Godfrey Serial Correlation

LM test (B-G LM(2) test), the RESET test for higher order omitted variables and the ARCH test

of heteroscedasticity show a p-values that are higher than the 5% level significance. This means

that for all these three tests the null hypothesis cannot be rejected which means, absence of serial

autocorrelation, absence of higher order omitted variables and absence of heteroscedasticity.

4.3.5. CUSUM and RS-CUSUM tests

After selecting the more appropriate specifications for both pollutants, the following step consists

in testing for residuals stability under each specification using the CUSUM and squared residuals

30

CUSUM tests. The results show that the solid blue lines of the cumulative sum of residuals and

squared residuals do not deviate from the critical bounds at the 5% level of significance (see

Figures 4 and 5). This results indicate the mean and variance stability of the residuals of each

specification which confirm that the selected specifications are more appropriate to describe the

evolution of the 𝐶𝑂2 emissions and EFP pollutants. This results allow us also to investigate the

-40

-30

-20

-10

0

10

20

30

40

76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06

CUSUM 5% Significance

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06

CUSUM of Squares 5% Significance Figure 4 : CUSUM and squared CUSUM test for the Footprint equation

-40

-30

-20

-10

0

10

20

30

40

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

CUSUM 5% Significance

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10

CUSUM of Squares 5% Significance

Figure 5 : CUSUM and squared CUSUM test for the 𝐶𝑂2 emissions equation

4.4. Granger causality analysis

The most interesting and important step after selecting the relationship between environment

degradation and its key determinants is the determination of the causality direction between each

couple of variables. The econometric theory suggests that at least one causal relationship should

exist if there exist a long run relationship between variables. In this paper, we use the standard

VECM Granger causality approach to investigate both the short and long run causal relationships.

The empirical results of the short and long run VECM Granger causality are reported in Table 8

and 9 for the 𝐶𝑂2 emissions and FP pollutants, respectively..

31

Our empirical findings concerning the causal relationship differ significantly between the two

pollutants employed in this paper. using the 𝐶𝑂2 emissions pollutant, the empirical results for the

long and short run causality is reported in Table 8. Empirical results show that bidirectional

causality is running from 𝐶𝑂2 emissions to the real GDP per capita, financial development and

openness trade. We found also bidirectional causality running from openness trade to real GDP

per capita and financial development. In the short run, our empirical findings show that

urbanization causes the 𝐶𝑂2 emissions, we found also that financial development and urbanization

causes real GDP.

Table 8 : The short and long run VECM Granger causality analysis

Dependen

t

Variable

Type of Granger causality

Short-run Long-run

𝛥𝐿𝐶𝑂2𝑡 𝛥𝐿𝑅𝐺𝐷𝑃𝑡 𝛥𝐿𝐸𝐿𝐸𝐶𝑡 𝛥𝐿𝐹𝐷𝑡 𝛥𝐿𝑈𝑅𝐵𝑡 𝛥𝐿𝑂𝑃𝐸𝑁𝑡 1ECM

t

F-statitics [p-values]

[t-

statistics]

𝛥𝐿𝐶𝑂2𝑡 - 7.564*

[0.000]

0.654

[0.522]

6.100*

[0.003

]

2.626**

*

[0.077]

5.929*

[0.000]

-0.100*

[-2.881]

𝛥𝐿𝑅𝐺𝐷𝑃𝑡 2.682**

*

[0.073]

- 0.679

[0.509]

16.63*

[0.000

]

3.049**

*

[0.052]

8.007*

[0.000]

-0.064**

[-2.276]

𝛥𝐿𝐸𝐿𝐸𝐶𝑡 0.695

[0.502]

1.699

[0.129] -

0.178

[0.837

]

0.028

[0.973]

0.525

[0.593]

0.006

[0.280]

𝛥𝐿𝐹𝐷𝑡 4.524**

[0.013]

12.39

[0.000]

0.333

[0.717] -

1.181

[0.311]

4.680**

[0.011]

-0.052

[-1.106]

𝛥𝐿𝑈𝑅𝐵𝑡

1.731

[0.182]

1.206

[0.312]

0.061

[0.941]

0.043

[0.958

]

- 0.008

[0.992]

0.001

[0.332]

𝛥𝐿𝑂𝑃𝐸𝑁𝑡 3.945**

[0.023]

4.249*

[0.003]

0.041

[0.959]

5.916*

[0.004

]

0.397

[0.673] -

0.025

[0.213]

*, **, *** indicate significance at the 1%, 5% and 10% level respectively.

Using the ecological Foot Print variable, empirical results reported in Table 9 show that, in the

long-run, only the ECT coefficient associated with the ecological foot print variable is significantly

different from zero and have a negative sign. This means that, for only this variable, there is an

evidence of an error correction mechanism which derives this variable back to their long-run

relationship. In the short-run, we found strong evidence for bidirectional causality between the

ecological foot print and real GDP per capita, electricity consumption and financial development

variables. Bidirectional causality is also running between the real GDP per capita, and electricity

consumption and urbanization. On the other hand, unidirectional causality relationship is found

between the urbanization and the foot print variable and between the financial development to the

real GDP per capita variable.

32

Table 9 : The short and long run VECM Granger causality analysis

Dependent

Variable

Type of Granger causality

Short-run Long-run

𝛥𝐿𝐹𝑃𝑡 𝛥𝐿𝑅𝐺𝐷𝑃𝑡 𝛥𝐿𝐸𝐿𝐸𝐶𝑡 𝛥𝐿𝐹𝐷𝑡 𝛥𝐿𝑈𝑅𝐵𝑡 1

ECMt

F-statitics [p-values] [t-statistics]

𝛥𝐿𝐹𝑃𝑡 - 3.162**

[0.047]

6.255**

[0.015]

5.419*

[0.006]

2.515

[0.116]

-0.0563**

[-2.165]

𝛥𝐿𝑅𝐺𝐷𝑃𝑡 2.378***

[0.098] -

5.830*

[0.004]

1.666

[0.194]

4.942*

[0.009]

0.005

[0.707]

𝛥𝐿𝐸𝐿𝐸𝐶𝑡 5.104*

[0.262]

3.852**

[0.025] -

2.641***

[0.100]

3.633**

[0.030]

-0.019

[-1.322]

𝛥𝐿𝐹𝐷𝑡 5.684*

[0.005]

28.93*

[0.000]

0.333

[0.718] -

1.092

[0.339]

-0.026

[-0.638]

𝛥𝐿𝑈𝑅𝐵𝑡

2.733***

[0.070]

3.278**

[0.015]

1.568

[0.214]

1.253

[0.213] -

0.0003

[0.231]

*, **, *** indicate significance at the 1%, 5% and 10% level respectively.

5. Concluding remarks and Policy implications

5.1. Summary

The papers examines possible long run relationship between 𝐶𝑂2 emissions and ecological foot

print pollutants considered as proxies of environment degradation and economic growth,

electricity consumption, financial development, urbanization and openness trade. In contrast to the

previous studies in this area, this paper is novelty at least in three elements. We investigate the

case of the Qatar country that is not yet examined as a single country. We use recent tests of unit

root and tests for cointegration with structural breaks rarely examined in the empirical studies such

as the Silvestre et al. (2009), and Gregory and Hansen (1996) and Hatemi-J (2008) respectively.

Moreover, when estimating the long run relationship we use the Markov switching equilibrium

correction model with shift in the intercept and slope coefficient of the real GDP per capita

variable. We investigate the Granger causality direction among variables within the context of

VECM framework.

The results of the unit root tests with structural breaks show that all series in level are non-

stationary and integrated with first order, e.g I(1), with the presence of structural breaks. This result

can be viewed as an indicator of possible existence of breaks in the cointegration vector if there

exist. After verifying that all variables are I(1) processes, the second step is to test for cointegration

with structural breaks using the Gregory and Hansen (1996) for one unknown break in the

cointegration vector and Hatemi-J (2008) test for cointegration with two unknown breaks.

Empirical findings show that both tests of cointegration, with one unknown or two unknown

breaks, cannot reject the alternative hypothesis of cointegration with shifts in both the intercept

and slope coefficients indicating the existence of long run relationship among investigated

variables. The results of estimation of the long run relationship with Markov shifts supports the

33

existence of a cointegration vector with two structural breaks as indicated in the probabilities

smoothing evolution. Moreover, we found that only the intercept and the slope coefficient of the

real GDP per capita variable shifts between regimes.

More precisely, our empirical results show that the inverted U-shaped hypothesis between the

environment degradation proxy and economic growth cannot be rejected for the two pollutants

examined, the CO2 emissions and ecological footprint. Moreover, we found that the impact of

electricity consumption is positive for the CO2 emissions environment degradation proxy and

negative for the ecological foot print proxy. We found also that urbanization and financial

development have positive and statistically significant impact on both pollutants. Concerning the

openness trade, we found that this variable determines positively the 𝐶𝑂2 emissions.

Concerning the Granger causality results, we found bidirectional causality running from 𝐶𝑂2 emissions to the real GDP per capita, financial development and openness trade. We found also

bidirectional causality running from openness trade to real GDP per capita and financial

development. In the short run, our empirical findings show that urbanization causes the 𝐶𝑂2 emissions, we found also that financial development and urbanization causes real GDP. For the

ecological footprint proxy, we found strong evidence for bidirectional causality between the

ecological foot print and real GDP per capita, electricity consumption and financial development

variables. Bidirectional causality is also running between the real GDP per capita, and electricity

consumption and urbanization. On the other hand, unidirectional causality relationship is found

between the urbanization and the foot print variable and between the financial development to the

real GDP per capita variable.

To summarize, this paper validate the EKC hypothesis for the Qatar state behavior differ across

regimes. Following this result, the location of the turning point depends on whether the Qatar

economy is in the first or second regime. This indicates also that the country economic structure

may play an important role in determining the speed and the time necessary to reach the turning

point.

5.2. Policy implications

The design of the appropriate policies required to improve the environmental quality is of great

importance for the Qatari government and Qatari policy makers. The effectiveness of the proposed

environmental strategy depends mainly on whether policy makers have completely understand the

variables that determine the environmental degradation and the causal relationships among these

determinants. The results of validating the EKC hypothesis and the Granger causality testing have

many policy implications. First, the results that validate the EKC hypothesis suggests that the rise

in economic growth in recent years has been associated with lower environmental degradation.

However, the challenge persists as Qatar remains one of the countries with highest CO2 emissions

in the world. Therefore, more efforts must be undertaken by policymakers to minimize the level

of emissions in the country. Second, it is important to recognize that policymakers are restricted

from imposing conservation policies due to the confirmed results of the bidirectional relationship

between economic growth and electricity consumption. This is because policies are likely to lower

not only CO2 emissions but also the level of economic growth. Accordingly, one measure that can

be undertaken to balance environmental quality and economic growth is to generate electricity

34

from renewable energy sources. According to the World Bank indicators, 100% of the electricity

production in Qatar in 2012 came from natural gas8. If the country were to diversify the production

of electricity by using renewable energy sources such as: solar power, wind power or wave power,

the emissions from electricity production will be reduced without negatively affecting economic

growth. Additionally, policies should be implemented to ensure saving and efficient use of energy.

This can be done by adopting energy efficiency technologies, and influencing the behavior of

consumers mainly by raising the prices of energy sources which is likely to lead to better

exploitation of available energy. Moreover, reducing the energy subsidy will lead to better

utilization of energy sources.

5.3. Limits and extensions

One of the major limits of our paper consists on allowing for a maximum of two breaks in the

cointegration vector of the long run relationships. As noted before, this choice is mainly explained

by the period of study that does not allows to consider more than 2 breaks. As possible extension

of this paper and in the case of collecting a data that can cover a more long period is to consider

the recent tests for cointegration with multiple breaks proposed by Arai and Kurozumi, 2007; and

Kejriwal and Perron, 2010. Another possible extension of our study is to include some others

socio-demographics and institutional variables as determinants of environment degradation

proxies such as fertility rate, life expectancy, political index as in Charfeddine and Khediri (2015).

References

Abler, D. G.; Rodriguez, A. G., and Shortle, J. (1999). Trade liberalization and the environment in

Costa Rica. Environment and Development Economics; vol. 4, pp. 357–73.

Al-mulali, U.; Che, Sab, C.N., (2012). The impact of energy consumption and CO2 emission on

the economic growth and financial development in the Sub Saharan African countries. Energy;

vol. 39, pp. 180–186.

Antweiler W.; Copeland, B.R., Taylor MS. (2001). Is free trade good for the environment?

American Economic Review, vol. 91, pp. 877–908.

Arai, Y., Kurozumi, E., (2007). Testing for the null hypothesis of cointegration with a structural

break. Econometric Reviews; vol. 26, pp. 705–739.

Beckerman, W. (1992). Economic Growth and the Environment: Whose Growth? Whose

Environment? . World Development, vol. 20(4): pp. 481-496.

8 http://wdi.worldbank.org/table/3.7

35

Carrion-i-Silvestre, J.L., Kim, D., and Perron, P., (2009). GLS-Based Unit Root Tests with

Multiple Structural Breaks Both under the Null and the Alternative Hypotheses. Econometric

Theory, vol. 25, pp. 1754-1792.

Charfeddine, L.; and Khediri, K. B. (2016). Financial development and environmental quality in

UAE: Cointegration with structural breaks, vol. 55, pp. 1322-1335.

Chikaraishi, M., Fujiwara, A., Shinji, Kaneko S., Poumanyvong, P., Komatsu, S.; Kalugin, A.,

(2015). The moderating effects of urbanization on carbon dioxide emissions: A latent class

modeling approach. Technological Forecasting and Social Change; 90 Part A:302-17

Cole, M.A., Neumayer, E. (2004). Examining the impact of demographic factors on air pollution.

Population and Environment; vol.26, pp. 5–21.

Matthew A. Cole; and Eric Neumayer (2004). Examining the impact of demographic factors on

air pollution. Population and Environment, vol. 26 (1), pp 5-21.

Davies, R.B. (1977): "Hypothesis testing when a nuisance parameter in present only under the

alternative," Biometrika, vol. 64, pp. 247-254.

Davies, R.B. (1987): "Hypothesis testing when a nuisance parameter in present only under the

alternative," Biometrika, vol. 74, pp. 33-43.

Dickey, D.A., Fuller, W.A., (1979). Distribution of the estimators for autoregressive time series

with a unit root. Journal of American Statistics Association, vol. 74, pp. 427–431.

Dodman, D. (2009). Blaming cities for climate change? An analysis of urban greenhouse gas

emissions inventories. Environment and Urbanization, vol. 21 no. 1 185-201.

Ehrhardt-Martinez (1998) Social Determinants of Deforestation in Developing Countries: A

Cross-National Study. Social Forces (1998) 77 (2): 567-586.

Elliot, G., Rothenberg, T.J., Stock, J.H., 1996. Efficient tests for an autoregressive unitroot.

Econometrica 64, 813–836.

Engle RF, Granger CWJ. (1987). Cointegration and error correction: representation, estimation,

and testing. Econometrica; vol. 55; pp. 251–76.

Esteve V, Tamarit C (2012). Is there an environmental Kuznets curve for Spain? Fresh evidence

from old data. Economic Modelling; vol. 29, pp. 2696-703.

36

Esteve, V., Tamarit, C.(2012). Threshold cointegration and nonlinear adjustment between CO2

and income: the environmental Kuznets curve in Spain, 1857–2007. Energy Economics. Vol. 34,

(6), pp. 2148–2156.

Fan Y, Lui L.-C, Wu G, Wie Y-M. (2006). Analyzing impact factors of CO2 emissions using the

STIRPAT model. Environmental Impact Assessment Review; vol. 26, pp. 377–95.

Frankel JA, Romer D. (1999). Does trade cause growth? American Economics Review, vol. 89

(3), pp. 379–99.

Gregory, A. W. and Hansen, B. E., (1996). Residual-based tests for cointegration in models with

regime shifts, Journal of Econometrics, vol. 70, pp. 99-126.

Grossman G.M., Krueger A.B., 1995. Economic growth and the environment. Quarterly Journal

of Economics 110. 353-377.

Hamilton, James D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time

Series and the Business Cycle,” Econometrica, 57, 357-84.

Hamilton, James D. (1990). “Analysis of Time Series Subject to Regime Changes,” Journal of

Econometrics, 45, 39-70.

Hansen, B.E.; Seo, B. 2002. Testing for two-regime threshold cointegration in vector error-

correction models". Journal of Econometrics 110.2, pp. 293-318.

Hatemi-J A. (2008). Tests for cointegration with two unknown regime shifts with an application

to financial market integration. Empirical Economics; vol. 35, pp. 497-505.

Holtz-Eakin, D., Selden, T.M., (1995). Stoking the fires? CO2 emissions and economic growth.

Journal of Public Economics 57, 85–101.

Horvath, R.J. (1997). “Energy consumption and the environmental Kuznets curve debate”. Mimeo,

Department of Geography, University of Sydney, Australia.

Hossain, Md. S., (2011). Panel estimation for CO2 emissions, energy consumption, economic

growth, trade openness and urbanization of newly industrialized countries, Energy Policy, vol. 39,

6991–6999.

Jayanthakumaran K, Liu Y. (2012). Openness and the environmental Kuznets curve: evidence

from China. Economic Modelling; vol. 29, pp. 566–76.

37

Kasman A, Duman YS. 𝐶𝑂2 emissions, economic growth, energy consumption, trade and

urbanization in new EU member and candidate countries: A panel data analysis. Economic

Modelling 2015;44:97–103.

Kejriwal, M., 2008. Cointegration with structural breaks: an application to the Feldstein–Horioka

puzzle. Studies in Nonlinear Dynamics & Econometrics 12 (1), 1–37.

Kejriwal, M., Perron, P., 2010. Testing for multiple structural changes in cointegrated regression

models. Journal of Business and Economic Statistics, vol. 28, pp. 503–522.

Kwiatkowski, D., Phillips, P., Schmidt, P., Shin, Y., (1992). Testing the null hypothesis of

stationary against the alternative of a unit root: how sure are we that economic time series have a

unit root? Journal of. Econometrics. Vol. 54, pp. 159–178.

Lee J, Strazicich MC. (2003). Minimum Lagrange multiplier unit root test with two structural

breaks. The Review of Economics and Statistics; vol. 85, pp.1082-9.

Liddle B, Lung S. (2010). Age-structure, urbanization, and climate change in developing countries

: revisiting STIRPAT for disaggregated population and consumption related environmental

impacts. Population Environment, Vol. 31, pp. 317–43.

World Wide Fund For Nature. Living Planet Report (2014). Species and spaces, people and places.

Ng S, Perron P. Unit root tests in ARMA models with data dependent methods for the selection of

the truncation lag. Journal of the American Statistical Association 1995;90:268–81.

Ozturk I, Acaravci A. (2013). The long-run and causal analysis of energy, growth, openness and

financial development on carbon emissions in Turkey. Energy Economics, Vol. 36, pp. 262-267.

Perron P. (1989). The great crash, the oil price shock and the unit root hypothesis. Econometrica;

vol. 57, pp. 1361-401.

Phillips, P.C.B., Perron, P., (1988). Testing for a unit root in time series regression.Biometrika 75,

335–346.

Poumanyvong, P., Kaneko, S., (2010). Does urbanization lead to less energy use and lower CO2

emissions? A cross-country analysis. Ecological Economics 70 (2), 434–444.

Sadorsky P. (2014). The effect of urbanization on CO2 emissions in emerging economies. Energy

Economics; vol. 41, pp. 147–53.

38

Shahbaz M, Lean HH, Shabbir MS. (2012). Environmental Kuznets curve hypothesis in Pakistan:

cointegration and Granger causality. Renewable and Sustainable Energy Reviews; vol. 16, pp.

2947–53.

Shahbaz M, Hye QMA, Tiwari AK, Leitão NC. (2013). Economic growth, energy consumption,

financial development, international trade and CO2 emissions in Indonesia. Renewable and

Sustainable Energy Reviews; vol. 25, pp. 109–21.

Shahbaz M., Lean H.H., Shabbir M.S., (2012), Environmental Kuznets curve hypothesis in

Pakistan: cointegration and Granger causality. Renewable and Sustainable Energy Reviews, vol.

16, pp. 2947–53.

Shahbaz M., Solarin S.A., Mahmood H., Arouri M., (2013a). Does financial development reduce

CO2 emissions in Malaysian economy? A time series analysis. Economic Modelling, vol. 35. pp.

145–152.

Shahbaz M., Tiwari A.K., Nasir M,. 2013c. The effects of financial development. economic

growth. coal consumption and trade openness on CO2 emissions in South Africa. Energy Policy

61. 1452–1459.

Shahbaz M., Lean H, (2012). Does financial development increase energy consumption? The

role of industrialization and urbanization in Tunisia, Energy Policy 40, 473–479

Sharma SS. (2011). Determinants of carbon dioxide emissions: empirical evidence from 69

countries. Applied Energy; vol. 88, pp. 376–382.

Shafik, N., Bandyopadhyay, S., (1992). Economic growth and environmental quality: time series

and cross-country evidence. Background Paper for the World Development Report 1992. World

Bank, Washington D.C.

Stern, D.I., Common, M.S., (2001). Is there an environmental Kuznets curve for sulfur? Journal

of Environmental Economics and Management 41, 162–178.

Stern, D.I., Common, M.S., Barbier, E.B., (1996). Economic growth and environmental

degradation: a critique of the environmental Kuznets curve. World Development 24, 1151–1160.

Stock, J.H., (1999). A class of tests for integration and cointegration. In: Engle, R.F., White, H.

(Eds.), Cointegration, Causality and Forecasting. A Festschrift in Honour of Clive W.J. Granger.

Oxford University Press, pp. 37–167.

39

Suri V, Chapman D. (1998). Economic growth, trade and the environment: implications for the

environmental Kuznets curve. Ecological Economics; vol. 25:195–208.

World Bank, (2011). World Development Indicators (WDI). Accessed from: http://www.

worldbank.org/data/onlinedatabases/onlinedatabases.html.

World Resource Institute, (2007). October 2006 Monthly Update: Fossil Fuel Consumption and

its Implications: http://earthtrends.wri.org/updates/node/100.

York R, Rosa EA, Dietz T. (2003). STIRPAT, IPAT and ImPACT: analytic tools for unpacking

the driving forces of environmental impacts. Ecological Economics; vol. 46, pp. 351–65.

Zivot E, Andrews D. (1992). Further evidence of great crash, the oil price shock and unit root

hypothesis. Journal of Business and Economic Statistics, vol. 10, pp. 251–70.