trade on carbon emission in developed, emerging
and less economically developed countries
Prof. Corazza
Gita Gonoody
Master Degree, Financial Econometrics
Ca’ foscari University
2 CONTENTS
Contents
1 Introduction 7
1.1 Describe and explain the subject matter . . . 7
1.2 Necessity and importance of research . . . 9
1.3 Research hypotheses . . . 10
1.4 Basic objectives of the research . . . 10
1.5 Research methodology . . . 10
1.6 Research data . . . 11
1.7 Research limits . . . 11
1.8 Expected results of the research . . . 12
2 Literature review 12 2.1 Introduction . . . 12
2.2 The process of forming opinions on the financial development and the environment . . . 14
2.3 Channels of influence of financial development on the environment . 15 2.4 Financial Development Indicators . . . 18
2.5 Summary . . . 19
3 Data 20 3.1 General equation . . . 20
3.2 The groups of countries. . . 21
3.3 Numerical analysis . . . 21
3.4 Graphical analysis . . . 24
3.4.2 Emerging countries . . . 27
3.4.3 Developing countries . . . 31
4 Methodology and empirical results 34 4.1 Unit root test on panel data . . . 34
4.1.1 Levin and Lin (1992) tests . . . 34
4.1.2 The Fisher’s type tests : Maddala andWu (1999) and Choi (2001) . . . 35
4.1.3 The results for Unit Root Tests . . . 37
4.2 Cointegration Test . . . 39
4.2.1 The results for Cointegration Tests . . . 40
4.3 The panel data model . . . 41
4.3.1 Models with fixed individuals effects . . . 42
4.3.2 Models with random individuals effects . . . 43
4.4 The Breusch-Pagan test . . . 48
4.5 The Hausman test . . . 48
5 Empirical result 49 5.1 Developed countries . . . 49 5.2 Emerging countries . . . 51 5.3 Developing countries . . . 52 5.4 Forecasts . . . 53 5.5 Developed countries . . . 53 5.6 Emerging countries . . . 54 5.7 Developing countries . . . 55
4 LIST OF TABLES 6 Conclusion 56 7 Appendix 59 7.1 Correlation . . . 59 7.1.1 On raw data . . . 59 7.1.2 Log. . . 60 7.1.3 Log difference . . . 61
7.2 Residuals on the training sample . . . 62
List of Tables
1 The groups of countries. . . 112 Descriptive statistics on raw data for developed countries.. . . 21
3 Descriptive statistics on raw data for emerging market countries. . . 22
4 Descriptive statistics on raw data for developing countries. . . 22
5 Stationarity test, developed countries, on first difference of the log-arithm. . . 37
6 Stationarity test, emerging countries, on first difference of the log-arithm. . . 37
7 Stationarity test, developing countries, on first difference of the log-arithm. . . 38
8 Stationarity test, developed countries, on first difference of the log-arithm. . . 38
9 Stationarity test, emerging countries, on first difference of the log-arithm. . . 38
10 Stationarity test, developing countries, on first difference of the log-arithm. . . 39
11 Cointegration test . . . 40
12 Random effect model estimation, developed countries . . . 49
13 Random effect model estimation, developed countries . . . 50
14 Additonnal information . . . 50
15 Random effect model estimation, emerging countries. . . 51
16 Random effect model estimation, emerging countries. . . 51
17 Additonnal information . . . 52
18 Random effect model estimation, developing countries . . . 52
19 Random effect model estimation, developing countries . . . 52
20 Additonnal information . . . 53
21 Correlation table, raw data, developed countries . . . 59
22 Correlation table, raw data, developing countries . . . 59
23 Correlation table, raw data, emerging countries . . . 60
24 Correlation table, log data, developed countries .. . . 60
25 Correlation table, log data, developing countries . . . 60
26 Correlation table, log data, emerging countries . . . 61
27 Correlation table, log diff data, developed countries . . . 61
28 Correlation table, log diff data, developing countries. . . 61
29 Correlation table, log diff data, emerging countries . . . 62
List of Figures
1 Channels of influence of financial development on the environment. 18 2 Energy consumption and CO2, developed countries. . . 246 LIST OF FIGURES
4 GDP and CO2, developed countries. . . 26
5 Trade openess and CO2, developed countries. . . 27
6 Energy consumption and CO2, emerging countries. . . 28
7 Financial development and CO2, emerging countries. . . 29
8 GDP and CO2, emerging countries. . . 30
9 Trade openess and CO2, emerging countries. . . 31
10 Energy consumption and CO2, developing countries. . . 32
11 Financial development and CO2, developing countries. . . 32
12 GDP and CO2, developing countries. . . 33
13 Trade openess and CO2, developing countries. . . 33
14 Forecasts over the periods from 2007 to 2010, developed countries. . 54
15 Forecasts over the periods from 2007 to 2010, developed countries. . 55
16 Forecasts over the periods from 2007 to 2010, developed countries. . 56
17 Residuals, developed countries. . . 62
18 Residuals, developed countries. . . 63
1
Introduction
In sustainable development, economic growth involves improvement in the quality of life and the social welfare of the community and equity in the use of welfare’s parameters (health, education, housing, food and entertainment) by quantitative and qualitative changes in the economy which is impossible except by conserve natural resources and the environment The United Nations Millennium Declara-tion identified principles and treaties on sustainable development, including eco-nomic development, social development and environmental protection. The waste control on the environment plays a key role in sustainable economic and social development. So the global trend towards sustainable development, according to environmental damage is essential.
Financial market development is one of the factors that influence the growth and development of economic and the environment. Therefore study the effect of fin-ancial development on the environment, is essential. The purpose of this chapter is to review the general research, in this context, first the necessity and importance of the subject of the research is addressed and then research hypotheses goals are stated and finally, research methods, research instruments, data collection proced-ures, defining the concepts and limitations of the research will be described.
1.1
Describe and explain the subject matter
Schumpeter (1911) established a so called relationship between financial sector development and economic growth by exploring the importance of finance as a blood in economic activity. Goldsmith, (1969); McKinnon and Shah (1973) and King and Levine, (1993) also argued that financial sector development can be con-sidered as an engine in an economy to stimulate economic growth. A developed and sound financial sector in an economy provides better access to financial services by reducing transactional, information and monitoring cost [Shahbaz et Lean(2012)].
8 1 INTRODUCTION
Financial sector development contributes to protection of environment by realloc-ating financial resources to environment friendly projects. Furthermore, this sector encourages firms to use advanced technology to enhance production level by emit-ting less energy pollutants [Tamazian et al.(2009)Tamazian, Chousa, et Vadlamannati]. Capital market is an important instrument in reducing environmental pollution. Because an efficient capital market provides easy access to the projects which are in line with the environment and relationship between financial development-CO2 emissions is weakened due to an increase in asymmetric information during the period of financial instability. [Richard(2010)]
So the development of the financial sector improves financial decisions and causes better distribution of resources and accelerates economic growth and reduces envir-onmental pollutants. Frankel and Romer (1999) pointed out that a well-developed financial sector attracts foreign direct investment (FDI), which in turns may stim-ulate economic growth and, hence, affect the environmental quality. Financial Sector Development, decreases the cost of borrowing (lending), and is conducive to promoting investment, also by increasing efficiency in the energy sector, envir-onmental emissions are reduced, and reduction of borrowing cost, enables national, regional and local governments, to make the projects complies with environmental [Muhammad et al.(2011)Muhammad, Faridul, et Muhammad Sabihuddin]. How-ever, some recent studies show that financial development has a direct impact on energy consumption[Sadorsky(2010)] and thereby impacts on greenhouse gas emissions[Tamazian et al.(2009)Tamazian, Chousa, et Vadlamannati] on the other hand Jensen 1996 showed that financial development increases greenhouse gas emissions through industrial growth. The above discussion indicates a lack of con-sensus regarding the effect of financial development on environmental pollution. In other words, the effect of financial development on the environmental pollution
de-pends on the specific country-specific conditions [Muhammad et al.(2011)Muhammad, Faridul, et Muhammad Sabihuddin]. It may have a different influence in different countries due to differences in
1.2
Necessity and importance of research
According to studies, ( King,1993 and. Demirguc-Kunt and Levine 2007 ), fin-ancial development plays a key role in economic growth of countries, so that it can be expressed, nation’s level of economic development expresses their level of financial development. On the other hand, financial development leads to the ad-option of advanced technologies in the energy sector and a significant reduction in greenhouse gases. Also financial development has led to advances in technology and increase productivity and stimulate asset and motivation and implementation of environmental regulations and thereby reduce environmental pollution has been followed. This study examines the relationship between financial development and environmental pollution in the three group of countries such as developed coun-tries (G7), emerging market councoun-tries and developing councoun-tries. The Group of 7 (G7), is a group consisting of the finance ministers and central bank governors of seven advanced economies: Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. The G7 are the seven wealthiest major developed nations on Earth by national net wealth, representing more than 63% of the net global wealth ($241 trillion) according to the Credit Suisse Global Wealth Re-port October 2013 An emerging market is a country that has some characteristics of a developed market but is not a developed market. This includes countries that may be developed markets in the future or were in the past. It may be a nation with social or business activity in the process of rapid growth and indus-trialization. As of May 2013, Russell Indexes considers the following countries as emerging markets, such as: Indonesia, India, South Korea, Mexico, Malaysia, Turkey, .... A developing country, also called less developed country (LDC) or less economically developed country (LEDC), is a nation with a lower living standard, underdeveloped industrial base, and low Human Development Index (HDI) relat-ive to other countries. Such as: Bangladesh, Egypt, Ghana, Nigeria, Pakistan, Senegal...
10 1 INTRODUCTION
1.3
Research hypotheses
In this study in order to demonstrate the effect of economic growth, financial devel-opment and trade on environmental pollution the data from 3 types of countries such as developed, emerging and developing (less developed) countries is used. This study intends to test the following hypotheses:
• H0: Financial development has a negative impact on the environment in
developed countries;
• H1: Financial development has a positive impact on the environment in
emerging countries;
• H2: Financial development has a positive impact on the environment in
developing countries which this amount is greater in compare to emerging market countries.
1.4
Basic objectives of the research
In order to test the hypotheses this study intends to pursue the following objectives: • Assessing the role of economic growth, financial development and trade on CO2 emission in developed, emerging and less economically developed coun-tries;
• Mechanism of financial development on environmental pollution in the de-veloped, emerging and developing countries;
• Provide policy recommendations to the Economic Policy.
1.5
Research methodology
In this study, three groups of countries: developed, emerging and developing(less developed) in order to investigate the research hypotheses have been considered
and then econometrics analysis of panel data is used to examine the effect of financial development on the environmental pollution in the selected countries.
1.6
Research data
The present study investigated the effect of financial development on environ-mental pollution in the developed, emerging and developing countries. Accord-ingly, the statistical population of this study is three groups of countries which are in different level of economic and financial development. Their names are listed below.
Developed countries (G7) Emerging countries Developing countries Canada Indonesia Bangladesh
France India Egypt
Germany South Korea Ghana
Italy Mexico Nigeria
Japan Malaysia Pakistan
United Kingdom Turkey Senegal United States
Table 1: The groups of countries
1.7
Research limits
The main limitation of this study is the lack of access to data for carbon dioxide emissions from the 2010 onwards and also the data for Germany before 1991, and also the lack of data for financial development indicators from 2008 onwards for Canada, are replaced by taking average from previous years for 2009 and 2010.
12 2 LITERATURE REVIEW
1.8
Expected results of the research
It is expected that the situation of financial development in countries plays a key role in environmental pollution, So that countries with low levels of economic development have a positive effect of financial development on environmental pol-lution, and development in financial sector more developed countries will lead to a reduction in environmental pollution.
2
Literature review
2.1
Introduction
This thesis intends to examine the effect of economic growth, energy consump-tion, financial development and international trade on environmental pollution in developed, emerging market and less economically developed countries and test the assumptions that the effect of financial development and economic growth on environmental pollution in developing countries is positive whereas in developed countries this effect is negative. In order to achieve the research objectives, we first need to define the concept of financial development. So first in the following words, the concept of financial development and economic growth are presented and then the ideas presented in this context will be considered. The concept of financial development was considered more after the concept of financial repression (A term that describes measures by which governments channel funds to themselves as a form of debt reduction. This concept was introduced in 1973 by Stanford econom-ists Edward S. Shaw and Ronald I. McKinnon). In the other hand the free role of banks and credit institutions in the regulation of the real value of financial instru-ments, and the possibility of rapid contribution to the trade off of the instruments has also been considered in this process.
to allow and promote activities like increased foreign direct investment (FDI) (An investment made by a company or entity based in one country, into a company or entity based in another country), increases in banking activity, and increases in stock market activity, presents one possible avenue for which economic growth can be increased and this will affect the demand for energy. Financial develop-ment is important because it can increase the economic efficiency of a country’s financial system. Financial development encourages a number of changes within a country including for example, a reduction in financial risk and borrowing costs, greater transparency between lenders and borrowers, access to greater financial capital and investment flows between borders and access to the latest energy ef-ficient products and cutting edge technology all of which can affect the demand for energy by increasing consumption and business fixed investment. Financial development can affect the demand for energy in several ways. One of the most direct ways that financial development can affect the demand for energy is by making it easier for consumers to borrow money to buy big ticket items like auto-mobiles, houses, refrigerators, air conditioners, and washing machines. In other words, financial development makes it easier for consumers to satisfy their wants and needs. These big ticket consumer items typically consume a lot of energy which can affect a country’s overall demand for energy. Businesses also benefit from improved financial development because it makes it easier and less costly to gain access to financial capital which can be used to expand existing businesses (by buying or building more plants, hiring more workers, and buying more machinery and equipment) or create new ones”.
Developed financial market is a market in which principles of freedom of trade off and transparency of information are respected correctly and suppliers and de-manders of financial services trade with enough information and liberty. Also financial development through accumulation and mobilization of capital resources, facilitate access to the projects in line with environment and to adopt advanced technology in the energy sector and will significantly reduce greenhouse gas
emis-14 2 LITERATURE REVIEW
sions and in the other hand financial development, Leads to technological advances, Increase the productivity and stimulate asset and incentive to create and enforce environmental regulations and thereby reduce environmental pollution has been followed.
The process of forming opinions on the financial development and the environment are described in following part and then the relationship between financial devel-opment and environmental pollution are mentioned and at the end this chapter the indicators which are used for financial development for the sample of countries considered in this study will be explained.
2.2
The process of forming opinions on the financial
devel-opment and the environment
The relationship between financial development and environmental pollution in
re-cent years has been considered by economists. First time [Tamazian et al.(2009)Tamazian, Chousa, et Vadlamannati] investigate the effect of economic and financial development on carbon dioxide
emissions as ”Does higher economic and financial development lead to environ-mental degradation: Evidence from BRIC countries”. After them, few studies have been conducted in this field. Thus, existing theories regarding the effect of financial development on the environment is particularly new. However, stud-ies related to environmental degradation first was introduced by Grossman and Krueger decades of the nineties, afterward Shafik and Bandyopadhyay in 1992 In their study, assuming increase in economic activities, ceteris paribus as cause of en-vironmental degradation. International Review of Bipolar Disorders (IRBD, 1992) they have benefited in their articles from the environmental Kuznets curve. Si-mon Kuznets on 1955 In a study entitled Economic growth and income inequality, concluded that on economic growth, the relationship between per capita income and income inequality, follow an inverted ”U” shape. Based on this assumption, the relationship between various indicators of environmental degradation such as
economic development and quality of environment, tend to get worse as modern economic growth occurs until average income reaches a certain level in the turning point. In the other words, according to this hypothesis, it is expected that envir-onmental effects of financial development variables in developing countries where financial markets are in their early stages of development and in the countries with emerging market will be positive and the effect of financial development variables on the environment in developed countries where financial markets are developed will be negative.
2.3
Channels of influence of financial development on the
environment
Since the issue of the effect of financial development the environment has recently received considerable attention, as far as this thesis studies show, any Theoret-ical with mathematTheoret-ical proof in this regard has not been formed. In this part of the study, channels of influence of financial development on the environment can be expressed as follows: Antle and Heidebrink 1995, in their studies argue that technology advances result in changes in the composition of manufactured goods, less polluting substitute inputs instead of environmentally damaging in-puts and reduced environmental pollution. Technological change beliefs that Poor nations in order to raise their incomes have tended to pollute the environment. In the early stages of development, Citizens to achieve higher revenue growth, cause more environmental pollution. In contrast, in higher income levels, increase in financial power of nations, has been caused the common use of less polluting technologies and taking advantage of new technologies coupled with increased rev-enue and decreased pollution. However, with rising income levels, the demand for environmental quality rises because environmental quality is a luxury item at this stage. Frankel and Rome 1999, in their review on the subject noted that financial market development contribute to attracting foreign direct investment, and acts
16 2 LITERATURE REVIEW
as a conduit for technology compatible with environment, financial sector devel-opment, decreases the cost of borrowing (lending), and is conducive to promoting investment, also by increasing efficiency in the energy sector, environmental emis-sions are reduced, and reduction of borrowing cost enables national, regional and local governments, to make the projects complies with environmental.[?]. Tadesse, 2005, implies that financial development, causes technological innovations by rais-ing capital and sharrais-ing the risks and returns that these new technologies may lead to pollution.
Claessens, Feijen 2007, also noted in their study the impact of financial market de-velopment on the environment. They believed that, greater financial dede-velopment can facilitate more financing at lower costs, including for investment in environ-mental projects. The ability to raise such financing may be especially important for governments at the local, state, and national levels, since much of environ-mental protection will be a public sector activity. It, however, also applies to the investment of private firms in (required) environmental-protecting activities such as: environmentally preferable purchasing and buying recycled programs, so that the firms can integrate environment and climate change in all activities that they undertake and ensure efficient implementation for sustainable development. Fur-thermore, it has been found that better governed firms are more willing to consider environmental considerations.
[Tamazian et al.(2009)Tamazian, Chousa, et Vadlamannati] were the first who ex-amined seriously effect of financial development on the environment. In 2009 they investigate the effect of financial development on carbon dioxide emissions for the 24 economies in transition; they concluded that higher degree of economic and financial development decreases the environmental degradation. Their analysis suggests that financial liberalization and openness are essential factors for the CO2 reduction. The adoption of policies directed to financial openness and liber-alization to attract higher levels of R&D-related foreign direct investment might reduce the environmental degradation in countries under consideration. Their
findings show that financial development is associated with decline in CO2 per capita emissions. Particularly, they find that capital market and banking sector development along with higher levels of foreign direct investment help to achieve lower CO2 per capita emissions. In this sense, it is not worthy that the govern-ment can help the markets by establishing a strong policy frame work that creates long-term value for green house gas emissions reductions and consistently supports the development of new technologies that lead to a less carbon-intensive economy. Moreover, well-developed capital markets are very important because firms can reduce the liquidity risk and can mobilize the funds required which is extremely useful in developing technology in the long run.
[?] did another study in this regard and stated that capital market is an important tool in reducing environmental pollution, because efficient capital market provides access in line with environmental projects and reduce the problems of asymmetric information. So the development of the financial sector improves financial decisions and better distribution of resources and accelerates economic growth and reduces environmental pollutants. Financial development acts as a conduit for technology compatible with environment, Financial Sector Development, decreases the cost of borrowing (lending), and is conducive to promoting investment, also by increasing efficiency in the energy sector, environmental emissions are reduced, and reduction of borrowing cost, enables national, regional and local governments, to make the projects complies with environment.
With regard to the content and opinions expressed by various economists, the channels of influence financial development on the environment can be shown in
18 2 LITERATURE REVIEW
Figure 1: Channels of influence of financial development on the environment.
As far as research for this thesis shows, the effect of economic growth on energy demand and thus reduce emissions, is an issue that less studied.
2.4
Financial Development Indicators
In this part financial development indicators are defined according to other studies. Four financial indicators are used to measure financial development. They are as follows:
• LLY measured by the ratio of liquid liabilities to GDP;
• BANK measured by the share of the domestic assets of private financial intermediaries in the total domestic assets of the private financial interme-diaries and the central bank;
• PRIVATE measured by the fraction of credit received by private enter-prises to total credit received by the government and the public and private enterprises;
• PRIVY measured by the ratio of credit received by private enterprises to GDP. PRIVY is a measure of the private access to the international credit
market.
The four financial indicators described above help us to examine different aspects of financial development. In particular, LLY measures the overall liquidity per unit of GDP, BANK measures the extent of private control over the domestic financial intermediaries, PRIVATE measures the fraction of overall liquidity available to the private enterprises and PRIVY measures the private access to total volume of national and international credit per unit of GDP.
All of these data are available from the World Bank World Development Indicators (WDI) online data base.
2.5
Summary
In recent years research has been carried out the effect of financial development on the environment. Tamazian in 2009 was the first one who considers this matter in his research. He concluded that financial development and economic growth the leads to reduced emissions of carbon dioxide. Financial sector encourages in-vestment activities through the issuance of loans to firms with lower costs and also led to the use of environment-friendly energy, which is effective in reducing environmental pollution. Also, financial development, leads environmental pol-lution by effecting energy consumption demand. Financial development impacts energy consumption demand in different ways, first, with direct effect on increase the consumer purchasing power that leads to buying appliances with high power energy consumption as a result, energy demand increases. Then, by improving the country’s trade leads to economic growth which results increase in energy demand. Financial sector encourages investment activities, to use environment-friendly technologies to increase production levels.
20 3 DATA
the research, regarding to the study of [Shahbaz et al.(2013)Shahbaz, Hye, Tiwari, et Leitão] PRIVY, real domestic credit to private sector per capita.
3
Data
3.1
General equation
Similarly to[Sadorsky(2010)] in order to examine and compare the linkage among economic growth, energy consumption, financial development and trade openness and CO2 , and as explained previously in literature, general form of empirical
equation is modeled as follow:
log(CO2it) = f (log(ECit), log(T Rit), log(GDPit), log(F Dit))
log(CO2it) = αit+ β1,itlog(ECit) + β4,itlog(T Rit) + β2,itlog(GDPit)+
β3,itlog(F Dit) + uit
(1) where ui ∼ N (0, 1), i = 1, . . . , N and t = 1, . . . , T .
where CO2 it indicates CO2 emission per capita in country i at time t; GDPit the
GDP per capita in country i at time t; F Dit the is financial development proxied
by real domestic credit to private sector per capita in country i at time t; T Rit
represents trade openness per capita in country i at time t. Theses set of variables are conforming the economic development uitindicate the country-specific random
effect and random error term, respectively. CO2 emissions (kt), carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.
I have to mention that the value of the GDP per capita and Energy consumption per capita is divided by 1000.
3.2
The groups of countries
In this section, statistical analysis, tables and graphs are presented in the three groups. The main purpose of this part is familiarization and identification of samples and evidence required to confirm the relationship between the variables and environmental pollution.
As mentioned in chapter two the first group is developed countries (G7) includes: Canada, France, Germany, Italy, Japan, the United Kingdom and the United States and the second group is countries with emerging economies includes : In-donesia, India, South Korea, Mexico, Malaysia, Turkey and finally in the third group is developing countries with less economically developed countries such as: Bangladesh, Egypt, Ghana, Nigeria, Pakistan, Senegal
3.3
Numerical analysis
CO2 EC TO GDP FD Mean 11.37 4.94 46.39 27.88 124.66 Max. 20.25 8.42 85.41 46.44 227.75 Min. 6.00 2.58 15.92 17.27 54.27 Std. 4.53 1.91 18.77 6.84 45.35 Kurt. 0.80 0.80 0.02 0.55 0.55 Skew. 2.15 1.92 2.16 2.37 2.18 JB. 0.00 0.00 0.13 0.03 0.02 ADF 0.02 0.00 0.41 0.75 0.6822 3 DATA CO2 EC TO GDP FD Mean 3.88 1.53 72.38 4.49 51.58 Max. 10.04 4.42 220.41 19.68 158.50 Min. 0.83 0.37 16.69 0.31 13.29 Skew. 2.69 1.09 56.42 4.05 42.28 Kurt. 0.82 1.24 1.59 1.36 1.10 Skew. 2.69 3.70 4.11 4.95 2.80 JB. 0.01 0.00 0.00 0.00 0.00 ADF 0.52 0.80 0.03 0.02 0.98
Table 3: Descriptive statistics on raw data for emerging market countries.
CO2 EC TO GDP FD Mean 0.68 0.43 53.33 0.57 22.09 Max. 2.45 0.91 116.05 1.51 54.93 Min. 0.14 0.11 18.89 0.15 3.66 Std. 0.57 0.22 20.49 0.32 12.33 Skew. 1.62 0.24 0.77 1.40 1.16 Kurt. 4.56 1.88 3.34 4.12 3.98 JB. 0.00 0.04 0.02 0.00 0.00 ADF 0.96 0.95 0.28 0.98 0.03
Table 4: Descriptive statistics on raw data for developing countries.
In order to descriptive analysis and compare the pollution between three groups of countries, we compare the mean and standard deviation of the mean for each group of countries. If we consider GDP, all statistical values of this variables in the different groups show that the average of GDP in developed, emerging, and
developing countries are 27.8, , 4.49, 0.57 , respectively. And also for financial development indicator these numbers are 124.65, 51.58, and 22.09, respectively. So it is clear that the significant differences exist between financial developments indicators of groups studied. Of course the sample standard deviation between the groups implies that the dispersion and fluctuation in developed countries is more than emerging and developing countries. The average of energy consumption in developed countries is 11 times more than the average of energy consumption in developing countries and is 3 times more in compare to emerging countries. If we compare this average in emerging and developing countries, it is 3.5 times more in emerging countries. The average of pollution (CO2) in developed countries is 16 times more than the average of pollution in developing countries and is 3 times more in compare to emerging countries. If we compare this average in emerging and developing countries, it is close to 6 times more in emerging countries. These results indicate that pollution level in industrialized countries is more than in developing countries, but they don’t reject the hypothesis of our study, because the purpose of this study is to evaluate and compare the effect of these variables on the level of pollution in different groups.
As it can be observed in tables in all groups for most of variables , the skewness coefficient is greater than zero and also for kurtosis coefficient this value is greater than 3, which imply that our row data are not normally distributed.
24 3 DATA
3.4
Graphical analysis
In the following diagrams, we try to plot the effect of these variables on the level of pollution in all the countries of studied groups by considering row data.
3.4.1 Developed countries
It can be seen easily in2that in all developed countries the energy consumption and
CO2 emission follow the same positive trend over the first 15 years and negative
trend for the last 5 years for most of the countries and positive correlation for all of them.
Figure 2: Energy consumption and CO2, developed countries.
3 we bring the graphs to compare the financial development indicator with CO2, the observation of these graphs are not clear but we can conclud that that for most of the countries except Japan the trend of financial development is positive, with negative correlation with CO2.
Figure 3: Financial development and CO2, developed countries.
In the next graphs,4we compare the trend of GDP per capita with CO2 emission in developed countries and for all the countries in this group GDP has positive trend with negative correlation in compare to CO2.
26 3 DATA
Figure 4: GDP and CO2, developed countries.
It can be observed in5the trend of Trade openness and CO2 at the same period of time with positive trend for Trade openness and negative correlation during most of the time in compare to CO2.
Figure 5: Trade openess and CO2, developed countries.
3.4.2 Emerging countries
In6,first of all it is obvious that the amount of CO2 emission during these 20 years for all the emerging countries is positive, to compare energy consumption with CO2 emission it is also clear that for all the countries they have positive trend with positive correlation.
28 3 DATA
Figure 6: Energy consumption and CO2, emerging countries.
From 7, we can see that financial development doesn’t follow the same trend in emerging countries.
Figure 7: Financial development and CO2, emerging countries.
From 8,it’s obvious that the trend of GDP during these years is positive and has a positive correlation with CO2 emission.
30 3 DATA
Figure 8: GDP and CO2, emerging countries.
In9, we can say that in most of the countries, during these years we have positive trend and positive correlation with CO2 emissions.
Figure 9: Trade openess and CO2, emerging countries.
3.4.3 Developing countries
In the Developing countries CO2 emission follows the same positive trend as in emerging market countries but with sharper slop and from10,11,12,13, the positive correlation between CO2 emission and all the independent variables in most of the countries is observable.
32 3 DATA
Figure 10: Energy consumption and CO2, developing countries.
Figure 12: GDP and CO2, developing countries.
34 4 METHODOLOGY AND EMPIRICAL RESULTS
4
Methodology and empirical results
4.1
Unit root test on panel data
When the number of time series observations on each of the sections is high, stationary analysis (unit root tests) for each of the sections can be examined. But the power of unit root tests when the period of data is very low have a bias towards accepting the null hypotheses. In these terms using unit root tests based on panel data is necessary to increase the power of the test. For example, usual unit root tests that are used for a time series such as Fuller, Augmented Dickey-Fuller, Phillips-Perron, have low power and have a bias towards accepting the null hypothesis. This is reinforced when the sample size is small (n < 50). One of the methods that have been proposed to resolve this problem is to increase the sample size by using panel data and panel unit roots test. So before the research model, it is necessary use stationary tests for all variables used in the model. However, it is necessary that we use one of the methods to panel unit root tests to apply our test. some methods are as follows:
1. Levin, Lin and Chu; 2. Im, Pesaran and Shin; 3. Breitung;
4. Fisher’s type tests; 5. Hadri.
4.1.1 Levin and Lin (1992) tests
Unit root tests as previously mentioned, check the stationary or non stationary of variables using an equation. Levin and Lin believed that individual unit root tests have limited power in compare to common unit root process. The power of the
test is the probability of rejecting the null when it’s false and the null hypothesis is unit root.
Where the lag order ρ is permitted to vary across individuals. The procedure works as follows:
∆xi,t = ρixi,t−1+ σt + αi+ i,t
i = 1, . . . , N t = 1, . . . , T
(2)
Which N is the number of section or countries, T is a number of time periods,σ is time effect, ρ is auto-correlation parameter for each section and i,t error terms
with normal distribution.
The hypothesis test is as follows:
H0 : ρ = 0;
H1 : ρ < 0;
(3)
HO hypothesis indicates that there is a unit root and if we can’t reject HO it
implies no stationarity of the variable .In this assumptions the larger the N and T the more the statistic will tend towards the standard normal distribution. In this article based on what [Hurlin et al.(2007)Hurlin, Mignon, et others] said in panel unit root tests we use Fisher’s type tests, and Levin and Lin test we try to investigate the stationary test in our panel.
4.1.2 The Fisher’s type tests : Maddala andWu (1999) and Choi (2001)
The panel unit root tests based on a heterogeneous model consist in testing the significance of the results from N independent individual tests. In this context, IPS uses an average statistic, but there is an alternative testing strategy based on combining the observed significant levels from the individual tests. This approach
36 4 METHODOLOGY AND EMPIRICAL RESULTS
based on p-values has a long history in meta-analysis. In panel unit root tests, such a strategy based on Fisher (1932) type tests, was notably used by Choi (2001) and Maddala and Wu (1999).
Let us consider a heterogeneous model:
∆yi,t = αi+ ρiyi,t−1+ pi X
z=1
βi,z∆yi,t−z + i,t (4)
We test the same hypothesis as IPS,H0 : ρi = 0 for all i = 1, . . . , N against the
alternative hypothesis H1 : ρi < 0 fori = 1, . . . , N 1 and
rhoi = 0 fori = N 1 + 1, . . . , N ; with 0 < N 1 ≤ N . The idea of the Fisher type
test is very simple. by considering pure time series unit root test statistics (ADF, Elliott-Rothenberg- Stock, Max-ADF, etc.) If these statistics are continuous, the corresponding p-values, denoted pi; are uniform (0; 1) variables. Consequently, un-der the crucial assumption of cross-sectional independence, the statistic proposed by Maddala and Wu (1999) defined as:
PM W = −2 N
X
i=1
log(Pi) (5)
Has a chi-square distribution with 2N degrees of freedom, when T tends to infinity and N is fixed. As noted by Banerjee (1999), the obvious simplicity of this test and its robustness to statistic choice, lag length and sample size make it extremely attractive. For large N samples, Choi (2001) proposes a similar standardized statistic: ZM W = √ N (N−1PM W − E(−2 log(Pi))) q (V ar(−2 log(Pi))) = − PN i=1log(P√ i) + N N (6)
This statistic corresponds to the standardized cross-sectional average of individual p-values. Under the cross-sectional independence assumption, the Lindberg-Levy theorem is sufficient to show that it converges to a standard normal distribution under the unit root hypothesis.
4.1.3 The results for Unit Root Tests Logarithm
developed ADF-F Proba. Levin-Lin-Chu Proba.
CO2 22.55 0.07 -2.36 0.01
FD 6.43 0.95 0.54 0.71
EC 25.36 0.03 -2.51 0.01
GDP 0.74 0.77 6.20 0.96
TO 14.14 0.44 -0.76 0.22
Table 5: Stationarity test, developed countries, on first difference of the logarithm.
Emerging ADF-F Proba. Levin-Lin-Chu Proba.
CO2 11.28 0.51 -1.41 0.08
FD 10.89 0.54 2.18 0.98
EC 8.10 0.78 -1.50 0.07
GDP 3.36 0.99 1.67 0.95
TO 11.41 0.49 -0.41 0.34
38 4 METHODOLOGY AND EMPIRICAL RESULTS
Developing ADF-F Proba. Levin-Lin-Chu Proba.
CO2 4.51 0.97 0.12 0.55
FD 17.58 0.13 -3.60 0.00
EC 4.93 0.96 0.88 0.81
GDP 4.27 0.98 0.56 0.71
TO 16.17 0.16 -2.21 0.01
Table 7: Stationarity test, developing countries, on first difference of the logarithm.
First log difference
developed ADF-F Proba. Levin-Lin-Chu Proba.
CO2 124.92 0.00 -5.43 0.00
FD 47.92 0.00 -2.31 0.01
EC 107.15 0.00 -4.53 0.00
GDP 33.83 0.00 -5.99 0.00
TO 35.64 0.00 -4.18 0.00
Table 8: Stationarity test, developed countries, on first difference of the logarithm.
Emerging ADF-F Proba. Levin-Lin-Chu Proba.
CO2 80.89 0.00 -7.55 0.00
FD 67.03 0.00 -1.42 0.08
EC 84.17 0.00 -4.63 0.00
GDP 56.34 0.00 -5.37 0.00
TO 76.08 0.00 -6.47 0.00
Developing ADF-F Proba. Levin-Lin-Chu Proba. CO2 100.42 0.00 -0.82 0.21 FD 58.76 0.00 -1.88 0.08 EC 53.05 0.00 -3.38 0.00 GDP 27.26 0.01 -4.92 0.00 TO 76.31 0.00 -3.03 0.00
Table 10: Stationarity test, developing countries, on first difference of the logar-ithm.
In order to investigate the statinarity in our panel, Levin Lin test and ADF Fischer test are used, the results imply that all our variables become stationary after apply-ing the first differences on logarithm. I have to mention that this transformation will provide us with growth rate of the series.
4.2
Cointegration Test
Investigation of cointegration on the panel data is important as well as time series data.Kao and Pedroni investigated the long run relationship between endogeneous and exogeneous variables. The DF-type test from Kao can be computed from the estimates residuals as :
ˆ
uit= γ ˆuit−1+ vit (7)
where ˆuit is the estimated residuals from the estimated static equation. In order
to test the null hypothesis of no cointegration, the null can be written as:
H0 : γ = 1; H1 : γ < 1. (8)
40 4 METHODOLOGY AND EMPIRICAL RESULTS
and the second hypotheses implies the existence of cointegration between variables.
ˆ γ = PN i=1 PT t=2uˆituˆit−1 PN i=1 PT t=2uˆ2it (9) The DF-type tests are constructed as follows:
1. DFγ = √ N T (ˆ√γ−1)+3√N 10.2 2. DFt= √ 1.25tγˆ+ √ 1.875N
The asymptotic distributions of DF and ADF converge to a standard normal dis-tribution N (0, 1).
4.2.1 The results for Cointegration Tests
To avoid spurious regression and because of the fact that all variables are integrated with the same order, I(1), we have to consider the result of cointegration test for all studied groups.
Developed Emerging Developing
DFγ 2.4 2.3 1.97
DFt 11.8 10.4 10.4
γ 0.97 0.99 0.89
Table 11: Cointegration test
As we can see from the table,11, there exist cointegration relationship between all the variables in all groups which imply that there is long run relationship between pollution(CO2) and all independent variables.
4.3
The panel data model
Given that this research is a cross-country study, therefore, the best approach is to estimate a panel data study. A longitudinal, or panel, data set is one that follows a given sample of individuals over time, and thus provides multiple observations on each individual in the sample. (Hsiao,2003, page 2). Isabelle Cadoret (2004),poin-ted out that, Panel data refers to the pooling of observations on a cross-section of households, countries, firms, ...over several times periods.
Suppose we have sample observations of characteristics of N individuals over T time periods denoted it
yit, xkit, i = 1, . . . , N , t = 1, . . . , T and k = 1, . . . , K.
Observations of y are assumed to be the random outcomes of some experiment with probability distribution conditional of vectors of the characteristics x and parameters b.
Several benefits from using panel data (Baltagi):
• Controlling for individual heterogeneity. Not accounting for individual het-erogeneity causes serious misspecification;
• Panel data give more informative data, more variability, less collinearity among the variables, more degree of freedom and more efficiency;
• Panel data are better able to study the dynamics of adjustment;
• Panel data are better able to identify and measure effects that are simply not detectable in pure cross sections or pure time-series data;
• Panel data models allows us to construct and test more complicated beha-vioral models than purely cross-section or time-series data;
• Many variables can be more accurately measured at the micro level, and biases resulting from aggregation over firms or individuals are eliminated.
42 4 METHODOLOGY AND EMPIRICAL RESULTS
4.3.1 Models with fixed individuals effects
The general specification of the fixed individual model is given by the equation10
yit= αi+ K X i=1 βikxit+ µit i = 1, . . . , N t = 1, . . . , T (10)
with yit the endogeneous variable, xit the exogeneous variables and it the error
term.
We have N T observation inside our data set. Because of the fact that we want to associated one constant for each individual in this specification, and because of the fact that we want to use K independent variables, we have to estimate
K + N parameters. Naturally, T must be sufficiently large to estimate our model.
In our case, we have 16 observations for 7 or 6 individuals, we have around 100 observations and 10 coefficients to estimate, we have 90 degrees of freedom. There exist two ways to estimate the coefficients for the regression. One in one step, by applying directly the ordinary least square procedure over the data.The other way is by applying the two steps procedure, in this situation, we estimate first the parameter associated with the independent variable, except the intercept, as explain in the equation 12.
yit− ¯yi. = β1(x1it− ¯x1i.) + · · · + βK(xKit− ¯xKi.) + (it− ¯i.)
i = 1, . . . , N
(11)
After that, we estimate the value of the intercept based on the value of the coeffi-cient wit estimated in the equation12. The intercept can be compute as follow:
ˆ
αi = ¯yi.− ˆβ1x¯1i.+ · · · + ˆβKx¯Ki.
i = 1, . . . , N
(12)
Note that, the pooled model, given by the equation 13 is a special case of the fixed individual effect. The coefficient are the same for all the countries for all the period of time. yit = α + K X i=1 βkxit+ µit i = 1, . . . , N t = 1, . . . , T (13)
4.3.2 Models with random individuals effects
Estimation with individual effects no correlated with the xit. The random effects
model is an appropriate specification if we are drawing N individuals randomly from a large population. The model is
yit = α + xitβ + µit (14)
with N = 1, . . . , N and t = 1, . . . , T and µit= ui+ it.
ui is assume random, it denotes the unobservable individual specific effect.
Plus, E(ui | x1,it, . . . , xK,it) = 0, E(u2i | x1,it, . . . , xK,it) = σu2 and E(uiuj |
x1,it, . . . , xK,it) = 0, i 6= j.
ij denotes the remainder disturbance.
E(it| x1,it, . . . , xK,it) = 0, E(2it | x1,it, . . . , xK,it) = σ2and E(itjt0 | x1,it, . . . , xK,it) =
0, i 6= j, t 6= t0. Consequently,
44 4 METHODOLOGY AND EMPIRICAL RESULTS
E(µit | x1,it, . . . , xK,it) = 0, E(µ2it | x1,it, . . . , xK,it) = σu2+σ2, E(µitµit0 | x1,it, . . . , xK,it) =
σ2
u, t 6= t
0 and E(µ
itµjt0 | x1,it, . . . , xK,it) = 0, t 6= t0 and j 6= j0.
Denoting µ, a vector(T, 1), i = 1, . . . , N. µi = µi1 µi2 .. . µiT (15)
For one individual, the variance covariance matrix (T, T) can compute:
V ar(µi | x1,i, . . . , xK,i) = E(µiµ0i | x1,i, . . . , xK,i)
= σ2 u+ σ2 σu2 . . . σu2 σ2 u σ2u+ σ2 . . . σu2 .. . . . . . .. ... σ2 u . . . . . . σ2u+ σ2 σ1t+ σu2ii 0 = Σ (16)
The variance covariance matrix (NT, NT) can compute:
V ar(µ | X) = E(µµ0 | X) = ω 0 . . . 0 0 ω . . . 0 .. . . . . . .. ... 0 . . . 0 ω 1O Ω = Λ (17) With:
µ = µ11 .. . µ1T .. . µN 1 .. . µN T (18)
The random effect model can be define as follow:
Y = X+β+µ (19)
X+ is a (NT, K+1) matrix of exogeneous variables:
X+ = 1 x111 . . . xk11 .. . . . . . . . ... 1 x11T . . . xk1T 1 x121 . . . xk21 .. . . . . . . . ... 1 x12T . . . xk2T .. . . . . . . . ... 1 x1N 1 . . . xkN 1 1 x1N T . . . xkN T (20)
46 4 METHODOLOGY AND EMPIRICAL RESULTS β = α β1 .. . βK (21)
The error variance is homoskedastic for all i and t, there is autocorrelation over time only between the disturbance of the same individual. GLS estimator is the best estimator of β+. ω1/2 = 1 − θ Tii 0 with θ = 1 − √ σ T σ2 u+σ2 and Ω−1/2Yi = 1 0 . . . 0 0 1 . . . 0 .. . . . . . .. ... 0 0 . . . 1 yi1 yi2 . . . yiT − θ T 1 1 . . . 1 1 1 . . . 1 .. . . . . . .. ... 1 1 . . . 1 yi1 yi2 . . . yiT = yi1− θ ¯yi. yi2− θ ¯yi. . . . yiT − θ ¯yi. (22) Ω−1/2Xi+=
1 − θ x1i1− θ ¯x1i. . . . xki1− θ ¯xki.
1 − θ x1i2− θ ¯x1i. . . . xki2− θ ¯xki.
..
. . . . . .. ... 1 − θ x1iT − θ ¯x1i. . . . xkiT − θ ¯xki.
(23)
The transform regression is :
yit− θ¯yi. = α(1 − θ) + β1(x1it− θbarx2i.) + · · · + β1(xkit− θbarxki.) + (µit− θ ¯µi.)
where i = 1, . . . , N and t = 1, . . . , T . And the GLS estimator is given by:
ˆ
β+ = (X+Λ−1
X+)−1X+Λ−1Y (25)
with
V ar( ˆβ+) = σw2(X+ΛX+)−1 (26)
GLS computed with the true variance components is BLUE (Best Linear Unbiased Estimator) but we have to apply feasible GLS as the true variance components are unknown.
The feasible GLS estimator is asymptotically efficient as ither N or T tend to infinity. It is given by:
ˆ β+ = (X+ˆλ−1 X+)−1X+Λˆ−1Y (27) With ˆω = 1 − √ σˆ T ˆσ2 u+ˆσ2
Swammy and Arora (1972) suggest to run 2 regressions to get an estimate of the variance components. The within regression:
yit− ¯yi.= β1(x1it− ¯x1i.) + β2(x2it− ¯x2i.) + · · · + βk(xkit− ¯xki.) + (it− ¯i.) (28)
Gives: ˆ σ2 = P i P te 2 wit
N T −N −K with wit= it− ¯i. and ei. the residuals.
The between regression with N observations and K+1 parameters ¯yi.= α+β1x¯1i.+
β2x¯2i.+ · · · + βkx¯ki.+ ¯i.+ ui Gives: ˆσ2 b = P ie 2 bi N −(K+1)
48 4 METHODOLOGY AND EMPIRICAL RESULTS
with bi = ¯i.+ ui and ebi the residuals.
ˆ σ2b is an unbiased estimator of σu2+ σ2 T . We obtain: ˆθ = 1 − √σˆ T ˆσ2 h .
4.4
The Breusch-Pagan test
Breusch and Pagan derived a Lagrange Multiplier test to to assess under the null hypothesis if the variance of the individual effects is equal to zero.
H0 : σu2 = 0; H1 : σu2 6= 0. (29)
We compute the LM statistics:
LM = N T 2(T − 1) P i( P tµˆit) 2 P i P tµˆ2it − 1 !2 (30) Where ˆµit are the least square residuals of the pooled model.
LM is asymptotically distributed as a X2(1).
If the LM is lower than 3.84 (α = 5%), we do not reject the null hypothesis and choose the pooled model.There is no individual random effect.
4.5
The Hausman test
Hausman test:
H0 : E(ui | x1it, . . . , xkit) = 0;
H1 : E(ui | x1it, . . . , xkit) 6= 0.
(31)
He suggest to compare te within and the FGLS estimators, both are consistent if
We compute:
W = ( ˆβw− ˆβ+)0( ˆV ar( ˆβw) − ˆV ar( ˆβ+))−1( ˆβw − ˆβ+).
W is asmptotically distributed as a X2(q) where q is the number of variables that can be correlated with ui.
5
Empirical result
After estimating both the random individual effects and the random effects model, we are able to compute the Preusch-Pagam test and the Hausman test. As we know, the first one will indicate if we have to use random or fixed effect model. The second one will provide us with the information that the GLS are convergent.
5.1
Developed countries
Stat. X2(1) p-value
Breusch-Pagan Test 775.28 3.84 0.00
Stat. X2(4) p-value
Hausman 0.48 0,97
Table 12: Random effect model estimation, developed countries
As we can read in the table13, it is clear that we have to consider a random effect model and that the GLS estimators are convergent. Respectivelly, we reject H0
for the Breusch-Pagam test and ewe can not reject H0 for the Hausman test. We
50 5 EMPIRICAL RESULT
coefficient std deviation p-value prob. Signif.
Intercept 1.59 0.15 10.91 0.00 ***
log(T O) -0.11 0.02 -4.55 0.00 ***
log(F D) 0.04 0.03 1.65 0.10 *
log(EC) 0.80 0.09 9.00 0.00 ***
log(GDP ) -0.07 0.02 -3.21 0.00 ***
Table 13: Random effect model estimation, developed countries
As we can observable in the table 13, all the coefficient are significant at 10% of significant. The trade openess and the GDP have a negative impact on the CO2 emission and the FD and the energy consumption have a positive impact on the CO2 emission. Note that, because of the fact that we estimate the coefficient on the logarithm of the series, the coefficient provide us directly with the elasticity. The latter means for example that an increase of 1% of the trade openess would decrease the CO2 emission for about 0.11%.
Sum of squared residuals 3.30
Log likelihood 38.47
AIC -66.95
SIC/BIC -53.35
Hannan-Quinn -61.43
Table 14: Additonnal information
The table14provide us with more information about the quality of the regression. The latter could be use to compare the performance of an other regression on the CO2 emission.
5.2
Emerging countries
Stat. X2(1) p-value
Breusch-Pagan Test 408.28 3.84 0.00
Stat. X2(4) p-value
Hausman 4,72 0,32
Table 15: Random effect model estimation, emerging countries
As for thr developed countries, the Breusch-Pagam test and the Hausman test for the Emerging countries group provide use with the result that we better have to use the random effect model. The parameters will be convergent. Indeed, we reject the null hypothesis for the Breusch-Pagam test and we can not reject the null for the Hausman test, as we can observe in the table 16.
coefficient std deviation p-value prob. Signif.
Intercept 0.86 0.15 5.58 0.00 ***
log(T O) 0.01 0.03 0.29 0.77
log(F D) -0.02 0.02 -1.20 0.23
log(EC) 0.75 0.06 12.73 0.00 ***
log(GDP ) 0.13 0.02 5.29 0.00 ***
Table 16: Random effect model estimation, emerging countries
From the table 16, we can observe that thhe trade openess and the Financial Development indicator are not significant. The coefficient associate with these two variables are not significantly different from 0. For the Energy Consumption, the impact is positive as for the GDP. When the Energy Consumption increased of 1%, the CO2 emissions increase of 0.75%.
52 5 EMPIRICAL RESULT
Sum of squared residuals 2.57
Log likelihood -37.55
AIC -65.10
SIC/BIC -52.28
Hannan-Quinn 59.92
Table 17: Additonnal information
As previously, the table17provides us with some information about the perform-ance of the model.
5.3
Developing countries
Stat. X2(1) p-value
Breusch-Pagan Test 26.37 26,37 0.00
Stat. X2(4) p-value
Hausman 4,68 0,32
Table 18: Random effect model estimation, developing countries
As we can read in the table19, we reject the null for the Breusch-Pagam test and we can not reject the null for the Hausman test.
coefficient std deviation p-value prob. Signif.
Intercept -0.48 0.40 -1.211 0.23
log(EC) 0.77 0.15 5.13 0 ***
log(GDP ) 0.27 0.05 5.50 0 ***
log(F D) 0.16 0.05 2.91 0.00 ***
log(T O) 0.08 0.08 1.05 0.30
In the table19, has we can see, the trade openess variable is not significant. The coefficient assocites with the variable is not significantly different from 0. For the other variables, the coefficients are significants. The Energy Consumption, the GDP and the Financial Development have a positive impact on the CO2 emission. An increase of 1% of the Energy Consumption will increase the CO2 emission for about 0.77%.
Sum of squared residuals 9.70
Log likelihood -26.20
AIC 62.40
SIC/BIC 75.22
Hannan-Quinn 67.58
Table 20: Additonnal information
5.4
Forecasts
In the section, we would like to observe the results out of sample of our model. As mentioned above, we keep four years of observations to do this.
5.5
Developed countries
As we know from the estimation part, the trade openess and the GDP have a negative influence on the CO2 emission and the financial development and the energy consumption have a positive impact on the CO2 emission.
Over the period of time from 2007 to 2010, the graph 14 provides us with the results of the out of sample forecasts.
54 5 EMPIRICAL RESULT
Figure 14: Forecasts over the periods from 2007 to 2010, developed countries.
It is interesting to point out the fact that in Canada, in Germany, in UK and in US the forecast undervalued the observed value. Opposite, in France the forecast is overrated over all the period. In Italy and in Japan, the true value is first undervalued and after overrated.
5.6
Emerging countries
As we know from the estimation part, all the variable of interest included in the model, which have a significant coefficient, have a positive impact on the CO2 emission. Over the period of time from 2007 to 2010, the graph 15 provides us with the results of the out of sample forecasts.
Figure 15: Forecasts over the periods from 2007 to 2010, developed countries.
As for the developed countries, for some countries the value is all the time overrated and in some other countries all the time underrated. For Indonesia and for Mexico, the value is all the time overrated for the other, for India, for Kenya, for Mexico, for Malaysia ans for Turkey, the value is underrated.
5.7
Developing countries
As we know from the estimation part, all the significant variables have a positive impact on the CO2 emission. Over the period of time from 2007 to 2010, the graph
56 6 CONCLUSION
Figure 16: Forecasts over the periods from 2007 to 2010, developed countries.
Here too, as previously observed in the two other groups of countries, the fore-casts overrated the observed value for Bangladesh, Ghanna, Nigeria, Pakistan and Senegal. For the other country, Egypt, the forecasts are lower than the observed value.
6
Conclusion
This study investigated the effect of economic growth, financial development and trade on CO2 emission in developed, emerging and less economically developed countries over the period of 1991-2010, all the data are taken from WDI. For this purpose we applied Levin Lin test and ADF Fisher test to examine the stationarity of our data, and we use Kao cointegration testing approach to examine the coin-tegration among the variables for long run relationship, and also to compare these three groups of countries which are completely different from financial and
econom-ical point of view, we use panel data, by using Breusch-pagan and Hausman test we understand that we have to consider random effect in our panel. At the end we estimate the model with random effect, over the period of 1991-2006 and apply it over our forecast sample from2007 to 2010 and we compare the forecast result with our observation. The empirical results show that energy consumption increase car-bon emission in all the studied groups, the coefficient of GDP and trade openness are negative in developed counties which explain that by increasing the volume of these variables in developed countries the CO2 emission is reduced, whereas it can be seen that these coefficients are positive in emerging and developing countries and also the values of these coefficient are higher in developing countries in com-pare to emerging countries, which state that in this groups of countries in order to achieve higher income, financial development and economic growth the govern-ments and the peoples are careless about the pollution and CO2 emission, they try to use all the facilities just to reach higher financial return, this point should be considered that in emerging countries by using newer technologies and better equipments they succeed to control the pollution and CO2 emission, also it should be noted that in developed countries, they try export semi finished goods or row materials which produce a lot of pollution in order to transfer to finished goods, to the developing countries, by this means not only they can reduce the CO2 emission in their countries but also they can benefit from cheaper cost of human resources in that countries, decrease the total cost of production and increase the amount of trade openness and financial development, as well, as explained before in develop-ing and emergdevelop-ing countries in order to access higher income and financial growth they produce a lot of pollution that imply the positive coefficient between GDP and trade openness and financial development. Nowadays in developed countries, they succeed to control energy consumption by using new technology which use less energy or by alternative energies such as wind, solar, and by waste sorting and materials recovery, as well by reallocating financial resources to environment friendly projects, they try to control and reduce CO2 emission with the aim of
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