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\def\makelabel##1{\upshape ##1}}% } {\end{list}\end{raggedright}} \newenvironment{specHead}[2]% {\vspace{20pt}\hrule\vspace{10pt}% \phantomsection\label{#1}\markright{#2}% \pdfbookmark[2]{#2}{#1}% \hspace{-0.75in}{\bfseries\fontsize{16pt}{18pt}\selectfont#2}% }{} \def\TheFullDate{2014-01-15 (revised: 15 January 2014)} \def\TheID{\makeatother } \def\TheDate{2014-01-15} \title{Carbon Emission and Economic Growth of SAARC Countries: A Vector Autoregressive (VAR) Analysis} \author{}\makeatletter \makeatletter \newcommand*{\cleartoleftpage}{% \clearpage \if@twoside \ifodd\c@page \hbox{}\newpage \if@twocolumn \hbox{}\newpage \fi \fi \fi } \makeatother \makeatletter \thispagestyle{empty} \markright{\@title}\markboth{\@title}{\@author} \renewcommand\small{\@setfontsize\small{9pt}{11pt}\abovedisplayskip 8.5\p@ plus3\p@ minus4\p@ \belowdisplayskip \abovedisplayskip \abovedisplayshortskip \z@ plus2\p@ \belowdisplayshortskip 4\p@ plus2\p@ minus2\p@ \def\@listi{\leftmargin\leftmargini \topsep 2\p@ plus1\p@ minus1\p@ \parsep 2\p@ plus\p@ minus\p@ \itemsep 1pt} } \makeatother \fvset{frame=single,numberblanklines=false,xleftmargin=5mm,xrightmargin=5mm} \fancyhf{} \setlength{\headheight}{14pt} \fancyhead[LE]{\bfseries\leftmark} \fancyhead[RO]{\bfseries\rightmark} \fancyfoot[RO]{} \fancyfoot[CO]{\thepage} \fancyfoot[LO]{\TheID} \fancyfoot[LE]{} \fancyfoot[CE]{\thepage} \fancyfoot[RE]{\TheID} \hypersetup{citebordercolor=0.75 0.75 0.75,linkbordercolor=0.75 0.75 0.75,urlbordercolor=0.75 0.75 0.75,bookmarksnumbered=true} \fancypagestyle{plain}{\fancyhead{}\renewcommand{\headrulewidth}{0pt}} \date{} \usepackage{authblk} \providecommand{\keywords}[1] { \footnotesize \textbf{\textit{Index terms---}} #1 } \usepackage{graphicx,xcolor} \definecolor{GJBlue}{HTML}{273B81} \definecolor{GJLightBlue}{HTML}{0A9DD9} \definecolor{GJMediumGrey}{HTML}{6D6E70} \definecolor{GJLightGrey}{HTML}{929497} \renewenvironment{abstract}{% \setlength{\parindent}{0pt}\raggedright \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt \textcolor{GJBlue}{\large\bfseries\abstractname\space} }{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Mirza Md. Moyen Uddin} \author[2]{Md. Abdul Wadud} \affil[1]{ Rajshahi University} \renewcommand\Authands{ and } \date{\small \em Received: 9 December 2013 Accepted: 3 January 2014 Published: 15 January 2014} \maketitle \begin{abstract} This paper examines the causal relationship between carbon ( 2 CO ) emissions and economic growth in seven SAARC countries using time series data for the period from 1972-2012. We applied Vector Error Correction Modeling (VECM) approach. We have also applied Augmented DickeyFuller (ADF) and Phillips-Perron (P.P) test and Johansen?s cointegration approach to check time series properties and cointegration relationship of the variables. Results exhibit a cointegration relationship between environmental pollution and economic growth. Results also show that the estimated coefficients of 2 CO emissions have positive and significant impacts on GDP in the long run. These results will help the environmental authorities to understand the effects of economic growth on environment for degradation and manage the environmental problems using macroeconomic methods. \end{abstract} \keywords{SAARC, emission, GDP, causality, VECM.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \par Regression (VAR) theory to analyze changes of SAARC environmental pressures in the process of economic growth.\par Emissions account for the largest share of total greenhouse gas emissions which are most largely generated by human activities {\ref (World Bank, 2007)}. Rapid increase of emissions is mainly the results of human activities due to the development and industrialization over the last decades. It is highly dependent to the energy consumption which is inevitable for economic growth. McKinesy Global Institute, {\ref (2008)} analyzed that the successful actions on solving climate change problems should meet at least two conditions, (i) curb the increase of global carbon emissions effectively and (ii) this actions of solving global warming problem should not at the expense of declining economic development and people's living standard. \hyperref[b16]{Kaplan et al.(2011)} found that the coefficients of the ECT terms for all models are statistically significant implying the longrun bi-directional causal relationship between energy and GDP shows that the higher the level of economic activity the higher the energy consumption and vice versa. The intergovernmental panel on climate change (IPCC, 2007) reported a 1.1 to 6.4 c increase of the global temperatures and a rise in sea level of about 16.5 to 53.8 cm by 2100. This would have tremendous negative impact on half of the world's population lives in coastal zones \hyperref[b13]{(Lau et al., 2009)}. In this respect most of the SAARC countries situated in coastal areas and for the global warming it has the vast and negative impact of climate change on SAARC countries.\par One of the crucial elements for continuous economic growth, it needed to consumption of more energy that generates huge amounts of 2 CO . Several studies emerged in this regard. {\ref Bloch, et al. (2012)} found that there is a unidirectional causality running from coal consumption to GDP both in short and long run under supply side analysis and bi-directional causality under demand side analysis between the variables in China. \hyperref[b11]{Jalil and Mahmud (2009)} found a unidirectional causality running from economic growth to 2 CO emissions in China. Andreoni, and Galmarini (2012) researched the decoupling relationship between economic growth and carbon dioxide ( 2 CO ) emissions in Italian by the way of making a decomposition analysis of Italian energy consumption. Holtz-Eakim and Selden (1995) found that there is a diminishing marginal propensity to emit as economies develop. Bhattachryya and ghoshal (2009) analyzed that the inter relationship between the growth rates of 2 CO emissions and economic development is mostly significant for countries that have a high level of 2 CO emissions and pollution. Asafu-Adjaye (2010) found in a study on economic growth and energy consumption in four Asian developing economies that a combination of unidirectional and bidirectional causality between the variables. Hye and Mashkoor (2010) found bidirectional causality between economic growth and environmental sustainability. Apergis and Payne (2009) examined the relationship between 2 CO emissions, energy consumption and output in Central America and they found unidirectional causality from energy consumption and real output to emissions in the short run but there appears bi-directional causality between the variable in the long run.\par This study designed to evaluate the causal relationship between 2 CO Emission and GDP growth in SAARC countries applying vector error correction modeling approach covering a period of data from 1972-2012 and suggest some policies to policy makers. \section[{a) Data}]{a) Data}\par This paper uses annual time series data of real per capita GDP and 2 CO emissions covering the period from 1972 to 2012 for the seven SAARC countries-Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. Real per capita GDP is taken as US dollar (\$) and 2 CO emissions variable is metric tons per capita. The data have been obtained from online version of World Development Indicators, the World Bank. \section[{b) Theoretical Issues}]{b) Theoretical Issues}\par This paper analyses the relationship between the long run causal relationships of economic growth and 2 CO emission in SAARC countries. The hypothesis tests in this paper is whether 2 CO Emission is related to the economic growth. We can express the relationship applying the following functional form between 2 CO emission and economic growth (GDP) as follows: Assessment of Granger causality between the variables and the direction of their causality in a vector error correction framework requires three steps. The first step is to test the nonstationarity property and determine order of integration of the variables, the second step is to detect the existence of long run relationship and the third step is check the direction of causality between the variables.) ( 2 GDP f CO ? (1) \section[{a) Testing for Nonstationarity Property and Order of Integration}]{a) Testing for Nonstationarity Property and Order of Integration}\par Examining the time series properties or nonstationarity properties of the variables is imperative as regression with nonstationary variables provides spurious results. Therefore, before moving further variables must be made stationary. This study applies two unit root tests-the Augmented Dickey Fuller test {\ref (Dickey \& Fuller, 1979)} and Phillips-Perron (Phillips-Perron, 1988) to test whether the variables are nonstationary and if nonstationary the order of integration is the same or not. \section[{b) Augmented Dickey Fuller (ADF) Test}]{b) Augmented Dickey Fuller (ADF) Test}\par The Augmented Dickey-Fuller (ADF) test is used to test for the existence of unit roots and determine the order of integration of the variables. The ADF test requires the equations as followst i t m i i t t y w y t y ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 1 1 0 (2)\par Where, ? is the difference operator, y is the series being tested, m is the number of lagged differences and ? is the error term. \section[{c) Phillips-Perron (P.P) Test}]{c) Phillips-Perron (P.P) Test}\par Phillips-Perron (1988) test deals with serial correlation and heteroscedasticity. Phillips and Perron use non parametric statistical methods to take care of serial correlation in the terms with adding lagged difference terms. Phillips-Perron test detects the presence of a unit root in a series. Suppose, is estimating ast t t u y t y ? ? ? ? ? ?1 * ? ? ? (3)\par Where, the P.P test is the t value associated with the estimated co-efficient of ?*. The series is stationary if ?* is negative and significant. The test is performed for all the variables where both the original series and the difference of the series are tested for stationary. \section[{d) Cointegration}]{d) Cointegration}\par We apply Johansen and Juselius (1990) and Johansen (1988) maximum likelihood method to test for cointegration between the series of carbon emission and economic growth. This method provides a framework for testing of cointegration in the context of Vector Autoregressive (VAR) error correction models. The method is reliable for small sample properties and suitable for several cointegration relationships. The cointegration technique uses two tests-the maximum Eigen value statistics and trace statistics in estimating the number of cointegration vectors. The trace statistic evaluates the null hypothesis that there are at most r cointegrating vectors whereas the maximal Eigen value test evaluates the null hypothesis that there are exactly r cointegrating vectors. Let us assume that follows I(1) process, it is an nX1 vector of variables with a sample of t. Deriving the number of cointegrating vector involves estimation of the vector error correction representation:t i t m i i m t t y y y ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 0 (4)\par The long run equilibrium is determined by the rank of ?. The matrix ? contains the information on long run relationship between variables, that is if the rank of ?=0, the variables are not cointegrated. On the other hand if rank (usually denoted by r) is equal to one, there exists one cointegrating vector and finally if 1