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\title{Carbon Emission and Economic Growth of SAARC Countries: A Vector Autoregressive (VAR) Analysis}
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             \author[1]{Mirza Md. Moyen  Uddin}

             \author[2]{Md. Abdul  Wadud}

             \affil[1]{  Rajshahi University}

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\date{\small \em Received: 9 December 2013 Accepted: 3 January 2014 Published: 15 January 2014}

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\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.}

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\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<r<n, there are multiple cointegrating vectors and there are nXr matrices of ? and such that ?=?? ?, where the strength of cointegration relationship is measured by ?, ? is the cointegrating vector and t y '? .\par
The tests given by Johansen and Juselius (1990) are expressed as follows. The maximum Eigenvalue statistic is expressed as:) 1 ln( ) 1 ( max ? ? ? ? ? r T ? ? (5)\par
While the trace statistic is written as follows:) 1 ln( ) ( 1 ? ? ? ? ? ? ? k r i i trace T r ? ? (6)\par
Where, r is the number of cointegrating vectors under the null hypothesis and ? i ? is the estimated value for the ith ordered eigenvalue from the matrix ?. To determine the rank of matrix ?, the test values obtained from the two test statistics are compared with the critical value from Mackinnon-Haug-Michelis (1999). For both tests, if the test statistic value is greater than the critical value, the null hypothesis of r cointegrating vectors is rejected in favor of the corresponding alternative hypothesis. 
\section[{e) Error Correction Mechanism}]{e) Error Correction Mechanism}\par
The direction of the causality of long run cointegrating vectors in a vector error correction framework can be conducted once the long run causal relationship between the variables is established. Assuming that the variables are integrated of the same order and cointegrated, the following Granger causality test with an error correction term can be formulated:t t j t m j j i t n i t ECT GDP Ep i Ep ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 1 1 0 (7) t t j t m j j i t n i i t ECT Ep GDP GDP ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 1 1 1 0 (8)\par
Where, ECT is error correction term. This provides the long run and short run dynamics of cointegrated variables towards the long run equilibrium. The coefficient of error correction term shows the long term effect and the estimated coefficient of lagged variables shows the short term effect between the variables. 
\section[{a) Results of Unit Root Test}]{a) Results of Unit Root Test}\par
The results of the Augmented Dickey Fuller (1981), ADF Stationarity test in levels show that some variables are stationary and some are non-stationary in level form. In the next step of difference form it is found that all the variables are stationary. The results of the stationarity test in levels and in difference form in shown is Table \hyperref[tab_1]{1}.  
\section[{CO}]{CO}\par
and GDP, we found that the calculated ADF statistic is greater than their critical value both in difference and level form respectively. So, null hypothesis can be rejected. For the Indian side we see that the Indian and 2 CO GDP calculated ADF are greater than their critical value both in difference and level form. So, null hypothesis rejected here and so on for Maldives, Nepal, Pakistan and Sri Lanka, it shows that the calculated ADF statistics are greater than their critical value. So, the null hypothesis is rejected and the variables are stationary. Phillips-Perron Test used to non parametric statistical methods to take care of the serial correlation in the terms without adding lagged difference terms.\par
Table \hyperref[tab_2]{2} shows the Phillips-Perron (1988) tests results.\par
It is evident from Table \hyperref[tab_2]{2} that the calculated Phillip-Perron (P.P.) statistics in respect of Bangladesh 2 CO and GDP are greater than their critical values (denoted by asterisks) both in difference and level form. In respect of Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka, we see that the calculated P.P statistics in respect of 2 CO and GDP are greater than their critical value. So, the null hypothesis can be rejected and the data series are stationary.  \hyperref[tab_3]{3} which indicates that the statistics value is greater than the critical value. This means that the hypothesis of no cointegration is rejected and hence they are cointegrated. The Trace statistics and Maximum Eigen value tests indicate that there is one cointegration eqn(s) at 5\% level. This means that the variables among environmental pollution (i.e. 2 CO emission) and economic growth (i.e. GDP) have the long run relationships. So, it is clear that there is one linear cointegration eqn(s) for each of the variables that there is one long run relationship and liner deterministic trend among the variables.\par
More specifically, Table \hyperref[tab_3]{3} shows that at 5 percent level of significance the likelihood ratios (trace statistics) for the null hypothesis having one (r=1) cointegration  {\ref (}  
\section[{c) Results of Error Correction Modeling}]{c) Results of Error Correction Modeling}\par
Engle and Granger  {\ref (1987)} showed that, if two variables (say X and Y) are individually integrated of order one [i.e. I (I)] and cointegrated then there is possibility of a causal relationship in at least one direction. That means cointegration with I (1) variables indicate the presence of Granger causality but it does not indicate the direction of causality. The vector error correction model is used to detect the direction of causality of long-run cointegrating vectors. Moreover, Granger Representation Theorem indicates how to model a cointegrated series in a Vector Auto Regressive (VAR) format. VAR can be constructed either in terms of level data or in terms of their first differences [I (0)] with the addition of an error correction to capture the short run dynamics.\par
If the two variables are cointegrated, there must exist an error correction mechanism. This implies that error correction model is associated with the cointegration test. The long term effects of the variables can be represented by the estimated cointegration vector. The adjusted coefficient of error correction term shows the long term effect and the estimated coefficient of lagged variables shows the short term effect. Causality test among the variables are based on Error Correction Model with first difference. Table \hyperref[tab_5]{4} shows the results of error correction model of the variables.  \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.85\textwidth}}
Level Form\end{longtable} \par
 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.083184011026878\textwidth}P{0.0029290144727773947\textwidth}P{0.10192970365265334\textwidth}P{0.07088215024121296\textwidth}P{0.07966919365954514\textwidth}P{0.11188835286009649\textwidth}P{0.09255685733976568\textwidth}P{0.11950379048931771\textwidth}P{0.18511371467953136\textwidth}P{0.0023432115782219157\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Level form}\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Difference Form}\\
\multicolumn{5}{l}{Difference Form Variables Statistics Critical Values}\tabcellsep \tabcellsep Statistics\tabcellsep \multicolumn{2}{l}{Critical Values}\\
\tabcellsep \tabcellsep With\tabcellsep 1\%\tabcellsep 5\%\tabcellsep 10\%\tabcellsep With\tabcellsep 1\%\tabcellsep 5\%\tabcellsep 10\%\\
\tabcellsep \tabcellsep Constant\tabcellsep \tabcellsep \tabcellsep \tabcellsep Constant\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep and\tabcellsep \tabcellsep \tabcellsep \tabcellsep and trend\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep trend\tabcellsep \tabcellsep \tabcellsep Bangladesh\tabcellsep \tabcellsep \tabcellsep \\
CO\tabcellsep 2\tabcellsep 1.723054 -\tabcellsep -4.211868\tabcellsep -3.529758\tabcellsep -3.196411\tabcellsep -13.90476\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep \multicolumn{2}{l}{6.026398 -}\tabcellsep -4.205004**\tabcellsep -\tabcellsep -5.186016\tabcellsep -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
\tabcellsep \tabcellsep \tabcellsep 4.205004*\tabcellsep \tabcellsep 3.194611***\tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Bhutan\tabcellsep \tabcellsep \tabcellsep \\
CO\tabcellsep 2\tabcellsep 1.475181 -\tabcellsep -4.205004\tabcellsep -3.526609\tabcellsep -3.194611\tabcellsep -5.799355\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep \multicolumn{2}{l}{0.813214 -4.219126}\tabcellsep -3.533083\tabcellsep -3.198312\tabcellsep -7.848361\tabcellsep -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep India\tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\multicolumn{2}{l}{2 IGDP CO}\tabcellsep \multicolumn{2}{l}{1.023785 -4.211868 4.425492 -}\tabcellsep -3.529758 -3.526609**\tabcellsep -3.196411 -\tabcellsep -3.705744 -5.145096\tabcellsep -4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411*** -3.529758** -3.196411***}\\
\tabcellsep \tabcellsep \tabcellsep 4.205004*\tabcellsep \tabcellsep 3.194611***\tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Maldives\tabcellsep \tabcellsep \tabcellsep \\
CO\tabcellsep 2\tabcellsep 0.571652 -\tabcellsep -4.234972\tabcellsep -3.540328\tabcellsep -3.202445\tabcellsep -25.76413\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep -\tabcellsep -4.226815\tabcellsep -3.536601\tabcellsep -3.200320\tabcellsep -14.22380\tabcellsep -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
\tabcellsep \tabcellsep 1.687696\tabcellsep \tabcellsep \tabcellsep Nepal\tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\multicolumn{2}{l}{Nepal 2 CO}\tabcellsep 2.849825 -\tabcellsep -4.234972\tabcellsep -3.540328\tabcellsep -3.202445\tabcellsep -7.410771\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep -\tabcellsep -4.219126\tabcellsep -3.533083\tabcellsep -3.198312\tabcellsep -8.621159\tabcellsep -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
\tabcellsep \tabcellsep 1.680807\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Pakistan\tabcellsep \tabcellsep \tabcellsep \\
CO\tabcellsep 2\tabcellsep 2.701688 -\tabcellsep -4.205004\tabcellsep -3.526609\tabcellsep -3.194611\tabcellsep -8.470362\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep -\tabcellsep -4.211868\tabcellsep -3.529758\tabcellsep -3.196411\tabcellsep -4.285085\tabcellsep -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
\tabcellsep \tabcellsep 2.243989\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 4.211868*\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Sri Lanka\tabcellsep \tabcellsep \tabcellsep \\
CO\tabcellsep 2\tabcellsep 2.116680 -\tabcellsep -4.205004\tabcellsep -3.526609\tabcellsep -3.194611\tabcellsep -6.955575\tabcellsep 4.211868* -\tabcellsep \multicolumn{2}{l}{-3.529758** -3.196411***}\\
GDP\tabcellsep \tabcellsep \multicolumn{2}{l}{6.686738 -}\tabcellsep -3.526609**\tabcellsep -\tabcellsep -3.653982\tabcellsep \multicolumn{3}{l}{-4.211868 -3.529758** -3.196411***}\\
\tabcellsep \tabcellsep \tabcellsep 4.205004*\tabcellsep \tabcellsep 3.194611***\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{The test is conducted using Eviews 7.1}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Note:}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.07799791449426485\textwidth}P{0.04431699687174139\textwidth}P{0.04431699687174139\textwidth}P{0.11788321167883212\textwidth}P{0.164859228362878\textwidth}P{0.11788321167883212\textwidth}P{0.11965589155370177\textwidth}P{0.16308654848800833\textwidth}}
\multicolumn{2}{l}{b) Cointegration Results}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Variable\tabcellsep H0\tabcellsep H1\tabcellsep Trace\tabcellsep 5\% Critical\tabcellsep Max. Eigen\tabcellsep 5\% critical\tabcellsep Hypothesis\\
\tabcellsep \tabcellsep \tabcellsep Statistics\tabcellsep value\tabcellsep value\tabcellsep value\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Bangladesh\tabcellsep \tabcellsep \tabcellsep \\
CO GDP 2\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 52.09660 1.202731\tabcellsep 15.49471 3.841466\tabcellsep 50.89387 1.202731\tabcellsep 14.26460 3.841466\tabcellsep Ho: Rejected H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Bhutan\tabcellsep \tabcellsep \tabcellsep \\
2 CO GDP\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 20.14684 0.354942\tabcellsep 15.49471 3.841466\tabcellsep 19.79190 0.354942\tabcellsep 14.26460 3.841466\tabcellsep Ho: Rejected H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep India\tabcellsep \tabcellsep \tabcellsep \\
CO GDP 2\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 31.24033 4.730134\tabcellsep 25.87211 12.51798\tabcellsep 26.51020 4.730134\tabcellsep 19.38704 12.51798\tabcellsep Ho: Rejected H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Maldives\tabcellsep \tabcellsep \tabcellsep \\
CO GDP 2\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 30.52002 8.876940\tabcellsep 25.87211 12.51798\tabcellsep 21.64308 8.876940\tabcellsep 19.38704 12.51798\tabcellsep Ho: Rejected H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Nepal\tabcellsep \tabcellsep \tabcellsep \\
2 CO GDP\tabcellsep r=0\tabcellsep r=1\tabcellsep 26.51150\tabcellsep 25.87211\tabcellsep 21.65528\tabcellsep 19.38704\tabcellsep Ho: Rejected\\
\tabcellsep r=1\tabcellsep r=2\tabcellsep 4.856219\tabcellsep 12.51798\tabcellsep 4.856219\tabcellsep 12.51798\tabcellsep H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Pakistan\tabcellsep \tabcellsep \tabcellsep \\
CO GDP 2\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 35.34613 3.800743\tabcellsep 25.87211 12.51798\tabcellsep 31.54539 3.800743\tabcellsep 19.38704 12.51798\tabcellsep Ho: Rejected H1: Accepted\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Sri Lanka\tabcellsep \tabcellsep \tabcellsep \\
2 CO GDP\tabcellsep r=0 r=1\tabcellsep r=1 r=2\tabcellsep 27.80299 1.938833\tabcellsep 15.49471 3.841466\tabcellsep 25.86416 1.938833\tabcellsep 14.26460 3.841466\tabcellsep Ho: Rejected H1: Accepted\end{longtable} \par
  {\small\itshape [Note: Cointegration]} 
\caption{\label{tab_3}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.0672316384180791\textwidth}P{0.008403954802259887\textwidth}P{0.0672316384180791\textwidth}P{0.15367231638418077\textwidth}P{0.037217514124293784\textwidth}P{0.12966101694915255\textwidth}P{0.050423728813559325\textwidth}P{0.008403954802259887\textwidth}P{0.05882768361581921\textwidth}P{0.26652542372881355\textwidth}P{0.0012005649717514123\textwidth}P{0.0012005649717514123\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Coefficient\tabcellsep t\tabcellsep F\tabcellsep \tabcellsep \tabcellsep \tabcellsep Coefficient\tabcellsep t\tabcellsep F\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Bangladesh}\tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep 0.012022\tabcellsep {}[ 0.42823]\tabcellsep 1.867654\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{51.52446** [ 7.74284] 50.44211}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Bhutan}\tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep 0.002749\tabcellsep {}[ 0.23656]\tabcellsep 0.364334\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{-22.31243** [-4.80641] 8.089451}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep India\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
{}[ 0.23656] GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep \multicolumn{2}{l}{-0.002613 [-0.43108]}\tabcellsep 9.506284\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{-10.77139** [-4.42385] 17.17979}\\
{}[-4.80641]\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Maldives}\tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep \multicolumn{2}{l}{-0.361661** [-3.72978]}\tabcellsep 7.365691\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{-79.42380 [-0.92433] 5.569285}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Nepal\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep \multicolumn{2}{l}{-0.197094 [-1.91152] [-1.91152]}\tabcellsep 1.160219\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{-106.6725** [-3.68314] 3.250268}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Pakistan}\tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep \multicolumn{2}{l}{-0.112020 [-0.57248]}\tabcellsep 4.644593\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{131.6173 [ 1.47971] 2.041946}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Sri Lanka}\tabcellsep \tabcellsep \tabcellsep \\
GDP ?\tabcellsep f\tabcellsep ? ? 2 CO\tabcellsep 0.000134\tabcellsep {}[ 0.06242]\tabcellsep 0.656019\tabcellsep CO ? 2\tabcellsep f\tabcellsep ? GDP ?\tabcellsep \multicolumn{2}{l}{-3.472699** [-3.81311] 16.65960}\end{longtable} \par
 
\caption{\label{tab_5}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.4934687953555878\textwidth}P{0.0012336719883889694\textwidth}P{0.014804063860667634\textwidth}P{0.3392597968069666\textwidth}P{0.0012336719883889694\textwidth}}
\multicolumn{3}{l}{shows the significance of Error}\\
\multicolumn{3}{l}{Correction Term (ECT) for carbon dioxide ( emission and economic growth (GDP) of SAARC CO ) 2}\tabcellsep This paper examines the long-run causal\\
\multicolumn{3}{l}{countries. It is evident from the Table that the error correction term (ECT) is significant for the country Bangladesh, India, Nepal, Bhutan and Sri Lanka in term}\tabcellsep relationships between growth in SAARC countries during the period of 1972-CO emissions and economic 2 2012. We apply cointegration and VECM to evaluate the\\
\multicolumn{2}{l}{of GDP, i.e. in these country GDP causes}\tabcellsep 2 CO for the\tabcellsep relationship. Empirical results suggest that a long run\\
\multicolumn{3}{l}{long term perspective. But in Maldives the ECT is}\tabcellsep relationship exist between\tabcellsep 2\\
significant in respect of\tabcellsep 2\tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 4}\end{figure}
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