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Modeling and Forecasting the Malaysian GDP and green GDP: A Comparative Analysis AS Abdul-Rahim1and AW Noraida Faculty of Economics and Management, Universiti Putra Malaysia, Malaysia Abstract: It is timely for every country to adjust or correct the measure of gross domestic product (GDP) for deterioration of the state of the environment and depletion of natural resources. GDP alone is not sufficient enough to use as an indicator for measurement of living standards and economic development. GDP do not necessary buy social progress such as basic needs, happiness and opportunity. It is meaningless of havingeconomic development without taking into consideration the negative impact of environmental degradation. In the case of Malaysia, Malaysia’s green economy faces a bright future with the various opportunities it offers and despite the gloomy global economic outlook. Its Prime Minister Datuk Seri NajibTunRazak had announced that renewable energy was expected to create RM70 billion economic activities by 2020, support 50,000 jobs and reduce carbon emission by around 40 per cent. Hence, the measurement of Malaysian green GDP is really important to reflect its green economy development. In the literature, there are a lot of ways of computing the green GDP. In this study the formula that we used to compute green GDP is by contracting the GDP with depletion of natural resources and costs of pollution. The objective of this study is to estimate and forecast the GDP and green GDP models by using Autoregressive Distributed Lag (ARDL) model. Data used were annual time series from 1980 to 2010. In this study, we develop models of green GDP and traditional GDP where the effects of the interaction parameters (lag dependent variables and CO2 emission), trade openness and urbanization are tested. In a case of Malaysia, we have found strong and robust results suggesting a positive correlation between all parameters used with green GDP and traditional GDP respectively.For the forecasting analysis, we have forecastedthe green GDP and traditional GDP for up to year 2050. The Forecasted results of Green GDP and traditional GDP reveal thatthe traditional GDP is persistently more than Green GDP until both of them share equal values in year 2045. Surprisingly, from that year onwards, green GDP outweigh traditional GDP. This suggests that, as Malaysia is really serious in implementing its green policies, this is proven that in the long-run the outcome of the vision can be realized. Keywords: Green GDP, Openness, CO2 emission, Urbanization,Green economy. 1 Corresponding author: [email protected] 1 Introduction Ecological and environmental degradation as well as natural resources depletion which lead to severe pollution on air, water and soil have generated much widespread debate on the ethical method of calculating Gross Domestic Product (GDP) and prompted an ecological frontier called green GDP. GDP in general is used to measure the economic growth of a country via the System of National Economic Accounting (SNA) structure prescribed under the United Nations (UN). Unfortunately, in calculating the economic activity of a nation, the GDP had ignored the natural wealth which has been subject to much exploitation and exhaustion in achieving economic development (Singh et al., 2009). Hence the UN Department of Economic and Social Affairs brought up the concept of Green GDP (Talberth and Bohara, 2006) when amending the SNA in 1993 which resulted in the introduction of Integrated Environmental and Economic Accounting (SEEA 1993) and SEEA 2003. At the forty-third session in 2012, the United Nations Statistical Commission (UNSC) adopted The System of Environmental-Economic Accounting (SEEA) - Central Framework, the first international statistical standard for environmental-economic accounting build on the previous framework (SEEA 1993 and 2003). The SEEA Central Framework is a multipurpose conceptual framework for understanding the interactions between the economy and the environment, and for describing stocks and changes in stocks of environmental assets. As the call for better environmental management and reduction in CO2 emissions gets louder each day via domestic and international platforms, policy-makers are responding with pledges for counter-control and uplifting measures in pursuit for a sustainable ecosystem. 2 What is green GDP? Green GDP is “defined as a measure of what is valuable about nature, excluding goods and services that are already captured in GDP” (Boyd, 2005). It should gauge welfare measurement according to an approach advanced by Maler (1991), Peskin, Angeles and Marian (2001) and Grambsch et al., (1993). What should be calculated in the green GDP? According to James Boyd, it should include ecosystem services and not ecosystem assets. Ecosystem services are “aspects of nature that society uses, consumes or enjoys to experience some benefits. They are the end products of nature that directly yield human well-being” (Boyd, 2006). Rapid industrialization and development alter the environmental scenario of a country with growing evidence of externalities associated with pollution and resource depletion that adversely affect the national growth. As GDP does not reflect the true value of economic growth due to environmental cost, the Chinese government issued the Green GDP Accounting Study Report in 2006 based on 2004 accounts and found that the economic losses from environmental pollution were estimated at RMB512 billion, i.e., 3.05 per cent of China’s GDP (Yang and Poon, 2009). The virtual abatement cost in containing and maintaining environmental impacts in 2004 constitute 1.8 per cent of China’s annual GDP, which is about 287.4 billion (Li and Lang, 2010). In some part of the Chinese province, the adjusted GDP growth was negative when externality cost was included. In the case of Malaysia, as one of the 'Asian tiger' economies, this country has enjoyed remarkable growth over the last few decades, with industrialization, agriculture and tourism 3 playing leading roles in this success story. The development of main sector in Malaysia was contributed to the growth rate of the GDP (Figure 1 and Figure 2). Source: 10th Malaysian Plan Figure 1: Average Annual Growth Rate for 2011-2015 period Source: 10th Malaysian Plan Figure 2: Share in GDP 2015 4 Global trade means that a country’s carbon footprint is international (Wang and Watson, 2007). The Kyoto Protocol provides a flexible clean development mechanism (CDM) for developed countries but evidence suggests the mechanism is ineffective (Wara, 2007). As this indicator is associated with domestic production, the developing countries in adhering to this obligation tend to relocate their businesses to developing nations, termed “carbon leakage”. However, despite a relatively positive environmental record, Malaysia faces problems of deforestation, pollution of inland and marine waters, soil and coastal erosion, overfishing and coral reef destruction, along with air pollution, water pollution and the problem of waste disposal. Based on Figure 3, it has been shown that one of the big problems faced by Malaysia is carbon dioxide emission. The quantity of carbon dioxide emission increases every year from 2000 to 2010. Source: Worldbank Figure 3: The Carbon dioxide emission from 2000 until 2010 5 Furthermore, based on the CO2 emission statistics calculated by the United States Department of Energy’s Carbon Dioxide Information Analysis Centre (CCDIAC) for UN, Malaysia ranked at 27th from the list of 218 countries in terms of highest contributor of CO2 emissions, an increase of 160,211 thousand metric tons from year 1990 to 2010 (Refer Table 1) Table 1: CO2 emission in 1990 and 2010 and according to highest ranking by ASEAN-5 Countries in 2010 Ranking as in 2010 Country Thousand metric tons/Year Per capita metric tons/Year 15 Indonesia 1990 149,566 2010 433,989 1990 0.8 2010 1.8 27 Malaysia 56,593 216,804 3.1 7.7 23 Thailand 95,833 295,283 1.7 4.4 42 Philippines 41,763 81,591 0.7 0.9 92 Singapore 46,941 13,520 15.4 2.7 Source: World Development Indicator (World Bank) and United States Department of Energy’s Carbon Dioxide Information Analysis Centre (CCDIAC) During the Copenhagen climate change summit in 2009, Malaysia has pledged to reduce CO2 emission up to 40 per cent by 2020 upon receiving transfer of technology and adequate financing from developed world (Oh and Chua, 2010). The objective of the study is to establish a causal relationship between Green GDP calculations, CO2 emission, trade openness and urbanization in Malaysia. In this study, the study focused on the environmental deterioration and natural resources depletion contributed from CO2 emission due to growing GDP, trade openness (export and import) and urbanization by factoring these two concepts in Green GDP calculations. Second objective was to forecast the green GDP and traditional in order to examine the relationship and the impact toward the variables. In doing so, a clear viewpoint can be obtained on the exact amount of environmental deterioration in terms of 6 pricing which could be instrumental in harmonizing environment and growth and for an amicable policy change and higher commitments. Literature Review The concept of green GDP was initially developed in the West in the 1960’s (Pearce et al., 1989) but was not practically implemented. Even before the concept of Green GDP and Eco Domestic Product (EDP) was introduced under SEEA, a variety of accounting methodologies and indicators was postulated by academicians and institutions in accounting ecological and environmental cost and for a sustainable development. Some of the approaches are ecological requirement index (ERI), net economic welfare (NEW), net national welfare (NNW), green accounting (GA), environmental accounts (EA), indices of sustainable economic welfare (ISEW) and many more. Unfortunately, all the above mentioned accounting indicators only focus on deducting the direct economic loss caused by resource consumption and environmental pollution without including potentially indirect economic losses hidden in the processes of resource consumption and environmental pollution. Moreover, neither the natural ecosystem’s inherent value, nor the natural capital obtained from ecosystem construction and environmental improvement, is involved in the accounting system (Shi et al., 2012). In favour of a more comprehensive ecosystem measure, Shi et al. (2012) proposed a new indicator “EcoDP” (consists of five sub-indicators: eco-service domestic product, eco-disaster domestic product, eco-construction domestic product, eco-threat domestic product and ecoprotection cost input) and evaluated the Chinese regional development for the past ten years and found that the country’s ecological problem lies mainly in gradual decrease of arable land 7 resources, degradation of grassland resources and ecosystem disruption related to road construction. China’s terrestrial ecosystems services value (ESV) in terms of total value (values of different functions and values provided by different type of ecosystem) showed growth from 1999 to 2008 with service value drop in farmland, grassland and road ecosystem. The authors attribute the increase in ESV between 1999 and 2008 due to accelerated pace of building ecological cities in China. Shahbaz et al. (2013) investigated the effects of financial development, economic growth, coal consumption and trade openness on CO2 emission in South Africa using the environmental Kuznets curve (EKC). The study which is based on the previous model developed by Menyah and Wolde-Rafael (2010) found that the major contributor to environmental degradation is the use of coal in energy production. The empirical exercise conducted by them pointed out that there is a positive linkage between financial development and environmental quality, indicating that financial development lowers energy pollutants. The higher the accessibility to domestic credit by private sector in the banking sector tends to help in lowering CO2 per capita emission. They also concluded that coal consumption deteriorates the environment whilst trade openness improves. Furthermore, their results also proved that EKC which demonstrates the relationship between economic growth and environmental degradation does exist for South Africa. A study on urban environment and resource planning based green GDP accounting system by Wuyishan et al. (2013) using analytic hierarch process (AHP) and modified Pearl curve model shows that energy use is the key factor that influences the ecological function of this city’s urban ecosystem. Ultimately, biodiversity and air quality are the most important factors influencing 8 green GDP in terms of ecosystem services. Based on this study, six ecological projects to promote urban sustainable development were put forward, namely water system corridor construction, special park construction, ecological landscapes restoration, rural biogas planning, vehicle control and, improvement and industrial energy structure adjustment. If these projects are implemented, the green GDP of Wuyishan City will gradually increase in the future. The final strand of literature is inclusive of the urbanization variable as one of the determinants of green GDP. Based on a report pertaining to world urbanization prospects by United Nations, more than half of the world’s population lived in urban areas by the end of 2008 (UNDP, 2010). Generally, levels of urbanization are closely correlated with levels of economic development (Henderson, 2003). Several literatures pertaining to urbanization have been linked to CO2 emissions as opposed to economic growth. Sharma (2011) examined a large panel of 69 countries (including high income, middle income and low income countries) and found that urbanization does have a negative and statistically significant impact on carbon emissions for the global panel. 9 Methodology The country that has been considered is Malaysia. In this study, there are two models developed according to the objective of the study. The first model referred to the green Gross Domestic Product (GGDP) as dependent variable and the other model has traditional Gross Domestic Product (GDP) as the dependent variable. Model 1: GGDP = α + β1 X1 + β2X2 + β3X3 Model 2: GDP = α + β1 X1 + β2X2 + β3X3 where both models shared similar variables. X1 is for carbon dioxide emissions (CO2) in metric tons per capita, X2 is trade openness in percentage of exports and imports as a portion of GDP and X3 is urbanisation in percentage of total urban population. The computation for green GDP (GGDP) for this research study was derived based on the methodological framework suggested by Liu and Guo (2005) that focused on the environmental aspect of sustainable development. At the present, the challenges impeding green GDP computation are common, due to the lack of wide acceptance in environmental accounting, unavailability of comprehensive survey in total natural resource stock, and difficulties in natural resource valuation. To address these challenges, Liu and Guo (2005) developed a method similar to the real green GDP, which is termed as Comparable green GDP. The Comparable Green GDP, in essence, calculates an approximate green GDP figure from a uniform formulation by fixing accounting components and prices, as the calculated values reflect sustainability differences among regions over time and therefore allows for comparison. Based on the Liu and Guo (2005), GGDP is defining as: 10 Green GDP = GDP – Depletion of Natural Resources – Costs of Pollution Equation 1 In order to substitute Equation 1 into the GGDP model (Model 1), the calculation of Comparable Green GDP in Malaysia is as follows; GDP includes the annual data of real Gross Domestic Product (GDP) (constant 2005 US$); depletion of natural resources encompasses (Energy Depletion + Mineral Depletion + Net Forest Depletion); energy depletion (current US$), is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years) that covers coal, crude oil, and natural gas. Mineral depletion (current US$), is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years) and covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. Net forest depletion (current USD), is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. With respect to costs of pollution, carbon dioxide damage (current USD) is estimated to be USD20 per ton of carbon (the unit damage in 1995 U.S. dollars) multiplied by the number of tons of carbon emitted. The data for the above variables were obtained from the World Bank’s Development Indicators. Additionally, the annual data for carbon dioxide emissions (CO2) (in metric tons per capita), trade openness, TOPEN (in percentage of exports and imports of GDP) and urbanisation, URBAN (percentage of total urban population) were also retrieved from the World Bank’s Development Indicators. The period of study spans from 1971 to 2010. In this study, we applied autoregressive distributed lagged bound test approach (ARDL). 11 There are several reasons why the ARDL bounds cointegration test have been used extensively in recent empirical modeling. First, as opposed to other multivariate cointegration techniques, it allows the cointegration relationship to be estimated by OLS method once the lag order of the model is identified. In addition, the ARDL approach does not need to undergo unit root test, as such, it permits to test for cointegration even when all the variables are mutually I(0), mutually I(1) or mixture of the two. Generally, the empirical results are very sensitive to the method and various alternative choices are available in the estimation procedure (Pesaran and Smith, 1998). With ARDL, it is possible for different variables to have different optimal lags, which is impossible with the standard cointegration test. Most importantly, the limited sample data ranging from 30 to 80 observations can be used in the model in which the set of critical values were originally developed by Narayan (2005). Secondly, the ARDL bounds cointegration test is not sensitive to valuing the endogenous and exogenous variables. These include the decisions regarding the number of endogenous and exogenous variables that need to be to be included, the treatment of deterministic elements, as well as the optimal number of lags that need to be used (Pesaran and Smith, 1998). Narayan (2005) and Odhiambo (2009) as quoted in Amusa et al. (2009) demonstrated that even when some of the independent variables are endogenous, the bounds testing approach generally provides unbiased long-run estimations and valid t-statistics. Moreover, the ARDL bounds test approach provides a method of assessing short-run and long-run effects of one variable towards other variables simultaneously (Bentzen and Engsted, 2001). 12 In order to determine the relationship between green GDP, CO2 emissions, trade openness and urbanisation for the Malaysia, the following model is specified: GGDPt = α0 COtα1 TOPENtα2 URBANitα3 eεt Equation 2 The purpose of adopting logarithms for each variable is to examine the elasticity between variables. Moreover, it provides a better fit and reduces the correlation between variables in the regression. The logarithmic transformation of Equation (1) is given by ln GGDPt = α0 + α1 lnCOt + α2 ln TOPENt + α3 ln URBANt + εt Equation 3 where GGDP is the computation of green GDP for Malaysia, CO2 is the per capita carbon dioxide emissions in metric tons, TOPEN represents trade openness (percentage of exports and imports values of total GDP) and URBAN indicates urbanisation (percentage of total urban population). Additionally, α1, α2 and α3 represents the long-run elasticity of Green GDP with respect to CO2, TOPEN and URBAN, respectively. The empirical study has several fundamental objectives to be examined. In the empirical analysis, we test for the existence of a long-run relationship amongst the variables (estimation of Equation 3), and the utilisation of the error-correction model (ECM) captures the short-run dynamics of the variables. At first, we would observe the long-run relationship between Green GDP, CO2 emissions, trade openness and urbanisation. Then, we would observe the dynamic causal relationship between the variables. The testing procedure entails four critical steps. The first step is to test whether the variables contain a unit root. The second step is to test whether there is a long-run co-integrating relationship between the variables. If a long-run relationship between the variables is found, the third step is to estimate Equation (3) using an appropriate 13 long-run estimator. If a long-run relationship between the variables is found, the final step is to forecast the two models (Model 1 and 2) using elasticity in excels spreadsheet. The processes are summarized in Figure 4: Process Method Software : Estimation of Model 1 and Model 2 : ARDL Bound Test Approach : Eview/Microfit Inputting - Coefficient - Data Process Method Software : Forecasting of Model 1 and Model 2 : Growth rate of the parameter (elasticity) : Excel Spreedsheet Result and Discussion On the basis of modern econometric techniques, the dynamic causal relationships between green GDP, CO2 emissions, trade openness and urbanization are examined in this study. We have excluded the unit root test as the ARDL model is applicable irrespective of whether the underlying regressors are purely I(0), purely I(1) or mutually cointegrated. Next, the ARDL co-integration test was performed on green GDP model and GDP model. This test was done to analyse the long-run co-integration between the variables in the models. The results were shown in Table 2. From the results, GGDP model and GDP model were cointegrated due to the nature of test statistics which was greater than the upper bound of the bounds test critical values. The F-statistics for each model was 7.1872 at 1% and 17.9562 at 1% 14 significant level, respectively. Hence, this indicated that both models were co-integrated and proved that there was a long-run relationship among the variables. Table 2: ARDLBound Test Variables: LCO2 LTOPEN LURBAN Critical Value Computed F- statistic; Lower Bound Upper Bound Green GDP Model: 7.1872*** GDP Model : 17.9562*** 1% significant level 5% significant level 10% significant level 5.333 3.710 3.008 7.063 5.018 4.150 The F-statistic from Wald coefficient test is used to test the joint coefficient of the lagged variables in the ARDL model. The critical values were referred from table case 3: unrestricted intercept and no trend, Narayan (2005), page 1988. There are 3 explanatory variables for each production model. P-value for diagnostic test in parentheses […].***significant at 1%, **significant at 5%, *significant at 10%. The estimations of the long-run and short-run coefficients of the CO2 emissions, trade openness and urbanization coefficients are presented in Table 3. When considering long-term coefficients, we obtain 1% significant level for all the coefficients of the variables of interest in both models. According to De Vita and Abbot (2002) and Kollias et al. (2008), the estimated coefficients obtained from the regression process represented the relationship between dependent and independent variables whereby strong relationship arose when the coefficients were significantly larger than one. On the other hand, the weak relationship was discovered when the coefficients were significantly less than one. Based on the long run coefficient, urbanization shows strong relationship and elastic in the GDP model. The result suggested that, a 10% increase in urbanization will influence the GDP to increase by 14.89%. The strong elasticity of the urbanization may not be unconnected with the increasing trend of Malaysian population. In order to fulfil the need of the populace, the provision of shelter and food to every Malaysian becomes paramount. According to Ahmad Fauzi et al., (2010), the accessible forestland in Malaysia has 15 slowly given way to agriculture especially in oil palm plantation, new satellite towns and other forms of land use, which simultaneously creates a conflict between agriculture production and forest management. It is clearly evident that in the long-run, CO2, trade openness and urbanization are pertinent determinants of green GDP and GDP in Malaysia. Table 3: ARDL Coefficient for Long-Run Elasticity GGDP Model GDP Model 1,1,0,0 1,2,2,0 Lag structure GGDP GDP Dependent variable Elasticities Independent Variables LCO2 0.3879*** 0.1959*** LTOPEN 0.1455** 0.2673*** LURBAN 0.6685** 1.4888*** Constant 11.3172*** 12.6143*** R-Squared 0.9987 0.9994 R-Bar-Squared 0.9985 0.9992 Notes: P-value for diagnostic test in parentheses […]. ***significant at 1%, **significant at 5%, *significant at 10%. R-squared values obtained from GGDP and GDP models were 0.9987 and 0.9994 respectively. This means that 99% of the variations in both models were explained by all its explanatory variables. After estimating the long-run coefficients of the variables, the estimates in Tables 3 provide more interesting results. Firstly, it is evident that the short-run coefficients of CO2 emissions and trade openness are significant in both models. However, the urbanization is not significantly related with the green GDP. This merely reflects a lack of short-run relationship between urbanization against green GDP. As the short-run coefficient mainly reflects adjustments of the variables to shocks, our results suggest that the contemporaneous co-movement of green GDP against urbanization does not react (or reacts less) to past shocks. 16 Then, the error correction model (ECM) was estimated to determine the existence of short-run relationship. ECM is the measurement for the speed of adjustment, which shows the speed at which the dependent variable adjusts to the change in the independent variables before converging to the equilibrium level. The ECMt for both model of GGDP and GDP were significant at 1% respectively which could be concluded that the short-run relationship existed among the variables. The estimated coefficient of ECMt for GGDP model and GDP models are 0.6822 and -0.6799 respectively. The results suggested that, the convergence to equilibrium of all independent variables for total domestic demand for each commodity in one year were corrected for about 68.22% and 67.99% respectively in the next year. ∆LCO2 ∆LCO2 1 ∆LTOPEN ∆LTOPEN 1 ∆LURBAN Constant ECM Table 4: Error Correction Representation of ARDL Model GGDP Model GDP Model 0.1292*** 0.0363 0.0617* 0.0993** 0.1960*** -0.1055* 0.4561 1.0124*** 7.7207*** 8.5772*** -0.6822*** -0.6799*** Notes: P-value for diagnostic test shown by; ***significant at 1%, **significant at 5%, *significant at 10% In addition, the estimated regression for GGDP model and GDP model underwent diagnostic tests to analyse the stability of the model. The tests used were; Breuch-Godfrey serial correlation LM tests (testing for first order autocorrelation), Ramsey RESET test, CUSUM test and CUSUM square test. Based on the tests employed, all regression models successfully obtained the same results. Firstly, the residuals of the models were normally distributed and free from autocorrelation problems in the first order of autocorrelation. Furthermore, the residuals were all 17 homoscedastic. Moreover, the Ramsey RESET test concluded that the models were in correct functional forms. Lastly, the CUSUM and CUSUM square tests showed that the models were stable at 5% a significant level. The summary of the diagnostic tests were presented in Table 5. Type of Test LM test Breuch-Pagan Functional Form Ramsey RESET test Cusum test Cusum Square test Table 5: Diagnostic Test Model 1 0.11688[0.735] 1.6150[0.213] 1.6150[0.213] Model 2 0.1647[0.688] 0.0263[0.872] 0.0263[0.872] 1.9172[0.175] 0.1575[0.694] Stable Stable Stable Stable Notes: The p-value for diagnostic test in parentheses […].***significant at 1%,**significant at 5%,*significant at 10% After the elasticities of all the variables of interest were obtained, the forecasting on green GDP and GDP was performed. In the forecasting procedures, both models were forecasted until 2050. From the Figure 1, the graph has been divided into three stages. The first stage (Stage I) referring to the historical data while Stage II and Stage III are the forecasted data. During the first stage (I Stage) and early the second stage (II Stage), the graph of GGDP and GDP showed increasing trend until 2036 year. In this both stage, the GDP’s trend was slightly higher than the GGDP’s trend. According to Safaai et al. (2011), the projections of energy demand increases until 2020 due to the increasing dramatic economic development and high demand for a better standard of living. However, in 2036 the forecasted value showed a turning point when the graph of GGDP keeps the trend on increase while the graph for GDP shows decreasing trend. Both models have intercepted at 2045. In third stage (III Stage) GGDP keeps on increasing in trend while the GDP shows decreasing trend. 18 The forecasted results of green GDP and traditional GDP reveal that the traditional GDP is persistently more than Green GDP until both of them share equal values in year 2045. Surprisingly, from that year onward, green GDP outweigh traditional GDP. This suggests that, as Malaysia is really serious in implementing its green policies, this is proven that in the longrun the outcome of the vision can be realized. I Stage II Stage III Stage Figure 4: Relationship between Green GDP and GDP in Malaysia from 1990 -2050. Conclusion This research paper attempts to empirically examine the short-run and long-run causal relationship between green GDP, traditional GDP, CO2 emissions, trade openness and urbanization for Malaysia, using the time series data for the period of 1971 to 2010. The study covered 39 years’ time periods, applied autoregressive distributed lag (ARDL) bounds testing approach developed by Pesaran et al. (2001) and critical values tabulated by Narayan (2005). Apart from that, this study also examined the forecasting value of green GDP and traditional GDP from 2011 to 2050. The trends of the both model were discussed over the years by dividing the analysis into three stages. 19 Based on the ARDL results, it is found that the long-run as well as the short-run CO2 emissions has significant positive impact on green GDP and traditional GDP for Malaysian economy. This clearly implies that due to expansion of the production of industrial output for rapid economic development within the countries in the region, it eventually leads to increased energy consumption in these nations. As such, it becomes vital for policy-makers to apply varied pollution control measures to mitigate the CO2 emissions. Additionally, it is also evident with respect to the results in the long-run that trade openness and urbanization are significantly related to the growth of green GDP and traditional GDP. This generally implies that in the absence of energy conservation policies, Malaysia is currently experiencing immense economic growth and trade is evidently consuming more energy that had led to environment degradation. From the analytical results, it can be concluded that several policies should be implemented by the country to curb further environment degradation and CO2 emissions. The Malaysian government and policy-makers should adopt and enforce strict environmental and energy conservation policies, such as imposition of taxes on the amount of CO2 emissions to mitigate the level of pollution. Furthermore, research and investment in clean energy and seeking alternative energy sources should be an integral vision for these nations to control the carbon dioxide emissions. By implementing energy conservation policies, our country would be able to reduce energy costs, which may invariably lead to financial cost savings to the consumers of this region. These savings may then be utilized to expand the energy saving projects or establish renewable energy projects. By implementing these energy efficient initiatives, the greenhouse gases emissions can be reduced substantially. 20 As increase in energy consumption leads to declining environmental quality, human health, agricultural productivity, water resources and ultimately economic growth will be affected in the long-run. Therefore, it is pertinent for policy-makers to develop strategic plans to reduce carbon emissions to protect the environment for future generations. As energy prices are relatively subsidized in Malaysia economies, price distortions maybe primarily responsible for the implausibly high energy intensity in this region. Therefore, it would be vital for the Malaysian government to effectively implement energy conservation policies through energy price reforms and fuel substitution. With increased energy prices, consumers and producers would decrease their consumption of energy; thus more energy-efficiency technology would be used, and invariably reduce the carbon dioxide emissions. Malaysia as country is blessed with renewable energy sources, progress towards utilizing them has been slow and remains underutilized. Based on the findings of this study, it is clearly imperative for the Malaysia to adopt multi-pronged strategy of increasing investment in energy infrastructure to expand energy inputs, and improved regulatory reform with respect to trade policies, energy infrastructure and energy conservation policies As urbanization is particularly significant in Malaysia in the long-run, urban planners should adopt proactive measures to gradually increase the process of urbanization, as opposed to rapid urbanization. This may eventually lead to reduced energy consumption in Malaysia. Lastly, based on the forecasted result, Malaysia takes more than 30 years for green GDP to overtake the traditional GDP. As such, tremendous effort needs to be made in order to make sure 21 that the number of years becomes much shorter so that the future generation can feel “green” in their life. References Ahmad Fauzi, P., Mohd Shahwahid, H.O., Mad Nasir, S., Zakariah, A.R. and Alias, R. (2010). 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Regional Analysis of China's Green GDP.Eurasian Geography and Economics, 50(5), 547-563. 23 Appendices Cusum test 20 15 10 5 0 -5 -10 -15 -20 1973 Green GDP Model Traditional GDP Model Plot of Cumulative Sum of Recursive Residuals Plot of Cumulative Sum of Recursive Residuals 1978 1983 1988 1993 1998 2003 2008 2010 20 15 10 5 0 -5 -10 -15 -20 1973 1978 1983 1988 1993 1998 2003 2008 2010 The straight lines represent critical bounds at 5% significance level The straight lines represent critical bounds at 5% significance level Plot of Cumulative Sum of Squares of Recursive Residuals Plot of Cumulative Sum of Squares of Recursive Residuals 1.5 1.5 1.0 Cusum Square test 1.0 0.5 0.5 0.0 0.0 -0.5 1973 1978 1983 1988 1993 1998 2003 2008 The straight lines represent critical bounds at 5% significance level 24 2010 -0.5 1973 1978 1983 1988 1993 1998 2003 2008 The straight lines represent critical bounds at 5% significance level 2010