Download Modeling and Forecasting the Malaysian GDP

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.
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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
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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
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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
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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
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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
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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
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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.
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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:
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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).
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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).
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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
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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%
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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
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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.
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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.
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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.
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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.
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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
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that the number of years becomes much shorter so that the future generation can feel “green” in
their life.
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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