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Does Electricity Consumption have Significant Impact towards the Sectoral Growth of Cambodia? Evidence from Wald Test Causality Relationship



It is recognized that energy-output relationship studies from an economic perspective have become popular among researchers recently. This study deals with the electricity consumption and outputs of some major economic sectors in Cambodia. The newly developed ARDL bound testing approach has been employed to examine the cointegration relationship. The Granger causality test of the aforementioned ARDL framework has also been used to investigate the corresponding causality effect. Analysis and discussion based on the data of Cambodia, for the period of 1980 to 2010, are presented. Interestingly, there is no any cointegration relationship identified between electricity consumption and sectoral outputs. But, causality effect has been identified from electricity consumption towards the sectoral outputs. Based on the findings, few recommendations have been made in the hope of systematizing the energy more efficiently within the scrutinized sectors.
© 2013 Research Academy of Social Sciences 59
Journal of Empirical Economics
Vol. 1, No. 2, 2013, 59-66
Does Electricity Consumption have Significant Impact towards the
Sectoral Growth of Cambodia? Evidence from Wald Test Causality
Thurai Murugan Nathan1, Venus Khim-Sen Liew2
It is recognized that energy-output relationship studies from an economic perspective have become popular among
researchers recently. This study deals with the electricity consumption and outputs of some major economic sectors in
Cambodia. The newly developed ARDL bound testing approach has been employed to examine the cointegration
relationship. The Granger causality test of the aforementioned ARDL framework has also been used to investigate the
corresponding causality effect. Analysis and discussion based on the data of Cambodia, for the period of 1980 to 2010,
are presented. Interestingly, there is no any cointegration relationship identified between electricity consumption and
sectoral outputs. But, causality effect has been identified from electricity consumption towards the sectoral outputs.
Based on the findings, few recommendations have been made in the hope of systematizing the energy more efficiently
within the scrutinized sectors.
Key words: Sectoral Outputs, Electricity Consumption, ARDL Bound Testing, WALD test.
1. Introduction
The modelling of electricity consumption in every country is important since this source is getting expensive. The
energy crisis during the 1970s and high energy prices had negatively affected the economic performance of every sector
within the Asian region. In Cambodia, high GDP growth rate over the past 10 years has stimulated substantially-
increasing demands for electricity within the country (Poch and Tuy, 2012). The recently published news from the
Business Monitor Online (2010) stated that the quality of electricity supply in Cambodia ranked at 121 out of 133
countries; the reason is that the country’s power supply services have been heavily damaged by war. Nevertheless, the
Cambodian government is recuperating under the support from the World Bank, ADB, Japan, USA, and European
Countries (General Directorate of Energy, MIME). At present, the electricity supply in Cambodia is fragmented into 24
isolated power systems centred in provincial towns and cities; all are fully reliant on diesel power stations. The per
capita consumption is only about 48 kWh/year and less than 15% of households have access to electricity (urban=53.6%
and rural=8.6%). The amount of electricity consumption in the private sector is 0.5%, the service sector takes up 40%,
and the industrial sector consumes 14% (World Bank, 2008).
MIME added that the main problem is that electricity-supplying companies such as Independent Power Producers
(IPP) and Electricite Du Cambogde (EDC) have distributed their cost energy into the smallest unit. This has made
energy expensive and the supply well below Cambodia's level of consumption3. In addition, since Cambodia is a
medium-income developing country, being heavily dependent on imported energies can restrain the country’s economic
growth4. This problem is further aggravated when the energy sources are not managed properly. So, the main challenge
for the Cambodia government now is in meeting the rising electricity demand of a growing economy. Efficient
management of electricity usage can ensure economic growth consistency. Thus, it is crucial that considerable
importance is put into identifying the relationship between electricity consumption and sectoral outputs, since this
1 Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia.
2 Universiti Malaysia Sarawak, Kota Samarahan, Sarawak, Malaysia
3Poch and Tuy (2012) added that electricity cost in Cambodia remains one of the highest in the region and also the
world. Meanwhile, the electricity import rate from neighboring countries is increasing.
4 Cambodia mainly imports electricity from Thailand and Vietnam. The total electricity import in 2009 and 2010 are
842.40 million kWh and 1547.31 million kWh, respectively. The percentage change is recorded as 83.68% (EAC
Annual Report, 2010).
T. M. Nathan and V.K. Liew
approach can help to identify the electricity dependent sectors in Cambodia (Nwosa and Akinbobola, 2012; Liew et al.,
2012; Chebbi and Boujelbene, 2008)5.
The focus of this study is to identify how electricity consumption can affect a sector’s contribution in Cambodia
by using the ARDL bound testing and also the causality effect. The findings of this study will identify the energy-led
sectors and sectors that are led by energy. If increased electricity consumption does not bring positive impact to the
output growth, then the sector is said to have inefficiently used the electricity supply; the electricity can thus be
allocated to other energy-dependent sectors to boost productivity. The critical part here is that this conservation policy
cannot be bluntly adopted since it will affect the production process and induce other side effects. A conservation
hypothesis has also been posed before this which states that policies implemented to reduce energy consumption may
have an adverse impact on the outputs. This has eventually become the driving force that motivates researchers to use
the causality test to identify the variables concerning a nation’s reliance on energy sources (Al-Iriani, 2006). To
conclude, it is anticipated that this study will become an asset to policy makers in managing the electricity consumption
of different sectors to overcome electricity shortage in Cambodia.
2. Methodology and Data
Data Description and model specification
Annual data from the year 1986 to 2010 were used in this study. These data on electricity consumption were
obtained from the U.S. Energy Information Administration (EIA) (2011). The data had been standardized using the
energy converter and were expressed in Million British Thermal Unit (Mmbtu). On the other hand, the data on sectoral
outputs for every sector were obtained from the United Nations (UN) (2011). The value added of the agricultural sector,
services sector, industrial sector, and transportation sector were used to represent sectoral outputs of the respective
sectors; this echoed with the method used by Liew et al., (2012) and Costantini and Martini (2010). All the variables
were changed into the logarithm form to reduce heteroskedasticity problems (Gujarati, 1995, pp.421).
The basic developed model for this study is as follows6. This model indicates that the total electricity consumption
is a function of sectoral growth.
  =( ), (1)
The sectoral outputs under study were agricultural, industrial, services, and transportation outputs. In logarithmic
form, the model can be expressed as follows:
=1+2+, (2)
where = {, ,, }.
Unit Root Tests
The purpose of this unit root test is to determine whether the time series is consistent with the I(1) process with a
stochastic trend or with the I(0) process, which means it is stationary with a deterministic trend. When the number of
observation is low (short-spanned data), the unit root test will have little power (Chebbi and Boujelbene, 2008). To
overcome this problem, two types of unit root tests were used to examine the results, i.e., Dickey-Fuller GLS (DF-GLS)
and Phillips-Perron (PP) unit root tests. According to Haug and Basher (2007), the DF-GLS test generally has higher
power than the standard Augmented Dickey-Fuller (ADF). Shahbaz et al., (2009) reported that the commonly used ADF
test tends to over-reject the null hypothesis of unit root, but the DF-GLS has performed in this sense. The general
regression of the DF-GLS test is:
= ++1+1
=1 +, (3)
where t is the time trend variable and k is the number of lags which are added to the model to ensure that the
residuals are white noise. The general regression for the PP test is:
=+1+, (4)
5 The disaggregation data allowed comparisons to identify the impact of different energy sources on output rates (Sari et
al., 2008). In addition, a few studies such as Costantini and Martini (2010); Galindo (2005); Ruth and Lin (2006); and
Hunt et al., (2003) used sectoral outputs or production in their estimations.
6 The empirical model for this study is adopted from Loganathan et al., (2010).
Journal of Empirical Economics
where denotes the first difference, is the time series being tested, is a vector of deterministic terms
(constant, treand etc.), and is I(0) and may be heteroskedastic. Both tests have the same null hypothesis integrated to
the order of one, I(1), as stationary.
Cointegration test
Traditionally, if the variables were to be cointegrated, two conditions must be satisfied: the series for the
individual variables must have the same statistical properties, and the variables must be integrated in the same order. So,
if the variables have mixed results (integrated in I(0) and I(1)), the widely used Johansen analysis cannot be employed
in finding the cointegration relationship. To overcome this constraint, Pesaran and Shin (1999) and Pesaran et al.,
(2001) have developed the autoregressive distributed lag (ARDL) model or bound testing approach; this is the most
extensively used method in energy based studies7.
According to Wang (2009), the advantages of the ARDL model are that it can identify long-run relationships and
solve small-sample problems. The author further added that the unit root pretesting is not that critical when the ARDL
model is used because the integrated order, whether it is I(0) or I(1) or a mixture of both, is not important. Shraetha and
Chowdhury (2005) further added that this approach can take sufficient numbers of lags to capture the data generating
procedure in a general-to-specific modelling framework. The formulated ARDL model is as follows:
+01+11+, (5)
=+0 ,
+01+11+, (6)
where is the first-difference operator; 1 and 2 are the lag lengths; 0, and 1, (Eq. 5) and 0, and 1, (Eq. 6)
represent the short-run dynamics of the model; 0 and 1 (Eq. 5) and 0 and 1 (Eq. 6) represent the long-run
relationship; and is a white noise error term. The null hypothesis in the ARDL is as follows:
0:0=1= 0 ;0 =1= 0, (7)
1:0 10 ;0 10. (8)
Besides that, this ARDL model also takes the error correction factors of previous periods into account. These error
correction terms, , and lag difference terms can test both short-term and long-term relationships (Wang, 2009). The
general error correction model is as follows:
ln =+0,
+1 ,
+ +
=+ 0,
 ++
where and are the speeds of the adjustment parameter and is expected to be negative as well as statistically
significant. It indicates how fast the current differences in a dependent variable respond to the error correction term in
7Energy consumption can then be explained by its own lags and lagged value of a number of explanatory variables.
Dependent variables are also taken into account because it will help in future adjustments when the frequently
characterized energy consumption reacts to changes in the explanatory variables (Bentzen and Engsted, 2001).
T. M. Nathan and V.K. Liew
disequilibrium within the previous period.  represents the residuals obtained from the estimated cointegration model
(Wang, 2009).
In this study, the cointegration relationship was examined using the F-test in the ARDL framework. The computed
F-test value was compared with the critical value in Table CI (iii) with unrestricted interception and no trend; tabulated
by Pesaran et al., (2001). In this ARDL framework, if the F-test value is higher than the upper bound of the critical
value, then the null hypothesis will be rejected and it can be concluded that there is a long-run relationship among the
variables. If the F-test value is less than the lower critical value, then the null hypothesis of the cointegration
relationship will be accepted, which means that no cointegration relationship exists among the variables. On the other
hand, if the F-test value is between the lower and upper bound value, it means that the results are inconclusive (Pesaran
et al., 2001). However, there are a few steps that have to be taken care of before the cointegration test can be conducted.
Firstly, the optimum lag should be identified based on the Schwarz-Bayesian criterion (SBC) and/or Akaike Information
criterion (AIC). Then, the ordinary least square (OLS) technique should be used with the selected model to find out the
F-test value. If this value is higher than the critical value, the null hypothesis of no long-run relationship among the
variables will be rejected. Finally, the long-run relationship and error correction model (ECM) can be estimated using
the selected lags. The ECM term should have a negative sign and should be statically significant.
Granger causality test
After the long-run relationship had been identified, the Granger Causality test was used to investigate the causal
relationships among the variables. This is because the cointegration test can only detect the long-run relationship, not
the direction of causality (Ozturk and Acaravci, 2010). According to Engle and Granger (1987), if the past value of is
able to predict the value, it is said that is the Granger cause of . Else, it can be said that is the Granger causes
of future values. The null hypothesis of the Granger causality test is that does not Granger cause and vice versa.
The following is the causality test model between the type of energy consumption and sectoral outputs:
 +2
 ++1
 +2
  +2+2
where is the lag operator; α0, δ0, ,β’s and øare the estimated coefficients; m and n are the optimal lags of the
series type of electricity consumptions and sectoral outputs (OUTPUT); ζ1t and ζ2t are serially uncorrelated random error
terms; μ1 and μ2 measure a single period responses of the variables to an exit to form equilibrium.  is the error-
correction term and the F-test on the lagged explanatory variables represents the short-run causal effect. If 2 is jointly
significant, then the null hypothesis which states that sectoral outputs does not Granger cause energy consumption can
be rejected (Odhiambo, 2009).
The Granger causality test based on ARDL framework (Wald test), known as the short-run causality test, was used
in this study to find out the causality effect among the variables. The causality relationships were discovered by using a
common factor that had restricted the lags of the variables’ coefficient, which should be zero in this case. If the null
hypothesis of no causality can be rejected, it means that the variable are Granger caused (less than 5% of significance
3. Empirical Evidence and Discussion
Table 1 summarizes the unit root test results of DF-GLS and PP tests. For the DF-GLS test, the agricultural and
electricity consumptions were integrated in I(1) and the rest were integrated in I(0). For the PP test, all variables were
integrated in I(1) except for the services outputs. This clearly indicates that there is a mixed order of I(0) and I(1) among
the variables.
Journal of Empirical Economics
Table 1: Unit Root Test Result
Dickey-Fuller GLS (ERS) test
Phillips-Perron (PP) test
First Difference
First Difference
Notes: All variables had been transformed to natural logs. Asterisks (*), (**) and (***) indicate statistical
significant at the 1%, 5% and 10% levels, respectively. The optimum lag length for DF-GLS test was auto-selected
based on the Schwarz Info Criterion (SIC) and for the PP test, this was automatically selected based on the Newey-
West Bandwidth.
Since the variables were integrated in a mixed order of I(0) and I(1), it is rational to adopt the ARDL model or
bound testing approach to investigate the cointegration relationship. Firstly, the bound testing process was executed to
obtain the F-test values for all the combined variables. Then, the minimum Schwarz-Bayesian Criterion (SBC) value,
which was in accordance to Ozturk and Acaravci (2010) and Abdelhak et al.,’s (2011) work, was chosen as the
optimum lag in the process of developing the F-statistic8. The chosen ARDL model must be superior in its statistical
properties so that its diagnostic tests will not demonstrate any substantiation of serial correlation, functional form,
normality, and heteroscedasticity (Verma, 2007)9. In this case, the chosen optimum lag was evaluated using the
diagnostic tests first. The F-test value is accepted if it passes all diagnostic tests. Otherwise, another minimum SBC
value is chosen and then evaluated again until the best fitted model has been identified (Akindoade et al., 2008).
Table 2: Bound Testing Results
Dependent Variable
Independent Variable
Optimum Lag
F-Test [Prob]
0.188 [0.834]
1.241 [0.339]
2.551 [0.139]
3.893 [0.096]
1.613 [0.288]
3.406 [0.117]
0.968 [0.508]
0.298 [0.747]
Notes: The critical values are; 1% (6.84-7.84), 5% (4.94-5.73) and 10% (4.04-4.78) significance level. The
optimum lag was selected using SBC. The maximum lag was fixed at 9.The critical values were obtained from
Table CI(iii) Case III: Unrestricted intercept and no trend reported in Pesaran et al., (2001).
Table 2 summarizes the bound testing results for all combined variables. The optimum lag chosen is the best fit
model for the combination where it passes all the diagnostic tests at 5% level of significance (Table 3). Surprisingly,
there was no evidence of cointegration relationship identified between the electricity consumption and sectoral outputs
in Cambodia since the obtained F-test values were less than the critical value at 1%, 5% and 10% significance level.
Next, the Granger causality test was conducted to identify the causality effect among the combined variables.
Table 4 shows the short-run causality test results obtained through the Wald test. The decision to reject the null
hypothesis is based on the probability value. If the probability value is less than that in the 5% significance level, the
null hypothesis which states that there is no causality relationship among the variables is rejected. In this study, it was
found that a unidirectional causality relationship running from electricity consumption towards industrial outputs in
short-run existed. Consistent with the study of Altinay and Karagol (2005), an increase in electricity consumption can
8The lag selection process did not include. It will be provided upon request.
9Ibarra (2011) supported Verma (2007) statement by adding that estimated equation must be confirmed by the battery
of diagnostic tests before moving on to the next step”.
T. M. Nathan and V.K. Liew
be viewed as the foremost indicator of industrial outputs growth in the country10. Such unidirectional causality
relationship was also found running from electricity consumption to agricultural and transport outputs; and at last
services from outputs to electricity consumption.
Table 3: Diagnostic Test Results
Serial Correlation
Form b
Normality c
0.435 [0.510]
0.643 [0.423]
0.208 [0.901]
0.604 [0.437]
0.571 [0.450]
0.044 [0.834]
0.714 [0.700]
0.071 [0.790]
0.003 [0.954]
0.323 [0.570]
0.203 [0.903]
1.179 [0.278]
1.939 [0.164]
7.396 [0.087]
1.036 [0.596]
0.437 [0.509]
0.008 [0.930]
0.061 [0.805]
0.531 [0.767]
0.051 [0.822]
2.851 [0.091]
7.772 [0.005]
2.182 [0.336]
0.309 [0.578]
0.616 [0.432]
0.695 [0.979]
0.403 [0.818]
0.028 [0.868]
0.430 [0.512]
3.555 [0.059]
2.802 [0.246]
0.106 [0.745]
Notes: aLagrange multiplier test of residual serial correlation. bRamsey's RESET test using the square of the fitted values.
cBased on a test of skewness and kurtosis of residuals. dBased on the regression of squared residuals on squared fitted values.
4. Conclusion and Recommendation
This study has applied the recently developed ARDL bound testing approach in identifying the cointegration
relationship and causality effect between the time series data of electricity consumption and sectoral growth in
Cambodia for the period covered from 1986 to 2009. The results of this study suggested that electricity consumption
does not give an impact in the long-run since there is no long-run relationship identified. Statistical evidences have also
showed that electricity consumption is an important component for sectoral growth in the short-run in Cambodia
because of its causality effects that runs from electricity consumption towards the agricultural, industrial, and
transportation sectors. In here, the electricity consumption is the leading indicator of growing economy. In reality, the
electricity generation had increased from 0.2 billion kWh in 1995 to 1.5 billion kWh in 2009 (EIA, 2010) but it has yet
to meet the growing electricity demand in Cambodia. According to EIA (2010), the electricity consumption in the
production process had increased from 0.7 million kWh in 1995 to 5.3 million kWh in 2008; this corresponded to an
increase in sectoral inputs within the same time period11. In term of policy implication, the government of Cambodia
10Tang (2008) also used the same explanation as Altinay and Karagol (2005) in his study. Since electricity consumption
affects economic growth, he explained that the former is an important element for Malaysia’s economic growth and that
Malaysia is an electricity-dependent country.
11The agricultural sector’s output increased from 1.32 billion in 1995 to 2.57 billion in 2008, that in the industrial sector
increased from 0.33 billion in 1995 to 1.78 billion in 2008, and the transport sector reported an increase in output from
0.15 billion in 1995 to 0.57 billion in 2008.
Table 4: Granger Causality Test Results for Cambodia (Wald Test F-statistic)
Null Hypothesis
Wald Test
LNELEC does not Granger-cause LNAGR
LNAGR does not Granger-cause LNELEC
LNELEC does not Granger-cause LNSER
LNSER does not Granger-cause LNELEC
LNELEC does not Granger-cause LNIND
LNIND does not Granger-cause LNELEC
LNELEC does not Granger-cause LNTRA
LNTRA does not Granger-cause LNELEC
Notes: (**) and (*) denote the rejection of the null hypothesis at 5% and 1% significance level. Prob represents the
probability value.
Journal of Empirical Economics
should be more aware of the importance of stable electricity supply, and better coalition has to be formed with the
electricity companies in Cambodia. Policies that can boost the electricity supply should be drafted, researched, and
Research grant (FRGS/05(16)/729/2010(15) funded by the Malaysian Higher Ministry of Education is gratefully
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... The Wald test shows that there is a long-term coefficient of information in the variable. According to Nathan and Liew (2013), the Wald test value can be obtained by comparing the F-statistic value with the critical value limit at the lower I (0) and upper I (1) bounds. The Wald test's null hypothesis is that the F-statistics are lower than the values of upper and lower critical bounds. ...
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The purpose of this paper is to find the relationship between public expenditure in the educational sector and the economic growth in Indonesia since the government decided to spend 20% of the state budget on education. We used time series data from 1988 to 2018 and the Cobb–Douglas production function as an economic theory for measurement. In the methodology, we employed Autoregressive Distributed Lag bound tests to find the relationship between variables. The results show that public expenditure on education has an insignificant relationship in the long- and short-term estimation. However, they both have different directions, which is a positive relationship in long-term and a negative relationship in short-term estimation. Meanwhile, gross fixed capital formation shows a positive relationship, and the labour variable has a negative relationship in the short and long terms. In conclusion, the Indonesian government should manage the education system regarding the relationship between education expenditure and economic growth.
... Nathan and Liew [37] in Cambodia, examined the causal link between energy consumption and economic growth in various sectors. Findings supported mixed results of unidirectional causality running from energy consumption to agricultural, industrial, and transportation sectors, and unidirectional causality from the services sector to energy consumption. ...
... Nathan and Liew [49] examine the causal relationship between electricity consumption and several economic sectoral outputs in Cambodia using autoregressive distributed lag (ARDL) model and Granger causality test. They conclude the existence of unidirectional causality running from electricity consumption to agricultural, industrial, and transportation sectors, and unidirectional causality from services sector to electricity consumption. ...
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The aim of this study is to investigate the causal relationship between electricity consumption and real output at the macro level and the sectoral levels in three main economic sectors, namely the agricultural, industrial, and services sectors in Egypt during the period 1971–2013. I use Johansen cointegration approach, vector error correction model, and Toda and Yamamoto (J Econom 66:225–250, 1995) approach. Empirical findings reveal the existence of a cointegration relationship between the variables at the macro level and the sectoral levels. At the macro level, there is bidirectional causality between real output and electricity consumption. Whereas at the sectoral levels, there exists bidirectional causality between electricity consumption and real output in the services sector and unidirectional causality running from real output in the industrial sector to electricity consumption. However, there is no causal relationship between electricity consumption and real output in the agricultural sector. These results can help policymakers in setting the appropriate electricity conservation policies that enhance economic growth at the macro level and the sectoral levels to prevent any possible adverse effect that may harm economic and social development. Additionally, ensuring a higher level of electricity generation needed for achieving high and sustainable economic growth is vital, where higher electricity generation can be provided through investing in clean technologies and renewable energy resources, such as wind and solar energy.
... Causality effect runs from electricity consumption to the output of agricultural, industrial, and transportation sectors. However, only the service sector output granger causes electricity consumption (Nathan & Liew, 2013). The earlier studies which scrutinised the relationship between economic growth and electricity focused the energy consumption data only and none of the study was carried out in India with the energy generation data. ...
... Before we directing the ARDL bound test, as a first step, the order of lags ought to be gotten from either utilizing the Akaike Information Criterion (AIC) or Schwartz-Bayesian Criterion (SBC). Taking after on Pesaran and Pesaran (1997), in this study have choose the ideal model by utilizing Schwartz-Bayesian Criteria (SBC) by selecting minimum lag length due this study give little size of observation which is 57 (Shrestha & Khorshed, 2005; Nathan & Liew, 2013) 4 . The ascertained F-statistics are contrasted and the critical values got from Pesaran, Shin and Smith (2001) and Pesaran and Pesaran (2009). ...
Conference Paper
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Financialization of the commodities " due to the globalization lead to the agricultural commodities are started to treat as a financial asset and securities where players in the global financial market view it as alternative investment areas rather than the real economic activities renewed the interest in reinvestigating the oil-commodity price relationship with the financial variables among researchers. Consequently, the main purpose of this study is to examine which financial variables having long-run co-movement and causality effect on oil price and energy incentive commodity prices such as corn, soybean, sugar and wheat in Malaysia. To extending the knowledge in literature, in this study the time period as be divided into three sub-period such as pre-crisis (2000-2005), crisis (2006-2008) and post-crisis (2009-2013) period to spot the effect of financial variables before, in crisis period and after the crisis period. ARDL method has been used to check for the long-run relationship and Toda Yamamoto method has been used to analyse the causality effect. The empirical findings of this study shows that the oil price and financial factor having close integration towards the commodity prices in the crisis period only which is contrary to other related studies whereby less in pre and post-crisis periods. Some implications and policies have been proposed based on the findings of this study.
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The spillover affects due to calamities caused by climate change has been distressing especially in the African countries and particularly in the Southern Africa states. The rising degree of calamities in this region affects not only the agricultural sector but also erodes the economic resources of families and communities. Previous studies have focused on the economic effects of natural disasters without differentiating neither between the types of disasters nor between the sectors of the economy. Pooling all natural disasters together would fail to consider the vast range of possible effects and could be misleading. Using the bonds testing or the Autoregressive Distributed Lags (ARDL) approach, this study investigated the long run effects of four types of natural disaster on the proportional GDP from three different sectors for five selected Southern Africa countries (Botswana, Lesotho, Namibia, South Africa and Swaziland). The results showed that drought, epidemic and storm have a negative effect on the agricultural GDP while flood affects positively the agricultural and manufacturing GDP in the long run. Meanwhile, the epidemic has a positive effect on the proportional GDP from services and negative sign with manufacturing sector in the long run. The estimated results suggested that the selected countries need to invest in developing and structuring flood protection systems as well as building water storage for water supply in drought periods as well as developing water resource management. The type of natural disasters is important key in determining the losses or gain of natural disasters in the long run.
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We investigate the relationships between energy consumption and the outputs of the main economics sectors in Pakistan, where energy shortage is a major challenge faced by the economy. It is found that services and industrial output, which make up of fourth-fifth of Pakistan gross domestic product, are not led by energy consumption in the country. Hence, the government of Pakistan could impose energy conservation measures on these two sectors with little or no adverse effect on the growth of those sectors.
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261 Abstract—During the past three decades, demand for energy in Malaysia grew rapidly, increasing at an average rate of 5% in the 1980s and 12% in 2009, surpassing the gross domestic product growth of 5%; and 3% over the corresponding period. The main objective of this study is to identify sustainability between energy consumption and economic performances during the past three decades by applying Ordinary Least Square Engel-Granger (OLS-EG), Dynamic Ordinary Least Square (DOLS), Autoregressive Distributed Lag (ARDL) bounds testing approach and Error Correction Model (ECM). Utilizing data from 1971-2008, the findings of this study reveals that there is a bidirectional co-integration effects between total energy consumption and Malaysia's economic performance. The key result from this study shows that, energy consumption in Malaysia is on sustainable limits with 57% speed of adjustment to reach long run equilibrium caused by short run shocks in Malaysia's economic performance.
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We re-examine the relationship between disaggregate energy consumption and industrial output, as well as employment, in the United States using the autoregressive distributed lag (ARDL) approach developed by Pesaran and Pesaran [Pesaran, M.H., Pesaran, B., 1997. Working with Microfit 4.0. Camfit Data Ltd, Cambridge] and Pesaran, Shin and Smith [Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16; 289–326] In particular, we focus attention on the following energy consumption variables: coal, fossil fuels, conventional hydroelectric power, solar energy, wind energy, natural gas, wood, and waste. The sample period covers 2001:1–2005:6. Our results imply that real output and employment are long run forcing variables for nearly all measures of disaggregate energy consumption.
The paper estimates and analyzes an equation for intermediate imports in Mexico during the 1988-2006 post-liberalization period. While some results are obtained from Johansen's VECM model, most of the analysis is carried out within an Error-Correction ARDL framework, following the bounds testing approach of Pesaran et al. (2001). Besides showing that an aggregate equation for intermediate imports can be satisfactorily estimated, the paper focuses on two specific results. First, exports have a very significant effect on imports, and failure to control for this effect (as in most previous studies) can yield misleading results, like an over-estimation of the output elasticity of imports. Second, the response of imports to variations in the real exchange rate has fallen over time, presumably because of the rising share of maquila in Mexico's export basket and the increasing "vertical specialization" of non-maquila export production. Some implications of the estimation results are briefly discussed, making reference to the possible external constraint on Mexico's economic growth.
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distributed lag (ARDL) model in estimating energy demand relationships. Recent research, however, shows that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are cointegrated. In this paper, we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using cointegration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the cointegration/ECM approach give very similar results.
The purpose of this study is to re-investigate the relationship between electricity consumption and economic growth in Malaysia from 1972:1 to 2003:4. This study adopted the newly developed ECM-based F-test [Kanioura, A., Turner, P., 2005. Critical values for an F-test for cointegration in the multivariate model. Applied Economics 37(3), 265–270] for cointegration to examine the presence of long run equilibrium relationship through the autoregressive distributed lag (ARDL) model. The empirical evidence suggests that electricity consumption and economic growth are not cointegrated in Malaysia. However, the standard Granger's test and MWALD test suggest that electricity consumption and economic growth in Malaysia Granger causes each other. This finding provides policymakers with a better understanding of electricity consumption and allows them to formulate electricity consumption policy to support the economic development and to enhance the productivity of capital, labour and other factors of production for future economic growth in Malaysia.
This paper explores potential impacts of climate change on natural gas, electricity and heating oil use by the residential and commercial sectors in the state of Maryland, USA. Time series analysis is used to quantify historical temperature–energy demand relationships. A dynamic computer model uses those relationships to simulate future energy demand under a range of energy prices, temperatures and other drivers. The results indicate that climate exerts a comparably small signal on future energy demand, but that the combined climate and non-climate-induced changes in energy demand may pose significant challenges to policy and investment decisions in the state.