ArticlePDF Available

Is there a cointegrating relationship between Australia's fossil-fuel based carbon dioxide emissions per capita and her GDP per capita?

Authors:

Abstract and Figures

Carbon dioxide (CO 2) emission per capita of Australia, a high-income economy with a fossil fuel-rich fuel-mix, is proven to have a strong cointegrating relationship with her gross domestic product (GDP) per capita. A conditional equilibrium correction model (ECM) has been developed to quantify the relationship between the two variables by employing the autoregressive distributed lag bound-testing approach to cointegration. The long-run income elasticity is estimated to be as high as 0.7, and 36% of any deviation from the long-run equilibrium is corrected within a year. In the short-run, 1% increase in GDP per capita growth in the previous year leads to 0.33% increase in the current growth in CO 2 emission per capita. The conditional ECM developed is robust against functional form misspecification and have stable regression coefficients over the sample period studied. Thus, it could be used to reliably predict the future CO 2 emissions in Australia.
Content may be subject to copyright.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 1 of 17
Is there a cointegrating relationship between Australia’s fossil-fuel based carbon dioxide
emissions per capita and her GDP per capita?
Rajaratnam Shanthini# and Kanthi Perera1
Department of Chemical & Process Engineering
1Department of Engineering Mathematics
University of Peradeniya, Peradeniya 20400, Sri Lanka
Abstract: Carbon dioxide (CO2) emission per capita of Australia, a high-income economy
with a fossil fuel-rich fuel-mix, is proven to have a strong cointegrating relationship with her
gross domestic product (GDP) per capita. A conditional equilibrium correction model
(ECM) has been developed to quantify the relationship between the two variables by
employing the autoregressive distributed lag bound-testing approach to cointegration. The
long-run income elasticity is estimated to be as high as 0.7, and 36% of any deviation from
the long-run equilibrium is corrected within a year. In the short-run, 1% increase in GDP per
capita growth in the previous year leads to 0.33% increase in the current growth in CO2
emission per capita. The conditional ECM developed is robust against functional form
misspecification and have stable regression coefficients over the sample period studied.
Thus, it could be used to reliably predict the future CO2 emissions in Australia.
Keywords: ARDL; Australia; carbon dioxide; cointegration; ECM; emission modelling;
equilibrium correction; GDP per capita; long-run equilibrium; short-run dynamics.
1 Introduction
Incipient research studies on carbon dioxide (CO2) emission modelling (Shafik, 1994;
Shafik and Bandyopadhyay, 1992) found the CO2 emission to monotonically increase with
rising income. Schmalensee, Stoker, and Judson (1998), however, contradicted the above and
showed that the relationship between per capita CO2 emission and per capita income describes
an ‘inverse-U-shaped’ (quadratic) relationship, known as the Environmental Kuznets Curve
(EKC). They located falling per capita CO2 emission with rising income at per capita income
levels reached in high-income economies during the 1970s. The ‘inverse-U-shaped’
relationship was foreseen by de Bruyn, van den Bergh, and Opschoor (1998) as a temporary
phenomenon that was on its way to grow into an ‘N-shaped’ (cubic) relationship.
Econometric evidence was found for the existence of ‘N-shaped’ relationship between CO2
and income for a single country (Friedl and Getzner, 2003) as well as for a group of countries
(Galeotti and Lanza, 2005).
In explaining the ‘N-shaped’ relationship between emission and income, de Bruyn (2000)
observed that pollution reduction initiatives taken by some economies may have ceased ‘once
the technological opportunities for further reductions have been exhausted or have become
too expensive’. Carrying out a comprehensive survey of the empirical evidence and of
# Email: rshanthini@pdn.ac.lk
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 2 of 17
possible causes of the EKC, Lieb (2003) concluded that ‘for a given pollutant an EKC will
only exist when policy measures are taken with respect to this pollutant’. Lieb also observed,
however, that the emission-income relationship monotonically rises for global pollutants, such
as CO2.
The phase diagram analysis of Unruh and Moomaw (1998) showed that the reduction in
the rate of increase in per capita CO2 emissions in some of the high-income economies during
the 1970s was caused by the ‘oil shocks of the 1970s’, during which the economically
prosperous countries looked for alternatives to raise their per capita incomes either in the
increase use of non-fossil fuel sources of energy, or in adapting innovative emission-reduction
technologies, or in the relatively less energy-intensive service sector.
The impact of the ‘oil shocks of the 1970s’ upon the fuel-mix of a number of high-income
economies was such that it has negated the otherwise strong relationships prevailed between
the CO2 emission of a country and her economic prosperity in high-income economies such as
Austria, France, Japan, Sweden, and United States (Aldy, 2005; Friedl and Getzner, 2003;
Lanne and Liski, 2004; Lindmark, 2002; Managi, 2006; Shanthini and Perera, 2007; Unruh
and Moomaw, 1998).
In case of Australia, however, the place of fossil-fuel in its fuel-mix has been so strong
that it has never fallen below 94% of the total energy consumption since 1965 (British
Petroleum, 2009). In 2006, for instance, about 44% of Australia’s total energy consumption
was met by coal, 33.6% by petroleum, 19% by natural gas, 2.8% by hydroelectricity, and
about 0.6% by other renewable energy sources (Energy Information Administration, 2008). It
is therefore highly likely that the fossil-fuel based CO2 emissions in Australia and her
economic prosperity may move together describing a cointegrating relationship (Engle and
Granger, 1987) between them.
The primary objective of this study is to seek for the probable existence of a cointegrating
relationship between Australia’s fossil-fuel based CO2 emission per capita and her gross
domestic product (GDP) per capita measured in market exchange rates, which is the proxy
used for economic prosperity. In case of firmly establishing a cointegrating relationship, the
next step is to develop a robust statistical model describing the long-run equilibrium
relationship and the short-run dynamic equation prevailing between the emission per capita
and GDP per capita for Australia. The existence of statistically significant long-run
equilibrium relationship and short-run dynamic equation would pave the way for forecasting
Australia’s fossil-fuel based CO2 emission per capita for hypothetical growth scenarios of her
GDP per capita (Amarawickrama and Hunt, 2008).
The econometric methodology used is the cointegration testing procedure advocated in the
autoregressive distributed lag (ARDL) bound-testing approach (Pesaran, Shin, and Smith,
2001). ARDL approach is adopted in this study since it is known to be better suited for
regressors of different order of integration (Pesaran, Shin, and Smith, 2001) and for small
sample sizes (Pesaran and Shin, 1999).
Cointegration is not new for the CO2 emission versus income research literature. Friedl
and Getzner (2003) showed evidence for the existence of cointegration between the Austrian
annual emission and income time series in the range of 1960 to 1999. They used the
augmented Dickey-Fuller test in the sense of Engle and Granger (1987) to arrive at the
conclusion, and then reverted back to ordinary least square (OLS) regression approach to
estimate the parameters of a simple linear model with a dummy variable accounting for
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 3 of 17
structural break in 1974, of an EKC model, and of an ‘N-shaped’ model. Aldy (2005) tested
for cointegration among the emission, income, and income-squared state-specific time series
using the Engle-Granger type augmented Dickey-Fuller test for United States using the state-
level annual data spanning 1960 to 1999. Aldy found evidence for cointegration in 8 of the 48
states for production-based CO2 emissions and in 7 states for consumption-based CO2
emissions, and estimated the parameters of the EKC-type models of these states using the
state-specific dynamic OLS regression. Both Friedl and Getzner (2003) and Aldy (2005),
however, failed to complement their cointegration analyses with the standard equilibrium-
correction modelling approach which combine the long-run equilibrium relationship with the
short-run dynamic equation (Engle and Granger, 1987; Pesaran, Shin, and Smith, 2001).
In this paper, for the first-time to the best of our knowledge, the ARDL modelling
approach has been employed to capture the long-run equilibrium relationship between the
CO2 emission and income time series. The rest of the paper is organized in the following
manner. Time series data used for developing the model and their characteristics are presented
in Section 2 along with the rationale behind the model developed. A brief account of the
ARDL bound-testing approach used to develop the model is presented in Section 3. Section 4
presents the results and discussion and Section 5 concludes.
2 Data characteristics
2.1 Data used
Historic time series data on the Australian annual CO2 emission estimates are available in two
independent sources which are the Carbon Dioxide Information Analysis Center (Marland,
Boden, and Andres, 2008), abbreviated CDIAC, and the International Energy Agency (2009),
abbreviated IEA. CDIAC uses the ‘Reference Approach’ which is based on the supply of
energy in a country and IEA uses the ‘Sectoral Approach’ which includes emissions only
when the fuel is actually combusted (International Energy Agency, 2009, pp.31-32).
Figure 1 shows the cumulative CO2 emissions stemming from the burning of solid, liquid
and gaseous fossil fuel obtained from both sources. It is evident in Figure 1 that the emissions
from the two sources somewhat differ from each other since 1980 and that a decline in
emission during 1998 to 2001 has been reported by CDIAC and not by IEA. CDIAC
emissions estimates are based on the fuel consumption data available in the Energy Statistics
Database of United Nations Statistics Division, which reports a 9.2% decline in Australia’s
gross production of coal during 1998 to 1999 coupled with the 5.9% increase in her coal
exports during the same period. This fact explains the decline in emission in 1998 reported by
CDIAC (private communication with Tomas A. Boden, CDIAC Director). However, such
decline in Australia’s coal production has not been reported in any data sources of Australian
origin or in the IEA database. It is therefore IEA data source was chosen as the primary
emission data source of this study with the emission data from CDIAC added to cover the
range of 1960 to 1973.
The CO2 emission per capita time series data used in this study were derived from
dividing the aforementioned CO2 emission data by the mid-year population data obtained
from World Development Indicators (World Bank, 2008). Time series data on the Australian
annual GDP per capita were obtained from the same source as well. Unit of CO2 emission per
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 4 of 17
capita used in this study is tonne of CO2 (which is equivalent to 1000 kg of CO2) and that of
GDP per capita is thousand of constant 2000 US$. It can be observed in Figure 2 that the data
used for model development, spanning the period 1960 to 2007, exhibit a tendency to move
together suggesting the probable existence of a cointegrating relationship between CO2
emissions per capita and GDP per capita. It is also to be noted in Figure 2 that both the CO2
emissions per capita growth and the GDP per capita growth slow down during the 1970s,
which is the decade of two major oil shocks, and that the emission appear to flatten out since
2000.
50
100
150
200
250
300
350
400
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year
CO
2
emissions
(million tonnes of CO
2
)
CDIAC
IEA
Figure 1 Australia’s estimated annual carbon dioxide emissions stemming from the burning of solid, liquid
and gaseous fossil fuel, obtained from CDIAC (Carbon Dioxide Information Analysis Center) and IEA
(International Energy Agency).
5
10
15
20
25
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Year
CO2
emissions
per capita
5
10
15
20
25
GDP per capita
GDP per capita
CO2 emissions per capita
Figure 2 Australia’s annual carbon dioxide emission per capita (in tonnes of CO2) and her annual GDP per
capita (in thousands of constant 2000 US$).
Figure 3 shows that the annual average marker crude oil price (British Petroleum, 2009)
experienced very little fluctuations till 1973, then a sharp increase during 1973 to 1974, and
another increase during 1978 and 1979. This decade of two major oil shocks is followed by a
general decline in oil price till 1998. From 1998 to 2008, oil price has increased once again.
The impact of the oil shock decade on Australian CO2 emission is such that the percentage
shares of CO2 emissions stemming from coal and oil burning switched their roles (Figure 4).
Emission from oil burning has been on the decline and that from coal burning has been on the
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 5 of 17
increase since the oil-shock decade. It is therefore the influence of oil price upon the
relationship between CO2 emissions per capita and GDP per capita is also researched into in
this study.
0
20
40
60
80
100
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year
Annual Average Crude Price
(constant 2008 $ per barrel)
Arabian Light
Brent
Figure 3
Figure 3 Variation in the marker crude price during 1960 to 2008.
0%
10%
20%
30%
40%
50%
60%
70%
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year
Percentage share of
CO2 emissions
Coal
Oil
Gas
Figure 4 Percentage shares of carbon dioxide emissions stemming from burning of solid, liquid and gas fossil
fuels.
2.2 Model rationale
The study of the data presented in Section 2.1 led us to hypothesize that there exists a
cointegrating relationship between CO2 emissions per capita and GDP per capita, and that
these two variables must be strongly tied up in a long-run relationship. We also hypothesize
that the inclusion of oil price might strengthen the long-run equilibrium relationship, even
though the impact of oil price on CO2 emissions per capita would be many folds smaller than
the impact of GDP per capita on it. Since we are interested in the temporal growths of the
variables concerned, we use natural logarithms of the variables for model development.
Natural logarithms of CO2 emissions per capita, GDP per capita, and oil price are denoted by
C(t), G(t), and O(t), respectively, where t represents the time in years.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 6 of 17
2.3 Stationary tests
Since the landmark contribution of Engle and Granger (1987) in the regression analyses of
time series, it has become a routine procedure to test if the time-series concerned are
stationary or not. It is because an OLS regression model developed with non-stationary time
series data violate the standard assumptions for asymptotic analyses such as hypothesis tests
about the regression parameters (Granger and Newbold, 1974). Time series data on C, G and
O were thus subjected to augmented Dickey-Fuller test (ADF), GLS-detrended Dickey-Fuller
test (DF-GLS), Phillips-Perron test (PP), Ng and Perron test (NP-MZt), and Kwiatkowski,
Phillips, Schmidt, and Shin test (KPSS) of Dickey and Fuller (1979), Elliott, Rothenberg, and
Stock (1996), Phillips and Perron (1988), Ng and Perron (2001), and Kwiatkowski et al.
(1992), respectively. All tests except the KPSS test have the null hypothesis that the data
series tested contains a unit root, i.e. the tested series is non-stationary. The KPSS test has the
null of the tested series being stationary. The test statistics obtained at levels and at first
differences of C, G and O, using the statistical package EViews6 from Quantitative Micro
Software LLC, are listed in Table 1. All test statistics confirm that C and G are non-stationary
at level and stationary at first difference. That is, C and G are I(1) series. All tests but the
KPSS test point out that O is an I(1) series. When considering the KPSS test statistics, O must
be taken as an I(0) series. Since O is used as a regressor in the ARDL procedure used in this
study, whether O is an I(1) series or an I(0) series does not effect the analyses (Pesaran, Shin,
and Smith, 2001).
Table 1 Unit root / Stationary test statistics for C, G, and O (which are the respective natural logarithms of
CO2 emissions per capita, GDP per capita, and oil price) and their first differences.
Variable ADF test DF-GLS test PP test NP-MZt test KPSS test
C -1.91 [0] 0.94 [0] -2.06 2.48 0.73**
ΔC -6.72 [0]*** -6.44 [0]*** -6.72*** -3.34*** 0.27
G -0.89 [0] 2.46 [0] -0.86 4.52 0.74***
ΔG -5.72 [0]*** -5.77 [0]*** -5.71*** -3.34*** 0.13
O -1.23 [0] -0.85 [0] -1.38 -0.90 0.29
ΔO -6.47 [0]*** -6.39 [0]*** -6.48*** -3.41** 0.09
Note: Symbol Δ denotes first difference. Symbols *** and ** indicate significance at the 1% and 5% levels,
respectively. Given within the brackets are the respective lag lengths of the ADF and DF-GLS test statistics,
selected automatically based on Hannan-Quinn Criterion with the user specified maximum lag of 9. The PP,
NP-MZt and KPSS test statistics are based on the automatically selected Newey-West bandwidth using Parzen
kernel.
3 Econometric methodology
In the ARDL bound-testing approach (Pesaran and Shin, 1999; Pesaran, Shin, and Smith,
2001), testing of cointegration among a dependent variable Y and regressors Xj (j = 1, 2, …, k)
begins with the unrestricted equilibrium correction model (ECM), given by Eq. (1), in which
the regressors Xj may be I(0) or I(1) series.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 7 of 17
ε(t)(t-i) Xb(t)Xb
Y(t-i)a)(tX)Y(tY(t)
j
p
i
k
jijj
k
jj
p
iij
k
jjy
+++
+++=
= ==
==
ΔΔ
Δ11Δ
1 11
11
0
ββα
(1)
where Δ symbolizes the first difference, α0 is the unrestricted intercept, βy is the coefficient of
the lagged level dependent variable Y and βj (j = 1, 2, …, k) are the coefficients of the lagged
level regressors Xj, t is time in year, ai are the coefficients of lagged ΔY, bj are the coefficients
of current ΔXj, bij are the coefficients of lagged ΔXj, k denotes the maximum number of
regressors used, p denotes the maximum lag length used, and ε(t) are the serially uncorrelated
residuals.
The first step in the ARDL bound testing approach is to determine the optimal value for
the lag length p in Eq. (1) so as to maintain the balance between mitigating the residual serial
correlation problem in Eq. (1) and refraining from over-parameterizing Eq. (1). This is done
by estimating Eq. (1) using the OLS procedure for different values of lag length p. For each
regression, Akaike’s Information Criterion (AIC) is determined. The lag length corresponding
to the regression with extreme value for AIC is chosen as the maximum lag length. The above
choice is further fortified by the determination of the Breusch-Godfrey Lagrange multiplier
test statistics for testing the null hypothesis of no residual serial correlation.
Having chosen the appropriate lag length p, the probable existence of a cointegrating
relationship in Eq. (1) is tested in the ARDL bound-testing procedure by calculating the F-
statistic under the null hypothesis that βy = βj (j = 1, 2, …, k) = 0 (that is, no cointegration)
against the alternative hypothesis that they are not. The F-statistic is then compared with the
asymptotic critical value bounds provided in Pesaran, Shin and Smith (2001) that are
reproduced in Table 2 for the cases of (i) a single regressor and (ii) two regressors.
If the F-statistic falls on the right-hand side of the upper bound critical value then the null
of no cointegration is rejected and cointegration among the variables is firmly established.
Consequently, a long-run equilibrium relationship among the dependent variable Y and the
regressors Xj shall be established in which the Xj are regarded as forcing Y. If the F-statistic
falls on the left-hand side of the lower bound critical value then the null cannot be rejected
and no cointegration among the variables is firmly established. Finally, if the F-statistic falls
between the lower and upper bound critical values, no conclusive decision could be reached.
Table 2 Asymptotic critical value bounds for F-statistic and t-ratio at 5% level of significance for the cases of
(i) a single regressor and (ii) two regressors.
Asymptotic critical value bounds at 5% level of significance
(i) with a single regressor (ii) with two regressors
Test
statistic Lower bound
I(0) Upper bound
I(1) Lower bound
I(0) Upper bound
I(1)
FIII 4.94 5.73 3.79 4.85
tIII -2.86 -3.22 -2.86 -3.53
Note: FIII is the F-statistic for testing βy = βj (j = 1, 2, …, k) = 0 in Eq. (1) and tIII is the t-ratio for testing βy = 0 in
Eq. (1). Critical values for FIII and tIII, are obtained from Tables CI(iii), and CII(iii) of Pesaran, Shin and Smith
(2001), respectively.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 8 of 17
The above test is complemented by the calculation of t-ratio under the null hypothesis of
βy = 0 in Eq. (1) against the alternative hypothesis that it is not. The t-ratio is then compared
with the asymptotic critical value bounds tabulated in Table 2. If the t-ratio falls on the right-
hand side of the upper bound critical value then the null of βy = 0 is rejected. If it falls on the
left-hand side of the lower bound critical value then the null cannot be rejected. If it falls
within the bounds then no conclusive decision could be reached.
Once the non-rejection of cointegration among the variables concerned are established,
the long-run equilibrium relationship is estimated using the ARDL approach detailed in
Pesaran and Shin (1999). First, the numerical values of the lag orders m and nj (j = 1, 2, … k)
of the ARDL(m,nj) model, expressed by Eq. (2), are estimated using the OLS procedure for
different combinations of m and nj (j = 1, 2, … k).
(t)(t-i)XY(t-i)Y(t) j
k
j
n
iij
m
ii
j
υτγσ
+++= = == 1 01
0 (2)
where σ0 is the constant term, γi are the coefficients of the lagged level dependent variable Y,
τij are the coefficients of the current and lagged level regressors Xj, k denotes the maximum
number of regressors used, m and nj denote the maximum lag lengths of Y and Xj,
respectively, and υ(t) are the serially uncorrelated residuals.
The lag lengths corresponding to the regression with minimum value for AIC or for
Schwarz Criterion (SC) give the ARDL(m,nj) model representing the long-run equilibrium
relationship. The coefficients of the long-run equilibrium relationship are estimated using the
OLS procedure, and the corresponding standard errors and t-statistics are estimated using the
Delta method as suggested in Pesaran and Shin (1999).
The residuals of the long-run equilibrium relationship is known as the equilibrium
correction term, which paves the way for estimating the short-run dynamic equation among Y
and Xj by setting up a conditional ECM corresponding to the ARDL(m,nj) model representing
the long-run equilibrium relationship. In the conditional ECM, the first difference of Y is
regressed on a one period lag of the equilibrium correction term, lagged first differences of Y
and current and lagged first differences of Xj using OLS regression (Pesaran, Shin, and Smith,
2001).
The short-run dynamic equation is considered statistically significant only if the residuals
of the model do not reject the null hypotheses of no residual serial correlation, no
heteroskedasticity among the residuals, and normally distributed residuals. These hypotheses
tests were carried out in this study using the Breusch-Godfrey Lagrange multiplier test,
Jarque-Bera normality test, and ARCH heteroskedasticity test, respectively. The stability of
the estimated parameters of the short-run dynamic equation is tested by employing the
Ramsey regression specification error test (RESET), which would reveal any misspecification
in the short-run dynamic equation such as non inclusion of all relevant variables. The stability
was further verified using the cumulative sum of recursive residuals (CUSUM) test (Brown,
Durbin, and Evans, 1975).
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 9 of 17
4 Results and discussion
4.1 Cointegration test results
Cointegration was first tested with C as the dependent variable and G as the regressor. Eq. (1)
with k = 1 was estimated using the OLS regression for different values of lag length p. Since a
limited number of annual data were used for the analyses, maximum value of p was limited to
3. For each regression, AIC statistics, P-values of Breusch-Godfrey Lagrange multiplier test
statistics at prescribed lag orders 1 and 4, F-statistic, and t-ratio were estimated. All statistics,
except the AIC statistics, for the cases of p = 0, 1, 2, and 3 were evaluated using the data sets
spanning the periods 1961-2007, 1962-2007, 1963-2007, and 1964-2007, respectively. In
estimating the AIC statistics for all values of p, the data set spanning the period 1964-2007
was used, which was a necessity to aid comparison among the AIC values estimated.
The results, tabulated in Table 3, show that the AIC statistic is at its minimum at p = 3.
The corresponding P-values of the Breusch-Godfrey Lagrange multiplier test statistics are
large enough to not reject the null hypothesis of no residual serial correlation even at 10%
level of significance. The corresponding F-statistic listed in Table 3 falls on the right-hand
side of the respective upper bound critical value (listed in Table 2) resulting in the rejection of
the null of no cointegration at 5% level of significance. The t-ratio given in Table 3 reveals
that the null of βy = 0 in Eq. (1) is also rejected at 5% level of significance. It is therefore the
existence of cointegration among the variables C and G for Australia is strongly established
with G forcing C. That is, GDP per capita forces CO2 emissions per capita.
Table 3 Statistics for testing the existence of a cointegrating relationship between C and G in Eq. (1) with k =
1, with C as the dependent variable and G as the regressor.
Maximum lag length p = 0 p = 1 p = 2 p = 3
AIC -4.545 -4.522 -4.436 -4.726
Probability )1(
2
SC
χ
0.605 0.493 0.564 0.375
Probability )4(
2
SC
χ
0.667 0.761 0.038 0.404
FIII 6.48right 5.49mid 6.21right 8.32right
tIII -3.27right -2.77left -3.02mid -3.96right
Note: Probability )1(
2
SC
χ
and Probability )4(
2
SC
χ
denote the P-values of the Breusch-Godfrey Lagrange
multiplier test statistics for the null of no residual serial correlation at pre-specified lag orders 1 and 4,
respectively. Superscripts right, left, and mid denote that the statistic concerned falls on the right-hand side of the
upper critical bound, on the left-hand side of the lower critical bound, and in the middle of the critical bounds
tabulated in Table 2, respectively.
When the above analyses were repeated with G as the dependent variable and C as the
regressor, the F-statistics and t-ratios of all cases studied fell on the left-hand side of the
respective lower bound critical values, and thereby resulting in the non-rejection of the null of
no cointegration. It can therefore be concluded that, in case of Australia, GDP per capita
forces CO2 emissions per capita and CO2 emissions per capita does not force GDP per capita.
The results of the tests carried out in search of a cointegration relationship among C, G
and O with C as the dependent variable, tabulated in Table 4, show that p = 3 was chosen.
However, the corresponding F-statistic and t-ratio fall within the critical bound values (listed
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 10 of 17
in Table 2) resulting in neither the rejection nor the non-rejection of the null of no
cointegration at 5% level of significance. It is therefore the existence of a long-run
relationship among C, G and O for Australia is not firmly established.
Table 4 Statistics for testing the existence of a cointegrating relationship among C, G and O in Eq. (1) with k
= 2, with C as the dependent variable and G and O as the regressors.
Maximum lag length p = 0 p = 1 p = 2 p = 3
AIC -4.571 -4.541 -4.417 -4.627
Probability )1(
2
SC
χ
0.498 0.247 0.853 0.395
Probability )4(
2
SC
χ
0.849 0.473 0.060 0.352
FIII 4.48mid 4.27mid 4.36mid 4.84mid
tIII -3.06mid -2.74low -2.82low -3.05mid
Note: Same as in Table 3.
4.2 Long-run equilibrium relationships
Since cointegration between the dependent variable C and the forcing variable G was firmly
established, as discussed in Section 4.1, as the next step, the long-run equilibrium relationship
between C and G was estimated. First the AIC and SC statistics were estimated for different
combinations of the lag orders m and n1 in the ARDL(m,n1) model, given by Eq. (2) with k =
1. Since we deal with annual data, the maximum lag length was limited to 3. We therefore
carried out 16 (= [3+1]2) regressions. Of which, the minimum AIC value was found to
correspond to the ARDL(1,2) model whereas the minimum SC value corresponded to the
ARDL(1,0) model. The coefficients of the levels relationship given by the ARDL(1,2) and
ARDL(1,0) models were estimated using the OLS procedure, and the corresponding standard
errors and t-statistics were estimated using the Delta method. The results are tabulated in
Table 5.
Table 5 shows that the coefficients and the standard errors of the ARDL(1,2) and
ARDL(1,0) models are very similar, and therefore the ARDL(1,2) model is chosen to represent
the long-run equilibrium relationship between C and G, and is expressed by Eq. (3).
ARDL(1,2): (t)tGC(t) 1
]5.13[]1.5[ ˆ
)(7020.07468.0
ν
++= (3)
where the numerical values given within the brackets are the t-statistics of the corresponding
coefficients and the residual (t)
1
ˆ
ν
is the equilibrium correction term.
Table 5 Coefficients and related statistics of the long-run equilibrium relationship between C and G.
Regressor Coefficient Standard Error t-statistic
ARDL(1,2):
Constant 0.7468 0.1473 5.07
G 0.7020 0.0519 13.5
ARDL(1,0):
constant 0.7070 0.1517 4.66
G 0.7202 0.0525 13.73
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 11 of 17
Since the null of no cointegration among the dependent variable C and the forcing
variables G and O was not rejected, as discussed in Section 4.1, the long-run equilibrium
relationship between C, G and O, given by Eq. (4), was also estimated following a procedure
similar to that described in the preceding paragraphs.
ARDL(1,2,0): (t)tOtGC(t) 2
]8.2[]6.19[]2.7[ ˆ
)(0396.0)(6842.06399.0
ν
+++= (4)
where the t-statistics are given within the brackets below the corresponding coefficients and
the residual (t)
2
ˆ
ν
is the equilibrium correction term.
The t-statistics in Eq. (3) and (4) prove that the coefficients of the forcing variables G and
O are statistically significant. Moreover, the long-run equilibrium relationships show that 1%
increase in GDP per capita causes about a 0.7% increase in CO2 emissions per capita, whereas
1% increase in oil price causes only an insignificant 0.04% increase in CO2 emissions per
capita.
4.3 Short-run dynamic equations
The short-run dynamic equation estimated from the conditional ECM corresponding to
ARDL(1,2), using the OLS procedure, is given in Table 6 along with the estimated essential
statistics. It is evident from the tabulated results that the coefficient of the equilibrium
correction term )(t 1
ˆ1
ν
, known as the adjustment parameter, not only has the expected
negative sign, but also is highly significant, which can be taken as further proof of the
existence of a stable long-run equilibrium relationship (Bannerjee, Dolado, and Mestre, 1998).
The numerical value of the adjustment parameter reveals that any deviation from the long-run
equilibrium following a short-run disturbance is corrected by about 36% in a year. Tabulated
P-values also show that the coefficient of ΔG(t-1) is statistically significant at 10% level and
that the coefficients of ΔG(t) and ΔG(t-2) must be taken as zero. Therefore, we conclude that
the impact of GDP per capita growth upon the CO2 emissions per capita growth is such that
1% increase in the GDP per capita growth in the previous year would lead to about 0.33%
increase in the CO2 emissions per capita growth in the current year.
Table 6 Equilibrium correction form of the ARDL(1,2) model of Eq. (3).
Regressor Coefficient Standard Error t-Statistic P-value
)(t 1
ˆ1
ν
-0.3640 0.0947 -3.84 0.0004
ΔG(t) -0.0116 0.1634 -0.07 0.9439
ΔG(t-1) 0.3247 0.1768 1.84 0.0736
ΔG(t-2) -0.0772 0.1570 -0.49 0.6256
adjusted R2 = 32.4%; Durbin-Watson statistic = 2.06
)4(
2
SC
χ
= 3.45 [0.49]; )2(
2
N
χ
= 0.49 [0.78]; )1(
2
H
χ
= 0.24 [0.62]; )1(
2
FF
χ
= 1.79 [0.18]
Note: The equilibrium correction term )(t 1
ˆ1
ν
is the residual of Eq. (3). )4(
2
SC
χ
, )2(
2
N
χ
, )1(
2
H
χ
and )1(
2
FF
χ
denote chi-squared statistics of Breusch-Godfrey serial correlation LM test, Jarque-Bera normality test, ARCH
heteroskedasticity test, and RESET, respectively. The corresponding P-values are given within the brackets.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 12 of 17
P-values corresponding to the chi-squared statistics of the residual tests, tabulated in Table
6, show that none of the test statistics was significant even at 10% level of significance. We
therefore concluded that the parameter estimates of the short-run dynamic equation are
statistically significant. P-values corresponding to the RESET ruled out any model
misspecification in the short-run dynamic equation.
In order to further verify the stability of the short-run dynamic equation, we subjected it to
the CUSUM test. Figure 5 reveals that CUSUM confines itself within the 5% critical lines and
that the departure of CUSUM from the zero line is insignificant since 1980. It is therefore we
conclude that the estimated coefficients have remained nearly constants from one sample
period to the other providing further verification for the stability of the short-run dynamic
equation considered.
-20
-15
-10
-5
0
5
10
15
20
1970 1975 1980 1985 1990 1995 2000 2005
CUSUM
5% Significance
CUSUM
Year
Figure 5 Cumulative sum of recursive residuals (CUSUM) of the conditional ECM given in Table 6. The
broken lines represent the 5% critical limit.
The short-run dynamic equation estimated from the conditional ECM corresponding to
ARDL(1,2,0) is given in Table 7. The adjustment parameter is highly significant and its
numerical value reveals that any deviation from the long-run equilibrium following a short-
run disturbance is corrected by about 51% in a year. Moreover, the coefficient of ΔG(t-1) is
statistically significant at 5% level, the coefficients of ΔG(t) and ΔG(t-2) must be taken as
zero, and the coefficient of ΔO(t) is statistically significant at 10% level,. Therefore, we
conclude that 1% increase in the GDP per capita growth in the previous year would lead to
about 0.33% increase in the CO2 emissions per capita growth in the current year and that 1%
increase in the growth in oil price would lead to a negligible 0.02% increase in the CO2
emissions per capita growth in the same year. The P-values corresponding to the chi-squared
statistics confer statistical significance of the parameter estimates of the short-run dynamic
equation considered as well as the absence of any model misspecification.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 13 of 17
Table 7 Equilibrium correction form of the ARDL(1,2,0) model of Eq. (4).
Regressor Coefficient Standard Error t-Statistic P-value
)(t 1
ˆ2
ν
-0.5109 0.1205 -4.24 0.0001
ΔG(t) 0.1364 0.1528 0.89 0.3773
ΔG(t-1) 0.3353 0.1651 2.03 0.0489
ΔG(t-2) 0.0560 0.1476 0.38 0.7064
ΔO(t) 0.0230 0.0121 1.90 0.0642
adjusted R2 = 38.9%; Durbin-Watson statistic = 1.88
)4(
2
SC
χ
= 4.59 [0.33]; )2(
2
N
χ
= 0.55 [0.76]; )1(
2
H
χ
= 0.019 [0.89]; )1(
2
FF
χ
= 0.17 [0.68]
Note: The equilibrium correction term )(t 1
ˆ2
ν
is the residual of Eq. (4). Chi-squared statistics are described in
Table 6.
4.4 CO2 emissions forecast
From the results presented in the preceding three subsections, it could be firmly concluded
that there exists a strong cointegrating relationship between CO2 emissions per capita and
GDP per capita and that the impact of oil price on CO2 emissions per capita is negligible.
Therefore, for forecasting purposes, we would consider only the long-run equilibrium
relationship given by Eq. (3) and the corresponding short-run dynamic equation given in
Table 6. Subjecting the latter for further statistical testing, we find that it has a Theil
inequality coefficient of 0.39 in the scale of 0, indicating perfect fit, to 1. The bias and the
variance proportions of the mean squared forecast error were estimated to be 0.0015 and
0.2075, respectively. The near zero bias proportion indicates that the mean of the forecast is
exactly the same as the mean of the actual series, and the 21% variance proportion indicates
that there is only a very small difference between the variations of the forecast and of the
actual series. Such small bias and variance proportions testify the forecasts of the short-run
dynamic equation considered are reliable.
The forecast equation is derived by substituting the equilibrium correction term from the
long-run equilibrium relationship given by Eq. (3) into the corresponding short-run dynamic
equation given in Table 6 as follows:
[
]
)(t.)G(t-.)-C(t-.(t) 1ΔG324707468.0170200136400ΔC += (5)
Figure 6 shows the CO2 emissions per capita obtained by dynamically simulating Eq. (5),
along with the actual co2pc values used for developing the model. Dynamical simulation of
Eq. (5) is carried out with the actual values of GDP per capita and with the actual value of
CO2 emissions per capita at 1960 as the initial emission input. The match between the model
predictions and the actual emissions seen in Figure 6 is commendable. Eq. (5) could therefore
be used for reliably forecasting Australia’s future CO2 emissions.
In forecasting CO2 emissions per capita after 2007, which is the end year of the data set
used, we used four different hypothetical GDP per capita growth rate scenarios, which were
the low-growth, reference-growth, average-growth, and high-growth scenarios. In these
scenarios, GDP per capita was assumed to grow at the rates of 0.7%, 1.4%, 2.5%, and 4.1%,
respectively, from 2007 onwards. The low, average and high GDP per capita growth rates
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 14 of 17
were the respective minimum, average and maximum GDP per capita growth rates prevailed
during the period 1992-2007. The reference growth scenario assumes current trends in
economic activity continue into the future (Commonwealth of Australia, 2008, pp.17).
8
12
16
20
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Year
CO2 emissions per capita
(tonnes of CO2)
Actual
Dynamical simulation
Figure 6 Dynamically simulated CO2 emissions per capita using the forecast equation, Eq. (5), compared
with the actual values used for model development.
Forecasts of cumulative CO2 emissions in 2010, 2020 and 2030 were calculated by
multiplying the forecasted CO2 emissions per capita values for these years by the respective
medium variant population projections of United Nations (2009), which were 21,512,000 in
2010, 23,675,000 in 2020, and 25,656,000 at 2030, respectively. The results are tabulated in
Table 8.
Gurney et al. (2007) observed that in the absence of any major regional or global climate
change initiatives and without any significant technological breakthroughs, greenhouse gas
(GHG) emissions in Australia would reach 549, 638, and 695 million tonnes of CO2
equivalent in 2010, 2020, and 2030, respectively. The composition of CO2 emissions in the
total GHG emissions of Australia was about 70% in 1990 (Australian Bureau of Statistics,
2007) and about 74% on 2005 (Australian Bureau of Statistics, 2007). If this composition is
assumed to vary in the range of 74% to 80% during 2010 to 2030, the CO2 emissions would
fall in the range of 406 to 439 million tonnes of CO2 in 2010, 472 to 510 million tonnes of
CO2 in 2020, and 514 to 556 million tonnes of CO2 in 2030.
Table 8 Forecast of cumulative CO2 emissions stemming from the burning of solid, liquid and gaseous fossil
fuel at hypothetical growth rates of GDP per capita since 2007 and at United Nations predicted
medium variant population projections.
Fossil-fuel based CO2 emissions
projection (in million tonnes of CO2)
Hypothetical
scenario
GDP per capita
growth rate
since 2007
(in %)
2010 2020 2030
Low-growth 0.7 425 492 560
Reference-growth 1.4 429 521 622
Average-growth 2.5 434 568 732
High-growth 4.1 443 644 925
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 15 of 17
Retuning to the results tabulated in Table 8, it could be seen that the projected CO2
emissions in 2010 for the low, reference and average-growth scenarios fall in the range of 406
to 439 million tonnes of CO2. Projected CO2 emission in 2020 falls in the range of 472 – 510
million tonnes of CO2 only in the low-growth scenario. Projected CO2 emission in 2030 is
slightly larger than 556 million tonnes of CO2 in the low-growth scenario. As it is highly
likely that the GDP per capita growth rate of Australia during 2008 to 2030 lie below the
reference-growth rate considered in this study, we conclude that the CO2 emissions forecasts
made by the model developed are comparable with the emissions predicted by Gurney et al.
(2007).
The cumulative CO2 emissions stemming from the burning of solid, liquid and gaseous
fossil fuel in 2000 was 337 million tonnes of CO2. Percentage growths in cumulative CO2
emissions at 2010, 2020 and 2030 over 2000 level would, therefore, become 26%, 36%, and
46%, respectively, for the low-growth scenario and 27%, 40% and 55%, respectively, for the
reference-growth scenario. It should be borne in mind that such high forecasted percentage
increases of CO2 emissions over the 2000 level would be realized only if the economic growth
and energy consumption paths pursued by Australia since 1960 undergo no appreciable
changes in the future.
5 Conclusion
Existence of a strong cointegrating relationship between Australia’s CO2 emission per
capita and her GDP per capita is firmly established in this study. It is also proven that growing
GDP per capita forces the CO2 emissions per capita to grow, whereas the reverse is not true.
Inclusion of oil price as a forcing variable is found to have insignificant impact on CO2
emissions per capita which is not surprising because of the persistent high place of fossil-fuel
in her fuel-mix owing to perhaps her indigenous fossil-fuel reserves.
In the long-run, 1% increase in Australia’s GDP per capita causes about 0.7% increase in
her CO2 emission per capita. In the short-run, 1% increase in GDP per capita growth in the
previous year leads to about 0.33% increase in the current growth in CO2 emission per capita.
Moreover about 36% of any deviation from the long-run equilibrium is corrected within a
year. These results clearly prove that Australia’s current economic development path is CO2
emission intensive.
Such a strong tie between income and CO2 emissions results in the projection of the
cumulative CO2 emissions stemming from the burning of solid, liquid and gaseous fossil fuel
to grow by 36 to 40% in 2020 over the 2000 level for GDP per capita growth rates in the
range of 0.7 to 1.4%. It should be borne in mind that such high forecasted percentage
increases in CO2 emissions would become a reality only in the absence of proactive actions
taken by the Australian government to weaken the strong cointegrating relationship existing
between CO2 emissions and economic prosperity, measured by GDP per capita.
References
Aldy, J.E. (2005) ‘An environmental Kuznets curve analysis of U.S. state-level carbon dioxide emissions’, The
Journal of Environment & Development, Vol. 14, pp.48-72.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 16 of 17
Amarawickrama, H.A. and Hunt, L.C. (2008) ‘Electricity demand for Sri Lanka: a time-series analysis’, Energy,
Vol. 33, pp.724-39.
Arrow, K., Bolin, B., Costanza, R., Dasgupta, P., Folke, C., Holling, C.S., Jansson, B.-O., Levin, S., Maler, K.-G.,
Perrings, C., and Pimental, D. (1995) ‘Economic Growth, Carrying Capacity, and the Environment’, Science,
Vol. 268, pp.520-521.
Australian Bureau of Statistics (2007) Year Book Australia, 2005. Obtained through the Internet:
http://www.abs.gov.au/ausstats/abs@.nsf/0/97155E66650851FBCA256F7200
832FFC?opendocument [accessed Dec. 04, 2009].
Australian Bureau of Statistics (2008) Year Book Australia, 2008. Obtained through the Internet:
http://www.abs.gov.au/ausstats/ABS@.nsf/0/201B70E9EE4974B1CA2573D
200106851?opendocument [accessed Dec. 04, 2009].
Bannerjee, A., Dolado, J., and Mestre, R. (1998) ‘Error-correction mechanism tests for cointegration in single
equation framework’, Journal of Time Series Analysis, Vol. 19, pp.267-283.
British Petroleum (2009) BP Statistical Review of World Energy June 2009. Obtained through the Internet:
http://www.bp.com/statisticalreview [accessed July 13, 2009].
Brown, R.L., Durbin, J., and Evans, J.M. (1975) ‘Techniques for testing the constancy of regression relationships
over time’, Journal of the Royal Statistical Society, Series B, Vol. 37, pp.149–192.
Commonwealth of Australia (2008) Australia’s Low Pollution Future: The Economics of Climate Change
Mitigation Summary Report. Obtained through the Internet:
http://www.treasury.gov.au/lowpollutionfuture/summary/downloads/Australias_Low_Pollution_Future_Sum
mary.pdf [accessed Dec. 04, 2009].
de Bruyn, S.M., van den Bergh, J.C.J.M., and Opschoor, J.B. (1998) ‘Economic growth and emissions:
reconsidering the empirical basis of environmental Kuznets curves’, Ecological Economics, Vol. 25,
pp.161-175.
de Bruyn, S.M. (2000) Economic Growth and the Environment: An Empirical Analysis, Series in Economy and
Environment, Vol. 18, Dordrecht, Boston, and London: Kluwer Academic Publishers.
Dickey, D.A. and Fuller, W.A. (1979) ‘Distribution of the estimators for autoregressive time series with a unit
root’, Journal of the American Statistical Association, Vol. 74, pp.427-431.
Elliott, G., Rothenberg, T.J., and Stock, J.H. (1996) ‘Efficient tests for an autoregressive unit root’,
Econometrica, Vol. 64, pp.813-836.
Energy Information Administration (2008) International Energy Annual 2006. Obtained through the Internet:
http://www.eia.doe.gov/iea/ [accessed July 13, 2009].
Engle, R.F. and Granger, C.W.J. (1987) ‘Co-integration and error correction: representation, estimation and
testing’, Econometrica, Vol. 55, pp.251-276.
Friedl, B. and Getzner, M. (2003) ‘Determinants of CO2 emissions in a small open economy’, Ecological
Economics, Vol. 45, pp.133-148.
Galeotti, M. and Lanza, A. (2005) ‘Desperately seeking environmental Kuznets’, Environmental Modelling &
Software, Vol. 20, pp.1379-1388.
Granger, C.W.J. and Newbold, P. (1974) ‘Spurious regressions in econometrics’, Journal of Econometrics, Vol.
2, pp.111–120.
Gurney, A., Ford, M., Low, K., Tulloh, C., Jakeman, G., and Gunasekera, D. (2007) Technology: Toward a Low
Emissions Future, ABARE Research Report 07.16 prepared for the Australian Government Department of
Industry, Tourism and Resources, Canberra, September.
International Energy Agency (2009) CO2 Emissions from Fuel Combustion, 2009 Edition, Paris: IEA. Obtained
through the Internet: http://www.iea.org/stats/index.asp [accessed Dec. 01, 2009].
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., and Shin, Y. (1992) ‘Testing the null hypothesis of stationarity
against the alternative of a unit root: How sure are we that economic time series have a unit root?’ Journal
of Econometrics, Vol. 54, pp.159-178.
Published in revised form in Int. J. Oil, Gas and Coal Technology 2010:3(2):182-200
Page 17 of 17
Lanne, M. and Liski, M. (2004) ‘Trends and breaks in per-capita carbon dioxide emissions, 1870—2028’,
Energy Journal, Vol. 25, pp.41-65.
Lieb, C.M. (2003) ‘The Environmental Kuznets Curve - a survey of the empirical evidence and of possible
causes’. Discussion Paper Series, No. 391. University of Heidelberg; Department of Economics.
Lindmark, M. (2002) ‘An EKC-pattern in historical perspective: Carbon dioxide emissions, technology, fuel
prices and growth in Sweden 1870-1997’, Ecological Economics, Vol. 42, pp.333-347.
Managi, S. (2006) ‘Pollution, natural resource and economic growth: an econometric analysis’, International
Journal of Global Environmental Issues, Vol. 6, pp.73-88.
Marland, G., Boden, T.A. and Andres, R.J. (2008) ‘Global, regional, national fossil fuel CO2 Emissions’,
Trends: A compendium of Data of Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge
National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee, U.S.A. Obtained through the
Internet: http://cdiac.ornl.gov/trends/trends.htm [accessed Nov 30, 2008].
Ng, S. and Perron, P. (2001) ‘Lag length selection and the construction of unit root tests with good size and
power’, Econometrica, Vol. 69, pp.1519-1554.
Pesaran, M.H. and Shin, Y. (1999) ‘An autoregressive distributed lag modeling approach to cointegration
analysis’, In: Strom, S. (ed.), Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch
Centennial Symposium, (pp.371-413), Cambridge: Cambridge University Press.
Pesaran, M.H., Shin, Y., and Smith, R. (2001) ‘Bounds testing approaches to the analysis of level relationships’,
Journal of Applied Econometrics, Vol. 16, pp.289–326.
Phillips, P.C.B. and Perron, P. (1988) ‘Testing for a unit root in time series regression’, Biometrika, Vol. 75,
pp.335-346.
Schmalensee, R., Stoker, T.M., and Judson, R.A. (1998) ‘World carbon dioxide emissions: 1950–2050’, Review
of Economics and Statistics, Vol. 80, pp.15-27.
Shafik, N. (1994) ‘Economic Development and Environmental Quality: An Econometric Analysis’, Oxford
Economic Papers, Vol. 46, pp.757-773.
Shafik, N. and Bandyopadhyay, S. (1992) ‘Economic growth and environmental quality: Time series and cross
country evidence’, World Bank Policy Research Working Paper, WPS 904, Washington DC: World Bank.
Shanthini, R. and Perera, K. (2007) ‘Oil price fluctuation incorporated models for carbon dioxide emissions and
energy consumption of high-income economies’, Ceylon Journal of Science (Physical Sciences), Vol. 13,
pp.45-59.
Unruh, G.C. and Moomaw, W.R. (1998) ‘An alternate analysis of apparent EKC-type transitions’, Ecological
Economics, Vol. 25, pp.221-229.
United Nations (2009) World Population Prospects: The 2008 Revision, Population Division of the Department
of Economic and Social Affairs of the United Nations Secretariat. Obtained through the Internet:
http://esa.un.org/unpp [accessed Dec. 04, 2009].
World Bank (2008) World Development Indicators Online. Obtained through the Internet:
http://publications.worldbank.org/susbcriptions/WDI/ [accessed Feb. 29, 2008].
... Our study found strong linear correlations between social-economic factors and carbon emissions on the provincial and city scales (Fig. 5a-d). Economic growth has a positive effect on carbon emissions, as shown in other related studies [75][76][77] . Although rapid economic growth led by industrialization and urbanization has remarkably enhanced per capita incomes and living standards, a considerable amount of energy consumption results in a relatively high, increasing trend of carbon emissions in the region 78 . ...
Article
Full-text available
This study analysed spatial-temporal dynamics of carbon emissions and carbon sinks in Guangdong Province, South China. The methodology was based on land use/land cover data interpreted from continuous high-resolution satellite images and energy consumption statistics, using carbon emission/sink factor method. The results indicated that: (1) From 2005 to 2013, different land use/land cover types in Guangdong experienced varying degrees of change in area, primarily the expansion of built-up land and shrinkage of forest land and grassland; (2) Total carbon emissions increased sharply, from 76.11 to 140.19 TgC yr-1 at the provincial level, with an average annual growth rate of 10.52%, while vegetation carbon sinks declined slightly, from 54.52 to 53.20 TgC yr-1. Both factors showed significant regional differences, with Pearl River Delta and North Guangdong contributing over 50% to provincial carbon emissions and carbon sinks, respectively; (3) Correlation analysis showed social-economic factors (GDP per capita and permanent resident population) have significant positive impacts on carbon emissions at the provincial and city levels; (4) The relationship between economic growth and carbon emission intensity suggests that carbon emission efficiency in Guangdong improves with economic growth. This study provides new insight for Guangdong to achieve carbon reduction goals and realize low-carbon development.
... Jalil and Feridun (2010) indicate that financial development lowers CO 2 emissions in China by investigating the impact of financial development, economic growth and energy consumption on environmental pollution. Shanthini and Perera (2010) suggest the probable existence of a co-integrating relationship between Australia's fossil-fuel based CO 2 emissions per capita and GDP per capita. In the short-run, 1 percent increase in GDP per capita growth in the previous year leads to 0.33 percent increase in the current growth in CO 2 emission per capita. ...
Article
Global environmental problems are getting more attention especially the increase in earth temperatures and change in climate. Increase in world average air and ocean temperatures, widespread melting of snow and ice, and rising global average sea level are some evidences of global warming. A CO2 emission, which is a global pollutant is the main greenhouse gas that causes 58.8 percent of global warming and climate change [The World Bank (2007a)]. The intergovernmental panel on climate change (IPCC) reported a 1.1 to 6.4 °C rise in the world temperatures and an increase in the sea level of about 16.5 to 53.8 cm at the end of 21st century [IPCC (2007)]. Combined global land and ocean surface temperature for January 2010 on the average was 0.60°C (1.08°F) above the 20th century average of 12.0°C (53.6°F) and the average global temperature for January 2010 at the surface air was recorded 0.83°C (1.49°F) above the 20th century average of 2.8°C (37.0°F). Global warming is partly resulting from higher night temperature and partly due to rapid urbanisation. Other factors adding to global warming are the continuously changing irrigation systems, desertification and variations in the use of local lands. The developing countries need more energy consumption for economic growth that’s why these economies face more environmental issues.
... They used a set of year-group dummy variables, the choice of which was solely guided by world crude real price fluctuations. A predictive model for Australia's per capita CO 2 emissions with per capita real GDP and world crude real price as explanatory variables was developed by Shanthini and Perera (2010) who used the ARDL bounds testing approach to cointegration for the first time to study the emissions-income-crude price nexus of a nation. Their study showed that world crude real price variations had very little influence on the emission-income nexus of Australia, which they attributed to Australia's possession of rich fossil fuel reserves. ...
Article
Full-text available
For the first time in the scientific literature, this research establishes empirical evidence for the existence of a causal relationship among fossil fuel-based carbon dioxide (CO 2) emissions in the United States (US), her gross domestic product (GDP) and world crude price. Estimated long-run income elasticity of CO 2 emissions is as high as 3.2% in the US. A strong bi-directional long-run causality is found between CO 2 emissions and GDP. Short-run causality runs from crude price to CO 2 emissions to GDP. Monte Carlo stochastic simulations of the model developed reveal even a small increase in the reference real GDP growth rate causes considerable increase in the future CO 2 emissions in the US. Results of this study therefore suggest urgent policy actions are imperative in the US to decouple the strong causal relationship existing between her CO 2 emissions and GDP, and to ensure sustainable economical development in the US.
... Tian and Zhang calculated China's per-capita factor analysis model for carbon emissions decomposition based on the Generalized Fisher Price Index method (GFI) [9]. Rajaratnam and Kanthi developed a conditional equilibrium correction model (ECM) for quantifying the relationship between Australia's percapita GDP and per-capita carbon emissions [10]. Lee and Chang used Panel Seemingly Unrelated Regressions Augmented Dickey-Fuller (SURADF) unit-root tests to determine whether the stochastic convergence and b-convergence of the per-capita carbon emissions were supported in OECD countries [11]. ...
Article
Full-text available
China is considered to be the main carbon producer in the world. The per-capita carbon emissions indicator is an important measure of the regional carbon emissions situation. This study used the LMDI factor decomposition model-panel co-integration test two-step method to analyze the factors that affect per-capita carbon emissions. The main results are as follows. (1) During 1997, Eastern China, Central China, and Western China ranked first, second, and third in the per-capita carbon emissions, while in 2009 the pecking order changed to Eastern China, Western China, and Central China. (2) According to the LMDI decomposition results, the key driver boosting the per-capita carbon emissions in the three economic regions of China between 1997 and 2009 was economic development, and the energy efficiency was much greater than the energy structure after considering their effect on restraining increased per-capita carbon emissions. (3) Based on the decomposition, the factors that affected per-capita carbon emissions in the panel co-integration test showed that Central China had the best energy structure elasticity in its regional per-capita carbon emissions. Thus, Central China was ranked first for energy efficiency elasticity, while Western China was ranked first for economic development elasticity.
... He used econometric techniques fit for stationary time series, and concluded that 1% growth in real GDP per capita caused 0.9% growth in CO 2 emissions per capita when holding technological progress, They used a set of year-group dummy variables, the choice of which was solely guided by world crude real price fluctuations. A predictive model for Australia's per capita CO 2 emissions with per capita real GDP and world crude real price as explanatory variables was developed by Shanthini and Perera [47] who used the ARDL bounds testing approach [20,21] for the first time to study the emissions-income-crude price nexus of a nation. A conditional equilibrium correction model (ECM) developed by them forecasted fossil fuel-based CO 2 emissions in Australia to grow by 36 to 40% in 2020 over the 2000 level even for per capita GDP growth rates as low as 0.7 to 1.4%. ...
Article
Full-text available
With the prime objective of learning from the fossil fuel based CO2 emissions-economic growth-world crude price nexus of a leading economy, the underpinning nature of the relationship among them is investigated for the United States (US). Autoregressive distributed lag bounds testing approach to cointegration provides empirical evidence for the existence of a long-run equilibrium relationship with 1% growth in GDP being tied up with 3.2% growth in CO2 emissions in the US. Increase in crude price and technological progress, proxied by time trend, are associated with decline in CO2 emissions in the long-run, though by comparatively small magnitudes. Short-run dynamics restore 25% of any disequilibrium in a year. Owing to the structural breaks identified in the individual series by the unit root tests, the stability of the model coefficients over the sample period is tested using the cumulative sum of recursive residuals test and ascertained. Error-correction based Granger causality tests provide evidence for fluctuating world crude real price Granger causing fluctuations in CO2 emission, and fluctuating CO2 emission Granger causing the rise and fall of real GDP. Deviations from long-run equilibrium are seen to Granger cause changes in both the CO2 emissions and the real GDP in the US.
Article
Energy-intensive industries are the primary sectors of energy and resources consumption and carbon emissions. Exploring the temporospatial pattern and influencing factors of carbon emission efficiency (CEE) of energy-intensive industries helps discover the contribution of energy-intensive industries to regional carbon emissions and formulate different regional low-carbon industry development strategies. Through three-stage data envelopment analysis (DEA) model, the CEE of energy-intensive industries of China was estimated from provincial level, and the temporospatial distribution and influencing factors of CEE were analyzed by spatial autocorrelation analysis and Tobit model. The results showed that CEE of energy-intensive industries decreased from 0.649 to 0.587 during 2005–2017 with a decreasing rate of 9.60% annually, while the carbon emissions increased from 0.80 to 1.31 billion tonnes during 2005–2017. CEE of energy-intensive industries in China has obvious spatial heterogeneity and spatial agglomeration. CEE of the eastern provinces is relatively higher, while that of the western regions is lower. It showed that the scale efficiency (SE) of energy-intensive industries is close to the optimal frontier, while the pure technical efficiency (PTE) of energy-intensive industries should be improved in the future. Change of CEE of China’s provinces may fall into five categories: high-high-high, low-high-high, low-low-high, low-low-low, and low-high-low. Industry scale (IS) and research and development (R&D) are promoting factors for the CEE of energy-intensive industries, while energy intensity (EI), capital formation rate (CF) and water intensity (WI) are negative factors. To improve CEE and promote cleaner production of energy-intensive industries, the technological innovation capacity should be further improved, energy structure should be adjusted, energy and water consumption should be restrained and the open economic system should be further promoted in central and western China in the future, which help to realize the dislocation, coordination, and high-quality development of energy-intensive industries among China’s provinces.
Article
China is undergoing rapid urbanization, enlarging the construction industry, greatly expanding built-up land and generating substantial carbon emissions. We calculated both the direct and indirect carbon emissions from energy consumption (anthropogenic emissions) in the construction sector and analyzed built-up land expansion and carbon storage losses from the terrestrial ecosystem. According to our study, the total anthropogenic carbon emissions from the construction sector increased from 3,905×104 to 103,721.17 ×104 t from 1995 to 2010, representing 27.87%-34.31% of the total carbon emissions from energy consumption in China. Indirect carbon emissions from other industrial sectors induced by the construction sector represented approximately 97% of the total anthropogenic carbon emissions of the sector. These emissions were mainly concentrated in seven upstream industry sectors. Based on our assumptions, built-up land expansion caused 3704.84×104 t of carbon storage loss from vegetation between 1995 and 2010. Cropland was the main built-up land expansion type across all regions. The study shows great regional differences. Coastal regions showed dramatic built-up land expansion, greater carbon storage losses from vegetation and greater anthropogenic carbon emissions. These regional differences were the most obvious in East China followed by mid-southern China. These regions are under pressure for strong carbon emissions reduction.
Article
This paper analyzed dual perspectives of production and resident, and quantified factors affecting the energy-related per capita carbon emission from cities. Tianjin is the largest coastal city in northern China with rapid economic development and urbanization. Analysis of the CO2 emission generated by driving forces in Tianjin can provide guidance for policy decisions on the CO2 emission mitigation in global metropolises. Using LMDI and Kaya extension methods, this study built a decomposition model of economic output, economic structure, energy intensity of the production sectors, energy mix of the production sectors, population structure of urban and rural distribution, energy consumption per capita and energy mix of the household sectors to analyze the per capita carbon emission factors. The empirical study of Tianjin from 2005 to 2011 showed: Economic output played a positive role in the per capita carbon emission increasing, while economic structure, energy intensity of the production sectors and energy mix of the production sectors played a negative role; population structure of urban and rural distribution and energy consumption per capita promoted in the per capita carbon emission increasing, while energy mix of the household sectors inhibited the per capita carbon emission increasing.
Article
Full-text available
The relationships among total investment, foreign direct investment (FDI) and growth are examined in this study for Pakistan. The ARDL technique is used to analyze these relationships during the period of 1974-2012. The ADF-test and Philips-Peron test are used to check the unit roots for stationary of the time series data. The economic growth is found as a significant determinant of total investment in both periods; the short run as well as long run. Therefore, it is suggested for economic policy makers to devise those economic policies which enhance the process of growth and development in the country. The coefficient of FDI inflows is not found significant in short run and the long run periods. It may be due to the high share of FDI to services sector than commodity producing sector in Pakistan.
Article
Full-text available
Carbon dioxide (CO 2) emissions increased almost linearly with increasing income in many high-income economies during the 1960s, and during the 1970s, the rate of increase of CO 2 emissions declined in many high-income economies. This decline was seen by some as the evidence for the existence of the Environmental Kuznets Curve (EKC) relationship, the inverted-U shape of which infers that increase in income beyond a certain threshold value has the capacity to reduce the pollution intensity of an economy. Considering the fact that the oil price experienced a sudden increase first in the late 1973 and then in the late 1979, this paper proposes an Oil-Price-Fluctuation-Incorporated (OPFI) regression model for CO 2 emissions and energy consumption with gross domestic product per capita as the predictor. The OPFI model is shown to well explain the per capita carbon dioxide emissions and the per capita energy consumption histories of high-income economies during 1960 to 1997. Moreover, the OPFI model is shown to have the in-built capacity to remove the autocorrelation otherwise strongly present in the per capita linear model or in the EKC-type model. The statistical significance of the OPFI model and the estimated positive growths of the emissions (and energy consumption) with per capita income during 1991 to 1997 attest that it is the oil price fluctuation that trigger changes in the degree of dependence of the per capita emissions (and energy consumption) upon the per capita income of a high-income economy.
Article
Full-text available
Let n observations Y 1, Y 2, ···, Y n be generated by the model Y t = pY t−1 + e t , where Y 0 is a fixed constant and {e t } t-1 n is a sequence of independent normal random variables with mean 0 and variance σ2. Properties of the regression estimator of p are obtained under the assumption that p = ±1. Representations for the limit distributions of the estimator of p and of the regression t test are derived. The estimator of p and the regression t test furnish methods of testing the hypothesis that p = 1.
Article
Full-text available
Most environmental Kuznets curve (EKC) theories do not apply to carbon dioxide (CO2)—an unregulated, invisible, odorless gas with no direct human health effects. This analysis addresses the hypothesis that the income-CO2relationship reflects changes in the composition of an economy as it develops and the associated role of trade in an emissions-intensive good (e.g., electricity). To test this hypothesis, I use a novel data set of 1960 to 1999 state-level CO2emissions to estimate pretrade (production-based) CO2EKCs and posttrade (consumption-based) CO2EKCs. Based on the first EKC analysis of CO2emissions in the United States, I find that consumption-based EKCs peak at significantly higher incomes than production-based EKCs, suggesting that emissions-intensive trade drives, at least in part, the income-emissions relationship. I have also investigated the robustness of the estimated income-CO2relationship through a variety of specifications. Estimated EKCs appear to vary by state, and the estimated income-emissions relationships could be spurious for some states with nonstationary income and emissions data. Finally, I find that cold winters, warm summers, and historic coal endowments are positively associated with states’ CO2emissions.
Article
Full-text available
National and international economic policy has usually ignored the environment. In areas where the environment is beginning to impinge on policy, as in the General Agreement on Tariffs and Trade (GATT) and the North American Free Trade Agreement (NAFTA), it remains a tangential concern, and the presumption is often made that economic growth and economic liberalization (including the liberalization of international trade) are, in some sense, good for the environment. This notion has meant that economy-wide policy reforms designed to promote growth and liberalization have been encouraged with little regard to their environmental consequences, presumably on the assumption that these consequences would either take care of themselves or could be dealt with separately. In this article, we discuss the relation between economic growth and environmental quality, and the link between economic activity and the carrying capacity and resilience of the environment.
Article
This study estimates electricity demand functions for Sri Lanka using six econometric techniques. It shows that the preferred specifications differ somewhat and there is a wide range in the long-run price and income elasticities with the estimated long-run income elasticity ranging from 1.0 to 2.0 and the long-run price elasticity from 0 to −0.06. There is also a wide range of estimates of the speed with which consumers would adjust to any disequilibrium, although the estimated impact income elasticities tended to be more in agreement ranging from 1.8 to 2.0. Furthermore, the estimated effect of the underlying energy demand trend varies between the different techniques; ranging from being positive to zero to predominantly negative. Despite these differences, the forecasts generated from the six models up until 2025 do not differ significantly. It is therefore encouraging that the Sri Lanka electricity authorities can have some faith in econometrically estimated models used for forecasting. Nonetheless, by the end of the forecast period in 2025 there is a variation of around 452 MW in the base forecast peak demand that, in relative terms for a small electricity generation system like Sri Lanka's, represents a considerable difference.
Article
The aim of the paper is to explore the relationship between economic development and carbon dioxide (CO2) emissions for a small open and industrialized country, Austria. We test whether an Environmental Kuznets Curve relationship also holds for a single country rather than concentrating on panel or cross-section data for a set of countries. A cubic (i.e. N-shaped) relationship between GDP and CO2 emissions is found to fit the data most appropriately for the period 1960–1999, and a structural break is identified in the mid-seventies due to the oil price shock. Furthermore, two variables are additionally significant: import shares reflecting the well-known pollution haven hypothesis, and the share of the tertiary (service) sector of total production (GDP) accounting for structural changes in the economy. Emission projections derived from this single country specification support the widely held opinion that significant policy changes are asked for when implementing the Kyoto Protocol in order to bring about a downturn in future carbon emissions.