To understand the sources of emissions, this study uses a vector autoregression model to examine the relationship among findings within the study indicate: 1 Granger Causality running fro
Trang 1An empirical analysis of the role of China’s exports on CO 2 emissions
Nyakundi M Michieka⇑, Jerald Fletcher, Wesley Burnett
Resource Management Program – Environmental and Natural Resource Economics, West Virginia University, Morgantown, WV 26505-6108, United States
h i g h l i g h t s
"We attempt to correct China’s coal consumption data
"We discover Granger Causality running from exports to CO2emissions
"We discover Granger Causality running from exports to trade-openness
"Policies aimed at controlling exports can control CO2emissions
"Policies aimed at controlling coal consumption will affect exports and CO2emissions
a r t i c l e i n f o
Article history:
Received 14 March 2012
Received in revised form 5 October 2012
Accepted 20 October 2012
Keywords:
China
Emissions
Vector autoregression
International trade
Coal consumption
a b s t r a c t
China is one of the world’s most rapidly growing countries and the largest consumer of energy in the world As a result, China’s pollution emissions almost doubled from 2002 to 2007, and in 2006 it sur-passed the United States to become the world’s top carbon dioxide emitter Understanding the sources
of emissions is essential towards designing policies aimed at curbing carbon emissions in China The surge in China’s exports has been partially blamed for this increase in emissions To understand the sources of emissions, this study uses a vector autoregression model to examine the relationship among
findings within the study indicate: (1) Granger Causality running from exports to emissions; (2) Granger Causality running from coal consumption to exports; and (3) GDP determines future variability in exports
could affect CO2emissions and exports Results from this study should assist in formulating policies to
Ó 2012 Elsevier Ltd All rights reserved
1 Introduction
China is one of the most rapidly growing countries in the world
and is the largest energy consumer[21] In 2006, China surpassed
the US as the world’s top gross carbon dioxide (CO2) emitter with
6.1 billion tons of annual emissions and by 2008 had already
out-distanced the US by 1.5 billion tons [40].1China’s CO2emissions
grew at 3.3% per year between 1990 and 1999, accounting for 13%
of global emissions These emissions doubled over the next decade,
growing at 8.9% per year between 2000 and 2007, and accounting
for 17% of global emissions Presently, China emits 21.3% of global
CO2emissions[17,55] The rapid expansion of the Chinese economy,
coupled with a coal-oriented energy structure, has made coal
consumption a major source of emissions[63] The country is mov-ing from a predominantly agricultural economy to one that is increasingly urbanized and industrialized[1] Moreover, growth in coal–fired electricity generation has been cited as a reason for the surge in emissions
New research indicates that about a third of all Chinese carbon dioxide emissions are the result of producing goods for export to developing and developed countries Weber et al.[58]found that
in 2005, around one-third of Chinese CO2emissions were gener-ated by the production of goods for export while Wang and Watson
[57]concluded that net exports from China accounted for 23% of its total CO2emissions in 2004 Shui and Harris[50]estimated that in
2003, close to 14% of China’s CO2emissions came from producing goods for export This problem is likely to persist owing to the ris-ing popularity of China’s exports which account for 10% of global exports Theory implies that for some countries a comparative advantage for production exists directly because of differences in environmental regulations The pollution heaven hypothesis posits that ‘‘pollution havens’’ will attract polluting industries that 0306-2619/$ - see front matter Ó 2012 Elsevier Ltd All rights reserved.
⇑Corresponding author.
E-mail addresses: nyakundi.michieka@mail.wvu.edu (N.M Michieka), jerry.
fletcher@mail.wvu.edu (J Fletcher), wesley.burnett@mail.wvu.edu (W Burnett).
1 The term ‘‘CO 2 emissions’’ and ‘‘emissions’’ are used interchangeably throughout
this paper.
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Applied Energy
j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / a p e n e r g y
Trang 2relocate from more stringent locales, although studies have found
little evidence to support this theory[13,23,9]
China’s exports are inexpensive and attractive because of the
low wages and low cost of raw materials that minimize
produc-tion costs In addiproduc-tion, costly polluproduc-tion controls are often not
implemented in China [57] This partially explains why China
has overtaken Germany to become the world’s top exporter
[62] Given the link between China’s exports and carbon
emis-sions, studies have sought to analyze this relationship using
econ-omy-wide modeling and econometric methods These studies
have not adequately addressed this analysis using accurate data;
i.e., some studies have employed data with questionable
reliabil-ity This study contributes to the existing literature by analyzing
the relationship between exports and emissions using improved
coal consumption data in China as a number of studies have cast
doubt on the validity of China’s reported coal consumption
fig-ures This issue is circumvented by employing a data-driven
inter-polation technique that gives more accurate figures over the past
three decades Given these accurate estimates the relationship
be-tween exports, coal consumption, GDP, CO2emissions and trade
openness is modeled within a multivariate time series model
The findings, from a global point of view, may provide ideas for
emission reduction strategies that China can adopt The analysis
will forecast the variability of emissions and coal consumption
to 10 years, while other studies have focused on constructing
forecasts of longer horizons of 50 or 100 years [48]; McCarthy
[38]
This study is important in some ways Understanding the key
drivers behind China’s growing CO2emissions is critical for
design-ing climate policies They provide an insight into how other
emerg-ing economies like India and Vietnam may develop low emission
economies in the future In addition, CO2 emissions in China
have a global impact, making its engagement in global climate
change mitigation essential Numerous studies have highlighted
the domestic and global environmental impacts of China’s
coal usage [25,53,69] Finally, given China’s integration in the
global economy, the study of China’s economic development and
energy infrastructure is vital to the health of global economic
development
This paper also seeks to investigate the relationship between
China’s exports and CO2emissions A fraction of emissions are
pro-duced by the manufacturing, electric power and transportation
industries needed for producing goods for export Others are
embodied in the exports themselves China’s energy infrastructure
is primarily coal based which accounts for 74% of total national
en-ergy consumption Consequently, large quantities of greenhouse
gases are emitted contributing to global climate change Further,
trade liberalization has been touted as a reason for China’s growth
in GDP The adoption of the open-door policy in China propelled
the country to become one of the fastest growing economies
in the world The direction of causality between economic
growth and trade openness has been a subject of extensive debate
Thus, the relationship between trade openness and GDP is sought
to be examined Trade openness is defined as the ratio of external
trade (imports plus exports) to GDP as used in the literature[34]
Therefore, a variable for trade openness is explored in this
analysis.2
The remainder of the study is organized as follows The next
section reviews the literature pertaining to this study while the
third section presents the model and data Section4presents the
results and Section5offers the conclusions
2 Literature review Energy consumption in China has attracted considerable re-search interest due to the environmental ramifications caused by the extensive use of coal, which has propelled high economic growth for the past two decades This interest spans many sub-branches of economics Past studies have looked into sources and ways of reducing CO2emissions while others have analyzed the relationship between emissions and exports The methods used vary – a majority employ economy-wide modeling that include in-put–output techniques while others use econometric techniques for analysis and forecasting Given the breadth of interests, this lit-erature review explores different litlit-eratures which relate directly
or indirectly to this particular study Since this analysis is broad,
we present a brief summary inTable 1of the major themes within the literature and key findings within particular studies.3
A number of regional and nationwide studies have sought to analyze the sources of emissions at the global and regional levels Using the Kaya identity, Raupach et al.[45]found that population and GDP are the main drivers of emissions The Kaya identity is a decomposition analysis that is used to explore the differing trends
of factors contributing to carbon dioxide emissions[26] The iden-tity is comprised of three primary factors: economic growth, pop-ulation growth, and energy consumption A majority of studies on China concluded that structural changes across sectors of the econ-omy are one of the main causes of emissions before the 1990s, whereas technological change within sectors has had a greater im-pact more recently[37] Streets et al.[54]used an atmospheric transport model to study emissions in the Pearl River Delta region This region is known to produce goods for export to North America, Europe and Asia Streets et al.[54]discovered that pollution in the area is caused by the manufacturing and transportation industries Their study found that the region is responsible for 5–30% of the ambient concentrations of various emissions A study by Gregg
et al [17]recommended that controlling fossil fuel combustion from electricity power generation and the cement manufacturing
in China would reduce emissions
Fang et al.[14]found that simple improvements to small indus-trial boilers could reduce CO2emissions in China by as much as
63 Mtons and save 34 Mtons of coal at an estimated cost of $10 per ton of CO2 Liang et al.[32]investigated stakeholder and public perceptions of deploying carbon capture and sequestration (CCS) technologies in China They found that a majority of those sur-veyed perceived climate change to be a major problem and viewed CCS as a necessary mechanism to reduce CO2emissions in China Choi et al.[7]use a data envelope analysis to estimate the potential reductions of CO2emissions through production and technological efficiencies They find that by adopting better efficiencies, CO2 emissions can, on average, be reduced by approximately 56.1M tons in each province and 1683 Mtons nationwide; but, the effi-ciencies will be easier to achieve in the well-developed eastern part of the country as opposed to the less well-developed western part
Two fairly recent studies highlight how the adoption of biofuels
in China may reduce greenhouse gas (GHG) emissions including
CO2 Hu et al.[20]consider the adoption of cassava-based ethanol
as an alternative automotive fuel in China and conduct a lifecycle analysis (LCA) to examine the impacts on energy consumption and CO2emissions from the adoption of such a policy They find that 10% mandate of blending the ethanol with conventional auto-motive fuel could significantly lower CO2emissions, but a nation-wide program would require an untenable 42% of total farmland in
2 Other energy sources of energy contribute to emissions but this study seeks to
examine the contribution of coal and exports to CO 2 emissions Future studies in this 3
This summary is not meant to be an exhaustive review of the literature, but rather
Trang 3China in order to meet the fuel demand Xunmin et al.[65]extend
the analysis of Hu et al.[20]by considering six different biofuel
pathways Like the previous authors, Xunmin et al.[65]conduct
a LCA to examine the impacts on energy consumption and GHG
emissions They argue that different biofuels pathways are
re-quired for China because each provincial region is geographically
unique for the country Ultimately, they find that these biofuel
pathways can reduce both energy consumption and GHG
emissions
The Hecksher–Ohlin theory of trade suggests that given free
trade, a developing country will specialize in the production of
goods in which it is abundantly endowed[15] This endowment
then will determine how intensive certain factors will be used in
the developing country China’s endowment constitutes an
abun-dant labor force China’s adoption of economic reforms in the latter
1970s, coupled with cheap labor, led to a huge increase in
manu-facturing, which in turn induced trade with foreign nations While
economic growth has benefitted from this large increase in
manu-facturing, a negative side effect is the pollution intensiveness of
manufacturing Not only is manufacturing pollution intensive in
output, but also in the pollution intensiveness in the coal-fired,
electricity inputs These combined intensities have led to the
in-crease in China’s CO2emissions over the past few decades
Researchers have employed economy-wide modeling
tech-niques such as input–output and structural decomposition
analy-ses to study the relationship between exports and emissions Lin
and Polenske[33]used a structural decomposition analysis to
ex-plain changes in China’s energy use between 1981 and 1987 They
found that increased expenditures on capital products were the
main cause of a rise in emissions They added that emissions could
be reduced by importing more goods than are currently exported
Weber et al [58] used a standard input–output model of the Chinese economy which reflected the amount of money flowing between sectors and found that in 2005, the export industry gen-erated 33% of China’s total emissions Chen and Chen[6]studied carbon emissions and resource use in the Chinese economy using
an ecological input–output model They found that international trade plays a significant role in redistributing carbon emissions More specifically, 3.59 or 5.54 gigatonnes of carbon dioxide equivalents of GHG emissions are embodied in exported products
A recent study by Yunfeng and Laike[66]examined the quantity of
CO2 emissions embodied in the trade between China and the European Union The paper identifies the sectors contributing most to these embodied CO2 emissions using the input–output approach; they find that the machinery and manufacturing sectors substantially contribute to emissions embodied in exports Chung et al.[8]conduct a similar input–output analysis but instead consider South Korea Through the use index decomposition analysis and an energy input–output model they found that the intermediate demand sector including the industrial sector accounted for approximately 85% of final energy demand, and as
a result the intermediate demand sector was largely responsible for GHG emissions within the country
In the future, China’s CO2emissions are projected to grow faster than the economy Hohne et al.[19] It is therefore important to forecast emissions to predict future paths of CO2 emissions in China Several econometric techniques have been employed for forecast analyses ZhiDong[68] used an integrated econometric model to perform a long-term simulation study in China for a
30 year period and found that China will sustain a 6% economic growth rate in the coming years, presenting challenges for CO2 emission reductions Using a panel dataset from 1985 to 2004,
Table 1
Existing studies on China’s emissions.
1 Cause of emissions
1.1 Raupach et al [45] Time series analysis and
kaya identity
China, region and the world Population and GDP were the main drivers of emissions 1.2 Streets et al [54] Atmospheric transport
model
Pearl river delta region in China Pollution in the region is caused by the manufacturing and
transportation industries
the cement manufacturing in China
2 Economy-wide modeling techniques
2.1 Lin and Polenske [33] Structural decomposition
analysis
cause of a rise in emissions.
carbon emissions.
2.4 Yunfeng and Laike [66] Input–output China and the European Union Machinery and manufacturing sectors substantially
contributed to emissions embodied in exports 2.5 Chung et al [8] Index decomposition
analysis and energy input–
output model
GHG emissions within the country.
3 Forecast emissions
model
years, presenting challenges for CO 2 emission reductions 3.2 Auffhammer and
Carson [3]
Using dynamic models with spatial dependence and provincial – level panel data
is several times larger than reductions embodied in the Kyoto Protocol.
4 Time series analysis
4.1 Lean and Smyth [30] Toda and Yamamoto [56] ,
and Dolado and Lütkepohl
[11] , VAR
and electricity consumption They also found Granger Causality running from exports to aggregate output.
electricity consumption The study also established a cointegrating relationship among electricity consumption, economic growth and exports.
cointegrated There is Granger Causality running from electricity consumption to real GDP
Trang 4Auffhammer and Carson[3]explore alternative econometric
spec-ifications for forecasting China’s CO2 emissions Using dynamic
models with spatial dependence and provincial–level panel data,
they found that the magnitude of the projected increase in Chinese
emissions (out to 2015) is several times larger than reductions
embodied in the Kyoto Protocol
A number of studies have incorporated time series analysis to
study interactions among variables that fuel increasing emissions
Lean and Smyth [30] examined the causal relationship among
aggregate output, electricity consumption, exports, labor and
cap-ital using a multivariate model for Malaysia They found
bi-direc-tional Granger Causality running between aggregate output and
electricity consumption They also found Granger Causality
run-ning from exports to aggregate output A study by Sami[47]
re-viewed the relationship among electricity consumption, exports
and real income per capita in Japan Using a vector error correction
model (VECM), their results indicated causality running from
ex-ports and real GDP per capita to electricity consumption The study
also established a cointegrating relationship among electricity
con-sumption, economic growth and exports A similar study by Shiu
and Lam[49]established similar findings in their study, and, more
specifically that electricity consumption and economic growth in
China are cointegrated
Halicioglu[18]finds that carbon emissions are determined by
energy consumption, income and foreign trade Other studies have
stated that coal consumption and emissions have been inextricably
linked in China for decades Increased coal consumption
acceler-ates environmental pollution, so there is very important practical
significance to study the causal relationship between the two Ma
[36] Coal is also used to produce electricity which plays a huge
role in China’s growth and GDP Therefore the method employed
by Wolde-Rufael[60]and Li et al.[31]is used to test the
relation-ship between coal consumption and GDP In addition, empirical
evidence from cross country comparisons suggests that there is a
relationship between economic growth and environmental
out-comes[46]
The studies here offer a comprehensive overview of the
struc-ture of China’s emissions and their sources; however, there is a
paucity of studies that use time series analysis to determine the
influence of exports on China’s emissions
3 Model and data description
3.1 Model
The vector autoregressive (VAR) system is constructed using the
following five variables: exports (xt), emissions (et), coal
consump-tion (ct), GDP (mt) and trade-openness (pt) The system of equations
with one lag is expressed as:
Xt¼a10þa11xt1þa12et1þa13ct1þa14mt1þa15pt1þext
et¼a20þa21xt1þa22et1þa23ct1þa24mt1þa25pt1þeet
ct¼a30þa31xt1þa32et1þa33ct1þa34mt1þa35pt1þect
mt¼a40þa41xt1þa42et1þa43ct1þa44mt1þa45pt1þemt
pt¼a50þa51xt1þa52et1þa53ct1þa54mt1þa55pt1þept:
ð1Þ
Eq.(1)can be rewritten in matrix notation as:
xt
et
ct
mt
pt
2
6
6
6
4
3
7
7
7
5
¼
a10
a20
a30
a40
a50
2
6
6
6
4
3
7
7
7
5
þ
a11 a12 a13 a14 a15
a21 a22 a23 a25
a31 a32 a33 a35
a51 a52 a53 a55
2 6 6 6 6
3 7 7 7 7
xt1
et1
ct1
ct1
pt1
2 6 6 6 4
3 7 7 7 5 þ
ext
eet
ect
emt
ept
2 6 6 6 4
3 7 7 7 5 :
ð2Þ
The multivariate generalization of the process is:
Yt¼ A0þ A1yt1þ þ Arytrþet; ð3Þ
where A0is a (5 1) column vector of intercepts and A1a (5 5) matrix of estimated coefficients on the first lag of the explanatory variables The system of equations can be extended to multiple lags,
as follows:
xt¼a10þXr
j¼1
a11;jxtjþXr
j¼1
a12;jetjþXr
j¼1
a13;jctj
þXr j¼1
a14;jmtjþXr
j¼1
a15;jptjþext
et¼a20þXr
j¼1
a21;jxtjþXr
j¼1
a22;jetjþXr
j¼1
a23;jctj
þ mr j¼1a24;jmtjþXr
j¼1
a25;jptjþeet
ct¼a30þXr
j¼1
a31;jxtjþXr
j¼1
a32;jetjþXr
j¼1
a33;jctj
þXr j¼1
a34;jmtjþXr
j¼1
a35;jptjþect
mt¼a40þXr
j¼1
a41;jxtjþXr
j¼1
a42;jetjþXr
j¼1
a43;jctj
þXr j¼1
a44;jmtjþXr
j¼1
a45;jptjþemt
pt¼a50þXr
j¼1
a51;jxtjþXr
j¼1
a52;jetjþXr
j¼1
a53;jctj
þXr j¼1
a54;jmtjþXr
j¼1
a55;jptjþept
ð4Þ
which implies the following generalization,
yt¼ A0þ A1yt1þ þ Arytrþet: ð5Þ
Given multiple lags a generalization of the coefficient matrix, Ar, would indicate the rth lag of the explanatory variables[12]
To test the null hypothesis that there is ‘‘Granger Causality’’ from exports to emissions, the null: H0:a21j= 0 is tested, where the a21j’s are the coefficients of xt1, xt1, , xtjrespectively in the second equation in the VAR system The causality from emis-sions to exports can be established through rejecting the null hypothesis which requires finding the significance of the Modified Wald (MWald) statistic for the group of the lagged independent variables identified above To complement the VAR, vector decom-positions were developed to check whether the variables affect one another in the ‘‘future,’’ which assist in confirming the results of Granger Causality For completeness, the impulse response functions are presented to provide a visual depiction of variable’s responses to shocks
3.2 Coal consumption data The past literature has reported that data on China’s coal con-sumption suffer from under-reporting Sinton[52]offers a compre-hensive overview relating to the accuracy and reliability of China’s coal statistics The point of contention in the data relates to the period between late 1990s and early 2000s During this time, offi-cial energy statistics showed a significant decrease in coal con-sumption despite increases in Chinese CO2 emissions Further, satellite data suggest that there was significant under-reporting
of coal consumption, which lead Akimoto et al.[2]to conclude that the official statistics should not be used for emission inventories
Trang 5To account for the potential under-reporting for that period, coal
consumption was scaled up to reflect a more accurate historic
trend
Values for coal consumption for the period between 1990s and
early 2000s were to be plotted However, the problem of fitting the
curve through finite sequence of points while preserving the shape
of the data was experienced The literature states that a piecewise
polynomial curve offers much more flexibility than a single
poly-nomial in preserving the shape of the data[16] In addition,
piece-wise cubic polynomials are used because their plots are smooth
and are the lowest degree polynomials that support inflection
points Given the nature of the data and shape of the curve, data
for the years 1995–2008 was constructed using a piecewise cubic
hermite interpolating polynomial This method was also employed
by Auffhammer and Carson[3] The interpolated data is shown in
Fig 1
3.3 The data
Data on coal consumption was obtained from BP statistical data
[4] Real gross domestic product (GDP), imports and exports were
obtained from the World Bank indicators database[61] Data on
emissions was obtained from the Carbon Dioxide Information
Analysis Center[5] The data set ranges from 1970 to 2010 In this
study, the relationship between exports, emissions, coal
consump-tion, GDP and trade openness are investigated within a (VAR)
framework A statistical summary of these variables is shown in
Table 2
4 Empirical results
The Granger no-causality test method applied in this analysis is
based upon the work of Toda and Yamamoto[56] This procedure
is expected to improve the standard F-statistic in the causality
test-ing procedure In determintest-ing whether some parameters of the
model are jointly zero, the traditional F-test is not valid when
the variables are integrated or cointegrated; in this case, the joint
distribution of the variables is not characterized by a normal
distri-bution In other words, if the data is integrated or cointegrated, the
usual tests for exact linear restrictions on the parameters (e.g the
Wald test) do not have their usual asymptotic normal
distribu-tions The procedure proposed by Toda and Yamamoto[56]
en-sures that the usual test statistics for Granger Causality have
standard asymptotic distributions This procedure can be used to
avoid the pre-testing distortions associated with prior tests for
non-stationarity and cointegration The basic idea of the approach
is to artificially augment the correct order, k, by the maximal order
of integration, d [44] Once this is done, a (k + d )th order of
VAR is estimated and the coefficients of the last lagged dmaxvectors are ignored To use this approach, the true lag length (k) and the maximum order of integration (dmax) of the series need to be ob-tained The advantage of using the Toda and Yamamoto[56]
meth-od is that it does not require a priori knowledge of cointegration within the system[67]
To ensure that the time series within the VAR model satisfy the assumption of normality, a number of stationarity tests were con-ducted Time series data is often characterized by unit root[39] In both raw and log-transformed data, it is found that all the variables have a non-zero mean Tests for unit roots were conducted using the Augmented Dickey and Fuller[10], Phillips–Perron[43] and Kwaitkowski–Phillips–Schmidt–Shin [27] The Phillips Perron (PP) test was used to complement the standard augmented dickey fuller (ADF) test in testing for unit root The PP procedure tests for unit roots in the presence of structural change[43] The Kwaitkow-ski–Phillips–Schmidt–Shin test (KPSS) was also used to comple-ment the ADF and PP tests; KPSS tests the null hypothesis of non-stationarity against the alternative of trend stationarity[41] The PP and KPSS tests are used together with the ADF tests for the sake of robustness The results of the unit root tests are re-ported inTable 3 Test results indicate that all the time series were I(1) except for coal consumption which was I(2)
The next step was to find out the appropriate lag length The ap-proach by Lütkepohl[35]was employed in which the optimal lag length (mlag) is based upon the number of endogenous variables
in the system (m) and the sample size (T) according to the formula:
m mlag = T1/3 With a sample size of 40 this rule implies a maxi-mal lag length of one.4 Using the Toda and Yamamoto[56] ap-proach, the Granger Causality tests were conducted using three lags (Using k = 1 and dmax= 2) and the results presented inTable 4 The Granger Causality results appearing inTable 4indicate one-way causality from coal consumption to exports They also indicate unidirectional ordering from coal consumption to emissions, con-firming hypotheses that past and present values of coal consump-tion help explain emissions Also, one-way causality running from GDP to coal consumption was discovered Regarding the energy-growth literature, results favored the conservation hypothesis which asserts that economic growth leads energy consumption, implying that energy conservation policies may not adversely af-fect GDP No causality was discovered between trade openness and economic growth
The results appearing onTable 4also indicate causality running from exports to emissions; this finding is consistent with other findings in the literature[42,58,64,66] The direction of causality between exports and emissions implies that the government can
0
200
400
600
800
1000
1200
1400
1600
1800
YEAR
(Million tonnes oil equivalent) Interpolated coal consumption
Reported coal consumption
Fig 1 China coal consumption 1970–2010.
Table 2 Summary table.
e (emissions
in million metric tonnes)
c (coal consumption
in million tonnes oil equivalent – Mtoe)
x (exports in current US$)
m (GDP in current US$)
p (trade openness)
Variance 3.91497E+12 148103.365 2.21E+23 1.99E+24 0.036063 Standard
deviation
4 The unit root test results provide the value of d max while the method by Lütkepohl
[35] is used to calculate k The sum of these values was used to select the number of lags to test for Granger Causality.
Trang 6regulate emissions by designing policies that regulate exports In
addition, policies aimed at curbing coal consumption can also
con-trol exports and emissions Finally, the results demonstrate
causal-ity running from exports to trade openness Therefore, it is possible
to forecast the future levels of trade openness from the past levels
of exports No relationship between trade openness and economic
growth was found
The causality tests indicate only Granger Causality within the
sample period, and do not allow us to gauge the relative strength
of the Granger Causality among the series beyond the sample
per-iod Thus, to complement the above analysis, the forecast error
var-iance of exports, consumption, emissions, GDP and trade openness
were decomposed into proportions attributed to shocks in all
vari-ables in the system This allows us to provide an indication of the
Granger Causality beyond the sample period [51] The variance
decomposition results are presented inTable 5
The cells in the variance decomposition represent percentages
of the forecast variance (error) in one variable at different time periods induced by innovations of the other variables These per-centages help determine the relative contribution the innovations make towards explaining movements in the other variables.5
Table 5also shows that GDP and coal consumption have the greatest effect on export variability over the forecast period GDP explains 20.34% variability at five years and 24.76% variability at a ten year horizon while coal consumption explains 14.81% and 19.19% of ex-port variability at five and ten year horizons respectively The shocks explained by coal consumption confirm the Granger Causality tests Emissions have an increasing effect on export variability, explaining 6.83% and 15.26% at the end of five and ten year horizons Trade openness has a decreasing effect on export variability of exports with time
While coal consumption and exports appear to be an important factor to emissions in China, the variance decomposition analysis shows that they explain 4.6% and 2.8% of variability at the end of the forecast period Furthermore, GDP is the most important factor
in explaining emissions variability It is beyond the scope of the pa-per to thoroughly examine the underlying reasons behind these weak consumption–emissions and export-emission relationships but it can be surmised that in the future, China will find more effi-cient ways of using of coal, which in turn affect electricity produc-tion and exports, and thereby reduce CO2emissions The table also indicates that GDP has an increasing effect on coal consumption explaining 11.59% and 44.44% of consumption variability in the short and long-run respectively In addition, exports explain 7.33% of consumption variability in the long run The relatively low contribution may be an indication that the causal relationship between exports and coal consumption is relatively weak over the long run compared to that of GDP and coal consumption For the forecast variance decomposition of trade openness, GDP has an increasing effect on variability, explaining 52.69% at the end of the tenth year – up from 43.74% in the fifth year Exports also have
a decreasing effect on trade openness, explaining 16.29% at the end
of the forecast period This result confirms the earlier Granger Causality tests Finally,Table 5shows that trade openness has a
Table 3
Unit root test results.
0.09516
6.7633 ***
6.5066 ***
0.1993⁄⁄
3.9261 **
5.2379 ***
ADF = Augmented dickey fuller Test, PP = Phillips Perron Test, KPSS = Kwiatkowski–Phillips–Schmidt–Shin test; C = Constant, CT = Constant and Trend.
* Denote significance at 10%.
** Denote significance at 5%.
***
Denote significance at 1%.
Table 4
VAR Granger Causality Tests.
* Denote significance at 10%.
** Denote significance at 5%.
*** Denote significance at 1%.
5 Each entry is the percentage of forecast error variance Due to rounding, the
Trang 7marginal effect on GDP, explaining 1.53% at the end of the forecast
period
Next the generalized impulse response functions (GIRFs) were
generated as shown inFig 2 The GIRF’s represent the reactions
of the variables to shocks in the system While the Toda and
Yamamoto[56]method tests the long-run Granger Causality
rela-tionships, it does not consider how variables respond to shocks in
other variables The generalized impulse response function
exam-ines how a shock to one variable affects another, and how long the
effect lasts Ordering of variables in the VAR system is important in
order to calculate the impulse response functions (IRFs) analyses
Different ordering may result in different IRF results The
general-ized GIRFs which are invariant to the ordering of the variables in a
VAR were employed[28] The charts inFig 2reflect the dynamic
properties of the system where without any shock, the response
plots would be flat The horizontal line in GIRFs shows the time
period after the initial shock The vertical line in GIRFs shows the
magnitude of response to shocks
Fig 2shows that the response of the emissions path to a one
standard deviation shock in GDP and coal consumption is positive
over the forecast period, whereas the response of emissions to a
one standard deviation shock in exports drifts around zero over
the forecast period This implies that GDP and coal will continue
to have a positive effect on emissions over the forecast period
while exports will have a marginal effect on the emissions path
The emissions path is negative in response to a shock in
trade-openness, suggesting that opening trade in China may reduce
emissions
The path of exports in response to a one standard deviation
shock in coal consumption is initially negative but then positive;
the effect levels off after the fifth period The response path is
re-versed when observing the effect of a shock of trade openness on
exports The response path is positive over the first four years
be-fore crossing zero and decreasing over time The response of
ex-ports to a shock in emissions and GDP is positive, implying that
coal consumption and GDP positively influence the path of exports
in the long run The path of coal consumption in response to a
shock in exports is negative until the ninth period when it
ap-proaches zero while its response from a GDP shock is positive over
the forecast period
The variance of GDP was shocked with coal consumption and
trade-openness but the path was unresponsive Of importance is
the finding that the path of GDP from a shock in trade openness
is negative This implies that some regions, especially those in the east, may experience a decrease to GDP when exposed to fur-ther liberalization of trade This may occur due to the lack of com-petitiveness in international markets as pointed by Jin [24] Nevertheless the GDP path remains positive and unresponsive when shocked by emissions and exports The unresponsive nature
of GDP to shocks from all the variables paths confirm the Granger Causality findings, implying that GDP is impacted by shocks out-side the system Finally, the response path of trade openness in re-sponse to GDP shock is negative over the forecast period, while remaining unresponsive when shocked by emissions This analysis was conducted using the interpolated coal consumption data.6
5 Conclusion China’s economic reforms have liberalized the economy, result-ing in remarkable economic growth and energy consumption since the late 1970s Evidence that China has overtaken the United States
to take the number one spot has led to renewed calls for China to act to reduce the environmental impact of its phenomenal growth
[29] As China observes a rapid increase in emissions, its policy makers may question why the country is criticized by the very con-sumers who import relatively inexpensive Chinese goods It has been argued that the steep rise in China’s emissions has been fuelled by exports of cheap goods to the rest of the world This study employed a vector autoregressive analysis to investi-gate the link between China’s exports and carbon dioxide emis-sions Based on the empirical analysis, Granger Causality running from exports to emissions was discovered These results imply that the government should consider policies aimed at controlling ex-ports to reduce emissions For example, the country can implement policies that place an environmental levy on exports to fund domestic GHG mitigation programs Such projects entail the installation of emission reducing technologies in industries that
Table 5
Variance decomposition results.
Variance decomposition of Ln_m:
Variance decomposition of Ln_c:
Variance decomposition of Ln_x:
Variance decomposition of Ln_p:
Variance decomposition of Ln_e:
Cholesky ordering: Ln_m Ln_c Ln_x Ln_p Ln_e
6 For a sensitivity analysis, coefficient estimates for the interpolated and the original coal consumption data were compared [59] All estimates except those for exports were similar in signage, magnitude, and significance, implying that the interpolation method was quite robust Also, results obtained using the original and interpolated data sets were compared but not presented to conserve space These
Trang 8manufacture goods for export Another potential policy may
encourage foreign direct investment in domestic, energy efficient
industries which emit less CO2emissions Also, unidirectional
causality running from coal consumption to exports, and coal
consumption to emissions was found China can consider
market-based mechanisms, such as cap-and-trade, which reduce coal
consumption and consequently reduce emissions Other policies
include renewable energy strategies or the use of clean coal
technologies in the formulation of a long–term emission reduction
portfolio Vector error decomposition analysis revealed that GDP
had the greatest effect on exports, emissions and coal consumption
variability
Predictions indicate that the increase in greenhouse gas
emis-sions from 2000 to 2030 in China alone will nearly equal the
in-crease from the entire industrialized world It is important for
China to take a lead in reducing CO2emissions[22] Their efforts
in taking responsibility for reducing the carbon emissions will
res-onate across countries following similar developmental patterns
In addition, China can invest returns from exports on projects that
promote use of renewables to mitigate emissions and further achieve better human health from reduced air pollution
One limitation of this study is that it uses interpolated data for coal consumption which may not reflect actual trends Future work should consider a different technique improving this data set An-other limitation is that the VAR is a reduced form model; therefore, IRFs may not capture shocks to the true underlying innovations
Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal lia-bility or responsilia-bility for the accuracy, completeness, or useful-ness of any information, apparatus, product, or process disclosed,
or represents that its use would not infringe privately owned rights Reference herein to any specific commercial product,
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of LN_GDP to LN_CONS
-.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_GDP to LN_EMISS
-.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_GDP to LN_EXPORTS
-.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_GDP to LN_TRADE
-.04
-.02
.00
.02
.04
1 2 3 4 5 6 7 8 9 10
Response of LN_CONS to LN_EMISS
-.04 -.02 00 02 04
1 2 3 4 5 6 7 8 9 10
Response of LN_CONS to LN_EXPORTS
-.04 -.02 00 02 04
1 2 3 4 5 6 7 8 9 10
Response of LN_CONS to LN_GDP
-.04 -.02 00 02 04
1 2 3 4 5 6 7 8 9 10
Response of LN_CONS to LN_TRADE
-.10
-.05
.00
.05
.10
.15
1 2 3 4 5 6 7 8 9 10
Response of LN_EXPORTS to LN_CONS
-.10 -.05 00 05 10 15
1 2 3 4 5 6 7 8 9 10
Response of LN_EXPORTS to LN_EMISS
-.10 -.05 00 05 10 15
1 2 3 4 5 6 7 8 9 10
Response of LN_EXPORTS to LN_GDP
-.10 -.05 00 05 10 15
1 2 3 4 5 6 7 8 9 10
Response of LN_EXPORTS to LN_TRADE
-.2
-.1
.0
.1
.2
1 2 3 4 5 6 7 8 9 10
Response of LN_TRADE to LN_CONS
-.2 -.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_TRADE to LN_EMISS
-.2 -.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_TRADE to LN_EXPORTS
-.2 -.1 0 1 2
1 2 3 4 5 6 7 8 9 10
Response of LN_TRADE to LN_GDP
-.04
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of LN_EMISS to LN_CONS
-.04 -.02 00 02 04 06 08
1 2 3 4 5 6 7 8 9 10
Response of LN_EMISS to LN_EXPORTS
-.04 -.02 00 02 04 06 08
1 2 3 4 5 6 7 8 9 10
Response of LN_EMISS to LN_GDP
-.04 -.02 00 02 04 06 08
1 2 3 4 5 6 7 8 9 10
Response of LN_EMISS to LN_TRADE Response to Generalized One S.D.Innovations ±2 S.E
Fig 2 Generalized impulse response functions LN_CONS is Ln_c; LN_GDP is Ln_m; LN_EXPORTS is Ln_x; LN_TRADE is Ln_p and LN_EMISS is Ln_e.
Trang 9process, or service by trade name, trademark, manufacturer, or
otherwise does not necessarily constitute or imply its
endorse-ment, recommendation, or favoring by the United States
Govern-ment or any agency thereof The views and opinions of authors
expressed herein do not necessarily state or reflect those of the
United States Government or any agency thereof
Acknowledgements
We are very grateful to two anonymous referees whose
con-structive comments have helped to improve upon the quality of
the paper We are also grateful to the Editor of the Journal, Prof
Jer-ry Yan, for his encouragement Errors and omissions, if any, are our
own
This material is based upon work supported by the Department
of Energy under Award Number DE-FC26-06NT42804
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