Journal of Cleaner Production xxx (2016) 1e13
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Journal of Cleaner Production
journal homepage: www.elsevier.com/locate/jclepro
How to improve the performance of carbon tax in China?
Zhe Zhang a, Aizhen Zhang b, Daoping Wang a, Aijun Li a, *, Huixuan Song c
a
The Center for Economic Research, Shandong School of Development, Shandong University, Jinan, 250100, China
School of Foreign Language, University of Jinan, Jinan, Shandong, 250022, China
c
School of Economics, Shandong University, Jinan, 250100, China
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 20 April 2016
Received in revised form
12 November 2016
Accepted 13 November 2016
Available online xxx
The Chinese government committed to controlling carbon dioxide (CO2) emissions and promoting
development of non-fossil fuels. Thus, it becomes a top priority for the government to develop effective
climate policies. Against such background, this paper develops a new multi-country computable general
equilibrium (CGE) model with detailed energy disaggregation. This study evaluates the impact of single
policy instrument (carbon tax) and combined policy mixes, in order to investigate driving factors
affecting the policy performance. The main findings are as follows. Firstly, the driving factors can be
classified into two types. The first type of factors affects cost-effectiveness significantly, while the second
type generates considerable effect on emission performance. Secondly, cross-border externalities function as important barriers to the CO2 emission reduction, since they imply extra competitiveness loss and
result in considerable carbon leakage. Finally, this paper considers two integrated policy mixes, wherein
carbon tax revenue is recycled to reduce capital tax or support clean energy subsidy. These policy mixes
can improve both cost-effectiveness and emission performance, and thus perform better than carbon tax
alone. In this way, carbon tax revenue can be used to improve the performance. Looking ahead,
complicated policy mixes are preferred to improve the performance of China's carbon tax.
© 2016 Elsevier Ltd. All rights reserved.
Keywords:
Renewable energy subsidy
CGE model
Rebound effect
Competitiveness issues
Carbon leakage
Carbon dioxide emission abatement costs
1. Introduction
Currently, China is the world's largest carbon emitter with large
annual increased carbon dioxide emissions. According to IEA
(2015), China's carbon dioxide emissions increased rapidly from
3299.7 million tons in 2000e9023.1 million tons in 2013. In the
meantime, there was a substantial increase in China's share of the
world's total carbon dioxide emissions, which increased from
14.15% in 2000 to 28.03% in 2013 (IEA, 2015). Against this background, China faces increasing international pressure to control its
domestic carbon dioxide emissions.
At present, it becomes a top priority for the Chinese government
to develop effective climate policies to curb domestic carbon dioxide emissions. According to Chinadaily (2014), the Chinese government made several commitments to control carbon dioxide
emissions. In 2009, the Chinese government committed to reducing
* Corresponding author. The Center for Economic Research, Shandong School of
Development, Shandong University, Jinan, 250100, China.
E-mail addresses: sduzhangzhe@126.com (Z. Zhang), sfl_zhangaz@ujn.edu.cn
(A. Zhang), wangdaoping@mail.sdu.edu.cn (D. Wang), liaijun@sdu.edu.cn (A. Li),
waxiaoguo150@163.com (H. Song).
its carbon intensity by 40e45% in 2020 as compared with the 2005
levels. In 2014, the Chinese government pledged to reach a peak in
carbon dioxide emissions by 2030 and committed to increasing the
share of non-fossil fuels to about 20% in primary energy consumption by 2030. To fulfill these commitments, the Chinese government should adopt effective climate policies to curb domestic
carbon dioxide emissions. Importantly, well-designed climate
policies require detailed information about the underlying factors
that affect the performance. For this purpose, this study develops a
new computable general equilibrium (CGE) model and investigates
the potential forces affecting the performance of carbon tax in
China (the performance, for short). Here, this study considers the
impact of both single policy instrument and aggregate policy mixes.
The climate policy mixes include an integrated tax-tax policy and
an integrated tax-subsidy policy. Carbon tax revenue is recycled to
reduce capital tax in the first policy mix and used to subsidize lowcarbon energy (or clean energy) in the second policy mix.
Great interest has been expressed in the potential of alternative
climate policies to mitigate carbon dioxide emissions. Carbon tax
and cap-and-trade policy are regarded as two important policy
instruments of curbing carbon dioxide emissions. A series of
studies seek to find out which policy instrument will perform
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
better, such as Pearce (1991); Shammin and Bullard (2009); AviYonah and Uhlmann (2009); Wirl (2012) and Strand (2013). It is
noteworthy that carbon tax can be motivated by double dividend
hypothesis, since carbon tax revenue can be used to reduce the
distorting effect of existing tax regime. Many studies have followed
this line of research, such as Goulder (1995); Proost and Regemorter
(1995); Parry (2000); Schwartz and Repetto (2000); ChiroleuAssouline and Fodha (2006, 2014); Jacobs and de Mooij (2015).
Meanwhile, some studies consider the impact of energy efficiency
improvement in China, such as Yao et al. (2015) and Li et al. (2017).
Under such circumstances, this study evaluates the combined
impact of carbon tax and other climate policies, since carbon tax
revenue can be used to reduce distorting effect of tax system or
support non-fossil fuel subsidy.
Meanwhile, some studies discuss optimal carbon tax, wherein
the main focus is optimal pathways of carbon tax rate over time. For
example, Wang et al. (2011) argued that low carbon tax rate might
be appropriate as a starting point. Zhang et al. (2013) investigated
optimal pathways in China's power sector. Fang et al. (2013) argued
that it would be important to determine the time to levy carbon tax.
Liu (2013) discussed tax evasion and optimal environmental taxes
by adopting a CGE model. Lin and Zeng (2014) calculated the
optimal gasoline tax in China. Duan et al. (2014) argued that the
optimal pathways of carbon tax in China were monotonically
increasing, and found that the optimal pathways formed a classical
S-shaped pattern.
In the meantime, some studies evaluate the effect of carbon tax
(or other climate policies) in different goal-settings. For example, Li
and Lin (2013) compared absolute-emission-reduction goals
(massed-based goals or absolute-volume-based goals) and
intensity-based emission goals (rate-based goals) in China, and
argued that there were significant differences in effect between the
two different goal-settings. Burtraw et al. (2015) considered rate
standard and the coordination problem among states in the United
States.
Another important topic about carbon tax is about distributional
effect across households, sectors or regions. For example, Zhang
and Baranzini (2004) argued that future carbon tax rates could
become higher than those already implemented and hence result in
more acute economic impacts, given the target of the Framework
Convention. Liang and Wei (2012) argued that carbon tax alone
would reduce standard of living of both urban and rural households
and widen urban-rural gap in China. Jiang and Shao (2014) pointed
out that distributional effect was crucial to the public acceptance of
carbon tax in China.
Some studies evaluate the effect of exogenous factors or settings, such as technological innovations and border measures.
Gomes et al. (2008) argued that reduction measures should be
taken to encourage solid waste treatment and wastewater treats et al. (2010) assessed the impact
ment in place of fossil fuel. Toma
of European carbon dioxide emissions trading scheme on Portuguese chemical industry. Jin (2012) argued that induced technical
improvement could help to reduce deadweight loss that was
caused by carbon tax in China. Zhang et al. (2014) argued that lowcarbon technologies alone would be insufficient to restrain China's
increasing carbon dioxide emissions. Li et al. (2012) adopted the
SICGE model to evaluate the impact of China's export carbon tax. Li
and Zhang (2012) argued that carbon motivated border tax adjustments (CBTA) would be relatively costly and inefficient to
reduce carbon dioxide emissions in comparison with carbon tax in
China. Chang (2013) argued that it would result in a decrease of 11%
in carbon dioxide emissions, by changing from production-based
responsibility to shared responsibility. Li et al. (2015) argued that
there would be considerable differences when different countries
adopted carbon tax to achieve the same carbon dioxide emission
reduction.
The final line of research includes studies of the general equilibrium modeling, especially those studies focusing on climate
policies. Liang and Wei (2012) used a recursive dynamic CGE model
and tested the distributional effect of carbon tax in China. Jin (2012)
developed a CGE model with endogenous technical change and
examined the impact of technological innovation on carbon dioxide
emissions. Guo et al. (2014) adopted a CGE model to evaluate the
effect of carbon tax in China and energy sectors were disaggregated
in detail in their model. Liu and Lu (2015) used a dynamic CGE
model to test the impact of carbon tax in China, wherein several
revenue recycling schemes were adopted.
Based on the previous studies, this paper seeks to provide two
contributions. Firstly, this study provides contributions in
modeling, since it develops a new multi-country general equilibrium model. The model is presented in detail in Section 2.1 One
important feature of this model is that there is detailed disaggregation of energy composites. Energy composites are disaggregated
into coal, oil, natural gas, nuclear, hydro, bioenergy and wind. In
such a setting, our model can be used to simulate the combined
effect of integrated tax-subsidy policy, which combines both carbon tax on fossil fuels and subsidy to low-carbon energy.
Secondly, this study seeks to provide fresh insights by focusing
on the following two questions. (1) Which factors affect the performance of carbon tax in China and how to improve the performance? To answer these questions, this study makes numerical
simulations and performs sensitivity analysis to explore the potential driving factors. (2) Are there significant differences in effect
between single policy instrument (carbon tax) and combined policy mixes? Here, this study addresses two policy mixes. The first
one is an integrated tax-tax policy, which means that carbon tax
revenue is recycled to reduce capital tax. The second one is an integrated tax-subsidy policy, wherein carbon tax revenue is used to
subsidize low-carbon energy. The purpose is to investigate whether
carbon tax revenue can be used to improve the performance.
This study is structured as follows. In Section 2, the model and
data are introduced. In Section 3, the results are presented. In
Section 4, discussion of the simulation results is provided. In Section 5, the conclusions and policy implications are reported.
2. Methods
This study develops a new multi-country (or world regions)
general equilibrium model, after referring to models in Rivers
(2010); Dong and Whalley (2012); Li and Zhang (2012); Lin and Li
(2012); Li et al. (2013, 2014, 2015). For readability, this model is
reported in detail.
2.1. The production
In this model, there are eight countries (or world regions), i.e.
China, the United States (US), Japan, India, the European Union (EU
or EU27), Brazil, Australia and Rest of the World (ROW). In such a
setting, this model covers the major trade partners of China, and
thus it is capable of incorporating the potential interaction between
China and other economies. Such potential interaction between
countries can be triggered by China's carbon tax and possibly
conducted through trade or energy channel. For simplicity of
statements, all world regions are treated as countries. Following
1
Corresponding author can provide the program of initial benchmark state to
readers who are interested in this paper. This program can provide complete
version of the CGE model. Other programs under different policy scenarios can be
derived easily.
Please cite this article in press as: Zhang, Z., et al., How to improve the performance of carbon tax in China?, Journal of Cleaner Production
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
3
2
Dong and Whalley (2012), ROW is not a producer of final goods and
is used to deal with the potential trade imbalances among
countries.
In this model, there are nine types of production inputs, i.e.
capital, labor, coal, oil, natural gas, nuclear, hydro, bioenergy and
wind. Various production inputs are combined to produce final
goods. Specifically, capital and labor are aggregated into nonenergy composites. Meanwhile, coal, oil and natural gas are
aggregated into fossil fuels, while nuclear, hydro, bioenergy and
wind are aggregated into low-carbon energy. Then, fossil fuels and
6
EC ¼ r3 4a31 ðFFÞ
3
ðs3 1Þ
s3
þ a32 ðLEÞ
6
FF ¼ r4 4a41 ðFF1 Þ
3
s 1
s 1
low-carbon energy are aggregated into energy composites.
Furthermore, non-energy composites and energy composites are
aggregated into final output. Final output is classified into two
kinds, i.e. industrial goods and non-industrial goods. In the meantime, capital and fossil fuels are assumed to be mobile among
countries and sectors. Labor and low-carbon energy are assumed to
be mobile between sectors in a country but immobile across
countries.
Production function transforms various production inputs into
final goods, and the nesting structure of production function is
illustrated in Fig. 1. Production function follows the constant elasticity of substitution (CES) form, which is in line with many studies,
such as Dong and Whalley (2012); Li and Zhang (2012); Lin and Li
(2012), and Li et al. (2015). Technically, production function can be
written as follows:
2
3
6
Y ¼ r1 4a11 ðVAÞ
ðs1 1Þ
s1
þ a12 ðECÞ
ðs1 1Þ
s1
2
7
5
3
ð2 Þ
ð2 Þ
6
7
VA ¼ r2 4a21 ðFKÞ s2 þ a22 ðFLÞ s2 5
s 1
s 1
s1
ðs1 1Þ
(1)
s2
7
5
(3)
3
ðs4 1Þ
s4
þ a42 ðFF2 Þ
ðs4 1Þ
s4
ðs4 1Þ
þ a43 ðFF3 Þ
s4
7
5
s4
ðs4 1Þ
(4)
s5
ðs5 1Þ
ð5 Þ
ð5 Þ
ð5 Þ
ð5 Þ
6
7
LE ¼ r5 4a51 ðLE1 Þ s5 þ a52 ðLE2 Þ s5 þ a53 ðLE3 Þ s5 þ a54 ðLE4 Þ s5 5
s 1
s3
s3
ðs3 1Þ
2
2
s 1
ðs3 1Þ
(5)
Where
Y represents final output.
VA represents non-energy composites and EC represents energy
composites.
FK represents capital and FL represents labor.
FF represents fossil fuels and LE represents low-carbon energy.
FF1 represents coal, FF2 represents oil and FF3 represents natural
gas.
LE1 represents nuclear, LE2 represents hydro, LE3 represents
bioenergy and LE4 represents wind.
ss represent substitution elasticity among various production
inputs.
rs represent scale parameters.
as represent share parameters.
Following Li and Zhang (2012); Lin and Li (2012) and Li et al.
(2015), energy supply is assumed to be a function of energy price
and price elasticity of energy supply. Technically, the function can
be written as follows.
t
ESFF;m ¼ EDFF;m PFF;m FF;m
(6)
t
ESLE;n ¼ EDLE;n PLE;n LE;n
(7)
ðs2 1Þ
(2)
Where
Outputs
Energy composites
Fossil Fuels
Coal
Oil
Non-energy composites
Low-carbon energy
Gas
Nuclear
Hydro
Capital
Bioenergy
Fig. 1. The nesting structure of production function.
Labor
Wind
Subscript m represents fossil fuels.
Subscript n represents low-carbon energy.
ES represents energy supply.
ED represents energy endowment at initial benchmark state
before carbon tax.
PFF represents fossil fuel price.
PLE represents low-carbon energy price.
t represents price elasticity of energy supply.
In competitive markets, producers make production decisions
according to the principle of profit-maximization. Under the assumptions of constant returns of scale, perfect competition and
profit maximization, zero-profit conditions will hold. Following
Rivers (2010), zero-profit conditions can be written as follows:
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
pj ¼ Pj Yj FKj PK FLj PL X
X
FFm PFFm 1 þ TFFm
m
LEn PLEn 1 SLEn ¼ 0
(8)
n
Here, ROW is not a producer of final goods. Thus, its income is
the sum of net energy export and trade balance, which can be
written as follows:
I¼
Subscript j represents final goods, i.e. industrial goods and nonindustrial goods.
Pj represents price of goods j.
PK represents capital price.
PL represents labor price.
T represents carbon tax rate of fossil fuels.
S represents subsidy rate of low-carbon energy.
(12)
In this model, there is one representative household in each
country. The households obtain income through collecting revenue
from final goods, production inputs and taxes. The households
spend their income on various final goods in order to obtain utility.
Final goods produced by different producers are treated as differentiated goods, since this model adopts Armington assumptions.
Armington assumptions imply that there is limited substitution
instead of perfect substitution among different final goods.
Meanwhile, utility of the households is assumed to be a function
of goods consumption. The utility function follows the CES form,
which is in line with many studies, such as Dong and Whalley
(2012); Li et al. (2014; 2015). Technically, utility function can be
expressed as follows.
2
3q q1
0
6
U ¼ 4ðb1 Þ ðx1 Þ
1
q0
q01
q0
1
q0
þ ðb2 Þ ðx2 Þ
q0 1
q0
7
5
(9)
qj
X 1 q
dij
j
xij
Where
EX represents net energy export of ROW.
The households pursue maximized utility subject to budget
constraints. Then, the utility-maximization problem can be written
as follows:
2
2.2. The household
xj ¼
PFF;m EXFF;m þ TI
m
Where
"
X
#qj 1
qjq1
j
(10)
3q0q1
q0 1
q0 1
1
1
6
7
MaxU ¼ 4ðb1 Þq ðx1 Þ q0 þ ðb2 Þq ðx2 Þ q0 5
X
s:t:
Pj xlj I
(13)
(14)
j
2.3. Price relationships
In this study, there is only one abating country (China), whereas
other countries do not adopt abating policies. For China, carbon tax
rate is treated as a variable until the given carbon dioxide emission
abatement target is satisfied. In this regard, this study follows Li
et al. (2015). In such a setting, there is a gap between China's energy price and international energy price. Technically, the following
price relationships hold.
C
PFF;m;CH
¼ PFF;m;W 1 þ TFF;m;CH
(15)
C
S
¼ PLE;n;CH
1 SLE;n;CH
PLE;n;CH
(16)
Where
i
Superscript C denotes price for consumers.
Superscript S denotes price for producers.
Subscript W denotes energy price in international market.
Subscript CH denotes energy price in China's market.
Where
Subscript i represents different producers.
U represents utility.
x represents goods consumption.
xij represents consumption of goods j from producer i.
bs and ds represent share parameters.
qs represent substitution elasticity among goods from different
producers.
In the meantime, the income is the sum of revenue from final
goods, energy balance, carbon tax and trade balance for each
household (i.e. China, the United States, Japan, India, the European
Union, Brazil and Australia). Here, this model incorporates exogenous trade balance to address potential trade imbalance between
countries, after following Dong and Whalley (2012).
I ¼ GI þ EI þ CI þ TI
(11)
2.4. Market clearing conditions
All markets clear, when supply is equal to demand at the same
time in all markets (i.e. markets in final goods and production inputs). Then, the following market clearing conditions hold, wherein
this study is line with many studies, such as Dong and Whalley
(2012); Li and Zhang (2012); Li et al. (2014, 2015). Specifically, the
market clearing conditions can be specified as follows:
X
Yij ¼
X
i
X
xkj
ESi;FF;m þ EXFF;m ¼
i
Where
GI represents revenue from final goods.
EI represents revenue from energy balance.
CI represents revenue from carbon tax.
TI represents revenue from trade balance.
(17)
k
XX
i
ESk;LE;n ¼
X
LEk;j;n
FFi;j;m
(18)
j
(19)
j
X
FLij ¼ LDi
(20)
j
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XX
i
FKij ¼
j
X
KDi
(21)
i
Where
Subscript k represents different consumers.
LD represents initial labor endowment before carbon tax.
KD represents initial capital endowment before carbon tax.
2.5. Data sources and scenarios
This study calibrates the data according to the method in Sancho
(2009), wherein the base year is 2012. The output data source is
World Bank (2015) and the trade data source is United Nations
Commodity Trade Statistics Database (2015). The consumption is
derived according to the data of output and trade. The data sources
of energy are BP (2014e2015); IEA (2013e2014, 2015) and Wind
Database (2015). The data sources of input-output tables are
OECD Database (2015). During calculation, this paper adjusts the
data based on the ratio of capital to labor so that the base year
becomes 2012. Due to data availability, low-carbon energy price is
calculated based on electricity price, wherein the data sources are
IEA (2012) and Wikipedia (2015). For simplicity, this paper assumes
that all carbon dioxide emissions are from fossil fuels (coal, oil and
natural gas). For each fossil fuel, emission factors of carbon dioxide
emissions are derived according to the data of IEA (2013e2014,
2015).
Table 1 reports the major parameters in this model. As for
substitution elasticity among production inputs, this study mainly
refers to Huang et al. (2003) and Burniaux and Martins (2012).
Huang et al. (2003) provided an econometrical estimation in China.
As for substitution elasticity among energy, this study refers to
Burniaux and Martins (2012). In terms of price elasticity, supply
elasticity for other fossil fuels and low-carbon energy is set 1.0,
which is in line with Burniaux and Martins (2012). Meanwhile,
supply elasticity for coal is set 5.0, which is different from that in
Burniaux and Martins (2012). In addition, this study performs
sensitivity analysis in order to show robustness of the results. As for
the range of parameters' values, this paper also refers to parameters’ values in other studies, i.e. Burniaux (2001); Hertel et al.
(2009); Rivers (2010); Dong and Whalley (2012); Li and Zhang
(2012) and Li et al. (2014).
In this study, the baseline policy scenario is set according to
China's commitment of carbon intensity reduction. Specifically, the
policy target of China's carbon intensity is 2.243 ton of carbon dioxide per thousand USD, which decreases by 40% relative to the
2005 levels (1.3458 ton per thousand USD).
In the meantime, two other scenarios are set according to the
sector coverage of carbon tax, since sector exemption is frequently
used for policy makers to protect domestic sectors from the loss of
international competitiveness. In this regard, the impact of sector
exemption is evaluated in many studies, such as Rivers (2010).
Specifically, one scenario of sector coverage is named as ALL-Sector,
wherein all sectors are taxed. The other scenario is named as INDSector, wherein only industrial sector is taxed.
3. Results
Based on the general equilibrium model, this study performs
numerical simulations. The main simulation results are presented
as follows.
Firstly, this study considers the impact of single policy instrument (carbon tax), which means that there is no other climate
policy in this scenario. This study focuses on output implications
and emission implications. Output implications are reported in
Table 2. Fig. 2 illustrates carbon dioxide emission changes across
countries, Fig. 3 depicts carbon dioxide emission changes across
sectors and Fig. 4 demonstrates carbon dioxide emission changes
across energy. Table 3 presents leakage rate and carbon dioxide
emission abatement cost.
Secondly, this study investigates combined effect of carbon tax
and other policy instruments. Two policy mixes are considered. The
first policy mix is an integrated tax-tax policy, meaning that carbon
tax is combined with capital tax. In this policy setting, carbon tax
revenue is recycled to reduce capital tax. The intent is to explore
whether revenue recycling can affect the performance. The implications of integrated tax-tax policy are reported in Table 3.
The second policy mix is an integrated tax-subsidy policy, which
combines carbon tax on fossil fuels and subsidy to low-carbon
energy. In this policy setting, carbon tax revenue is used to subsidize low-carbon energy. The purpose is to investigate whether lowcarbon energy subsidy can improve the performance. The implications of integrated tax-subsidy policy are reported in Fig. 5.
Finally, this study performs sensitivity analysis of major parameters in order to identify the potential driving factors affecting
the performance. Fig. 6 illustrates the impact of carbon intensity
reduction on the performance. Fig. 7 makes sensitivity analysis of
Armington trade elasticity in order to investigate whether
Armington trade elasticity can have a significant effect on the
performance. Figs. 8e10 depict the effect of price elasticity of energy supply on the performance. Three types of energy supply
elasticity are included, i.e. coal supply elasticity, oil supply elasticity
and natural gas supply elasticity.
Table 2
Output implications of carbon tax alone.
Scenarios
ALL-Sectora
Table 1
Values for major parameters used in modeling.
Parameters
Substitution elasticity among
production inputs
s1
s2
s3
s4
s5
Values
Parameters
0.88
0.40
0.50
0.40
1.26
Price elasticity
of energy supply
Values
t1
t2
t3
t4
t5
t6
t7
5.0
1.0
1.0
1.0
1.0
1.0
1.0
Sources: Burniaux (2001), Huang et al. (2003), Hertel et al. (2009), Rivers (2010),
Dong and Whalley (2012), Burniaux and Martins (2012), Li and Zhang (2012) and
Li et al. (2014).
5
IND-Sectorb
Regions
China
US
Japan
India
EU
Brazil
Australia
Worldc
China
US
Japan
India
EU
Brazil
Australia
World
Output changes (%)
Industrial
goods
Non-industrial
goods
Overall
output
7.34
0.72
0.39
2.55
0.54
0.92
1.33
1.77
9.44
1.02
0.05
2.81
0.24
1.22
1.57
2.10
1.09
0.21
0.89
0.43
0.83
0.87
0.14
0.63
1.69
0.21
0.79
0.50
0.79
0.69
0.01
0.67
2.70
0.31
0.56
1.10
0.49
0.88
0.48
0.02
3.32
0.38
0.57
1.23
0.53
0.82
0.45
0.08
a
ALL-Sector represents scenario where all sectors are taxed (hereafter).
IND-Sector represents scenario where only industrial goods are taxed
(hereafter).
c
World refers to the sum of China, US, Japan, India, EU, Brazil and Australia.
b
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
Table 3
Leakage rate and carbon dioxide emission abatement cost of carbon tax alone.
Carbon emissions changes
10%
Scenarios
0%
Carbon tax without
revenue recycling
Carbon tax with
revenue recycling
-10%
-20%
ALL-Sector
IND-Sector
ALL-Sector
IND-Sector
Leakage
rate (%)
Carbon dioxide emission
abatement cost (USD/t)
40.23
34.39
38.52
32.78
100.00
120.98
19.54
62.19
-30%
ALL-sector
IND-sector
-40%
-50%
China
US
Japan
India
EU
Brazil
Australia
4.1. The single effect of carbon tax
World
Fig. 2. Impact of carbon tax on carbon dioxide emissions across countries. Note: For all
scenarios, China's carbon intensity decreases by 40% in terms of 2005 levels. In this
case, the target of carbon intensity is about 1.346 (ton/1000USD).
This subsection considers the impact of carbon tax alone in
China. Output implications and carbon dioxide emission implications are discussed in Subsection 4.1.1 and Subsection 4.1.2 separately. In Subsection 4.1.3, this study focuses on two important
indicators, i.e. leakage rate and carbon dioxide emission abatement
cost.
4. Discussion
In this section, this study discusses the simulation results. Section 4.1 considers the implications of carbon tax alone. Section 4.2
evaluates the effect of the integrated tax-tax policy and the integrated tax-subsidy policy. The purpose is to investigate whether
carbon tax revenue can be used to improve the performance. Section 4.3 performs sensitivity analysis in order to identify the driving
factors affecting the performance.
Carbon emissions changes
10%
Emissions of China
4.1.1. The output implications
The output implications of carbon tax in China are reported in
Table 2. This study begins with discussing the impact of carbon tax
on China's output. In the scenario of ALL-Sector, China experiences
output loss of industrial goods (7.34%) and output improvement
of non-industrial goods (1.09%). In the meantime, there is overall
output loss (2.70%). As for the scenario of IND-Sector, industrial
goods face output reduction (9.44%) and non-industrial goods
Emissions of other countries
Emissions of world
0%
-10%
-20%
-30%
ALL-sector
IND-sector
-40%
-50%
From IND
From NIND
From IND
From NIND
From IND
From NIND
Fig. 3. Impact of carbon tax on carbon dioxide emissions across sectors. Note: Other countries represent the sum of other countries (i.e. the United States, Japan, India, the European
Union, Brazil and Australia) (hereafter).
Carbon emissions changes
10%
Emissions of China
Emissions of other countries
Emissions of world
0%
-10%
-20%
-30%
ALL-sector
IND-sector
-40%
-50%
From Coal
From Oil
From Gas
From Coal
From Oil
From Gas
From Coal
From Oil
From Gas
Fig. 4. Impact of carbon tax on carbon dioxide emissions across fossil fuels. Note: Other countries represent the sum of other countries (i.e. the United States, Japan, India, the
European Union, Brazil and Australia) (hereafter).
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a
b
60
140
Carbon emission abatement cost (USD/t)
7
ALL-sector
IND-sector
55
Leakage rate (%)
120
100
80
50
45
40
35
60
30
40
25
20
0%
20%
40%
60%
80%
20
0%
100%
Rate of low-carbon energy subsidy
20%
40%
60%
80%
100%
Rate of low-carbon energy subsidy
Fig. 5. Combined effect of carbon tax and low-carbon energy subsidy.
a
b
60
Carbon emission abatement cost (USD/t)
140
ALL-sector
IND-sector
55
Leakage rate (%)
120
100
80
50
45
40
35
60
30
40
25
20
25%
30%
35%
40%
20
45%
25%
Reduction of carbon intensity
30%
35%
40%
45%
Reduction of carbon intensity
Fig. 6. Sensitivity analysis of carbon intensity reduction.
b
140
60
ALL-sector
IND-sector
55
120
Leakage rate (%)
Carbon emission abatement cost (USD/t)
a
100
80
50
45
40
35
60
30
40
20
25
0
1
2
3
4
5
6
7
8
9
10
Armington trade elasticity
20
0
1
2
3
4
5
6
7
8
9
10
Armington trade elasticity
Fig. 7. Sensitivity analysis of Armington trade elasticity.
experience output improvement (1.69%). Meanwhile, there is
overall output reduction (3.32%).
From the above results, one can see that carbon tax affects the
structure of economy, resulting in production shift from industrial
goods to non-industrial goods. These results are not surprising and
the explanations are as follows. Industrial goods are intensive in
terms of fossil fuel consumption, while non-industrial goods are
extensive in terms of fossil fuel consumption. Then, industrial
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
a
b
Carbon emission abatement cost (USD/t)
140
60
ALL-sector
IND-sector
55
Leakage rate (%)
120
100
80
50
45
40
35
60
30
40
20
25
0
1
2
3
4
5
6
7
8
9
20
10
0
1
2
3
4
5
6
7
8
9
10
Coal supply elasticity
Coal supply elasticity
Fig. 8. Sensitivity analysis of coal supply elasticity.
a
b
60
Carbon emission abatement cost (USD/t)
140
ALL-sector
IND-sector
55
Leakage rate (%)
120
100
80
50
45
40
35
60
30
40
20
25
0
1
2
3
4
5
6
7
8
9
20
10
0
1
2
3
4
5
6
7
8
9
10
Oil supply elasticity
Oil supply elasticity
Fig. 9. Sensitivity analysis of oil supply elasticity.
a
b
60
Carbon emission abatement cost (USD/t)
140
ALL-sector
IND-sector
55
Leakage rate (%)
120
100
80
50
45
40
35
60
30
40
20
25
0
1
2
3
4
5
6
7
8
Gas supply elasticity
9
10
20
0
1
2
3
4
5
6
7
8
9
10
Gas supply elasticity
Fig. 10. Sensitivity analysis of natural gas supply elasticity.
goods face relatively high carbon tax rate compared with nonindustrial goods. Thus, industrial goods undergo relatively large
output loss.
It is noteworthy that there is other effect, driving up output of
non-industrial goods. Overall output decline (particularly for fossilfuel-intensive sectors) leads to demand reduction of various production inputs. In this case, demand decrease drives down price of
production inputs, given that market supply remains constant.
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Then, low price of production inputs encourages output of nonindustrial goods. When such effect becomes dominant, nonindustrial goods experience output gain instead of output loss.
Furthermore, one can also see that output implications are
qualitatively similar between scenarios of ALL-Sector and INDSector. Quantitatively, however, there are considerable differences
between the two scenarios, since taxation extension of sector
coverage reduces overall output loss. Quite interestingly, these results imply that overall output loss can be reduced by welldesigned sector coverage of carbon tax, when the carbon intensity reduction target remains to be the same.
Then, this study considers output implications in the nonabating countries, as presented in Table 2. The non-abating countries include all countries except China, since China is the only
abating country in this paper. In the scenario of ALL-Sector, there is
industrial output gain in the United States (0.72%), India (2.55%),
Brazil (0.92%) and Australia (1.33%), while there is industrial output
loss of Japan (0.39%) and the European Union (0.54%). These
differences are induced by cross-country variations, such as trade
openness and the structure of economy. In contrast, there is nonindustrial output improvement in all non-abating countries, i.e.
the United States (0.21%), Japan (0.89%), India (0.43%), the European
Union (0.83%), Brazil (0.87%) and Australia (0.14%). In addition,
there is overall output improvement in all non-abating countries,
i.e. the United States (0.31%), Japan (0.56%), India (1.10%), the European Union (0.49%), Brazil (0.88%) and Australia (0.48%). In
addition, it can be observed from Table 2 that there are qualitatively
similar results in the scenario of IND-Sector.
From the above discussion, it can be concluded that all nonabating countries are affected by carbon tax in China, in spite of
cross-country variations. Undoubtedly, all non-abating countries
are positively affected in terms of overall output. These results are
not surprising and the explanations are mainly two folds. On one
hand, there exists competitiveness issue through trade channel,
since China is a large economy with high trade openness. In this
case, China's carbon tax results in competitiveness loss of domestic
producers, causing output loss of domestic sectors and output gain
in international competitors.
On the other hand, there is rebound effect through energy
channel, since China is the largest primary energy consumer in the
world. Carbon tax in China adds new cost to domestic producers
and thus drives down energy demand in China. As a consequence,
energy price falls in international market, given that there is no
change in the world's market supply. Furthermore, low international energy price reduces production cost and thus promotes
output growth in the non-abating countries, since energy is an
important production input. Such effect is termed as cross-border
rebound effect. It is noteworthy that cross-border rebound effect
implies relocation of energy consumption from the abating country
to the non-abating countries and thus generates benefits to the
non-abating countries.
Finally, this study considers output implications in the world. In
the scenario of ALL-Sector, the world shows output loss of industrial goods (1.77%) and overall output (0.02%), which are mainly
induced by output reduction in China. By contrast, there is an
output increase in non-industrial goods (0.63%). In this way, the
structure of economy can be affected in the world. Furthermore,
similar results can be found in the scenario of IND-Sector.
4.1.2. The carbon dioxide emission implications
This subsection looks into carbon dioxide emission implications.
Here, this study begins with discussing carbon dioxide emission
changes across countries. According to the results in Fig. 2, China
shows a reduction of 27.77% in carbon dioxide emissions in the
scenario of ALL-Sector and a decrease of 28.23% in IND-Sector.
9
These results show that carbon tax contributes to carbon dioxide
emission reduction in China. Meanwhile, it can be seen that there
are some quantitative differences in carbon dioxide emission
reduction between the two scenarios, wherein the two scenarios
face the same level of carbon intensity reduction target. These results imply that taxation extension of sector coverage can be relevant to explaining part of differences in emission implications
across scenarios.
From Fig. 2, it can also be observed that the non-abating countries experience an increase in carbon dioxide emissions. For
example, the United States shows an increase of 6.81% carbon dioxide emission in the scenario of ALL-Sector and 5.90% in INDSector. For Japan, there is an increase of 7.32% in carbon dioxide
emissions in the scenario of ALL-Sector and an increase of 6.13% in
IND-Sector. These results imply that carbon tax in China results in
carbon leakage from the abating country (China) to the non-abating
countries.
In the meantime, the world shows a reduction of 6.60% in carbon dioxide emissions in the scenario of ALL-Sector and of 7.36% in
IND-Sector. The results suggest that China's carbon tax contributes
to the world's carbon dioxide emission mitigation. By comparison,
there are slight differences in the size of the world's carbon dioxide
emission reduction, implying that taxation extension of sector
coverage may be relevant to explaining the world's carbon dioxide
emission reduction.
Then, this study considers carbon dioxide emission changes
from the perspective of sector, as illustrated in Fig. 3. The main
purpose is to find out whether carbon dioxide emissions are
affected by production shift across sectors or across countries. As
for the scenario of ALL-Sector, there is a decrease of 34.09% in
carbon dioxide emissions of China's industrial goods and a decrease
of 16.28% in China's non-industrial goods. In terms of all nonabating countries, there is an increase of 8.61% in carbon dioxide
emissions of industrial goods and an increase of 6.06% in carbon
dioxide emissions of non-industrial goods. As for the scenario of
IND-Sector, there is a decrease of 46.74% in carbon dioxide emissions of industrial goods and a reduction of 5.42% in non-industrial
goods. Meanwhile, the non-abating countries show an increase of
7.97% in carbon dioxide emissions of industrial goods and 4.76% in
non-industrial goods.
From the above discussion, it can be concluded that production
shift across sectors is not in proportion to carbon dioxide emission
changes across sectors. This conclusion is quite interesting, since it
implies that production shift across sectors is not the driving factor
explaining carbon leakage across scenarios. Similarly, it can also be
concluded that output relocation across countries may be insignificant in explaining differences in carbon leakage across scenarios.
Finally, this study considers emission changes from the viewpoint of fossil fuels, as depicted in Fig. 4. The main purpose is to
explore whether inter-fuel substitution and relocation of fossil fuel
consumption across countries are important in explaining carbon
leakage.
As for the scenario of ALL-Sector, there is a large drop in carbon
dioxide emissions from coal (31.47%), a significant decrease from oil
(8.26%) and a large reduction from natural gas (18.77%) in China. In
the non-abating countries, there is a significant increase in carbon
dioxide emissions from coal (9.70%), and a moderate increase from
oil (5.94%) and natural gas (5.89%).
As for the scenario of IND-Sector, there is a large decrease in
carbon dioxide emissions from coal (33.43%), a small decrease from
oil (1.08%) and a large reduction from natural gas (14.41%) in China.
In the meantime, the non-abating countries experience a moderate
increase in carbon dioxide emissions from coal (9.03%), and a small
increase from oil (4.60%) and natural gas (5.08%) respectively.
In general, there are two types of effect which generate
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considerable impact on carbon dioxide emissions. The first type of
effect is termed as inter-fuel substitution effect. Carbon tax in China
leads to relative price changes among fossil fuels. Then, relative
price change results in inter-fuel substitution among fossil fuels. It
is important to note that inter-fuel substitution effect does not
affect carbon leakage directly, since leakage rate is calculated according to carbon dioxide emission changes across countries.
The second type of effect is termed as rebound effect. In China,
carbon tax curbs domestic fossil fuel consumption. Then, consumption reduction leads to price decrease in fossil fuel, when
market supply remains constant. Low fossil fuel price encourages
fossil fuel consumption, indicating rebound in fossil fuel consumption. Such rebound effect is mainly conducted within the
abating country.
It is noteworthy that rebound effect can be conducted across
borders. Fossil fuel consumption reduction in China's market indicates total consumption reduction in international market. Consumption reduction drives down international fossil fuel price,
when market supply remains constant. Low international fossil fuel
price encourages fossil fuel consumption in the non-abating
countries, thus implying rebound in fossil fuel consumption in
these countries. Importantly, cross-border rebound effect implies
relocation of fossil fuel consumption from the abating country to
the non-abating countries. Relocation of fossil fuel consumption
corresponds to relocation of carbon dioxide emissions across
countries, thus inducing carbon leakage directly.
4.1.3. Leakage rate and carbon dioxide emission abatement cost
This subsection focuses on two important indicators. The first
indicator is leakage rate, which is calculated as the ratio of
increased carbon dioxide emissions in the non-abating countries
(the United States, Japan, India, the European Union, Brazil and
Australia) to decreased carbon dioxide emissions in the abating
country (China). In such a setting, leakage rate reflects the degree of
carbon leakage.
As presented in Table 3, the estimated leakage rate is 40.23% in
the scenario of ALL-Sector, while leakage rate is 34.39% in INDSector. These results indicate that there is a moderate gap in
leakage rate between these two scenarios. Therefore, taxation
extension of sector coverage may be relevant to determining carbon leakage across countries.
There are significant differences in the size of leakage rate
among existing studies. Branger and Quirion (2014) found that
leakage rate ranged from 5% to 25% with the mean of 14%, which
was based on 25 existing studies. By comparison, the estimated
leakage rate in this study is greater than that in existing studies.
However, the leakage rate in this paper is still reasonable, since
there is only one abating country in this manuscript. Burniaux and
Martins (2012) held that the size of leakage rate was affected by
many factors, such as the size of abating-country coalition, the
shape of production function and coal supply elasticity.
The second indicator is carbon dioxide emission abatement cost,
which is defined as ratio of GDP loss relative to carbon dioxide
emission reduction. In this study, carbon dioxide emission abatement cost refers to average abatement cost, instead of marginal
abatement cost. Carbon dioxide emission abatement cost is used to
capture the effectiveness of policy instruments.
According to Table 3, carbon dioxide emission abatement cost is
100.00 USD/t in the scenario of ALL-Sector and 120.98 USD/t in INDSector. It can be found that our results are reliable, after making a
comparison with the results of other studies. For example, Li and
Lin (2013) estimated average carbon dioxide emission abatement
cost of carbon tax and carbon tax with revenue recycling. Their
estimated carbon abatement cost is about 400 RMB/t in the scenario of carbon tax and 330RMB/t in the scenario of carbon tax with
revenue recycling. Cui et al. (2014) found that average carbon dioxide emission abatement cost ranged from 7.14 RMB/t to 98.80
RMB/t, covering the following provinces, i.e. Beijing, Tianjin,
Shanghai, Hubei, Guangdong and Chongqing.2 They also found that
there were rapid increasing trends of average carbon dioxide
emission abatement cost with the increase of carbon intensity
reduction. When intensity reduction target increased from 40% to
60%, average carbon dioxide emission abatement cost increased
sharply from 16.18 RMB/t to 184.36 RMB/t.
From the simulation results, it can be concluded that there are
considerable differences in carbon dioxide emission abatement
cost between the two scenarios. To put it differently, taxation
extension of sector coverage can significantly reduce carbon dioxide emission abatement cost. Therefore, taxation extension of
sector coverage may be important in determining costeffectiveness of carbon tax.
4.2. Combined effect of policy mixes
This subsection discusses the combined impact of two policy
mixes. The first policy mix is an integrated tax-tax policy, wherein
all carbon tax revenue is recycled to reduce capital tax. The second
policy mix is an integrated tax-subsidy policy, wherein carbon tax
revenue is used to subsidize low-carbon energy. Overall, this subsection seeks to find out whether carbon tax revenue can be used to
improve the performance.
4.2.1. Impact of integrated tax-tax policy
The impact of integrated tax-tax policy is discussed in this
subsection. The purpose is to investigate whether revenue recycling can improve the performance. For simplicity, this study focuses on two performance indicators, with leakage rate reflecting
carbon dioxide emission reduction efficiency and carbon dioxide
emission abatement cost showing cost-effectiveness. The simulation results are presented in Table 3.
As for the scenario of ALL-Sector, leakage rate (38.52%) in carbon
tax with revenue recycling is slightly lower than that in carbon tax
without revenue recycling (40.23%). In terms of the scenario of INDSector, leakage rate in carbon tax with revenue recycling (32.78%) is
slightly lower than that in carbon tax without revenue recycling
(34.39%), too. Then, the above results imply that revenue recycling
can reduce carbon leakage to a certain degree, thus contributing to
the world's carbon dioxide emission reduction.
Meanwhile, as for the scenario of ALL-Sector, carbon dioxide
emission abatement cost is 19.54 USD/t in carbon tax with revenue
recycling, as is significantly lower than that in carbon tax without
revenue recycling (100.00 USD/t). Furthermore, in terms of the
scenario of IND-Sector, carbon dioxide emission abatement cost is
62.19 USD/t in carbon tax with revenue recycling, as is substantially
lower than that in carbon tax without revenue recycling (120.98
USD/t). From the above results, it can be concluded that revenue
recycling can reduce carbon dioxide emission abatement cost
considerably, implying high performance of cost-effectiveness.
Hence, revenue recycling may be important in determining carbon dioxide emission abatement cost.
In sum, revenue recycling can reduce carbon leakage slightly
and improve cost-effectiveness significantly. These results are as
expected and the explanations are as follows. Revenue recycling
implies low capital tax rate, which can encourage capital to replace
energy during production process. Such inter-factor substitution
2
Cui et al. (2014) evaluated the scenario of the carbon emissions trading, which
covers pilots (PETS). Total cost across provinces referred to the sum of cost of carbon
dioxide emission abatement and the expenditure to buy emission rights.
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results in low carbon dioxide emissions. In this way, revenue
recycling can reduce carbon leakage and improve the emission
performance. Furthermore, low capital tax rate encourages new
capital inflow from other countries to China. More capital inflow
advances domestic production. Then, revenue recycling can reduce
carbon dioxide emission abatement cost and improve costeffectiveness of carbon tax.
Nevertheless, China still suffers from overall output loss rather
than overall output gain in the scenario of carbon tax with revenue
recycling. Put it differently, these simulation results do not support
the argument of “double dividends”. The results are not surprising,
since carbon tax adds new distorting effect to the economy. Tax
interaction between carbon tax and capital tax may function as a
barrier to “double dividends”.
4.2.2. Impact of integrated tax-subsidy policy
In this subsection, this study discusses the impact of integrated
tax-subsidy policy. The purpose is to investigate whether lowcarbon energy subsidy can improve the performance.
As presented in Fig. 5a, there are significant downward trends of
carbon dioxide emission abatement cost, when low-carbon energy
subsidy rate increases. In the scenario of ALL-Sector, carbon dioxide
emission abatement cost is 98.53 USD/t, when low-carbon energy
subsidy rate is 5%. In comparison, carbon dioxide emission abatement cost reduces to 41.05 USD/t, when low-carbon energy subsidy
rate increases to 95%. In the scenario of IND-Sector, carbon dioxide
emission abatement cost reduces from 119.76 USD/t to 73.76 USD/t,
when low-carbon energy subsidy rate increases from 5% to 95%.
Therefore, low-carbon energy subsidy reduces carbon dioxide
emission abatement cost dramatically.
From Fig. 5b, one can see that leakage rate tends to reduce, when
low-carbon energy subsidy rate rises. In the scenario of ALL-Sector,
leakage rate decreases from 40.18% to 37.38%, when low-carbon
energy subsidy rate rises from 5% to 95%. In the scenario of INDSector, leakage rate reduces from 34.30% to 29.67%, when lowcarbon energy subsidy rate rises from 5% to 95%. From the above
discussion, it can be concluded that low-carbon energy subsidy can
have a moderate effect on carbon leakage.
4.3. Sensitivity analysis and the driving factors
In this section, this study performs sensitivity analysis in order
to identify the potential driving factors which affect the performance considerably.
4.3.1. Carbon intensity reduction
This subsection discusses the impact of carbon intensity
reduction, as illustrated in Fig. 6. The aim is to explore whether the
size of carbon intensity reduction can affect the performance.
Fig. 6a illustrates the relationships between carbon intensity
reduction and carbon dioxide emission abatement cost. In the
scenarios of both ALL-Sector and IND-Sector, there are significantly
increasing trends of carbon dioxide emission abatement cost, when
the size of carbon intensity reduction increases. Furthermore, carbon dioxide emission abatement cost increases drastically, especially when reduced carbon dioxide emissions account for a
relatively high proportion of total carbon dioxide emissions. The
results are not surprising, since increasing carbon dioxide emission
intensity reduction implies the declining potential and growing
difficulty in carbon dioxide emission reduction.
Fig. 6b demonstrates leakage rate as a function of carbon intensity reduction. In the scenarios of both ALL-Sector and INDSector, there are moderately increasing trends of leakage rate,
when the size of carbon intensity reduction increases. The explanations are as follows. When carbon intensity reduction rises, there
11
are relatively large energy price changes in China. Larger energy
price changes in domestic market result in greater reduction in
domestic energy consumption. Greater reduction in China's energy
consumption leads to larger drops in international energy price.
Consequently, larger drops in international energy price result in
relatively large size of cross-border rebound effect, implying high
carbon leakage.
4.3.2. Armington trade elasticity
This subsection evaluates the impact of Armington trade elasticity on the performance, as demonstrated in Fig. 7.
Fig. 7a depicts carbon dioxide emission abatement cost as a
function of Armington trade elasticity. Clearly, there are dramatically increasing trends of carbon dioxide emission abatement cost
in the two scenarios, when Armington trade elasticity rises. Fig. 7b
displays leakage rate as a function of Armington trade elasticity. In
the two scenarios, there are moderately growing trends of leakage
rate, with the increase of Armington trade elasticity. Meanwhile,
there are slight differences in the impact of Armington trade elasticity on leakage rate between the two scenarios.
These above results are not surprising and the reasons are as
follows. Higher Armington trade elasticity implies increasing substitution possibility between domestic goods and imported goods.
Therefore, higher Armington trade elasticity corresponds to more
competitiveness loss of China's producers in international competition. Additional competitiveness loss leads to larger output relocation from China to the non-abating countries. More output loss
leads to higher carbon dioxide emission abatement cost. In addition, large output relocation across countries results in high carbon
leakage across borders.
4.3.3. Energy supply elasticity
This subsection considers the impact of energy supply elasticity
on the performance.
Figs. 8a, 9a and 10a illustrate the impact of price elasticity of
three fossil fuels on carbon dioxide emission abatement cost
respectively. As demonstrated in Fig. 8a, carbon dioxide emission
abatement cost tends to decrease moderately, when coal supply
elasticity rises. These results are not surprising, since large values of
coal supply elasticity imply that coal supply can be elastic to coal
price. In this case, carbon tax might result in smaller changes in coal
price. Then, small coal price changes can generate small output loss.
In this case, carbon dioxide emission abatement cost tends to
decline. From Figs. 9a and 10a, it can be found that there is a
negligible impact of oil supply elasticity and natural gas supply
elasticity on carbon dioxide emission abatement cost. Therefore, oil
supply elasticity and natural gas supply elasticity may be insignificant in determining carbon dioxide emission abatement cost, and
have an insignificant effect on cost-effectiveness of carbon tax.
Figs. 8b, 9b and 10b demonstrate leakage rate as a function of
price elasticity of various fossil fuels. As illustrated in Fig. 8b, there
are considerably diminishing trends of leakage rate, when coal
supply elasticity increases. These results are not surprising and the
explanations are mainly related to cross-border rebound effect.
Large values of coal supply elasticity imply that coal supply is elastic
to price changes. In other words, small changes in coal price can
result in significant changes in coal supply. In this case, the same
reduction in China's energy consumption causes smaller drops in
coal price in international market. Then, there is a relatively small
increase in coal consumption in the non-abating countries, indicating lower carbon leakage from China to the non-abating countries. Similarly, leakage rate tends to decline slightly, when oil or
natural gas supply elasticity rises, as presented in Figs. 9b and 10b.
In comparison with other fossil fuels, coal supply elasticity can play
an important role in determining the size of leakage rate, because of
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Z. Zhang et al. / Journal of Cleaner Production xxx (2016) 1e13
coal-dominant energy consumption mix in China.
5. Conclusion and policy implications
The Chinese government committed to reducing carbon intensity, controlling carbon dioxide emissions and promoting
development of non-fossil fuels. Thus, it becomes a top priority for
the Chinese government to adopt effective climate policies to curb
domestic carbon dioxide emissions. It is noteworthy that welldesigned climate policies require detailed information regarding
the potential driving forces that affect the performance. Under such
circumstances, this paper develops a new multi-country general
equilibrium model with detailed energy disaggregation. Here, this
study evaluates the impact of both single policy instrument (carbon
tax) and aggregate policy mixes. The purpose is to find out whether
carbon tax revenue can be used to improve the performance.
Furthermore, this study performs sensitivity analysis to identify the
driving factors affecting the performance of carbon tax in China.
According to the simulation results and the above-mentioned
discussion, this study puts forward the following conclusions.
Firstly, cross-border externalities function as important barriers
to unilateral carbon dioxide emission reduction. Through trade
channel, competitiveness loss results in new output loss of the
abating countries. In addition, through energy channel, crossborder rebound effect generates additional competitiveness loss.
Furthermore, carbon leakage makes it difficult for the world to
achieve anticipated carbon dioxide emission reduction target.
Secondly, the integrated policy mixes can perform better than
carbon tax alone, implying that carbon tax revenue can be used to
improve the performance. In this paper, two integrated policy
mixes are covered. Carbon tax revenue is recycled to reduce capital
tax in the integrated tax-tax policy and is used to subsidize lowcarbon energy in the integrated tax-subsidy policy. Two policy
mixes show better performance than carbon tax alone, which implies that carbon tax revenue can be used to improve the
performance.
Finally, all driving factors affecting the performance can be
classified into two types. The first type of factors has a significant
effect on cost-effectiveness, such as the size of carbon intensity
reduction and Armington trade elasticity. The second type of factors generates a considerable effect on the emission performance,
such as coal supply elasticity.
Based on the above discussion and conclusions, this paper
proposes the following policy implications regarding how to design
China's carbon tax:
Low carbon tax and low-carbon energy subsidy can be implemented simultaneously, given that policy mix can perform better
than carbon tax alone. Meanwhile, high carbon tax rate has a
substantial adverse effect on the economy and some sectors may be
highly adversely affected. To overcome this barrier, carbon tax
should begin with low tax rate. Besides, low-carbon energy subsidy
can result in energy substitution from fossil fuels to low-carbon
energy, without considerable negative impact on the economy. In
this case, policy-makers should consider how to combine carbon
tax and low-carbon energy subsidy in order to improve the
performance.
Looking ahead, more complicated policy mixes are preferred, in
order to mitigate negative impact of cross-border externalities and
high carbon tax rate. According to the simulation results, carbon tax
revenue can be used to improve the performance. In this regard,
other policy instruments can be combined with carbon tax, as long
as new integrated policy mixes can improve the performance.
In the future research, some limitations in the model can be
overcome. Firstly, detailed analyses of low-carbon energy subsidy
are quite interesting, which go beyond the focus of this study.
Secondly, more countries can be added and the sectors should be
further disaggregated. Thirdly, the existing climate policies outside
of China should be added to the model. After overcoming the above
limitations, researchers can produce more realistic simulation results and provide more useful policy suggestions for policy makers.
Acknowledgements
The authors are grateful to the anonymous referees for their
valuable suggestions. This paper is supported by the National
Natural Foundation of China (Grant No. 71403147), the Ministry of
Education Research of Social Sciences Youth Funded Projects (Grant
No. 13YJC790065), Shandong Social Science Planning Fund Program
(Grant No. 12DJJJ12) and Young Scholars Program of Shandong
University (Grant No. 2016WLJH02).
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