Impact of Emission Trading Market Linkage on Carbon Price:
Findings of the GTAP-E Model
Mengfei Jiang, Xi Liang
University of Edinburgh Business School
19 June 2017, the 40th IAEE Singapore Conference
Carbon Pricing
• 1998 - Kyoto Protocol
– International Emissions Trading (IET)
• The European Union ETS
– Clean Development Mechanism (CDM)
– Joint Implementation (JI)
• 2009 – Copenhagen Summit
– Negotiating position of the European Union
• European Union revised its carbon allowances system for the post-Kyoto period
• 2015 - Paris Agreement
– Internationally Transferred Mitigation Outcomes (ITMOs)
– Nationally Determined Contributions (NDCs)
• Potential global carbon market
2
Existing and Emerging ETS
Source: the World Bank, 2014
• Timeline of ETS developing in China
Pilot ETS in 7 regions
2011
National ETS
2017
Link with other ETS
Near future
(Anger N., 2008; Flachsland et al, 2011; Guoyi et al., 2012; Qi et al., 2013; Gavard et al., 2013; Kachi et al., 2015;
Wang et al., 2015; Tao et al., 2015;)
3
Efficiency Gain from Linkage
$/t
MAC B
$/t
PautB
X
MAC A
PlinkA
PlinkB
Y
PautA
Qaut
Reduction Target A
Qlink
Reduction Target B
• The efficiency gain
from linkage in a
compliance period is
equal to the total
difference between the
sum of the mitigation
costs of the two ETS
before and after
linkage, which is the
total area of (𝑋 + 𝑌).
Figure: Simplified Illustration of Distribution of Efficiency
Gains when Linking Two ETSs (assuming carbon prices of
two system converge after linkage)
4
GTAP-E
multi-sector, multi-regional CGE model
Taxes
Taxes
Goods and
Services
Goods and
Services
5
GTAP-E: fundamental features
Representative Agents Behaviors
– Household behaviour: maximize utilities
𝑚𝑎𝑥𝑐 𝑈(𝑐1 , … , 𝑐𝑛 ), subject to 𝑚 =
𝑖
𝑁
𝑖=1 𝑝𝑖
𝑖𝑗 ,𝑣𝑖𝑗
𝜋𝑗 = 𝑝𝑗 𝑦𝑗 −
𝑁
𝑖=1 𝑝𝑖 𝑥𝑖𝑗
−
(𝑐𝑖 + 𝑠𝑖 )
𝐹
𝑓=1 𝑤𝑓 𝑣𝑓𝑗 ,
Equilibrium Conditions
𝐶
∆𝑖 =
– Market clearance
𝐹
∆𝑓 =
𝛾𝑓𝑗
𝑗=1
– Zero profit
𝑝𝑗 𝑦𝑗
𝑁
𝑤𝑓
𝜋
𝑚
+ 𝑝𝑖 𝑠𝑖 − 𝑝𝑖 𝑦𝑖
𝑨
𝜶
𝑤𝑓 𝑉𝑓 − 𝑚
𝜷
𝛾𝑓𝑗
(𝑝𝑖 /𝛽𝑖𝑗 )
𝑖=1
∆ =
𝑁
𝑗=1 𝑝𝑗 𝑠𝑗
𝐹
𝛽𝑖𝑗
∆𝑗 = 𝑝𝑗 − 𝐴𝑗
−
𝒙
𝒚
𝑼
𝝅
∅
− 𝑉𝑓
𝐹
– Income balance
𝐹
𝑓=1 𝑤𝑓 𝑉
𝑓
𝑝𝑗 𝑦𝑗 +𝛼𝑖
Consumption of
commodity
Households’ income
Commodity price
Households’ saving
Households’ primary
factors
Households’ wages for
factors
Intermediate inputs
Outputs of industry
Households’ utility
Enterprises’ profit
Constraints of production
technology
Scaling parameter
Share of each good in
expenditure on
consumption
Share of each
intermediate inputs in the
cost of production
Share of each factor
inputs in the cost of
production
𝒘
Subject to 𝑦𝑗 = ∅𝑗 (𝑋1𝑗, , … , 𝑋𝑁𝑗 ; 𝑣1𝑗 , … , 𝑣𝐹𝑗 )
𝑁
𝑗=1 𝛽𝑖𝑗
𝑁
Denotation
𝒄
𝒎
𝒑
𝒔
𝒗
– Industry behaviour: maximize profits
𝑚𝑎𝑥𝑥
Variables
(𝑤𝑓 /𝛾𝑓𝑗 )
𝑓=1
𝜸
𝑓=1
Elasticity of Substitution
– Trade-offs
6
Model Calibration
•Benchmark database (GTAP 8 Database
Year 2007);
•Define sets, parameters, variables and
equations;
•Assign values to elasticity parameters and
initial values to variables from the
database
Solve model
Re-solve
model
•Simulation
•“Shock” by changing
exogenous variables or
parameters:
•Substitution
coefficients, etc.
• Find new values for the
endogenous variables:
• Emission price
• Emission reduction
• GDP, etc.
Results
7
Aggregated Regions & Sectors
REGIONS DESCRIPTION
TYPE
SECTORS
DESCRIPTION
NONENERGY
Agriculture
Aggregation
of
all
agriculture
products, plus managed forest land
and logging activities;
USA
United States
EU27
European Union
Transportation
Pipeline transport, and water, air and
land transport;
EEFSU
Eastern Europe and Former Soviet Union
Energy-Intensive
Industry
Iron and steel, non-metallic minerals
products,
non-ferrous
metals
products, chemical rubber products
and fabricated metal products;
JPN
Japan
Other Industries
ROA1
Other Annex 1 Countries
Other Services
EEX
Net Energy Exporters
All other industries not included
elsewhere, e.g. food, tobacco,
construction, mining, equipment and
others;
All other services not included
elsewhere,
e.g.
communication,
finance, public services, dwellings
and others;
Extraction of petroleum;
CHN
IND
ENERGY
Oil
Coal
China
India
Nature Gas
Petroleum
Electricity
ROW
Rest of the World
Mining and agglomeration of hard
coal, lignite and peat;
Extraction of natural gas;
Refined oil and petro chemistry
products;
Electricity and heat generation,
transmission and distribution. Electric
generation technologies include:
Coal, Gas, Refined Oil, Hydro,
Nuclear, Wind, Solar, Biomass
8
Scenarios: policy constraints
Scenario
CHN
EU27
JPN
RoA1
ETS in the USA
model
EEFSU
EEx
IND
RoW
Multilateral Trading
Description
1
2
3
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

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
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


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
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

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
ETS adopted
separately in these
countries
Chinese ETS is NOT Chinese ETS is
covered in global
covered in global
carbon market
carbon market
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Results: emission price (USD/ton CO2)
Scenario 1
Scenario 2
Scenario 3
CHN
29.97
0
40.08
EU27
40.37
27.62
40.08
JPN
121.91
27.62
40.08
RoA1
126.19
27.62
40.08
USA
24.1
27.62
40.08
EEFSU
0
0
0
EEx
0
0
0
IND
0
0
0
ROW
0
0
0
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Results: emission reduction (%)
Scenario 1
Scenario 2
Scenario 3
CHN
-40*
0.32
-40*
EU27
-17*
-17*
-17*
JPN
-30*
-30*
-30*
RoA1
-40*
-40*
-40*
USA
-17*
-17*
-17*
EEFSU
1.91
0
-28.79
EEx
1.47
0.96
1.36
IND
-0.15
-0.13
-0.16
RoW
1.63
1.07
1.5
* Fixed emission reduction targets
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Results: GDP changes (%)
CHN
EU27
JPN
RoA1
USA
EEFSU
EEx
IND
RoW
0.2
0.11
0
-0.2
Scenario
-0.06 1
0.08
0.02
-0.03
-0.04 2
Scenario
-0.14
-0.2
-0.22
-0.14
-0.15
-0.17
-0.22
0.03
0.11
0.03
-0.04
Scenario
-0.06 3
-0.21
-0.22
-0.26
-0.32
-0.4
-0.46
-0.6
-0.8
-0.8
-0.87
-0.88
-1
-1.1
-1.2
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Findings highlight
•
A strong and robust carbon price could give investors a right price signal on the
value of carbon emission, thus incentive the carbon market activity and improve
the liquidity;
•
Allowing multilateral trading of emission among shifts the burden of the reduction
away from the relatively carbon-efficient economies (EU, Japan and the rest of
Annex 1 countries) towards coal in the USA;
•
Additional emissions will be emitted in those countries with no binding constraint
relative to the emission reduction in countries with binding constraints
•
A linkage between Chinese carbon market and the international carbon market
leads to a significant lower decrease in the GDP in China, resulting as 0.04%.
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Contributions
 Contribute to methodology
Assessing applicability and limitation of CGE model and their use in economy-wide climate
change policy analysis. (Wing, 2004; Biabiker et al., 2004; Qi et al., 2013)
 Contribute to policy-making
Understanding the impacts of carbon market linkage will benefit the development and
improvement of climate change policy, maximise efficiency of carbon trading market, and
effectively promote carbon abatement with clear carbon price signal.
 Contribute to theory
With a modified and extended CGE model, this research will verify and replenish the
multiple policy interaction and carbon market linkage analysis. This work also fill the
literature gap of climate change policy analysis in China. (Cheng and Zhang, 2011; Zhang, et al., 2014;
Duan et al., 2014; Liu and Wang, 2014; Fan and Wang, 2014; Wu et al., 2014; Zhou, et al., 2013; Qi, et al., 2014)
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Thank you very much!
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