Low carbon economy 2030 in China - Jiangxi Province as an example
Hancheng DAI
2012.12.16
NIES
Overview
2012/12/16 18th AIM International Workshop 2
— Motivation: develop methodology for provincial level low-carbon studies;
— Questions Future scenario of energy & emissions without policy intervention;
Economic cost of carbon reduction;
Measures to lower the economic cost;
Co-benefits of low carbon economy.
— Methodology A 2-region CGE model including Jiangxi province and rest of China;
A hybrid model including various technologies in power sector;
2005-2030 period;
— Findings Energy and emission would increase due to GDP growth
carbon price and GDP loss would be quite high in the most stringent
carbon reduction scenarios
With low-carbon countermeasures, carbon price and GDP loss would be
lowered substantially.
A lot of co-benefits associated with low-carbon economy, including air
pollutants reduction and energy security improvement
This model is relatively robust and can be applied to any other province
given the data.
1. Introduction: energy and emissions in China
— Past (IEA, 2009)
— Future (EIA, 2011)
2012/12/16 18th AIM International Workshop 3
0%
20%
40%
60%
80%
100%
2005 2011 2017 2023 2029 2035
CO2 emissions OtherBrazilSouth KoreaJapanEuropeCanadaUnited StatesIndiaChina0%
20%
40%
60%
80%
100%
2005 2011 2017 2023 2029 2035
Primary energy OtherBrazilSouth KoreaJapanEuropeCanadaUnited StatesIndiaChina
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0200400600800
100012001400160018002000
1971 1978 1985 1992 1999 2006
Mto
e/ye
ar
Primary Energy OtherRenewableHydro
Nuclear
Gas
Oil
coal
share toworld 0%
5%
10%
15%
20%
25%
0
1000
2000
3000
4000
5000
6000
7000
1971 1978 1985 1992 1999 2006
Mt-
CO
2/y
ear
CO2 Emissions Other
Transport
Industry
OtherEnergyElectricity&heatShare toWorld
1. Introduction: Motivation and objective
2012/12/16 18th AIM International Workshop 4
To develop methodology at provincial level 1
To develop a framework for integrated and
quantitative assessment of low-carbon policies 2
To assess the effectiveness and economic
impacts of low-carbon policies 3
To provide policy recommendations for
promoting low carbon economy (LCE) 4
1. Introduction: Question Statement
2012/12/16 18th AIM International Workshop 5
Future scenario of energy & emissions
under BaU 1
Key technologies /countermeasures to
achieve LCE 2
Economic cost of LCE 3
How to soften the economic cost? 4
Benefits and co-benefits ofof LCE 5
2. Literature review: existing studies on LCE
2012/12/16 18th AIM International Workshop 6
— Country level • Japan: it has the technological potential to reduce its CO2 emissions by 70%
compared to the 1990 level in 2050, quantitative roadmaps which include over 600 options. (NIES, 2008) ;
• China: possible for China to reduce its emissions to 2005 levels in 2050 (Jiang,
2009).
• India: transiting to low carbon future either through advanced technologies like
CCS and nuclear energy, or through renewable technologies on the supply side and dematerialization, sustainable consumption and end use device efficiency on the demand side (Shukla, 2009) ;
• South Korea: "Low Carbon, Green Growth Policy“, reduce carbon emissions by
30% by 2020 relative to BaU (Jones and Yoo, 2010);
• Vietnam: low carbon society for Vietnam in 2030 for the residential,
commercial, industrial, passenger and freight transport sectors, emissions can be decreased by approximately 45% from BaU (Nguyen et al, 2010);
• Indonesia: LCS visions 2050, emissions would be 48-85% lower than the BAU,
clean energy, low carbon lifestyle, electricity and fuels in industry and sustainable transport (Dewi et al, 2010);
• Thailand: emissions in 2030 can be decreased approximately by 42.5% to 324
million t-CO2 (Limmeechokchai, 2010).
2. Literature review: existing studies on LCE
2012/12/16 18th AIM International Workshop 7
— Country level • UK: Pathways to a low-carbon economy, the CO2 reduction by 40%, 60% and 80% by
2050 compared to 1990 levels, deep reduction in power and transport sectors. Higher GDP in mitigation scenario due to faster adoption of new carbon- reducing technologies and higher investment (Dagoumas, 2010);
• Australia: reduce accumulated CO2 during 2006–2051 by 50%, through renewable
electricity or advanced fossil fuel with CCS, nuclear and natural gas, GDP loss 14-37% (Foran, 2011).
— Sub-country level • Ahmedabad, India: low carbon vision toward 2035 through "Eight Actions“, by 67%
over 2035 BaU level (Shukla, 2009);
• Iskandar, Malaysia: Low-carbon Region by 2025, emission decreasing by 60% and
suppressed to 19.6 million t-CO2 (Ho, 2009);
• Australia: transitioning to low carbon communities through behaviour change in
households which result in less carbon intensive lifestyles & institutional and infrastructure systems (Moloney et al., 2009);
• Broward, USA: policies and responses on planning for low carbon city at county level
(Feliciano, 2011);
• Jilin city, China: emissions reduction by 25-42% in 2030 against BaU (Jiang et al,
2009);
• Guangdong Province, China: CO2 lowered by 29-50% in 2030 against BaU
(Jiang, 2009).
3. A two-region CGE model for Jiangxi Province
2012/12/16 18th AIM International Workshop 8
Production block
Income block
Market block
Expenditure block
Household
Government
Goods
Goods
Goods
Goods
Goods
Goods
Government
Household
Tax on products
GHG
Income Tax
Gross saving
Domestic consumption
Local market Provincial inflow σ >4
Local Production Import σ≈4
Domestic
Export
Local market
Provincial outflow
T = 3
T = 2 Output Energy & VA
Output
Energy Value added
Electricity Fossil energy
Solid Gas Liquid Capital Labor Land
Resource GHG
σ = 0
σ = 0.4
σ = 0.7
σ = 1.5
σ = 0.6
Non Energy
NE1 NEk …
σ = 0
Income /Endowment
σ = 1
σ = 1
σ = 1
Capital Formation
3. Methodology: CGE model
2012/12/16 18th AIM International Workshop 9
Production: 40 sectors (excl. electricity)
• Input: Intermediate inputs, capital, labor, land, resource, traditional biomass; • Land reliant: Agriculture, Forest, Livestock; • Resource reliant: Coal mining, natural gas, crude oil, mineral mining, other agriculture • Energy transformation : Coking, oil refinery, town gas • Other sectors
Technology
CCS technology Coal, Gas, Biomass with CCS; Cement, Chemistry, Iron and Steel
Bio-fuel Biomass to liquid, to gas Need land inputs (competing with agriculture)
• Five Coal • Oil power • Natural gas • Hydro
• Nuclear • Wind • Solar • Biomass
Power generation
Data
• Input-output table • Energy balance table • GHG emission factors of fossil fuels • Data on characteristics of electricity
generation technologies; • …
Region & time & GHGs
• Two regions: Jiangxi Province, rest of China • Time: 2005-2030, one year time step • Gas type CO2, CH4, N2O, CO, NH3, NMVOC, NOX, SO2; Energy related, process related, biomass
related.
3. Data requirement of CGE model
2012/12/16 18th AIM International Workshop 10
Base year
• Input-output table: Statistics; • Energy balance table: Statistics; • CO2 emission factors: IPCC recommendation; • Energy price of coal, oil and gas: Statistics;
• Cost of renewable energy technology: Investigation & estimation.
Future scenario
Economy • GDP growth rate; • Labor force growth rate; • Elasticity of substitution among inputs; • Total factor productivity (TFP) improvement; • Production trend of key energy intensive
products (cement, iron & steel etc.); • Domestic consumption scenario;
Energy & Technology • Autonomous energy efficiency improvement; • Future international energy price; • Extraction cost change of fossil fuels; • Resource potential of renewable energy. • Utilization target of each renewable energy type in
different years;
3. Jiangxi Province
2012/12/16 18th AIM International Workshop 11
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
Tib
et
Qin
ghai
Nin
gxia
Hain
an
Gansu
Guiz
hou
Xin
jiang
Yunnan
Jiangxi
Chongqin
g
Jilin
Shanxi
Shaanxi
Tia
njin
Guangxi
Helo
ngjiang
Mongolia
Anhui
Beijin
g
Fujian
Hunan
Hubei
Sic
huan
Shanghai
Lia
onin
g
Hebei
Henan
Zhejiang
Shandong
Jiangsu
Guangdong
GDP Share
Jiangxi
Jiangxi Province and Rest of China
2012/12/16 18th AIM International Workshop 12
0
5
10
15
20
25
30
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
[20
00
US
$]
per capita GDP a
0
2
4
6
8
10
12
14
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
[tC
O2]
per capita CO2 b
0
5
10
15
20
25
30
35
40
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
[MJ/
$]
Energy intensity c
0
20
40
60
80
100
120
140
19
71
19
75
19
79
19
83
19
87
19
91
19
95
19
99
20
03
20
07
[t
CO
2/G
J]
Carbon intensity d
• Evolution of Kaya factors of world, OECD, China and Jiangxi
Per capita GDP steady increases • Jiangxi < China < World < OECD Per capita CO2 increases • Jiangxi < China ≈ World < OECD Energy intensity falls • OECD < World < Jiangxi < China • In 2007 almost the same Carbon intensity • China increases • Jiangxi slightly decreases • Higher than world and OECD
4. Scenario towards 2030
2012/12/16 18th AIM International Workshop 13
— Consumption pattern (2 types)
• High carbon style
• Low carbon style
— Carbon constraints (3 types)
• Level 1: carbon intensity of GDP reduces as Copenhagen target
• Level 2: ERI’s Enhanced Low Carbon Scenario
• Level 3: Global collective reduction derived from 2 degree target
— Counter measures (4 types)
• None
• Non-fossil energy development
• Low carbon consumption pattern
• Carbon Capture and Storage (CCS) technology
• Inter-provincial emissions trading
Reference Scenario
Mitigation Scenario
4. Scenario towards 2030
2012/12/16 18th AIM International Workshop 14
Nr. Scenario Non-foss il
energy
Consumpt ion
pat tern
CCS Emiss ion
trading
1 RS_HC conventional scale High carbon ��off off off No constraint
2 RS_LC conventional scale Low carbon off off off No constraint
3 CM_CAP1 2005 level High carbon ��off off on Level1: intensity target
4 CM_CAP2 2005 level High carbon off off on Level2: mild reduction
5 CM_CAP3 2005 level High carbon ��off off on Level3: most stringent
6 CM_CAP3_HC 2005 level High carbon off off on Level3, GDP intensity convergence
7 CM_CAP3_HC_RE Large scale develop High carbon ��off off on Level3, GDP intensity convergence
8 CM_CAP3_LC_RE Large scale develop Low carbon off off on Level3, GDP intensity convergence
9 CM_CAP3_LC_CCS Large scale develop Low carbon on off on Level3, GDP intensity convergence
10 CM_CAP3_LC_ET Large scale develop Low carbon on on on Level3, GDP intensity convergence
CO2 emiss ion constraint
Reference
Carbon constraints
Counter- measure
4. Scenario: household consumption pattern
2012/12/16 18th AIM International Workshop 15
Jiangxi Urban Rural Urban Rural
Whole
Region High Carbon Low Carbon
Per capita expenditure (2005 US dollar) 746 303 5446 2214 4250 4250 4250
Food 40.8% 49.1% 27.8% 32.4% 28.7% 28.0% 32.0%
Clothing 10.6% 5.0% 10.1% 5.9% 9.3% 10.0% 6.0%
Housing 10.6% 13.1% 9.7% 22.8% 12.2% 10.0% 12.0%
Furnishings 7.0% 3.9% 8.0% 5.3% 7.5% 8.0% 5.0%
Health care and Medical services 5.3% 6.2% 6.5% 6.6% 6.5% 6.6% 6.5%
Transport and Communications 9.3% 9.2% 18.5% 13.0% 17.4% 18.0% 13.0%
Education and recreation 13.2% 11.1% 14.6% 11.3% 13.9% 15.0% 11.0%
Miscellaneous goods and services 3.2% 2.2% 5.0% 2.7% 4.5% 4.4% 14.5%
Rest of ChinaPer capita expenditure (2005 US dollar) 970 312 10280 3307 7700 7700 7700
Food 36.7% 45.1% 26.5% 31.7% 27.4% 26.0% 32.0%
Clothing 10.1% 5.8% 10.1% 5.9% 9.4% 10.0% 6.0%
Housing 10.2% 14.3% 9.6% 23.0% 11.7% 10.0% 12.0%
Furnishings 5.6% 4.4% 8.2% 5.3% 7.8% 8.0% 5.0%
Health care and Medical services 7.6% 6.4% 6.4% 6.5% 6.4% 6.4% 6.5%
Transport and Communications 12.6% 9.8% 19.1% 13.3% 18.2% 19.0% 13.0%
Education and recreation 13.8% 12.0% 14.9% 11.5% 14.4% 15.0% 12.0%
Miscellaneous goods and services 3.5% 2.3% 5.1% 2.7% 4.8% 5.6% 13.5%
2005 2030 Direction
5. Results: Reference scenario
2012/12/16 18th AIM International Workshop 16
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
10
Billio
n Y
uan
Jiangxi: GDP
1RS_GAP_HC
2RS_GAP_LC
0
2000
4000
6000
8000
10000
12000
14000
2005 2010 2015 2020 2025 2030
10
Billio
n Y
uan
Rest of China: GDP
1RS_GAP_HC
0
50
100
150
200
250
300
350
400
2005 2010 2015 2020 2025 2030
CO
2:
Millio
n t
on
Jiangxi: CO2
1RS_GAP_HC
2RS_GAP_LC
0
5000
10000
15000
20000
25000
2005 2010 2015 2020 2025 2030
CO
2:
Millio
n t
on
Rest of China: CO2
1RS_GAP_HC
2RS_GAP_LC
0
20
40
60
80
100
120
2005 2010 2015 2020 2025 2030m
tce
Jiangxi: TPES
1RS_GAP_HC
2RS_GAP_LC
0
1000
2000
3000
4000
5000
6000
7000
8000
2005 2010 2015 2020 2025 2030
mtc
e
Rest of China: TPES
1RS_GAP_HC
2RS_GAP_LC
+170%
+220%
+180%
+200%
+560%
+620%
-8.7%
-13.2%
-11.9%
-11.7%
Energy saving and carbon reduction effect by low-carbon consumption.
5. Results: Mitigation scenario
2012/12/16 18th AIM International Workshop 17
Carbon constraints • CAP1: Carbon intensity of GDP reduces as Copenhagen target; • CAP2: ERI’s Enhanced Low Carbon Scenario; • CAP3: Emissions in 2030 are reduced by 9.5% compared with 2005’s
level, derived from 2 degree target.
0
5000
10000
15000
20000
25000
2005 2010 2015 2020 2025 2030
CO
2:
Mill
ion
to
n
China: CO2
Ref
CAP1
CAP2
CAP3-17%
-61%
-72%
Nr. Scenario Non-foss il
energy
Consumpt ion
pattern
CCS Emiss ion
trading
3 CM_CAP1 2005 level High carbon ��off off on Level1: intensity target
4 CM_CAP2 2005 level High carbon off off on Level2: mild reduction
5 CM_CAP3 2005 level High carbon ��off off on Level3: most stringent
CO2 emiss ion constraint
5. Results: Mitigation scenario
2012/12/16 18th AIM International Workshop 18
0
50
100
150
200
250
300
2005 2010 2015 2020 2025 2030
10
Billio
n Y
uan
Jiangxi: GDP
Reference
CAP1
CAP2
CAP3
0
2000
4000
6000
8000
10000
12000
14000
2005 2010 2015 2020 2025 2030
10
Billio
n Y
uan
Rest of China: GDP
Reference
CAP1
CAP2
CAP3
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 20302
00
5 $
/t
CO
2
Jiangxi: CO2 Price
CAP1CAP2CAP3
0
100
200
300
400
500
600
700
800
900
2005 2010 2015 2020 2025 2030
20
05
$/
t C
O2
Rest of China: CO2 Price
CAP1CAP2CAP3
-1.4%
-12%
-19%
-1.0%
-14%
-21%
841
499
48
807
510
34
0
50
100
150
200
250
300
350
400
2005 2010 2015 2020 2025 2030
CO
2:
Millio
n t
on
Jiangxi: CO2
ReferenceCAP1CAP2CAP3
0
5000
10000
15000
20000
25000
2005 2010 2015 2020 2025 2030
CO
2:
Millio
n t
on
Rest of China: CO2
ReferenceCAP1CAP2CAP3
-14%
-52%
-65%
-17%
-62%
-72%
Carbon emissions in mitigation scenarios are exogenously assumed.
Carbon prices increase with more reduction.
GDP loss is higher under more stringent constraint.
5. Results: Mitigation scenario
2012/12/16 18th AIM International Workshop 19
Five types of responses
• None;
• Non-fossil energy development;
• Low carbon consumption pattern;
• CCS technology;
• Free inter-provincial emissions trading.
0
2
4
6
8
10
12
14
16
2005 2030RS 2030CM
Bill
ion
kW
h
Jiangxi: Non-fossil
Solar
Biomass
Wind
Nuclear
Hydro
0
200
400
600
800
1000
1200
1400
2005 2030RS 2030CM
Bill
ion
kW
h
Rest of China: Non-fossil
Solar
Biomass
Wind
Nuclear
Hydro
Reference: Energy Research Institute, 2009.
Assumptions on CCS: • Absorb 80% CO2; • Need 1.5-2 times more capital and labor input;
• Need 30% more electricity input; • Annual penetration rate: less than 2% of existing power plant capacity.
Nr. Scenario Non-foss il
energy
Consumpt ion
pattern
CCS Emiss ion
trading
6 CM_CAP3_HC 2005 level High carbon off off
7 CM_CAP3_HC_RE Large scale develop High carbon ��off off
8 CM_CAP3_LC_RE Large scale develop Low carbon off off
9 CM_CAP3_LC_CCS Large scale develop Low carbon on off
10 CM_CAP3_LC_ET Large scale develop Low carbon on on
5. Overall impacts of countermeasures
2012/12/16 18th AIM International Workshop 20
841
684
560 501
391
0
100
200
300
400
500
600
700
800
900
0
50
100
150
200
250
300
CO
2 p
ric
e:
US
$/
ton
GD
P:
10
Billio
n y
uan
Jiangxi 2030
GDP Carbon price
807
595
451 389 391
0
100
200
300
400
500
600
700
800
900
1000
0
2000
4000
6000
8000
10000
12000
14000
CO
2 p
ric
e:
US
$/
ton
GD
P:
10
Billio
n y
uan
ROC 2030
GDP Carbon price
• Under carbon constraint scenario, carbon price and GDP loss would be quite high;
• With all countermeasures introduced, carbon price will fall by half, and GDP loss would be about 9% instead of 20%.
Comparing with historical trends
2012/12/16 18th AIM International Workshop 21
Per capita GDP steady increases • China: higher than current OECD; • Jiangxi: higher than world level Per capita CO2 increases • China: close to OECD • Jiangxi: close to world level • Falling in mitigation scenario Energy intensity further falls • China and Jiangxi lower than current
world level; • Jiangxi lower than China Carbon intensity decreases • Due to non-fossil energy
development. • But still higher than world and OECD
due to coal domination.
- Oil import dependency - Air pollutants emission
Co-benefits of low-carbon economy
2012/12/16 18th AIM International Workshop 22
5. Co-benefits: Oil import dependency
2012/12/16 18th AIM International Workshop 23
0%
20%
40%
60%
80%
0
10
20
30
2005 2011 2017 2023 2029
Im
po
rt
(m
il.
ton
)
Jiangxi
Import (RS)
Import (LC)
0%
20%
40%
60%
80%
0
500
1,000
1980 2003 2014 2024
Im
po
rt
rati
o
China
Import (RS)
Import (LC)
5. Co-benefits: air pollutants
2012/12/16 18th AIM International Workshop 24
0
50
100
150
200
250
300
350
2005 2030RS 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CO2: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CO2: Rest of China
0
1
2
3
4
5
6
7
8
9
2005 2030RS 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CO: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal 0
50
100
150
200
250
300
350
2005 2030RS 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CO: Rest of China
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NOX: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal0
10
20
30
40
50
60
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NOX: Rest of China
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
SO2: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
Agriculture0
10
20
30
40
50
60
70
2005 2030RS 2030CM
Emis
sio
ns
(Mill
ion
To
n)
SO2: Rest of China
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
Agriculture
5. Co-benefits: air pollutants
2012/12/16 18th AIM International Workshop 25
0
0.5
1
1.5
2
2.5
3
3.5
4
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CH4: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal0
20
40
60
80
100
120
140
160
180
200
2005 2030RS 2030CM
Emis
sio
ns
(Mill
ion
To
n)
CH4: Rest of China
0
0.2
0.4
0.6
0.8
1
1.2
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NMVOC: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal0
10
20
30
40
50
60
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NMVOC: Rest of China
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
N2O: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal0
0.5
1
1.5
2
2.5
3
3.5
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
N2O: Rest of China
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal 0
0.1
0.2
0.3
0.4
0.5
0.6
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NH3: Jiangxi
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
-1%
0
5
10
15
20
25
2005 2030CM
Emis
sio
ns
(Mill
ion
To
n)
NH3: Rest of China
Household
Service
Transport
OthEnergy
Electricity
OtherManu
Chemicals
Metal
Conclusions
2012/12/16 18th AIM International Workshop 26
• Energy and emission would increase due to GDP growth in future reference scenarios; therefore, China’s participation is crucial for global climate mitigation;
• At national level, economic impacts are closely related to stringency of carbon constraints; at provincial level, economic impacts are also determined by burden sharing scheme;
• Without additional low-carbon countermeasures, carbon price and GDP loss would be quite high in the most stringent carbon reduction scenarios;
• With introduction of the low-carbon countermeasures, carbon price and GDP loss would be lowered substantially.
• There are a lot of co-benefits associated with low-carbon economy, including air pollutants reduction and energy security improvement.
Future work
2012/12/16 18th AIM International Workshop 27
• Inter-provincial flow of labor and capital;
• Technology in transport sector;
• Apply to other provinces, or developing 31-region model.
Publications
2012/12/16 18th AIM International Workshop 28
Journal papers Dai Hancheng, Masui Toshihiko, Matsuoka Yuzuru, Fujimori Shinichiro (2012). "The Impacts of
China’s Household Consumption Expenditure Patterns on Energy Demand and Carbon Emissions towards 2050." Energy Policy 50(Special Issue): 736-750.
Dai Hancheng and Masui Toshihiko (2012). "Assessing the Contribution of Carbon Emissions Trading in China to Carbon Intensity Reduction." Energy Science and Technology 4(1): 1-8.
Dai Hancheng, Masui Toshihiko, Matsuoka Yuzuru, Fujimori Shinichiro (2011). "Assessment of China's Climate Commitment and Non-Fossil Energy Plan towards 2020 Using Hybrid AIM/CGE Model." Energy Policy 39(5): 2875-2887.
Proceeding papers and presentation
Assessing the Contribution of Inter-provincial Carbon Emissions Trading in China to Carbon Intensity Reduction in 2020. The 2nd Congress of the East Asian Association of Environmental and Resource Economics, Bandung, Indonesia, Feb 2-5, 2012.
Contribution of China’s Renewable Energy Development in Power Generation to Carbon Intensity Reduction. The 1st Congress of the East Asian Association of Environmental and Resource Economics, Sapporo, Hokkaido, Aug. 18-19, 2010.
Impact Assessment of China's Climate Target towards 2020. The 15th Asia-Pacific Integrated Model International Workshop, Tsukuba, Japan, Feb. 20-22, 2010
Thank you for your attention!
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