Low carbon economy 2030 in China

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)


0%

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2005 2011 2017 2023 2029 2035

CO2 emissions OtherBrazilSouth KoreaJapanEuropeCanadaUnited StatesIndiaChina0%

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2005 2011 2017 2023 2029 2035

Primary energy OtherBrazilSouth KoreaJapanEuropeCanadaUnited StatesIndiaChina

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0200400600800

100012001400160018002000

1971 1978 1985 1992 1999 2006

Mto

e/ye

ar

Primary Energy OtherRenewableHydro

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coal

share toworld 0%

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OtherEnergyElectricity&heatShare toWorld

1. Introduction: Motivation and objective


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


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


— 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


— 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


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


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


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


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


0

5

10

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25

30

19

71

19

75

19

79

19

83

19

87

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91

19

95

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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

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40

19

71

19

75

19

79

19

83

19

87

19

91

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95

19

99

20

03

20

07

[MJ/

$]

Energy intensity c

0

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19

71

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75

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79

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83

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87

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91

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95

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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


— 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


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


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


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

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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


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%


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



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.



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.


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


841

684

560 501

391

0

100

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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


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


5. Co-benefits: Oil import dependency


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


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


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


• 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


• Inter-provincial flow of labor and capital;

• Technology in transport sector;

• Apply to other provinces, or developing 31-region model.

Publications


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

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Low carbon economy 2030 in China

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