Emission for 6W/m2...300 400 500 600 700 800 900 2000 2020 2040 2060 2080 2100 EJ Wind, Solar,...

Emission Pathway for 6W/m2

Toshihiko Masui, Kenichi Matsumoto, Yasuaki

Hijioka, 

Tsuguki

Kinoshita, Toru Nozawa, Sawako Ishiwatari, 

Mikiko

Kainuma

(National Institute for Environmental Studies)

Etsushi

Kato

(Japan Agency for Marine‐Earth Science and Technology)

IAMC Meeting Tsukuba, Japan

September 15, 2009

Flowchart of RCP6.0Flowchart of RCP6.0

AIM/Impact 

[Policy]

AIM/CGE [Global]

Emission 

downscaling model

Landuse

downscaling 

model

Population/GD

P scenario

GHG/Aerosol emission path

Base year 

data

Radiative

forcing

Population/GDPDownscaling model

Pop./GDPGrid cell data

Emission scenario[region]

Global

RegionNational

Grid cell

Land‐use 

change

Ecosystem 

model

Land‐cover/land‐use

Emission [fire, land‐

use change]

Emission[others]

production factormarket

capital

laborland

Final demand sector

resource

Production sectors

produced commoditymarket

food

serviceenergy

...

tradeJapan

China...

Energy technology model: energy efficiencyAgriculture model: land productivity

...

Annual parameter change

GHGsemissions

GHGsemissions

climatechange

feedbackeg. land productivity change due to climate changescenarios: population, GDP, ... 

structure of AIM/CGEstructure of AIM/CGE

0

50

100

150

200

250

2000 2020 2040 2060 2080 2100

trillion US$2000

Results of AIM/CGE (Reference)Results of AIM/CGE (Reference)

0

2000

4000

6000

8000

10000

12000

2000 2020 2040 2060 2080 2100

miillio

n

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

GDP Population

0

5000

10000

15000

20000

25000

30000

2000 2020 2040 2060 2080 2100

TgC

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

CO2

Results of AIM/CGE (6W/mResults of AIM/CGE (6W/m22))

0

50

100

150

200

250

2000 2020 2040 2060 2080 2100

trillion US$2000

0

2000

4000

6000

8000

10000

12000

2000 2020 2040 2060 2080 2100

miillio

n

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

GDP Population

0

2000

4000

6000

8000

10000

12000

14000

16000

2000 2020 2040 2060 2080 2100

TgC

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

CO2

0

200

400

600

800

1000

1200

2000 2020 2040 2060 2080 2100

EJ

Wind, Solar, Geoth., othehydro

nuclear

coal

biomass

gas

oil

0

100

200

300

400

500

600

700

800

900

1000

2000 2020 2040 2060 2080 2100

EJ

Electricity

Liquids

Solids

other

gas

Results of AIM/CGE (Reference)Results of AIM/CGE (Reference)

Primary energy

Final energy

0

100

200

300

400

500

600

700

800

900

1000

2000 2020 2040 2060 2080 2100

EJXAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

0

200

400

600

800

1000

1200

2000 2020 2040 2060 2080 2100

EJ

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

Primary energy

Final energy

0

100

200

300

400

500

600

700

800

900

2000 2020 2040 2060 2080 2100

EJ

Wind, Solar, Geoth., otherhydro

nuclear

coal

biomass

gas

oil

0

100

200

300

400

500

600

700

800

2000 2020 2040 2060 2080 2100

EJ

Electricity

Liquids

Solids

other

gas

Results of AIM/CGE (6W/mResults of AIM/CGE (6W/m22))

Primary energy

Final energy

0

100

200

300

400

500

600

700

800

900

2000 2020 2040 2060 2080 2100

EJ

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

0

100

200

300

400

500

600

700

800

2000 2020 2040 2060 2080 2100

EJ

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

Primary energy

Final energy

0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

TgSO

2

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

TgSO

2

SAV

AWB

RES

IND

WST

ENE

ISH

TRA

LUC

0

50

100

150

200

250

300

350

400

2000 2020 2040 2060 2080 2100

TgCH

4

SAV

AGR

AWB

RES

IND

WST

ENE

ISH

TRA

LUC

Results of AIM/CGE (Reference)Results of AIM/CGE (Reference)

0

50

100

150

200

250

300

350

400

2000 2020 2040 2060 2080 2100

TgCH

4XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

CH4 CH4

SO2 SO2

0

50

100

150

200

250

300

350

400

2000 2020 2040 2060 2080 2100

TgCH

4

SAV

AGR

AWB

RES

IND

WST

ENE

ISH

TRA

LUC

0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

TgSO

2

SAV

AWB

RES

IND

WST

ENE

ISH

TRA

LUC0

20

40

60

80

100

120

2000 2020 2040 2060 2080 2100

TgSO

2

XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

Results of AIM/CGE (6W/mResults of AIM/CGE (6W/m22))

SO2 SO2

CH4

0

50

100

150

200

250

300

350

400

2000 2020 2040 2060 2080 2100

TgCH

4XAF XLMXRE XE10XE15 XRAzaf xmerus braarg mexusa canxsa indxse thaidn korjpn chnnzl aus

CH4

Spatial explicit population/GDP scenarioSpatial explicit population/GDP scenario

DataData PopulationPopulation GDPGDP

UN population（UN Long range）

UN population（UN Long range）

224

countriesPopulation data

(2000-2050)

183

countriesGDP

data(2000-2100)

224

countriesPopulation data

(2000-2100)

３０

secondGrid cell pop.(2000-2100)

UN population（UN shot range)

UN population（UN shot range) IFs

GDP scenarioIFs

GDP scenario

224 countriesGDP

data(2000-2100)

UN UrbanizationUN Urbanization

30 secondGrid cell GDP(2000-2100)

Income gapIncome gap

GTAP GDP in 2001GTAP GDP in 2001

IIASAPopulation 

scenario

Population 

rank‐size 

rule

Urban area 

rank‐size 

rule

DataData

Population scenarioPopulation scenario

2000

2050

2100

LanduseLanduse

downscaling modeldownscaling model

1. Urban (GDP, crop price…)2. Cropland (yield, slope angle…)3. Pasture (NPP, slope angle…)4. Harvest forest (population density..)

Geophysical constraint・Built‐up area < 5 degree・Forest < 20 degreeetc.

0.5 degree

1km

Results (LandResults (Land--use scenario)use scenario)

CroplandCropland

20002000 20502050

21002100

Results (LandResults (Land--use scenario)use scenario)

0.00E+00

5.00E+06

1.00E+07

1.50E+07

2.00E+07

2.50E+07

3.00E+07

3.50E+07

4.00E+07

2000 2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Pasture [km2]

XAF XLM XRE XE0 XE5 XRA zaf xmerus bra arg mex usa can xsa indxse tha idn kor jpn chn nzl aus

PasturePasture

20002000 20502050

21002100

Sector region indicator for downscale

electricity Japan population

electricity China population

… … …

agriculture USA agricultural area

… … …

Summed upFrom IAM

AIM

24 region

・Power plant & energy conv. （by population）・Industry: process & combustion（by GDP）・Solvent use（by GDP）・Residential & commercial （by rural pop）・Waste（by population）・Agriculture: waste （by agriculture）・International shipping・Aviation・Transportation (road & railroad)・Agriculture : Animal & Soil

Downscale byindicator

Global distributionRegional distribution

Emission downscaling modelEmission downscaling model

：spatially explicit indicator for region    and for sector

：regional emissions for region    and for sector    estimated by IAM

Case 1Case 1

r sr

srsrsrss dxdytyxw

tyxwttetettyxEtyxE

),,(),,(

)()(),,(),,(,

,,,

r sx y t

),,( tyxEs

)(, te srr s

),,(, tyxw srr s

：region ：sector：longitude ：latitude ：year

：gridded emissions from a sector s

Changes in regional emissions are downscaled according to spatially explicit indicators for each sector and each region.ENE（total population）, IND（GDP）, SLV（GDP）, DOM（rural population）, WST（total population）

& AWB（cropland area）

Spatial explicit emission scenariosSpatial explicit emission scenarios

Case 1 (Industry, NO2)

20002000 20502050

21002100

：global emissions for sector    estimated by IAM

：gridded emissions from a sector

Case 2Case 2

)()(),,(),,(

00 te

tetyxEtyxEs

sss

sx y t

),,( tyxEs

)(tess

：sector：longitude ：latitude ：year

s

Global distribution at year 2000 is scaled by world total emissions.SHP & AIR

Spatial explicit emission scenariosSpatial explicit emission scenarios

20002000 20502050

21002100

Case 2 

(International shipping, SO2)

：

spatially explicit indicator for region    and for sector

：regional emissions for region    and for sector    estimated by IAM

Case 3Case 3

r sr

srsrs te

tetyxEtyxE )()(),,(),,(

0,

,0,

r sx y t

),,( tyxEs

)(, te srr s

),,(, tyxw srr s

：region ：sector：longitude ：latitude ：year

：gridded emissions from a sector s

),,(, tyxE sr ：gridded emissions region     and for sector sr

Regional distribution at year 2000 is scaled by regional total emissions for each region.TRA & AGR

Spatial explicit emission scenariosSpatial explicit emission scenarios

20002000 20502050

21002100

Case 3 (Agriculture, NH3)

0

200

400

600

800

1000

1200

1400

2000 2020 2040 2060 2080 2100

CO

2 e

mis

sion [TgC

/yr

]

Year

6W stabilize

Reference

Ecosystem modelEcosystem model

Assessing carbon cycle

Transition Matrix (λ)

agricultureagriculture

primaryforest

primaryforest

2nd forest2nd forest

Forest Harvesting

(λPS )

Natural Disturbance

Forest Harvesting(λss )

Land clearing

Land abandonment

(λSA )

(λP

A)

(λAS )CO2 emission by landuse

change

Spatial explicit emission scenariosSpatial explicit emission scenarios

20002000 20502050

21002100

Case 4 (savanna burning, BC)

• Emissions from landuse

change are diverse  among models. 

• Extension to 2300. 

Remaining worksRemaining works