An Integrated Assessment of Global
Warming Mitigation and Adaptationand the Development of Multi-regionaland Multi-sector IAM- Project Phoenix -Shunsuke MORI (RITE, Tokyo Univ. of Sci.)
Toshimasa Tomoda, Hiromi Yamamoto, KeigoAkimoto, Koji Tokimatsu, Takanobu Kosugi, Ayami
Hayashi, Takashi Honma (RITE)

Integrated Assessment Models as a platform ofthe policy and technology assessments• Integrated assessment models (IAMs) have beendeveloped since 1990s as a powerful tool for this subject.
However,• Economic models and technology assessments deal withnear future (until 2020) while existing IAMs mainly talkabout near 2100.
• Economic models and technology assessments mainly
analyze country level while existing IAMs mainly
aggregate the world into 10-15 regions.
• Globalization, civilization, penetration of IT, industrial
structure changes etc. are not well discussed in the global
environmental context.

Project Phoenix- Paths toward Harmony Of Environment,
Natural resources and Industry complex –• Developed by the RITE - Research Instituteof Innovative Technology for the Earth• Supported by the Ministry of Economy,
Trade and Industry as a part of an“International Research Promotion Funds
for the Global Environment”• A project for 2002-2006 (five years)

Structure of Project Phoenix – three WGs
Multi region and sector model GTAP+ Easy to connect with GAMS
- Dynamics- Aggregated energy technologies and sources(Model development WG)

Energydemand,economicactivities, structural changes+ Data availability (trade andeconomic statistics)- Societal structural change(Warming factors WG)

Assessments of global warming+ Availability on food, water,
climate change studies- Uncertainties of global
warming damages(Warming impacts WG)

K: Mitigationinvestment
ex-post expenditure
ex-ante investment
cost-benefit integrated
assessments

E：Assessments of global
warming
water resource
ocean, river and lakes
land usefood production
vegetation etc.
F: Food supply and demand

G：Energy demandtransportation
public and household
long-term growth
patterns
structural changes

H: Assessments ofregional
options
CGS, distributed
energy systems
renewable sources
recycling andwaste
managements

A: Economic activities GTAP model
multi regional and multi-sectoral- CGE model- static model- energy flow and technologies
should be integrated

B: Energy flow model
existing research
activities in RITE
DNE-21 and LDNE-21
Energy demand
scenarios should be
provided based on theeconomic and societalstory-lines.

I: Regional structurechangecivilizationsocial structure
modeling methods

J: Biospherehuman healthimpacts on biosphere
D: Assessments ofregional climatechange
GCM data
GIS

C: Assessments of
Global climatechangesimple climate
models(MAGICC, BERN)
L: GHG emission scenariodetailed regional emission
scenario
subjects in 2004
subjects after 2005

Tentative IAM structure and results• GTAP (Purdue Univ.) incorporates more than 60 regions andsectors and is still being expanded.
• GTAP is designed to assess the international trade andproduction impacts of various policy options.
• GTAP-EG includes energy flow subsystems.
• GTAP provides comprehensive and consistent world economic
data base.
In Phoenix Project,• We aim at the assessments of the certain technologies such asenergy conversion technologies, carbon capture options,biomass production and utilization, etc.
• Dynamic model simulation is also needed.
• We impose the bottoming up technology model into the GTAP
model simplifying the frame, if necessary.

Conceptual Frame of the Model
Intermediate Inputs

tradeInvestments

Con
sumpTion

OutputSecondarymI

CQ

X11=
X12=
Q1・
Q2・
VA_Epre=Σ(capitalanda11
a12
1００ｍ1

I1

C1

Q1

labor costs of primary
X21=
X22=
energyextraction
and
Q1・
Q2・costs)+(others)
a21
a22
2 production
００ｍ2

I2

C2

Q2０

Ce=
PeEc

EC=
PeE

Non-energy
sectors
1Int.
InputsNonenergy
Sectors

Primary

Value
AddedOutput

ary

2００

PeE1

Xe2=
PeE2

V=f(K,L,E)-(secondary
Energyinput
Secondenergycosts) Xe1=
Sectors

Final demand

KPk・K1

Pk・K2

L

PL・L1

PL・L2

Q

Q1

Q2

Energy sectors
Primary０VA_E=Σ(capital and laborEC̲pre=costs of energy conversion
PpS
Xpe
ｍp００technologies)+(others)

００

VA̲preVA̲E０Y
EC̲pre=PpS

EC=PeE(Total secondary energy supply)=Σ(Conv. Eff.)＊(primary energy inputs)

QIntegration of energy flow
Intermediate inputs
Non energy sectors
(j)
EIS
EIS
I Nonn energyYt sectors
AGR
(i)
.
SERin Energy CRU
p sectors OTRuCOLtGASsOIL
ELE
VA capital
KDD labor
LQOutput

YFinal demand

Energy sectors (e')

AGR SER CRU OTR COL GASOIL

ECCRU,OIL,tELE

M

ICQ

xi,t − mi,t I i , t

C i ,tQ

0

ECes ,ELE,t0

EC s , j ,t = e sj Q j ,tPe,t ⋅ Ee,M,t00

ECs ,C ,tEC e ,t0V j ,t = K j ,t + L j ,tV e ′,tQj,t = Qi,tECe′,t = ECe,tECRU,OIL,tTrade
ELE0Consump Produc Pricestiontion

00Es, j,ti,t0COL

OILInvest Consump
menttion0CRUGAS

ELEbie ECe′,taij Q j ,tNon energy sectors (j)Energy flow
EISY
AGR SER CRU OTR COL GASOILOTR

TradeOutput

00

Ees ,ELE,t E e , M ,t0

E e ,tE s ,C ,tPe ,tEnergy flow in DNE-21 model:
simplified structure will be imposed.
From Other Regions

To Other Regions

Coal
Synthetic Oil

Crude Oil
Synthetic. Oil
Methane (pipeline)
Methane (tanker)
Hydrogen (pipeline)
Hydrogen (tanker): Electricity Use

Crude Oil
MethaneHydrogen
Natural Gas

CH4 Liquefaction

Natural Gas Splitting

H2Water Electrolysis
Methane Synthesis

CH4

Gaseous FuelOil Refinery (Gasoline)
Oil Refinery(Distillation)

Crude Oil

Oil

Synthetic Oil

Syn.Oil
Coal Gasification

Coal

Coal Liquefaction

COMethanol Synthesis

H2 Liquefaction

SOx Rec. in End UseGasoline

Oil Refinery (Gasoline)
Oil Refinery(Distillation)

H2 power gen.
N.Gas power gen.

Light Fuel Oil

Methanol Upgrading(into Gasoline)

IGCC with CO2 Rec.

Heavy Fuel Oil

Energy Crops

Modern Fuel Woods
Wood Residues
Sawmill Residues
Timber Scraps
Cereal Harvest Residues
Sugarcane Harvest Resid.
BagasseCoal Cleaning

Biomass Liquefaction

Biomass Liquefaction

SOx Rec.
in End Use

Coal power gen.

Paper Scraps

Black Liquor

Ethanol(Fermentation)

Shift Reaction

CH3OH

Biomass Gasification

Meth. power gen.

Biomass Gasification

Biomasspower gen.

Sorbent Injection
Wet limestone scrub.
Wellman-Lord

Oil power gen.

Kitchen Wastes

Oil power gen.

Animal Wastes

Human Feces

Photovoltaics(Anaerobic Digestion)

CO2

Methanol Synthesis

Photov. power gen.

Wind&O.R.E.

Hydro power gen.
Geoth. power gen.
Nuclear power gen.

Hydro

Geothermal

SOx
Sorbent Injection
Wet limestone scrub.
Wellman-Lord

CO2 Recovery

Water Electrolysis

Solid FuelElectricity

Electricity

CO2 Compression

Nuclear
CO2 Injection

Methanol
CO2From Other Regions

CO2 Liquefaction

Ocean
EOR
Gas WellAquifer
CO2 Disposal

Coal
Methanol
CO2 (pipeline)
CO2 (tanker)

To Other Regions

Aggregation of GTAP data into 18 regions and 18 nonenergy sectors
18 regions
USA
CAN
MCM
BRA
SAM

USA

CAF
SAF
JPN
CHN
IND

Middle African countries

WEP

West and middle Europeancountries

ASN

Asia NIES countries

EEP

Hungary, Poland and other eastEuropean countries

TME

Turkey and Middle-Eastcountries

FSU

Former USSR

ANZ

Australia, New Zealand andPacific Island countries

NAF

North and Middle Africancountries

XAP

Other countries

CanadaMiddle American countries
BrazilPeru, Argentina, Chile, Uruguay
and other south Americancountries

South African countries
Japan
China, Hong kong, Taiwan
India

Aggregation of GTAP data into 18 regions and 18 nonenergy sectors
18 non-energy sectors
I_S
CRP
NFM

Iron and steel

NMM
TRN
OME
OMN
FPR
PPP

Non- metaric materials

Chemical industry
Non-ferrous metals

Transport equipments
Other machinary
Minings
Food Products
Paper, pulp and printings

LUMCNSTWL

Wood, Pulp and printing

OMF
AGR
T_TATP
BSR
SSR

Other manufacturings

Construction
Textiles, wearing, apparel andleather

Agricultural products
Transportation
Aviation
Business services
Social services

Current Stage：
Two preliminarily dynamic models are developed.
(1)18-region and 18-sector model with 2 energy
sectors (Primary and secondary)(2) 1-region and 18-sector model with 6 energy
sources and 4 power generation technologies

These two are being integrated.

Total Value-added

T o ta l V a lu e - a dde d

USA

CAN

14000

MCM BRA

12000
10000

SAM

WEP

EEP

FSU

NAF

CAF

8000
6000
4000
2000

SAF

JPN

CHN

IND

0ASN

TME

ANZ

XAP

1997

2007

2017

2027
Year

2037

2047

2057

Preliminarily Simulation Results: GDP
18 region - 18 sector with 2 energy input model

Energy Consumption
USA

CAN

350.0

MCM

BRA

300.0

SAM

WEP

250.0

EEP

FSU

NAF

CAF

SAF

JPN

CHN

IND

ASN

TME

ANZ

XAP

Final E nergy Consumption(E J)

400.0

200.0
150.0
100.0
50.0
0.0
1997

2007

2017

2027
Year

2037

2047

2057

Preliminarily Simulation Results:
Final Energy Concumption
18 region - 18 sector with 2 energy input modelDomestic Production(10million US$)JPN Sectoral Domestic Production

I̲S

CRP

1400

NFM

NMM

1200

TRN

OME

OMN

FPR

PPP

LUM

CNS

TWL

OMF

AGR

ATP

T̲T

BSR

SSR

1000
800
600
400
2000
1997

2007

2017

2027
Year

2037

2047

2057

Preliminarily Simulation Results:
Outputs by sector (Japan)
18 region - 18 sector with 2 energy input modelDom estic Production(10m illion US$)WEP Sectoral Domestic Production
4500
4000
3500
3000
2500
2000
1500
1000
50001997

2007

2017

2027
Year

2037

2047

2057

I̲S

CRP

NFM

NMMTRN

OME

OMN

FPR

PPP

LUM

CNS

TWL

OMF

AGR

ATPT̲T

BSR

SSR

Preliminarily Simulation Results:
Outputs by sector (WEP)
18 region - 18 sector with 2 energy input modelDom estic Production(10m illion US$)USA Sectoral Domestic Production

I̲S

CRP

6000

NFM

NMM

5000

TRN

OME

4000

OMN

FPR

3000

PPP

LUM

2000

CNS

TWL

1000

OMF

AGR

0

ATP

T̲T

BSR

SSR

1997

2007

2017

2027
Year

2037

2047

2057

Preliminarily Simulation Results:
Outputs by sector (USA)18 region - 18 sector with 2 energy input model

CHN Sectoral Domestic Production

Domestic Production(10million US$)

1400
1200
1000
800
600
400
20001997

2007

2017

2027
Year

2037

2047

2057

I̲S

CRP

NFM

NMM

TRN

OME

OMN

FPR

PPP

LUM

CNS

TWL

OMF

AGR

ATP

T̲T

BSR

SSR

Preliminarily Simulation Results:
Outputs by sector (CHN)
18 region - 18 sector with 2 energy input modelDomestic Production(10million US$)IND Sectoral Domestic Production
1000
900
800
700
600
500
400
300
200
10001997

2007

2017

2027
Year

2037

2047

2057

I̲S

CRP

NFM

NMM

TRN

OME

OMN

FPR

PPP

LUM

CNS

TWL

OMF

AGR

ATP

T̲T

BSR

SSR

Preliminarily Simulation Results:
Outputs by sector (India)
18 region - 18 sector with 2 energy input model

2500
GDP (10 billion USD)

Withpolicy
制約無し

2000

制約有り

Without policy

1500
1000
500
01997

2007

2017
2027
YEAR
西暦年

2037

2047

Preliminarily Simulation Results:GDP (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

(USD/MWh)Electric電力価格
power price(USD/Mwh)

100
80
60
4020

With
policy
制約無し
Withoutpolicy
制約有り

01997

2007

2017
2027
YEAR
西暦年

2037

2047

Preliminarily Simulation Results:electric power price (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

5

炭素排出量Carbon emission(GtC)(GtC)

Withpolicy
制約無し

4Withoutpolicy
制約有り

3210
1997

2007

2017
2027
YEAR
西暦年

2037

2047

Preliminarily Simulation Results:carbon emission (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

Shadow priceof Carbon emission($/tC)
炭素潜在価格（USD/tC）

700
600
500
400
300
200
10001997

2007

2017
2027
YEAR
西暦年

2037

2047

Preliminarily Simulation Results:
Shadow price of carbon emission (USA)1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

送電端 発 supply
電 電 力 (Tkwh)
量 （T k W h）
Electricpower

141210石油
Oil
Gas
天然ガス
石炭
Coal
Others
その他

86420
1997

2007

2017
2027
YEAR
西暦年

2037

2047

Preliminarily Simulation Results:
Power generation mix without carbon policy (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policyElectricsupply(Tkwh)
送 電power
端発電
電力量（TkW h）

98765Oil
石油
Gas
天然ガス
Coal
石炭
Othersその他

432101997

2007

2017
2027
西暦年
YEAR

2037

2047

Preliminarily Simulation Results:
Power generation mix with carbon policy (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

産業
Industry

SSR
BSR
ATP
T̲T
AGR
OMF
TWL
CNS
LUM
PPP
FPR
OMN
OME
TRN
NMM
NFM
CRP
I̲S
合計
Total
02

468 10 12 14
付加価値生産額減少率
Lossof product (%) （%）

1618

Preliminarily Simulation Results:
Impacts of carbon policy; loss of sector products in 2047 (USA)
1 region - 18 sector with 6 energy input modelwith and without carbon emission stabilization policy

Integration : Scenario Generation and Simulations
2:Structure Analysis among factors
Lower warming
factors & events

Exogenousconditions

3:Scenario generation by X-I method

Lower warming
factors & events

1:TAR-assumptions
Upper warming
factors & events
Quantitative IAM- economy- technology- energy- naturalresource- etc.

Upper warming
factors & events

5:Feedback:consistency
check

4:parameters,
constraints,exogenous
variables

Structure Analysis for the Narrative Scenario Generation

Scenario Generation using X-I method -1
Originally developed by Gordon (1965) to see thecomplicated interactions among the events .
(1) Estimating the probability of occurrence of each technology(2) Evaluate the degrees of impact among events(3) Revise occurrence probabilities using Monte Carlo simulation.
Dalky pointed out the mathematical consistency in 1972.

Duperrin and Godet (Duperrin, 1975) proposed a new
method to guarantee the mathematical consistency.
Kaya et. al. (Kaya, 1979) expanded their method；(1)using causality probabilities instead of conditional probabilities
based on the Markovian probability model.
(2)sequential linear programming method to assess the range of highdimensional state probabilities.

Dynamic expansion has been developed (Mori, 1984).

Scenario Generation using X-I method -2
1.
Determine the set of events to be considered during the forecastingperiod.
2.
Define the exogenous conditions affecting the event occurrences
one-sidedly.
3.
Estimate the occurrence probability of event i (i=1,2…n) at the endof the forecasting period P(i).
4.
Estimate the “impact probability” P(i →j) : the occurrence probabilityof event j given the condition that the event i occurs solely in the beginningof the period.
5.
Calculate the two-dimensional probability applying Markoviantransition model.
6.
Construct the mathematically consistent probabilities modifying
estimated two dimensional probability data set by{}{}min . J = ∑iw i P(i ) − P (i ) + ∑ j≠i w ij P(i, j) − P (i, j)
*

2

*

2

where the consistent probabilities P*(i), P*(i,j) are the linear combinationsof n-dimensional state probabilities {πk}.

Scenario Generation using X-I method -3
7.

min .

Calculate the ranges of {πk} using linear programming.

π k subject to P * (i ) = ∑k d ik π k , P * (i, j ) = ∑k d ik d kj π k , ∑k π k = 1 , π k ≥ 0max .
π k :n-dimensional probability P(d1k , d k2 ,Λ d kn ) (k = 1,2,Λ 2 n ) .
d ik =1 if even i occurs in the state k else d ik =0.

ex. Nuclear power technology forecasting for 1990-2010(Mori and Kaya, 1984)(1)FBR: some FBR(Fast Breeding Reactor)s are already developed.
(2)ATR: some ATR(Advanced Thermal Reactor)s are already developed.
(3)CAND: some CANDU-PHW reactors are developed.
(4)LWR-Pu: share of Plutonium recycling comes to 33% of total LWR fuel.
(5) Repro: some reprocessing systems for LWR-Pu or ATR are operating.
(6) Cent: some centrifugal separation plants are developed.
(7) Coal: the share of coal fired power generation comes to more than 20%of world electric power supply.

Example of X-I method - continued
1990(0000000)
0.688

1995

(0000001) (0000000) (0000010).22-.42 A: .25-.45 .18-.37
B: .27-.51

2000(0000011).42-1.0(0001110)12-.932005(0000011).32-.81(1001110).33-.822010(1000111).09-.17(1001110).09-.12

Scenario AScenario B

upper:occurrence state lower: range of probability
Scenario A: Coal-nuclear scenario
Scenario B: Nuclear oriented scenario
Dynamic scenario sequences from 1990-2010(1) FBR(2) ATR(3) CAND(4) LWR-Pu(5) Repro(6) Cent(7) CoalExpected outcomes• The changes of the energy supply-demand systems,
industry structure changes and the international industryallocation scenarios will provide the basic information toassess the policy measures.
• The outcomes of the project will give the helpful
information on the energy technology developmentstrategies.
• The most preferable burden sharing scenario on thecarbon emission reduction can be generated.
• Industry policies on the R&D on the energy andenvironmental technologies, technology transfer, andother industry strategies can be assessed under theglobal warming mitigation policies.