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An Integrated Assessment of Global Warming Mitigation and Adaptation
and the Development of Multi-regional and Multi-sector IAM
- Project Phoenix -
Shunsuke MORI (RITE, Tokyo Univ. of Sci.)Toshimasa Tomoda, Hiromi Yamamoto, Keigo
Akimoto, Koji Tokimatsu, Takanobu Kosugi, Ayami Hayashi, Takashi Honma (RITE)
Integrated Assessment Models as a platform of the policy and technology assessments
• Integrated assessment models (IAMs) have been developed since 1990s as a powerful tool for this subject.
However,
• Economic models and technology assessments deal with near future (until 2020) while existing IAMs mainly talk about 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 Institute of 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 WGsMulti region and sector model GTAP
+ Easy to connect with GAMS- Dynamics- Aggregated energy technologies and sources
(Model development WG)
Energy demand, economic activities, 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)
A: Economic activities GTAP modelmulti regional and multi-sectoral
- CGE model- static model- energy flow and technologies
should be integrated
B: Energy flow modelexisting research activities in RITEDNE-21 and LDNE-21Energy demand scenarios should be provided based on the economic and societal story-lines.
H: Assessments of regional
optionsCGS, distributed energy systemsrenewable sourcesrecycling and waste managements
G:Energy demandtransportationpublic and householdlong-term growth patternsstructural changes
I: Regional structure change
civilizationsocial structuremodeling methods
C: Assessments of Global climate changesimple climate models(MAGICC, BERN)
D: Assessments of regional climate
changeGCM dataGIS
E:Assessments of global warming
water resourceocean, river and lakesland usefood productionvegetation etc.
J: Biospherehuman healthimpacts on biosphere
F: Food supply and demand
K: Mitigation investmentex-post expenditureex-ante investmentcost-benefit integrated assessments
subjects in 2004subjects after 2005
L: GHG emission scenariodetailed regional emission scenario
Tentative IAM structure and results• GTAP (Purdue Univ.) incorporates more than 60 regions and
sectors and is still being expanded.• GTAP is designed to assess the international trade and
production 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 as
energy 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 Final demand
Non-energy sectors Energy sectors trade
Investments
ConsumpTion Output
1 2 PrimarySecondary m I C Q
1
X11=Q1・a11
X12=Q2・a12 0 0 m1 I1 C1 Q1
2
X21=Q1・a21
X22=Q2・a22 0 0 m2 I2 C2 Q2
Primary 0 0 0 Xpe mp 0 0EC_pre=PpS
Secondary
Xe1=PeE1
Xe2=PeE2 0 0 0 0
Ce=PeEc
EC=PeE
K Pk・K1 Pk・K2
L PL・L1 PL・L2
Output Q Q1 Q2EC_pre=PpS
EC=PeE Q
VA_pre VA_E
Y
ValueAdded
EnergySectors
Non-energySectors
Int.Inputs
V=f(K,L,E)-(secondary energy input costs)
VA_E=Σ(capital and labor costs of energy conversion
technologies )+(others)
VA_Epre=Σ(capital and labor costs of primary energy extraction and
production costs)+(others)
(Total secondary energy supply)=Σ(Conv. Eff.)*(primary energy inputs)
Integration of energy flow
Trade Investment
Consumption
EIS Y AGR SER CRU OTR COL GAS OIL ELE M I C Q
YAGRSERCRU 0OTR 0COLGAS 0OIL 0ELE 0
capital Klabor L
Q
Intermediate inputs Final demand Output
Non energy sectors(j)
Energy sectors (e')
Int.inputs
Non-energysectors(i)
EIS
Energysectors 0
0
VADD
Output
tjijQa , teie ECb ,′ titi mx ,, − tiI , tiC , t,iQ
tOILCRUEC ,,
tMete EP ,,, ⋅ teEC ,tELEesEC ,,
tjsjtjs QeEC ,,, = tCsEC ,,
tjtjtj LKV ,,, += teV ,′
titj QQ ,, = tete ECEC ,, =′
EIS Y AGR SER CRU OTR COL GAS OIL ELECRU 0
OTR 0
COL
GAS 0
OIL
ELE 0
Non energy sectors (j) Energy flow Trade Consumption
Production
Prices
0
0
tOILCRUE ,,
tMeE ,,teE , teP ,tELEes
E ,,
tjsE ,, tCsE ,,
Energy flow in DNE-21 model:simplified structure will be imposed.
Natural Gas Splitting
Coal Gasification
Biomass Gasification
Coal Liquefaction
IGCC with CO Rec.2
Biomass Liquefaction(Fermentation)
Shift Reaction
CO2 Recovery
CO Liquefaction2
CO Compression2
CO Injection2
Methane Synthesis
Methanol Synthesis
EORGas WellAquifer
Ocean
Methanol Synthesis
CoalMethanolCO (pipeline)2
CO (tanker)2
Water Electrolysis
Water Electrolysis
H Liquefaction2 Hydrogen (tanker)
Methane (tanker)
Synthetic. Oil
Hydrogen (pipeline)
Methane (pipeline)
CH Liquefaction4
Methanol Upgrading (into Gasoline)
Oil Refinery(Distillation)
Oil Refinery (Gasoline)
Natural Gas
Methane
Crude Oil
Synthetic Oil
Oil
Coal
Coal
Energy Crops
Wind&O.R.E.
Photovoltaics
Nuclear
H2
CO
CH4
CO2
CO2
CH OH3
Methanol
Electricity
CO Disposal2
Hydrogen
From Other Regions
From Other Regions To Other Regions
To Other Regions
Hydro
Geothermal
Electricity
Gaseous Fuel
Light Fuel Oil
Solid Fuel
: Electricity Use
Gasoline
Heavy Fuel Oil
Modern Fuel WoodsWood Residues
Sawmill ResiduesTimber Scraps
Cereal Harvest ResiduesSugarcane Harvest Resid.
Bagasse
Paper Scraps
Black Liquor
Kitchen Wastes
Animal Wastes
Human Feces
Synthetic Oil
Crude Oil
SOx
Syn.Oil
Oil Refinery(Distillation)
Oil Refinery (Gasoline)
Biomass Liquefaction
Biomass Gasification(Anaerobic Digestion)
Crude Oil
EthanolCoal Cleaning
N.Gas power gen.
Meth. power gen.
Coal power gen.
H power gen.2
Biomasspower gen.
Oil power gen.
Oil power gen.
Photov. power gen.
Hydro power gen.Geoth. power gen.Nuclear power gen.
Sorbent InjectionWet limestone scrub.Wellman-Lord
Sorbent InjectionWet limestone scrub.Wellman-Lord
SOx Rec. in End Use
SOx Rec.in End Use
Aggregation of GTAP data into 18 regions and 18 non-energy sectors 18 regions
USA USA CAF Middle African countries
CAN Canada SAF South African countries
MCM Middle American countries JPN Japan
BRA Brazil CHN China, Hong kong, Taiwan
SAM Peru, Argentina, Chile, Uruguay and other south American countries
IND India
WEP West and middle European countries
ASN Asia NIES countries
EEP Hungary, Poland and other east European countries
TME Turkey and Middle-East countries
FSU Former USSR ANZ Australia, New Zealand and Pacific Island countries
NAF North and Middle African countries
XAP Other countries
Aggregation of GTAP data into 18 regions and 18 non-energy sectors 18 non-energy sectors
I_S Iron and steel LUM Wood, Pulp and printing
CRP Chemical industry CNS Construction
NFM Non-ferrous metals TWL Textiles, wearing, apparel and leather
NMM Non- metaric materials OMF Other manufacturings
TRN Transport equipments AGR Agricultural products
OME Other machinary T_T Transportation
OMN Minings ATP Aviation
FPR Food Products BSR Business services
PPP Paper, pulp and printings SSR 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
0
2000
4000
6000
8000
10000
12000
14000
1997 2007 2017 2027 2037 2047 2057
Year
Total Value-added
USA CAN
MCM BRA
SAM WEP
EEP FSU
NAF CAF
SAF JPN
CHN IND
ASN TME
ANZ XAP
Preliminarily Simulation Results: GDP18 region - 18 sector with 2 energy input model
Energy Consumption
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
1997 2007 2017 2027 2037 2047 2057
Year
Final Energy Consumption(EJ)
USA CAN
MCM BRA
SAM WEP
EEP FSU
NAF CAF
SAF JPN
CHN IND
ASN TME
ANZ XAP
Preliminarily Simulation Results: Final Energy Concumption
18 region - 18 sector with 2 energy input model
JPN Sectoral Domestic Production
0
200
400
600
800
1000
1200
1400
1997 2007 2017 2027 2037 2047 2057
Year
Domestic Production
(10million US$)
I_S CRP
NFM NMM
TRN OME
OMN FPR
PPP LUM
CNS TWL
OMF AGR
ATP T_T
BSR SSR
Preliminarily Simulation Results: Outputs by sector (Japan)
18 region - 18 sector with 2 energy input model
WEP Sectoral Domestic Production
0
500
1000
1500
2000
2500
3000
3500
4000
4500
1997 2007 2017 2027 2037 2047 2057
Year
Domestic Production
(10million US$)
I_S CRP
NFM NMM
TRN OME
OMN FPR
PPP LUM
CNS TWL
OMF AGR
ATP T_T
BSR SSR
Preliminarily Simulation Results: Outputs by sector (WEP)
18 region - 18 sector with 2 energy input model
USA Sectoral Domestic Production
0
1000
2000
3000
4000
5000
6000
1997 2007 2017 2027 2037 2047 2057
Year
Domestic Production
(10million US$)
I_S CRP
NFM NMM
TRN OME
OMN FPR
PPP LUM
CNS TWL
OMF AGR
ATP T_T
BSR SSR
Preliminarily Simulation Results: Outputs by sector (USA)
18 region - 18 sector with 2 energy input model
CHN Sectoral Domestic Production
0
200
400
600
800
1000
1200
1400
1997 2007 2017 2027 2037 2047 2057
Year
Domestic Production
(10million US$)
I_S CRP
NFM NMM
TRN OME
OMN FPR
PPP LUM
CNS TWL
OMF AGR
ATP T_T
BSR SSR
Preliminarily Simulation Results: Outputs by sector (CHN)
18 region - 18 sector with 2 energy input model
IND Sectoral Domestic Production
0
100
200
300
400
500
600
700
800
900
1000
1997 2007 2017 2027 2037 2047 2057
Year
Domestic Production
(10million US$)
I_S CRP
NFM NMM
TRN OME
OMN FPR
PPP LUM
CNS TWL
OMF AGR
ATP T_T
BSR SSR
Preliminarily Simulation Results: Outputs by sector (India)
18 region - 18 sector with 2 energy input model
0
500
1000
1500
2000
2500
1997 2007 2017 2027 2037 2047
西暦年
GDP (10 billion USD)
制約無し
制約有り
YEAR
With policy
Without policy
Preliminarily Simulation Results:GDP (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
0
20
40
60
80
100
1997 2007 2017 2027 2037 2047
西暦年
電力価格 (USD/MWh)
制約無し
制約有り
YEAR
With policy
Without policy
Ele
ctric
pow
er p
rice
(US
D/M
wh)
Preliminarily Simulation Results:electric power price (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
0
1
2
3
4
5
1997 2007 2017 2027 2037 2047
西暦年
炭素排出量 (GtC)
制約無し
制約有り
YEAR
With policy
Without policyC
arbo
n em
issi
on (
GtC
)
Preliminarily Simulation Results:carbon emission (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
0
100
200
300
400
500
600
700
1997 2007 2017 2027 2037 2047
西暦年
炭素潜在価格 (USD/tC)
Preliminarily Simulation Results:
Shadow price of carbon emission (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
YEARSha
dow
pric
e of
Car
bon
emis
sion
($/
tC)
0
2
4
6
8
10
12
14
1997 2007 2017 2027 2037 2047西暦年
送電端発電電力量 (TkWh)
石油
天然ガス
石炭
その他
Preliminarily Simulation Results:
Power generation mix without carbon policy (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
YEAR
Ele
ctric
pow
er s
uppl
y (T
kwh)
Oil
Gas
CoalOthers
0
1
2
3
4
5
6
7
8
9
1997 2007 2017 2027 2037 2047西暦年
送電端発電電力量 (TkWh)
石油
天然ガス
石炭
その他
Preliminarily Simulation Results:
Power generation mix with carbon policy (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
YEAR
Ele
ctric
pow
er s
uppl
y (T
kwh)
Oil
Gas
CoalOthers
0 2 4 6 8 10 12 14 16 18
合計I_SCRPNFMNMMTRNOMEOMNFPRPPPLUMCNSTWLOMFAGRT_TATPBSRSSR
産業
付加価値生産額減少率 (%)
Preliminarily Simulation Results:
Impacts of carbon policy; loss of sector products in 2047 (USA)1 region - 18 sector with 6 energy input model
with and without carbon emission stabilization policy
Loss of product (%)
Indu
stry
Total
Integration : Scenario Generation and Simulations
Exogenous conditions
Lower warming factors & events
Upper warming factors & events
Lower warming factors & events
Upper warming factors & events
4:parameters, constraints, exogenous variables
5:Feedback:consistency
check
3:Scenario generation by X-I method
1:TAR-assumptions2:Structure Analysis among factors
Quantitative IAM- economy- technology- energy- natural resource- etc.
Scenario Generation using X-I method -1
Originally developed by Gordon (1965) to see the complicated 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 -21. Determine the set of events to be considered during the forecasting period.
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 end of the forecasting period P(i).
4. Estimate the “impact probability” P(i →j) : the occurrence probability of event j given the condition that the event i occurs solely in the beginning of the period.
5. Calculate the two-dimensional probability applying Markovian transition model.
6. Construct the mathematically consistent probabilities modifyingestimated two dimensional probability data set by
( ) ( ){ } ( ) ( ){ }∑ ∑ ≠−+−=
i ij
2*ij
2*i j,iPj,iPwiPiPwJ.min
where the consistent probabilities P*(i), P*(i,j) are the linear combinations of n-dimensional state probabilities {πk}.
Scenario Generation using X-I method -37. Calculate the ranges of {πk} using linear programming.
kπ.max.min
subject to ( ) ∑= k kkidiP π* , ( ) ∑= k k
kj
ki ddjiP π,* , 1=∑k kπ , 0≥kπ
kπ :n-dimensional probability ( ) ( )nkn
k2
k1 2,2,1kd,d,dP ΛΛ = .
kid =1 if even i occurs in the state k else k
id =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
B: .27-.51.18-.37
2000 (0000011) (0001110).42-1.0 12-.93
2005 (0000011) (1001110).32-.81 .33-.82
2010 (1000111) (1001110).09-.17 .09-.12
Scenario A Scenario B
upper:occurrence state lower: range of probability
Scenario A: Coal-nuclear scenarioScenario 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 industry allocation scenarios will provide the basic information to assess the policy measures.
• The outcomes of the project will give the helpful information on the energy technology development strategies.
• The most preferable burden sharing scenario on the carbon emission reduction can be generated.
• Industry policies on the R&D on the energy and environmental technologies, technology transfer, and other industry strategies can be assessed under the global warming mitigation policies.