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Integrated Assessment PHOENIX- Land-use Modeling and Global Warming
Impacts on Agriculture -
Keigo Akimoto, Shunsuke Mori and Toshimasa Tomoda Systems Analysis Group Research Institute of Innovative Technology for the Earth (RITE)
Energy Modeling Forum (EMF) 22: Climate Policy Scenarios for Stabilization and In TransitionDecember 12-14, 2006, Tsukuba
RITE’s Study for Climate Change Assessment
- Post-Kyoto frameworks- Assessments of Regional and sectoral frameworks, e.g. APP
- Transition scenarios
- DNE21+ Model: -Y2030/205077 world regions; detailed bottom-up energy modeling
- DEARS Model: -Y205018 world regions; 18 non-energy sectors (GTAP base);bottom-up energy modeling
Others- DNE21-ITC model
4 world regions- Country model for Japan
focusing particularly on CCS
PHOENIX
- Addressing the Article 2 - Stabilization scenarios
Integrated assessment
- DNE21 Model: -Y220010 world regions
- Non-CO2 GHG Models- Climate change model
(MAGICC+GCM results)- Global warming impact models
- Water resources- Agriculture (GAEZ base)- Human health- Biodiversity (Biome base)- Sea level rise
- Land use models- GLUE Model: bioenergy pot.- Forestation pot. estim. model
Land-use Model – GLUE (1/2)
18 18 world divided regionsworld divided regions
Biomass flows considered in GLUE
Land-use Model – GLUE (2/2)Estimation procedures for bioenergy potentials in GLUE
Estimated Bioenergy Supply Potentialsby Region
0
500
1000
1500
2000
2500
3000
3500
400020
05
2010
2015
2020
2025
2030
2035
2040
2045
2050
YEAR
Bio
ener
gy s
uppl
y po
tent
ial o
f dry
biom
ass
resi
dues
(Mto
e/ye
ar)
Other world
Australia & New Zealand
Turkey & Middle East
ASEAN & Korea
India
China
Japan
South African
Central African
North Africa
Former Soviet Union
Eastern Europe
Western Europe
Rest of South America
Brazil
Mexico & Central America
Canada
USA
Estimated Potentials of Bioenergy Supply in 2050
0
100
200
300
400
500
600 U
SA
Can
ada
Mex
ico
& C
entra
l Am
eric
a
Bra
zil
Res
t of S
outh
Am
eric
a
Wes
tern
Eur
ope
Eas
tern
Eur
ope
For
mer
Sov
iet U
nion
Nor
th A
frica
Cen
tral A
frica
n
Sou
th A
frica
n
Jap
an
Chi
na
Indi
a
AS
EAN
& K
orea
Tur
key
& M
iddl
e E
ast
Aus
tralia
& N
ew Z
eala
nd
Oth
er w
orld
Region
Bio
ener
gy s
uppl
y po
tent
ial o
f dry
bio
mas
sre
sidu
es in
Y20
50 (M
toe/
year
)
Bagasse
SugarcaneharvestingresiduesCereal harvestingresiduesTimber scrap
Paper scrap
Sawmill residues
Black liquor
Fuelwood harvestingresiduesIndustrial roundwoodharvesting residues
Estimations of Carbon Sequestration Potential by Afforestation/Rehabilitation
0 - 1
00
100
- 200
200
- 300
300
- 400
400
- 500
500
- 600
600
- 700
700
- 800
800
- 900
900
-100
0
1000
-110
0
1100
-120
0
1200
-130
0
1300
-140
0
1400
-150
0
1500
-160
0
1600
-170
0
1700
-180
0
1800
-190
0
1900
-200
0
2000
-210
0
2100
-220
0
2200
-230
0
2300
-240
0
2400
-250
0
2500
-260
0
2600
-270
0
2700
-280
0
2800
-290
0
2900
-300
0
500-1000
400-500
300-400
150-300
50-150
25-50
12.5-25
5-12.5
2.5-5
0-2.5
面積 (百万ha)
年間降水量 (mm/yr)
単位面積当りのバイオマス資源
(ton
/ha) 450-480
420-450390-420360-390330-360300-330270-300240-270210-240180-210150-180120-15090-12060-9030-600-30
Precipitation (mm/yr)
Area (Mha)
Phyt
omas
s st
ocks
(ton
/ha)
0
100
200
300
400
500
600
0 500 1000 1500 2000 2500 3000
Annual precipitation (mm/yr)
Aver
aged
sto
cks
of p
hyto
mas
s (to
n/ha
)
The area having the stock under the averaged stock for each precipitation level is assumed to achieve the increase in the stock up to the averaged one by afforestation/rehabilitation.Land use, soil types, slope, temperature conditions are also considered for the estimation.
Estimated Carbon Sequestration Potential by Afforestation/Rehabilitation in 1990
The global potentials of carbon sequestration: 170 The global potentials of carbon sequestration: 170 GtCGtC
0
5000
10000
15000
20000
2000 2010 2020 2030 2040 2050Year
CO
2 em
issi
ons
& re
duct
ions
(MtC
/yr) Energy Saving
Fuel Switching among Fossil FuelsNuclear PowerHydroWindPhotovoltaicsBioenergyForestationCO2 Seq - Oil Well (EOR)CO2 Seq - Depleted Gas WellCO2 Seq - Deep Saline AquiferCO2 Seq - Coalbed (ECBM)Net Emission
Net emission in Reference Case
Net emission for the stabilization at 550 ppmv
0
5000
10000
15000
20000
2000 2010 2020 2030 2040 2050Year
CO
2 em
issi
ons
& re
duct
ions
(MtC
/yr) Energy Saving
Fuel Switching among Fossil FuelsNuclear PowerHydroWindPhotovoltaicsBioenergyForestationCO2 Seq - Oil Well (EOR)CO2 Seq - Depleted Gas WellCO2 Seq - Deep Saline AquiferCO2 Seq - Coalbed (ECBM)CO2 Seq - OceanNet Emission
Net emission in Reference Case
Net emission for the stabilization at 550 ppmv
Without ETAnnex I: 60%
reduction in 2050
Cost-effective Options for Emission Reductions at 550 ppmv by Using DNE21+ Model
With ET
PHOENIX Project
♦ PHOENIX: Pathways toward Harmony Of Environment, Natural resources and Industry compleX
♦ Integrated assessment of global warming impacts, adaptations and mitigations
♦ Addressing the ultimate target of Article 2 of UNFCCC
Assessment Procedure in PHOENIX
Reference emission pathways
Tolerable emission pathways
(Long-term target)
Eval. of climate change Eval. of mitigation measures
Eval. of impacts Eval. of adapt. measures
Comprehensive Assess.
Type II events*; prevented to occur (Precaution approach)
Expert judgment (Finally, based on world wide agreement)
Emission to be suppressed until catastrophic events do not occur regardless of mitigation costs
Type I events
Using a high CS value
Using a medium CS value
Emission to be suppressed considering mitigation costs, vulnerable regions etc.
(No climate policy)
* Type II: abrupt and catastrophic events (THC, WAIS etc.)
The Climate Model- Integration of SCM and the results of AOGCM -
CO Emission2
SOx Emission
MethaneEmission
N O Emission2
Halocarbons(27 types)Emissions
Atmospheric Concentration
CO2
Oceans LandSurface
RadiativeForcing of CO2
MethaneConcentration
N OConcentration
2
Halocarbons (27 types)
Concentration
Radiative Forcingof Methane
RadiativeForcing of N O2
Radiative Forcingof Halocarbons
(27 types)
RadiativeForcing of SOx
RadiativeForcing of H O2
RadiativeForcing of Ozone
Global & Annual MeanTemperature
Rise
Sea LevelChange
Total RadiativeForcing
Monthly averagetemperature
by grid. . . . .
Monthly averageprecipitation
by grid
AOGCM Results (grid data)
SCM: MAGICC base
ECHAM4, MIROC etc.
0
1
2
3
4
5
6
2000 2020 2040 2060 2080 2100 2120 2140 2160 2180 2200
Year
Glo
bal m
ean
tem
pera
ture
cha
nge
from
the
pre-
indu
stria
l leve
l (de
gC)
SRES B2-base
WGI S650 (Non-CO2 GHG: B2)
WGI S550 (Non-CO2 GHG: B2)
WGI S450 (Non-CO2 GHG: B2)
Global mean temperature change
Atmospheric CO2 concentration
Climate sensitivity: 2.5 ºC
300
500
700
900
1100
2000 2020 2040 2060 2080 2100 2120 2140 2160 2180 2200
Year
Atm
osph
eric
CO
2 co
ncen
tratio
n (p
pmv) SRES B2-base
WGI S650
WGI S550
WGI S450
CO2 Concentration & Temperature Change
Annual Mean Temperature Change in 2150
Global mean temperature change+4.2 ºC from pre-industrial levels
Global mean temperature change+2.7 ºC from pre-industrial levels
SRES B2-base Reference
IPCC WGI S550
GCM results: ECHAM4
Annual Mean Precipitation Change in 2150
SRES B2-base Reference
IPCC WGI S550
GCM results: ECHAM4
Overview of Crop Potential Model
♦ The model is based on the GAEZ (Global Agro-ecological Zones) framework developed by IIASA/FAO.
♦ Crop production potentials are estimated by matching between climate, soil condition etc. and characteristics of crops.
♦ AEZ has a detailed database of crop characteristics. ♦ AEZ provides the Leaf area index (LAI) and harvest index
depending on the agriculture input levels.
♦ Consideration of the productivity increase (LAI and harvest index) of agriculture depending on economic levels
♦ Maximizing the production potentials considering the changes in implantation crops and month, which can evaluate the adaptation effects for global warming
Estimation Procedure of Production Estimation Procedure of Production Potentials of CropsPotentials of Crops
Elevation
PotentialEvapotranspirationMonthly average
Temperature
From Climate Model
Monthly averageprecipitation
Monthly averagewind speed
Soils
Terrain slopes
Max temperature
Min temperature
Crop yieldspotentials
Crop characteristics
Historical monthlyMax/Min temperature
Historical monthly averagecloud cover
ActualEvapotranspiration
SRES B2-base Reference
IPCC WGI S550
Change in Production Potentialof Wheat in 2150
Optimal Implantation Month of Wheat
e.g.,The optimal implantation month shifts from April-May in 1990 to January-February in 2150
Year 2150: IPCC WGI S550Year 1990
Year 2150: SRES B2-base Reference
IPCC WGI S550
SRES B2-base Reference
Change in Production Potential of Rice in 2150
Optimal Implantation Month of Rice
Year 2150: IPCC WGI S550Year 1990
Year 2150: SRES B2-base Referencee.g.,The optimal implantation month shifts from April in 1990 to March in 2150
Change in Production Potential of Wheat from 1990
-60%
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
2050 2150
Cha
nge
in p
rodu
ctio
n po
tent
ial/p
er-c
apita
pro
duct
ion
pote
ntia
l of
whe
at fr
om 1
990
leve
ls Reference
WGI S650
WGI S550
WGI S450
Change in productionpotentials of wheat
Change in productionpotentials of wheat
Change in per-capitaproduction potentials of wheat
Change in per-capitaproduction potentials of wheat
Change in Production Potential of Rice from 1990
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
2050 2150
Cha
nge
in p
rodu
ctio
n po
tent
ial/p
er-c
apita
pro
duct
ion
pote
ntia
l of r
ice
from
199
0 le
vels
Reference
WGI S650
WGI S550
WGI S450
Change in productionpotentials of rice
Change in productionpotentials of rice
Change in per-capitaproduction potentials of rice Change in per-capita
production potentials of rice
Effects of Increase in Crop Productivity
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
Cha
nge
in p
rodu
ctio
n po
tent
ial o
f whe
at fr
om 1
990
leve
ls
2050 2150
No considerationof productivity increase
Under considerationof productivity increase
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
70%
Cha
nge
in p
rodu
ctio
n po
tent
ial o
f whe
at fr
om 1
990
leve
ls
Reference
WGI S650
WGI S550
WGI S450
2050 2150
No considerationof productivity increase
Under considerationof productivity increase
Wheat Rice
Alternative Socio-Economic Scenarios for Sensitivity Analysis
Temperature Change
0
1
2
3
4
5
6
7
2000 2025 2050 2075 2100 2125 2150
Year
Glo
bal m
ean
tem
pera
ture
cha
nge
from
the
pre-
indu
stria
l lev
el (°
C)
SRES A1FI-baseSRES B2-baseWGI S650 (Non-CO2 GHG: B2)WGI S550 (Non-CO2 GHG: B2)WGI S450 (Non-CO2 GHG: B2)
0
2000
4000
6000
8000
10000
12000
1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150Year
Wor
ld p
opul
atio
n (m
illion
peo
ple)
SRES-A1
SRES-B2
OECD/IEA Statistics(World Bank)
DNE21 - Scenario(IPCC SRES)
0
200000
400000
600000
800000
1000000
1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 2110 2120 2130 2140 2150Year
Gro
ss w
orld
pro
duct
s (b
illion
US9
0 $)
SRES-A1 SRES-B2
OECD/IEA Statistics(World Bank)
DNE21 - Scenario(IPCC SRES)
Population Assumptions
World GDP
Sensitivity to Socio-Economic Conditions
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
2050 2150
Cha
nge
in p
rodu
ctio
n po
tent
ial/p
er-c
apita
pro
duct
ion
pote
ntia
l of
whe
at fr
om 1
990
leve
ls
SRES B2-baseReference
SRES A1FI-baseReference
Change in productionpotentials of wheat
Change in per-capitaproduction potentials of wheat
- Wheat -
However, the potential production in A1FI will be decrease in 2150, due to large temperature rise.
Although large temperature rise is estimated in A1FI, the decrease in the per-capita potential productivity is smaller than in B2, due to a smaller population assumption in A1FI.
Higher economic growth and improvements of agriculture productivity are assumed in A1FI than in B2, and therefore, the potential production is larger than in B2 instead of larger temperature rise.
Final RemarksFinal Remarks
♦ PHOENIX is conducting consistent assessments for different levels of stabilization scenarios.
♦ Bioenergy and forestation potentials are evaluated.
♦ Global warming impacts on potential productions of crops are also evaluated.
♦ However, Socio-economic conditions would be more influential on crop production potentials than stabilization levels.
♦ Harder linkages among sea level rise, water resources, agriculture, bioenergy supply potentials, forestation potentials, socio-economic estimates etc. are needed.
♦ The linkage between DEARS model (using GTAP database) and global warming impacts on agriculture is also an important future work.