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Asia Pacific Integrated Model (AIM)Asia Pacific Integrated Model (AIM)
International Workshop on URBANIZATION, DEVELOPMENT International Workshop on URBANIZATION, DEVELOPMENT
PATHWAYS AND CARBON IMPLICATIONSPATHWAYS AND CARBON IMPLICATIONS
2828--30 March 2007, NIES, Tsukuba, Japan30 March 2007, NIES, Tsukuba, Japan
National Institute for Environmental StudiesNational Institute for Environmental Studies
Mikiko KainumaMikiko Kainuma
-- Urbanization in the context of global Urbanization in the context of global
integrated assessment models integrated assessment models --
ContentsContents
� Introduction of AIM
� Top-down and bottom-up
� Model integration in AIM
– Assessment of air pollution policy in China
– Assessment of long-term global environmental change
� Study on Low-Carbon Society (LCS)
AIM, NIES
� India’s road passenger transport sector analysis by Strategic Database (SDB)
� UNEP/GEO4 scenario analysis on air pollution
Basic structure of AIMBasic structure of AIM
IPCCUNEPOECD
ECO-AsiaAPEISIEA
UNU/IAS
Japan team
China Team
India Team
Population
Lifestyle
GHG Emission Modules
Land use Energy
TechnologyEconomics
Atmospheric OceanCarbon Cycle
AIM, NIES
IEAUNU/IASAPNEMFWWFGovern-mentsCompa-nies
Korea Team
Thailand Team
Malaysia Team
Climate ChangeModules
Supporting Policy
process
Model
Developm
ent
Atmospheric Chemistry
Climate Change
OceanDynamics
Vegetation
Sea LevelRise Human
Health
Agriculture
Water Resources
Vegetation
Sea LevelRise Human
Health
Agriculture
Water Resources
Impacts and Adaptation Modules
Global warming issueGlobal warming issue
Adaptation
•Stab. GHG concentration
•Stab. temperature
•Reducing impacts
•…
Mitigation (developed countries)
Mitigation (developing countries)
TargetTarget
to protectto protect
globalglobal
warmingwarming
Achievement ofKyoto Target
Participation ofdeveloping countries
Integrated assessment
of mitigation and adaptation
as climate policy
Economic development Economic development
Domestic environmental problemsDomestic environmental problems
Present 2010 - 30 2100 -
•Solution of air pollution
•Ensuring safe water
•Energy security
•Food security (e.g. Impact on agriculture)
•…
Policy assessment torealize long term targetto avoid climate change,
short/medium term domesticenvironmental policy, andeconomic development
MillenniumMillennium
developing goalsdeveloping goalsMedium termMedium termenvironmentalenvironmental
targettarget
BottomBottom--up model and Topup model and Top--down model in AIMdown model in AIM
� "Model" reflects the real world.
� But "Model" is just a model, not real world. – Select appropriate model to meet aim,
• Reality of technology change: End use model• Consistency of economic activity: CGE model• Estimation of climate impact: Physical process model
– Integration of appropriate models
� Bottom-up model
AIM, NIES
� Bottom-up model– Inputs: economic activity (service demand)
– Outputs: diffusion of detailed technologies, environmental burdens
– Partial equilibrium: energy flow, agricultural potential production change, water flow, ...
� detailed descriptiondetailed description
� Top-down model– Inputs: technology level
– Outputs: economic activity, environmental burdens
– General equilibrium: whole economic activity, environmental policy, ...
� consistent descriptionconsistent description
Relationship bottomRelationship bottom--up and topup and top--down in AIMdown in AIM
Top-down
Environmentwater, air, land, ...
Bottom-up• Land use change• Crop productivity change• Municipal solid waste generation• Water demand
Environmental serviceFeedback from ecosystem
OutputsEnvironmental indicators, economic development, ...
AIM, NIES
water, air, land, ...
DriversGDP, population, technologies, ...
• Water demand• Water availability• Risk of water shortage• Access to safe water/sanitation• Potential crop productivity• Risk of hunger• Diarrhea incidence• Air pollutant emission• Urban air quality• Energy system
Feedback from ecosystemto socio-economy
Environmental damageMaintenance
EconomyProduction
ConsumptionInvestment
MarketPrice
Strategic DatabaseEnvironmental Options
Model integration in AIM- Assessment of air pollution policy in
China -China -
AIM, NIES
‰
-14
-12
-10
-8
-6
-4
-2
0
200
0
20
02
2004
20
06
20
08
2010
20
12
2014
2016
20
18
2020
mean
lower
upper
Health impacts on national economy (reference case)Health impacts on national economy (reference case)
Meta-analysis
Concentration-response
relationship
Air pollutioninduced health
effects
Laborloss
Medicalexpenditure
Top-downmodel
(AIM/MaterialChina)
Health impact
Economic systemresponse
Example of model integration in AIMExample of model integration in AIMAssessment of air pollution policy in ChinaAssessment of air pollution policy in China
AIM, NIES
AIM, NIES
BaU
Policy
BaU
Geographical distribution of PM10
emissions across China in 2020 (unit: ton)
Policy
Ambient concentration of PM10
in urban areas of China in 2020
PM10PM10 emissions and concentrationemissions and concentration
in in BaUBaU and policy caseand policy case
Urban populationexposed to ambient
air pollution
Rural populationexposed to ambient
air pollution
Ambientair pollution
concentration
Atmosphericpollution
concentration model
Pattern of rural energyconsumption
Emissions byLPS and AS
Bottom-upmodel
(AIM/LocalChina)
Energy service demandEnergy price
Emission constraints
Emission distribution
Background
Damage of air pollution
Forest and crop, river and stream and soil, building materials,
clothes, health ……
Health effects of air pollutionHealth effects of air pollution
Verified by toxicology, clinics and epidemiology inside and outside
of China
- Impairs functions of respiratory system and circulatory system
- Increase people’s risks to mortality and morbidity
Background
Rural residents are still relying on biomass and coal for cooking and heating
Indoor air pollution in rural areas
Indoor PM10 concentrations in the monitored rural families
assumed urbanization in Chinaassumed urbanization in China
800
1000
1200
1400
1600
mil. pers
onsestimates
AIM, NIES
0
200
400
600
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
Year
mil. pers
ons
urban rural
Quantification of health effects
� PM10 is selected as the surrogate air pollutant
--- avoid double counting
--- most hazard pollutant: complicated chemical composition
� China Air Quality Standard is taken as benchmark� China Air Quality Standard is taken as benchmark
--- Outdoor: average annual concentration of PM10 cannot exceed
100µg/m3
--- Indoor: daily average concentration of PM10 cannot exceed 150
µg/m3
Scenario design
No. Scenario
Code of Sub-scenario
Without health
feedback
With health
feedback
1 No pollution control BAU BAU_H
2 Environmental tax TAX TAX_H
3 Emission cap CAP CAP_H
4 Emission cap + investment CAP_INV CAP_INV_H
1.Environmental tax
2. Environmental cap
3. Environmental cap + investment
Enhancement investment on cleaner energy: natural gas and renewable energy
Annual investment will increase 2%
TAX
BAU
CAP_INVCAP
Geographical distribution of PM10 emissions across China in 2020 (unit: ton)
TAX
BAU
Ambient concentration of PM10 in urban areas of China in 2020
CAP CAP_INV
Pattern of energy consumption of rural household in 2020
electricity
3.11%
biomass
55.76%
coal
40.75%
gas
0.39%
TAX
electricity
3.11%
biomass
56.38%
coal
40.51%
gas
0.00%
BAU
electricity
7.34%
biomass
37.61%
coal
28.03%
gas
27.01%
electricity
7.36%
biomass
37.69%
coal
27.87%
gas
27.07%
CAP_INVCAP
TAXBAU
Saved labor loss under the different policies
1.5
2
2.5
(‰)
TAX
CAP
CAP_INV
0
0.5
1
2000
2005
2010
2015
2020
2000
2005
2010
2015
2020
(‰)
Urban Rural
Saved medical expenditure under the different policies
15
20
25
(billi
on y
uan)
TAX
CAP
CAP_INV
0
5
10
15
2000 2005 2010 2015 2020 2000 2005 2010 2015 2020
Urban Rural
Economic impact of air pollution management Economic impact of air pollution management
and recovery from countermeasureand recovery from countermeasure
BaUPollutionreduction
Pollution
-1.00
0.00
1.00
trill
ion
yu
an
, 1
99
7 p
rice
TAX_H CAP_H CAP_INV_H
AIM, NIES
Pollutionreductionwith clean
energy policy
Although only introduction of environmental constraint will bring economic damage, appropriate countermeasures will be able to mitigate the economic damage.
-4.00
-3.00
-2.00
20
00
20
05
20
10
20
15
20
20
20
00
20
05
20
10
20
15
20
20
20
00
20
05
20
10
20
15
20
20
trill
ion
yu
an
, 1
99
7 p
rice
health benefit
health damage
environmental policy
Model integration in AIM- Assessment of long-term global
environmental change -environmental change -
Overall of AIM/EcosystemOverall of AIM/Ecosystem
AIM/Ecosystem is a model in order to assess the interaction among economic activities and natural services from the view point of long-term global environmental change.
� Global CGE model with recursive dynamics
� Based on GTAP ver.6 and IEA energy balance table
� Air pollutant emissions and GHG emissions from fuel and land use/ land use change
AIM, NIES
land use change
� Land is treated as one of production factors
� Feedback from climate change
– Agriculture, water, ...
� Linked with other models
– With country model: Key parameters between global model and country model are international price and trade.
– With simple climate model: GHG emissions and global mean temperature change.
– With agriculture productivity change model: global mean temperature and land productivity change.
Structure of AIM/EcosystemStructure of AIM/Ecosystem
production and service/goods
sector
produced goodsand service
intermediate, energy, capital, laborEnvironmental service/goods
Environmental service/goods
Drivers ofecosystem change
AIM/Agricultureland productivity
AIM, NIES
household
government
abroadmarketimport
export
final demandRecreational and cultural services
Environmental service/goods
production sectors
capital & labor
Natural capital natural capital
maintenance sectors
intermediate,energy,capital, labor
maintenance and augmentation service/goods
maintenancecosts
Feedback mechanism of ecosystem to socio-economy
Based on CGE model supported by other AIM models
China
KoreaJapan
Interaction between global model
and country modelsAssessment of Impacts of• countermeasures such as CDM• international trade• ...
water
agricul-
ture
landusesolid
waste
energy
resource
CGE
air
other
AIM/Ecosystem and other modelsAIM/Ecosystem and other models
AIM, NIES
::::
Global enduse model
IndiaOther
country
Thailand
Water resource modelGlobal agriculture model
Interaction amongglobal models
Global CGE model (AIM/Ecosystem)Global CGE model (AIM/Ecosystem)
AIM, NIES
BaU GHG-475ppm GHG-500ppm GHG-550ppm GHG-650ppm
0
5
10
15
20
25
19
90
200
0
201
0
202
0
20
30
204
0
205
0
206
0
207
0
20
80
209
0
21
00年温室効果ガス排出量 (二酸化炭素換算:GtC/年)
0.0
1.0
2.0
3.0
4.0
5.0
19
90
200
0
20
10
202
0
203
0
20
40
205
0
20
60
207
0
208
0
20
90
210
0
21
10
212
0
213
0
21
40
215
0年気温上昇 (1990年=0.6℃) GHG475ppm
650
550
500
BaU
650
550500
BaU
GHG475ppm
Global GHG emissions(GtC-eq/yr)
Global mean temperature(degree centigrade)
Outputs Outputs GHGGHG emissions and temperatureemissions and temperature
Year Year
Climate change and agricultural productivityClimate change and agricultural productivity
CO2 emissionschange in
global meantemperature
Simple climate modelin AIM/Ecosystem
downscalebased on
GCM results
AIM, NIES
AIM, NIES
Climate change impact under various constraintsClimate change impact under various constraints
on on GHGGHG concentrationconcentration(Potential productivity change in India)(Potential productivity change in India)
BaU GHG-425ppm GHG-450ppm GHG-475ppm
GHG-500ppm GHG-550ppm GHG-650ppm
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100年C
hange o
f rice p
roductivity (
%)
-30
-25
-20
-15
-10
-5
0
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100年C
hange o
f w
heat
pro
ductivity (
%)
Rice Wheat
Year Year
Main module inAIM/Ecosystem
(CGE model)
change in precipitation &temperaturein country
Potentialcrop productivitychange model
(AIM/Agriculture)
GCM results
crop productivitychange
in country
agriculturalproductivitychange in
model region
Preliminary modelPreliminary model
Goods (16 goods)
� AGR Agriculture
� LVK Livestock
� FRS Forestry
� FSH Fishing
� COL Coal
� CRU Oil
� OIL Petroleum coal products
� GAS Gas
Region (14 regions)
� CAN Canada
� USA USA
� JPN Japan
� OCN Australia and New Zealand
� WEU OECD Europe
� EEU Eastern Europe
� CIS CIS
� MEA Middle East
AIM, NIES
� GAS Gas
� WTR Water
� OMN Minerals nec
� EIS Energy intensive industry
� IDY Other industry
� CNS Construction
� ELE* Electricity
� SER Service
� T_T Transport
* Electricity is supplied by various power sectors such as coal fire, nuclear, hydro and so on.
� MEA Middle East
� NAF Northern Africa
� SAF Sub-saharian Africa
� LAM Latin America
� EAS East Asia
� SAS South Asia
� SEA South East Asia
Rice productivity change in 2100Rice productivity change in 2100
50
100
150
Without Adaptation
With Adaptation
chan
ge
to r
efer
ence
cas
e (%
)
AIM, NIES
-50
0
-100
AN
Z
CH
N
JPN
XE
A
XS
E
IND
XS
A
CA
N
US
A
XN
A
XS
M
XC
A
XE
U
XE
E
RU
S
XM
E
XN
F
XA
F
XS
S
chan
ge
to r
efer
ence
cas
e (%
)
Calculated by Mr. Ishibashi (Titech)
GDP change in 2100GDP change in 2100
-2
-1
0
1
AN
Z
CH
N
JPN
XE
A
XS
E
IND
XS
A
CA
N
US
A
XN
A
XS
M
XC
A
XE
U
XE
E
RU
S
XM
E
XN
F
XA
F
XS
S
Worl
d
chan
ge
to r
efer
ence
cas
e (%
)
AIM, NIES
With Adaptation
-7
-6
-5
-4
-3
-2
Without Adaptation
-8
With Adaptation
chan
ge
to r
efer
ence
cas
e (%
)
Calculated by Mr. Ishibashi (Titech)
Global GDP change Global GDP change
-0.4
-0.2
0.0
0.2ch
ange
to r
efer
ence
cas
e (%
)
AIM, NIES
-1.4
-1.2
-1.0
-0.8
-0.6
2001 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Without Adaptation
With Adaptation
chan
ge
to r
efer
ence
cas
e (%
)
Calculated by Mr. Ishibashi (Titech)
Study on Low-Carbon Society (LCS)
Technology development,socio-economic change projected by historically trend
Forecasting Reference future world
Mitigation Technology developmentRequired
Policy
Environmental pressure
Required intervention
3. We need
Forecasting from now and Forecasting from now and Backcasting from future Backcasting from future
prescribed/normative worldprescribed/normative world
http://2050.nies.go.jp
2020 20502000
Long-te
rm ta
rget y
ear
Release of AIM result
Back-casting
Normative target world
Service demand change
by changing social behavior, lifestyles
and institutions
Policy intervention and Investment
required intervention policy and measures
Environmental pressure
Checking
year(2
015)
Checking
year(2
025)
Required intervention
“Trend Breaks”
to realize visions
2. We need
“Visions”
1.Target may
be tough50% reductions
In the world
10
15
20
25
温室効果ガス排出量 (二酸化炭素換算:GtC/年
)
2.0
3.0
4.0
5.0
気温上昇 (1990年=0.6℃)
GHG475ppm
650550500
BaU
650
BaU
GHG
475ppm
Determination of climate targetDetermination of climate targetusing AIM/Climate [Policy]using AIM/Climate [Policy]
AIM, NIES
BaU GHG-475ppm GHG-500ppm GHG-550ppm GHG-650ppm
0
5
19
90
200
0
20
10
202
0
20
30
20
40
205
0
20
60
20
70
20
80
20
90
21
00年
温室効果ガス排出量
0.0
1.0
19
90
20
00
20
10
20
20
20
30
20
40
20
50
20
60
20
70
20
80
20
90
21
00
21
10
21
20
21
30
21
40
21
50年
気温上昇 GHG475ppm
50% reduction
650550500
475ppm
Global GHG emissions(GtC-eq/yr)
Global mean temperature(degree centigrade)
Burden sharing of GHG emissions
Depicting
socio-economic
visions
in 2050
Estimating
energy
service demands
Exploring
innovations for energy
demands and energy
Quantifying
energydemand
Checking
potentials for energy
supply
Scenario Approach to Develop
Japan Low-Carbon Society (LCS)
Step1Step5
and energy supplies
demand and supply
to estimate
CO2
emissions
Achieving energy-related CO2
emissions target
Step4
Step2
Step3
Step5
Vision A “Doraemon” Vision B “Satsuki and Mei”
Vivid, Technology-driven Slow, Natural-oriented
Urban/Personal Decentralized/Community
Technology breakthroughCentralized production /recycle
Self-sufficientProduce locally, consume locally
As for LCS visions, we prepared two As for LCS visions, we prepared two different but likely future societiesdifferent but likely future societies
Doraemon is a Japanese comic series created by Fujiko F. Fujio. The series is about a robotic cat named Doraemon, who travels back in time from the 22nd century. He has a pocket, which connects to the fourth dimension and acts like
Step1
/recycle locally
Comfortable and Convenient Social and Cultural Values
Akemi Imagawa
fourth dimension and acts like a wormhole.
Satsuki and Mei’s House reproduced in the 2005 World Expo. Satsuki and Mei are daughters in the film "My Neighbor Totoro". They lived an old house in rural Japan, near which many curious and magical creatures inhabited.
Super high
efficiency air-
conditioner
LED lightPV on roof
Heat insulation
house
66% reduction of
lighting demand
60% reduction of
heat demand
3-4kW
Depict Future Image: Residential sector in 2050Step1 and
Step3
Efficient use
New energy
Infrastructure
Eco-lifestyle
イメージを表示できません。メモリ不足のためにイメージを開くことができないか、イメージが破損している可能性があります。コンピュータを再起動して再度ファイルを開いてください。それでも赤い x が表示される場合は、イメージを削除して挿入してください。conditioner
Stand-by energy
reduction
Fuel cell
cogeneration
Hot water supply by heat pump or
solar heating
HEMS (Home Energy
Management System)
Eco-life Navigation
COP=8
for cooling
Environment
Education
10-20% reduction
33% reductionCOP=5 for warming10-20% reduction
Maho Miyashita
Population and Household ModelPopulation and Household Model
Total population
(Period T-1)
[Sex, Age]
Province-wisePopulation
(Period T-1)[Sex, Age]Life table
[Sex, Age] Consistency
Adjustment
Province-wiselife table[Sex, Age]
• Drastic change is projected in Japan’s population structure by 2050. Downturn in birthrate, depopulation and aging will continue until 2050, and they affect greatly the future vision.
• A cohort component model for population, a household headship rate modelfor household types, with spatial resolution of provinces, land-use types and climate zones and five family types was developed, and is used to analyze effects of depopulation and changes in family composition on the realization of LCS.
Step2
Total population
(Period T)
[Sex, Age]
Province-wisePopulation(Period T)[Sex, Age]
[Sex, Age]
InternationalNet-migration(Japanese)
International net-migration
(Outsider)
Fertility rate
[Age]Province-wise
fertility rate[Age]
Total number ofHousehold(Period T)
[Family-wise]
Headship rate
[Sex, Age, Family]
Province-wiseheadship rate
[Sex, Age, Family]
Landuse Cls.-wisePopulation(Period T)[Sex, Age]
Province-wiselanduse Cls.
share
Province-wiseclimatic division
share
Province-wisehousehold(Period T)[Family]
Climatic zone-wise household(Period T)[ Family]
Consistency
Consistency
Consistency
Adjustment
Adjustment
: Data flow
: Exogenous variable: Endogenous variable
[Sex, Age]
Province-wisenet-migration
[Sex, Age]
Flowchart of PHM
40
60
80
100
120
140
Popula
tio
n (
Th
ousan
d) 80-
60-79
40-59
20-39
0-19 40%
60%
80%
100%
Others
Parent-Children
One-Person
Couple-Only
Type of household (%)
(Million)
age
Projection Japan population and households in
scenario A
0
20
2000 2010 2020 2030 2040 2050
Popula
tio
n (
Th
ousan
d)
0%
20%
2000
2005
2010
2015
2020
2025
2030
2035
2040
2045
2050
Couple-Children
Type of household (%)
year 2000 2050A B
Population (million) 126.9 94.5 100.3Aged population ratio (%) 17.4 38 35.8Average number of household 2.71 2.19 2.38Single-person households (%) 27.6 42.6 35.1
Projection of urbanization
40
60
80
100
120
140
Population (mill.)
Forest-rural
Forest-central city
Forest-Metropolitan
Agricultural-rural
Agricultural-central city
Agricultural-metropolitan
Urban-Regional
Urban-Central
Rural
Step2
0
20
2000 2010 2020 2030 2040 2050
Urban-Central
Urban-Core
Urban-MetropolitanUrban
year 2000 2050A B
Population (million) 126.9 94.5 100.3Urban population(%) 78.1 84.2 76.7Agricultural area population(%) 8.2 7.1 8.5Forest area population(%) 13.7 8.7 14.8
A B A B A B A B A B A B A B A B A B A B A B
Final energy demands
70% reduction: Combination of demand side energy reduction
++++low carbon energy Seconday energy demands (Mtoe)
IndustrialResidential
Commercial
Trans. Prv.
Trans. Frg.
- 50 100 150 200 250 300 350 400
2000
2050A
2050B
Industrial Residential Commercial Trans. Prv. Trans. Frg.
Decrease of Energy
Demand
Primary energy supply
Step4 and Step5
Coal Oil Gas
Biomass
Nuclear
Solar and Wind
0 100 200 300 400 500 600
2000(Actual)
2050(Scenario A)
2050(Scenario B)
Primary Energy Consumption (Mtoe)
Coal Oil Gas Biomass Nuclear Hydro Solar and Wind
Main Main Main Main factorfactorfactorfactors to reduce COs to reduce COs to reduce COs to reduce CO2222 emissionsemissionsemissionsemissions Factors Class.Factors Class.Factors Class.Factors Class.
Soci
Soci
Soci
Soci-- --
ety
ety
ety
ety
• High economic growthHigh economic growthHigh economic growthHigh economic growth
• Decrease of population and number of householdsDecrease of population and number of householdsDecrease of population and number of householdsDecrease of population and number of households
Demand growth by Demand growth by Demand growth by Demand growth by
activity level changeactivity level changeactivity level changeactivity level change
Industrial
Industrial
Industrial
Industrial
• Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.Energy Efficiency Energy Efficiency Energy Efficiency Energy Efficiency
Imp. (EE)Imp. (EE)Imp. (EE)Imp. (EE)
• Fuel switching from coal and oil to natural gasFuel switching from coal and oil to natural gasFuel switching from coal and oil to natural gasFuel switching from coal and oil to natural gasCarbon Intensity Carbon Intensity Carbon Intensity Carbon Intensity
Imp. (CI)Imp. (CI)Imp. (CI)Imp. (CI)
Residential and
Residential and
Residential and
Residential and
com
mercial
com
mercial
com
mercial
com
mercial
• High insulation dwelling and buildingHigh insulation dwelling and buildingHigh insulation dwelling and buildingHigh insulation dwelling and building
• Home/Building energy management systemHome/Building energy management systemHome/Building energy management systemHome/Building energy management system
Reduction of service Reduction of service Reduction of service Reduction of service
demands (SD)demands (SD)demands (SD)demands (SD)
• Efficient airEfficient airEfficient airEfficient air----conditioner, Efficient water heater, conditioner, Efficient water heater, conditioner, Efficient water heater, conditioner, Efficient water heater, Energy Efficiency Energy Efficiency Energy Efficiency Energy Efficiency
Activity
31
em
issio
ns in
20
00
SD
29
EE
84
CI
27
CO
2re
ductions in
energ
y
end-u
se s
ecto
r(MtC)
reductions in
energ
y
MtC
)
Re
du
ctio
n o
f C
O2
em
issio
n(M
tC)
Incre
ase o
f
CO
2E
mis
sio
n
GHG 70% reduction in 2050 Scenario A: Vivid Techno-driven Society
22
9
19
28
610
34
12
Demand side energy -40% + Low carbonization of primary energy+CCS
Residential and
Residential and
Residential and
Residential and
com
mercial
com
mercial
com
mercial
com
mercial
• Efficient airEfficient airEfficient airEfficient air----conditioner, Efficient water heater, conditioner, Efficient water heater, conditioner, Efficient water heater, conditioner, Efficient water heater,
Efficient lighting systemEfficient lighting systemEfficient lighting systemEfficient lighting system
• Fuel cell systemFuel cell systemFuel cell systemFuel cell system
• Photovoltaic on the roofPhotovoltaic on the roofPhotovoltaic on the roofPhotovoltaic on the roof
Energy Efficiency Energy Efficiency Energy Efficiency Energy Efficiency
Imp. (EE)Imp. (EE)Imp. (EE)Imp. (EE)
Carbon Carbon Carbon Carbon
Intensity Imp. (CI)Intensity Imp. (CI)Intensity Imp. (CI)Intensity Imp. (CI)
Trans
Trans
Trans
Trans-- --
portation
portation
portation
portation
• Intensive landIntensive landIntensive landIntensive land----use, Concentrated urban functionuse, Concentrated urban functionuse, Concentrated urban functionuse, Concentrated urban function
• Public transportation systemPublic transportation systemPublic transportation systemPublic transportation system
Reduction of service Reduction of service Reduction of service Reduction of service
demands (SD)demands (SD)demands (SD)demands (SD)
• MotorMotorMotorMotor----driven mobiles: Electric battery vehicles, Fuel driven mobiles: Electric battery vehicles, Fuel driven mobiles: Electric battery vehicles, Fuel driven mobiles: Electric battery vehicles, Fuel
cell battery vehiclescell battery vehiclescell battery vehiclescell battery vehicles
EE & CIEE & CIEE & CIEE & CI
Energy
Energy
Energy
Energy
Transform
ation
Transform
ation
Transform
ation
Transform
ation
• Nuclear energyNuclear energyNuclear energyNuclear energy
• Use of electricity in night time, Electric storageUse of electricity in night time, Electric storageUse of electricity in night time, Electric storageUse of electricity in night time, Electric storage
• Hydrogen supply systemHydrogen supply systemHydrogen supply systemHydrogen supply system
Carbon Intensity Carbon Intensity Carbon Intensity Carbon Intensity
Imp. (CI)Imp. (CI)Imp. (CI)Imp. (CI)
• Advanced fossil fueled plants + CCSAdvanced fossil fueled plants + CCSAdvanced fossil fueled plants + CCSAdvanced fossil fueled plants + CCS
• Hydrogen supply using fossil fuel + CCSHydrogen supply using fossil fuel + CCSHydrogen supply using fossil fuel + CCSHydrogen supply using fossil fuel + CCS
Carbon Capture and Carbon Capture and Carbon Capture and Carbon Capture and
Storage (CCS)Storage (CCS)Storage (CCS)Storage (CCS)
CO
2em
issio
ns in 2
050
CO
2e
mis
sio
ns in
20
00
EE & CI
73
CCS
42
CO
2re
ductions in
energ
y
transfo
rmation s
ecto
r(MtCR
ed
uctio
n o
f C
O
73
42
Step4
em
issio
ns in
20
00
CO
2re
ductions in
energ
y
end-u
se s
ecto
r(MtC)
Re
du
ctio
ns o
f C
O2
em
issio
n (
MtC)
Main factors to reduce COMain factors to reduce COMain factors to reduce COMain factors to reduce CO2222 emissionsemissionsemissionsemissions Factors Class.Factors Class.Factors Class.Factors Class.
Society
Society
Society
Society
• Reduction of final demand by material saturationReduction of final demand by material saturationReduction of final demand by material saturationReduction of final demand by material saturation
• Reduction of raw material productionReduction of raw material productionReduction of raw material productionReduction of raw material production
• Decrease of population and number of householdsDecrease of population and number of householdsDecrease of population and number of householdsDecrease of population and number of households
Demand growth by Demand growth by Demand growth by Demand growth by
activity level changeactivity level changeactivity level changeactivity level change
Industrial
Industrial
Industrial
Industrial
• Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.Energy efficient improvement of furnace and motor etc.
Energy Efficiency Energy Efficiency Energy Efficiency Energy Efficiency
Imp. (EE)Imp. (EE)Imp. (EE)Imp. (EE)
• Increase of Fuel switching from coal and oil to natural Increase of Fuel switching from coal and oil to natural Increase of Fuel switching from coal and oil to natural Increase of Fuel switching from coal and oil to natural
gas and biomassgas and biomassgas and biomassgas and biomass
Carbon Intensity Carbon Intensity Carbon Intensity Carbon Intensity
Imp. (CI)Imp. (CI)Imp. (CI)Imp. (CI)
• High insulation dwelling and buildingHigh insulation dwelling and buildingHigh insulation dwelling and buildingHigh insulation dwelling and building
• EcoEcoEcoEco----life navigation systemlife navigation systemlife navigation systemlife navigation system
Reduction of service Reduction of service Reduction of service Reduction of service
demands (SD)demands (SD)demands (SD)demands (SD)
109
19
18
21
32
23
7
Demand reductionsby activity level
change10
SD25
EE
53
CI
GHG 70% reduction in 2050 Scenario B: Slow-nature oriented Society
Demand side energy -45% + Low carbonization of primary energy
CO
2em
issio
ns in 2
050
CO
2e
mis
sio
ns in
20
00
CO end
CO
2re
ductions in
energ
y
transfo
rmation s
ecto
r(MtC)Re
du
ctio
ns o
f C
O
Residential and com
mercial
Residential and com
mercial
Residential and com
mercial
Residential and com
mercial
• EcoEcoEcoEco----life navigation systemlife navigation systemlife navigation systemlife navigation system demands (SD)demands (SD)demands (SD)demands (SD)
• Efficient airEfficient airEfficient airEfficient air----conditioner, Efficient water heater, Efficient conditioner, Efficient water heater, Efficient conditioner, Efficient water heater, Efficient conditioner, Efficient water heater, Efficient
lighting systemlighting systemlighting systemlighting system
Energy Efficiency Energy Efficiency Energy Efficiency Energy Efficiency
Imp. (EE)Imp. (EE)Imp. (EE)Imp. (EE)
• Photovoltaic on the roofPhotovoltaic on the roofPhotovoltaic on the roofPhotovoltaic on the roof
• Expanding biomass energy use in homeExpanding biomass energy use in homeExpanding biomass energy use in homeExpanding biomass energy use in home
• Diffusion of solar water heatingDiffusion of solar water heatingDiffusion of solar water heatingDiffusion of solar water heating
Carbon Intensity Carbon Intensity Carbon Intensity Carbon Intensity
Imp. (CI)Imp. (CI)Imp. (CI)Imp. (CI)
• Shortening trip distances for commuting through Shortening trip distances for commuting through Shortening trip distances for commuting through Shortening trip distances for commuting through
intensive land useintensive land useintensive land useintensive land use
• Infrastructure for pedestrians and bicycle riders Infrastructure for pedestrians and bicycle riders Infrastructure for pedestrians and bicycle riders Infrastructure for pedestrians and bicycle riders
(sidewalk, bikeway, cycle parking)(sidewalk, bikeway, cycle parking)(sidewalk, bikeway, cycle parking)(sidewalk, bikeway, cycle parking)
Reduction of service Reduction of service Reduction of service Reduction of service
demands (SD)demands (SD)demands (SD)demands (SD)
Trans
Trans
Trans
Trans-- --
portation
portation
portation
portation
• BiomassBiomassBiomassBiomass----hybrid engine vehiclehybrid engine vehiclehybrid engine vehiclehybrid engine vehicle EE and CIEE and CIEE and CIEE and CI
• Expanding share of both advanced gas combined cycle Expanding share of both advanced gas combined cycle Expanding share of both advanced gas combined cycle Expanding share of both advanced gas combined cycle
and biomass generationand biomass generationand biomass generationand biomass generation
Carbon Intensity Carbon Intensity Carbon Intensity Carbon Intensity
Imp. (CI)Imp. (CI)Imp. (CI)Imp. (CI)
23
28
55
CI
79
EE & CI
55
Step4
Residential sector
CO2 reduction potential: 50%
109
3 43 4
50
60
70
En
erg
y d
em
an
ds (
Mto
e)
Change of numbers of
householdsIncrease of service
demand per householdDecrease of service
demand per household
Improvement of energy
efficiencyHi-Insulated housingSocial
Innovationsイメージを表示できません。メモリ不足のためにイメージを開くことができないか、イメージが破損している可能性があります。コンピュータを再起動して再度ファイルを開いてください。それでも赤い x が表示される場合は、イメージを削除して挿入してください。
1723
0
10
20
30
40
2000 2050A 2050B
En
erg
y d
em
an
ds (
Mto
e)
efficiencyElectricity
H2
Solar
Biomass
Gas
Oil
Energy demands in
2000
Energy Effiency
Innovations
Technological
Innovations
Step4
Some results of India’s road
passenger transport sector
analysis by Strategic Databaseanalysis by Strategic Database
India’s Road Passenger Transport:
Demand scenario
Factors considered implicitly for projecting road
passenger demands in Strategic Database
(SDB) and AIM/Enduse:
• GDP/capita growth
• Urbanization
• Improvement of roads/flyovers in cities
India’s Road Passenger Transport:
Demand scenario in SDB
Private 4-wheeler vehicles (Billion-Prs-km)
600
800
1000
1200
1400
1600
1800
2000
イメージを表示できません。メモリ不足のためにイメージを開くことができないか、イメージが破損している可能性があります。コンピュータを再起動して再度ファイルを開いてください。それでも赤い x が表示される場合は、イメージを削除して挿入してください。CARG: 4.9% CARG: 3.8%Mainly
Urban and
Semi-urban
modes
0
200
400
2000 2010 2020 2030 2040 2050イメージを表示できません。メモリ不足のためにイメージを開くことができないか、イメージが破損している可能性があります。コンピュータを再起動して再度ファイルを開いてください。それでも赤い x が表示される場合は、イメージを削除して挿入してください。 イメージを表示できません。メモリ不足のためにイメージを開くことができないか、イメージが破損している可能性があります。コンピュータを再起動して再度ファイルを開いてください。それでも赤い x が表示される場合は、イメージを削除して挿入してください。CARG: 2.7% CARG: 3.5%
Urban,
Semi-urban
and Rural
modes
India’s Road Passenger Transport:
Innovative options considered in SDB
Technological Options:
• Electric vehicles
• Hybrid vehicles
• Biodiesel• Biodiesel
• Ethanol(Note: above technological options also require institutional/management interventions,
e.g. to set up infrastructure for plug-in points; rural infrastructure for sourcing and
transportation of biomass from farmlands to processing facilities, etc.)
Management Option:
• Improved management and countdown timer at traffic
signals and railway crossings
Electric vehicle
Innovations and Prospect for Electric
Vehicles in India:
•Reva Electric Car Company, headquartered in Bangalore, has designed a low-price electricity driven small car
•Cost of Indian electric car is low even at low volume production due to several product and process innovations like running chassis platform, body panel technology, process modifications to suit supplier
1: Motor
2: Powerpack
3: Charger
4: Controller
5: Energy Management System
technology, process modifications to suit supplier capabilities in India (for example, one-piece rotationally molded bumpers), and computerized energy management and diagnostics system
• Currently the car has 80 km range (on a single charge) and therefore confined to markets in big cities where private charging facilities are available
•For expansion of markets for such electric cars in the future, infrastructure of plug-in points needs to be developed. If such an infrastructure is developed and the government provides incentives to customers in initial stage of market penetration, then electric vehicles have a good market potential in urban India.
Biofuels
Innovations and Prospect for Biofuels in
India:
•The Indian government is in the process of setting up a regulatory regime to promote biodiesel and ethanol
•Out of three possible biodiesel blends – B5, B20 and B100 – B5 and B20 may be used in the initial periods of regulatory introduction but the higher blends like B100 will begin to enter as markets for producing and distributing biodiesel become competitive with distributing biodiesel become competitive with experience
•5% blend of ethanol with gasoline (E5) is being produced on pilot scale in some states; There are plans of producing higher blends like E15, E24, E85 and E100 in the future
•Several private companies including the largest energy sector firm – Reliance Industries Limited – have entered biodiesel and ethanol sector. Reliance has set up a pilot scale collection and processing (transesterification) facilities in the state of Andhra Pradesh for producing biodiesel. Some farmers gave begun to cultivate biodiesel crops like Jetropha and Pongamia
Jetropha – a widely available
source of biodiesel in India
Countdown Timers at Urban Traffic
SignalsProspect for Traffic Light Countdown
Timers in India:
•Countdown timers at traffic lights have been installed at major traffic junctions in a few big Indian cities like Mumbai and Bangalore
•This system indicates the time remaining for light to switch colour; It helps drivers to avoid unnecessary idling while waiting at traffic signals
•Expanding this system to all traffic signals in all cities and introducing other similar practices for improving traffic signal management can help reduce fuel combustion during the most inefficient stage (i.e. idling).
Timer at traffic light
SDB for Road Passenger Transport Sector in India: Scenarios
• RF: BaU or Reference Scenario
• CM-1: Higher Diffusion of Electric and Hybrid Vehicles, with – share of electric vehicles increasing from 5% in 2020 to 20% in 2050, and – share of hybrid vehicles increasing from 10% in 2020 to 25% in 2050.
• CM-2: Higher Diffusion of Biofuels, with– ethanol-gasoline 15% blend (E15) used in 5% of gasoline vehicles in 2020 and remaining at
10% from 2030 onwards,– ethanol-gasoline 15% blend (E15) used in 5% of gasoline vehicles in 2020 and remaining at
10% from 2030 onwards,– pure ethanol (E100) used in 5% of gasoline vehicles in 2020, increasing to 30% in 2050,– biodiesel-diesel 20% blend (B20) used in 5% of diesel vehicles in 2020 and remaining at
10% from 2030 onwards, and– pure biodiesel (B100) used in 5% of diesel vehicles in 2020, increasing to 30% in 2050.
• CM-3: Improved Management and Countdown Timer at Traffic Signals, with– 5% resultant reduction in fuel use, and covering 20% of all vehicles in 2020, increasing to
80% in 2050.
• CM-4: Simultaneous introduction of all measures outlined in scenarios CM-1, CM-2 and CM-3.
Results of SDB for Road Passenger Transport in India
40
60
80
100
120
140
160
180
t-CO2 RF
CM-1
CM-2
CM-3
CM-4
50
100
150
200
250
kg-SO2 RF
CM-1
CM-2
CM-3
CM-4
0
20
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
CO2 emissions:
•Maximum reduction (30% over 50 years) occurs in Biofuels Scenerio (CM-2)
•Simultaneous introduction of all measures leads to 40.3% reduction over 50 years and 20.6% in 2050
SO2 emissions:
•Maximum reduction (5% over 50 years) occurs in both Biofuels Scenario (CM-2) and Electric/Hybrid Vehicles Scenerio (CM-1)
•Simultaneous introduction of all measures leads to 24.2% reduction over 50 years and 9.6% in 2050
Co-benefits: Very high correlation between CO2 and SO2 reduction is observed in scenarios
with Electric/Hybrid Vehicles Introduction (CM-1) and Traffic Signal Management (CM-3)
0
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Results of SDB for Road Passenger Transport in India
•In Biofuels Scenario (CM-2), CO2
emissions reduction is achieved mainly
by decline in carbon intensity of energy
use (33.6% decline from 2000 to 2050,
and by 30% in 2050 as compared to BaU)
40.00
50.00
60.00
70.00
80.00
kg
-CO
2/G
J
RF
CM-1
CM-2
CM-3
•In Electric/Hybrid Vehicles Scenario
(CM-1), CO2 emissions reduction is
achieved mainly by decline in energy use
(by 20% in 2050 as compared to BaU),
and carbon intensity of energy use does
not change much (due to carbon intensity
of electricity generation)Carbon intensity of energy use in road
passenger transport in India
0.00
10.00
20.00
30.00
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
kg
-CO
2/G
J
CM-3
CM-4
UNEP/GEO4 scenario analysis on air pollution
The input of the model:
�Basic data of cities
such as urban area,
population and income
�Traffic data including
road network length, car
Structure of a model on urban air pollutant emissions from
passenger transportation
Total daily trip per
capita
GDP Population Urban area
GDP per capita
Population density
Passenger carownership
Length of roadper pass. car
Ratio of rail network length
to road
Length of roadper capita
Number of trips
Mean temperature
Share of vehicle technology
Passenger car
Motorcycle
Road network
Emission factor
0.57
-0.14
PC:0.51MC:-0.06BS:-0.31
RL:0.20PC: 0.24
MC: 0.08-
0.42
-0.33
0.37
0.23 0.18-0.53
AIM, NIES
road network length, car
ownership
Non motorized
Passenger car(PC)
Motorcycle(MC)
Rail(RL)
Bus(BS)
Passenger car
Motorcycle
Bus
Rail
Motorcycle
Passengercar
Bus
Rail
Motorcycleownership
Ratio of trip share of rail on pass. car
Number of bus
per capita
Number of rail per capita
Modal share of trip Trip distanceNumber of personson unit of vehicle
Traffic volumein vehicle-km
Passenger car
Bus
Rail
Air pollutantemission
NOx
CO
CO 2
PM
Motorcycle
Passengercar
Bus
Rail
Road network speed
RL:0.20
PC:0.03MC:0.60BS:- 0.10RL:-0.30
MC: 0.08-BS:0.17
RL:0.34 PC:0.30MC:-0.06BS:-0.22RL:-0.26
-0.54
0.21
-0.15
0.23-0.34
0.32
-0.61
0.31
0.65
-0.70
0.74
-0.52
0.75
0.81
069
Endogenousvariable
Exogenousvariable
Definitional relation
Estimated relation
Standardizedcoefficient
Cities examined
Markets First
(MF)
Policy First
(PF)
Security First
(SeF)
Sustainability First
(SuF)
City GDP per Capita*1
(annual change)
2.23%/year
(Average of all
cities)
2.31%/year
(Average of all
cities)
1.30%/year
(Average of all
cities)
1.96%/year
(Average of all
cities)
City population
(annual change)
0.75%/year
(Average of all
cities)
0.67%/year
(Average of all
cities)
0.76%/yaer
(Average of all
cities)
0.59%/year
(Average of all
cities)
Scenario assumptions in UNEP/GEO4
AIM, NIES
Population density Low density Low density Low density High density
Urban area Spread out Spread out Spread out Compact
Emission regulationNo additional
regulation
Gradually
strengthened
No additional
regulation
Gradually
strengthened
Advanced technologies Introduced Introduced Not introduced Not introduced
*1: Based on GEO country scenario
*2: Based on GEO country scenario and UN urbanization prospects
Scenario analysis for UNEP/GEO4
0
500
1000
1500
2000
2500
3000
3500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year
CO
2 e
mis
sio
n (
kg/p
ers
on) MF0
500
1000
1500
2000
2500
3000
3500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year
CO
2 e
mis
sio
n (
kg/p
ers
on) PF
Market First: Air pollutants emissions decrease in long-term future because of wide diffusion of advanced technologies such as hybrid vehicle and fuel cell vehicle.Policy First: Air pollutants emissions sharply decline with gradually strengthened emission control.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year
CO
2 e
mis
sio
n (
kg/p
ers
on) SeF
0
500
1000
1500
2000
2500
3000
3500
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
Year
CO
2 e
mis
sio
n (
kg
/pe
rso
n) SuFstrengthened emission control.
Security First:Progress made in controlling pollution at the beginning of the century is reversed around 2015 as the volume of air pollution begin to riseSustainability First: Air pollutant emission sharply decline with gradually strengthened emission control and effective transportation demand management at city level
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