Asia Pacific Integrated Model (AIM) - Urbanization in the … · · 2016-06-22Relationship bottomRelationship bottom-up and top up and top-down in AIMdown in AIM Top-down Environment

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 (%

)


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 (%

)


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

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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


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



• 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


• 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.



• 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



• 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



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



• 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



• 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)



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



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


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


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

Thank you !Thank you !

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