13. AIM/Trend: Policy Interface
Junichi Fujino1, Shigekazu Matsui2, Yuzuru Matsuoka3, and Mikiko Kainuma1
Summary. The model for assessing the future environmental loads based on thepast socio-economic trends has been developed. By using this model, theenvironmental trends until 2032 in the Asia-Pacific 42 countries have been
estimated. This AIM/Trend model has characteristics of econometric model andcommunication tool to enhance discussions and search for countermeasures byenvisaging environmental conditions in a consistent way. Results of AIM/Trend
model show that Asia-Pacific countries have a wide range of economy, energy,
and environment conditions. It makes the country-level study in this region more
important to deal with the diversity of Asia-Pacific countries.

13.1 Introduction
The Asia-Pacific region is expected to lead global economy due to its rapid
increase of population and economic growth in the earlier of 21st century. On theother hand, it is said this economic development will cause significant damage on
regional and global environment without any appropriate measures.
Though the study on its economy, energy, and environment is becoming more
important, the variety of Asia-Pacific culture, society, and natural resources in acountry-level makes it difficult to understand policy and future projection. It isnecessary to develop the model that evaluates the effects of each country’s policy
to country-level, regional, and global environment. It is also important that eachcountry’s policymakers who know well the country can use the model easily. Our
team has been developing the model for assessing the future environmental loads
based on the past socio-economic trends and future scenarios. By using thismodel, the environmental trends until 2032 in the Asia-Pacific countries have been
estimated (Fig.1).
AIM/Trend model is built as a simple econometric model and written in ATPL
(AIM/Trend Program Language) using VBA in Microsoft Excel. The user canchange the model structure and make sensitivity analysis. Results of AIM/Trend
model are used as Asia-Pacific scenario in UNEP/GEO-3 (UNEP 2002).

1

National Institute for Environmental Studies, Tsukuba 305-8506, Japan
Fuji Research Institute Corporation, Tokyo 101-8403 Japan
3
Kyoto University, Kyoto 606-8501, Japan2

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Scenario generation & AIM/Trend
past economic trend
past environmental
trend

Input

China India Korea Japan
ThailandDiscussion

Renewable Energy
energyPopulation Growth
growthEconomic Growth
growthAIMAIM/
TREND
Trend

OutputEnergy Efficiency
efficiency
Improvement
improvements

Narrative Scenarios
scenarios
Asian Quantitative
quantitative
FutureScenarios
scenarios
economic trend

Fe
ed
b

Future estimates
Estimatesack

Futureeconomic trend

Futureenvironmental trend

Futureenvironmental trend

Fig. 1. Concept of AIM/Trend model (see color plates)

13.2 Structure of AIM/Trend Model
13.2.1 Framework
AIM/Trend model is an econometric model to estimates future conditions of
economy, energy, and environment (Fujino 2002). It calculates the relationships
between each parameter by regression method and extrapolates those relationships
for the future projection. It makes simulations of energy supply and demand, GHG
emissions, waste emission, water supply and demand, and so on by setting basic
data –population GDP, GDP per capita, GDP share, etc. – as driving forces(Fig.2). In this chapter, the module of energy supply and demand is mainly
focused.
Energy Supply and Demand

Population
EconomyGHG Emissions

Fig. 2. Framework of AIM/Trend model

Driving
ForcesWater Supply and Demand
Wastes13. AIM/Trend: Policy Interface

219

Target countries
This model covers 42 countries in the Asia-Pacific region shown in Table 1. They
are divided into two categories: Model A and Model B. Model A uses detailedenergy data, IEA energy statistics data. Model B uses simple energy data, UN
energy data. The countries can be grouped in five regions: South Asia, Southeast
Asia, East Asia, Central Asia, and ANZ and South Pacific.
Table 1. Target countries
Detailed Data (Model A)
CodeCountry

Simple Data (Model B)
Group
SA

Code

1

BGD

Bangladesh

1

2

IND

India

SA

2

3
4

IRN
LKA

Iran
Sri Lanka

SA
SA

3
4

5
6

NPL
PAK

Nepal
Pakistan

SA
SA

5
6

7
8

IDN
MMR

Indonesia
Myanmar

SEA
SEA

9
10

MYS
PHL

Malaysia
Philippines

11
12

SGP
THA

13
14
15
16

AFG

CountryGroup

Afghanistan

SA

BTN

Bhutan

SA

MDV
BRN

Maldives
Brunei

SA
SEA

KHM
LAO

Cambodia
Laos

SEA
SEA

7
8

MNG
FJI

Mongolia
Fiji

EA
SP

SEA
SEA

9
10

KIR
NRU

Kiribati
Nauru

SP
SP

Singapore
Thailand

SEA
SEA

11
12

PLW
PNG

Palau
Papua New Guinea

SP
SP

VNM
CHN

Vietnam
China

SEA
EA

13
14

PYF
SLB

French Polynesia
Solomon Islands

SP
SP

JPN
KOR

Japan
Korea, Rep

EA
EA

15
16

TON
VUT

Tonga
Vanuatu

SP
SP

17

PRK

Korea, Dem

EA

17

WSM

Samoa

SP

18
19

TWN
KAZ

TaiwanKazakhstan

EA
CA

20

KGZ

Kyrgyz Republic

CA

Code

Group

21

TJK

Tajikistan

CA

SASouth Asia

22
23

TKM
UZB

Turkmenistan
Uzbekistan

CA
CA

SEA
EA

Southeast Asia
East Asia

24 AUS Australia
SP
CA
Central Asia
25 NZL New ZealandSP
SP
ANZ and South PacificNote: Though it has IEA energy statistics data, Brunei is treated in Model B because of its
special structure of energy supply and demand.

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Fujino, J. et al.

Simulation period
The Johannesburg summit was held at August 2002. The target year is set as 2032,
30 years after the summit.
Target indices
Following indices are selected and will be evaluated by AIM/Trend model (sign“*” means the element under consideration):
•
•
•
•
•
•
•
•
•
•

Population: population, rate of urbanization
Economy: GDP (growth rate, per capita), GDP share (agriculture, industry,
service, PFC (private final consumption), car numbers
Energy: primary energy supply by fuel, final energy demand by fuel andsector, energy plant, economic intensity, carbon intensity
GHG emissions: CO2, SOx, NOx, CH4, N2O, CO
Waste: waste emission, landfill*, recovery of waste*
Water: withdrawal, consumption (agriculture, industry, domestic), populationin water stress*
Food and Agriculture*: average daily consumption, vegetable foodconsumption, animal food consumption, fraction of meat from feedlots, fish
production, crop production, feed production, nitrogen fertilizer consumption
Land use*: crop land, irrigated cropland, potential cultivable land, mature
forest, growing forest, pasture, protected, other land
Human Health*: SPM (PM10, PM2.5)
Biodiversity*: species, degree of threat to biological diversity, area of habitat
remaining

13.2.2 Model structure
AIM/Trend model consists of Modal A with detailed energy data and Model B
with simple energy data. The model structures of Model A and Model B are
explained in following sections.
Detailed data model (Model A)
Model A covers 25 countries that have the IEA energy statistics. The calculation
flow of Model A focused on energy is shown in Fig. 3. Model A is built up by
following order: final energy demand, energy conversion, and primary energy.
(1) Energy classification
Following 10 energies are treated in this Model A: COL (coal), OIL (crude oil andpetroleum products), GAS (gas), CRW (combustible renewables and waste), NUC

13. AIM/Trend: Policy Interface
Regression
Assumption

Regression
Assumption

Driving Force
Population
GDP*IVASHR*AVASHR*PFCSHRCARCAP

Elasticity
IND: IVA
TPR: CAR
TPO: GDP
AGR: AVAOTH: PFC

221

AEEI
Final Energy Demand
Regression
IND, TPR, TPO, AGR, OTH Assumption
Electricity ShareHeat Share
IND, TPR, TPO, AGR, OTH

Final Energy Demand:
Final Energy Demand:
Electricity and Heat
excluding Electricity and Heat
IND, TPR, TPO, AGR, OTH
IND, TPR, TPO, AGR, OTH
Assumed generation
Assumed share
Efficiency
Assumed shareefficiency
Scenario
Primary Energy Supply
Input for Electricity plant,
COL, OIL, GAS, CRW
Heat plant, and CHP
NUC, HYD, GEO, NEW
IND: Industry
TPR: Road transport
TPO: Other transport
AGR: Agriculture
OTH: Other

IVA: Industry Value Added
AVA: Agriculture Value Added
PFC: Private Final Consumption
CARCAP: Car numbers per capita

NUC: Nuclear
COL: Coal
CRW: Combustible HYD: Hydro power
and renewables GEO: Geothermal
NEW: Wind, PV, and so on

Fig. 3. Calculation flow of detailed data model (Model A)(nuclear), HYD (hydro power), GEO (geothermal), NEW (wind, PV, and so on),
HET (heat), and ELE (electricity).
(2) Energy sector classification
Energy conversion sector has following 5 sub-sectors: ELP (power generation),
HTP (heat supply system), CHP (combined heat and power plant), DST(distribution of energy), and TFM (transformation). The final energy demand
sector has following 6 sectors: IND (industry), TPR (transport on road), TPO(other transport), AGR (agriculture), OTH (other), and NEU (non-energy use).
(3) Estimation of final energy demand
Final energy demand except NEU is described as the function of driving force(DRV). Driving force of IND, TPR, TPO, AGR, and OTH is basically defined asindustrial value-added (IVA), numbers of car (CAR), GDP, agricultural valueadded (AVA) and private final consumption expenditure (PFC), respectively.
Elasticity between each final energy demand and driving force is calculated byregression analysis using historical data. If these data are not available, GDP canbe used for the driving force. Following equation is assumed:

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Fujino, J. et al.

TFE i (t ) = Ai (t ) × TFE i (t 0 ) × {DRV i (t ) / DRV i (t 0 )}

ELS i

(1)

Ai (t ) = (1 − AEEI i (t ) / 100) (t −t0 )where,TFE i (t ) : total final energy demand for sector i , time period t
DRV i (t ) : driving force for sector i, time period t
ELS i : elasticity for sector i
AEEI i (t ) : autonomous energy efficiency improvement for sector i, time period t
i : final energy demand sector, i ={IND,TPR,TPO,AGR, OTH}t : simulation time period, t0: initial time period
NEU (Non-energy use demand) is estimated from the following equations. It is
supposed that NEU consists of oil by historical data.
NEU (t ) = FE IND ,OIL (t ) /FE IND , OIL (t 0 ) × NEU (t 0 )(2)where,
FE i , e (t ) : final energy demand for sector i , energy e , time period te : energy, e ={OIL, COL, GAS, CRW, NUC, HYD, GEO, NEW, ELC, HET}
(4) Estimation of driving force
To estimate the trajectory of driving force (IND, TPR, TPO, AGR, and OTH),IVASHR (GDP share of IVA), CARCPT (car numbers per capita), PFCSHR(GDP share of PFC), and AVASHR (GDP share of AVA) are estimated from
regression analysis using GDP per capita for independent variables. To estimate
IVASHR in other way SVASHR (GDP share of SVA (service value added) is also
estimated. The relationships between AVASHR, SVASHR and GDP per capita
using South Asia, East Asia, and South East Asia data from 1980 to 1995 of
HYDE/RIVM are shown in Figs. 4 and 5 (Klein, et al. 1995).
(5) Share of energy in final energy demand
Final energy demand consists of electricity, heat, and others.
a) Share of electricity: The share of electricity in each final energy sector isestimated by using the regression analysis using driving force for independent
variable.
b) Share of heat: The share of heat is fixed at that in the latest data existed year inall sectors.
c) Share of other energy: The share of fossil fuel energy is estimated by using theregression analysis using driving force for independent variable.
(6) Fuel share and energy efficiency in energy conversion sector
CHP and HTP are only used in specific countries in the Asia-Pacific region. Shareof fuel input into HTP and share of fossil fuel input into CHP are assumed to be13. AIM/Trend: Policy Interface

AVA share of GDP (%)

80%

223

y = 9.8494×x -0.5724
R 2 = 0.9778

70%
60%
50%
40%
30%
20%
10%
0%
0

500

1000

1500

2000

GDP/cap [1995US$/cap]

Fig. 4. Relationship between GDP per capita and AVA share of GDP

SVA share of GDP (%)

50%
40%
30%

y = 0.0618~ ln(x) + 0.0108
2
R = 0.8363

20%
10%
0%
0

500

1000

1500

2000

GDP/cap [1995US$/cap]

Fig. 5. Relationship between GDP per capita and SVA share of GDP

constant. Share of fossil fuel input into ELP is calculated by regression analysis.
Non-fossil fuel input into CHP and ELP are depended on scenario assumption.
The electricity generation efficiency of ELP and CHP with COL, OIL, GAS, and
CRW is assumed by using exogenous energy efficiency improvement parameter.
The generation efficiency of NUC, HYD, GEO, and NEW is fixed as IEA’s
definitions (NUC: 0.33, HYD: 1.0, GEO. 0.1, NEW: 0.1). The heat generation
efficiency is assumed to be fixed as that in initial simulation period.
(7) Total primary energy supply
Primary energy supply is calculated with final energy demand, energy conversion
process, and distribution loss. Distribution loss of fossil fuel, electricity, and heatis assumed to be constant as that in initial simulation period.

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Fujino, J. et al.

(8) GHG (CO2, NOX, SOX, CH4, N2O, CO) emissions
Energy related GHG emissions are calculated by simulation result and assumed
emission factor. NOx and SOx emissions are assumed to be reduced according toincrease of GDP per capita, known as Kuznets curve.
Simple data model (Model B)
For the several countries, that are not available of IEA energy statistics data,
Model B is constructed for estimation of environmental loads in the future.
UN Energy Statistical Yearbook 2000 (UN 2000) is used as energy data.
Energies are categorized as Liquids, Solids, Gas, Electricity and Traditional
fuelwood. Liquids, Solids, and Gas correspond to OIL, COL, and GAS in Model
A respectively. Electricity (ELC) consists of supply from geothermal, hydro,
nuclear, solar, tide, wind, wave, import and export. Traditional fuelwood (TRF)
corresponds to CRW in Model A. Each energy supply is assumed to be decided by
GDP and AEEI.
This can be given by the following equation:
PE e (t ) = Ae (t ) × PE e (t 0 ) × {GDP(t ) /GDP (t 0 )}Ae (t ) = (1 − AEEI e (t ) / 100) (t −t0 )(3)PE e (t ) : primary energy supply for energy e , time period t
GDP(t ) : GDP, time period t
AEEI e (t ) : autonomous energy efficiency improvement for energy e , time periodte : energy, e ={OIL, COL, GAS, ELC, TRF}
t : simulation time period, t0: initial time period
Model data
Following data are mainly used:••••

Population: historical data (WB 2000), projection (UN 1998)
GDP: historical data (WB 1999), projection (IMF 2001) (EIA/DOE 2001),
IPCC/SRES scenario (IPCC 2000)
Energy: for Model A (IEA 2001), for Model B (UN 2000)
Emission factor: (IPCC 1996)

13. AIM/Trend: Policy Interface

225

13.2.3 Model interface
AIM/Trend model is executed with ATPL (AIM/Trend Program Language) that isbuilt with VBA of Microsoft Excel. Major commands consist of load, save, future
parameter setting, future projection, format and regression. An illustration of
AIM/Trend detailed data model (Model A) interface is shown in Fig. 6. Users canchoose country and select cases for load and save. Clickable buttons such as
Projection All, and Pam Set All are designed to perform simulations effectively.
They are written in ATPL and users can write or change programs for their own
purpose.

File Operation
Load DataJapan

Worksheet OperationCaseload M F
save M F

show sheets

Save datadelete sheets

Future Projection
Projection All

Write Output to"Pro" Sheet

Projection ofEnergy Use

Projection of
Water Use

Future Parameter

Population, GDP
Scenario Setting

Pam Set All

Pam

Ene

Population, GDP
Scenario Setting

Projection ofEnergy Share

Projection ofDriving Force

Energy scenario
Setting

Projection of
Final Enegy Demand

Energy related
Parameter Setting

FormatPamhide sheets

Ene

Fig. 6. AIM/Trend interface for detailed data model (Model A)226

Fujino, J. et al.

13.3 Middle-term Economy, Energy, Environment
Scenario Simulation in the Asia-Pacific Region
13.3.1 Scenario study
UNEP published GEO-3: Global Environment Outlook 3 (UNEP 2002) before
Johannesburg Summit. It focused on future scenarios from 2002 to 2032. The
results of AIM/Trend model were adopted as the future projections in the AsiaPacific region. GEO-3 prepared following 4 scenarios: Market First scenario,
Policy First scenario, Security First scenario, and Sustainability First scenario. The
Market First scenario envisages a world in which market-driven developments
coverage on the values and expectations that prevail in industrialized countries; In
the Policy First world, strong actions are undertaken by governments in an attempt
to reach specific social and environmental goals; The Security First scenario
assumes a world of great disparities, where inequality and conflict prevail, brought
about by socio-economic and environmental stresses; and Sustainability First
pictures a world in which a new development paradigm emerges in response to thechallenge of sustainability, supported by new, more equitable values andinstitutions.
To address GEO-3 scenarios with AIM/Trend model, several parameters (GDP,population, AEEI, energy conversion efficiency, non-fossil energy supply, GHG
emission control and so on) are set for each scenario.
13.3.2 Overview results
Figure 7 shows the historical data and future projection of major economy, energy,
and environment indices in China. GEO3/Sustainablity First scenario is used toestimate future conditions of environmental loads.
Population will reach 1,500 million around 2030, though rate of increase inpopulation will be decreasing annually. GDP will increase steadily and GDP per
capita will become $5,000 in 2030. Population and GDP assumptions are given as
scenario.
GDP share, which is driving force of final energy demand, is projected byregression. According to growth of GDP per capita, AVA share will decrease and
SVA share will increase. This is a common phenomenon observed in developed
countries. Though IVA share increased in the 4th quarter of 20th century, it will
be replaced by SVA share in 21st century.
Total final energy demand will increase due to the growth of driving forces. It
mainly consists of industry sector and other sectors. Total primary energy supply
will also increase and it will reach at around 2,500MTOE in 2030. equivalent to
30% of 1999 total primary energy supply in the world. It is mainly formed fromcoal, oil, and CRW. Though CO2 emissions will become close to 2 Gt-C in 2032,
its rate of increase will slow down because of introduction of nuclear andexpansion of natural gas. CO2 intensity will also decrease after 2015. Energy13. AIM/Trend: Policy Interface

227

2,000

TPE ,TFE (MTOE)

1,000

1,000

500

500
Year 2010

2030

TPE

TFE

CO2
CO2

TPE (total primary energy supply), TFE (total final energy demand)

2000
OTH

1500
1000

TPO

500

TPR

IND

IND
2030

0

25

50

0.8

20

40

CO2 intensity

3.0
2.0

0.6

Energy intensity

0.4

NOx emissons(Mt-NO2 )

1.0

4.0

CO2 intensity(t-C/TOE)

5.0

15

30

NOx intensity
10

20

SO2 intensity

Year

Fig. 7. Projection of each index for China

Year

2035

2030

2025

2020

2015

2010

2005

2000

1995

1990

2040

2030

0

2020

0

2010

0.0
2000

10

0.0
1990

5

1980

0.2
1970

1.0

(Mt-SO2 )Year

SO2 emissions

Year

Energy intensity(TOE/1000$/capita)

AGR

OTH

2025

NEW
GEO
HYD
NUC
CRW
GAS
OIL
COL

0
1990

2020

2030

2025

2020

2010

2005

2000

1,500

0
1970

SVASHR

COL

1,500

2,000

2030

CRW
OIL

2,000

2015

2010

IVASHR

3000
2500
2000
1500
1000
500
0

GDPCPT

2,500

1995

AVASHR

2030

GDP

3,000

Final energy demand bysector (MTOE)

1990 Year

1990 Year 2010

2030

2005

2010

Year

2000

1990

60
50
40
30
20
10
0
1970

1995

4,000

0
1970

0
1970

GDP share (%)

6,000

2010

400

8,000

CO2 emissions (Mt-C)

GDP (Bill.US$)

800

2015

Population (million)

1,200

Primary energy supply by
fuel (MTOE)

6
5
4
3
2
1
0

10,0001,600

GDP per capita(1000US$/cap)

intensity decreased after 1970 dramatically. This decreasing trend is projected tocontinue in future.
Though SO2 emissions will increase after 2000, but it will start to decrease after
2015. NOx emissions will also begin to decline around 2020. It is well-known as
Kuznets effect that air polluting sources such as SO2 and NOx will be reduced
according to economic growth. Kuznets effect is more pronounced and is
projected to occur earlier for SO2 emissions than for NOx emissions.

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Fujino, J. et al.

Figure 8 shows the projection of CO2 emissions per capita for each country in
GEO3/Sustainability First scenario. Countries are arranged in order of CO2
emissions per capita in 2032. China and India, which have larger population in
Asia-Pacific countries, will be located in the middle position in 2032. Singapore
and Brunei, which have special energy structure, will stay at larger CO2 per capita
emissions through 2032.
Asia-Pacific countries exhibit a wide range of CO2 per capita emissions. Thishighlights the importance of country-level study in this region.

CO2 emissions per capita(t-C/capita)
12

10

8

4

Laos

Solomon Islands

BhutanCambodia

2005

2015

Year

1995

2032

2025

0

Kiribati

2

Bangladesh
Vanuatu
Papua New Guinea
Pakistan
Nepal
MyanmarVietnam
Sri Lanka
Maldives
Samoa
Indonesia
Tajikistan
Philippines
India
Fiji
Kyrgyz Republic
French Polynesia
Tonga
China
ThailandIran
New Zealand
Uzbekistan
Mongolia
MalaysiaJapan
Turkmenistan
Nauru
TaiwanAustralia
Korea, Dem
Korea, Rep
Kazakhstan
Palau
Singapore
Brunei6Country

Fig. 8. Projection of CO2 emissions per capita for each country

13. AIM/Trend: Policy Interface

229

13.3.3 Energy related GHG emissions results
Energy related CO2 emissions
Figure 9 shows the energy related CO2 emissions in the Asia-Pacific region in thefour scenarios. Compared to Policy First and Sustainability First, emissions of
CO2 increase more rapidly in Markets First circumstances because of higheconomic growth. In Policy First, advanced technologies are introduced to reduce
CO2 emissions. Because a Sustainability First society shifts from conventional tosustainable lifestyles, CO2 emissions are somewhat mitigated. On the other hand a
Security First society holds on to technologies with low energy efficiency. CO2
emissions increase most rapidly in this scenario everywhere except in Central Asia
where low economic activities mitigate CO2 emissions vis-à-vis Markets First.
Figure 10 shows change in energy related CO2 emissions by 2032 relative to2002 in sub-regions of the Asia-Pacific region. China in the East Asia sub-region,
India in South Asia, and Kazakhstan in Central Asia experience maximum change
in CO2 emissions over the 30 year period while the ANZ and South Pacific subregion including Australia and New Zealand experiences least change in all the
scenarios. An interesting observation is that the ANZ and South Pacific sub-region
and Japan show a decline in CO2 emissions in the Policy First scenario. In all subregion but Central Asia, CO2 emissions are higher in the Security First scenario
than in the Markets First scenario for the reason mentioned in the previous
paragraph.
Energy related SO2 emissions
As with the CO2 emission factor, SO2 emission factor is set for each country.
Kuznets curve, which postulates an inverse U relationship between income leveland pollution, are assumed to deal with the change of emission reduction rateaccording to economic growth. In modeling this phenomenon, thresholds are set
as the function of GDP per capita and the emission reduction rate changes at thesethresholds.
SO2 emissions will increase most rapidly in the Security First scenario because
little money is invested to reduce SO2 emission in a low economic growth world(Fig.11). On the other hand, in the other scenarios, the increase of SO2 emissions
will be slow as measures are taken to avoid severe air pollution. This is especiallytrue in Policy First and Sustainability First where SO2 emissions will be controlled
more strictly. Due to the Kuznets effect, a downward trend is observed in PolicyFirst and Sustainability First scenarios from around 2015 while it is observed inMarkets First from around 2025. It is not observed in Security First scenario.
Results for changes in SO2 emissions between 2002 and 2032 (Fig.12) provide
some interesting observations. In the ANZ and South Pacific, SO2 emissions are
reduced by 40 to 50 percent between 2002 and 2032 in all scenarios. In modelingthe scenarios, it is assumed that all the industrialized countries would reduce theirSO2 emissions regardless of the scenario. Australia dominates emissions in the
ANZ and South Pacific and therefore the whole area appears to be decreasing its

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Fujino, J. et al.

8,000

Historical data
Market First
Policy First
Security First
Sustainability First

CO2 emissions (Mt-C)

7,000
6,000
5,000
4,000
3,000
2,000
1,000
0
1980

1990

2000

2010

2020

2030

Year

Fig. 9. Energy related CO2 emissions in the Asia-Pacific region (see color plates)

South Asia

MK: Market First
PO: Policy First

SC: Security First
SU: Sustainability First250

Afghanistan
BhutanIran
Nepal
Sri Lanka

Bangladesh
India
Maldives
PakistanSoutheast Asia

200
150

Brunei

Cambodia

Indonesia
Malaysia

Laos
MyanmarPhilippines
ThailandSingapore
Vietnam

East Asia
China
Japan
Mongolia

100

Korea, Dem
Korea, Rep
Taiwan

MK
PO
SC
SU

Central Asia

50

Kazakhstan
Tajikistan
Uzbekistan

Kyrgyz Republic
Turkmenistan

MK
PO
SC
SU

MK
PO
SC
SU

ANZ and South Pacific

MK
PO
SC
SU

0

MK
PO
SC
SU

Change in energy-related CO2 emissions
by 2032 relative to 2002 (%)

300

East Asia

Central Asia

-50
South Asia South East Asia

ANZ and
South Pacific

Australia
Fiji
Nauru
Papua New Guinea
Tonga
Samoa

New Zealand
Kiribati
Palau
French Polynesia
VanuatuSolomon Islands

Fig. 10. Energy related CO2 emissions in sub-regions of the Asia-Pacific region (see color
plates)13. AIM/Trend: Policy Interface

160

Historical data
Market First
Policy First
Security First
Sustainability First140
SO2 emissions (Mt-SO2)

231

120
100
80
60
40
20
0
1980

1990

2000

2010

2020

2030

Year

Fig. 11. Energy related SO2 emissions in the Asia-Pacific region (see color plates)

MK: Market First
PO: Policy First

300

SC: Security First
SU: Sustainability First

Afghanistan
BhutanIran
Nepal
Sri Lanka

Bangladesh
India
Maldives
PakistanSoutheast Asia

200

BruneiIndonesia
Malaysia

150

Philippines
Thailand
China
Japan
Mongolia

MK
PO
SC
SU

50

MK
PO
SC
SU

MK
PO
SC
SU

MK
PO
SC
SU

0
-50

Cambodia
Laos
MyanmarSingapore
Vietnam

East Asia

100

MK
PO
SC
SU

Change in energy-related SO2 emissions
by 2032 relative to 2002 (%)

250

South Asia

East Asia

Central Asia

Central Asia
Kazakhstan
Tajikistan
Uzbekistan

Kyrgyz Republic
Turkmenistan

ANZ and South Pacific-100
South Asia South East Asia

Korea, Dem
Korea, Rep
TaiwanANZ and
South Pacific

Australia
Fiji
Nauru
Papua New Guinea
Tonga
Samoa

New Zealand
Kiribati
Palau
French Polynesia
VanuatuSolomon Islands

Fig. 12. Energy related SO2 emissions in sub-regions of the Asia-Pacific region (see color
plates)

232

Fujino, J. et al.

SO2 emissions. In the East Asia sub-region, China’s emissions reduce at a slower
rate than those of Japan. As a result, the sub-region on the whole increases its
emissions in all scenarios except Policy First scenario. Emissions also reduce forsome countries in Southeast Asia and East Asia sub-regions under Policy First andSustainability First scenarios. In all the regions except ANZ and South Pacificemissions increase most rapidly in Security First scenario because of relatively
low investment in emissions reduction, especially in India and China where theemissions more than double over the 30 year period.

13.4 Concluding Remarks
In this chapter, the framework of AIM/Trend model and its results are presented.
This model has been developed to assess the future environmental loads based on
the past socio-economic trends and scenarios though 2032 in the Asia-Pacific 42
countries of the Asia-Pacific region. It is built as a simple econometric model andwritten in ATPL using VBA in Microsoft Excel. The user can change the model
structure and make sensitivity analysis easily. Results of AIM/Trend model showthat the Asia-Pacific region is going to experience a rapid growth in CO2emissions in all UNEP/GEO-3 scenarios, and Asia-Pacific countries have a wide
range of economy, energy, and environment conditions. It makes the country-levelstudy in this region more important to deal with the diversity of Asia-Pacificcountries. In future, AIM/Trend model will be improved to make people interested
in immediate actions to control the incidence of increased environmental loads on
air, land as well as water.

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