GHG Emissions and Climate Change- Trends and Mitigation Potentials -Summary
It has been acknowledged that climate change is one of the important issues facing humanity
in the present century and needs to be studied over a long-term horizon. The effects of climatechange are already visible in different parts of the world. Analysis of climate data observed inlast century and projected by climate models toward the end of this century have shown thatmuch higher drastic changes in climate change indicators like annual mean temperature andprecipitation is expected over the next century than that witnessed in the past century. Thesechanges are likely to adversely impact the ecosystems in the region and have been a major
cause of concern for both the developed and the developing countries. This study highlights
the likely impacts of climate change on the ecosystems of China, India and Korean Peninsula.
China has been prone to floods frequently and has been a cause of concern forpolicymakers. Impact studies have shown that farsighted investments in infrastructure forpreventing flood disaster from early decades in this century have a potential to mitigate not
only additional flood disasters caused by future climate change but also flood disasters whichcurrently occur because of climate variability. The impact analysis also covers the impact ofclimate change on the vegetation in the Korean Peninsula and on the water resources in India.
The Korean country study highlights the extent of vegetation damage caused by climatechange while the Indian analysis brings out the acute risk of drought that India would face if
the country grows in an unsustainable manner.
However technological development resulting from both market driven investments inresearch and development and push policies from national governments has resulted indevelopment of technologies with the potential to abate green house gas (GHG) emissions
and thus mitigate adverse impacts on the environment. A section of this study using the AIM
Global framework analyses the GHG emission trends -both past and future- and the reduction
potentials in countries in the Asia Pacific region. The countries studied in detail include China,
India, Korea and Thailand.
The dynamic optimization model provides global greenhouse gas emissions paths under
different socioeconomic scenarios with various constraints on multiple greenhouse gas
emissions, temperature increases, rates of temperature increases and rises in sea levels. It
identifies the short-term mitigation targets under different long-term goals. Stabilization of
GHG concentrations at 500 ppmv is required in order to limit any temperature increase as
close as possible to 2ºC in 2150 relative to the 1990 level. The GHG reductions required toachieve such a 500 ppmv cap on total GHG concentrations are 7.1 Gt-CO2eq in 2020 and
19.0 Gt-CO2eq in 2030 compared to the BaU case.
The AIM Global modeling results show GHG reduction potentials in different sectors forworld regions for the year 2020. The study brings out the potential of mitigating GHG
emissions and the costs involved. To achieve this reduction potential it is essential to set up amechanism that would facilitate transfer of advanced technologies and financial aid to lessdeveloped nations of the world.

Contents

Summary
A. Climate Change and Its Impacts in Asia
A.1 Climate Change Observed in 20th Century and Simulated for 21st Century ·················· 2
A.2 Climate Change Impacts in Asian Countries ························································ 6
B.GHG Emissions and Reduction Potentials in Asia
B.1 China ···································································································· 8
B.2 India ···································································································· 12
B.3 Korea ··································································································· 16
B.4 Thailand ······························································································· 20
C.Long-term Emissions Scenarios and Short-term Targets
C.1 Global GHG Emissions Paths ······································································· 24
C.2 GHG Emissions Reduction Potentials ····························································· 26

Appendix: Outline of Asia-Pacific Integrated Model (AIM) ······················· 37
AIM Network ············································································· 43··

A. Climate Change and Its Impacts in Asia

A. Climate Change and Its Impacts in Asia
A.1 Climate Change Observed in 20th Century (20c) and Simulated for 21stCentury (21c)Trends of Annual Mean Temperature and Precipitation
・ Temperature increase during 21c is larger in China, Japan and Korea than in India, while
it is smaller in Thailand. Difference of temperature increase among models (model
uncertainty) is also large where temperature increase is large.
・ Temperature change by the end of 21c is the largest in SRES-A2 scenario, followed by
A1B, B2, and B1 scenarios.
・ While future precipitation is quite uncertain, increase in precipitation seems to be acommon future trend in China both among models and among emission scenarios.
Figure A1-1 shows time-series of annual-mean temperature and precipitation observed for theperiod 1901 to 2002 and simulated for the period 2003 to 2100. Thick-colored lines denote
10-year moving average of annual mean value based on CSIRO-mk2 AO-GCM, while thincolored lines denote annual mean value. Error-bars at the right-end show the range of decadalmean for the period from 2091 to 2100 among 6 different AO-GCMs (CCSR/NIES, CGCM,CSIRO-mk2, ECHAM4, GFDL-R30, and HADCM3) distribute at IPCC Data Distribution
Centre. Black-lines show the observed values based on CRU2.1 time-series dataset distributed
at Climate Research Unit in University of East Anglia, UK. Using the difference between
observed and simulated values for the period from 1991 to 2000, bias of model simulationfrom observation is removed.
In India, temperature change from 1990s to 2040s ranges between 1.04 oC (B2) and 1.75 oC(A2) and temperature change from 1990s to 2090s ranges from 2.1 oC (B1) to 4.05 oC (A2)due to the emission scenario diversity (SRES-A1B, A2, B1, B2) according to CSIRO-mk2's
result. However, difference among climate models is quite large, and temperature change
from 1990s to 2090s under A2 scenario ranges between 2.83 oC and 5.31 oC. This uncertainty
in range resulting from model diversity is larger than that resulting from emission scenarios
used by one model. In China, temperature change from 1990s to 2090s ranges from 3.23 oC(B1) to 5.48 oC (A2) according to CSIRO-mk2's result, and it is higher than in India.
Difference among climate models is also larger than that of India. On the other hand,temperature change and its difference among models are smaller in Thailand than in India. In
all the 5 countries, temperature change by the end of 21c is the largest in A2 scenario,
followed by A1B, B2, and B1 scenarios. In the first half of 21c, the largest temperature
increase is expected to occur under B2 scenario. Future precipitation is quite uncertain and it
is difficult to tell whether it would increase or decrease in each country. However, it can beout of the range experienced in 20c. Only in China, increase in precipitation seems to be acommon future trend among emission scenarios. Annual precipitation in China will increase
from 611 mm/year in 1990s to 624 – 660 mm/year in 2040s, and it will reach 664 – 715
mm/year in 2090s according to CSIRO-mk2’s result. The error bars at the right-end also tellthat most climate models agree with the trend in China. It is a common result among the fivecountries that the uncertainty range derived from model diversity is larger in A2 scenario thanin B2 scenario. It means that behavior of precipitation under the same degree of temperature
increase is still quite different among climate models.

2

Observed and Simulated Climate Change

16

900

Precipitation (mm/year)

China

Temperature ( oC)

14
12
10
8

800

700

600

500

6
1900

1950

2000

2050

1900

2100

1950

Precipitation (mm/year)

Temperature ( oC)

28

26

24

22
1950

2000

2050

2050

2100

2050

2100

2050

2100

1000
800

1900

1950

2000
Year

2000

Precipitation (mm/year)

16
Temperature ( oC)

2100

1200

2100

18

14
12
10
8

1800

1600

1400

1200
1900

1950

2000

2050

2100

1900

1950

Year

2000
Year

20

2000

18

1800
Precipitation (mm/year)

Temperature ( oC)

2050

1400

Year

Korea

2100

600

1900

Japan

2050

1600

30

India

2000
Year

Year

16
14
12
10
8

1600
1400
1200
1000
800

1900

1950

2000

2050

2100

1900

1950

Year

2000
Year

32

2200

Precipitation (mm/year)

Thailand

Temperature ( oC)

2000
30

28

26

24

1800
1600
1400
1200
1000

1900

1950

2000

2050

2100

Year

1900

1950

2000
Year

Figure A1-1: Time-series of annual-mean temperature and precipitation(Red: SRES-A1B, Yellow: A2, Green: B1, Blue: B2, Black: Observed)3

A. Climate Change and Its Impacts in Asia

Rate of Temperature Change
・ Drastic GHGs mitigation is required for keeping temperature increase per decade below
0.2oC.
Figure A1-2 shows the rate of temperature change in 5 countries for 2 emission scenarios(SRES-A2 and B2). Plotted data is based on CRU2.1 for 20c and mean of 6 AO-GCMs’
results for 21c. High rate of climate change is considered to affect vulnerable sectors such as
natural ecosystem significantly, and some studies have defined 0.2oC/decade as the criticallimit. It can be found that decadal rate of temperature will continue to increase even in thelatter half of 21c in A2 scenario, while it will be rather stable in B2 scenario. Even in B2
scenario, the rate would be more than 0.4 oC/decade in the first several decades of 21c and it
may not cross the critical line of 0.2 oC/decade during the century in most countries. In orderto fulfill the climate control target of 0.2 oC/decade, more drastic mitigation of GHGs is
required.
1.0

Rate of temperature change (oC/decade)

0.8
0.6
0.4
China (A2)
India (A2)Japan (A2)
Korea (A2)
Thailand (A2)

0.2
0.0

China (B2)
India (B2)Japan (B2)
Korea (B2)
Thailand (B2)

-0.2
-0.4
-0.6
1900

1950

2000

2050

2100

Year

Figure A1-2: Rate of temperature change in 5 countries
Fluctuation of Monthly-Mean Temperature during a Year・ In China and India, fluctuation of monthly temperature during a year has been decreasing.
Figure A1-3 shows decadal-mean of intra-annual temperature fluctuation index for A2 and B2.
The index is defined as an annual-mean of absolute values of difference between annual-meantemperature and monthly-mean temperature and it is described in the equation below.
12

Intra-annual temperature fluctuation index = ∑ temp m − temp ave / 12m =1Here, tempm denotes monthly-mean temperature and tempave denotes annual-meantemperature. Thick-lines show the trend of observation (20c) and mean value of 6 GCMs’
simulation results (21c), while dotted thin lines show the range of 6 GCMs’ simulations.
China (green) and India (blue) show a clear trend of decrease in the index through both theobservation and simulation periods. It means that future temperature increase in cold season islarger than that in hot season and fluctuation of monthly temperature during a year decreases.
In Korea and Japan, there is a decreasing tendency in 20c but may not continue to decrease in
21c. In Thailand, through both the observation period and the simulation period, the index
will not change significantly.

4

10

10

9

9

8

8
Intra-annual fluctuation ( oC)

Intra-annual fluctuation ( oC)

Observed and Simulated Climate Change

7
6
5
4
3

7
6
5
4
3

2

2

1

1

0

China
India
Japan
Korea
Thailand01900 1950 2000 2050 2100

1900 1950 2000 2050 2100

Year

Year

Figure A1-3: Intra-annual temperature fluctuation index (Left:A2, Right:B2)
Fluctuation of Annual-Mean Temperature during a Decade
・In India and China, extreme events may occur more often in future.

Figure A1-4 shows inter-annual temperature fluctuation index of each decade. This index isdefined as decadal-mean of absolute values of difference between decadal-mean temperature
and annual-mean temperature. The index of 1990s is described in the equation below.
2000∑ tempInter annual fluctuation index =y =1991

ave , y− temp dec ,1990 s / 10

0.8

1.0

0.7

0.9
Inter-annual fluctuation ( oC)Inter-annual fluctuation ( oC)

Here, tempave,y denotes annual-mean temperature in the year y and tempdec,1990s denotes
decadal-mean temperature in 1990s. The meaning of line-style is the same as in Fig. A1-3.
Although the future trend of inter-annual fluctuation is quite different among GCMs, it may
increase significantly in India and China according to the mean of the 6 GCMs’ results.
Extremely hot summer and its disastrous impact are reported in many regions of the worldthese days. Increasing tendency of the index implies that such an extreme climate event will
occur more often in future.

0.6
0.5
0.4
0.3
0.2
0.1

0.8
0.7
0.6
0.5
0.4

China
India
Japan
Korea
Thailand

0.3
0.2
0.1

0.0

0.0

1900

1950

2000

2050

2100

1900

Year

1950

2000

2050

2100

Year

Figure A1-4: Inter-annual temperature fluctuation index (Left:A2, Right:B2)

5

A. Climate Change and Its Impacts in Asia

A.2 Climate Change Impacts in Asian Countries
In China, we might as well invest in flood prevention infrastructure from early decadesin 21st century with supposing future flood damage increase because of climate change.

(a)

3.5
3
2.5
2
1.5
1
0.5
0
2000

Utility (relative to CnAn, %Cropland damage (% )Flood, one of the main natural disasters in China, occurs frequently, not only in southern
China where a humid monsoon climate prevails but also in arid and semiarid northern China.
Changes in flood risk is considered to be one of the potential impacts of climate change, sincesome studies estimate the increase in frequency/intensity of heavy rain. On the other hand,additional investment in infrastructure for preventing flood disaster from early decades in thiscentury have a potential to mitigate not only additional flood disasters caused by futureclimate change but also flood disasters which currently occur because of climate variability.
We evaluated the optimal amount of investment in infrastructure for preventing flood
disasters by considering the uncertainty of future flood occurrence. Figure A2-1 shows (a)estimated flood damage to cultivated land under four alternative scenarios (CnAn (baseline),
CyAn, CyAy and CnAy) and (b) the estimated changes in consumer’s utility compared withbaseline case. Flood is expected to damage 1.13% of cultivated land in 2100 even if
investment takes climate change into consideration and climate changes do occur (CyAy).
The damage will gradually increase to 3.11% by the end of the century when there is no
investment in flood prevention infrastructure to combat projected climate change and climatechange unfortunately occurs (CyAn). Comparing with the consumer’s utility per capita in
CnAn case, it will decrease by 0.83% when no adaptation investment is implemented while
climate change occurs (CyAn). The decrease of utility can be reduced to 0.1% if adaptation
investment is implemented (CyAy). Even if investment is larger than the optimal level(CnAy), consumer’s utility per capita will not be lost significantly. According to this analysis,
it doesn’t seem to be a bad strategy to invest in flood prevention infrastructure with supposing
flood damage increase because of climate change even if the occurrence and magnitude ofclimate change is still uncertain. In other words, we can consider this strategy to be fairly
robust.
CnAn
CyAn
CyAy
CnAy

2020

2040

Year

2060

2080

(b)

0.2
0
-0.2
-0.4
-0.6
-0.8

CnAn
CyAn
CyCy
CnAy

-1

2100

2000

2020

2040

Year

2060

2080

2100

[CnAn] Policymakers do not arrange adaptation investment for the
projected climate change and climate change does not occur.
( = Baseline case)[CyAn] Policymakers believe climate change will not occur and thus noadaptation investment is implemented to mitigate flood damage fromclimate change, but unfortunately climate change occurs. (The amountof adaptation investment is assumed to be same as that in CnAn.)[CnAy] Policymakers believe that climate change will occur andadaptation investment is arranged to mitigate flood damage fromclimate change, but climate change does not occur. (The amount ofadaptation investment is assumed to be same as that in CyAy.)[CyAy] Policymakers believe climate change will occur and thus
adaptation investment is arranged to mitigate flood damage fromclimate change and actually climate change occurs.

Figure A2-1: (a) Ratio of cultivated land damaged by flood (%) and (b) Consumer’s utility
per capita relative to the baseline scenario (%).

6

Climate Change Impacts in Asian Countries

In India, drought risk will go up in future if high rate of population increase continues.
Climate change may exacerbate the risk in some regions.
Figure A2-2 depicts the stress on India’s water resources resulting from climate change andsocio-economic change. The water stress index is the ratio of water withdrawal to renewable
water resource in each basin. Under the unsustainable scenario, which reflects high-rate of
population growth and low rate of water-use efficiency improvement, water stress index will
significantly increase in India, leading to increasing drought risk. On the other hand, under thesustainable scenario, water stress index will decrease slightly in most basins reflecting highrate of water-use efficiency improvement (there are small number of exceptional basins wherewater stress index increases because of the regional pattern of climate change).
2000

2100

2100

1 5 100(a) Current(b) Sustainable society scenario

20 40 60 80 100 ～(%)(c) Unsustainable society scenarioiFigure A2-2: Stress on India’s water resources in 2100

Forest loss caused by climate change may reach 2.08% of the area of Korean Peninsula
Figure A2-3 shows the extent of forest vegetation damage caused by climate change on the
Korean Peninsula by 2100. Whether current forest vegetation will be damaged or not is
identified by comparing potential velocity of forest moving (VFM) with the velocity ofvegetation zone shift that is estimated considering climate change scenario. VFM is assumed
to be in the range from 0.25 to 2.0km/year in our assessment. In SRES A2 scenario, whose
temperature increase is higher than the other SRES scenarios, the extinction area will be
2.08% of the Korean Peninsula if VFM is assumed to be 0.25km/year.
Area whose vegetation zone will change
from forest to non-forest (Forest extinction)
Area where VFM is slower than a rate ofvegetation zone shift (Possibility of forest
extinction)
Area where VFM is faster than a rate ofvegetation zone shift (Success inautonomous adaptation of forest)
No impact(a) CCCma SRES-A2(b) CCCma SRES-B2

Figure A2-3: Climate change impact on Korean natural vegetation in 2100
7

B. GHG Emissions and Reduction Potentials in Asia

B. GHG Emissions and Reduction Potentials in Asia
B.1 China
By 2002, population in China is 1.28 billion, when it is 0.987 billion in 1980, with annual
growth rate 1.18. Studies for population growth in China suggested it will be around
1550million by 2030. Urbanization rate could reach 60% by 2030. GDP in 2002 is 10.47
trillion Chinese yuan with annual growth rate 9.5% after 1980. Among industries, firstindustry account for 15.4%, second industry and tertiary industry account for 51.1% and33.5%, respectively.
In terms of future development, developing the economy and improving the living standard
are the first rank of one short- and long-term targets set out by the Chinese government. At
same time sustainableS1
development
is
S2
S3
recognised
asanS4
important issue.
200
S5

Trillion Yuan (1990Price)

S6
CS:
BaU
CS:基准?

150

100

50

0
1990

1,800
1,600
1,400

Mtce

1,200
1,000
800

Figure B1-1 gives GDP
development
trend
presented by various
studies. All studies show
GDP growth will keep
fast for next several
decades. Basic trend
follows
government
targets which aim GDP
per capita will reach thelevel of OECD countries
at beginning of 1990s by
2050. Recently new
2000 2010 2020 2030 2040 2050
economic development
target was given by
Figure B1-1: GDP scenario for China
Chinese government. It
is planned to be quadrupled of the
GDP of the year 2000 by 2020, with awell-off society in an all-round way.
That means the average GDP growthrate will be 7.18% for next twenty
years, giving China one of the rapidest
countries in the world.
CS:
Opt-Medium
CS:替代方案
CS:低替代方案
CS: Opt-High
ERI-CEEDLES
ERI:CESS-Low
ERI:CESS-Medium
ERI:CESS-High
CCE2000:BaU
CCE2000:ED
CCE1997:A
CCE1997:B
Tsinghua-MARKAL
AIM/SRES-C
AIM/SRES-D
AIM/SRES-S
AIM/SRES-E
TIMER-A1
TIMER-A2
TIMER-B1
TIMER-B2

600

Energy and Emission Profile

400

Production

200

Consumption

0
1950

1960

1970

1980

1990

2000

Figure B1-2: Energy production and consumption

8

Because of rapid economy growth,
total primary energy consumption
increased from 400 Mtoe in 1978 to
1036 Mtoe in 2002, with an annual
average rate 4% (see Fig. B1-2).

ChinaCoal is the major energy in all energy use, taking share of 70.7 % in 1978 and 66.1% in 2002
in total primary energy use (see Fig. B1-3).
By using IPAC-AIM/China withconsideringvariousGDP,
80
population
andtechnology
assumption,thescenario
results on
60
energy demand and CO2 emission
40
by different cases for China arepresented in Fig. B1-4 and Fig. B120
5. There will be a significant
0increase of primary energy demand,
1957
1978
1988
1993
1998
2003
which ranges from 2.3billion tce to
3.7billion tce in 2020, and
Coal
Crude OilNatural Gas
Hydro-power
2.7billion tce to 4.4billion tce inFigure B1-3: Energy mix
2030. And it also can be seen
obviously from Fig. B1-5 that CO2
emission will increase quickly witheconomic development in China. Compared with base year 1990, CO2 emissions will be 1.7,
2.5 and 3.8 times in 2000, 2010 and 2030 for medium case which can be regarded as apossible development case, 1.6, 2.1 and 3.3 times for policy case which is the lowest growthrate of CO2 emission. Figures B1-6 presents the distribution of CO2 emission in 2010 in China,
which show the coast region account for most emissions.
%

100

3000

6,000

2500
Mt-C

5,000

Mtce

4,000

2000
1500

3,000

1000

2,000

500
0

1,000

1990

2010

2030

Year

2000

2010

2020

2030

Technology frozen
MarketFigure B1-4: Primary energy demand
scenarios in China

PolicyFigure B1-5: CO2 emissions in China

9B. GHG Emissions and Reduction Potentials in Asia

0.00
200.00400.00600.00800.001000.00
1200.00
1400.00
1600.00
1800.00
2000.00
2200.00
2400.00
2600.00
2800.00
3000.00>=3200.00

Figure B1-6: CO2 emission distribution in 2010
Emission Reduction Potential
From Fig. B1-4 and Fig. B1-5 we can see the potential for energy saving and CO2 emission
reduction. There could be significant potential, ranged by 8% to 46% in 2030. Figure B1-7
presents the CO2 emission reduction curve by major sectors.
The reduction potential by
sector was simulated with awider cost range of up to
US$50/tC. Emission reduction
potential by these sectors withcost less than US$50/tC isshown in Fig. B1-8. This could
be used as basis to identify CDM
projects in China.

Urban

600

Co m
merc
e
Rural

Indu

700

Agriculture

800

Transport

Average cost for CO2 reduction, yuan/t-C

900

stry

1000

500
400

Technology
measures

300
200
100
0
0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Rate of accumulated CO2 reduction

Figure B1-7: reduction cost by sectors

10

40.0%

and

PolicyTechnology play key role inclimate change mitigation in the
scenario studies. As a large
country at the stage for economy
to take off, technologies is very
important in the sake of energy,
environment and climate change.

ChinaMost of these technologies are also match the demand for energy conservation andenvironment both in short-term and long-term. Therefore technology strategy could wellcombined with energy and environment policies.

Other

Power

Rural

Urban

Transport

Paper

Commerce

CopperGlass

Aluminum

Brick

CementCalcium

fertilizer

Ethylene

Ammonia

Steel

Mt-C

Climate policies should be combined with domestic sustainable development strategy.
Sustainable development is an important factor in national development for both short- andlong-term plans. Policy options, such as clean energy utilisation, including natural gas, andnuclear and renewable energy, could well match the targets described in these national plans.
Win−win opportunities
90
should be developed.
80
Much of the potential
70
emission
reductions
60
discussed
above
could
50
be achieved through40
marketmechanisms
30
20(often even with finding
10
benefits larger than0
costs). Many energysaving oriented projects
could contribute to CO2emission
reduction.
SectorEfficiency improvement
No
Cost
Less than US$50/t-C
M arketdiscussed above could
be well-integrated withenergy-saving strategies
Figure B1-8: Emission reduction potentials by sectors
in China.
Climate policies should be integrated with the national energy development plan. As shownin some scenarios, it is expected that future energy supply modes will slowly become cleaner,
based on imports of natural gas and oil, implementation of clean coal technology and limited
introduction of renewables. Such trends build on existing energy policies in China. The
climate policy options could be seen as a stronger effort in these directions.
International mechanisms such as CDM should be used as a way for technology transfer.
These mechanisms focus on GHG emission reduction and domestic sustainable development
and could help reducing some of the political and financial barriers to greenhouse gas
mitigation in China.
Climate policies should match domestic economic instruments. Tax reform in China started
10 years ago. So far there is no energy tax. However, energy subsidies have been reduced anda fuel tax for transport will be established soon. A carbon tax may not be possible in the shortterm, but could be implemented through a mixed energy tax. Such effects could be enhanced
through double-dividends, as discussed in the IPCC Third Assessment Report.

11

B. GHG Emissions and Reduction Potentials in Asia

B.2 India
Expanding industrialization, modernizing agriculture, and rising incomes have led to rapidly
rising energy use in India. The Indian GDP grew at an average of about 6.5% during theperiod 1990-2000. Driven by 7.3 % growth in the manufacturing sector and over 9 % in
agriculture, economic growth stood at 8.2 per cent in 2003-04. Also with 70% of India’s
population expected to be in the working age group by 2015, economists are predicting higher
levels of savings, capital investments and growth. This growth is expected to be energy
intensive resulting in a policy need to push for a sustainable growth path.
Energy and Emission Profile
Total primary energy consumption for India increased 2.7 times during the period 1970-2000
and is expected to increase by about 2.1 times during the period 2000-2030. The share of thedominant energy resource-coal-increases from 22% in 2000 to 27% in 2030 (Fig. B2-1). Also
due to reduced availability and switch to commercial fuels, the consumption of biomass
remains almost constant at about 5 Exa Joules, reducing its share in primary energy
consumption from about 45 % in 2000 to 24 % in 2030. Figure B2-2 gives the share andgrowth of different green house gases for the same period.
4,000
3,000

18

Mt-CO2Eq

Exa Joules

24

12
6

1,000

0
1970 1980 1990 2000
Coal
Fuel Oil
Biomass

2,000

Gas
Gasoline2010 2020 2030

DieselKerosene
Aviation turbine fuel01970 1980 1990
CO2

Figure B2-1: Primary energy consumption

CH42000 2010
N2 O

2020

2030

Other Gases

Figure B2-2: Share of GHGs emissions

CO2 equivalent GHG emissions grow about 2.6 times from 1454 Tg to about 3507 Tg
during 2000-2030 (as compared to about seven times during the period 1970-2030). This ispropelled by high growth in CO2 and N2O emissions. The share of other gases, which include
PFC, HFC and SF6, increases to about 4.5% in 2030 from an insignificant 0.77% in 2000 (Fig.
B2-2). The major sources of CO2 emissions are the industrial sector followed by theresidential and the transport sectors.
Figure B2-3 gives the regional distribution of CO2 emissions in India for 2000 and 2030.
CH4 emission sources include Paddy and enteric fermentation from livestock. Agriculture
sector also contributes to a majority of N2O emissions. Also a major local pollutant in India is
SO2, which has profound health impacts. These emissions are expected to grow from about
5.5 Tg in 2000 to 8.4 Tg in 2025 and then show a declining trend in line with Kuznets effect.
12

India2030

2000

Million Ton

Million Ton

15

< 3
3-6
6-9
9 - 12
12 -1515 - 1820

18 - 21

40

> 21

0
5
10

Figure B2-3: Regional distribution of CO2 emissions in India
Potential and Costs for Mitigating Greenhouse Gases

Marginal Cost (US$/t-CO2)

Preceding analysis highlighted the growing trend in green house gases and its impact on theregions natural resource. However technological development resulting from bothautonomous investments in research and development and push policies from national
governments has resulted in development of technologies with the potential to abate green
house gas emissions. Figures B2-4
and B2-5 present abatement
Automatic ingniter
Hot charge rolling
800 High efficiency heating furnace Heat recovery of hot blast stove
potential and marginal costs for
LDG recovery
Dry top pressure gas recovery
green house gas abatement for
700 Coal injection
Coller waste heat recovery
some important sectors.
W et top pressure reovery
Hot direct rolling
COG latent heat recovery
600 turbine
India produced 27.1 million metric
LDG latent heat recovery
Regenerative burner
Mainly waste heat recovery
Coal wet adjustment
tonnes of finished steel from main
500
Continous annealing lines
and secondary producers in 2000.
DC electric furnace
400 Continuous caster
Scrap pre-heat
The Indian steel industry has grownHigh efficiency ingiter
Large size blast furnace
above 7% (compounded) per
Coke dry type quenching
300 Coke gas recovery
Dry top pressure reovery
Automatic combustion
annum during the 1990s and isturbineLarge size blast furnace
200 Retire of OHF
Hot charge rollingpoised to grow even faster during
Dry top pressure gas recovery
the present decade due to thrust on
100
infrastructure development and0
housing by the Government. The
0
20
40
60
80
total steel production is projected to-100reach 52.5 Mt (in 2010), 81 Mt-200(2020) and 103 Mt (2030).
CO2 Reduction (Mt-CO2)

Figure B2-4: MAC curve (Steel Sector) in 2020

13

B. GHG Emissions and Reduction Potentials in Asia

Presently, the Indian steel industry is about 1.5 times more energy intensive than the averageinternational steel producers, both for the integrated steel plants and the medium and small
steel-rolling mills. Therefore it has considerable potential for energy conservation. Figure B24 shows the CO2 reduction potential in the steel sector and the associated costs of thetechnologies for the year 2020. Also, the cumulative carbon emissions from the Indian steel
sector for 2000-2030 are projected to be about 1.5 billion tce. Energy conservation and carbon
mitigation technologies may mitigate about one-third of these. The main technologies for
India are indicated in Table B2-1.

Marginal Cost (US$/t-CO2Eq)

Figure B2-5 depicts the abatement options and the costs involved in mitigating CH4 in 2020.
Investigation on coal bed methane for its commercial exploitation is a comparatively recent
phenomenon in India. The government announced in October 2003 that 14 bids for eightblocks had been received
100
under the second round of
Replace Urea with Ammonium Sulphatebidding for coal bed methane
Complet e mix digester for electricity
exploitation. Also, Indian80
P lug flow digest er for elect ricit y
cities are introducing private
sector participation in Solid
60
Waste Management. The aim
Covered anaerobic digest er for elect ricit yis to attract funding and new40
Coal Bed Met hane Vent ilat ion Oxidizer for Heattechnologies for better service
Coal Bed Met hane Degasificat ion
delivery. The waste to energy
for Nat ural Gas P ipeline Inject ion
technologies in India include
20
Use of Low Bleed P neumat ic Devices
incineration, pelletization and
Use of Inst rument Air
bio-methanation. The other0
Landfill gas to direct gas use
sectors that have also been
0
2
4
6
8
10
12
14
Coal Bed Met hane Degasification for Electricit yconsidered for estimating
Coal Bed Met hane Ventilat ion for Elect ricit y-20
reduction potential are theLandfill gas to elect ricity
paddy and the natural gas
production and distribution-40
CH4 Reduction (Mt-CO2Eq)
sectors. The biomass andenteric fermentation sectors
were outside the scope of thisFigure B2-5: MAC curve for CH4 in 2020 across sectors
analysis.
Policymakers have been pushing for cleaner environment through various laws andincentives. The recent policy initiatives to use compressed natural gas for public transport in
Delhi, emission limiting performance standards for passenger vehicles, and stricter
enforcement of existing environmental laws has helped bring down the related emissions fromthe transport sector.
Also some examples of innovations at the local level that have helped save energy, water
use and reduced emisions include innovative arrangement of sugar cane plants (agriculture),use of improved chulahs(rural households) and introduction of electric cars (by a privatecompany) and design of pollution free motor cycles (through individual initiative).
As an example of state inititaive, the state of Andhra Prades has provided thrust for thedevelopemnt of Eco Tourism in the state. The Andhra Pradesh Tourism Development
Corporation (APTDC) and the Andhra Pradesh Forest Development Corporation (APFDC)
have both adopted their own strategies in developing this sector. APTDC has decided to14

IndiaTable B2-1: Energy conservation and carbon
emission reduction technologies for Indian iron andsteel industry
Procedures
Prior-iron system

Process control

Cumulative mitigation(MtCE) for 2000-2030

Technology description
Coal grade mixing

9

Coal washing and drying

13

Coal grinding

20

Coal injection

14

Better plant management toauxiliary energy consumption

reduceExcess air control

41
32

Temperature regulation in the furnace

37

Computerized controls

35

Residual heat recovery from blast furnace

31

Residual heat recovery from hot air
furnace

26

Residual heat recovery from steel dregs

13

Steel-rolling

Pre-heating (Steel rolling technologies)

26

systemComprehensive

Variable speed drives for motors

18

Electric arc furnace penetration

14

Technology

Electric arc furnace (existing) energy

develop eco-tourism in three
aspects: natural wonders such as
caves and waterfalls; wilderness
camping and trekking; and wildlife
tourism.
Clean Development Mechanism(CDM) has developed a lot ofinterest from industry in India.
During the period 2002-2012 nearly
120 TgC emissions could be
reduced at below $25/t-C withdominationofefficiency
improvements on demand andsupply side (see Fig.B2-6).

Technology transfer and financial
aid would have importance for the35
conservation
higher cost measures. In case of
Fuel switching (coal to gas)
53
India, technology penetration is also
Future technology Deoxidized steel making technologies
32
of significance since some new(new)
CO capture and storage
6
technologies are available but their
Cumulative carbon mitigation (MtCE) over 2000-2030
455
penetration is low. Mechanisms tobring in financial aid that is not part of ODA need to be put in place in order to reap the
benefits of carbon funding.
2

Figure B2-6: CDM potential in India for the period 2002-2012
Discussions
Developing countries like India may also be coupled with competing mitigation options fordifferent green house gases and scarce resources. Policy initiatives to introduce technology
options for utilizing gases like methane as an energy source can provide the much-needed
flexibility in the mitigation exercise. Also autonomous investments in industries (like steel
and power) to improve energy efficiency can help reduce emissions.
15

B. GHG Emissions and Reduction Potentials in Asia

B.3 Korea
Korean economy has been rapidly developed since 1970s with average of about 8.8% GDP
growth rate during the period 1986-1995. In 2001, manufacturing sector takes GDP share by
36.0 % and 46.8% was for the service sector. However, in 1998 Korean economy faced severe
financial crisis, which inevitably downgraded its sovereign credit rating. After the painful
restructuring its economy, Korean economy has been successfully recovered.
Energy and Emission Profile
Total primary energy consumption for Korea was 192.9 million toe (tones of oil equivalent)
in 2000, which was the tenth largest consumption in the world. More than 97% of totalenergy consumption was imported. In 2000, Korea was the world sixth largest oil consuming,
the third largest oil importing, and the second largest coal and natural gas importing country.
The total primary energy consumption will be expected to increase by about two times during
the period 2000 – 2030 under BaU scenario. Also due to the high demand for the cleaner
energy source, the share of natural gas, which was about 9% in 2000, will be expected toincrease by 13% in 2030. In 2030, the share of oil will be still more than 40% of total
primary energy source (Fig. B3-1).
CO2 equivalent GHG emissions grow about 1.7 times during 2000-2020. This is mainly
due to the high growth in CO2 emissions and relatively stable growth of N2O emissions. The
share of other gases, which include methane and PFC, HFC and SF6, decrease to about 40%
in 2020. Therefore in 2020, most of GHG emissions (CO2) related to energy will take theshare of 94.9% from 93.5% in 2010. This trend will continue and in 2030, almost all GHG
emissions will come from energy. The major sources of CO2 emissions are the industrial
sector followed by energy transfprmation sector such as power generation, the transport andresidential sectors (Fig. B3-2).

100%
100%

500

80%
80%

Mtoe

400

60%
60%

300
200

40%
40%

100

20%
20%

0

0%
0%

1970 1980 1990 2000 2010 2020 2030
Petroleum

LNG

Coal

Hydro

NuclearFire Wood & Other

1990
1990

2000
2000

CO2

Figure B3-1: Primary energy consumption

2010

2010

CH4

2020
2020

2030
2030

N2O

Figure B3-2: Share of GHGs emissions

16

KoreaCH4 and N2O emissions are from agricultural sector, which is affected by land use for ricecultivation, fertilizer, so on. However, in Korea, the area of rice field is to decline gradually.
Also, the number of livestock is to decline. Another source of CH4 is waste. However, due tothe very tight waste management policies in Korea, the total volume of waste is relatively
stable, which contributes the stable emissions of CH4 and N2O.
Figure B3-3 shows the regional distribution of CO2 emissions in Korea for 2000 and 2030.
During this period, overall CO2 emissions in all prefectures in Korea will increase with 1.5% 3.3% growth rates. Especially, the CO2 emissions in Kyunggi-Do (Metropolitan Seoul Area)
will increase with highest growth rate. The main reason for the rapid increase in thisprefecture is the increase of the population and the expansion of its economic activities.

Figure B3-3: Regional distribution of CO2 emissions in Korea
Emission Reduction Potential
Since energy is the very important component for the sustainable economic development andhigh living standard, it is somewhat inevitable for one country, whose economy is to grow, toincrease GHG emissions. However, the GHG emission reductions can be achieved by energy
efficiency improvement, advance technology adaptation and other policy and measures.
Figures B3-4 and B3-5 show the results of some options to reduce GHG emissions and theirimplementation costs.

17

B. GHG Emissions and Reduction Potentials in Asia

600
PV

Marginal Cost (US$/t-CO2)

500
400

Wind

300
200

ACC52%

100
PFBC

0
0
-100

5
10
Retire of low efficiency plant

Biomass
15

20

25

CO2 Reduction (Mt-CO2)

Figure B3-4: MAC curve for CO2 reduction in power sector
The introduction of advanced technologies and some policy measures are good enough toreduce GHG emissions at costs shown above. One of important findings is that some options
show even negative cost of reducing CO2 emissions.
Many options are to introduce
advanced technologies in various sectors, while some of them are fuel substitutions and GHG
sink options.
Importance of Financial Aid, technology transfer, R&D and capacity building
Korea is one of active supporters in the area of global environmental issues. Korea has been amember of Global Environment Facility (GEF) since 1994. Korea has contributed
US$5.5million to GEF in every five-year term. During the period of 1998 – 2001, Korea
contributed more than US$4 million to multilateral institutions and projects that are related toenvironment. Furthermore through the Korean International Cooperation Agency (KOICA),
total US$455 million grant aids were assisted to individual country and internationalorganization in 2001
As a member of Organization for the Economic Cooperation and Development (OECD),
Korea launched many energy efficiency improvement and training programs via KOICA.
These programs included energy conservation policies, energy efficiency management
systems, voluntary agreements, energy-service companies (ESCO). In 1998, Korea andUnited States agreed to start a bilateral cooperation in four major technology areas, so called
Climate Technology Partnership Korea (CTP-Korea).

18

Korea150

Marginal Cost (US$/t-CO2)

100
Hot direct rolling
Heat furnace with regenerative burner
Continous annealing lines etc.

50
0
0.0
-50

-100

2.0

4.0

6.0

8.0

10.0

DC electric furnace
Next generation coke oven
Coke dry quenching
Increase of coal injection-150
CO2 Reduction (Mt-CO2)

Figure B3-5: MAC curve for CO2 reduction in steel sector
Discussions
Korea as one of OECD and advanced developing countries supports UNFCCC, developing
aggressive energy conservation policy and measures to mitigate GHG emissions. At the sametime, Korea also fully recognizes the sustainability of its economic development. It is themost difficult challenge for every country how to harmonize economic development andenvironmental preservation. More precise policy analysis and emission projections for the
future are very necessary process for a country to tackle this climate change issues. For the
better understanding the climate related issues, providing proper policy analysis of mitigating
GHG emissions, it is crucially important for both developed and developing countries to work
together, sharing information and knowledge. As a part of such effort, Korea has actively
participated into AIM research network for more than ten years.
Especially in Asia, the heterogeneous situation of each country in this region requires more
information sharing and joint effort to tackle global environmental issues such as climatechange. In this sense, the AIM research network has contributed a lot in leading the research
activity in this region.

19

B. GHG Emissions and Reduction Potentials in Asia

B.4 Thailand
Thailand experienced one of the world’s highest economic growth rates starting from mideighties until the year 1996 with an average annual growth rate (AAGR) of 9.5 per centduring 1986-1996. Although the financial crisis from 1997 to 1999 reduced the momentum of
the economy, it has experienced strong growth over the past few years, mainly due toimproved demand for Thai exports and growth in domestic demand. The country's real grossdomestic product (GDP) grew by 6.7 per cent in 2003, up from only 2.1 per cent in 2001. The
country has the second largest economy among the ASEAN1 countries with a total GDP of
US$ 143 billion and the population of 64 million in 2003. Thailand is also the second largest
consumer of energy consumption in the region in 2000.
Energy and Emission Profile
Although slowed by the 1997-1998 financial crisis, the total primary energy consumption(TPEC) in Thailand is expected to grow rapidly in the future. As can be seen from Fig. B4-1,
the TPEC is estimated to increase from about 6 Mtoe in 1970 to almost 187 Mtoe by 2030
under business-as-usual (BAU) case. In 2000, fossil fuels (coal, oil and natural gas) accounted
for 81 per cent of TPEC and they are expected to continue to be the dominant source ofenergy even in year 2030. About half of the TPEC was from oil in 2000. The share of oil isestimated to decrease to one-thirds of the TPEC by 2030, whereas the share of natural gas willremain unchanged in 2030. The share of coal, however, is estimated to increase to about 30
per cent.
The sectoral anthropogenic emission profiles of CO2 and selected local pollutants (SO2 andNOx) from 1970 to 2030 are presented in Fig. B4-2 to B4-4. CO2 emissions are estimated toincrease from 18 million tons in 1970 to about 511 million tons by 2030 at an AAGR of 6.9
per cent. In 2000, the power and the transport sectors, combined together, contributed about
64 per cent of total CO2 emissions. By 2030, the industrial sector are expected to have thelargest share (33 per cent) in the total CO2 emissions, whereas the share of the power sector
would be declining and that of the transport sector would remain almost the same.
A similar picture arises for emissions of SO2 and NOx as well. Total SO2 emission isestimated to increase by more than four times the level from 2000 to 2030. The power sector
will contribute the most in total SO2 emissions during 2000-2030. It is also estimated that theindustrial sector’s share will be increasing. Likewise, NOx emission is estimated to increaseby almost three folds during 2000-2030. The transport and power sectors were the twoleading contributors to NOx emissions in 2000. By 2030; however, transport sector alone is
expected to contribute most to NOx emission.
Most of these emissions are concentrated in and around the capital of Thailand, Bangkok,
and few other areas (such as Ayutthaya, Lampang, and Rayong provinces), where major
power plants and energy intensive industries are located. Figures B4-5 to B4-6 show theregional distributions of CO2 and SO2 in Thailand. The points marked by circles show thespatial distribution of emissions from power plants and major industries (such as steel, cement
and paper) based on their emission intensities.
1

The Association of South East Asian Nations (ASEAN) includes Brunei, Cambodia, Indonesia, Laos, Malaysia,
Myanmar, Philippines, Singapore, Thailand and Vietnam.

20

Thailand

Potential and Costs for Mitigating Carbon Dioxide Emissions

540
Agriculture
Commercial
480
Power
Industry
420
Residential
Transport
360
300
240
180
120
60
0
1970 1980 1990 2000 2010 2020 2030

Figure B4-1: Primary Energy supply during
1970 – 2030

Thousand tons

1,250
1,000

Agriculture
Power
Residential

160
120

0
1970

500
250

Natural Gas

Oil

Primary Electricity

1980

1990

2000

2010

2020

2030

Figure B4-2: CO2 emissions during 1970 –
2030

1000

750

Coal

80

1200

Commercial
Industry
Transport

Biomass

40

Thousand tons

1,500

200

Mtoe

Million tons

In view of the major environmental problems caused primarily by the use of fossil fuels, it isimportant to evaluate the effects of possible mitigation options. The graphical representationof CO2 emission abatement cost curve during 2010-2030 is shown in Fig. B4-7. As can beseen from the figure, at the incremental abatement cost (IAC) of 28$/ton of CO2, about 142
million tons of CO2 could be mitigated, while 1 billion tons of CO2 emission could be reduced
at the IAC of 111$/ton of CO2 .

800

Agriculture
Power
Residential

Commercial
Industry
Transport

2005

2015

600
400
200

0
2000 2005 2010 2015 2020 2025 2030

Figure B4-3: SO2 emissions during 2000 –
2030

21

0
2000

2010

2020

2025

2030

Figure B4-4: NOX emissions during 2000 –2030

B. GHG Emissions and Reduction Potentials in Asia

Figure B4-5: Regional distribution of CO2 emissions inThailandA study by Shrestha
analyzed the role oftechnological options for
GHG reductions under
Clean
Development
Mechanism (CDM) in the
power sector of threeAsian
countries,
including Thailand. This
study shows that cleaner
thermal power generation
technologies
involvingfuel switching from coal
to gas or oil would be themain source of CO2
reduction not only at thepresently
prevailing
pricesof
Certified
Emission
Reduction(CER), i.e., 3 to 6 dollars,
but also at significantly
higher prices.
Environmental
Innovations andStrategies, and Capacity
Building

Severalapproaches
including environmental
innovation and strategy
programs for sustainable
development are initiatedin Thailand to reduce theimpact of GHG emissions.
Figure B4-6: Regional distribution of SO2 emissions in
DemandSide
ThailandManagement (DSM) andenergy efficiency programs in energy sector mandated by Energy Conservation andPromotion Act of 1992, new emission standards and mass transit systems in transport sector,
and forest protection programs are some of the examples. Also, most of the proposed CDM
activities in Thailand are based primarily on two issues: (i) electricity generation frombiomass energy and (ii) waste management project.
As part of the collaborative Integrated Assessment Modeling (IAM) project activities, aworkshop in Bangkok, Thailand suggested that the financial support should be explored forresearch and course development in developing Asian institutes to involve researchers anddoctoral students in such activities. It is also important to recognize a need for coordinationand cooperation between existing expertise and institutions, as well as national and regionalorganizations for the assessment of mitigating climate change in developing countries of Asia.

22

Thailand

Discussions
As with other developing countries in Asia, both Thailand's energy intensity and carbon
intensity levels have increased over the past decade and they are expected to increase in theforeseeable future. Deteriorating air quality in the urban areas (such as Bangkok) andconcerns of the country's other low-lying coastal areas in the event of rising oceans due toclimate change are some of the environmental concerns of Thai government.
One option to reduce GHGs would be to diversify its energy sources from currently heavy
dependence on fossil fuels to increasing use of renewable energy sources. Among therenewable energy sources, biomass power generation seems to be one of the attractive costeffective options in future. In addition, the further development of new innovation strategies
and technology transfer seems necessary for mitigating GHGs and to promote sustainable
development in the country.

120

Incremental Cost
(1995 US$/t-CO2)

100
80
60
40
20
0
100

300

500

700

900

Cumulative CO2 Reduction for 2010-2030

Mt-CO2

Figure B4-7: Incremental cost of CO2 abatement in constant 1995 US$

23

C. Long-term Emissions Scenarios and Short-term Targets

C. Long-term Emissions Scenarios and Short-term Targets
C.1 Global GHG Emissions Paths
The dynamic optimization model included in AIM/Impact[policy] provides global greenhousegas emissions paths under different socioeconomic scenarios with various constraints on
multiple greenhouse gas emissions, temperature increases, rates of temperature increases andrises in sea levels. The model incorporates four sub-models: economic-energy model,
simplified climate model, sea level rise model, and GHG emissions model. The world istreated as a single region. The time periods are the decades from 1990 through 2200. In thismodel, the focus is on several greenhouse gases: CO2, CH4, N2O, SO2, CFCs, PFCs, SF6, BC,
and O3. Several future projections of GHG emissions paths under reduction constraint
strategies are described in this report.

Required GHG reductions to achieve a 500 ppmv stabilization of GHG
concentrations are 7.1 Gt-CO2eq in 2020 and 19.0 Gt-CO2eq in 2030
This model provides a framework to assist policymakers in their decisions on meeting the
UNFCCC’s ultimate objective; “stabilization of greenhouse gas concentrations in theatmosphere at a level that would prevent dangerous anthropogenic interference with theclimate system.” To discuss the time frame and volume of GHG reductions, this study took
six cases into consideration as follows:
・ Simulation cases (reference Scenario: SRES B2)
9 Case 1: Business as usual (BaU)
9 Case 2: 450 ppmv cap on CO2 concentration
9 Case 3: 550 ppmv cap on CO2 concentration
9 Case 4: 650 ppmv cap on CO2 concentration
9 Case 5: 500 ppmv cap on total GHG concentrations
9 Case 6: 550 ppmv cap on total GHG concentrations
With respect to Cases 2 to 6, constraint optimization calculations were carried out in which
CO2 or total GHG concentrations do not exceed the constraint levels from 1990 to 2200. Total
GHG concentrations were calculated based on their global warming potential as reported by
the IPCC. Figure C1-1 compares carbon emissions for the six alternatives. Figure C1-2
indicates CO2 concentrations. These concentrations indicate CO2 itself, not converted CO2
concentrations from the total for GHGs. Figure C1-3 shows the results of global mean
temperature changes from 1990 for the six alternatives. Under the business as usual case, CO2
24

Global GHG Emissions Paths

40

30

20

10

BaU

Case 2

Case 3

Case 4

Case 5

2150

2140

2130

2120

2110

2100

2090

2080

2070

2060

2050

2040

2030

2020

2010

2000

0
1990

The WBGU suggested from a review of
the available literature that “above 2ºC
the risks increase very substantially,
involving potentially large extinctions oreven ecosystem collapses, major
increases in hunger and water shortage
risks as well as socioeconomic damage,
particularly in the developing countries.”

50

CO2 emission (Gt-CO2)

emissions continue to rise up to 2050 andin 2150 the CO2 concentration is almost
double the 1990 level. The temperature
increases by nearly 2ºC in 2060 and by
4.2ºC in 2150.

Case 6

Figure C1-1: Global CO2 emissions
750

550

450

350

BaU

Case 2

Case 3

Case 4

Case 5

2150

2140

2130

2120

2110

2100

2090

2080

2070

2060

2050

2040

2030

2020

2010

250
2000

for Cases 2 to 4, in which the maximum
CO2 concentrations are restricted, in eachcase the increase surpasses 2ºC in 2150.
This is because there are no constraints
on the emissions of other GHGs except
for CO2. In Case 2, which represents themost severe constraint, the temperature
increases by 3.1ºC in 2150.

1990

In investigating the temperature increases

CO2 concentration (ppmv)

650

Case 6

Figure C1-2: CO2 concentrations
5.0

Temperature increase (1990=0)

4.0

3.0

2.0

1.0

BaU

Case 2

Case 3

Case 4

Case 5

2150

2140

2130

2120

2110

2100

2090

2080

2070

2060

2050

2040

2030

2020

2010

2000

0.0
1990

In the case of constraints on GHG
concentrations, Case 5 (500 ppmv
stabilization of GHG concentrations)indicates a temperature increase ofaround 2ºC relative to 1990 in 2150
(2.2ºC). The GHG reductions required toachieve a 500 ppmv cap on total GHG
concentrations are 7.1 Gt-CO2eq (CO2
reduction: 6.1 Gt-CO2) in 2020 and 19.0
Gt-CO2eq (CO2 reduction: 17.0 Gt-CO2)
in 2030, compared to the BaU case.

Case 6

Figure C1-3: Global mean temperature changes
25

C. Long-term Emissions Scenarios and Short-term Targets

C.2 GHG Emissions Reduction Potentials
The reduction potentials in 2020 in steel and residential sectors are shown in this section.
AIM/Enduse is used to measure global and regional reduction potentials and required direct
costs. They are estimated for 21 regions (see Table AP-1).

C.2.1 Steel Sector
800 Mt-CO2 emission increase in 20 years from steel sector
The world steel production in 2000 was about 800 Million tons, contributing to the largest
CO2 emissions share in the industry sector. It is expected that world steel production willcontinue to increase, with high growth in steel production in the developing countries. In thisstudy, steel production is assumed to increase by 40% over the next 20 years. In the absenceof technology improvement emissions are projected to reach about 800 Mt-CO2 emission,
which is 50% more than the 2000 emission levels .

CO2 Emission (Mt-CO2)

2,500

2,307

1,500

(-17%)(-25%)

+787

2,000

(-21%)(-24%)

1,520
Reduction
Emission

1,000
500

AC < 100

AC < 0AC < 0AC < 100

DR=Discount rate
AC = Abatement Cost (US$/tCO2)

2020 Frozen

2000

0

2020 DR= 5% 2020 DR= 33%

Figure C2.1-1: CO2 emission and reduction from steel sector in 2020

Reduction potential of abatement technology
The dotted blue areas in Fig. C2.1-1 indicate the reduction potential of CO2 emissions
resulting from use of energy efficient technologies in 2020. Two discount rates are assumed;
5% and 33%. With 5% discount rate, market driven investment is expected to reduce 395
Mt-CO2. Also advanced technologies at marginal abatement cost of 100US$/t-CO2 could
26

GHG Emissions Reduction Potentials

reduce 571 Mt-CO2 emissions. This reduction corresponds to 17% and 25% of emissions in
the frozen case scenario.
As private industries take into account high investment risk for energy conserving
technologies, a payback period of 3-years is usually assumed. The discount rate
corresponding to 3-years payback is about 33% based on the assumption of 30 years lifetimefor steel plants. The reduction potential with 33% discount rate is less than 5% discount ratecase. The reduction in emissions (with a 33% discount rate) corresponds to 338 Mt-CO2 in themarket-driven case and 486 Mt-CO2 at marginal abatement cost of 100US$/t-CO2.

Country-wise reduction potential
Figure C2.1-2 shows country-wise reduction potential from steel industry with 33% discountrate (see also Table C2.1-1). The reduction potential is large in countries where the current
energy efficiency is low and the expected production in 2020 is large. For example, thereduction potentials for China, Russia and India are 115 Mt-CO2, 58 Mt-CO2 and 46 Mt-CO2
in market case, and 175 Mt-CO2, 74 Mt-CO2 and 67 Mt-CO2 at abatement cost 100US$/t-CO2,
respectively. ,600

CO2 Reduction (Mt-CO2)

500

32

Developed

97

Developing Others

17

21
21

Brazil

74

67

14
14
46

74

400

300

200

58

EU-10 in Eastern
EuropeIndia
Russia

100

175

115

China

0
< US$0/t-CO2
Discount rate = 33%< US$100/t-CO2

Figure C2.1-2: Country-wise reduction potential from steel sector

Technology-wise reduction potential
Figure C2.1-3 shows technology-wise reduction potential from steel industry with 33%
discount rate (see also Table C2.1-2). Examples of market driven technologies include large
27

C. Long-term Emissions Scenarios and Short-term Targets

plant technologies such as furnace gas recovery, blast furnace and coke oven. Most of thesetechnologies are installed in developed countries. Examples of technologies installed atmarginal abatement costs of 100US$/t-CO2 are LDG recovery, LDG latent heat recovery andcoal injection.
600

OthersCO2 Reduction (Mt-CO2)

500

26
25
47

400

300

66

14
15
28

15
28

68

68

88

88

124

124

200

100

Coke dry typequenchingHigh efficiency heating
furnace
Increase of coal
injection
LDG & latent heatrecoveryContinuous caster
Retire of OHF
Large size blast furnace
Large size coke oven
Furnace gas recovery0< US$0/t-CO2
Discount rate = 33%< US$100/t-CO2

Figure C2.1-3: Technology-wise reduction potential from steel sector
Table C2.1-1: Country-wise reduction potential of steel sector
CO2 Emission
CO2 Reduction
DR=33%
DR=5%
2000
2020
< 0US$/t-CO2< 100US$/t-CO2< 0US$/t-CO2< 100US$/t-CO2
2000
FrozenMt
Mt-CO2 Mt-CO2 Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen
106
156
147
1
0.8%
5
3.6%
2
1.6%
7
4.8%
127
347
635
115
18.2%
175
27.5%
115
18.2%
205
32.2%
27
73
219
46
20.8%
67
30.4%
54
24.5%
75
34.4%
3
2
3
0
4.7%
0
11.7%
0
12.9%
1
22.7%
43
58
86
1
0.9%
2
2.8%
4
4.9%
5
6.0%
2
1
2
0
0.0%
0
8.4%
0
7.7%
0
20.8%
5
3
4
0
0.2%
0
9.0%
1
16.3%
1
21.3%
1
3
5
1
28.8%
2
38.5%
2
33.7%
2
42.2%
4
2
3
0
0.0%
0
7.9%
1
15.9%
1
20.4%
7
17
22
2
9.5%
3
15.6%
2
10.7%
4
18.8%
1
1
2
0
18.2%
1
29.0%
1
29.2%
1
32.6%
17
20
24
1
2.9%
1
5.1%
1
4.8%
2
7.0%
102
144
181
10
5.4%
13
7.1%
15
8.4%
18
9.7%
163
217
240
3
1.4%
8
3.4%
14
5.9%
17
6.9%
20
50
78
13.62
17.4%
20.71
26.5%
14
17.7%
24
31.2%
59
156
241
58
24.2%
74
30.7%
71
29.5%
81
33.8%
4
7
10
2
18.3%
3
28.5%
3
29.5%
3
32.9%
28
52
74
14
18.9%
21
28.8%
21
28.9%
24
31.9%
24
28
41
7
18.5%
12
29.0%
8
20.7%
14
34.9%
14
27
38
6
16.5%
10
26.2%
6
16.8%
12
31.8%
79
155
252
56
22.1%
67
26.7%
58
23.0%
74
29.3%
836
1,520
2,307
338
14.6%
486
21.1%
395
17.1%
571
24.7%

Steel

Production

Japan
China
India
Indonesia
Korea
Thailand
Other South-east Asia
Other South Asia
Middle East
Australia
New Zealand
CanadaUSA
EU-15 in Western Europe
EU-10 in Eastern Europe
RussiaArgentine
BrazilOther Latin America
AfricaRest of the World
Total

28

GHG Emissions Reduction Potentials

Table C2.1-2: Technology-wise reduction potential of steel sector
CO2 Reduction
DR=33%
DR=5%< 0US$/t-CO2< 100US$/t-CO2< 0US$/t-CO2< 100US$/t-CO2
Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share
Large size coke oven
88
25.9%
88
18.0%
88
22.2%
88
15.3%
Coke gas recovery
84
24.8%
84
17.3%
84
21.3%
84
14.7%
Automatic combustion
2
0.6%
2
0.4%
2
0.5%
2
0.3%
Coke dry type quenching
0
0.0%
0
0.0%
8
1.9%
21
3.6%
Coal wet adjustment
0
0.0%
0
0.0%
0
0.0%
0
0.0%
COG latent heat recovery
0
0.0%
0
0.0%
0
0.0%
0
0.0%
Next generation coke oven
12
3.7%
12
2.6%
12
3.1%
12
2.2%
Automatic ingniter
0
0.0%
4
0.9%
2
0.5%
4
0.7%
Coller waste heat recovery
0
0.0%
0
0.0%
0
0.0%
12
2.1%
Mainly waste heat recovery
0
0.0%
5
0.9%
1
0.3%
7
1.2%
High efficiency ingiter
0
0.0%
0
0.0%
0
0.0%
1
0.2%
Large size blast furnace
68
20.2%
68
14.0%
68
17.3%
68
12.0%
Blast furnace gas recovery
40
11.9%
40
8.3%
40
10.2%
40
7.1%
Wet top pressure reovery turbine
0
0.0%
2
0.4%
4
1.0%
10
1.8%
Dry top pressure reovery turbine
0
0.0%
0
0.0%
1
0.1%
2
0.4%
Heat recovery of hot blast stove
0
0.0%
0
0.0%
0
0.0%
4
0.7%
Coal injection
0
0.0%
47
9.6%
16
4.1%
47
8.1%
Dry top pressure gas recovery
0
0.0%
0
0.0%
0
0.0%
1
0.2%
Retire of OHF
28
8.3%
28
5.8%
28
7.1%
28
4.9%
LDG recovery
0
0.0%
31
6.3%
6
1.4%
31
5.4%
LDG latent heat recovery
0
0.0%
35
7.2%
2
0.5%
35
6.1%
Continuous caster
15
4.5%
15
3.2%
15
3.9%
15
2.7%
Hot charge rolling
0
0.0%
0
0.0%
0
0.0%
10
1.7%
Hot direct rolling
0
0.0%
0
0.0%
0
0.0%
6
1.1%
High efficiency heating furnace
0
0.0%
25
5.1%
4
1.1%
25
4.4%
Heat furnace with regenerative burne
0
0.0%
0
0.0%
0
0.0%
0
0.0%
Continous annealing lines
0
0.0%
0
0.0%
0
0.0%
0
0.0%
DC electric furnace
0
0.0%
1
0.1%
12
2.9%
15
2.7%
Scrap pre-heat
0
0.0%
0
0.0%
1
0.4%
2
0.3%
Total
338 100.0%
486 100.0%
395 100.0%
571 100.0%

29

Marginal abatement cost curve of steel sector in 2020 (1/2)
100,000 t

147 Mt-CO2

233,000 t

China

600

700

700

400

600

600

-200

0

2

4

6

8

10

12

14

-400
-600

Marginal Cost (US$/t-CO2)

800

0

500
400
300
200
100

-800

0

-100

50

CO2 Reduction (Mt-CO2)

4,622 tIndonesia

500
400
300
200
100

100

150

200

250

0

-100

64,000 t

Korea, Republic Of

86 Mt-CO2

700

600

600

200
100

Marginal Cost (US$/t-CO2)

700

600

Marginal Cost (US$/t-CO2)

700

300

500
400
300
200
100

0.0

0.2

0.4

0.6

0.8

1.0

1.2

-100

0.0

2.0

8,130 t8.0

4 Mt-CO2

300
200
100

10.0

0
-100

1,671 tOther South Asia

5 Mt-CO2

800

700

700

600

600

600

400
300
200
100
0

Marginal Cost (US$/t-CO2)

800

500
400
300
200
100
0

0.0

0.5

1.0

1.5

-100

0.5

22 Mt-CO2

1.5

2.0

1,600

600

1,400

Marginal Cost (US$/t-CO2)

700

500
400
300
200
100

998 t

New Zealand
1,800

2.5

0.0

1.0

2.0

3.0

CO2 Reduction (Mt-CO2)

4.0

5.0

2 Mt-CO2

0.5

0.6

3 Mt-CO2

500
400
300
200
100

-100

0.0

0.2

0.4

0.6

0.8

19,911 t

Canada1.0

1.2

24 Mt-CO2

1,200
1,000

1,200
1,000
800
600
400

800
600
400
200
0

0
-200

0.4

CO2 Reduction (Mt-CO2)

200

0
-100

1.0

Marginal Cost (US$/t-CO2)

9,296 t0.3

5,723 t

CO2 Reduction (Mt-CO2)

800

0.2

0
0.0

CO2 Reduction (Mt-CO2)

Australia

0.1

Middle East

700

-100

0.0

CO2 Reduction (Mt-CO2)

800

500

2 Mt-CO2400

CO2 Reduction (Mt-CO2)

Marginal Cost (US$/t-CO2)

Marginal Cost (US$/t-CO2)

6.0

-200

CO2 Reduction (Mt-CO2)

Other South-east Asia

4.0

100

500

0

0

80

3,408 tThailand
800

400

60

CO2 Reduction (Mt-CO2)

800

500

40

-200

800

-100

20

CO2 Reduction (Mt-CO2)

3 Mt-CO2219 Mt-CO200-1,000Marginal Cost (US$/t-CO2)

81,000 t

India

800

200

Marginal Cost (US$/t-CO2)

635 Mt-CO2

800

Marginal Cost (US$/t-CO2)

Marginal Cost (US$/t-CO2)

Japan

0.0

0.1

0.2

0.3

0.4

0.5

CO2 Reduction (Mt-CO2)

0.6

0.7

0.0

0.5

1.0

1.5

2.0

2.5

3.0

-200
CO2 Reduction (Mt-CO2)

Discount Rate = 33%
Discount Rate = 5%30

Marginal abatement cost curve of steel sector in 2020 (2/2)
127,567 t

181 Mt-CO2

700

700

600

600

600

400
300
200
100

Marginal Cost (US$/t-CO2)

700

500

500
400
300
200
100

0

5

10

15

20

25

30

0

5

300
200

20

25

100

30

0

241 Mt-CO2

700

600

600

Marginal Cost (US$/t-CO2)

700

500
400
300
200
100

10 Mt-CO2

0
40

60

80

33,489 t

39,610 t

100

500

0.0

1.0

2.0

3.0

0

4.0

0

-200

19,819 t

38 Mt-CO2

600

600

0
0

5

10

-200

15

20

500
400
300
200
100
0

-100
CO2 Reduction (Mt-CO2)

Marginal Cost (US$/t-CO2)

700

600

Marginal Cost (US$/t-CO2)

800

100

15

20

128,495 t

Rest of the World

700

200

10

25

30

CO2 Reduction (Mt-CO2)

800

-100

5

-500

700

300

74 Mt-CO2

1,000

200

800

400

30

1,500

300

Africa

500

25

2,000

400

CO2 Reduction (Mt-CO2)

41 Mt-CO2

20

2,500

CO2 Reduction (Mt-CO2)

Other Latin America

15

3,000

500

-100

100

10

Brazil0
20

5

CO2 Reduction (Mt-CO2)

6,367 tArgentine
800

0

0

-100
CO2 Reduction (Mt-CO2)

800

-100

400

Marginal Cost (US$/t-CO2)

91,185 t

15-200

CO2 Reduction (Mt-CO2)

Russia1078 Mt-CO2

500

0
-100

31,134 t

EU-10 in Eastern Europe
800

-100

Marginal Cost (US$/t-CO2)

240 Mt-CO2

800

0Marginal Cost (US$/t-CO2)

180,681 t

EU-15 in Western Europe

800

Marginal Cost (US$/t-CO2)

Marginal Cost (US$/t-CO2)

USA

252 Mt-CO2

500
400
300
200
100
0

0.0

2.0

4.0

6.0

8.0

10.0

CO2 Reduction (Mt-CO2)

12.0

14.0

-100

0.0

20.0

40.0

60.0

80.0

100.0

CO2 Reduction (Mt-CO2)

Discount Rate = 33%
Discount Rate = 5%31

C. Long-term Emissions Scenarios and Short-term Targets

C.2.2 Residential Sector
1,500 Mt-CO2 emission increase in 20 years from residential sector
World population has been increasing approximately 1.5% per year, and energy consumption
and CO2 emission per capita themselves are also expected to increase rapidly based on theeconomic growth and improvement of the standard of living, especially in developing
countries. This study shows that CO2 emission from residential sector increases about 50% toreach 4,500 Mt-CO2 over the next 20 years. However, it also shows that energy efficiency
equipments have a potential to reduce CO2 emission approximately 800 Mt-CO2 in 2020.
5,000(-9%)(-18%) (-18%) (-3%) (-9%) (-14%)

4,500+1,486

CO2 Emission (Mt-CO2)A

4,000
3,500
3,000
2,500

4,660

2,000
1,500 3,174

Reduction

1,000

Emission2020 DR= 5%

AC < 300

AC < 100

AC < 0AC < 300

AC < 100

AC < 0

DR=Discount rate
AC = Abatement Cost (US$/t-CO2)2020 Frozen

0

2000

500

2020 DR= 33%

Figure C2.2-1: CO2 emission and reduction from residential sector in 2020

Reduction potential of abatement technology
The dotted blue areas in Fig. C2.2-1 indicate the reduction potential of CO2 emissions byapplying energy efficient equipments in 2020. In this study, two discount rates are assumed;
5% and 33%. The higher discount rate is set based on the assumption that high investment
risk would be a barrier to the promotion of energy efficient equipments in the residential
sector as well.
With 5% discount rate, it’s estimated that 412 Mt-CO2 is reduced in market driven case. In
addition, advanced equipments at marginal abatement cost of 100US$/t-CO2 and300US$/t-CO2 could reduce 824Mt-CO2 and 847Mt-CO2 respectively. These reductions
correspond to 9%, 18% and 18% of emissions in the frozen case scenario respectively.

32

GHG Emissions Reduction Potentials

The reduction potential with 33% discount rate is less than that of 5% discount rate case. The
reduction in emissions (with a 33% discount rate) corresponds to 157 Mt-CO2 in the market
driven case, 424 Mt-CO2 at marginal abatement cost of 100US$/t-CO2 and 668 Mt-CO2 atmarginal abatement cost of 300US$/t-CO2.

Country-wise reduction potential
Figure C2.2-2 shows country-wise CO2 reduction potential from residential sector with 33%
discount rate (see also Table C2.2-1). As shown in the figure, most of the reduction potentials
in market driven case are seen in Russia and in developed countries such as EU or Japan. On
the other hand, the reduction potentials in developing countries are very small because of theinexpensive fuel price. While the reduction potentials at marginal abatement cost of 100 or
300US$/t-CO2 are very large in Russia, China and India.
800
Developed others

CO2 Reduction (Mt-CO2)A

700

Developing others

600

111

500

103

400

87

23
34
36
36

300

73

61

EU-10 in Eastern Europe

20
33
17
10
36

91

Middle East

Latin America

200
27
28

100

57

0

< 0US$/t-CO2

Discount rate=33%

USA
India

China

53

173

96

< 100US$/t-CO2

Russia<300US$/t-CO2

Figure C2.2-2: Country-wise reduction potential from residential sector

Technology-wise reduction potential
Figure C2.2-3 shows technology-wise reduction potential from residential sector with 33%
discount rate (see also Table C2.2-2). In market driven case, efficient heaters and water
heaters are predominant. In 100 and 300US$/t-CO2 cases, wall insulation, fluorescent ofincandescent type and efficient refrigerator have great contribution to the reduction.

33

C. Long-term Emissions Scenarios and Short-term Targets

800
Others700
25

CO2 Reduction (Mt-CO2)A

600

165

400

14
57

300

89

200

-

Efficient air conditioner
Insulated window

500

100

Efficient refrigerator

14
60
96

179

127
10
29
35

10
29
35

10
29
35

56

56

56

< 0US$/t-CO2
Discount rate=33%

Fluorescent ofincandescent type
Wall insulation
Efficient kerosene water
heaterEfficient kerosene heater
Efficient gas water heater
Efficient gas heater< 100US$/t-CO2 < 300US$/t-CO2

Figure C2.2-3: Technology-wise reduction potential from residential sector
Table C2.2-1: Country-wise reduction potential of residential sector

Japan
China
India
Indonesia
Korea
Thailand
Other South-east Asia
Other South Asia
Middle East
Australia
New Zealand
CanadaUSA
EU-15 in Western Europe
EU-10 in Eastern Europe
RussiaArgentine
BrazilOther Latin America
AfricaRest of the World
Total

CO2 Emission
CO2 Reduction
2000
2020
DR=33%
DR=5%
Frozen< 0US$/tCO2< 100US$/tCO2<300US$/tCO2< 0US$/tCO2< 100US$/tCO2< 300US$/tCO2
Mt-CO2 Mt-CO2 Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen Mt-CO2 vs Frozen
124.6
160.6
15.1
9.4%
26.0
16.2%
36.6
22.8%
29.1
18.1%
38.3
23.8%
38.3
23.8%
308.4
441.1
4.0
0.9%
53.0
12.0%
90.6
20.5%
7.6
1.7%
90.6
20.5%
97.6
22.1%
97.5
165.6
3.5
2.1%
16.9
10.2%
35.6
21.5%
16.1
9.7%
35.6
21.5%
36.2
21.8%
29.6
53.5
3.2
5.9%
9.2
17.2%
11.3
21.1%
10.2
19.0%
12.8
23.9%
13.5
25.2%
36.4
51.0
1.3
2.6%
3.3
6.5%
3.5
6.8%
2.5
4.9%
5.2
10.2%
7.4
14.6%
8.9
19.7
0.7
3.6%
2.8
14.2%
3.8
19.0%
3.3
16.9%
4.5
22.6%
4.7
23.7%
18.4
27.8
0.5
1.9%
1.5
5.5%
5.5
19.8%
3.3
11.7%
6.7
24.1%
7.2
26.0%
14.4
29.3
1.0
3.3%
1.0
3.6%
5.4
18.4%
5.3
18.1%
6.2
21.1%
6.7
22.7%
133.7
207.9
19.3
9.3%
35.9
17.3%
61.2
29.4%
50.5
24.3%
63.1
30.3%
67.0
32.2%
35.0
45.6
0.0
0.0%
3.1
6.9%
8.3
18.2%
4.4
9.7%
8.6
18.8%
8.6
18.8%
1.4
1.8
0.0
0.0%
0.1
5.5%
0.1
7.2%
0.2
9.2%
0.3
16.2%
0.3
17.4%
61.2
81.3
0.3
0.4%
4.6
5.7%
5.0
6.1%
3.5
4.3%
8.8
10.8%
11.0
13.5%
490.6
871.8
2.1
0.2%
33.1
3.8%
34.2
3.9%
37.8
4.3%
90.9
10.4%
90.9
10.4%
547.1
755.2
10.0
1.3%
49.8
6.6%
57.4
7.6%
44.3
5.9%
91.2
12.1%
91.2
12.1%
96.0
139.1
4.2
3.0%
9.9
7.1%
36.0
25.8%
4.5
3.2%
41.6
29.9%
42.6
30.6%
870.8 1,163.1
57.5
4.9%
95.5
8.2%
173.3
14.9%
99.7
8.6%
204.0
17.5%
204.1
17.5%
9.0
17.5
1.5
8.4%
2.0
11.4%
3.4
19.3%
3.3
19.1%
3.5
19.9%
3.8
21.5%
8.1
13.2
2.9
21.8%
3.0
22.3%
4.5
33.8%
4.8
36.1%
4.8
36.5%
5.4
40.8%
39.3
78.1
7.3
9.4%
15.4
19.7%
15.4
19.7%
16.8
21.5%
17.2
22.0%
18.0
23.0%
61.2
133.9
5.1
3.8%
16.4
12.2%
25.9
19.3%
22.1
16.5%
32.7
24.5%
33.4
25.0%
182.9
202.7
17.5
8.7%
41.6
20.6%
51.5
25.4%
42.4
20.9%
57.6
28.4%
59.4
29.3%
3,174.3 4,659.9
157.1
3.4%
424.1
9.1%
668.1
14.3%
411.7
8.8%
823.9
17.7%
847.0
18.2%

Table C2.2-2: Technology-wise reduction potential of residential sector

Efficient refrigerator
Efficient television
Efficient air conditioner
Fluorescent of incandescent type
Efficient gas cooking stove
Efficient kerosene water heater
Efficient gas water heater
Efficient electricity water heater
Efficient kerosene heater
Efficient gas heater
Wall insulation
Insulated window
Total

CO2 Reduction
DR=33%
DR=5%< 0US$/t-CO2< 100US$/t-CO2 < 300US$/t-CO2< 0US$/t-CO2< 100US$/t-CO2 < 300US$/t-CO2
Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share Mt-CO2 Share
0.0
0.0%
7.6
1.8%
165.4
24.8%
107.8
26.2%
189.0
22.9%
189.0
22.3%
0.0
0.0%
0.0
0.0%
4.5
0.7%
7.1
1.7%
23.1
2.8%
35.8
4.2%
8.6
5.4%
13.8
3.2%
13.8
2.1%
15.1
3.7%
19.9
2.4%
20.2
2.4%
14.0
8.9%
88.5
20.9%
95.6
14.3%
79.0
19.2%
95.6
11.6%
95.6
11.3%
0.9
0.5%
0.9
0.2%
2.6
0.4%
2.6
0.6%
3.9
0.5%
11.6
1.4%
9.8
6.2%
9.8
2.3%
9.8
1.5%
10.4
2.5%
11.4
1.4%
11.4
1.3%
34.8
22.1%
34.8
8.2%
34.8
5.2%
40.3
9.8%
46.2
5.6%
48.6
5.7%
0.0
0.0%
0.0
0.0%
17.5
2.6%
56.9
13.8%
110.7
13.4%
110.7
13.1%
28.7
18.2%
28.7
6.8%
28.7
4.3%
28.7
7.0%
28.7
3.5%
28.7
3.4%
56.5
35.9%
56.5
13.3%
56.5
8.5%
56.5
13.7%
56.5
6.9%
56.5
6.7%
3.9
2.5%
126.8
29.9%
179.1
26.8%
7.6
1.8%
179.1
21.7%
179.1
21.1%
0.0
0.0%
56.9
13.4%
59.9
9.0%
0.0
0.0%
59.9
7.3%
59.9
7.1%
157.1 100.0%
424.1 100.0%
668.1 100.0%
411.7 100.0%
823.9 100.0%
847.0 100.0%

34

Marginal abatement cost curve of residential sector in 2020 (1/2)

161 Mt-CO2

Japan

441 Mt-CO2

China

166 Mt-CO2

India

1,500

1,500

1,500

1,000

Marginal Cost (US$/t-CO2)

2,000

Marginal Cost (US$/t-CO2)

2,000

Marginal Cost (US$/t-CO2)

2,000

1,000

500

0

1,000

500

0
0

10

20

30

40

50

0
0

-500

20

40

60

80

100

120

0

-500

53 Mt-CO2

51 Mt-CO2

Korea, Republic Of

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

1,000

500

0
15

500

20

2

4

6

8

500

10

0

-500

28 Mt-CO2

29 Mt-CO2

Other South Asia

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

1,000

500

0
6

8

500

10

2

4

6

8

0

46 Mt-CO2

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

1,000

1,000

0

500

6

-500

8

10

0.0

0.1

0.2

0.3

0

0.4

2

4

6

8

10

12

-500

-500
CO2 Reduction (Mt-CO2)

500

0

0
4

80

81 Mt-CO2

Canada
2,000

500

60

CO2 Reduction (Mt-CO2)

2 Mt-CO2

New Zealand

1,000

40

-500

2,000

2

20

CO2 Reduction (Mt-CO2)

2,000

0

208 Mt-CO2

500

10

-500
CO2 Reduction (Mt-CO2)

Australia

6

0
0

-500

5

1,000

0
4

4

Middle East
2,000

1,000

3

CO2 Reduction (Mt-CO2)

2,000

2

2

CO2 Reduction (Mt-CO2)

2,000

0

1

-500

CO2 Reduction (Mt-CO2)

Other South-east Asia

20 Mt-CO2

0
0

-500

50

1,000

0
10

40

Thailand
2,000

1,000

30

CO2 Reduction (Mt-CO2)

2,000

5

20

CO2 Reduction (Mt-CO2)

2,000

0

10

-500

CO2 Reduction (Mt-CO2)

Indonesia

500

CO2 Reduction (Mt-CO2)

CO2 Reduction (Mt-CO2)

Discount Rate = 33%
Discount Rate = 5%

35

Marginal abatement cost curve of residential sector in 2020 (2/2)

872 Mt-CO2

USA

755 Mt-CO2

EU-15 in Western Europe

1,500

1,500
Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

2,000

Marginal Cost (US$/t-CO2)

2,000

1,000

1,000

1,000

500

500

0

500

0

0
0

20

40

60

80

100

0

-500

20

40

60

80

100

120

0

140

1,163 Mt-CO2

18 Mt-CO2

Argentine

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

Marginal Cost (US$/t-CO2)

1,500

500

100

150

200

250

0

-500

1

2

3

4

0

5

78 Mt-CO2

134 Mt-CO2

Africa1,500

0Marginal Cost (US$/t-CO2)

1,500
Marginal Cost (US$/t-CO2)

1,500
Marginal Cost (US$/t-CO2)

2,000

500

1,000

10

15

-500

20

500

25

5

6

203 Mt-CO2

500

0
0

5

10

15

20

25

-500
CO2 Reduction (Mt-CO2)

4

1,000

0
5

3

Rest of the World

2,000

1,000

2

CO2 Reduction (Mt-CO2)

CO2 Reduction (Mt-CO2)

2,000

0

1

-500

-500
CO2 Reduction (Mt-CO2)

Other Latin America

500

0

0
50

50

1,000

1,000

0

40

13 Mt-CO2

2,000

500

30

Brazil

2,000

1,000

20

CO2 Reduction (Mt-CO2)

CO2 Reduction (Mt-CO2)

2,000

0

10

-500

-500
CO2 Reduction (Mt-CO2)

Russia139 Mt-CO2

EU-10 in Eastern Europe

2,000

30

35

0

40

10

20

30

40

50

60

70

-500
CO2 Reduction (Mt-CO2)

CO2 Reduction (Mt-CO2)

Discount Rate = 33%
Discount Rate = 5%36

Appendix: AIM Model

Appendix: Outline of Asia-Pacific Integrated Model (AIM)The Asia-Pacific Integrated Model (AIM) is one of the main tools of developing policy
options for the Asia-Pacific region. It is a set of integrated computer simulation models used
to assess policy options for sustainable development in this region. It started as a tool toevaluate policy options to mitigate climate change and its impacts, and extended its function
to analyze other environmental issues such as air pollution control, water resourcesmanagement, land use management, and environmental industry encouragement. More than
20 modules have been developed so far, and models to evaluate climate policy options areclassified into emission models, climate models and impact models from the viewpoints ofclimate policy assessment. These models have been used as single models or in combination
depending on the policy needs.
Models Used in This Pamphlet
AIM models used in this pamphlet are
AIM/Enduse, AIM/CGE, AIM/Impact and
AIM/Impact[policy]. Brief explanations of eachmodel are written in the following pages. They
were separately or jointly used to get the outlookof climate change impacts and mitigation
potential in the future. AIM/Enduse was used tomeasure global and regional reduction potential
and required direct costs. AIM/CGE was used toestimate economic burdens of emission reduction
to comply with the path. AIM/Impact was used toestimate country level climate impacts, and
AIM/Impact[Policy] was used to project
allowable emission paths from the view point ofclimate stabilization. These four models are usedin this pamphlet following the paths illustrated inFig. AP-1.

AIM/Impact[Policy
Policy] ]AIM/Impact[
Simplifiedintegrated
integrated
Simplified
assessmentmodel
model
assessment
forclimate
climatestabilization
stabilizationfor

Global emission paths to climate
Global emission paths to climate
stabilization
stabilization

AIM/CGE
AIM/CGE

Globalmulti-regional
multi-regional
Global
CGEtype
typeenergyenergyCGE
Economicmodel
model
Economic

Abatement cost curves and potentials
Abatement cost curves and potentials
of reductionof reduction

Regional Impact andRegional Impact and
Adaptation details
Adaptation details

AIM/Impact
AIM/Impact

Processmodel
modelforfor
Process
assessingdetailed
detailed
assessingclimateimpacts
impacts
climateRegional economic details on
Regional economic details on
climate vulnerability andclimate vulnerability andadaptation potential
adaptation potential

Macro-economic driving forces
Macro-economic driving forceson energy consumption andon energy consumption and
GHG emissions
GHG emissions

AIM/Enduse
AIM/Enduse

Bottom-uptype
type
Bottom-up
energy-emission
energy-emission
model
model

Figure AP-1: AIM models used inthis pamphlet

AIM/Enduse Model
AIM/Enduse is a technology selection framework for analysis of greenhouse gas emissions
mitigation policies. It simulates flows of energy and materials in an economy, from supply of
primary energy and materials, through conversion and supply of secondary energy and37

Appendix: AIM Modelmaterials, to satisfaction of enduse services. AIM/Enduse models these flows of energy andmaterials through detailed representation of technologies.
Selection of technologies
takes place in a linear- Heating- Oil- Boiler- Lighting- Coal- Power generation
optimization
framework- Steel products- Gas- Blast furnace- Cooling- Solar- Air conditioner- Transportation- (Electricity)- Automobile
wheresystemcostisEnergy Consumption
minimized under several
Technology
Service Demand
CO2 Emissions
constraints like satisfaction ofservice demands, availability
Energy Database Technology Database Socio-economic Scenario- Population Growthof energy and material- Technology price- Energy type
- Economic Growth- Energy consumption- Energy price- Industrial Structure- Service supplied- Energy constraints
supplies, and other system- Employees- Share- CO2 emission factor- Lifestyle- Lifetime
constraints. System cost
includes fixed costs andFigure AP-2: Structure of AIM/Enduse model
operatingcosts
oftechnologies, energy costs, and other costs like taxed or subsidies. The model can perform
Table AP-1: Regional classification calculations simultaneously for multiple years.
of global model
Variousscenarios
includingpolicyAsia
Japan
countermeasures can be analyzed in AIM/Enduse.
China
Figure AP-2 illustrates AIM/Enduse model.
India
EnergyEnergy Technology

Energy Service

Indonesia
Korea
ThailandRest of South East Asia
Rest of South Asia
Oceania

Australia
New Zealand

North America USA
CanadaSouth America Argentine
BrazilAfricaAfricaEuropeEU15
EU10
RussiaThe first version of AIM/Enduse focused on
analysis of country-level policies on CO2 mitigationin Asia region. It extended to analyze co-benefits of
CO2 and SO2 mitigation objectives by collecting
local information such as large point sources
sector-by-sector and revising model structure. Thenregions of interest are extended to global level. The
regional classification is listed in Table AP-1.

Rest of Europe
Middle East

Middle East

AIM/Enduse model permits analysis of various
countermeasures. These are characterized asfollows:
Enduse stage countermeasures like efficiency improvement or better use and managementof devices.
Rest of the World

•

•
•
•

Emission tax on fuels for greenhouse gases and air pollutants.
Energy tax on a (polluting) fuel to discourage4 its use.
Regulatory constraint on quantity of emission of a gas in selected group of sectors in an
38

Appendix: AIM Modeleconomy.
Regulatory constraint on quantity of use of an energy-type in selected group of sectors inan economy.
Subsidy on capital cost or operating cost of a device to promote its selection.
Subsidy to promote attachment of an emission removal process to a device.
Regulatory constraint on use of a lean or efficient device or its combination with anemission removal process.

•

•
•
•

AIM/CGE Model
AIM/CGE model is a recursive dynamic equilibrium model of the world economy used toanalyze the effects of climate stabilization policies. The model divides the world into 21
geopolitical regions. The structure of AIM/CGE is described in Fig. AP-3. The updated model
includes framework for both CO2 and non-CO2 gases.

investment

consumption

carbonemissionright

primaryfactors

consumption

rent

The model has three sectors- the production, household, and government sectors- in eachregion. CO2 and non-CO2 gases are emitted by each of these sectors. The production ofelectricity and non-energy goods involves
the production and use of fossil fuels
input
CO2
leading to emission of these gases. In
Fuels
input
Electricity
input
addition, the use of automobiles and otherNon-energy
goods
CO2
input
direct use of fossil fuels by the household
input
production sectors
and government sectors lead to emissions.
It is assumed that the household sector has
CO2
multi gas emissions rights and distributes
Governments
them to other sectors and within theCO2household sector itself. Fossil fuels cannot
Householdbe used without having such rights. The
price of these rights depends on several
Figure AP-3: Structure of AIM/CGE Modelfactors such as emission targets and themethod of emissions trading. The household sector also supplies primary factors to the
production and government sectors. An agent in the household sector determines consumption
and savings.
The bottom-up AIM/Enduse and topdown AIM/CGE models are used interactively. The
outputs of AIM/CGE such as energy price and service demands can be used by AIM/Enduse
and the information on technology efficiencies is an input to AIM/CGE.

39

Appendix: AIM Model

AIM/Impact Model
The AIM/Impact model has been developed in order to evaluate future climate change
impacts to support decision making on the Global/Asia scale. The AIM/Impact model consistsof sub-models for evaluating impacts on major vulnerable sectors and linkages among them.
Figure AP-4 shows the linkages between the sub-models.
The FOOD sub-model consists of a productivity model for 12 major crops and an agricultural
trade model. The potential productivity changes caused by climate change are estimated using
a 5o x 5o spatial resolution. Next, based on the estimated changes in crop productivity, theagricultural trade model calculates the allocation of the production of, and demand for, crops
and other commodities that maximize social welfare. The HEALTH sub-model examines theimpact of malaria infection. It evaluates the suitability of climatic factors for the malaria
causing mosquito to reproduce, and estimates the extent of malaria infection. The VEG
sub-model estimates the impact of climate change on several forest and other vegetation types.
The model simulates forest collapse in regions where the rate of climate change is too high forthe existing vegetation patterns to continue. Work to modify the model so that it simulates
dynamic changes to the vegetation is continuing. The HYDRO sub-model uses information
on climate, soil and terrain to simulate surface runoff and river discharges. The WATER
sub-model estimates the future water demand at the national level and assigns that demand toeach grid block, thereby creating a spatial distribution of water demand. CLIMATE
sub-model provides future climate scenarios for the sub-models of AIM/Impact withprocessing of the spatial GCM projections and observed climatology. Results of AIM/Impact
are contained in the impact database in AIM/Impact[Policy] and used for further policy
analysis.
Assessment tools for country-scale impact have also been developed by refining
AIM/Impact’s global-scale modules through the collaboration between Japanese and eachcountry’s team members. Results introduced in the pages 6 and 7 are the outputs of thosecountry-scale models.

40

Appendix: AIM Model

AIM/Climate

HEALTH
Health impacts ofenvironmental
changePOP

CLIMATE

Population, fertility and mobility

Radiation,energy balance,
temperature andsea level rise

ENV

ENERGYCYCLE

Environmental pressure andcounter-measure

Energy technology
and resources

Chemistry of
GHGs

CGE
AIM/Emission

Supply and demand equilibrium of goods,
energy, water, land and labor

FOOD
Production anddemandOCEAN
Energy andcarbon budget of
ocean

LAND

WATER

Land-use allocation
and GHGs emission

Supply infrastructures
Demand

HYDRO

VEG

Surface water balance
Routing module

Vegetation dynamics

AIM/Impact

Figure AP-4:

Framework of AIM/Impact model

AIM/Impact[Policy]
AIM/Impact [policy] is an integrated assessment model which provides a framework forevaluating climate change impacts management under stabilization strategies for GHG
emission, concentration and temperature. The objectives of model development are (1) toprovide a platform to integrate past impacts studies of climate change on several sectors andanalyze climate change impacts on dangerous level, economical damage and adaptation
strategy comprehensively, (2) to provide a platform to investigate GHG emission reduction
strategies for achieving climate stabilization goals and to analyze the effects of burden sharing
scheme and flexibility scheme for GHG emission reduction.
This model consists of a series of linked sub models representing the major processes of GHG
emission and climate change impacts (Fig. AP-5). The GHG emission part includes four sub
models. The dynamic optimizing model is used to analyze global GHG emission path under
different socio-economic scenarios and reduction constraint strategies of multi GHGs. In thismodel, the world is treated as single region and economic and simplified climate modules areincorporated. The burden sharing scheme model provides quantitative information on burdenof GHG reduction at country level. Input data of global GHG reduction volume on various
scenarios are provided by the dynamic optimizing model. This scheme includes several typesof burden sharing approach, e.g. Contraction and Convergence, Brazilian Proposal,
Multi-stage, etc. The global CGE model is a recursive dynamic equilibrium model of world
economy at regional and country level. This model enables quantitative evaluation of
economic damages caused by GHG reduction and climate change impacts. The flexibility
41

Appendix: AIM Model

scheme model is incorporated in the global CGE model and enables to evaluate range ofemission reduction effects utilizing emission trading and carbon tax scheme.
Climate change impacts part include one sub model, impact assessment and adaptation modelfor global climate change. This is a database type model and composed of pre-simulated
results of process type models. In this model, country-averaged climate change derived from
GCM and projected quantitative impacts derived from detailed impact studies in sensitivity
analysis are linked with the global temperature increase projected by a simple climate module.
This module is built as a dynamic optimizing model in order to estimate country-wise impact
on several sectors. Results of external impact research group as well as the results of
AIM/Impact are contained in the impact database. This model indicates the sector-wise
severity of the impacts based on the relationship among socio-economic scenario, adaptation
capacity and sector-wise potential impacts.
Climate change impact threshold

Dynamic optimizing model for global
multi-GHG emission constraint（single region, economic and simplified
climate model）

Impact assessment and adaptation modelfor global climate change（Multi region and sector, database typemodel）

Global GHG emission permit
path

Global CGE model with energy-economic
module for global multi-GHG emission
constraint（Multi region）
Flexibility scheme for GHG emission
constraint

Burden sharing scheme model

GHG emission permit
path at country level

Figure AP-5:

Framework of AIM/Impact[Policy]

42

AIM Network

AIM Network
The research project of AIM started in 1990 and has been specifically developed using acollaborative approach of Asian region, based on international collaboration programs withparticipation of governments and developing countries to get their own Integrated
Assessment tools, and these countries have already applied own Integrated Assessment
Models to their actual policy making processes
Asian Institute of Technology, Pathumthani, Thailand (http://www.ait.ac.th/)Ram M. Shrestha, Sunil Malla, Migara Liyanage, Wongkot Wongsapai
Energy Research Institute, Beijing, China (http://www.eri.org.cn/)
Xiulian Hu, Kejun Jiang, Hongwei Yang, Songli Zhu
Indian Institute of Management, Ahmedabad, India (http://www.iimahd.ernet.in/)Priyadarshi R. Shukla, Amit Garg
Indian Institute of Management, Lucknow, India
Rahul Pandey(http://www.iiml.ac.in/)

Institute of Geographical Sciences and Natural Resources Research, Beijing, China(http://english/igsnrr.ac.cn/)
Jiulin Sun, Zehui Li, Songcai You
Institute for Global Environmental Strategies, Hayama, Japan (http://www.iges.or.jp/)
Tae Yong Jung
Korea Environment Institute, Seoul, Korea (http://www.kei.re.kr/eng/)
Seongwoo Jeon, Hui Cheul Jung
Kyoto University, Kyoto, Japan (http://www.kyoto-u.ac.jp/)
Yuzuru Matsuoka, Takeshi Fujiwara, Reina Kawase, Hiromi Nishimoto, Osamu Akashi
Maulana Azad National Institute of Technology, Bhopal, India(http://www.manit.ac.in/)
Manmohan Kapshe
Mizuho Information & Research Institute, Tokyo, Japan(http://www.mizuho-ir.co.jp/english/)
Go Hibino, Hisaya Ishii, Maho Miyashita
National Institute for Environmental Studies, Tsukuba, Japan (http://www.nies.go.jp/)
Mikiko Kainuma, Hideo Harasawa, Toshihiko Masui, Junichi Fujino, Kiyoshi Takahashi,
Yasuaki Hijioka, Tatsuya Hanaoka, Rajesh Nair
Ritsumeikan University, Kyoto, Japan (http://www.ritsumei.ac.jp/eng/)
Koji Shimada
Seoul National University, Seoul, Korea (http://www.dola.snu.ac.kr/leng/)
Dong Kun Lee
43

AIM Network

Winrock International, New Delhi, India (http://www.winrockindia.org/)
Ashish Rana

Collaborating Institutes
International Institute for Applied Systems Analysis (http://www.iiasa.ac.at/)
Joint Global Change Research Institute (http://www.globalchange.umd.edu/)
Acknowledgement
The research has been funded by the Ministry of Environment, Japan. This funding isgreatly appreciated and has served to create an international network of researchers.
Authors, who have contributed to the publication of this brochure, would like to express
their deep gratitude for late Dr. Tsuneyuki Morita who was the founder of the AIM Networkand contributed to build capacities for developing models in developing countries. Although
he passed away on September 4, 2003, his passioned enthusiasm for global environment
protection still remains in authors and is passed on to the next generation.

The 9th AIM International Workshop (13-15 March 2004, Tsukuba)

44