CO2 Reduction Potential by Energy Efficient Technology in Energy ...

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CO2 Reduction Potential by Energy Efficient Technology in Energy Intensive Industry* Kanako Tanakaa**, Ryuji Matsuhashib, Masahiro Nishioc and Hiroki Kudoa a

The Institute of Energy Economics, Japan, b The University of Tokyo, c The National Institute of Advanced Industrial Science and Technology.

1. Introduction There should be a variety of approaches to improve problems existing Kyoto protocol and make the future framework widely acceptable to most of countries. However, various national circumstances, such as economy, political/social structure, capacity of industry/technology, resources and geographical features, cause confliction of interests so that the realization of equitable decision and discussion of future framework are expected to be difficult. Therefore it is necessary that various information is grasped more precisely while considering circumstances between each countries. For example, information of energy technology such as energy efficiency or cost is uneven in a value by various difference (for example, available technology/fuel, a nature and ability of a market, regulation, a difference of a demand side) and evaluation objects (facilities, a factory, a complex) by an area or difference in way of boundary setting. Accordingly, we focus at a framework based on technology, which ensures more equitable scheme, attempt to establish evaluation criteria for them, and provide useful information to domestic/international discussion on this issue. In this research, through doing quantification of CO2 reduction potential with technology in industry, problems and requirements for the indexization were identified.

2. Concept of Calculation of Reduction Potential 2.1 Outline of calculation The outline of calculation, including targeted sector and basic assumption of baseline, was shown as follows. ・ Estimation of the amount of possible CO2 emissions reductions in 2030 ・ Iron and steel, cement, and pulp and paper industry ・ Under the premise that the best available technology(BAT) today would have been adopted. ・ Future production assumed to be proportion to energy consumption in industry described in IPCC-SRES-A1 and B2 marker scenarios. ・ The introduction of high-efficiency and energy-conserving technologies at the manufacturing stage (including the utilization of unused heat) was in large part envisaged.

2.2 Difference in the evaluation depending on sectors/type of industries Due to the variation in data available, different methodologies were used for each type of industry. (see BOX 1 relating to this issue.) ・Iron and steel industry: energy savings from several of the energy-saving technologies were *

This paper was originally presented as a poster and orally at the breakout session at the Industry Expert Review Meeting to the Fourth Assessment of Working Group 3 IPCC, Cape Town, 17-19 January 2006. ** Climate Change Policy Research Group, Global Environment & Sustainable Development Unit, The Institute of Energy Economics, Japan, Email: [email protected] 1

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combined. ・Pulp/paper industry and the cement industry: macro indicators were used.

BOX 1 Importance of considering “boundary” Energy consumption and CO2 emissions are subject to how assessment boundaries are set and how materials and energy coming in and out over the boundary are counted, namely the utilization of waste heat from each process, giving and receiving of by-products and electricity and/or heat with other industries, recycling levels, etc. In the case of transfer of such materials/energy over any boundaries, what kind of conversion factors is used is also critical issue. Therefore, the given results of international comparisons of unit energy consumption, etc. can serve as reference but require cautious observation. CO G Po w e r

Cok e

St a t io n

Fu el

BF G

Ta r /O il W astes

BO FG Coke

Co al

Co k e

O ven

F u rn a c e PC

Co m p a n y

Fu el fo r st ee l p ro ce ss

B高 la炉 st

Ta r /O il

Po w e r

O n -s ite u t ilitie s

BO F

CC

E AF

IC

H o t R o llin g F uel

W astes

U t ilitie s

C oal C oke

Sin te r in g Pla n t

© JISF

W a s t e e n e rg y b a s e d e le c t ric it y , s te a m

Σ E n e rg y in p u t – Σ E n e rg y o u tp u t C ru d e S te e l P ro d u ctio n

In th e c a s e o f a c o u n try in w h ic h w a s te s a re in c in e ra te d , w a s te s u s e d a s a s u b s titu tio n o f c o a l a re n o t c o u n te d a s e n e rg y c o n s u m p tio n s in th e s e c to r b y a n in te rp re t o f “s y s te m e x te n s io n ”.

Figure 1 Energy flow at typical integrated steel making plant in Japan As an example of problems caused when different boundary conditions are considered, energy consumption at coke oven process in iron and steel industry are estimated using different definitions due to different boundary setting here. When only looking at input energy to a process (e.g. definition 1 of Figure 3), energy consumption amount may be overestimated in such a case that energy, which was abandoned, from one process is utilized in another process as recovered heat/energy. A1.Coal B1.Coke oven gas 896 B2.BOF gas 88 B3.BF gas 1,390 B4.Steam 151 B5.Power 331 B6.Nitrogen 8 B7.Compress Air 8 B8.Others 4 (1) (C+D)/(A+B)= 92% (2) C/(A+B)= 88%

30,935

(Unit : MJ -LHV/ t-coal put -in)

Annual production=45,427kt -coal put-in

Coke Oven

D1.Steam

CDQ 2,876 C1.Coke 21,935 C2.Coke oven gas 6,137 C3.Tar 1,227 C4.Light oil 393

29,692 © JISF

Figure 2 Specific Energy Consumption of Coke Oven of Japan’s Steel (National average of the fiscal year 2002)

2

1,365

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( MJ-LHV/t-coal put-in) 2876

1511

-862

1243

2754

Energy recovery by CDQ △1365

1

2

3 Definition

4

5 © JISF

Figure 3 Energy Consumption of Coke Oven per 1 ton-coal in Japan based on various definitions. “Specific energy consumption (SEC) of coke oven can have many definitions” -Definition 1: energy used for coking，SEC= ΣBi -Definition 2: recovered energy by CDQ(=D1) considered, SEC=ΣBi – D1 -Definition 3: by-product gas (B1,B2,B3) neglected as they are internal use of energy, SEC= ΣBi – D1 -B1-B2-B3. -Definition 4: focusing on material loss through coke oven, SEC=A1 - ΣCi -Definition 5: focusing on whole energy/materials balance, SEC=A1+ ΣBi - ΣCi -D1 Other than national average, each works has own boundaries because of separate company for coke oven operation etc.

2.3 Definition of Potential Potential estimated here was defined as follows. ・ Technical potential was estimated. ・ Social and economic barriers were not taken into account. E.g., social factors (regulations, economic situation, public acceptance, etc.), differences in the availability of energy (energy sources, energy prices), equipment-specific constraints (weather conditions, geographic conditions), differences in product demand (the manufacturing process may be affected in the industrial sector), and cultural differences between countries, etc.

3. Estimation of Reduction Potential 3.1 Iron and Steel 3.1.1 Calculation methodology Calculation scheme was shown in Figure 4 and methodology was summarized.

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U nit energy saving

×

P roduction

×

T arget diffusion level

−

C urrent diffusion level

Q uantities of energy conservation

R eduction potential of C O 2 em issions CO2 emission reduction quantity in 2030 =∑ [Unit energy-saving of the technology × Production quantity in 2030 × (Targeted diffusion level of the technology – Current diffusion level of the technology) × CO2 emission quantity per energy consumption quantity ]

Figure 4 Calculation scheme (iron and steel industry) ・ Energy saving technology adopted: CDQ(Coke Dry Quenching), TRT(Top-pressure Recovery Turbine), CC(Continuous Casting), BOFG(Basic Oxygen Furnace Gas) recovery, BOFG WH (waste heat) recovery, HS(Hot stove) WH Recovery, SC ( Sinter cooler ) WH Recovery, SP ME ( Sinter plant Main Exhaust)WH Recovery, which are considered to be particularly effective and of which introduction is regarded as technologically feasible. (The information required to assess them is also readily available or can be reasonably assumed.) ・ Current diffusion level was decided based on data from the IISI (2004) and experts’ judgment from iron and steel industry (Japan Iron and Steel Federation (JISF)). ・ Targeted diffusion level: 100 % ・ Energy saving amount per unit production quantity was based on the existing survey (NEDO, 2001). ・ Type of fuel for the offset energy: CDQ and TRTÆ Electricity, CC and BOFG recovery Æ Oil ・ CO2 emission coefficients for fuels and for electricity were based on IEA data (OECD/IEA, 2004(a)). ・ Production quantity: TRT, BOFG recovery, and CCÆ based on the crude steel production quantity (IISI, 2005). CDQÆ based on the coke production quantity (IISI, 2005), estimated from the quantity of pig iron using the ratio between the pig iron quantity and coke quantity in 2002 in Japan (0.4)(JISF, 2003).

3.1.2 Results ・ 0.26, 0.21 billion ton of CO2 emissions per year an be reduced globally, at A1 and B2 scenarios respectively, through eight technologies. ・ High potential in Central Planned Asia including China in light of future increase in production volume and the current low diffusion rates. ・ CDQ and BOFG recovery reduce the largest amount of CO2, because of current low diffusion rates and unit energy saving amount. CC, which has highest unit energy saving amount, has comparatively small potential because of current very high diffusion rate. TRT is also expected to demonstrate high energy-saving effects. ・ Even though the rest four technologies don’t have high unit energy saving amount, some reductions can be expected because of low diffusion rates.

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CO2 reduction potential (Mt-CO2/yr)

120 100 80 60 40 20

SP-ME-WH Recovery SC-WH Recovery HS-WH Recovery BOFG-WH Recovery BOFG recovery CC TRT CDQ

0

e a ica a a D rica N. Afr pe Europ Union d Asi sia c c f i i o C r r r A e e e E u t A . n ic O rth Am tern E and E Sovie Plann ther tin Am ahara st and f i c O s y r l La ub S le Ea Pa No We entra orme entrall d S F C C Mid Figure 5 CO2 reduction potential of eight technologies (iron and steel, 2030, B2 scenario)

3.2 Cement 3.2.1 Calculation methodology Calculation scheme was shown in Figure 4 and methodology was summarized.

U nit C O 2 em issions

×

Production C O 2 em issions w hen using of B A T

C O 2 em issions

D ifference = C O 2 reduction

CO2 emission reduction quantity in 2030 = ∑(Current unit of CO2 emission in region- the unit for Japan) × Predicted production quantity in 2030 in region)

Figure 6 Calculation scheme (cement industry) ・ CO2 emissions per unit of production was assumed to be reduced to Japanese level. Data was referenced from the existing literature (Battelle, 2002) ・ Cement production quantity: data of 2004 for each country (International Cement Review, 2005).

3.2.2 Results ・ 0.76, 0.62 billion ton of CO2 emissions per year can be reduced globally, at A1 and B2 scenarios. ・ High potential in Central Planned Asia including China of more than 0.3 billions ton –CO2 because of future increase in production volume.

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CO2 reduction potential (Mt-CO2/yr) Pa ci f N ic O or E t W h A CD m e C en ste er r n ica tr Eu Fo al a ro rm nd pe C er E. en So Eu tra v r lly iet ope U Pl an nio O ne n th d La er As tin As ia S Am ia M ub Sa id er dl h ic e a Ea ara n st A an fri c d N a .A fri ca

350 300 250 200 150 100 50 0

Figure 7 CO2 reduction potential (cement, 2030, B2 scenario)

3.3 Pulp and Paper 3.3.1 Calculation methodology Calculation scheme was shown in Figure 4 and methodology was summarized. P ro d u ctio n o f p u lp , p ap er, recy cled fib er

E n erg y C o n su m p tio n by F u el

C O 2 em issio n s

E n erg y C o n su m p tio n w h en u sin g B A T b y p ro d u ct(p u lp , p ap er, recy cled fib er) C O 2 em issio n s w h en u sin g B A T

D ifferen ce = C O 2 re d u ctio n

CO2 emission reduction quantity in 2030 =Σ[CO2 emission quantity in 2030 × (1 - Unit energy consumption in Japan by product/Unit energy consumption in the region by product)] Unit energy consumption by product =Σ(energy consumption quantity by fuel) by product /Current production quantity by product

Figure 8 Calculation scheme (pulp and paper industry) ・ Items: paper, paper and paperboard, and recovered paper ・ Production quantity: the FAO data (FAO, 2005) ・ Total energy consumption quantity: estimated from the fuel input quantity according to the types of fuels (OECD/IEA, 2005) ・ BAT energy consumption: pulpà literature data (JAPAN TAPPI, 2001), recycled fiber à literature data (Farla et al, 1997) , paper à derived from the differences of energy consumption between total value and the summation of pulp and recycled fiber in Japan. ・ Energy consumption in 2030 using BAT in each region/country was estimated from these energy consumptions per production by product and each future production quantity. (The energy consumptions per production of several countries were estimated to be lower than that in Japan. In this case, the potential was not calculated.) ・ CO2 emission quantity of the fossil fuel used :by multiplying the energy consumption quantity and the unit CO2 emission for each energy source (OECD/IEA, 2004).

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・ CO2 emission derived from biomass and waste use systems: assumed to be zero. ・ Fuel composition and products ratio was assumed to be unchanged

3.3.2 Results ・ 0.17, 0.15 billion ton of CO2 emissions per year can be reduced in several countries, at A1 and B2 scenarios. ・ Approximate 0.1 billions ton –CO2 emission reductions is available in North America.

N

or th

CO2 reduction potential (Mt-CO2/yr) Am W es C er en t t r a er n ic a Eu la nd ro pe E. Eu ro p O th e er La As tin ia Am er ic a

120 100 80 60 40 20 0

Figure 9 CO2 reduction potential (pulp and paper, 2030, B2 scenario)

4. Economic assessment 4.1 Assumptions for the assessment Cost per 1 t-carbon reduction was calculated based on the following assumptions. ・ Discount rate: 10% ・ Annual maintenance cost: 5% of initial investment cost. ・ Investment cost, energy reduction using technology: literature data (NEDO, 2001) ・ Energy price: values of 2004 in Japan. (IEA, 2005)

4.2 Results Table 1 shows the results of the economic assessment. Only technologies listed in yellow cells in the tables are linked to the CO2 reduction potential assessment in the section 3. ・ Among eight technologies for iron and steel industry in this research, TRT, CDQ, HS-WH recovery, and SP-ME-WH recovery, which have negative cost, are most cost-effective. Secondly, BOFG recovery of less than 100 US$/t-C. ・ Most of technology adopted here in pulp and paper has negative cost. ・ For cement industry, SP kiln is most cost-effective. For iron and steel industry, both potential and economic assessments were done for same technologies (listed in the yellow part of table 1) so that those results can be interlinked. Figure 10 shows the relations between cost and CO2 reduction potential, where only negative cost options are shown. There are less reduction potential left at highly beneficial options but still much potential exists at negative cost options in many regions, especially Asia and FSU.

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Table 1 Results of economic assessment for iron and steel, cement and pulp and paper industries Ce m e nt

Iron and steel CDQ TRT CC BOFG recovery BOFG WH Recovery HS WH Recovery SP Cooler WH Recovery SPMain Exhaust WH Recovery Pulverized coal injection Direct hot rolling Regenerative Burner on Scrap preheating in EAF

heating

+++ +++ + +++ +++ +++ ++ +++ +

S u s p en s io n p reh ea ter (S P ) kiln

+++

N ew s u s p en s io n p reh ea ter kiln

++

V ertic a l ro ller m il fo r grin d in g E ffic ien t c lin ker c o o ler

+++

P u lp a n d P a p e r R o d s iz e p res s H ea t rec o very m ec h a n ic a l p u lp S h o e p res s

+++ of

th irm o +++ +++

R ep o w erin g s y s tem , g a s tu rb in a n d b o iler u s in g w a s ted h ea t

+++

“+++”: benefit > cost, “++”: US$0 – $100/tC, “+”:US$100 – $300/tC, “–“: >US$300/tC

0

Reduction Potencial[Mt-C] 4 6

2

8

10

0

Cost[US$/t-C]

-50 -100 -150 -200

Pacific OECD Western Europe Former Soviet Union Other Asia Sub Saharan Africa

North America Central and E. Europe Centrally Planned Asia Latin America Middle East and N. Africa

-250

Figure 10

Cost and potential of CO2 reduction by technology at iron and steel industry (2030, B2 scenario)

5. Discussion -- comparison of macro assessment using statistical data (top-down type) and bottom-up assessment based on technologies 5.1 Top down type macro assessment In this study, top down type macro assessment means the way using unit energy consumption/CO2 emissions from statistical data, which was applied to pulp and paper, and partly to cement industries. The character, merit and demerit were summarized as follows. ・ Easy to provide the indicator, such as unit energy CO2 emissions as a benchmark, which is used in assessment. ・ Difference in evaluation boundaries by regions – namely, the difference in the size of the

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countries/regions/business institutions, the difference in contents of facilities/processes and structural differences by countries cannot be dealt with properly. ・ How to categorize the evaluation boundary and subdivide the factors affecting the indicator are critical. (By regions, by fuel types or by ages of facilities.) ・ In case of looking at technical potential (not social/economic one), potential is likely to be overestimated. ・ In some case, low reliability and credibility of statistical data. 5.2 Technology based bottom-up assessment In this study, technology based bottom-up assessment is the way focusing on the energy-saving effect of each technology, which was applied to iron and steel. The character, merit and demerit were summarized as follows. ・ Helps clarify and quantify the reduction effect of the technology, and has the additional advantage of making the economic cost easier to be grasped. ・ Provides a quantitative indicator of action-based cooperation between countries, such as transfer of measures. ・ Has the advantage that it is not so affected by the difference in boundaries. ・ Effective in case that there are difficulties in setting common evaluation boundary, even if manufacturing process is similar among countries. ・ Requires the data of diffusion rate and unit energy saving / CO2 emissions reduction amount of targeted technologies. ・ Limitation of kinds of targeted technology limits the range of CO2 emission reduction potential. ・ Methodology of developing database should be considered: Cooperation with industry circles is important. Combination with nationally introduced mechanism, e.g., Top-Runner scheme in Japan, Dutch Benchmark Covenant, Labelling scheme in US or Singapore.

6. Conclusion ・ 1.0-1.2 billion tons of CO2 emissions can be reduced by use of energy efficient technologies in iron and steel, cement and pulp and paper industry, at global scale. (However, not all applicable technologies and countries were considered because of data limitation.) ・ It is suggested that the importance of development of framework which draws CO2 reduction potentials through utilization of efficient technology as much as possible, as an effective mitigation measure for climate change and new international technology based cooperation. ・ Merit and demerit of top-down and bottom-up type methodologies were clarified. ・ More detailed analysis of specific aspects in each industry is required to seek better ways to assess its CO2 emissions and its reduction potential. ・ To obtain more accurate and consistent database, more systematic data preparation methodology should be discussed. ・ More detailed economic assessment, quantitative assessment of social barrier, and “dynamic” assessment considering timeframe are worth taking into account. Table 2 summarised all results estimated in this study.

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Table 2

Summary of CO2 reduction potential in 2030

Mt-CO2 Pacific OECD North America Western Europe Central and E. Europe Former Soviet Union Centrally Planned Asia Other Asia Latin America Sub Saharan Africa Middle East and N. Africa

Iron and steel A1 B2 2 2 15 12 18 12 14 10 38 28 110 95 31 25 21 14 3 4 5 3 260 210

Cement Paper and pulp A1 B2 A1 B2 13 9 45 36 100 91 36 25 22 18 9 7 5 4 12 9 380 330 170 140 32 29 22 14 9 7 13 16 63 32 760 620 170 150

Acknowledgement This report was developed based on the part of the project of FY 2004, “A Basic Study and Analysis of the Post-Kyoto International Framework, -Evaluation of the CO2 Reduction Potential of High-Efficiency Technology Introduction” funded by The New Energy and Industrial Technology Development Organization (NEDO). We highly appreciate for NEDO’s understanding and cooperation on this publication. We also very much appreciate the following people and associations for their contributions in many ways– discussion, data provision: Japan Iron and Steel Federation; Japan Cement Association; Japan Paper Association; Dr Campbell of UNICE/ICC(ARKEMA; Mr Hyvarinen,CEPI; Dr Worrell, ECOFYS; Dr Reinstein, Reinstein & Associates International.

References Battelle, 2002, Toward a sustainable cement industry, WBCSD FAOSTAT data, 2005, (last undated August 2005) Farla, J., Blok, K., and Schipper, L., 1997, "Energy efficiency developments in the pulp and paper industry", Energy Policy, 25(7-9), 745-758 IISI, 2005, Steel Statistical Yearbook 2003, IISI International Cement Review, 2005, The Global Cement Report Sixth Edition, Tradeship Publications Ltd. TAPPI , 2001, “Survey on Energy Consumption in the Pulp and Paper Mills in Japan-Part 1：Energy Conversion and Energy Consumption in Pulp Production-“, Energy Committee, JAPAN TAPPI JOURNALVol. 55, No. 5 NEDO, 2001, FY 2000 Research Report, “Research on Domestic Energy-Saving Technology,” commissioned to the Japan Consulting Institute. OECD/IEA：2004, CO2 Emissions from Fuel CombustionOECD/IEA; 2005, Energy Statistics of OECD Countries, Non OECD Countries; 2005, Energy Prices and Tax Contact: [email protected]

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Appendix As an extension1 of the paper, we identified uncertainty ranges in reduction potentials of GHG by several technologies.

For this purpose, Monte-Carlo simulations were adopted based on the following

assumptions. (1) Energy-saving potentials in energy intensive industries are expressed in the following equations, in which all factors include uncertainties. Energy saving potentials = Unit-energy-saving ① × Production ② × (Target-diffusion-level ③ – Current-diffusion-level ④) (2) In the above equation, actual data were available for factors ①, ③ and ④, although some of them are insufficient to statistically estimate uncertainty. (3) Regarding ①, we evaluated uncertainty ranges based on actual operation data of the specific technologies. (4) Regarding ②, we did not include uncertainty this time, since we follow scenario A1b or B2 of SRES, which project future production without uncertainty ranges. (5) Regarding ③, we evaluated uncertainty ranges based on actual diffusion data of continuous casting. Since continuous casting is cost-effective enough and well established, its diffusion was also assumed to express the target level of the other technologies. (6) Regarding ④, we assessed present diffusion data of each technology.

Then standard deviation in

each data was assumed to be half of the present diffusion level. Monte-Carlo simulations were performed on coke dry quenching (CDQ), blast furnace top pressure recovery turbine (TRT), and basic oxygen furnace gas recovery (BOFG recovery) in iron and steel industry.

We will

also deal with the other technologies in our future work, although actual operation

data were not presently available on them. Identified uncertainty ranges on CDQ, TRT, and BOFG recovery are shown in the tables 1, 2 and 3, 1

This additional assessment on uncertainties was done by authors after the main body of text was

published at IEEJ WEB in March 2006, and updated in June 2006. It is noted that this part was, therefore, not reviewed at the Industry Expert Review Meeting to the Fourth Assessment of Working Group 3 IPCC, Cape Town, 17-19 January 2006, but peer-reviewed by some experts Japan including Iron and Steel Federation. 11

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respectively. Table 1. Ranges of uncertainty in GHG reduction potentials by introducing CDQ. CDQ A1

B2

Range of reduction potentials（90%）

range of reduction potentials（90%）

1000t-CO2

1000t-CO2

PacificOECD

300∼410

310∼420

North America

2660∼3120

2110∼2460

Western Europe

2260∼3510

1540∼2410

Central and E.Europe

1700∼2350

1270∼1800

Former Soviet Union

1880∼4700

1330∼3370

Centrally Planned Asia

24400∼33400

21200∼28800

Other Asia

5600∼6740

4570∼5460

Latin America

1550∼1820

1040∼1210

Sub Saharan Africa

950∼1110

1110∼1300

320∼370

180∼210

Middle East & N.Africa

Table 2. Ranges of uncertainty in GHG reduction potentials by introducing TRT. TRT A1

B2



1000t-CO2

1000t-CO2

pacificOECD

20∼60

20∼60

North America

1190∼1370

960∼1100

Western Europe

1040∼1590

700∼1070


930∼1110

720∼850

Former Soviet Union

1430∼2230

1020∼1600


10400∼13200

9100∼11500

Other Asia

2580∼3030

2110∼2480

Latin America

720∼830

480∼550

Sub Saharan Africa

440∼510

520∼590


150∼170

80∼100

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Table 3. Ranges of uncertainty in GHG reduction potentials by introducing BOFG recovery. BOFG recovery A1

B2

range of reduction potentials

range of reduction

（90%）

potentials（90%）

1000t-CO2

1000t-CO2

pacificOECD

0

0

North America

2440∼3650

1860∼2820

Western Europe

220∼3770

150∼2540


1750∼2890

1300∼2170

Former Soviet Union

4760∼6720

3340∼4750


31100∼44800

27400∼38700

Other Asia

5040∼7950

4070∼6400

Latin America

860∼1910

600∼1310

Sub Saharan Africa

0∼960

0∼1120


30∼370

20∼220

Although histogram could be depicted for each of the above uncertainty ranges, we show only three histograms due to page limits.

140 120 100 80 60 40 20

32300

31600

30900

30200

29500

28800

28100

27400

26700

26000

25300

24600

23900

23200

22500

21800

21100

20400

19700

19000

18300

17600

16900

16200

15500

14800

0

GHG reudction potential (1000t-CO2)

Figure1．Histogram in GHG reduction potentials by introducing CDQ in Centrally Planned Asia.(SRES B2)

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120 100 80 60 40 20

12700

12300

11900

11500

11100

10700

10300

9900

9500

9100

8700

8300

7900

0

GHG reduction potential (1000t-CO 2)

Figure2．Histogram in GHG reduction potentials by introducing TRT in Centrally Planned Asia.(SRES B2)

140 120 100 80 60 40 20

46200

45200

44200

43200

42200

41200

40200

39200

38200

37200

36200

35200

34200

33200

32200

31200

30200

29200

28200

27200

26200

25200

24200

23200

22200

21200

0

GHG reudction potential (1000t-CO2)

Figure3．Histogram in GHG reduction potentials by introducing BOFG recovery in Centrally Planned Asia. (SRES B2)

15