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March 10-12, 2005 ,NIES, Tsukuba, Japan Activities in the Fiscal Year 2005 in Korea The 10 10th AIM International Workshop th AIM International Workshop The Tae Yong Jung IGES, Japan Dong Kun Lee Seoul National University, Korea So Won

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Tae Yong Jung IGES, Japan Dong Kun Lee Seoul National University, Korea So Won Yoon Seoul National University, Korea Ｅｕｎ Ｙｏｕｎｇ Ｋｉｍ Seoul National University, Korea

The The 10 10th AIM International Workshop th AIM International Workshop

Activities in the Fiscal Year 2005 in Korea

March 10-12, 2005 ,NIES, Tsukuba, Japan

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Table of Contents

  • I. AIM/Korea local Model: Transport

Sector in Seoul

  • Introduction
  • Input data projection
  • Scenario (setting/Results)
  • Policy Implication
  • II. AIM/Enduse (MAC) Model
  • Introduction
  • Analysis Results
  • Policy Implication
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  • 1. Introduction
  • 2. Input data projection
  • 3. Scenario (setting/Results)
  • 4. Policy Implication

AIM/Korea local Model AIM/Korea local Model

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  • 1. Introduction

I . AIM/Korea local Model

The Ministry of Environment (MoE) of Republic of Korea (ROK) enacted the Special Act on Metropolitan Air Quality Improvement in December 2003 The new legislation of the Special Act on Metropolitan Air Quality Improvement is expected to affect the whole emission profiles of air pollutants in this area with the introduction of diesel passenger cars. To discuss the possible impact of this Special Act on emissions of sulfur-dioxide (SO2), nitrogen-oxide (NOx), carbon monoxide (CO), particulate matter (PM), and carbon dioxide (CO2) from the transport sector in Seoul. To analyzes the various policy scenarios along with projections of key determinants in the transport sector in this area.

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  • 2. Input Data Projection

I . AIM/Korea local Model

2000000 4000000 6000000 8000000 10000000 12000000 1 9 7 1 9 7 3 1 9 7 6 1 9 7 9 1 9 8 2 1 9 8 5 1 9 8 8 1 9 9 1 1 9 9 4 1 9 9 7 2 2 3 2 6 2 9 2 1 2 2 1 5 2 1 8 2 2 1 2 2 4 2 2 7 2 3 m an wom an

Population Population

100 200 300 400 500 600

  • 4

a g e 5

  • 9

a g e 1

  • 1

4 a g e 1 5

  • 1

9 a g e 2

  • 2

4 a g e 2 5

  • 2

9 a g e 3

  • 3

4 a g e 3 5

  • 3

9 a g e 4

  • 4

4 a g e 4 5

  • 4

9 a g e 5

  • 5

4 a g e 5 5

  • 5

9 a g e 6

  • 6

4 a g e 6 5

  • 6

9 a g e 7

  • 7

4 a g e 7 5

  • 7

9 a g e a b

  • v

e 8 a g e 1980 m an 1980 wom an 2030 m an 2030 wom an (thousand pop.)

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  • 2. Input Data Projection

I . AIM/Korea local Model

GRDP GRDP Vehicle Vehicle

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  • 2. Input Data Comparison

I . AIM/Korea local Model

Tokyo/Seoul/Beijing Tokyo/Seoul/Beijing Vehicle(Seoul) Vehicle(Seoul)

100 200 300 400 500 600 1940 1950 1960 1970 1980 1990 2000 Vehicles per 1000 people Japan Tokyo Tokyo Ward area Korea Seoul China Beijing 9 72 317 557 226 356 216

100 200 300 400 500 2002 2005 2008 2011 2014 2017 2020 2023 2026 2029 359.5 423.5

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  • 3. Scenario (Setting/Results)

I . AIM/Korea local Model

Scenario Description BAU Business-As-Usual (BAU) Scenario BAU_IMP Scenario that the new emission standard is applied D10 Scenario that diesel passenger cars will take 10 % shares in 2030 H30 Scenario that new advanced technology vehicles will take 30% shares in 2030 D10H30 (D10 + H30) Combined Scenario

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Energy use Energy use

  • 3. Scenario (Setting/Results)

I . AIM/Korea local Model

4,000 4,100 4,200 4,300 4,400 4,500 4,600 4,700 2001 2005 2009 2013 2017 2021 2025 2029

10^3TOE

BAU BAU_IMP D10 H30 D10H30

Energy use

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NOx NOx

  • 3. Scenario (Setting/Results)

I . AIM/Korea local Model

10,000 12,000 14,000 16,000 18,000 20,000 22,000 24,000 26,000 28,000 2001 2005 2009 2013 2017 2021 2025 2029 TO N BAU BAU_IMP D10 H30 D10H30

NOx

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SO2 SO2

  • 3. Scenario (Setting/Results)

I . AIM/Korea local Model

2,150.0 2,200.0 2,250.0 2,300.0 2,350.0 2,400.0 2,450.0 2 1 2 3 2 5 2 7 2 9 2 1 1 2 1 3 2 1 5 2 1 7 2 1 9 2 2 1 2 2 3 2 2 5 2 2 7 2 2 9 BAU BAU_IMP D10 H30 D10H30 TON S O 2

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CO2 CO2

  • 3. Scenario (Setting/Results)

I . AIM/Korea local Model

3,100,000 3,200,000 3,300,000 3,400,000 3,500,000 3,600,000 3,700,000 2001 2004 2007 2010 2013 2016 2019 2022 2025 2028 T O N BAU BAU_IMP D10 H30 D10H30

CO 2

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  • 4. Policy Implication

I . AIM/Korea local Model

The new legislation of the Special Act on Metropolitan Air Quality Improvement will affect the emission profiles of air pollutants in this area especially with the introduction of diesel passenger cars. The environmental policies and measures would shift to more market-

  • riented approaches rather than the conventional ‘command-and-control’

type. The relative energy prices between gasoline and diesel should be re- examined (energy tax issues). Policy balance among sectors and policy integration is considered in more systematic way to achieve multi-targets and goals. To boost the R&D of advanced technologies in the transport sector with financial and tax incentives will contribute to the formulation of overall framework for environmentally sustainable society .

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  • 1. Introduction
  • 2. Analysis Results
  • 3. Policy Implication

AIM/Enduse (MAC) Model AIM/Enduse (MAC) Model

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  • 1. Introduction

Modeling the cost of abating greenhouse gases (GHGs) is crucial to demonstrate an economy’s ability to reduce GHG emissions cost-effectively with specific options. The results of the analysis are presented as marginal abatement cost curves for 2030 in transport and residential sector. Starting year : 2001 Ending year : 2030 Sector : transport sector, residential sector Area : Korea

II . AIM/Enduse (MAC) Model

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Marginal Abatement Cost Curve Marginal Abatement Cost Curve

  • 15
  • 10
  • 5

5 10 15 0.0E+00 1.0E+12 2.0E+12 3.0E+12 4.0E+12 5.0E+12 6.0E+12 Reduction Potential M arginal C

  • st

kg-CO2 Won/kg-CO2

  • Transport sector
  • 2. Analysis Results

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Marginal Cost Marginal Cost

  • Transport sector

Won/kg-CO2 Technology_name

CPP(New) compact private passenger Cars(New) SPP(CNG) small private passenger Cars(CNG) JEEP(LPG_New) Jeeps(LPG_New) BL15P(LPG_New) Buses less than 15 persons(LPG_New) CPP(electricity) compact private passenger Cars(electricity) CPP(fuel cell-meth) compact private passenger Cars(fuel cell- meth) LPP(gasoline hybird) Large private passenger Cars(gasoline hybird) LPP(full cell-meth) Large private passenger Cars(full cell- meth) TL1(LPG_New) Trucks less than 1.0 tons(LPG_New) MPP(CNG) Medium private passenger Cars(CNG) MPP(gasoline hybrid) Medium private passenger Cars(gasoline hybrid) MPP(electricity) Medium private passenger Cars(electricity) BL15P(CNG) Buses less than 15 persons(CNG) SPP(electricity) small private passenger Cars(electricity) BM25P(CNG) Buses more than 25 persons(CNG) Jeep(gasoline_New) Jeeps(gasoline_New) BM15P(Electricity) Buses more than 15 persons(Electricity) BM15P(gasoline_New) Buses more than 15 persons(gasoline_New) SPP(full cell-meth) small private passenger Cars(full cell-meth)

Marginal_Cost 0.0000 2.0000 4.0000 6.0000 8.0000 10.0000 12.0000

C P P ( N e w ) S P P ( C N G ) J E E P ( L P G _ N e w ) B L 1 5 P ( L P G _ N e w ) C P P ( e l e c t r i c i t y ) C P P ( f u e l c e l l

  • m

e t h ) L P P ( g a s

  • l

i n e h y b i r d ) L P P ( f u l l c e l l

  • m

e t h ) T L 1 ( L P G _ N e w ) M P P ( C N G ) M P P ( g a s

  • l

i n e h y b r i d ) M P P ( e l e c t r i c i t y ) B L 1 5 P ( C N G ) S P P ( e l e c t r i c i t y ) B M 2 5 P ( C N G ) J e e p ( g a s

  • l

i n e _ N e w ) B M 1 5 P ( E l e c t r i c i t y ) B M 1 5 P ( g a s

  • l

i n e _ N e w ) S P P ( f u l l c e l l

  • m

e t h )

Won/kg-CO2

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Marginal Cost Marginal Cost

  • Transport sector

Won/kg-CO2

Marginal_Cost

  • 16.0000
  • 14.0000
  • 12.0000
  • 10.0000
  • 8.0000
  • 6.0000
  • 4.0000
  • 2.0000

0.0000

L P P ( d i e s e l _ N e w ) S P P ( D i e s e l ) M P P ( D i e s e l ) C P P ( L P G _ N e w ) S P P ( L P G _ N e w ) L P P ( L P G _ N e w ) M P P ( L P G _ N e w ) T L 5 ( N e w ) T M 5 ( N e w ) M P T ( N e w ) M P T ( N e w ) B M 2 5 ( N e w ) S P P ( N e w ) B M 1 6 P ( N e w ) M P P ( N e w )

Technology name

LPP(diesel_New) Large private passenger Cars(diesel_New) SPP(Diesel) small private passenger Cars(Diesel) MPP(Diesel) Medium private passenger Cars(Diesel) CPP(LPG_New) compact private passenger Cars(LPG_New) SPP(LPG_New) small private passenger Cars(LPG_New) LPP(LPG_New) Large private passenger Cars(LPG_New) MPP(LPG_New) Medium private passenger Cars(LPG_New) TL5(New) Trucks less than 5.0 tons(New) TM5(New) Trucks more than 5.0 tons(New) MPT(New) Medium private Taxi(New) MPT(New) Medium company Taxi(New) BM25(New) Buses more than 25persons(New) SPP(New) small private passenger Cars(New) BM16P(New) Buses more than 16persons(New) MPP(New) Medium private passenger Cars(New)

Won/kg-CO2

II . AIM/Enduse (MAC) Model

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200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 2,000,000

T M 5(N e w ) M P T a xi(N e w ) T L 5(N e w ) S P P (N e w ) M C T(N e w ) M P P (N e w ) M P P (L P G _ N e w ) M P P (D ie s e l) B M 2 5P (N e w ) S P P (D ie s e l) S P P (L P G _ N e w ) B M 1 6P (N e w ) C P P (L P G _ N e w ) L P P (L P G _ N e w ) L P P (d ie s e l_ N e w )

10^6kg-CO2

  • 2. Analysis Results

Reduction Potential Reduction Potential

  • Transport sector

Technology_name

TM5(New) Trucks more than 5.0 tons(New) MPTaxi(New) Medium private Taxi(New) TL5(New) Trucks less than 5.0 tons(New) SPP(New) small private passenger Cars(New) MCT(New) Medium company Taxi(New) MPP(New) Medium private passenger Cars(New) MPP(LPG_New) Medium private passenger Cars(LPG_New) MPP(Diesel) Medium private passenger Cars(Diesel) BM25P(New) Buses more than 25persons(New) SPP(Diesel) small private passenger Cars(Diesel) SPP(LPG_New) small private passenger Cars(LPG_New) BM16P(New) Buses more than 16persons(New) CPP(LPG_New) compact private passenger Cars(LPG_New) LPP(LPG_New) Large private passenger Cars(LPG_New) LPP(diesel_New) Large private passenger Cars(diesel_New)

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Reduction Potential Reduction Potential

  • Transport sector

20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000

B L15P(CNG) MPP(gasoline hybrid) TL1(LPG_New) Jeep(LPG_N ew) SPP(electricity) SPP(CN G) SPP(full cell-meth) Jeep(gasoline_New) MPP(CNG) B M25P(CNG) B L15P(LPG_New) CPP(New) CPP(fuel cell-meth) B M15P(gasoline_New) CPP(electricity) LPP(full cell-meth) LPP(gasoline hybird) MPP(electricity) B M15P(Electricity)

10^6kg-CO2

Technology_name BL15P(CNG) Buses less than 15 persons(CNG) MPP(gasoline hybrid) Medium private passenger Cars(gasoline hybrid) TL1(LPG_New) Trucks less than 1.0 tons(LPG_New) Jeep(LPG_New) Jeeps(LPG_New) SPP(electricity) small private passenger Cars(electricity) SPP(CNG) small private passenger Cars(CNG) SPP(full cell-meth) small private passenger Cars(full cell- meth) Jeep(gasoline_New) Jeeps(gasoline_New) MPP(CNG) Medium private passenger Cars(CNG) BM25P(CNG) Buses more than 25 persons(CNG) BL15P(LPG_New) Buses less than 15 persons(LPG_New) CPP(New) compact private passenger Cars(New) CPP(fuel cell-meth) compact private passenger Cars(fuel cell- meth) BM15P(gasoline_Ne w) Buses more than 15 persons(gasoline_New) CPP(electricity) compact private passenger Cars(electricity) LPP(full cell-meth) Large private passenger Cars(full cell- meth) LPP(gasoline hybird) Large private passenger Cars(gasoline hybird) MPP(electricity) Medium private passenger Cars(electricity) BM15P(Electricity) Buses more than 15 persons(Electricity)

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Reduction Potential Reduction Potential

  • Transport sector

10,000,000 20,000,000 30,000,000 40,000,000 50,000,000 60,000,000

2001 2030 GHG Emission Emission Reduction

10^6Kg-CO2

▶ GHG Emission

  • 2001 : 29.998.45010^6
  • 2030 : 45,884,380 10^6

▶ Reduction 5,276,466 10^6kg-CO2 II . AIM/Enduse (MAC) Model

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Marginal Abatement Cost Curve Marginal Abatement Cost Curve

kg-CO2 Won/kg-CO2

  • Residential sector
  • 2. Analysis Results
  • 10
  • 5

5 10 15 20 0.0E+00 1.0E+12 2.0E+12 3.0E+12 4.0E+12 5.0E+12 Reduction Potential M a rg in a l C

  • st

Won/kg-CO2 kg-CO2

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Marginal Cost Marginal Cost

Won/kg-CO2 Won/kg-CO2

Marginal_Cost

  • 10
  • 5

5 10 15 20

W ASHIN DHETW 1 DHEAT2 DB OIL4 TELEV1 DHEAT1 DLIGI2 DCOM2 DLIGI5 DB OIL6 DLIGI4 W ASHI2 TELEV2 DHEAT3 DSORAR DZISN2 REFRI2 DCOOL2 Won/Kg-CO2

Technology_name WASHIN Regular washing mashine DHETW1 LPG oven range(LPG) DHEAT2 LNG heater(LNG) DBOIL4 Boiler(LNG) TELEV1 Regular Television DHEAT1 Kerosene pan heater DLIGI2 Fluorescent lamp(luminous) DCOM2 Efficient computer DLIGI5 Compact Fluoresen lamp(saving 2) DBOIL6 Condensing Boiler DLIGI4 Efficient fluorescent lamp(saving) WASHI2 Efficient washing mashine TELEV2 Efficient Television DHEAT3 LPG heater(LPG) DSORAR Solar energy DZISN2 Insulation REFRI2 Efficient Refrigeration DCOOL2 High efficient air conditioner

  • Residential sector

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

Reduction Potential Reduction Potential

Reduction_Potential 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000

DSORAR DZISN2 DBOIL6 REFRI2 DLIGI2 DCOM2 DLIGI4 DLIGI5 TELEV2 DHEAT3 DHEAT2 DBOIL4 DHETW1 WASHI2 DCOOL2 DHEAT1 TELEV1 WASHIN 10^ 6kg-CO2

Technology_name DSORAR Solar energy DZISN2 Insulation DBOIL6 Condensing Boiler REFRI2 Efficient Refrigeration DLIGI2 Fluorescent lamp(luminous) DCOM2 Efficient computer DLIGI4 Efficient fluorescent lamp(saving) DLIGI5 Compact Fluoresen lamp(saving 2) TELEV2 Efficient Television DHEAT3 LPG heater(LPG) DHEAT2 LNG heater(LNG) DBOIL4 Boiler(LNG) DHETW1 LPG oven range(LPG) WASHI2 Efficient washing mashine DCOOL2 High effiecient air conditionaer DHEAT1 Kerosene pan heater TELEV1 Regular Television WASHIN Regular washing mashine

  • Residential sector

II . AIM/Enduse (MAC) Model

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  • 2. Analysis Results

II . AIM/Enduse (MAC) Model

Reduction Potential Reduction Potential

5,000,000 10,000,000 15,000,000 20,000,000 25,000,000

2001 2030 GHG Emission Emission Reduction

10^6Kg-CO2

▶ GHG Emission

  • 2001 : 14,396,73010^6
  • 2030 : 16,109,950 10^6

▶ Reduction 3,933,938 10^6kg-CO2

  • Residential sector
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  • 3. Policy Implication

II . AIM/Enduse (MAC) Model

Negative cost of reduction options implies that such options are feasible without extra cost of implementation, The list of such

  • ptions will be a good information for policy makers to launch

specific action programs to mitigate GHG emissions in a specific sector. In transport sector, most of options are to improve the energy efficiency in various vehicles. If high advanced technologies in this sector is considered, the potential of GHG reduction will be much bigger with much higher MAC. Even if the potential of GHG reduction in transport sector is bigger, relatively, it is easier to do it in residential sector. This finding implies that some policies will be implemented in residential sector.