the 10th aim international workshop · 2020-02-06 · tae yong jung iges, japan dong kun lee seoul...

Tae Yong Jung

IGES, Japan

Dong Kun Lee

Seoul National University, Korea

So Won YoonSeoul National University, Korea

ＥｕｎＹｏｕｎｇＫｉｍSeoul National University, Korea

The The 1010th AIM International Workshopth 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

1. Introduction

2. Input data projection

3. Scenario (setting/Results)

4. Policy Implication

AIM/Korea local ModelAIM/Korea local Model

<4>

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 ofdiesel 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

0

2000000

4000000

6000000

8000000

10000000

12000000

1970

1973

1976

1979

1982

1985

1988

1991

1994

1997

2000

2003

2006

2009

2012

2015

2018

2021

2024

2027

2030

man woman

PopulationPopulation

0

100

200

300

400

500

600

0-4

age

5-9a

ge10

-14a

ge15

-19a

ge20

-24a

ge25

-29a

ge30

-34a

ge35

-39a

ge40

-44a

ge45

-49a

ge50

-54a

ge55

-59a

ge60

-64a

ge65

-69a

ge70

-74a

ge75

-79a

ge

abov

e 80

age

1980 man 1980 woman 2030 man 2030 woman

(thousand pop.)

<6>

2. Input Data Projection I . AIM/Korea local Model

GRDPGRDP VehicleVehicle

<7>

2. Input Data Comparison I . AIM/Korea local Model

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

0

100

200

300

400

500

600

1940 1950 1960 1970 1980 1990 2000

Vehi

cles

per

100

0 pe

ople

Japan

Tokyo

Tokyo Ward area

Korea

Seoul

China

Beijing

9

72

317

557

226

356

216

0

100

200

300

400

500

2002 2005 2008 2011 2014 2017 2020 2023 2026 2029

359.5

423.5

<8>

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

D10Scenario that diesel passenger cars will take 10 %

shares in 2030

H30Scenario that new advanced technology vehicles will

take 30% shares in 2030

D10H30 (D10 + H30) Combined Scenario

<9>

Energy useEnergy use


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

<10>

NOxNOx


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


NOx

<11>

SO2SO2


2,150.0

2,200.0

2,250.0

2,300.0

2,350.0

2,400.0

2,450.0

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029


TON SO 2

<12>

CO2CO2


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

TON


CO 2

<13>

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-oriented 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 .

1. Introduction

2. Analysis Results

3. Policy Implication

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

<15>

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 CurveMarginal Abatement Cost Curve

-15

-10

-5

0

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

Mar

gina

l Cos

t

kg-CO2

Won/kg-CO2

- Transport sector

2. Analysis Results II . AIM/Enduse (MAC) Model

<17>

2. Analysis Results

Marginal CostMarginal 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

CPP

(New

)SP

P(CNG

)

JEEP

(LPG

_Ne w)

BL15

P(LP

G_N

ew)

CPP

(ele

c tric

ity)

CPP

(fue l c

e ll-

meth

)

LPP(

gaso

line h

ybird

)

LPP(

full

c e ll-m

eth )

TL1(

LPG

_Ne w)

MPP

(CNG

)

MPP

(ga so

line

hybrid

)

MPP

(ele

c tric ity

)BL

15P(

CNG

)

SPP(

e lec tri

c ity

)

BM25

P(C

NG)

Jee p (g

a solin

e_Ne w)

BM15

P(El

ectri

c ity)

BM15

P(g a so

line_N

ew)

SPP(

full

c e ll-m

e th)

Won/kg-CO2


<18>

2. Analysis Results


- 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

LPP(d

iesel_

New)

SPP(Dies

el)MPP(D

iese l)

CPP(LPG_N

ew)

SPP(LPG _N

ew)

LPP(L

PG_New

)MPP(L

PG_New

)TL

5 (New

)TM

5(New

)MPT(

New)

MPT(New

)BM25(

New)

SPP(New

)BM16P

(New

)MPP(N

ew)

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


<19>

0

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

TM

5(N

ew

)

MP

Ta

xi(Ne

w)

TL

5(N

ew

)

SP

P(N

ew

)

MC

T(N

ew

)

MP

P(N

ew

)

MP

P(L

PG

_N

ew

)

MP

P(D

ies

el)

BM

25

P(N

ew

)

SP

P(D

ies

el)

SP

P(L

PG

_N

ew

)

BM

16

P(N

ew

)

CP

P(L

PG

_N

ew

)

LP

P(L

PG

_N

ew

)

LP

P(d

ies

el_

Ne

w)

10 6̂kg-CO2

2. Analysis Results

Reduction PotentialReduction 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)


SPP(Diesel) small private passenger Cars(Diesel)

SPP(LPG_New) small private passenger Cars(LPG_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)


<20>

2. Analysis Results


- Transport sector

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

BL15P

(CN

G)

MP

P(g

aso

line h

ybrid

)

TL1(L

PG

_N

ew

)

Jeep

(LP

G_N

ew

)

SP

P(e

lectric

ity)

SP

P(C

NG

)

SP

P(fu

ll cell-

meth

)

Jeep

(gaso

line_N

ew

)

MP

P(C

NG

)

BM

25P

(CN

G)

BL15P

(LP

G_N

ew

)

CP

P(N

ew

)

CP

P(fu

el c

ell-

meth

)

BM

15P

(gaso

line_N

ew

)

CP

P(e

lectric

ity)

LP

P(fu

ll cell-

meth

)

LP

P(g

aso

line h

ybird

)

MP

P(e

lectric

ity)

BM

15P

(Ele

ctric

ity)

10 6̂kg-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_New)

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)


<21>

2. Analysis Results


- Transport sector

0

10,000,000

20,000,000

30,000,000

40,000,000

50,000,000

60,000,000

2001 2030

GH

G E

mis

sion

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


<22>

Marginal Abatement Cost CurveMarginal Abatement Cost Curve

kg-CO2

Won/kg-CO2

- Residential sector

2. Analysis Results

-10

-5

0

5

10

15

20

0.0E+00 1.0E+12 2.0E+12 3.0E+12 4.0E+12 5.0E+12

Reduction Potential

Mar

gina

l Cos

t

Won/kg-CO2

kg-CO2


<23>

2. Analysis Results


Won/kg-CO2

Won/kg-CO2

Marginal_Cost

-10

-5

0

5

10

15

20

WA

SH

IN

DH

ETW

1

DH

EA

T2

DB

OIL

4

TE

LE

V1

DH

EA

T1

DLIG

I2

DC

OM

2

DLIG

I5

DB

OIL

6

DLIG

I4

WA

SH

I2

TE

LE

V2

DH

EA

T3

DS

OR

AR

DZ

ISN

2

RE

FR

I2

DC

OO

L2

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



<24>

2. Analysis Results


Reduction_Potential

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

DS

OR

AR

DZ

ISN

2

DB

OIL

6

RE

FR

I2

DLIG

I2

DC

OM

2

DLIG

I4

DLIG

I5

TE

LE

V2

DH

EA

T3

DH

EA

T2

DB

OIL

4

DH

ETW

1

WA

SH

I2

DC

OO

L2

DH

EA

T1

TE

LE

V1

WA

SH

IN

10 6̂kg-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



<25>

2. Analysis Results II . AIM/Enduse (MAC) Model


0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

2001 2030

GH

G E

mis

sion

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


<26>

3. Policy Implication II . AIM/Enduse (MAC) Model

Negative cost of reduction options implies that such options arefeasible without extra cost of implementation, The list of such options will be a good information for policy makers to launch specific action programs to mitigate GHG emissions in a specificsector.

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.

the 10th aim international workshop · 2020-02-06 · tae yong jung iges, japan dong kun lee seoul...

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