The 10th AIM International Workshop

Transcription

1 March 10-12, 2005,NIES, Tsukuba, Japan Activities in the Fiscal Year 2005 in Korea The 10th AIM International Workshop 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

2 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 <2>

3 AIM/Korea local Model 1. Introduction 2. Input data projection 3. Scenario (setting/results) 4. Policy Implication

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 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. <4>

5 2. Input Data Projection I. AIM/Korea local Model Population (thousand pop.) man woman age 5-9age 10-14age 15-19age 20-24age 25-29age 30-34age 35-39age 40-44age 45-49age 50-54age 55-59age 60-64age 65-69age 70-74age 75-79age above 80age 1980 man 1980 woman 2030 man 2030 woman <5>

6 2. Input Data Projection I. AIM/Korea local Model GRDP Vehicle <6>

7 2. Input Data Comparison I. AIM/Korea local Model Tokyo/Seoul/Beijing Vehicle(Seoul) Japan Tokyo Vehicles per 1000 people Tokyo Ward area Korea Seoul China Beijing <7>

8 3. Scenario (Setting/Results) I. AIM/Korea local Model Scenario BAU Description Business-As-Usual (BAU) Scenario BAU_IMP Scenario that the new emission standard is applied D10 H30 D10H30 Scenario that diesel passenger cars will take 10 % shares in 2030 Scenario that new advanced technology vehicles will take 30% shares in 2030 (D10 + H30) Combined Scenario <8>

9 3. Scenario (Setting/Results) I. AIM/Korea local Model Energy use 10^3TOE 4,700 Energy use 4,600 4,500 4,400 4,300 4,200 4,100 4, BAU BAU_IMP D10 H30 D10H30 <9>

10 3. Scenario (Setting/Results) I. AIM/Korea local Model NOx TON 28,000 NOx 26,000 24,000 22,000 20,000 18,000 16,000 14,000 12,000 10, BAU BAU_IMP D10 H30 D10H30 <10>

11 3. Scenario (Setting/Results) I. AIM/Korea local Model SO 2 TON SO 2, , , , , , , BAU BAU_IMP D10 H30 D10H30 <11>

12 3. Scenario (Setting/Results) I. AIM/Korea local Model CO 2 TON CO 2 3,700,000 3,600,000 3,500,000 3,400,000 3,300,000 3,200,000 3,100, BAU BAU_IMP D10 H30 D10H30 <12>

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 marketoriented approaches rather than the conventional command-and-control type. The relative energy prices between gasoline and diesel should be reexamined (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. <13>

14 AIM/Enduse (MAC) Model 1. Introduction 2. Analysis Results 3. Policy Implication

15 1. Introduction II. AIM/Enduse (MAC) Model 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 <15>

16 2. Analysis Results II. AIM/Enduse (MAC) Model - Transport sector Marginal Abatement Cost Curve Won/kg-CO25 Marginal Cost 0 0.0E E E E E E E Reduction Potential kg-co2 <16>

17 2. Analysis Results II. AIM/Enduse (MAC) Model Won/kg-CO Won/kg-CO CPP(New) SPP(CNG) J EEP( LPG_New) BL15 P(LPG _Ne w) CPP(electricity) C PP(fuel c ell- meth ) Marginal Cost LPP(full c e ll- m eth ) LPP(g asolin e hybird ) Marginal_Cost TL1 (LPG_New) MPP(C NG ) MPP(g a so lin e h ybrid) MPP(electric ity) BL15P(CNG) SPP(elec tric ity) BM25P(CNG) BM 15P(Ele ctricity) Jeep(gasoline _Ne w) - Transport sector SPP(full cell- me th) BM15P(g aso line_new) CPP(New) SPP(CNG) JEEP(LPG_New) BL15P(LPG_New) CPP(electricity) CPP(fuel cell-meth) LPP(gasoline hybird) LPP(full cell-meth) TL1(LPG_New) MPP(CNG) MPP(gasoline hybrid) MPP(electricity) BL15P(CNG) SPP(electricity) BM25P(CNG) Jeep(gasoline_New) BM15P(Electricity) BM15P(gasoline_New) SPP(full cell-meth) Technology_name compact private passenger Cars(New) small private passenger Cars(CNG) Jeeps(LPG_New) Buses less than 15 persons(lpg_new) compact private passenger Cars(electricity) compact private passenger Cars(fuel cellmeth) Large private passenger Cars(gasoline hybird) Large private passenger Cars(full cellmeth) Trucks less than 1.0 tons(lpg_new) Medium private passenger Cars(CNG) Medium private passenger Cars(gasoline hybrid) Medium private passenger Cars(electricity) Buses less than 15 persons(cng) small private passenger Cars(electricity) Buses more than 25 persons(cng) Jeeps(gasoline_New) Buses more than 15 persons(electricity) Buses more than 15 persons(gasoline_new) small private passenger Cars(full cell-meth) <17>

18 2. Analysis Results II. AIM/Enduse (MAC) Model - Transport sector Marginal Cost Marginal_Cost Won/kg-CO2 Won/kg-CO LPP(diesel_New) SPP(Diesel) MPP(Diese l) CPP(LPG_New) SPP(LPG_New) LPP(LPG_New) MPP(LPG_New) TL5(New) TM5(New) MPT( New) MPT(New) B M25(New) SPP(New) BM16P(New) MPP(N ew) LPP(diesel_New) SPP(Diesel) MPP(Diesel) CPP(LPG_New) Technology name Large private passenger Cars(diesel_New) small private passenger Cars(Diesel) Medium private passenger Cars(Diesel) compact private passenger Cars(LPG_New) SPP(LPG_New) LPP(LPG_New) small private passenger Cars(LPG_New) Large private passenger Cars(LPG_New) MPP(LPG_New) Medium private passenger Cars(LPG_New) TL5(New) TM5(New) MPT(New) Trucks less than 5.0 tons(new) Trucks more than 5.0 tons(new) Medium private Taxi(New) MPT(New) Medium company Taxi(New) BM25(New) SPP(New) Buses more than 25persons(New) small private passenger Cars(New) BM16P(New) Buses more than 16persons(New) MPP(New) Medium private passenger Cars(New) <18>

19 2. Analysis Results II. AIM/Enduse (MAC) Model - Transport sector Reduction Potential Technology_name 10^6kg-CO2 2,000,000 1,800,000 TM5(New) MPTaxi(New) TL5(New) Trucks more than 5.0 tons(new) Medium private Taxi(New) Trucks less than 5.0 tons(new) 1,600,000 SPP(New) small private passenger Cars(New) 1,400,000 1,200,000 1,000, , ,000 MCT(New) MPP(New) MPP(LPG_New) MPP(Diesel) Medium company Taxi(New) Medium private passenger Cars(New) Medium private passenger Cars(LPG_New) Medium private passenger Cars(Diesel) 400,000 BM25P(New) Buses more than 25persons(New) 200,000 0 TM 5(N ew) M PTaxi(N ew) TL5(N ew) SPP(New) MCT(New) MPP(New) MPP(LPG_New) MPP(Diesel) BM25P(New) SPP(Diesel) SPP(LPG_New) BM16P(New) CPP(LPG_New) LPP(LPG_New) LPP(diesel_New) SPP(Diesel) SPP(LPG_New) BM16P(New) CPP(LPG_New) small private passenger Cars(Diesel) small private passenger Cars(LPG_New) Buses more than 16persons(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) <19>

20 2. Analysis Results II. AIM/Enduse (MAC) Model - Transport sector 10^6kg-CO2 160, , , ,000 80,000 60,000 40,000 20,000 0 BL15P(CNG) MPP(gasoline hybrid) TL1(LPG_New) Reduction Potential Jeep(LPG_New) SPP(electricity) SPP(CNG) SPP(full cell-meth) Jeep(gasoline_New) MPP(CNG) BM25P(CNG) BL15P(LPG_New) CPP(New) CPP(fuel cell-meth) BM15P(gasoline_New) CPP(electricity) LPP(full cell-meth) LPP(gasoline hybird) MPP(electricity) BM15P(Electricity) 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) small private passenger Cars(full cellmeth) SPP(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) compact private passenger Cars(fuel cellmeth) CPP(fuel cell-meth) BM15P(gasoline_Ne w) Buses more than 15 persons(gasoline_new) compact private passenger CPP(electricity) Cars(electricity) Large private passenger Cars(full cellmeth) LPP(full cell-meth) Large private passenger Cars(gasoline LPP(gasoline hybird) hybird) MPP(electricity) Medium private passenger Cars(electricity) BM15P(Electricity) Buses more than 15 persons(electricity) <20>

21 2. Analysis Results II. AIM/Enduse (MAC) Model - Transport sector GHG Emission 10^6Kg-CO2 60,000,000 50,000,000 40,000,000 30,000,000 20,000,000 Reduction Potential GHG Emission : ^ : 45,884,380 10^6 Reduction 5,276,466 10^6kg-CO2 10,000, Emission Reduction <21>

22 2. Analysis Results II. AIM/Enduse (MAC) Model Marginal Abatement Cost Curve 20 - Residential sector 15 Won/kg-CO2 Won/kg-CO2 10 Marginal Cost E E E E E E kg-co2 kg-co2 Reduction Potential <22>

23 2. Analysis Results II. AIM/Enduse (MAC) Model - Residential sector Marginal Cost WASHIN Technology_name Regular washing mashine Won/Kg-CO2 Won/kg-CO2 20 Won/kg-CO2 Marginal_Cost DHETW1 DHEAT2 DBOIL4 LPG oven range(lpg) LNG heater(lng) Boiler(LNG) 15 TELEV1 DHEAT1 Regular Television Kerosene pan heater 10 DLIGI2 DCOM2 Fluorescent lamp(luminous) Efficient computer 5 DLIGI5 Compact Fluoresen lamp(saving 2) DBOIL6 Condensing Boiler 0-5 WASHIN DHETW1 DHEAT2 DBOIL4 TELEV1 DHEAT1 DLIGI2 DCOM2 DLIGI5 DBOIL6 DLIGI4 WASHI2 TELEV2 DHEAT3 DSORAR DZISN2 REFRI2 DCOOL2 DLIGI4 WASHI2 TELEV2 Efficient fluorescent lamp(saving) Efficient washing mashine Efficient Television -10 DHEAT3 DSORAR LPG heater(lpg) Solar energy DZISN2 Insulation REFRI2 Efficient Refrigeration DCOOL2 High efficient air conditioner <23>

24 2. Analysis Results II. AIM/Enduse (MAC) Model - Residential sector Reduction Potential DSORAR Technology_name Solar energy DZISN2 Insulation 10^6kg-CO2 Reduction_Potential DBOIL6 REFRI2 Condensing Boiler Efficient Refrigeration 1,400,000 DLIGI2 Fluorescent lamp(luminous) 1,200,000 1,000, ,000 DCOM2 Efficient computer DLIGI4 Efficient fluorescent lamp(saving) DLIGI5 Compact Fluoresen lamp(saving 2) TELEV2 Efficient Television 600,000 DHEAT3 LPG heater(lpg) 400,000 DHEAT2 LNG heater(lng) 200,000 0 DSORAR DZISN2 DBOIL6 REFRI2 DLIGI2 DCOM2 DLIGI4 DLIGI5 TELEV2 DHEAT3 DHEAT2 DBOIL4 DHETW1 WASHI2 DCOOL2 DHEAT1 TELEV1 WASHIN DBOIL4 DHETW1 WASHI2 DCOOL2 Boiler(LNG) LPG oven range(lpg) Efficient washing mashine High effiecient air conditionaer DHEAT1 Kerosene pan heater TELEV1 Regular Television WASHIN Regular washing mashine <24>

25 2. Analysis Results II. AIM/Enduse (MAC) Model Reduction Potential - Residential sector 10^6Kg-CO2 25,000,000 20,000,000 GHG Emission : 14,396,73010^ : 16,109,950 10^6 Reduction 3,933,938 10^6kg-CO2 GHG Emission 15,000,000 10,000,000 5,000, Emission Reduction <25>

26 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 options 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. <26>