INTEGRATED ENVIRONMENTAL STRATEGIES (IBS)
PHASE II PART II
ntegrated
nvironmental
Strategies
Cost-Benefit Analysis of Integrated
Environmental Strategies for Air Quality
Improvement and Greenhouse Gas Emission
Reductions
Final Report
June, 2007
Korea Environment Institute
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Cost-Benefit Analysis of Integrated Environmental
Strategies for Air Quality Improvement and
Greenhouse Gas Emission Reductions
Yeora Chaea, Sangyeop Leea, Jeongim Parka, Nanjeong Moona,
Jongbum Leeb, Jeongeun Kima and Sooyoung Baea
a Korea Environment Institute
b Kangwon National University
June 2007
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TABLE OF CONTENTS
EXECUTIVE SUMMAY ES-1
CHAPTER 1 INTRODUCTION
1.1 BACKGROUND AND PURPOSE 1
1.2 RESEARCH ACTIVITIES AND SCOPE OF STUDY 3
1.2.1 Research Activities 3
1.2.2 Scope of Study 5
1.3 SPECIAL ACT ON SEOUL METROPOLITAN AIR QUALITY IMPROVEMENT AND
GHG REDUCTION MEASURES 6
1.3.1 Special Act on Seoul Metropolitan Air Quality Improvement 6
1.3.2 GHG Reduction Measures 10
CHAPTER 2 ANALYSIS AND PROJECTION OF GHG AND AIR
POLLUTANT EMISSIONS FROM FUEL USE IN SEOUL METROPOLITAN
AREA 11
2.1 METHODOLOGY FOR EMISSION ESTIMATION 11
2.1.1 Estimating Air Pollutant Emissions 11
2.1.2 Estimating GHG Emissions 16
2.2 CURRENT STATUS OF AIR POLLUTANT AND GHG EMISSIONS IN SEOUL
METROPOLITAN AREA 24
2.3 PROIECTED EMISSIONS OF AIR POLLUTANTS AND GHG IN SEOUL
METROPOLITAN AREA 25
2.3.1 Methodology for Projection 25
2.3.2 Projected Emissions of Air Pollutants and GHG in Seoul Metropolitan Area 31
CHAPTER 3 SEOUL METROPOLITAN AREA AIR QUALITY
MANAGEMENT PLAN (SAQMP) AND GHG EMISSION MITIGATION
PLAN (GHG): EFFECTS ON EMISSION REDUCTIONS AND COST
ANALYSIS 36
3.1 ESTIMATING EMISSION REDUCTIONS AND COSTS FOR SAQMP 37
3.1.1 Area Sources 37
3.1.2 Mobile Sources 60
3.1.3 Industrial Sources 91
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3.2 ESTIMATING UNIT EMISSION REDUCTIONS AND UNIT COSTS FOR GHG
EMISSION REDUCTION PL AN 108
3.2.1 Waste 108
3.2.2 Transportation Sources 116
CHAPTER 4 INTEGRATED ENVIRONMENT STRATAGIES TO REDUCE
AIR POLLUTANTS AND GREENHOUSE GASES 124
4.1 COST BENEFIT AND CORRELATION ANALYSIS FOROF AIR POLLUTANT AND
GHG EMISSION REDUCTION PLANS 124
4.2 OPTIMIZATION MODEL 138
4.3 SCENARIOS 139
4.3.1 GHG Mitigation Plan (GHG 2014) Scenario 139
4.3.2 Seoul Air Quality Management Plan (SAQMP 2014) Scenario 140
4.3.3 Integrated Environment Strategies (IES 2014) Scenario 142
CHAPTER 5 AIR QUALITY MODELING 144
5.1 INTRODUCTION 144
5.1.1 Background Information 144
5.1.2 Purposes 144
5.2 METHODOLOGY 145
5.2.1 Meteorological Model 145
5.2.2 Photochemical Model 145
5.2.3 TERRAIN Generation 153
5.2.4 Execution of Virtual Model 153
5.2.5 Emission Input File Generation 154
5.2.6 Current Air Quality Modeling 155
5.2.7 Projected Air Quality Modeling 157
5.3 RESULTS OF AIR QUALITY MODELING 158
5.3.1 Evaluating Model with Measured Concentrations 158
5.3.2 Results from BASE 2004 Scenario 164
5.3.3 Results from BAU 2014 Scenario 171
5.3.4 Results from SAQMP 2014 Scenario 172
5.3.5 Results from GHG 2014 Scenario 173
11
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5.3.6 Comparison of Air Quality Improvement by Three Scenarios 174
5.3.7 Limitations of Modeling Results 174
CHAPTER 6 VALUATION OF HUMAN HEALTH BENEFITS 175
6.1 BenMAP INPUT DATA FOR VALUATION OF HUMAN HEALTH BENEFITS 175
6.1.1 Data Setup 175
6.1.2 Population Data on Seoul Metropolitan Area 176
6.1.3 Geological Data on Seoul Metropolitan Area 178
6.1.4 Measured Air Pollutant Concentration Data on Seoul Metropolitan Area 179
6.1.5 Base Mortality 181
6.1.6 Concentration-Response Function 181
6.1.7 Economic Valuation Function 183
6.2 BenMAP CONFIGURATION 184
6.3 PM10 - HEALTH BENEFITS ASSOCIATED WITH PREMATURE MORTALITY
(BAU2014-SAQMP2014) 186
6.3.1 Distribution of PM10 Concentration 186
6.3.2 Distribution of Reduced Premature Death 188
6.3.3 Estimating Economic Benefits from Reduced Premature Deaths 191
6.4 PM10 - HEALTH BENEFITS ASSOCIATED WITH PREMATURE MORTALITY
(BAU2014-GHG2014) 194
6.4.1 Distribution of PM10 Concentration 194
6.4.2 Distribution of Reduced Premature Death 196
6.4.3 Estimating Economic Benefits from Reduced Premature Deaths 199
CHAPTER 7 CONCLUSIONS AND IMPLICATIONS 203
7.1 COST BENEFIT ANALYSIS 203
7.2 CONCLUSIONS AND IMPLICATIONS 205
REFERENCES 212
APPENDIX 214
in
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Cost-Benefit Analysis of Integrated Environmental
Strategies for Air Quality Improvement and
Greenhouse Gas Emission
EXECUTIVE SUMMARY
Korea, like many countries, is trying to balance environmental and public health concerns
against economic growth. Previous government policies to improve air quality in the Seoul
metropolitan area have achieved remarkable outcomes; however, many measures have reached
their limits of effectiveness due to the soaring number of vehicles and other pollution sources in
the region. Recognition of these serious challenges led to the legislation of the Special Act on
Seoul Metropolitan Air Quality Improvement Plan and the implementation of the Basic Plan for
Seoul Metropolitan Area Air Quality Management (SAQMP) in December 2004.
In addition to aggressively pursuing improved air quality, Korea joined the international efforts
to reduce greenhouse gases (GHGs) by signing the United Nations Framework Convention on
Climate Change (UNFCCC) in Rio, Brazil, in 1993. Although Korea is not a member of the
Annex I group under the Kyoto protocol (1997), Korean ministries put together action plans to
meet the Kyoto Protocol's goals.
For a county with limited economic resources and severe air pollution problems like Korea,
implementing integrated measures, which address both local air pollution and GHG emissions, is
essential to achieving necessary air pollution reductions and preparing for future agreements on
climate change. Integrated strategies have been implemented worldwide as cost-effective
mechanisms to reduce air pollutant impacts on ecosystems and human health and risks associated
with climate change, especially when GHGs and local air pollutants are co-generated by fossil
fuels combustion.
To assess the potential for integrated measures to help Korea achieve its environmental goals, the
Integrated Environmental Strategies (lES)-South Korea program was initiated in February 1999.
This program is a collaboration between the U.S. Environmental Protection Agency (EPA), the
Republic of Korea's Ministry of Environment, the Korean Environment Institute (KEI), and the
National Renewable Energy Lab (NREL). Its objective is to assess and quantify the
environmental and public health benefits resulting from integrated measures to reduce GHGs and
local air pollution.
This study report completes Phase II of IBS-South Korea, which assesses the co-benefit potential
of measures from the SAQMP aimed specifically at improving air quality and selected measures
targeting GHG emissions. This co-benefit analysis includes an estimation of health benefits and
associated economic valuation, yielding a cost-effectiveness value for each measure. Based on
cost effectiveness optimization, the study develops an alternative scenario of emission reduction
measures to achieve Korea's goals for both air quality improvement and GHG reduction.
ES-1
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Specific outcomes of this study include the following.
o Identification of cost-effective integrated strategies to improve air quality and reduce GHG
emissions
o Estimation of co-benefit potential of the air quality improvement plan and GHG mitigation
measures
o Cost-benefit assessment for each measure that encompasses implementation and operating
costs, GHG reduction effects, and public health benefits
The major research activities undertaken to achieve these objectives are shown in Table ES-1.
Table ES-1. Major Research Activities
Area
Current and projected air pollutant and
GHG emissions from fuel use in Seoul
metropolitan area
Cost benefit analysis for metropolitan air
quality improvement plan
Cost benefit analysis for GHG mitigation
plan
Development of IBS scenario
Air quality modeling
Valuation of health benefits with
BenMAP model
Cost-benefit analysis
International workshops
Research Activities
o
o
o
o
o
o
o
o
o
o
o
o
o
0
0
0
0
0
Conduct GHG emission analysis and projection based on fuel use
in Seoul metropolitan area
Conduct air pollutant emission analysis and projection based on
fuel use in Seoul metropolitan area
Quantify air pollutant emission reductions for each measure
Quantify GHG emission reductions for each measure
Quantify cost for each measure
Quantify air pollutant emission reductions for each measure
Quantify GHG emission reductions for each measure
Quantify cost for each measure
Conduct cost benefit analysis for each measure
- Recommend measures based on the best benefit-cost ratio
Identify the most cost effective IBS scenarios
Run model to project emissions for the target year with each
scenario
Convert air quality modeling outcome to BenMAP -ready format
Perform estimation of health benefits with BenMAP for each
scenario
Conduct valuation of health benefits from air quality improvement
Conduct cost benefit analysis for integrated valuation of health and
GHG mitigation benefits
Korea-US Environmental Protection Agency (EPA) joint
workshops
Workshops on international case studies for experts, policy
makers, NGOs and press
Korea Environment Institute (KEI) coordinates workshops and
participants in consultation with Korean Ministry of Environment
(MOE) and EPA
The geographic scope of this study was the Seoul Metropolitan area including Seoul, Incheon
and Kyonggi. Power plants in the Chuchung area were included in the air quality modeling due
to their impact on the air quality in the Seoul metropolitan area. The base year of the study was
2003, and 2014 was the year used for analyzing impacts of emission reduction scenarios
compared to business as usual (BAU). The pollutants included in the analysis were sulfur
oxides (SOx), nitrogen oxides (NOx), particulate matter with aerodynamic diameter less than 10
microns (PMio), carbon dioxide (CO2), and methane (CH/t).
ES-2
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Measures examined in this study were selected from the SAQMP and additional GHG mitigation
measures associated with fuel consumption. The sources impacted by these measures included
industrial energy combustion, non-industrial combustion, manufacturing industry combustion,
and mobile sources. Emission changes resulting from each measure were estimated from
published studies on emission factor changes. Air quality impacts of these emission reductions
were estimated using EPA's Models-3/Community Multiscale Air Quality (CMAQ) modeling
system, and the impact of changes in air quality on human health were estimated using EPA's
Environmental Benefits Mapping and Analysis Program (BenMAP).
The estimated costs of implementing a measure included the capital and operating costs, which
were summed and converted into an Equivalent Annual Value (EAV) for each measure. The
economic values associated with reduced morbidity and mortality were estimated using the value
of a statistical life (VSL) suggested by EPA and adjusted for Korea using purchasing power
parity (PPP) for a value of approximately 2.5 billion won. The economic value associated with
GHG reductions was determined using the marginal damage cost of 12 USD per ton of CC>2
suggested by the Intergovernmental Panel on Climate Change (IPCC) 4th Report.
Table ES-2 shows the most effective measures from the SAQMP and GHG scenarios for
reducing emissions of individual pollutants and pairs of pollutants. Conversion of busses to
compressed natural gas (CNG) and installation of diesel particulate filters (DPF) were the most
effective measures for reducing NOx and PMi0. Measures with the greatest potential to reduce
CC>2 were fuel switching from coal to liquefied natural gas (LNG), use of solar energy systems,
and advanced passenger vehicle technologies. When looking at NOx and PMio in combination
with CO2 reductions, the same four measures showed the greatest combined potential, including
two categories of accelerated vehicle retirement.
Table ES-2. Rank of Measures by Pollutant Emission Reductions
1
2
3
4
NOX
Promotion of
CNG Intra-city
Buses
DPF Install. -
Intra-city
Buses
DPF Install. -
Chartered
Buses
DPF Install. -
Large Trucks
PM10
Promotion of
CNG Intra-city
Buses
DPF Install. -
Intra-city
Buses
DPF Install. -
Chartered
Buses
DPF Install. -
Large Trucks
C02
Fuel Control
(Anthracite for
Residential
Use^LNG)
Solar Energy
Systems
Promotion of
Electric Vehicles
Promotion of
Hybrid Vehicles
NOX-C02
Accel. Veh.
Retirement -
Large Trucks
Promotion of
Low-NOx
Boilers
CNG Intra-city
Buses
Accel. Veh.
Retirement -
Large Pass.
Vans
C02- PM10
Accel. Veh.
Retirement -
Large Trucks
Promotion of
Low-NOx
Boilers
CNG Intra-city
Buses
Accel. Veh.
Retirement -
Large Pass.
Vans
NOX-PM10
Accel. Veh.
Retirement -
Large Trucks
Promotion of
CNG Intra-city
Buses
DPF Install. -
Large Trucks
DPF Install. -
Midsize Pass.
Vans
Analysis of the cost-effectiveness of measures for reducing each pollutant of interest revealed
that the most cost effective measures were often those associated with fuel switching and
restrictions on idling.
The cost and emission reduction potential for each measure were used as inputs to an
optimization model. The output of the model was a list of cost-optimized measures that would
ES-3
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meet GHG and air pollutant reduction goals. These measures were used to form the IBS
Scenario, which is shown in Table ES-3.
Table ES-3. Emission Reductions and Costs for IES2014 Scenario
Measure
Landfill Gas for Energy
Low-NOx Boilers
CNG Intra-city Buses
District Heating & Cooling
Fuel Switching in Industry
Idling Regulation (Gasoline)
Idling Regulation (Diesel)
LGP Conversion (Midsize
trucks)
Accelerated Vehicle
Retirement program (Midsize
trucks)
Accelerated Vehicle
Retirement program (Large
trucks)
Total Allowable Emissions
System NOX BACT
Total Allowable Emissions
System SOX BACT
Total Allowable Emissions
Sy stem PM10 BACT
Total
CO2 Emission
Reduction (kg)
1,559,675,000
5,738,577,000
856,893,612
108,204,000
2,042,097,140
19,134,615
15,027,336
36,485,400
152,189,100
114,901,304
0
0
0
10,643,184,507
NOX
Emission
Reduction
(kg)
0
74,666
6,955,892
13,200,888
11,440,665
62,439
123,558
14,534,184
55,477,800
27,790,924
42,930,000
0
0
172,591,01
6
PM10 Emission
Reduction (kg)
0
725
375,447
1,262,380
1,082,558
0
9,740
1,152,039
3,998,400
1,265,964
0
0
1,245,000
10,392,25
Cost (won)
-17,345,358,519
-1,417,760,012,487
-131,551,371,941
-4,802,420,836,720
-32,794,111,782
-11,398,216,416
-6,323,684,047
45,318,513,695
368,250,191,064
163,286,279,348
588,484,440,000
361,490,210,000
9,034,965,000
-4,883,728,992,805
Note: Values are rounded off to the nearest integers, and the total values may not be the sum of each value.
Emission reductions estimated for the IBS scenario in 2014 are compared with the BAU,
SAQMP, and GHG scenarios in Table ES-4.
Table ES-4. Emissions in 2014 and Cost by Scenario
BAU 20 14
SAQMP 2014
GHG 20 14
IBS 2014
NOX (kg)
353,943,649
181,949,649
322,085,542
181,352,634
SOX (kg)
91,114,932
25,213,534
77,597,622
49,507,622
PM10 (kg)
17,384,277
7,791,651
14,653,428
6,992,025
C02 (kg)
103,084,826,000
95,758,809,994
92,745,217,297
92,441,641,493
Cost (won)
0
295,610,922,711
-6,419,593,591,912
-4,883,728,992,805
CMAQ modeled concentration outputs were used as inputs to BenMAP, where changes in
morbidity and mortality and the associated economic values were estimated to obtain a net
benefit associated with reductions in air pollutant emissions. The value of 12 USD per ton CC>2
was used to estimate economic benefits from GHG reductions.
Figure ES-1 shows the costs, individual benefits (air quality and CO2), and net benefits of each
scenario. The GHG scenario costs the least to implement with a net savings of 6 trillion won
before the value of benefits is added. When net benefits are examined, however, the benefits
ES-4
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associated with the IBS scenario exceed those of the SAQMP or GHG scenarios. Although the
IBS scenario realizes slightly less savings from project implementation and operation compared
to the GHG scenario, the air quality and GHG savings exceed those in either scenario for the
greatest net economic benefit. This result was expected, given that measures in the IBS scenario
were selected using a cost-effectiveness optimization model.
160.0
140
(unit: trillion won)
i Cost
i Economic Benefit from Air
Quality Improvement
Economic Benefits from
CO2 Reduction
i Net Benefit (Air Quality
Improvement-Cost)
Net Benefit (Air Quality
Improvement+Climate
Change-Cost)
Figure ES-1. Cost and Benefit from Air Quality Improvement and GHG Reduction by Scenario
The results of this study have several implications. First, GHG reductions are possible without
any additional costs due to the close linkage with fuel use and the potential for net cost savings
associated with fuel savings. Second, air quality improvement is closely related to GHG
emission reductions, as was shown by the estimated 7,320,000 tons CC>2 reduction expected
through SAQMP and the reductions of 30,000 tons NOx and 2,700 tons PMio expected from the
GHG measures. Third, integrated strategies satisfy both air pollutant and GHG reduction targets
and generate economic benefits from fuel cost savings well above initial installation costs.
This study demonstrates that connecting GHG mitigation with air quality management measures
is effective. For example, current air quality management measures such as mandatory use of
clean fuel and bans on solid fuel use are effective in significantly reducing GHGs. Promoting
CNG use in intra-city buses was also shown to be effective in air quality improvement and GHG
reduction. The approximately 7,320,000 tons of CC>2 reductions expected from SAQMP is
equivalent to about 8% of the total GHG emissions in the Seoul metropolitan area in 2003.
Beyond SAQMP, the IBS scenario showed the potential to both achieve greater GHG reductions
(approximately 10,339,608 tons CO?) and exceed air quality improvements and do so at a lower
cost.
ES-5
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Figure ES-2 shows the percent reductions by pollutant for the three control scenarios (SAQMP,
GHG, and IBS) compared to BAU and the sectors where the emission reductions are occurring.
This shows the importance of mobile sources in achieving reductions of both air pollutant and
GHG emissions under the IBS scenario.
10
i Nonroad Mobile
Source
1 Industrial Source
Mobile Source
i Area Source
Waste
Figure ES-2. Reduction of Emissions by Scenario Compared to Emissions under BAU 2014 Scenario
Other measures not included in this study for analysis, such as improvement of fuel efficiency,
connecting air pollutant and GHG emission trading, and voluntary agreements on emission
reductions, also have great potential as integrated strategies. Additional studies are needed to
clarify their effects on emissions and cost effectiveness.
Several areas require further investigation to improve understanding of Korea's potential for
GHG and air pollutant reductions. Better definition of the maximum level of penetration for
each measure would assist in more concretely defining emission reduction potential; to do this
would require thorough consideration of the social, economic and political aspects of each
measure. Unified standards for cost assessments could improve comparability of benefits
between measures. Additional health benefits may be realized through reductions in air pollutant
concentrations beyond PM10, which were not explored in this study. Further, GHGs other than
carbon dioxide, such as nitrous oxide and methane, have a high global warming potential and
should be analyzed for completeness. Studies of the effects of integrated strategies for Korean
regions beyond the Seoul metropolitan area could reveal additional low- to no-cost opportunities
for integrated planning.
ES-6
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CHAPTER 1
INTRODUCTION
1.1 BACKGROUND AND PURPOSE
Fossil fuels have provided convenience and mobility but have also caused serious environmental
problems through emissions of air pollutants and greenhouse gases during production and
consumption processes. Climate change and air pollution from fossil fuel consumption have
great adverse effects on ecosystems, the ambient environment and public health. Many countries
are trying to balance environmental and public health concerns against economic growth.
Integrated strategies are the most cost-effective in reducing air pollution and its downstream
impacts on ecosystems and human health in the short-term as well as reducing increased risks
due to climate change in the long-term. Worldwide, integrated measures have been implemented
to reduce local air pollution and GHG emissions simultaneously where they are generated
together by fossil fuel combustion.
Integrated strategies refer to actions to reduce both local air pollution and GHG emissions at the
same time. Among the measures to improve air quality, retrofit technologies such as catalytic
converter and desulfurization reduce only local air pollutants; integrated measures such as
promotion of compressed natural gas (CNG) buses and district heat and cooling systems reduce
both local air pollutants and associated global GHGs by switching fuel type and increasing fuel
efficiency. The objectives of this study are to identify cost-effective integrated strategies to
address both local air quality issues and GHG mitigation concerns and to conduct economic
valuation and analysis of integrated planning compared with current air quality improvement
plans and GHG mitigation approaches.
A combination of high population density and an economic structure with high energy demand
very likely leads to serious air pollution problems in Korea. Especially in Seoul metropolitan
area, air quality improvement is critical due to high population density and a heavy volume of
traffic. Air quality management measures such as fuel control and stringent emission standards
led to significant decreases in the concentrations of some air pollutants common in developing
countries (e.g. SOx and CO). In Seoul, SOx concentration was reduced from 0.094ppm in 1980
to O.OOSppm in 2001, and CO concentration from 3.2ppm in 1989 to 0.9ppm in 2001.
However, the concentrations of other air pollutants common in developed countries (e.g. NO2,
PM10 and Os) either stay the same or tend to increase. NO2 and Os concentrations increase
mainly due to increase of vehicle exhaust emissions. PM10 concentration decreased till 1998
because of economic slowdown, but it has been on the rise since then. For the last decade, Seoul
metropolitan area Gross Regional Domestic Product (GRDP) increased by more than 6% every
year. The fact that PM10 and NO2 concentrations continued to rise for the last 3 years indicates
current air pollution control approaches are insufficient.
Various government policies to improve air quality in the Seoul metropolitan area have achieved
remarkable outcomes. However, current air quality improvement measures reached to the limits
due to soaring number of vehicles and various sources. Thus, the efforts to cope with these
serious challenges led to the legislation of the Special Act on Seoul Metropolitan Air Quality
1
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Improvement Plan and implementation of Basic plan for Seoul Metropolitan Area Air Quality
Management to execute it (December, 2004). Municipal governments will conduct local level
implementation of air quality improvement measures.
As environmental problems become global issues, international treaties on climate change
increase. Korea joined the international efforts to reduce GHGs by signing the United Nations
Framework Convention on Climate Change (UNFCCC) in Rio, Brazil in 1993. Although Korea
is not a member of Annex I group of Kyoto protocol (1997), Korean ministries put together
action plans to meet the Kyoto Protocol's goals.
For a county with limited economic resources and severe air pollution problems, implementing
integrated measures is essential to achieve co-benefits (air pollution and GHG reduction) and to
prepare for future agreement on climate change.
Specific objectives of this study are to
o Identify cost-effective integrated strategies to improve air quality and to reduce GHG
emissions
o Conduct co-benefit analysis of air quality improvement plan and GHG mitigation measures
o Conduct cost-benefit analysis considering costs of IBS programs, GHG reduction effects and
public health benefits in connection with previous Phase I, II and III IBS studies
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1.2 RESEARCH ACTIVITIES AND SCOPE OF STUDY
1.2.1 Research Activities
The major research activities and framework of this study are illustrated in Figure 1.1.
Analysis and prospect of air pollutant and GHG emissions in Seoul metropolitan area
Effects of air
quality
improvement plan
on air pollutant
emission reduction
Effects of air
quality
improvement plan
on GHG emission
reduction
Cost analysis for
air quality
improvement
measures
Develop IES scenarios
Prioritize measures
based on costs and
benefits
Identify integrated
measures with the
best benefit-cost
ratio
Effects of GHG
mitigation plan on air
pollutant emission
reduction
Effects of GHG
mitigation plan on
GHG emission
reduction
Cost analysis for
GHG mitigation
measures
Air quality modeling
Health effect analysis
Economic valuation and analysis
Figure 1.1 Framework of the Study
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Table 1-1. Major Research Activities
Area
Research Activities
Current and projected air pollutant
and GHG emissions from fuel use
in Seoul metropolitan area
o
o
Conduct GHG emission analysis and project from fuel
use in Seoul metropolitan area
Conduct air pollutant emission analysis and project
from fuel use in Seoul metropolitan area
Cost benefit analysis for
metropolitan air quality
improvement plan
o Quantify air pollutant emission reduction for each
measure
o Quantify GHG emission reduction for each measure
o Quantify cost for each measure
Cost benefit analysis for GHG
mitigation plan
o Quantify air pollutant emission reduction for each
measure
o Quantify GHG emission reduction for each measure
o Quantify cost for each measure
Development of IBS scenarios
o Conduct cost benefit analysis for each measure
- Recommend measures based on the best benefit-
cost ratio
o Identify the most cost effective IBS scenarios
Air quality modeling
o Run model to project emissions for the target year with
each scenario
o Convert air quality modeling outcome to BenMAP -
ready format
Valuation of health benefits with
BenMAP model
o Perform valuation of health benefits with BenMAP for
each scenario
o Conduct valuation of health benefits from air quality
improvement
Cost-benefit analysis
o Conduct cost benefit analysis for integrated valuation
of health and GHG mitigation benefits
International workshops
o
o
o
Korea-US Environmental Protection Agency (EPA)
joint workshops
Workshops on international case studies for experts,
policy makers, NGO and press
Korea Environment Institute (KEI) coordinates
workshops and participants in consultation with Korean
Ministry of Environment (MOE) and EPA
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1.2.2 Scope of Study
Geographical scope
- The geographical scope of this study is limited to Seoul metropolitan area including
Seoul, Incheon and Kyonggi
For the air quality modeling analysis, power plants in Chuchung area are included due to
their impacts on the air quality of Seoul metropolitan area
Time period
Year 2003 plays the role of base year and 2014 was selected for projection
- Year 2003 is selected as the base year due to the availability of reliable data for other
years
Only if necessary and available, more recent data than those in 2003 are used
Pollutants of concern
- Air pollutants: SOX, NOX, PM10
- GHGs: CO2, CH4
Policies
- Air pollutant reduction plan: Seoul metropolitan air quality improvement plan
GHG reduction plan: GHG mitigation measures associated with environmental fuel
consumption
Sources
- In this study, sources for analysis are limited to those from fuel combustion (Table 1-2).
Table 1-2. Sources for Analysis
Emission source
Energy industry combustion
Non-industrial combustion
Manufacturing industry combustion
Road mobile source
Samples of source
Public power plants
District heating facilities
Oil refineries
Commercial and public buildings
Residential buildings
Combustion facilities
Furnaces
Cars
Taxies
Passenger vans
Buses
Trucks
Special vehicles
Two-wheeled vehicles
Analysis Model
- Quantitative models are adopted to model air quality and health effects.
- Air quality model: EPA-MODELS3/CMAQ
- Health effect model: B enMAP
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1.3 SPECIAL ACT ON SEOUL METROPOLITAN AIR QUALITY IMPROVEMENT
AND GHG REDUCTION MEASURES
1.3.1 Special Act on Seoul Metropolitan Air Quality Improvement
The emission reduction policy to improve air quality can be classified as regulations for
industrial site management (total allowable emissions system, emission trading, etc), area source
control (including VOC control), on-road mobile source control and non-road mobile source
control. Components of each category are described below.
- Industrial source control: total allowable emissions system and emission trading system play
the key role. For other facilities not under total allowable emissions system, fuel control,
more stringent emission standards, voluntary air quality management and support and
guidance from government organization will be promoted. Small-sized incinerating facilities
should be closed down and products banned from incineration should be expanded.
- Area source control: fuel control, expansion of district air conditioning and heating systems,
spread of lowNOx boilers, distribution of new recycling energy, and energy demand control
- VOC control: mandatory stage II controls for gasoline service stations, regulations on
arsenic emission in production process, stringent standards of organic solvent content in
consumer products, low arsenic emission standards for industrial surface coating and
cleaning operations and limitation of cutback asphalt usage
- Vehicle source control: low emission standards for new vehicles, supply of low emission
vehicles, distribution of emission control technologies like Diesel Particulate Filter (DPF),
Diesel Oxidation Catalyst (DOC), support for conversion to gas vehicles, support for
accelerated vehicle retirement program, on-road motor vehicle emission and fuel standards,
more stringent emission test procedures, clean fuels programs, enhancement of regulation
on idling, designation of green (clean air) area as a policy for demand control, improvement
of public transportation infrastructure, parking demand control, industrial traffic demand
control
- Non-road mobile source control: stringent emission standards for construction machinery,
marine vessels and farm equipments, retrofit technologies like Selective Catalytic Reduction
(SCR) and DPF, limits on sulfur content in fuel
The measures to reduce air pollutants and CO2 and their effects are presented in Table 1-3. Those
for which CO2 reductions can be quantified are highlighted.
-------
Table 1-3. Effects of Seoul Metropolitan Area Air Quality Management Plan on Air Pollutants
and GHG Reduction
Source
Industrial
Sources
Area Sources
Measure
Total
allowable
emissions
systems
Fuel control
Stringent
emission
standards
Solid waste
combustion
facility
management
Voluntary
environment
management
Support/
Education
Fuel control
Description/Example
Total allowable emissions
system and emission trading
Expand the regions for low-
sulfur fuel usage
Stringent emission standards
and penalty on emission of
nitrous oxide
Stringent emission standards
Closedown of small-size solid
waste incinerators
Expansion of products
prohibited for incineration
(incineration volume)
More environmentally friendly
companies
Agreement on voluntary
environment management
Distribution of manual for
emission facility management
and guidance
Diagnosis of air quality
control, consulting and
financial aid for environmental
investment for small- and
medium-sized businesses
Emission reduction partnership
between large enterprises and
their collaborate firms
Support for investment on
infrastructure
Switch from residential
smokeless coal to city gas
Expansion of region to use
low-sulfur gasoline and clean
fuels
Air Pollutant
Reduction
SOX, NOX,
PMio
SOX, PM10
NOX, PMio
SOX, NOX,
PMio, VOC
SOX, NOX,
PMio
SOX, NOX,
PMio
SOX, NOX,
PMio, VOC
SOX, NOX,
PMio, VOC
GHG
Reduction
CO2
C02
C02
C02
-------
Area Sources
continued...
On-Road
Mobile
Sources
District air
conditioning
and heating
NOX
regulation
Demand
control
Regulations
on
manufacturer
On-road
vehicle
regulations
Expansion of district air
conditioning and heating
system
Revitalization of small-size
community energy system
(CES)
Supply low-NOx boilers
Better management of LNG
facilities
Distribution of alternative
energy: solar energy
Regulation on indoor air-
conditioning and heating
Environment friendly (energy
saving) building standards and
certification programs
Stringent emission standards
for new vehicles
Distribution of low emission
vehicles
Emission reduction plan for
specified-diesel-vehicles a:
SCR/DPF installation
Emission reduction plan for
specified-diesel-vehicles: DOC
installation
Emission reduction plan for
specified-diesel-vehicles: LPG
conversion
Emission reduction plan for
specified-diesel-vehicles:
Support for accelerated vehicle
retirement program
Improvement of on-road
vehicle emission management:
inspection program for on-road
vehicles, introduction of
remote sensing devices (RSD),
occasional emission inspection
program for on-road vehicles,
defect inspection program,
introduction of on-board
diagnostics (OBD), etc
SOX, NOX,
PMio
SOX, NOX,
PMio, VOC
SOX, NOX,
PMio, VOC
SOX, NOX,
PMio, VOC
NOX, PMio,
VOC
NOX, PMio,
VOC
NOX, PMio,
VOC
PMio, VOC
NOX, PMio,
VOC
NOX, PMio,
VOC
PMio, VOC
C02
C02
C02
C02
CO2
-------
On-Road
Mobile
Sources
continued...
Two-wheeled
vehicle
regulations
Fuel Control
Traffic
demand
control
Stringent emission standards
Higher quality standards for
engine oil
Mandatory regular inspection
program
Higher quality standards for
gasoline fuels
Designation of green (clean
air) district
Taxation on causing heavy
traffic
Improvement of public
transportation infrastructure
Industrial traffic demand
control
Parking demand control
Encouragement of bicycle ride
VOC, NOx
SOX
SOX, NOX,
PMio
CO2
a specified-diesel-vehicle: vehicle that passed the guaranteed period for specified emission rate among the diesel
vehicles registered in Seoul metropolitan area
-------
1.3.2 GHG Mitigation Measures
o
o
o
GHG emissions are closely associated with fuel consumption, so the GHG mitigation plan
mainly focuses on reduction of fuel consumption by improving fuel efficiency and
developing alternative energy sources.
In this study, GHG reduction measures associated with fuel consumption are analyzed.
Effects of the GHG reduction measures on the reduction of air pollutants and GHGs are
presented in Table 1-4. The measures associated with fuel consumption are highlighted.
Table 1-4. Effects of GHG Reduction Measures on Air Pollutants and GHG Emission Reduction
Area
Building energy
management
Transportation
energy
management
Waste
management
Measure
Environmentally-friendly building
certification program
Stringent regulation on vehicle
idling
Distribution of low emission
vehicles like hybrid cars
Expansion of waste water treatment
facilities
Expansion of livestock waste
matter treatment facilities
Expansion of sewage disposal
plants
Energy industrialization of landfills
Building facilities for energy
industrialization of food wastes and
management
Distribution of green fuel such as
biodiesel
Air Pollutant
Reduction
SOX,NOX,PM10
SOX,NOX, PM10
SOX,NOX,PM10
Odor
Odor
Odor
SOX,NOX,PM10,
Odor
Odor
SOX,NOX,PM10
GHG
Reduction
CO2
C02
CO2
CH4
CH4
CO2, CH4
CO2, CH4
CO2; CH4
CO2
10
-------
CHAPTER 2
ANALYSIS AND PROJECTION OF GHG AND AIR POLLUTANT
EMISSIONS FROM FUEL USE IN SEOUL METROPOLITAN AREA
2.1 METHODOLOGY FOR EMISSION ESTIMATION
2.1.1 Estimating Air Pollutant Emissions
Data from Clean Air Policy Support System (CAPSS, 2005) by Korean Ministry of Environment
(MOE) was utilized in order to characterize the emissions of air pollutants. Emissions of air
pollutants from various source categories were estimated using methodologies that were
consistent with the method presented in CAPSS1.
A. Methodology for Estimating Air Pollutant Emissions from Stationary Combustion
Major sources contributing to air pollutant emissions from fossil fuel combustion are fossil fuel
combustion for energy use (CAPSS category 01), non-industrial combustion (CAPSS category
02) and industrial combustion (CAPSS category 03). The equation to calculate emissions from
fossil fuel combustion is presented below.
= EFij>
-------
Air pollutant emission factors by fuel type and source are presented in Table 2-1.
Table 2-1. Air Pollutant Emission Factors
Fuel type
Anthracite
Coal
Bituminous
Coal
B-A Oil
B-B Oil
Unit
kg/ton
kg/ton
kg/kL
kg/kL
Source
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
NOX
2.4
2.4
2.4
1.3
2.4
2.4
2.89
7.5
7.5
-
7.5
7.5
3.84
4.81
4.81
2.4
4.81
4.81
3.84
4.81
4.81
sox
19.5S
19.5S
19.5S
103
19.5S
19.5S
19S
19S
19S
-
19S
19S
18S
18S
18S
18S
18S
18S
18.84S
18.84S
18.84S
PM10
134
134
134
0.402
134
134
33.5
33.5
33.5
-
33.5
33.5
0.596
0.521
0.521
0.521
0.521
0.521
0.852
0.744
0.744
12
-------
B-B Oil
B-C Oil
LSWR
Diesel
Kerosene
kg/kL
kg/kL
kg/kL
kg/kL
kg/kL
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
2.4
4.81
4.81
5.87
6.64
6.64
6.63
6.64
6.64
5.87
6.64
6.64
-
6.64
6.64
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
2.4
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
18.84S
-
18.84S
18.84S
17S
17S
17S
17S
17S
17S
17S
17S
17S
0.744
0.744
0.744
0.718S+0.277
0.682S+0.242
0.682S+0.242
0.682S+0.242
0.682S+0.242
0.682S+0.242
0.718S+0.277
0.682S+0.242
0.682S+0.242
-
0.682S+0.242
0.682S+0.242
0.17
0.149
0.149
0.105
0.149
0.149
0.17
0.149
0.149
13
-------
Kerosene
LPG/
Propane/
Butane
LNG
kg/kL
kg/kL
kg
/1000m3
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
Public electricity generation
District heating/oil
refineries/solid fuels
Nonindustrial combustion
point sources
Nonindustrial combustion
area sources
Industrial combustion point
sources
Industrial combustion area
sources
5.46
2.4
2.4
2.28
2.28
2.28
2.18
2.28
2.28
1.43
2
2
2.62
2
2
17S
17S
17S
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.105
0.149
0.149
0.069
0.069
0.069
0.035
0.069
0.069
0.029
0.029
0.029
0.029
0.029
0.029
14
-------
B. Methodology for Estimating Air Pollutant Emissions from Mobile Sources
o Estimating Hot-Start emissions
- The method to estimate emissions from road mobile sources is presented below.
Emission (vehicle type, road type) = Emission Factor (vehicle type, road type)
x Vehicle Kilometer Traveled (vehicle type, road type)
- Not all the traffic volume could be measured. Thus, total vehicle kilometer traveled
(Total VKT) was determined with number of registered vehicles and average vehicle
kilometer traveled. The difference between total VKT and VKT with monitored data
becomes VKT without monitored data.
D Total VKT
Total VKT (vehicle type) = Average daily VKT (vehicle type)
x Number of Registered Vehicles (vehicle type) x 365
D VKT by Road Type
VKT by road type (vehicle type) = Traffic Volume by Road Type (vehicle type)
x Road Section
D VKT without Monitored Data (vehicle type)
= Total VKT (vehicle type) - VKT by Road Type (vehicle type)
Allocating VKT without monitored data
= Based on the length of lanes for the section without any monitored data on traffic volume
- Estimates for emissions from mobile sources were calculated from VKT and emission factors.
Emission factors were dependent on vehicle speeds and vehicle speeds on road types.
- Emissions of air pollutants were calculated from emission factors by model year presented by
Automobile Pollution Research Center (APRC, Emission factors of on-road vehicles by
model year, 2002.5).
15
-------
Table 2-2. Emission Factors for Road Mobile Sources (g/km)
Vehicle type
Passenger
Cars
Extra-
Small
Small
Midsize
Large
Taxies
Passenger
Vans
Trucks
Small
Midsize
Large
Small
Midsize
Large
Fuel
Gasoline
Gasoline
Gasoline
Gasoline
LPG
Gasoline
Diesel
LPG
Diesel
Diesel
Gasoline
Diesel
LPG
Diesel
Diesel
CO2
137.8
180.9
212.9
235.7
231.0
251.7
243.3
190.2
315.1
1382.4
247.3
245.5
187.9
334.9
1388.2
CO
0.656
0.821
0.962
0.994
2.31
0.633
0.39
1.717
0.513
2.424
0.627
0.364
1.642
0.252
3.068
voc
0.069
0.042
0.037
0.031
0.126
0.022
0.039
0.231
0.719
0.051
0.491
0.824
CH4
0.03
0.02
0.02
0.02
0.042
0.033
0.004
0.032
0.019
0.041
0.032
0.012
0.031
0.034
0.036
NOX
0.19
0.132
0.131
0.135
0.586
0.196
0.556
0.447
2.494
6.647
0.135
0.536
0.397
0.573
10.305
N2O
0.03
0.05
0.06
0.04
0.038
0.059
0.007
0.026
0.007
0.095
0.058
0.008
0.025
0.007
0.075
PM
-
-
-
-
-
0.064
-
0.069
0.154
-
0.061
-
0.06
0.331
2.1.2 Estimating GHG Emissions
A. Methodology for Estimating GHG Emissions from Stationary Combustion
o CO2 emissions are directly related to fuel consumption. The method to estimate CO2
emissions is presented below.
o Estimating CC>2 Emissions
CC>2 Emission
= Fuel Consumption x Heat Value x Carbon Content Coefficient
x Fraction of Carbon Oxidized x 44/12
- Data: fuel consumption by fuel type (kg), heat value (kcal/kg), fuel efficiency (kW/kcal),
and CC>2 emission (tCO2)
Calculation:
1) Determine fuel consumption by fuel type
2) Calculate TOE (Ton of oil equivalent) by multiplying fuel consumption by fuel type
and TOE conversion factor
3) Determine tC (Ton of Carbon) by multiplying TOE and CEF (Carbon Emission
Factor)
4) Emission = tC x 44/12
16
-------
Table 2-3. TOE Conversion Factors and Carbon Emission Factors
Fuel Type
Bituminous coal (ton)
lower than 0.3%
Cokes (ton)
Wood (ton)
Diesel (kL)
Kerosene (kL)
Distillate oil (kL)
LSWR (kL)
lower than 0.3%
Naphthenic oil (kL)
Reduced crude oil
(kL)
B-C Oil (kL)
Lower than 4.0%
Lower than 1.0%
Lower than 0.5%
Lower than 0.3%
LNG( 1000m3)
LPG( 1000m3)
Refinery gas (1000m3)
TOE Conversion
Factor
0.66
0.65
0.45
0.92
0.87
0.99
0.99
0.80
0.99
0.99
0.99
0.99
0.99
0.99
1.05
1.50
1.50
Unit
kg/kg
kg/kg
kg/kg
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/L
kg/Nm3
kg/Nm3
kg/Nm3
Carbon Emission
Factor
(Ton C/TOE)
1.132
1.132
1.132
0.832
0.812
0.875
0.875
0.829
0.875
0.875
0.875
0.875
0.875
0.875
0.637
0.713
0.713
Data: Korea Energy Management Cooperation (KEMCO) and Intergovernmental Panel on Climate Change (IPCC)
B. Methodology for Estimating GHG Emissions from Mobile Sources
- Daily emissions of CO2 from a vehicle (g/day-vehicle) were calculated by multiplying
vehicle kilometer traveled (VKT) and emission factors (g/km).
- Annual emissions of CO2 from all the vehicles (ton/yr) were then computed as below2.
Annual C02 Emission = Nt x M, x EFt x Iff6 x 365
Where
EFj = Emission factor for vehicle type / at average speed (g/km)
Nt = Number of vehicles of vehicle type /
MJ = kilometer traveled for vehicle type /' (km)
o Emission Factor
2 Prospective air pollutant emissions from vehicles, Ecofrontier
17
-------
- Emission factors at average speed of 20km/hr, 60km/hr and 80km/hr were derived from
the method presented in CAPSS (Table 2-4).
Table 2-4. Estimating CC>2 Emission Factors by Vehicle Type
Vehicle Type
Passenger
Cars
Extra- Small
Small
Midsize
Large
Taxies
Passenger
vans
Trucks
Small (Diesel)
Midsize
Large
Small (Diesel)
Midsize
Large
Emission Factor (g/km)
y = 595.32 * VA(-0.404)
y = 937.56* VA(-0.4506)
y = 1248.4 * VA(-0.4845)
y = 1563.9* VA(-0.5194)
y = 1397.4 * VA(-0.5475)
y = 1103.7* VA(-0.413)
y= 1086.2* VA(-0.3249)
Intra-city buses (< 50km/hr)
y = 2804.7 * VA(-0.3 105)
Other than intra-city buses,
use emission factors for large trucks
y = 1073.8 * VA(-0.4009)
y = 0.1029 * VA(2) -14.937 * V + 798.9
y = 624.04 * VA(-0.3829)
R2
0.87
0.93
0.97
0.99
0.98
0.89
0.91
0.88
0.86
0.89
0.97
Reference: NIER. 2005. Measures for Reduction of GHG Emissions from Vehicles; V = velocity in km/hr
Table 2-5. CC>2 Emission Factors by Vehicle Type
Vehicle Type
Passenger Cars
Extra- Small
Small
Midsize
Large
Taxies
Passenger Vans
Trucks
Small (Diesel)
Midsize
Large
Small (Diesel)
Midsize
Large
Emission Factor by Speed (g/km)
20 km/hr
177
243
292
330
271
320
410
1106
60 km/hr
114
148
172
186
149
203
287
80 km/hr
101
130
149
161
127
181
262
Other than intra-city buses,
use emission factors for large trucks
323
541
1982
208
273
1301
185
263
1166
18
-------
o The ratios of vehicle speeds differ by the region. Thus, average emission factors were derived
by multiplying the ratios of vehicle speeds for each region (Urban (20km/hr), Rural
(60km/hr), Highway (80km/hr)) (data from CAPSS) and weighted average of emission
factors at each speed3.
- For small passenger cars in Kyonggi, 20km/hr 36.3%, 60km/hr 37.5%, 80km/hr 26.2%
- Average emission factor = [(weighted average emission factor at 20km/hr x 36.3%) +
(weighted average emission factor at 60km/hr x 37.5%) + (weighted average emission
factor at 80km/hr x 26.2%)] / 100
3MOE. Estimation of Air Pollutant Emissions from Vehicles
19
-------
Table 2-6. VKT Ratios by Region and Vehicle Type
Vehicle type
Extra- Small
Cars
Small
Passenger Cars
Midsize
Passenger Cars
Large
Passenger Cars
Taxies
Small
Rvs
Midsize RVs
& Passenger
Vans
Midsize
Passenger
Vans
Intra-city
Buses/Diesel
Intra-city
Buses/CNG
Intercity
Buses
Chartered
Buses
Express
Buses
Other
Buses
Diesel/Small
Trucks
Midsize
Trucks
Large
Trucks
Special
Vehicles
Seoul
20
km/hr
79.5
82.2
85.2
85.3
98.7
85.3
84.2
33.7
98.8
98.8
14.6
23.3
52.4
33.7
81.6
67.7
25.8
25.8
60
km/hr
16.2
14.1
11.8
11.8
1.3
11.8
12.9
53.0
1.2
1.2
82.5
63.2
0.0
53.0
13.6
20.9
45.8
45.8
80
km/hr
4.2
3.7
2.9
2.9
0.0
2.9
3.0
13.4
0.0
0.0
3.0
13.5
47.6
13.4
4.8
11.5
28.4
28.4
Incheon
20
km/hr
50.2
50.2
47.6
45.3
87.0
45.3
58.1
17.6
87.1
87.1
0.0
33.2
0.0
17.6
44.5
29.2
5.8
5.8
60
km/hr
18.5
18.2
19.8
21.3
0.6
21.3
13.6
32.4
0.6
0.6
50.0
16.8
50.0
32.4
21.4
35.4
47.1
47.1
80
km/hr
31.3
31.6
32.7
33.4
12.5
33.4
28.2
50.0
12.3
12.3
50.0
50.0
50.0
50.0
34.0
35.4
47.1
47.1
Kyonggi
20
km/hr
37.3
36.3
35.1
35.3
100.0
35.3
41.9
2.1
100.0
45.9
45.9
2.1
3.2
2.1
31.2
18.1
2.3
2.3
60
km/hr
38.5
37.5
38.2
38.1
0.0
38.1
35.0
59.0
0.0
48.3
48.3
56.9
0.0
59.0
39.5
34.4
38.5
38.5
80
km/hr
24.2
26.2
26.7
26.7
0.0
26.7
23.2
38.8
0.0
5.8
5.8
41.0
96.8
38.8
29.3
47.5
59.1
59.1
Data: CAPSS internal data, Ecofrontier
20
-------
Table 2-7. Average CC>2 Emission Factors for Gasoline Passenger Cars by Vehicle Type and
Model year
Gasoline vehicle
type
Extra- Small
Small
Midsize
Large
Model year
All
< 1996
1997-2001
2002 <
All
< 1996
1997-2001
2002 <
All
< 1996
1997-2001
2002 <
All
< 1996
1997-2001
2002 <
Average emission factor (g/km)
Seoul
164
152
167
162
226
223
226
224
274
272
262
303
308
264
323
314
Incheon
142
132
144
142
190
188
189
200
222
218
211
256
243
205
254
250
Kyonggi
135
125
136
135
178
176
176
189
203
204
197
244
230
194
240
238
21
-------
Table 2-8. CC>2 Emission Factors by Vehicle Type (g/km)
Vehicle type
Passenger
Cars
Taxies
Passenger
Vans
Large
Passenger
Vehicles
Trucks
Extra- Small
Small
Midsize
Large
Midsize/Large
Extra- Small
Small
Midsize
Intracity buses
Intercity buses
Chartered buses
Express buses
Other buses
Extra- Small
Small
Midsize
Large
Fuel Type
Gasoline
LPG
Gasoline
LPG
Gasoline
Diesel
LPG
Gasoline
Diesel
LPG
LPG
Gasoline
LPG
Gasoline
Diesel
LPG
Diesel
Diesel
LPG
Diesel
Diesel
Diesel
Diesel
Gasoline
LPG
Gasoline
Diesel
LPG
Diesel
Diesel
Seoul
164
131.2
226
180.8
274
205.5
219.2
308
231
246.4
269
251.7
190.2
251.7
301
190.2
326
1106
Incheon
142
113.6
190
152
222
166.5
177.6
243
182.3
194.4
253
251.7
190.2
251.7
265
190.2
296
1106
Kyonggi
135
108
178
142.4
208
156
166.4
230
172.5
184
271
251.7
190.2
251.7
247
190.2
247
1106
34.35 (ton/yr-vehicle)
143.8
143.8
143.8
143.8
247.3
187.9
247.3
301
187.9
454
1438
1277
1277
1277
1277
247.3
187.9
247.3
251
187.9
348
1277
123.5
123.5
123.5
123.5
247.3
187.9
247.3
237
187.9
317
1235
o Number of Registered Vehicles
- Projected numbers of registered vehicles by year, vehicle type and vehicle age were used
to estimate CC>2 emissions.
o Annual Vehicle Kilometer Traveled
- Annual vehicle kilometer traveled was calculated by multiplying daily vehicle kilometer
traveled (VKT) by number of registered vehicles and 365 days.
22
-------
Table 2-9. Daily VKT in Seoul by Year (km)
Vehicle type
Extra- Small
Passenger
Cars
Small
Passenger
Cars
Midsize
Passenger
Cars
Large
Passenger
Cars
Midsize
Taxies
Large Taxies
Small RVs
Midsize
RVs/
Passenger
Vans
Midsize
Passenger
Vans
Intra-city
Buses
Intercity
Buses
Chartered
Buses
Express
Buses
Other Buses
Extra-small/
Small Trucks
Midsize
Trucks
Large Trucks
Year
2004
37.2
37.2
38.3
42.7
221.0
161.3
42.7
62.9
50.9
210.9
342.6
189.8
525.1
117.3
61.5
67.8
102.4
2005
37.1
37.1
38.2
42.6
220.6
161.0
42.6
62.8
50.8
210.9
341.9
189.4
524.0
117.0
61.4
67.6
102.2
2006
37.1
37.1
38.1
42.5
220.1
160.9
42.5
62.6
50.7
210.9
341.3
189.0
522.9
116.8
61.3
67.5
102.0
2007
37.0
37.0
38.1
42.4
219.7
160.3
42.4
62.5
50.6
210.9
340.6
188.6
521.9
116.5
61.1
67.3
101.8
2008
36.9
36.9
38.0
42.3
219.2
160.0
42.3
62.4
50.5
210.9
339.9
188.2
520.8
116.3
61.0
67.2
101.5
2009
36.8
36.8
37.9
42.2
218.8
159.7
42.2
62.3
50.4
210.9
339.2
187.9
519.8
116.1
60.9
67.1
101.3
2010
36.8
36.8
37.8
42.1
218.3
159.3
42.1
62.1
50.3
210.9
338.5
187.5
518.7
115.8
60.8
66.9
101.1
2011
36.7
36.7
37.8
42.1
217.9
159.0
42.1
62.0
50.2
210.9
337.8
187.1
517.7
115.6
60.6
66.8
100.9
2012
36.6
36.6
37.7
42.0
217.5
158.7
42.0
61.9
50.1
210.9
337.1
186.7
516.6
115.4
60.5
66.7
100.7
2013
36.6
36.6
37.7
42.0
217.5
158.7
42.0
61.9
50.1
210.9
337.1
186.7
516.6
115.4
60.5
66.7
100.7
2014
36.6
36.6
37.7
42.0
217.5
158.7
42.0
61.9
50.1
210.9
337.1
186.7
516.6
115.4
60.5
66.7
100.7
Data: MOE. 2004. Total Allowable Emissions System in Seoul Metropolitan Area
23
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2.2 CURRENT STATUS OF AIR POLLUTANT AND GHG EMISSIONS IN SEOUL
METROPOLITAN AREA
The emissions of air pollutants and GHGs by source in the Seoul metropolitan area were
analyzed to identify major sources and to characterize emissions from those sources. Emissions
of air pollutants and GHG by source are presented in Table 2-10.
Table 2-10. Emissions of Air Pollutants and GHGs in year 2003 (ton)
Source
Combustion
For Energy
Nonindustrial
Combustion
Industrial
Combustion
Public Power Plants
District Heating
Facilities
Oil Refineries
Private Power Plants
Subtotal
Commercial/Municipal
Buildings
Residential Buildings
Agricultural/ Animal
Farming/ Fishing
Facilities
Subtotal
Combustion Facilities
Industrial Furnaces
Subtotal
Industrial Processes
Road Mobile Sources
Non-road Mobile Sources
Waste Management
Total
NOX
35,951
2,037
789
1,847
40,624
11,350
23,483
1,517
36,350
12,863
6,446
19,309
6,764
144,968
51,514
7,135
306,664
SOX
11,316
1,830
1,204
4,748
19,098
5,341
6,616
926
12,884
17,749
7,523
25,272
4,865
3,182
6,757
788
72,846
PM10
299
33
17
151
501
43
697
78
818
1,185
179
1,364
257
9,058
2,054
32
14,084
C02
8,603,583
1,468,538
10,072,122
7,312,125
8,569,342
925,088
925,087
10,514,850
16,806,553
5,663,898
33,206,684
10,578,091
1,901,888
88,741,086
Note: Values are rounded off to the nearest integer. Total numbers may not be the same as the summation of values
from difference sources.
24
-------
2.3 PROJECTED EMISSIONS OF AIR POLLUTANTS AND GHG IN SEOUL
METROPOLITAN AREA
Statistical data from the government as well as materials on socio-economic projections were
used to project future fuel demand. Socio-economic indices influencing energy demand were
adjusted to the year of the project. Projected fuel demand was estimated by multiplying energy
demand for base-year by a growth factor.
Projected Fuel Consumption = Fuel Consumption for Base-year x Growth Factor
x Control Factor
Fuel consumption by sector and the corresponding GHG emissions for the year 2014 were
estimated using CAPSS data, projected emissions from MOE (Korean Ministry of Environment,
2005) and growth factors.
2.3.1 Methodology for Projection
A. Industrial Combustion for Energy, Nonindustrial Combustion, Industrial Combustion
and Industrial Process
0 Setting Socio-Economic Indices
The socio-economic indices used to project future energy demand were from the report titled the
Countermeasures against Convention on Climate Change and Kyoto Protocol. The socio-
economic indices are presented below.
1) Economic Growth Rate
The economic growth rate in the report, Countermeasures against Convention on Climate
Change and Kyoto Protocol, was determined from data in the reports (short-term economic
outlook by The Bank of Korea, projected potential economic growth rate and economic outlook
in basic plan for national energy demand phase II by Korea Development Institute) and historical
trends.
Table 2-11. Projected Economic Growth Rates
Period (year)
2001-2010
2011-2020
2001-2005
2006-2010
2011-2015
2015-2020
2001-2020
Projected economic growth rates
5.1%
5.1%
4.5%
4.0%
4.7%
Data: Korean Ministry of Commerce, Industry and Energy, Korea Energy Economics Institute, 2003, Counter
Measures against Convention on Climate Change and Kyoto Protocol
25
-------
2) Industrial Structure
In the report of Countermeasures Against Convention on Climate Change and Kyoto Protocol,
the industrial structure was projected by using a long-term outlook of industrial structure by the
Korea Institute for Industrial Economics and Trade4, the 2nd year report of Countermeasures
against Convention on Climate Change and Kyoto Protocol and historical trends. When the
industrial structure was projected, upper and lower limits were set (Table 2-13) to reflect the
difference in decrease rate for share of manufacturing industry in total GDP as presented in
Table 2-12.
Table 2-12. Comparison of Two Reports on Projection of Industrial Structure
Reference
A
B
Projected industrial structure
Similarity
G Share of manufacturing industry
in total GDP has consistently
decreased.
G Share of service industry in total
GDP has consistently increased
Difference
Share of manufacturing industry in total
GDP has decreased relatively slowly.
Share of manufacturing industry in total
GDP has decreased relatively fast.
A: Prospect and Vision of Industrial Development in 2010
B: Basic plan for National Energy, Phase II
Table 2-13. Projected Industrial Structure (proportion, %)
Industry
Agricultural and
fishing industry
Mining and
manufacturing
industry
(Manufacturing
industry)
Service industry
GDP
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
2001
5.2
5.2
34.1
34.1
33.8
33.8
60.7
60.7
100.0
100.0
2005
4.2
4.2
33.6
32.7
33.4
32.5
62.2
63.1
100.0
100.0
2010
3.3
3.0
33.0
31.0
32.8
30.8
63.7
66.0
100.0
100.0
2015
2.7
2.4
32.4
29.0
32.3
28.9
64.9
68.6
100.0
100.0
2020
2.2
2.0
31.8
28.0
31.7
27.9
66.0
70.0
100.0
100.0
Reference: Korean Ministry of Commerce, Korea Energy Economics Institute. 2003. Counter measures against
convention on climate change and Kyoto Protocol
4 MCIE, Korea Institute for Industrial Economics and Trade. 2001. Prospect and Vision of Industrial Development
for the Year 2010
26
-------
3) Projected Residential Energy Demand
GDP, population, total number of households, and number of capita per household are the key
elements for projection of residential energy demand (Countermeasures Against Convention on
Climate Change and Kyoto Protocol). Future population was projected using population
estimates by Korea National Statistical Office under the assumption that increase rate of total
number of households was consistent with that of number of general households by Korea
Institute for Health and Social Affairs.
Table 2-14. Projections of Key Elements in Residential Energy Demand
GDP
(1995 constant,
1000 won)
Population
(million)
Household
(million)
Number of People
per Household
Year
2001
493.0
47.3
14.7
3.2
Year
2005
614.8
48.5
15.9
3.0
Year
2010
786.9
49.6
17.7
2.9
Year
2015
980.7
50.4
18.1
2.8
Year
2020
1,193.1
50.7
18.7
2.7
Annual
Increase (%)
2002
-2010
5.3
0.5
1.7
-1.2
2011
-2020
4.2
0.2
0.9
-0.7
Reference: Korean Ministry of Commerce, Korea Energy Economics Institute. 2003. Counter measures against
Convention on Climate Change and Kyoto Protocol
4) Projected Commercial Energy Demand
Key elements used to project commercial energy demand are presented in Table 2-15.
Commercial building area was projected using data by Korea National Statistical Office and
applying the correlation between GDP and building area.
Table 2-15. Projection of Key Elements in Commercial Energy Demand
GDP
(1995 constant
value, trillion won)
Area of Buildings
(million m2)
2001
216.4
281 3
2005
276.4 -
280.4
353.7-
358.6
2010
356.8-
369.9
446.3
-461.5
2015
455.1
-480.5
523.3
-547.9
2020
562.3
-596.6
589.2
-618.6
Annual
increase (%)
2002
-2010
5.7
-6.1
5.3
-5.7
2011
-2020
4.7
-4.9
2.8
-3.0
Reference: Korean Ministry of Commerce, Korea Energy Economics Institute. 2003. Counter measures against
Convention on Climate Change and Kyoto Protocol
27
-------
0 Determining Growth Factor
The growth factor was derived from growth rate of future energy demand (excluding public
electricity generation sector). A Top-down method of breaking projected national energy demand
by sector into energy demand by region, sector and fuel type was adopted.
o Steps to Determine Growth Factor
1) Aggregate data on energy demand by sector and fuel type from 1997 till 2001 in a
country, Seoul, Incheon and Kyonggi using annual report on regional energy statistics5.
2) Assess ratios of energy demand by sector and fuel type to national energy demand
3) Compute increase/decrease rate of regional energy demand by sector and fuel type from
1997 till 2001 to reflect the characteristics of energy demand by sector and fuel type
4) Calculate ratios of national to regional energy demand by sector and fuel type for the
period of 2002 to 2015 by applying the increase/decrease rate for the same period to the
ratios of regional to national energy demand in 2001
5) Project national energy demand by sector and fuel type for the period of 2005 to 2014
using projections of national energy demand for the year 2001 (base-year), 2005, 2010
and 2015 as presented in the report, countermeasures against convention on climate
change and Kyoto Protocol6
6) Project energy demand for the period of 2005 to 2014 by applying results from step 4 to
results from step 5
7) Project energy demand growth by region, sector and fuel type for the period of 2005 to
2014
B. Industrial Combustion for Energy, Nonindustrial Combustion, Industrial Combustion,
Industrial Process
Increase rate of energy demand by sector, fuel type and region was considered as growth rate.
C. Industrial Process
Growth rate was derived from projected energy demand for each corresponding industry.
D. Petrochemical Industry
Growth rate of energy demand was determined by considering all the fuel types of petroleum,
coal, LNG and etc.
5 Korea Institute of Commerce, Industry and Energy, Korea Energy Economics Institute. 1998. 1999. 2001. 2004.
Annual Report on Regional Energy Statistics
6 Korea Institute of Commerce, Industry and Energy, Korea Energy Economics Institute. 2003. Counter Measures
against Convention on Climate Change And Kyoto Protocol
28
-------
E. Other Industries than Petrochemical Industry
Only growth rate of petroleum energy demand was used for growth rate.
0 Control Factor
o
o
Regulations and policies related to air may have great impacts on future emissions. It is
necessary to review acts on national air quality fuel control, acts on factory location
regulation, acts and regulations on industrial complex (applied only to regulations and
policies approved by December 31, 2005)
Factors that may influence on emissions of air pollutants in the future: act on air quality
management (regulations on air pollutant concentrations from emission facilities),
mandatory green fuel use
Past National
and Regional
Energy
Consumption by
Sector/Fuel
Type
Ratio of
Regional to
National
Energy
Consumption
by Sector/Fuel
Type
Average
Increase/Decrease
Rate for Ratio of
Regional to
National Energy
Consumption by
Sector/Fuel Type
Assessment of
Increase/Decrease
Rate for Future
Regional Energy
Demand by
Sector/Fuel Type
As 2003 Base-
year
Projected National Energy Demand by Sector/(Fuel Type) As 2003 Base-year
Projected Future Regional Energy
Demand Growth Rate by Sector/Fuel
Type As 2003 Base-year
Projected Future Regional Energy
Demand by Sector/Fuel Type
As 2003 Base-year
Figure 2-1. Flowchart for Growth Rate
29
-------
Table 2-16. Steps to Estimate Fuel Consumption
Source
Facilities
Steps
Combustion
for Energy
Public
Power
Plants
Emissions from historical data x Growth of electricity generation
District
Heating
Facilities
1) Determine past average increase/decrease rate for the share of
regional (Seoul, Kyonggi and Incheon) heat generation in
national heat generation
2) Determine future regional share of heat generation
3) Future national petroleum energy demand x regional share of
heat generation
projection on petroleum production in Incheon (oil refineries)
4) Determine regional growth rate
Nonindustrial
Combustion
Commercial
and
Municipal
Buildings
1) Determine average increase/decrease rate for the share of
regional (Seoul, Kyonggi and Incheon) fuel consumption in
national fuel consumption for commercial sector in the past
2) Determine future share of demand by region and fuel type
3) Future estimates of national fuel demand is multiplied by
value from 2) to obtain future regional fuel demand by fuel
type for commercial sector
4) Determine regional growth rate
Residential
Buildings
1) Determine average increase/decrease rate for the share of
regional (Seoul, Kyonggi and Incheon) fuel consumption in
national fuel consumption for residential sector in the past
2) Determine future share of demand by region and fuel type
3) Future estimates of national fuel demand is multiplied by
value from 2) to obtain future regional fuel demand by fuel
type for residential sector
4) Determine regional growth rate
Agricultural,
Animal
Farming and
Fishing
Facilities
1) Determine average increase/decrease rate for the share of
regional (Seoul, Kyonggi and Incheon) fuel consumption in
national fuel consumption for agricultural/animal
farming/fishing sector in the past
2) Determine future share of demand by region and fuel type
3) Future estimates of national fuel demand is multiplied by
value from 2) to obtain future regional fuel demand by fuel
type for agricultural/animal farming/fishing sector
4) Determine regional growth rate
Industrial
Combustion
1) Determine average increase/decrease rate for the share of
regional (Seoul, Kyonggi and Incheon) fuel consumption in
national fuel consumption for manufacturing sector in the past
2) Determine future share of demand by region and fuel type
3) Future estimates of national fuel demand is multiplied by
value from 2) to obtain future regional fuel demand by fuel type
for manufacturing sector
4) Determine regional growth rate
30
-------
2.3.2 Projected Emissions of Air Pollutants and GHG in Seoul Metropolitan Area
Projected emissions of air pollutants and GHG for the year 2014 are presented in Table 2-17.
Table 2-17. Projected Emissions of Air Pollutants and GHG by Source and Region for the Year
2014 (ton/yr)
Source
Combustion
for Energy
Nonindustrial
Combustion
Industrial
Combustion
Industrial
Processes
Road Mobile
Sources
Non-road
Mobile
Sources
Waste
Management
District
Seoul
Incheon
Kyonggi
Subtotal
Seoul
Incheon
Kyonggi
Subtotal
Seoul
Incheon
Kyonggi
Subtotal
Seoul
Incheon
Kyonggi
Subtotal
Seoul
Incheon
Kyonggi
Subtotal
Subtotal
Seoul
Incheon
Kyonggi
Subtotal
Total
NOX
1,284
20,780
25,037
47,101
17,022
10,181
32,479
59,682
6,057
16,981
7,507
30,545
0
8,508
952
9,460
46,694
16,701
66,678
130,073
64,179
1,582
1,214
10,107
12,903
353,943
SOX
689
9,671
19,242
29,601
660
4,968
2,911
8,539
4,836
18,332
8,260
31,428
0
5,422
742
6,164
1,696
203
2,937
4,836
9,183
282
66
1,016
1,364
91,116
PM10
18
1,129
409
1,556
187
77
465
729
41
854
1,696
2,591
0
343
33
376
3,137
1,175
5,195
9,507
2,572
8
5
41
54
17,385
CO2
570,846
3,968,972
5,065,463
9,605,281
4,140,498
3,554,054
8,415,033
16,109,585
3,467,719
2,886,278
5,720,517
12,074,514
0
1,325,059
488,148
1,813,207
17,164,965
5,035,621
21,733,498
43,934,084
13,178,186
614,562
387,007
2,368,401
3,369,970
103,084,826
-------
Emissions of air pollutants and GHG by source for the year 2003 and the year 2014 are presented
in Table 2-18.
Table 2-18. Emissions of Air pollutants and GHG for the Year 2003 and Year 2014 (ton/yr)
Source
Combustion for Energy
Nonindustrial
Combustion
Industrial Combustion
Industrial Processes
Road Mobile Sources
Non-road Mobile
Sources
Waste Management
Total
Year
2003
2014
2003
2014
2003
2014
2003
2014
2003
2014
2003
2014
2003
2014
2003
2014
NOX
40,624
47,101
36,350
59,682
19,309
30,545
6,764
9,460
144,968
130,073
51,514
64,179
7,135
12,903
306,664
353,943
SOX
19,098
29,602
12,884
8,539
25,272
31,428
4,865
6,164
3,182
4,836
6,757
9,183
788
1,364
72,846
91,116
PM10
501
1,556
818
729
1,364
2,591
257
376
9,058
9,507
2,054
2,572
32
54
14,084
17,385
C02
10,072,122
9,605,281
16,806,553
16,109,585
10,514,850
12,074,514
5,663,898
1,813,207
33,203,684
43,934,084
10,578,091
13,178,186
1,901,888
3,369,970
88,741,086
103,084,826
Emissions of air pollutants and GHG by source in Seoul metropolitan area for the year 2003 are
illustrated in Figure 2-2.
32
-------
SOx NOx PM10 CO2
Waste management
Nonroad mobile source
1 Road mobile sources
1 Industrial processes
Industrial combustion
1 Nonindustrial
combustion
i Combustion for energy
Figure 2-2. Emissions of Air Pollutants and GHG in Seoul Metropolitan Area for the Year 2003
For the year 2003, industrial combustion constituted 35% of SOx emissions, and road mobile
sources were the most significant sources for emissions of NOx and PM10 that accounted for
47% and 64% of emissions, respectively. Road mobile sources also contributed to GHG
emissions the most.
SOx NOx PM10 CO2
Waste management
Nonroad mobile
sources
i Road mobile sources
1 Industrial processes
Industrial combustion
1 Nonindustrial
combustion
1 Combustion for energy
Figure 2-3. Projected Emissions of Air Pollutants and GHG in Seoul Metropolitan Area for the
Year 2014
33
-------
Emissions of air pollutants and GHG for the year 2014 were not much different from those for
the year 2003. However, while the contribution of road mobile sources to PM10 emissions
decreased slightly, road mobile sources had greater impact on CC>2 emissions.
A comparison of emissions between year 2003 and year 2014 is illustrated in Figure 2-4.
ton
400,000
350,000
300,000
250,000
200,000
150,000
100,000
50,000
D
Waste management
N onto ad mobile sources
Road mobile sources
I Industrial processes
Industrial combustion
Nonindustrial combustion
i C ombustion for energy
2003 2014 2003 2014 2003 2014
SOx
NOx
PM10
Figure 2-4. Comparison of Air Pollutant Emissions in Seoul Metropolitan Area for the Year
2003 with Those for the Year 2014
As presented in Figure 2-4, it was anticipated that emissions of air pollutants for the year 2014
would not be much different from those for the year 2003. For both years 2003 and 2014,
industrial combustion accounted for the most SOx emissions, and on-road mobile sources were
the most important contributors for both NOx and PM10 emissions.
34
-------
ton
120,000,000
100,000,000
80,000,000
60,000,000
40,000,000
20,000,000
D
2003
2014
CO2
Waste management
Nonroad mobile sources
Roadmobile sources
Industrial processes
Industrial combustion
Noiindustrial combustion
C ombustion for energy
Figure 2-5. Comparison of GHG Emissions in Seoul Metropolitan Area for the Year 2003 with
Those for the Year 2014
Like air pollutant emissions, GHG emissions for year 2014 slightly increased, and road mobile
sources contributed to GHG emissions the most.
35
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CHAPTER 3
SEOUL METROPOLITAN AREA AIR QUALITY MANAGEMENT PLAN
(SAQMP) AND GHG EMISSION MITIGATION PLAN (GHG): EFFECTS
ON EMISSION REDUCTIONS AND COST ANALYSIS
Air pollutant emission reductions as well as the associated costs were analyzed to evaluate the
efficiencies of specific measures under Seoul metropolitan area Air Quality Management Plan
(SAQMP) and GHG emission mitigation plan (GHG).
Unit cost is the summation of equipment cost and fuel cost savings. Equipment cost was
converted to Equivalent Annual Value (EAV) with equipment longevity to estimate future unit
cost more accurately. The equation for converting equipment cost to EAV is presented below.
Fuel cost savings can be estimated from differences in fuel consumption of each measure.
Unit Cost = Equipment Cost + Fuel Cost Saving
t
Annual Equipment Cost =
Cj^j: Total Equipment Investment Cost
r: Discount Rate
«: Equipment Longevity
Discount rate (r) of 0.05 was used in this study1.
36
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3.1 ESTIMATING EMISSION REDUCTIONS AND COSTS FOR SAQMP
3.1.1 Area Sources
A. Switching from Anthracite for Residential Use to Clean Fuels
[] Summary
A regulation on fuel use like mandatory use of clean fuels is underway in Korea. Facilities
obliged to use clean fuels (Notification of Clean Fuel Use No 13) are apartments and row houses
whose sizes are bigger than those specified in annexed list 6 in district heating facilities among
integrated energy supply facilities based on Integrated Energy Supply Act, Article 2, business
boilers with evaporation loss greater than 2 ton (including commercial and public boilers and
excluding industrial boilers) and power plants (excluding industrial cogeneration plants).
Regulation on fuel use by some apartments and row houses in Seoul, Incheon and Kyonggi is
presented in Table 3-1.
Table 3-1. Fuel Use of Apartments and Row Houses with Central Heating or District Heating
District
Seoul
Metro
politan
Area
Seoul
Incheon, Suwon,
Bucheon,
Gwacheon,
Seongnam,
Gwangmyeong,
Anyang,
Uiwang, Gunpo,
Siheung, Guri,
Goyang
Pyeongtaeg,
Osan, Yongin
Status
Current
New
Current
New
Boiler Capacity
- >25 pyeonga
- >12.1 pyeong and <24 pyeong
- >25 pyeong
- >1 8 pyeong and <25 pyeong
- >25 pyeong (Building permits
approved before Jan. 1st, 1991
for apartments and before April
11th, 1991 for row houses)
- >1 8 pyeong and <25 pyeong
(Building permits approved after
May 1st, 1994)
- >18 pyeong
- >12.1 pyeong
(Building permits approved after
Jan. 1st, 1997)
Fuel
Clean Fuel
Clean Fuel or
Diesel
Clean Fuel
Clean Fuel or
Diesel
Clean Fuel
Clean Fuel or
Diesel
Clean Fuel or
Diesel
Clean Fuel or
Diesel
Data: Notification on Clean Fuel Use, Annexed
Note: a Pyeong is a unit of area commonly used
List 6
for buildings in Korea and it's equivalent to 3.3058m2
37
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[] Major Sources
This is one of fuel control methods and the major emission sources impacted by this measure are
all the facilities using anthracite for nonindustrial combustion in residential buildings in Seoul
metropolitan area.
[] Air Pollutant Emission Reductions
Anthracite for residential heating is switched to urban gas to reduce air pollutant emissions in the
Seoul metropolitan area. CC>2 emission reductions are achieved by switching from anthracite
with high C content to gas fuels such as LNG with lower C content. Emission factor for a fuel
per calorie was calculated by multiplying emission factor by the amount of fuel consumed to
produce the same amount of heat as anthracite for residential use did when anthracite for
residential use was assumed to have consumption of 1.
Projected anthracite consumption for nonindustrial combustion/residential buildings for the year
2014 is presented in Table 3-2.
Table 3-2. Projected Anthracite Consumption for Nonindustrial Combustion/Residential
Buildings
District
Seoul
Incheon
Kyonggi
Seoul Metropolitan Area
Year 20 14 (toe)
6,770
0
6,177
12,947
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Table 3-3. Emission Factors for Residential Use of Anthracite and LNG (kg/4,600Kcal)
Fuel
Anthracite for Residential Use
LNG
NOX
1.300
1.148
SOX
10.300
0.0004
PM10
0.402
0.013
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Note: 1) Based on anthracite for residential energy use
2) CAPSS Data, Sulfur content of kerosene used for boilers was assumed to be 0.1%.
38
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When the same amount of heat is produced from two fuels, emissions vary as much as the ratio
of emission factors does. Emission reduction is the difference in emissions from anthracite for
residential use and a clean fuel.
EfEFc = En:EFn
Ef: Projected Emission in 2014
EFc: Emission Factor per Calorie for Anthracite for Residential Use
En: Emission from New Fuel
EFn: Emission Factor per Calorie for New Fuel
Emission Reduction (ER) = Projected Emission in 2014 (Ef)
- Emission from New Fuel (En)
The results are presented in Tables 3-4 and 3-5.
Table 3-4. Emissions from Anthracite for Residential Use and Urban Gas (kg/yr)
Business As Usual 2014 (No Control)
Switching to Urban Gas (2014)
NOX
30,301
26,754
SOX
240,069
10
PM10
9,370
296
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Table 3-5. Emission Reductions from Fuel Switching (kg/yr)
Reduction (20 14)
NOX
3,547
SOX
240,059
PM10
9,074
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
With projected anthracite consumption of 12,947 toe for nonindustrial combustion/residential
buildings in 2014, air pollutant emission reductions are computed as:
NOX Reduction: 3,547 kg/12,947 TOE = 0.27 kg/toe
SOX Reduction: 240,059 kg/12,947 TOE = 18.54 kg/toe
PM10 Reduction: 9,074 kg/12,947 TOE = 0.7 kg/toe
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0 CO2 Emission Reductions
Steps to evaluate CO2 emission reductions from fuel control are
- Project anthracite consumption for nonindustrial combustion/residential buildings
- Convert a fuel consumption unit of kg/yr to toe
- Ton Oil Equivalent (toe) - calculate the converted value by setting heat value of crude oil
of 10,000 kcal to 1
- Estimate emission using emission factor by fuel type.
Table 3-6. Toe by Fuel Type
Crude Oil: 1
Gasoline: 0.83
Diesel: 0.87
Kerosene: 0.92
LNG: 1.03
Anthracite: 0.45
Bituminous coal: 0.66
Electricity: 0.25
Table 3-7. CO2 Emission Factors for Anthracite for Residential Use and LNG
Fuel Type
Anthracite for Residential Use
(Primary Solid Fossil Fuel)
LNG (Gas Fossil Fuel)
C02 (kg C/GJ)
26.80
15.30
CO2 (ton C/toe)
1.100
0.637
Data: IPCC Carbon Emission Factor (CEF)
The same amount of heat is produced from the two fuels being used, emissions vary by the ratio
of emission factors per calorie, and the emission reduction is the difference in emissions.
Emission Reduction = CO2 Emission from Anthracite for Residential Use
- CO2 Emission from Switched Fuel of LNG
- CO2 Emission from Anthracite for Residential Use in 2014
12,947 TOE x 1.100 TonC/toe x 44CO2/12C = 52,220 Ton CO2
- CO2 Emission from Fuel Switching in 2014
12,947 TOE x 0.637 TonC/toe x 44CO2/12C = 30,240 Ton CO2
- CO2 Emission Reduction from Fuel Switching in 2014
(Anthracite for Residential Use >LNG)
52,220 Ton - 30,240 Ton = 21,980 Ton CO2
When 12,947 TOE of fuel is consumed in 2014, CO2 emission is reduced by 21,980 Ton. Thus,
unit CO2 emission reduction from switching fuel is 1.7 ton per 1 toe.
40
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Air Pollutant Emission Reductions from Fuel Control (kg/toe)
Fuel Control
NOX
0.27
SOX
18.54
PM10
0.7
CO2
1,700
Q Unit Cost
Equipment cost for this measure includes the price of a gas boiler as well as installation cost.
Prices of residential gas boilers are summarized in Table 3-8, and boiler longevity is assumed to
be 10 years.
Table 3-8. Equipment Prices of Gas Boilers by Manufacturer (won/equipment)
Manufacturer
Kiturami Gas Boiler
Rinnai Gas Boiler
Daesung Gas Boiler
Average
Price a
400,000
410,000
380,000
397,000
Note: a Cost includes both equipment price and installation cost. The values for a building space of 32 pyeong or
smaller is adopted. Pyeong is a unit of area and equivalent to 3.3058m2.
With boiler longevity of lOyears, EAV for a boiler was estimated to be 51,413 won. EAV for
investment cost was divided by annual household fuel consumption of 1.247 toe/household7 to
obtain investment cost per toe of 41,229 won/toe. The same amount of heat is generated with
each fuel type, and fuel cost saving is the difference of fuel cost between anthracite for
residential use and urban gas. The price of anthracite for residential use is 81 won/kg and that of
urban gas is 629.59 won/m3. Fuel cost saving can be calculated from the difference in fuel costs
after they are converted to a unit of won/toe using TOE conversion factor; fuel cost of anthracite
for residential use (=81won/kg/0.45) and urban gas (=629.59 won/m3/1.5) in won/toe. The EAV
value for equipment cost was divided by annual fuel consumption and added to fuel cost saving
to obtain unit cost for fuel switching from anthracite for residential use to urban gas.
Unit Cost for Fuel Control (Anthracite for Residential Use » Urban Gas) (won/TOE)
Measure
Fuel Control (Anthracite for Residential Use -» Urban Gas)
Cost
280,960
7 Korea Energy Economics Institute. 2004. First Year Report on Mid- and Long-Term Strategies to Cope with
Convention on Climate Change
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B. Low-Sulfur and Green Fuels
[] Summary
This is a measure of fuel control. The summary of switching from anthracite for residential use
to clean fuels applies to this section as well. Diesel and heavy oil (B-A oil, B-B oil and B-C oil)
are commonly used for nonindustrial combustion (commercial and municipal buildings,
residential buildings and agricultural, animal farming and fishing facilities) in Seoul, Incheon
and Kyonggi. SOx emission can be reduced by switching from diesel and heavy oil to low-sulfur
fuel. Sulfur contents of diesel, B-A oil, B-B oil and B-C oil supplied in Seoul metropolitan area
are lower than 0.1%, 0.5%, 0.5% and 0.3% respectively.
[] Major Sources
The main emission sources impacted by this measure are industrial combustion/combustion
facilities/others according to CAPSS classification.
[] Air Pollutant Emission Reductions
Other than SOx and PM10, emission factors don't change according to sulfur content of fuel.
Thus, emission reductions from different sulfur contents were determined only for SOx and
PM10.
Table 3-9. Emission Factors for NOX, SOX and PM10 by Fuel Type (kg/kL)
Fuel type
Diesel (1.0%)
B-AOil(1.0%)
B - A Oil (2.0%)
B-B Oil (1.0%)
B-B Oil (3.0%)
B - C Oil (0.5%)
B-C Oil (1.0%)
B - C Oil (4.0%)
Diesel (0.1%)
B-AOil(0.5%)
B-B Oil (0.5%)
B - C Oil (0.3%)
NOX
2.400
2.400
2.400
6.600
6.600
6.630
6.630
6.630
2.400
2.400
6.600
6.630
SOX
17.000
18.000
36.000
18.840
56.520
9.420
18.840
75.360
1.700
9.000
9.420
5.652
PM10
0.105
0.521
0.521
0.744
0.744
1.017
1.569
2.970
0.105
0.521
0.744
1.017
Data: CAPSS, Phase III: Methodology for emission estimation
42
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Table 3-10. Emissions from Nonindustrial Combustion with No Control (kg/yr)
Fuel type
Diesel (1.0%)
B-AOil(1.0%)
B - A Oil (2.0%)
B-BOil(1.0%)
B-BOil(3.0%)
B-COil(0.5%)
B-COil(1.0%)
B - C Oil (4.0%)
Total
SOX
1,945,695
97,103
1,239,122
32,937
657
368,948
530,039
1,559,698
5,774,199
PM10
3,386
489
3,688
680
12
18,662
35,712
46,030
108,659
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Table 3-11. Emissions from Nonindustrial Combustion with Low-Sulfur Fuel (kg/yr)
Fuel Type
Diesel (0.1%)
B-AOil(0.5%)
B-BOil(0.5%)
B - C Oil (0.3%)
Total
SOX
194,570
358,332
16,578
497,358
1,066,837
PM10
3,386
4,177
692
57,572
65,827
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Table 3-12. Emission Reductions from Low-Sulfur Fuel (kg/yr)
Emission Reductions from Supply of Low-Sulfur Fuel
SOX
4,707,362
PM10
42,832
When fuel was switched to low-sulfur fuel, SOx emissions were reduced by the difference in
emission factors.
Table 3-13. SOx Emission Reductions from Fuel Switching to Low-Sulfur Fuel (kg/kL)
Fuel Switching
Diesel 1.0% ^0.1%
B-AOil 1.0% ^0.5%
B-AOil2.0%^0.5%
B-BOil 1.0%-» 0.5%
B-B Oil 3.0% ^0.5%
B - C Oil 0.5% -»0.3%
B-COil 1.0%-»0.3%
B - C Oil 4.0% -» 0.3%
SOx Emission
Factor
17.000
18.000
36.000
18.840
56.520
9.420
18.840
75.360
SOx Emission
Factor for Low-
Sulfur Fuel
1.7000
9.000
9.000
9.420
9.420
5.652
5.652
5.652
Difference in
Emission Factors
(Reduction)
15.3
9
27
9.42
47.1
3.8
13
69.7
43
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For PM10, only emissions from B-C oil are reduced by the differences in emission factors.
Table 3-14. PM10 Reductions from Switching to Low-Sulfur Fuel (kg/kL)
Fuel Switching
B-C Oil 1.0%-»0.3%
B - C Oil 4.0% -» 0.3%
PM10 Emission
Factor
1.569
2.970
PM 10 Emission
Factor for Low-
Sulfur Fuel
1.017
1.017
Difference in
Emission Factors
(reduction)
0.55
1.95
Air
Pollutant Emission Reductions from Fuel Switching to Low-Sulfur and Clean Fuels (kg/kL)
Fuel Switching
Diesel 1.0%-»0.1%
B-AOil 1.0%-»0.5%
B-AOil2.0%^0.5%
B-BOil 1.0%-»0.5%
B-B Oil 3.0% ^0.5%
B-C Oil 0.5% ^0.3%
B-C Oil 1.0%-»0.3%
B - C Oil 4.0% -» 0.3%
NOX
0
0
0
0
0
0
0
0
SOx
15.3
9
27
9.42
47.1
3.8
13
69.7
PM10
0
0
0
0
0
0
0.55
1.95
CO2
0
0
0
0
0
0
0
0
44
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Q Unit Cost
There is no equipment cost associated with fuel switching. Thus, total costs for this measure
were computed from differences in fuel prices between current fuel and switched fuel. Average
values of prices by oil refineries (March, 2007) are used to estimate costs.
Table 3-15 Wholesale Prices by Oil Refinery (won/1)
Fuel Type
Diesel (0.003%)
Diesel (0.05%)
Diesel (1.0%)
B-AOil(0.5%)
B-AOil(1.0%)
B - A Oil (2.0%)
B-BOil(0.5%)
B-BOil(1.0%)
B-BOil(3.0%)
B - C Oil (0.3%)
B - C Oil (0.5%)
B-COil(1.0%)
B - C Oil (4.0%)
SK
1,165
1,165
1,140
588.68
580.28
571.13
528.69
513.14
495.33
489.86
469.57
452.51
421.95
GS Caltex
1,169
0
1,144
591.14
582.72
573.42
527.60
514.17
496.00
491.82
466.82
452.77
421.70
Hyundai
Oilbank
1,173
0
1,140
595.00
586.07
575.87
533.03
517.53
497.53
489.42
471.42
454.72
420.72
S-Oil
0
0
0
579.70
571.30
562.10
522.30
508.10
490.30
487.40
464.80
450.50
418.70
Unit Cost for Fuel Control (Low-Sulfur and Clean Fuel Use) (won/kL)
Fuel Switching
Diesel 1.0%-»0.1%8
B-AOil 1.0%-»0.5%
B-AOil2.0%^0.5%
B-BOil 1.0%-»0.5%
B-B Oil 3.0% ^0.5%
B - C Oil 0.5% -»0.3%
B-COil 1.0%-»0.3%
B - C Oil 4.0% -» 0.3%
Unit Cost
25,667
8,538
18,000
33,115
14,670
21,473
37,000
68,858
8 Total cost is calculated from the difference between 1.0% diesel price and average of 0.003% and 0.05% low-
sulfur diesel prices.
45
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C. District Heating and Cooling Expansion
[] Summary
District heating and cooling systems provide commercial and residential buildings and offices
heating, air conditioning, hot water and electricity from a central plant to individual buildings.
1,177,000 households used district heating and cooling systems nationally in 2002, which
constituted 9.5% of the total number of households (12,358,000). According to the report on
integrated energy systems by Korea Energy Management Cooperation (2002), 939,000
households in Seoul metropolitan area were served by district heating systems in 2001. The
Korean government plans to expand district heating and cooling systems to 1,590,000
households by the year 2006, a 36.5% increase compared to year 2002. By the year 2010, 2
million households, 11.3% of total households, will be served by district heating and cooling
systems according to the government plan.
[] Major Sources
Major emission sources impacted by this measure are industrial combustion for energy
use/district heating systems and nonindustrial combustion/residential buildings.
[] Air Pollutant Emission Reductions
The plan is to replace existing heating systems with district heating systems for 90,000
households per year in the Seoul metropolitan area until reaching 2,109,000 households served
by district heating systems by the year 2014. This estimate was derived from the national plan on
supply of integrated energy systems (Public Notice 2002-240) by the Korean Ministry of
Commerce, Industry and Energy (MCIE), and the annual plan of expanding district heating and
cooling system supply by year for Korea is presented below (Table 3-16). The average annual
increase of number of households provided by district heating systems is 105,000 household/yr.
Table 3-16. District Heating and Cooling Systems Expansion Plan by Year (Basic plan for
Supply of Integrated Energy Systems)
Year
Increase of Number of Households with
District Heating and Cooling Systems
(1000 households)
2002
101
2003
79
2004
117
2005
152
2006
78
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
Average annual increase of number of households in Seoul metropolitan area was estimated from
percentage of integrated energy systems in Seoul metropolitan area (88%) (Status of district
heating system supply in 2001, References in integrated energy systems business (2002)).
However, this value needed to be modified to consider the ratio of residential area (0.0464) in
46
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Kyonggi not under air quality management. The modified percentage became 86%. Annual
increase in households served by district heating systems in the Seoul metropolitan area became
105,000 household/yr x 86% = 90,000 household/yr. If additional 90,000 households were
served by integrated energy systems every year, a total of 2,109,000 households (=939,000 +
(90,000* 13)) would be served by the integrated energy systems by the year 2014.
Emissions were estimated based on the data in year 2001. 939,000 households were served by
district heating systems among 4,966,000 total households in Seoul metropolitan area in 2001.
The number of households utilizing current heating systems was calculated by subtracting the
number of household with district heating systems (939,000) from the total number of
households in Seoul metropolitan area (4,027,000). When emissions were assessed, the
contribution from local power plants (13% of power plant output was used for residential heating
in 2001) was also included.
Table 3-17. Emission Comparison between District Heating and Existing Heating (kg/yr 2001)
District Heating
Systems
Existing Heating
Systems
Industrial Combustion for
Energy /District Heating Facilities [1]
Electric Power Plants [2]
Total (Total = [!] + [2] x 0.13)
Nonindustrial Combustion/Residential
Buildings
NOX
2,267,478
10,067,673
3,576,276
21,233,407
SOX
1,685,162
31,752
1,689,290
7,839,950
PM10
30,249
80,147
40,668
742,510
Air pollutant emission per household by heating type was estimated from dividing emissions by
the number of households by heating type (939,000 households of district heating systems and
4,027,000 households of existing heating systems) in year 2001.
Table 3-18. Emission Reductions from Switching to District Heating Systems in 2001 (kg/1000
households)
District Heating Systems [1]
Existing Heating Systems [2]
Emission Reduction (=[2]-[l])
NOX
3,809
5,723
1,464
SOX
1,799
1,947
148
PM10
43
184
141
Data: MOE. 2004. Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
By the year 2014, 1,170,000 households will be served by district heating systems. Air pollutant
emissions reductions (kg/1000 household) were multiplied by the number of households
switching to district heating systems to obtain emission reduction in 2014.
Table 3-19. Projected Emission Reductions from District Heating Systems Expansion (kg/yr)
Emission Reduction (2014)
NOX
1,713,067
SOX
172,944
PM10
165,055
47
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0 CO2 Emission Reductions
The steps to estimate CC>2 emission reductions from expansion of district heating systems are:
Estimate CCVtoe, toe/household, and CCVhousehold due to fuel consumption from
existing heating system
Calculate CC^/toe, toe/household, and CCVhousehold due to fuel consumption from
district heating system
Project toe by fuel type » CC>2 emissions from existing heating systems in year 2014
CC>2 emissions from district heating systems in year 2014
Compute emission reductions by multiplying unit emission reduction by number of
households with district heating systems
Emission Reduction = Unit Reduction x Number of Households Converting to District Heating
Systems
Table 3-20. Comparison of CC>2 Emissions from District Heating Systems and Existing Heating
Systems in 2001 (kg)
Year 2003
District Heating [1]
Electric Power Facility [2]
Total Heating Emissions (Total =[l]+[2]xQ.13)
Unit Emission from District Heating (per 1000
households) [4]
Total Emissions Existing Heating System
Unit Emission from Existing Heating System (per
1000 households) [5]
Unit Emission Reduction (per 1000 households)
([5H4D
Emission (kg)
110,991
26,363,322
3,441,661
3,665
63,764,851
15,834
12,169
Data: MOE, 2004, Action Plan for Total Allowable Emissions System in Seoul Metropolitan Area
48
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Unit emission reduction ([5]-[4]) of 12,169kg/yr per lOOOhouseholds was multiplied by number
of households converting to district heating systems by 2014 of 2,109 (1000 households) to
obtain CC>2 emission reduction of 25,664 tons. The unit emission reduction per household is 12
kg/household. Emission reductions from switching to district heating systems are presented in
Table 3-21.
Table 3-21 Emission Reductions from Switching to District Heating (kg/household)
Emission reduction
NOX
1.5
SOX
0.15
PM10
0.14
CO2
12
Em
lission Reductions from Expansion of District Heating and Cooling Systems (kg/household)
Converting to District Heating
and Cooling Systems
NOX
1.5
SOX
0.15
PM10
0.14
CO2
12
Q Unit Cost
District heating and cooling in residential and commercial buildings and offices were considered
for cost estimation. Equipment cost was evaluated by dividing annual investment cost by annual
supply plan (Integrated energy system supply by MCIE (2002)). Total equipment cost9 was
estimated to be 1,530,000 won/household in 2005. With heating and cooling system longevity of
15 years10, EAV for this measure became 147,404 won.
9 Total Equipment Cost = Annual Investment - Annual Supply Plan = 2321000000000won/152000household =
15300000won/household (2005)
10 Yunho Song and etc.. 2006. Cost-Benefit Analysis of Utilization of the Heat of the Earth from Private and Public
Perspective, The Korean Society for Geosystem Engineering
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Table 3-22. Annual District Heating Cooling System Supply Plan (1000 household)
Year
Household (Increase)
Household (Cumulative Total)
Percentage (%)
2002
101
1,166
9.4
2003
79
1,245
9.7
2004
117
1,362
10.3
2005
152
1,514
11.0
2006
78
1,592
11.3
Data: MCIE. 2002. Basic Plan for Integrated Energy Supply
Table 3-23. Investment Costs by Year (100 million won)
Year
Investment Cost (Increase)
Investment Cost (Cumulative Total)
2002
3,103
3,103
2003
4,683
7,786
2004
3,026
10,812
2005
2,321
13,133
2006
1,000
14,133
Data: MCIE. 2002. Basic Plan for Integrated Energy Supply
Fuel cost savings for this measure were based on number of district heat supplied residences in
2005 as well as energy cost savings from switching existing heating systems to district heating
systems. Number of residences served by district heat in the Seoul metropolitan area in 2005 was
670,425 households, and the energy cost difference between two heating systems was 453,510
million won. Therefore the energy cost savings11 became 680,000won/household12.
Table 3-24. Number of Households with District Heating Systems in 2005
Location
Jungang
Bundang
Goyang
Gangnam
Suwon
Sangam
Yongin
Hwaseong
Total
Residential Building (household)
49430
100723
152963
144695
94472
6929
108568
12645
670425
Data: Korea District Heating Corp. 2006. Energy Consumption Reduction and Its Effects on Environmental
Improvement
11 Korea Heating Corp. 2005. Energy Saving and Effects on Environmental Improvement
12 Energy Cost Saving = (Energy Cost for District Heating - Energy Cost for Existing Heating) + Number of Heat
Supply = -453,510,000,000won/679,425household = -680,000won/household.
Note: Negative value means saving of energy cost.
50
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