nleg rated
H nvironmental
Q^trategies
Ancillary Benefits Due to Greenhouse Gas Mitigation, 2000-
2020
Seunghun Joh, Yunmi Nam, Sanggyoo Shim, Joohon Sung and Youngchul
Shin
June 2001
0N?SL
-------
ANCILLARY BENEFITS DUETO GREENHOUSE
GAS MITIGATION, 2000 to 2020
-The International Co-Control Analysis
Program for Korea
June 2001
Seunghun Joha
Yunmi Nama
Sauggyoo Shimb
Joohon Sung0
Youngchul Shind
: Global Environment Research Center, Korea Environment Institute
: Global Environment Research Center, Korea Institute of Science and Technology
: Department of Preventive Medicine, Kangwon National University
: Department of Economics, Daejin University
Korea Environment Institute
-------
Foreword
Climate change is recognized one of the most important issues to overcome in the Earth in
the 21st century in the international society. Taking actions to prevent climate change
seem to result in harmful consequences of economy. Meanwhile controlling greenhouse
gas yields additional benefits among them an improvement of local air quality. This
aspect deserves to draw attention in Korean situation in which the air quality is becoming
serious problem as economic activities are expanding. In 1998, Korea Environment
Institute and the United States Environmental Protection Agency launched a program of
extended research on the ancillary benefit issues in an effort to reach policy
recommendation on climate change issue. This is a series of International Co-control
Analysis Program(ICAP) which was initiated by US EPA. The ICAP-Korea assessment
found that the ancillary benefits of implementing GHG mitigation measures were useful in
informing policy makers and the public of the co-benefit impacts of policy decisions and
assisting with the development of cost-effective integrated strategies to address both local
air quality issues and GHG mitigation concerns simultaneously.
This report is the result of a collaborative effort by many people. First of all, as president of
lead institute of the project, I express my profound gratitude to especially Mr. Paul
Schewengels and Dr. Jin Kim at US EPA and Mr. Ron Benioff and Mr. Collin Green at
National Renewable Energy Laboratory for financial assistance and technical guidance.
In addition to principal investigator Dr. Seunghun Joh, Ms. Yunmi Nam at KEI, Dr. Shang
Gyoo Shim at KIST, Prof. Joohon Sung at Kangwon National University and Prof.
Youngchul Shin at Daejin University worked for the project as Korea ICAP team. Dr. Alan
Krupnick at RFF played a key role in designing a CVM survey and Prof. Sungwhee Shin at
University of Seoul made a timely and useful contribution. Ms. Soohee Chung and Yonhee
Cho gave editorial assistance.
Opinions expressed here are the authors', and do not represent the opinions of KEI.
June 2001
Korea Environment Institute
President
Suh Sung Yoon
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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION 1
1.1 Background and Purpose 1
1.2 Relationship to Other Related Studies 1
1.3 Project Team 2
1.4 Schedule of Key Activities 2
1.5 Key Scoping Decisions 3
1.6 Analytical Design 3
CHATER2: GHG MITIGATION SCENARIOS 5
2.1 GHG PROJECTIONS 4
2.2 Mitigation Options Considered in the ICAP 10
2.3 Sectoral Mitigations 11
2.4 Data processing for ICAP 16
CHAPTER 3: AIR POLLUTION ANALYSIS 21
3.1 Key Assumptions 21
3.2 Emission inventory 21
3.3 Scenarios 22
3.4 Air quality modeling 24
CHAPTER 4: HEALTH EFFECTS 25
4.1 Observation and Projection 25
4.2 Exposure Assessment of PM10 25
4.2.1 General Consideration about PM10 Monitoring in Korea 25
4.2.2 Indirect Assessment of PM10 Data Validity 26
4.2.3 Exposure Assessment of PM10 levels in This Study 31
4.3 Methods and Data Sources 31
4.3.1 Scopes of this study 31
4.3.2 Data Sources 32
4.3.3 Health Outcomes Definition 33
4.3.4 Parameters for Valuing Cost of Illness attributable to PM10 34
4.3.5 Analytic Methods 35
4.3.6 Review of Korean Studies 38
4.4 Health Effects Results 39
CHAPTER 5: ECONOMIC VALUATION 45
5.1 Valuation Methods of Health Effects 45
5.1.1 Introduction 45
5.1.2 Defining and Measuring Changes in Health 47
5.1.3 Methods Used to Value Health and Welfare Effects 48
5.2 Benefit Transfer 53
5.2.1 Introduction 53
5.2.2 QUANTIFICATION OF HEALTH EFFECTS(MORTALITY) 47
5.2.3 Transferred Monetary Value of Statistical Life 55
5.2.4 Benefits of Premature Mortality Risk Reduction 57
5.3 Contingent Valuation Method 59
5.3.1 Introduction 59
5.3.2 The Value of Reductions in Mortality Risks 60
i
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5.3.3 Valuing Mortality Risks Among Older Persons 62
5.3.4 Results 67
5.4 Cost of Illness 71
5.4.1 Introduction 71
5.4.2 A Structural Model of Illness 71
5.4.3 Results 73
5.4.4 Total Medical Cost of Respiratory Disease 67
5.5 Total Benefits of Health Effect due to GHG Mitigation. 68
CHAPTER 6: CONCLUSIONS AND IMPLICATIONS FOR POLICY 82
REFERENCE 87
APPENDIX: CONTENTS OF SURVEY QUESTIONNAIRE 91
LIST OF TABLES
Table 1.1: Activities ofKorea-ICAP 2
Table 2.1: Projections of economic and social indicators 5
TABLE2.2 : GHG PROJECTIONS UNDER BAU 5
TABLE2.3 : POLICIES AND MEASURES FOR PROMOTION MAJOR OPTIONS 6
TABLE2.4 : GHG MITIGATION POTENTIAL AND COST-EFFECTIVENESS PER UNIT OF OPTION 8
TABLE2.5 : COVERAGE OF ICAP DATA OUT OF NATIONAL DATA 16
TABLE2.6 : COMPARISON OF NATIONAL WITH ICAP BY AREA 18
Table 2.7 : Comparison of national with ICAP by sector 19
Table 3.1: TSP and PM10 emission factors used Tins study 23
Table 3.2 : TSP and PM10 emission factors for transportation 24
Table 3.3 : GHG emission estimates for each scenario 24
Table 4.1Number of PM10 and TSP (in parentheses) monitoring sites since 1995 25
Table 4.2: NAAQS in Korea vs in US and their measuring method for major air pollutants. ..26
TABLE4.3 : RELATIVE RISKS FROM PM10 BY THE ORGAN SYSTEMS, SEVERITY AND
CHRONICITY OF HEALTH EFFECTS. EFFECT SIZE WAS ESTIMATED PER 50 UG/M3 INCREASE
OF PM10 LEVEL 40
TABLE 4.4 : MORTALITY RATE AND PREVALENCE RATE OF HEALTH OUTCOMES(PER 100,000
PERSON-YEAR) 40
TABLE 4.5 : HEALTH EFFECTS OF PM10 ON MORTALITY 44
TABLE 4.6 DECREASES IN ANNUAL MORTALITY AND MORBIDITY UNDER GHG REDUCTION
SCENARIOS 44
Table 5.1: Population Projections (people) 53
TABLE5.2: ANNUAL OCCURRENCE REDUCTION PREMATURE DEATH 47
TABLE 5.3 : TRANSFERRED MONETARY VALUES OF VSL(VALUE OF STATISTICAL LIFE) 56
TABLE 5.4: ESTIMATED ANNUAL BENEFITS OF MORTALITY AVOIDED(BENEFIT TRANSFER)(1999
MILLION WON) 58
TABLE 5.5 : SURVEY DESIGN 66
TABLE 5.6 : BID STRUCTURE IN THE MORTALITY RISK SURVEY(2000 KOREAN WON) 67
TABLE 5.7 : MEAN WTPS FOR CURRENT RISK AND FUTURE RISK REDUCTIONS AND IMPLIED
VALUE OF STATISTICAL LIFE, BOTH WAVES 68
TABLE 5.8 : ESTIMATED ANNUAL BENEFITS OF MORTALITY AVOIDED(CVM) (1999 MILLION $) ...70
TABLE 5.9 : ANNUAL OCCURRENCES OF ASTHMA AND ACUTE RESPIRATORY DISEASE AVOIDED ....74
TABLE 5.10 : WAGE FUNCTIONS ESTIMATED FROM 1995 WAGE SURVEY DATA 76
TABLE 5.11 : UNIT VALUES OF MORBIDITY 67
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Table 5.12 : Estimated annual benefits of morbidity avoided(COI) 68
Table 5.13 : Values of statistical life 69
Table 5.14 : Estimated annual health benefits of mortality(CVM) and morbidity
AVOIDED 69
Table 5.15 Economic benefit per GHG emission avoided 70
Table 5.16 Cumulative results 2000 to 2020 of total excess occurrence of mortality
AND MORBIDITY AVOIDED AND THE CORRESPONDING BENEFITS 70
LIST OF FIGURES
Figure 1.1: Overview of ICAP methodology 4
Figure 2.2 : Derivation of area factors 17
Figure 4.1: Comparison of annual grand levels (1-A) and 98 percent highest levels
(1-B) BETWEEN TSP AND PM10 ALONG THE YEAR 1995-1998. MEDIAN LEVELS OF PM10 IN
1995 ARE EVEN HIGHER THAN TSP LEVELS 28
Figure 4.2 : Trend of PM10 and TSP level for the year 1995 and 1995. Each line
CONNECTS THE POLLUTANT LEVEL OF THE SAME MONITORING SITE 29
Figure 4.3 : Seasonal variation of TSP and PM10 (monthly average value, um/m3).
Both pollutants show similar pattern, and PM10 levels are slightly lower
THAN TSP LEVEL 30
Figure 4.4 : Strategic frame work for COI calculation 35
Figure 4.5 : Non-linear relation between, relative humidity, temperature and
ASTHMAATTACK 36
Figure 4.6 : A, B. Marked weekly variation of morbidity (A) and mortality (B)
PATTERNS. AS SHOWN IN NORMAL QQ PLOT, ADJUSTMENT OF DAY OF THE WEEK MARKEDLY
IMPROVED MODEL FITTING ESPECIALLY FOR MORBIDITY (A) 37
Figure 4.7 : Schematic Flow of Cohort Construction in this study 39
Figure 4.8 : GAM results 41
Figure 4.9 : PM10 - loess(PMIO) (=log RR) plot of PM10 for cardiopulmonary
MORTALITY. 42
FIGURE 4.10 : PM10 - LOESS(PMIO) (=LOG RR) PLOT OF PM10. FOR ASTHMA ATTACK
(HOSPITAL ADMISSION) 43
ill
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1. INTRODUCTION \
CHAPTER 1: INTRODUCTION
1.1 Background and Purpose
It is widely recognized that developing countries will make the most progress in
reducing the growth of their greenhouse gas emissions by implementing measures
that are consistent with their development objectives and that provide near term
economic and environmental benefits. While many developing countries have
conducted extensive analysis of possible greenhouse gas measures, little attention
has been given to full characterization of the more immediate environmental and
health benefits that would result from these measures. The International Co-
Control Analysis Program or ICAP is a new initiative to assist developing countries in
evaluating the environmental benefits of technologies and policies for reducing
greenhouse gas emissions. ICAP is a cooperative program involving the U.S.
Environmental Protection Agency (U.S. EPA) and government agencies in Argentina,
Brazil, Chile, China, Korea, and Mexico. The National Renewable Energy Laboratory
(NREL) and the World Resources Institute together with other cooperators and
contractors will implement the program. The mission of the International Co-
control Benefits Analysis Program of Korea is primarily two folds;
• Estimate ancillary benefits: Assess and quantify the environmental benefit
resulting from greenhouse gas mitigation.
• Provide policy recommendation for climate change and air quality programs:
Help government officials and stakeholders understand the air pollution benefits
of energy technologies that will reduce greenhouse gas emissions, thus the
results of this analysis can enhance support for appropriate policy for UNFCCC
and air quality control program.
1.2 Relationship to Other Related Studies
The first cost-benefit study of air quality control programs that applied the impact
analysis approach was carried out by Joh(2000) for the Kyonggi area (a part of the
Seoul Metro.) in 1999. Continuing to apply the impact analysis framework, KEI is
currently conducting a project funded by Korean Ministry of Environment targets to
quantify the ancillary benefits of reduction of SOx and NOx at the national level.
This project will last through August 2001.
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2 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
1.3 Project Team
The Korean team includes the following institutions and experts:
- Lead Institution: Korea Environment Institute(KEI)
- Team Members:
Principal Investigator: Dr. Seunghun Joh, KEI
Energy : KEI
Air Quality: Dr. Shang Gyoo Shim, Korea Institute of Science and
Technology(KIST)
Health Effect : Prof. Joohon Sung, Department of Preventive Medicine,
Kangwon National University College of Medicine
Economic Valuation: Prof. Yeongchul Shin, Daejin University
- International Collaboration:
Technical advice: National Renewable Energy Laboratory
CVM: Dr. Alan Krupnick, Resources for the Future
1.4 Schedule of Key Activities
Tables 1.1 describes the schedule of key project activities:
Table 1.1 : Activities of Korea-ICAP
Date
Activities
Feb. 1999
Scoping meeting in Korea
Aug.1999
Contract made between Korea and NREL
Mar. 2000
IPCC Expert Workshop on Assessing The Ancillary Benefits
and Costs of Greenhouse Gas Mitigation Strategies
Sep. 2000
Final report on Health Effect
Oct. 2000
Policymaker review workshop
Nov. 2000
COP6 meeting
June 2001
Final Report
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1. INTRODUCTION 3
1.5 Key Scoping Decisions
The following project scoping decisions were made through a scoping workshop
and further consultations with climate change, air pollution, health, and economic
valuation experts.
- Area : Largely due to data availability, the metropolitan area(Seoul, Kyounggi,
Inchon), was chosen which covers about a half of all Korean population (22
million out of 47 million, 46.5%)
- Time Period : 1 995, 2000, 2010, 2020. Year 1 995 plays the role of base year
and 2010 and 2020 were selected to consider the potential timing of GHG
mitigation under the UNFCCC.
Pollutants of Concern: PM10 was the only pollutant considered in this initial
analysis. Here, only direct PM10 was considered and that the effects of
secondary PM10 such as sulfates and nitrates were excluded from the analysis.
In estimating PM10 health effects, S02 effects were considered
simultaneously. Ozone was not considered in this study, as the ozone
pollution modeling/projection could not be supported.
Economic Valuation Methods: A CVM survey to develop unit values for
premature mortality was administrated only in Seoul because of cost
restrictions.
1.6 Analytical Design
Starting from GHG mitigation scenarios applied in the Seoul Metro., emission
inventories and concentration levels for PM10 are estimated. Reductions in
occurrences of premature mortality and morbidity of asthma and respiratory
diseases are calculated based on concentration-response functions. Contingent
valuation method for premature mortality is employed along with benefit transfer
method. Cost of illness is applied for morbidity effects. Figure 1 illustrates a
methodology applied to the study.
[ Output ]
[D/B]
[ Methodology ]
Mitigation
SI ~S4
MOCIE
Bottom-Up
I
Emission
1 56Grid
ICAP
Area coef. - GHG,
NIER, EPA
I
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4 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Concentrati
on
1 56 Grid
UR-BAT
I
Health
C-R
Function
KNSO, KNHI
Poisson Regression
I
Valuation
COI, WTP
NHS
GIS, Benefit Trans.(Mort.)
Opportunity Cost(Morb.)
Figure 1. 1 : Overview of ICAP methodology
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2. GHG MITIGATION SCENARIOS
5
CHATER 2: GHG MITIGATION SCENARIOS
2.1 GHG Projections
The Rational Energy Utilization Act made it mandatory to establish a "Basic 10-Year
National Energy Plan" and to revise it every fifth year to reflect changes in economic
circumstances and population growth as a rolling plan. As a part of the plan, an
energy demand forecast has been made and updated (Table 2. 1).
Table 2.1 : Projections of economic and social indicators
1995
2000
2010
r.DP
257.5
.157.7
610.9
(1990 consl;n)l prices, trillion won)
(100)
(139)
(237)
Population
44.851,000
(100)
46,789,000
(104)
49,683,000
(111)
Number of households
12,961,000
(100)
13,967,000
(108)
16,561,000
(128)
Number of cars
8,469,000
(100)
15,148,000
(179)
30,062,000
(355)
Source: Table 4-1, National Communication of the Republic of Korea, 1 998.
The energy demand forecast used a modified version of LEAP (Long-range Energy
Alternative Program) which was developed by the TELLUS Institute in the U.S.A. The
model adopts a bottom-up approach and forecasts energy consumption by sector
and projects national energy demand by summing up sectoral energy consumption.
The LEAP model is one of the most widely used models in the world. It is similar to
other models, such as MEDEE and STAIR.
The projected C02emissions are shown in Table 2.2 C02 emissions in Korea are
expected to grow from 101.1 million TC in 1995 to 148.5 million TC in 2000, to
187.4 million TC in 2005, and to 217.0 million TC in 2010 as energy demand for
economic growth increases. The annual average growth rate of C02emissions from
1996 to 2010 is projected at 5.2%. Tables 2.3 and 2.4 show newly updated
policies and measures for mitigation options and GHG mitigation potential and
cost-effectiveness.
Table 2. 2 : GHG projections under BAU
(Unit: M
illion TC, %)
AAGR
1995
2000
2010
2020
1995-
2020
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6 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Energy-Related
102.65
121.64
168.43
206.16
2.8
Emission
(82.4)
(84.0)
(83.8)
(82.9)
Industrial
11.5B
1 1.79
1 5.70
18.24
1.9
Processes
(9.B)
(8.1)
(7.8)
(7.3)
Agriculture
4.04
(3.2)
4.02
(2.8)
4.02
(2.0)
4.02
(1.6)
0.0
Waste
12.21
14.74
19.60
26.82
3.2
Management
(9.8)
(10.2)
(9.7)
(10.8)
Managed Forest
-5.95
-7.40
-6.64
-6.66
0.0
(Removed)
(-4.6)
(-5.1)
(-3.3)
(-2.7)
Total net
124.64
144.79
201.11
248.58
2.8
(100.0)
(100.0)
(100.0)
(100.0)
Source: Table 11, National Action Plan, 1 998.
Table 2. 3 : Policies and measures for promotion major options
Option
Barriers and Constrains
Policies and Measures
Efficient lighting
High price
Introduction of financial
Product quality
incentive program
reliability
Strengthening minimum
efficiency standard
Introduction of energy
saving design standard
in building code
Recommending public
buildings to use efficient
lighting
- Activating energy service
company
Green Lighting Program
Solar Water heater
Space
R&D support
High cost
Financial aid for
establishment
Efficient home
- R&D support
appliances
(Air-con. Refrigerator)
Condensing gas boiler
Domestic technology is
- Technology development
in building
not developed yet
Promoting consumer
High price
awareness
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2. GHG MITIGATION SCENARIOS
7
Strengthening
- Coordination of position
- Making energy
insulation standard
between gov't agencies
regulations consistent
Vehicle fuel efficiency
R&D support
CNG vehicle
Lack of infrastructure
Basic infrastructure such
High price
as recharging station
Incentives to the
purchase and use of CNG
vehicle
Revision of related laws
such as high-pressure
gas safety and
management law,
petroleum business law,
and urban gas law
LPG vehicle
- Lack of infrastructure
Build LPG station
LPG compact car
Electric vehicle
- Technology
Support for technology
Lack of infrastructure
development
High price
Basic infrastructure
Financial support to
purchasers
Public sector's purchase
Replacement of old
Low fuel price
Financial support
furnace and kiln with
High investment
Increase fuel price
new ones
Low production tech.
Industrial condensing
Low credibility for
- Advertisement
boiler
equipment
- Advice for use of high
Consumer's low
efficient equip.
acknowledgment
Replacement of old
Low fuel price
Financial support(Fund
furnace and kiln with
for Rational Energy Use)
new ones
Upward adjustment of
fuel prices(Carbon tax)
Efficient motor
Lack of technology
- Technical assistance
development
program for small
Product reliability
manufacturers
Setting minimum
efficiency standard
Green Motors Program
Inverter
- High investment
Financial support for
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8 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
purchase
Pressurized fluidized
bed combustion(PFBC)
Integrated gasification
combined cycle
Fuel cell
High capital cost
Lack of technology
development
- Technology transfer from
developed countries
R&D investment
LNG combined cycle
power plant
High fuel cost
Lack of LNG
infrastructure
LNG infrastructure
- Adjustment of fuel prices
Forest conservation
Urbanization
Forest planning system
Designation of "Reserve
Forests"
Control of forest fire and
insects/diseases
Afforestation
- Low profitability
Financial support
Waste minimization
Unit pricing
system(Volume-Based
waste fee system)
Deposit fund System
Clean technologies
Changing consumption
pattern
Waste recycling
Insufficient market
value of recyclables
Quality problems of
recyclables
Cost ineffectiveness
Expanding recycling
facilities
Setting up source
separations-
Incineration
Emissions of hazardous
air pollutants
siting problems(NYMBY
syndrome)
Low level of incineration
technology
Support of R&D
Source: Table 1 2, National Action Plan, 1 998.
Table 2.4 : GHG mitigation potential and Cost-effectiveness per unit of option
(1 US$ =1,200 Korean Won as of 1 998)
Annual GHG Mitigation
Incremental Cost
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2. GHG MITIGATION SCENARIOS 9
Potential
(Thousand TC, 2020)
(Thousand Won/TC)
1. Compact car
608
-2,210
2. Continuously variable
transmission
1,168
-1,234
3. Efficient Air
Conditioner(Commercial)
60
-979
4. Industrial condensing
Boiler
12
-333
5. Replacement of old boilers
102
-295
6. Lean burn engine
1,797
-284
7. Power saving motor
(commercial)
34
-280
8. Power saving motor
(manufacture)
70
-212
9. Efficient Florescent
Lamp(Commercial)
554
-187
10. Efficient fluorescent
lamp
1 77
-178
11. Efficient motor
37
-143
1 2. Efficient Air
Conditioner(Residential)
26
-122
1 3. Efficient motor
(manufacture)
276
-96
14.Nuclear power
9,480
-46
1 5. Condensing gas
boiler(Commercial)
46
-5
16.Afforestation
21
3
1 /.Reforestation
25
3
1 8.Forestry conservation
55
19
1 9. Weight reduction of
Vehicle
929
31
20. Genetic Improvement of
Performance(Livestock)
53
39
21. LNG C/C(power
generation)
1,109
59
22. Methane restraint animal
diet
21
67
23. Efficient fluorescent
lamp(residential)
81
142
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10 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
24. Inverter system(other
Industrial)
61
147
25. Pressurized fluidized bed
combustion(PFBC)
0.5
165
26. Strengthening insulation
standard
512
173
27. Inverter
system(Commercial)
50
234
28. Efficient refrigerator
354
280
29. Integrated gasification
combined cycle
1
323
30. Condensing gas
boiler(Residential)
56
436
31. Bulb type fluorescent
76
466
32.Small Thermal
generation(200kw)
13
543
33. Washing machine
10
1,023
34. Solar water heater
31
1,922
Source: Table 1 3, National Action Plan, 1 998.
2.2 Mitigation Options Considered in the ICAP
1. Household Sector
-Condensing boiler
-Solar heating
-Insulation
-Town gas
-Energy efficiency appliances (compact fluorescent lamp, 32W fluorescent lamp,
refrigerator, TV, air-conditioner, washing machine, PC)
2. Commercial Sector
-Condensing boiler(l OT/h)
-Inverter
-Air conditioner
-Motor
-Co-generation(200KW, 1 MW)
-Compact fluorescent lamp,
-B2W fluorescent lamp,
3. Transportation
-CNG bus
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2. GHG MITIGATION SCENARIOS \ \
-Lean burn engine
-Weight reduction
-Continuously variable transmission
-Electric vehicle
-LPG vehicle
4. Other Industry
-Replacement of out-of-date boiler
-Condensing boiler
-Efficient motor
-Inverter
5. Industry sector
: Classification
Major industry(steel, non-metalic minerals, petrochemical); 70% energy
consumption. Option-specific data are not available but dealing with in sector
anlaysis
Other industry: foods, textiles, pulp, and electronics,
Agriculture, fishery, mining, and construction: 9% of energy consumption: excluded
in the study
2.3 Sectoral Mitigations
^ Household
- Condensing Boiler
: improvement of energ
y efficiency form 89% to 99%
Year
1995
2000
2010
2020
Market size(l OOOea)
1,323
1,463
1,743
2,144
Replacement_BAU(%)
0.0
0.4
25.4
38.0
R_control(%)
0.0
1.1
46.9
58.5
- Insulation
:applies to floor, ceiling, and wall,
:energy savings: e.g. for 82.5 m2 housing energy requirement amounts to
90B1.9Mcal/year, resulting in 18% saving, 9% decrease in heating energy
consumption. Here assumption made include for household heating light oil and
gas boiler are utilized with 85% efficiency.
Year
1995
2000
2010
2020
Market size(l OOOea)
8,954
10,879
13,750
1 5,788
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12 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Replacement_BAU(%)
0
0
0
0
R_control(%)
0
11
44
64
-Air-conditioner
: 15% improvement of energy in case of air-con for 29.7 m2 from 1.94kwh
1.65kwh
-Florescent lamp(with bulb type)
:Substitution 1 7W florescent lamps for 60W bulbs
Year
1995
2000
2010
2020
Market size(l OOOea)
43,634
50,629
66,489
82,025
Replacement_BAU(%)
-
5.9
36.4
44.2
R_control(%)
-
10.0
50.4
61.6
-Efficient florescent lamp
Year
1995
2000
2010
2020
Market size(l OOOea)
18,991
22,036
28,938
35,700
Replacement_BAU(%)
0.1
2.6
59.8
74.3
R_control(%)
0.1
3.0
80.2
99.8
-Refrigerator
Year
1995
2000
2010
2020
Market size(l OOOea)
21,859
29,093
50,072
59,904
Replacement_BAU(%)
0.0
0.0
0.0
0.0
R_control(%)
0.0
0.1
16.3
40.4
Energy saving in household sector
Options
BAU
Control
Air-con(Kw/hr)
1.94
1.65
Effi. Florescent(W/hr)
92.6
59.7
Con.boiler(kw/hr)
0.00180
0.00162
Bulb. FlorescentW/hr)
60
1 7
Refregerator/hr)
53.9
39.0
lnsulation(TOE/hr)
0.0012
0.0001
^ Commercial and Public sector
-Condensing boiler
10% improvement of energy efficiency in base-case of 10 ton of boiler
Year
1995
2000
2010
2020
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2. GHG MITIGATION SCENARIOS \ 3
Market size(l OOOea)
10,190
10,950
13,142
1 5,035
Replacement_BAU(%)
0.0
2.1
71.5
89.2
R_control(%)
0.0
2.8
93.0
100.0
-Energy saving windows
10% improvement of energy efficiency by installing energy saving windows
-Air-conditioner
: 15% improvement of energy in case of air-con for 29.7 m2 from 1.94kwh
1.65kwh
Year
1995
2000
2010
2020
Market size(l OOOea)
653
1,194
2,519
4,1 53
Replacement_BAU(%)
-
17.2
57.5
57.5
R_control(%)
-
17.2
100.0
100.0
-Motor
3% improvement of efficiency in case of 22.5kw(30HP)
motor
Year
1995
2000
2010
2020
Market size(l OOOea)
6,850
1 1,400
21,713
31,749
Replacement_BAU(%)
0.0
0.8
38.3
52.0
R_control(%)
0.0
2.6
69.5
72.0
-Inverter
3% improvement of efficiency in case of 22.5kw(30HP)
motor
Year
1995
2000
2010
2020
Market size(l OOOea)
2.626
4,104
7,816
1 1,428
Replacement_BAU(%)
0.6
0.7
6.1
10.0
R_control(%)
0.6
0.8
19.7
21.0
-Energy saving motor
:16% improvement of efficiency
-Co-generation for 200kw and I MW
: 30-40% energy saving
-Florescent lamp(with bulb type)
:Substitution 17W florescent lamps for 60W bulbs, 105kwh with assumption of
yearly use of 2,434hours
Year
1995
2000
2010
2020
Market size(l OOOea)
18,860
26,942
45,925
63,551
Replacement_BAU(%)
11.1
18.1
63.3
65.0
R_control(%)
11.1
18.7
85.5
88.0
-------
14 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
-Efficient florescent lamp
Year
1995
2000
2010
2020
Market size(l OOOea)
43,721
62,457
106,463
147,324
Replacement_BAU(%)
1.0
10.6
73.4
75.7
R_control(%)
1.0
11.2
96.8
100.0
-Energy saving in household sector
Options
BAU
Control
e-saving motor(Kw/hr)
14.63
23.29
Air-con(Kw/hr)
1.94
1.65
Eff.florescent(W/hr)
62.6
59.6
Bulb flo.(W/hr)
60
1 7
Eff.motor(Kw/hr)
1 5.75
1 5.28
lnverter(Kw/hr)
1 5.75
10.3
Condensing boiler(TOE/hr)
0.758
0.686
Windows
(TOE)
Heating
10.8
9.5
Cooling
70.6
63.0
> Transportation
-Fuel Efficiency Improvements with Lean Burn Engine
: 20% improvement of fuel in local driving
-Weight reduction
: 10% reduction results in 1 0% efficiency
-Continuously variable transmission
;10% increase in efficiency
-Compact car
Fuel efficiency (km/I)
Options
BAU
Control
Compact car
11.0
1 5.3
CVT
9.9
10.8
Lean burn
11.0
13.1
Weight reduction
11.0
12.0
> Industry sector
-------
2. GHG MITIGATION SCENARIOS \ 5
: Classification
Major industry(steel, non-metalic minerals, petrochemical); 70% energy
consumption. Option-specific data are not available but dealing with in sector
analysis
Other industry: foods, textiles, pulp, and electronics,
Agriculture, fishery, mining, and construction: 9% of energy consumption: excluded
in the study
> Other industry
-Replacement of out-of-date boiler
;1 5% of efficiency improvement
Year
1995
2000
2010
2020
Market size(l OOOea)
720
490
1197
18BB
Replacement_BAU(%)
50
50
50
50
R_control(%)
50
61.1
83.B
94.5
-Condensing boiler
;1 5%p of efficiency improvement (90%-->99%)
Year
1995
2000
2010
2020
Market size(l OOOea)
14
60
129
194
Replacement_BAU(%)
0.0
13.6
37.6
61.7
R_control(%)
0.0
21.8
65.4
98.0
-Efficient Motor
: 5% of efficiency improvement
Year
1995
2000
2010
2020
Market size(l OOOea)
42,770
59,214
87,509
109,729
Replacement_BAU(%)
0.0
4.1
24.8
25.0
R_control(%)
0.0
24.9
71.8
72.0
-Inverter
36% energy saving
Year
1995
2000
2010
2020
Market size(l OOOea)
5,859
8,1 12
11,989
13,937
Replacement_BAU(%)
3.1
4.5
9.8
10.0
R_control(%)
3.1
6.8
20.9
21.0
-------
16 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
-Energy saving motor
:16% energy saving
Year
1995
2000
2010
2020
Market size(l OOOea)
6407
8870
1 3109
1 5239
Replacement_BAU(%)
0.8
0.9
2.9
3.0
R_control(%)
0.8
2.9
7.0
7.0
Energy saving in other industry sector
Options
BAU
Control
Condensing boiler(TOE/hr)
0.758
0.686
Boiler replacement (TOE/hr)
0.251
0.212
e-saving motor(kw/hr)
14.63
12.29
Eff. Motor(kw/hr)
1 5.4
14.63
lnverter(kw/hr)
1 5.75
10.6
2.4 Data processing for ICAP
:Geographic scope of ICAP includes Seoul, Inchon, and Kyoggi
:The data set utilized is of national level
:Based on the national data, area unit(g«, shi, g«n)data have been obtained in
following way (See Figure 2.2)
a. National -> Seoul, Inchon, and Kyonggi(Al)
:Portion of National energy to A1 's (See Table 2.6 and 2.7)
b. Seoul, Inchon, and Kyonggi(Al) -> area unit(g«, city, gun)(A2)
:A1 ^(portion of population and/or productions in A2)
Table 2.5 : Coverage of ICAP data out of national data.
Household: All except for renewables, generation, and local heating
Commercial: All except for renewables, generation, and local heating
Transportation: All except for renewables and generation
Industry
Major and Other Industry : All except for renewables
Non-manufacturing: not included in ICAP
Power transformation
Generation: All except for nuclear, hydro, and other
-------
2. GHG MITIGATION SCENARIOS \ 7
Figure 2.2 : Derivation of area factors
-------
18 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Table 2.6 : Comparison of national with ICAP by area
ICAP_Seoul
ICAPJnchon
ICAP_Kyonqqi
National
1. Total (1000 TOE)
11B60.02
7642.67
1 7053.90
1 50222.281 7
2. Selected (1000
TOE)
1 1360.02
7642.67
1 7053.90
126071.9594
3. ICAP/National (%)
7.56
5.09
11.35
4. ICAP/National (%)
9.01
6.06
13.53
*"1.Total" indicates total energy consumption covered in ICAP and National,
"2.Selected" indicates sum of covered components in National,
"3. ICAP/National " implies ICAP/National using "1. Total "figures
"4. ICAP/National " implies ICAP/National using "2.Selected "figures
'"The above relations applied to Inchon and Kyonggi as well.
-------
2. GHG MITIGATION SCENARIOS \ 9
Table 2.7 : Comparison of national with ICAP by sector
National Data
-energy consumption
(1000 TOE)
1 000 TOE
1995
2000
2005
2010
2020
Household
18477.04
20495.60
21719.82
24201.99
29392.78
Commercial,
public
7026.80
7684.41
9160.84
10030.17
12296.51
Transportation
2701 3.60
30800.47
40033.97
47144.19
55332.29
Industry
54652.82
63495.57
70643.29
77828.06
93544.97
Transformation
43052.02
58338.79
76652.92
90025.53
1 1 5761.74
Sum
1 50222.28
180814.83
218210.84
249229.93
306328.30
ICAP
-energy consumption
(1000 TOE)
1 000 TOE
1995
2000
2005
2010
2020
Household
18212.04
20275.60
21404.82
23746.99
2851 5.78
Commercial,
public
6977.20
7570.41
9004.84
9806.17
1 1 855.52
Transportation
2701 3.60
30800.47
40033.97
47144.19
55332.29
Industry
48944.10
561 56.46
62036.30
67741.98
79431.1 3
Transformation
24925.02
32067.54
44797.67
48910.03
56672.75
Sum
126071.96
146870.48
1 77277.60
197349.35
231807.47
- energy consumption covered ICAP/National (%)
Household
98.57
98.93
98.55
98.12
97.02
Commercial,
public
99.29
98.52
98.30
97.77
96.41
Transportation
100.00
100.00
100.00
100.00
100.00
Industry
89.55
88.44
87.82
87.04
84.91
Transformation
57.90
54.97
58.44
54.33
48.96
Sum
83.92
81.23
81.24
79.18
75.67
Primary Data Sources:
- The second-year study of planning national actions for the UNFCCC, KEEI, May
1999.
Study for National Action Plan in Response to Climate Change, 1 997.
-------
ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
National Communication of the Republic of Korea, 1 998.
Report on Energy Census, 1 996.
Yearbook of Energy Statistics, various years.
Unpublished government documents.
-------
3. AIR POLLUTION ANALYSIS 21
CHAPTER 3: AIR POLLUTION ANALYSIS
3.1 Key Assumptions
The target region for the analysis is the Seoul Metropolitan Area, which includes
Seoul, Inchon, and most part of Kyonggi Province. Only primary TSP and PM10 (not
secondary particulates) from fuel combustion and fugitive dusts from paved roads
are considered. Emissions are calculated with emission factors and activity data for
each economic sectors relying on fuel consumption data for the sectors and data on
vehicle use. The atmospheric PMio concentrations are calculated with the UR-BAT
model, which is a revised urban scale version of ATMOS used in RAINS-Asia, with
emission inventory and meteorological data compiled in this study.
Key assumptions include:
• The background atmospheric concentration of PM10 is assumed as 20ug/m3
• The number of registered vehicles in a domain is calculated based on the
assumption that there will be the growth rate of oil price of 4% and low
economic growth rate of 2% every year.
• The same meteorological input data of 1 995 are used for other future years.
• Relative patterns of energy use in each region of analysis do not change from
2000 to 2020 for any reason other than the impact of energy policies in the
reduction scenarios
It is important to note that in Korea, PM10 has been measured only since 1995 (20
sites in study area). This relative short history and sparse networks make it difficult
to precisely assess the health effects from PM10 pollution. There are only a few
studies evaluating the health effect from PM10 to date in Korea, although a growing
body of evidence is being established about the health effects of TSP. For this
analysis, we started with the ambient concentration and monitoring system of PM10
and focused on PM10 data since 1 996, which is considered the most reliable.
3.2 Emission inventory
Emission factors for TSP and PMio are mainly based on Korea NIER(National Institute
of Environment Research) and U.S. EPA. Summary of emission factors by fuels and
by sectors are shown in Table B.l and B.2.
Reference Scenario: National date from the Ministry of Commerce, Industry and
-------
22 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Energy (MOCIE) (MOCIE 1 998) were used to develop bottom-up estimates for energy
consumption and GHG emissions through 2020. Table 2.6 shows the proportion of
national energy consumption that is covered by the study areas, with the three
areas accounting for 24% of national total in energy consumption.
3.3 Scenarios
Reference scenario: Based on National data and mitigation options described
: The National data have been made through bottom-up type for energy
consumption, and GHG emission. The limitation of the given data set is that it
enables only one scenario. Making, thus, alternative scenarios is almost impossible
unless obtaining back data.
In order for alternative scenarios to be carried out, a simple assumption is made
such that based on national data set the identical energy input change applies to all
sectors in ICAP data . Three alternative scenarios include 5 percent reduction of
energy input, 10 percent, and 1 5 percent.
Reduction scenario 1 - Assumptions include a portfolio of energy efficiency
measures for all major energy sub-sectors including introduction of high-
efficiency facilities, replacement of fuels according to MOCIE, and increasing
efficiency of PM10 emission controls at industrial manufacturing facilities.
Reduction scenario 2 - Assumes 5% reduction in energy use across economic
sectors regardless of measures and the use of CNG fueled buses (CNG fueled
buses are assumed to replace commercial buses by 10% in 2000, 75% in 2005,
and 100% to 2010
Reduction scenario 3 - Assumes 10% reduction in energy use across economic
sectors regardless of measures and the use of CNG fueled buses
Reduction scenario 4 - Assumes 1 5% reduction in energy use across economic
sectors regardless of measures and the use of CNG fueled buses
Scenario 1 involves assumptions regarding an enhanced program for improved air
quality control. Thus, we propose that reduction scenarios 2-4 be considered for
analysis of GHG mitigation activities in this analysis. Scenario 1 applies additional
levels of air pollution control for PM10.
-------
3. AIR POLLUTION ANALYSIS 23
Table 3.1 : TSP and PM10 emission factors used this study
Z3
Anthracite
Bituminous
Gasoline
Kerosene
~ iesel
Bunker
Jet oil
Naphtha
LPG
LNG
City gas
Unit
Kg/ton
kg/ton
kg/kl
kg/kl
Kg/kl
kg/kl
kg/kl
kg/kl
kg/kl
kg/1000
m3
kg/1000m
3
-
0.60
-
-
0.24
0.24
-
-
-
0.05
0.003
0.003
PMio
0.54
-
-
0.13
0.13
-
-
-
0.05
0.003
0.003
TSP
0.60
-
-
0.24
0.24
1.256
-1.478
0.240
-
0.05
0.003
PMio
0.54
-
-
0.13
0.13
0.779
-0.916
0.130
-
0.05
0.003
TSP
20
5.0
0.02
0.02
0.024
0.126
-0.209
-
-
0.007
-
0.01
PMio
12
3.3
0.01
0.01
0.012
0.119
-0.197
-
-
0.007
-
0.01
TSP
-
-
-
-
0.024
0.071
-0.1 55
-
-
-
-
0.005
PMio
-
-
-
-
0.012
0.050
-0.110
-
-
-
-
0.005
TSP
-
-
-
-
-
0.126
-0.148
-
-
-
0.003
0.003
PMio
-
-
-
-
-
0.078
-0.092
-
-
-
0.003
0.003
-------
24 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Table 3.2 : TSP and PM10 emission factors for transportation
Passenger
Small
Heavy
Small
Heavy
Ship-
Ship - Heavy
Fuel
(ex. B-C)
car
bus
bus
truck
truck
Diesel
Unit
g/km
g/km
g/km
g/km
g/km
kg/kl
kg/kl
TSP
0.01
0.B5
2.02
0.37
2.07
1.80
1.20
PMio
0.01
0.B5
2.02
0.B7
2.07
1.80
1.14
Table 3.3 : GHG emission estimates for each scenario
1995
2000
2010
2020
1000TCE
(%)
1000TCE
(%)
1000TCE
(%)
1000TCE
(%)
Nationwide
BAU
102,132
100
117,539.9
100
160,349.3
4
100
188,323.1
2
100
Metropolitan
area
BAU
28,036
27.45
31498.91
26.80
45023.43
28.08
56372.70
29.9
3
Control
28,036
27.45
30963.45
26.34
42976.20
26.80
52113.75
27.6
7
5%
Reduction
29923.97
25.46
42772.25
26.67
53554.06
28.4
4
10%
Reduction
28349.02
24.12
40521.08
25.27
50735.43
26.9
4
15%
Reduction
26774.08
22.78
38269.91
23.87
47916.79
25.4
4
3.4 Air quality modeling
This UR-BAT(Urban Branching Atmospheric Trajectory) model is a three
dimensional multi-layered Lagrangian model revised from ATMOS model. The
resolution of this model is 5 minutes. Same meteorological data of 1995 are
also used all future years, 2000, 2010 and 2020. Background atmospheric
PMio concentration of PMio is assumed as 20 |ig/m3.
-------
4. HEALTH EFFECTS 25
CHAPTER 4: HEALTH EFFECTS
4.1 Observation and Projection
Health effect analysis was performed based on both observation and projection.
To get an effect size of PM10, we used actual PM10 level and morbidity data of
the past (=observation). Then we projected the effect size (Concentration-
Response function, C-R function) to predicted level of PM10 (=projection).
First, we concentrated on acquiring epidemiologically sound health effects of
PM10 from available data sources. Then, we tried to estimate best estimators
of magnitude of health effects from PM10, based on given effect size,
prevalence and emission scenarios.
To get epidemiologically sound effect size of PM10, we estimated actual
exposure from PM10 as the first step of analyzing "past observation"
4.2 Exposure Assessment of PM10
4.2.1 General Consideration about PM10 Monitoring in Korea
- Monitoring Sites: 22-36 sites between 1995 and 1998, nationwide.(Table
4.1)
Table 4.1Number of PM10 and TSP (in parentheses) monitoring sites since 1995*.
Year
1995
1996
1997
1998
Number of PM10 Sites
22
22
28
36
(TSP sites)
(57)
(57)
(80)
(96)
- The PM10 monitoring was first introduced in 1995 (about 20 monitoring
sites). The PM10 data was not fully validated for an epidemiologic studies, yet.
Especially, PM10 levels in 1995 have been augued, since even the
administrative authorities does not guarantee that standard PM10 sampling
method (tape sampler method) was uniformly applied from the beginning.
- NAAQS (National Ambient Air Quality Standards) in Korea and the standard
measuring methods are shown in Table 4.2 PM2.5 is not being measured in
Korea. There are still more TSP monitoring sites than PM10 sites in Korea,
-------
26 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
although PM10 sites are gradually replacing TSP sites.
Table 4.2 : NAAQS in Korea vs in US and their measuring method for major air pollutants.
Pollutants
Standard
US EPA standard
Method
S02
Annual 0.03ppm
24h average 0.14ppm
lh average 0.25ppm
Same
Pulse U.V. Fluorescence
Method
CO
8h average 9ppm
lh average 25ppm
Same
Non-Dispersive Infrared
Method
Nox
Annual 0.05ppm
24h average 0.08ppm
1 h average 0.1 5ppm
Same
Chemiluminescent Method
PM
TSP
Annual 150D/D
24h average 300D/D
Annual 75 ~/~
(geometric)
24h average 260 ~/~
P-Ray Absorption Method
Sampled by High Volume
Air Sampler
PM10
Annual 80D/D
24h average 1 50D/D
Annual 50D/D
(arithmetric)
24h average 150D/D
P-Ray Absorption Method
Sampled by Tape Sampler
Method
PM2.
5
Not monitored
Annual 15D/D
(arithmetric)
24h average 50D/D
OB
8h average 0.06ppm
lh average 0.1 ppm
8h average 0.08ppm
lh average 0.12ppm
U.V. Photometric Method
Pb
3 months average
1.5D/D
Atomic Absorption
Spectrophotometry
- In this study, PM10 exposure levels were assessed based on those from fixed
monitoring sites
4.2.2 Indirect Assessment of PM10 Data Validity
- As PM10 data was not fuly validated for studies like ours, we tried to
estimate the PM10 data in Korea. Since we did not have any gold standard for
PM10 data, we evaluated PM10 data indirectly by comparing the trends of
-------
4. HEALTH EFFECTS 27
PM10 and TSP around the year of 1 995.
- Annual average value of overall monitoring sites and trends in every
monitoring site was plotted to see the reliability of (especially 1 995) PM10 data
(Figure 4.1)
- As shown in Figure 4.1 there is a possibility that PM10 levels in 1995 were
overestimated, if judging from the differences in their trend. However, the
areas with PM10 monitoring are generally more polluted, we cannot rule out
the possibility that they are the actual PM10 values.
130 -
1 E0
1 1 0
I 00
90
80
70 :
go :
50
HO
30 :
eo
1 0 :
0
A. Annual Average Values
TSP
PM10
1995
1996
1997
1998
-------
2 8 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
300
B. Annual 98 percent Highest Values
TSP
PMO
£00
1998
Figure 4.1 : Comparison of annual grand levels (1-A) and 98 percent highest levels (1-B)
between TSP and PM10 along the year 1995-1998. Median levels of PM10 in 1995 are even
higher than TSP levels.
- When we plotted the trend of PM10 and TSP level by the monitoring site,
more sites monitoring TSP showed increased pollution level in 1996
compared with those in 1 995 (Figure 4.2)
- The seasonal variation pattern of PM10 was same with that of TSP (Figure
4.B). Monitored PM10 level was slightly lower than the level of TSP
-------
4. HEALTH EFFECTS 29
5SD
EQQ
1SD
SD
(AnnualTfl percenthighestvalie)
TS
T7
3SD
3DD
ESD
EDD
1SD
SD
(AnnualTfl percenthighestvalie)
TS
T7
Figure 4.2 : Trend of PM10 and TSP level for the year 1995 and 1995. Each line connects
the pollutant level of the same monitoring site.
-------
3 0 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
onQ
j i^O
S
P i no
MONTH
? n o
P i ^ o'
M
1
0
i n o
a i in
MONTH
Figure 4.3 : Seasonal variation of TSP and PM10 (monthly average value, um/m3). Both
pollutants show similar pattern, and PM10 levels are slightly lower than TSP level.
Considering the substantial uncertainty of PM10 values in 1 995, selected
the PM10 data of the years between 1 996-1 998_for this study.
Although the PM10 level in this study was relatively higher than could be
-------
4. HEALTH EFFECTS 3 \
expected from the conversion factor of "0.6" used in US, we could not
conclude PM10 level was overestimated, because PM10 and TSP levels
were not monitored in the same area. And areas where PM10 was
monitored usually were usually more polluted areas.
For a next step, more extensive validation of PM10 level would be
needed, by comparing the PM10 level with modeled TSP level of the same
area (for the areas where both PM10 and TSP levels are available), or by
comparing with the past TSP level after controlling seasonal-diurnal
variation and long term trends.
4.2.3 Exposure Assessment of PM10 levels in This Study
A. PM10 levels were assigned as following methods Daily average level
was used for the representative exposure level of the area
B. Daily value less than 75% completeness (less than 1 8 hours) was treated
as missing data
C. In case the daily average value of less than 75% completeness day
exceeded 24-hour standard (of US EPA, 150D/D), it was included in the
analysis (if not exceeded standard, then discarded).
D. Air pollution data and health outcome data were merged based on
administrative unit area: "Gu" (ward-level subdivision of a larger city),
"Gun" (county-level subdivision of a rural province) or the city itself (for
smaller cities) when possible. All unit areas are as large as a typical county
in the US, with 200 to 400 thousand residents.
- We assumed that centrally monitored PM10 levels represented average
exposure level of the subjects in study areas.
Same method of exposure assessment was used for S02, which was
considered in the model (PMlO-helath effects)
4.3 Methods and Data Sources
4.3.1 Scopes of this study
-------
3 2 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Area : Greater Seoul Area (Seoul, Kyoungi, Incheon)
Period: Between 1 996-1 998Pollutants of concern: PM10 / PM10 adjusted for
SXDI2e(ceireiidHrBtknt^faob©icfcei)onsidered in this study:
meteorological factors
temperature : Daily average temperature
humidity: Daily average relative humidity
Health outcomes of this study:
A. Mortality: cardiovascular mortality (ICD 10, 100-199), and
respiratory mortality (ICD— 10, J00-J99)
B. Morbidity: Asthma, Chronic Obstructive Pulmonary Diseases (COPD)
C. Short-term health effects rather than long-term health effects
(Long-term health effects like occurrence of new COPD from normal subjects
were not considred)
4.3.2 Data Sources
- Mortality data:
A. Death registry data of all Korean people between 1996-1998 (Korean
National Statistical Office)
B. With individual information
a. the cause of death (international classification of diseases lOh revision
code, ICD— 10)
b. date and area of death : address code (as large as "Gu"-"Gun"-"City"
level, equivalent to county level in US).
c. Age when death and gender
d. Occupation and marital status
- Morbidity data
A. Medical claim data of Nationwide Health Insurance data (KNHI) between
1996-1998 for the diseases of Asthma and COPD. KNHI covers more
than 95% of all Koarean people (about 450 million people).
B. Providing
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4. HEALTH EFFECTS 33
a. IB-digit ID number: enables exact data matching among various data
sources (unified ID system)
b. ICD-10 code for the diagnosis
c. date of treatment (starting data) and duration of treatment
d. hospital (location and size of hospital)
e. inpatient or outpatient care
f. total cost for a spell of diasese.
C. About 45% of all KNHI members have insurance number based on their
residence area so that residence area could be found. Finally we used the
morbidity data whose residence area could be identified.
- Air quality data:
A. Continuously measuring particles (TSP or PM10), SO2, ozone, nitrogen
dioxide (NO2), carbon monoxide (CO), and ambient lead level
B. There were about 110 (1 996) -140 (1 998) monitoring sites, nationwide.
C. There were 49 monitoring sites (1998) in the study area (greater Seoul
area)
D. United States Environmental Protection Agency standard methods are
used for measuring
- Meteorological data.
A. Meteorological data, including hourly temperature (degree C), relative
humidity (percent) was gathered from 71 sites.
B. There were 3 major (KMA, Korean Meteorological Agency) and 3 minor
meteorological monitoring sites in the study area. Data from 3 major site
(KMA) were utilized.
4.3.3 Health Outcomes Definition
- Total Mortality: The count of daily deaths from non-external causes of each
study area
- Cardiovascular mortality: deaths caused from any cardiovascular diseases
(100-199 for ICD-1 0)
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3 4 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
- Respiratory mortality: deaths caused from any respiratory diseases (J00-J99
for ICD-10)
- Asthma morbidity: Any medical uses claimed by Korean National Health
Insurance Corporation from ICD-10 J45-J46 in study area
- COPD morbidity : Any medical uses claimed by Korean National Health
Insurance Corporation from ICD-10 J42-44 (chronic bronchitis) J47
(emphysema) in study area
4.3.4 Parameters for Valuing Cost of Illness attributable to PM10
(1 -Relative Risk)* x (disease prevalence) x (PM10 change) x (pop. size) x (unit
cost per disease spell)
* Relative risk, RR (dimensionless)
-increased risk of diseases-deaths per unit increase of pollution level
~ Disease prevalence/mortality rate (cases/person-year)*
-New estimation by age group, severity and diseases
ex) Asthma admission rate (spell based) among F, 65 or over
~Average cost/duration for a spell of asthma/COPD*
assumed to be constant over years
- This schemetic diagram of is as follows
Average Time Lost per a Spell of
Disease
(events can be recurred )
- Time Lost d/t admission
- Time Lost d/t outpatient visit
- Time lost d/t medication at
home?
Average cost per a death
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4. HEALTH EFFECTS 35
Figure 4.4 : Strategic frame work for COI calculation
4.3.5 Analytic Methods
- A Robust Poisson Regression Model was selected for several reasons
A. to control non-linear relation between temperature, humidity and
health effects (by smoothing functions like loess)
B. the count of health outcomes could be assumed to follow Poisson
distribution
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3 6 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Figure 4.5 : Non-linear relation between, relative humidity, temperature and asthma attack.
C. this model allows considering couple of other factors like S02, date
of the week, year and so on which were related to either health
outcomes or PM10 level.
- Model Selection
A. Meteorological Variables:
a. Daily average temperature and relative humidity was used as a
smoothing function (loess)
b. As there were strong but non-linear relation between health
effects and temperature, health effects and humidity, we
selected a smoothing function for controlling weather factors
(figure 4.5).
- We also used indicator variables for day of the week, year, first and last
days of the month (for morbidity data only).
- There were marked cyclic variations in a week especially for morbidity
data (admission as well as outpatient)(figure 4.6)
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4. HEALTH EFFECTS 37
A. Control of Marked Weekly Variation by modeling (Morbidity)
ex) weekly variation of hospital use
rsons
Num berofOPD vEis from Asthm a
000 r CUJ eekVCyck)
600
400
200
000
800
600
400
200
JZL
Fri
N on -Accident Total Death
(average daily count)
5DD
ns
IflS
IflD
175
17D
lb 5
IbD
¦
1
1
1
SUN
HON
TUE
Id ED
THU
FRI
SAT
Figure 4.6 : A, B. Marked weekly variation of morbidity (A) and mortality (B) patterns. As
shown in Normal QQ plot, adjustment of day of the week markedly improved model fitting
especially for morbidity (A).
- To fit the daily count of health outcomes on air pollution levels (PM10).
Meteorological factors (average temperature and relative humidity), S02
effect, time trends, days of weak were considered.
- This model included loess smooth function on the time and meteorological
factors to capture the seasonal/long-term time trend and any possible
nonlinear relationship between the health outcome and meteorological factors.
After adjustment
mal QQ plots of the pearson residuals
Nor
Before adjustment
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3 g NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Example of Analytical model for total mortality was expressed as:
log E(death) = S(time) + S(temperature) + S(relative humidity) + days of week
(indicative variables) + PM10 level + other pollutants (if needed)
where log E(death) is a logarithm of expected deaths, S is a smoothing function
to adjust for possible nonlinear and seasonal trends.
And an example of analytical model for morbidity was expressed as
log E(death) = S(time) + S(temperature) + S(relative humidity) + (Sunday and
Holidays)* + Monday* + 1 st day of a month* + 2nd day of a month* + 3rd day
of a month* + 4th day of a month* +
First week of a month* + last 2 days of a month* + PM10 level + other
pollutants levels (S02)
(* indicative variables)
Generalized Additive Model (GAM) was used for this analysis
Loess smoother (S-PLUS ver 4.5) was used for a smoothing function.
4.3.6 Review of Korean Studies
(Studies since 1 995)
-More than 1 5 qualified studies
-Growing body of evidence for the TSP, S02, and Ozone relations
-More evidence is needed for quantitative functional/symptomatic chances,
chronic health ef
fects, health effects other than respiratory and cardiovascular effects
- Studies in Western Countries or other countries were also considered, with
less weighting scores for the evidence from them.
Proposed Method of Chronic Health Effect estimation for further study- Cohort
Study Approach(figure 4.7)
• Reconstructing Cohorts for estimating chronic health effects from air
pollution
• About 350 thousand peoples with individual information of smoking
history, residence area, basic risk factors of health were being
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4. HEALTH EFFECTS 39
reconstructed
• Exposure level from air pollution is assigned according to the residence
area office area
• The follow-up begins at 1 988
• Follow-up of disease-death status is based on medical insurance data and
death certificate data (Not yet fully combined)
• Analysis would be performed with survival analysis (Cox's proportional
hazard model or other appropriate analytical models)
The Insured of Korean Medical (about 1,000 thousand peoples)
Insurance Corporation
Health examination and questionnaire
Disease status follow-up
(between 1 986-1 990)
Confirmed to be disease free at 1 986
(About B50 thousand people)
:Respiratory diseases, cardiovascular
diseases, cancers, congenital
malformations
Com bining airpollution data with
individual cohort members
Figure 4.7 : Schematic Flow of Cohort Construction in this study
4.4 Health Effects Results
Some parameters were calculated separately to get epidemiologically sound
values for health benefit estimation; 1) relative risk (RR) from unit increase of
PM10. Table 4.3) prevalence or mortality rate of health outcomes in Korea
(Table 4.4). By combining projected PM10 levels and population size and
structure in 2020, we could calculate estimated mortality and cases
attributable to PM10. As for milder health outcomes, meta-analysis was
applied such as respiratory symptoms and lung function (forced expiratory
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40 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
volume 1 second, FEV1). For references cases, employed are studies in Korea,
Asian countries (China, Taiwan), western countries.
Table 4.3 : Relative risks from PM10 by the organ systems, severity and chronicity of
health effects. Effect size was estimated per 50 ug/m3 increase of PM10 level.
sDrgans
Severity
Functional
change
Respiratory system
Acute
Chronic
Cardiovascular system
Acute
Chroni
Etc
Birth outcomes
Cancer
3-5% decrease
nf FFV1
Symptom
and signs
RR: 1.32
(RR: 1 21 1 13)
-low birth weight
(Under pilot study)
-congenital anomaly
-increase of lung
cancer
(Under pilot study)
Morbidity
aggravation of
asthma
RR: 1.011 (RR:
1.007-1.015)
-aggravation
of CHF
(premature)
mortality
Respiroatory
mortality
RR: 1.053
(1 022-1 085)
Cardiovascular
mortality
RR:1.053
(1 038-1 068)
Increase of total non
accident mortality
RR: 1.024
(RR: 1 016-1 032)
Mortality rate and Prevalence rate (spell based, not person based) was
estimated independently to provide 'basal rate" or "reference rate" of mortality
and morbidity (table 4). Note that we intentionally estimated spell-based
prevalence to get more valid estimator of total medical cost.
Table 4.4 : Mortality rate and prevalence rate of health outcomes
(per 100,000 person-year)
Morta
ity
Age
Croups
All (non-external)
Cardiovascular
Respiratory
Male
Female
Male
Female
Male
Female
0-14
22
20
1.5
1.7
1.8
1.6
15-64
289
125
67.7
37.0
10.8
3.6
65-
5,657
4,142
1 573.3
1287.1
447.9
227.9
Morbidity
Ast
ima
n
o
PD
Admission
OPD visit
Admission
OPD visit
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4. HEALTH EFFECTS 4 \
Male
Female
Male
Female
Male
Female
Male
Female
0-14
216
1 31
6307
5345
21
16
2496
2294
15-64
28
32
603
1105
30
19
1846
2731
65-
286
219
3664
3497
709
288
6809
5640
Results of GAM model
Quantiles of standard normal After adjusting for meteorological factors
and cyclic variations like day of the week, we got relatively well fitted
model (Figure 4.8 )
Figure 4.8 : GAM results
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42 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
PM10 - Loess (PM10) = log(relative risk) plotting, which can be used as
continous risk profile along the increase of PM10 level, was estimated as below
for total mortality and cardiopulmonary mortality (figure 4.9)
We could assume a linear incrase of health effects within the windows of
probable PM10 level (less than 1 50 ug/mB)
Health effects above this leve (150 ug/mB), there were few observations so
that the estimated risk was unreliable.
50
mi ii mi i in i|i i
100 150
200
250
300
PM10
Figure 4.9 : PM10 - loess(PMlO) (=log RR) plot of PM10 for cardiopulmonary mortality.
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4. HEALTH EFFECTS 43
For morbidity (asthma attack), the shape of the curve was basically same, but
the increase of risk was more blunted, (figure 4.10)
Also in the effect for asthma and COPD morbidity, we could assume linear
increase of risk below the level of 150 ug/mB (PM10). Above this point, best
estimator of the risk profile was not reliable.
PM10
Figure 4.10 : PM10 - loess(PMlO) (=log RR) plot of PM10. for asthma attack (hospital
admission)
Also, as the case in mortality, increase of morbidity related to PM10 level was
not conclusive above level of 1 50 g/mB of PM1 Olevel)
The four GHG reduction scenarios result in significant decreases in mortality
and occurrences of asthma and other respiratory diseases. Key results from
the health effects analysis include(Table 4.5 and 4.6)
• The decreases in premature deaths range from 40 deaths/yr for scenario 2
to 1 20 deaths/yr. in scenario 4 in 2020.
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44 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
• The reductions in asthma and respiratory diseases range from 2800
occurrences/yr. to over 8B00 occurrences/yr. in 2020.
Table 4.5 : Health effects of PM10 on mortality
AREA
1996
1997
1998
Sum
Seoul
Cardiovascular
9,441
8,587
8,708
26,736
Respiratory
1,872
1,891
2,008
5,771
Non-Accident
19,174
16,629
30,085
65,888
Total cause
37,923
37,498
32,197
107,618
Incheon
Cardiovascular
2,267
2,398
2,331
6,996
Respiratory
443
480
475
1,398
Non-Accident
4,699
4,427
7,742
16,868
Total cause
9,534
10,080
8,378
27,992
Kyunqqi
Cardiovascular
7,478
7,652
8,170
23,300
Respiratory
1,364
1,524
1,723
4,611
Non-Accident
17,085
1 5,357
28,686
61,128
Total cause
34,334
35,418
31,043
100,795
Further results are depicted in Table 7.
Table 4.6 Decreases in annual mortality and morbidity under GHG reduction scenarios
2000
2010
2020
Mortality by Cardiovascular
6.22
55.46
83.37
Scenario 1
Mortality by Respiratory
0.71
6.36
9.56
Asthma
471.54
4,207.48
6,324.48
Respiratory Diseases
9.59
85.57
128.63
Mortality by Cardiovascular
22.27
29.16
36.01
Scenario 2
Mortality by Respiratory
2.55
3.34
4.13
Asthma
1,689.71
2,212.28
2,731.60
Respiratory Diseases
34.37
44.99
55.56
Mortality by Cardiovascular
44.55
58.32
72.01
Scenario 3
Mortality by Respiratory
5.1 1
6.69
8.26
Asthma
3,379.43
4,424.56
5,463.21
Respiratory Diseases
68.73
89.99
1 1 1.1 1
Mortality by Cardiovascular
66.82
87.48
108.02
Scenario 4
Mortality by Respiratory
7.66
10.03
12.39
Asthma
5,069.14
6,636.84
8,194.81
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4. HEALTH EFFECTS 45
CHAPTER 5: Economic Valuation
5.1 Valuation Methods of Health Effects
5.1.1 Introduction
• One of the basic services provided by the environmental is the support of
human life. Changes bin the life support capacity of the environmental brought
about, for example, by reducing the pollution of air or water, can lead to
decreases in the incidence of disease, reduced impairment of activities, or
perhaps, increased life expectancy.
• The standard economic theory for measuring changes in individuals' well-
being was developed to interpret changes in the prices and quantities of goods
purchased in markets. This theory has been extended and applied to a wide
variety of nonmarket or public goods and social programs, including public
housing and other transfer programs, public investments in parks,
transportation, the development of water resources, and improvements in
environmental quality and health(Freeman, 1979). This theory is based on the
assumption that individuals' preferences are characterized by substitutability
between income and health. The trade-offs that people make as they choose
among various combinations of health and other consumption goods reveal
the values they place on health.
• According to the simplest models of individual choice, researchers can
interpret an individual's observed trade-off between income and health as a
measure of his willingness to pay(WTP) for improvement in his health.
• However, there are two qualifications to this statement. First, society has
developed several mechanisms for shifting some of the costs of illness away
from the individual who is ill and onto society at large. An individual's
expressed willingness to pay to avoid illness would not reflect those
components of the costs of his illness borne by or shifted to others. But the
value to society of avoiding his illness includes these components.
• The second qualification concerns the emphasis given to the individual's
concern for his own illness. This emphasis does not preclude altruism because
an individual may have preferences about the health and well-being of others,
especially close relatives and his spouse.
• Environmental pollution that impairs human health can reduce people's well-
being through at least the following five channels: 1) medical expenses
associated with treating pollution-induced diseases, including the opportunity
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46 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
cost of time spent in obtaining treatments; 2) lost wages; B) defensive or
averting expenditures associated with attempts to prevent pollution-induced
disease; 4) disutility associated with the symptoms and lost opportunities for
leisure activities; and 5) changes in life expectancy or risk of premature death.
• The first three of these effects have readily identifiable monetary
counterparts. The latter two may not. Since reducing pollution may be
benefical to individuals because it reduces some or all of these adverse effects,
a truly comprehensive measure of benefits should capture all of these effects
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5. ECONOMIC VALUATION 47
Measures based solely on decreases in medical costs or lost wages are
inadequate because they omit major categories of beneficial effects.
5.1.2 Defining and Measuring Changes in Health
• Health has many dimensions, and environmental changes can affect people's
health in a variety of ways, ranging from changes in the frequency of mild
illness or irritating symptoms to increases in the risk of contracting a serious
or fatal disease. This chapter follows the conventional economic practice in
distinguishing between mortality and morbidity effects, where in the former
case the primary endpoint of concern is death, while in the latter case, the
focus is on nonfatal illness or a set of symptoms. This section describes the
major categories of health effects and how they typically are measured in
empirical economic research.
1. Mortality
• For mortality, the measurement of a change in health is the change in the
probability of dying, or more specifically, the change in the conditional
probability of dying at each age, for an identified group of individuals at risk.
The conditional probability of dying at age t is the probability that one dies
before his t + 1 st birthday, given he is alive on his fth birthday. Age-specific
mortality rates provide empirical estimates of the conditional probability of
death.
• A number of environmental contaminants ingested through various routes
are known to cause or are suspected of causing increases in the incidence of
fatal diseases such as cancer. One problem in valuing changes in risk of death
due to exposure to environmental carcinogens is that there is typically a lag
between exposure to the substance and the production of cancerous cells.
Because the individual is safe from cancer during this latency period, the
benefits of reduced exposure do not occur until the end of the latency period.
2. Morbidity
• Morbidity is defined by the U.S. Public Health Service as "a departure from a
state of physical or mental well-being, resulting from disease or injury, of
which the affected individual is aware." The last phrase in the definition is the
key to answering an important question in air pollution control policy: What
constitutes an adverse health effect from an economic perspective ?
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4 8 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
• From an economic perspective, the answer to this question depends on
whether the changes are perceived by the individual and whether the individual
reveals or expresses a willingness to pay to avoid the effect.
• From the viewpoint of valuation, an important distinction to make
concerning the health outcome that is affected by pollution is whether it occurs
often enough and to a sufficient percentage of the population that it may be
viewed as certain from the viewpoint of a single individual, or whether it is rare
enough that its occurrence to an individual must be viewed as uncertain.
5.1.3 Methods Used to Value Health and Welfare Effects
• Methods for obtaining monetary values for improvements in health can
broadly be categorized as those that rely either on observed behavior and
choices (revealed preferences) or on responses to hypothetical situations
posed to individuals (contingent valuation). The first category includes all of
those techniques that rely on demand and cost functions, market prices, and
observed behavior and choices. The second category includes asking people
directly to state their willingness to pay or accept compensation for a
postulated change, how their behavior would change, or how they would rank
alternative situations involving different combinations of health and income or
consumption.
• Benefit transfer suggests the possibility that some results of valuation study
in other countries can be adopted for the valuation in country under
consideration, given proper adjustments. Benefit transfer can be easily
accepted when the population at risk and the sample population are
considered to be close to identical (e.g., within one country). Problems arise
when the population at risk and the sample population for whom WTP is known
do not have similar characteristics and their preferences are not identical.
However, various approximations can be made for such a transfer.
1. Methods of Valuing Reduced Mortality
• Two alternative approaches to defining a measure of the value of lifesaving
activities.
• The first approach is based on measurements of the economic productivity
of the individual whose life is at risk. This is often referred to as the human
capital approach because it uses an individuals discounted lifetime earnings as
its measure of value.
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5. ECONOMIC VALUATION 49
• The second approach is to use some indicator of the individuals willingness
to pay to reduce his risk of death as the measure of value.
• The benefits of risk reductions are usually measured using the concept of
the value of a statistical life (VSL) estimates are derived from aggregated
estimates of individual values for small changes n mortality risks.
• This approach avoids the issue of valuing life, per se, by recognizing that
what people actually buy and sell through their choices and trade-offs is
not life versus death, but small changes in the probability of dying.
• In this approach, the economic value is derived by focusing on choices ex
ante; that is, before the uncertainty about whether or not one will die is
resolved.
¦ The Human Capital Measure of Value
• This approach is based on measurements of the economic productivity of
the individual whose life is at risk.
• The human capital measure is based on two assumptions: that the value of
an individual is what he produces and that productivity is accurately measured
by earnings.
• The human capital approach calculates the value of preventing the death of
an individual who is presently of age j as the discounted present value of that
individual's earnings over the remainder of his expected life.
• The change in expected lifetime earnings is a lower bound to willingness to
pay to reduce risk of death.
• Lave and Seskin(l 971, 1 977)
• The most important criticism of the human capital approach is that it is
inconsistent with the fundamental premise of welfare economics; namely, that
it is each individual's own preferences that should count for establishing the
economic values used in benefit-cost analysis.
• Furthermore, both theoretical reasoning and empirical evidence suggest that
human capital measures are poor approximations of the desired willingness-
to-pay measures of value for small changes in the risk of death.
¦ Compensating Wage Studies
• An alternative approach is to infer the value of a statistical life from wage
premia that workers receive to compensate them for risk of accidental death.
• To estimate the risk premium, which is the partial derivative of the market
wage function with respect to risk of death, requires having data on wages, job
attributes, and worker attributes. These data are used to estimate an hedonic
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5 0 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
wage function, an equilibrium relationship between the wage, job
characteristics, and variables affecting worker productivity.
¦ The Willingness-to-Pay Approach
• In keeping with the assumption that individual's preferences provide a valid
basis for making judgements concerning changes in their economic welfare,
increases in longevity or reductions in the probability of death due to accident
or illness should be valued according to what an individual is willing to pay to
achieve them. This presupposes that individuals treat longevity more or less
like any other good rather than as a hierarchical value.
• The most important conclusion to be drawn from the review of theorectical
models of individual choice and willingness to pay is that the value each
person attaches to a small reduction in his probability of dying is likely to
differ because of differences in underlying preferences, age, wealth, number of
dependents, degree of aversion to risk, and level of risk to which he is
currently exposed.
• A second conclusion is that in the case of multiple risks of death, where the
individual can "purchase" reductions in one component of risk can usually be
taken as a close approximation of the individual's willingness to pay for
reductions in other components of risk.
• U.S. EPA identified 26 policy-relevant risk VSL studies as part of an extensive
assessment titled The Benefits and Costs of the Clean Air Act, 1970 to
1990(EPA, l 997). Five of the 26 studies are contingent valuation studies; the
rest are compensating wage(wage-risk) studies. To allow for probabilistic
modeling of mortality risk reduction benefits, the analysts reviewed a number
of common distributions to determine which best fit the distribution of mean
values form the studies. A Weibull distribution was selected with a central
tendency(or mean) of $ 5.8 million( in l 997 dollars).
2. Methods of Valuing Reduced Morbidity
• There are three techniques for valuing reduced morbidity
• The first, the cost of illness(COI) approach, uses data on lost earnings and
medical expenditures to infer a lower bound to willingness to pay for reduced
air pollution.
• The second technique, the averting behavior method, infers peoples
willingness to pay to reduce ambient pollution levels from the amounts of
money they spend to avoid exposure to air pollution (for example, by installing
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5. ECONOMIC VALUATION 5 \
air filters) or to mitigate its effects (for example, by taking an antihistamine to
reduce nasal discharge).
• The third technique for valuing reduced morbidity is the contingent valuation
method, involves asking people what they would pay to reduce the number of
symptom or restricted-activity days they experience.
¦ Cost of IIIness(COI)
• The value of work and leisure time lost due to illness plus any change in
averting and mitigating expenditures constitute a lower bound to willingness
to pay for reduced exposure to pollution.
• If these costs of illness are to constitute a lower bound to individual WTP,
then the relevant prices are those that the individual faces. This measure is
referred to as the private cost of illness.
• Since the rest of society's WTP to reduce health risks must be added to the
sum of individual WTPs if individuals do not face the full social cost of medical
care or lost productivity, it is also of interest to value lost time plus averting
and mitigating expenditures at their true social cost. This is termed the social
cost of illness.
¦ Averting Behavior Method
• To implement the averting behavior approach requires having data in the
following five categories for a cross-section of individuals:
1. Frequency, duration, and severity of pollution-related symptoms.
2. Ambient pollution levels to which the individual is exposed.
3. Actions which the individual takes to avoid or mitigate the effects of air
pollution.
4. Costs of avoidance and mitigating activities.
5. Other variables affecting health outcomes (age, general health status,
presence of chronic conditions, and so on.)
• These data are used to estimate health production and input demand
functions, which, in turn, are used to calculate willingness to pay for a
marginal change in ambient pollution.
• Gerking and Stanley(l 986), Dickie et al.(l 986), Chestnut et al.(l 988b)
¦ Contingent Valuation Method
• The contingent valuation method involves asking people either what they
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5 2 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
would be willing to pay to reduce pollution or what value they place on
reducing symptoms, and then multiplying this answer by the reduction in
symptoms corresponding to a change in pollution, pay to reduce the number
of symptom or restricted-activity days they experience.
• Loehman et al.(l 979), Rowe and Chestnut(l 985), Tolley, Babcock, et
al.(l 986), Dickie et al.(l 987), and Chestnut et al.(l 988b).
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5. ECONOMIC VALUATION 5 3
5.2 Benefit Transfer
5.2.1 Introduction
• To estimate the health benefits of a reduction in ambient air pollution, four
components were determined: (1) the quantitative relationship between
ambient concentrations and the health response or concentration-response
functions; (2) the size and identification of susceptible populations, (B) the
projected change (between BAU and reduction scenarios) in air pollution
concentrations under consideration, and (4) the economic value of the
reduction in health effects incidence.
• Firstly, Epidemiologic study provides the basis for the concentration-
response relationships between ambient PM10 and several adverse health
outcomes used in this analysis including: premature mortality, asthma, and
acute respiratory diseases. The relative risks of premature mortality, asthma,
and acute respiratory diseases which are suggested in the analysis of health
effect are utilized.
• Secondly, the susceptible populations of our research are given in Table 1.
Population for individual grid (total grid amounts to 1 56 covering ICAP area) is
calculated as follows:
1. population data in 1995 for Seoul, Incheon, and Kyonggi(Al) are
projected for 2000, 2010, and 2020 based population projection data and the
populations of total 76 administrative units belonging to the three A1 in 1 996
have been selected as a base year population
2. then for the projection of the 76 unit, projection rates of A1 have been
made to individual administrative unit, respectively ,
3. using GIS projected population is derived for individual grid,
4. age and sex ratios of Seoul in 1995 are applied to total 76
administrative units.
Table 5.1: Population Projections (people)
1995
2000
2010
2020
1 11 L* vl
Seoul
10.342.224
9.981.649
9.625.060
9.409.018
Incheon
2.333.769
2.559.424
2.886.504
3.114.402
Kyonggi
7,737,864
9,364,923
11,727,264
13,188,852
Total
20,413,857
21,905,996
24,238,828
25,712,272
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5 4 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
5.2.2 Quantification of Health Effects(Mortality)
• Thirdly, the projected changes of 1 56 grids between BAU and GHG reduction
scenarios in annual average PM10 concentrations are estimated using data
from air quality modeling. Equation [1] is used as the basis for calculating the
numbers of cases of premature mortality.
• Occurrence reduction in annual mortality in each grid
= (RR-l)xBa X PMa X POP, [1]
where
RR: relative risk
Ba : baseline annual mortality, and
PMa: change in annual average PM10 concentration
POP : population.
• In order to calculate an estimate of the change in the number of premature
Scenario
Deaths by
2000
2010
2020
Reduction Scenario 1
Cardiovascular Disease
6.22
55.46
83.37
Respiratory Disease
0.71
6.36
9.56
Reduction Scenario 2
Cardiovascular Disease
22.27
29.16
36.01
Respiratory Disease
2.55
B.B4
4.13
Reduction Scenario 3
Cardiovascular Disease
44.55
58.32
72.01
Respiratory Disease
5.1 1
6.69
8.26
Reduction Scenario 4
Cardiovascular Disease
66.82
87.48
108.02
Respiratory Disease
7.66
10.03
12.39
deaths expected as a result of a change in PM10 in a given location, a baseline
mortality rate must be used. For this assessment, the estimates are made in
terms of annual cases of premature deaths reduced, so we use national annual
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5. ECONOMIC VALUATION 5 5
average mortality rates as each baseline. Annual occurrences reduction in
annual mortality are given in Table 5.2.
Table 5.2 : Annual occurrence reduction in premature death
5.2.3 Transferred Monetary Value of Statistical Life
• Finally, the economic value of the reduction in health effects incidence.
• The valuation of these full impacts is usually referred to as the maximum
willingness to pay (WTP) to prevent the health effects. The basic analytical
approaches used in welfare economics to estimate WTP are based on situations
in which individuals are observed making tradeoffs between health effects
(measured as incidence or risk) and some financial benefit, such as income.
• Reductions in risk of death are arguably the most important societal benefit
underlying many environmental programs. In two recent analyses of the
benefits of U.S. air quality legislation, The Benefits and Costs of the Clean Air
Act, 1 970 to 1 990(U.S. EPA, 1 997) and The Benefits and Costs of the Clean Air
Act, 1990 to 2010(U.S. EPA, 1999), more than 80% of monetized benefits were
attributed to reductions in premature mortality.
• Individual WTPs for small reductions in mortality risk are summed over
enough individuals to infer the value of a statistical life saved.
• There are two sources of empirical estimates of individuals willingness to
pay(WTP) for mortality risk reductions: revealed preference studies, based on
compensating wage data or consumer behavior, and stated preference studies,
including those employing contingent valuation methods.
• Benefit transfer suggests the possibility that some results of valuation study
in other countries can be adopted for the valuation in country under
consideration, given proper adjustments. Benefit transfer can be easily
accepted when the population at risk and the sample population are
considered to be close to identical (e.g., within one country). Problems arise
when the population at risk and the sample population for whom WTP is known
do not have similar characteristics and their preferences are not identical.
However, various approximations can be made for such a transfer.
• A simple adjustment method for transferring the monetary values of health
effects from United States to Korea is proposed, applying the following
relationship:
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5 6 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
VSL(Korea) = VSL(US)*Radj [2]
Monetary
Values in the
U.S. or Canada
Adjusted 1
(million
won)
Adjusted 2
(million
won)
Non
Adjusted
(million
Average
(million
won)
Low
1,300,000
n qqo C
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5. ECONOMIC VALUATION 5 7
5.2.4 Benefits of Premature Mortality Risk Reduction
• The estimated benefits of premature mortality risk reduction due to GHG
mitigation scenarios in Korea are calculated by using the unit value of VSL and
excess occurrence of premature deaths. These estimated benefits are given
Table 5.4.
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5 g NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Table 5.4: Estimated annual benefits of mortality avoided(Benefit transfer)(1999 million
won)
Adjusting
Method
Scenario
2000
2010
2020
Adjusted 1
Scenario 1
5,345.2
47,682.6
71,678.1
Scenario 2
19,144.0
25,067.7
30,960.5
Scenario 3
38,BOB.4
50,143.1
61,913.3
Scenario 4
57,447.4
75,510.7
92,873.8
Adjusted 2
Scenario 1
8,526.6
76,062.7
1 14,340.1
Scenario 2
30,538.3
39,987.7
49,387.8
Scenario 3
61,101.2
79,987.6
98,763.4
Scenario 4
91,639.4
1 19,975.3
148,1 51.2
NonAdjusted
Scenario 1
20,608.4
183,839.8
276,354.4
Scenario 2
73,809.5
96,648.2
1 19,368.0
Scenario 3
147,678.5
193326.2
238,706.2
Scenario 4
221,488.0
289,974.0
358,074.2
Average
Scenario 1
1 1,493.4
102,528.3
1 54,124.2
Scenario 2
41,163.9
53,901.2
66,572.1
Scenario 3
82,361.0
107,819.0
1 33,127.6
Scenario 4
123,524.9
161,720.1
199,699.7
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5. ECONOMIC VALUATION 5 9
5.3 Contingent Valuation Method1
5.3.1 Introduction
• Much of the justification for environmental rulemaking rests on estimates of
the benefits to society of reduced mortality rates. Reductions in risk of death
are arguably the most important benefit underlying many of the United States
Environmental Protection Agency's (U.S. EPA) legislative mandates, including
the Safe Drinking Water Act, the Resource Conservation and Recovery Act and
the Clean Air Act. For example, in two recent analyses of the benefits of U.S. air
quality legislation, The Benefits and Cost of the Clean Air Act, 1970-
1990 (U.S. EPA, 1997) and The Benefits and Cost of the Clean Air Act,
1990-2010 (U.S. EPA, 1999), over 80 percent of monetized benefits were
attributed to reductions in premature mortality. These benefits are equally
important in environmental cost-benefit analyses performed in
Canada(Environment Canada, 1999).
• There are two sources of empirical estimates of individuals willingness to
pay (WTP) for mortality risk reductions: revealed preference studies, based on
compensating wage data or consumer behavior, and stated preference studies,
including those employing contingent valuation methods. From the perspective
of valuing lives saved by environmental programs both estimation
techniquesas applied to dateshare a common shortcoming. They focus on
measuring the value that prime-aged adults place on reducing their risk of
dying, whereas the majority of statistical lives saved by environmental
programs, according to epidemiological studies, appear to be the lives of older
people and people with chronically impaired health. It has been conjectured
that older people should be willing to pay less for a reduction in their risk of
dying than younger people on the grounds that they have fewer expected life
years remaining. Theory, however, cannot predict exactly how WTP varies
with age, and, to our knowledge, few empirical studies have been conducted
that include subjects over the age of 65. Likewise, there are no studies that
examine the impact of health status on WTP for mortality risk changes.
• The goal of this research is to estimate what older people are willing to pay
to reduce their risk of dying, and to examine the impact of current health
status on WTP. We accomplish this through a contingent valuation survey that
1 As we used the modified version of Canada survey, the most contents in this
chapter are cited from Krupnick et al.(2000).
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60 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
is administered to persons 40 to 79 years old. Targeting this age range
allows us to examine the impact of age on WTP, thus providing an empirical
answer to the above speculations, and allows us to compare our WTP estimates
with those from previous studies. We measure health status in two ways.
Respondents are asked whether they have ever been diagnosed as having one
of several chronic heart or lung diseases, or cancer. To further capture the
severity of the disease (or other chronic health conditions) we ask respondents
to complete a detailed health questionnaire, Standard Form 36 (Ware et al.,
1997), which has been shown to correlate well with severity of various chronic
illnesses (Bousquet et al., 1 994).
• The survey uses audio and visual aids to communicate both baseline risk of
death and risk changes. Respondents are given experience with graphical
representations of risks of death (depicted by colored squares on a rectangular
grid) and are tested for comprehension of probabilities before being asked
WTP questions.
5.3.2 The Value of Reductions in Mortality Risks
1. The Nature of Mortality Risk Reductions from Environmental Programs
• Life saving benefits from environmental regulations have been quantified for
the conventional air pollutants, especially particulate matter, and for
carcinogens. These studies suggest that life-saving benefits are concentrated
among persons 65 years of age and older and may disproportionately benefit
people with pre-existing chronic conditions. Other health and safety
regulations, such as those intended to reduce foodborne pathogens, also
disproportionately benefit older persons and persons in compromised health.
• Epidemiological evidence for the link between older people and air pollution
comes from two directions. First, epidemiological studies typically assume
that the effect of a change in pollution concentrations is proportional to
baseline mortality rates. This assumption is implicit in time-series models in
which deaths on day t are assumed to be an exponential function of air
pollution on day t-s, weather and other variables. It is also embodied in the
prospective cohort study of Pope et al. (1995), which assumes that the impact
of air pollution is proportional to the probability of dying at each age (given
that one survives to that age). Since death rates are higher for older persons,
this implies that the benefits of reducing exposure to air pollution accrue
primarily to older people. Based on Pope et al. (1995), the EPA (1997)
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5. ECONOMIC VALUATION g \
estimates that three-quarters of the statistical lives saved by the Clean Air Act
in 1 990 as a result of reducing particulate matter are persons 65 years of age
and older. Second, epidemiological studies have found larger changes in
mortality rates for people over 64 than for younger people (Schwartz 1991,
199B).
• Reducing exposure to pollution may also reduce risk of cancer. Cancer is the
health endpoint most often quantified in connection with hazardous waste
sites, pesticide regulations and drinking water standards. Although the
toxicological studies that are used to quantify cancer risks provide only an
estimate of lifetime cancer risk, rather than age-specific risk estimates, it is
reasonable to assume that the age distribution of deaths from environmentally
induced cancers follows the same pattern as cancer mortality rates from all
causes. Since cancer mortality rates are concentrated among individuals aged
65 and over, the statistical lives saved by reducing exposure to carcinogens
will be concentrated among people in the same age group. In 1996, 71 percent
of all cancer deaths in the U.S. were concentrated among residents aged 65
years and over (US. Census Bureau, 1 999).
• Epidemiological studies also suggest that persons with chronic heart or lung
conditions are likely to benefit disproportionately from improvements in air
quality. For example, Schwartz (1991), Schwartz and Dockery (1989), and
Pope et al. (1995) find that changes in particulate concentrations have a larger
impact on deaths due to cardiovascular disease and chronic obstructive lung
disease than on all deaths. This has caused some observers to suggest that
the value of lives saved by air pollution should reflect the compromised health
of the beneficiaries (EOP Group, Inc., 1997). It is not, however, clear that
people with chronic heart and lung disease would pay less than healthier
individuals to reduce their risk of dying.
2. Current Approaches to Valuing Mortality Risk Reductions
• In benefit-cost analyses of health and safety regulations, including
environmental regulations, it is standard practice to ignore the health status of
people whose lives are extended by the regulation. The age of persons saved
is sometimes incorporated by converting the value of a statistical life from a
labor market study (or other source) into a value per life-year saved. To
illustrate this calculation, suppose that the value of a statistical life based on
compensating wage differentials is $5 million, and that the average age of
people receiving this compensation is 40. If remaining life expectancy at age
40 is 35 years and the interest rate is zero, then the value per life year saved is
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62 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
approximately $140,000. If, however, the interest rate is 5 percent, then
discounted remaining life expectancy is only 16 years, and the value per life-
year saved rises to approximately $300,000. The value of a life-year can then
be multiplied by discounted remaining life expectancy to value the statistical
lives of persons of different ages. This procedure is, however, ad hoc. It
assumes that the value per life-year saved is independent of age, and it is
sensitive to the rate used to discount the value of future life-years, which is
usually assumed by the researcher rather than estimated on the basis of actual
behavior. Moore and Viscusi (1988) have used labor market data to infer the
rate at which workers discount future utility of consumption; however, their
models make very specific functional form assumptions in order to infer a
discount rate from a single cross section of data.
• Evidence from contingent valuation studies (Jones-Lee et al., 1 985) suggests
that willingness to pay is not proportional to remaining life expectancy;
however, policymakers may be reluctant to rely on such studies unless it can
be demonstrated that they pass tests of internal and external validity. One
measure of the success of a contingent valuation survey is that, when different
groups of respondents are asked to value risk changes of different magnitudes,
WTP increases with the size of the risk change. An external scope test is
passed when the mean WTP of respondents faced with the larger risk change is
significantly greater than the mean WTP of the respondents faced with the
smaller risk change. An internal scope test is passed when a respondents WTP
increases with the size of the risk reduction. In the context of valuing risk
changes, however, a more stringent criterion can be applied. If respondents
maximize expected utility or, more generally, if their utility function is linear in
probabilities, WTP for small risk changes should increase in proportion to the
size of the risk change.
• As a recent literature review by Hammitt and Graham (1999) demonstrates,
few contingent valuation studies of mortality risks pass either internal or
external scope tests. In some cases (e.g., Jones-Lee et al., 1985; Smith and
Desvousges, 1 987) WTP fails to increase at all with the size of the risk change.
Only three contingent valuation studies designed to value mortality risks pass
external scope tests. All of these studies were conducted in the context of
traffic safety and two involved extremely small samples (N < 110). None of
these studies focused on valuing mortality risk reductions among older people
and none examined the impact of health status on WTP for risk reductions.
5.3.3 Valuing Mortality Risks Among Older Persons
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5. ECONOMIC VALUATION 53
1. Goals of the Survey
• The goal of our survey is to estimate what older people would pay for a
reduction in their risk of dying and to examine the impact of health status on
WTP. We target a population ranging in age from 40 (the mean age of workers
in compensating wage studies) to 79 years and collect extensive information
on health status. We ask respondents to value annual risk reductions on the
order of 10 4 . Risk changes valued in labor market studies are on the order of
1 in 10,000 per year. A risk change of this order of magnitude could also be
delivered by an environmental program (e.g., air pollution control). For
instance, the Pope et al. study (1 995) predicts that a 10 D/m3 change in PM10
results in an average risk change of 2.4 in 10,000, whereas studies based on
time series generally predict that the same change in pollution levels results in
a 0.8 in 10,000 risk change.
• For use in benefit-cost analyses, it is important that risk reductions be a
private good; that is, that we estimate each respondents WTP to reduce his or
her own risk of dying. For this reason, we have chosen an abstract product (not
covered by health insurance) as the mechanism by which risk reductions are
delivered. In practice, most environmental programs reduce mortality risks for
all persons in an exposed population: In other words, risk reductions are a
public good. Johansson (1994) and Jones-Lee (1991) have shown, however,
that when people exhibit pure altruism, maximization of net social benefits
calls for equating the sum of individuals' marginal WTP to reduce risks to
themselves to the marginal cost of the risk reductions. Therefore, the
appropriate measure of benefits is the sum of private WTP for reductions in
risk.
2. Avoiding Past Pitfalls
• The failure of many contingent valuation studies to pass tests of internal and
external validity may be traced to three types of problems:
1. Respondents may not understand the risk changes they are asked to value.
2. Respondents may not believe that the risk changes (or baseline risks)
apply to themselves.
3. Respondents may lack experience in trading money for quantitative risk
changes or lack the realization that they engage in this activity.
• Our approach to dealing with each problem is described below.
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64 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
• Communication of Risk Changes. Our survey relies on a graph containing
1,000 squares to communicate probability of dying. White squares denote
chances of surviving, red squares represent chances of dying. Reductions in
the risk of dying are represented by changing red squares to blue.
• Because we value annual risk changes on the order of 10 4 , the graph
represents the chances of dying (surviving) over a 10-year period with risks on
the order of 10~3. The use of a 10-year period is motivated by two
considerations. When respondents are told their baseline risk of dying over the
next 12 months, they often believe that the risks do not apply to them. In
focus groups, respondents more readily accepted baseline risks over longer
periods. Secondly, the use of a 10-year period makes it possible to represent
risks using 1,000 squares. In our questionnaire development, we found that
respondents regarded grids with more squares (e.g., 10,000 or 100,000)
confusing and tended to dismiss such small risk changes as insignificant.
• Understanding of Risk Changes. Each respondent goes through the survey
on a computer screen, at his own pace. We encourage respondents to think
about changes in mortality risks by showing them side-by-side depictions of
the risks with and without the product, and by asking them questions to test
their understanding of how risks(and risk changes) are represented. If the
respondent answers a question incorrectly, he or she is provided additional
educational information and is asked an additional, similar question.
• Experience Trading Quantitative Risk Changes. Although most respondents
engage in activities or purchase goods to reduce their risk of dying, they often
fail to associate quantitative risk reductions with these activities. We acquaint
respondents with the quantitative risk reductions associated with medical tests
and products with which the respondent may be familiar (e.g., mammograms,
colon cancer screening tests, and medicine to reduce blood pressure) prior to
asking what he or she would pay for a product that will reduce risk of dying. In
doing so, we keep the cost information provided to the respondent qualitative
in nature (e.g., expensive, moderate, and inexpensive).
• Communication of Payments. Tests in focus groups and one-on-one
interviews suggested that payments for risk reductions should be made
annually, over a 10-year period. We use graphs to convey the timing of the
payments and the relationship between the timing of payments and risk
reductions. This relationship is especially important when eliciting WTP today
for a future risk reduction.
3. Survey Protocols
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5. ECONOMIC VALUATION 55
• The survey instrument used in this project was developed over several years.
The development effort included extensive one-on-one interviews in the
United States, pretests in the United States and Japan, and several focus groups
in Hamilton, Ontario, including one at a senior citizen recreation center,
followed by another pretest.
• Korean survey was administered to around 1,000 subjects in Seoul, in 2000.
Research 21, a survey research firm, administered the survey over a two-
month period. Our target population consisted of persons between 40 and 79
years of age.
• The survey was administered on a computer with a simplified keypad, which
was color-coded and especially labelled for use with the survey (e.g., Press
the BLUE key to see the next screen. ). Respondents moved through the
survey at their own pace. Words on each screen appeared in large font and
were read to the respondent by a voice-over.
4. Description of the Questionnaire
• The questionnaire is divided into five parts. Part I elicits personal information,
including health information about the respondent and his or her immediate
family.The questionnaire is divided into five parts. Part I elicits personal
information, including health information about the respondent and his or her
immediate family.
• Part II introduces the subject to simple probability concepts through coin
tosses and roulette wheels. The probabilities of dying and surviving over 10-
year periods are then depicted using a 1,000-square grid. The respondent
goes through simple exercises to become acquainted with our method of
representing the probability of dying. The respondent is then shown two 25 by
40 grids: one for person 1, with red squares (representing death), and one for
person 2, with 10 red squares (see Figure 5.1). The respondent is asked to
indicate which person faces the higher risk. If the respondent picks person 1,
he or she is provided with additional information about probabilities and is
asked again. The respondent is then asked which person he or she would
rather be. Individuals responding Person 2 (the person with the higher
risk) are asked a followup question to verify this answer and are given the
opportunity to change their answer if they wish. The baseline risk of death for
a person of the respondents age and gender is then presented numerically and
graphically.
• Part III presents the leading causes of death for someone of the respondents
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66 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
age and gender. Common risk-mitigating behaviors are listed, together with
the quantitative risk reductions they achieve and a qualitative estimate of the
costs associated with them ( inexpensive, moderate, and
expensive ). The purpose of this section is twofold. We wish, first, to acquaint
the respondent with the magnitude of risk changes delivered by common risk-
reducing actions and products (cancer screening tests, medication to reduce
high blood pressure) and, second, to remind the respondent that such actions
have a cost, whether out-of-pocket or not.
Table 5.5 : Survey design
Group of
respondents
Initial Risk
Reduction Valued
Second Risk
Reduction Valued
Future Risk
Reduction Valued
Wave 1 ( N = 484)
5 in 1,000
1 in 1,000
5 in 1,000
Wave 2( N = 51 3)
1 in 1,000
5 in 1,000
5 in 1,000
• Part IV elicits WTP for risk reductions of a given magnitude, occurring at a
specified time, using dichotomous choice methods. (Table 5 summarizes our
survey design.) In one sub-sample (Wave 1), respondents are first asked if they
are willing to pay for a product that, when used and paid for over the next 10
years, will reduce baseline risk by 5 in 1,000 over the 10-year period (WTP5);
that is, by 5 in 10,000 annually. In the second WTP question, risks are reduced
by 1 in 1,000 (WTP1); that is, by 1 in 10,000 annually. In a second subsample
(Wave 2), respondents are given the 1 in 1,000 risk change question first.
Baseline risk is age- and gender-specific, and increases with age and for males.
The baseline risks are shown as red squares on the 1,000-square grid. The red
squares are first randomly scattered over the grid, and then grouped together.
The risk reductions delivered by the products are shown by changing the
appropriate number of squares from red to blue.
• After the first two questions, respondents in both subsamples under age 60
years are asked their WTP over the next 10 years for a 5 in 1,000 risk reduction
over 10 years beginning at age 70 years (WTP70)(Table 5.6). This question
serves two purposes. First, it tests whether respondents are willing to pay
anything today for a future risk reduction what one would like to measure to
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5. ECONOMIC VALUATION (¦,7
value reduced exposure to a pollutant with a latency period. Second, it
provides a test of internal consistency of responses because WTP today for a
future risk change should be less than WTP today for an immediate risk change.
This question is preceded by a question that asks the respondent to estimate
his or her chances of surviving to age 70.
Table 5.6 : Bid structure in the mortality risk survey(2000 Korean won)
Group of
respondents
Initial payment
question
Follow-up question
(if "yes")
Follow-up question
(if "no")
~
40,000
90,000
20,000
~
90,000
300,000
40,000
~
300,000
450,000
90,000
~
450,000
600,000
300,000
• All WTP dichotomous choice questions answered by No-No or Yes-Yes
responses were followed by a question asking how much the respondent is
willing to pay. With bids secured, respondents were then asked, on a 1 to 7
scale, their degree of certainty about their responses.
• Part V asks an extensive series of debriefing questions, followed by some
final socio-demographic questions (e.g., education and household income).
The debriefing questions are used to identify respondents who had trouble
comprehending the survey or did not accept the risk reduction being valued.
• The 36-question quality of life survey (Standard Form-36, abbreviated SF-
36), which is used routinely in the medical community to gauge physical
functionality and mental and emotional health states (Ware et al. 1997). The 36
health questions supplement those posed at the beginning of the interview and
can be used to construct eight indices commonly used in the health literature.
5.3.4 Results
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6 g NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
1. WTP and VSL Estimates: Current and Future Risk Reductions
• Since we have three rounds of payment questions, we can form three
different sets of estimates for mean WTP. All sets of estimates recode not
sure responses as no responses.
• The first set of estimates utilizes only the responses to the initial payment
questions, and is thus safe from undesirable response effects sometimes
observed in the presence of follow-up questions (Herriges and Shogren, 1996;
Alberini, Kanninen and Carson, 1997).
Table 5.7 : Mean WTPs for current risk and future risk reductions and implied value of
statistical life, both waves
Type of Risk
Reduction
Risk Reduction
Single bound Model
(Weibull distribution)
Mean WTP
(won1*
VSL
(million wnn)
Current Risk
5 in 1,000
333,067
(203,702
- 544.588V)
666.13
(407.40
- 1 .089.1 7V>
1 in 1,000
1 33,297
(90,068
- 197.276V)
1,332.97
(900.68
- 1.972.8V)
Average
999.56
Future Risk
5 in 1,000
(WTP70)
271,671
(179,013
- 412.349V)
543.38
(358.03
- 824.69V)
1) 95 % confidence interval
• As shown in Table 5.7, mean WTP for a 5 in 1,000 risk reduction ranges from
203,702 won to 544,588 won a year. The corresponding figures for the 1 -in-
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5. ECONOMIC VALUATION 59
1,000 risk reduction are 90,068 won to 197,276 won per year. In the case of
future risk reduction, the mean WTP for the risk reduction of 5 in 1,000
beginnign at age 70 is 271,671 and the 95% confidence interval ranges from
1 79,01 3 won to 41 2,349 won per year.
• The WTP figures can be used to compute the corresponding value of a
statistical life (VSL). We computed VSL by dividing annual WTP by the size of
the annual risk reduction (5 in 10,000 or 1 in 10,000). The respective VSLs of
current risk reductions, also reported in Table 8, range from 407.40 million
won to 1972.76 million won. The average VSL of current these results is
999.56 million won. The VSL from future risk reduction is 543.38 million won
and the 95% confidence interval ranges from 358.03 million won to 824.69
million won.
• To summarize, the corresponding VSLs are generally lower than those used
by those of Canada or U.S. in benefit-cost analyses.
2) Benefits of Mortality Risk Reduction due to GHG Mitigation
• The VSL based on future mortality risk reduction in Table 5.7, that is 543.4
million won(0.47 million $) is applied to estimate annual benefits of mortality
avoided, because the VSL inferred from future mortality risk reduction is more
appropriate for health effects of our GHG mitigation scenarios(Table 5.8).
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70 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Table 5.8 : Estimated annual benefits of mortality avoided(CVM)
(1999 million $)
Year
Scenario
2000
2010
2020
Reduction scenario 1
B.29
29.33
44.09
Reduction scenario 2
1 1.77
1 5.42
19.04
Reduction scenario 3
23.56
30.84
38.08
Reduction scenario 4
35.33
46.26
57.12
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5. ECONOMIC VALUATION 7 \
5.4 Cost of Illness
5.4.1 Introduction
• Conservative estimates of the benefits of improving air quality can be
obtained by focusing on the cost of illness, that is the sum of medical
expenditures and lost earnings attributable to the illness associated with
pollution. In some cases, one can obtain estimates of the averting
expenditures incurred by the individual to control exposure to pollution and
hence illness.
• It is widely recognized that the cost of illness and averting expenditure
provide only a lower bound for the correct measure of willingness to pay
(Harrington and Portney 1987).
5.4.2 A Structural Model of Illness
• Suppose that the health outcome of interest is the number of hours S during
a year or a month that a person spends ill with some respiratory ailment. The
health production function relates time spent ill to exposure to pollution, E,
and to activities that mitigate the effects of exposure, M. Mitigating activities
include taking antihistamines or visiting a doctor, and have a unit cost of pm,
which includes time as well as out-of-pocket costs. Pollution exposure is a
function of ambient pollution and activities A termed averting or avoidance
activities, that affect exposure given ambient pollution levels; that is, E = E{A,
P). Let pa denotes the unit cost of A. The health production function may be
written
S = S[E(A,P),M\ [4]
• Time spent ill directly affects the individual's utility by producing discomfort;
it indirectly affects it by reducing the amount of time (and possibly money)
available for leisure activities and consumption. Formally, S enters the utility
function, together with all other goods Xand leisure time L.
U=U(X,L,S) [5]
• S also enters the budget constraint by reducing the amount of time spent at
work, and hence, the amount of income earned. The individual's budget
constraint says that nonwage income I plus earnings must equal total
I + w (T - L - S )= p xX + p aA + p M M [6]
expenditure. Formally,
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72 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
where w is the wage rate and T - L - S is the time spent at work( T is total
time).
• The health production model assumes that the individual allocates his time
between work and leisure activities and his income between defensive (averting
and mitigating) expenditures and expenditures on other goods to maximize
utility. The problem for the individual is to choose the mitigating and averting
activities M and A, the expenditures on all other goods X, and the leisure time
L that will maximize function [5] subject to [4] and [6].
• An individual's willingness to pay for a small reduction in ambient pollution
P is defined as the largest amount of money that can be taken away from him
without reducing his utility. Formally, economists define the
pseudoexpenditure function as the minimum value of expenditure minus the
wage income necessary to keep utility at U°, or
where m is a Lagrangian multiplier. Applying the envelope theorem to [7] and
substituting from the first-order conditions for utility maximization,
willingness to pay for a marginal
E = mm[pxX + pAA + pmM -w(T-L-S) + m[U° -U[X,L,S(A,P,M)]]\ [7]
change in P, dE/dP, is given by
• Willingness to pay is given by the reduction in sick time associated with the
reduction in pollution, 3S/3P , times the marginal cost of sick time. The latter
is given by the cost of an additional mitigating input Pm divided by the
reduction in sick time that input produces -3S/3M , or alternatively, by the
cost of averting behavior Pa divided by the reduction in sick time that
averting behavior produces -dS/dA .
• In the health production model, in which pollution affects utility only
through health, this amount of money is the reduction in the cost of achieving
the optimal level of health made possible by the decrease in pollution.
• According to Harrington and Portney(l 987), WTP can be written as the sum
of the value of lost time w(3S/3P) and the disutility of the change in illness
(dS/dP)(3U/3S)/X plus the observed changes in averting and mitigating
expenditures, Pm (3M* /DP) and Pa (3A* /DP)
WTP = -(dS /dP)pM/(dS IdM ) = pM (dM IdP)
= -(dS /dP)p A/(dS /dA) =p A (dA / dP)
= (dS !dP)WTP s
[8a]
[8b]
[8c]
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5. ECONOMIC VALUATION 73
dP Pm
dM' dA' dU/dSdS
-^T = Pa^z : ^
dP dP 2 dP
[9]
where A, the marginal utility of income, converts the disutility of illness 3U/
3S into dollars, and where M* =M* (l,w,px , Pa , Pm , P) and A* =A* (l,w,px , Pa ,
Pm , P) are the demand functions for M and A to a change in pollution. WTP
is a function of the total derivative of illness with respect to pollution, dS/dP ,
which incorporates the effect of pollution on averting behavior and averting
behavior on illness. To compute dS/dP , it is not necessary to estimate a health
production function; rather it is possible to estimate a dose-response
function, which is a reduced-form relationship between illness, ambient
pollution, and variables that affect averting and mitigating behavior. In the
health production framework, a dose-response fucntion is obtained by
substituting the demand functions for M and A into the health production
function.
• As a practical matter, the first three terms in [9] can be approximated after
the fact by using the observed changes in illness and averting and mitigating
expenditures. In this way, equation [9] can be used to derive a lower bound to
individual WTP. Because the last term in the equation is negative (3U/3S < 0),
the first two terms - the value of lost time plus the change in averting and
mitigating expenditures - give a lower bound to WTP. These terms are referred
to as the private cost of illness, or the cost borne by an individual of
mitigating and averting expenditure and lost time.
• In practice, the cost of these items to an individual may differ from their cost
to society due to medical insurance and paid sick leave. Therefore, the social
cost of mitigating and averting expenditures plus lost time will be referred to
as the social cost of illness.
• In the health literature, the expression cost of illness typically refers only to
the social cost of lost earnings plus the recuperative (mitigating) medical
expenditures associated with illness. This expression therefore ignores two
components of our social cost of illness - the social value of averting
expenditures and the cost of leisure time that results from illness.
5.4.3 Results
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74 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
1. Data
• 1995 National Health Interview Survey : The Health Interview Survey, which
also incorporated the health behavior survey, was aimed at estimation of the
national prevalence of selected diseases and risk factors and included a set of
questions on topics such as morbidity, limitation of activity, medical utilization
and health behaviors asked by interviews through personal household
interviews. A stratified multistage probability sampling design was used in
this survey. A total number of 6,791 Korean households with 22,450
household members took part in this survey.
• 1 995 Occupational Wage Survey Data : wage data of 39,891 workers
2. Quantification of Health Effects(Morbidity)
• The projected changes of 156 grids between BAU and GHG reduction
scenarios in annual average PM10 concentrations are estimated using data
from air quality modeling. Equation [10] is used as the basis for calculating the
numbers of cases of morbidity(asthma and acute respiratory disease)
• Occurrence reduction in annual morbidity in each grid
= (RR-l)x Ba x PMa x POP, [10]
where
RR: relative risk
Ba : baseline annual morbidity, and
PMa: change in annual average PM10 concentration
POP : population.
• In order to calculate an estimate of the change in the number of asthma and
acute respiratory disease occurrences expected as a result of a change in PM10
in a given location, a baseline mortality rate must be used. For this assessment,
the estimates are made in terms of annual cases of premature deaths reduced,
so we use national annual average morbidity rates as each baseline. Excess
occurrences in annual morbidity are given in Table 5.9
Table 5.9 : Annual occurrences of asthma and acute respiratory disease avoided
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5. ECONOMIC VALUATION 75
Scenario
Illness of
2000
2010
2020
Reduction Scenario 1
Asthma
472
4207
6324
Acute Respiratory
10
86
129
Reduction Scenario 2
Asthma
1690
2212
2732
Acute Respiratory
Disease
34
45
56
Reduction Scenario 3
Asthma
3379
4425
5463
Acute Respiratory
Disease
69
90
1 1 1
Reduction Scenario 4
Asthma
5069
6637
8195
Acute Respiratory
Disease
103
135
167
B) Estimation of Wage per Hour
• The wage per hour of respondent with occupation is estimated through three
assumption using the wage function 1 (Table 5.10) which was estimated from
[1 995 occupational wage survey data]
• The wage per hour of respondent without occupation is estimated using
wage function 2(Table 5.10) which was estimated from [1995 occupational
wage survey data]
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76 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
Table 5.10 : Wage functions estimated from 1995 wage survey data
^^Function
Variable
Wage function 1
Wage function 2
variable
Definition
coefficient
t-value
coefficient
t-value
CONST
C onstant
-7991 93
-38 7
-7197 00
-33 4
SEX
Sex
798.37
21.0
607.67
15.5
MAR
No marriage = 0,
670.83
14.8
622.25
12.8
AGE
Ano
331 95
29 9
373 59
31 8
AGES
An°2
-2 64
-19 3
-3 03
-21 0
MID
Graduated
909.96
12.5
1282.03
16.5
HIGH
Graduated
2362.19
32.9
3480.20
46.9
COLL
Graduated from
2209.97
24.7
4268.26
49.0
UNIV
Graduated
4270.95
49.6
6911.36
89.9
OC1
Occupationl d =1
8195.25
69.4
OC2
Occupation21> =1
4376.00
50.7
OCB
Occupations1' =1
4022.55
48.6
OC4
Occupation^) =1
3290.55
43.7
OC5
Occupations1' =1
2421.00
25.6
OC6
OccupationG1' =1
775.82
1.2
OC7
Occupation/1' =1
2217.00
30.1
OC8
Occupations1' =1
2258.99
32.3
R-square
0.5044
0.4366
Adi R-square
0.5042
0.4365
1) occupation 1: legislators, senior officials and managers, occupation 2:
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5. ECONOMIC VALUATION 77
professionals, occupation 3: technicians and associate professional, occupation
4: clerks, occupation 5: service workers and shop and sales workers,
occupation 6: skilled agriculture and fishery workers, occupation 7: craftmen
and related trade assemblers, occupation 8: plant and machine operator and
occupations, occupation 9: laborers
5.4.4 Total Medical Cost of Respiratory Disease
• Total medical costs of outpatient and inpatient was calculated by equations
[11],[12].
• Total medical cost of outpatient = personal expenses for treatment +
expenses from insurance + traffic expenses + {(number of visit x required
time for visit x 2) + waiting time for treatment} * (wage per hour) [11]
• Total medical cost of inpatient = personal expenses for hospital treatment
+ expenses from insurance + expenses for come-and-go + expenses for
nursing + rewards or supplementary expenses +{(required time for visit x 2)
+ days of hospital treatment x 8} x (wage per hour) [12]
• The costs of admission and outpatient visit are calculated by using data from
the National Health Insurance data(NHIC) and 1995 National Health Interview
Survey. In order to get the mean costs of asthma and respiratory disease, the
prevalence rates of each diseases are utilized as weight factors(Table 5.11).
Table 5.11 : Unit values of morbidity
Cost of
Adm.(KW)
Cost of
OPD.(KW)
Mean Cost
(KW)
Mean Cost
(US $)
Prevalence
rate
(spell based)
Asthma
913,534
40,1 57
70,973
62.0
Adm:OPD=
203 : 5,359
Respiratory
Disease
1,040,488
33,959
63,845
55.7
Adm:OPD=
196 : 6,405
Note: Adm.: admission OPD.: outpatient
1) Benefits of Morbidity Reduction due to GHG Mitigation
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7 8 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
The annual benefits of asthma and other respiratory diseases avoided due
to GHG mitigation scenarios are presented in Table 5.1 2
(1999 million $)
Year
Scenario
2000
2010
2020
Reduction scenario 1
0.03
0.27
0.40
Reduction scenario 2
0.11
0.14
0.17
Reduction scenario 3
0.21
0.28
0.34
Reduction scenario 4
0.32
0.42
0.52
Table 5.12 : Estimated annual benefits of morbidity avoided(COI)
5.5 Total Benefits of Health Effect due to GHG Mitigation
As for benefit estimation, only morbidity and mortality were calculated in
connection with PMio. Cost of illness figures were employed for economic
valuation of diseases while a range of values of statistical life was used to
calculate the value of the avoided premature deaths(Table 5.1 B). As for the
values of the avoided cases of asthma and other respiratory diseases COI
estimates were appplied(Table 5.14). All numbers are in 1999 present values
with annual discount of 7.5 percent and with converted as 1 US$ = 1,145.4
Korean Won (KW). Key results of the aggreagte values of mortality and
-------
5. ECONOMIC VALUATION 79
morbidity include :
- The economic value(inferred from CVM of future mortality risk reduction) of
the deaths avoided from the climate change mitigation scenarios ranges from
3.32 million (2000, scenario 1) to 57.64 million (2020, scenario 4) US$/yr.
- The economic value of the cases of asthma and other respiratory diseases
avoided for the climate change mitigation scenarios range from 0.03(2000,
scenario 1) million to 0.52 million(2020, scenario 4) US$/yr.
- The economic benefits per GHG emission avoided range $6.21(2000,
scenario 1 to $14.4(2010, scenario 1) for the climate change scenarios(Table
5.1 5).
- The cumulative value of these avoided health effects is estimated to range
from 342.16(scenario 2) to 1,026.57(scenario 4) million US$(Table 5.16).
Table 5.13 : Values of statistical life
VSL
(M KW)
VSL
(M US $)
Reference
Human
Capital
Approach2
283.3
0.25
Average remaining
expected life time between
40 and 79: 27.5 years
Per capita GDP : 10.3
(MKW)
Transferred
Value
1,658.5
1.45
range of values :
246.1 - 5,066.6 (M KW)
CVM
Current
Risk
999.6
0.87
range of values :
407.4 - 1,972.8 (M KW)
Future
Risk
543.4
0.47
range of values :
358.0 - 824.7 (M KW)
Table 5.14 : Estimated annual health benefits of mortality(CVM) and morbidity avoided
2 The value of life based on human capital approach is calculated by the average remaining
expected life time of target people(between 40 and 79 years old persons) and the population of
each age in Seoul.
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8 0 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
(99 million US $)
Benefits from decreases of
2000
2010
2020
Scenario 1
Asthma and respiratory
disease
0.0B
0.27
0.40
Premature deaths
3.29
29.33
29.59
Total benefit
B.B2
29.60
29.59
Scenario 2
Asthma and respiratory
disease
0.11
0.14
0.17
Premature deaths
1 1.77
1 5.42
19.04
Total benefit
11.88
1 5.56
19.21
Scenario 3
Asthma and respiratory
disease
0.21
0.28
0.34
Premature deaths
23.56
30.84
38.08
Total benefit
23.77
31.12
38.42
Scenario 4
Asthma and respiratory
disease
0.32
0.42
0.52
Premature deaths
35.33
46.26
57.12
Total benefit
35.65
46.68
57.64
Table 5.15 Economic benefit per GHG emission avoided
$/ton of carbon
avoided
2000
2010
2020
Scenario 1
6.2
14.4
10.4
Scenario 2,3,4
7.5
6.9
6.8
Table 5.16 Cumulative results 2000 to 2020 of total excess occurrence of mortality and
morbidity avoided and the corresponding benefits
Scenario
Cumulative
Decreases
from 2000
to 2020
(occurrence
)
Value
(M US$)
Total Value
Scenario
1
Mortality
Cardiovascul
ar Disease
1,102.81
523.1 7
588.44
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5. ECONOMIC VALUATION g \
Respiratory
Disease
126.45
59.99
Morbidity
Asthma
83,660
5.18
Respiratory
Disease
1,701
0.09
Scenario
2
Mortality
Cardiovascul
ar Disease
641.30
304.23
342.16
Respiratory
Disease
73.48
34.86
Morbidity
Asthma
48,652
3.01
Respiratory
Disease
990
0.06
Scenario
3
Mortality
Cardiovascul
ar Disease
1,2 82.60
608.47
684.41
Respiratory
Disease
147.1 3
69.80
Morbidity
Asthma
97,305
6.03
Respiratory
Disease
1,979
0.11
Scenario
4
Mortality
Cardiovascul
ar Disease
1,923.90
912.70
1,026.57
Respiratory
Disease
220.61
104.66
Morbidity
Asthma
145,957
9.04
Respiratory
Disease
2,969
0.17
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g2 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
CHAPTER 6: CONCLUSIONS AND IMPLICATIONS FOR POLICY
The Korea ICAP work applies a bottom-up impact analysis approach to
evaluate the ancillary benefits resulting from greenhouse gas mitigation
polices and measures. This work initially has focused on the impact of these
greenhouse gas mitigation measures on PM10 levels in the Seoul Metropolitan
area and the corresponding impact on premature mortality and morbidity of
asthma and respiratory diseases in 1995 through 2020. The greenhouse gas
scenarios considered in this preliminary analysis focus primarily on energy
efficiency and use of compressed natural gas for vehicles. More aggressive
greenhouse gas reduction scenarios that include fuel substitution outside of
the transportation sector would likely generate greater air pollution health
benefits.
The results reveal that modest greenhouse gas reduction scenarios (5-
1 5% reductions in 2020) can result in significant air pollution health benefits
through reductions in PM10 concentrations. For instance, these greenhouse
gas reduction measures for Korea's energy sector could avoid 40 to 120
premature deaths/yr. and 2,800 to 8,B00 cases/yr. of asthma and other
respiratory diseases in the Seoul Metropolitan Area in 2020. The cumulative
value of these avoided health effects is estimated to range from 7 to 103
million US$/yr (in 1999 dollars with annual discounting rate 7.5%). This is
equivalent to a benefit of $10 to $42 per ton of carbon emissions reduced in
2020 for the climate change scenarios.
Policy Implications
A review meeting for the ICAP-Korea project was held on 16 October 2000.
This meeting was attended by the Korean ICAP study team lead by KEI, Korean
policy makers from Ministry of Environment and the Korean legislature, Korean
technical experts, and technical experts from the USA. The objectives of the
meeting were to present the analytical methodology and the outcome of the
project to Korean policy makers and technical experts and to obtain feedback
on the usefulness of the project approach and results for enhancing effective
policy making in Korea in the areas of GHG mitigation and air quality
management.
The ICAP-Korea assessment found that the ancillary benefits of
implementing GHG mitigation measures in Seoul Metro. Korea between 2000
and 2020 would, on average, result in human health benefits of reduced air
-------
6. CONCLUSIONS AND IMPLICATION FOR POLICY g 3
pollution of $US10-42/ton C mitigated, a significant figure when considering
the costs of potential GHG mitigation measures. Policy makers agreed that
the ICAP approach and the results of this project were useful in informing
policy makers and the public of the co-benefit impacts of policy decisions and
assisting with the development of cost-effective integrated strategies to
address both local air quality issues and GHG mitigation concerns
simultaneously.
Study limitations that effect magnitude of results;
The average ancillary health benefits of $US10-42/ ton C were viewed as
conservative due to several limitations of the current studies analytical
approach and methodology which tended to lead to underestimates of the total
benefits which could be realized. The meeting recognized these study
limitations and concluded that if these limitations could be successfully
addressed in future work, the expected ancillary benefits of the GHG mitigation
scenarios would likely increase. The discussion of the key limitations
identified by the policy makers and experts and their effect on the assessment
outcome is summarized below.
Mitigation scenarios:
The meeting noted that the GHG mitigation scenarios assumed a modest level
of implementation of effective GHG mitigation measures and that these
measures were not specifically targeted toward "integrated strategies" which
would be most effective in simultaneously reducing GHG emissions and
emissions of air pollutants. A greater focus in the mitigation scenarios on
harmonized strategies that target both GHG and air pollution emissions from
specific sectors and fuel types would likely have resulted in greater emission
reductions of both types of pollutants, and hence greater health benefits.
Assessment considered a limited set of key air pollutants:
The only air pollutant considered under the assessment methodology was
directly emitted PM10, which Korean researchers estimate make up only about
50% of total air pollution health effects in Seoul. Other pollutants which are
have been determined to have important impacts on human health include fine
particulate matter (PM2.5 and secondary particulate matter such as sulfates
and nitrates), S02, NOx, and OB. Atmospheric concentrations of these other
pollutants would also be expected to be reduced as a result of implementation
of the GHG mitigation strategies, along side PM10. Thus, the meeting
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g4 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
recognized that consideration of a wider range of air pollutants would allow
the project to quantify an increasingly larger set of ancillary health benefits
resulting from implementation of GHG mitigation measures.
Health effects estimation includes some known uncertainties.
For social impact estimation, mortality should be estimated as a probability of
death among total population. In this study and similar studies else, health
effect was analyzed as short-term premature deaths probably from the already
diseased pools. This discrepancy is most important source of overestimation,
considering the larger proportions that mortality occupies in total
impact/benefits (more than 80%).
This study also has some important sources of underestimation:
Besides the fact that we did not consider the "main effect" from the beginning
(direct health effect from GHG, i.e., heat wave, extreme weathers and newly
emerging infection), we limited pollutants to PM10. This restriction ruled out
the ozone and S02 effects, which in Korea had shown stronger adverse health
effects. Particularly, ozone has been observed to adversely affect mortality and
respiratory diseases by the factor of 3-4 compared with PM10. To top on this,
there has been strong evidence that ozone modifies the effect of heat wave
effect on mortality. This restriction of pollutants may be the largest source of
underestimation.
Many health outcome cells are still left empty, not included in
calculating benefits. Ischemic heart diseases, congestive heart failure and
lung cancer may be the most probable health effects to be included in next
step. And there are important but often neglected source of underestimation
from using annual average value in pollutant level projection. We believe that
strong adverse health effect may be caused in the day of extremely high
pollution levels. Small increase in the average pollution level almost always
parallels wider fluctuation of pollution level. In this study we only considered
change in annual average level, which dampened actual daily health effects. As
a result, the meeting concluded that the assessment, by associating health
effects with daily average PM10 concentrations, underestimated the health
impacts resulting from increased PM10 concentrations and hence the ancillary
benefits of reducing these concentrations were also underestimated.
Relevance and usefulness of the ICAP approach and results for policy making:
There was an overwhelming consensus that the approach and results of this
project were very useful for policy making at both local levels (on air quality
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6. CONCLUSIONS AND IMPLICATION FOR POLICY g 5
management) and national levels (on GHG mitigation). Policymakers noted that
the project demonstrated the potential for real, positive economic and social
ancillary benefits from mitigation scenarios and commended the project efforts
activities to provide these estimates. An important next step in this process
would be to more widely disseminate the outcome and results of this project to
achieve greater recognition and understanding of the results in the policy
making community and the general public.
Representatives from the Ministry of the Environment (MOE) noted that
while in general in Korea, policy makers place greater value on actions to
improve local air quality than on actions to mitigate GHG emissions, the
approach followed in this project could be used to develop cost-effective
integrated strategies to address both types of concerns simultaneously.
The representative from Congress pointed out that the Korean government
already expressed a keen interest in climate change issues and lawmakers are
very interested in the issue of ancillary benefits of climate change mitigation
actions. Under consideration is establishment of a special committee on
climate change in congress to investigate policy matters related to climate
change issues in greater detail. However, the problem of awareness extends
beyond the policymakers to the general population who view climate change as
a complicated, difficult and potentially costly problem. Thus, one benefit of
this project and it's results would be to assist with educating the general
public about the potential economic and social benefits of taking action on
climate change issues in a way that allows them to better relate to these issues
on a personal level and comprehend the costs and benefits of policy decisions.
The ICAP project affords the benefit of allowing the policy issues of climate
change to be viewed in the context of sustainable development. Through
linking strategies to address local air quality and improve human health with
GHG emissions reductions, the relationship between sustainable development
and climate change policy becomes more apparent. As those linkages are
further developed, it becomes clear that practical measures to address climate
change are also practical measures to help achieve sustainable development
goals as well.
It was also pointed out that in Korea, as in the US and many other
developed countries, pollution regulation has traditionally addressed one
criteria pollutant at a time often resulting in a overall regulatory strategy which
is not optimal nor cost effective. The ICAP project is useful for air pollution
regulation in Korea as it aids policymakers in integrating the regulation of
multiple pollutants simultaneously, resulting in more effective, and more cost-
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8 6 NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
effective strategies.
The policy makers also noted that to be useful in practical application,
the ICAP project should attempt to prioritize specific measures and strategies
in terms of their benefit potential and cost effectiveness in achieving
simultaneous GHG mitigation and human health improvement. To address
this concern, ICAP would need to develop and analyze more specific mitigation
measures and technologies related to specific sectors and fuel types to
determine the overall impact and benefit ratio for these measures. In this way,
the ICAP approach could more effectively communicate to policymakers and
the general public the anticipated level of ancillary benefits of specific
measures and build support for implementation of these measures.
-------
REFERENCE 37
Reference
Alberini, A., B. Kanninen and R. T. Carson. 1997. Modeling Response Incentive Effects in
Dichotomous Choice Contingent Valuation Data, Land Economics, 73(3), 309-324.
Chestnut, L.G., et. al, 1988, Risk to heart disease patients from exposure to
carbon monoxide: assessment and evaluation, Draft report for U.S. Environmental
Protection Agency, Washington, D.C.
Dickie, M., et al., 1986, Values of symptoms of ozone exposure: an application of the
averting behavior method, In Improving accuracy and reducing costs of
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REFERENCE 39
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-------
NCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
-------
REFERENCE 9\
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118 ANCILLARY BENEFITS DUE TO GREENHOUSE GAS MITIGATION
AGE:
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Appendix: SURVEY QUESTIONNAIRE | |Cj
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120 ancillary benefits due to greenhouse gas mitigation
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Appendix: SURVEY QUESTIONNAIRE | 2 1
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122 ancillary benefits due to greenhouse gas mitigation
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AGE:
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Appendix: SURVEY QUESTIONNAIRE | 23
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Appendix: SURVEY QUESTIONNAIRE | 25
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126 ancillary benefits due to greenhouse gas mitigation
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Appendix: SURVEY QUESTIONNAIRE | 27
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128 ancillary benefits due to greenhouse gas mitigation
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Appendix: SURVEY QUESTIONNAIRE | 29
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130 ANCILLARY benefits due to greenhouse gas mitigation
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Appendix: SURVEY QUESTIONNAIRE |3 |
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Appendix: SURVEY QUESTIONNAIRE | 33
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Appendix: SURVEY QUESTIONNAIRE | 35
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Appendix: SURVEY QUESTIONNAIRE | 3 7
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Appendix: SURVEY QUESTIONNAIRE | 39
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Appendix: SURVEY QUESTIONNAIRE 14 |
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Appendix: SURVEY QUESTIONNAIRE 143
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