Assessing Health Externalities for
Fossil Fuel Power in Taiwan
Meng-Ying Lee, Meng-I Liao, Pei-Hao Li, Ming-Lung Hung,
Hwong-wen Ma
Green Energy and Environment Laboratories, Industrial
Technology Research Institute (ITRI), Taiwan
Graduate Institute of Environmental Engineering, National
Taiwan University, Taipei, Taiwan
International Energy Workshop, Cork
June 1st, 2016
Outline
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Introduction
Objectives
Methods
Results and scenario study
Conclusion
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Introduction: Overview of Taiwan’s energy status
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The annual growth rate (1995-2014) for electricity consumption: 3.84%
The annual growth rate for electricity intensity: -0.61%
The share of LNG-fired power significantly increased
Fossil fuel power accounts for 78.7% of total electricity generated in Taiwan
Trend of electricity mix (1995 – 2004)
Source: Energy statistics, 2015
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Introduction: Energy transition in Taiwan
• National Energy development strategy: big change on electricity mix
by May 20th, 2016
To be discussed
Nuclear debates
- 5.1GW existing
- 2.7GW planned
No nuclear in 2025?
Renewable energy
- 17GW in 2030
Coal-fired power
Oil-fired power
LNG-fired power
Enhanced goal in 2025?
Strict regulations on air pollutants
• Local air pollution problems
– Emission capping for air pollutants in heavily polluted areas
– Coal-fired power curtailment based on air quality
– Prohibition on coal combustion from local governments
• Understanding local differences of health impacts is crucial for future
energy deployment
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Introduction: Air pollution and power plants in Taiwan
Sources of major air pollutants in Taiwan in 2010
• Utilities, manufacturing, and
transportation account for significant
contribution to air pollution in Taiwan.
• 17 fossil fuel power plants are in
operation, most of which are located in
populated areas.
Source: TEDS, 2015; National Statistics, 2015
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Objectives
• To develop an health impact assessment module for Taiwan TIMES model
to estimate external cost for fossil fuel power plants in Taiwan
• To evaluate health cost per kWh for each fossil fuel power plant
considering their individual properties and locations
• To provide information on local health burdens under different energy
development scenarios
Taiwan
TIMES model
Health impact
assessment module
Local health impact
costs of different
energy development
scenarios
Scenariobased
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Regionalbased
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Method: Taiwan TIMES model
• Taiwan MARKAL model was established in 1993 with funding support from
Bureau of Energy. It was transformed into Taiwan TIMES model in 2010.
• Taiwan MARKAL/TIMES model has been supporting energy policy making
including nuclear debates, national energy development planning, INDC target
setting, etc.
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Assessment Methodology: Impact Pathway Approach (IPA)
• IPA was developed within the EU ExternE project series and widely adopted
in energy system impact studies.
• IPA is a bottom-up approach connecting source emissions to the receptors
exposed to the pollutants with monetary evaluation of the potential health
impacts.
SOURCE
RECEPTOR
Emission source
Pollutant
Distribution
Health impact
Cost of Impact
Source specification &
estimating emissions
Air dispersion
modeling
Dose-response
function
Monetary
evaluation
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Method: Analysis flowchart and integrated mechanism
• The electricity mix output from TIMES model was used as input parameters
for the health impact assessment module.
Policy
scenarios
Technology
scenarios
Taiwan
TIMES
Model
Electricity
generation from
each plant
Emission level of
air pollutants
Socioeconomic
outlook
Health impact
assessment module
Air pollutant
emissions factors
AERMOD air
dispersion model
Re-enter
optimization
process if not
meeting
evaluation
targets
Spatial
distribution of
pollutants
Health impact
analysis
Monetary
evaluation
Pollutant dispersion
matrix
ERF slope
(dose-response function)
Population distribution
External cost factors
Local health cost
evaluation
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Method: Health impact valuation process
9 major pollutants: SO2, NOX, PM10, PM2.5, Dioxin, Cd, As, Cr(VI), Ni
10 health endpoints: Long-term mortality, Restricted activity days, chronic bronchitis,
Bronchodilator usage, Lower Respiratory Symptoms, Congestive Heart Failure, Cardiac
Hospital Admin., Respiratory Hospital Admin., Cancers
1. Source emission:
- Existing plants: Calculated from
Taiwan Emission Data System (TEDS)
database with electricity generation
statistics;
- Planned plants: Committed emissions
factors in the Environmental Impact
Assessment Report
2. Pollutant dispersion:
AERMOD air dispersion modeling system:
- considering meteorological condition,
elevation of sources and receptors,
land use, stack properties, physiochemical properties of pollutants at the
resolution of 200m x 200m
- Generate per kWh dispersion matrix
4. Monetary valuation:
Due to lack of comprehensive data in
Taiwan, the monetary value for each
health impact in ExternE program was
used.
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Results: Source emissions factors
• Specify emission factors of air pollutants for each power plant
• Variations by fuel types and technologies employed
Average emissions factors of fossil fuel power plants in Taiwan (g/kWh)
Generation unit type
Existing Coal (steam)
New Coal (USC)
Existing Oil (steam)
Existing Gas (steam+CCGT)
New Gas (CCGT)
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PM2.5
PM10
SO2
NOx
Cd
As
Cr(VI)
Ni
Dioxin
3.86E-08 3.12E-08 3.56E-07 5.44E-07 3.34E-12 2.36E-11 1.45E-11 5.15E-11 6.35E-17
1.26E-08 6.55E-09 2.20E-07 1.82E-07 2.66E-12 1.86E-11 1.14E-11 4.05E-11 5.18E-17
4.87E-08 3.39E-07 1.55E-06 7.32E-07 6.61E-13
1.08E-12 4.12E-17
1.77E-09 2.07E-08 8.64E-09 2.17E-07
1.34E-12
1.66E-09 1.40E-09 7.77E-09 2.08E-07
1.24E-12
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Results: Aggregate concentration of pollutants
• AERMOD modeling results based on 2014 electricity generation.
• Pollution hotspots form in mid-western, mid-southern, and northern areas.
PM10 (µg/m3)
Cd (µg/m3)
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PM2.5 (µg/m3)
As (µg/m3)
SO2 (µg/m3)
Cr-VI (µg/m3)
NOX (µg/m3)
Ni (µg/m3)
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Results: External Cost per kWh for each power plant
• New types of generators pose less health impacts than the existing ones.
• Fuel type is one of the determining factors for overall performance.
• Variations among the same fuel type are mainly from source locations.
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Results: Contributions of external costs for each fuel type
• The external cost of oil-fired units is mostly attributed to SO2 (74.8% of total
health cost), while NOX accounted for the majority of external costs of gasfired units (89.0% of total health cost).
• Long-term mortality accounts for 65% of total external cost, followed by
chronic bronchitis and restricted activity days.
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Scenario study: assumptions and electricity mix
• Based on scenarios in national energy development planning under discussion
• The share of LNG-fired is 21% more in gas-based scenario compared with coalfired scenario, while oil-fired is phased out in 2030 in both scenarios.
Scenario
assumptions
Shared
assumptions
Case 1: Coal-based
Case 2: Gas-based
LNG supply for power
Increase of LNG supply by 70%
generation remains the same
by 2030
level through 2030
 Coal-fired power ensures reserve capacity
 No oil-fired power generation in 2030
 The same capacities on nuclear power and renewables
TIMES modeling
output:
electricity mix of
fossil fuel power
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Scenario study: Health costs in 2014 and 2030
• The TIMES integrated health impact module can readily reflect detailed
difference of health impacts based on scenario deployment.
• Demonstrating effects on efficiency improvement and fuel switch:
– Higher share of gas-fired power generation reduce overall impacts.
– Increase of new plants with lower emission factors help reduce overall
impacts, while plant location is also an determining factor.
Electricity mix by fossil fuel
power
Generation unit type
Coal
Gas
Existing (steam)
New (USC)
2030
2014
Case 1: Coal-based
Case 2: Gas-based
electricity per-kWh cost electricity per-kWh cost electricity per-kWh cost
share
(NTD/kWh)
share
(NTD/kWh)
share
(NTD/kWh)
53%
0.070
33%
0.065
33%
0.065
0.070
0.063
0.068
0%
30%
0.061
9%
0.078
Existing (steam+CCGT)
44%
0.029
New (CCGT)
Oil
Total
0%
3%
100%
0.407 0.407
0.064
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0.029
13%
0.052
24%
0%
100%
0.008
0.048
0.024
27%
31%
0.00%
100%
0.032
0.019
0.008
0.040
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Scenario study: Local health impact analysis
• Health impacts in 2030 are generally higher than those in 2014 due to the
increase of electricity demand; the coal-based scenario pose higher impacts
than the gas-based scenario.
• Local health impacts reflect variations of power plant deployment between
scenarios (eg. County D, E, F).
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Scenario study: Local health impact versus electricity
demand
• Significant imbalance between local electricity demand and health
burden
• providing a more comprehensive reference for regional development
planning to reduce further impacts on heavily burdened areas
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Conclusion
• The variation of external cost per kWh for each power plant is a
combined consequence of types of fuels, emissions factors of pollutants,
local air dispersion properties, and local population densities.
• Efficiency improvement and fuel switch from coal to gas contribute to
overall external cost reduction.
• Significance of the results:
– To provide information on aggregated local health impacts of fossil
fuel power as a reference for policy makers on local energy
development planning
– To signify the importance to look into the balance between local
electricity demand and health burdens to local population for future
energy policy making
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Ongoing work – database hard linkage and application
• Integrating health impact assessment databases into Taiwan TIMES
model optimization process
• Enhancing model capabilities for analyzing the impacts of air
pollution-related regulations for power sector in Taiwan
Taiwan TIMES-Air model
Air pollutant
emissions factors
Pollution
control
technology
database
Air pollutant
emissions levels
Plant-based air
pollutant emission
cap
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Air pollutant
dispersion matrix
Local distribution
of pollutants
Air quality
contribution from
power sector
Local health burden
analysis
Health impact
assessment module
Comprehensive impact
analysis for power
sector under air
pollution regulations
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Thank you for your attention
Meng-Ying Lee, ITRI
[email protected]
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Appendix: Input parameters for AERMOD modeling
Type
Parameters
Parameter description and source
Digital Terrian Model(DTM) from Center for Space and Remote Sensing
Stack location & elevation
Emission source
Research, National Central University
properties
Monitoring data(including stack emission rate, emission quantity, stack height,
Stack emission factors
outlet temperature, stack diameter) from TEDS8.1(2014)
Particle size distribution
Particle mass distribution Kaupp & Mclachlan (1999, 2000)
Particle density
Diffusivity in air
Diffusivity in water
Henry’s Law constant
Cuticular resistance
Record time
Wind direction
Wind speed
Temperature
Meteorological Precipitation
data
Atmospheric Stability
Mixing height
Friction velocity
Monin-Obukov length
surface heat flux
Grid size for receptors
Receptor terrain
Receptor’s elevation
Deposition
parameters
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Carbonell et al. (2010)
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Surface hourly meteorological data from local weather stations
Upper-air sounding data from Taipei and Hualien stations
Calculated by AERMET in AERMOD modeling system
200m grid
Digital Terrian Model(DTM) from Center for Space and Remote Sensing
Research, National Central University
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Appendix: ERF slope of pollutants to corresponding health
endpoints
Health Endpoints
Long-term Mortality
(YOLL/(pers.yr.μg/m3))
Restricted Activity Days
(days/(pers.yr.μg/m3))
Chronic Bronchitis
(cases/(pers.yr.μg/m3))
Bronchodilator usage
(cases/(pers.yr.μg/m3))
Lower Respiratory
Symptoms
(cases/(pers.yr.μg/m3))
Congestive Heart Failure
(cases/(pers.yr.μg/m3))
Cardiac Hospital Admin.
(cases/(pers.yr.μg/m3))
Respiratory Hospital Admin.
(cases/(pers.yr.μg/m3))
Cancers
(cancers/(pers.yr.μg/m3))
CAP
PM10
PM2.5
SOx
NOx
Cd
Metals
As
Cr(VI)
Ni
Dioxin
2.88E-04 4.80E-04 4.80E-04 2.88E-04
-
-
-
-
-
3.95E-02 6.60E-02 6.60E-02 3.95E-02
-
-
-
-
-
2.65E-05 4.43E-05 1.01E-04 5.81E-05
-
-
-
-
-
2.06E-02 3.44E-02 3.44E-02 2.06E-02
-
-
-
-
-
3.25E-02 5.42E-02 5.42E-02 3.25E-02
-
-
-
-
-
2.59E-06 4.33E-06 4.33E-06 2.59E-06
-
-
-
-
-
6.92E-06 1.15E-05 1.15E-05 6.92E-06
-
-
-
-
-
7.03E-06 4.28E-06 4.28E-06 2.56E-06
-
-
-
-
-
-
-
-
-
2.57E-05 6.14E-05 1.71E-05 3.43E-06 6.02E-01
Source: Leksell & Rabl, 2001; Rabl, 2001; Spadaro, 2003; NEEDS, 2009; Spadaro and Rabl, 2000 & 2004
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Appendix: Unit cost for each health endpoint
Health endpoint
Long-term Mortality (NTD/YOLL)
Restricted Activity Days (NTD/day)
Chronic Bronchitis (NTD/case)
Bronchodilator usage (NTD/case)
Lower Respiratory Symptoms (NTD/case)
Unit cost (NTD)
3,005,760
3,452
5,291,328
1,250
238
Congestive Heart Failure (NTD/case)
101,779
Cardiac Hospital Admin. (NTD/case)
523,776
Respiratory Hospital Admin. (NTD/case)
135,110
Cancers (NTD/case)
60,570,000
Source: Spadaro, 2003; Spadaro and Rabl, 2000
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Appendix: Health external costs in IPCC and EU CASES
studies
Per-kWh external cost (unit: NTD)
Generator type
Taiwan
IPCC
EU CASES
Coal-fired
0.070
0.140
0.486
Oil-fired
0.407
-
0.692
Gas-fired
0.029
0.064
0.164
Source:
IPCC, 2011, Special report on renewable energy sources and climate change mitigation
ExternE, 2008, Cost assessment of sustainable energy systems (CASES)
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