Benefit-Cost Assessment of Aviation Environmental Policies
by
Christopher K. Gilmore
B.S.E., Mechanical Engineering, Duke University, 2010
Submitted to the Department of Aeronautics and Astronautics in partial fulfilln ent of the
requirements for the degree of
ARCHNES
Master of Science in Aeronautics and Astronautics
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@ Massachusetts Institute of Technology 2012. All rights reserved.
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.......................
Signature of Author: ...............................................
Department of Aeronautics and Astronautics
May 24, 2012
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Certified by: .............................
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.......................
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Steven R.H. Barrett
Charles Stark Draper Assistant Professor of Aeronautics and Astronautics
Thesis Supervisor
Accepted by: ....................................................
I
7
A
E a.
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Modiano
Eytan H.
Professor of Aeronautics and Astronautics
Chair, Committee on Graduate Students
Benefit-Cost Assessment of Aviation Environmental Policies
by
Christopher K. Gilmore
Submitted to the Department of Aeronautics and Astronautics on May 24, 2012 in Partial
Fulfillment of the Requirements for the Degree of Master of Science in Aeronautics and
Astronautics
ABSTRACT
This thesis aids in the development of a framework in which to conduct global benefit-cost
assessments of aviation policies. Current policy analysis tools, such as the aviation
environmental portfolio management tool (APMT), only consider climate and air quality impacts
derived from aircraft emissions within the US. In addition, only landing and takeoff (LTO)
emissions are considered. Barrett et al., however, has shown that aircraft cruise emissions have
a significant impact on ground-level air quality. Given the time-scale and atmospheric lifetimes
of species derived from aircraft emissions at these higher altitudes, a global framework for
assessment is required.
This thesis specifically investigates the global as well as regional implementation of an ultra-low
sulfur jet fuel (ULSJ). The expected result from this policy is a reduction in aircraft SOx
emissions, which in turn would reduce the atmospheric burden of primary and secondary sulfate
aerosols. Sulfate aerosols have both climate and air quality impacts as they reflect incoming
solar radiation (and thus provide atmospheric cooling) and are a type of ground-level pollutant
that have generally been correlated to premature mortalities resulting from cardiopulmonary
disease and lung cancer.
Benefit-cost techniques are applied in this analysis. The framework developed within this thesis
includes the ability to calculate expected avoided premature mortalities outside of the US. In
addition, a monetization approach is used in which different values of statistical lives (VSLs) are
applied depending on the country in which a premature mortality occurs. Also, the economic
impact of increased fuel processing to reduce the FSC is estimated. This analysis is performed
using Monte Carlo techniques to capture uncertainty, and a global sensitivity analysis (GSA) is
utilized to determine the primary sources of uncertainty.
The benefit-cost analysis results show that for US and global implementation, there is -80%
chance of ULSJ implementation having a not cost beneficial outcome when climate, air quality,
and economic impacts are included. On average, however, the air quality benefits do exceed
the climate disbenefits. In addition, the GSA reveals that the largest contributor to the
uncertainty in this analysis is the assumed US VSL distribution, where approximately 60% of the
variance in the final output distribution can be attributed to this uncertainty.
In addition, a fast policy tool approach is investigated using sensitivity values calculated from an
adjoint model built-in to the global chemical transport model (GCTM) used for the atmospheric
modeling within this analysis. From this fast policy tool, first order estimates of the impact of
ULSJ on premature mortality are calculated.
Thesis Supervisor: Steven R.H. Barrett
Title: Charles Stark Draper Assistant Professor of Aeronautics and Astronautics
i
Acknowledgements
First I would like to thank my advisor, Professor Steven Barrett, for his guidance throughout my
first two years at MIT. He has helped me develop into a capable researcher, a better writer, and
a more critical thinker, all of which I greatly appreciate.
I would also like to thank Dr. Steve Yim for his patience with me and tolerating all of my
questions when I first got to PARTNER. His help day in and day out has been invaluable. I also
want to thank Dr. Jim Hileman for all of his help. His door was always open, and he was always
there to provide his insights. In addition, I want to thank Dr. Christopher Wollersheim and Dr.
Robert Malina for their wisdom with regards to all things related to economics. Their help and
guidance with the ULSJ project made my job easier. I also would like to thank Dr. Jon Levy for
his help with the public health analysis and everyone at the FAA, including Chris Sequeira and
Daniel Jacob, for their advice and help with regards to the ULSJ project.
I also want to thank all the students in PARTNER for helping to make my experience at MIT so
far a great one. In particular, I want to thank Akshay and Jamin for always being willing to help
me with whatever problem I found too daunting to take on by myself, thanks to Kristy, Naki, (Lt.)
Nick, Tony, and Gideon for putting up with my sense of humor, Phil for answering all of my
climate code related questions, and finally thanks to Fabio, Seb, and Sergio for always
lightening the mood.
I also would like to thank Dr. Jon Protz, my advisor from Duke University for helping me get this
far and convincing me that grad school was the right choice for me.
Finally, and most importantly, I would like to thank my parents, Mike and Ai-Chi, for always
being there and providing encouragement and advice whenever I needed it. I would not be here
without them.
ii
Contents
Chapter 1: Introduction...............................................................................................................
1
1.1. Aircraft and the Environment ........................................................................................
1
1.2. Policy Objectives .......................................................................................................
4
1 .3 . Mo tiva tio n ........................................................................................................................
5
1.4. Thesis Structure ..........................................................................................................
6
Chapter 2: Background ..........................................................................................................
8
2.1. Atmospheric Modeling .................................................................................................
8
2.1.1. Global and Nested GEOS-Chem Model Descriptions............................................
2.2. Impact of Aerosols on Climate .....................................................................................
8
9
2.3. Air Quality and Mortality.............................................................................................
11
2.4. Elements of EBCA......................................................................................................
13
2.4.1. Benefit/Cost Analysis ..........................................................................................
14
2.4.2. Discounting ..............................................................................................................
15
2.4.3. Premature Mortality...............................................................................................
16
2.4.4. Valuing Lives........................................................................................................
16
2.4.5. Cost Analysis ........................................................................................................
18
2.5. Ultra-Low Sulfur Fuels .................................................................................................
18
2.5.1. Ultra-Low Sulfur Diesel Case Study .....................................................................
18
2.5.2. QinetiQ Report on Jet Fuel Sulfur Limit Reduction ..............................................
20
2.5.3. Energy Information Administration (EIA) Report on Market Effects Due to ULSD.....21
iii
2.5.4. Other Transportation Sectors ..............................................................................
21
2.6. Role of Sensitivity Analysis ........................................................................................
21
Chapter 3: EBCA Methodology Development .......................................................................
26
3.1. Em issions Scenarios .................................................................................................
26
3.2. Determ ining Climate Impacts......................................................................................
27
3.2.1. Sulfate RF Calculation...........................................................................................28
3.2.2. RF Uncertainty ......................................................................................................
30
3.2.3. Sulfate Lum ping ...................................................................................................
31
3.2.4. W TW GHG Emissions..............................................................................................
32
3.2.5. APMT-Impacts Climate Module............................................................................
33
3.3. Country Dependent VSLs ..........................................................................................
34
3.4. Concentration Response Functions ............................................................................
36
3.5. Econom ic Analysis .....................................................................................................
39
3.5.1. Price History Analysis...........................................................................................
40
3.5.2. Cost Buildup Approach........................................................................................
42
3.5.3. Cost Distribution....................................................................................................46
3.6. Sensitivity Analysis ...................................................................................................
46
3.6.1. Monte Carlo Analysis Framework........................................................................
46
3.6.2. Nom inal Range Sensitivity Analysis .....................................................................
47
3.6.3. Global Sensitivity Analysis ...................................................................................
47
3.7. Additional Operational Concerns .................................................................................
48
iv
3.7.1. Change in Fuel Properties....................................................................................
48
3.7.2. Fuel Lubricity.........................................................................................................49
Chapter 4: EBCA Results......................................................................................................
52
4.1. Aerosol RF Results......................................................................................................
52
4.2. Mortality Results by Country......................................................................................
53
4.3. VSL Results by Country.............................................................................................
55
4.4. Global and US ULSJ O utcom es ................................................................................
56
4.4.1. Assum ptions for Global and US Im plem entation Analysis ....................................
56
4.4.2. Assum ed Uncertainty Distributions......................................................................
58
4.4.3. Global Im plem entation Analysis Results ..............................................................
61
4.4.4. US Implem entation Analysis.................................................................................
62
4.4.5. US-Only Im plem entation Analysis ........................................................................
64
4.4.6. Constant VSL Analysis ........................................................................................
65
4.4.7. Cost Effectiveness Analysis .................................................................................
66
4.5. Policy Im plications ......................................................................................................
66
4.6. Nom inal Range Sensitivity Results ............................................................................
68
4.6.1. Discount Rate .....................................................................................................
70
4.7. Global Sensitivity Analysis (GSA) Results ...................................................................
71
Chapter 5: Fast Policy Analysis.............................................................................................
75
5.1. Adjoint Model and Policy Tool....................................................................................
75
5.2. USLJ Analysis Com parison ........................................................................................
76
V
Chapter 6: Conclusions and Future W ork.............................................................................
82
6.1. Global ULSJ Implementation..........................................................................................82
6.2. Fast Policy Analysis...................................................................................................
83
6.3. Limitations ......................................................................................................................
83
6.4. Future W ork....................................................................................................................84
Appendix A: Tables...................................................................................................................87
W o rks Cited ..............................................................................................................................
vi
97
List of Figures
Figure 1: Radiative forcing estimates of aircraft emissions.....................................................
2
Figure 2: Multi-branch hysteresis behavior of an ammonium sulfate aerosol particle. Plot from
0
W ang et a l.................................................................................................................................1
Figure 3: The product supplied of ULS, LS, and HS diesel fuel plotted simultaneously with the
price differential for ULS-HS and LS-HS for Jan 2001 to February 2011
..............................
41
Figure 4: NG Price history required for ULSJ hydroprocessing where prices are presented
based on 2.37 scf/gal (0.018 scm/L) of NG per gallon of ULSJ. ............................................
44
Figure 5: Capital costs for hydrotreating and SMR units as a function of HDS capacity
depreciated over 30 years...................................................................................................
45
Figure 6: Benefit-cost distribution for global implementation analysis for three different discount
ra tes (DRs ). ..............................................................................................................................
61
Figure 7: Benefit-cost distribution for US implementation analysis. .......................................
63
Figure 8: Benefit-cost distribution for US-only implementation analysis................................ 64
Figure 9: Benefit-cost distribution for a constant US VSL analysis. .......................................
65
Figure 10: NRSA results for global implementation of ULSJ................................................
68
Figure 11: NRSA results for US implementation of ULSJ. ...................................................
69
Figure 12: Net benefit-cost plotted against discount rate of the deterministic model used in the
NR SA ........................................................................................................................................
70
Figure 13: Global Implementation GSA main effect sensitivity index results......................... 72
Figure 14: Global Implementation GSA total effect sensitivity index results........................... 72
Figure 15: US Implementation GSA main effect sensitivity index results ...............................
73
Figure 16: US Implementation GSA total effect sensitivity index results ................................
73
vii
Figure 17: Using sensitivities to compute the total impact from an aircraft emissions policy
s ce n a rio .'...................................................................................................................................7
Figure 18: Forward model premature mortality results from the ULSJ EBCA. .......................
6
77
Figure 19: Adjoint policy tool results and adjusted results for global ULSJ implementation. ...... 78
Figure 20: Adjoint policy tool results for monetized avoided premature mortalities with an
assumed income elasticity of 1. ............................................................................................
80
Figure 21: Proposed structure of multi-year study adjoint policy tool.................................... 85
viii
List of Tables
Table 1: Assumed properties of "sulfates" optical bin in GEOS-Chem..................................
11
Table 2: On-road implementation timeline of ULSD in the US ............................................
19
Table 3: Off-road implementation timeline of ULSD in the US.' ............................................
20
Table 4: Source-receptor mortality impact from full-flight aircraft emissions. .........................
24
Table 5: Emission indices methodology for air quality simulations, where bolded variables are
from AE DT . ...............................................................................................................................
27
Table 6: Coefficients in Eq. (11) and associated values and uncertainties. ...........................
30
Table 7: Percentage increase in avoided mortalities given a 1 pg/m 3 increase in ground-level
PM2 5 concentration. Values from Pope et al. and Laden et al. ..............................................
38
Table 8: Process energy shares required for jet fuel production...........................................
44
Table 9: Aviation sulfate RF by component and region. ........................................................
52
Table 10: Avoided mortalities by country due to global ULSJ implementation. .....................
54
Table 11: Regional simulations avoided mortalities results for the US from global
im plem entation of ULSJ .....................................................................................................
. . 54
Table 12: VSL and valuation of avoided premature mortalities (when cruise emissions are
included) due to ULSJ implementation by country in US 2006 $. ..........................................
56
Table 13: Brief description of each input parameter. .............................................................
59
Table 14: Monte Carlo Input Values and Distributions (Triangular: [Low, High, Nominal]).........60
Table 15: Global implementation EBCA results, given in 2006 US $ Billion.......................... 61
Table 16: Global implementation results from EBCA where no implementation cost has been
included, given in 2006 US $ Billion. ....................................................................................
ix
62
Table 17: Global implementation results from EBCA where no climate cost has been included,
given in 2006 US $ Billion. ...................................................................................................
Table 18: US EBCA results for global implementation, given in 2006 US $Billion. ................
. 62
63
Table 19: Valuation of US health impacts due to global implementation from CMAQ and GEOSC hem Nested sim ulations. ....................................................................................................
63
Table 20: US-only implementation EBCA results, given in 2006 US $ Billion. ......................
64
Table 21: Constant US VSL EBCA results, given in 2006 US $ Billion. .................................
65
Table 22: Cost effectiveness analysis results, given in 2006 US $ Million. ...........................
66
Table 23: Aircraft SO x Em issions..........................................................................................
77
x
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xi
Chapter 1: Introduction
While aircraft emissions represent only -1% of total fossil fuel use in the world,' aircraft
operations are expected to increase by -5% annually.2
If this growth is realized, current
aviation operations will double by 2025 and represent the fastest growing mode of
transportation. As with any transportation sector, the environmental impact of aircraft emissions
is of growing concern. The prioritization of improving air quality is reflected historically in
legislation passed within the United States (US), such as the Clean Air Acts of 1970 and 1977,5
which motivated the restrictions placed on allowable pollutant concentrations. With regards to
the transportation sector, the US has also required the use of desulfurized diesel fuel for on and
off highway purposes to reduce ground-level pollutant concentrations. 6 Similar regulations also
exist in many European nations. Specifically regarding aviation, the International Civil Aviation
Organization's (ICAO) has routinely recommended increased aviation NOx stringencies to
reduce negative health impacts derived from aircraft operations through an improvement in air
quality.7
Aircraft emissions have both climate impacts, through their impact on the atmospheric radiative
balance, and human health impacts, due to increased ground-level pollution concentrations by
way of the chemical reaction of emissions with the ambient atmosphere and vertical transport.
Policymakers have placed increased emphasis on mitigating the environmental impacts of
aviation where the Federal Aviation Administration (FAA) has defined a set of prospective policy
8
goals in their Destination 2025 plan. This thesis aims to provide a framework in which to
evaluate potential aviation policies within the context of a global environmental benefit-cost
analysis (EBCA) with an emphasis on quantifying and monetizing air quality and climate impacts
in order to achieve these policy goals.
1.1. Aircraft and the Environment
Aircraft emissions are comprised mostly of carbon dioxide (CO2) and water vapor (H20), each
making up 71 % and 28% of emissions, respectively. A small, but significant, portion of these
emissions are comprised of nitrogen oxides (NOx), sulfur oxides (SOx), hydrocarbons (HCs),
carbon monoxide (CO), and primary PM2.5. PM 2 .5 refers to particulate matter with a diameter
less than or equal to 2.5 pm, and primary PM 2 .5 is particulate matter that is directly emitted from
jet engines and most notably consists of black carbon (BC), or soot.9 With regards to climate,
1
aircraft emissions on the whole have a net warming effect on the atmosphere as estimated
based on the most current understanding of how aircraft perturb the state of the atmosphere.
Figure 1 shows the estimated radiative forcing (RF) contribution of these emissions as well as
species (e.g. aerosols) and phenomena (e.g. contrail formation) derived from aircraft emissions
with uncertainties.
I
2
Spatial
) m)
(W m (W
sale
RF Terms
Global
Carbon dioxide
NOx
00
Total NOx
Water vapour
I
iI
fl
Linar ontail
I
-0.0048
Local to
Low
0.0034
(0.002S)
Local
to global
(0.010)
Local to
continental
1..
-0.04
.
OW
LO
Local to
Very
hemispheric Low
1
I
global
(IPCC AR4 values)
1
Total aviation
(Excl. induced cirrus)
-0.08
Low
L
(-0.0035)
I0.0118
Induced cirrus
cloudiness
Total aviation
(Incl. Induced cirrus)
*lBest estimate
jEstimate
S
Global
Hemispheric
to global
I(0.0020)
-90% confidence
Soot aerosol
Linear contrails
High
0.0263 Continental
(0.219) hemiheric
-0.0125
Global
(-0.0104)
Ozone
production
Methane
reduction
Sulphate aerosol
LO
SU
.J...........
.
0.055
(0.0478)
Global
Low
0.078
Global
Low
,1
0.08
0.04
0
Radiative Forcing (W M-2)
0.12
Figure 1: Radiative forcing estimates of aircraft emissions. 10
RF is a metric used to quantify the net perturbation to the atmospheric radiative balance from a
particular atmospheric species. It is typically taken at its top-of-the-atmosphere (TOA) value, i.e.
the net incoming minus the net outgoing radiative flux at the border between the atmosphere
and space, although measures at other levels of the atmosphere are also possible. RF is not the
only way by which to measure the climate impact of atmospheric species, where global warming
potential and integrated temperature change are also common metrics." For the purposes of
this analysis, however, RF provides the most convenient impact measure.
2
Greenhouse gases (GHGs), such as carbon dioxide (CO2), ozone (03), methane (CH4), and
water vapor, warm the atmosphere. As can be seen from Figure 1, direct emissions of C02 and
water vapor both lead to warming as aircraft directly increase the concentration levels of these
species, although the net RF impact (where red/positive denotes warming) of C02 is an order of
magnitude larger than water vapor. The formation of ozone also leads to a positive RF impact
by way of the NOx cycle:
NO +034 N0
2 +0
2
NO2 + hv + NO + 0
O + 0 2 + M+ 0 3 + M
NOx emissions, which are primarily NO (except at low thrust), destroy ozone, but can also
produce ozone given the production of NO2. In addition, NO and NO2 are cycled between one
another and can result in ozone production and loss through the following reactions:
HO 2 + NO
H0
2
+0
3
--
OH + NO 2
-+OH+0 2 +0
2
OH + 03 4 HO 2 + 02
Thus, ozone formation is a function of not only NOx emissions, but also ambient concentrations
of OH and HO2 , two very important atmospheric oxidizing agents. Ozone formation is also a
strong function of altitude where ozone production efficiency (i.e. proportion of NOx that is
ultimately converted to ozone) is higher at cruise altitudes than at ground-level. NOx molecules
also interact with the methane cycle, where aircraft emissions lead to an atmospheric decrease
in methane (thus an associated cooling), but an associated decadal loss of tropospheric ozone
leads to further cooling.9 NOx emissions from aircraft that are closer to the ground can either
create or destroy 03, and this behavior is a function of the ambient hydrocarbon (HC)
concentration in a particular region.
Aircraft emissions also lead to the formation of primary and secondary aerosols. As mentioned
previously, primary BC is a direct emission from combustion and leads to a warming effect.
Secondary sulfate aerosols (as well as ammonium nitrate aerosols) are formed from either
direct SO 4 emissions or oxidized SO 2 emissions, where inorganic aerosols of this type refract
3
solar radiation back into space, thus providing a net cooling effect. Linear contrail formation in
the wake of the aircraft provides an additional net warming effect. The most uncertain climatic
impacts of aircraft emissions, however, are the induced cirrus cloudiness and soot-cirrus. Cirrus
cloud formation is caused by the spreading and shearing of linear contrails as a result of
increased particulate matter concentrations in the atmosphere, which provide nuclei from which
clouds can grow. 9 The effect of this indirect climate impact can be either warming or cooling.
Figure 1 shows it as a net warming effect, but with an associated large uncertainty.
Primary and secondary particulate matter is also harmful to human health through ground-level
population exposure. Exposure to PM, such as sulfate aerosols and BC, has been shown to
lead to increased risk in cardiopulmonary disease (CP) as well as lung cancer (LC), and longterm PM exposure have been associated with overall increases in human premature mortality,
where premature mortality comprised approximately 85% of all monetized health impacts from
Although population exposure to ozone also has human health impacts, it is
not considered in this thesis. In addition, morbidity impacts (hospital admissions, missed work
PM 2.5 exposure.
days, etc.) are also not considered.
1.2. Policy Objectives
In their Destination 2025 document, the FAA has stated 6 distinct goals to improve sustainability
8
and reduce the overall impact of aircraft transportation operations by 2018. These goals are the
following:
-
Reducing those exposed to aircraft noise to less than 300,000 people
-
Implementing an alternative fuel for current leaded general aviation fuel
-
Improve fuel efficiency by 2% annually
-
Reduce the health impacts of aircraft emissions by 50% relative to a 2005 baseline
-
Set aviation on a trajectory for carbon neutral growth relative to a 2005 baseline
-
Use at least one billion gallons of renewable jet fuel
This thesis focuses primarily on the impact of aviation emissions on the environment and human
health, thus topics concerning other aspects of sustainability or aircraft noise reduction will not
4
be discussed. The major focus of this thesis is, however, on global as well as regional
implementation of an ultra-low sulfur jet fuel (ULSJ), where the emphasis is placed on reducing
ground-level PM 2.5 concentrations with the ultimate goal of reducing the human health impact of
aviation operations. ULSJ would serve as a drop-in and immediate replacement fuel where
rulemaking would mandate a fuel sulfur content of less than 15 ppm by mass.
1.3. Motivation
In order to determine the viability of a particular piece of environmental legislation or rulemaking,
standard benefit/cost analysis (BCA) techniques are employed in which potential economic,
climate, and health benefits, disbenefits, or societal costs are monetized and aggregated in
order to determine a net benefit/cost outcome. The general BCA framework will be discussed in
greater detail in Section 2.4. Analysis currently conducted within the Partnership for AiR
Transportation Noise and Emissions Reduction (PARTNER), such as that performed for the
NO) stringency analysis,
is regionally focused on the US, and BCA has only historically
considered landing/take-off (LTO) emissions. A suite of tools known as the aviation
environmental portfolio management tool (APMT) has been developed by PARTNER that
determine noise, air quality (LTO emissions, only), and climate impacts and monetization of
damages of aviation policy scenarios within the US. APMT can assess policies from a US
perspective, but does not currently have the ability to account for a global-scale analysis.
Barrett et al.,14 however, has shown that cruise emissions have a significant impact on groundlevel air quality, where -8000 premature mortalities per year are attributable to aircraft cruise
emissions, which constitutes 80% of the total mortality impact derived from all aircraft
emissions. Given the time scale of removal and transport of PM 2.5 and its associated precursors,
aircraft emissions then have an inherently global impact. As such, it becomes necessary to
adjust the scope of policy analysis to incorporate the global atmospheric impact of a particular
aviation policy. This requires global atmospheric modeling through the use of global chemical
transport models (GCTMs), rather than only more regional or localized air quality models. In
addition to other difficulties, it is also necessary to determine the ground-level health impact in
countries outside of the US where epidemiological data may not be available.
The primary goal of this thesis is to expand the analytical capabilities of PARTNER and the
APMT tool suite to include global scale BCA studies of aircraft policy emissions scenarios. This
is not only important for the specific case of ULSJ, but also relevant for any future alternative jet
5
fuel study where a comprehensive BCA may be necessary to validate or invalidate a transition
away from today's standard jet fuel. This requires the development of a comprehensive global
environmental benefit/cost analysis framework in which to streamline current and future aviation
policy analyses in order to achieve the afore stated FAA policy goals.
1.4. Thesis Structure
This thesis has the following structure:
Chapter 1 generally addresses how aircraft impact the environment and more specifically
discusses some of these prospective policy goals and potential aviation policy options.
Chapter 2 provides background on general EBCA methodology, including current benefit-cost
analysis techniques employed by the Environmental Protection Agency (EPA).
Chapter 3 discusses the EBCA methodology developed for the purposes of global policy
analysis, including determining aviation emissions impacts and monetizing these impacts. This
is developed within a framework to address the environmental and economic impacts of an
ultra-low sulfur jet fuel (USLJ) scenario that would require all commercial jet fuel to have a fuel
sulfur content (FSC) of less than 15 ppm.
Chapter 4 presents the results of an EBCA as applied to a case study of global and US
implementation of ULSJ.
Chapter 5 extends this policy analysis framework to a discussion of a fast policy tool derived
from an adjoint sensitivity analysis. A comparison between the previous EBCA mortality
outcomes versus those calculated from the adjoint fast policy tool is also provided.
Chapter 6 presents results from an adjoint sensitivity analysis as applied to temporal variations
in aviation emissions impacts and a discussion of the potential policy implications of these
variations.
Finally, chapter 7 concludes this thesis and provides a discussion of possible future work.
6
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7
Chapter 2: Background
This chapter provides background information for the EBCA methodology that will be developed
in Chapter 3. It includes a discussion on the types of atmospheric models used as well as some
basic information concerning how aerosols (i.e. PM2.5 ) impact the radiative balance of the
atmosphere. In addition, literature is reviewed related to the link between PM2.5 concentrations
and premature mortality risk and current EBCA practices. Information is also presented from an
ultra-low sulfur diesel (ULSD) case study that was performed given the similarities between
diesel and jet fuel. Finally, a brief description of adjoint sensitivity analysis and how it relates to
policy analysis is provided.
2.1. Atmospheric Modeling
EBCA is directly dependent on the ability to chemically and physically model the perturbation to
the atmosphere resulting from anthropogenic emissions. Once emissions scenarios have been
accurately defined, it is then necessary to incorporate these scenarios into the appropriate
physical models. Two different models are generally employed within PARTNER to conduct air
quality studies. On a global scale, GEOS-Chem, a GCTM, is typically used, although the nested
version can also be applied to smaller, higher resolution domains. For more local studies (i.e.
US, only), the Community Multiscale Air Quality (CMAQ) model is used. CMAQ has been used
by the EPA in their air quality policy analyses, but because CMAQ was used as a sensitivity
study compared to GEOS-Chem and nested GEOS-Chem simulations for the ULSJ analysis, no
detailed description will be provided in this thesis.
2.1.1. Global and Nested GEOS-Chem Model Descriptions
GEOS-Chem is a tropospheric model (with an approximated stratosphere) that simulates global
gas and aerosol phase chemistry (including aerosols that are relevant to aircraft emissions),
accounts for wet and dry deposition, and models transport on an intercontinental scale. 15 The
model takes in emissions and meteorology as inputs and at the end of each time step computes
new tracer concentrations. A typical chemistry time step is 60 minutes and a year simulation
with a 3 month spin-up period, which is required to eliminate the impact of the initial conditions,
takes approximately 12 hours to perform.
8
GEOS-Chem generates three dimensional, gridded data. For the ULSJ analysis, a 40 x 50
horizontal grid is typically used, while nested simulations use a 0.5*
x
0.6660 horizontal grid
where boundary conditions are provided by the coarser resolution simulation. These simulations
also use the GEOS-5 reduced vertical grid that is defined up to 0.01 hPa, where the atmosphere
is split into 47 layers. GEOS-Chem also has a built-in aerosol optical property module in which it
calculates optical depths of the primary aerosol species, but it currently does not have an
integrated radiative transfer model (RTM) to allow for climate studies. An aircrafts emissions
module was previously implemented into GEOS-Chem where species concentrations in the
appropriate grid cells were perturbed given aircraft emissions at that location. The model
currently allows the user to switch on or off particular aircraft emissions types, to choose to
include full aviation emissions or only LTO emissions, and to set the FSC.
2.2. Impact of Aerosols on Climate
Inorganic aerosols, such as ammonium nitrate and ammonium sulfate, scatter incoming solar
radiation. In atmospheric models, sulfate aerosols are considered to be purely scattering, i.e.
extinction of incoming solar radiation is achieved by only refraction and not through
absorption.
16
With sulfate aerosols, the amount of solar radiation that is refracted back into
space is determined by a given particle's backscattering coefficient, 0.
1is a function of several
parameters, including solar incidence angle and radiation wavelength, but most notably it is a
function of particle size.
In general, aerosols can exist as a solid or as an aqueous particle. Transition between solid and
aqueous states for sulfate aerosols is governed by a hysteresis cycle. In this cycle, aerosol
particle radius is a function of relative humidity given the hygroscopic growth (i.e. water vapor
condensation) that occurs on the particle, which in turn impacts the optical properties of the
particle.17 Thus, an increase in relative humidity can cause an increase in particle radius through
water condensation in order to maintain thermodynamic equilibrium, but this growth behavior is
specific to the relative humidity history of the particle. Figure 2 gives an illustration of this
hysteresis cycle.
9
s-.
2.5
0-
DRo
DRH 0
0z~
U
CR1-I
0- 1.0
0
20
40
60
80
100
Relative Humidity (%)
Figure 2: Multi-branch hysteresis behavior of an ammonium sulfate aerosol particle. Plot from
Wang et al."
From Figure 2, there are two distinct pathways that the ammonium sulfate aerosol particle can
follow, which are in general called the upper and lower hysteresis branches. In the lower branch
(solid line), no hygroscopic growth is experienced until a threshold RH value of 80% (DRHo) is
reached if the particle is initially solid. Similarly, an aqueous particle will not begin to crystallize if
it exists on the upper branch (dashed line) until a threshold RH value of 35% (CRHo) is reached.
Thus, relative humidity history dictates on which branch a given aerosol particle exists given
some assumption concerning the initial phase of the particle of interest. In addition, different
aerosol types have different hysteresis pathways, which add to modeling complexity when
aerosol mixing ratios need to be determined or assumed.
From this particle growth data as well as total atmospheric burdens, the aerosol optical depth
can be calculated, which is a dimensionless measure of the atmospheric burden of the aerosol
as it relates to extinction of solar radiation and is mathematically defined as
(x1, x 2 ;
b(x,2) dx
2=f
Eq. (1).16
As can be seen in Eq. (1), the optical depth, -r, is a function of the two integration bounds as well
as the wavelength of the incoming solar radiation. b is known as the extinction coefficient and is
proportional to the burden of the species within the atmosphere as well as the optical properties
of that particular species. Optical depth is directly related to the RF contribution of that species.
10
The methodology implemented for this thesis for RF due to sulfate aerosol scattering is
discussed further in Section 3.2.1.
GEOS-Chem takes a simplified approach to the treatment of inorganic aerosols and its
calculation of optical depth. GEOS-Chem currently tracks atmospheric burdens of nitrates,
sulfates, and ammonium individually, but they are lumped together within the "sulfates" bin
when performing optical calculations, where the primary underlying assumption is that the three
different species are internally mixed (i.e. mixed aerosols are homogenous). GEOS-Chem,
however, does not consider more complex internally mixed aerosols, such as liquid H2 SO4
deposited on BC particles. The particle size distributions for dry "sulfates" (i.e. 0% RH) are
assumed to be as shown in Table 1.
Table 1: Assumed properties of "sulfates" optical bin in GEOS-Chem.
Property
Density
Geometric Mean Radius
Geometric Standard Deviation
Effective Radius
Refractive Index
Value
1.7g/cm
0.07 pm
1.6
0.12 pm
1.43-10-8i
Additionally, the hygroscopic growth behavior of sulfate aerosols within GEOS-Chem is outlined
in Martin et al.
The assumed refractive index is that of sulfuric acid aerosol and no complex
hysteresis behavior is assumed. Different mixing fractions, however, and the impact on the
assumed refractive index are not considered and is the subject of ongoing development within
the model.
2.3. Air Quality and Mortality
This section contains significant contributions from Dr. Jon Levy from Boston University School
of Public Health.
Many studies have linked PM 2.5 exposure to adverse health end-points in the US, most notably
the American Cancer Society (ACS)
19,20
and Harvard Six Cities2
cohort studies in which
populations were monitored over time and the associations between several health incidences
and long-term exposure to ground level PM2.5 concentrations were monitored. For this analysis,
only premature mortality is considered given that approximately 85% of all monetized health
impacts are due to premature mortality.12 Changes in concentrations of PM 2.5 can be related to
11
avoided mortalities through a concentration-response function (CRF). For this analysis, a linear
CRF is used based on follow-up analyses of the aforementioned cohort studies and an EPA
expert elicitation study,23 which comprehensively describes current interpretation of
epidemiological evidence based on expert opinion.
Studies, as outlined in Pope et al. 24 provide evidence of a link between premature mortality and
long-term exposure to PM2.5 . The EPA expert elicitation study2 showed that several leading
experts believe that the main contributors to mortality from short-term exposure are acute
cardiac or respiratory events, possibly from pre-existing health conditions. Mortality due to longterm exposure is most likely a result of cardiovascular disease, chronic respiratory disease, and
lung cancer. The biological drivers of these premature mortalities are still uncertain, although it
is strongly believed that PM exposure can lead to health issues such as chronic obstructive
pulmonary disease (COPD)25,26 and atherosclerosis.27 An impact on lung cancer risk due to PM
exposure is thought to exist, but the relationship remains uncertain as compared to
cardiopulmonary illnesses,28,29,30 although relative risks have been calculated for lung cancer.
Given these established links to human health, deaths resulting from cardiopulmonary (CP)
diseases and lung cancer (LC) are assumed to be the dominating health impact pathways given
a change in ground-level PM2.5 concentrations. Uncertainties for each disease are captured in a
range of relative risk values obtained through literature review.
A linear CRF is assumed given the results from multiple analyses of long-term studies. In
addition, no substantial evidence has been found for a concentration level at which health
impacts exhibit a significantly different concentration response.
24
Specifically, Schwartz et al.
31
conducted an analysis that included threshold and non-linear models, and also concluded that a
linear fit does indeed best fit the data. This interpretation concerning CRF shape is also
reiterated within the EPA expert elicitation study. Nonlinearities are observed, specifically in the
Pope ACS cohort analysis. Goodness-of-fit tests, however, have shown that the probability of a
non-linear fit being statistically different from that of a linear fit is not significant.24 These cohort
studies, however, did not consider very high pollutant concentration levels that may be seen in
portions of the developing world (-50 pg/m 3 versus -10 pg/m 3 in the US). In these cases, the
assumption of a linear CRF may be inappropriate as the marginal impact of pollution at very
high concentration levels may be decreasing and the number of premature mortalities predicted
may be overestimated. To account for this, the CRF applied in Ostro86 as part of a World Health
Organization (WHO) study assumes a log-linear relationship with changes in concentrations,
12
which results in a lower premature mortality response at higher background PM2.5
concentrations.
Differential toxicity among PM 2.5 species is a subject of ongoing research and is relevant given
that the change in ground-level PM 2.5 due to ULSJ implementation is seen primarily in S04
species. The relative impact of S04 species on air quality becomes important in determining the
32
magnitude of avoided mortalities seen due to a reduction in FSC. Hedley et al. showed that a
reduction in SOx emissions in Hong Kong due to a sulfur content restriction of 5000 ppm for fuel
used in power plants and road vehicles led to 80% and 41% reductions in S02 pollution and
SO4 particulate concentrations, respectively. This improvement in overall air quality was
accompanied by a 2.1% reduction in all-cause mortality, a 3.9% reduction in respiratory disease
related mortalities, and a 2.0% reduction in cardiovascular related mortalities in annually
averaged trends after the fuel regulation was enforced. In addition, some studies have shown a
link between sulfate aerosols and CP related health incidences,3 3 34 3, 5 and committees have
concluded that there is currently not enough evidence to discredit the assumption of equal
toxicity. 53,36
More recent results presented by Levy et al.,37 where a probabilistic analysis was applied to
determine the relative toxicity of constituent species relative to PM 2.5 as a whole, however, show
that sulfate has only a 42.7% of being more toxic than PM2.5 with regards to cardiopulmonary
illnesses. This is in stark contrast to elemental carbon and nitrate, which have a 100% and
97.7% chance of being more toxic than PM2.5. Probabilities are lower in general for respiratory
illnesses. Levy et al.,37 however, does acknowledge the large uncertainty present in this
analysis and state that the results only suggest the possibility of differential toxicity. In this
thesis, differential toxicity is not directly considered and all PM2.5 species, including those
derived from SOx emissions, are assumed to have equal health impacts. This is, however, a
significant uncertainty.
2.4. Elements of EBCA
For the purposes of this thesis, many of the guidelines set by the EPA have been incorporated
into the techniques employed within this particular analysis. This section will serve as a brief
overview of these practices and any assumptions that were required when these guidelines
could not be strictly met.
13
2.4.1. Benefit/Cost Analysis
BCA (or also commonly, CBA) is a general framework in which policy analysis is commonly
performed. It requires the identification of both benefits and costs, each of which can be positive
or negative for society (negative benefits are also referred to as "disbenefits"). Distinguishing
between benefits and costs is, in some cases, not always straightforward. For the case of
environmental policy analysis, a general rule of thumb is that positive or negative outcomes
derived from the actual implementation of a policy fall under the benefits category (e.g.
decrease in premature mortality or increase in climate damages) while costs are assigned
towards the actual implementation of the policy, be it the need for additional infrastructure, land,
etc.
Furthermore, BCA requires a definition of both a baseline and policy scenario. Within aviation
policy, the baseline scenario has been defined using aviation in the year 2006 where the most
complete set of aircraft emissions inventory data available. The policy scenario is then defined
relative to this baseline aviation scenario. As an example, the ULSJ policy scenario would have
aircraft SOx emissions reduced by a factor of 15/600, where this factor is the ratio of the
proposed FSC of 15 ppm to the current average FSC of 600 ppm. As the atmosphere is a highly
nonlinear system, the definition of the background emissions, i.e. emissions from all other
sources not including aviation, is also important for both the baseline and policy scenario
outcomes. As the focus of this thesis is on aviation policy, the topic of background emissions will
not be addressed in detail, but a brief description of the source for these background emissions
is provided in Section 3.
Sensitivity analysis and uncertainty quantification also play a vital role in BCA. From the
atmospheric modeling to metric monetization, policy analysis is a highly uncertain process. This
is due in large part to the limitations of the physical models used and, particularly for
environmental analysis, the lack of real markets in which to obtain monetary values that can be
easily assigned to climate damages or premature mortalities. As such, it becomes necessary to
utilize Monte Carlo techniques in order to provide both a policy outcome distribution as well as a
comprehensive sensitivity analysis to determine the primary sources of uncertainty given the
assumptions within the analysis.
14
2.4.2. Discounting
A key component of BCA is the discounting of all future benefits/costs. In an economic sense,
money now is worth more to a person than money in the future and the degree to which a
person experiences that opportunity cost is contained within the discount rate, which is directly
tied to the concept of a net present value (NPV). NPV is defined in the following equation:
NPVCt
t1(1 +r)t
Eq.(2)
where Ct is the cash flow for that time period, t is the time period, and r is the discount rate.
Within the base year of the analysis, the time period is 0 (i.e. costs in the initial year of a policy
are not discounted). The cash flow can be defined as the relevant benefit or cost quantity within
that time period. The US government has identified a range of appropriate discount rates (27%)38 to be applied in BCA, where this range is also applied for aviation environmental policy
analysis. The higher the discount rate, the more current costs are valued against future costs,
and vice versa. Thus, different choices in discount rate will cause different policy assessment
outcomes, especially when the time scales of benefits and/or costs are significantly different.
Discounting plays an important role in valuing both climate damages as well as human health
impacts. Aviation emissions in a given year can have impacts out to hundreds of years, thus it
becomes necessary to take the NPV of all costs over a time horizon that captures these impacts
to fully quantify the climate benefit/disbenefit associated with a given policy scenario. In the
case of climate damages, Eq. (2) can be directly applied where the cash flow for a specific year
is the climate damage in that year, all of which is computed within APMT. A more detailed
description of how APMT assigns climate damages will be discussed in Section 3.2.5.
As suggested by the EPA, it is also necessary to discount health impacts based on a prescribed
lag schedule. It assumes that 30% of avoided mortalities are seen in the year of implementation,
50% in years 2-5, and the remaining 20% spread out over years 6-20.39 The assumed discount
rate can then be applied to each year as required, and then premature mortalities can be
summed over the full 20 years to obtain a "discounted" premature mortality outcome. This thesis
applies the same discount rate for all discounting purposes as the EPA does not provide any
insight on application-specific discount rates.
15
2.4.3. Premature Mortality
Based on the information presented above, the EPA recommends the use of a linear CRF with
a nominal estimate of 1.06% for the CRF coefficient. A Weibull distribution was assumed for the
CRF coefficient within The Benefits and Costs of the Clean Air Act from 1990 to 2020,39 but no
distribution parameters were provided. For this thesis, upper and lower bounds were determined
from the EPA expert elicitation study concerning CRFs.
2.4.4. Valuing Lives
Valuing premature mortalities is a very complex issue, both from an economic as well as social
justice perspective. The US recommends that the value of a statistical life (VSL), largely
calculated from wage-risk studies, be applied in matters of policy analysis when mortality
monetization is required. As this tends to be a sensitive issue within policy analysis, a brief
overview of VSL use standardization within the US is provided below.
Standardizing VSL use within US cost-benefit analyses was first addressed in OMB Circular A4.40 A literature review concluded that the "substantial majority of the resulting estimates of VSL
vary from roughly $1 to $10 million per statistical life." Based on a review by the EPA's science
advisory board (SAB), two important conclusions were drawn: it is appropriate to adjust VSL
relative to income and a lag structure for health effects should be applied. OMB advised a
standard value of $5 million, but acknowledged the value was lower than other estimates in
federal agencies, specifically the EPA.
In DOT's 2009 VSL guidance memorandum, 4 1 the standard estimate applied within DOT was
updated from a previous value provided in a 2008 memorandum. In this update, five different
US VSL studies were taken and adjusted for real income growth (with an assumed income
elasticity of 0.55 based on a literature review) as well as inflation using the consumer price
index (CPI). $5.8 million was given as the mean value adjusted to 2007 prices. While the
original OMB range of $1 to $10 million was not ruled out, DOT suggested a more specific
uncertainty range of $3.2 to $8.4 million. A normal distribution with a standard deviation of 2.6
million was recommended, although due to the unrealistic negative values that this
encompassed, other distributions, such as Weibull or lognormal, were also recommended, but
no distribution parameters are provided. In terms of policy applications, DOT stated "the same
standard is to be applied to all individuals at risk, regardless of age, location, income, or mode
16
of travel," thus setting requirements that all individuals within the US be monetized by the same
VSL.
In EPA's Guidelines for Preparing Economic Analyses,38 a central estimate of VSL is given as
$7.4 million. This value is based on 26 VSL studies. VSLs are adjusted year to year by a GDP
deflator, although it appears that no adjustment is made for real income growth or decline. A
Weibull distribution was then fit to these 26 studies, resulting in a scale parameter of 7.75 and a
shape parameter of 1.51. The EPA lists several limitations to the provided estimate, including its
lack of specificity to environmental hazards, as is true of all current VSL estimates. It concludes
that for now, the abovementioned central estimate is the most appropriate estimate of US VSL
and should be applied uniformly without the consideration of differences of income within the
US.
In order to be consistent with previous analyses conducted on the impact of changes in air
quality on human health, the EPA approach was applied in both calculating and monetizing
avoided premature mortalities. Due to the global scope of this thesis, differentiating between
premature mortalities in different regions in the world by applying different VSLs could be
interpreted to suggest that a person's life in a higher income country is "worth" more than a life
in a lower income country. Heinzerling42 argues that VSL is in fact an inappropriate valuation to
apply to premature mortalities as it does not capture the value of lost (or saved) life but rather
just the value of the increased risk associated with a more "unsafe" environment. In this thesis,
country specific VSLs are applied, and the justification for this approach is provided in Section
3.3, although a constant VSL approach is also applied as a comparison.
One of the disadvantages of using VSL as a mortality monetization metric is that it does not
capture the temporal aspect of when the premature mortality occurs. For instance,
complications from CP illnesses may place the elderly at much greater risk than younger
members of the population, but a premature mortality from either age group is valued equally.
Thus, an alternative metric, as applied in the ExternE43 study, is the value of lost life years
(VOLLY). This involves calculating the expected age of mortality relative to a reference life
expectancy, and then monetizing each of the years "lost" due to premature mortality. Although
this helps resolve the temporal issue related to the VSL, this approach is not applied in this
thesis given the lack of data related to value of a life year lost on a disease specific basis as well
as a lack of precedent in US environmental policy analysis.
17
2.4.5. Cost Analysis
Here it is important to distinguish between a financial BCA, in which "real" prices are analyzed,
versus a societal BCA, in which the "true" costs seen by society are analyzed.4 As
environmental BCA certainly falls under the latter category, it is necessary to obtain the
"shadow" prices (i.e. prices that are not distorted by market effects) when determining the cost
of policy implementation. An example of a market distortion might be the oligarchical pricing
effect due to the small number of firms within the oil refining industry. As such, the cost analysis
portion of this thesis attempts to determine the "true" costs seen by society as a result of the
implementation of ULSJ through both a top-down and bottom-up approach.
2.5. Ultra-Low Sulfur Fuels
2.5.1. Ultra-Low Sulfur Diesel Case Study
Given the similarities between diesel and jet fuel, 45 ultra-low sulfur diesel (ULSD)
implementation within the United States was used as a comparative case study for ULSJ,
especially for the cost analysis. ULSD, through EPA rulemaking, was phased-in to production
for on and off-road uses. Table 2 shows the timeline for on-road implementation of ULSD, while
Table 3 shows the timeline for off-road implementation of ULSD. US implementation of ULSD
exhibited a pattern of a gradual increase in fuel sulfur content (FSC) stringency as the fuel
passed from refineries to retail outlets. This implementation was seen over a time period of 10
years for both on and off-road uses.
18
Table 2: On-road implementation timeline of ULSD in the US.46,47
Requirement
Announcement of Diesel Fuel Sulfur
Content Regulation for On-Road
Vehicles
Proposed Heavy-Duty Engine and
Vehicle Standards and Highway
Diesel Fuel Sulfur Control
Requirement
Heavy-Duty Engine and Vehicle
Standards and Highway Diesel Fuel
Sulfur Control Requirements Final
Rule
Refiners and Importers: 80% of
Diesel Fuel Imported/Produced must
be ULSD
Fuel Terminals: Fuel listed as ULSD
must meet 15 ppm specification
Retail Outlets: Fuel listed as ULSD
must meet 15 ppm specification
Refiners and Importers: 100% of
Diesel Fuel Imported/Produced must
be ULSD
Fuel Terminals: All highway diesel
must be ULSD
Retail Outlets: All highway diesel
must be ULSD
Date
May 1997
May 2000
January
2001
Description
Reducing sulfur content of diesel fuel for heavyduty diesel engines is identified as a potential
pathway to improve air quality.
Proposed requirement to reduce sulfur content of
diesel fuel for highway vehicles to no greater
than 15 parts per million (ppm) with a start date
of June 1, 2006.
Final rule requires that refiners begin producing
15 ppm sulfur content diesel fuel beginning June
1, 2006.
June 2006
N/A
September
2006
October
2006
June 2010
N/A
October
2010
December
2010
N/A
19
N/A
N/A
Based on a ULSD pump survey, 85% of pumps
were dispensing ULSD in the 4th quarter of 2006.
100% of highway diesel fuel pumps are now
dispensing ULSD as of the 3 rd quarter of 2010.
Table 3: Off-road implementation timeline of ULSD in the US.46,47
Requirement
Proposed Clean Air NonRoad Diesel Rule
Date
April 2003
Final Clean Air Non-Road
Diesel Rule
May 2004
Description
Reduce diesel fuel sulfur content to a maximum of
500 ppm starting in 2007 for non-road applications
(including locomotive and marine applications).
Reduce diesel fuel sulfur content to a maximum of
15 ppm by 2010.
Non-road diesel fuel sulfur content must be
reduced from current levels (about 3000 ppm) to
15 ppm by 2010.
Fuel must meet 500 ppm standard.
Refiners and Importers: Non- June 2007
Road, Locomotive, and
Marine Fuel
Fuel Terminals: Non-Road,
August 2007
Locomotive, and Marine Fuel
Retail Outlets: Non-Road,
October 2007
Locomotive, and Marine Fuel
Refiners and Importers: Non- June 2010
Road Fuel
Refiners and Importers:
June 2010
Locomotive and Marine Fuel
Fuel Terminals: Non-Road
August 2010
Fuel
Fuel Terminals: Locomotive
August 2012
and Marine Fuel
Retail Outlets: Non-Road
October 2012
Fuel
Retail Outlets: Locomotive
October 2012
and Marine Fuel
Fuel must meet 500 ppm standard.
Fuel must meet 500 ppm standard.
Fuel must meet 15 ppm standard.
Fuel must meet 15 ppm standard.
Fuel must meet 15 ppm standard.
Fuel must meet 15 ppm standard.
Fuel must meet 15 ppm standard.
Fuel must meet 15 ppm standard.
2.5.2. QinetiQ Report on Jet Fuel Sulfur Limit Reduction
A report by QinetiQ48 addressed ULSJ implementation in Europe. It estimated that due to the
additional hydroprocessing required, there will be a 0.01 to 0.015 EUR/liter additional required
cost in ULSJ production, which is approximately 4 to 7 cents (2006 US$) per gallon. The report
also outlined many of the potential impacts the additional hydroprocessing would have on fuel
properties as well as operational effects due to the reduction in fuel sulfur content. Potential
climate impacts were described, but were not quantified. SO2 emissions as a function of FSC
were estimated for a representative local airport by scaling against a previous dispersion model
study at Heathrow Airport49 based on emissions derived from the First Order Approximation
methodology.50 The report concluded there is unlikely to be any measurable health effect due to
FSC reduction when only LTO emissions are considered. Full-flight emissions impacts were not
addressed.
20
2.5.3. Energy Information Administration (EIA) Report on Market Effects Due to ULSD
An EIA report from 200151 analyzed the possible effects of ultra low sulfur diesel (ULSD)
implementation on the diesel fuel market within the US. Based on a cost curve analysis, a 6.5 to
8.2 (2006 US$) cent/gal marginal cost increase for ULSD production was estimated to cover
additional capital and hydrotreating costs for an assumed future supply and demand. In
comparison, the EPA52 predicted a full compliance US average cost of 5.2 cents/gal (2006
US$).
2.5.4. Other Transportation Sectors
Marine fuels have also received significant attention in terms of their global air quality impact.
A policy analysis performed by Corbett and Winebrake 53 estimated a 70 to 85% reduction in
marine SO,, emissions due to marine gas oil (MGO) and marine diesel oil (MDO) implementation
(lower sulfur alternatives) over the standard marine residual oil (RO). Additional C02 emissions
were estimated to be less than 1%. In a human health policy analysis, Winebrake et al.54
estimated the total health impacts due to a global marine fuel sulfur content limit. Findings
showed a 41,200 reduction in premature mortalities for a global fuel sulfur content limit of 5000
ppm as compared to 87,000 premature mortalities with the assumed baseline emissions
scenario. Marine fuel use is defined as an off-road diesel fuel and all marine fuel must meet the
15 ppm standard by the end of 2012, as outlined in Table 3, for all US marine applications
except for RO used by ocean-going ships.
2.6. Role of Sensitivity Analysis
In general, sensitivity analysis is a useful tool to supplement policy analysis. In the case of
performing large scale atmospheric chemistry simulations, sensitivity analysis allows for the
determination of perturbations to atmospheric concentrations given a small perturbation in
emissions without the need to completely rerun entire GCTM simulations. For GCTMs in
particular, however, sensitivity calculation to emissions is not always a simple task, either due to
model complexity or excessive computation time. Sensitivity analysis begins by the definition of
a cost function, J, which can be any function of the inputs to a given model. For instance, one
can define the cost function of a GCTM simulation to be the total atmospheric concentration of a
particular trace gas (e.g. ozone) or aerosol (e.g. sulfate) at the final time step, N, of the
simulation. Thus, through sensitivity analysis techniques, one can determine the impact of
21
emissions in each of the time steps (i.e. 1 to N) on the atmospheric concentration of a particular
species in the final time step.
There are two primary methods by which sensitivities can be calculated. In the forward
calculation, each input is perturbed individually, and for each perturbation, a simulation is
performed to determine the impact on the defined cost function, J. While simple, this approach
very quickly becomes computationally expensive given the number of forward model
simulations required. For GCTMs such as GEOS-Chem, a forward calculation of sensitivities for
just one particular emission would require the number of forward simulations equal to the
number of grid boxes times the number of time steps. In a standard GEOS-Chem simulation
performed for this thesis, there are a total of 72
x
46 x 47 grid boxes (155,664) and within a
year simulation, there are 8,760 time steps, where the standard time step for emissions is 60
minutes. Given that a single year long simulation takes about 12 hours to complete, this
problem then becomes far too large given the relatively limited computational resources
available.
An alternative procedure for sensitivity calculation is the adjoint method, which has already been
implemented within GEOS-Chem. Adjoint methods take a "backward integration" approach to
sensitivity calculation. First, the governing equations of the GCTM are linearized so that the
following approximation holds true:
Ax" = xn+
Eq. (3),
where A represents the approximate linear operator, xn is the input vector (i.e. grid concentration
values) for the current time step, and
x"*<
is the output vector (i.e. grid concentration values to
serve as inputs in the next time step). As mentioned previously, the cost function, J, can be
defined in terms of the output, x''. In that case, the perturbation to J, or 5J, can be computed
by the following relationship:
SJ = axn+l
J
xn+1
Eq. (4),
where the transpose notation is a result of the matrix multiplication required given that x is a
vector input. From Eq. (3), given that it is a linear approximation, small perturbations in the
output can be calculated from the following equation:
8xn+1 = ASx"
22
Eq. (5).
Substituting Eq. (5) into Eq. (4) and rearranging some of the notation, the following relationship
is obtained:
Sx"
]
SJ =AT
Eq. (6).
Equivalently, Eq. (4) can be written in terms of sensitivity to inputs:
6J =
axn
6x'
Eq. (7).
Comparing Eq. (6) to Eq. (7), the first order derivative becomes
a = AT
Eq. (8),
where it can be observed that
A =axni
Eq. (9),
which, based on the previous definitions, is the Jacobian of the system. From Eq. (8), it is then
clear that the sensitivity of the cost function to concentrations in previous time steps can be
calculated iteratively by multiplying the sensitivity at the current time step by the Jacobian
evaluated at that time step. Determining the Jacobian can be a difficult task if the GCTM is
inherently complex or poorly organized, where substantial understanding of the underlying code
structure is required to implement the adjoint method. The primary advantage to this
"backwards" approach, however, is the wealth of sensitivity data obtained from a single adjoint
simulation. It is possible to obtain full 3-D sensitivity data, as well as temporal sensitivity data,
for, in the case of GEOS-Chem, an approximate 2.5 times increase in runtime over a standard
forward model simulation. These equations are relevant for discrete adjoint method
implementation. The topic of continuous adjoint methods will not be discussed in this thesis.
Given that the adjoint method has been implemented within GEOS-Chem, there is considerable
freedom in how J is defined. Currently, GEOS-Chem is only compatible with cost functions that
are first order differentiable to the outputs. For instance, the cost function can be the total
atmospheric ozone burden in the final time step or, alternatively, in all time steps. Thus, J is a
sum of the ozone mass in every grid cell over the appropriate time horizon, which is first order
differentiable given it is a linear cost function. J can also be defined as a weighted sum of
concentration, where the cost function has the following general form:
23
I = 2%=1
L=1X%
k4=1 Wi,jkXi,j,k,n Eq. (10),
where w is the grid (defined by i, j, k) specific weight value (assumed to be constant in time), x is
the grid and time specific concentration of the species relevant to the cost function, n denotes
the time step index, i denotes the longitudinal index, j denotes the latitudinal index, and k
denotes the altitudinal index, and N, /, J, and K are the limits on these indices, respectively. The
weighting term is defined based on the policy metric of interest. If, for instance, the total
atmospheric burden is the desired output, then the weighting in each grid cell will be 1
(assuming the concentration is outputted as a mass). Alternatively, if a population weighted
metric is required, then the weighting will be the population specific to that grid. In addition, the
definition of the cost function can be restricted to a certain domain, where grid cells within the
domain (e.g. the contiguous US) have a non-zero weighting and grid cells outside of the domain
have a zero weighting. The exact definition of the weighting functions used in this thesis will be
addressed in Section 5.1.
The adjoint model was developed and validated within GEOS-Chem by Henze et al.55 Previous
research performed by Koo5 focused on the application of the GEOS-Chem adjoint model on
the air quality impact of aircraft emission, where both spatial and temporal studies were
conducted and validated. For these aviation air quality studies, the cost functions were defined
as the (weighted or non-weighted) PM 2.5 burden at ground-level. One of the primary results from
Koo showed the impacts of aviation emissions from one region (source) on the expected
premature mortality in another region (receptor) based on adjoint sensitivity simulations. For
instance, aviation emissions in the US have the largest premature mortality response in the
Asian domain given the intercontinental nature of the transport of PM 2.5 and its precursors. This
data is presented in Table 4, where the regions considered are the US, North America (NA),
Europe (EU), Asia, and the entire world.
Table 4: Source-receptor mortality impact from full-flight aircraft emissions. 56
US
NA
EU
Asia
World
To
US
180
210
20
30
320
NA
220
310
50
60
490
24
EU
490
750
2010
380
3600
Asia
1620
2470
1630
2830
8760
World
2240
3400
3010
3300
12150
THIS PAGE INTENTIONALLY LEFT BLANK.
25
Chapter 3: EBCA Methodology Development
This chapter provides a detailed description of the EBCA methodology that was developed or
adapted for the specific case of ULSJ implementation globally as well as just within the US. It
covers the entire policy analysis pathway, from the definition of the emissions scenario to the
final monetization and comparison of policy outcomes. While the methodology in this section
was developed for the ULSJ case, it is also helps to establish a general framework in which to
conduct EBCA on global aviation policies, particularly those that are air quality and human
health motivated.
3.1. Emissions Scenarios
Emissions were derived from output from the FAA's Aviation Environmental Design Tool
(AEDT). AEDT calculates aircraft fuel burn and emissions on a flight-by-flight basis, covering the
majority of civil aviation. A procedure similar to that applied by Barrett et al.57 was used to
modify AEDT output for use in this thesis, which is outlined in Table 5 where AEDT outputs are
bolded. For the baseline case, a FSC of 600 ppm was assumed, while for the ULSJ (i.e. policy)
scenario, a FSC of 15 ppm was applied. The relationship between FSC and SO 2 and SO 4
emissions is also provided in Table 5.
The background emissions inventory used within GEOS-Chem will not be discussed in-depth
within this thesis, but a description of these background emissions is provided in Donkelaar, et
al. 58 There are also several expected changes in fuel properties that will be discussed in greater
detail in Section 3.7.1, but these expected changes were not considered in the definition of the
policy emissions scenario.
26
Table 5: Emission indices methodology for air quality simulations, where bolded variables are
from AEDT.
Species
CO2
Baseline Emissions (g)
3159 x FUEL
H20
NOx as NO2
1231
NO,
CO
HC as CH 4
TOG
BC < 3000ft
CO
HC
1.16 x HC
PMNV
BC > 3000ft
OC < 3000ft
0.03 x FUEL
PMFO
OC > 3000ft
SO 2
0.03 x FUEL
(FSC/1 000) x [(100 - E)/100] x
FUEL x (64/32)
(FSC/1000) x (E/100) x FUEL x
(96/32)
Svi as S04
x
Description/Notes
C02 aircraft emissions, constant is adjusted to 3150
FUEL
for ULSJ, FUEL represents the fuel burn value
obtained from AEDT
Aircraft H20 emissions
AEDT default value, NO/NO 2 mole fraction partitioning
changes between LTO/non-LTO
AEDT default value
CH 4 equivalent, AEDT default value, speciated
Total organic gases aircraft emissions, speciated
Black carbon emissions below 3000 ft, AEDT default
value for Black carbon with small amounts of metals
(PMNV)
Black carbon emissions above 3000 ft
Organic carbon emissions below 3000 ft, AEDT
default value for organic PM from fuel (PMFO)
Organic Carbon emissions above 3000 ft
S02 emissions, based on fuel sulfur content (FSC) in
ppm (by wt) and wt-% of fuel sulfur emitted at Sv (E)
Assumes Sv' emitted as SO 4
3.2. Determining Climate Impacts
Two changes in emissions due to ULSJ implementation are considered in this analysis: a
decrease in aircraft SOx emissions directly due to fuel desulfurization and an increase in well-towake (WTW) greenhouse gas (GHG) emissions due to increased fuel processing requirements.
Because atmospheric sulfate aerosol concentrations are reduced given the decrease in the
sulfur content in the jet fuel, the impact on RF, a metric used to quantify the net effect of a
particular species on the global radiation energy balance, is determined. Lee et al.59 provides
values for aviation RF impacts, estimating the sulfate aerosol impact as -4.8 (90% Cl: -0.79 to 29.3) mW/m 2 for 2005 aviation, where the negative RF value implies cooling. Sulfate aerosols
have a cooling effect on the atmosphere,
thus a decrease in the sulfur content of fuel is
expected to cause a net warming effect when comparing ULSJ to standard aviation jet fuel
when only direct effects are considered. The increase in WTW GHG emissions was previously
analyzed in Stratton et al.61
While direct climate impacts are the primary focus, indirect effects are also possible. In general,
the formation of particulate matter within the atmosphere provides nuclei from which clouds may
27
propagate, also known as cloud condensation nuclei (CCN). Thus, there is a possibility that a
decrease in sulfate aerosols would remove possible CCN, thereby decreasing overall cloud
cover as CCN formation is a function of specie particle number, not specie mass. Whether or
not this leads to a net warming or cooling, however, is difficult to determine as it depends largely
on the change in size distribution of the aerosol particles. In general, smaller particles are not as
easily removed and generate longer-living clouds.16 Thus, a decrease in sulfate aerosol
concentration may or may not have a significant impact on cloud formation and/or lifetimes.
Additionally, when H2SO 4 condenses on BC, a phenomenon known as optical focusing occurs,
which results in a net warming. Thus, desulfurization of jet fuel can have a cooling effect to the
extent that optical focusing is important. This phenomenon, however, is not considered as it is
not modeled within GEOS-Chem. As mentioned in Section 1.1, indirect climate effects are
highly uncertain, and given this uncertainty, these indirect effects are not addressed in any
detail within this thesis. An analysis of this type may be possible in the future given sufficient
development of a climate-coupled GCTM and a better understanding of aerosol mass impact on
particle number and size distributions.
3.2.1. Sulfate RF Calculation
While higher fidelity calculation of RF can be accomplished through the use of a radiative
transfer models (RTM), GEOS-Chem does not currently have a built-in RTM to allow for such
calculations, thus a simplified approach is taken here. Eq. (11) is used to determine the globally
averaged direct radiative forcing due to sulfate aerosols, or sulfate direct radiative forcing
1
(DRF), from aerosol optical depth (-) quantities calculated in GEOS-Chem 7,62:
DRF =
where
4
FT
-FTT2(1
- Ac)2(1 -RS)
2
(saTsd + /aqTaq)
Eq. (11)
is the global mean top-of-the-atmosphere radiative flux, T is the fraction of incident
light transmitted by the atmospheric layer above the aerosol layer, Ac is the fractional amount of
cloud cover, f, is the area averaged albedo of the underlying surface, #,d is the backscattering
coefficient of a solid particle of interest,
Paq
is the backscattering coefficient of an aqueous
particle of interest, -rd is the optical depth of a solid particle of interest, and Taq is the optical
depth of an aqueous particle of interest. This equation assumes the aerosol is a purely
scattering particle (i.e. no absorption of solar radiation) and is optically thin (i.e. - << 1), 17,62
28
which are appropriate assumptions for the sulfate aerosol species present in the atmosphere.' 6
A derivation of Eq. (11) can be found in Seinfeld and Pandis.16
Eq. (11) is a simplified one box model representation of the atmosphere. In this one box
representation, a single aerosol layer is assumed through which the net flux is determined by
using globally and temporally averaged parameter values present in Eq. (11). Because a more
rigorous calculation of DRF which incorporates speciated hysteresis behavior, as was done in
Wang et al.,17 is outside the scope of this thesis, it is assumed that this one box model approach
is first order accurate. As described in Section 2.2, transition between solid and aqueous states
for sulfate aerosols is governed by a hysteresis cycle,' 7 where the relative humidity (RH) history
of a particle is related to the hygroscopic growth that occurs. No hysteresis loop behavior is
assumed in the GEOS-Chem simulations and sulfate aerosol particles are assumed to always
63,64
Several studies have attempted to quantify the impact of
sulfate hysteresis behavior on sulfate aerosol RF.17,64,65
be on the upper hysteresis branch.
can be estimated based on a particle's asymmetry factor, g, where g is an intensity-weighted
average of the cosine of the scattering angle 16,66and is also a function of RH.65,67 Wiscombe and
Grams66 estimate the average value of f to be the following:
--
g Eq. (12)
where, g is a function of the size of the particle, thus fl (overbar denotes time average) is a
function of RH given the hygroscopic growth that occurs due to water condensation.
Aerosol optical depth (AOD) values are obtained from GEOS-Chem simulations for the
background (not including aviation), baseline aviation (background + aviation with standard jet
fuel), and ULSJ aviation (background + aviation with ULSJ fuel) cases. These AOD values,
however, are presented for a 400 nm wavelength of incoming solar radiation. Wang et al. 17
evaluates AODs at 550 nm, "a wavelength that is representative of the mean across the solar
spectrum." In general, RFs are calculated by taking a weighted average over the entire solar
radiation spectrum as aerosol optical properties are wavelength dependent.6 7 A simplified
weighted RF calculation is described in Nemesure et al.67 For this analysis, however, AODs at
just 550 nm are computed to avoid significant computation times and are used to determine
sulfate DRF. The version of GEOS-Chem used in this analysis cannot compute AODs at a
specified wavelength other than at the default wavelength of 400 nm, although a recently
29
developed post-processing module, FlexAOD, 68 does have this functionality. FlexAOD can also
compute asymmetry factors, which can be used to determine the backscattering coefficient
based on Eq. (12). Using FlexAOD, sulfate aerosol AODs are recomputed at 550 nm and used
in Eq. (11).
As mentioned in Section 2.2, the sulfate species bin within GEOS-Chem and FlexAOD also
includes nitrates and ammonium where no distinction is made in optical properties between the
different species. Limited research has been performed on the direct RF impacts of nitrates and
ammonium alone, but nitrate contributions to overall aerosol mass is small relative to sulfate
when background concentrations are considered and impact on direct RF is uncertain.
69
Within
GEOS-Chem, all three of these species are treated identically (i.e. purely scattering), thus Eq.
(11) is still applicable. Using this bin to compute RFs also captures the nitrate bounce-back
effect and its potential impact on direct climate forcing due to a reduction in atmospheric sulfate
concentrations. This lumping, however, assumes that sulfate, nitrate, and ammonium aerosols
are identical in their optical properties, which is certainly not the case. This issue is addressed in
greater detail in Section 3.2.3.
RF values for standard aviation minus the background and ULSJ aviation minus the background
are calculated for four regions: global, northern hemisphere, Europe, and Asia. Values for all
anthropogenic and biogenic sources of sulfate are also calculated. These RF values are area
weighted to reflect the differences in grid box size given that GEOS-Chem uses a uniform polar
grid (40
x
50).
3.2.2. RF Uncertainty
The IPCC Third Assessment Report70 provides uncertainty values and ranges (based on
Penner et al. 71) for all of the coefficients in Eq. (11). The minimum and maximum values
provided in the paper are used as bounds for a triangular distribution. No uncertainty estimate
was provided for FT.
Table 6: Coefficients in Eq. (11) and associated values and uncertainties.
Coefficient
7
1 -Ac
(1 - Rs)2
Nominal Value
0.58
0.39
0.72
30
Uncertainty Range
0.4 - 0.83
0.35-0.44
0.65 - 0.8
Wiscombe and Grams66 provide a high, low, and mean value for the backscattering coefficient,
f. The mean value is previously shown in Eq. (12). The upper and lower bounds can be
estimated as the following:
#high
- g
Eq. (13)
Aw - - g
Eq. (14)
=
Uncertainty in the optical depth values from GEOS-Chem is not easily obtained due to the
inherent complexity that exists in the model. Rather than attempt to determine how uncertainty
propagates through the model based on initial uncertainties in the input data, the uncertainty for
optical depth was determined by survey. IPCC Fourth Assessment Report72 provides sulfate
aerosol optical depth values across nine different models that used identical input emissions.
These nine values are used to define an uncertainty factor for the optical depth values. This
approach also captures uncertainty related to atmospheric processing and removal of SOx
emissions.
3.2.3. Sulfate Lumping
Again, aerosols are assumed to be internally mixed, thus different mixing fractions of sulfate,
nitrate, and ammonium are not considered. The potential effect of different mixing fractions,
however, is approximated in this section. In this case, differences in hysteresis behavior are not
considered as this is rather complex. Instead, the focus is placed on the magnitude of the
backscattering coefficient for a given particle. Wang et al.
provides estimates of the
backscattering coefficient given differences in the refractive index and particle size. It can be
seen that if a composition of purely ammonium sulfate is assumed as compared to sulfuric acid
(which is the assumed composition of sulfates in GEOS-Chem), the change in the
backscattering coefficient would increase results by about 13%. Similarly, Jarzembski et al.73
shows that for short-wave radiation, the calculated backscattering coefficients for ammonium
sulfate versus ammonium nitrate are nearly equal, assuming ammonium nitrate is
representative of the nitrate aerosol present in the atmosphere. Given that variations in the
calculated RFs are -10%, the lumping was assumed to be reasonable given the large
aforementioned uncertainty in premature mortality calculation and monetization as well as
climate impact monetization.
31
3.2.4. WTW GHG Emissions
61
As a part of their analysis of alternative jet fuels, Stratton et al. performed a life cycle green
house gas (GHG) emissions analysis of standard jet fuel and ULSJ using the Greenhouse
Gases, Regulated Emissions, and Energy Use in Transportation (GREET) framework
developed by Argonne National Laboratories. Their approach used a weighted average of GHG
emissions from all potential crude oil sources (12 different countries/regions) feeding into US
refineries, i.e. just the extraction and raw material transportation aspects of the cycle. The
baseline case was further defined by the assumptions made concerning the refining efficiency. It
was assumed that the refining energy efficiency of conventional jet fuel is 93.5% (i.e. MJ of fuel
for a unit quantity of jet fuel/MJ input to refinery). A life cycle analysis of standard jet fuel using
2005 production data yielded a total WTW GHG emissions value of 87.5 gCO 2e/MJ. This value
includes the extraction and transportation emissions as well as refining and combustion
emissions.
For the corresponding baseline case, ULSJ differs from conventional jet fuel in terms of its life
cycle analysis in two important ways. First, from a 2001 General Motors study, 74 a 2% energy
penalty is assumed for reducing the sulfur content in diesel fuel from 350 to 5 ppm, i.e. 2% more
energy is required during the processing and refining stage of the life cycle given the additional
HDS of the diesel fuel that is required. Given the similarities between diesel and jet fuel, this
same 2% penalty assumption was extended to the case of ULSJ. This penalty was seen in the
refining energy efficiency, which was reduced to 91.5% for ULSJ. Second, there is an expected
0.4% decrease in combustion C02 emitted per unit of fuel energy due to a change in the
hydrogen-carbon ratio as a result of the additional hydroprocessing. Both of these factors were
accounted for in the life cycle analysis for ULSJ and yield a WTW GHG emissions total of 89.1
gCO 2e/MJ. Thus, this 2% assumed reduction in refining efficiency caused a 2% increase in
WTW GHG emissions was seen between the baseline cases for conventional jet fuel and ULSJ.
A high and low value of 90.7 and 87.5 gCO 2e/MJ are assumed as the uncertainty range in this
analysis. Given that regional jet fuel data for the US shows an average FSC of 600-700 ppm
and the ULSD study that is the basis of this work used a FSC of 350 ppm, it can then be
expected that the assumed energy penalty could be as great as 4%. As determined in the
baseline emissions scenario, a 2% energy penalty yielded an 89.1 gCO 2e/MJ WTW GHG lifecycle emissions value. By extrapolating this expected change, it then follows that a 4% energy
penalty yields an additional 1.6 gCO 2e/MJ, or 90.7 gCO 2e/MJ. The low value is equal to the
32
WTW GHG emissions value of conventional jet fuel where the FSC of the inputted crude oil is
assumed to be less than 15 ppm, thus no additional processing would be required. A higher
baseline energy penalty could be assumed given the higher FSC of jet fuel on average
compared to diesel fuel. There is, however, considerable uncertainty in what energy penalty will
be seen in ULSJ production because the chemical make-up of jet fuel (as outlined in Hileman et
al. 75) in general has simpler hydrocarbon structures than diesel fuel. Thus the energy input
required to desulfurize jet fuel from 350 to 5 ppm is potentially less than the energy input
required to desulfurize diesel fuel the same amount.
3.2.5. APMT-Impacts Climate Module
As part of the aviation environmental portfolio management tool (APMT) project that focuses on
quantifying and valuing the environmental effects of aviation activity, a framework in which
climate impacts are assessed in a computationally inexpensive manner was initially developed
76
77
by Marais et al. and Mahashabde et al. This section will briefly describe the overall structure
of the APMT-Impacts Climate module and explain the relevant portions of code important to this
analysis.
The APMT-Impacts Climate Module (from here on referenced as "APMT-Climate") is used to
value climate impacts on a global scale in this analysis. APMT-Climate takes as inputs fuel
burn, C02, and NOx emissions. Climate impacts due to a variety of species and effects,
including sulfate aerosol cooling, are derived or scaled from these inputs. Sulfate aerosols are
designated "short-lived" effects, i.e. effects that are scaled directly from fuel burn and whose RF
effects are assumed only to last the year in which it is emitted. For this climate analysis, only
two climate impacts are considered: reduction in sulfate cooling due to FSC reduction and an
increase in CO 2 RF due to the increase in WTW GHG emissions.
The RF due to S04 can be calculated using the following relationship:
(RF
RFso4(t) = RFGEOS-Chem
10,
Ashort~J
C
= emission year
t > emission year
Eq. (15),
where RFGEOS-Chem is the RF value for sulfate aerosols calculated from the GEOS-Chem
simulations as outlined in Section 3.2.1, A is the climate efficacy value for the short-lived and
C02 effect, and t is a time variable in integer years. The climate efficacy relates the proportional
change between the RF of the given species and the resulting temperature response of the
33
system. Given the uncertainty in these estimates, this analysis assumes all efficacy values to be
one, i.e. each effect produces the same proportional response in temperature given a unit RF
input to the system.
The increase in C02 emissions is reflected in the emissions inputs required by the code, as
mentioned previously. Although these are not direct aviation emissions, the method by which
the C02 RF value is determined is based on total atmospheric C02 concentrations, and thus the
source of the C02 is inconsequential. APMT-Climate deals with C02 effects by determining the
overall change in concentration given the input emission index based on an impulse-response
function and then integrating the product of these values over time. A logarithmic relationship is
then used to determine the RF of C02 in the atmosphere for the study year relative to preindustrial C02 levels. Further details are provided in Marais et al.
76
7
and Mahashabde et al.77
Once RFs have been computed, surface temperature changes are calculated by using a
simplified heat transfer model.78 After the induced temperature changes are calculated, these
values are then passed to a damage function that computes the impact on global GDP, as is the
case with the DICE-2007 model. 79 Although RF effects for short-lived terms are only considered
for the year of emission as atmospheric lifetimes of these species are assumed to be short, the
temperature effects can last multiple years resulting from the transient heat model, thus the total
damage is provided as a net present value (NPV) of damages taken in relation to the base year
of 2006.
This analysis is limited to a one-year pulse emission scenario for the policy (ULSJ) and baseline
(standard jet fuel) cases. As mentioned previously, RF effects for sulfate aerosols last only in
the year they are emitted, but C02 RF impacts are tracked into the future due to the lifetime of
the species. Temperature effects are also tracked into the future for all species given the
analytical heat transfer model. A time horizon of 800 years is assumed.
3.3. Country Dependent VSLs
The EPA suggests the use of the value of a statistical life (VSL) as a means to value avoided
premature mortalities when conducting benefit-cost analyses (BCA).
80
Many studies have
explored VSL values in the United States and other relatively high income countries,
81,82,83
but
uncertainty remains in determining how to apply VSLs from higher income countries to lower
34
income countries in order to provide an appropriate estimate in these countries where no VSL
estimates have been made.
VSL is constructed from a person's willingness to pay (WTP) for an arbitrarily small but finite
reduction in risk. Wage-risk studies estimate WTP by comparing an individual's perceived risk
within a certain type of employment versus the amount of compensation the individual receives,
i.e. wage. Beyond higher income countries, few credible VSL estimates exist. Miller 8 ' provided
estimates for 49 countries, including several low income countries. For this analysis, it is
necessary to extrapolate a VSL from one country and apply it to another country in which no
estimate has been made. From Hammitt and Robinson,83 who conducted a study on applying
US VSL specifically to sub-Saharan Africa, the following relationship can be used to extend a
VSL from one country to another.
VSLB
=
VSLA - ()IE
Eq. (16),
where A and B denote the base country and country of interest, respectively, VSL are the VSLs
for the respective countries, / is a measure of income for each country, and IE is the income
elasticity associated with VSL.
This method requires the selection of a base VSL. For the purposes of this thesis, the US is
used as the base country with a VSL of $7.4 million in 2006 dollars as suggested by the EPA.80
This value is derived from 26 US VSL studies where values have been adjusted for inflation. A
Weibull distribution was then fitted to the data with a scale parameter of 7.75 and a shape
parameter of 1.51.80 Hammitt and Robinsons3 recommend using gross national income (GNI)
per capita as an income measure for each country. The major source of uncertainty lies in the
value of IE used. IE is a reflection of the proportion of an individual's income that is used
towards risk reduction. Within higher income countries, it has been shown that lEs less than one
are appropriate,83 meaning that an increase in income level does not cause a proportional
increase in VSL. When performing cross-country comparisons where there is a large
discrepancy in income level, however, lEs greater than one are plausible given that as the
average income of a person is reduced, a reduction in the proportion of income used towards
risk reduction follows. What this value for IE should be, however, remains highly uncertain.
Hammitt and Robinson83 suggest applying a range of lEs from 1 to 2, where a uniform
distribution is assumed in this analysis.
35
Hammitt and Robinson8 3 also suggest comparing calculated VSL values to the expected future
earnings and consumption so as not to undervalue VSLs in low income countries since VSLs
should be at least equal to the net present value of future earnings lost due to premature
mortality. Hammitt and Robinson 83 make an estimate of expected future earnings by taking the
NPV of unadjusted GNI per capita for half the expected lifetime for a person in the country of
interest assuming a 3% discount rate, where NPV was previously defined in Eq. (2). For this
analysis, the cash flow for the period is defined as the GNI per capita for the base year of 2006
for each country. Where appropriate (i.e. when VSL falls below the NPV of future earnings), this
value is substituted for the VSL.
Valuing lives across countries, however, has ethical implications in that it may be interpreted as
a life being more highly valued in higher income countries than in lower income countries. VSLs
are rather a reflection of an individual's willingness to reduce his or her own risk in premature
mortality subject to the economic constraints present in that country.
83
Policymakers may object
on moral grounds to a variable VSL approach. As such, the Department of Transportation
(DOT) sets guidelines so that all individuals in the US are valued equally, as previously
discussed in Section 2.4.4. This idea can also be extended across countries, i.e. assume a
constant VSL valuation for all avoided mortalities as a result of ULSJ implementation where this
may be viewed as a policy choice rather than a concept strongly supported by economic theory.
Because no "global" VSL exists, a valuation using a constant US VSL is provided.
The benefit of ULSJ implementation is valued by multiplying the number of avoided mortalities
for a given country by that country's corresponding VSL and summing while also taking into
account discounted health benefits in the future. The standard mortality lag structure as defined
in Section 2.4.3 is used in this analysis. Non-discounted costs are also presented in Error!
Reference source not found. as a comparison.
3.4. Concentration Response Functions
For this analysis, the EPA defined CRF is extrapolated to other countries in order to evaluate
global health impacts of ULSJ implementation, but several assumptions are required. First, a
linear CRF is applied across all pollutant concentration levels. Second, it is assumed that all
avoided mortalities that result from ULSJ implementation will be seen in a reduction in
cardiopulmonary disease and lung cancer related deaths. Third, consideration is limited to
36
members of the population with an age greater than 30 as the original cohort studies focused
their analysis to this age bracket. The CRF then has the following form:
A(Premature Mortalities)
= Zj[fl
fk,30+
PijAxijBcP + fp/Cfk, 3 0+PijAxijBkc]
Eq. (17),
where k denotes the country of interest, which is a function of the grid cell location, ij (i.e. k =
k(ij)), fk,30+ is the fraction of the population above 30 years of age in a specific country, B is the
disease specific baseline per capita mortality rate in a specific country, CP denotes values in
terms of cardiopulmonary disease, LC denotes values in terms of lung cancer, # is the fractional
increase in mortality given one ptg/m
3 increase
in annual average PM2.5 (i.e. risk coefficient) and
is a function of the disease of interest, Pij is the total population in grid cell, ij, and AXij is the
change in PM 2.5 concentration in a grid cell, ij with units of Ig/m3 . In Eq. (17), the summation
symbol and indices, ij, show that in order to obtain the total avoided mortalities, it is necessary
to sum across all grid points defined by the gridded population data, GRUMP, 84 which has a
finer grid resolution (2.5' x 2.5') than global GEOS-Chem (40 x 5*). Each population grid cell is
assigned to the closest corresponding GEOS-Chem grid cell (by grid center point) to determine
population/concentration products as required by Eq. (17).
As stated in Section 2.3, only CP and LC related premature mortalities are considered. The
change in the number of premature mortalities is split between both disease groups with two
different # values, which are assumed not to change across countries and are based on
epidemiological data for the US. Because it is not appropriate to apply the US all-cause risk
coefficient globally given that countries in different parts of the world can have very different
mortality profiles, it is necessary to scale the disease-specific risk coefficients appropriately.
Under the assumption that CP and LC premature mortalities dominate and comprise all
premature mortalities seen by a change in ground-level PM2.5 concentration, the diseasespecific risk coefficients within the US are related to the all-cause (AC) values by
#
B=
BC
#3lBfc + #!%B&c
Eq. (18),
where flcP and fls are unknown. Baseline incidence rates for the US are computed using data
from the WHO Global Burden of Disease (GBD)85 database and flC is assumed to be the
distribution of values obtained through the EPA expert elicitation study,23 which is centered on a
1.06% increase in premature mortality given a 1 pg/m 3 increase in ground-level PM2.5
concentration. This central estimate is based on the EPA recommended Weibull distribution, a
37
distribution based on the ACS and Six Cities studies.20, This CRF approach will be here-on
referred to as the "EPA CRF."
The number of CP related deaths is much greater than LC related deaths in the US, and the CP
risk coefficient is known with more certainty. As a result, Eq. (18) can be defined in terms of flI
and a ratio between the uncertain disease specific relative risks, y. Eq. (18) then becomes
flAC
=
fl[BP + yBL]
=
(RRLC-1)
RR LC
Eq. (19), where
(RRCP-1)
Eq. (20),
RRCP
and RR is the ratio between the number of health incidences in the baseline pollution case to
the number of health incidences when only background pollution is considered. (RR-1)/RR is
then the percentage change in mortality given a change in ground-level PM2.5 concentration.
Rearranging Eq. (19) produces the following equation for the risk coefficient.
#s =L[C
us[BUS+yBbU]
Eq. (21).
To solve for flcP, an appropriate value of y must be determined. Table 7 shows the central
estimates for these RR, adjusted to percent increase per pg/m
3
increase in PM 2.5 .
Table 7: Percentage increase in avoided mortalities given a 1 pg/m 3 increase in ground-level
PM 2.5 concentration. Values from Pope et al.20 and Laden et al.
Pope
Laden
All Cause
0.6
1.4
Cardiopulmonary
Lung Cancer
0.8
1.2
2.2
2.1
Based on the values in Table 7, if each RR value is allowed to vary within the specified range
above, y can range from 0.5 (1.2/2.2) to 2.6 (2.1/0.8) where a uniform distribution is assumed
for each RR. Eqs. (9) and (12) yield the following result:
A(Premature Mortalities) = Z'ijfk,30+ PijAXi j f CP(Bf + yBLc)]
Eq. (22).
As a comparison to the EPA CRF approach, the WHO methodology described by Ostro86 was
also used and it was applied by Barrett et al.87 to determine the number of mortalities that result
from full-flight operations by aircraft. This function has the following form:
38
RRk =kXB+1)
Premature Mortalities = Ek
Eq. (23)
RRk
BkPk
Eq. (24),
where XA and XB are the concentrations for the policy and baseline cases, which for this study
would be ULSJ aviation and standard aviation, respectively, fl is a disease specific power
coefficient, Bk is the baseline incidence rate for a disease category, Pk is the exposed
population, and k corresponds to a given country and total mortalities are determined by
summing across all countries. Thus, this methodology uses background concentrations
compared across policy cases in order to determine a relative risk per country (RRk), which is
then related to a percent increase in premature mortality given some change in PM 2 .5
concentration. This method results in a lower marginal risk at higher concentrations.
Baseline incidence rates are a function of grid cell location (i.e. the country that coincides with
that grid cell) and are determined using the WHO GBD database, which provides cause specific
mortality information bracketed by age group for each country. Given that the ACS and Harvard
Six Cities cohort studies focused on populations 30 and 25 years and older, respectively,
country specific mortality data is required specifically for the 30 years and older (30+) age
bracket. WHO GBD data, however, are provided only for the 15+ bracket. As a first
approximation, it is assumed that no CP and LC deaths occur in the 15-30 age bracket, and that
the mortality data provided for the 15+ bracket can be exactly applied to the 30+ bracket.
Relative uncertainties for all cause mortality rates are also provided for each country. Applying
these uncertainties to cause-specific deaths underestimates the uncertainty for CP and LC
related deaths, but no cause-specific relative uncertainties are provided. 30+ population data for
each country are obtained using the US Census IDB.
88
Given that similar data is used to
determine the WHO GBD database values as are used to perform the population projections in
the US Census IDB, the relative uncertainties from the GBD database are also applied to the
population projections.
3.5. Economic Analysis
Two separate cost analyses are performed. The first uses historical price data from ULS diesel
to estimate ULSJ costs while the second relies on cost-curve estimation based on petroleum
refining data.
39
3.5.1. Price History Analysis
This section includes significant contributions from Dr. Robert Malina and Dr. Christoph
Wollersheim.
The only cost aspect considered in the price history analysis is the expected increase in price
due to the additional processing required to desulfurize jet fuel. For this analysis, it is assumed
that any price increase for ULS production is a function of increased capital and refining costs
and not a function of any other market factors that may be relevant. This price analysis is based
on price history data of ULS diesel as it has recently been implemented for on-road use in 2006
and is currently being phased into off-road use (ships, locomotives, etc.) as detailed previously
in Section 2.5.
The US Energy Information Administration (EIA)89 provides price history data for three diesel
fuel types: high sulfur (HS) (500+ ppm), low sulfur (LS) (15-500 ppm), and ultra low sulfur (ULS)
(<15 ppm). The price differential between ULS/LS and LS/HS are plotted against the amount of
ULS/LS and HS diesel fuel supplied, and is shown in Figure 3. Note that throughout this section,
references to HS, LS, and ULS refer to diesel fuel.
The black and red lines show the price differentials and correspond to the right-hand axis, while
the purple, green, blue, and orange lines show the product supplied and correspond to the lefthand axis. There appears to be a spike in the price differential beginning in 2005 and ending in
early 2008. This spike coincides with a change in supply of ULS and LS fuel, which are
assumed to be direct substitutes given that a decrease in LS supply is accompanied by an
increase in ULS supply, while the total amount of ULS and LS supplied remains approximately
constant over the entire time period. This price spike also coincides with the initial phase-in of
ULS diesel in 2006 shown in the timeline of standards for on-road implementation provided in
Table 2. The cause of this price spike is unclear. It could be a result of market fluctuations given
the shift in supply away from LS to ULS. This shift in supply, however, may be exaggerated in
Figure 3 given that ULS diesel in Jan. 2005 may already have been produced in non-negligible
quantities. No ULS diesel was reported due to the fact that it was not required to be labeled as
ULS diesel by regulations at this point.
40
Product Supplied and Price Differential
140000
0.3
120000
- 0.25
- 015
80000 -
--
*
-
L Supplied
LS Supplied
2 160000 -- 0.05
a.
0
40000
-
60000
ULS + LS Supplied
0
2-0.05
-- HS Supplied
-ULS-HS
0
r-
r-I
o
o
4 N
M
0000
<
Lfn '0
r_
r-
<
,
0
0
,
o
0
0
00
0
0)
0
<
0
-'-
0
-0.1
-
Price Duff.
LS-HS Price Diff.
0
Date
Figure 3: The product supplied of ULS, LS, and HS diesel fuel plotted simultaneously with the
price differential for ULS-HS and LS-HS for Jan 2001 to February 2011.
All price history data is condensed into a single representative price differential. For this
analysis, the observed price differential in ULS and HS diesel fuel (after adjusting the nominal
prices by inflation to the real prices for a base year of 2006) is weighted against the amount of
ULS and HS diesel supplied for a given month in order to capture the interaction between price
and quantity within the fuel market. HS diesel fuel is defined by a FSC of greater than 500 ppm,
and because jet fuel FSC is between 600 and 700 ppm, the ULS/HS price differentials are used
rather than the ULS/LS price differentials. One limitation of this method is weighting against
negligible amounts of HS fuel (which would drive price differentials downward) due to a phasing
out of HS diesel fuel for non-road applications beginning in January 2007. Figure 3 shows a
decline in HS diesel production starting around this time, but its production is currently nonnegligible and thus not an issue in this analysis.
Three different weighted estimates are determined by considering three distinct time periods.
The first time period considered is the "steady state" period in which the price spike stabilizes
relative to the noise already present in the data. This period is set from January 2008 to
February 2011 (where the end date was based on the most up-to-date data available given the
time the price analysis was conducted). The second time period includes all ULS price data,
which is only reported from January 2007 and onward although ULS production numbers are
41
provided starting in January 2004. Not all relevant price data is available (i.e. price data for ULS
since introduction of ULS production), for the entire price spike period. For this reason, a third
period is analyzed from January 2005 to the present where the LS/HS price differential is used
as a substitute for the ULS/HS price differential from 2005-2007 when ULS price data was
unavailable. Regardless, all price differentials for this third price scenario are weighted against
ULS and HS production quantities. For the three scenarios described above, the following
weighted averages have been calculated: 3.7 cents for the steady state period (low price
differential), 5.6 cents for all ULS price data, and 6.5 cents for all price data (high price
differential).
3.5.2. Cost Buildup Approach
This section includes an analysis originally performed by Dr. Jim Hileman, Russ Stratton, and
Matt Pearlson and then supplemented by the thesis author.
An alternative approach to estimating production costs is used to corroborate the price
differentials determined above. The analysis includes estimating the capital costs for the
hydrotreater unit, the steam methane reformer (SMR) unit, and the natural gas (NG) feedstock
costs for a representative refinery. In hydrodesulfurization (HDS), or more generally
hydroprocessing, hydrogen gas (H2 ) is used to desulfurize jet fuel. Steam methane reformation
is used to create H2 at the expense of NG consumption. The additional NG requirement for HDS
is determined from the GREET model90 as follows.
GREET is used to calculate the differences in NG consumption (in units of energy of NG
consumed per energy of ULSJ produced) based on the refining efficiencies and process shares
defined for ULSJ and conventional jet fuel. Refining efficiency for a specific petroleum product is
defined as
7
EF
EF
EF+E1
Eq. (25),
where EF is the specific energy of the fuel and Er is energy input to the refinery per unit mass of
jet fuel produced. The total change in input specific energy between standard jet fuel and ULSJ
is required for this analysis, which is defined as
A = E1,u - E1,1
42
Eq. (26).
U indicates values for ULSJ, and J indicates values for standard jet fuel. Inverting Eq. (25) and
taking the difference between the standard jet fuel and ULSJ cases produces the following
relationship:
1
1
EFU+EIU
EF,J+EJ
f7u
77j
EF,U
EF,J
Eq. (27).
If the specific energy of standard jet fuel and ULSJ are assumed to be approximately equal
(within 0.3% as outlined in Section 3.2.4), then Eq. (27) reduces to
1
lU
E,-Eg
7j
Eq. (28).
-
EF
EF
To determine the change in energy associated with additional NG consumption per mass of jet
fuel, it is necessary to multiply Eq. (28) by the appropriate process energy share.
ANG = (-
- EF 'fNG Eq. (29),
where fNG is the total process energy share associated with NG use within the refinery. To
determine the additional amount of NG required, Eq. (29) must be multiplied by the density of jet
fuel, where energy density is equal to the product of specific energy and density of the jet fuel
as reflected in Eq. (30). This expression is then divided by the energy density of NG to acquire
the additional volume of NG required (at standard conditions) per unit volume of jet fuel.
ANGvolume =
where
DF
-1 -
-)-DF
-f
DN
Eq. (30),
is the energy density of jet fuel, and DNG is the energy density of natural gas.
61
The process energy shares assumed in Stratton et al.
(see Table 8 below) are used for this
analysis. The process fuels shown in Table 8 are used to operate the SMR to produce H2 for
HDS and also power the hydroprocessing itself. Use of natural gas and refinery gas in refining
processes accounts for 80.9% of the total energy consumption, thus the costs due to electricity,
coke, and residual oil consumption are not considered. The OECD defines refinery gas as a
"non-condensable gas obtained during distillation of crude oil or treatment of oil products (e.g.
cracking) in refineries. It consists mainly of hydrogen, methane, ethane and olefins. It includes
gases which are returned from the petrochemical industry."
43
Table 8: Process energy shares required for jet fuel production. 6 1
Type of process fuel
Process energy share
(%)
3.5
41.3
39.6
14.3
1.3
100
Electricity
Natural Gas
Refinery Gas
Coke
Residual Oil
Total
The energy density of NG is 983 BTU/ft3, or 3.66x104 kJ/m 3 (lower heating value (LHV), GREET
v1.8A). The energy density of ULSJ is 34.3 MJ/L.
8
From Stratton et al., 6' the assumed jet fuel
refinery efficiency is 93.5% and ULSJ refinery efficiency is assumed to be 91.5% due to the
energy penalty described with the WTW GHG emissions calculation.
Using Eq. (30) and the above stated values, the amount of refinery and natural gas required for
the additional hydroprocessing required to desulfurize jet fuel is 2.37 standard cubic feet (scf)
per gallon, or 0.018 standard cubic meters (scm) per liter.
The NG price history is plotted in Figure 4 where the price shown is the cost for 2.37 standard
cubic feet (scf), the amount of NG computed previously that is required for HDS for each gallon
of jet fuel. Because refinery gas is created and used within the refineries, there is no market for
refinery gas from which to obtain a per volume price. Thus, refinery gas is assumed to have the
same cost as natural gas given they are used for the same purpose within the refinery.
0.03
I
-. 12
0.025
810
0.02
a
ii:
-
0
00
C
1000
0.01
ULSJ.
0
(Nf
C
~
0
C
C
C
C
0
C
4
C
C
C
-
C
Figure 4: NG Price history8 required for ULSJ hydroprocessing where prices are presented
based on 2.37 scf/gal (0.018 scm/L) of NG per gallon of ULSJ.
Capital expansion of hydrotreating and steam methane reformation capacity is required to
handle the increase in ULSJ production. Here it is assumed that the 2.37 scf of natural gas
44
calculated previously is used only for the H2 production required for hydroprocessing. Cost
curves presented in Gary and Handwerk 200791 are used to derive the additional capital costs
for increasing the hydrotreater and SMR unit capacities for ULSJ production. Capacity is varied
from 5,000 to 50,000 barrels per day (bpd), or 800,000 to 8,000,000 liters per day for the
hydrotreater and a corresponding SMR capacity is determined from the per gallon requirement
of NG. Steam methane reformation is assumed to yield 3.8591 moles of H2 per mole of NG
where a water gas shift reaction is assumed; therefore the product of hydrotreating capacity and
the required NG/gallon is multiplied by 3.85 to obtain the total additional amount of H2 required
to treat a gallon of ULSJ fuel.
The cost curves of the hydrotreater and the corresponding SMR are aggregated and plotted in
Figure 5 (blue line). For example, the total capital cost associated with steam methane
reformation and hydrotreating for a capacity of 22,000 bpd of ULSJ is $58 million. Straight-line
depreciation is then applied to these capital costs. The US mandates a 10-year depreciation
time horizon for tax purposes,92 but capital costs in this analysis are depreciated over 30
years'l to reflect the useful lifetime of an average refinery to yield a per gallon capital cost (red
line).
For 22,000 bpd of capacity, the additional per-gallon cost for increasing the hydrotreating
capacity to accommodate full scale ULSJ production is $0.017 per gallon when a 10 year
depreciation time horizon is considered, but $0.006 per gallon when depreciated over 30 years.
0.030
120
,
0
0.025
.100
80
0.020
L860
2
0.015
40
0.010
20
0.005 '
0.000
0 -.
0
10,000
20,000
30,000
40,000
50,000
Hydrotreating Capacity (bpd)
Figure 5: Capital costs for hydrotreating and SMR units as a function of HDS capacity
depreciated over 30 years.
45
The average capacity of ULS fuel at US refineries, according to the EIA, is approximately
22,000 bpd, where this value was obtained by dividing the total amount of ULS diesel produced
in 2009 (3.1 million bpd) by the total number of US refineries producing ULSD (141). For this
representative capacity, the per gallon additional capital cost ranges from 0.004 and 0.006
$/gal. A more rigorous cost buildup approach would involve estimating the additional per gallon
cost of ULSJ using cash flows that includes capital cost factors such as loan payments,
depreciation over 10 years, escalation from 2005 costs (the year in which this analysis is
based), a change in location factor seeing as capital costs would rise outside of the US Gulf
Coast, a compounding of increased capital costs due to loan payments and depreciation/capital
recovery, inclusion of fixed operating expenses (additional staff, insurance, maintenance, etc.),
additional supporting utilities, and discounted cash flows for the "true value of money" for an
economic investment. This approach will result in a higher cent/gal cost than estimated above.
3.5.3. Cost Distribution
From the price history analysis, minimum and maximum price differentials of 3.7 and 6.6
cents/gallon are calculated. From the cost-buildup approach, minimum and maximum price
differentials of 1.6 and 3.6 cents/gallon are calculated. While the cost-buildup approach is useful
in validating the price history analysis, it provides a minimum price differential as it only
considers the additional methane required for H2 production for hydroprocessing and additional
refinery processes that may use the methane as a fuel. Other costs that the refineries see as a
result of ULSJ implementation that may be hidden within the price history analysis are not
captured. Thus, 1.6 cents/gallon is taken as the minimum price differential, while the 6.6
cents/gallon is taken as the maximum price differential. 3.7 cents/gallon is chosen as the
nominal price differential given it is the expected price differential when production reaches a
steady state as defined in Section 3.5.1. A triangular distribution is assumed, which is described
in Section 4.4.2.
3.6. Sensitivity Analysis
3.6.1. Monte Carlo Analysis Framework
Monte Carlo techniques are used to quantify the uncertainty present within the EBCA. From
Allaire and Willcox, 9 3 within a general model, f(x), with an arbitrary number of input parameters,
the expected outcome can be determined from a Monte Carlo simulation with the form
46
1E
=1f(xm)
-+
IE[f(x)] as N -> oo Eq. (31),
where x denotes a vector of input parameters. Eq. (31) states that as the number of Monte
Carlo simulations, N, goes to infinity, then the mean value of all simulations will approach the
expected outcome of the model system. For each simulation, the input parameters are randomly
selected based on distributions assigned to each variable. The outputs presented in each of the
results section have all been produced using Eq. (31) unless otherwise noted. Given
computational time constraints, N is chosen to be 2000. Due to the complexity of GEOS-Chem,
it is not practical to perform 2000 air quality simulations. Rather, a 60% uncertainty is assumed
for ground-level concentrations and these results are scaled for each Monte Carlo (MC)
simulation based on a triangular uncertainty distribution.
3.6.2. Nominal Range Sensitivity Analysis
A nominal range sensitivity analysis (NRSA) as detailed in Jun 94 is used. A NRSA is a local, first
order sensitivity analysis that is used for a deterministic model and shows changes in the final
output given these deterministic inputs. Each input is varied from a nominally low to high value
as it is inputted into the deterministic model while all other parameters not being tested are held
at their modal values. This type of analysis does not capture any interaction sensitivities and is
most effective for linear systems. The high, low, and nominal values are defined in Table 14,
except for the US VSL, which for the purposes of this analysis, is assumed to have a high, low,
and nominal value of $12 million, $1million, and $7.4 million, respectively. Also, nominal values
for the uniform distributions are assumed to be the value midway between the defined
endpoints.
3.6.3. Global Sensitivity Analysis
While the results of an NRSA may be useful to understand first order and absolute effects of
input parameter values on the output value, it provides no information concerning how much
uncertainty each parameter contributes to the total output uncertainty. A global sensitivity
analysis (GSA) serves to quantify the contribution to variance. GSA is detailed in Allaire and
Willcox, 9 3 but the method used for this analysis is implemented as specified by Salteli. 95 A
description outlining this approach is provided here.
Three matrices are defined with random variables: A, B, and C. A is an N x k matrix that
contains a set of randomly generated variables, where N is the number of MC simulations and k
47
is the number of input parameters. B is an N x k matrix that contains a different set of randomly
generated variables from those in A. C is an N x k matrix that is formed by all columns of B
except for the
fh
column, which is replaced by the
/h column
of A. Based on these constructed
matrices, Monte Carlo simulations are performed where each column of each matrix is one
simulation defined by randomly defined input parameters and each unique matrix defines one
complete Monte Carlo run. Thus, because there are k + 2 unique matrices formed, a total of
Nx(k + 2) runs are required. From these simulations, the variance can be computed as
Var(Y) = Var[Y(A); Y(B)]
Eq. (32),
meaning the expected variance of the output is defined by both set of randomly generated
variables and Y is the vector of expected outcomes generated by the simulations. The main
effect index can be computed as
Si = 1Z[Y(A) - Y(Ci) - Y(A) - Y(B)]/Var(Y)
Eq. (33),
where the multiplication shown above is scalar component-wise multiplication which gives the
fraction of output variance explained by that input parameter alone, but does not include
interaction effects between parameters.93 The total effect sensitivity index is defined as
STi
=
1
-
N=
1
[Y(B) - Y(Ci) - Y(A) - Y(B)]/Var(Y)
Eq. (34),
which does account for input interactions.93 The results from a GSA can indicate which input
parameters require further research and understanding to reduce overall uncertainty in a model
or associated analysis. To improve convergence times and reduce the value of N, Salteli9 5
suggests the use of Sobol quasi-random numbers instead of completely random variables.
Sobol sets are used in this analysis.
3.7. Additional Operational Concerns
3.7.1. Change in Fuel Properties
One of the operational concerns of ULSJ is the impact on fuel energy density and specific
energy given the additional processing required for desulfurizing jet fuel. From Hileman et al.,75
a 1% reduction in energy density and a 0.3% increase in specific energy of the fuel are
expected post-processing of a fuel with an average FSC of 600-700 ppm. These changes in fuel
properties are most likely due to the breakdown of aromatic rings that constitute approximately
48
20% of jet fuel.
Thus, more fuel will be burned by volume, but less by mass. This has two
potential consequences. First, if more fuel is required to be burned by volume, then it is possible
that airlines will have to purchase more fuel by the gallon at a given price. The impact of this
effect is unclear as market adjustments could take place given that the consumers know the fuel
has reduced energy density, and thus airlines may not incur any cost penalty as a result.
Second, if less fuel is burned by mass (assuming that the total fleet energy requirement remains
the same and there are no airline operational impacts given the increase in fuel volume carried),
there is a potential reduction in climate and health impacts due to a reduction in overall
emissions, although this is uncertain and not considered in this analysis.
The reduction in fuel energy density may cause a higher percentage of fuel to fall below
standard jet fuel specifications, which in turn has an effect on profits seen by the refineries given
that less fuel can be sold as jet fuel. Based on ASTM turbine fuel specifications for Jet A or
A1,96
jet fuel must have a specific energy content of at least 42.8 MJ/kg and a density between
0.775 and 0.840 kg/L, which implies that the lowest possible energy density that is within
specification is 33.2 MJ/L. Fuel data for JP-8 was obtained through the Petroleum Quality
Information System (PQIS) database.97 Given the similarities between Jet A and JP-8, it is
assumed that the fuel specifications mentioned above could also be applied to the PQIS data.
The values in the data set are shifted by the expected reduction in energy density. A 1 % energy
density reduction corresponds to an energy density of 34.4 MJ/L, thus a 0.4 MJ/L shift is applied
to all values in the data set and compared against the minimum fuel specification value of 33.2
MJ/L. No energy density values fall below the fuel specification. As a limiting case, a 2% energy
density reduction shift is also applied to all the data points. Of the available data where energy
density could be computed, three points fell below the fuel specification, which corresponds to
0.07% of the total fuel volume. It is then assumed that no significant additional costs would be
seen by refineries or consumers with regards to meeting fuel specification standards.
3.7.2. Fuel Lubricity
One other operational issue is a potential decrease in fuel lubricity. Decrease in fuel lubricity can
lead to engine fuel pump failure as these components tend to be partially fuel lubricated.
Decreased fuel lubricity can cause more rapid fatigue of the mechanical components due to an
increase in wear scar diameter (WSD). An example of this was the full or partial failure of at
least eight pumps on New Zealand airlines flights due to poor lubricity fuel delivered from a local
49
New Zealand refinery. Tests indicated excessive wear in the spline-drives, which connect the
fuel-pumps to the fuel-control units, from at least three different manufacturers. These splines
were expected to have a service life of 3000-5000 hours, but were wearing out in 150 hours.
98
This issue was addressed in several ways. First, the refinery added 5% (although 30% has also
been suggested99 ) straight-run (non-hydroprocessed) kerosene to production when possible to
hydroprocessed streams which resulted in a reduction of hydrotreater severity. As a result,
WSD decreased from 0.78 mm to 0.65 mm from June 1994 to December 1996. Of the airlines
affected, one was supplied with DCI-4A (corrosion inhibitor for use in jet fuels) doped fuel, one
added a different, unspecified corrosion inhibitor, and one did not use any additive. In addition,
hardware modifications were made by the engine manufacturers by offering improved pump
splines. There have not been any further reported lubricity issues with this fuel since these
changes were implemented.
100
If it were needed, a fuel additive could be used with ULSJ to improve its lubricity. The US
military currently uses a Corrosion Inhibitor/Lubricity Improver (CI/LI) additive in all of its JP8
fuel. This additive is obtained through a contract price of $19.706/gallon when purchased in a 55
gallon drum. CI/LI is typically added at 20 mg/L (25 ppm m/m) of JP8. 101 It then follows, on a per
gallon volumetric basis, the additional price as a result of the CI/LI additive is 0.05 cents/gallon.
Given that this additional price is two orders of magnitude less than the cost of HDS, it is
neglected in the benefit-cost analysis. There is also a possible air quality impact due to the
additive since it is burned with the fuel during engine operations, but this is unknown.
50
THIS PAGE INTENTIONALLY LEFT BLANK.
51
Chapter 4: EBCA Results
This chapter presents the results from the EBCA conducted for global and US implementation of
ULSJ. First, results pertaining to the climate and air quality impacts of the policy
implementations are presented in Sections 4.1, 4.2, and 4.3. Section 4.4 presents the BCA
outcomes when all impacts are monetized and aggregated. Finally, results from the sensitivity
analysis are shown to determine the main contributors to the uncertainty present within this
EBCA. All assumptions made within this analysis are also provided in this chapter.
4.1. Aerosol RF Results
Table 9 shows the calculated RF values for the baseline minus the ULSJ scenario, which
corresponds to sulfate aerosol formation from direct aviation SOx emissions. These values
correspond to a global implementation of ULSJ. Total background sulfate RF values are also
provided in order to further compare against values from the literature.
Table 9: Aviation sulfate RF by component and region.
RF in m W/m2
Region
Global
Northern Hemisphere
Europe
Asia
Background (W/m 2)
2.5%
Percentile
-6.0
-11.2
-15.9
-8.2
-1.44
Average
Median
-3.4
-6.3
-9.0
-4.7
-0.82
-3.3
-6.1
-8.8
-4.5
-0.80
97.5%
Percentile
-1.4
-2.6
-3.7
-1.9
-0.35
The total direct radiative forcing for sulfate, nitrate, and ammonium aerosols estimated from this
analysis is -0.82 W/m 2 . Results from Kiehl et al. 65 and Wang et al.1 7 are not directly comparable
as they only consider direct RF due to sulfate aerosols alone, where the latter only considers
anthropogenic aerosols, although direct RF estimations of -0.56 W/m 2 and -0.389 W/m 2,
respectively, show that the estimates made in this analysis are on the same of magnitude.
Directly comparable results are found in Martin et al.,10 2 which also used GEOS-Chem and
reported a direct RF of -0.605 W/m 2 for sulfate, nitrate, and ammonium aerosol.species when
biogenic and anthropogenic emissions are both considered under the assumption that aerosols
were on upper hysteresis branch. The results from this analysis show a 36% bias when
compared to Martin et al. Liao et al.1 03 reports a sulfate aerosol RF of -0.49 W/m 2, but the
annually and globally weighted average AOD at 550 nm is reported to be 0.024, while the
simulations for this analysis produce an AOD of 0.034. Also, the use of RTMs (which were
52
implemented in all of the aforementioned studies) more accurately models the flux between
aerosol layers as well as accounting for the attenuation in solar radiation intensity as light
penetrates the atmosphere and interacts with other molecules.
The SOx pathway median RF of -3.3 mW/m 2 is about 45% lower than the Lee et al.59 RF of -4.8
mW/m 2 , although this value is captured within the 95 percentile range. The Lee et al. 59 value is
based off of a scaling from fuel burn indices relative to a reference value determined in Sausen
et al. 104 and differences could be a result of different assumptions in the underlying chemistry or
emissions indices. In addition, the above forcing calculation results also take into account the
nitrate bounce back effect where a decrease in sulfate aerosol concentration actually causes an
increase in nitrate aerosol concentration due to the availability of free ammonia in the
atmosphere, which is in general a limiting factor for aerosol formation. Thus, the warming
experienced due to a reduction in FSC may be partially mitigated by this bounce back effect. It
can also be seen that the change in RF is very regionally dependent. As expected, the change
in RF is larger in the northern hemisphere (NH) given that the majority of aircraft emissions
occur in this region. Also, the largest RF change is in the European domain, most likely due to
the fact that nitrate aerosols are more prevalent in this region and thus a not as strong bounce
back effect is observed.
4.2. Mortality Results by Country
Avoided mortalities for select countries due to ULSJ implementation are presented in Table 10
for both EPA and WHO CRFs. The WHO values are deterministic. Results are generated from
GEOS-Chem and CMAQ simulations performed by Dr. Steve Yim.
53
Table 10: Avoided mortalities by country due to global ULSJ implementation.
EPA-derived CRF, Full-Flight
Emissions
EPA-derived CRF, LTO
Emissions
WHO CRF
Full-
LTO
FliahtI
Country
Australia
Canada
China
Egypt
Germany
India
Japan
Kenya
Saudi Arabia
United Kingdom
United States of
America
Total
2.5%
Percentile
0.4
2.3
85
15
32
340
5.7
0.5
4
9.7
Mean
0.9
6
220
39
83
870
15
1.5
11
25
97.5%
Percentile
1.6
11
390
70
150
1600
27
2.9
22
45
2.5%
Percentile
0.1
0.3
-270
1.1
1.8
-93
1.3
0
0.4
2.8
Mean
0.2
0.7
-150
3
4.7
-55
3.4
0
1
7.2
97.5%
Percentile
0.3
1.2
-59
5.2
8.4
-20
6.3
0.1
1.9
13
1.4
5.9
72
46
25
390
14
4.2
13
14
0.3
0.8
-13
3.4
1.4
-15
3.6
0.1
1.1
4.2
46
890
120
2300
210
4200
12
-390
31
-130
56
100
140
1500
34
58
Note: positive values indicate avoided mortalities (i.e. saved lives) while negative numbers
indicate an increased mortalities (i.e. lives lost) after ULSJ implementation.
Avoided mortality numbers from the US nested GEOS-Chem and CMAQ are provided in Table
11. Note that based on the EPA CRF formulation, for the US specifically, the avoided mortalities
are independent of y, i.e. the ratio between the disease specific relative risks.
Table 11: Regional simulations avoided mortalities results for the US from global
implementation of ULSJ.
Full-Flight
LTO
Emissions
2.5%
Nested GEOSChem
CMAQ
Emissions
97.5%
2.5%
97.5%
Percentile
Mean
Percentile
Percentile
Mean
Percentile
56
93
140
230
260
430
18
33
44
83
81
150
When compared to the global GEOS-Chem simulation avoided mortality numbers, the nested
GEOS-Chem values are 17% higher than the global GEOS-Chem results, while the CMAQ
results are 92% higher on average when full-flight emissions are considered.
54
Table 10 shows that when using the EPA CRF formulation, considering only LTO emissions
results in a global net increase in mortalities (net health disbenefit), while using the WHO
formulation results in a global net decrease in mortalities (net health benefit) when compared to
the baseline scenario. Based on the previously defined CRFs, any increase in ground-level
PM2.5 concentrations will result in an increase in mortalities (i.e. negative values). The
differences in global avoided mortalities between the two CRFs can be explained by the nonlinearity of the WHO function. For instance, China's (the US's) change in concentration due to
ULSJ implementation for LTO emissions is +0.002 pg/m 3 (-0.001 pg/m 3) while the background
concentration is 45.5 pg/m 3 (6.2 pg/m 3), where the values are population weighted. A gradient
can be defined for each CRF, where the WHO gradient is defined as (RR-1)/RR, and the EPA
gradient is defined as the product of the risk coefficient and the change in ground-level PM2.5
concentration. Applying the data that was used to derive the population weighted values, the
WHO CRF gradient is 12 times lower than the gradient assumed by the EPA CRF in China and
8 times lower than the gradient assumed by the WHO CRF in the US. As a result, the WHO
CRF predicts -13 avoided mortalities compared to the -150 avoided mortalities predicted by the
EPA CRF (whereas in the US, it is 34 versus 31 avoided mortalities, WHO and EPA,
respectively). Thus, countries such as China, including India where background PM 2.5
concentrations are also high, cause an overall increase in mortalities when health impacts are
scaled linearly to ground-level concentration changes for LTO impacts (-130 on average, not
discounted).
4.3. VSL Results by Country
Table 12 presents the VSLs determined for select countries as described previously as well as
total valuation from health impacts when no lag structure is considered (non-discounted health
impacts). All values are in 2006 US dollars, and the GNI data, which is purchasing power parity
adjusted, was obtained from the World Bank database.10 5
55
Table 12: VSL and valuation of avoided premature mortalities (when cruise emissions are
included) due to ULSJ implementation by country in US 2006 $.
ULSJ
Country
Australia
Canada
China
Egypt
Germany
India
Japan
Kenya
Saudi Arabia
United Kingdom
United States of America
Total
GNI/capita
2006pUS$00 3,0
2.5%
d
Percentile
33,010
930,000
36,410
500,000
4,790
51,000
4,700
30,000
34,410
430,000
2,540
5,100
32,770
400,000
1,430
1,700
22,590
220,000
35,110
450,000
45,640
690,000
77
VL
2006 US$
Mean
4,400,000
5,100,000
290,000
280,000
4,600,000
130,000
4,300,000
61,000
2,500,000
4,800,000
7,100,000
Valuation
2006 US$
97.5%
Percentile
11,000,000
13,000,000
1,100,000
1,100,000
12,000,000
560,000
11,000,000
300,000
6,800,000
12,000,000
18,000,000
Mean
4,000,000
30,000,000
63,000,000
11,000,000
390,000,000
110,000,0001
64,000,000
89,000
28,000,000
120,000,000
830,000,000
2,500,0000
From the selected countries, the estimated VSL and avoided premature mortality valuations are
both largest for the US. The predicted VSLs vary by several orders of magnitude and although
China has almost twice as many avoided premature mortalities as the US, the valuation of these
mortalities is -10 times lower than the US.
4.4. Global and US ULSJ Outcomes
4.4.1. Assumptions for Global and US Implementation Analysis
For each of the Monte Carlo (MC) simulations, the following assumptions are made for the
global implementation of ULSJ analysis:
" Changes in energy density and specific energy are not considered within this analysis.
" APMT-Climate input parameters are used as described in Table 13 and the distributions
and associated values are provided in Table 14.
" Three discount rates are applied to climate costs deterministically: 2, 3, and 7%.
*
The EPA CRF methodology is used to calculate the number of avoided mortalities due to
ULSJ implementation in this analysis where a mortality lag structure is implemented
assuming the same discount rates as applied to climate costs.
56
0
Full-flight health impacts are considered.
"
Gross National Income (GNI) per capita adjusted for purchasing power parity (PPP) is
used as the income measure to determine VSLs across countries.
*
Price differentials are assumed to be applicable on a global level although they are
based on US price history data.
The primary assumptions for the US implementation analysis are very similar to those used in
the global implementation analysis. The only additional or differing points are the following:
e
The calculations provided in the US implementation analysis are the costs seen by the
US due to a global implementation of ULSJ.
*
Climate costs are scaled by a regional GDP factor to obtain climate costs for just the US
(7-23% of total damages"').
*
Avoided mortality benefits are those seen by the US due to global implementation.
e
Implementation costs are a result of US fuel burn, only (6.74x 1010 kg for aviation year
2006), i.e. the amount of fuel burn seen in the US region as defined by the nested
GEOS-Chem grid.
A US-only implementation analysis is also performed. The overall structure of the analysis is the
same as the global implementation analysis, except for the following distinctions:
" Climate costs are calculated based on US fuel burn, only. Given that the DICE-2007
damage function calculates impact on global GDP, the 7-23% fraction for US damages
is again applied.
*
Avoided mortality benefits are those seen by the US due to US-only implementation,
where US-only implementation is approximated by a nested GEOS-Chem simulation
with baseline boundary conditions and ULSJ for all flights within the domain
*
Implementation costs are a result of US fuel burn.
57
4.4.2. Assumed Uncertainty Distributions
Table 13 provides a brief description of each input parameter used in the MC analysis. As
described in the cost build-up section, the additional price associated with increased
hydroprocessing and hydrogen gas capacity determines the amount of additional lifecycle C02
emissions. The amount of additional hydroprocessing is also directly related to the expected
change in fuel energy density and specific energy, as described in the operations section below.
Table 14 shows the values and assumed distributions for each of the described input
parameters in Table 13.
58
4.4.3. Global Implementation Analysis Results
Table 15 presents the primary results from the EBCA for global implementation of ULSJ. Note
that values in parentheses are non-cost beneficial values.
Table 15: Global implementation EBCA results, given in 2006 US $ Billion.
Component
-Climate
2%
3%
7%
Air Qualit
_2%
3%
7%
Implementation
Total
2%
3%
17%
Mean
Median
95% Interval
2.35
1.64
(0.82)
2.07
1.46
(0.73)
0.13 0.10 -34.26
0.06 - 20
2.34
2.27
2.05
(2.52)
1.83
1.77
1.60
(2.49)
0.21 -7.55
0.20 -7.32
0.18 -6.59
(1.31) - (3.80)
(2.53)
(1.89)
(1.29)
(2.63)
(2.11)
(1.62)
(7.70) - 3.37
(5.98)- 3.59
(4.15) - 3.48
% Cost
Beneficial
16.32
15
17
20
Figure 6 shows the benefit-cost distribution produced by the MC simulations for each of the
three scenarios described in Table 15. Positive values represent net cost beneficial scenarios
while negative values represent net non-cost beneficial scenarios. From Table 11, there are an
estimated -2300 avoided premature mortalities resulting from global ULSJ implementation.
2% DR
-10
-8
6
-4
-2
0
2
4
6
8
10
2
4
6
8
10
2
4
6
8
10
3% DR
-10
-8
-6
-4
-2
0
7% DR
-10
-8
-6
-4
-2
0
Net Benefit-Cost (US 2006 $ billion)
Figure 6: Benefit-cost distribution for global implementation analysis for three different discount
rates (DRs).
61
Table 16 shows statistics when climate impacts are weighed against air quality impacts only.
Note that climate and air quality statistics are the same as in Table 15.
Table 16: Global implementation results from EBCA where no implementation cost has been
included, given in 2006 US $ Billion.
Component
Climate
2%
3%
7%
Air Qualit
2%
3%
7%
Total
2%
3%
7%
Mean
Median
95% Interval
(2.35)
(1.64)
(0.82)
2.07)
(1.46)
0.73
(0.13) - (6.32
(0.10) -. (4.26)
(0.06) - 2.081
2.34
2.27
2.05
1.83
1.77
1.60
0.00
0.63
1.23
(0.18)
0.31
0.85
%Cost
Beneficial
10.21 -7.55
10.20 -7.32
10.18 -6.59
4.68)- 5.65
3.06 - 5.84
1.16 - 5.78
46
57
77
Alternatively, implementation costs are weighted against air quality impacts only due to the
uncertainty in global climate impacts. This is shown in Table 17.
Table 17: Global implementation results from EBCA where no climate cost has been included,
given in 2006 US $ Billion.
Com onent
Mean
Air Quali
2%
2.34
3%
2.27
7%
2.05
Implementation 12.521
Total
1
2%
0.18
3%
0.25
7%
0.47
Median
95% Interval
1.83
1.77
1.60
2.9)
0.21 -7.55
0.20 -7.32
0.18 -6.59
(1.31) -13.80
0.61
0.66
0.84
2.94 - 5.20
2.95 - 4.98
3.00 - 4.26
% Cost
Beneficial
37
35
31
4.4.4. US Implementation Analysis
Table 18 presents the primary results from the EBCA for US implementation of ULSJ. Note that
values in parentheses are not cost beneficial values. Table 19 provides health impacts and
valuations for the US from global implementation for the other two models i.e. nested GEOSChem and CMAQ. Valuations are discounted for the lag in health impacts and a nominal VSL of
2006 US $7.4 million is assumed. All values in Table 19 are nominal values.
62
Table 18: US EBCA results for global implementation, given in 2006 US $Billion.
Component
Climate
2%
3%
7%
Air Quality
2%
3%
108
I
% Cost
"Ran~
Mean
Median
0.75
0.60
89)
0.06 -2.37
(0.56)
(1.7) - (1.326
2
(0.50)
(0.45)
(1.46- 1.32
(1.26) - 1.20
23
2
i
0)
2%pl(0.48)io
(0.40)
(.35)
3%
7%
Table 19: Valuation of US health impacts due to global implementation from CMAQ and GEOSChem Nested simulations.
Valuation
2006 US $Million
940
300
1,500
560
Avoided
Mortalities
140
44
230
83
Nested GEOS-Chem
Nested GEOS-Chem, LTO
CMAQ
CMAQ, LTO
Figure 7 shows the benefit-cost distribution produced by the MC analysis for each of the three
scenarios described in Table 18. Positive values represent cost beneficial scenarios while
negative values represent not cost beneficial scenarios (i.e. plotted as benefit minus cost).
2%DR
-4
-3
-2
-1
0
1
2
3
4
1
2
3
4
1
2
3
4
3%DR
-4
-3
-2
-1
0
7%DR
-4
-3
-2
-1
0
Net Benefit-Cost (US 2006 $ billion)
Figure 7: Benefit-cost distribution for US implementation analysis.
63
4.4.5. US-Only Implementation Analysis
Table 20 presents the primary results from the EBCA for US-only implementation of ULSJ. Note
that values in parentheses are non-cost beneficial values.
Table 20: US-only implementation EBCA results, given in 2006 US $ Billion.
n
IMa~nn
Component
udn
s n
I
I 4501. Inta nual
Madian
Cost
I RianafiniaI
Climate
2%
3%
7%
Air Qualit
2%
3%
7%
Implementation
0.09
0.04
0.07
0.00) - (0.24
(.40.00)1-(0.12)
0.50
0.48
0.43
(0.90)
0.38
0.38
0.35
(0.89)
0.04-1.57
0.04-1.52
0.04 -1.37
(0.47) - (1.36)
2%
(0.53)
(0.58)
(1.33) - 0.65
1
3%
(0.51)
(0.56)
(1.27) - 0.64
1
7%
(0.51)
(0.56)
(1.20) - 0.52
1
Figure 8 shows the benefit-cost distribution produced by the MC simulations for each of the
three scenarios described in Table 20. Positive values represent net cost beneficial scenarios
while negative values represent net non-cost beneficial scenarios.
2%DR
-2
-1.5
-1
-0.5
0
0.5
1
0
0.5
1
0.5
1
3% DR
-2
-1.5
-1
-0.5
7%DR
-2
-1.5
-1
-0.5
0
Ne t Benefit-Cost (US 2006 $ billion)
Figure 8: Benefit-cost distribution for US-only implementation analysis.
64
4.4.6. Constant VSL Analysis
As mentioned previously, the US VSL can be applied to all avoided mortalities to reflect a policy
choice that values all premature mortalities equally. The results from this analysis are presented
in Table 21 and Figure 9.
Table 21: Constant US VSL EBCA results, given in 2006 US $ Billion.
7.41
(4.94) - 43.02
(3.73) - 42.55
(2.64) - 38.94
7.81
7.33
2% DR
-10
0
10
20
30
40
50
60
30
40
50
60
50
60
3%DR
-10
0
10
20
7% DR
-10
0
10
20
30
40
Net Benefit-Cost (US 2006 $ billion)
Figure 9: Benefit-cost distribution for a constant US VSL analysis.
65
4.4.7. Cost Effectiveness Analysis
As an alternative to a benefit-cost analysis, implementation costs and climate disbenefits are
presented on a per premature mortality basis within a cost effectiveness framework. Results are
presented in Table 22 and are expressed in 2006 US $ million and are presented for the global
implementation, US implementation, and US-only implementation scenarios.
Table 22: Cost effectiveness analysis results, given in 2006 US $ Million.
Discount Rate
Global Implementation
2%
3%
7%
Median
95% Interval
2.57
2.26
2.02
2.18
1.97
1.78
0.72 - 6.75
0.73 - 5.66
0.73 - 4.72
13.0
12.3
12.2
13.4
10.8
10.8
4.29 - 32.2
4.40 - 30.0
4.43 - 28.9
Mean
1_7117_71
Global Implementation-US
2%
3%
7%
US-Only Implementation
b
2%
16.7
14.8
5.86 - 40.3
3%
16.6
17.6
14.6
15.5
5.95 - 38.9
6.43-4.12
7%
4.5. Policy Implications
The results from the global implementation analysis, shown in Table 15, indicate that the
majority of predicted outcomes are not cost beneficial, although the 95% confidence interval
does include the cost neutral outcome (i.e. costs and disbenefits are equal to benefits). All three
monetized components of the analysis (climate, air quality, and implementation) are on a similar
order of magnitude, which indicates no single component, be it climate damages or health
benefits, dominates the outcomes. While higher discount rates tend to narrow the distribution
shown in Figure 6, for this analysis, higher discount rates only slightly increased the percentage
of cost beneficial outcomes. Increased cost beneficial outcomes is generally expected given that
it will reduce the overall impact of C02 climate damages given their time scale, but increasing
discount rate also reduces the avoided premature mortality benefit due to the implemented
health benefit lag structure.
Because implementation cost reflects the economic impact of ULSJ policy implementation,
Table 16 shows the results from an analysis where only the environmental components are
considered. When comparing climate damages to air quality benefits on a global scale, the
average outcome of the analysis shows either cost-neutral or cost beneficial outcomes,
66
indicating that on average, the benefits from air quality will outweigh the damages seen by the
expected increase in atmospheric warming. Alternatively, if the air quality impacts are measured
against only the implementation cost, Table 17 shows that on average, the predicted cost
required to produce ULSJ will exceed the air quality benefit from reduced ground-level PM 2.5
concentrations.
From a US implementation outlook, where, again, the US implementation scenario is defined by
global implementation of ULSJ but costs and benefits are considered only within the US
domain, similar results as seen in the global implementation are found. All three discount rates
show that the majority of outcomes (77-78%) are not cost beneficial, although the proportion of
costs and benefits seen by the US are different than the relative magnitude of the costs and
benefits seen in the global implementation analysis. In the case of US implementation, about
1/3 of the implementation cost and air quality benefit is taken on by the US, but only 1/7 of the
climate cost is attributed to US emissions, thus resulting in a slightly larger percentage of cost
beneficial outcomes. This proportionally small amount of climate damages seen by the US is a
result of the assumed climate damage fraction stated in Section 4.4.1. When US-only
implementation is considered, which is defined by ULSJ implementation only within the US
domain, the air quality benefits dominate climate damages, but the implementation cost is a
factor of 2 larger than the benefit achieved from a reduction in premature mortalities, thus only
11-13% cost beneficial scenarios are observed.
Given the wide range in VSLs obtained from the methodology developed in Section 3.3 and the
ethical considerations of valuing all avoided premature mortalities equally, US VSLs are also
applied to all premature mortalities in the global study. As expected, ULSJ implementation on
average is significantly cost beneficial (-US $10 Billion net outcome) where the majority of
outcomes are cost beneficial. A constant VSL approach, however, should be considered
carefully as it does not reflect current economic practice.
It is difficult to say, however, whether or not this policy "should" or "should not" be implemented.
Besides the constant VSL approach, where air quality benefits are likely being overvalued, all
implementation scenarios show an expected cost detrimental outcome. The uncertainty ranges,
however, do always capture the $0 outcome, indicating that there is a certain likelihood of an
overall cost beneficial outcome, albeit this likelihood is at or under -20% for all cases. A
decision on the actual policy implementation will then largely be a result of the priorities of the
policymakers. If the goal is to reduce the environmental impact of aviation, then the climate
67
versus air quality approach indicates that ULSJ implementation may be a viable option. If the
goal is to simply reduce the air quality impact of aviation, then ULSJ, given the expected air
quality impacts, is a very attractive option given that it can be used as a drop-in fuel and can be
implemented quickly given the trends and rulemaking for on and off-road diesel fuel. If,
however, implementation cost is an issue given that refineries would incur the capital costs to
update their refineries to meet ULSJ demand, then the economic impact of ULSJ
implementation may need to be investigated further given the uncertainties present in this
aspect of the analysis.
4.6. Nominal Range Sensitivity Results
This section presents the results of the NRSA for both global and US implementation, where the
primary results are shown in Figure 10 and Figure 11, respectively. Again, US implementation
here refers to the US net benefit-cost due to a global implementation of ULSJ.
Blue and green bars represent the change in net benefit-cost attributed to a low or high
parameter value, respectively. Only the change in net benefit-cost relative to a base
deterministic model output is shown.
Tornado Plot of Net Cost/Benefit Sensitivities
Price Differential
US, VSL
I
$0.066
I
$1 Million
US, All Cause, Beta
0.0139
GEOS-Chem PM Conc.
-60
E
U
$0.016
$12 Million
0.0145
+60%
Global Income Elasticity
2
Discount Rate, Climate Costs
2%
1
Low
4.5
Climate Sensitivity Param.
0.0015
+75%
Aerosol Optical Depth
-2.5
-2
-1.5
-1
-0.5
-70%
0
NPV Change
Figure 10: NRSA results for global implementation of ULSJ.
68
High
2
0.0041
Damage Function Coeff.
-3
7%
0.5
1
1.5
2
2.5
x10
Tornado Plot of Net Cost/Benefit Sensitivities, US
US, VSL -
$1 Million
Price Differential -
$0.066
US, All Cause, Beta a.
$12 Million
$0.016
0.0139
GEOS-Chem PM Conc.
0.0145
-60%
+60%
US GDP Scale Factor -
23%
7%
High
Discount Rate, Climate Costs
2%
Climate Sensitivity Param. -
4.5
Damage Function Coeff. -
0.0041
-10
-8
-6
-4
-2
0
NPV Change
%
2
0.0015
2
4
6
8
x 10
Figure 11: NRSA results for US implementation of ULSJ.
In the global and US analysis, the total net benefit-cost output is most significantly impacted by
the US VSL, the price differential, the assumed percent change in premature mortality given a
1 pg/m 3 change in PM 2.5 concentration, and the uncertainty assumed for the ground level PM
concentration change found in GEOS-Chem. Other important input parameters are the
assumed global income elasticity and components specific to the climate impacts such as the
climate sensitivity parameter, damage function coefficient, and the various components of the
sulfate RF calculation method. The other APMT-Climate inputs do not appear as significant
parameters. The global income elasticity, CP percent increase in premature mortality, and LC
percent increase in premature mortality values have no effect on the US analysis because no
values applied in the EBCA are derived from those parameters as they are in the full global
analysis. Likewise, the GDP fraction associated with US climate costs has no impact on the
global analysis. Uncertainty analysis of APMT-Climate has been performed previously and can
be found in Jun. 108
This sensitivity analysis is useful in that it provides a method in which to gauge the response of
the system for a perturbation in an individual parameter. The values shown in the tornado plots
above can be used to estimate the benefit-cost response to an increase in US VSL or change in
69
ground PM 2 .5 concentration relative to the nominal value. For instance, if the US VSL is actually
$12 million rather than $7.4 million, then one would expect a $1.5 billion increase in net benefit
for the global implementation case, which is shown as a positive $1.5 billion shift in NPV of the
net benefit-cost value. This type of analysis, however, is potentially misleading. This analysis
provides relatively little insight into each components contribution to uncertainty, i.e. which
parameters have the largest impact on the distribution seen from the MC analysis. It is
misleading in the sense that the US VSL and the price differential are shown to have the largest
impact on the value of the output metric, but the range in values applied in the analysis is
significant compared to the other inputs. While this NRSA approach is useful as it provides
some insight into what the most influential factors in this CBA within a deterministic framework
are, to further understand the major sources of uncertainty in this analysis, a GSA is performed.
The results of the GSA are discussed in the next section.
4.6.1. Discount Rate
Within the US NRSA, both endpoints for discount rate produce a decrease in net benefit-cost.
This is possible due to the interaction of discounting health benefits and climate disbenefits. Net
benefit-cost is plotted against discount rate for deterministic outcomes to better understand this
relationship.
Discount Rate vs. Net Benefit-Cost
10 9
S-0.5
Co4
0
C
~ 1.5
Global
z
- - - us
1
7
2
8
9
10
Discount Rate (%)
Figure 12: Net benefit-cost plotted against discount rate of the deterministic model used in the
NRSA.
70
Figure 12 shows that in the global and US analyses, the net benefit-cost increases through the
nominal rate of 3% (as defined, net benefit-cost at 3% will be 0) and then plateaus as the
discount rate increases to 10%. The plateau can be explained by the decrease in value in future
costs/benefits, thus the net benefit-cost of the system approaches the benefit-cost in the year of
implementation. Also, the US analysis (dashed line) never produces a net benefit, as shown in
Figure 11. The shape of the response can be explained by the decreasing climate disbenefits
coupled with decreasing health benefits as the discount rate increases. At lower discount rates,
climate costs more rapidly decrease compared to the decrease in health impacts. After a local
minimum is reached at approximately 6% in the global analysis and 3% in the US analysis, the
decrease in climate costs no longer outweighs the decrease in health benefits and a slight
downturn is observed. The net result of each analysis then approaches a steady state value as
the discount rate continues to increase.
4.7. Global Sensitivity Analysis (GSA) Results
A GSA was performed in order to determine the contribution of each input parameter to the total
output variance. Main and total effect indices are reported. Main effect indices report the specific
input parameters direct impact on the output variance while the total effect indices also account
for input parameter interaction. The results for the most significant factors for the global
implementation are shown in Figure 13 and Figure 14. The results for the most significant
factors for the US implementation are shown in Figure 15 and Figure 16.
Only input parameters with main effect indices of greater than 2% are plotted. Both the US and
Global implementation results yield similar results, although the climate factors were less
significant in the US analysis than in the global analysis. It is clear that the US VSL input
parameter has the largest impact on output variance with a main effect sensitivity index of
approximately 55% and 60% for the global and US analysis, respectively, while all other
significant effects are approximately 10% or below. This is not surprising given that the US VSL
forms the basis for all potential benefits derived from ULSJ implementation as well as being a
highly uncertain value in itself due to the assumed Weibull distribution as defined by the EPA.
This analysis also shows that the same parameters shown to be significant in the NRSA are
also shown to be significant in the GSA, but relative impacts on the output variance are much
different than in the differences seen on the absolute value of the output. Total effect sensitivity
indices are not significantly higher than the main effect sensitivity indices, indicating that second
order interaction effects between the input parameters are present but not significant.
71
US, VSL
Price Differential
US, All Cause, Beta
GEOS-Chem PM Conc
I
I
Damage Function Coeff
Global Income Elasticity
0
0.1
0.5
0.3
0.4
0.2
Main Effect Sensitivity Index
0.6
0.7
Figure 13: Global Implementation GSA main effect sensitivity index results.
US, VSL
US, All Cause, Beta
Price Differential
GEOS-Chem PM Conc
Global Income Elasticity
Damage Function Coeff
I
I
0
0
0.1
0.1
0.2
0.3
0.4
0.5
0.4
0.5
0.2
0.3
Total Effect Sensitivity Index
0.
0..6
Figure 14: Global Implementatio n GSA total effect sensitivity index results.
72
0.7
US, VSL
Price Differentia
US, All Cause, Beta
GEOS-Chem PM Cor
I
0
0.1
0.2
0.3
0.4
0.5
Main Effect Sensitivity Index
0.6
0.7
Figure 15: US Implementation GSA main effect sensitivity index results.
US, VSL
US, All Cause, Beta
GEOS-Chem PM Conc.
Price Differential
0
0.1
0.2
0.3
0.4
0.5
Total Effect Sensitivity Index
Figure 16: US Implementation GSA total effect sensitivity index results.
73
0.6
0.7
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74
Chapter 5: Fast Policy Analysis
5.1. Adjoint Model and Policy Tool
As stated in Section 2.1, a single GEOS-Chem forward model simulation takes approximately
12 hours to complete. Thus, if any reanalysis needs to be performed, a complete rerun of a
forward simulation is not a trivial task and using GCTMs such as GEOS-Chem as the direct
policy analysis tool can be both cumbersome and inefficient. If variations in the emissions
scenario are small, such as is often the case with aviation, it may be more effective to use
calculated sensitivities of outputs to input emissions. The task of calculating these sensitivities
was described in detail in Section 2.6 through the use of the GEOS-Chem adjoint model.
A policy tool is currently being developed based on these computed adjoint sensitivities. The
general approach is based off of the work done previously by Koo.56 His work focused on
determining a set of "source-receptor" matrices, the source referring to the geographical source
of aircraft emissions, and receptor referring to the geographical receptor of health impacts
based on the defined cost function. The primary functions of the policy tool are to compute the
number of premature mortalities resulting from a particular policy scenario as well as to
monetize these premature mortalities. For premature mortalities, the tool uses the following
defined cost function:
=
=
1=
_1 j=lf 3 0+,i,j,kPi,J,kBi,j,kXi,j,k,n
Eq. (35),
where f3o. is the fraction of the population greater than 30 years of age, P is the population, B is
the baseline incidence rate for either CP or LC related mortalities, and X is the concentration of
PM 2 5 , where all of these values are a function of the grid cell, ijk,where X is also a function of
time step, n. As can be seen, Eq. (35) is essentially identical to Eq. (22), except that it does not
include non-grid specific values (such as the relative risk ratio), which are multiplied later on
within the tool. For monetization, the tool uses the following defined cost function:
=
k=1(f30+,i,j,kPi,I,kBi,j,kXi,j,k,n)
=1 =
1X 1 _1 E=
I)
ijk
Eq. (36),
where Eq. (36) is Eq. (35) multiplied by the income ratio used to determine the extrapolated
VSL. Again, these cost functions can be limited to the desired receptor region by limiting the
summation limits in the above equations.
75
Once the appropriate source-receptor matrix has been calculated from an adjoint simulation, the
total impact can be calculated as described in Figure 17.
3D Gridded
Emissions Data
3D Gridded
Sensitivity Data
Inner Product
Defines Policy
Scenario
=
0
Total
Atmospheric
Impact of
Aircraft
Emissions
Figure 17: Using sensitivities to compute the total impact from an aircraft emissions policy
scenario.109'110
Figure 17 shows that using sensitivities, the total impact can be computed very easily as it is
just a matrix inner product between the 3D sensitivity data and the defined emissions scenario.
Currently, the tool also has many of the uncertainty aspects as described in Chapter 3 built-in to
the code so that MC simulations can be simultaneously performed. The primary assumption that
allows the use of first-order sensitivities to compute the approximated perturbation to the
atmosphere is that GEOS-Chem is indeed linear to small perturbations in input emissions. This
was shown to be an appropriate assumption in Koo.
56
5.2. USLJ Analysis Comparison
This section provides a policy comparison between the forward model calculated premature
mortalities and those calculated by the newly implemented policy tool. Here, premature
mortalities from global ULSJ implementation are determined. The distribution in Figure 18
shows the MC results from the forward model data where the mortality lag-structure has not
been taken into account.
76
Avoided Mortality Distribution Based on Sensitivity Analysis
200
180 -160 140 120a)
100_
CD
-
80 6040 -
20 0
0
1000
2000
3000
4000
Avoided Mortalities
6000
6000
Figure 18: Forward model premature mortality results from the ULSJ EBCA.
Again, these results were based on the methodology described in Section 3.4 where the data
show a modal value of approximately 1800 premature mortalities derived from global ULSJ
implementation.
Next, the adjoint sensitivity policy tool is applied. First, an appropriate aircraft emissions
scenario needs to be applied. Again, the empirical relationship between total aircraft SOx
emissions and FSC is shown in Table 5, which is shown again in Table 23.
Table 23: Aircraft SOx Emissions
S02 1(FSC/1000) x [(100 - E)/100] x FUEL x (64/32)
S04
(FSC/1000) x (E/100) x FUEL x (96/32)
Given that Table 23 shows a linear relationship between SOx emissions and FSC, a very simple
emissions scaling factor can be applied to the baseline emissions scenario. Again, the ULSJ
scenario assumes a decrease in FSC of approximately 600 to 15 ppm, meaning the SOx scaling
factor would be 15/600. Applying this scaling factor and using the tool based on the
methodology presented in Figure 17, the following distribution is calculated and plotted against
the distribution shown in Figure 18:
77
Comparison of Distributions
200
|
180
Adjoint
Forward
160140
120-
100
60
40
200
1000
2000
4000
3000
Avoided Mortalities
6000
5000
Comparison of Distributions, Adjusted
200
Adjoint
180 -
Forward
-
160 140 120
S100-
u-
80
-
60
-
40 20
0
1000
2
300
4000
Avoided Mortalities
6000
6000
Figure 19: Adjoint policy tool results and adjusted results for global ULSJ implementation.
As can be seen in top half of Figure 19, the shapes of the distributions are very similar, but the
modal value produced from the adjoint policy tool is only 1500 avoided premature mortalities. If
the distribution produced from the adjoint policy tool is multiplied by the ratio of the average
predicted avoided premature mortalities, the forward model distribution is accurately
reproduced, as shown in the bottom half of Figure 19. This outcome indicates that the policy tool
is systematically underestimating the avoided premature mortalities for this particular policy
scenario, suggesting that further development of the policy tool will require some sensitivity
tuning. This systematic underestimation relative to the forward model results may be a
78
consequence of several factors. First, while the linear approximation relative to changes in
aircraft emissions has been shown to be appropriate, there are still non-negligible second-order
errors (possibly up to 10%). In addition, this analysis did not incorporate any sort of temporal
variations in sensitivities nor aircraft emissions given the increase in data storage that would be
required for the tool. Thus, taking the inner product between yearly averaged sensitivities and
aircraft emissions could bias the results given in or out of phase temporal patterns in both the
sensitivities and emissions. Although this bias does exist, the results from the adjoint policy tool
do show a first-order accurate estimate of the resulting decrease in premature mortalities given
a global implementation of ULSJ. Future implementation of temporal sensitivities (on the order
of a month or week rather than a year) and its impact on this bias is currently being investigated.
The premature mortality valuation portion of the code is still being developed and is based on
the cost function defined by Eq. (36). The primary difficulty in the development of this aspect of
the code is the inability to post-process the adjoint sensitivities. Eq. (36) can only be defined for
a set income elasticity value, and this elasticity cannot be altered after the simulation has been
performed given that the cost function is a global summation. Thus, it will be necessary to
perform several adjoint simulations with different assumed income elasticities and then
interpolate to approximate the mortality valuation. A comparison, however, is provided between
the forward model results when a constant income elasticity of 1 is assumed and the adjoint
policy tool. A distribution comparison is shown in Figure 20.
79
Comparison of Valuation Distributions
450
Adjoint
Forward
400 350
300
u 250
I 200
U-
150
100
50
0'-
0
0.5
1
1.5
Avoided Mortality Valuatoin
2
2.5
10 10
Figure 20: Adjoint policy tool results for monetized avoided premature mortalities with an
assumed income elasticity of 1.
Figure 20 again shows a systematic underestimation of the valuation of avoided premature
mortalities for global ULSJ implementation. Even when adjusting for the bias, the distribution
produced by the adjoint policy tool does not exactly match the distribution based on the forward
model results. Differences in this case can be attributed to the fact that for this sample analysis,
some of the uncertainty factors pertaining to disease specific premature mortalities (such as a
variable relative risk ratio) were excluded based on the definition of the cost function.
80
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81
Chapter 6: Conclusions and Future Work
As the aviation sector of transportation continues its growth, the effect of aircraft operations on
the environment will have increased importance with regards to its impacts on climate and air
quality. As such, policies proposed to address these environmental issues need to be evaluated
within a consistent framework. This thesis introduced the concepts of environmental benefitcosts analysis as it pertains to aviation policy. It also helped to develop a framework in which to
conduct global policy analysis given that tools currently available can perform regional analysis,
only.
6.1. Global ULSJ Implementation
This thesis focused largely on the global implementation of an ultra-low sulfur jet fuel (ULSJ).
Government rulemaking would require the fuel sulfur content of jet fuel to be reduced to at most
15 ppm, a measure already in place for on and off-road diesel fuel. There are two primary
expected consequences of ULSJ implementation: a reduction in ground-level PM2.5 as a result
of a decrease in atmospheric sulfate aerosol burden and an increase in global temperature
given the loss in cooling as sulfate aerosols reflect incoming solar radiation and an overall
increase in C02 emissions due to additional fuel processing requirements. After determining the
magnitude of these potential impacts, the valuation of climate damages as well as air quality
benefits as it pertained to decreases in premature mortality were calculated and the economic
impact of increased fuel processing to bring ULSJ to specification was estimated.
It was calculated that ULSJ would have a global benefit of approximately 2300 avoided
premature mortalities, of which 120 are a result of decreased PM2. concentrations within the
US. Using the variable VSL approach and without discounting the premature mortalities, these
2300 mortalities were valued at approximately 2006 US $2.5 billion. In terms of climate
damages, a 3.3 mW/m2 increase in global RF was calculated due to the sulfate aerosol
reduction. This RF increase, as well as the one associated with the 2% increase in WTW C02
emissions due to the fuel processing requirements, resulted in expected climate damages of
2006 US $0.8 - 2.4 billion, where variations are due to the assumed discount rate. The
economic impact of global ULSJ implementation was valued at approximately 2006 US $2.5
billion for the year of policy implementation.
82
While an air quality benefit due to ULSJ implementation is expected, the total aggregated
outcome from this analysis indicates that there is between an 80-85% likelihood that global
ULSJ implementation will result in a not cost beneficial outcome based on the MC uncertainty
analysis. If, however, the health benefits are weighted against only the climate disbenefits, then
there is between a 46-77% chance that there will be a cost beneficial outcome. Thus, the
decision of whether or not to implement ULSJ will be a largely motivated by the priorities of the
policymaker. The FAA has already stated their goal of a 50% reduction in air quality impacts of
aircraft emissions by 2018, a goal that ULSJ implementation would help achieve. If a stronger
priority is placed on achieving this goal rather than about the underlying cost-effectiveness, then
the policymakers may indeed opt to implement ULSJ.
6.2. Fast Policy Analysis
Based on the adjoint model currently implemented within the GEOS-Chem GCTM, a fast policy
tool was developed in order to provide rapid policy analysis. The ULSJ study was burdened by
the computational intensity of GEOS-Chem. The calculation of a particular cost function's
sensitivity to aircraft emissions allows for a first order approximation of the effect of a particular
policy scenario. While the tool is still being developed, initial estimates of the amount of
expected premature mortalities due to a sample ULSJ case are promising, where the tool
estimates a modal value of 1500 avoided premature mortalities compared to 1800 calculated
based on the forward model analysis. The underestimation is thought to occur due to the
annually averaged approach taken by the adjoint policy tool. Temporal resolution is lost due to
the desire to reduce overall data storage and to reduce simulation times, and given that
temporal variations in sensitivities as well as aircraft emissions are no longer captured, an
underestimation occurs. This will be investigated further in future work.
6.3. Limitations
An obvious limitation for any policy analysis is the amount and quantification of uncertainty
within a given study. Based on the global sensitivity analysis performed for the global ULSJ
implementation case, nearly 60% of the uncertainty present within this analysis was a result of
the distribution assumed for the base (US) VSL value. This indicates that reductions in
uncertainty within the total analysis can be achieved given more refined estimates of US VSL
values.
83
The time-scale of the ULSJ study was also a major limitation in this analysis. Impacts due to
ULSJ implementation were only considered for the year of implementation. Aviation policies,
however, have much longer time scales (on the order of decades), thus a more informed policy
decision may be obtained by considering ULSJ implementation out to 20 or 30 years in order to
incorporate changes in the atmospheric state that could increase or decrease the air quality and
climate impacts relative to the baseline. Lower economic costs could also occur in the future as
production efficiency of ULSJ improves. The primary limiting factors for why a multi-year study
was not performed within this thesis were the lack of global background (as well as aircraft)
emissions data for future years and the significant computation times that would be associated
with such simulations. A potential option for addressing these issues, however, is presented in
Section 6.4.
In addition, higher fidelity options must be available in the future when considering the climate
impacts of different scenarios. For this particular case, the APMT-Climate tool was sufficient as
it was not difficult to obtain a first order estimate of the RF impact due to a reduction in sulfate
aerosols as well as the expected increase in CO 2 emissions. For other scenarios where there
may be significant changes in other trace gases (such as ozone), a full radiative transfer model
will be required to provide more accurate RF calculations. Also, coupling a radiative transfer
model with a GCTM such as GEOS-Chem will allow for multi-year climate studies, making
policy studies on the order of decades easier to conduct.
6.4. Future Work
As mentioned above, integration of an RTM is of great importance, especially for more refined
calculations in RF change across policy scenarios as well as for climate impact due to aviation
studies. Future work will focus on the development of a simplified environment in which results
from GEOS-Chem simulations can be passed to an RTM and higher fidelity RF calculations can
be obtained. This work, however, will not focus on direct implementation of an RTM coupled
within GEOS-Chem as this is currently in development within the GEOS-Chem community.
With regards to the adjoint policy tool, the premature mortality monetization component needs to
be fully implemented. In addition, given that policies have on the order of 20 year lifetimes,
development of the policy tool to predict policy impacts out to several decades would greatly
improve the ULSJ policy analysis performed here as well as improve overall decision making by
policymakers. Figure 21 shows the proposed structure of a multi-year adjoint policy tool.
84
Define Emissions
Scenarios
I
Scale Emissions
Pre-Defined Bg
Scenario 1
Pre-Defined Bg
Scenario n
Pre-Defined Bg
Scenario N
Determine First
Order Impact
Compute Air
Quality Impact
Year 1
---
Interpolate
Compute Air
--Quality ImpactQuality
Year n
Interpolate
Compute Air
Impact
Total
Impact
Year N
Figure 21: Proposed structure of multi-year study adjoint policy tool.
A defined policy emissions scenario would be passed to several pre-defined background
scenarios in which the total atmospheric impact for each of the years corresponding to the
background scenarios would be calculated based on sensitivities computed from corresponding
adjoint model simulations. Each background scenario would be defined by the predicted
increase or decrease in background anthropogenic emissions over time. Growth and decline in
anthropogenic emissions and its impact on air quality for the US have been analyzed by
Ashok."' Impacts in years where these is no pre-defined background scenario can be
interpolated from the years built-in to the policy tool, and the impacts can be monetized and
discounted as appropriate to obtain the total air quality impact of the policy. The primary
difficulty is determining the interpolation scheme that will be required. Sensitivities are a function
of the defined background scenario as the adjoint tool produces a linear projection relative to
the total amount of emissions present at a given time step. Thus, to minimize the overall error
based on these first order estimates, it will be necessary to study how nonlinearly sensitivities
are related to each type of emissions given defined variations in the background scenarios.
85
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86
Appendix A: Tables
Full table of estimated premature mortalities due to global ULSJ implementation.
Table Al: Premature mortalities by country.
EPA-derived CRF from Eq.
(13), Full-Flight Emissions
EPA-derived CRF from Eq.
(13), LTO Emissions
'
Country
Afghanistan
Albania
Algeria
Angola
Antigua and
Barbuda
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
BosniaHerzegovina
Botswana
Brazil
Brunei
Darussalam
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Cape Verde
Central African
Republic
Chad
Chile
China
Colombia
'
WHO CRF
from Eq. (7)
FullFlight
LTO
16
1
7.3
2.7
97.5%
Percentile
31
1.9
13
5.5
2.5%
Percentile
0.0
0.1
0.4
-0.20
Mean
0.0
0.3
1.2
-0.10
97.5%
Percentile
0.0
0.4
1.9
0.0
14
0.6
12
1.20
-0.1
0.1
1.6
0.0
0.0
0.7
1.6
0.4
2.3
1.8
0.1
32
5.2
4.1
0.0
0.4
0.4
0.1
0.0
1.7
4.1
0.9
5.8
4.5
0.2
81
13
11
0.0
1
1.3
0.3
0.0
3.1
7.9
1.6
11
8.3
0.4
150
24
20
0.1
2
2.6
0.5
0.0
0.1
0.0
0.1
0.1
0.0
0.0
0.7
-0.5
0.1
0.0
0.1
0.0
0.0
0.0
0.3
0.0
0.2
0.4
0.1
0.0
1.8
-0.3
0.3
0.0
0.3
0.0
0.0
0.0
0.5
0.0
0.3
0.6
0.2
0.0
3.2
-0.1
0.6
0.0
0.5
0.1
0.0
0.0
2.8
2.6
1.4
1.8
3.7
0.2
39
4.8
3.1
0.1
1
0.6
0.4
0.0
0.4
0.0
0.3
0.2
0.1
0.0
0.8
-0.1
0.1
0.0
0.2
0.0
0.0
0.6
0.0
3.2
1.5
0.1
8.2
2.7
0.1
15
0.1
0.0
0.6
0.3
0.0
1.6
0.6
0.0
2.9
0.7
0.1
15
0.2
0.0
2.4
0.0
1.4
1.8
0.1
0.4
2
2.3
0.0
0.0
3.5
5
0.4
1.2
5.4
6
0.0
0.0
6.3
9.6
0.8
2.4
11
11
0.1
0.0
0.1
0.2
0.0
0.0
0.4
0.3
0.0
0.0
0.3
0.4
0.0
0.1
0.9
0.7
0.0
0.0
0.5
0.8
0.0
0.2
2
1.2
0.0
0.0
1.6
7.9
0.5
1.7
5.1
5.9
0.2
0.0
0.1
0.4
0.0
0.1
0.9
0.8
0.0
-2.6
2.2
0.1
85
0.6
-1.3
5.8
0.2
220
1.5
-0.5
12
0.4
390
2.7
0.6
0.8
0.0
-270
0.0
1.5
2
0.1
-150
0.0
3.2
4.5
0.1
-59
0.0
-0.3
6.9
0.3
72
4.1
0.6
1.7
0.1
-13
0.1
2.5%
Percentile
5.2
0.4
2.9
1.0
Mean
87
EPA-derived CRF from Eq.
(13), Full-Flight Emissions
' EPA-derived CRF from Eq.
(13), LTO Emissions
WHO CRF
from Eq. (7)
Full-
LTO
linh*
Country
Commonwealth of
Dominica
Comoros
Congo
Congo Democratic
Republic
Costa Rica
Croatia
Cyprus
Czech Republic
Denmark
Djibouti
Dominican
Republic
East Timor
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
Federated State of
Micronesia
Fiji
Finland
France
FYROM/Macedonia
Gabon
Gambia
Georgia
Germany
Ghana
Greece
Grenada
Guatemala
Guinea
Guinea-Bissau
Guyana
Honduras
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
2.5%
Percentile
Mean
97.5%
Percentile
2.5%
Percentile . Mean
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
1.2
0.1
1
0.1
4.3
1.2
0.1
3.3
0.2
2.5
0.3
11
3.1
0.2
6.3
0.3
4.6
0.5
20
5.8
0.4
-0.7
0.0
0.3
0.0
-1.3
0.1
0.0
-0.4
0.0
0.7
0.0
-0.7
0.3
0.0
-0.1
0.0
1.3
0.1
-0.3
0.5
0.0
2.6
0.7
1.2
0.3
2.9
1.5
0.5
-0.2
0.0
0.3
0.0
-0.2
0.1
0.0
0.8
0.0
0.0
15
0.2
0.0
0.3
0.3
6.5
1.9
0.0
0.1
39
0.5
0.0
0.9
0.8
18
3.4
0.0
0.2
70
0.9
0.1
1.7
1.5
36
0.0
0.0
0.0
1.1
0.0
0.0
0.0
-0.2
0.3
0.1
0.0
0.0
3
0.0
0.0
0.0
-0.1
0.8
0.1
0.0
0.0
5.2
0.0
0.0
0.1
0.0
1.8
5.7
0.0
0.4
46
1.2
0.1
1.3
0.5
30
0.2
0.0
0.1
3.4
0.0
0.0
0.0
-0.1
1
0.0
0.0
0.4
8.3
0.3
0.0
0.1
1.1
32
0.7
1.2
0.0
0.3
0.6
0.1
0.0
0.2
2.7
0.0
340
4.8
12
5.8
0.2
0.0
0.0
1.1
22
0.9
0.1
0.4
2.8
83
2
3.1
0.0
0.8
1.7
0.3
0.0
0.6
7
0.0
870
12
29
15
0.6
0.0
0.0
2
39
1.6
0.1
0.8
5.1
150
4
5.7
0.0
1.5
3.2
0.7
0.1
1.1
13
0.0
1600
22
54
28
1.1
0.0
0.0
0.1
1.1
0.1
0.0
0.0
0.0
1.8
0.2
0.2
0.0
0.0
0.2
0.0
0.0
0.0
0.2
0.0
-93
0.5
0.1
0.2
0.1
0.0
0.0
0.2
2.9
0.2
0.0
0.2
0.0
4.7
0.5
0.5
0.0
0.0
0.5
0.1
0.0
0.0
0.6
0.0
-55
1.3
0.3
0.6
0.2
0.0
0.0
0.3
5.1
0.3
0.0
0.2
0.0
8.4
1
0.9
0.0
0.0
1.1
0.1
0.0
0.0
1.1
0.0
-20
2.3
0.5
1.1
0.3
0.0
0.0
1
11
0.5
0.1
0.6
1.9
25
2.3
2.8
0.0
2
1.6
0.5
0.1
1.6
2.2
0.0
390
20
28
15
0.7
0.0
0.0
0.1
1.8
0.1
0.0
0.2
0.0
1.4
0.4
0.5
0.0
0.1
0.3
0.1
0.0
0.0
0.2
0.0
-15
2.6
0.4
0.6
0.2
88
97.5%
Percentile
EPA-derived CRF from Eq.
(13), Full-Flight Emissions
Country
Israel
Italy
Ivory Coast
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Korea
Kuwait
Kyrgyz Republic
Lao People's
Democratic
Republic
Latvia
Lebanon
Lesotho
Liberia
Libyan Arab
Jamahiriya
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Mauritania
Mauritius
Mexico
Mongolia
Morocco (includes
Western Sahara)
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Panama
EPA-derived CRF from Eq.
(13), LTO Emissions
WHO CRF
from Eq. (7)
FullFlight
LTO
:2I
2.5%
Percentile Mean
0.8
2
9.8
25
0.7
1.8
0.3
0.7
5.7
15
0.6
1.5
2.9
7.5
0.5
1.5
0.0
0.0
3.3
8.5
0.2
0.5
2.3
0.9
97.5%
Percentile
3.7
46
3.6
1.3
27
2.8
14
2.9
0.0
16
1
4.2
2.5%
Percentile
0.1
3.2
0.0
0.0
1.3
0.0
0.3
0.0
0.0
0.5
0.0
0.1
Mean
0.2
8.4
0.1
0.0
3.4
0.1
0.7
0.0
0.0
1.3
0.0
0.2
97.5%
Percentile
0.3
15
0.2
0.0
6.3
0.2
1.2
0.1
0.0
2.3
0.0
0.5
2.5
15
2.6
2.1
14
1.9
6.9
4.2
0.0
2.1
0.5
2.3
0.2
4.9
0.1
0.1
3.6
0.1
0.6
0.1
0.0
0.3
0.0
0.2
0.3
0.6
0.7
0.0
0.1
0.9
1.5
2
0.1
0.2
1.7
2.8
3.6
0.1
0.5
-0.1
-0.3
0.1
0.0
0.0
-0.1
-0.2
0.2
0.0
0.0
0.0
-0.1
0.3
0.0
0.0
0.6
0.9
2.1
0.1
0.6
0.0
-0.1
0.2
0.0
0.0
0.5
1.5
0.1
0.1
0.1
0.4
0.0
2.1
0.1
0.4
0.0
6.5
0.1
1.4
3.9
0.3
0.2
0.3
1.1
0.0
5.7
0.2
1
0.0
17
0.3
2.6
7.1
0.6
0.4
0.6
2.1
0.1
11
0.3
2.2
0.0
30
0.6
0.1
0.0
0.0
0.0
0.0
0.0
0.0
-0.3
0.0
0.0
0.0
0.5
0.0
0.2
0.0
0.0
0.0
0.0
0.1
0.0
-0.2
0.0
0.0
0.0
1.3
0.0
0.3
0.1
0.0
0.0
0.0
0.2
0.0
-0.1
0.1
0.0
0.0
2.3
0.0
2.1
1.4
0.1
0.7
0.6
1.5
0.1
9.9
0.2
2.5
0.1
26
0.5
0.3
0.0
0.0
0.0
0.0
0.1
0.0
-0.2
0.0
0.0
0.0
2.1
0.0
3.9
0.1
0.0
7.3
3
0.0
0.2
2.7
11
0.3
0.3
37
0.0
10
0.4
0.0
18
7.7
0.0
0.4
8
30
0.7
0.8
95
0.1
18
0.7
0.1
34
14
0.0
0.8
15
60
1.3
1.6
170
0.2
0.3
0.0
0.0
-1.9
0.3
0.0
0.0
0.2
0.9
0.0
0.0
-18
0.0
0.7
0.0
0.0
-1.1
0.8
0.0
0.0
0.5
2.2
0.1
0.0
-9.7
0.0
1.3
0.0
0.0
-0.4
1.5
0.0
0.0
1.1
4.9
0.2
0.1
-3.8
0.0
19
0.8
0.1
8.3
2.8
0.1
1.5
15
37
0.9
1
51
0.5
1.4
0.0
0.0
-0.3
0.4
0.0
0.0
1.1
2.9
0.2
0.1
-3.1
0.0
89
EPA-derived CRF from Eq.
(13), Full-Flight Emissions
Country
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Republic of
Moldova
Romania
Russia
Rwanda
Saint Kitts and
Nevis
Saint Lucia
Saint Vincent
EPA-derived CRF from Eq.
(13), LTO Emissions
WHO CRF
from Eq. (7)
FullFliaht
LTO
2.5%
Percentile
0.0
0.1
0.2
1.7
11
0.7
Mean
0.0
0.2
0.5
4.5
27
1.7
97.5%
Percentile
0.1
0.4
0.9
8.3
50
3.1
2.5%
Percentile
0.0
0.0
0.0
0.1
-1.6
0.2
Mean
0.0
0.0
0.0
0.2
-0.9
0.5
Percentile
0.0
0.0
0.1
0.3
-0.3
0.9
U.2
U.U
0.3
1.1
13
7.5
2.6
0.0
0.1
0.5
-0.5
0.7
0.9
3.7
28
0.1
2.3
9.3
73
0.4
4.2
17
140
0.7
-0.5
-4.8
-18
0.0
-0.3
-2.8
-11
0.0
-0.1
-1
-3.6
0.0
1
3
37
0.4
-0.1
-0.8
-2.7
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
4
1.3
0.0
11
3.6
0.0
22
7.2
0.0
0.4
0.3
0.0
1
0.8
0.0
1.9
1.8
0.0
13
5.3
0.0
1.1
1.3
3.1
0.0
0.5
0.1
2.3
0.4
0.0
0.7
5.8
1.5
5.8
0.0
0.0
1.1
1.2
7.9
0.0
1.6
0.3
5.8
1.1
0.0
1.8
15
3.8
16
0.0
0.0
2.8
3
14
0.0
3.3
0.6
11
2
0.0
3.4
27
7.1
31
0.0
0.0
5.1
5.5
0.5
0.0
0.2
0.0
0.1
0.1
0.0
0.1
1.5
0.0
0.2
0.0
0.0
0.0
0.3
1.2
0.0
0.4
0.1
0.3
0.3
0.0
0.2
4.1
0.1
0.7
0.0
0.0
0.0
0.8
2.2
0.0
1.1
0.1
0.5
0.6
0.0
0.3
7.1
0.2
1.2
0.0
0.0
0.0
1.3
3.6
0.0
1.7
0.3
1.3
0.5
0.0
2.2
14
9.2
22
0.1
0.0
2.2
1.1
0.6
0.0
0.3
0.1
0.1
0.1
0.0
0.2
3.7
0.3
0.8
0.0
0.0
0.1
0.2
1.6
0.9
2.6
0.3
0.0
4.1
2.4
6.8
0.7
0.0
7.4
4.6
12
1.4
0.0
0.1
0.1
0.1
0.1
0.0
0.3
0.2
0.2
0.3
0.0
0.5
0.4
0.3
0.5
0.0
3.9
2.2
7.1
0.7
0.0
0.3
0.2
0.4
0.2
0.0
0.0
1.3
8.3
1.4
0.7
0.1
3.4
21
3.5
1.8
0.2
6.1
39
6.4
3.4
0.0
0.3
0.8
0.1
0.0
0.0
0.7
2.3
0.3
0.1
0.0
1.2
3.8
0.5
0.1
0.5
5
16
4.1
2.9
0.0
0.9
1.5
0.3
0.1
97.5%
Sao Tome and
Principe
Saudi Arabia
Senegal
Serbia and
Montenegro
Seychelles
Sierra Leone
Singapore
Slovakia
Slovenia
Solomon Islands
South Africa
Spain
Sri Lanka
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian Arab
Republic
Tajikistan
Thailand
Togo
Tonga
Trinidad and
Tobago
Tunisia
Turkey
Turkmenistan
Uganda
90
EPA-derived CRF from Eq.
(13), Full-Flight Emissions
Country
Ukraine
United Kingdom
United Rep. of
Tanzania
United States of
America
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet Nam
Western Samoa
Yemen
Zambia
Total
WHO CRF
from Eq. (7)
EPA-derived CRF from Eq.
(13), LTO Emissions
FullFlight
LTO
2.5%
Percentile
23
9.7
Mean
57
25
97.5%
Percentile
100
45
2.5%
Percentile
-22
2.8
Mean
-13
7.2
97.5%
Percentile
-4.9
13
21
14
-4.1
4.2
0.6
1.7
3
0.0
0.0
0.0
4
0.1
46
0.0
3.9
0.0
0.6
4
0.0
3.3
0.2
890
120
0.1
9.9
0.0
1.5
10
0.0
9.6
0.5
2300
210
0.2
18
0.0
2.7
19
0.0
19
1
4200
12
0.0
0.6
0.0
0.0
-0.8
0.0
0.1
0.0
-390
31
0.0
1.5
0.0
0.0
-0.5
0.0
0.2
0.0
-130
56
0.0
2.6
0.0
0.1
-0.2
0.0
0.5
0.0
100
140
0.3
8.9
0.0
4.3
9.4
0.0
14
0.4
1500
34
0.0
1.2
0.0
0.1
0.2
0.0
0.4
0.0
58
Full table of calculated VSLs by country.
Table A2: VSLs and non-discounted monetizations by country.
GNlIcapita
Country
Afghanistan
Albania
Algeria
Angola
Antigua and Barbuda
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahrain
Bangladesh
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia-Herzegovina
Botswana
Brazil
2006 USS
VSL
2006 US$
990
7,010
7,160
3,780
17,060
11,710
4,950
33,010
35,810
5,390
29,410
1,240
9,760
34,450
5,870
1,330
3,740
4,300
7,350
11,740
8,810
Mean
45,000
530,000
540,000
220,000
1,700,000
1,000,000
320,000
4,400,000
5,000,000
350,000
3,700,000
58,000
770,000 4,700,000
390,000
60,000
220,000
260,000
530,000
990,000
670,000
21,000
250,000
240,000
60,000
500,000
340,000
130,000
930,000
950,000
120,000
710,000
26,000
190,000
740,000
110,000
20,000
61,000
68,000
130,000
150,000
140,000
91
ULSJ
Valuation
2006 US$
97.5%
Percentile
200,000
1,700,000
1,700,000
870,000
4,800,000
3,100,000
1,200,000
11,000,000
13,000,000
1,300,000
9,700,000
250,000
2,500,000
12,000,000
1,400,000
270,000
860,000
1,000,000
1,800,000
3,100,000
2,200,000
Mean
700,000
530,000
3,900,000
590,000
15,000
1,700,000
1,300,000
4,000,000
28,000,000
1,600,000
900,000
4,700,000
10,000,000
49,000,000
14,000
61,000
270,000
63,000
780,000
50,000
5,400,000
Country
Brunei Darussalam
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Cape Verde
Central African Republic
Chad
Chile
China
Colombia
Commonwealth of
Dominica
Comoros
Congo
Congo Democratic
Republic
Costa Rica
Croatia
Cyprus
Czech Republic
Denmark
Djibouti
Dominican Republic
East Timor
Ecuador
Egypt
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
Federated State of
Micronesia
Fiji
Finland
France
FYROM/Macedonia
Gabon
Gambia
Georgia
Germany
Ghana
Greece
Grenada
Guatemala
Guinea
ULSJ
Valuation
2006 US$
VSL
2006 US$
GNI/capita
2006 US$
Ou, -Ifu
10,790
1,090
350
1,570
2,010
36,410
2,880
690
1,080
11,380
4,790
7,640
Z.7o
Percentile
8uu,uuu
160,000
11,000
3,800
18,000
19,000
500,000
34,000
5,400
7,900
130,000
51,000
79,000
880,000
45,000
12,000
71,000
95,000
5,100,000
150,000
26,000
44,000
950,000
290,000
550,000
21,UUU,UUU
2,800,000
220,000
65,000
330,000
430,000
13,000,000
640,000
140,000
220,000
3,000,000
1,100,000
1,900,000
Mean
5,UUU
3,100,000
220,000
4,800
84,000
520,000
30,000,000
7,500
-34,000
250,000
220,000
63,000,000
810,000
7,490
1,150
2,480
75,000
9,700
17,000
530,000
47,000
120,000
1,800,000
240,000
540,000
4,200
350
16,000
270
9,630
16,310
25,060
21,230
36,700
2,180
6,620
1,990
6,810
4,700
5,920
13,550
630
17,930
700
1,800
86,000
140,000
260,000
200,000
490,000
13,000
48,000
12,000
45,000
30,000
36,000
99,000
3,100
160,000
3,000
8,400
750,000
1,600,000
2,900,000
2,300,000
5,100,000
100,000
450,000
93,000
470,000
280,000
390,000
1,200,000
22,000
1,800,000
25,000
49,000
2,400,000
4,600,000
7,900,000
6,300,000
13,000,000
470,000
1,600,000
430,000
1,600,000
1,100,000
1,400,000
3,700,000
120,000
5,100,000
140,000
27,000
140,000
4,000,000
850,000
25,000,000
16,000,000
20,000
850,000
900
54,000
11,000,000
190,000
33,000
19,000
1,500,000
450,000
3,240
4,310
33,410
31,950
8,520
11,050
1,100
4,130
34,410
1,270
26,410
7,650
4,270
870
16,000
20,000
410,000
390,000
45,000
72,000
3,700
17,000
430,000
3,800
280,000
37,000
14,000
1,900
170,000
250,000
4,400,000
4,200,000
630,000
910,000
44,000
240,000
4,600,000
52,000
3,100,000
550,000
250,000
33,000
730,000
1,000,000
11,000,000
11,000,000
2,100,000
2,900,000
220,000
960,000
12,000,000
260,000
8,400,000
1,900,000
990,000
170,000
470
1,400
4,700,000
89,000,000
560,000
62,000
18,000
660,000
390,000,000
100,000
9,800,000
6,600
200,000
53,000
92
Mean
o,2UU,UUU
, Percentile
ULSJ
Valuation
2006 US$
VSL
2006 US$
97.5%
Country
Guinea-Bissau
Guyana
Honduras
Hungary
Iceland
India
Indonesia
Iran
Iraq
Ireland
Israel
Italy
Ivory Coast
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Kiribati
Korea
Kuwait
Kyrgyz Republic
Lao People's Democratic
Republic
Latvia
Lebanon
Lesotho
Liberia
Libyan Arab Jamahiriya
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Mauritania
Mauritius
Mexico
Mongolia
Morocco (includes
Western Sahara)
Mozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
/,( ( U
3,350
17,300
33,570
2,540
3,040
9,880
2,850
36,670
24,840
30,170
1,520
7,040
32,770
4,850
8,690
1,430
3,630
24,320
51,130
1,790
6,600
8,700
150,000
410,000
5,100
7,200
59,000
6,400
490,000
260,000
350,000
2,000
32,000
400,000
17,000
47,000
1,700
10,000
250,000
820,000
2,700
Mean
38,000
140,000
180,000
1,700,000
4,500,000
130,000
160,000
780,000
150,000
5,100,000
2,900,000
3,800,000
65,000
490,000
4,300,000
300,000
650,000
61,000
200,000
2,800,000
8,400,000
81,000
1,710
14,540
9,870
1,660
250
14,910
15,610
60,210
920
650
12,240
4,650
980
21,470
1,740
10,900
13,520
2,850
2,500
110,000
59,000
2,300
210
120,000
130,000
1,100,000
770
400
83,000
16,000
860
210,000
2,500
70,000
99,000
6,400
3,790
670
5,810
1,010
39,070
25,130
2,400
11,000
420
22,000
910
550,000
260,000
4,600
93
, Percentile , Mean
200,000
620,000
760,000
4,900,000
12,000,000
560,000
680,000
2,500,000
640,000
13,000,000
7,800,000
10,000,000
320,000
1,700,000
11,000,000
1,100,000
2,200,000
300,000
830,000
7,500,000
22,000,000
380,000
13,000
4,700
100,000
12,000,000
34,000
110,000,000
1,900,000
23,000,000
2,200,000
3,000,000
5,800,000
96,000,000
120,000
350,000
64,000,000
450,000
4,900,000
89,000
100
24,000,000
4,400,000
180,000
76,000
1,300,000
780,000
73,000
7,200
1,400,000
1,500,000
11,000,000
35,000
23,000
1,000,000
280,000
38,000
2,300,000
78,000
890,000
1,200,000
150,000
360,000
4,000,000
2,500,000
350,000
45,000
4,100,000
4,300,000
28,000,000
180,000
130,000
3,200,000
1,100,000
200,000
6,400,000
370,000
2,800,000
3,700,000
640,000
66,000
2,000,000
1,500,000
3,800
1,800
1,900,000
5,700,000
3,700,000
7,200
7,400
1,200,000
7,700
210,000
350,000
81,000
14,000
20,000,000
48,000
210,000
24,000
380,000
39,000
5,600,000
2,900,000
120,000
870,000
130,000
1,400,000
200,000
14,000,000
7,900,000
530,000
2,100,000
8,900
17,000
710,000
43,000,000
76,000
51,000
ULSJ
Valuation
2006 US$
VSL
2006 US$
Mean
Percentile
Mean
Niger
b4u
39jU
22,UUU
1,0uuuu
IOU,UUU
Nigeria
Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Republic of Moldova
Romania
Russia
Rwanda
Saint Kitts and Nevis
Saint Lucia
Saint Vincent
Sao Tome and Principe
Saudi Arabia
Senegal
Serbia and Montenegro
Seychelles
Sierra Leone
Singapore
Slovakia
Slovenia
Solomon Islands
1,790
53,330
20,480
2,390
9,380
1,690
4,080
6,360
3,090
14,640
22,180
2,860
10,870
14,560
940
13,270
8,830
7,690
1,560
22,590
1,650
9,935
18,160
670
46,950
17,700
25,140
2,230
2,700
880,000
190,000
4,500
54,000
2,400
13,000
26,000
7,400
110,000
220,000
6,400
70,000
110,000
800
96,000
49,000
37,000
2,100
220,000
2,300
60,000
160,000
420
720,000
160,000
260,000
4,000
81,000
9,000,000
2,200,000
120,000
720,000
75,000
230,000
420,000
160,000
1,300,000
2,400,000
150,000
890,000
1,300,000
36,000
1,200,000
660,000
550,000
68,000
2,500,000
73,000
780,000
1,800,000
24,000
7,400,000
1,800,000
2,900,000
110,000
380,000
23,000,000
6,000,000
520,000
2,400,000
360,000
940,000
1,500,000
700,000
4,000,000
6,700,000
640,000
2,800,000
4,000,000
190,000
3,600,000
2,200,000
1,900,000
330,000
6,800,000
350,000
2,500,000
5,200,000
130,000
19,000,000
5,000,000
7,900,000
480,000
2,400,000
6,300,000
1,800,000
11,000,000
90,000
2,900
46,000
200,000
730,000
37,000,000
4,100,000
330,000
8,200,000
97,000,000
13,000
6,700
6,900
7,000
440
28,000,000
260,000
6,100,000
4,600
38,000
2,400,000
10,000,000
3,100,000
260
South Africa
9,090
51,000
690,000
2,300,000
1,200,000
Spain
Sri Lanka
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Thailand
Togo
Tonga
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Uganda
Ukraine
United Kingdom
29,810
3,850
1,660
6,360
4,580
36,140
42,510
4,070
1,550
6,970
790
4,310
22,180
6,650
12,250
4,970
970
6,130
35,110
350,000
11,000
2,300
26,000
15,000
470,000
620,000
12,000
2,000
31,000
580
14,000
220,000
29,000
84,000
17,000
850
25,000
450,000
3,800,000
220,000
73,000
420,000
270,000
5,000,000
6,400,000
230,000
67,000
480,000
29,000
250,000
2,400,000
450,000
1,000,000
300,000
37,000
400,000
4,800,000
9,900,000
890,000
350,000
1,500,000
1,100,000
13,000,000
16,000,000
940,000
330,000
1,700,000
160,000
1,000,000
6,700,000
1,600,000
3,200,000
1,200,000
200,000
1,500,000
12,000,000
55,000,000
820,000
1,100,000
6,800
6,700
14,000,000
19,000,000
940,000
160,000
3,200,000
21,000
130
310,000
1,500,000
22,000,000
1,100,000
65,000
23,000,000
120,000,000
94
ULSJ
United Rep. of Tanzania
United States of America
Uruguay
Uzbekistan
Vanuatu
Venezuela
Viet Nam
Western Samoa
Yemen
Zambia
Total
1,140
45,640
10,170
2,170
3,630
11,010
2,310
3,990
2,120
1,070
# 3t:$:y7jf?
1,100
690,000
62,000
3,800
10,000
71,000
4,200
12,000
3,600
1,000
VSL
2006 US$
Valuation
2006 US$
Mean
Percentile
46,000
230,000
7,100,000
18,000,000
810,000
2,600,000
100,000
470,000
200,000
830,000
900,000
2,900,000
110,000
500,000
230,000
920,000
100,000
460,000
42,000
220,000
%
%.1%
$
Mean
75,000
830,000,000
91,000
1,000,000
230
1,300,000
1,100,000
310
940,000
22,000
2,500,000,000
Not all countries and regions are considered in Tables Al and A2. This is due to either a lack of
mortality data within the WHO GBD database or a lack of economic data from the World Bank
database. To maintain consistency within the analysis, values from other sources were not
used. The following countries or territories have been omitted: American Samoa, Andorra,
Anguilla, Aruba, Bahamas, Barbados, Bermuda, British Virgin Islands, Cayman Islands, Cook
Islands, Cuba, Faeroe Islands, Falkland Islands, French Guiana, French Polynesia, Gibraltar,
Greenland, Guadeloupe, Guam, Guernsey, Haiti, Hong Kong, Isle of Man, Jersey, North Korea,
Lichtenstein, Macao, Marshall Islands, Martinique, Mayotte, Monaco, Montserrat, Myanmar,
Nauru, Netherland Antilles, New Caledonia, Niue, Norfolk Island, Northern Mariana Island,
Occupied Palestinian Territory, Palau, Pitcairn, Puerto Rico, Qatar, Reunion, Saint Helena,
Saint Pierre and Miquelon, San Marino, Somalia, Svalbard, Taiwan, Tokelau, Turks and Caicos
Islands, Tuvalu, United Arab Emirates, United States Virgin Islands, Wallis and Futuna, and
Zimbabwe. Given the low population densities as well as geographical locations of many of
these omitted countries and territories, the impact on the overall EBCA is expected to be
minimal.
95
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96
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