Simplification of the Environmental Life Style Analysis (ELSA) Model
By
Anthony D. Teixeira
SUBMITTED TO THE DEPARTMENT OF CHEMICAL ENGINEERING IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
BACHELOR OF SCIENCE IN MECHANICAL ENGINEERING
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
JUNE 2008
©2008Anthony D. Teixeira. All rights reserved.
The author hereby grants to MIT permission to reproduce
and to distribute publicly paper and electronic
copies of this thesis document in whole or in part
in any medium now known or hereafter created.
Signature of Author:
Department of Mechanical Engineering
May 9, 2008
Certified by:
Timothy Gutowski
Professor of Mechanical Engineering
Thesis Supervisor
A rrtpntp• hx. -
[. Lienhard
V
IEngineering
s Committee
ARCHIVES
11
Simplification of the Environmental Life Style Analysis (ELSA) Model
By
Anthony D. Teixeira
Submitted to the Department of Mechanical Engineering
on May 9, 2008 in Partial Fulfillment of the
Requirements for the Degree of Bachelor of Science in
Mechanical Engineering
ABSTRACT
Accurately determining the environmental impacts of specific lifestyles is a critical part of moving
to reduce those impacts. However, this process is often complicated and tedious. This study
strives to simplify that process by improving upon the Environmental Life Style Analysis (ELSA)
model put forth by Gutowski et al. To accomplish this goal, data on the expenditures, divided
between eight categories such as transportation or taxes/government services, of 24 different life
styles was collected and analyzed. First, a quantity called the impact intensity, defined as the
impact per dollar of expenditure, was calculated for each life style in each category. After being
deflated to 1997 values, which is required by the ELSA model, these values were then averaged
over each of the eight categories to correlate spending in a specific category to impact. This
allowed the simplification of the model into a simple eight input version. When this model was
tested using values of average expenditure for 1997, it was found to accurately calculate CO 2
emissions. While the energy calculations were not particularly accurate, this is a significant step
forwards. In addition, the model was able to calculate impacts for a very minimal life style, in this
case a homeless man. Data pertaining to the homeless man's expenditures provided insight into
the basic environmental impacts caused simply by surviving as a resident of the United States.
Although more work needs to be done on this model, this study made strides forward and showed
that there is significant potential for this model as a simple but accurate environmental impact
calculator.
Thesis Supervisor: Timothy Gutowski
Title: Professor of Mechanical Engineering
1. Introduction
Every day, each resident of the United States impacts his environment in some way with the
choices and actions that he takes. Over the course of a full year and the full population of the
country, these impacts become significant. However, to the everyday person, the impact of these
choices may not be immediately clear. The goal of this study is to make this decision clearer to the
average consumer. Informing the population of the difference in environmental impact between
common everyday products and services is the first step to motivating a greater change in the
habits of the country as a whole. The goal of this study is first and foremost to provide an accurate
tool for estimating the environmental impact of any resident of the United States. However, a
secondary goal is to make it very user friendly and simple, so that it can be made accessible to the
average person. Ideally, any user would be able to simply fill out a short survey related to annual
expenditures and end up with an accurate measure of environmental impact.
2. Background
This study is a revision of the Environmental Life Style Analysis (ELSA) model created by
Gutowski et al. [2] in 2007. The ELSA model is basically a questionnaire that takes the form of a
spreadsheet. The user is asked to fill in values for expenditures for a number of different
categories and is given a final calculation for environmental impact. The spreadsheet is divided
into eight categories taken from the Bureau of Labor Statistics (BLS) categories for expenditures in
its annual report [1]: 1) food, diet, and alcohol, 2) housing, furniture, and maintenance, 3) home
utilities and fuel, 4) apparel and services, 5) transportation, 6) services/personal, 7) insurance and
investment, and 8) taxes/government services. Each category has its own sheet where the user is
prompted to input itemized expenditures. The sheet for insurance and investments is shown below
in Figure 1 as an example. From these expenses, the impacts are determined in five different areas:
economic impact in dollars, energy in MJ, global warming potential (GWP) in equivalent MT of
CO 2, CO 2 emissions in megatons (MT), and toxic releases in kg. These impacts are determined by
the EIO-LCA [2] model formulated at Carnegie Mellon University. This model exists in an online
format. The user is able to select an industry sector for which he wants to determine a certain
impact. The model calculates the impacts based on the expenditures in that area. The data for the
specific categories in the ELSA are then complied and displayed on the front page for the user to
examine.
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The ELSA model provides a very accurate calculation of the impacts of a specific user
according to the expenditures that they have. However, the major drawback of the model is its
complexity. While a drastic reduction in complexity when compared to its predecessor the EIOLCA model, which had a choice of 491 industry sectors [3], the ELSA model has a lot of room to
improve. Currently, the model has a total of 254 fields [2] requiring user input in order to calculate
the impacts for a certain lifestyle. However, when examining the progress made to this point, it
must also be noted that the ELSA model provides the user with a much friendlier interface, posing
the 254 fields as simple to answer questions instead of the list-like format of the EIO-LCA model.
At any rate, significant improvements can be made to the model. A questionnaire with over 250
fields to fill out is somewhat of a daunting task for the average user. By determining a clear set of
important quantities and avoiding extraneous information, the model can be streamlined into a
simple to use tool.
3. Results and Discussion
3.1. Impact Intensity Analysis
In order to determine the best course of action to take in simplifying the model, data was
collected about 24 different lifestyles, listed below in Table 1. These lifestyles represent the full
cross section of American lifestyles, ranging from the homeless to Bill Gates. This large sample
range ensured that localized trends for high income or low income cases would not be assumed to
explain the whole range. Methods used to obtain this information ranged from personal interviews
to referencing publically available information. More specifically, the data obtained pertained to
the annual expenditures of each lifestyle in the categories specified in the ELSA model. These
values were then input into the master spreadsheet, yielding the environmental impacts for the
specific lifestyles. As a final step in preparation for analyzing the data, the dollar values of the
expenditures were deflated to represent 1997 dollars. These values of impact are shown in
Appendix A. The reason for this is that the CMU model is all in terms of 1997 dollars, so any
current day expenditures would have to be translated to their approximate 1997 values.
Table 1: Lifestyles for which expenditure data was collected.
Child
Veg Student
Mgmt Consultant
Coma - ICU
Coma - mix
Coma - support
Retiree
TFA - Chicago
TFA - Houston
Inv Banker
Golfer - High
Golfer - Low
Homeless
TFA - NYC
CEO "Average"
Monk 2
Artist
CEO "Great"
Nursing Home
Monk 1
Engineer
Soccer Mom
Oprah
Gates
The first step towards determining where the ELSA model could be shaved down was looking
at general trends across its eight main categories. Since the entire point of the model, as well as
the CMU model, is to correlate monetary expenditures to their corresponding environmental
impacts, it only makes sense to look at this correlation in more depth. To this end, for each
category, the total impacts for each lifestyle were divided by the total expenditures for that
category and lifestyle. This value of impact per dollar of expenditure is These values are
extremely valuable for the analysis of the model itself, providing valuable information about the
correlation between expenditure and impact. Tables 2-9 below show the impact intensities across
the eight categories. Erroneous values are notated according to the legend at the bottom of Table
2.
Table 2: Impact intensities for food, diet and alcohol.
Child
Energy
(MJ/$)
11.5
GWP (KG
CO2E /$)
1.91
CO2
(KG/$)
0.76
Toxics
(G/$)
0.49
7.8
0.68
2.08
0.59
1.68
1.19
1.04
0.94
1.21
1.65
1.27
1.13
1.08
0.82
1.61
2.14
0.75
0.98
1.00
1.08
1.03
1.2
0.45
0.53
0.81
0.50
0.74
0.62
0.55
0.52
0.57
0.71
0.63
0.58
0.54
0.50
0.68
1.08
0.38
0.55
0.56
0.59
0.54
0.6
0.15
0.26
Coma - ICU *
Coma - mix *
Coma - support*
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student
Retiree
12.4
7.3
11.1
9.3
8.2
TFA - Chicago
7.8
TFA- Houston
8.6
10.7
9.4
8.6
8.1
7.4
10.2
TFA - NYC
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer- Low
9.7
Golfer - High
9.5
CEO "Average"
CEO "Great"
Oprah
Gates
Mean
Standard Dev
8.2
Leaend
8.3
9.0
8.1
9.1
1.42
II
Leen
Cakcutation
FGrprr11f
0.61
0.20
0.49
0.37
0.33
0.30
0.35
0.47
0.41
0.38
0.35
0.28
0.46
0.42
0.43
0.42
0.44
0.52
0.39
0.4
0.10
I Zero Exnenditur&1
-
eoEnni
'
I
I
-
--
--
I
Table 3: Imnact intensities
----- `-for
- hno.inc
- V U- II· filritiirp
-·LI~L ~~qnd
IIU mnintpnanrp
IIUI· LIIY I ~ .
Child
Energy
(MJ/$)
GWP (KG
CO2E /$)
C02
(KG/$)
Toxics
(G/$)
8.7
0.70
0.59
0.08
0.48
0.60
0.59
0.40
0.54
0.53
0.06
0.30
0.49
0.58
0.58
0.35
0.11
0.50
0.46
Coma - ICU *
Coma - mix *
Coma - support*
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student *
Retiree
0.06
0.35
0.57
0.64
0.64
0.41
TFA - Chicago
TFA - Houston
TFA - NYC
Artist
0.04
0.47
0.59
0.54
0.53
0.40
Engineer*
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer - Low
Golfer - High
CEO "Average"
CEO "Great"
Oprah
Gates
Mean
Standard Dev
6.7
1.84
0.49
0.63
0.63
2.15
0.12
0.57
0.49
0.59
0.39
0.6
0.41
0.42
0.57
0.57
1.93
0.10
0.49
0.43
0.51
0.34
0.5
0.38
0.38
0.52
0.53
0.32
0.53
0.65
0.48
0.62
0.36
0.4
0.17
.
Table
4:
I
act
Child
intens
Energy
(MJ/$)
GWP (KG
CO2E /$)
CO2
(KG/$)
Toxics
(G/$)
61.1
6.93
5.16
1.38
34.6
42.3
34.7
17.78
5.45
4.74
13.45
4.10
2.90
0.84
1.03
1.14
Coma - ICU *
Coma - mix *
Coma - support*
Homeless *
Monk 2
Nursing Home
Monk 1
Veg Student*
Retiree
-
.a~la~sssl~aa
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer- Low
Golfer- High
CEO "Average"
CEO "Great"
Oprah
Gates
16.0
26.4
36.5
17.8
31.2
25.2
19.2
17.7
23.8
55.4
8.9
65.6
55.9
23.5
17.2
3.89
3.56
4.41
2.33
3.17
3.80
3.52
3.30
3.06
7.39
0.96
6.38
5.39
2.58
1.18
2.72
2.11
2.95
1.42
2.55
3.23
2.36
2.41
1.94
9.42
0.68
5.93
4.93
2.88
2.56
0.38
0.70
0.75
0.49
0.67
0.73
0.60
0.42
0.76
1.42
0.40
1.32
1.11
0.47
0.33
Mean
Standard Dev
32.3
16.71
4.7
3.61
3.9
3.03
0.8
0.35
TFA - Chicago
TFA - Houston
TFA - NYC
Table 5: Impact intensities for apparel/services.
'
Child
Coma - ICU*
Energy
(MJ/$)
9.5
GWP (KG
CO2E /$)
0.78
CO2
(KG/$)
0.61
Toxics
(G/$)
0.59
0.58
0.63
0.59
0.59
0.10
0.62
0.57
0.57
0.57
0.51
0.59
0.56
0.57
0.56
2.03
0.13
0.48
0.48
0.45
0.54
0.6
0.36
0.39
0.56
0.75
0.58
0.10
0.79
0.56
1.07
0.94
1.88
0.92
1.19
0.92
0.56
4.95
5.98
1.46
5.53
1.70
6.21
1.8
1.98
Coma - mix *
Coma - support*
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student
Retiree
TFA - Chicago
TFA- Houston
TFA - NYC
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer - Low
Golfer - High
CEO "Average"
CEO "Great"
Oprah
Gates
Mean
Standard Dev
9.2
9.8
9.1
9.0
1.5
9.5
8.8
8.8
8.9
7.8
9.2
8.6
8.8
8.7
7.7
7.5
7.3
7.1
6.9
8.0
8.2
1.74
0.73
0.78
0.73
0.75
0.13
0.77
0.71
0.72
0.75
0.68
0.75
0.73
0.73
0.71
2.87
0.18
0.62
0.68
0.62
0.77
0.8
0.51
6:
Table
I
-&-
Imoact
Child
.
inte
Energy
(MJ/$)
GWP (KG
CO2E /$)
CO02
(KG/$)
Toxics
(G/$)
39.4
3.10
2.77
1.23
11.1
19.8
11.1
20.5
39.5
29.8
35.3
17.8
38.1
32.4
46.7
18.6
16.1
0.48
1.37
0.48
1.41
1.24
2.99
2.18
2.64
1.13
2.89
2.44
3.58
1.11
0.97
0.44
1.27
0.44
1.31
1.14
2.69
1.97
2.38
1.05
2.60
2.19
3.20
1.01
0.89
0.15
0.16
0.15
0.16
0.16
0.33
0.26
0.31
0.15
0.34
0.32
1.02
0.20
0.15
51.4
5.2
4.1
0.22
0.43
0.34
0.19
0.37
0.29
2.25
1.15
0.75
25.3
14.17
1.6
1.09
1.5
0.98
0.5
0.57
Coma - ICU *
Coma - mix *
Coma - support*
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student
Retiree
TFA - Chicago
TFA - Houston
TFA - NYC
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
18.7
Golfer - Low *
Golfer - High
CEO "Average"
CEO "Great"
Oprah *
Gates *
Mean
Standard Dev
Table 7: Imnact
intensities for service.
nptrnnal
---- r`
Y--~----,
~-·-UVI~UI·
Child
Coma - ICU
Coma - mix
Coma - support
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student*
Retiree
TFA - Chicago
TFA - Houston
TFA - NYC
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer- Low*
Golfer- High
CEO "Average"
CEO "Great"
Oprah
Gates
L
Mean
Standard Dev
Energy
(MJ/$)
GWP (KG
CO2E /$)
C02
(KG/$)
Toxics
(G/$)
3.2
5.2
5.1
5.0
4.9
3.8
5.6
4.7
177.1
4.3
4.7
4.3
5.2
3.5
4.0
5.0
4.3
4.4
0.26
0.42
0.40
0.39
0.40
0.30
0.43
0.37
13.62
0.35
0.39
0.36
0.43
0.28
0.33
0.40
0.35
0.36
0.21
0.33
0.32
0.32
0.32
0.26
0.35
0.32
11.52
0.29
0.32
0.29
0.36
0.23
0.27
0.34
0.29
0.30
0.23
0.30
0.32
0.32
0.29
0.21
0.33
0.30
7.90
0.29
0.41
0.34
0.33
0.34
0.36
0.31
0.24
0.29
4.0
3.9
3.7
3.5
4.6
4.4
0.66
0.02
0.31
0.29
0.28
0.40
0.3
0.09
0.01
0.26
0.24
0.24
0.31
0.3
0.07
0.22
0.32
0.25
0.18
0.63
0.3
0.09
I
Table 8: Impact intensities for insurance and investment.
Energy
(MJ/$)
Child
Coma - ICU *
Coma - mix *
Coma - support*
Homeless
GWP (KG
CO2E /$)
CO02
(KG/$)
Toxics
(G/$)
0.07
0.07
0.07
0.07
0.09
0.06
0.06
0.06
0.06
0.07
0.03
0.03
0.03
0.03
0.03
0.09
0.07
0.03
0.80
0.15
0.10
0.10
0.10
0.08
0.14
0.20
0.16
0.10
0.05
0.09
0.30
0.32
0.11
0.05
0.1
0.08
0.65
0.13
0.09
0.09
0.09
0.06
0.11
0.17
0.14
0.09
0.04
0.07
0.16
0.16
0.09
0.04
0.1
0.04
0.33
0.05
0.04
0.04
0.04
0.03
0.05
0.07
0.05
0.04
0.01
0.07
0.08
0.08
0.04
0.03
0.0
0.02
Monk 2 *
Nursing Home
Monk 1 *
Veg Student*
Retiree
TFA - Chicago
TFA - Houston
TFA - NYC
Artist
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer - Low
Golfer - High
CEO "Average"
CEO "Great"
Oprah
Gates
Mean
Standard Dev
9.8
1.9
1.3
1.3
1.3
0.9
1.7
2.5
2.0
1.3
0.2
2.4
2.4
2.5
1.4
0.6
1.5
0.68
Table 9: Im act intensities for tax and overnment services.
Child
Coma - ICU
Coma - mix
Coma - support
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student
Retiree
TFA - Chicago
TFA - Houston
TFA - NYC
Energy
(MJ/$)
GWP (KG
CO2E /$)
CO2
(KG/$)
Toxics
(G/$)
4.0
2.0
2.9
3.0
5.2
2.0
5.8
2.0
2.0
5.3
2.6
2.0
2.0
0.33
0.23
0.23
0.23
0.44
0.23
0.49
0.23
0.23
0.45
0.23
0.23
0.23
0.29
0.21
0.20
0.20
0.38
0.21
0.42
0.21
0.21
0.38
0.21
0.21
0.21
0.12
0.06
0.08
0.08
0.15
0.07
0.16
0.07
0.06
0.15
0.07
0.07
0.07
2.0
2.0
2.0
2.0
2.0
2.0
2.1
2.1
0.23
0.23
0.23
0.23
0.24
0.23
0.23
0.23
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.07
0.07
0.07
0.06
0.06
0.07
0.07
0.07
2.3
2.1
2.7
1.19
0.24
0.24
0.3
0.08
0.20
0.21
0.2
0.07
0.08
0.07
0.1
0.03
Artist *
Engineer
Soccer Mom
Mgmt Consultant
Inv Banker
Golfer- Low
Golfer- High
CEO "Average"
CEO "Great"
Oprah
Gates
Mean
Standard Dev
The average impact data for each category is shown below in Table 10. Extreme outliers were
omitted from these calculations for various reasons. The most common reason was the simple case
of suspected calculation or input error. For example, in the case of the vegetarian student's
impacts in the category of housing, furniture, and maintenance, there seemed to be suspiciously
low values. This was due to the fact that the researcher neglected the effects of amortized values
of expenditure included in the cost of building the house, including counters, heaters, etc.
Table 10: Im act intensity mean by catego.
Food, Diet, Alcohol
Housing, Furniture, Maintenance
Home Utilities, Fuel
Apparel, Services
Transportation
Services, Personal
Insurance and Investment
Tax, Government Services
Energy
(MJ/$)
9.1
6.7
32.3
8.2
25.3
4.4
1.5
2.7
GWP (KG
CO2E /$)
1.23
0.61
4.73
0.77
1.61
0.34
0.13
0.27
CO02
(KG/$)
0.61
0.54
3.88
0.59
1.46
0.28
0.10
0.24
Toxics
(G/$)
0.40
0.45
0.79
1.79
0.51
0.31
0.04
0.08
(G CO2/
MJ)
68
81
120
72
57
64
64
88
As can be seen, the amount of impact per dollar of expenditure varies greatly from category to
category. The categories contributing the largest impacts as far as energy usage and CO2 emissions
are home utilities and fuel and transportation. Given that these categories account for most of the
consumption of fossil fuels, this is not a surprising fact. It is of some interest however to note the
large difference between categories, which can be as high as an order of magnitude. These values
are graphed by category in Figures 2-5 below.
Figure 2: Mean energy use per dollar graphed by category with standard deviation noted by error bars.
Mean GWP
Food, Diet, Alcohol Housing, Furniture,
Maintenance
Home Utilities,
Fuel
Apparel, Services
Transportation
Seices. Personal
Insurance and
Investment
Tax, Govemment
Serices
Category
Figure 3: Mean GWP per dollar graphed by category with standard deviation noted by error bars.
Mean CO2
8.00
7.00
6.00
5.00
4.00
0
U 3.00
2.00
1.00
0.00
Food, Diet, Alcohol Housing, Fumiture.
Maintenance
Home Utilities,
Fuel
Apparel. Services
Transportation
Serices, Personal
Insurance and
Inestment
Tax, Go\emment
Sernces
Category
Figure 4: Mean CO2 emissions per dollar graphed by category with standard deviation noted by error barS.
Mean Toxics
4.00
3.50
3.00
2.50
2.00
i
1.50
1.00
0.50
0.00
-0.50
Category
Figure 5: Mean toxic release per dollar graphed by category with standard deviation noted by error bars.
In addition to the mean values of impact intensity, standard deviations were calculated. These
values are displayed below in Table 11, as well as on Figures 2-5, represented by error bars. As is
to be expected, the categories of home utilities and fuel and transportation see the largest values of
standard deviation. However, one surprisingly unstable category is represented by the toxics
released from apparel and services. This is due mainly to jewelry, which is made by processes that
release a significant amount of toxics.
Table 11: Impact intensity standard deviation b category.
Food, Diet, Alcohol
Housing, Furniture, Maintenance
Home Utilities, Fuel
Apparel, Services
Transportation
Services, Personal
Insurance and Investment
Tax, Government Services
Energy
(MJ/$)
GWP (KG
CO2E /$)
CO2
(KG/$)
Toxics
(G/$)
1.42
1.84
16.71
1.74
14.17
0.66
0.68
1.19
0.45
0.41
3.61
0.51
1.09
0.09
0.08
0.08
0.15
0.38
3.03
0.36
0.98
0.07
0.04
0.07
0.10
0.17
0.35
1.98
0.57
0.09
0.02
0.03
For those categories with significant standard deviations, impact was plotted versus disposable
income. This would determine whether the deviations were random, or based on some trend
relating to income. These plots are shown below in Figures 6-10. The pink lines on the vertical
axis represent the average values of impact intensity. As can be seen in Figures 6, 8, and 9, home
utilities and fuel present a challenge when trying to derive correlations. The cause of this difficulty
is most likely the varying fuels used for home utilities. For example, heating oil and natural gas
have significantly different impacts per dollar. This makes it very difficult to correlate impacts
directly to expenditures. Transportation presents a very similar problem, especially when looking
at higher income lifestyles. When differing modes of transportation are introduced, differing
significantly from the standard mode of automobile transportation, the impact intensities vary
wildly with very little correlation to income. Apparel and services presents a similar problem, but
for different reasons. Since the toxic releases due to expenditures in this category rely heavily on
the type of product being purchased, it is once again difficult to determine impact intensity based
on disposable income. For example, the high income, relatively low toxic release data points
probably correlate to low expenditures on jewelry. In contrast, the other four high income data
points probably correlate to high expenditures on jewelry. It is clear that despite the high amount
of disposable income available in both lifestyles, it is impossible to determine impact with that
figure alone.
Energy Intensity vs. Income
Home Utilities, Fuel
70.0
60.0
50.0
. 40.0
>
30.0
w 20.0
10.0
0.0
0
-10.0
Disposable Income ($)
Figure 6: Energy intensity versus disposable income for home utilities and fuel.
Energy Intensity vs. Income
Home Utilities, Fuel
70.0
60.0
50.0
o
40.0
S30.0
w
20.0
10.0
0.0
0
-10.0
Disposable Income ($)
Figure 7: Energy intensity versus disposable income for transportation.
GWP Intensity vs. Income
Home Utilities, Fuel
20.00
18.00
16.00
A 14.00
12.00
0 10.00
8.00
O
6.00
4.00
2.00
0.00
0.00
1
10
100
1,000
10,000
100,000 1,000,0 10,000, 100,000
00
000
,000
1,000,0 10,000,
00,000 000,000
Disposable Income ($)
Figure 8: GWP intensity versus disposable income for home utilities and fuel.
Figure 9: CO 2 intensity versus disposable income for home utilities and fuel.
Toxics Intensity vs. Income
Apparel, Services
7.00
6.00
5.00
1 4.00
U
x 3.00
I-
2.00
1.00
0.00
1
10
100
1,000
10,000
100,000 1,000,00 10,000,0 100,000, 1,000,00 10,000,0
0
00
000
0,000
00,000
Disposable Income ($)
Figure 10: Toxic releases intensity versus disposable income for apparel and services.
3.2. Floor Decomposition
Another valuable use for the ELSA model is the calculation for the floor values for
environmental impacts. In other words, what is the environmental impact of someone living the
most meager lifestyle possible? In the case of this study, the lowest impact lifestyles were that of
the Buddhist monk who spent six months a year in the forest and the homeless man. What was
interesting though was the fact that one had the lowest value of CO 2 emissions while the other
showed the lowest value of energy use. To determine the cause of this discrepancy, the amount of
CO 2 emitted per MJ of energy expended was calculated. The second portion of the analysis of the
floor values of impacts was to determine what that floor consisted of. To answer this question, the
homeless man's expenditures were divided up into the industry categories defined by the EIOLCA and input into a composite model of expenditures representing the man (Table 12). These
values were multiplied by a million dollars to ensure the calculations are carried out with an
appropriate amount of resolution. Then, inputting the values into the EIO-LCA model, I was able
to determine the major impacts caused by the homeless man.
12:
Table
.
.
Ex
-~-
enditur
Expenditures By Industry;;; Sector
;
:
:: :
Expenditures
n
.---
Impact Intensity
GWP (g
C02
Energy
(Mi$)
(g/$)
CO02E$)
167.35
10.8
699
1420
163
65
47
137.93
111.46
23.27
5.15
9.33
3.90
7.80
7.80
46.25
46.25
17.8
11.5
8.46
7.9
3.8
8.57
13.1
8.99
12.1
8.88
11.1
9.32
9.3
1230
829
576
518
251
557
840
591
788
574
606
642
640
1310
899
685
880
391
688
1070
891
970
773
735
730
717
278.64
5.92
369
449
7.80
10.9
666
821
57
5.12
342
406
15
10.4
633
685
696.6
13.8
542
597
7106.55
74.68
6333.64
371.52
7.36
3.94
9.34
6.94
501
272
668
477
816
324
809
539
74.26
8.62
548
674
31
28
28
83
355
3519
19901.19
1.94
9.77
21.6
39.9
8.03
132
164
1490
4280
610
192
283
1780
4600
732
0.0001
0.06
0.06
After calculating the homeless man's impacts by industry sector, his major impact categories
can be analyzed. This data is displayed below in Figures 13-16, displaying the top five impact
sectors for each type of impact. For example, although the man's main expenditures were in the
categories of food and lodging, the most significant impact he made in energy was actually in
power generation. We can see that despite the seemingly simple expenditures of the homeless
man, there are actually some other underlying or background processes which create larger
impacts. From this analysis, we can see the essential impacts caused by living a baseline lifestyle
in the US. In the case of energy for example, power generation, accommodation, food, and
transportation are the biggest causes of impact. This is consistent with what one would think to be
the case. Currently, there's some discrepancy between the values calculated for these impacts and
the original values calculated for these impacts. These discrepancies can be attributed to entry and
calculation errors when looking at the homeless man's impacts in the original report. For example,
the original data reported that the homeless man had a negative expenditure in the category of
insurance and investments. Not only is this inconsistent with the written reports, but it is clearly
impossible.
Another source of error may have been the disparities between manufacturing cost
and retail cost. For example, an expenditure of $30.06 for cut-and-sew apparel manufacturing was
entered when in fact the expenditure was for purchasing finished cut-and-sew apparel. The
impacts therefore were higher than expected. However, the data is still useful as it shows the
leading causes of impact for a baseline type of lifestyle.
Table 13: Top five contributing sectors for toxics.
Sector
Toxics (kg)
Total for all sectors
4.52
Copper, nickel, lead, and zinc mining
1.24
Power generation and supply
1.02
Waste management and remediation services
0.32
Gold, silver, and other metal ore mining
0.22
Foam product manufacturing
0.17
Table 14: Top five contributing sectors for CO 2.
Sector
C02 (MT)
Total for all sectors
9.66
Power generation and supply
4.94
State and local government electric utilities
0.55
Truck transportation
0.42
Other accommodations
0.36
Food services and drinking places
0.35
Table 15: Top five contributing sectors for global warming potential.
Sector
Total for all sectors
Power generation and supply
Cattle ranching and farming
Waste management and remediation services
Grain farming
State and local government electric utilities
GWP (MT
CO2E)
13.20
5.00
1.02
0.71
0.71
0.55
Table 16: Top five contributing sectors for energy.
Sector
Total for all sectors
Power generation and supply
Other accommodations
Food services and drinking places
Transit and ground passenger transportation
State and local government electric utilities
Energy (MJ)
141000
59326
8493
7931
7769
4930
One final case to look at is the discrepancy between the two floor values: the homeless man for
CO 2 and the monk for energy. To determine this, graphs of g of CO 2 per MJ of energy were
plotted as shown below in Figure 11. The reason for the discrepancy between CO 2 emissions and
energy use can clearly be seen in the home utilities and fuel category, where the monk has almost
six times the CO2 emissions per MJ of energy of the homeless man. Where does this value come
from considering that the monk's expenditures for utilities and fuel totaled only 221 1997 dollars?
When divided up between standard household utilities, this expenditure makes sense in terms of
energy, but no sense in terms of CO 2 emissions, which totaled 2.97 MT. The reason for this
discrepancy is not immediately apparent, but on closer inspection a probable explanation reveals
itself. The monk's lifestyle includes a six month per year stay in the forest, where there is a large
chance that he started at least a few fires for warmth or safety. Since the wood is free to him, it
would not show up in his expenditures. However there is a field in the utilities and fuel category
for the dollar equivalent of wood purchased or collected per person per year. The impact in terms
of energy for this category is not very significant, but burning the wood creates a substantial
amount of CO 2. The amount of CO 2 emissions per energy produced for burning wood can be
higher than 100 g C0 2/MJ compared to the next highest value of 60.58 g C0 2/MJ for natural gas.
In the sample case in which the monk devotes all of his expenditures for utilities to heating his
home with natural gas, the impact only comes out to be 0.828 MT of CO 2. This leaves a remaining
2.14 MT of CO 2 that must have been caused by burning firewood. According to the ELSA model,
it would require less than $100 worth of firewood to create the 2.14 MT value of CO2, so this
explanation is a very probable cause of the major discrepancy seen between CO 2 emissions
between the monk and the homeless man. After subtracting this 2.14 MT from the monk's total
CO 2 impact, the remaining value is 8.35 MT, which is more consistent with the numbers
previously calculated for minimum CO 2 emissions for a resident of the US. Despite the fact that
these calculations provide a plausible explanation for the discrepancy, they are based upon
incomplete information. To determine the cause concretely, more research needs to be done on the
specific spending habits of the monk.
450.0
400.0
350.0
2
300.0
250.0
• Homeless
E Monk 2
S200.0
150.0
100.0
50.0
0.0
Food,Diet.
Alcohol
Housing,Fum, HomeUtilities,
Maint.
Fuel
Appareland
Senices
Transportation SeNces,
Personal
Insuranceand
Investment
Tax, GoAt
Services
Totals
Category
Figure 11: Chart of CO2/Energy by category for the homeless man and monk.
3.3. Model Verification
To verify the values in our model, they were compared to the average values of environmental
impacts for 1997. To do this, the average percentages for expenditures in the eight categories
found in the BLS report for that year (citation) were used. By multiplying these percentages by the
average income in 1997, average expenditures in each of the categories was determined. These
values were then multiplied by their corresponding average impact intensities to obtain a value for
average impacts in each category. The average environmental impacts per category according to
the average impact intensities are displayed below in Table 17. The major values of interest are
the values for total impact in energy and GWP. The accepted average values of impact for 1997
are 370 GJ of energy and 24 T CO 2E. This analysis shows that the average impact intensity model
nails the GWP perfectly. However, there is more of a problem when it comes to the energy, where
an error of about 30% can be seen. This is not entirely surprising considering that the impact
intensity averages for energy showed the most amount of deviation from the average. This shows
that the model developed in this study has some potential as an accurate but simplified version of
the full ELSA model. However, some more work needs to be done to determine where the error in
the energy estimate comes from.
1dnn vPurnc
17. Ax
Alcohol
Housing,
Furniture,
Maintenance
Home Utilities,
Fuel
Apparel, Services
Transportation
Services,
Personal
Insurance and
Investment
Tax, Government
Services
Totals
1Q7
e~nenditiire
and
Energy
(MJ/$)
GWP (KG
CO2E/$)
CO2
(KG/$)
Toxics
(G/$)
Energy
(GJ)
GWP (T
CO2E)
C02
(T)
Toxics
(KG)
5524.07
6.7
0.61
0.54
0.45
37
3
3
2
1494.75
32.3
4.73
3.88
0.79
48
7
6
1
1083.15
8.2
0.77
0.59
1.79
9
1
1
2
4007.66
25.3
1.61
1.46
0.51
101
6
6
2
4375.93
4.4
0.34
0.28
0.31
19
1
1
1
2014.66
1.5
0.13
0.10
0.04
3
0
0
0
4391.00
2.7
0.27
0.24
0.08
12
1
1
0
25898
90.1
9.69
7.69
4.37
257
24
20
11
I
4. Conclusions
The goal of this study was to analyze the data collected for 24 varied lifestyles and determine
ways to use it to simplify the ELSA model of environmental impact analysis. To accomplish this,
impact intensities were averaged across the board for all of the eight categories defined by the
BLS. When calculating these averages, care was taken to avoid values thought to be erroneous,
mostly due to probable input errors. Using these values in conjunction with average values for
expenditures in the eight categories for 1997, it was found that by simply using these average
impact intensities, the GWP could be accurately estimated. Although the energy impact had
significantly more error, this proves that the model has substantial potential as a useful tool for
environmental impact analysis. However, significant steps must first be taken to improve the
model. Areas known to cause large variations in impact intensity must be analyzed in greater
depth to determine what critical quantities cause these variations. The most significant cases that
need to be looked at are home utilities and fuel and transportation. In these cases, the largely
varying impacts due to varying types of fuel impart large variations to the data. It may be
impossible to make large simplifications to the model for these categories due to this inevitable
variability, but there are probably still some significant improvements to be made. In the case of
toxic releases for apparel and services, jewelry plays a large role in increasing variability in impact
intensity. To fix this problem, apparel and services should be divided into jewelry and non-jewelry
categories with unique impact intensities. After taking these measures, this model could be made
to very accurately predict environmental impacts with a very simple, single page interface.
A secondary goal was to determine the baseline level of environmental impact for lifestyles
living very frugal lifestyles. The two lowest impact lifestyles turned out to be the homeless man
and the monk, for CO 2 emissions and energy use respectively. The homeless man's expenditures
were analyzed to determine what types of impacts were necessary for survival. A major impact in
every category turned out to be power generation. This makes sense considering the critical role
that electricity plays in our world today. As for the discrepancy between the minimum CO 2
emissions and minimum energy usage, a probable explanation was found. Plotting CO 2 per energy
showed that the monk was in fact using some form of fuel with extremely high CO 2emissions.
Analyzing his lifestyle, the probable conclusion is that he burned some wood as fuel while living
in the woods. This is due to the fact that with his expenditures, it is impossible to create a CO 2
impact as high as he did without burning wood as fuel.
As a tool for calculating environmental impacts, the ELSA model proves to be very useful. On
top of that, this study shows that the model has significant areas where it can be simplified. In its
final form, it's not inconceivable to think that it may be as simple as a 20 question survey on
expenditures in the eight categories. While there is still a significant amount of work to be done,
this study shows that the work is indeed worthwhile and that the moving to simplify the model is
the right course of action to be taking. This has significant implications for calculating
environmental impacts for the average consumer. Hopefully this work will enable common US
citizens to determine exactly how to reduce their environmental impacts in meaningful ways.
References
[1] BLS (Bureau of Labor Statistics), U. S., 1998, "1997 Consumer Expenditure Survey"
[2] T. Gutowski, et al., 2007, "Environmental Lifestyle Analysis (ELSA)".
[3] C. T. Hendrickson, L.B. Lave, and M. H. Scott, Environmental Life Cycle Assessment of
Goods and Services An Input - Output Approach, Resources for the Future Press, 2006.
Appendix A: Deflated Impact Charts
Table A-1: GWP in MT CO 2 by category for each lifestyle.
Child
Home
Utilities,
Diet.
Food,
Foo
Housing,
FAp
Alcohol
Fum, Maint.
Fuel
2.39
0.25
6.07
0.68
Transportation
0.20
-
Coma Coma- mix
Coma -
Apparel and
and
Services
Person
Insurance
n and
Tax,
Govt
Investment
Services
Totals
0.15
0.08
3.44
13.27
219.81
0.08
1.02
220.91
170.06
0.25
22.47
192.78
-
-
-
143.99
0.08
36.78
180.85
support
Homeless
5.07
-
0.14
0.10
0.33
0.22
(0.30)
4.93
10.51
Monk 2
1.55
0.26
3.92
0.08
6.00
0.11
0.06
1.04
13.02
Nursing Home
8.15
5.07
4.96
0.94
2.23
10.98
(0.04)
7.82
40.12
2.25
5.58
0.12
11.90
0.09
0.02
1.04
25.69
5.90
0.14
0.85
7.98
0.15
1.02
19.14
1.63
7.08
5.16
34.41
1.44
0.67
2.82
26.82
1.43
0.69
1.03
38.28
22.28
Monk 1
Veg Student
4.69
2.96
0.12
Retiree
4.49
0.57
12.66
0.24
2.58
TFA - Chicago
5.65
2.70
2.92
1.95
8.67
TFA - Houston
6.61
8.49
11.07
1.72
7.25
1.12
5.25
2.27
0.70
1.03
17.04
1.31
11.12
2.58
1.20
1.04
45.69
4.86
0.91
5.72
1.22
6.69
1.05
26.69
6.46
9.31
1.42
4.09
14.39
2.41
1.73
2.07
6.55
1.05
36.58
1.69
1.05
48.65
1.03
60.95
667.68
TFA - NYC
3.55
5.29
3.07
Artist
7.97
3.42
Engineer
5.82
0.41
Soccer Mom
Mgmt
Consultant
2.30
14.22
2.67
13.82
5.36
3.48
4.50
2.87
11.28
141.25
15.56
16.11
169.47
270.43
34.26
1.03
25.65
14.17
4.04
9.44
13.55
13.71
1.02
93.28
105.83
336.91
59.83
62.10
156.19
154.23
395.88
1.12
1,272.10
410.02
3,763.80
265.01
556.75
812.71
1,061.54
7,560.45
1.12
14,431.40
28,636.48
334,335.40
Inv Banker
23.48
8.94
Golfer - High
19.57
Golfer - Low
11.69
CEO
rage
"Average"
CEO "Great"
Oprah
415.57
304.61
534.41
410.87
5,136.07
330.16
21,502.77
2.03
Gates
223.75
346.98
179.15
22.64
5,083.45
135.67
328,342.54
1.22
Table A-2: CO 2 in MT by categor for each lifestyle.
Food,
Diet.
Alcohol
Child
0.95
Housing,
Furn,Fuel
Home
Apparel
Furn, Maint.
Utilities,
and
Services
Transportatio
n
0.21
4.51
0.54
-
Coma - ICU
Coma - mix
Coma - support
-
Services,
Personal
Insurance and
Investment
Tax, Govt
Services
0.18
0.13
0.07
3.03
9.61
-
175.20
0.07
0.91
176.18
Totals
-
139.70
0.21
19.86
159.77
-
-
-
118.83
0.07
32.49
151.39
(0.24)
4.22
8.47
Homeless
3.90
-
0.03
0.08
0.30
0.18
Monk 2
0.60
0.22
2.97
0.07
5.57
0.09
0.05
0.93
10.49
Nursing Home
6.90
4.57
3.73
0.75
2.03
9.03
(0.03)
6.67
33.64
Monk 1
2.05
2.01
3.41
0.09
11.04
0.07
0.02
0.94
19.64
Veg Student
1.54
0.11
2.81
0.11
0.79
6.75
0.12
0.92
13.16
Retiree
2.39
0.48
8.84
0.19
2.32
1.35
6.21
4.41
26.20
TFA - Chicago
3.14
2.33
1.73
1.55
7.85
1.17
0.56
2.50
20.84
TFA- Houston
3.12
7.66
7.41
1.35
6.54
1.16
0.57
0.92
28.72
TFA - NYC
1.52
4.77
1.87
0.86
4.86
1.90
0.59
0.92
17.28
Artist
3.94
2.95
13.71
0.98
10.00
2.15
1.02
0.93
35.68
Engineer
2.98
0.35
4.13
0.72
5.15
0.99
5.47
0.94
20.73
Soccer Mom
1.15
2.29
4.33
1.10
12.86
1.45
5.53
0.94
29.66
Mgmt Consultant
8.58
12.46
6.79
3.18
2.18
1.74
1.49
0.94
37.34
Inv Banker
9.85
8.06
3.39
2.76
4.15
2.39
9.36
0.92
40.89
Golfer - High
9.88
127.29
19.83
11.40
150.83
226.62
25.18
0.93
571.97
73.50
Golfer - Low
5.85
23.07
10.05
2.92
8.21
11.32
11.15
0.91
CEO "Average"
59.14
288.39
55.61
48.29
134.53
130.15
203.52
1.00
920.63
CEO "Great"
228.29
3,279.67
242.16
389.24
690.52
900.05
3,872.14
1.00
9,603.07
Oprah
229.25
262.88
595.00
301.15
4,714.03
277.75
18,594.65
1.69
24,976.40
Gates
117.54
298.62
389.20
16.06
4,451.64
104.69
269,492.78
1.07
274,871.59
Table A-3: Energy in MJ by category for each lifestyle.
Child
Food, Diet.
AlcoholD
Housing,
Fum,
Maint.
Home
Utilities,
Fuel
Apparel
and
Services
Transportation
14,421
3.096
53.508
8.328
-
Coma - ICU
Coma - mix
Coma -
-
-
Services,
Personal
Insurance and
Investment
Tax, Govt
Services
2,518
1.894
1,020
41,973
126,758
-
2,732.378
1.020
8.738
2,742.137
-
2.206,978
3,061
286,449
2.496,489
1,877,133
1,020
471,590
2.349,744
123,785
-
support
Homeless
57.740
-
444
1.280
7.739
2,740
(3,620)
57.462
Monk 2
9.229
3.244
7.638
1.063
86,816
1,346
728
8,965
119,029
Nursing Home
101,564
62,634
38.444
11.690
51,590
144,168
(520)
93,478
503,049
Monk 1
31,024
27,733
40,840
1.443
172.990
1,086
291
9.049
284.456
Veg Student
23,112
1.537
27,400
1.745
12,907
103.782
1,864
8,804
181,152
Retiree
35,506
6,818
52,098
2,949
34,101
20,153
91,498
60,300
303,423
TFA - Chicago
46,656
32,639
21,636
24,006
118,637
17,355
8,389
32,005
301,322
TFA -Houston
46,772
104,822
91.441
20,805
96,668
17.157
8.567
8,862
395,094
TFA -NYC
23,015
65,284
23,413
13,297
82,494
27,819
8,754
8,862
252,939
Artist
59,008
41,487
168,007
14.961
146,461
32,526
15.128
9.010
486,589
Engineer
44,503
5,001
32,250
11,093
76.209
14,671
81,557
9,154
274,438
Soccer Mom
Mgmt
Consultant
17,202
126,989
32,698
170.590
35,269
49.808
16.922
49,035
187,633
40,328
21.502
25,533
82,436
9,153
402,815
21,906
9.070
493,258
InvBanker
148,741
110,417
41,699
42,521
74,768
34.903
139,973
8,842
601.865
Golfer - Low
88,527
316,607
116,601
43,450
118,527
165,572
167.534
8.749
1,025,567
Golfer - High
148,240
1.743,528
130,102
168,397
2.202,612
3,286,929
377,031
8.905
8,065.744
CEOrage
"Average"
884,784
3,987,246
614.812
735.389
1.880.520
1,946,581
3,136,829
9.873
13,196.034
CEO "Great"
3,424.519
45,891,907
2,745,678
5,750.088
9.923,465
13.547,478
59.702,576
9,873
140,995.583
Oprah
3.477,877
3.681,301
4,856.616
4,554.560
66.570,586
4.102,195
274.828.845
19.470
362,091.450
Gates
1,756.515
4.229,239
2.619.094
237.949
60,812.379
1.573,632
3.985.630,450
10,843
4.056.870.101
Table A-4: Total toxic releases in KG by category for each lifestyle.
Food,
Housing,
AlDooi
Furn, Maint.
0.62
0.03
Home
Apparel
Transportatio
Services,
Utilities,
Serndes
n
Personal
0.52
0.08
Insurance and
Tax, Govt
Investment
Services
Totals
0.14
0.04
1.21
3.84
Coma -ICU
158.71
0.04
0.28
159.03
Coma - mix
137.17
0.11
7.55
144.83
Coma - support
118.72
0.04
12.39
131.15
3.84
Child
Homeless
Monk 2
1.96
0.45
-
0.06
1.21
0.06
0.19
0.05
0.10
0.16
(0.11)
1.62
0.06
0.70
0.07
0.02
0.29
1.84
(0.02)
2.60
20.67
0.01
0.29
6.25
6.84
Nursing Home
2.77
4.21
0.93
0.96
0.68
8.53
Monk 1
1.37
1.73
1.34
0.09
1.34
0.07
Veg Student
0.91
0.07
0.66
0.11
0.11
4.63
0.06
0.29
Retiree
1.42
0.77
1.23
0.25
0.29
1.35
2.24
1.69
9.23
TFA - Chicago
1.82
2.79
0.57
1.54
1.05
1.51
0.24
0.89
10.42
7.10
1.88
2.55
0.84
1.37
0.25
0.29
16.16
0.64
1.40
0.71
1.77
0.26
0.29
10.49
3.62
1.32
3.15
0.47
0.29
18.38
0.76
1.30
2.35
0.30
9.09
0.30
14.23
TFA - Houston
TFA - NYC
Artist
Engineer
1.88
1.01
2.59
1.96
4.42
3.34
0.38
3.61
0.94
1.11
Soccer Mom
Mgmt
Consultant
0.75
4.84
2.07
11.56
1.11
1.20
2.32
5.11
4.08
1.32
2.28
0.43
1.44
0.53
0.29
25.41
Inv Banker
6.75
7.48
1.33
2.77
0.70
2.32
4.05
0.29
25.70
10.10
11.47
5.18
0.28
82.65
0.29
551.9
Golfer - Low
3.87
20.95
2.99
27.80
Golfer - High
6.76
117.63
5.86
135.02
96.59
178.60
11.13
CEO "Average"
45.35
381.51
12.41
146.72
416.05
162.97
102.10
0.33
1.267
CEO "Great"
181.53
3,679.88
54.32
4,502.67
1,809.36
919.15
1.953.86
0.33
13.101
199.18
323.45
96.70
1,127.71
730.84
209.25
7,566.64
0.70
10,254
50.66
183.81
10,083.71
212.98
156,704.51
0.37
167.637
Oprah
Gates
84.14
316.43
Table A-5: Economic activity in dollars by categor for each lifestyle.
Food
Housing,
Diet.Alcohol
Furn, Maint.
Alcohol
Child
3,221
925
Home
Fuel
Fuel
Apparel
Services
Services
Transportatio
n
Services,
Personal
Insurance and
Investment
Tax, Govt
Services
Totals
1,185
2,.199
223
973
1,902
14,545
25,174
159
0
0
159
827.996
5,707
236,219
1,069,921
1,105.030
Coma - ICU
Coma - mix
Coma - support
-
-
-
-
712,109
1,902
391,019
Homeless
14,598
-
38
285
1,456
1,055
(3,724)
16,623
30,331
Monk 2
1,890
972
422
257
9,149
646
749
4,089
18,175
132.845
Nursing Home
26,053
16.730
1,297
2,672
9,706
51,159
(535)
25,764
Monk 1
6,709
7,513
1,869
403
17.634
432
300
4,130
38,991
Veg Student
5,626
434
1,754
473
1,441
47.423
2,685
4,039
63,874
Retiree
9.325
2.227
3,816
684
1,720
8,162
62,069
17.257
105,261
TFA - Chicago
12,655
10,642
1,372
6,721
8,049
7,590
8,016
23,409
78,454
TFA - Houston
11,735
27,997
3,839
5,829
5,461
7.761
8,199
4,059
74,879
TFA - NYC
5,108
17,437
1,867
3,630
9,706
10,430
8,391
4,059
60.627
Artist
14,486
13,214
7,104
4,485
7,552
15,303
23,026
4,141
89,312
Engineer
11.390
1,574
1,630
3,005
4,583
7,087
73,345
4,183
106,796
Soccer Mom
4.521
10,287
2,568
4,691
15,903
8,331
39,872
4,169
90,341
Mgmt
Consultant
35,446
45,569
3,648
12.666
4,974
10,973
14,214
4,141
131,631
Inv Banker
35,227
29,489
2,488
12,087
9,706
14,174
130.713
4,057
237,941
Golfer - Low
20,630
84,917
4,056
13,366
31,383
60,276
184,419
4,024
403,072
Golfer - High
36.294
466,041
7,077
54,148
277,761
1,263,010
848.514
4.089
2,956,935
CEO "Average"
230,790
1,249,530
11,120
232.776
602,147
791,568
1,758,190
4,565
4,880,687
CEO "Great"
875,504
14,629,272
53,420
1,914,824
4.293,476
5,738,416
32,785,815
4.565
60,295,292
Oprah
812.924
1,192,439
127,828
1.452,961
3,108,745
1.956.263
275,097,712
9,303
283,758.175
Gates
467,208
1,311,533
11,263
72,994
13,668.135
794,635
9,486,215,692
5,064
9,502.546,524
Dietoo,
Alcohol
ousing,
Maint
1.254
356
I Alcohol
Child
Maint.
Uome
Fuelt
Fuel
875
Apparei
Services
Transportatio
n
875
64
Services
Coma - ICU
Coma - mix
Coma - support
Homeless
Monk 2
Nursing Home
Monk 1
Veg Student
Retiree
7,412
-
Services,
Personal
Insurance and
Investment
Tax, Govt
Services
Totals
600
1,238
10,384
15,646
528,510
1,238
4,391
534,139
430,395
3.715
97,271
531,381
372,112
1.238
159,191
532,541
11,096
16,669
-
-
139
697
557
221
108
4,378
356
-
4,428
10,783
743
550
13,932
8,452
909
1,285
4,644
25,650
(464)
16,020
70,428
2,786
3,793
1,176
161
8,437
232
-
4,459
21,045
1,131
690
586
190
4,401
38,823
310
864
4,680
47,360
11.470
73,896
12,153
40,568
2,482
4,337
1,623
3,253
TFA - Chicago
6,019
4,740
820
2,742
3,980
3,709
6,405
TFA - Houston
5,452
13,269
2,508
2,378
2,742
4.011
6.564
4,413
41,335
TFA - NYC
2,146
8,264
1,316
1,495
4,644
5,311
6,714
4,413
34,303
6,260
8,446
5,379
1,920
3,848
9,177
15,966
1,281
1,211
2,349
3,639
48,869
4,498
72,207
32,942
4,483
57,144
Artist
Engineer
Soccer Mom
Mgmt
Consultant
5,146
2,135
17,276
5,215
5,451
22,051
52,074
1.836
1,958
4,020
4,319
2.819
5,565
2,164
5,965
10,859
4,467
71,165
4,644
7,916
109,033
4,415
161,322
34,149
691,801
4,394
814,451
14.545
14,113
1,749
4,907
9,139
65,793
2,105
5,615
15,604
220,593
14,698
22,570
42,838
821,570
154,052
4,438
1,296,362
CEO "Average"
108.050
588,831
9,377
100,646
362,745
501,676
1,299,911
4,789
2,976.026
CEO "Great"'
410,288
7,614,767
49,149
814,102
2,420,870
3,707,770
23,697,050
4,789
38,718,785
8,290
204,416,539
5,165
6,179,283,058
Inv Banker
Golfer - Low
Golfer - High
Oprah
385,545
519,700
206,879
663,241
1,008,250
1,167,502
200,457,134
Gates
217,153
890,814
152,246
29,590
473,386
339,167
6.177,175,537
Indicates robable erroneous data