Analysis of Energy Use and Carbon Emissions from
Automobile Manufacturing
by
Sumant S. Raykar
B.E in Mechanical Engineering, University of Pune, 2009
M.Eng in Manufacturing, Massachusetts Institute of Technology, 2011
Submitted to the Department of Mechanical Engineering
in Partial Fulfillment of the Requirements for the Degree of
Master of Science
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Signature of Author..................
Sumant S. Raykar
Department of Mechanical Engineering
May 18, 2015
Certified By ............................
Signature redacted
V
Timothy G. Gutowski
Professor of Mechanical Engineering
Thesis Supervisor
Accepted By .......................
Signature redacted......
David E. Hardt
Ralph E. and Eloise F. Cross Professor of Mechanical Engineering
Department of Mechanical Engineering
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Analysis of Energy Use and Carbon Emissions from Automobile
Manufacturing
by
Sumant S. Raykar
Submitted to the Department of Mechanical Engineering on May 18, 2015
in Partial Fulfillment of the Requirements for the Degree of Master of
Science in Mechanical Engineering
Abstract
In this thesis, we study the energy use and emissions arising from
automobile manufacturing. The automobile manufacturing sector is the 11th
largest industrial sector globally in terms of energy use and emissions. The
IPCC has set targets for reduction in emissions so that the average
concentration of carbon dioxide in the atmosphere does not exceed dangerous
levels. The materials production sectors have achieved significant reduction
in energy use in the last few decades. Progress in the production and
assembly of components has been harder to prove. Base load energy use
continues to be a high fraction of energy use at manufacturing facilities.
We study the energy use and emissions reported by automobile companies in
voluntary disclosures to the Carbon Disclosure Project (CDP), and in their
sustainability reports. A model of a typical global vehicle assembly plant is
created by using data published in literature. We find a good fit of this data
with the CDP data. A certain fraction of parts manufacturing is included inhouse. Then, a simple thermodynamic model of the factory is developed. This
shows that air exchange causes a significant heating and cooling load at
factories. Internal heat gains contribute to the cooling load. We then test
various emissions reduction scenarios to see their effectiveness in reducing
energy use or emissions.
We find that most of the reduction in emissions intensity in the last few years
is likely due to the economies of scale effect, in spite of significant emissionreduction efforts by some companies. We predict a trend towards higher
manufacturing energy consumption due to use of low weight, high energy
intensity materials in order to reduce use phase emissions. At some point,
manufacturing emissions might become as significant as use phase
emissions. Even if emissions intensity of manufacturing can be decreased,
increased demand means that absolute emissions will continue to grow. Right
now, it does not appear that this sector is on a pathway towards meeting
climate change goals.
3
Thesis Supervisor: Timothy G. Gutowski
Professor of Mechanical Engineering
4
Acknowledgements
This thesis is the culmination of an unusual, eventful, long-winded journey,
and it could not have been possible without the encouragement, strength and
companionship provided by so many people.
I extend my gratitude to my advisor, Prof. Gutowski, for being a great
educator and a great mentor. He provided numerous opportunities, and
showed immense faith in me which drove me to do my best. I learned a lot
about research and teaching from him, and these lessons and his kindness
will always be cherished.
Equally cherished will be the memories with Prof. Gutowski's research group,
Environmentally Benign Manufacturing, which I called home for almost two
years. I will be forever grateful to Michael Lloyd for his friendship at a time
when I was lonely and not my own self. Dan Cooper and Katie Rossie
energized the lab, and we all went from being office-mates to friends because
of their ability to connect people so effectively. Dan's advice on matters work
and otherwise was always on point. And Katie has been a good friend, a
gracious host and a charming story-teller. Sheng Jiang, my comrade-in-arms
in spending late nights at the office, going swimming or sailing, and sharing
life stories, has been a great friend. And I will fondly remember the times
spent in the office and outside with Marta Baldi, Gero Corman, Michael
Hausmann, Anne Raymond and Mathias Schmieder. Thanks go especially to
Mathias with whom I worked on this project, and who is a wonderful
collaborator.
I would also like to thank Karuna Mohindra, David Rodriguera and Carissa
Leal in the Laboratory for Manufacturing and Productivity. Also, thanks to
Leslie Regan at the MIT Mechanical Engineering Graduate Office, and the
5
International Students Office who kept me out of trouble and provided
terrific support. This extends to Prof. David Hardt as well to whom I have
gone often over the last five years seeking direction and counsel.
This thesis benefited from the work done by my peers in the MIT class
2.813/2.83 Energy, Materials and Manufacturing in the spring 2014 term in
gathering data from various automobile companies. Thanks again to Katie for
motivating the class project and my own thesis in the first place.
MIT presents lot of challenges, and often these challenges lie not in the realm
of research, but in overcoming fear, doubt, stress and periods of crippling
hopelessness. Alexandra Prior helped me get over these challenges, and in
the process made me a more reflective and considerate person, more aware of
my weaknesses and ways to overcome them. When I experience doubt or
confusion, I remember her advice, and it always gets me through. I cannot
thank her enough.
I also thank Cassandra Donnelly, who I wish I had met earlier, because every
day that I know her has been better than the last. She kept me going through
the thesis-writing process, and always puts a smile on my face.
I would be remiss not to mention my mother and father, my sister, her
husband and their two beautiful boys, who provide immense happiness and
strength. They are all an inspiration to me. Thanks also go to my school and
college friends from Pune, and my current roommates Kit and Kat.
Finally, thanks to all the wonderful people and resources at MIT - the
Sailing Pavilion, the Z-Center, the libraries and the Thirsty Ear Pub. And
thank you to everyone else who I may have missed mentioning here, but who
made this journey possible.
6
Contents
C hapter 1: Introduction................................................................................
15
1.1 State of the global automobile industry..................................
15
1.2 M otivation ...............................................................................
17
1.3 Problem Statem ent ..................................................................
27
1.4 Thesis Structure ......................................................................
28
Chapter 2: Literature Review ......................................................................
30
Chapter 3: Analysis of CDP Reports ...........................................................
36
3.1 Introduction to the CDP ..........................................................
36
3.3 C D P Q uestionnaire..................................................................
38
3.4 D ata A nalysis...........................................................................
41
Chapter 4: Case Studies of Component Production and Vehicle Assembly
62
4.1 Engine Manufacturing Plant Energy Use ..............................
62
4.2 Regression Models for Renault Factory Emissions................
67
Chapter 5: Surrogate Global Assembly Plant Model..................................
73
5.1 A ssem bly plant m odel..............................................................
73
5.2 Assembly plant and the automobile supply chain..................
80
Chapter 6: Thermodynamic model and evaluation of emission reduction
a ctiv itie s ........................................................................................................
91
6.1 Basic therm odynam ic m odel ...................................................
6.2 Scenario A nalysis.....................................................................
Chapter 7: Modern Vehicles - Materials, Manufacturing and Use ...........
91
112
136
7.1 An LCA of the Tesla Model S ..................................................
136
7.2 An eco-audit of the Volkswagen XL1......................................
152
7
Chapter 8: Conclusions and Future Work...................................................
157
8 .1 C on clu sion s ..............................................................................
157
8.2 F uture W ork.............................................................................
162
Referen ces .....................................................................................................
16 4
Appendix A: Energy Use for Vehicle Manufacturing in Literature ...........
174
Appendix B: Renault Factory-Level Data ...................................................
177
Appendix C: Material Content of the Vehicle Modeled ..............................
180
Appendix D: Solar Radiation on Flat-Plate Collectors in Detroit ..............
182
Appendix E: Assumed Bill-of-Materials for the Volkswagen XL1 .............
183
8
List of Figures
Figure 1.1: Global C02 emissions from fossil fuel combustion by sector,
... .
2 0 1 1 ...........................................................................................................
18
Figure 1.2: Global industrial (a) primary energy use and (b) CO 2
em issions by end-use sector, 2005...............................................................
20
Figure 1.3: Primary energy use in the U.S manufacturing sector, 2006 ...
21
Figure 1.4: Non-process energy use in U.S manufacturing, 2006..............
22
Figure 3.1: Total number of responses for the Climate Change and
Supply C hain program s ...............................................................................
38
Figure 3.2: Global production numbers from 2008-2012 for eleven
a u tom ak ers ...................................................................................................
43
Figure 3.3: Absolute Scope 1+2 emissions from 2008-2012 for eleven
au tom ak ers ...................................................................................................
44
Figure 3.4: Scope 1+2 emissions per vehicle for eleven automakers .........
45
Figure 3.5: Scope 1+2 emissions intensity over the years for eleven
automakers plotted against their global production numbers ...................
48
Figure 3.6: Scope 1+2 and Scope 3.1 emissions per vehicle for eleven
autom akers, 20 12 .........................................................................................
51
Figure 3.7: Use phase emissions in grams C02 per km for eighteen
companies shown against the 2012 CAFE and NEDC standards, 2012....
53
Figure 3.8: Use phase emissions for U.S fleets, and emissions standards
for different regions, in NEDC gram C02 per km ......................................
54
Figure 3.9: Use phase emissions for European fleets, and emissions
standards for different regions, in NEDC gram CO 2 per km .....................
55
Figure 3.10: Average of manufacturing and use phase emissions for five
autom akers in 20 12 ......................................................................................
56
Figure 3.11: Purchased electricity per vehicle for eleven automakers,
2 0 12 ...........................................................................................................
9
... . 5 7
59
Figure 3.12: Fuel use (MJ per vehicle) for eleven automakers, 2012 ........
Figure 3.13: Emissions intensity of purchased electricity in kg CO 2 per
k W h, 2 0 12 ....................................................................................................
.
60
Figure 3.14: Emissions intensity for fuel use, in kg CO 2 per MJ, 2012.....
61
Figure 4.1: Scaled electricity use vs. scaled engine production..................
63
Figure 4.2: Scaled natural gas use vs. scaled engine production ...............
64
Figure 4.3: Total natural gas use vs. mean monthly temperature ............
65
Figure 5.1: Sketch of the surrogate factory, its emissions and products ...
77
Figure 5.2: Scope 1, Scope 2 and Scope 1+2 emissions for fifteen
autom akers, 20 12 .........................................................................................
79
Figure 5.3: Comparison of Sullivan's VMA model to literature .................
84
Figure 5.4: Sankey diagram of energy used per vehicle at the factory......
86
Figure 5.5: Calculated in-house energy compared to reported energy use
by auto com panies, 2012 ..............................................................................
87
Figure 5.6: Calculated Scope 1+2 emissions compared with reported
2012 Scope 1+2 em issions ............................................................................
88
Figure 5.7: Calculated Scope 1+2 and Scope 3.1 emissions compared with
values reported to the CDP by five companies............................................
89
Figure 6.1: A histogram of employees at some U.S plants .........................
104
Figure 6.2: Carbon intensities of the U.S and Japanese electric grids
over th e y ears ...............................................................................................
12 5
Figure 7.1: Schematic of the operations covered under the GREET model 140
Figure 7.2: Vehicle cycle primary energy use for various vehicles, GJ per
v ehicle ...........................................................................................................
14 5
Figure 7.3: Lifecycle primary energy use for various vehicles, MJ per km
146
Figure 7.4: Vehicle cycle CO 2 emissions for various vehicles, metric tons
p er v ehicle .....................................................................................................
14 7
Figure 7.5: Life-cycle CO2e emissions, gram per km ..................................
148
Figure 7.6: Model S Life-cycle emissions over 50 states and D.C, gram
C O 2e p er k m .................................................................................................
149
Figure 7.7: Tesla Model S compared to other vehicles from Ashby for use
phase energy and CO 2 em issions.................................................................
151
10
Figure 7.8: Lifetime manufacturing and use phase emissions for the
M odel S in th e U .S ........................................................................................
152
Figure 7.9: V olksw agen XL1 ........................................................................
153
Figure 7.10: Estimated lifetime energy use for the XL1 ............................
155
Figure 7.11: Estimated lifetime CO 2 emissions for the XL1 ......................
155
Figure 8.1: Tailpipe emissions: historical and proposed targets for
variou s cou n tries ..........................................................................................
158
Figure 8.2: Pathways for manufacturing and use phase emissions till
2 0 5 0 ...............................................................................................................
16 0
11
List of Tables
Table 4.1: Results of regression analysis for scaled electricity and scaled
natural gas use vs. scaled production and temperature .............................
65
Table 4.2: Average values of the plant variables in the Renault model ....
67
Table 4.3: Scope 1 emissions intensity regression results..........................
70
Table 4.4: Scope 2 emissions intensity regression results ..........................
70
Table 4.5: Scope 2 emissions intensity regression results..........................
71
Table 4.6: Scope 2 emissions intensity regression results..........................
71
Table 5.1: Activities performed at the surrogate assembly plant ..............
74
Table 5.2: Emissions per vehicle from natural gas and electricity use......
76
Table 5.3: Comparison of the CDP data to Sullivan's data for emissions
in te n sity ........................................................................................................
77
Table 5.4: Materials and vehicle manufacturing results from Sullivan
(19 9 8) ...........................................................................................................
. 80
Table 5.5: Vertical integration at Chrysler, Ford and G.M in the late
1 9 9 0s . ............................................................................................................
80
Table 5.6: Energy use and emissions for the entire automobile
manufacturing cycle based on Sullivan's data............................................
82
Table 5.7: Estimated energy required for in-house operations in an
assem b ly p lant..............................................................................................
85
Table 5.8: Estimated emissions from in-house operations in an assembly
p la n t ..............................................................................................................
85
Table 6.1: Detroit heating and cooling Fahrenheit degree days for 2014..
92
Table 6.2: O utdoor air flow rates .................................................................
94
Table 6.3: Sensible heating and cooling loads for a year ............................
95
Table 6.4: Latent heating and cooling loads for a year...............................
97
Table 6.5: Air exchange heating and cooling loads .....................................
98
12
Table 6.6: Average sol-air temperatures for external surfaces for the
w inter and sum m er season ..........................................................................
100
Table 6.7: Heating and cooling loads across different external surfaces ...
102
Table 6.8: Net process equipment heat release ...........................................
106
Table 6.9: Summary of the internal heat gains on a per vehicle basis ......
107
Table 6.10: Heating load, energy requirement and CO 2 emissions............
109
Table 6.11: Cooling load, energy requirement and CO2 emissions............
109
Table 6.12: Comparison of our model to literature .....................................
110
Table 6.13: Energy use at the surrogate plant in Detroit ..........................
113
Table 6.14: CO 2 emissions from the surrogate plant in Detroit .................
113
Table 6.15: Winter heating loads in the base case and the warmer winter
119
case due to air exchange...............................................................................
Table 6.16: Winter heating loads in the base case and the warmer winter
120
case due to conduction through external surfaces ......................................
Table 6.17: Summer cooling loads in the base case and the less humid
sum m er case due to air exchange ................................................................
121
Table 6.18: Maserati energy and emissions data, 2010-2013.....................
122
Table 6.19: Comparing emissions from purchased electricity for vehicle
assem bly in Japan and the U S ...................................................................
123
Table 6.20: Aggregate production, Scope 1+2 emissions and emissions
intensity of eleven companies from 2008 to 2012 .......................................
131
Table 6.21: Estimates of the impact of scenarios on emissions..................
134
Table 6.22: Change in CO 2 emitted per vehicle for a change in production
volume and capacity, given that all else remains the same .......................
135
Table 7.1: Assumptions for the Model S with lightweight and
conventional m aterials.................................................................................
142
Table 7.2: Results for the Tesla Model S vehicle cycle................................
143
Table 8.1: Manufacturing and use phase emissions comparison between
average European vehicle and the Volkswagen XL1..................................
159
13
14
Chapter 1: Introduction
In this section, we introduce the topic of automobile manufacturing, its
impact on the environment in terms of fossil fuel depletion and carbon
emissions, and why this sector of the global economy deserves closer
attention if we are to meet global C02 emissions reduction targets.
1.1 State of the global automobile industry
Global vehicle production has seen tremendous growth in the last two
decades. Assembly of cars and light trucks rose to 83 million vehicles in 2013
from 45 million vehicles in 1990 [1]. This rapid growth in the late 1990s and
early 2000s was interrupted by the global recession of 2008-09 in which
demand fell dramatically. Since then, the industry has consolidated and has
exceeded pre-recession production volumes. However, the effects of the
recession are still being felt by the industry. European factories are
underutilized, with an average utilization rate 15% points lower than what it
was in 2000 [2]. In North America, several production facilities were closed,
and entire product divisions (like General Motors' Pontiac and Saturn) were
dissolved. In 2009, Chrysler and GM filed for bankruptcy protection with the
U.S government to allow them to restructure [3] [4]. In 2009, U.S automobile
15
utilization dropped down to 60% and China displaced the U.S as the top
automobile producer in the world. China has maintained the top spot since
then, but U.S automobile manufacturing has made a strong recovery and
utilization rates reached 90% in 2013. Most of the growth in automobile
capacity and production in the last few years has happened in China, South
Korea, India, Brazil and Mexico [5].
The type of vehicles being manufactured is also changing. In 2012, small
vehicles accounted for about 30% of global vehicle sales or about 24 million
vehicles, and they are expected to reach 30 million vehicles by 2020. Most of
this growth is driven by the markets of developing countries [2].
Carmakers are also responding to legislation mandating minimum fuel
economy standards in many countries. The New European Driving Cycle
(NEDC) standard for European vehicles has set the target at 95 grams CO 2
per km for the year 2025. In the US, the Corporate Average Fuel Economy
(CAFE) standards originally set a target of 99 grams CO 2 per km for the year
2025. However,
calculated
CAFE
standards
have
been
reworked
and
are
now
based on vehicle footprint. Thus, each automaker has its own
target based on the composition of its fleet. A fine is imposed if the fleet does
not meet the prescribed standards. Carmakers have started using lightweight materials in vehicles to increase fuel efficiency. Aluminum, plastics
and composites are increasingly used in cars, replacing conventional steel.
Often, the low-weight material used as a replacement for steel has a higher
material production energy and carbon footprint. However, the savings in
energy and emissions which happen over the vehicle use phase are more than
enough to offset this higher impact. Hybrid vehicles, electric vehicles, and
alternative fuel technologies are also being developed to reduce tailpipe
emissions.
16
1.2 Motivation
Brief introduction to climate change
The Intergovernmental Panel on Climate Change (IPCC) was established by
the United Nations in 1988 to study climate change - its causes, potential
effects, and mitigation strategies. The IPCC's most recent assessment report,
published in 2014, warned that the concentration of greenhouse gases (GHG)
in the atmosphere had exceeded levels measured or estimated for as far back
as 800,000 years. The report warned that the rate of increase of GHG
concentration had not been witnessed in the past 20,000 years. The three
(N 2 0). A most certain conclusion is that anthropogenic emissions of CO 2
-
main GHGs are carbon dioxide (C0 2 ), methane (CH 4) and nitrous oxide
from fossil fuel burning and land use change - are the main cause of the
increase in its concentration in the atmosphere [6, p. 467].
When CO 2 is present in the atmosphere, it absorbs long-wave radiation
emitted from the earth's surface thereby causing global warming. The IPCC
reports that most of the increase in CO 2 concentration in the atmosphere has
happened due to fossil fuel burning. Over the period 2002-2011, annual
emissions from fossil fuel burning and cement production averaged (8.3
0.7)
Gt of carbon [6, p. 486]. From 2005 to 2011, the CO 2 concentration in the
atmosphere increased by (11.66
0.13) ppm. The 2011 global average CO 2
concentration was (390 +- 0.28) ppm [7, p. 166]. The IPCC also reports that
the first decade of the 21st century was the warmest on record [7, p. 161].
The global warming potential (GWP) of various GHGs over a certain time
period can be expressed in terms of how much CO 2 would add the same
amount of energy to the atmosphere. As an example, consider methane. The
17
GWP potential of methane 20 years after its emissions is 86 whereas after
100 years it is 28 [8, p. 714]. The GWP of a compound depends among other
things on its lifetime in the atmosphere, how much radiative forcing it
causes, its stability and reactions with other compounds in the atmosphere.
Energy use in industry
Global industrial use of fossil fuels is the largest contributor to CO 2
emissions. Figure 1.1 shows how CO 2 emissions from fossil fuel burning broke
down by end-use sector.
o Residential and Other
o Industrial
o Transportation
23%
38%
Total:
31,342
rnillion tons
Figure 1.1: Global CO 2 emissions from fossil fuel combustion by sector, 2011
In 2011, industrial emissions accounted for almost 39% of total CO 2
emissions. This was followed by emissions from transportation at almost
23%. Of the total transportation emissions, about 72% were from road
transport. The category "Other" includes commercial services, public services,
agricultural activities among others [9, p. 11].
18
Total industrial emissions in 2010 amounted to 13.1 GT CO 2 out of which 5.3
GT were from direct energy-related emissions, 5.2 GT were indirect emissions
from the generation of electricity and heat, 2.6 GT were from process
emissions, and the rest were from waste. Total emissions of greenhouse gases
in 2010 consisted largely of CO 2 (85%) and methane (8.6%) [10].
Globally, industrial energy use (and CO 2 emissions) is dominated by a few
sectors - iron and steel, chemicals and petrochemicals, cement, paper, pulp
and print, and food and tobacco. These sectors account for 70% of global
energy use [11, pp. 476-477]. Their high energy use is attributable to the
energy-intensive processes in these industries.
Automobile manufacturing falls under the broader sector of transportation
equipment. This sector ranks 11th globally in terms of energy use and CO 2
emissions. In 2005, it used 1,423 PJ of primary energy, and emitted 49
million metric tons of CO 2 [11, p.
481].
Figure
1.2 shows the
major
manufacturing sectors by energy use and CO 2 emissions.
Food and
Paper, pulp and
print
tbcO Non-ferrous
5%
metals
6% Machinery
30%
4%
Textile and
leatheMining
2% and
quarryin
Construction 2%
1%/
Transport
equipment
ood and
19
1%
(a)
Paper, pulp and
rintV
z
3%
Non-ferrous
metals
2%
d
Food
to cco
4%
Textile and
inery leather
M
2%
1%
Mining and
quarrying
onstruction
1%
1%
Transport
equipment
1%
Wood and wood
products
0%
(b)
Figure 1.2: Global industrial (a) primary energy use and (b) CO 2 emissions
by end-use sector, 2005
In 2006, the transportation equipment sector was the 6th largest in the U.S in
terms of annual primary energy use at 904 TBtu [12, p. 17]. It produced 53
million metric tons of C02-equivalent (CO2e) emissions of which 15 million
metric tons were generated on-site [12, p. 37]. Figure 1.3 shows the energy
consumption breakdown by sector for the United States. Of the total
transportation equipment sector, 8% is due to passenger vehicles and light
truck manufacturing (North American Industry Classification System sectors
336111 and 336112). Note that this does not include heavy duty truck
manufacturing
Nevertheless,
or
the
motor
vehicle
parts
manufacturing
sectors.
we can draw important lessons by studying automobile
assembly, about which sufficient data is available, that can then be applied to
the transport equipment sector in general.
20
Computers,
electronics and
electrical eq.
Textiles
2%
Cement Glass
2%
2%
Foundri
Machinery
2%
Alumina and
aluminum
Fabricated
metals
Plastics
4%
4%
Transportation
equipment
5%
Figure 1.3: Primary energy use in the U.S manufacturing sector, 2006
In the materials production industries, energy costs can be significant,
around 20% of the total costs in the steel industry [13, p. 22]. The non-process
load (for example, heating, ventilation and air-conditioning (HVAC)) is small
compared to the process load [13, p. 17]. Since energy costs are high, these
industries have made notable improvements in their processes and reduced
their energy footprint. For example, in the U.S, the steel industry has
reduced energy intensity by 60% over two-and-a-half decades going back from
2006 [13, p. 19]. The U.S cement industry reduced energy intensity by 30% in
the period from 1970 to 1999 [14, p. 10].
Gutowski et al studied energy use for some of the most energy consuming
materials - steel, cement, aluminum, paper and plastics - and found that
energy use for these materials could not be halved by 2050, as demand for
these materials doubles [15]. This puts additional pressure on other sectors of
manufacturing, as well as on residential and transportation sectors to do
better than a 50% reduction.
21
Process energy needs do not always dominate non-process energy needs. As
we move down the supply chain i.e., away from materials production, nonprocess energy requirements get more significant. While transportation
equipment ranks 6th in terms of overall energy use, it ranks
3rd
in terms of
energy use for non-process needs, at 196 TBtu, only behind the forest
products and chemicals sectors [12, p. 31]. This is shown in Figure 1.4 below.
In 2006, the U.S automobile and light truck sectors consumed 44.3 TJ of
primary energy to meet HVAC needs [16].
Alumina and
aluminum
1%
Glass
2%
Cement
1%
~
Foundries
2%
Textiles
3%
Petroleum
refining
4%
Iron and steel
5%
Plastics
5%
Fabricated
metals
6%
Computers,
electronics
and electrical
equipment
7%
Machinery
6%
Figure 1.4: Non-process energy use in U.S manufacturing, 2006
The residential sector is a major end-use contributor to CO 2 emissions from
fossil fuel burning, as seen in Figure 1.1 above. A residence uses energy for
HVAC, water heating, lighting and appliances. To minimize residential
energy needs, the concept of passive houses was developed. Careful attention
is given to the building construction, exposure to the elements, air flow,
employing
energy-efficient
lighting and appliances
and heat recovery
techniques. The use of renewable energy sources is emphasized. The passive
house standard requires heating demand to be less than 15 kWh per square
22
meter of treated floor area, total primary energy demand to be less than 120
kWh and air infiltration to be less than 0.6 air exchanges per hour.
Thousands of passive houses have been built so far, most of them in Europe.
Even conventional residences in cold-climate countries have made notable
improvements in reducing heating energy intensity in the past few decades.
Harvey [17] presents a summary these improvements.
The same level of attention to heating and cooling loads, and air exchange
rates has not been seen in factories. In 2006, the automobile and light truck
manufacturing sectors of the U.S economy purchased $886 million of energy,
about half of which was spent on purchasing electricity, 43% on natural gas,
and the rest on other fuels [16]. According to Galitsky et al [18], about 11% to
20% of electricity use is for HVAC needs, and half of fossil fuel is used for
space heating.
The heating load in an automobile assembly plant is
estimated to be about 555 to 860 kWh per square meter of floor area' [16] [19]
[20]. Paint shop air handling requirements are the most stringent.
A few examples of factories incorporating passive house concepts are the
SurTec factory [21] and a factory which builds components for passive houses
[22]. However, these are small factories. One noteworthy example is the
Daimler vehicle assembly plant in Rastatt, Germany. This plant has a floor
area of 539,000 m 2 and it makes Mercedes Benz A- and B-class cars. Daimler
reports
that this plant has eliminated
conventional heating and air
conditioning systems. They utilize groundwater heat by means of a heat
pump, and recover factory waste heat to maintain comfortable operating
conditions year round [23].
The Kaya identity
1 To estimate this, we assume that the factory floor area is 250,000
production is 250,000 vehicles.
23
M2,
and annual
The IPCC presents emissions pathways or scenarios for various levels of
CO2e concentrations in the atmosphere predicted for the year 2100, and the
reduction in absolute emissions needed to meet that limit.
To limit CO2e
concentration in the atmosphere between 430-480 ppm by the year 2100, CO2e
emissions in 2050 would have to be reduced by 41% to 72% compared to
emissions in the year 2000, or 78-118% lower in 2100 compared to 2000 levels
[24, p. 431]. We can use these emission pathways to allocate an emission
reduction target for different sectors. Grimes-Casey et al [25] did this for U.S
automobile use-phase emissions. We can use the Kaya identity (also known
as the
IPAT
equation) to
determine
how energy-efficient
automobile
production needs to be, even as it grows over the next few decades, in order to
meet its emissions target.
The IPAT identity relates the environmental impact (1) to causative factors
like population (P), affluence (A), consumption (C), and technology (T). We
write,
I=PxAx Cx T.
For small changes in each factor, we can write the per cent change as,
AI
I
AP
P
AA
A
AC
C
AT
T
Writing the equation this way helps to identify which controls we can operate
to avoid a certain impact. For example, reducing consumption of a certain
resource might require a political or social action, whereas reducing the
intensity of consumption might require a technological effort. Note that
written this way the equation assumes that the parameters on the right side
are independent of each other.
Here, we use the IPAT equation in different ways to see how global economic
growth poses increasing challenges in reducing energy use and emissions. We
24
consider the global average economic growth as well as that of the United
States economy and how motor vehicle production fits into overall growth. We
use C02-only pathways since data on CO 2 emissions per dollar of GDP or per
unit kg oil-equivalent of energy used are readily available.
In the IPCC's fourth assessment report (AR4), the most stringent target of a
CO 2 concentration of 350-400 ppm in 2100 required reducing global C02
emissions in 2050 by 50-85% compared to 2000 levels [26]. Assuming a
compounded annual rate, this would require an annual decrease of 1.38 to
-
3.72% a year. In the IPAT differential equation, we have, A/I= -0.0138 to
0.0372.
Instead of using the term population literally, we use it here to mean global
value-added in terms of dollar value. We use the world and U.S average gross
domestic product (GDP) figures, calculated by the purchasing power parity
(PPP) method and presented for a constant 2011 international dollar [27].
From 1990 to 2013, world GDP grew at 3.37% and U.S GDP grew at 2.48%
annually.
For representing the affluence and consumption term, we use energy use in
terms of kg of oil equivalent per $1,000 of GDP, with GDP defined the same
way as above. World average energy use defined this way decreased at 1.37%
year-on-year (YOY) from 1990 through 2010 while U.S energy use decreased
at 1.76% YOY [27].
The technology term is represented by the carbon intensity of fuel use (kg
CO 2 per MJ of energy). This is calculated based on World Bank data on CO 2
use per 2011 international dollar of GDP and the energy use term we used
above. World carbon intensity increased from 1990 to 2010 at a rate of 0.14%
a year whereas the U.S carbon intensity decreased at 0.07% over the same
period.
25
Entering the values of AP/P, AA/A and ATIT in the IPAT equation, we get,
AI/I = 2.14% for the world, and AI/ = 0.63% for the U.S. Thus, over the past
few years, global and U.S emissions have not been on the pathways
prescribed by the IPCC. Meeting the targets would still be possible after this
initial overshoot if future emissions could be reduced at drastic rates.
For the second case study, we look at the global picture and separate
automobile manufacturing from global output, which is different from global
GDP. According to the United Nations Industrial Development Organization,
global manufacturing output in 2008 totaled $32 trillion out of which, motor
vehicles output was $2.7 trillion [28]. From 2000 through 2008, global
manufacturing output grew at 9.35% a year, whereas the share of motor
vehicle manufacturing fell at 2.66% a year. Now, we consider the IPCC AR4
target of reducing emissions at 1.38% to 3.72% a year. To achieve this, motor
vehicle manufacturing would have to improve its energy efficiency (defined as
energy use per dollar of sector output) at 7.72% to 10% a year, every year till
2050, if growth continued at the same rate. We can see that allocating the
IPCC target to motor vehicle production presents a steep challenge for the
industry.
Determining efficacy of emission reduction activities
In the past few years, companies, bowing to societal, regulatory or investor
pressures,
have
businesses.
The
begun disclosing
the environmental
CDP is a successful
example
impact
of companies
of their
sharing
information about their operations. In their CDP reports, companies describe
their operations, the nature of risks and opportunities they expect from
climate change, the management structure and incentives they have in place
to reduce their environmental footprint, and actual data on their energy use,
emissions, and savings from these impacts due to the implementation of
26
emission reduction projects. However, often it is not easy to discern the
actual benefit of these emission reduction activities, whether these can be
replicated elsewhere, and whether they make a significant dent in emissions.
For example, automobile manufacturing companies may claim that they
planted trees on a site adjacent to their factory to offset their direct and
indirect emissions. Or an assembly plant may replace existing lighting by
energy-efficient light bulbs, and claim that this reduced their CO 2 emissions.
To really understand the effect of such activities in a factory and to determine
if there are any unintended side-effects, we need a model of a factory which
operates like a typical global automobile factory so we can determine typical
energy loads, and test various scenarios. Such a model can make it possible to
compare and prioritize emission reduction activities.
1.3 Problem Statement
The goal of this thesis is to develop a model of a typical global automotive
assembly plant to predict its direct and indirect energy usage and CO 2
emissions.
There have been surprisingly few studies on energy use in
automobile manufacturing. The most comprehensive ones published over the
past few years rely on data going back several decades. Nonetheless, we use
this data as a starting point to construct our model. We validate the model by
comparing the data to more recent data published voluntarily by automobile
companies to the Carbon Disclosure Project (CDP) and in their sustainability
reports. The CDP data on emissions are more detailed than the energy data.
So we estimate CO 2 emissions from the energy model and compare it with the
CDP data.
The ultimate utility of such a model would be in determining the impact of
emissions reduction activities, or the effect of outside factors, like weather or
27
the carbon intensity of the electric grid, on factory emissions. To achieve this,
we construct a simple bottom-up thermodynamic model of the automobile
factory. We estimate the magnitude of heat loss in the winter (or gains in the
summer) due to air exchange, transmission through walls, and other sources
of heat drains (or gains). We can then evaluate scenarios like the effect of a
warm winter on the heating load of a factory, the effect of moving a plant
from one location to another, or installing more efficient lighting in the
factory, to estimate their impact.
This
thesis
will
also
present
several
case
studies
of
automobile
manufacturing which illustrate important concepts like the heat-replacement
effect in a factory to the move towards lightweight but more energy-intensive
materials. We also show how publically available data, including the CDP
data, can be used to build linear regression emissions models of major OEM
factories. We can identify which factors are significant and how a company
can operate different technology levers to reduce the carbon footprint of its
factory.
1.4 Thesis Structure
This thesis is structured as follows. In Chapter two, we review existing
literature on energy use in automobile
manufacturing.
Chapter three
contains an introduction to the Carbon Disclosure Project and an analysis of
automakers reports. Chapter four contains case studies of a component
manufacturer and a vehicle assembler for whom we have some factory-level
data. In Chapter five, we develop a model of a typical assembly plant. We also
determine the level of vertical integration at such a plant. In Chapter six, we
develop an energy model to determine the energy needed for heating and
cooling activities in the factory. We then test the impact of various emission
reduction activities. In Chapter seven, we present studies of the Tesla Model
28
S and the
Volkswagen
XL1 which represent
a dramatic
shift from
conventional vehicles. Chapter eight concludes the thesis with our evaluation
about the future of energy use in automobile manufacturing and use, and
whether climate change targets can be achieved.
29
Chapter 2: Literature Review
Literature on automobile production broadly falls into the following mutually
non-exclusive categories:
1. Automobile lifecycle assessment (LCA) studies,
2. Plant-level energy use surveys,
3. Bottom-up models of automobile production,
4. Aggregate motor vehicle production (sector-level or company-level)
data.
Automobile lifecycle assessment (LCA) studies
Automobile LCAs are the most common studies on the topic. LCA studies
have been done for different kinds of vehicles - fictional, generic vehicles
(Sullivan 1998 [29]), to conventional gasoline vehicles (Schuckert et al's Polo
LCA [30]), to LCAs of hybrid and electric vehicles (Hawkins 2012 and
Hawkins 2013 [31] [32]). The materials production and use phases of the
automobile lifecycle are perhaps the best understood. Vehicle component
production and final assembly are harder to analyze. This is because a typical
30
automobile consists of several hundred sub-assemblies and components,
manufactured by a vast supply chain. So, actual data on component
production or even vehicle assembly is difficult to gather. Nonetheless,
Sullivan et al (1998) attempted to narrow down a 20,000 part vehicle into 644
components, and got data on energy use for some of the major components of
an automobile. They also obtained actual energy data from assembly plants.
Their results for energy use were 94 GJ per vehicle for materials production,
and 39 GJ per vehicle for vehicle manufacture. The CO 2 emissions were 4.4
tons C02 per vehicle from materials production, and 2.5 tons C02 per vehicle
from vehicle manufacturing.
A different approach to LCA is the Economic Input-Output environmental
LCA (EIOLCA) as used by Hendrickson et al [33]. It relies on economic
interactions between various sectors of an economy. Various products can be
analyzed for their environmental impact if its producer or purchaser price is
known. For example, Samaras and Meisterling [34] used this model to
determine the impact of a Toyota Corolla whose producer price they
estimated to be $13,500. They use the 1997 producer price model and give a
value of 102 GJ of primary energy use, out of which 22 GJ is electricity use,
26 GJ is coal use, 43 GJ is natural gas use, and 8.5 metric tons of CO2e
associated with vehicle production. Note that this includes the entire
automobile supply chain. If we use only CO 2 (not CO2e), the result is 7.2
metric tons of CO 2 . The EIOLCA results tend to be higher compared to
typical LCA estimates. This is partly because of the aggregate nature of the
data, but also because EIOLCA includes lot more activities than a typical
LCA. We often consider the EIOLCA estimate to be an upper bound.
Plant level energy use surveys
31
Boyd et al [35] collected and analyzed three years of data from 35 assembly
plants in the U.S to construct an Energy Star Energy Performance Indicator.
They focused only on body weld, painting and final assembly activities, and
presented electricity use and fuel use data. The mean value of electricity use
per vehicle was 6.9 GJ per vehicle, and fuel use per vehicle was 4.8 GJ per
vehicle. They found a strong correlation between energy use and parameters
like vehicle wheelbase, weather conditions, and utilization. In this thesis, we
will use a similar approach to construct a regression model for emissions from
factories.
Galitsky et al [18] reviewed published data to get some rough estimates of
electricity use in vehicle assembly plants, and a simple breakdown of fuel use
in the factory. They quote a value of 9.2 GJ per vehicle of electricity use.
Using the MECS data, they estimate fuel use for automobile production to be
6.8 GJ per vehicle. Their work is cited by others who attempt to construct
bottom-up models of assembly plants.
Bottom-up models of automobile production
One of the earliest efforts to construct a bottom-up energy use model of
automobile assembly was by Brown et al in 1985 [36]. Their analysis was
based on by sector-level surveys like the Annual Survey of Manufacturers
(ASM). They developed process flow diagrams for "Motor Vehicles and Car
Bodies" as well as "Motor Vehicles Parts and Accessories" sectors under the
old Standard Industrial Classification (SIC). Visits to two factories for each
sector informed this process. They present mass, heat and energy flow
diagrams normalized by weight of the vehicle. Their estimates for production
energy use are electricity use of 31 GJ per vehicle, and fuel use of 25 GJ per
vehicle. These values are much greater than other sources. This approach
suffers from two problems: the data are highly aggregated, and it is difficult
32
to support the argument that automobile assembly energy use has a strong
correlation to vehicle weight.
The most comprehensive model of energy use in automobile manufacturing is
developed by Sullivan et al [20], and called the VMA (part manufacturing and
vehicle assembly) model. They were able to account for 92.5% of a vehicle's
weight by considering the major materials and transformation processes.
They found value from literature for each material-transformation pairing.
When data on a certain material was not available, they represent the
material by a "surrogate" material. Most importantly, they use data from
Galitsky et al to represent the base load energy in an assembly plant - for
heating, HVAC, lighting and compressed air. However, similar data for
plants making components is hard to find. Their estimates for automobile
energy use and emissions are 13.8 GJ and 852 kg CO 2. Including material
transformation, the estimates are 32 GJ and 1.9 tons C02. However, they
used a high value of carbon intensity of the electric grid, about 0.77 kg CO 2
per kWh. So the CO 2 estimate might be on the high side.
The Argonne National Laboratory's "Greenhouse gases, Regulated Emissions,
and Energy Use in Transportation (GREET)" model combines data on vehicle
assembly from Boyd with data on unit processes like stamping from other
sources. For vehicle assembly, they cite Sullivan's VMA model. Their model is
fairly easy to use and can compare different types of vehicles' lifecycles. The
assembly data is common to all vehicles, so the differences lie mostly in
materials and use phases [37].
Aggregate motor vehicle production (sector level or company level)
data
33
For U.S sector-level information on energy use, we refer to the Manufacturers
Energy
Consumption
Survey
(MECS)
published
by
the
U.S
Energy
Information Administration. The survey, conducted approximately every four
years, goes back to 1985. In the more recent survey from 2006, over 15,000
establishments, representing some of the largest companies by payroll, were
included. The survey is mandatory and it collects information on energy
consumed by end use, region, and fuel type. For automobile manufacturing,
we look at sectors 3361111 Automobile Manufacturing, and 336112 Light
Truck Manufacturing, under the North American Industry Classification
System
(NAICS).
manufacturing
is
For some
included,
sectors, data on Canadian
but the
auto
sectors
only
and Mexican
consider
U.S
manufacturing. In 2006, the two sectors combined consumed 80 trillion BTU
of energy (this includes net electricity and is not primary energy). We get
automobile production data from other sources, and we estimate that the fuel
use per vehicle produced was 5,060 MJ, and electricity use was 8,174 MJ per
vehicle (primary energy) [16].
Company-level emissions information can be obtained from the CDP and
sustainability reports. The CDP observes the Greenhouse Gases Protocol in
defining sources of emissions. Scope 1 emissions or direct emissions are those
arising from use of fuels on-site. Scope 2 emissions or indirect emissions are
from purchased fuels and electricity. Scope 3 emissions are also indirect
emissions but arising from business activities like emissions associated with
purchased goods, business travel, use of sold products etc. Companies also
may report division-level or company-level energy use by fuel source.
However, these are not further split by end-use and it is difficult to analyze
these numbers meaningfully. We discuss CDP reports in detail in Chapter 3.
Other literature on automobile production
34
The existence of a base load component and a variable component to
equipment or factory energy use has been shown before [38]. Bolin [39]
investigated energy use in an engine production facility and found a similar
pattern of energy use. We extend Bolin's analysis for the engine production
plant, discovering unintended consequences of increased production, and
utilize the observations of base loads and process loads in constructing a
model of a typical automobile assembly plant.
While models of automobile assembly plants have been developed before, it
has been difficult to make comparisons between plants, accounting for
variables like utilization, vehicle type, and weather conditions. Moreover, a
comparison of the models with recent factory data has been missing. Finally,
analysis of emission reduction activities has not received enough attention.
This is in part because robust thermodynamic models of assembly plants
have not been constructed. In this research, we address these deficiencies. A
more in-depth look at assembly plant thermodynamics and an exergy
analysis of automobile plants is being undertaken by Schmieder [40].
A summary of the references mentioned here, and a breakdown of the energy
use they provide by end-use is given in Appendix A.
35
Chapter 3: Analysis of CDP Reports
In this chapter, we give an introduction to the Carbon Disclosure Project
(CDP) and then study disclosures made by select automakers in the past few
years. Some trends on emissions and energy use become apparent. In chapter
5, we will use the CDP data to test our assembly plant model. This chapter is
based on work done by Eaton and Raykar for the MIT class 2.83 in the spring
of 2014 [41]. We also benefited from data compiled by students in that class
for several automobile companies.
3.1 Introduction to the CDP
The CDP is a London-based non-profit whose mission is to "transform the
global economic system to prevent dangerous climate change and value our
natural resources by putting relevant information at the heart of business,
investment, and policy decisions" [42].
In order to advance its mission, the CDP provides "the only global system for
companies
and
cities to
measure,
disclose,
manage
and share
vital
environmental information" [43]. The CDP issues questionnaires for climate
36
change, water and forest impacts, goals, risks, and opportunities. In order to
motivate company participation, the CDP uses the backing of institutional
investors [43].
The CDP is known for its fast growth and high participation rate. Figure 3.1
shows the increase in total responses for the sum of the Climate Change and
Supply Chain (starting in 2008) programs. For the first three years, only the
Financial Times' Global 500 list of companies received questionnaires.
Participation quickly grew to over 70% (and was 81% in 2013) [44] [45]. After
2007, the response rate dropped and remained relatively flat thereafter.
However, in each of these years significantly more companies were targeted
[44]. In general, a public participation rate tends to increase the year after
being targeted. Companies have been increasing participation in other ways
as well. For example, from 2011 to 2013 the number of Global 500 companies
reporting emissions verification doubled to 71% [45].
Of course, it has not
been a steady path towards increased disclosure. Most companies continue to
not report Scope 3 emissions (besides business travel). And in a particularly
striking case, the percentage of U.S firms reporting Scope 1 emissions
5000
-
dropped from 82% in 2009 to 25% in 2010 [44].
4000
---------------------
0C
CL
-- - - - - - - - - ----1000
M)
----------------------
---
-
(IC)
2 0 00
NT
o>N
(1)C0
--
(O
cr
020303 2--4-2----2----2--7-2----2----201o
0820921
20620
200
200
2003
37
2 11-2-12
0121
Figure 3.1: Total number of responses for the Climate Change and Supply
Chain programs
3.3 CDP Questionnaire
The CDP questionnaire is divided into three main modules: Introduction,
Management, Risks and Opportunities, and Emissions. The introductory
module asks for general description of the organization, the year for which
the data is being reported, the list of countries for which data will be
provided, and the currency in which financial information will be reported.
Here we will only focus on the Emissions module.
Emissions Reporting
Sections 7 to 14 of the questionnaire deal with emissions reporting. A few
questions deserve special mention. Section 7.2 asks for the methodology or
protocol being used to collect Scope 1 and 2 emissions data. Scope 1 emissions
are the emissions arising from consumption of fuel on-site. The on-site energy
is supplied by burning fuels, usually natural gas, on the factory premises.
Scope 2 emissions are arising from purchased and consumed energy, often
electricity. Section 7.3 asks for a reference used to determine Global Warming
Potentials (GWP) of different emissions.
Section 8.1 deals with how boundaries are drawn by the company to
determine Scope 1 and 2 emissions. Companies typically use Financial
Control or Operational Control boundaries. Sections 8.2 and 8.3 are where
the Scope 1 and 2 emissions will be entered. Section 8.6 asks to report the
status and standards used for verification of emissions reporting. The CDP
accepts several standard of emissions verification, including the California
Mandatory GHG Reporting Regulations standard, the Climate Registry's
General Verification Protocol and the ISO 14064-3 standard.
38
In sections 9 (and 10), Scope 1 (and Scope 2) emissions can be reported on per
country and by business division or facility-wise basis. Section 11 deals with
the use of direct and indirect energy. In section 11.1, companies can report
what fraction their energy costs are of their operational costs. Section 11.2
asks for a breakdown of electricity, fuel, steam, heating and cooling energies
in MWh. The type of fuel use is elaborated in section 11.3.
In section 12.1, the reasons for changes in emissions values are listed. These
could be due to emission reduction activities, mergers, divestments, changes
in boundaries, changes in methodologies or other reasons. In sections 12.2
and 12.3, emissions are reported on a per unit revenue, and per full time
equivalent employee basis. Automobile companies also often choose to report
emissions on a per vehicle basis in section 12.4.
Note that carbon removal efforts (say, by planting trees) are not to be
included in section 12.1. These efforts can be reported as additional
information, but section 12.1 only deals with emission reduction efforts.
In section 14, Scope 3 emissions are documented. Scope 3 emissions are
segmented into 15 categories, including purchased goods and services,
business travel and use of sold products. The flexibility of the CDP
questionnaire allows companies to use various methods to determine these
emissions.
Of particular interest from an automobile manufacturing perspective is the
purchased goods and services section (called Scope 3.1). An automobile is
assembled from hundreds of sub-assemblies which in turn may have several
hundred parts. Auto companies source these components from hundreds of
suppliers. This makes it challenging to actually measure CO 2 emissions from
part manufacturing. Thus, companies typically use Life-Cycle Assessment
(LCA) to get an estimate of the Scope 3.1 emissions. In 2013, BMW, Daimler,
Nissan, Renault and Volkswagen reported using LCA software to determine
39
Scope 3.1 emissions. Since companies usually know the material content and
processing steps involved in making the car components, this can lead to a
reasonably good estimate. Some companies also participate in the CDP's
Supply Chain Program which applies the emissions reporting questionnaire
to the upstream supply chain. General Motors (GM) is one such company,
requesting its suppliers to document and report their emissions. As was said
before, this is a challenging activity, and in 2013, GM reported that its
estimate covered only 10% of its suppliers.
Several studies have shown that the use of sold products constitute the
significant fraction of lifecycle emissions from an automobile. Burnham et al
report that over a lifetime of an internal combustion engine vehicle, vehicle
operation
constitutes
73% of CO 2 emissions,
the
fuel production
and
distribution constitutes 16%, and the vehicle, and the vehicle manufacturing
(from raw material extraction to final assembly) constitutes 11%. Regulations
in several countries also require companies to make efforts to reduce the
emissions from combustion of fuel by the vehicles. Companies typically report
the use of sold products emission value by determining the well-to-tank
emissions which relate to the production and distribution of gasoline or
diesel, and the tank-to-wheel emissions for the fleet of vehicles sold in the
preceding year.
AU section
Automakers are asked to provide additional information about their sales
across different vehicle platforms and engine types in different regions,
emissions from those vehicles (in gram CO 2 per km or gram C02 per mile)
and the deployment of clean technologies in the vehicles. Sales-weighted CO 2
emissions for different regions and vehicle segments are reported in sections
AU2.3 and AU2.4
40
Section AU3.1
(a-h) offers choices of different clean technologies
and
companies can report what fraction of their fleet implements that technology.
For I.C engine vehicles, the technologies include turbocharger downsizing,
exhaust recovery and Flexfuel among other options. For hybrid vehicles,
choices include start and stop regenerative braking, full hybrid or plug-in
hybrid.
Sustainability Reports
Many automobile companies have begun publishing sustainability reports in
recent years. Often, companies extend their corporate social responsibility
(CSR) reports to add environmental management data, describing how their
design and manufacturing processes incorporate
environmental-friendly
thinking. We rely on sustainability reports to fill in gaps when data from
CDP reports is incomplete.
3.4 Data Analysis
This section contains the analysis of the data gathered from CDP reports. In
some cases, data was drawn from sustainability reports of the company. For
example, if a company disclosed information to the CDP but did not make it
public (for example, Volkswagen in 2012), or if an error was observed in a
CDP report but the sustainability report seemed to have the right number,
the sustainability report number was chosen. And in cases where a company
did not report data to the CDP (for example, Fiat and Ford in 2008) but
disclosed that information in their sustainability reports, that data was used.
The CDP has restricted access to the 2010 CDP reports, but it has published
summary of the data, which along with sustainability reports was used to
populate the 2010 numbers.
41
Of the companies studied over the course of the MIT class, the eleven
companies studied here have the most complete data - geographically and
over time. Usually, the parent company discloses information of all its
divisions and subsidiaries and so we see some consolidation in the numbers.
For
example,
Audi's
and
Lamborghini's
numbers
are
published
by
Volkswagen, and Fiat's results include data for Chrysler (from 2011) and
Maserati.
Figure 3.2
shows the annual global production of eleven automobile
companies from 2008 to 2012 [46]. With the exception of Hyundai and Fiat,
all companies reduced production in response to the global recession which
struck in 2008-09. Since 2009, companies have largely returned to their prerecession production levels, and in some cases exceeded those levels. The
smallest automaker in terms of production numbers studied here is BMW
with 2 million vehicles, and the largest is Toyota with 10.1 million vehicles
produced in 2012. We notice some other large shifts in this plot. Fiat's
production numbers increase by more than 2 million after it formed an
alliance with the Chrysler group. And the effect of the 2011 Fukushima
earthquake in Japan and the floods in Thailand on Honda, Toyota, and to
some extent Nissan is evident.
42
12
c 10
-o-BMW
- GM
- Renault
--- Daimler
--Honda
-e- Toyota
--------------- -- - -
-+Fiat
-+-Ford
-Hyundai
-0-Nissan
o Volkswagen
- -------- - -- - - --- - - ---------
C
0
-0
00
0
2008
2009
2010
Production Year
2011
2012
Figure 3.2: Global production numbers from 2008-2012 for eleven
automakers
Figure 3.3 shows the absolute emissions reported by auto companies from
2008 onwards [47] [23] [48] [49] [50] [51] [52] [53] [54] [55] [56]. There are a
few missing data points: Renault's 2009 absolute emissions could not be
found from their CDP or sustainability reports. GM's emissions data are
available from the year 2010. Daimler's numbers are reported only from the
year 2010 onwards because prior to that they did not disclose information to
the CDP, and it is not possible to separate Daimler's passenger vehicle data
from that of their trucks and heavy commercial vehicles.
43
10
C
S
o
C
-e- BMW
-e- Daimler
--- GM
- Renault
--Honda
-0-Toyota
--
0
-Fiat
Hyundai
o Volkswagen
-+- Ford
-e-
Nissan
0
8
------- - - -- - - - - - - - - - - - 0
2
----
- - -
-----
---
C
LU
0
00
0
- - - - - - - - -- - - - - - - - - - - - - - - - - - - - - -
-
0
0
U)
0.
0IF
0
2008
2009
2010
2011
2012
Production Year
Figure 3.3: Absolute Scope 1+2 emissions from 2008-2012 for eleven
automakers
Comparing Figure 3.2 and Figure 3.3 we notice some outliers. Toyota's
reported emissions stayed almost constant from 2009 even though their
production volume went from 7.2 million to 10 million in that time period.
BMW, Daimler and Fiat reported a decrease in absolute emissions from 2011
to 2012 though their production volumes increased over that time frame.
These companies attribute this reduction -- BMW (5.1%), Daimler (6.4%) and
Fiat (4.2%) -- to emissions reduction activities. Along the same lines, Ford's
absolute emissions fell by 3.2% from 2010 to 2011 although its production
volumes increased by 3.1%. Ford attributes this to emissions reduction
activities,
particularly to the implementation
of "Three Wet"
painting
technology which it claims reduces CO 2 emissions by 40%. The boundaries of
this project are not clear from Ford's CDP report. Meanwhile, Honda's
absolute emissions increased from 2008 to 2009 although their production
44
volume decreased. No reason was found for this behavior. Other noticeable
shifts are Fiat's absolute emissions almost doubling with its acquisition of
Chrysler.
Toyota's production was affected in 2011 due to the Fukushima earthquake
and the Thai floods but their emissions increased from 2010 to 2011, which
could be attributed to the change in electric grid sourcing in the aftermath of
the 2011 earthquake. The company's operations bounced back in 2012. While
Toyota's production increased by 2 million (or 25%), their absolute emissions
increased only by 2,000 metric tons (or 0.028%). Because of this, Toyota's
emissions intensity decreased by 20%. This is seen in Figure 3.4. In sections 4
and 5, we attempt to explain this effect.
S
1.6
-e
| --
BMW
GM
Renault
+
-e--
Daimler
Honda
Toyota
--
o
Fiat
Hyundai
Volkswagen
-9-
==O=
Ford
Nissan
Average
0
0
+7% p.a
1.2
C-)
-3.2% p.a
-2.3% p.a
-3.7% p.a
-==-5.73% p.a
-6.2% p.a
-1.7% p.a
-7.2% p.a
-5.7% p.a
E
a)
0.8
C*%j
0L
0
0.4
-4.5% p.a
U)
0.0
2008
2009
2010
Production Year
2011
2012
Figure 3.4: Scope 1+2 emissions per vehicle for eleven automakers
Figure 3.4 shows the Scope 1+2 emissions on a per vehicle basis for the
eleven automakers. The per cent change in emissions from the first available
data point to the 2012 intensity is also shown. The overall trend is towards
45
decreasing intensity of emissions for most automakers. In their reports,
companies attribute
this to emissions reduction
activities,
changes in
production output and change in boundaries among other reasons. The major
exception here is Honda whose emissions trend upwards since 2008. The
impact of the 2008-09 recession, the Japan earthquakes and Thai floods on
Honda, and to some extent on Toyota is striking. Each company moved up
and down on emissions intensity from 2008 to 2009, and then from 2010 to
2011 as their operations - and those of their suppliers - were affected.
We can plot the emissions intensity data over the years against production
volume to obtain curves along which companies move as their production
output changes. This is shown in Figure 3.5. There, the Scope 1+2 emissions
intensity is plotted against annual production for the years 2008-2012. As
discussed earlier, some data points are missing. Linear fits are done and the
trend lines are shown for each automaker.
As expected, we see a decrease in the emissions intensity with increase in the
production volumes. But companies seem to move along different slopes. For
example, the per cent decrease of emissions for BMW is markedly higher
than, say, Volkswagen. At lower production volumes, the spread of the
emissions intensity goes from 0.39 metric tons CO2e per vehicle for Renault
to 1.28 metric tons CO2e per vehicle for Daimler, a factor of 3.3. The spread is
narrower at higher volumes. For example, Volkswagen's emissions intensity
of 1.31 metric tons CO2e per vehicle is only 1.8 times Toyota's 0.72 metric
tons CO2e per vehicle. We suspect this very wide divergence
at low
production volume is due to the degree of outsourcing done by companies.
Our hypothesis is that Daimler retains more manufacturing than Renault.
Other contributing factors could be vehicle size, local temperature and
climate, and regional differences in carbon intensity of electric grids. We can
also see the effect of the 2011 Japan earthquakes on Nissan. Nissan's
46
emissions intensity increased from 0.68 in 2010 to 0.72 metric tons CO2e per
vehicle in 2011.
47
1.6
1.44
0(2011)
Daimler
Honda
1.28
0)
E
1.359
(2009)
1.31
(2010)
1.2
E
1.20
1.20
(2012
(2012)
Volkswagein
q
1.070
(2009)
6
(2011)
(2009)
1.11
06Fat
0
_
0.8
06
R0
0.47
04
Renault
(2010)
(
1
0
)(
(20)
0
)0
0 (2012
)2010
0.39
0.4 2009) (2010) 0.56
0.4!W 0.39
(2012) (2011)
Toyota
10
3
0.72
(2012)
0.55
Hyundai
0.0111
12
0.92
1
(.61Hy86
(04
0.620
0.1M208)6
(2012)
C,
1
(
0.6
(2
0.0
02)
2)Ty83
8)
0 201(.0
20 0)
00(09)
0.2)
(20
120280(2008)
.
(21.
111.
1.01
2(0
(:(2009)
1.03
o0
20
(2011) 0.98 Ford
(
(2010)
4
6
5
7
8
9
10
Global Vehicle Production in Millions
Figure 3.5: Scope 1+2 emissions intensity over the years for eleven automakers plotted against their global
production numbers
48
So far, we have only looked at a company's in-house operations and its Scope
1+2 emissions. These figures may differ for companies depending on the level
of capacity utilization and outsourcing. Therefore, factoring in Scope 3.1
emissions might enable better comparisons between companies. The raw
material extraction and primary material processing have been shown to be
the more energy (and carbon) intensive processes [37], accounting for as
much as 75% of the vehicle manufacturing cycle.
The Scope 1+2 and Scope 3.1 emissions are shown in Figure 3.6. BMW,
Daimler, Honda, Nissan, Renault and Volkswagen report that they perform
life-cycle analyses (LCA) to estimate their Scope 3.1 emissions. Honda's
numbers include motorcycles and power equipment and it was not possible to
isolate the automobile manufacturing emissions from the aggregate. GM
reports that its Scope 3.1 emissions are 10% of the actual emissions. Hyundai
estimated its Scope 3.1 emissions by using a carbon footprint of ten of its
models and using their sales data. However, only a fraction of its total Scope
3.1 emissions are reported. None of the other companies studied here report
Scope 3.1 emissions.
We provide three values from literature to relate to the CDP data. Ashby [57]
lists the material content of a 1,361 kg conventional I.C engine vehicle. We
used this material content information and Ashby's material properties
tables to determine the CO 2 emissions associated with material production
and component manufacturing. In addition to this, we need to consider the
emissions associated with vehicle assembly. These estimates were taken from
Sullivan's [20] vehicle part manufacturing and assembly (VMA) model.
Sullivan's work yields a value of 889 kg CO 2 emitted per vehicle. The total
CO 2 emissions for vehicle manufacture estimated this way are 5.4 tons CO 2
per vehicle. Also, Sullivan et al [29] present a lifecycle analysis of a "generic
vehicle" of mass 1,532 kg. Their estimate is 7 tons CO 2 per vehicle. Finally,
49
Samaras
and Meisterling
[34]
use
the
Economic
Input-Output
LCA
(EIOLCA) model to determine CO2e emissions from vehicle production. The
vehicle they consider is the Toyota Corolla. They quote a value of 8.5 metric
tons of C02e per vehicle.
50
12
EScope 3.1 perveh.
mScope 1+2 perveh.
*: Incomplete data
A:
Scope 3.1 not reported
10
EIOLCA
CN
Scope 1+2+3.1 C0 2e = 8.5
I--,
a)
Sullivan 1998
Scope 1+2+3.1 C02
a
=
7
C,,
0
Ashby Eco-Audit and
Sullivan 2010 VMA model
Scope 1+2+3.1 C02 = 5.4
E
C,,
I
0
C,,
E
0,
*
2
-
0
A
0
*
A
A
I-
BMW
Daimler
Fiat
Ford
GM
Honda
Hyundai
Nissan
Renault
Toyota
Figure 3.6: Scope 1+2 and Scope 3.1 emissions per vehicle for eleven automakers, 2012
51
Volkswagen
Use of sold products (Scope 3.11)
We recognize that the largest portion of an automobile's life-cycle emissions is
from its use phase. Sullivan (1998) estimated the use phase emissions of a
generic gasoline vehicle to be 51 tons or about 86% of the lifecycle emissions
[29]. More recently, Burnham et al developed a model which put the well-topump emissions for gasoline at 47 grams CO 2 per km, and the tailpipe
emissions at 234 grams CO 2 per km for a gasoline vehicle with a fuel
economy of 24.8 mpg, over a lifetime of 160,000 km [37]. By this estimate, the
use phase emissions for a gasoline vehicle over its lifetime are about 45 tons.
Companies often invest in materials, processes and technologies which draw
more energy at the manufacturing phase to reduce the use phase impact of a
vehicle. This section provides some comparison between the use phase
emissions reported by companies.
In CDP reports and elsewhere, companies report the gram CO 2 per km (or
gram C02 per mile) metric of their vehicle fleet over a certain assumed
vehicle lifetime. This number depends on the composition of the fleet, the
driving conditions and laws in different countries, the test cycles under which
the measurement is made, and the assumed vehicle lifetime. The reported
fleet average emissions in gram CO 2 per km for eighteen companies for the
year 2012 are shown in Figure 3.7 below. Note that some companies report
global fleet averages whereas others report emissions only for certain
markets. The New European Driving Cycle (NEDC) 2012 standard is
converted to the U.S Corporate Average Fuel Economy (CAFE) standard
using the
method published by the International
Council
on Clean
Transportation [60]. With the exception of Lamborghini and Maserati, all
other fleet average emissions fall between the European and the U.S
standards.
52
Ann
370
-CAFE
NEDC 2012: 121 gCO2 per km
- -- - - ----------
~ -
-
300
--
-
---- -- -- - ---
-
0
0
C"
2012: 263 gCO2 per km
0
C,)
204
199
- - - - --
--~ ~ - - -7- - - -- - 163 169
-
200
164
164
C)
145
140
0
134
130 137 126
100
0
CL
co
L0
C C
ccZ
C)
C
0
0
0)
Cu
S C:
0
Of
0)
LL
Cu
c
>
-$--~
Cu
C
Cu
0
Cu
C
Cu
0
C
CU
.
2
=3
c"'
F-
C,)
Cu
-
0
E
CD
CU
-j
Figure 3.7: Use phase emissions in grams C02 per km for eighteen
companies shown against the 2012 CAFE and NEDC standards, 2012
We now want to see the changes in fleet average emissions and standards
over the years. We selected data for the U.S and European markets. All
tailpipe emissions are reported in terms of NEDC gram CO 2 per km.
For
companies which instead reported a miles per gallon or equivalent fuel
efficiency metric, we converted that number into the equivalent NEDC gram
CO 2 per km value by using the guidelines published by the United Nations
[59]. The data set is sparser than before because companies have not
53
completely disclosed the fleet efficiency values for all markets. We also
sourced the emissions regulations in place or proposed by a select few
countries and converted these to a gram CO 2 per km value. The data set is
plotted in Figure 3.8 and Figure 3.9.
The Japanese target for tank-to-wheels emissions for the year 2020 is 105
gram CO 2 per km, and the European target for the year 2022 is 95 gram CO 2
per km. In the U.S, the proposed target for tank-to-pump emissions was 99
gram CO 2 per km by the year 2025. However, CAFE standards have been
reworked and are now calculated based on vehicle footprint. Thus, each
automaker has its own target based on the composition of its fleet. This
makes a direct comparison with other standards difficult.
E
300
----
BMW
-4
Ford
-E
CO
e- Honda
- NEDC
--
- Japan
GM
-- 4--Nissan
Toyota
---- o
Fiat
--- c-
US
-Australia
CAFE
-
------- - - --R - 00-- - --------------------------------
200
-
(5)
-o-
--
190
z
1
--- -- -- -- -060
a)
06iZ~
>
0L95
0
Zo
E
1
1
0 1
1
1
1
1
1
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024
Year
Figure 3.8: Use phase emissions for U.S fleets, and emissions standards for
different regions, in NEDC gram C02 per km
54
300
BMW
Fiat
-4-- GM
-- p-- Renault
------0
---
-o
---
E
o
E
us
-- + -Australia
CAFE
0
--
Daimler
Ford
Honda
Volkswagen
-NEDC
-Japan
00
0
1(M
C99
_1
-- - - - - - - - - - - - ------- ---------------- ---- _---
-- -- --
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024
Year
Figure 3.9: Use phase emissions for European fleets, and emissions
standards for different regions, in NEDC gram CO2 per km
We now contrast the vehicle manufacturing emissions with the use phase
emissions by taking the average of the emissions reported by BMW, Daimler,
Nissan, Renault and Volkswagen. We choose these companies because we
have their Scope 1, Scope 2, Scope 3.1 and Scope 3.11 values for the year
2012. The average tailpipe emission reported by these companies is 141 gram
CO 2 per km. We assume a vehicle lifetime of 150,000 km over ten years since
BMW, Daimler, Renault and Volkswagen assume this as the vehicle lifetime
usage. The averaged lifecycle emissions per vehicle are shown in Figure 3.10.
The manufacturing emissions amount to 6 tons per vehicle while the use
phase emissions are 21 tons or 78% of the total. The graph also shows values
55
from literature for vehicle manufacturing emissions, and the emissions if the
vehicle fuel economy equaled European or CAFE standards.
a)
45
* 2012 Average Scope 3.11 emissions per vehicle
a)
* 2012 Average Scope 3.1 emissions per vehicle
a)
At CAFE 2012
limit: 39.4 tons
132012 Average Scope 1+2 emissions per vehicle
C
0
a)
E
30
C
0
Co
a)
IEDC 2012
t: 18.3 tons
0
15
T
EIOLCA: 8.5 tons
y + Sullivan: 5.4 tons
0
Use Phase
Manufacturing
Figure 3.10: Average of manufacturing and use phase emissions for five
automakers in 2012
The trend of use phase emissions reduction and increase in manufacturing
emissions is discussed in Chapter 8.
Energy use and intensity of energy of manufacturing
In questions 11.1 through 11.4 of the CDP questionnaire, companies report
their annual energy usage on a company-wide basis. Companies disclose their
purchased and consumed electricity, consumed fuel, heat, steam and cooling
energy in units of MWh. Figure 3.11 shows the purchased electricity per
56
vehicle calculated using the 2012 CDP energy data and the production
numbers available from OICA.net.
3,000
-Sullivan
-
U)
2010 Assembly kWh per veh.
Sullivan 2010 Processing and Assembly kWh per veh.
*entire Daimler group
C)
2,000
I-
C')
1,532
i
2012 Average:
1198
~__
-------
---
- - --
----
C
(ID
-
-
1,000
kWh per veh.
-
-
--
a
Mt
00
(0
-
*
(V)L
04C
0
COI..1
C!
Ln
Figure 3.11: Purchased electricity per vehicle for eleven automakers, 2012
Also shown in Figure 3.11 are the values from Sullivan for electricity use per
vehicle for assembly only (684 kWh) and the electricity use per vehicle for
material transformation, machining and assembly i.e., the VMA cycle (1,532
kWh). We observe that Hyundai's purchased electricity figure is the smallest,
well below the average. We could not find a reasonably explanation for this
apparent self-sufficiency of electricity. Daimler reports aggregate energy use
for all its divisions which include trucks and heavy commercial vehicle
manufacturing.
Consequently,
we
could
not
calculate
the
purchased
electricity use per passenger vehicle, and instead report here the purchased
electricity per automobile that Daimler manufactured.
57
Except for Daimler, all companies fall between the two values from Sullivan.
This seems reasonable since the level of outsourcing and therefore the
electricity use directed towards pre-assembly processes will vary from
company to company. We hypothesize that companies retain final vehicle
assembly and some aspects of component or sub-assembly manufacturing.
Daimler as the outlier can be explained this way since trucks and heavy
commercial vehicle manufacturing would require more electricity than the
average passenger car.
We repeat the above exercise for fuel consumption. The fuel consumption in
terms of MJ per vehicle is shown in Figure 3.12. We also show the values
from Sullivan for natural gas use for assembly alone, and for the VMA cycle.
Note that Volkswagen reports fuel use data only for a few of its German
plants. So its fuel use intensity reported in Figure 3.12 is lower than what
might be expected. In this plot, only Daimler group's fuel use per vehicle
exceeds the assembly-only fuel use per vehicle number estimated by Sullivan.
Most companies fall well below this lower bound. In Sullivan's model, the
natural gas use for assembly supplies the paint shop and heating needs of the
factory. We cannot explain why companies' fuel use is significantly less than
Sullivan's numbers.
58
a
--
a)
9
12,000
Sullivan 2010 Assembly MJ per veh.
--
16,000
Sullivan 2010 Processing and Assembly MJ per veh.
*entire Daimler group
3, -
---------------
--
C
=3
%4-
------
-
-
- ----
-
8,000
5,499
4,000
-
-
-
-
--
-
2012 Average:
4 ereh
-3,899_MJ
U-
0
Figure 3.12: Fuel use (MJ per vehicle) for eleven automakers, 2012
We can evaluate how consistent the emissions and energy reporting are by
using the CDP data to go back and calculate the emissions factor for
purchased electricity and the consumed fuel. We expect the calculation to
reflect global electric grid emission factors, and emissions factors of the fuels
used by companies, of which natural gas use is predominant. We divide the
Scope 1 emissions by the fuel use, and the Scope 2 emissions by purchased
electricity. Figure 3.13 shows the C02 emissions per kWh of purchased
electricity for the carmakers.
59
1.2
--US
Q.
China 2011 kg C02 per kWh
2011 kg C02 per kWh
*entire Daimler group
0
c,4 0.8
0.76
--------------
(N
0.50
Figur--
0
Thure
----
E-s- --- ts--- -f- p-r-hased
13Emissions intensity ofprhsed
Wt iper kh,
a triaker i
kg C
per
kWh. This is in line with the global average grid intensity. The IEA reports a
world average intensity of 0.536 kg C02 per kWh for 2011. Renault's
emissions intensity is very low. This could be explained by the geographical
location of its production plants. Renault's major production facilities are
located in countries with very low emissions factors, for example, France
(0.061 kg C02 per kWh), Spain (0.291 kg CO2 per kWh) and Brazil (0.068 kg
C02 per kWh) among others.
The emissions intensity for fuel use is plotted in Figure 3.14. The emissions
intensity of anthracite coal (0.098 kg per MJ) and natural gas (0.05 kg C2
per MJ) are also shown for comparison [57]. We see most companies fall
between the two bounds set by anthracite coal and natural gas. However,
again, Toyota and Hyundai are outliers because of their low reported energy
60
use values. Volkswagen also has emissions intensity higher than coal, but
this is because Volkswagen's fuel use is reported only for a few of its German
factories. Therefore, its emissions intensity is inflated. BMW's fuel emissions
intensity is less than that of natural gas, although the bulk of BMW's fuel
supply is derived from natural gas.
0.4
CN
*entire Daimler group
0
0
C)
-.,
(N
Coal kg C02 per MJ
Natural Gas kg C02 per MJ
0.3 I-
0.2 I-
0.098
0.0502
CD0
-
0.1
(C
CD
iL1i
65
0
<((\
CL)
C)
"I
6*
o
oi
0)
Mv
6
0O
04
'0
VIO
-.\O
Figure 3.14: Emissions intensity for fuel use, in kg CO 2 per MJ, 2012
Conclusion
In this chapter, we studied the emissions and energy use reported by
automakers to the CDP. We found several trends in the reported values over
the years. In chapter 5, we will use these values to benchmark our model.
61
Chapter 4: Case Studies of Component
Production and Vehicle Assembly
In this chapter, we analyze energy use by a manufacturer of large engines.
Following the work of Bolin [39], we correlate electricity and natural gas use
to
production
and
weather
conditions.
We
discover
an
interesting
consequence of the increase in production on natural gas use. The second case
study deals with data reported to the CDP by Renault. We build regression
models to correlate plant-level emissions reported by Renault with variables
like average heating and cooling degree days, factory utilization, vertical
integration and average wheelbase which are found from other, often public,
sources.
4.1 Engine Manufacturing Plant Energy Use
In this section, we discuss the effect of seasonal weather conditions and
production volumes on the electricity and fuel use at a Cummins engine
manufacturing plant. The plant manufactures engines for heavy trucks,
construction equipment as well as stationary power equipment. Machining of
62
the cylinder head, cylinder block, crankshaft and camshafts is done in the
machine shop, and the painting and final assembly is performed at the same
facility. The data in this section has been anonymized and scaled to protect
confidential business information.
Figure 4.1 and Figure 4.2 show the scaled electricity use plotted against the
scaled production data over two years and the scaled natural gas use plotted
against the scaled production data over one year.
_0 2.0
CU
C,
0
0
a')
0
0
1.5
o
R = 0.64
000
0
-- 0------
0-
1.0
0.5
nn
0
1
3
2
Engines Produced, Scaled
4
Figure 4.1: Scaled electricity use vs. scaled engine production
63
5
3
o
aI)0
01
0
E
su0
0
z
'
0o
0
----0---------------------------
0
I
I
1.0
1.5
1-
0.5
0.0
2.0
2.5
3.0
Engines Produced, Scaled
Figure 4.2: Scaled natural gas use vs. scaled engine production
While the electricity use appears to scale linearly with production, the
natural gas use does not. Figure 4.3 shows the natural gas consumption
plotted against the mean monthly temperature over a year at the site. The
correlation
is
stronger
here,
indicating
the
weather
conditions
and
concomitant heating and cooling loads have a larger impact on natural gas
use which is used for powering heating and HVAC units.
We build regression models for the scaled electricity use and natural gas use
with
the
scaled production
and mean
monthly temperature
as the
independent variables. The results of these are shown in Table 4.1. All the
2
variables are significant at 99% or more. The R -adjusted value for the
electricity model is 0.81 and that for the natural gas use model is 0.86.
64
3
cu~
a)
C,
0 February
January,
0 March
Min. production (5s
volume
CU
49
2
0 April
December?-
0.74
'..R2
Yearend
z
November,
Max. production
volume
0June
1
O May
0
0
'
-------------------------------
-
August
September
-October
July
0
10
20
40
30
50
60
70
80
Mean Temperature (OF)
Figure 4.3: Total natural gas use vs. mean monthly temperature
Table 4.1: Results of regression analysis for scaled electricity and scaled
natural gas use vs. scaled production and temperature
Coefficient
t-Statistic
p-value
0.8
11.7
IE-10
Scaled Production
0.15
7.6
1E-07
Temperature
0.005
4.8
8E-05
Intercept
3.86
13.73
2E-07
Scaled Production
-0.48
-3.53
6E-03
Temperature
-0.02
-6.46
1E-04
Scaled Electricity Use
Intercept
Scaled Natural Gas Use
We can predict the scaled electricity use by the equation,
Scaled Electricity Use = 0.8 + 0.15 x Production + 0.005 x Temperature.
65
And the scaled natural gas use can be predicted using,
Scaled Natural Gas Use = 3.86 - 0.48
x
Production - 0.02 x Temperature.
In both models, we notice a strong significance of the intercept or the base
load energy component. Electricity use increases with production output and
with temperature. This seems reasonable since all process activities in the
factory use electricity. And cooling, ventilation and air conditioning systems
also use electricity, the use of which increases in the summer months.
The natural gas use is negatively correlated with temperature. This is
because a warmer winter means less fuel use for factory heating. An
interesting observation is the negative correlation of natural gas use with
production output. Natural gas is used for process activities (for example,
painting) as well as for non-process activities, like factory heating.
This suggests that the so-called heat-replacement effect might be at play
here. Production activities tend to generate waste heat which raises indoor
temperature
and
reduces
the
heating
load.
This
is
an unintended
consequence of increased activity in the factory. Thus, improvement in
IAL*_ 1-___ __-1 -1I1- _ L1- - - efP P1I .'
equipMenIt ur lighting efficiency would have the eiect of reducing the waste
heat, and therefore increasing the fuel consumed for heating. In chapter 6, we
develop
a
model
to
be
able
to
determine
improvements offer a net benefit.
66
whether
such
efficiency
4.2 Regression Models for Renault Factory
Emissions
In this section, we construct regression models for Scope 1 and Scope 2
emissions reported by Renault in its CDP reports. Recall that Scope 1
emissions are those arising from fuel consumption on site, and Scope 2
emissions are those from purchased and consumed energy carriers. Renault
is chosen since they report emissions on a per plant basis, for four years:
2008, 2010, 2011 and 2012. We can independently find data on weather
conditions, production and capacity numbers, and the type of vehicles being
manufactured at each plant location. While Renault reports emissions of over
forty plants, not all plant data can be used. This is because we do not always
have reliable weather data for each site; or accurate production and capacity
data might be missing; or the plant may manufacture products other than
cars and light trucks. So we select ten plants for which we have four years of
emission data. Thus, we have forty data points. The average values of the
variables are given in Table 4.2 below. The complete data set is presented in
Appendix B.
Table 4.2: Average values of the plant variables in the Renault model
Variable
Value
Unit
Annual scope 1 emissions
24,874
metric tons CO2e
Annual scope 2 emissions
Annual HDD
Annual CDD
Electric grid intensity
Annual assembly
Annual capacity
Plant utilization
Wheelbase
Vertical integration
23,176
3,952
702
0.313
146,201
202,847
72.6
2,636
1.4
metric tons CO2e
F-day
F-day
kg CO 2 per kWh
67
units
units
percent
millimeters
Variables and data sources
Scope 1 and 2 emissions
Scope 1 and Scope 2 definitions were discussed earlier. These are obtained
from Renault's CDP reports for the years 2008, 2010, 2011 and 2012 [54]. The
2009 year data was not available on CDP's website and so it could not be
included. The emissions are C02-equivalent, not CO 2 . The GHGs included in
Scope 1 emissions are C0 2, CH4 , N 2 0 and HFCs. Carbon dioxide is by far the
most dominant GHG, about ten times higher than the second highest GHG.
Annual degree days
The weather conditions at each plant site are represented by heating and
cooling degree days. Weather conditions influence heating and cooling loads
in factories. Boyd [35] makes the distinction between air-conditioning and
air-tempering. In their survey, Boyd found that all plants conditioned the air
i.e., controlled the humidity, but few plants cooled the air. Boyd says that few
plants in North America temper the air. The data are sourced from the
website wunderground.com [60] which presents annual Fahrenheit-days
calculated at a base temperature of 65 F. Often, heating and cooling degree
days (HDD and CDD) for the exact plant location are not available so we use
the nearest weather station data.
Electric grid intensity
Average annual grid intensity data is obtained from the IEA's 2013 report
[61]. Grid data for the 2012 calendar year was not available in this report and
so it was taken from Renault's CDP reports. It is worth mentioning here that
the world average intensity during this time was about 0.53 kg CO 2 per kWh
whereas Renault's average for the plants considered here is 0.31 kg CO 2 per
68
kWh. Most of the plants studied here are in countries like France and Spain
which have low carbon intensity grids.
Production, capacity and utilization
Car and light truck assembly and capacity data was obtained from
Pricewaterhouse Coopers' Autofacts database [1]. The utilization is calculated
for each plant for each year by dividing annual assembly by and annual
production capacity.
Wheelbase
Vehicle wheelbase is the distance between the front and rear axles. Boyd
found that assembly energy use was correlated to the vehicle surface area,
not weight. This is possibly because of increased energy expenditure on
welding, assembly and painting. So, vehicle wheelbase is used as a proxy for
the surface area. The vehicle models manufactured at each site and the
model year is known from the Autofacts database. We can find the vehicle
wheelbase of each model from public sources on the internet. For each plant,
we then calculate a production-weighted vehicle wheelbase.
Vertical integration
The plants studied here have varying levels of vertical integration. For
example, some plants only perform body weld, chassis assembly, paint and
final assembly, whereas others might have casting and machining operations.
To account for this, we introduce an ordinal variable, ranging from 1 to 3. A
value of 1 implies only final assembly, with possible body shop processes. A
value of 2 is assigned to a plant if, in addition to plant 1 tasks, it also
performs powertrain production and assembly. A value of 3 is assigned to
plants that manufacture components in addition to tasks that a 2-rated plant
does. The level of vertical integration is obtained based on public sources,
often from Renault's website for each plant.
69
Analysis
We build regression models for Scope 1 emissions per vehicle (tons CO2e per
vehicle) and Scope 2 emissions per vehicle (tons CO2e per vehicle). The
results for Scope 1 and 2 emissions intensity are shown in Table 4.3 and
Table 4.4.
Table 4.3: Scope 1 emissions intensity regression results
Coefficients
t-statistic
p-value
-0.693
-1.94
0.060
Avg. CDD
2.3E-05
1.54
0.132
Avg. HDD
1.5E-05
4.02
3E-04
Utilization
-0.233
-5.23
8E-06
Vertical integration
-0.034
-2.94
5E-06
3.8E-04
2.95
5E-06
Intercept
Wheelbase
Table 4.4: Scope 2 emissions intensity regression results
Coefficients
t-statistic
p-value
Intercept
0.278
0.89
0.375
Avg. CDD
2E-05
1.12
0.271
Avg. HDD
6E-06
1.89
0.067
Grid intensity
0.534
9.47
6E-11
Utilization
-0.182
-4.57
6E-05
Vertical integration
-0.023
-2.23
0.032
Wheelbase (mm)
-6E-05
-0.52
0.601
For Scope 1 emissions intensity, except the cooling degree days, all factors
are significant at more than 99.9%. The CDD non-significance is reasonable
70
since typically fossil fuels are not used for factory space conditioning. The
coefficient for vertical integration is negative which is counter-intuitive. With
greater vertical integration, we would expect higher emissions per vehicle
because more activities are being in-house. The model is significant with an
adjusted-R 2 value of 71%.
For Scope 2 emissions intensity, the model has several variables which are
significant at less than 95%. We drop the wheelbase variable which is the
least significant and re-run the analysis. The results are presented in Table
4.5 below.
Table 4.5: Scope 2 emissions intensity regression results
Coefficients
t-statistic
p-value
Intercept
0.115
4.36
1E-04
Avg. CDD
2E-05
1.08
0.288
Avg. HDD
6E-06
1.84
0.075
Grid intensity
0.532
9.56
4E-11
Utilization
-0.172
-5.04
2E-05
Vertical integration
-0.023
-2.22
0.033
We see higher significance of the variables now. The weather parameters,
CDD and HDD, still seem not very significant. If we remove those too, we get
a model in the variables grid intensity, utilization and vertical integration.
Table 4.6: Scope 2 emissions intensity regression results
Coefficients
t-statistic
p-value
Intercept
0.127
5.03
1E-05
Grid intensity
0.566
17.1
0
Utilization
-0.16
-4.7
3E-05
Vertical integration
-0.01
-1.8
0.08
71
Again, we see that vertical integration
is negatively
correlated with
emissions, which is counter-intuitive. However, it is only significant at 92%.
Grid intensity is highly significant as is utilization.
With these models, we can characterize Renault's plants. Knowing factors
like average HDD and CDD, grid intensities, vehicle wheelbase and factory
utilization, we can closely predict the emissions intensity at any plant. It also
reveals where the most effective controls lie for reducing emissions. For
example, a company can reduce its Scope 2 intensity by buying certain
percentage of its electricity from renewable sources. Closer attention to air
exchanges, heat losses and gains through factory walls, roofs and ceilings,
and effectively using waste heat would reduce the effect adverse weather
conditions have on on-site energy use.
Similarly, a model such as this reveals how emissions intensity depends on
seemingly peripheral factors. As we will see in chapter 6, a plant may look
like it is improving on emissions, say, by increasing its utilization. However,
while the emissions intensity might decrease with higher utilization, absolute
emissions might still grow. Controlling for extraneous factors like utilization
and weather conditions is important when comparing emissions reduction
activities.
Conclusion
In this chapter, we studied energy use and emissions from component
manufacturing and vehicle assembly. We find that automobile factories can
be modeled like
unit processes.
Knowing which factors contribute
to
emissions, we can precisely control factory emissions, much like optimizing
unit processes.
72
Chapter 5: Surrogate Global Assembly
Plant Model
In this chapter, we develop a model of a globally representative assembly
plant. We use Sullivan's VMA model and we benchmark it to the CDP
reports. We then determine the level of vertical integration in the factory,
and develop a model for the automobile supply chain.
5.1 Assembly plant model
In this section, we develop a simple model to represent an average global
automobile assembly plant. We take advantage of two observations: 1. The
average Scope 1 and Scope 2 emissions from the eleven companies with
complete data closely matches the CO 2 emissions from the Sullivan report on
automobile manufacturing, and 2. Factory CO 2 emissions and energy use can
be roughly segmented into fixed (also called base load) and variable
components. This approach reveals an energy-"economies of scale" effect.
That is, when a plant is operated below capacity, the energy use and CO 2
73
emissions per vehicle are much larger than when it is operated at higher
volumes. Most notably, we see this for Maserati as reported by Fiat in their
2013 sustainability report. Maserati's addition of a plant in 2013 saw its
Scope 1 emissions increase from 1,138 tons for one plant to 15,776 tons for
the two plants. Its Scope 2 emissions increased from 1,975 tons to 26,145
tons.
Let the company have N factories which are identical in all respects. We
assume that the factory only does vehicle assembly and painting. Sullivan
[20] presents a model for the vehicle component manufacturing and assembly
(VMA) phase of automobile manufacturing. The processes performed in the
factory and the fixed components are listed in Table 5.1 by drawing upon
Sullivan's VMA model.
Table 5.1: Activities performed at the surrogate assembly plant
Type of activity
Activities
Base load
Lighting, heating, HVAC, compressed air
Process
Painting, welding, material handling
Sullivan assumes some in-house material transformation and machining
processes and quotes a figure of 2,227 kg C02 per vehicle for the VMA phase.
We looked at some of the largest automobile assembly plants in the U.S to
assume a suitable factory area and annual production volume for the
surrogate factory. The average throughput per unit area of the factory for the
large automobile assembly plants was found to be 0.94 [62]. Based on this
data, we assume an annual production volume of 250,000 vehicles and a
factory floor area of 250,000 m 2 for the surrogate factory. The volume is
measured at two shifts running eight hours with 250 working days a year.
74
Let us denote annual emissions from an assembly plant by c. These emissions
can be split into base load and process emissions as follows:
d
b + ki
... (5.1)
Where,
b represents the base load emissions (kg CO 2 per plant per year) which
are independent of the production volume, but depend on conditions which
affect things like heating, cooling, lighting requirements of the factory;
i) represents the annual production volume of the plant; and
k is a process parameter (kg C02 per vehicle) which depends on the
type of processes
being performed
at the factory, vehicle parameters
(example, wheelbase, weight, material content), and the fuel(s) used for these
processes.
Let V be the company's annual output from these factories. That is,
V = Nv
... (5.2)
Then, at the company level, we can write equation (5.1) as:
C = Nb+ kV
... (5.3)
On a per-vehicle basis, we can write,
Nb
C~- . -.
+k
V
V
Let CB= Nb/V be the base load emissions per vehicle, and Op
... (5.4)
k be the
process emissions per vehicle. That is,
S= OB
75
+P
...
(5.5)
For our model, we assume that only vehicle assembly is being done in-house.
Activities like HVAC, lighting, heating and compressed air supply constitute
base load processes. We assume that these activities stay on and draw energy
regardless of the status of production. Using Sullivan's numbers, we get the
emissions listed in Table 5.2.
Table 5.2: Emissions per vehicle from natural gas and electricity use
Base load
Process
Total
Emissions (kg CO 2 per vehicle)
Scope 1 emissions
Scope 2 emissions
(from natural gas use)
(from electricity use)
195
317
150
212
345
530
513
362
875
The emissions resulting from natural gas constitute Scope 1 emissions, and
the emissions from purchased electricity constitute Scope 2 emissions. From
Table 5.2, we have CB = 513 kg and Op = 362 kg. Annually, the surrogate
factory contributes to 86,372 tons of CO 2 from natural gas combustion, and
132,525 tons C02 from purchased electricity. The total annual CO 2 emissions
from the plant therefore amount to 218,897 tons
The surrogate assembly
plant, its energy inputs and products are depicted in a sketch in Figure 5.1.
76
86,372 tons C02
On-site Natural Gas
Automobile Assembly Plant
Floor Area = 250,000 m2
132,525 tons C02
250,000 Vehicles Produced
Each Year
Electric Utility
Figure 5.1: Sketch of the surrogate factory, its emissions and products
From the CDP data in Chapter 3, in general, we saw an absolute emissions
increase with the production rate. We plot the 2012 Scope 1, Scope 2, and
Scope 1+2 emissions for fifteen manufacturers against their production rate
and we get the chart seen in Figure 5.2. We get a reasonably good fit for all
the three plots, but especially for the Scope 1+2 linear models.
From Figure 5.2 and Table 5.2, we can compare the emissions intensity
determined from the CDP data and what Sullivan estimated for a generic
vehicle. The slopes of the linear fits for Scope 1, Scope 2 and Scope 1+2
emissions intensity are shown alongside Sullivan's natural gas, electricity
and total emissions in Table 5.3.
Table 5.3: Comparison of the CDP data to Sullivan's data for emissions
intensity
Scope 1
Scope 2
Scope 1+2
From CDP
326
514
839
kg CO 2 per vehicle
From Sullivan's report
data
345
530
875
77
The Scope 1+2 emissions reported to the CDP are within 4.2% of Sullivan's
numbers. Part of the reason that Sullivan's number is higher could be the
higher emission factor that Sullivan uses for the U.S grid (0.77 kg CO 2 per
kWh). Sullivan's estimate of Scope 1+2 emissions intensity would equal the
CDP Scope 1 plus 2 emissions intensity for a grid intensity of 0.72 kg CO 2 per
kWh. For this grid intensity, the Scope 1 intensity is 0.345 ton CO 2 per
vehicle and the Scope 2 intensity is 0.493 ton CO 2 per vehicle. The average
grid intensity for the thirteen states where U.S automobile manufacturing is
concentrated is 0.67 kg CO 2 per kWh delivered.
78
L.
C').1~M
_0
0)
L
C
ff
0
(D
'..
o
>.C:
a)
AScopel1
a)C
0
Cl)
0
o
L
>
=
emissions
-32
.37
0.J
0)
*~Scope 1+2
CD
Cl)
Cl)
emissions
E
---------------------
5---------------------------------------------------------------5
7
7
7
0.55
3
------ ---------
y0.5123x + 95820R 2 =0.875
0.29
R7
--------------------------------7-------------
--
648
A
A ------- ------
Global Annual Production in Millions
Figure 5.2: Scope 1, Scope 2 and Scope 1+2 emissions for fifteen automakers, 2012
79
5.2 Assembly plant and the automobile supply chain
The automobile production supply chain extends from material mining,
refining and production, to final assembly of the vehicle. As mentioned in
Chapter 2, Sullivan et al (1998) did an LCA of a 1,532 kg generic vehicle and
presented results for energy use and CO 2 emissions. Their results are shown
in Table 5.4 below.
Table 5.4: Materials and vehicle manufacturing results from Sullivan (1998)
Energy use (MJ)
CO 2 (kg)
Materials
production
94,460
4,440
.
39,217
2,562
Total
production
133,677
7,002
Note that manufacturing includes production of parts, sub-assemblies and
final assembly. We see that 70% of the energy use and 63% of the emissions
for the production of automobiles is attributable to materials production.
Schuckert et al studied the Volkswagen Polo in 1997 and published an LCA
analysis. Their estimate for total energy of production was 62,000 MJ per
vehicle and the associated emissions were 3,700 kg CO 2 . The estimate is
lower because of the smaller dimensions and weight (1,040 kg) of the car.
Also, base load energy and emissions do not seem to have been included in
this study.
For companies, the level of vertical integration is an important strategic
decision. In the late 1990s, the estimate for in-house component production
was as shown in Table 5.5 below [63].
Table 5.5: Vertical integration at Chrysler, Ford and G.M in the late 1990s.
Component
Automatic transmissions
Axles for rear-wheel-drive
Chrysler
100%
50
Ford General Motors
65%
100%
70
30
Axles for front-wheel-drive
Body panel stampings and assemblies
Brakes (excluding antilock type)
0
73a
0
50
66a
0
Cylinder blocks and foundry products
50b
100
82
0
65
70a
0
0
0
50
Suspensions
a Minimum estimate. b Maximum estimate.
(Reproduced from The New York Times)
80
50
100
100
0
0
100
100
Engines
Fuel-injection systems
Glass
Heating/cooling systems
Lighting systems
Seat tracks and fasteners
Steering gears
100
90a
100
100
98
100
0
100
100
100
100
100
In the previous section, we only included welding, painting and final
assembly activities in the factory boundaries. Assembly plants will typically
do these operations. Some plants also do upstream operations in the same
facility. Galitsky et al present a list of vehicle manufacturing plants in the
U.S in the year 2000. While the U.S auto industry has seen a lot of
reorganization since 2000 with many plants being shut down, we assume
that most plants are operating the same way now as they did then. Most
plants perform assembly and painting activities, and some do stamping, but
very few do machining or casting.
We can empirically determine the stamping percentage done in-house as a
percentage of total stamping. We use the SGAP model developed in section
5.1 and we can determine what percent of stamping done in-house would give
us the average Scope 1+2 emissions for a year, say, 2012. To do this, we need
to adjust the numbers we used in the SGAP model. As mentioned in section
5.1, Sullivan seemed to use a high value of electric grid intensity of about
0.77 kg CO 2 per kWh. So we scale down the Scope 2 emissions by applying the
81
world average grid intensity of 0.536 kg CO 2 per kWh in 2011 [63]. We also
assume that all other energy is derived from natural gas with an intensity of
0.055 kg CO 2 per MJ [59].
In their 2010 work, Sullivan et al provided details about the energy use and
emissions associated with the manufacturing phase mentioned above i.e.,
processing metals, polymers and glass for use in vehicle assembly. Processes
like casting, forging, stamping, machining employed for different materials
were studied. We build on Sullivan's results for energy and emissions from
vehicle assembly as shown in Table 5.6 below. The materials production
numbers are calculated using Sullivan's vehicle inventory data and Ashby's
material profiles. The details are provided in Appendix C. A clear distinction
is made in material transformation and the vehicle assembly stages. The
base load energy and emissions for material transformation are assumed to
be equal to those for vehicle assembly, following Sullivan's lead [64].
Table 5.6: Energy use and emissions for the entire automobile
manufacturing cycle based on Sullivan's data
Stage
Base load energy
MJ/veh
From
Fuels
From
Electricity
Process energy
MJ/veh
From
Fuels
From
Electricity
Material
Production
Total
energy
MJ/veh
69,204
Material
Transformation
3,10
3,5
11,315
7,717
25,476
Vehicle
Assembly
3,110
3,335
2,664
4,493
13,602
Total
108,282
82
Process emissions
kg CO2/veh
Base load emissions
kg CO2/veh
Stage
From
Fuels
From
Electricity
From
Fuels
From
Electricity
Material
Production
Total
emissions
kg
CO2/veh
3,876
Material
Transformation
171
166
622
383
1,342
Vehicle
Assembly
171
166
147
223
706
5,924
Total
We compare these values with Sullivan (1998) and Schuckert et al. Figure 5.3
illustrates the spread of values reported in literature.
160,000
Sullivan et al 1998 LCA of a Generic
U.S. Sedan (1,532 kg)
133,677 MJ/vehicle
120,000
----------------------------------------
-------------------------------
80,000 I----------------------------
S huckert et al 1997 Volkswagen
Plo LCA
(1 ,040 kg)
6 ,000 MJ/vehicle
------------------------------
40,000 F------------------------------
108,282
0
Total MJ per vehicle
83
8,000
Sullivan et al 1998 LCA of a Generic
U.S. Sedan (1,532 kg)
7,002 kg C02 per vehicle
6,000
------------------------------------------------------------------------
4,000 ----------- ~-~~-----------~~~~
--..
S
We---aTI99
Vol swagen Polo LCA
-
-~~
(1,(40 kg)
3,7 0 kg C02 per vehicle
---------------------------
2,000 -----------------------------
5,924
0
Total kg C02 per vehicle
Figure 5.3: Comparison of Sullivan's VMA model to literature
In 2012, the average CO 2 emissions intensity for the eleven automakers
whose CDP reports we studied was 795 kg per vehicle. The breakdown of
average Scope 1 and 2 emissions intensity was 266 kg per vehicle and 529 kg
and per vehicle. We assume that all energy needs for stamping are met by
electricity.
Knowing the energy intensity of stamping per kilogram of output, and the
amount of stamped material used in cars, we need to determine what fraction
of stamping will raise the vehicle assembly emissions shown above equal to
706 kg to be equal to 795 kg. A simple calculation shows that 61% of total
stamping needs to be done in the same assembly plant for our model to match
the 2012 average CDP Scope 1+2 emissions intensity.
We can verify this stamping percent result by comparing it to Sullivan's
bottom-up model. We assume that a plant would stamp only the major body
84
panels, the ones which are galvanized. From Sullivan's materials inventory
for his vehicle, we find that 22.7% of the curb weight is stamped galvanized
steel. The total percentage of the vehicle weight assumed to be stamped steel
is 37.9% according to Sullivan. So, exactly 60% of the stamped steel content
might be assumed to be done in-house. This compares well with our empirical
estimate above.
The final energy and emissions results for our model are given in Table 5.7
and Table 5.8 below.
Table 5.7: Estimated energy required for in-house operations in an assembly
plant
Machining
Painting
Welding
Material
Handling
Process Sum
Sum
Electricity MJ/veh
(Primary Energy)
3,110
-
-
3,110
3,335
3,335
3,110
1,380
4,715
1,380
7,825
2,664
-
1,792
1,503
920
1,792
2,664
5,774
4,905
9,620
690
-
Base load
Heating
HVAC and
Lighting
Compressed Air
Base load Sum
Process
Stamping
Total
MJ/veh
Natural Gas MJ/veh
(Primary Energy)
4,167
920
690
7,569
15,394
Table 5.8: Estimated emissions from in-house operations in an assembly
plant
Scope 1 Emissions
kg CO2/veh
85
Scope 2 Emissions
kg C02/veh
Total kg
C02/veh
Machining
Painting
Welding
Material
Handling
Process Sum
Sum
171
-
166
166
171
69
235
69
406
147
-
89
75
46
89
222
46
34
34
147
318
243
477
390
795
171
-
Base load
Heating
HVAC
and
Lighting
Compressed Air
Base load Sum
Process
Stamping
The energy use per vehicle can be shown as a Sankey diagram to highlight
the relative magnitudes of the requirements of different processes. The width
of the bands is proportional to the energy use. Figure 5.4 below shows the
metered energy use at the factory level.
Heating
Metered
Natural Gas
5774 MJ
Painting
Stamping
Welding
Material Handling
Metered Electricity
Compressed Air
Lighting
3207 MJ
Ventilation and Air-conditioning
Electricity [MJ]
Natural Gas [MJ]
Figure 5.4: Sankey diagram of energy used per vehicle at the factory
86
We compare our estimate to the eight auto companies for whom reliable data
is available from the CDP. Figure 5.5 shows the primary energy comparison.
a,
40,000
2012 CDP Average Energy Use:
18,572 MJ per vehicle
a)
30,000
C
LU
CU
E
- -
20,000
-
- --
---
-----
Calculated in-
- ~~--h6U
eenergy
use:-
15,394 MJ/vehicle
C)
CN
10,000
-
--
LO
C
L-----
1.
LO)
0
BMW
Daimler
Fiat
Ford
0
GM
PU'
Honda
Nissan
Renault
Figure 5.5: Calculated in-house energy compared to reported energy use by
auto companies, 2012
The estimated in-house energy use is 17% lower than the 2012 average of the
energy use reported by these companies. This is because the energy use
reported by companies is aggregated over all factories and business divisions.
Energy use for non-manufacturing operations would also be reported here.
Also, Daimler's reporting includes divisions other than their passenger car
division. If we do not include Daimler, the 2012 average energy use intensity
is 16,675 MJ per vehicle, and our estimate is within 8% of this value.
For a more direct comparison, we compare our calculated emissions to the
2012 reported emissions. This is shown in Figure 5.6 below. Since we fit our
model to this data, on average we get the same result for Scope 1+2 emissions
87
intensity. Differences in emissions intensities can be attributed to different
vehicle types, differences in grid efficiencies, among other reasons.
1,600
-
1,200 [ -
-
-
--
-
-
----
-----
-
2012 CDP
2012 CDP
Calculated
Calculated
---
Scope 1 Emissions
Scope 2 Emissions
Scope 1 Emissions
Scope 1+2 Emissions
---------
a)
a)80
795
CL80
(N
0
0
0)
400
---qC0q _1
-~~
---N
- -------
0
IN
0
40
Figure 5.6: Calculated Scope 1+2 emissions compared with reported 2012
Scope 1+2 emissions
Finally, from the CDP data, we have data on Scope 3.1 emissions for six auto
companies for 2012. The companies are BMW, Daimler, Nissan, Renault and
Volkswagen. In Figure 5.7, we compare our model for Scope 1+2 and Scope
3.1 emissions intensity with the reported emissions. The Scope 1+2 intensity
for these companies is close to the average we used to determine our in-house
stamping percentage. So these numbers match up well. The Scope 3.1
emissions estimate is also fairly close, up to 2% of the reported emissions.
One reason for this could be that we do not include the emissions associated
88
with components assembly. Another reason could be differences in electric
grid intensities, since we assumed world average grid intensity in our model.
8,000
L
0 Scope 3.1
0,
0
MScope 1+2
0)
0
0)
U)
LU
6,000
(>
(D
4,000
CU)
Cu
2,000
I
0
2012 CDP Average of 5 companies
Calculated
Figure 5.7: Calculated Scope 1+2 and Scope 3.1 emissions compared with
values reported to the CDP by five companies
Conclusions
In this chapter, we built and extended the global assembly plant model to
include some amount of stamping. Our model matches well with data
reported by auto companies to the CDP. Supply chain energy use is
complicated because of the vast network of suppliers required for each car.
Our model does not have reliable figures for supply chain overhead energy
use. Reliable data on component assembly is also missing. It can be difficult
to determine this because the production of sub-assemblies
89
does not
necessarily scale with the masses of the components. A detailed study of the
supply chain energy use, both base load and process, is needed.
90
Chapter 6: Thermodynamic model and
evaluation of emission reduction
activities
In this chapter, we build a model to evaluate heating and cooling loads in a
factory by focusing on some of the more important heat transfer phenomena.
We combine this model with our basic factory model developed earlier to
evaluate how different emission reduction activities affect total factory
energy requirements. We also consider the effect of environmental conditions
as well as operational decisions on factory energy use.
6.1 Basic thermodynamic model
We base our surrogate factory in Detroit, Michigan. Historical weather and
solar radiation data are obtained for Detroit. The Fahrenheit heating and
91
cooling degree days, determined for a base temperature of 65 F, for Detroit in
2014 are shown in Table 4.1 below [60].
Table 6.1: Detroit heating and cooling Fahrenheit degree days for 2014
January
February
March
April
May
June
July
August
September
October
November
December
HDD
1430
1240
1106
469
162
5
2
3
99
330
815
935
CDD
0
0
0
0
66
213
210
247
79
1
0
0
Based on this table, we divide the year in a heating and a cooling season. We
assume only a heating load in a heating season and a cooling load in the
cooling season. The heating season for 2014 is considered to be January
through May and then September through December. The cooling season is
therefore the months June through August.
We consider three mechanisms which cause heating or cooling loads. These
are air exchange, transmission through walls, roof and floor, and internal
heat gains from people, equipment and lighting. These mechanisms occur in
both seasons. We describe how we determine heat loss or gain for each
mechanism and then determine the per vehicle fuel or electricity use. We also
consider the energy needed to move air by means of fans.
We do not partition these shops into separate zones. That is, no difference in
temperature, humidity or ventilation parameters is assumed between these
92
zones. This is a modeling simplification. We recognize that often these zones
can be in different buildings with separate air control systems, or they might
be adjacent and exchange heat with each other. Furthermore, we only
consider loads during the first and second shifts, which are assumed to be the
only operational shifts.
Air exchange
In a factory, air is circulated to maintain ergonomic conditions. Unlike most
commercial or residential buildings, a factory has bays to allow material
entry and exit. Since outside air might constantly enter and leave the system,
it may constitute a significant thermal load on the ventilation system.
Sensible heating and cooling loads
The sensible load is associated with controlling the temperature of air. Latent
loads are those associated with controlling the humidity of air. We use the
degree day methodology to determine the sensible heating loads on a monthly
basis. The degree days are calculated on a daily basis and added for each
month. This is a reasonable resolution for us since we do not expect HVAC
control systems to react to daily changes in temperature. We expect to get a
reasonable average estimate of heating and cooling loads. We calculate the
sensible heating load,
Qs
Qs,
by using the following expression:
= (air flow rate)
x
(specific heat) x (degree-days)
A ventilation guide has been developed for automobile factory HVAC by
industry professionals in which some data on about six plants is available
[65]. An important variable is the rate at which air is exchanged with the
93
surroundings. This depends on the shop within a factory, whether it is a body
shop, paint shop or a final assembly shop. Values are presented for the air
flow in cubic meters per hour per square meter of factory floor area. For the
summer season, this depends on whether the plant is tempered. Usually
plants will condition the air, i.e., control for humidity. Some plants temper
the air i.e., the also cool the air in the summer. Tempered plants have lower
air flow rates. Table 6.2 shows some of the air flow rates given in the
ventilation guide.
Table 6.2: Outdoor air flow rates
40
36.5-73
42
-
15
40
-
18
23-32
18.3
23
40
36.5-73
-
25
23-32
-
25
11-23
29.3
15.8
38
23.7
38
-
Body shops
Ford
General Motors
Toyota, KY
Daimler, Germany
Daimler, Ontario
Paint shops
Ford
Toyota, KY
Assembly Shop
Ford
General Motors
Toyota, KY
Daimler, Germany
-
Plant
Outdoor air flow rate (m 3 /h per m 2 of floor area)
Winter
Summer
Non-tempered
Tempered system
system
18
23-32
22
23
(Reproduced from the Ventilation guide)
We will assume that our surrogate plant has a tempered system. We assume
an average air flow rate of 25 m 3 per hour per m 2 for both the seasons.
94
Degree days were presented in Table 6.1 above. These are in Fahrenheit days
added up for month. We can write the sensible load for each month by using
the following expression:
Qs = pa x f x A x wr x Cp x DD x d,,.
Where,
QS =sensible heating or cooling load each month, MJ,
pa
density of air = 1.2 kg/M 3 of air,
,
f = air flow rate = 25 m 3/h/m 2
,
A = factory floor area = 250,000 M 2
= working hours in a month. We assume 250 working days a year divided
equally over twelve months. We assume two work shifts of eight hours
duration each. So Wni = 333 hours a month.
Wni
Cp = specific heat of air = 1.005 kJ/kg-K = 0.558 x 10-3 MJ/kg-F,
DD = heating or cooling degree days, F-days/month,
dni = month/days, assumed to be 1/30 for simplicity.
The results for sensible heating load for both seasons are shown in Table 6.3
below. Note again that we assume no overlap of heating and cooling loads in
a month. In reality, for Detroit, the months of May and September might
have small cooling loads.
Table 6.3: Sensible heating and cooling loads for a year
Month
January
February
March
Sensible Heat Load (MJ)
69,304,248
60,095,991
53,601,747
95
Sensible Cooling Load (MJ)
Sensible Heat Load (MJ)
22,729,855
7,851,251
-
-
Sensible Cooling Load (MJ)
10,322,940
10,177,547
11,970,734
-
-
-
4,797,986
15,993,288
39,498,574
45,314,316
319,187,257
-
Month
April
May
June
July
August
September
October
November
December
Total
32,471,221
Latent heating and cooling loads
In addition to its temperature,
the moisture content of air has to be
controlled. We use the following equation from the ASHRAE Handbook [68,
p. 18.13] to determine the latent load, Qi, for each month:
Qi = f x Dh x (Win
-
Wo)
Where,
Q1 = latent heat load, MJ,
,
Dh = latent heat of evaporation = 3,010 kJ/m 3
Win =
Wo
desired indoor humidity ratio, kg of water/kg of air,
outdoor air humidity ratio, kg of water/kg of air.
Relative humidity (RH) of air is the ratio of the mole fraction of water in the
given sample of air to the mole fraction of water in saturated air sample at
the same temperature and pressure [66, p. 1.8]. The ASHRAE handbook
96
suggests keeping the relative humidity between
30 to 60% [66,
pp.
18.30,9.12]. We assume the inside desired RH to be 50%. The relative
humidity can be converted to the humidity ratio at a known temperature
using the following equation [66, p. 1.8]:
WO =
W"'
1 + (1 - #)W 8t,/0.62945 x W
8
Where,
Wst,P = humidity ratio of moist saturated air at the same temperature and
pressure.
#
= relative humidity.
For a factory inside temperature of 18
0 C,
Wst,p is 1.2
x
10-2, and 50% RH
translates to a humidity ratio of 6.4 x 10-3.
We then determine the value of Wst,p at median summer (i.e., June through
August) and winter (i.e., September through May) temperatures. For Detroit,
based on the data from 2010 to 2014, the median temperature in the winter is
2'C whereas it is 280 C in the summer. We assume an atmospheric pressure
of 101 kPa. For these values, Wst,p is 4.3 x 10-3 in the winter and 2.4 x 10-2 in
the summer. We use the formula above to convert the 5-years of relative
humidity data for Detroit, available from wunderground.com, to humidity
ratio. The median relative humidity in winters is 2.2 x 10-3 and it is 2.1
x
10-2
in the summer. We use these values for 0 in the two seasons as a
simplification. The results for 2014 are shown in Table 6.4 below.
Table 6.4: Latent heating and cooling loads for a year
Latent Heat Load (MJ)
238,682,961
Latent Cooling Load (MJ)
274,114,013
97
So, the total heating and cooling loads due to air exchange are as shown in
Table 6.5 below:
Table 6.5: Air exchange heating and cooling loads
Sensible + Latent Load, MJ
Winter
Summer
557,870,217
306,585,234
2,231
1,226
Air exchange load, MJ/vehicle
Heat transfer through walls, roof and floor
Conduction of heat through the external surfaces of the factory building is
the other mechanism we model.
Walls and roof
The heat exchange across a surface can be written by the general equation
given below:
Qt = U x A x AT.
Where,
U is the overall heat transfer coefficient, W/m 2/K,
,
A is the surface area, M 2
A-T is tue temperature difference across the surface which causes t
exchange, K.
98
heeau
In the equation above, U depends on the construction of the surface as well as
inside and outside air conditions. The U-values can range from 0.22 to 3.12
W/m 2 -K. We assume the wall and roof have an insulated
steel frame
construction. The U-value for this surface is 0.86 W/m 2 -K [68, p. 27.4].
The equation above is used in the ASHRAE handbook for determining heat
gain by using the conduction time-series method. Note that ASHRAE [66, p.
18.23] does not consider solar heat gain for design considerations in the
winter. We adapt their heat gain equation for the winter to determine the
heat loss in the summer. Also, our calculations are on a monthly basis and we
do not consider time-series effects. That is, heat gain (or loss) during one day
does not affect heat (or cooling) loads the following day.
We use the sol-air temperature method to model convection and radiation at
the walls and roofs. The sol-air temperature is the temperature which in the
absence of radiation would cause the same heat exchange at the surface as
would occur due to convection, global incident radiation [66, p. 18.22] and
radiant heat exchange with the sky and surroundings. In the equation above,
in the winter, AT= (Tin - Tsoi), and in the summer AT = (Tsol - Tin), where Tsoi
is the sol-air temperature. The sol-air temperature for each above-ground
surface is given by:
T1 = To +
EAR
ho
ho
Where,
To is the outside air temperature, 'C,
a is the absorptance of a surface to solar radiation,
,
Et is the total solar radiation incident on a surface, W/m 2
99
ho is the heat transfer coefficient for convection and long-wave radiation,
W/m 2/K,
e is the hemispherical emittance of a surface,
AR is the difference between long-wave radiation incident on a surface from
the sky and surroundings and the radiation emitted by a black body at
.
outdoor air temperature, W/m 2
For each day of the month, we know the outside air temperature for Detroit.
The value of a/ho is typically taken to be 0.026 for light-colored surfaces [66,
p. 18.23]. To determine Et, for simplicity, we use monthly average incident
radiation on a building which is provided by the National Renewable Energy
Laboratory (NREL) [67]. We assume the roof to be perfectly horizontal and
the walls to be perfectly vertical. Note that there is one value of Et for the roof
for each month, but the incident radiation on each wall depends significantly
on its orientation (also known as the surface azimuth). South-facing surfaces
receive more radiation than north-facing ones. NREL provides incident
radiation data based on surface orientations. We assume that the factory has
a square layout and is oriented perfectly north-south. So we determine Tsol for
the roof and Tsoi for each wall on a monthly basis. For horizontal surfaces like
roofs, ASHRAE suggests using e = 1 and AR = 63 W/m 2 , and using eAR = 0 for
vertical surfaces like walls. The sol-air temperatures for the five surfaces for
each season were estimated and the seasonal averages are shown in Table
6.6 below. The actual average air temperatures for Detroit in 2014 were
about 10C in the winter and 27 0 C in the summer.
Table 6.6: Average sol-air temperatures for external surfaces for the winter
and summer season
100
Average sol-air
Average sol-air
temperature in
temperature in
the winter (0 C)
the summer (0 C)
0.58
29.95
2
29.32
East Wall
3.01
31.04
South Wall
3.97
30.47
West Wall
2.96
30.97
Surface
Roof
North Wall
Floor
The heat transfer across the floor is relatively simpler to determine since
there is no solar radiation effect, and the ground temperature stays relatively
stable through the year. ASHRAE suggests the following expression for heat
loss through the slab perimeter [66, p. 18.31]:
Qip =p
X F
X
(Tin
-
Tgr)
Where,
Qp is the heat loss through the slab perimeter, W,
p is the factory perimeter which is (500 x 4) = 2000 m.
Fp is the heat loss coefficient per meter of slab perimeter, W/m-K. Its value
depends on the type of wall construction. We assume a poured concrete wall
with duct near perimeter for which is a value of 3.67 W/m-K is provided by
ASHRAE.
Tgr is
the ground temperature. The ground temperature remains fairly steady
throughout the year. We consider the heat loss to the floor for the winter
101
season only. In the summer, this would be a cooling load credit, but we ignore
this for now. The minimum ground temperature, used to get a conservative
estimate, is determined by the following expression:
Tr = Tr - A
Where,
Tg, is the mean ground temperature, 'C. We estimate this to be equal to the
annual average air temperature as suggested by ASHRAE. The annual
average air temperature for Detroit for the last 5 years is 10.5'C.
A is the ground surface temperature amplitude. This depends on the
geographic location of the site. The ASHRAE handbook provides a map of
values for the United States [66, p. 18.30]. For Detroit, the amplitude is
about 12 K.
All the variables in the equation above to calculate floor heat loss are
assumed to stay constant for the nine months of the winter season.
The estimates for heat loss and gains across different external surfaces for
the heating and cooling seasons are summarized in Table 6.7 below:
Table 6.7: Heating and cooling loads across different external surfaces
Heating load in the
winter (MJ)
Cooling load in the
60,350,081
13,270,004
North Wall
1,111,218
East Wall
1,048,187
250,857
290,192
983,830
277,258
1,050,956
288,637
Surface
Roof
South Wall
West Wall
102
summer (MJ)
Floor
1,573,813
Total
66,118,085
14,376,948
Therefore, the heating and cooling loads on a per vehicle basis are 264 MJ
per vehicle and 57 MJ per vehicle. Compared to the heating and cooling loads
due to air exchange in Table 6.5, these are an order of magnitude smaller.
Internal heat gains
We now consider heat generated inside the factory by human activity,
equipment and lighting.
Heat gain from human activity
ASHRAE has collected data on the rate at which heat and moisture are
released by human beings in various states of activity. For the category
"Heavy machine work, lifting" in a factory, the sensible heat generation rate
is 185 W and the latent heat generation rate is 285 W [66, p. 18.4]. Since the
data are in units of power, we can calculate the heat gain in each working
shift on a per vehicle basis by using the following expression:
Qih = (heat generation rate) x (number of operators in the factory) x (working
hours per day) / (vehicles produced per day).
We have some data on number of people employed in factories. In the U.S.,
AutoAlliance [68], a group representing major automobile manufacturers,
presents data on about 40 factories. The spread of the data on number of
people employed is shown in the histogram in Figure 6.1 below.
103
10
-
----- ------- ---
--- - - - - - - -
-
-----
-
-- - -
8 6
6C)
0
500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500 6000 6500 7000
Number of employees at each plant
Figure 6.1: A histogram of employees at some U.S plants
Note that this figure may include staff and support service employees. We
assume a conservative figure of 3,000 employees, who are performing actual
operational tasks, divided equally in two shifts.
Recall that we assume 250 working days in a year and a production rate of
250,000 vehicles. Each day has two shifts. So a factory produces 500 vehicles
per shift. Using the expression above and substituting the values, we get a
sensible heat load of 64 MJ per vehicle and a latent heat load of 98 MJ per
vehicle.
Heat gain from lighting
From Galitsky et al, we have a range on how much electricity is consumed for
lighting in assembly plants. The range is 130-140 kWh per vehicle. We
104
assume that 250,000 vehicles are produced in a year, and that the factory
floor area is 250,000 M 2 . So, we can say the lighting power density (LPD) is
around 130 kWh per m 2 -year. Assuming 250 working days and 16 working
hours a day, we get a power density of 32.5 W/m 2 . This is higher than the
standard of 13.2 W/M 2 quoted by ASHRAE for manufacturing establishments
with bays higher than 25 feet but lower than 50 feet. We use the 32.5 W/m 2
LPD in the equations provided by ASHRAE [66, p. 18.4] to determine heat
gain from lighting:
q11
W x Ful x Fsa
Where,
qu is the heat gain, W,
W is the light wattage, W,
Fua is the lighting use factor. It is the ratio of the actual to installed wattage.
It is assumed to be equal to one for commercial applications. We assume the
same here.
Fsa is the special allowance factor which is the ratio of power used by the
fixture to the rated power consumption of the lamp. For ballasts of sodiumvapor lamps, this value is 1.1 which will be our assumption.
In addition, we need to determine how much sensible heat actually goes to
the conditioned space, and how much to the roof. This is determined by the
space fraction ratio. ASHRAE provides this value for different kinds of light
fixtures. We assume a "recessed fluorescent luminaire with lens" which has
an average space fraction of 0.45.
105
Finally, we assume that 80% of the electricity consumption is during the two
operational shifts. The rest is assigned to the third shift when aisles and
docks or areas under maintenance are illuminated. We ignore the thermal
loads associated with the third shift and so we do not calculate the third shift
heat gain from lighting.
The rate of heat gain from lighting is calculated to be:
qil = 32.5 (W/m 2 )
x
1
x
1
x
0.45
X
250,000 m 2 = 4,021,875 Watts.
Considering only the two production shifts and daily production, we therefore
get a lighting heat gain per vehicle of 185 MJ per vehicle. Note that this
estimate is likely on the high side since we assume that the entire factory
floor area of 250,000 m 2 is illuminated at the same intensity.
Heat gain from equipment
From the Ventilation Guide mentioned earlier, we have some data on the net
heat released by process equipment in automobile assembly shops. We
reproduce that data below in Table 6.8.
Table 6.8: Net process equipment heat release
Net process equipment heat release (W/m 2
)
Plant
Body shops
Ford
Daimler, Ontario
Paint shops
Ford
Assembly Shop
Ford
32
50
28
29
(Reproduced from the ventilation guide)
106
The German standard VDI 3802 suggests that the value is between 25-45
W/m 2 of assembly floor area. We take the average of the three Ford values in
Table 6.8. We further assume that of the total floor area of 250,000 M 2 , 80%
is for equipment. We consider two shifts of production each day when this
heat gain occurs. For three months of the summer season, this leads to
26,700,000 MJ of heat released. This constitutes a cooling load which spread
over the annual production volume of 250,000 vehicles means a cooling load
of 107 MJ per vehicle. The heating load is simply three times this number
since the heating season is taken to be nine months.
A summary of the internal heat gains is presented in Table 6.9 below.
Table 6.9: Summary of the internal heat gains on a per vehicle basis
Heat source
Heat gain (MJ per vehicle)
Human activity
162
Lighting
185
Equipment
342
Total
690
Total heating and cooling loads
Finally, we can add up the heating loads minus the internal heat gains and
the cooling loads and see how our model compares with literature. Note that
the values given in literature are usually metered fuel or electricity. To get to
that stage, we need to consider how fuel and electricity are utilized to meet
heating and cooling requirements.
We assume the fuel burned is natural gas which is the fuel predominantly
reported to be used by companies in CDP reports as well as in the MECS.
107
Natural gas is combusted, and a boiler generates steam which goes towards
meeting the heating requirement of the factory. We assume a boiler efficiency
of 80%. Furthermore, we assume that production of natural gas is 91%
efficient. That is, the primary energy associated with fuel use is 10% higher
than the actual fuel requirement [69, p. 259]. So a heating load of 1 MJ per
vehicle translates into 1.34 MJ of primary energy requirement per vehicle.
For the cooling system, we assume a "Large Commercial Packaged AirConditioning and Heating Equipment (Water-Cooled, Evaporatively-Cooled,
and Water-Source) A/C". The efficiency of air conditioning equipment is
defined in the U.S by the Energy Efficiency Ratio (EER). The EER is a ratio
of the cooling effect (in BTU) to the input electric energy (in Wh). The
relationship between EER and the coefficient of performance (COP) is: EER =
COP x 3.41. The EER ranges from 9-13 BTU/Wh [70]. For the chosen system,
the EER is 11. We assume the efficiency of electricity generation to be 33%.
We convert the cooling load calculated earlier to the electrical energy
requirement (primary energy) by using the following formula:
Electricity needed [MJ/veh] = Cooling load [MJ/veh] x 1/1055.87
[MMBTU/MJ]
x 106 [BTU/MMBTU]
x 1/EER [Wh/BTU] x 1/1000
[kWh/Wh] x 10.8 [MJ primary energy/kWh].
That is, a cooling load of 1 MJ per vehicle translates to a primary energy
requirement of 0.93 MJ per vehicle.
We present the final results in Table 6.10 and Table 6.11. We include the CO 2
emissions calculation assuming average natural gas intensity of 0.055 kg CO 2
per MJ consumed, and the average grid intensity for Michigan from 2011 of
0.667 kg CO 2 per kWh [71].
108
Table 6.10: Heating load, energy requirement and CO 2 emissions
Load
Heating load
Air exchange
Loss through walls,
MJ/veh
Fuel
needed
kg C02/veh
MJ/veh
2,231
3,069
153
264
363
18
-690
-949
-47
1,806
2,485
124
roof and floor
Internal heat gains
Table 6.11: Cooling load, energy requirement and C02 emissions
Cooling load
Air exchange
Cooling load
MJ/veh
Electricity
needed
kg C02/veh
MJ/veh
1,055
981
61
58
53
3
690
641
40
1,973
1,835
113
Gain through
walls, roof and
floor
Internal heat
gains
Ventilation electricity load
109
Finally, we need to consider the electricity required to move the outdoor air
and the air within the factory. We assume that 60% of the air in the factory is
recirculated. The air flow rates considered in the air exchange rate section
are 40% of the total air flow rate. Recall that we assumed outdoor air flow
rates of 25 m 3/h/m 2 in summer and winter. Considering the recirculated air,
the amount of air being moved is therefore 62.5 m 3/h/m 2 . Assuming a factory
height of 10 meters, this means 6.2 air exchanges are done each hour.
We assume roof mounted fans which have a maximum air flow rate capacity
of 52,000 m 3 per hour, and the power rating is 11 kW [74]. For our air flow
requirements, we need 300 such fans in the factory. Considering two
operational shifts a day over 250 days, this translates into an energy
requirement of 190 MJ per vehicle of electricity. That is, the primary energy
requirement is 571 MJ per vehicle. This energy is then added to the
electricity used for cooling.
Discussion
The pivotal attempt at estimating factory heating and cooling loads was by
Galitsky et al. They used the industry average data from MECS 1994 to
make their estimates. Galitsky et al also cite studies going back several
decades on how fuel use and electricity use is divided between base load and
process activities. Sullivan in turn used Galitsky's data in his VMA model.
We compare our model to Sullivan's model and to more recent MECS data
from the years 2006 and 2010.
Table 6.12: Comparison of our model to literature
Model/Source
Fuel for
Electricity
110
CO 2
CO 2
heating
for HVAC
emissions
emissions
(MJ/veh)
(MJ/veh)
from (a)
from (b)
(a)
(b)
(kg/veh)
(kg/veh)
2,485
2,406
124
149
Sullivan (2010)
3,110
1,840
195
113
MECS 2006
2,250
1,690
MECS 2010
1,910
1,660
Basic
thermodynamic
model
Our model estimates are higher than the MECS 2006 and 2010 values for
both heating and cooling. They are lower than Sullivan's heating estimate
and higher than their cooling estimate. The MECS data for 2010 might be
skewed since the U.S economy was still recovering from the recession, and
automobile production, utilization and operations are not representative of
normal
years.
So
the
MECS
2006
data
can
be
considered
more
representative.
The Sullivan paper uses data from several sources, many of them going back
a few decades. The fuel use for heating data is attributed to Galitsky who in
turn consider MECS data from 1994. Galitsky cites Leven et al [73] who did a
survey of several German automobile assembly plants and found an even
split in the fuel use for painting and factory heating. The MECS 1994 data
quoted by Galitsky puts the total fuel use in the factory at 6,863 MJ per
vehicle. We calculate this value from the 2006 and 2010 MECS data and we
estimate it to be 5,254 MJ per vehicle or 6,093 MJ per vehicle. That is, it
seems like fuel use in the factory on a per vehicle basis seems to have
declined over the interval 1994-2010. If we revise Sullivan's estimate by
111
using the 2006 MECS data and assuming a 50-50 split between paint shop
use and heating use of fuel, our estimate of fuel use for heating is within 7%.
For electricity use for HVAC, too, Sullivan cites Galitsky who in turn cites
Price and Ross (1989) [74]. Price and Ross state that about 20% of electricity
use, of a total of 2,240 kWh, is for space conditioning and ventilation.
Galitsky et al apply a 15% fraction to an electricity use estimate of 1030 kWh
per vehicle from Leven. Thus the range could be from 1,668 to 3,628 MJ per
vehicle for HVAC. This is a wide range and it doubtless depends on factory
geographic location, choice of air conditioning systems (tempered vs. nontempered) and efficiency of factory equipment. Our estimate falls between
these two bounds.
We conclude that this is a reasonably accurate model, in terms of both energy
use and CO 2 emissions. Further work, studying energy loss or gains by other
mechanisms such as heat transfer through windows and heat gain by
material, and dividing the factory into separate zones, is currently being
undertaken [40]. At this point, we have enough confidence to be able to
determine how emission reduction activities might impact factory energy use
and emissions.
6.2 Scenario Analysis
In this section, we use the model developed in earlier sections in order to test
the efficacy of various emission reduction activities. Many of these activities
were reported in automakers' CDP reports. Wherever possible, we cite the
source of those claims and the impact that was claimed. We then use our
model to see how our model predicts the impact of energy use and emissions,
112
and we try to identify which activities truly make a difference. Other
scenarios tested here deal with issues like starting a new factory, the effect of
improving electric grid intensity, or how economies of scale might affect
emissions intensity. The results are summarized in Table 6.21 at the end of
this section.
We assume that our factory is based in Detroit. Therefore, the supply chain
model developed in section 5.2 has to be updated to reflect the higher carbon
intensity of the electric grid. The energy and CO 2 summary for the plant are
given in Table 6.13 and Table 6.14 below. We use these values when we
present changes in factory energy use or emissions due to emission reduction
activities.
Table 6.13: Energy use at the surrogate plant in Detroit
Natural Gas MJ/veh
(Primary Energy)
Machining
Painting
Welding
Material
Handling
Process Sum
Sum
Total
MJ/veh
2,485
2,485
2,485
2,664
-
4,074
4,074
1,380
5,454
1,380
7,939
1,792
1,503
920
1,792
-
Base load
Heating
HVAC and
Lighting
Compressed Air
Base load Sum
Process
Stamping
Electricity MJ/veh
(Primary Energy)
4,167
920
690
2,664
5,149
4,905
10,359
690
7,569
15,508
Table 6.14: CO 2 emissions from the surrogate plant in Detroit
113
Scope 1 Emissions
kg CO2/veh
Scope 2 Emissions
kg CO2/veh
124
-
124
252
252
Base load
Heating
HVAC and
Lighting
Total kg
CO2/veh
Compressed Air
-
85
85
Base load Sum
Process
Stamping
124
337
461
Painting
Welding
Material
Handling
Process Sum
Sum
147
-
93
57
240
57
43
43
147
271
303
640
450
910
-ii
111
Planting Trees
Several companies report tree-planting activities in their sustainability
reports and press releases. GM reports planting 1,800 trees and 1,500 bushes
at its CAMI assembly plant in Ingersoll, Ontario [75]. Toyota reported
planting 11,000 seedlings at various Japanese plants [76].
We assume that a company plants a temperate forest equaling the factory
floor in area adjacent to the factory on its site. Recall from our surrogate
model that the area of the factory floor is 250,000 M 2 . Plants absorb
atmospheric CO 2 in the photosynthesis reaction to produce sugar molecules.
Net primary productivity (NPP) is amount of CO 2 absorbed by plants minus
the CO 2 which plants release when they process the sugar molecules. The
NPP of a temperate
forest can vary
significantly
depending on its
composition. From the Oak Ridge National Laboratory Distributed Active
Archive Center, we have NPP values ranging from 0.3 kg-C/m 2/year to 2.57
114
kg-C/m 2/year [77]. We assume a value of 1.131 kg-C/m 2/year which was
reported for a young forest [78]. Then, the factory tree plantation effort can
fix 282,750 kg carbon every year, which translates to 1037 tons C02 every
year.
For the surrogate factory located in Detroit, the annual are 227,611 tons.
Thus, the tree plantation would fix 0.46% of the annual CO 2 emissions from
the plant. Note that as the trees mature, the net primary productivity
decreases. So we expect this effort to make a progressively smaller impact
over time.
Installing photovoltaic panels on the roof
In its 2012 CDP report, GM reported that its Baltimore plant, which
manufactures transmission components, installed about 7,690 m 2 of PV
panels on its roof.
The rated capacity of the panels is 1.2 MW and they
generated 955 MWh of electricity from June to December 2011. On its
website, GM says that the panels supply about 9% of their annual energy
requirements [79]. Daimler also reports that 45,000 m 2 of roof area at various
plants is being used for electricity generation by PV cells.
We now model PV panel installations on the factory roof. NREL reports 30year averages of incident solar radiation for most major U.S cities [80]. The
maximum, minimum and average values are reported on a monthly basis for
various configurations of collectors. We use the average values for Detroit,
MI. We assume flat-plate collectors facing true south, at a fixed tilt of 15
degrees minus the latitude. Then, the annual average incident solar radiation
on such a panel at Detroit is 4.3 kWh per m 2 per day or 179 W/m 2 . Monthly
115
incident radiation values for various tilts for flat-plate collectors are provided
in Appendix D.
The NREL National Center for Photovoltaics reports photovoltaic panel
efficiencies ranging from 8.6% to 46% depending on cell design [81].
Note
that these are efficiencies of best available technologies, not necessarily of
panels in actual use. We expect these latter to be lower. We assume an
efficiency of 20% for the panel.
Next, we assume the company installs PV arrays on its factory roof i.e., on an
area of 250,000 M 2 . It is assumed that there is no shading of the panels and
no snow or other material collects on the panels. Then, based on incident
radiation and cell efficiency data, we estimate that 280 TJ of electricity would
be generated annually. From the surrogate model, we estimated annual
primary energy consumption of 3.8 PJ. So, the PV output under these
conditions is about 7.2% of the annual energy consumption of the factory. The
maximum amount of electricity is generated in July (34 TJ) and the least is
in December (10 TJ).
Suppose this PV output partially replaces electricity consumption. We know
the carbon intensity of the Michigan grid is 0.667 kg C02 per kWh. Given
that 280 TJ or 77,795 MWh of energy is provided by PV, it means PV would
reduce the factory's Scope 2 emissions by 51,889 tons each year. This would
mean a 23% reduction in the annual emissions from the factory. Note that
these results depend strongly on location, panel area, panel efficiency and
maintenance, but otherwise are quite aggressive.
Installing more efficient lighting
116
In its 2013 CDP report, Fiat reports installing energy-efficient lighting as
part of broader efforts to reduce energy consumption. Ford also reports
replacing older lighting with high efficiency "T8 and T5H" fluorescent lights.
Energy requirement for lighting is contributes to half of the base load
electricity use. Light fixtures generate heat in addition to light. Also, some
lighting technologies are more efficient than others. We study how a 50%
improvement in lighting efficiency might affect factory energy requirements.
Recall from the thermodynamic model that lighting constitutes a heating
gain, which reduces the fuel requirement for heating in the heating season,
but increases the electricity requirement in the cooling season. We need to
evaluate whether the net effect of this efficiency improvement is positive.
Suppose the existing lighting system requires W1 energy and has an
efficiency qi. We replace the entire system with a more efficient system which
requires less energy, W2, because it has an efficiency
q2 > q].
Since the
amount of illumination is the same, we write,
Wi Il =
If 712= 1.5qi, W2
= 2/3Wi.
W2
12.
According to Galitsky et al, lighting and cooling loads
contribute equally to the electricity base load. So from the SGAP model from
Chapter 5, the electricity needed for lighting is 154 kWh per vehicle and the
resulting emissions are 103 kg CO 2 . Due to the 50% efficiency improvement,
the new electricity requirement and emissions are 102.9 kWh and 68.6 kg.
Note that all light is eventually
converted into heat. The efficiency
improvement reduces the heat gain by 33% on a per vehicle basis, or by 61.8
MJ per vehicle. The total reduction in electricity use is therefore the,
117
Reduction in electricity use = Savings from lighting + Savings from HVAC
kWh
MJ
MJ primary energy
x 3
x 3.6
= (154 - 102.9)
veh
kWh
MJ electricity
+
MJ cooling load
veh
613 MJ per vehicle.
6 1 .8
0 93
MJ primary energy
MJ cooling load
This is a 15% reduction in the electricity use for lighting and cooling. The
reduction in emissions is the same, amounting to 38 kg CO 2 per vehicle.
The reduction of heating gain of 61.8 MJ per vehicle applies for the heating
season. To supply this additional heat, we would need an additional 85 MJ
per vehicle of fuel after considering the boiler efficiency. This causes an
additional 4.2 kg per vehicle of CO 2 emissions. The net effect is therefore a
decrease in primary energy requirement by 471 MJ per vehicle and a
decrease in emissions of 33.6 kg CO 2 per vehicle. Thus, the energy use is
reduced by 3.4% and the emissions by 3.7%. So, even after considering the
internal heat gain of the lights, this measure reduces save energy and
emissions.
Favorable weather conditions
Renault, in its 2013 CDP report, reports that adverse weather conditions in
Europe caused a 3.6% increase in Scope 1+2 emissions from 2011 to 2012. On
the other hand, GM, in its 2013 CDP report, says that their Scope 1+2
emissions decreased by 1% in 2012 due to fewer heating degree days. We now
model favorable weather conditions in the summer and winter and how they
affect factory energy use.
118
Case I: Average winter temperature is 10 F higher
An increase in the outside temperature in winter would reduce the heat loss
across the walls and roof. Also, the air being exchanged every hour is warmer
and does not need to be heated to the same extent. We determine these
effects separately. We assume that for each day of the winter month is
warmer by 10 F. This is a large temperature shift. But we assume this to get
a sense of scale of how factory energy and emissions change.
Effect on air exchange heat loss
Recall from section 6.1 that we determined the air exchange rate by using the
degree day method. Since each winter day is 10 F (or 5.44 C) warmer, the
HDD in each month are reduced by (10 x no. of days in that month). We
assume that the latent heat load is unaffected. The effects of this change are
shown in Table 6.15 below.
Table 6.15: Winter heating loads in the base case and the warmer winter case
due to air exchange
Base Case
Warmer Winter Case
Sensible heat load (MJ)
319,187,257
203,793,261
Latent heat load (MJ)
238,682,961
238,682,961
Total heating (MJ)
557,870,217
442,476,221
2,231
1,770
Total heating load
(MJ/vehicle)
Effect on transmission heat loss
119
Recall that we used the sol-air temperature method to determine the heat
transfer through walls and the roof. The sol-air temperature changes linearly
with change in outside temperature. Thus, the AT term can simply be scaled
as required to determine the new heat loss. The heat loss through the floor is
assumed to stay constant since ground temperatures are relatively stable
throughout the year. The results are shown in Table 6.16 below.
Table 6.16: Winter heating loads in the base case and the warmer winter
case due to conduction through external surfaces
Base Case
Warmer Winter Case
4,194,190
2,846,790
60,350,081
42,949,736
1,573,813
1,573,813
66,118,084
47,370,339
267
189
Heat loss through walls (MJ)
Heat loss through the roof (MJ)
Heat loss through the floor (MJ)
Total heating load (MJ)
Total heating load (MJ/ vehicle)
In the base case, the total heating load is 1,806 MJ per vehicle after
subtracting the internal heat gains of 690 MJ per vehicle. In the warmer
winter case, the heating load is 1,270 MJ per vehicle. The CO 2 emissions
decrease from 124 kg per vehicle to 87 kg per vehicle. This reduces overall
factory energy use by 4.3% and CO 2 emissions by 4.1%.
Case II: Average outside relative humidity in the summer is 10
percentage points lower
120
In the basic thermodynamic model, the median humidity in the summer
months of Detroit was 0.021 kg/kg of air which translated to 88% relative
humidity (RH). Recall that the indoor desired RH is 50%. We compare how
energy use changes if outside RH is 78% for the whole summer. This
corresponds to a humidity ratio of 0.0187 kg/kg of air. This should reduce the
air exchange latent heat load.
In section 6.1, we saw that the latent heat load is calculated by a formula
which involves a difference in outside humidity ratio and inside desired
humidity ratio. We simply scale the original latent heat load by the lower
difference in humidity ratios. The air temperature is assumed to stay the
same.
Table 6.17: Summer cooling loads in the base case and the less humid
summer case due to air exchange
Base Case
Less Humid Summer
32,471,221
32,471,221
Latent cooling load (MJ)
274,114,013
231,596,342
Total cooling load (MJ)
306,585,234
264,067,563
1,226
1056
Sensible cooling load (MJ)
Total cooling load
(MJ/vehicle)
The latent heat load decreases by 15% and the total cooling load by 13.9%.
The effect on total factory energy use and emissions is lower - a 3% decrease
in energy use and a 1% decrease in C02 emissions.
New factory started
121
In 2013, the luxury vehicle brand Maserati inaugurated a new plant in
Grugliasco, Italy. This plant and their plant in Modena, Italy produce the
Maserati Quattroporte and Maserati Ghibli models. In 2013, Fiat, which
owns the Maserati brand, reported energy use and emissions data for these
plants in its sustainability report [82]. The effect on energy and emissions of
the new plant starting operations is noticeable. Table 6.18 shows the absolute
and per vehicle energy use and emissions from 2010 through 2013.
Table 6.18: Maserati energy and emissions data, 2010-2013
Year
Fuel
Purchased
Scope 1
Scope 2
Cars
Scope 1
Scope 2
Use
Electricity
emissions
emissions
Produced
intensity
intensity
(GJ)
(GJ)
(tons)
(tons)
(tons/veh)
(tons/veh)
2010
29,027
27,596
1,548
3,213
6,033
0.26
0.53
2011
21957
26751
1,232
2,262
6,231
0.20
0.36
2012
20,278
25,936
1,138
1,975
6,204
0.18
0.32
2013
280,846
160,019
15,776
26,145
15,993
0.99
1.63
Natural gas use went up almost 14 times while electricity use went up 6
times from 2012 to 2013. Maserati's installed capacity increased from 10,000
in 2012 to 35,503 in 2013. But their utilization decreased from 62% to 45%. If
Maserati could restore its utilization to pre-2013 levels, even then its energy
intensity would be three times more what it was before 2013. Even a 100%
utilization rate does not bring its energy intensity to pre-2013 levels. We
suspect this could be due to high base load energy use, but also perhaps a
change in operational details like doing more operations in-house. We could
not find any explanation for this drastic increase in energy use.
Effect of the carbon intensity of the electric grid
122
Automakers purchase electricity from the regional grid. Changes in the fuel
sources and efficiency of the grid affect emissions reported by companies. For
example, in the aftermath of the Fukushima disaster in Japan, Honda and
Nissan both reported increased Scope 2 emissions due to reduction in nuclear
energy input to the grid. Honda reported a 4.8% increase whereas Nissan
reported a 9.5% increase, from 2011 to 2012.
One operational decision a company might make is to move a factory to
another country. Let us consider the hypothetical case of a company moving
one plant from Japan to the U.S. Assuming the HVAC and process needs are
the same, we expect the fuel consumption to stay the same. However, the
Japanese and U.S electric grids have markedly different compositions of
energy sources. From the IEA data set, we know that the Japanese grid emits
0.41 kg CO 2 per kWh delivered whereas the U.S grid is at 0.5 kg CO 2 per
kWh. From the surrogate model, we estimated the annual electricity
consumption of the factory to be 226.5 GWh. Thus the Japanese factory
electricity use would generate 99,748 tons CO 2 a year compared to 120,369
tons CO 2 for the American grid. When we consider natural gas emissions of
70,793 tons, the absolute CO 2 emissions change by 12% with this factory
move.
The comparison on a per vehicle basis using the Michigan grid intensity is
shown in Table 6.19. On a per vehicle basis, a U.S plant is 12.3% more carbon
intensive.
Table 6.19: Comparing emissions from purchased electricity for vehicle
assembly in Japan and the U.S
kg C02 per vehicle
Japan
123
U.S
Emissions from electricity use for
Base load activities
210
253
Process activities
189
228
Base load activities
124
124
Process activities
147
147
Total
670
752
Emissions from natural gas use for
The carbon intensity of a grid is dynamic. Sourcing data from the IEA which
is plotted in Figure 6.2, we see a gradual improvement in the U.S electric
grid, and a worsening of the Japanese grid over the years. According to IEA
data, the Japanese grid intensity increased from 0.418 kg CO 2 per kWh in
2010 to 0.497 kg CO 2 per kWh in 2011, whereas the U.S grid improved and
its intensity decreased to 0.503 kg CO 2 per kWh [61].
0.7
--- U.S
-*-Japan
t5
a)
0.582
0.59
*
0.593
0
0.579 0.577 0.574
04
a)
CL
0.552 056
N)
0.5170.522
0)
0
ED
- - ---- - - - - - --------
0.5
-d
0.435
0
0.412
0.446 0.429 0.431
0
04
---
0.503
- --0.497
.540.44 046
.1
0.402
0.3
1990
1995
2000
2003 2004 2005 2006 2007 2008 2009 20102011
Year
124
Figure 6.2: Carbon intensities of the U.S and Japanese electric grids over
the years
Let us think about the grid intensity as a variable which increases or
decreases at a certain annual rate. From 2007 to 2011, the world average
carbon intensity of the grid decreased at 0.46% a year. From the data shown
in Figure 6.2, the U.S grid intensity fell at 1.74% per year while the Japanese
grid intensity rose at 1.36% per year. We want to examine how this affects
the carbon intensity of automobile manufacturing. Recall from equation 5.3
that the annual emissions from a factory can be written as:
C = Nb+ kV
...
(6.1)
Let us inspect b and k. The baseline emissions from a plant result from
burning fossil fuels on site and from the purchase of electricity, both of which
go towards powering baseline activities like lighting, heating, HVAC and
supplying compressed air.
Thus, we can write:
b=f
x e+(pe)bxg
... (6.2)
Where,
fb
is a vector of energy input in MJ of fossil fuel combustion per plant on site
which powers base load processes;
e is the vector of emission factors for each fuel in kg CO 2 per MJ;
(pe)b is the electricity purchased in kWh per plant for base load needs; and
g is the intensity of the grid in kg CO 2 per kWh where the factory draws
electricity from.
125
Companies typically report their fuel and electricity purchases in CDP
reports. Emissions factors for fuels are widely published (see: IPCC Second
Assessment Report), and carbon intensity of electricity grids of various
regions of the world are well documented.
Similarly, for k, we can write,
f
k
x e+ (pe) xg
(6.3)
Where,
fp
is a vector of energy input in MJ per vehicle of fossil fuel combustion by the
factory on site which powers assembly processes; and
(pe)p is the electricity purchased in kWh per vehicle for process needs.
From our notation of CB = Nb/V as the base load emissions per vehicle, and
Cp = k as the process emissions per vehicle, we can write,
N
CB =
. ( bo
X e -+(Xpe
+...
f
x)
Xo
(6.4)
And,
Cp
=k
-
fT
x e + (pe)p x g
...
(6.5)
No-growth scenario
Let us now consider small improvements in the electricity grid over the years,
and its impact on base load emissions and overall emissions for the surrogate
factory. We assume that fossil fuel use, emissions factors and electricity use
126
do not change. This is the no-growth scenario. Thus, the only variable in the
expressions for b and k is the term g. Thus, we can write,
Ab = A9(Pe)b
...
(6.6)
Ak = Ag(pe)p
...
(6.7)
...
(6.8)
...
(6.9)
And,
Recall that
-
C-.
o
V
-
Nb
.
V
+k
Therefore,
AC=ANbW 1'+
N
Nb
V
V2
AC
-
N
ANbV' + -- Ag(pe)b
V
-
This becomes,
Nb
NbAV
+Ag(pe),
V2
...(10
(6.10)
We divide equation (6.10) by equation (6.8) and replace CB and Cp by their
equivalents from equations (6.4) and (6.5) to write:
N
AC
0
ANbV 1 + - Ag(pe)b
V
Nb
V
NT
. (fb x e+(pe)b x g)+ f'
AV+ Ag(p>P
XeC+(pe)p x g
...
(6.11)
Now, if a company's capacity and production volumes do not change, then AN
0 and AV = 0. Then, the above equation becomes,
127
N
A
((fb
Ag(pe)b + Ag(pe)p
+ (PC)b X g) +
x
+...
f,
p)
(6.12)
Or,
AC
(fb+
+ .(P)b +(Pe)p)
C
e
NN
9
EV
C
N
(Pe)b +(Pe)P)
Ag
_
+
V
... (6.13)
Rearranging the terms, we get,
Ag
AC _g
1+-f
+i
1+ N
A +
... (6.14)
The multiplier of the term e/g in the denominator is a ratio of fuel consumed
in MJ per vehicle to the electricity use in kWh per vehicle. Recall from earlier
sections that the natural gas consumption is (2,258 + 2664) = 4922 MJ, and
the electricity consumed is 959 kWh. So the ratio is 5.13. Therefore, we get,
Ag
...
C
(6.15)
1+5.13-
9
Furthermore, we assume that e is a scalar because the factory only uses
natural gas. And we use e = 0.055 kg CO 2 per MJ, and g = 0.536 kg CO 2 per
kWh for the world average grid intensity. Therefore,
128
AC
Ag
=
-0.65
C
... (6.16)
g
Now, we can use the U.S and Japanese grid improvements to determine their
effect on carbon intensity. Substituting the values in equation (6.16) under
the no-growth scenario the per vehicle carbon intensity would decrease
annually by 1.1% for a carmaker in the U.S, while for a Japanese carmaker it
would increase annually by 0.89%.
"Economies of scale" effect
We can now use estimates of base load and process emissions on a per vehicle
basis to understand how economies of scale affect carbon intensity per vehicle
produced. From equation 6.1, we can differentiate C with respect to N, b, k
and V. We assume these parameters are independent of each other. For
infinitesimal changes in these parameters we can estimate the change in
emissions intensity. A partial differentiation of equation 6.1 gives:
ANb-
1
+ NAb V-- - Nb . 2 + Ak
V
,
AC
Or,
AC=
ANN
CB+
N
Ab-
b
AV-
C
-
b
V
Dividing both sides by equation (6.8), we get,
129
V
B+
Ak,
bAV
N
AC
C
0
CB~-P
CB +CP
...
(6.17)
From OICA global production data [83], we calculate the rate of growth of
production volumes. From 2009 to 2012, global car production increased at a
rate of 9.7%, from 47.9 million in 2009 to 63 million in 2012.
An estimate
of growth
of production capacity is available
from the
Worldwatch Institute [84]. They quote a growth in production capacity from
95 million in 2012 to an estimated 100 million in 2016. This means that
production capacity is growing at a rate of 1.3% every year. In our model, the
number of company factories, N, is a proxy for production capacity. On
average, we can say for each company, AN/N = 0.013.
Let us now formulate a business-as-usual scenario. In this scenario, the
company does not carry out any emissions reduction activities. Thus, Ab/b
and Ak are both zero. From equation (6.17), we get,
CB
SCB
A
+ CP
...
(6.18)
Substituting the values of CB, CP, AN/N and AV/Vfrom 2009 to 2012 we
get,
AC
C
_
461
461+449 (0.013 - 0.097) = -0.043.
... (6.19)
Thus, if companies simply keep increasing their production output without
increasing installed capacities at the same rate, and without investing in
emissions reduction activities, they can reduce the carbon intensity of their
130
operations at a rate of 4.3%. Hyundai and Daimler disclose in their 2013 CDP
reports that production increases contribute to some extent to decrease in
emissions intensity. The same effect is observed in the opposite direction in
the case of Renault which attributes increase in emissions intensity in part to
a drop in production level from 2011 to 2012. We term this the production
elasticity of carbon.
We now compare our model to aggregated data of the eleven companies
whose CDP reports we studied in Chapter 3. The aggregate production and
CDP data are summarized in Table 6.20. Note that not all companies
reported emissions in 2008 and 2009. So the production total in those years
does not include the production output of those companies.
Table 6.20: Aggregate production, Scope 1+2 emissions and emissions
intensity of eleven companies from 2008 to 2012
Year
2010
2011
2012
Aggregate global
production
(units)
48,606,385
52,291,784
58,452,808
Aggregate Scope 1+2
emissions
(metric tons)
43,450,887
45,943,772
47,388,961
Scope 1+2
emissions per
vehicle
0.89
0.88
0.81
Thus, we see that from 2010 to 2012, the emissions intensity declined at a
rate of 4.8% a year. Our model predicts a 4.3% decrease in emissions. Thus,
we suspect that most of the reduction in emissions intensity is due to the
economies of scale effect.
The change in emissions intensity under the business-as-usual scenario is
very sensitive to the ratio of the base load emissions intensity to the
aggregate emissions intensity. If CB is zero or if it is negligible compared to
131
Cp then the emissions intensity will stay constant under the business-asusual scenario. On the other hand, if Op is zero or if CB significantly
dominates the emissions intensity, then for the assumed values of AN/Nand
AV/V, we get,
AC
-
-(0.013
0.097) -=-0.084.
Thus, we estimate 8.4% is the limit of the business-as-usual scenario. As long
as capacity growth is outpaced by growth in production volume, it is
beneficial to have the base load emissions dominate and process emissions
minimal. However, as in the case of Maserati discussed earlier, we see large
shifts when capacity becomes saturated, new plants are needed and when the
new plants are under-utilized.
Business-as-usual and improving grid intensity
Rearranging the terms from equation (6.11), we get,
N( AN,
A
V
C
- -
Ng
eN
g
YV
Aq
,
:AV7
V/-A.
+
)+
~(Pe)b
fC
() ++fT +(.(pe)b + (pe),
P
--
Vg
-
N g
(Pe-)p
T
V
... (6.20)
This is the general form of the linearized equation for change in carbon
intensity of vehicle assembly. Now, in addition to the economies of scale
effect, we want to consider the effect of decreasing grid intensity. This is the
business as usual scenario. From Vital Signs, we have AN/N= 1.3% and
AV/V= 9.7%. Let us assume the grid improves at a rate of 1% every year.
132
That is, Ag/g= -0.01. The term V/N is the production volume of one plant
and it equals 250,000. So for the surrogate factory,
b = (461 x 250,000) kg C02 for the plant,
fbT=
(2,258 x 250,000) MJ per plant,
fpT=
2,664 MJ per vehicle,
(pe)b =
(505 x 250,000) kWh per plant,
(pe)p = 454 kWh per vehicle,
e
0.055 kg C02 per MJ for natural gas consumed,
g
0.5 kg CO 2 per kWh.
Then from equation (6.20) we get,
0.013
250,000
C
x 461 x 250,000 - 0.01 x 505
x 250,000
0.5
005(
0.5
1 2258 x 250, 000 + 2664)
250,000
-
+
0.097 x 461 x 250, 000
0.01
x 250, 000 x 454)
250,000 505 x 250, 000 + 454)
Or,
AC
-87
C1500-
-0.058.
That is, under a business-as-usual scenario with a grid which improves
steadily at 1% per year, the carbon intensity of vehicle assembly decreases at
5.8% per year.
/
Note that equation (6.20) decomposes to the original linear model when A
= 0. If we use g = 0.667 kg C02 per kWh for Michigan, we get the same result
for AC/C = 4.3% annual decrease.
133
Summary of emission reduction activities
The estimated impact of emissions reduction activities is shown in Table
6.21. Note that these changes would have to be implemented for every factory
in order to obtain the magnitude of improvement estimated by the company
in their CDP reports. If only a fraction of plants implement the changes, the
improvement will consequently be a fraction of the estimates for the whole
company.
Table 6.21: Estimates of the impact of scenarios on emissions
Scenario
Capturing CO 2 by planting
250,000 m 2 of trees
Changein
Change in
factory CO 2
emissions in
factory
emissions in
tons
percent
-1,037
-0.5%
-51,889
-23%
-8,400
-3.7%
-9,250
-4.1%
-2442
-1%
Installing 250,000 m 2 of 20%
efficient PV arrays
Installing 50% more efficient
lighting
Lower heating load due to 10 F
warmer winter
Lower cooling load due to 10%
points lower summer humidity
Moving a factory from Japan to the
U.S (effect of the grid)
134
Table 6.22: Change in CO 2 emitted per vehicle for a change in production
volume and capacity, given that all else remains the same
Annual percentage
Annual percentage
Annual percentage
change in
change in
change in C02 per
production volume
production capacity
vehicle
10%
1%
-4.5%
135
Chapter 7: Modern Vehicles - Materials,
Manufacturing and Use
In this chapter, we study vehicles which have gained attention in recent
years for demonstrating impressive energy efficiency in their use phase. We
attempt to investigate the whole production supply chain associated with
these cars, and compare them with conventional vehicles. We see remarkable
changes in how energy use is spread over the vehicle lifetime, with use phase
energy use declining and materials production energy on the rise.
7.1 An LCA of the Tesla Model S
Tesla Motors, Inc. is a U.S automobile company founded in 2003.
The
company manufactures electric vehicles and electric vehicle powertrain
components.
The company is headquartered in Palo Alto, California. Tesla
conducts vehicle component manufacture and assembly operations at its
136
factory in Fremont, California. It also has a manufacturing facility and parts
warehouse in Tilburg, Netherlands to supply vehicles to markets in the
European Union.
In 2008, the company launched its first production vehicle, the Tesla
Roadster.
The Roadster was in production till January 2012, having sold
2,500 units. The company currently produces the Model S sedan which has
sold 25,000 units in North America and Europe as of December 2013. The
company has unveiled a new SUV-minivan crossover, the Model X, which it
expects to deliver beginning the spring of 2015. Tesla has also announced its
intention to develop a low price-point, high-volume model to be in production
by 2017 [85].
Model S Specifications
The Model S is the only Tesla model currently in production. This model is
available in two battery pack options, 60kWh and 85kWh, which have an
effective base price of $62,400 and $72,400 in the United States.
The
company also offers an 85kWh "performance" variant for an effective price of
$85,900. The prices are obtained after applying the $7,500 federal tax credit
for the purchase of alternative fuel vehicles [85].
The curb weight of the Model S is 4,647.3 lbs [86]. The 60kWh model has a
U.S. Environmental Protection Agency (EPA) 5-Cycle Certified Range [87] of
208 miles with a rated power of 302 HP. The EPA 5-cycle range for the 85
kWh model is 265 miles, with a rated power of 362 HP [88].
The EPA has
certified the 60 kWh model to have a fuel efficiency of 95 Miles per Gallon
Equivalent (MPGe), and 89 MPGe for the 85 kWh model [87]. This is based
137
on the assumption that one gallon of gasoline delivers 33.7 kWh of energy.
The MPGe is a wall-to-wheels measure of fuel economy. That is, it does not
consider the losses which take place in generating electricity.
According to
EPA tests, the 60 kWh model battery requires 10 hours to charge at 240
volts, whereas the 85 kWh model requires 12 hours at 240 volts [89].
Environmental Reporting
To date, Tesla Motors has not published a sustainability report. The company
has also not responded to requests from the Carbon Disclosure Project to
disclose the emissions from its business activities. Furthermore, no emissions
reports were found for Tesla's Netherlands operations.
The EPA provides an online tool which can be used to estimate the use phase
C02 emissions from vehicles. The estimate includes tailpipe emissions and
the emissions associated with the production and distribution of fuel,
averaged over 26 U.S. regions. The Model S has zero tailpipe emissions since
it is an all-electric vehicle. As per EPA estimates, the 60 kWh model emits
230 grams C02 per mile, and the 85 kWh model emits 250 grams CO 2 per
mile. In contrast, the EPA estimates that an average gasoline vehicle emits
480 grams CO 2 per mile, and the plug-in hybrid vehicle (PHEV) Toyota Prius
emits 220 grams CO 2 per mile drive [89]. Note that use-phase emissions
differ widely depending on the carbon intensity of the electric grid. As per the
U.S Environmental Protection Agency (EPA) eGrid data [90], Wyoming has
the most carbon intensive grid with 0.94 kg C02 emitted per kWh of
generated electricity, whereas Vermont is the least carbon intense with only
0.0013 kg CO 2 emitted per kWh of generated electricity. The U.S average is
0.5 kg CO 2 per kWh generated. Since the U.S grid can be quite carbon
138
intensive in certain regions, the PHEV Prius seems to be cleaner in usephase than the Model S.
We know that use-phase emissions dominate the life-cycle emissions for an
automobile. Burnham et al report that for a certain lifetime of an I.C Engine
vehicle, vehicle
operation constitutes 73% of CO 2 emissions, the fuel
production and distribution constitutes 16%, and the vehicle, and the vehicle
manufacturing (from raw material extraction to final assembly) constitutes
10.7% [37]. The distribution changes for hybrid electric vehicles (HEV) with
70% CO2 emissions coming from the use phase, and the remaining i.e., 30%
divided equally between fuel production (and distribution) and vehicle
manufacturing.
Sullivan reports that the component manufacturing and
vehicle assembly accounts for about 4% of the lifecycle impact (energy and
C0 2 ) of a vehicle [20].
A life-cycle analysis (LCA) of the Tesla Model S is not found in literature. The
main roadblock seems to be the accurate estimation of the Model S material
content. We make some assumptions about material content and get upper
and lower limits on life-cycle emissions for the Model S.
Model S LCA
Model Assumptions
We use the Argonne National Laboratory's Greenhouse Gases, Regulated
Emissions, and Energy Use in Transportation Model (GREET) to perform the
LCA of the Model S. The GREET model consists of two main modules - the
fuel cycle (GREET1) and the vehicle cycle (GREET2). A schematic of the
model as depicted on the GREET website is reproduced in Figure 7.1 [91].
139
The GREET1 model - also known as the fuel cycle model - includes the
production and distribution of fuels and the generation of electricity. The
GREET2 model - also known as the vehicle cycle - covers the production of
raw material, component manufacturing and assembly, use phase impacts
(derived in part from GREET1) as well as disposal and recycling of the
materials. The results are reported for the vehicle cycle for the following
processes - component manufacturing, assembly, disposal and recycling
(ADR), batteries and fluids.
VEHICLE CYCLE
IGREET 2 S
As
m
de
f
WELL TO PUMP
Figure 7.1: Schematic of the operations covered under the GREET model
Analysis of the vehicle cycle requires the following inputs:
140
1. Vehicle Type - options are passenger cars, sports utility vehicles, and
pick-up trucks - and powertrain system - whether I.C engine, HEV,
PHEV, all-Electric Vehicle (EV) or Fuel Cell Vehicle (FCV).
2. Curb Weight of the vehicle.
3. Weight of battery, its specific energy (Wh/kg), and chemistry - whether
Ni-MH or Lithium ion - and replacements over the life time.
4. Weight of vehicle fluids, and replacements over lifetime.
5. Weights of vehicle components
-
like powertrain,
transmission,
chassis, motors and generators, electronic systems, body, and glass - in
percentages.
6. Estimated number of tire replacements over the lifetime of the vehicle.
7. Vehicle lifetime Miles Traveled (VMT)
The model provides default values for different kind of vehicles. It also uses
data from Sullivan (2010) for vehicle assembly processes like painting and
welding,
as
well as
overhead
impact
for
assembly
and
component
manufacturing.
Since the precise material composition of the Model S is not known, we
consider two scenarios. In the first scenario - called the Model S (lightweight
EV) -- the Model S is assumed to have high aluminum and carbon fiber
reinforced plastic (CFRP) content and low steel content in its body and
chassis for the same weight of the car. In the second scenario - called the
Model S (conventional EV), we assume that the Model S has the material
composition of the "conventional materials" EV default in GREET2. The
relevant values for both scenarios are listed in Table 7.1.
141
Table 7.1: Assumptions for the Model S with lightweight and conventional
materials
Parameter
1.
2.
3.
4.
5.
6.
Tesla Model S
Tesla Model S
- lightweight
- conventional
EV
4,647
1,302
Li-ion
85
89
EV
4,647
1,302
Li-ion
85
89
Vehicle Weight
Battery Weight
Battery Chemistry
Battery Energy
Fuel Economy
Number of battery
replacements over the
0
vehicle lifetime
7. Tire replacements over
3
vehicle lifetime
8. Vehicle body material composition
0
Comment
pounds [88]
pounds [94]
kWh [88]
MPGe
GREET2
default
values
3
GREET2
i.
Steel
10.3%
68%
default
value
ii. Wrought Aluminum
42.6%
6.6%
iii. CFRP
iv. Average plastic
23.8%
9. Vehicle chassis material composition
14.3%
i. Steel
0.7%
0%
18%
84%
ii. Cast Iron
9.2%
6.9%
iii. Wrought Aluminum
22%
0%
iv. Cast Aluminum
10.VMT
34.7%
1%
160,000
160,000
142
GREET2
default
value
GREET2
default
value
miles
Wherever a reference is not indicated for the material content values, the
default values were modified for the lightweight Model S so that the steel
content was replaced by aluminum. For simplicity, only the major materials
are listed here. Burnham et al [37] describe the assumptions made in the
GREET2 model in detail.
Model S LCA Results
Based on these assumptions, GREET2 presents the manufacturing and
lifecycle energy use and emissions. Table 2 shows the results for the vehicle
cycle. We discuss the energy and C02 impacts in detail below.
Table 7.2: Results for the Tesla Model S vehicle cycle
Tesla Model S
lightweight EV
conventional EV
132,910
82,387
ii. ADR
16,438
16,438
iii. Batteries
42,637
42,820
iv. Fluids
2,954
2,954
Total Primary Energy
194,939
144,598
8.15
5.72
ii. ADR
1.08
1.08
iii. Batteries
2.54
2.55
iv. Fluids
0.1
0.1
-
Tesla Model S -
Paramete rl
Units
1. Primary Energy
i.
Components
MJ per
vehicle
2. CO 2 emissions
i.
Components
Metric
143
tons per
vehicle
Total CO
Tota
C022
11.9
11.9
9.48
9.48
Primary energy consumption
We find a wide range for the primary energy consumption for component
manufacturing for the two scenarios due to the assumptions of material
content made for the lightweight and conventional Model S.
Sullivan et al
(1998) conducted an LCA study on a generic 1,532 kg I.C engine car and
reported a value of 134 GJ for the material production,
component
manufacture and vehicle assembly. The Model S energy estimates do not
bracket this value, most likely due to the high energy intensity materials
assumed to be present in the vehicle, along with the added intensity of
battery manufacturing.
The Model S results are plotted for the default vehicles in GREET2 for
comparison and to gauge if the model performs adequately. The default
vehicles are comparable in weight and differ mainly in powertrain systems,
material composition and battery weights and chemistries. The vehicle-cycle
energy use is shown in Figure 7.2. In all cases, component manufacture
dominates the energy use, constituting as much as 82% of total energy for
FCVs. Note that the ICEV primary energy is 103 GJ per vehicle which is on
the low side compared to Sullivan's 1998 estimate of 134 GJ. The disparity
seems largely to come from differences in assumptions of replacement of
components and fluids.
The ADR figure is the same for all vehicles - 16,438 MJ per vehicle -- based
on data from Sullivan et al [20]. Sullivan's value for the component
manufacturing and assembly phase is 13,602 MJ. This does not include
material transformations and machining. The ADR number is higher since it
144
includes paint production in addition to impacts of disposal and recycling. We
observe that battery manufacturing constitutes a significant portion of the
vehicle cycle for the electric Model S which has a large Li-ion battery.
250
al)
o Fluids
[ Batteries
o Components
*ADR
0
a)
D150
CD
a)
3
C:
LU
100
50
133
82
133 1
82
Model S
Model S
(lightweight) (conventional)
-
-11-
177
1141
FCV
PHEV
78
781
HEV
74
ICEV
Vehicles
Figure 7.2: Vehicle cycle primary energy use for various vehicles, GJ per
vehicle
We now look at the entire vehicle lifecycle, adding the inputs from the
GREET1 model and compare the same set of vehicles as before. The
comparison is shown in Figure 7.3. Now we see that the Model S outperforms
all other vehicles. Due to its high reported range of 265 miles, the Model S
requires less energy per km driven. We observe that the vehicle cycle
constitutes between 21 to 31% of the lifecycle energy. Note that this value
depends on the assumed vehicle lifetime of 160,000 miles. The energy
efficiency of electric vehicles becomes apparent over longer lifetimes which
145
compensates for the energy intensive component and battery manufacturing
processes.
5.0
oVehicle Operation oVehicle Cycle oWell-To-Pump
4.0
E
-----
-
- ------
-
- -------
---- - -- - ----
a)
C
W 2.0
0.76
1.0
---
0.85
-- 0.70
0.5
------
------
1.53 -
--
23.22
2.02
--
-
2.30
-
0.44
i.Q1.21
-
3.0
------ 0.40
0.42
.207
1.Z250.1
0.0
Model S
Model S
PHEV
FCV
HEV
ICEV
(lightweight) (conventional)
Vehicles
Figure 7.3: Lifecycle primary energy use for various vehicles, MJ per km
C02 emissions
The vehicle cycle C02 emissions are shown in Figure 7.4. The emissions are
reported as the sum of Scope 1, Scope 2 and Scope 3 emissions in metric tons
CO 2 per vehicle. The ADR emissions minus the disposal emissions are
counted as Scope 1+2.
Some values for these emissions from literature are shown on the graph.
These references were discussed in Chapter 3. Ashby's Eco-Audit combined
146
with Sullivan's VMA model gives a value of 5.41 tons CO 2 per vehicle.
Sullivan (1998)
estimated 7 tons CO 2 per vehicle from the material
production and vehicle manufacturing phases. The EIOLCA estimate is 8.5
tons CO2e. Our estimates of the Model S exceed even the EIOLCA value. This
12
,
is attributable again to the high energy intensity of the components used.
o Scope 1+2+3.1
EIOLCA
0
8
Sullivan, 1998
0.
CI
0
As by Eco- Audt
E
cn
C:
0
4
-
---
97
71
6
E
U)
11.
9.2
8.8
7.2
7.1
6.5
Model S
FCV
PHEV
HEV
ICEV
0
Model S
(lightweight) (conventional)
Vehicles
Figure 7.4: Vehicle cycle C02 emissions for various vehicles, metric tons per
vehicle
The emissions for the entire life-cycle of the vehicle in terms of grams CO 2
per km are shown in Figure 7.5. As was observed for energy use, the Model S
performs better over a long lifetime since its tailpipe emissions are zero. Note
that this data is based on a U.S average and in some states it might be better
to drive a PHEV than an all-electric vehicle. For example, in Figure 7.6 we
observe the lifecycle emissions associated with driving a Model S in the 50
147
states and District of Columbia. In states where coal and oil constitute a
large fraction of the energy source for electricity generation, driving an EV
would produce more emissions than a PHEV.
400 1
1
aVehicle Operation
oVehicle Cycle
oWell-To-Pump
P 300
CD
0
U,
In 200
E
-----
-
- - --
--
-------
0
0
a)
-5 100
0
Model S
Model S
(lightweight)
(conventional)
PHEV
FCV
HEV
Vehicles
Figure 7.5: Life-cycle CO2e emissions, gram per km
148
ICEV
300
(N
0
4-
0
-
------------------------------------- -- - ---------- - - -
-
-
E 2 50
E
U>
-- -- - - - - -- - - -- - - ----
-- --- - - - - - - - ------ ---
-- - -- -
-
-0 -
2 0-
100
5 0
-- - - - -- -- -
-
1 0- - --
- ------
0
e-
z2DUzore-
Q2OZ2220
L<ZM-<2a<<
2z>
OWzzozo2o-
States
Figure 7.6: Model S Life-cycle emissions over 50 states and D.C, gram CO2e per km
-
-
50
For a final comparison, we plot the Tesla Model S LCA estimates on Ashby's
graphs [57, pp. 145-146] as shown in Figure 7.7. Considering its weight, the
Tesla Model S does better than comparable vehicles in terms of energy use
per km. When we factor in CO 2 emissions, the Model S falls right on the
trajectory of most popular vehicles. However, we also notice that its energy
intensive manufacturing processes and all-electric drive drawing upon the
U.S grid make it a fairly carbon-intensive vehicle.
-
10
Gasoline, LPG, and hybrid-engine cars
*
V
4d Rover2 Land
0
Land Rover 6Bentley
Discovery V6
Rolls-Royce
6.75 V12
Audi A6
Jeep Cherokee 4.0 -D
Maybach
5.5 V12
M b0
Alpha Romeo 2.5,
Mitsubishi
Saab 9-3 2.0
Shogun 3.5
0
Vauxhall Astra 1.6
c-m
E
5
0
A
.
Fiat Bravo 1
E
Citroen Saxa 1.4
C
0
Fiat 1.1
2)
a)
Ford Galaxy 2.3
0 O
BMW 318 Ci
2
Tesla Model S
Skoda Fabia 1.2
Smart 0.7
, -
2.,
Vauxhall Corsa
1r
Toyota Pnus
Suzuki Alto 1.1
Toot'
i
o
Gasoline
* LPG
o Hybrid
1
600
(A)
4
MFA' 11
1000
1500
Unladen weight, kg
(a)
2000
2500
3000 3500
500
Gasoline, LPG, and hybrid-engine cars
Range Rover 4.0
A N
JM
Jeep
-
400
Ferrari 360 Spider
I/
Maserati 4.2 V8
aserati 4.24.
Bentley Continental 6.0
Chrokee 4
J
-Volkswagen
Phaeton 6.0
Toyota Land Cruiser 4.2MrcdsBnML5
Mercedes.Benz MOW5
Chrysler Voyager 2.4 0 0oO
\
rr
aay.2.8
Ford Galaxy
-
300
Audi A4 3.0
Honda NSX 3.2
0
,
Renault Megane 2.0
0,
200
-
0
Ferrari 360 Modena
resla MOCersaxo 1.4
Smart 0.7
Toyota Prus
,
Volvo S60 LPG
Nissan Primera 1.8 LPG
Gasoline: CO 2 = 68 x Energy
0 Hybrid: C02 = 68 x Energy
0 LPG:
C02 = 46 x Energy
(C02 in g/km, energy in MJ/km)
,0
-
100
Volvo V70 LPG
0
0-
MFA 1T
0
(A)
1
2
3
4
5
Energy consumption, MJ/km
6
7
8
(b)
Figure 7.7: Tesla Model S compared to other vehicles from Ashby for use
phase energy and CO 2 emissions
Finally, the manufacturing and use phase lifetime emissions are shown in
Figure 7.8 below. The use phase emissions assuming a U.S average grid are
shown as the column and the upper and lower limits, depending on the
carbon intensities of states are shown as well. Vermont has a very low carbon
intensity of the grid and its use phase emissions are of the same order as the
manufacturing of fluids. The grid for the District of Columbia, on the other
hand, is quite carbon intensive and the use phase emissions would be 3.8
times the manufacturing emissions.
151
50
0 Assembly and Recycling
o Batteries
* Fluids
- -- --- - -
-
- D.C use phase:
44 tons
-
40
* Component
0 U.S average
30
20
0.1
2.5
10
Ve mont use phase:
1
0
Manufacturing emissions
0.1 tons
Use phase emissions
Figure 7.8: Lifetime manufacturing and use phase emissions for the Model S
in the U.S
7.2 An eco-audit of the Volkswagen XL1
Introduction
The Volkswagen XL1 is a diesel-powered plug-in hybrid vehicle unveiled by
Volkswagen in 2011. Volkswagen demonstrated that the car, a two-seater,
could achieve a fuel economy of about 283 miles per U.S gallon (MPG). Its
certified fuel economy under the NEDC cycle is 260 MPG. This impressive
feat is achieved by reducing the vehicle mass, reducing the drag resistance,
and greater transmission control. The car has a curb weight of 795 kg and a
cross-sectional area of 1.5 M 2 . The top speed of the vehicle is limited
152
electronically to 99 miles per hour. The vehicle is being produced on a limited
basis at Osnabruck, Germany, and went on sale with a sticker price of
111,000 [93]. Figure 7.9 below shows the XL1 [94].
Figure 7.9: Volkswagen XL1
Specifications
Volkswagen has reduced the weight of the XL1 by reducing its size and
attempting to maximize the use of low-weight materials. Of the total vehicle
weight of 795 kg, only 23% or 184 kg is iron or steel [95]. The body weight is
230 kg, largely made of carbon fiber reinforced plastic (CFRP). The rest of the
weight is for the engine and battery (227 kg), the transmission system (153
kg), the electrical system (105 kg) and rest of equipment (80 kg).
The vehicle body design is streamlined to achieve a drag coefficient of 0.189.
This is achieved by removing projections from the surface like rearview
mirrors which are instead replaced by cameras. The external dimensions of
the car are 3,888 mm x 1,665 mm x 1,153 mm (length x width x height).
The car has an 800 cc, 35 kW diesel engine and an electric motor with a rated
power of 20 kW. The 5.5 kWh lithium-ion battery can by charged by plugging
the car to a wall socket and by the engine during lean loads. The rated fuel
153
consumption is 0.9 liter per 100 km or 260 miles per gallon. The car can be
powered solely by energy stored in the battery for a range of up to 50 km.
Modeling the lifecycle of the XL1
We now attempt to quantify the energy use and emissions over the lifecycle of
the XL1. We do not have a detailed bill-of-materials for the car although some
data on the main materials - CFRP and iron and steel - are available. Ashby
presents the material content for a lightweight material car weighing 836 kg
[57, p. 212]. We use this bill-of-material whenever data on the XL1 is
unavailable. Note that Ashby does not include the impact of manufacturing
the battery or vehicle fluids. The battery on the XL1 is smaller than those
typically seen on PHEV sedans or all-electric cars, and we do not include it
for this analysis. The assumed bill-of-materials is shown in Appendix E.
Average material production and processing energies are taken from chapter
15 of Ashby. These are intended to provide an approximate measure of the
material and component production energy use and emissions. For vehicle
assembly, we use Sullivan's data. For determining use phase emissions, we
use the rated fuel efficiency for the XL1 of 260 miles per gallon. The energy
intensity of diesel is 38 MJ per liter and the carbon intensity is 3.1 kg CO 2
per liter. We assume a vehicle lifetime of 160,000 km. Maintenance and
repair are not considered here. The energy and emissions estimates over the
lifetime of the vehicle are shown in Figure 7.10 and Figure 7.11 below.
154
160,000
127,604
120,000
--------------
t-- -------------------------------------103,863
80,000
--
--------------
---------------------------------
-
C
--
--------
--------------------------------
-
40,000
-
54,720
w
10,140
13,602
Manufacturing
Assembly
0Materials
Total
manufacturing
Use phase
Figure 7.10: Estimated lifetime energy use for the XL1
12,000
10,748
9,072
U>)
8,000
-----------------
.-------------------------------------
0)
U>
0
E
4,000
---------
------------------------------------
787
889
Manufacturing
Assembly
-
4,464
0
Materials
Total
Use phase
manufacturing
Figure 7.11: Estimated lifetime C02 emissions for the XL1
155
We notice that the energy use and emissions for manufacturing exceed those
estimated for the vehicle use phase over a lifetime of 160,000 km. This is not
the case for conventional vehicles. In Sullivan (1998), use phase emissions for
a conventional gasoline-engine powered sedan by a factor of 6. In Figure 3.10,
the ratio of use phase to manufacturing emissions for five companies was
3.6:1. For plug-in hybrid vehicles or for electric vehicles, the energy use for
manufacturing is exceeded by the use phase emissions only after a certain
miles have been driven, which is typically significantly smaller than the
vehicle lifetime. For the Volkswagen XL1, we see that in the quest for fuel
economy and reducing tailpipe emissions, more energy has to be expended in
the manufacturing phases. However, even then, the XL1 is remarkably
efficient. The total lifetime energy use estimate above is about 182 GJ and
the emissions amount to 15 tons. The vehicle Sullivan modeled in 1998 used
954 GJ of energy and emitted 58 tons C0 2 , over 85% of it in the use phase.
The
significance
of this
trend
towards
high-intensity
manufacturing is discussed in the concluding chapter.
156
material
and
Chapter 8: Conclusions and Future Work
In this chapter, we summarize our findings and present the way forward for
the automobile manufacturing industry. We predict the challenges in meeting
climate change goals, and effective ways of reducing emissions. Finally, tasks
which need more attention are highlighted.
8.1 Conclusions
Meeting climate change goals
For present-day vehicles, the use phase of a vehicle contributes most, often as
high as 86% - to its lifecycle emissions. As discussed in Chapter 1, the
transportation sector contributes almost a quarter of all fossil fuel emissions.
In many countries, fuel economy and greenhouse gas regulations have been
enacted to reduce this large impact. Figure 8.1 shows the trends for emissions
on a gram CO 2 per kilometer basis till the early 2010s and the proposed or
enacted targets for the next decade (reproduced from ICCT) [58].
157
280
---
US
'EU
Japan
-e-China
India
260
E
240
0
,
220
c 1 200
2C.
180180
(0
(E
120 68
0o2 0 --04%e
undestu8 ies
1r7
natdtag
80
60
performance
20521
lines: historical
8:Solid
r 000
F4h
nDashed lines: enacted targets
Dotted lines: proposed targets or
20 ga targets under study
0 1
2010
2005
2000
01
2015
022
2020
2025
Figure 8.1: Tailpipe emissions: historical and proposed targets for various
countries
Of the countries shown here, the US has the second-most stringent reduction
rate for light-duty vehicles, after China, requiring a 4.2% annual reduction
till 2025. This would reduce emissions intensity by 43% over the 2012 level of
207 gram Of C02 per km to the 2025 target of 118 gram CO 2 per km. The fuel
economy would go from 33 MPG in 2012 to 50 MPG in 2025, a 3.9% annual
increase, 52% overall. Grimes-Casey et al [25] constructed emission pathways
to achieve 450 ppm CO 2 concentration in the atmosphere. They translated
those into requirements for US light-duty vehicles, which came to about 25
MPG by 2025 and 136 MPG by 2050. Thus, the standards till 2025 would
meet the target. The 2050 goal requires a 665% improvement in fuel economy
158
compared to 2007 levels i.e., 4.8% annually after accounting for growth in
demand (in terms of vehicle miles travelled) and fuel-mix scenarios.
These improvements in fuel efficiency are being driven to a large extent by
light-weighting of cars. This requires materials with high strength-to-weight
ratios, and often these tend to require higher material production energy. In
the Table 8.1 below, we show an example of this dynamic. We show the 2012
average Scope 1+2+3.1 emissions and use phase emissions for five companies:
BMW, Daimler, Renault, Nissan and Volkswagen. We saw this data earlier in
Chapter 3, but it is adjusted to show the use phase emissions for a lifetime of
160,000 km. Also shown are the same values estimated for the Volkswagen
XL1. The average miles per gallon fuel economy is shown for both data
points, using carbon intensity of gasoline from Ashby.
Table 8.1: Manufacturing and use phase emissions comparison between
average European vehicle and the Volkswagen XL1
Emissions in kg for
2012 average European car
Volkswagen XL1
(Scope 1+2+3.1)
5,953
10,748
Lifetime use phase
22,400
4,464
Total
28,500
15,212
Manufacturing
Suppose that the Volkswagen XL1 represents a typical car in the year 2050.
In fact the annual per cent decrease in use phase emissions required from
2012 to achieve the XL1's emissions in 2050 is 4.2%, which is equal to the
CAFE requirement till 2025 and close to the requirement estimated by
Grimes-Casey et al for 2050. We notice that to achieve the XL1's high fuel
economy, the manufacturing emissions had to increase. The increase, on an
159
annual basis would be 1.5%. The total emissions therefore decrease at a lower
rate, at 1.6% a year. The reduction in absolute emissions is 47%. It is
straightforward to determine the point at which manufacturing emissions
will cross the use phase emissions for a vehicle. Figure 8.2 is a plot of the
manufacturing, use phase and total emissions for the data set shown in Table
8.1 above. According to these numbers, around the year 2034, manufacturing
emissions will exceed use phase emissions, if they continue to change at the
predicted rate. Thus, more attention should be given to manufacturing
emissions in order to make a bigger impact on overall emissions. Note that
the manufacturing and use phase emissions reported here are lower than the
global average. So the annual percent change required for use phase would be
higher, and that for manufacturing would be lower.
30,000
-.- Mfg. Emissions
-o--Use Phase Emissions
-*-Total Emissions
U,
C
(0 20,000
-n
--- - - - - -
-
-------------
-- - ---- ------------------ ------
U)
-1.64% a year
(DJ
0
E
10,000
-- -
-- -- -
-
--- ---
+1.57% a year
- ---
I
-4.16% a year
0
Year
Figure 8.2: Pathways for manufacturing and use phase emissions till 2050
160
From our analysis of automakers' CDP reports, we found that Scope 1+2
emissions per vehicle were decreasing at a rate of about 4.8% a year for the
last five years. However, we suspect most of this, around 4.3%, is due to the
economies of scale effect. We do not have enough data on Scope 3.1 emissions
to conclude if outsourcing has contributed to increased emissions. We do see
for BMW, Daimler and Renault that their Scope 1+2 emissions per vehicle
decreased at 5.3% a year whereas their Scope 3.1 emissions per vehicle
increased only at 0.12% from around 2008-2009 to 2012. For these companies,
Scope 1+2+3.1 emissions per vehicle have decreased at 0.57% a year. For
BMW, Daimler, Nissan, Renault and Volkswagen, Scope 1+2 emissions per
vehicle have decreased at 2.1% a year whereas Scope 3.1 emissions per
vehicle have increased at 1.5% a year. For these companies, Scope 1+2+3.1
emissions per vehicle have increased at 0.62% a year.
From CDP reports, we see that absolute Scope 1+2 emissions have increased
at a rate of 4.4% a year from 2010 to 2012. If emissions increase at this rate,
they will have increased by 467% of their 2010 levels by 2050. For the
companies we studied, emissions intensity has decreased slowly in the last
few years. Meanwhile, we can expect production volumes to increase as
developing countries reach the same standards of living as developed
countries. Also, automobile manufacturing might get more energy intensive
in order to save on use phase emissions. To meet overall goals, it seems that
use phase emissions targets would have to be even more stringent in order to
make up for the increase in absolute emissions from vehicle manufacturing.
Effectiveness of emission reduction activities
161
In Chapter 6, we saw the impact of emission reduction activities. Installing
50% more energy efficient lighting would reduce factory emissions by 3.7%.
However, this would be a one-time benefit, not a year on year improvement.
The same applies to PV panel installation. It displaces 7.2% of primary
energy use most of which is currently obtained from fossil fuels. The 23%
reduction in emissions would
also not be a cumulative
benefit. The
improvement in utilization provides cumulative benefits. We estimate that if
production volume outpaces capacity growth by 9% year after year, emissions
intensity would keep on decreasing at 4.5% a year. However, absolute
emissions would still increase. Persistent efforts at improving efficiency of
processes and base load activities are required.
8.2 Future Work
This work focused on automobile assembly plants, with the assumption that
other than some stamping work, welding, painting and assembly activities,
all other work is done upstream in the supply chain. The biggest impact of
energy and emissions happens here, primarily in the production of materials.
However, the material transformation part of the supply chain has not
received as much attention. We suspect that base load emissions constitute
as significant a portion of total emissions if not more as they do for
automobile assembly. However, we were not able to find a comprehensive
study modeling this part of the supply chain. The complexity of the supply
chain inhibits this. But perhaps with better data communication and storage
technologies this challenge could be overcome. We believe the energy savings
potential in the supply chain to be substantial, and as is the case for
162
assembly,
it
can be an
important
area for cost
reduction,
without
compromising on quality or output.
We constructed a basic thermodynamic model of the factory, focusing on
energy flows. A model with a higher resolution would enable analysis of
individual shops and highlight opportunities for efficiency. Moreover, an
exergy analysis would lead to better understanding of process efficiencies,
and a shift away from using high energy quality fuels like electricity or fossil
fuels for low energy quality tasks like heating. The potential here for
identifying efficiency improvements is tremendous. To some extent, this has
been studied for residential and commercial buildings. It would be as
effective and worthwhile at automobile assembly plants.
163
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Appendix A: Energy Use for Vehicle
Manufacturing in Literature
A summary of energy use reported in literature is given in Table A.1.
Wherever possible, the breakdown of fuel use and electricity use is given.
Studies differ in boundaries and things they include within those boundaries.
Some comments on each reference explain the values. This table was
prepared in collaboration with Schmieder.
Table A.1: Energy use for vehicle manufacturing quoted in literature
Energy Use (GJ per vehicle)
Authors and Year
Brown et al, 1985 [36]
Included manufacturing steps
Title of Publication
Energy Analysis of 108
Industrial Processes
Fuel
Electricity
Total
31
24.8
55.8
Gray Iron Foundry, Motor
Vehicle Parts and Accessories,
Motor Vehicles and Car Bodies
8.2
15.1
Engine and Parts Manufacture,
Vehicle Body Production,
Chassis, Painting, Assembly
Energy Efficiency
Galitsky et al, 2008
[18]
Improvement and Cost
Saving Opportunities
for the Vehicle
Assembly Industry
6.8
Pressing, Welding, Coating,
Resin molding, Plating, Body
Kobayashi, 1997 [96]
assembly, Casting, Forging,
Car Life Cycle
e
Car ifeCy
Inventory Assessment
Schuckert et al, 1997
[30]
Life Cycle Inventories New experiences to
save environmental
loads and costs
Sullivan et al, 2010
[20]
Energy-Consumption
and Carbon-Emission
Analysis of Vehicle and
Component
19.9
Heat treatment, Machining,
Parts assembly, Power source
and other, also parts
manufacturers
5.5
7.3
24.1
Press shop, Car cassing, Paint
shop, Assembly, Plastic parts,
Engine, Gearbox
12.8
Material transformation,
machining, assembly and base
load
Energy Use (GJ per vehicle)
Authors and Year
Title of Publication
Included manufacturing steps
Fuel
Electricity
Total
5.8
7.5
13.3
Body weld, paint and assembly
39.2
Part and sub-assembly
manufacturing, vehicle
assembly
All fuel and electricity use of 7
companies: BMW, Fiat, Ford,
GM, Honda, Nissan, Renault.
Manufacturing
Boyd, 2005 [35]
Sullivan et al, 1998
[29]
Development of a
Performance-based
Industrial Energy
Efficiency Indicator for
Automobile Assembly
Plants
Life Cycle Inventory of
a Generic U.S. Family
Sedan Overview of
Results USCAR AMP
Project
Carbon Disclosure
Project, 2013
U.S EIA
Manufacturers
Energy Consumption
Survey
1994 [97]
4.2
12.3
16.5
6.6
7.9
14.6
2006 [16]
5
7.8
12.9
2010 [19]
6
8.9
14.9
176
Industry-wide fuel use and
electricity use data. Production
data taken from PWC Autofacts
Appendix B: Renault Factory-Level Data
The Renault plant level data used in section 4.2 is given in Table B.1.
Table B.1: Plant-level data for Renault for 2008, 2010, 2011 and 2012
Year
Plant
Scope 1
Emissions
(tons)
Scope 2
Emissions
(tons)
Avg.
HDD
(F-days)
Avg.
CDD
(F-days)
2008
Bursa
31,225
74,951
3088
2008
Casablanca
7,106
14,936
2008
Douai
43,997
2008
Envigado
2008
Flins
2008
Moscow
2008
Vertical
integration
Avg.
Wheelbase
(mm)
1373
Grid
intensity
(kg C02
per kWh)
0.511
2
2575
1575
1242
0.787
1
2659
6,948
5005
119
0.072
1
2685
4,616
3,756
670
5
0.107
1
2557
30,427
5,249
4774
224
0.072
2
2575
8,964
23,807
7880
177
0.426
1
2634
Palencia
33,820
36,323
4807
287
0.327
1
2559
2008
2008
2008
2010
2010
Pusan
Sandouville
Santa Isabel
Bursa
Casablanca
29,329
30,655
15,160
32,353
6,486
42,197
5,939
19,992
65,226
17,791
3861
4810
1442
47
3
1
2672
2765
1622
2809
1234
1382
1487
1546
0.487
0.072
0.369
0.460
0.687
1
2
1
2633
2580
2661
2010
2010
Douai
Envigado
56,245
4,064
9,647
1,342
5841
658
185
92
0.077
0.176
1
1
2697
2575
2010
Flins
29,500
7,452
5416
293
0.077
2
2575
2010
Moscow
19,694
18,259
8977
756
0.412
1
2634
2010
Palencia
38,025
26,149
5262
373
0.237
1
2575
2010
Pusan
39,129
62,417
4053
1461
0.534
3
2690
2010
Sandouville
26,944
6,335
5496
48
0.077
1
2762
2010
20,062
19,921
1955
1407
0.366
1
2584
2011
2011
Santa Isabel
Bursa
Casablanca
36,011
8,845
65,709
20,540
3644
1599
1363
1631
0.472
0.729
2
1
2578
2652
2011
Douai
43,515
8,691
4491
115
0.061
1
2696
Table B.1: Plant-level data for Renault for 2008, 2010, 2011 and 2012
Vertical
integration
Avg.
Wheelbase
(mm)
41
Grid
intensity
(kg C02
per kWh)
0.108
1
2576
4070
8438
4424
221
415
353
0.061
0.437
0.291
2
1
1
2575
2635
2569
60,867
6,029
.19,035
62,024
4095
4314
1597
3184.0
1254
70
1416
1716
0.545
0.061
0.390
0.480
3
1
1
2
2706
2762
2620
2592
9,688
37,613
17,830
8,092
1979
0.638
0.090
1
2647
5208
1530
149
1
2697
Envigado
6,240
3,242
850
8
0.175
1
2621
2012
Flins
36,659
8,776
4747
253
0.090
2
2565
2012
Moscow
31,416
25,526
8874
219
0.317
1
2649
5038
412
0.299
1
2580
1354
89
0.498
0.090
3
1
2695
2766
1540
0.355
1
2630
Scope 1
Emissions
(tons)
Scope 2
Emissions
(tons)
Avg.
HDD
(F-days)
Avg.
CDD
(F-days)
Envigado
4,665
1,518
941
2011
2011
2011
Flins
Moscow
Palencia
20,322
28,600
28,972
7,548
24,193
23,786
2011
2011
2011
2012
Pusan
Sandouville
Santa Isabel
Bursa
34,480
20,477
18,828
32,874
2012
2012
Casablanca
Douai
2012
Year
Plant
2011
2012
Palencia
27,555
18,892
2012
Pusan
52,086
2012
Sandouville
26,315
16,185
5,862
4217
5003
2012
Santa Isabel
17,907
18,195
1581
179
Appendix C: Material Content of the
Vehicle Modeled
Here we present the details on the material content of the vehicle used in all
models. The material content of the vehicle was taken from Sullivan's VMA
model. The material energy and emissions data is taken from Ashby. Table
C.1 presents energy and emissions data for the material production phase.
Table C.1: Material composition of the vehicle, and energy and emissions calculations
Primary Production
Primary CO 2
Primary Energy
weight
Energy MJ/kg
kg/kg
MJ/veh
Carbon Steel
54%
32
2
26,277
1,478
Iron
11%
17
2
2,735
241
Aluminum
6%
149
9
14,381
863
Brass
1%
59
4
542
34
Lead
1%
27
2
331
25
Copper
1%
59
4
1,085
68
Glass
3%
11
1
450
32
HDPE
2%
81
3
2,234
76
Rubber
7%
118
7
13,377
748
Polyurethane
3%
48
2
1,923
84
Polyvinyl Chloride
0%
59
3
181
8
Polypropylene
5%
79
3
5,688
220
69,204
3,876
% of Curb
Material Group
Total
92.5%
C02 kg/veh
Appendix D: Solar Radiation on FlatPlate Collectors in Detroit
The monthly averages of incident solar radiation (W/m 2 ) for flat-plate
collectors at Detroit, used in section 6.2, are given in Table D.1. These are
provided by NREL for various fixed tilt angles in degrees with reference to
the latitude.
Table D.1: Incident solar radiation in W/m 2 for Detroit for various tilt angles
Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Year average
0
67
104
142
192
233
258
254
221
171
117
71
54
158
Tilt (degrees)
Lat - 15
Lat
Lat + 15
100
138
171
208
238
254
254
233
200
154
100
79
179
113
150
175
204
225
233
233
225
200
163
108
88
175
117
154
171
188
200
204
204
204
192
163
108
92
167
90
108
138
133
125
117
113
117
129
138
133
96
83
121
Appendix E: Assumed Bill-of-Materials
for the Volkswagen XL1
Table E.1 presents the bill-of-materials used to make estimates of material
and manufacturing energy and emissions for the Volkswagen XL1 in section
7.2.
Table E.1: Bill-of-materials assumed for the Volkswagen XL1
Material type
Mass (kg)
Carbon steel
Stainless steel
Cast iron
Wrought aluminum (10% recycled content)
Cast aluminum (35% recycled content)
Copper/Brass
Magnesium
Glass
Thermoplastic polymers (PU,PVC)
Thermosetting polymers(Polyester)
Rubber
CFRP
161
4
20
55
124
49
4
36
71
44
18
169
22
3.3E-03
1.8E-01
GFRP
Platinum, catalyst (Table 6.2)
Electronics, emission control etc.
20
Other (proxy material: Polycarbonate)
795
Total
183
184