Carbon Footprint Measurement and Analysis of a Multi-Modal Logistics
Network
by
Adam J. Miller
B.S. Mechanical Engineering Technology, Purdue University, 2007
Submitted to the MIT Sloan School of Management and the Department of Mechanical Engineering in
Partial Fulfillment of the Requirements for the Degrees of
Master of Business Administration
and
Master of Science in Mechanical Engineering
MASSACHUSETT'S
In conjunction with the Leaders for Global Operations Program at the
Massachusetts Institute of Technology
June 2014
INS
JUN 18 2014
LIBRARIES
©2014 Adam J. Miller. All rights reserved.
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any medium now known or hereafter created.
Signature redacted
Signature of Author
MIT Sloan School of Management, MIT Deparpent of Mechanical Engineering
Signature redacted
May9,2014
Certified by
Timothy G. Gutowski, Thesis Supervisor
Professor, MIT Department of Mechanical Engineering
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Certified by
TJay, Thesis Supervisor
an School of Management
Lecturer
Directo;Vainablity itijyve at.Mj Tloan
Signature redacted
Accepted by
David E. Hardt, Chairman, Committee on Graduate Students
Department of Mechanical Engineering
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Accepted by
Maura Herson, DirectbcjvlBA Program
MIT Sloan School of Management
ME
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2
Carbon Footprint Measurement and Analysis of a Multi-Modal Logistics
Network
by
Adam J. Miller
Submitted to the MIT Sloan School of Management and the Department of Mechanical Engineering on
May 9, 2014 in Partial Fulfillment of the Requirements for the Degrees of Master of Business
Administration and Master of Science in Mechanical Engineering
Abstract
General Motors (GM) is one of the world's largest automobile manufacturing companies and does
business in over 120 countries, requiring a complex operations network. Operating with a focus on
environmental impact has become a strategic pillar within the company, both in its products and in its
supply chain. Specifically, the GM global logistics organization is driving toward greater emissions
visibility and the identification of carbon dioxide reduction opportunities within its network.
Key objectives of this thesis work include creating business tools and processes to record global
logistics emissions data, which will allow GM to more accurately report logistics emissions and reduction
efforts to shareholders, track network emissions over time, pinpoint carbon reduction opportunities that
align with cost savings efforts, and understand and mitigate future risks to the business.
The approach taken to address the above objectives unfolds into three distinct work streams: (1)
implementation of industry-recognized methods and processes, (2) development of a global carbon
footprint measurement model, and (3) emissions analysis of network change activities.
Industry research and data analysis along with internal cost and network data were used to develop
carbon measurement tools. These tools are capable of estimating mass emissions (tons C02) generated by
logistics operations globally as well as the increase or decrease in mass emissions generated by individual
network change events (e.g., changes in mode, mileage, shipment frequency, etc.). Furthermore, through
close collaboration with logistics providers, GM fulfilled the necessary requirements to become an
official shipper partner of the USEPA SmartWay program.
Immediate benefits of the project work include using the resulting data for global reporting and
benchmarking purposes, providing management with a new set of information that can be used to
strengthen network change proposals, and tracking improvements in overall network emissions as well as
the performance of individual providers. Long term benefits include stronger relationships with providers,
reputational and governmental risk mitigation, and cost savings from increased fuel efficiency of
operations.
Thesis Supervisor: Jason Jay, Lecturer, MIT Sloan School of Management
Thesis Supervisor: Timothy G. Gutowski, Professor, MIT Department of Mechanical Engineering
3
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4
Acknowledgments
I would first like to thank everyone at General Motors who helped make this research fellowship a
success, starting with my supervisors Jeffrey Morrison and Elliot Swiss, who provided guidance and
support for both my work and my professional development within the Global Purchasing and Supply
Chain Organization. I am also grateful to David Tulauskas, a forward-thinking leader in sustainability at
GM, with whom I met weekly to discuss progress and direction of my research. Furthermore, I wish to
acknowledge the steering committee comprised of GM leaders who oversaw and gave helpful input to my
work on a monthly basis. In addition to those mentioned above, members of the steering committee
included Edgard Pezzo, Bill Hurles, Bryan Burkhardt, Grace Overlander, Al Hildreth, Doug Ravas, Jorge
Arinez, Levon Hachigian, Mari Kay Scott, Alex Burnett, Nate Lawson, Vince Kretschmer, Leon Pozen,
Colin Dill, and Mike Rhadigan.
Without the help of trusted partners of GM, significant outcomes of this work could not have been
achieved. Therefore, great thanks goes to the teams at Ryder System Inc., Penske Corporation, Maersk
Line, DSV, FedEx, Union Pacific Railroad, and the U.S. Environmental Protection Agency.
I would like to thank my MIT advisers Jason Jay and Timothy Gutowski for their academic
perspective and guidance throughout my research and thesis preparation. With strong influences provided
by Professor Jay's action-oriented sustainability focus and Professor Gutowski's carbon and life cycle
analysis expertise, the results of this work are both practical and credible. Also, both advisers were
instrumental in helping me apply research findings in an industry setting.
Finally, I would like to thank my wife Grace for her encouragement and unending support, which
continues to be my greatest source of motivation. Over the two years spent at MIT, Grace and I made
many lifelong friends, shared countless new experiences, and welcomed our first child Alex into the
world, which has made my grad school experience far more enriching and memorable that I could have
ever envisioned.
5
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6
Table of Contents
Abstract.........................................................................................................................................................
3
Acknow ledgm ents.........................................................................................................................................
5
Table of Contents..........................................................................................................................................7
List of Figures.............................................................................................................................................
10
List of Equations .........................................................................................................................................
11
1
12
Introduction.........................................................................................................................................
1.1
Background and M otivation....................................................................................................
12
1.2
Purpose of Research....................................................................................................................
13
1.3
Overview of Approach................................................................................................................
13
1.4
Research M ethodology ..........................................................................................................
14
2
Literature Review and Industry Research........................................................................................
2.1
15
2.1.1
Global Warm ing and Energy Consum ption.....................................................................
16
2.1.2
Profitable Sustainability.................................................................................................
18
2.1.3
Additional Motivation for Corporate Sustainability .......................................................
18
2.2
3
Corporate Sustainability..............................................................................................................
15
Logistics Carbon Footprint.....................................................................................................
19
2.2.1
MIT CTL and the Environm ental Defense Fund ............................................................
19
2.2.2
Em issions Measurem ent M ethods...................................................................................
21
Logistics Carbon Footprint Initiative at General M otors ................................................................
24
3.1
Objectives ...................................................................................................................................
3.2
Scope of Research.......................................................................................................................24
24
3.2.1
Included...............................................................................................................................25
3.2.2
Excluded .............................................................................................................................
25
Current State of Em issions Reporting at General Motors.......................................................
26
3.3
7
3.3.1
3.4
4
27
3.4.2
A Tripartite Solution ...........................................................................................................
32
Environm ental Industry Group Participation..................................................................................
34
4.1
Environm ental Industry Group Selection ...............................................................................
34
4.2
U SEPA Sm art W ay Transport Partnership ..................................................................................
35
M ethods and Tools.........................................................................................................
35
4.3
Process to Onboard General Motors as a SmartWay Shipper Partner ....................................
41
4.4
Results.........................................................................................................................................
41
4.5
D iscussion ...................................................................................................................................
42
G lobal Em issions Measurem ent M odel...........................................................................................
45
5.1
M ethodology ...............................................................................................................................
45
5.2
M odel Inputs ...............................................................................................................................
46
5.2.1
Environmentally-Extended Input Output Emissions Factors..........................................
46
5.2.2
Logistics Spend by M ode of Transportation...................................................................
48
5.2.3
Analysis of Inputs ...........................................................................................................
49
5.3.1
5.4
M odel Outputs ............................................................................................................................
Sensitivity of Outputs.......................................................................................................
Results.........................................................................................................................................
N etwork Change Analysis ..................................................................................................................
6.1
Building a Carbon Change M odel for Logistics .....................................................................
50
51
51
56
56
6.1.1
M odel Inputs .......................................................................................................................
60
6.1.2
M odel Outputs ....................................................................................................................
61
6.2
7
27
Attempting a Single G lobal Approach..........................................................................
5.3
6
Approach: Evaluating Emissions of a Complex, Global Logistics Network ..........................
3.4.1
4.2.1
5
Competition.........................................................................................................................27
Results.........................................................................................................................................62
Conclusions and Recom mendations ...............................................................................................
8
64
7.1
Key Findings ...........................................................................................
64
7.1.1
Global Emissions Reporting ..........................................................................................
65
7.1.2
Logistics Provider Relationships and Sourcing Decisions..............................................65
7.1.3
Cost Reduction Reinforcement........................................................................................
66
7.1.4
Risk Reduction ..................................................................................
66
7.2
8
A ppendix
8.1
9
Recommendations and Future Development..........................................................................
67
.............................................................................................................................................. 69
Media Coverage Report for GM-SmartWay Partnership........................................................69
R eferences...........................................................................................................................................72
9
List of Figures
Figure 1: Historical Levels of Greenhouse Gasses (C02, N20, CH4) [4].............................................
16
Figure 2: End-Use Sector of Total Energy Consumption, 2011 [7] ......................................................
17
Figure 3: Triple Bottom Line M odel.......................................................................................................
18
Figure 4: Top Factors Contributing to CEO Action on Sustainability Integration [10] ..........................
19
Figure 5: Industry Group Participation and Employed Framework for Carbon Footprint Measurement of
Key GM Logistics Partners Serving North America ..................................................................................
28
Figure 6: Current Global Freight Transportation Emissions Reduction (a.k.a "Green Freight") Efforts [18]
....................................................................................................................................................................
31
Figure 7: Information Flow of the SmartWay Shipper Tool...................................................................
36
Figure 8: C02 Emissions Factors (EF) by Fuel Type (assumes 100% combustion) [19].......................
37
Figure 9: Sample of Carrier Performance Data Adapted from SmartWay Website [20]........................
38
Figure 10: Example of SmartWay Shipper Activity Data Entry............................................................
39
Figure 11: Home-Screen Menu of SmartWay Shipper Tool [21]..........................................................
40
Figure 12: Summary of 2012 Reporting Year SmartWay Shipper Tool Submission for GM................42
Figure 13: Components of a Complex International Shipment ..............................................................
45
Figure 14: EEIO Emissions Factors for Reporting Ocean Carriers .......................................................
47
Figure 15: Outlier Analysis of EEIO Emissions Factors for Ocean Transportation...............................
47
Figure 16: Average EEIO Emissions Factors by Mode ..........................................................................
48
Figure 17: Regional Logistics Spend by Mode of Transportation (SR,M), 2012...-........
-......
48
Figure 18: Mass Emissions (Eco2) of GM's Global Logistics Network, 2012.......................................
51
Figure 19: GM Global Logistics: Relative GHG Emissions by Region .....................................................
52
Figure 20: GM Global Logistics: Relative GHG Emissions by Mode .......................................................
52
Figure 21: GM Vehicle Sales by Region, 2012 [23]...................................................................................
53
10
Figure 22: C02 Emitted by Source: GM Production Operations [source: GM Energy Management Team]
....................................................................................................................................................................
55
Figure 23: Components and Influences on Logistics Cost and C02 Emissions .....................................
57
Figure 24: Natural Gas Consumption in the Transportation Sector, 1995 - 2040 [24]..........................
58
Figure 25: Activity-based Emissions Factors by Mode (EFM) ...............................................................
61
Figure 26: C02 and Cost Savings for ~800 GMNA Inbound Network Change Initiatives ...................
62
Figure 27: Correlation of Emissions Change vs. Cost Savings for GMNA Inbound Logistics Network
C hange Initiatives .......................................................................................................................................
63
Figure 28: The Virtuous Cycle of Organizational Efficiency [29] .........................................................
67
List of Equations
Equation 1: C02 Calculation based on Fuel Consumption [19]............................................................
37
Equation 2: SmartWay Shipper Partner Mass Emissions Calculation [22] ...........................................
40
Equation 3: EEIO Emissions Factor Calculation for a Carrier Company..............................................
46
Equation 4: Mass Emissions as Calculated by EEIO Method ................................................................
50
Equation 5: Change in C02 Resulting from a Network Change Activity ..............................................
61
11
1
Introduction
General Motors (GM) is one of the world's largest automobile manufacturing companies and does
business in over 120 countries, requiring a complex operations network. The company operates in four
distinct regions: North America (GMNA), Europe (GME), South America (GMSA), and Asia and rest of
world, which is referred to as International Operations (GMIO). Operating with a focus on environmental
impact has become a strategic pillar within the company, both in its products and in its supply chain. Dan
Akerson, GM's Chairman and CEO during the time of our study, was quoted in the company's annual
sustainability report saying, "Our sustainability strategy is guided by this simple truth: energy diversity,
resource conservation and C02 reduction are business imperatives [1]." Specific to our study, the GM
global logistics organization is driving toward greater emissions visibility and the identification of carbon
dioxide reduction opportunities within its network.
1.1
Background and Motivation
Recently, executives within the GM logistics organization have been stressing cost reduction as a
critical business priority. The team has prided itself on waste elimination, which was a key contributor to
cost savings in 2013 and will continue to drive savings in the future. In addition, the executive team has
provided sound leadership in guiding the organization to deliver on cultural priorities such as nurturing
strategic relationships with logistics providers and focusing on the total enterprise cost of doing business.
Through network optimization, route analysis, production facility waste walks, and countless other
studies and analyses, the logistics team has been able to save millions of dollars while continuing to
provide superb customer satisfaction.
But it's not only dollars that are being saved. The team is also helping decrease damage to the
environment by reducing GM's carbon footprint, creating further value for customers. Paying attention to
carbon footprint can help achieve business and cultural goals. This has been exemplified at competing
auto manufacturers such as Ford, who is actively reporting and reducing its carbon footprint by working
12
hand-in-hand with logistics providers on network optimization and emissions-related reduction activities
and investments. In addition, the Fiat Group (Chrysler) employs a logistics sustainability team that has
partnered with the United States Environmental Protection Agency's (USEPA) SmartWay program (see
2.2.2.2) to track and reduce logistics carbon emissions, leading the automaker to work with only the most
efficient carriers. In Europe, by prioritizing rail transport, BMW has secured long-term rail arrangements
with its partners. These firms don't hesitate to promote their efforts. The GM Logistics team is doing
some of the very same things operationally that its competitors are; however, there are two key
differences. First, GM Logistics has not highlighted its environmental successes well in the past and must
do a better job of this going forward. Second, the team is missing out on business benefits that can be
achieved by looking at their operations through a sustainability lens. GM can gain a competitive
advantage through sustainability initiatives that strengthen provider relationships, reduce long-term costs,
and reduce business risk.
1.2
Purpose of Research
The purpose of our research is to develop an approach for GM to measure and analyze its logistics
carbon footprint in order to drive business value by instituting simple, low-resource processes. This will
allow the organization to win on more than just cost. GM has a prime opportunity to lead all major
automakers in operating the most efficient, lowest-cost, and most environmentally responsible logistics
network; the team is working hard to win in the first two categories, and our research, recommendations,
and deliverables will help them with the third. We will achieve this by enabling GM logistics to report
logistics emissions, take action on emissions data, and champion sustainability in logistics.
1.3
Overview of Approach
Globally and regionally, the logistics group needs to formalize its emissions reporting and tracking.
To do this globally is very complex, but we create a simplified process that can be used to estimate total
emissions and concentrate on reduction year-over-year. Global tracking would be considered a pioneer
13
effort in the industry. Regionally, an internal method and process for detailed carbon footprint analysis is
difficult without a dedicated team in logistics, therefore we pursue an externally-based, industryrecognized solution. We endorse and establish a partnership with the USEPA SmartWay program in
conjunction with third party logistics (3PL) partners that will allow GM to see specific data on the
emissions output of its carrier network with minimal resource investment.
Once the data and reporting is established, the company can use it as a powerful business tool and
take action. GM can ask providers and carriers key questions about emissions reduction and efficiency,
and asking such questions can lead to more optimization opportunities, technology investment, and carrier
improvements in fuel consumption. In addition, management can leverage this new information as
another data point in carrier sourcing, ensuring only the best, most efficient carriers are used. We
highlight these opportunities to management through our conclusions and recommendations.
Finally, the logistics team has the opportunity to champion sustainability within the organization. Our
work will allow employees to take credit for current efforts by using carbon calculation tools to convert
cost-saving network changes into carbon savings. The ability to view cost-savings work in a new light
will provide additional workplace motivation and help to impart a cultural change that encourages
employees to think about each situation through a sustainability lens no matter what the job is.
1.4
Research Methodology
Our work reflects efforts to solve a problem for GM, an auto industry giant. Although the data and
organizational descriptions are unique to GM, our research methodology is intended to provide an
framework that can be applied to any company seeking to undertake a large-scale multi-modal carbon
footprint measurement initiative for its logistics organization. Furthermore, not only does our work
address the mechanics of how a logistics organization can measure its carbon emissions, but we also
describe ways to use emissions data to take action and realize the business benefits of increased network
efficiency.
14
2
Literature Review and Industry Research
This chapter gives an overview of various works that were used to both clarify the problem and
motivation as well as provide a foundation for our approach. While the review is broad in scope, it is
intended to be a holistic, yet concise, preamble for anyone attempting to introduce carbon footprint
measurement and analysis into a logistics organization.
2.1
Corporate Sustainability
Sustainability initiatives within corporations have become more popular in recent years for a number
of reasons. One major contributor to this trend comes from the increased level of attention that has been
given to serious environmental topics such as global warming and resource conservation. Environmental
concerns have been discussed for decades, but have recently sparked to heightened public attention with
the help of activists such as Al Gore, whose campaign to educate citizens about the severity of such issues
was featured in the 2006 documentary An Inconvenient Truth. These types of public awareness
campaigns, along with recent natural disasters that seemingly support activist claims, have put the
spotlight on pollution. Especially targeted are businesses and corporations, who are typically viewed as
major contributors to global warming, resource depletion, and environmental waste.
Evidence of global warming, combined with internal and external pressure to take responsibility for
environmental impact, has led major companies to take action toward reversing climate change trends.
These actions may be as modest as tracking energy consumption internally, or as assertive as taking a
public stance toward supporting climate change mitigation efforts. For example, in 2009, both Nike and
Apple made strong statements by vacating their memberships within the U.S. Chamber of Commerce due
to opposing views on climate change. Specifically, the firms vacated because the Chamber was not in
support of the Waxman-Markey bill, which would have placed a cap-and-trade system on greenhouse
gasses in the United States [2][3].
15
With increased publicity on the topic of corporate sustainability, individual citizens have become
more passionate about resource conservation, waste reduction, and repurposing materials. These citizens
comprise the human resources that drive business practices, so it is no surprise that product life cycle
planning and waste elimination initiatives have sprung up in many corporations.
2.1.1
Global Warming and Energy Consumption
Understanding the importance of sustainable business practices related to greenhouse gas emissions
begins with a brief introduction of global warming. Greenhouse gases, such as carbon dioxide (C02),
Methane (CH4), and Nitrous Oxide (N20) exist naturally in Earth's atmosphere, but due to human
activities such as burning fossil fuels to produce energy and increasing agricultural activity, mass
quantities of these gases have been unnaturally released into the atmosphere over the past 200 years. This
contributes to the "greenhouse effect," a phenomenon in which a blanket of greenhouse gases trap heat
within earth's atmosphere. Figure 1 below indicates a startling increase in the amount of greenhouse
gasses measured in the atmosphere beginning around the industrial revolution through 2005. Essentially,
as soon as humans began conducting large scale business and began burning fossil fuels to meet
increasing energy demands, greenhouse gas emissions began to increase at a highly accelerated rate.
.2000
400................
-.
--
350
-
1800
Carbon Dioxide (CO 2
Methane (CH 4)
Nitrous Oxide (N2O)
1600
..
1400
:1
1200
0 300-
-1000
800
0
500
1000
Year
1500
2000
Figure 1: Historical Levels of Greenhouse Gasses (C02, N20, CH4) [41
16
-
According to the Intergovernmental Panel on Climate Change (IPCC), the increase of greenhouse
gases has been scientifically linked to a steady increase in global mean surface temperature at rate of
0. 12*C per decade from 1951-2012 [5]. If this trend continues, it could have hugely negative implications
including drastic changes to weather patterns, rise of sea levels, and intense risk to the economy, human
health, and infrastructure.
As stated previously, the increased rate of global warming appears to be a direct result of humans'
consumption of energy, specifically energy created by burning fossil fuels. Figure 2 illustrates the
breakdown of energy consumption in the United States. Corporate operations consume energy in three of
the four major consumption categories and account for 100 percent of the commercial and industrial
categories. In addition, logistics and freight operations make up approximately 30 percent of the energy
used for transportation, the balance of which is associated with passenger transportation [6].
Transportation energy consumption is especially relevant to an automotive OEM such as GM, since its
products contribute to both freight and passenger-use consumption components. In an effort to reduce
energy consumption and carbon dioxide emissions, corporations have been under pressure to become
more environmentally sustainable.
Industrial
Transportation
Figure 2: End-Use Sector of Total Energy Consumption, 2011 [71
17
2.1.2
Profitable Sustainability
In response to the growing concern of global warming, among other social and environmental
concerns, businesses have begun to make changes to the way they operate. For some businesses, the
pursuit of sustainability is solely to demonstrate social responsibility, and they implement changes
without much hope of financial benefits [8]. However, many businesses are charting new routes in
profitable sustainability. The ideal state in which corporations can intelligently make changes to their
operations to (1)
improve the environment, (2) manage human resources more responsibly, and (3) make
a profit has been referred to as achieving a "Triple Bottom Line" [9], illustrated in Figure 3. Sustainability
initiatives with the potential to be profitable are far more persuasive and likely to be approved by senior
leaders who must face cost-cutting pressures. This concept was incredibly important to keep in mind as
we formulated our approach for GM.
Figure 3: Triple Bottom Line Model
2.1.3
Additional Motivation for Corporate Sustainability
A number of other motivating factors have been pushing corporations toward reducing their
environmental footprint. A survey conducted by consulting firm Accenture of 766 CEOs worldwide
18
identified the top factors contributing to action on integrating sustainability strategies into their
businesses. The results can be seen in Figure 4.
Brand, trust and reputation
72%
Potential for revenue
growth/cost reduction
Personal motivation
42%
Consumer/customer
demand
Employee engagement
39%
31%
and recruitment
Impact of development
gaps on business
29%
Governmental/regulatory
24%
environment
Pressure from investors/
shareholders
12%
Figure 4: Top Factors Contributing to CEO Action on Sustainability Integration [101
2.2
Logistics Carbon Footprint
Reducing the carbon emissions associated with logistics operations can be done in several ways.
Companies may choose to solely focus on optimizing their logistics networks, while others may choose to
invest in fuel-saving technology for their freight transport equipment. Regardless of what method is
chosen, one must first generate a baseline measurement of emissions in order to track and measure
improvements over time. Our initial research led to a few significant findings that helped shape our
approach at GM. These findings are summarized below.
2.2.1
MIT CTL and the Environmental Defense Fund
Research performed by the MIT Center for Transportation and Logistics (CTL) and sponsored by the
EDF suggests that a strong focus on optimizing logistics networks can lead to greater carbon efficiency.
Collaborating with logistics providers as well as competitors to generate creative transportation solutions
is one way to achieve this goal. Applying this concept, agricultural cooperative Ocean Spray gained
19
significant logistics efficiencies by partnering with one of its competitors [11]. Jason Mathers, an expert
in supply chain and logistics at EDF, concluded,
"Ocean Spray has shown that concrete and measurable sustainability results can be
found within projects that were previously identifiedfor cost savings only. We encourage
all companies who identify cost cutting opportunities within their logistics operations to
also calculate potential emissions reductions to add greater overall value to their
organizations."
We agree with Mr. Mathers in his assessment and also recognize that by quantifying emissions
reductions associated with the numerous network change initiatives identified for cost-savings at GM, the
company can yield greater value from its efforts. This concept is a major driver in our network change
analysis process implementation and results discussed in Chapter 6.
The MIT CTL has dedicated multiple research initiatives to advance sustainability efforts in logistics
operations and supply chains. In addition to the study mentioned above, two other works influenced the
approach for our work with GM: "The Value of Detailed Logistics Information in Carbon Footprints"
[12] and "System Dynamics Modeling of the SmartWay Transport Partnership" [13].
First, the research paper entitled "The Value of Detailed Logistics Information in Carbon Footprints"
provides a comparison of estimating, or screening, the carbon emissions associated with a product's
logistics operations. The study compares results of calculating logistics-related carbon emissions using
public information versus using actual, detailed information from the 3PL provider managing the
shipment of the products. Public information included road distances and ocean distances calculated from
online map tools, mass emissions factors published by the GHG Protocol (see section 2.2.2.1), and port
emissions published by the Port of Seattle. Detailed information included specifics on carriers, ocean
routes, ports of call, truck road distances, warehouse emissions factors, and in some cases actual fuel
consumption of equipment. The study proceeds to compare the emissions calculation from a 'screening
20
model' (based on public information) to a presumably more accurate 'detailed model' (based on actual
activity data) for products transported by five different global logistics networks. The results of the study
show misalignment and variation between the two methods of calculation. In some instances, the detailed
model suggests lower emissions than the screening model, and in some instances, the opposite is true. The
results of the comparison study are beneficial for our work at GM as they reinforce the preference for
using activity-based data when calculating emissions in logistics.
Second, the paper entitled "System Dynamics Modeling of the SmartWay Transport Partnership"
provides deep insights into the USEPA SmartWay Transport Partnership. The work includes detailed
system dynamics models of stakeholder interactions and factors driving the SmartWay program's success.
While evaluating potential solutions for measuring the carbon footprint of GM's logistics network, this
work from the CTL was helpful in providing further clarification of the benefits of becoming a SmartWay
shipper partner.
2.2.2
Emissions Measurement Methods
For those attempting to measure carbon emissions produced by a corporation, the first question often
is, which method should be employed? Countless methods and models exist for measuring emissions
from operations and supply chains, including freight transportation and logistics. These methods have
been published by all levels of governments around the world (national, regional, and local) as well as by
individual companies and industry groups. A quick internet search for 'greenhouse gas measurement
methods' will provide evidence for this claim. Despite the growing popularity of emissions management,
no single method has emerged as a universal standard. This is because few countries, including the United
States, have not yet mandated the practice of reporting GHG inventories for municipalities and
corporations. On the other hand, some individual countries have created mandates for GHG reporting,
driving the use of specific methods for quantifying such emissions. An example of this is France and its
Decree n' 2011-1336.
21
Through the course of this research, we studied numerous methodologies for measuring GHG
emissions in logistics operations. The following sections describe three groups that stand out as leaders in
carbon emissions measurement and analysis. The fundamentals of their methodologies have provided a
solid foundation for our work.
2.2.2.1
Greenhouse Gas (GHG) Protocol
The GHG Protocol is recognized as one of few globally-used accounting tools for measurement of
greenhouse gases. The protocol outlines three primary source types, called scopes, of emissions for a
reporting organization [14]. In our case, GM is considered to be the reporting organization. Scope 1
emissions are those created as a result of the reporting organization consuming primary fuel sources onsite within its owned assets. An example of Scope 1 emissions for GM would be C02 generated by
burning natural gas to heat facilities. Scope 2 emissions are those that are created by an outside
organization but consumed directly by the reporting organization. An example of Scope 2 emissions is
C02 created by power plants that produce electricity for GM-owned buildings. Scope 3 emissions are
those created by outside organizations in order to provide a purchased good or service to the reporting
organization. Emissions created by contracted logistics services, the focus of our research, are considered
Scope 3. As GM does not operate its own logistics fleet, we do not need to focus on any Scope 1
generated emissions. The GHG Protocol and its Scope 3 calculations guidance is essential to
understanding how to evaluate carbon emissions within a logistics network.
2.2.2.2
USEPA SmartWay Transport Partnership
The SmartWay Transport Partnership is a public-private collaboration between the United States
Environmental Protection Agency and the freight transportation industry. It was started in 2004 and its
primary goal is to reduce the amount of carbon emissions produced by transporting materials and finished
goods, thus creating more efficient logistics networks [15]. Through our research, we have concluded that
the program's tools and methods are best-in-class for truck and rail freight transportation networks. As a
result of our work, GM logistics has joined SmartWay. We explore this program in detail in Chapter 4.
22
2.2.2.3
Carbon Disclosure Project (CDP)
CDP is an international organization that provides an information sharing platform for companies and
cities to disclose carbon emissions. It holds the largest set of climate change data globally, which can be
used to support strategic business and policy decisions with regards to environmental impact [16]. GM is
a current member of CDP's supply chain program, which requires annual reporting of global emissions
data. Building on existing efforts, our work has enhanced GM's external reporting processes to
organizations such as CDP.
23
3
Logistics Carbon Footprint Initiative at General Motors
Proposed by executives within GM's logistics, sustainability, and energy management organizations,
the idea for the logistics carbon footprint initiative was conceived to add business value and achieve
greater alignment with the company's overall sustainability strategy. Though multiple sustainability
initiatives had been pursued with regards to GM's products, facilities, and supply chain (sourcing of
material), minimal and unstructured effort had been given to understanding and reporting the
environmental impact of its global logistics network. The project was loosely defined at first, as the
company chose to leverage its partnership with the MIT Leaders for Global Operations program to lead
the research effort.
Objectives
3.1
Desired outcomes of this research are:
1)
Create a recommendation for GM to implement a global method for measuring and reporting
carbon footprint for inbound and outbound logistics operations.
2)
Design business processes for capturing and reporting carbon footprint data from logistics
providers, allowing GM to:
3)
3.2
*
Accurately report emissions and reduction efforts to shareholders
*
Track network emissions over time
*
Pinpoint carbon reduction opportunities and align with cost reduction efforts
*
Understand future risks to the business
Work with GM's sustainability team to integrate our project into the overall company strategy.
Scope of Research
A clearly defined scope of work is imperative to successfully achieving our objectives. In
sustainability and emissions measurement initiatives, defining a scope (or boundary) can greatly alter
results. For example, when quantifying logistics emissions, whether or not empty miles (the required
24
miles traveled by a freight vehicle when it does not contain goods, sometimes called return miles) are
included in the calculation could yield results that differ by nearly a factor of two. Therefore, we define
what is included and excluded in the scope of this work.
3.2.1
Included
The scope of this project spans GM global logistics, inbound and outbound. This includes
transportation of parts and material from the shipping dock of tier 1 parts suppliers to the receiving dock
of GM assembly plants (inbound) as well as transportation of finished vehicles from the shipping dock of
GM assembly plants to dealer locations (outbound). Empty miles are included when applicable. All
modes of transportation will fall within the project scope, including truck, rail, air, ocean, and intermodal
(any combination of truck, rail, and ocean). Research extends from global logistics to operations,
engineering, and business planning groups as required. Direct interaction with logistics providers
(external to GM) is required. Logistics data within scope are those necessary to report global greenhouse
gas emissions per the Greenhouse Gas Protocol's Scope 3 emissions classification, within the 'upstream'
and 'downstream' transportation categories (excluding warehousing and distribution center emissions see exclusions below).
3.2.2
Excluded
Analysis of any emissions related to facilities used to manufacture, warehouse, or consolidate GMowned material is not included in the project scope. This is because: (1)
emissions produced from
manufacturing and storage of products in GM owned facilities is currently captured and reported in GM's
Scope 1 and Scope 2 emissions categories, and (2) data availability restrictions prevent direct tracking of
emissions from third party facilities. Emissions of products sold are not included in this analysis. These
are classified in the 'use of sold products' category of the GHG Protocol and are currently tracked by
GM's energy management team separately.
25
3.3
Current State of Emissions Reporting at General Motors
GM has an impressive and robust annual sustainability report that includes a wide range of ongoing
environmental and social responsibility efforts at the company [1]. Regarding emissions measurement not
related to logistics, GM reports annually on its global facilities and product portfolio. The facilities
organization has a structured energy management team that leads energy efficiency projects around the
globe. The energy management team has been in place for over a decade, and has implemented various
business processes as well as a comprehensive, global energy management IT system that collects
consumption data for all GM-owned facilities. Through constant monitoring and analysis, the team can
recommend critical energy and cost reduction projects for the company. This is a clear benefit that can be
gained from having access to energy consumption data and the resulting emissions dataset. As for GM's
product portfolio, the product development team pays close attention to the emissions generated by the
products it designs. This is not only critical for consumer satisfaction but also to adhere to governmental
regulation and corporate average fuel economy (CAFE) standards for passenger cars and light trucks.
Prior to applying the approach resulting from our research, GM's logistics emissions reporting was in
its beginnings. The company had already been reporting supply chain activity to the CDP, but it did not
yet have a robust method for collecting logistics emissions data nor did it have any method instilled to
analyze such data and guide business decisions. The process for reporting logistics emissions data to the
CDP originated from the facilities energy management team, who solicited data from the logistics
organization on an annual basis. Information such as 'planned network miles traveled' and 'dollars spent
on logistics services' (called "spend") was collected to satisfy reporting requirements. Due to data
inconsistencies, along with the use of planned miles instead of actual miles and the need for global
extrapolation based on unit sales, the results of GM's initial GHG emissions estimates in logistics were
not accurate to a high level of confidence.
26
3.3.1
Competition
An initial analysis of competitors, through public sources, indicated that Ford, Chrylser (Fiat),
Toyota, and Volkswagen all have ongoing logistics-targeted emissions reduction initiatives. Sections
detailing logistics carbon footprint reduction efforts were found within each competing firm's
sustainability report. Through 2012, GM's sustainability report did not include sustainability efforts
specifically related to logistics. Furthermore, Ford has partnered with Odette to publish common
guidelines for freight carbon dioxide reporting for the European auto industry. Finally, both Chrysler and
Toyota participate in the USEPA SmartWay program as shipper partners.
Approach: Evaluating Emissions of a Complex, Global Logistics Network
3.4
Considering our objectives, we focus on creating a lasting system to measure and analyze logistics
emissions and view our approach as an ongoing initiative rather than a one-time project. Additionally,
working within an organization whose goals are heavily focused on cost savings, and given that negligible
budget is reserved for implementation of our approach, it is critical that our approach be one that can be
easily understood, easy to implement in a complex business environment, and does not require large
capital or human resources. From this vantage, a solution including a robust internal global transportation
management system (TMS) requiring software and IT investment would be cost-prohibitive and human
resource intensive and thus could not be considered. Such a system would be ideal for a company with
logistics operations that are primarily internal; however, most global corporations, including GM,
leverage 3PL partners that closely manage logistics operations using their own TMS. Therefore, in
addition to internal data and information, we place high importance on working closely with 3PL partners
to develop and implement our approach.
3.4.1
Attempting a Single Global Approach
Adopting a single global method for measuring and analyzing GM's logistics carbon footprint is the
ideal solution to achieve the stated objectives. Whether this solution is created using internal company
data or by leveraging external logistics providers' databases is a major decision that must be addressed
27
early in developing a successful approach. In order to answer this question, our research began with
interviewing GM's key logistics providers. Our intention was to better understand the capabilities,
methods, and processes that were being used currently and to determine if any method or methods stood
out as common among industry providers or mode types globally. Common themes were identified as a
result of over a dozen interviews and further industry research. A graphical representation of these
findings for GMNA is shown in Figure 5.
CEVA
UNION
PACIFIC
RYDER
PENSKE
FEDEX
ILG
CSX
HA G-DSV
LLOYD
N4C
APL
MAERSK
Figure 5: Industry Group Participation and Employed Framework for Carbon Footprint Measurement of Key GM
Logistics Partners Serving North America
Two main conclusion can be drawn from our GMNA findings. First, we see a clear preference among
truck and rail carriers to use the USEPA SmartWay program for emissions reporting. Second, and
although not as predominate, we see major ocean carriers choosing the Clean Cargo Working Group for
emissions reporting. Furthermore, the underlying measurement frameworks for providers serving North
America were found to be either adaptations of the GHG Protocol and/or an exclusive or proprietary
method. Exclusive methods use custom calculations based on company-specific data or modal intricacies.
An example of this is found within the Clean Cargo Working Group's methods, which use loads
28
measured in TEUs (or twenty-foot equivalent units, a common unit of measure for container ships)
instead of units of weight. Proprietary methods, such as those used at FedEx, are kept secret but are
verified by a third party audit firm. The sole firm using the Euro Standard EN 16258 is based in Europe.
This mapping process was attempted for GM's three other regions (GME, GMSA, and GMIO), but
substantial issues arose as internal and external interviews revealed clear differences in the maturity of
carbon footprint measurement capabilities and data availability among the regions as compared to North
America. The gaps most crippling to our work were found in the truck, rail, and short sea (moving goods
by sea without crossing an ocean, typically intra-continental) modes of transportation outside of North
America. Global maturity of the top ten largest ocean carriers was not a problem, as these carriers serve
the entire globe, and it is with these large carriers that GM contracts its intercontinental ocean freight. The
following issues were identified, by region, which prevented one consolidated approach for all regions:
Europe (GME): We originally anticipated Europe to be as advance, if not more advanced, than North
America in its ability to measure and analyze carbon footprint given the region's reputation shaped by
countries like Sweden and Germany, for example. This indeed was the case. Organizations such as Odette
and Green Freight Europe mirror the U.S.'s Automotive Industry Action Group (AIAG) and SmartWay,
respectively. In fact, SmartWay and Green Freight Europe consistently work together, cooperating toward
a common goal of reducing global emissions produced by freight transportation. However, we were
unable to develop a comprehensive map similar to our GMNA illustration due to a GM logistics transition
within the European network. Although data availability was limited due to the transition during the time
of our study, we developed important contacts within the new management structure who we recommend
to co-lead future global synchronization efforts. Once the European network data systems are complete,
GM logistics will easily be able to develop a partnership with Green Freight Europe.
South America (GMSA): Anecdotes had been given by GM leaders who have spent time working in
the company's South American region that there is a trending focus to operate in a more sustainable
29
manner. Work performed by GMSA teams with regards to facilities energy management, factory waste
reduction, and responsible sourcing prove this to be true. However, the region significantly lacks
organized industry groups dedicated to helping companies measure and reduce carbon emissions in
logistics. Brazil has created a program based on the GHG Protocol that provides 'how-to' training and
workshops for organizations that wish to create a greenhouse gas emissions inventory for voluntary
reporting. The program is a step in the right direction, but it is still in its early stages of development and
does not provide a common database or collaborative environment for providers and procurers of logistics
services like the SmartWay and Green Freight Europe programs do. We determined that far too few of the
logistics providers that GM works with in South America were a part of Brazil's GHG Protocol program
or had the technical knowledge required to be able to provide emissions data.
Asia and Rest of World (GMIO): Currently, the GMIO logistics group functions in a unique way that
works well for its operating structure. Each manufacturing facility largely controls the logistics operations
needed to sustain production. The centralized regional logistics team is much smaller than in North
America and is more involved with logistics strategy for the region. This is problematic in that mapping
major logistics providers and researching what, if any, environmental industry groups or emissions
measurement methods they employ would mean interfacing with each manufacturing facility and the
smaller logistics providers that service each facility. Two main barriers to progress arise because of this.
First, the amount of time that would be required to make the connections required to sufficiently map the
GMIO network was not anticipated nor allocated to the initial phase of our effort. Second, we realized
that even if we allocated time to map the GMIO network, emissions measurement of logistics services in
Asia is also in its infancy, and robust tools and processes such as those found within the SmartWay and
Green Freight Europe program do not yet exist to aggregate regional data for large procurers of logistics
services. Green Freight Asia, based in Singapore, and Green Freight India, based in New Delhi, are
gaining momentum and show promise to evolve into practical, data-driven industry groups in the near
future as more members join.
30
Initial conclusions were that a single global model for measuring and analyzing the carbon footprint
of GM's logistics network was not yet feasible at the time of the study. However, we have identified
numerous independent initiatives around the globe aimed at addressing and reducing freight transport
emissions. Figure 6 shows the world's leading initiatives geographically. In the coming years, we
anticipate a stronger and more untied global effort. As noted in its Green Freight Call to Action [17], an
effort by the partners of the Climate and Clean Air Coalition (CCAC) is underway to "...collaborate with
stakeholders to develop and deploy a coordinated Global Green Freight Action Plan that can be
implemented through public-private partnerships worldwide. The Action Plan will provide a common
roadmap that can help to harmonize and coordinate the development of green freight programs, ease
implementation, and incorporate a large knowledge base of previous efforts. It will also provide a
platform for companies to share best practices, promote innovation, and communicate sustainability
improvements on road freight." The CCAC is supported by various global governments, NGOs, IGOs,
and members from the private sector.
_
NKSmartWa
CENTRE
-
WOR LD
EC4N MIC
sa
fRE
NDN
FREEIGHT
SClean
~
"~~i
IATA
Figure 6: Current Global Freight Transportation Emissions Reduction (a.k.a "Green Freight") Efforts 1181
31
3.4.2
A Tripartite Solution
Our initial research findings suggested that a single global approach would not be feasible in the short
term, but that this ideal state will likely exist in the near future. Therefore, we propose a tripartite solution
that includes participating in one of the major environmental industry groups focused on logistics
emissions measurement for North America, while creating an interim estimation model for the rest of the
globe. These two parts generate separate work streams and together quantify the mass emissions
generated by GM's global logistics network. A third work stream is also added in which we leverage the
existing cost savings work done in the logistics organization to show the positive relationship between
cost savings and emissions reduction.
3.4.2.1
Part One: Environmental Industry Group Participation
Part one of our approach solidifies GM's efforts to implement industry-recognized methods and
processes for logistics-related emissions measurement. Here, we take the steps necessary for GM to
become a shipper member of the USEPA SmartWay program. SmartWay was selected among all other
industry groups due to its strength in membership within North America and its robust, industryrecognized methodology. Chapter 4 is dedicated to explaining the work completed to achieve this
partnership and discusses the current and future benefits of joining SmartWay.
3.4.2.2
Part Two: Global Emissions Measurement
Part two of our approach creates a model for estimating GM's logistics carbon footprint for all
regions of the globe. The model is based on the amount of spend on each mode of transportation and uses
emissions factors in terms of carbon dioxide per U.S. dollar (C02/$USD) to equate mass emissions
generated by operating the network. Chapter 5 is dedicated to explaining the work completed to build this
model and discusses the outputs of the model.
32
3.4.2.3
Part Three: Network Change Analysis
In part three of our approach, we develop a tool that translates cost savings from network change
activities into increases or decreases in carbon dioxide emissions generated by the change activities. Our
analysis of these emissions changes shows a positive correlation with cost savings, providing further
reinforcement and motivation for implementing carbon reduction projects in logistics. Chapter 6 is
dedicated to explaining the work completed to create this tool and its use within the organization.
33
4
Environmental Industry Group Participation
After researching and benchmarking other automakers, logistics service providers, and individual
transport carriers, we concluded that there many environmental industry groups across the globe (see
Figure 6) that are dedicated to measurement and reduction of logistics-related carbon emissions. These
groups, however, are not yet unified in their efforts and still operate with independent processes and
membership pools. Yet, we offer that becoming a member of at least one of these environmental industry
groups is critical for sustaining carbon footprint measurement and reduction efforts within GM logistics
as well as driving a cultural shift. We selected the USEPA SmartWay Transport Partnership, and this
chapter explains the selection process, onboarding effort, and benefits of participating in the program.
4.1
Environmental Industry Group Selection
By participating in an environmental industry group, GM can leverage external expertise, methods,
tools, and participant networks. For an organization with constrained budgetary and human resources,
adopting an existing and externally supported process is superior to creating an internal data solution.
Most of the environmental industry groups manage and update their systems on a yearly basis, which
delivers a more sustainable business process for GM and is more likely to live-on and evolve even as
corporate stakeholders change roles or leave the organization.
Because of the issues identified in Chapter 3 (3.4.1) with global data acquisition, we determined that
it would be best to pilot GM's participation in such a group in North America. With success, efforts
would be mirrored around the globe as other regions' environmental industry groups become more mature
and required data becomes available.
After narrowing our search to the North American region, two groups became front-runners for
selection. First, the USEPA SmartWay program, which spans the United States and Canada and focuses
efforts on truck and rail networks, has been in existence for over a decade and has a large and established
member base with over 3,000 participating firms. Second, the Clean Cargo Working Group, which has a
34
smaller number of members (fewer than 50 at the time of this study) around the globe, focuses solely on
ocean carrier networks. Two primary considerations led us to select SmartWay initially instead of Clean
Cargo: (1)
most importantly, truck and rail make up the vast majority of the North American network,
while ocean remains small in comparison, and (2) joining SmartWay requires no monetary membership
fee, while Clean Cargo charges nominal membership dues annually.
4.2
USEPA SmartWay Transport Partnership
The SmartWay Transport Partnership program is a public-private collaboration between the United
States Environmental Protection Agency and the freight transportation industry. The program is free of
charge and voluntary. It was started in 2004 and its primary goal is to reduce the amount of carbon
emissions produced by transporting materials and finished goods, thus creating more efficient logistics
networks [15]. Active participation in the program can lead to environmental, operational, and cost
efficiencies. SmartWay pioneered methods and developed tools whereby information is voluntarily shared
among freight carriers, logistics service providers, and shippers, yet data regarding actual fuel
consumption (and hence, financial information) is kept confidential. This fosters a collaborative
environment in which all parties can work together toward achieving a common goal of emissions
reduction. Currently, the program has members in the United States and Canada and focuses on truck and
rail transportation. Each year, the EPA recognizes member firms that have achieved excellence in
reporting transparency and reduction efforts. The use of the program's logo is encouraged in company
reports and press releases, and it has been likened to the Energy Star logo for appliances. In addition to
providing methods and tools for carbon measurement, SmartWay also provides guidance for technology
selection and network optimization.
4.2.1
Methods and Tools
SmartWay offers a platform for companies to measure transportation-related emissions. For freight
carriers operating owned assets, these classify as Scope
1 emissions per the GHG Protocol. For shipper
partners, like GM, these emissions comprise the upstream and downstream transportation components of
35
Scope 3 emissions (i.e., emissions resulting from purchased goods or services). The tools are capable of
calculating C02 emissions as well as NOx and particulate matter; however, for the purposes of our
research, we discuss only C02. We explain the process from the perspective of a shipper partner.
At a high level, the SmartWay Shipper Tool requires two sets of information to generate detailed
fleet classification and fuel consumption data from
mass emissions data useful to a shipper partner: (1)
individual freight carriers, and (2) activity data from shippers or the 3PL providers managing operations
on behalf of the shipper. This data is processed through the tool and the resulting mass emissions data can
be used by shippers for benchmarking and reduction tracking. The process is illustrated in Figure 7.
Submit Fleet
Classification and Fuel
ow)-SmartWay,
Emissions Data
Transport Partner
Getting Theme With Clop."rAir
e
Figure 7: Information Flow of the SmartWay Shipper Tool
4.2.1.1
Logistics Carrier Data
To begin the process, individual carriers must submit a fleet classification describing equipment and
service type. For truck mode, this includes options such as standard truckload, flat bed, refrigerated, etc.
For rail, this distinction is not relevant, as SmartWay rail carriers are simply classified as Class 1. Carriers
must also submit fuel consumption data for their fleet vehicles over the course of the reporting year. The
tool uses this data to categorize each company to a specific fleet type and calculates C02 emissions based
on emissions factors found in Figure 8.
36
Gasoline
Diesel
Biodiesel (B100)
Ethanol (E100)
CNG
LNG
LPG
g/gal
8,887
10,180
9,460
5,764
7,030
4,394
5,790
Figure 8: C02 Emissions Factors (EF) by Fuel Type (assumes 100% combustion) 1191
These factors are used to calculate C02 based on type and amount of fuel consumed. The equation used
to calculate mass emissions is:
Eco2
=
(F x EFF)+(B X EFB)
Equation 1: C02 Calculation based on Fuel Consumption 1191
List of Variables
Eco2 = Mass emissions (grams C02/year)
F = Fossil fuel (gallons/year)
B = Biofuel (gallons/year)
EFF = Fossil fuel emissions factor based on fuel type (grams/gallon)
EFB = Biofuel emissions factor based on fuel type (grams/gallon)
Mass emissions are totaled for each carrier and divided by the total miles traveled during the
reporting year to generate an average value for the firm in units of grams of C02 per mile (g C02/mile).
The mass emissions are also divided by the total mileage multiplied by the average weight per shipment
during the reporting year to generate an average value for the firm in units of grams of C02 per ton-mile
(g C02/ton-mile). These metrics are referred to as "carrier performance data" and are used by SmartWay
as indicators of efficiency, normalized and comparable across firms operating similar equipment types.
These metrics are not only used in SmartWay's tool suite, but they are also published and made available
37
to the public. A selection of this report has been adapted from the SmartWay website and can be found in
Figure 9.
Company Division Name
Penske Logistics LLC
Ryder Supply Chain Solutions
Cassens Transport Co.
Centurion Auto Transport, Inc.
DHL Express USA
Fedex Express
Old Dominion Freight Line, Inc.
J.B. Hunt Transport, Inc.: Truck
Kraft Foods Group, Inc.: Private Fleet
Tyson Foods, Inc.
Union Pacific Railroad
Mode
Sub Mode
Logistics Logistics
Logistics Logistics
Truck
Auto Carrier
Truck
Auto Carrier
Truck
Package
Truck
Package
Truck
LTL/Dry Van
Truck
TL/Dry Van
Truck
Refrigerated
Truck
Refrigerated
Rail
Rail
g C02/mile
g C02/ton-mile
Figure 9: Sample of CarrierPerformance Data Adapted from SmartWay Website [201
It is important to understand that the performance value measured in units of g C02/ton-mile accounts
for the weight capacity utilization of an average shipment for the firm. For example, we see that
companies DHL Express USA and Fedex Express post the best performance in terms of g C02/mile, but
these companies offer package/parcel services that typically carry lightweight yet bulky consumer
packages and end up maximizing volume of shipment while underutilizing weight of shipment. This leads
to a greater amount of emissions released into the atmosphere in terms of g C02/ton-mile of shipment. In
contrast, Old Dominion Freight Line, Inc. reports an average performance in terms of g C02/mile,
however they seem to do well at maximizing weight utilization as they post exceptional performance in
terms of g C02/ton-mile. Moreover, rail firms utilize locomotives and uninterrupted momentum to pull
shipments far heavier than a single truck can, resulting in superior emissions performance values per tonmile. In most industrial cases, transport logistics firms will seek to optimize both weight and volume of
shipment, but the limiting factor is often weight due to transporting heavy-goods. Therefore, in the auto
industry, it is preferred to use performance values in terms of g C02/ton-mile when possible.
38
The SmartWay carrier partner network is vast and growing, but not all carriers that are contracted to
service GM's network have joined the program. Therefore,
discrete, company-specific
carrier
performance data cannot be obtained for such carriers. In order to accommodate for this, a generic "nonSmartWay" carrier performance metric is generated in terms of both miles and ton-miles, which are set at
the
9 9 th
4.2.1.2
percentile of all reporting carrier partner submissions [20].
Shipper Activity Data
The second set of data required to generate an accurate emissions inventory for shippers through the
SmartWay process is shipper activity data. Activity data consists of individual transport moves that have
taken place to serve the shipper's logistics needs. This data includes carrier firm name, total miles
traveled (empty miles included), and weight transported. Activity data for a single carrier is combined for
all shipments so that only one data entry line is required per carrier.
Shown below in Figure 10 is an example of a shipper activity data entry submission. A firm the size
of GM could have hundreds of line submissions, indicating a vast operating network supported by many
freight carriers. In addition, one aggregate non-Smartway data entry line is submitted to represent all
carriers that are not participants of the program. It should be noted that GM only operates with Class 1 rail
carriers, all of whom are members of the program.
Carrier ID
12345
Carrier Name
Carrier ABC
Mode
Truck
Equipment Inbound/Outbound
TL/Dry Van
Inbound
Calc Metric
g/ton-mile
Ton Miles Total Miles
91963
7357
Average Payload
12.5
Figure 10: Example of SmartWay Shipper Activity Data Entry
4.2.1.3
SmartWay Shipper Tool and Mass Emissions Inventory
The Shipper Tool uses Microsoft Excel driven by macros for data input, calculations, data output, and
display. Figure 11 shows the home-screen user interface of the tool from which all input and output
screens can be accessed. Imbedded in the tool is the updated annual carrier performance dataset (see
4.2.1.1).
39
Figure 11: Home-Screen Menu of SmartWay Shipper Tool 1211
Through the tool's upload function, the shipper company submits its activity data and its mass
emissions inventory is calculated in the following way:
Eco 2
CPj x AD
=
Equation 2: SmartWay Shipper Partner Mass Emissions Calculation 1221
List of Variables
Eco2 = Total mass emissions of shipper company (grams C02/year)
CP = Carrier performance value (grams C02/mile or g C02/ton-mile)
AD = Activity data submitted by shipper (total miles or total ton-miles)
n = Total number of carriers used by shipper for reporting year
i
=
Individual carrier index
NOTE: Units of AD must match denominator units of CP
40
4.3
Process to Onboard General Motors as a SmartWay Shipper Partner
The requirements to become a shipper partner are not extensive. The program is funded by the U.S.
government and does not require any fees to join or for membership maintenance. Essentially, there are
only two obligations of membership: (1) the shipper must annually submit a completed Shipper Tool that
contains all (or a disclosed percentage of) shipper activity data and the firm's resulting mass emissions
inventory (see 4.2.1.2 and 4.2.1.3), and (2) the shipper firm must agree to have its name listed as an
official member on the SmartWay website.
In order to acquire data with the proper level of detail, we approached a small number of logistics
providers who manage the majority of the logistics truck carrier contracts for GM. Only through
partnering with these providers could we obtain the data needed to complete the requirements of the
SmartWay partnership. This will be the case for the foreseeable future as strengthening external logistics
partnerships is a continuing priority for GM. With the leadership of 3PL partners Ryder System Inc. and
Penske Corporation, both of whom are viewed as highly influential in the field of green logistics, a data
acquisition protocol and submission cadence was established. This allowed GM to collect activity data
from various sources in the same format and at the same time each year, giving way to easy data
consolidation and submission into the Shipper Tool.
4.4
Results
In 2013, the SmartWay process was implemented for GM's inbound truck network for the United
States and Canada. The data submitted represents approximately 75 percent of all inbound truck activity,
and the remaining 25 percent of data was either inbound freight from Mexico (not currently covered by
SmartWay) or data that we were unable to acquire during our study. Membership provides GM with a
new and powerful set of data that the company can use for reporting and strategic decision making. This
data consists of:
.
Total mass C02 emissions of network activity reported
41
*
Normalized C02 performance for each reporting carrier (in g/mile and g/ton-mile)
*
Composite normalized C02 performance for GM (in g/mile and g/ton-mile)
*
Contribution of emissions from SmartWay members versus non-SmartWay members
*
Non-emissions-related: total miles and ton-miles of activity by carrier
A summary table of the results for the 2012 reporting year (submission in November 2013) follows in
Figure 12:
GM North America Inbound Truck*
Mass Emssons
1,573,000
Total C02 (short tons):
Normalized CompositePerfornance
1848
C02 per Mile (grams):
199
C02 per Ton-Mile (grams):
Percent'SmartWay Carriers
based on Miles:
based on Ton-Miles:
based on C02 Emissions:
*Approximately 75% of activity data
77.0%
76.6%
43.2%
reported
Figure 12: Summary of 2012 Reporting Year SmartWay Shipper Tool Submission for GM
4.5
Discussion
Of all the work completed in our research, the implementation of the SmartWay Transport
Partnership was by far the most rewarding. This accomplishment generated significant "buzz" within the
company and also triggered a press release announcement, which itself generated eight additional articles
and made over 87,000 impressions (see Appendix 8.1). This is important to igniting a cultural change that
promotes sustainability efforts in logistics. Becoming a partner was both time consuming and challenging.
This was not due to the creation of complex tools or algorithms, but rather due to the organizational effort
and data collection process orchestrated among multiple logistics providers to make the partnership a
success.
42
Simultaneously, implementation support from GM leaders was critical, but it was not unanimous at
first. Executive leadership concerns included the investment of human resources and the actual value that
would be gained from participation. Alleviation of resource concerns is given by 3PL partners whose data
acquisition capabilities contribute to the efficiency of completing the annual requirements, making it a
sustainable business practice. The value of participating in an environmental industry group continues to
be backed by the GM corporate sustainability team as well as the GM energy management team, both of
whom are stakeholders in the company's overall environmental impact. These teams cite SmartWay as an
industry-recognized organization that brings further credibility to the company's sustainability efforts. In
addition, active carrier participation leads to a more efficient network for GM with reduced fuel
consumption, which can ultimately lead to less impactful fuel surcharges. Therefore, by encouraging all
freight carriers to participate, GM can help proliferate best-practices in increasing fleet efficiency,
resulting in mutual benefits for itself and its logistics partners. This ties sustainability efforts directly to
the strategic goals of the organization, including cost savings and supplier relationship enhancement.
Discussion of the data generated by the SmartWay model should naturally lead to a question of how
representative this work is of the true emissions generated by GM's network. Two key points must be
addressed: (1)
the percentage of "non-SmartWay" carriers and its effect on total mass emissions, and (2)
the portion of the entire network activity data that is being reported.
A quick glance at the data found in the "Percent SmartWay Carriers" section of Figure 12 indicates
that there is a vast dissimilarity between distance-based and C02-based values. This illustrates the penalty
that is assigned to non-SmartWay carriers under contract whose activity is submitted into the tool. Even if
a non-SmartWay carrier truly operates at average C02 emissions as compared to member firms, its
performance values are unknown and therefore assigned as the
99'
percentile of all reporting firms. We
suspect the EPA has structured its method in this way for ease of calculation and fairness to reporting
firms. Despite the inaccuracy that this may cause in the data, we also notice that this creates a stronger
incentive for shipper firms, like GM, to encourage 100 percent of their carriers under contract to join
43
SmartWay. In the spirit of data integrity, we recommend the EPA apply a probabilistic approach to
assigning performance values to non-members; however, from the perspective of driving urgency and
faster adoption of the program, we applaud the stark contrast that the EPA has been able to "build-in" to
their measurement methodology.
Capturing 75 percent of activity data for GM's North American inbound truck network is a significant
accomplishment for the logistics organization. The amount of activity data reported was enough to
establish the company as a SmartWay partner, but it only represents just over half of all activity that could
potentially be reported for the region. Inbound rail, outbound truck, and outbound rail are all components
of activity that were not reported in 2013, but are on track to be added in future reporting years. As a
reminder, air and ocean modes are not considered in the current SmartWay model, thus further reducing
the true representation of total emissions accounted for. Because of these reasons, we focused efforts on
creating a model that could estimate the all-inclusive total emissions for the globe, which we discuss in
Chapter 5.
We firmly believe that the SmartWay Transport Partnership will evolve at GM in both a local and
global way. Locally, GM has the knowledge and people in place to continue to increase the amount of
network activity reported. Globally, the EPA has been involved with numerous international initiatives
that look to bridge gaps and link SmartWay to similar "sister" programs across Europe, Asia, and the rest
of the world.
44
5
Global Emissions Measurement Model
Ideally, global emissions measurement would be achieved by collecting detailed, activity-based
information on every logistics move made within the network across all modes and all regions. Weight
and volume of shipment, distance traveled, equipment type, and fuel consumption would be necessary for
each step in both the domestic and international shipping processes in order to precisely calculate carbon
emissions. Figure 13 illustrates the steps involved in a complex international shipment.
TIER 1
SUPPLIER
'
PORT OF
DEPARTURE
TRUCK
SHIPMENT
RAIL
SHIPMENT
OCEAN OR AIR
SHIPMENT
TRUCK
SHIPMENT
PORT OF
ARRIVAL
RAIL
YARD
>_DESTINATION
Figure 13: Components of a Complex International Shipment
Computing emissions based on detailed shipment information produces an actionable dataset that
management can use to investigate the areas that are most impactful to the carbon footprint of the
logistics network. This principle is what makes the SmartWay program successful. If a company has a
globally integrated TMS, it may be possible to obtain these details; however, as firms utilize 3PL
providers, data is not as readily available or housed in one location. This challenge exists at GM, and the
capabilities to obtain the necessary detailed information have not been developed. Development of these
capabilities would require significant IT investment, and therefore was not scoped within our research.
Because of this, we developed a method to approximate the emissions generated by GM's global logistics
network using readily available data. Our method takes internally reported spend data by mode for each
region and uses modal emissions factors measured in C02 generated per unit of spend to compile an
approximation of emissions.
5.1
Methodology
Conceived from within the Technical Guidance for Calculating Scope 3 Emissions published by the
World Resources Institute and based on the Greenhouse Gas Protocol [14], the methodology we adapted
45
for our approach is referred to as an environmentally-extended input output (EEIO) model. The model
uses reported mass emissions inventories resulting from the activities of public companies focused on
transportation and logistics. The revenues generated by these public firms are also reported, thereby
allowing us to utilize EEIO emissions factors in terms of metric tons C02 per U.S. dollar (mt
C02/$USD). We develop EEIO emissions factors of individual transportation firms, segregated by mode,
which best represent the GM network and generate an average emissions factor for each mode. In tandem,
internal GM logistics spend data is collected from each region by mode. The average EEIO emissions
factor and spend values are used to calculate an estimate of the mass emissions produced globally. The
process is completed on an annual basis and used primarily for reporting and benchmarking purposes.
5.2
Model Inputs
Two primary inputs are necessary to achieve the desired output of the model: (1)
EEIO emissions
factors for each mode of transportation used by GM, and (2) the amount of money spent on each mode of
transportation for each region. Inputs are calculated annually for the reporting year being analyzed.
5.2.1
Environmentally-Extended Input Output Emissions Factors
We first compile the EEIO factors. The data consists of carrier companies, segregated by mode, and
their self-reported yearly C02 emissions per unit of revenue. Carrier revenue is synonymous with GM
spend; therefore, the resulting emissions factors are normalized to the U.S. dollar. The underlying
equation used to calculate EEIO emissions factors is shown in Equation 3.
EEIOEFL
EC2
EC02 1
Equation 3: EEIO Emissions Factor Calculation for a Carrier Company
List of Variables
EEIOEF=Environmentally-extended Input Output Emissions Factor (metric tons C02/$USD)
Ecoz = Mass Emissions of reporting carrier company (metric tons C02/year)
46
R = Revenue of reporting carrier company ($USD/year)
i = Reporting carrier company index
Using ocean shipment mode as an example, the resulting factors can be found in Figure 14. A similar
table is composed for all modes of transportation.
Ocean Carier #1
Ocean Carrier #2
Ocean Carier #3
Ocean Carier #4
Ocean Carier #5
Ocean Carier #6
Ocean Carrier #7
Ocean Carrier #8
0.00000149
0.00005200
0.00006120
0.00011779
0.00012520
0.00013336
0.00025500
0. 00096720
mt
mt
mt
mt
mt
mt
mt
mt
CO2/$USD
CO2/$USD
CO2/$USD
CO2/$USD
CO2/$USD
CO2/$USD
CO2/$USD
CO2/$USD
Figure 14: EEIO Emissions Factors for Reporting Ocean Carriers
Outliers are eliminated using a simple boxplot analysis. For example, in the dataset above we find one
outlier (max outlier), shown in Figure 15, and eliminate it.
I
0.0010
0.0008
2 0.0006
0
E 0.0004
0.0002
0.0000
Ocean
x Min Outlier
z Max Outfier
Figure 15: Outlier Analysis of EEIO Emissions Factors for Ocean Transportation
47
EEIO emissions factors for non-outlier values are averaged. Continuing with our example, the
resulting average emissions factor for ocean transportation is 0.000107 mt C02/$USD. A summary of
average EEIO emissions factors, denoted by EEIOEF, for each mode of transportation is given in Figure
16.
0.000960
Truck
Rail
Ocean
Air
mt
mt
mt
mt
0.000344
0.000107
0.000269
GU2/UbD
CO2/$USD
C02/$USD
CO2/$USD
Figure 16: Average EEIO Emissions Factors by Mode
5.2.2
Logistics Spend by Mode of Transportation
The second critical input to our model is the amount of money spent for logistics services. Working
with GM logistics leaders around the globe, we acquire regional spend data by mode (SRM). These data
are highly confidential and cannot be published. However, Figure 17 shows the relative proportion of
logistics spend data for GM. The purpose of showing Figure 17 is to illustrate the format in which
inbound and outbound logistics spend data is collected by the regions. Outbound Air is not a form of
transportation that is used, as air freight cost for shipping a finished vehicle is extremely uneconomical.
REGION [R]
Spend (S R m)
IBAir
IB Ocean
:B Rail
lB Truck
0 OB Ocean
OB Rail
OB Truck
U
" Region Total
I
% Global Total
1
0%
23%
0%
U
0%
0%
12%
I
3.1%
-
0%
12%
0%
1%
25%
1%
21%
1
17.8%
I
19
U
6%
24%
0%
14%
35%
0%
20%
6%
5%
%
11n0
0%
28%
15%
31.4%
j6%
2%
*GM Brazil is a subsetof GMSA, accounting for 61% of GMSAvolume
Figure 17: Regional Logistics Spend by Mode of Transportation (SR,M), 2012
48
5.2.3
Analysis of Inputs
Because financial data is acquired by internal GM logistics teams, we are fairly confident in using the
non-zero numbers reported for our model. However, we have some skepticism with regards to the values
omitted for certain region/mode combinations, since we expect the use of these modes even if only
occasionally. This is especially true for GM Brazil, the only subset of the GMSA region from which we
could successfully acquire data. Follow up discussions indicated that this data could not be obtained by
the regions or was deemed negligible.
In greater question, however, is the accuracy of the EEIO factors. This is not simply a concern that
has arisen with our model, as accuracy concerns are frequently discussed when employing EEIO models.
The Technical Guidance for Calculating Scope 3 Emissions [14] discusses some of the benefits and
concerns of using such a model. When comparing EEIO models to more detailed, activity-based models,
the guidance document highlights simplicity and reduced time investment as advantages of the EEIO
method, yet it points out a lack of specificity and potential inaccuracies of using broad sector averages as
drawbacks. It goes on to reason that while employing an EEIO method can help an organization achieve a
directionally correct estimation, assuming a linear attribution between monetary and environmental flows
does not give visibility to nuances associated within each sector (e.g., standard truck service versus
refrigerated truck service).
Using an EEIO model may introduce confusion into an analysis process. For instance, it may not
make sense on the surface that our EEIO emissions factor for air shipment mode is lower than those for
truck transportation and rail transportation. However, traditional logic may not apply when using an EEIO
model. In the case of air shipment mode, the cost (spend) associated with using this mode is
disproportionately high when compared to that of truck or rail with respect to emissions output.
Therefore, even though the use of air transportation results in higher emissions per activity (i.e., per tonmile transported), it emits less carbon dioxide per unit of spend. Despite this, the proper amount of mass
49
emissions is accounted for as the disproportionately high air freight costs are multiplied with the
relatively low EEIO factor, thus correcting the perceived misalignment.
5.3
Model Outputs
Our simple model accepts two inputs and generates the estimated amount of mass C02 emissions
released into the atmosphere by operating the GM logistics network. These inputs are: (1) logistics spend
by region and mode, and (2) average EEIO emissions factors for each mode of transportation. For our
analysis, the EEIO emissions factor for a given mode is the same regardless if used for inbound or
outbound transportation. The following equation is used to generate output values of regional mass
emissions by mode:
Eco 2 = SR,M x EFIOEF
Equation 4: Mass Emissions as Calculated by EEIO Method
List of Variables
Eco 2 = Mass Emissions generated (metric tons C02/year)
SRM =
Regional spend value by mode ($USD)
EEIOEF = Average modal EEIO emissions factor (metric tons C02/$USD)
Note: EEIOEF and SR,M must match in mode
Applying Equation 4, the resulting mass emissions estimates for calendar year 2012 are found. The
results are shown as relative percentages by mode for each region in Figure 18. Again, to protect
confidentiality of financial data, actual GHG emissions values cannot be disclosed at this level of detail.
A summation of all modes and all regions yields the total global estimate of mass emissions for GM
logistics.
50
REGION rR1
I
Mass Emissions
(E co2)
lB Air
IB Ocean
JIlB Rail
0%
0%
lB Truck
I
0
0 OB Ocean
OB Rail
OB Truck
0%
I%
Region Total
% Global Total
0%
II
3%
34%
_0M
4.0%
&
I
4%
IN
9%
19.1%
M
21.7%
1
*GM Brazil is a subset of GMSA, accounting for 61% of GMSA volume
Figure 18: Mass Emissions (Eco2) of GM's Global Logistics Network, 2012
5.3.1
Sensitivity of Outputs
The mass emissions data generated by our model is a good first-pass estimate for GM's entire global
logistics network. Quantifying this number is a primary objective for the company on an annual basis.
This type of EEIG model is an accepted method of measuring logistics-related emissions in the absence of
more detailed network data [14], but its shortcomings (as described in 5.2.3) leave our results exposed to
error. Especially critical to the accuracy of our outputs is the average EEIO emissions factor for each
mode. The ocean carrier data shown in Figure 14 shows only eight representative carriers, from which we
eliminated one outlier and used the remaining seven to calculate an average value for the EEIO emissions
factor for the mode. Sample sizes for the remaining modes were similar, representing only a small
fraction of the total population of carriers. With such limited data, we must be aware of the risk of
inaccuracy in our results.
5.4
Results
Our model gives insight into GM's logistics operations that could not previously been seen. Other
than simply providing the mass emissions output of the network, the results point to regional differences
in operations and highlight areas that the company should target in order to make the largest emissions
reduction impact. Figure 19 and Figure 20 illustrate the relative network emissions generated by GM's
51
logistics network. These figures are proportionally true and meant to serve as visuals for discussion of
regional and modal outputs of the model. Total mass emissions values are not displayed, as these values
are generated based on confidential financial data.
N
0
Z
0
Li
I-
GMNA
GM Brazil
GME
GMIO
REGION
N IB Truck a lB Rail a IB Ocean m IB Air ; OB Truck a OB Rail
U
OB Ocean
Figure 19: GM Global Logistics: Relative GHG Emissions by Region
N
0
0
IB Truck
IB Rail
IB Ocean
IBAir
OB Truck
OB Rail
MODE
0 GMNA a GME
U GM Brazil
U GMIO
Figure 20: GM Global Logistics: Relative GHG Emissions by Mode
52
OB Ocean
Figure 21 gives unit sales information for each region, which will provide further clarity to the
analysis of our results. Note that regional sales percentages add up to 100 percent. This represents 100
percent of the vehicle sales used in our study, and should not be interpreted as 100 percent of GM global
vehicle sales.
2012 Vehicle Sales
642,000
7%
% Vehicle Sales [
1,607,000
18%
3,616,000
41%
3,019,000
34%
*GM Brazil is a subset of GMSA accounting for 61% of GMSAvolume
Figure 21: GM Vehicle Sales by Region, 2012 123]
We observe that GMNA operations release by far the most emissions from logistics than any other
region. This can be attributed to the region's heavy use of truck mode and the distances traversed,
extending from Canada to Mexico. The primary sources of emissions for GMNA are inbound truck,
outbound truck, and outbound rail. An important fact that is difficult to glean from the presented charts is
that the majority of finished vehicles (outbound) are transported by rail. By favoring a more efficient
mode for outbound transportation, outbound logistics achieves a smaller total carbon footprint than its
inbound counterparts.
The next critical observation is that the GMIO region produces more vehicles than GMNA, yet it
produces far fewer carbon emissions. This is due to GMIO's heavy use of ocean transportation. Regional
spend for inbound and outbound ocean freight rank #1
and #2 for the region, and since ocean
transportation benefits from the lowest average EEIO emissions factor, a huge savings in mass emissions
is generated. Although the choice to use an abundance of ocean transport is likely the result of a greater
number of coastal manufacturing facilities, the savings in emissions with respect to vehicles produced is
apparent.
53
When observing the modal contributions to emissions output for all regions combined, it becomes
very clear that truck transportation contributes by far the most carbon emissions in GM's logistics
network. Given that truck transport is used extensively in all regions and its use carries a relatively high
carbon intensity when compared to ocean and rail, its impact on GM's total carbon footprint is not a
surprise. Moving forward, GM should continue to focus on making network improvements to reduce the
emissions resulting from truck transportation while moving to ocean and rail transportation whenever
possible. These types of network change activities and their impacts on carbon emissions are discussed in
Chapter 6.
Looking at the big picture, it helps to understand how the logistics-related portion of GM's Scope 3
emissions compares to the company's Scope 1 and Scope 2 emissions, as well as how it compares to the
other categories found within Scope 3 related to production. Put into this perspective, the importance of
focusing on reducing emissions produced by logistics operations becomes even more evident. Figure 22
shows the contributions of various C02 emissions sources found in the vehicle production process. From
this breakdown, we see transportation and logistics to be the third leading source of emissions from the
company's production operations. The summation of all categories (all scopes) adds to 100 percent, and
we see that approximately 9 percent is attributed to Scope 1 emissions (facility fuel), 21 percent is
attributed to Scope 2 emissions (facility electric), and the remaining 70 percent is attributed to various
Scope 3 emissions found within the production process. Note that the Scope 3 category "Use of Products"
has been omitted here. This is because we are analyzing the sources of C02 from production, not from
consumer use. Further observation reveals that transportation (inbound and outbound) accounts for
approximately 19 percent of all production-related emissions, and nearly 30 percent of GM's Scope 3
emissions. Comparatively, transportation emissions are equal to nearly two-thirds of those produced by
Scope 1 and Scope 2 combined, which reveals the carbon intensity associated with logistics operations
versus assembling vehicles. The main takeaway from this pareto analysis is that logistics-related
emissions are relatively large when compared to other major sources of emissions in the production
54
process. In addition, the current contribution of emissions from transportation should serve as a
benchmark to be evaluated and reduced annually.
35%
30%
25%
20%20
715%
10%
5%
a.
--
0%
10
4t
-
-
'
Sb~
0%,
Fa
:P
Fiur
226%
mte
ySuc:G
prtosIore:G
Pouto
55
nryMngmn
em
6
Network Change Analysis
At GM, the logistics organization is constantly looking for ways to make its transportation network
more efficient. In doing so, the management team looks to reduce operating costs and improve critical
metrics such as meeting production and delivery schedules, maintaining proper inventory levels, and
protecting the goods being transported. In order to achieve worthwhile reductions in cost, a substantial
number of initiatives, small and large, are under consideration or deployment at all times. These
initiatives range from minor changes in route frequencies, to major changes that result in shifting
locations of parts suppliers to achieve closer proximity to final assembly operations (called "supplier
footprint" change). Each network change activity is carefully logged along with the net cost savings
realized from implementation. Although the team has exceled at tracking and achieving cost reductions,
viewing logistics network efficiency in terms of carbon emissions is something that, until now, the
organization had not explored.
6.1
Building a Carbon Change Model for Logistics
Mileage reduction, fuel usage, shipment frequency reduction, mode choice, and supplier footprint
improvement are a few common themes associated with cost-beneficial network change initiatives at GM.
Each choice that is made with regards to these factors affects total shipment cost. We hypothesize that in
logistics, total shipment cost has a positive correlation with total shipment C02 emissions. We base this
hypothesis on the premise that both operational costs and fuel costs are reduced as network efficiency is
improved, thus fuel consumption and carbon emissions are also reduced. As the influences on logistics
cost components and C02 emissions drivers are analyzed, we see substantial overlap, which is shown in
Figure 23.
56
$
shipment
C02
shipment
$
gallons miles traveled
gallon ton . mile # of shipments
C02
-gallon
Fuel
Tye
gallons miles traveled
ton -mile # of shipments # of tons
Fuel conomy
Miles Per Shipment
W ih
Figure 23: Components and Influences on Logistics Cost and C02 Emissions
We begin by looking at fuel cost and fuel type. The cost of fuel is dependent upon market rates.
Because these rates fluctuate, a "fuel component" of each contracted shipment lane is determined, and
fuel surcharges, which are adjusted based on market rate, are applied as a percentage of the fuel
component. This standard practice introduces variability into the cost of each contracted lane, and one
clear way to reduce this variability is to reduce the size of the fuel component of the shipment lane cost.
For our study, fuel type is fixed per transportation mode. That is, diesel fuel is used for heavy-haul trucks
and locomotives, aviation fuel is used for jets, and bunker fuel is used for ocean container ships. Of
course, there are exceptions such as biodiesel and natural gas, but these alternative fuels make up a
relatively small, yet growing, portion of GM's network at this time. Until alternative fuels become more
prevalent and cost-effective, these fixed fuel type generalizations will be sufficient for carbon emissions
estimation.
However, with the rapidly changing alternative fuels market, it is important to understand the impact
that fuel switching could have on GM's network in the future. The U.S. Energy Information
Administration (EIA) projects a combined average annual growth rate in the use of compressed natural
gas (CNG) and liquefied natural gas (LNG) of 14.6 percent in heavy-duty vehicles (HDVs) between 2011
57
and 2040 [24]. This growth will be aided by the price gap between lower-cost natural gas and higher-cost
diesel fuel. The HDV category includes class 8 trucks, which make up the majority of GM's truck
transportation network. Overall, the EIA estimates that natural gas consumption for HDVs will grow from
near zero in 2011 to just over 1 quadrillion Btu by 2040. If these projections pan out, natural gas would
account for approximately 13 percent of all fuel consumed in HDVs in 2040. This growth is not linear
over time, but is predicted to increase rapidly beginning around 2025, as shown in Figure 24. Switching
to natural gas for truck transportation has potential to save on both cost and C02 emissions, as the amount
of C02 emitted from CNG and LNG versus diesel fuel is reduced by 31 percent and 57 percent,
respectively, per diesel gallon equivalent [19].
12
History
Projections
2011
_
1.0
.?
co
0.8
0
~ L.t 0.6
Heavy-duty vehicles
cr 0-4-
Buses\
0.2
0
1995 2000
2010
2020
2030
2040
Figure 24: Natural Gas Consumption in the Transportation Sector, 1995 -2040 [24]
Extrapolated to the GMNA network today, if 13 percent of all truck transportation ran on CNG, the
resulting C02 savings would be approximately 3.3 percent. If the same 13 percent were run on LNG, the
resulting C02 savings would be 6.1 percent. Another consideration when comparing CNG and LNG to
diesel is the amount of non-combustion-related emissions generated from using the alternative fuels. For
CNG, compressor operation for storage is one such source of additional emissions. For LNG, one should
58
consider the greenhouse gas effects of "venting" stored methane, which can occur even in well-insulated
LNG tanks as unsafe pressure, caused by heat transfer between ambient temperature and LNG's
cryogenic storage temperature, is released [25]. In summary, understanding fuel switching from a life
cycle perspective is essential to evaluating different fuel options for logistics. Currently, there is an
abundance of research underway to better comprehend the overall emissions impact of alternative fuel
types [26] [27] [28].
Next, we analyze the influences on fuel economy, miles traveled, and weight for a given shipment. To
transport parts from point A to point B, the largest influence in fuel economy is mode choice. Mode
choice heavily depends on accessibility of the mode to the locations being served. For example, if a route
is accessible by both road and rail, it is worthwhile to investigate using rail since it often costs less than
truck and is a less carbon intensive mode of transportation. Thus, mode switching can result in cost and
emissions savings, but negative impacts to lead time or delivery requirements are a possible consequence
and may diminish the business value of switching. When modal change is not an option, logistics teams
should strive to optimize the weight and volume utilization of each shipment. If freight containers are
shipped at low utilization (i.e., less than 100 percent capacity) due urgent production needs or poor
planning, there is a negative impact on fuel economy.
Continuous reassessment of production needs and part order quantities may reveal inefficient
shipment frequencies and provide opportunities for improvement, while combining multiple partial
shipments into one load can also' reduce frequency and total miles traveled. In addition, meticulous
analysis into the most efficient routes available given variables such as highway congestion and weather
events can help assist in reducing idle time. Furthermore, changes in the supply base, including supplier
relocation, are substantial undertakings that may result in drastic mileage reduction.
Lastly, weight considerations other than percent utilization include the design of the product and
shipment containers themselves. By engineering weight out of these components while maintaining
59
product quality, sizeable efficiency gains in logistics can result. Highway weight regulations for freight
transportation must also be considered in logistics network planning.
These are the types of questions and analyses that currently take place at GM. They are very complex,
cross-functional studies that require approval from multiple stakeholders and executive leaders. Business
cases for change initiatives must be soundly developed before approvals are given and can take months
before being implemented. The unending focus on such projects is cost and complexity reduction, while
the environmental impact reduction and emissions savings had been left unnoticed and unquantified. Our
work unites ongoing network change efforts and builds off existing datasets to provide the organization
with new tools and methods for evaluating network changes in terms of C02 output.
6.1.1
Model Inputs
To start, we data mine the following pieces of information from internal sources regarding network
change activities:
*
Description of network change - used for reporting and understanding the primary and most
impactful drivers of carbon change
*
Mode(s) associated with change - some changes will occur within a single mode, while others
will involve multiple modes or modal shifts
*
Cost savings - recorded in order to analyze the relationship between changes in logistics cost and
changes in carbon emissions
*
Mileage change - necessary for calculating carbon impact associated with the network change
This dataset is used in conjunction with average modal emissions factors obtained from the USEPA
SmartWay program that describe the amount of carbon dioxide produced by transporting materials and
finished goods. The factors measure C02 per mile traveled by a particular mode of transportation based
on activity-based data reported from members of the transport partnership. These factors were taken from
a database consisting of over 3,000 truck carriers and Class 1 rail carriers. Greater than 85 percent of all
60
change activity at GM occurs within the truck and rail modes; therefore, we limited our focus to these two
modes as well as a combination of these modes, referred to as intermodal. The intermodal emissions
factor is calculated and is weighted equally based on reported truck and rail factors. Emissions factors
used for our model are shown in Figure 25.
Rail
0.00107
Truck
Intermodal
0.00169
0.00138
mt GU2/mIle
mt C02/mile
mt CO2/mile
Figure 25: Activity-based Emissions Factors by Mode (EFM)
6.1.2
Model Outputs
We calculate the estimated carbon impact of a single discrete network change activity. A simple
example would be reducing shipment frequency of a particular truck route, thus decreasing the weekly
miles traveled by the route. A more complex example would include a mode change from truck to rail,
thereby reducing truck miles traveled but increasing rail miles traveled. We use the following equation:
AEco 2 = Y(AMiles,
x EFm,)
Equation 5: Change in C02 Resulting from a Network Change Activity
List of Variables
AEco
2
= Mass emissions change resulting from a network change activity (metric tons C02)
AMilesm, = Mileage change resulting from network change of mode i (miles)
EFM, = Activity-based emissions factor associated with mode i (metric tons C02/mile)
Note: Modes (i) consist of: Truck, Rail, Intermodal (see Figure 25)
61
Our results from the model are presented to the company in two ways, tabular and graphical,
satisfying two different objectives: (1)
tabular results are totaled and reported as part of GM's
environmental impact and transparency effort, recognizing logistics carbon-reduction activities, and (2)
graphical results are shared internally with the team and generate additional motivation for network
efficiency efforts. Graphical results are illustrated in Figure 26 for inbound logistics activity for a portion
of 2013 (initiatives are plotted in chronological order from January 2014 to September 2014).
$180,000,000.00
140,000
$160,000,000.00
Z
120,00
0
$140,000,000.00
Z
40
JA U
100,0( 30
0
$220,000,000.00
U
LU
0
02Z
LU
U
80,00 0
$100,000,000.00
60,000
$80,000,000.00
'I
LU
$60,000,000.00
40,000
$40,000,000.00
20,00 0
$20,000,000.00
0
$1
51 101 151 201 251 301 351 401 451 501 551 601 651 701 751 801
NUMBER OF INITIATIVES
-
Cumlative Savings (USD$)
Cumulative Savings (Metric Tons C02)
-
Figure 26: C02 and Cost Savings for -800 GMNA Inbound Network Change Initiatives
6.2
Results
We found our network change model to be well received by management. Practically, this data is
useful for the energy management team that reports total emissions as well as C02 reduction activity to
shareholders annually. By creating a process to collect this data in a systematic way, we are able to reduce
the time it takes to aggregate the data each year. Organizationally, management thoroughly enjoyed the
62
visual representation of the emissions savings as a motivator for employee engagement. We included an
equivalence reference as well so that employees could better understand the amount of carbon emissions
actually saved. For example, approximately 114,000 metric tons of C02 were mitigated by implementing
the -800 initiatives sampled above. This amount of emissions is difficult to visually quantify, so we also
represented this savings in terms of emissions resulting from nearly 24,000 passenger vehicles per year.
Quantitatively, we find that our initial hypothesis is correct: in the logistics sector, most cost savings
activities will also result in emissions savings. Occasionally, business needs may drive a course of action
that reduces cost while increasing C02 emissions. An example of this may be observed when switching
from a long rail route to a shorter truck route that costs less and provides improved delivery time, yet
generates a greater amount of carbon emissions. We also see examples, such as the first fifty initiatives of
2014 as well as the 4601 initiative of the year (which saved -$8.5MM), where change initiatives save the
organization cost, but do not reduce emissions. These changes are generally contract changes or
renegotiations prior to a vehicle launch. However, even with the inclusion of these negative and neutral
impacts on carbon emissions, the network change data from GM's inbound operations suggests a
correlation of +0.61 between cost savings and emissions savings, seen in Figure 27.
s,000
0
U
B
'low
,E
00
2,000
U
21
~
1,000
LU
*
$11,000,000)
$1000,000
$2,000000
$3,000,000
$4,00,000
$S10001000
$6,000,000
$7,000,000
$8,000,000
$9,000,000
(1,000)
(2,00)
Cost Savings ($USD)
Figure 27: Correlation of Emissions Change vs. Cost Savings for GMNA Inbound Logistics Network Change Initiatives
63
7
Conclusions and Recommendations
Our approach to implementing methodologies and tools to give GM's logistics organization better
visibility of its carbon footprint yields a new set of data that management can use to make better business
decisions. In this final chapter, we summarize the benefits that the organization will gain from now on by
having access to more accurate and more complete carbon emissions data. We also look to the future and
identify ways in which GM, through continuous improvement, can be viewed as a leader in carbon
efficient logistics.
7.1
Key Findings
To reiterate the objectives of our research, we set out to create a recommendation for GM to
implement a global method for measuring and reporting its carbon footprint for inbound and outbound
logistics operations. In doing so, we developed an approach and designed business processes for capturing
and reporting carbon footprint data from logistics providers, while working with GM's sustainability team
to integrate our initiative into the overall company strategy. Our approach unfolded into three distinct
work streams:
1)
Implementation of industry-recognized methods and processes
2) Development of a global carbon footprint measurement model
3)
Emissions analysis of network change activities
The high-level results of these work streams include GM joining the USEPA SmartWay Transport
Partnership, implementation of a method to estimate the logistics-related emissions produced by the
company's global, multi-modal freight transportation network, and a new tool to track changes in carbon
emissions resulting from individual network change activities. Combined, these results provide benefits to
the company that previously did not exist. Our research and findings have given GM logistics the ability
to use structured processes to measure and analyze its carbon footprint. Greater alignment with the
64
corporate sustainability strategy has also been achieved, and we anticipate that the logistics team's efforts
will be included in GM's 2013 sustainability report (released in 2014) for the first time.
7.1.1
Global Emissions Reporting
Carbon emissions reporting is becoming increasingly more important to consumers and investors.
Exemplifying sustainability efforts and their impacts generates value for shareholders that is becoming
more quantifiable with the advent of market indices such as the Dow Jones Sustainability Index. In
addition, the accuracy and transparency of reported data is becoming more critical as voluntary reporting
requirements, and possibly future reporting mandates, become stricter. Using our global emissions
measurement model, GM can more accurately report the sources of carbon emissions for all regions.
Furthermore, management can review regional dissimilarities of emissions output both in magnitude and
by modal proportion, and by investigating these differences, they may identify best-practices and
opportunities for improvement in each region.
7.1.2
Logistics Provider Relationships and Sourcing Decisions
Emissions reduction is a common goal that GM and its logistics providers can achieve together.
Because of this, we found that best practices are freely shared between customers, 3PL providers, carriers,
and industry groups, making sustainability an easy topic for all parties to see eye-to-eye. This was critical
in achieving our objectives and will be very important as sustainability reporting becomes more prevalent
for global supply chains in general. With countless interactions among GM and its logistics providers,
information sharing is the only way to truly gain a complete view of global activity. Therefore, it becomes
imperative that the company chooses to partner with firms that are willing to share common goals as well
as information. As a member of SmartWay, GM can now choose to urge its contracted providers that are
not currently members to join, thereby helping them embarking on a path to more efficient operations. We
recommend that SmartWay membership serve as a sourcing requirement in the future. If GM can achieve
100 percent SmartWay member sourcing for its logistics provider base, then it can more closely work
with each provider and align efficiency goals.
65
7.1.3
Cost Reduction Reinforcement
Cost reduction will always be at or near the top GM's business priorities list. By aligning
sustainability efforts with cost savings activities, we are able to generate a greater amount of executive
engagement. As management employs the methods and processes resulting from our research, we urge
them to use the data in ways that provide core business value, including cost savings. One such way is to
use detailed emissions performance data of logistics providers to adjust fuel surcharge fees based on their
relative efficiencies. By doing this, large consumers of logistics services, like GM, can help stimulate
efficiency-related initiatives in the logistics sector, as it would become financially beneficial for providers
to invest in carbon reduction projects.
Our carbon change methodology can also be used to strengthen cost savings proposals. One such
example came just weeks after implementation. The network change team was seeking permit approvals
from the state of Indiana to allow heavy-hauling, or transportation of a payload in excess of current permit
regulations. Approval would have allowed the team to reduce the frequency of shipments per week on a
particular route and substantially reduce the route's annual cost. Using our network change methodology,
we were able to calculate an estimated 7 percent reduction in carbon dioxide emissions over the course of
a year. This estimate was used to reinforce the team's proposal to the state.
7.1.4
Risk Reduction
With increased visibility to carbon emissions, GM can manage future risk out of its logistics business.
One such risk is fuel volatility. By using carbon emissions as a proxy for efficiency, GM can choose to
partner with the most efficient transportation providers, resulting in reduced fuel consumption and
reduced cost variability associated with unstable fuel prices. In addition, as competitors continue to tout
their logistics-related environmental efforts, GM can now do the same and reduce pressure from
shareholders and customers that may have otherwise put the company's reputation at risk. Finally, recent
legislative proposals have threatened to require carbon dioxide reporting or to attach a cost to carbon that
66
must be paid by large corporations. If similar such bills are passed into law in the future, GM logistics
will have an established process for compliance and excellence, resulting in minimal business impact.
7.2
Recommendations and Future Development
In a joint study, sustainability experts at the Environmental Defense Fund (EDF) and the MIT Sloan
School of Management propose that the actions of individuals supporting and implementing sustainability
projects can reinforce one another over time [29]. The success of such projects, and their resulting
outcomes and stories, creates a virtuous cycle that drives further employee and management engagement,
which in turn increases the resources invested in additional projects. The basic construction of these
dynamics can be found in Figure 28.
Executive
engagement
Results and
Resource
R
stories
+
investment
A Virtuous Cycl, of ivsmn
owgnhsonal
Energy Efclency
Identification,
implementation
and measurement
+
People and
tools
Figure 28: The Virtuous Cycle of Organizational Efficiency [291
Keeping this Virtuous Cycle in mind, we come to the realization that continued engagement to create
a more carbon efficient network remains solely within the hands of the GM logistics organization. The
tools and processes provided by our work is a solid foundation that must be continuously improved and
developed. Internally, global commitment to sustainability in logistics must increase. Our interaction with
regions outside of North America was limited during our study, and we feel that global success in carbon
footprint management will only be attained with a united international effort.
67
Active engagement with the USEPA SmartWay team is important for future success. We see this
team as an active leader in collaborating with similar environmental industry groups globally. The
activity-based method in which emission data is collected through the program provides the most detailed
and actionable data for GM, and thus the company should seek to develop this process for all regions and
all modes of transportation. If it is able to do so, then our global carbon footprint measurement model can
be phased out, simplifying the process.
Finally, the management team must be dedicated to evolving the organization's culture to instill a
desire and responsibility in each employee to reduce environmental impact as well as cost. This type of
cultural change can take years and must be communicated on a regular basis through both words and
actions. To assist this cultural enhancement, we recommend that GM create a position with responsibility
to unite logistics teams globally with a goal to drive profitable sustainability. We believe that with a
small, incremental amount of human resources allocated to further understanding the organization's
carbon footprint, additional opportunities in reducing risk, strengthening relationships with logistics
providers, motivating employees, and increasing network efficiency will present themselves.
68
8
8.1
Appendix
Media Coverage Report for GM-SmartWay Partnership
Media Coverage
GM Focuses on Logistics to Reduce Carbon Footprint
Last updated Dec. 23, 2013 at 10 a.m.
Summary:
On Dec. 18, GM announced the company has voluntarily joined the U.S. Environmental Protection
Agency SmartWay Partnership, which will drive benchmarking of fuel consumption and reduction of
emissions by its freight shippers and carriers with the goal of further shrinking the company's carbon
footprint.
Promotion included collaboration with the EPA to announce the partnership, a press release and bloq
post.
As a result, GM received more than 87,000 impressions from original coverage in 5 outlets, including
Environmental Leader, Canadian Manufacturing and GM Authority.
The press release appeared in 3 online outlets.
Altogether, this announcement generated 8 articles and more than 87,000 impressions.
Topline Coverage:
1. Dec. 18, 2013, Canadian Manufacturing: GM takes aim at carbon footprint of logistics operations
2. Dec. 18, 2013, GM Authority: GM Joins EPA's SmartWay Partnership
3. Dec. 19, 2013, Environmental Leader: GM Focuses on Freight to Reduce Carbon Footprint
4. Dec. 20, 2014, Logistics Manager: GM Enters SmartWay Partnership
5. Dec 23, 2014, Corporate Eco Forum: General Motors ioined the EPA SmartWay Partnership,
pledging to measure and reduce C02 emissions associated with freight shipping
Press Release Pick-up
1. Dec. 18, 2013, Paddock Talk: GM Focuses on Logistics to Reduce Carbon Footprint
2. Dec. 18, 2013, 4-Traders: General Motors Company: GM Focuses on Logistics to Reduce
Carbon Footprint
3. Dec. 19, 2013, AutoCar India: GM Aims to Reduce Carbon Footprint
Full Text:
Canadian Manufacturing
GM takes aim at carbon footprint of logistics operations
Dec. 18, 2013
UMV: 9,128
General Motors said it will optimize its logistics operations as a way to reduce carbon emissions as part of a voluntary
partnership it is joining in the United States.
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According to the automaker, it is joining the voluntary U.S. Environmental Protection Agency's SmartWay partnership
as a way to mitigate its carbon footprint.
Under the EPA program, GM will collect its shipping activity data, including which carriers the company uses to ship
freight, the number of miles traveled and freight weight.
Combining this information with carrier data, including equipment and service type, GM and its SmartWay partner
carriers can develop plans to further reduce carbon emissions.
"Our environmental impact extends from our supply chain to the use of our products," GM vice-president of
sustainability and global regulatory affairs Mike Robinson said in a statement.
"This EPA SmartWay Partnership provides a useful tool to help our company and carriers ... to reduce emissions and
save fuel and money."
According to the EPA, 28 per cent of greenhouse gas emissions are produced by the transportation sector.
Of that percentage, the organization estimate that approximately 30 per cent is freight related.
"By joining SmartWay, GM is on the road to improve the environmental performance of goods movement and reduce
greenhouse gas emissions from its supply chain," said Christopher Grundler, director of EPA's Office of
Transportation and Air Quality.
GM said it will encourage its logistics carriers that are not part of the SmartWay partnership to become members and
take advantage of tips and training to help save fuel and money, and reduce air pollution and emissions that
contribute to climate change.
In its 2012 Sustainability Report, GM identified its supply chain as one of 10 material issues facing the company with
respect to its economic, environmental and social impacts.
That year the company engaged its logistics suppliers to make the most of routing, reducing greenhouse gases by
62,000 tons.
This is the equivalent of the C02 emissions from the annual electricity use of 8,530 homes, the automaker said.
GM Authority
GM Joins EPA's SmartWay Partnership
Dec. 18, 2013
UMV: 56,829
General Motors has signed on to join the U.S. Environmental Protection Agency's SmartWay Partnership, a global
coalition of nearly 2,900 companies aimed at improving fuel-efficiency and reducing greenhouse-gas emissions.
According to the EPA, transportation alone accounts for 28% of all greenhouse gas emissions, with approximately
30% of that coming from shipping freight. Since 2004, SmartWay Partners have saved 65 million barrels of fuel ( est.
$8.1 billion), and prevented 28 million metric tons of carbon dioxide from going into the atmosphere (or, according to
their website, "the equivalent to taking 5 million cars off the road for an entire year").
According to Christopher Grundler, director for EPA's Office of Transportation and Air Quality, "By joining SmartWay,
GM is on the road to... reduce greenhouse gas emissions from its supply chain." GM will collect all data on its
shipping activities and work with SmartWay partner carriers to see how they can further reduce their carbon
emissions and thereby save money. Any of GM's current logistics carriers that are not members of the SmartWay
Partnership will be encouraged to join so that they too can help to reduce air pollution.
"This EPA SmartWay Partnership provides a useful tool to help our company and carriers - who already share our
environmental commitment - to reduce emissions and save fuel and money," said Mike Robinson, vice president of
GM Sustainability and Global Regulatory Affairs. "It's a significant win-win situation."
Environmental Leader
GM Focuses on Freight to Shrink Carbon Footprint
Dec. 19, 2013
UMV: 21,900
70
General Motors is joining the voluntary EPA SmartWay partnership, which GM says will drive benchmarking of fuel
consumption and reduction of emissions by its freight shippers and carriers with the goal of further shrinking the
company's carbon footprint.
GM will collect its shipping activity data, including which carriers the company uses to ship freight, the number of
miles traveled and freight weight. Combining this information with carrier data, including equipment and service type,
GM and its SmartWay partner carriers can develop plans to further reduce carbon emissions.
GM will encourage its logistics carriers that are not part of the SmartWay partnership to become members and take
advantage of tips and training to help save fuel and money, and reduce air pollution and emissions that contribute to
climate change.
According to the EPA, 28 percent of greenhouse gas emissions are produced by the transportation sector. Of that
percentage, approximately 30 percent is freight related.
In its 2012 Sustainability Report, GM identified its supply chain as one of 10 material issues facing the company with
respect to its economic, environmental and social impacts. In 2012, the company engaged its logistics suppliers to
make the most of routing, reducing greenhouse gases by 62,000 tons.
Best Buy, Johnson & Johnson and Lowe's are among the 55 companies honored by the EPA as industry leaders in
supply chain environmental and energy efficiency with its 2013 SmartWay Excellence Awards.
Logistics Manager
GM Enters SmartWay Partnership
Dec. 20, 2013
UMV:
General Motors has joined the Environmental Protection Agency's SmartWay Partnership in the US, to reduce its
carbon footprint.
The company offers a range of vehicles to more than 120 countries around the world, from electric and mini-cabs, to
heavy-duty full-size trucks and convertibles. It produces cars and trucks for brands such as Chevrolet, Cadillac, Opel,
and Vauxhall.
By joining the voluntary partnership, General Motors hopes to drive the benchmarking of fuel consumption and the
reduction of emissions of its freight shippers and carriers.
It will now collect all shipping data, including the carrier used, number of miles travelled, and freight weight, for
SmartWay to analyse and in turn develop plans to further reduce carbon emissions.
"Our environmental impact extends from our supply chain to the use of our products," said Mike Robinson, vice
president of Sustainability and Global Regulatory Affairs.
"This EPA SmartWay Partnership provides a useful tool to help our company and carriers - who already share our
environmental commitment - to reduce emissions and save fuel and money. It's a significant win-win situation."
71
9
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