Greenhouse gas emissions from milk production and
consumption in the United States: A cradle-to-grave life
cycle assessment circa 2008
, Jennie Popp
, Darin Nutter
, David Shonnard
, Richard Ulrich
, Dae Soo Kim
, Zara Neiderman
, Nathan Kemper
, Felix Adom
Ralph E. Martin Department of Chemical Engineering, University of Arkansas, 3202 Bell Engineering Center, Fayetteville, AR 72701-1201, United States
Department of Agricultural Economics and Agribusiness, University of Arkansas, 217 Agriculture Building, Fayetteville, AR 72701, United States
Department of Mechanical Engineering, University of Arkansas, 204 Mechanical Engineering Building, Fayetteville, AR 72701, United States
Department of Chemical Engineering and Sustainable Futures Institute, Michigan Technological University, 1400 Townsend Drive,
Houghton, MI 49931-1295, United States
Department of Biological and Agricultural Engineering, University of Arkansas, 203 Engineering Hall, Fayetteville, AR 72701, United States
Received 31 October 2011
Received in revised form
6 May 2012
Accepted 30 August 2012
This article presents a cradle-to-grave analysis of the United States ﬂuid milk supply chain greenhouse gas
(GHG) emissions that are accounted from fertilizer production through consumption and disposal of milk
packaging. Crop production and on-farm GHG emissions were evaluated using public data and 536 farm
operation surveys. Milk processing data were collected from 50 dairy plants nationwide. Retail
and consumer GHG emissions were estimated from primary data, design estimates, and publicly
available data.Total GHG emissions, based primarily on 2007 to 2008 data, were 2.05 (90% conﬁdencelimits:
e per kg milk consumed, which accounted for loss of 12%at retail and an additional 20% loss
at consumption. A complementary analysis showed the entire dairy sector contributes approximately 1.9%
of US GHG emissions. While the largest GHG contributorsare feed production, enteric methane, and manure
management; there are opportunities toreduce impacts throughout the supply chain.
Ó2012 Elsevier Ltd. All rights reserved.
Consumers are growing more aware of their impact on the
environment, and this may result in changes to their consumption
habits. To that end, the US dairy industry is working to further
improve the environmental performance of its supply chain in
a way that is also economically sustainable. In 2007, approximately
17.4 Tg (million metric tons) of ﬂuid milk of all types were
consumed (USDA, 2010a). Analysis of the dairy supply chain from
production through ultimate disposal of packaging was necessary
to provide the industry with a documented baseline of the carbon
footprint of ﬂuid milk an analysis of the dairy supply chain from
production through ultimate disposal of packaging was performed.
These results serve to identify reduction opportunities through best
practices and creation of decision-support tools for producers,
processors, and others throughout the dairy supply chain that can
be used to foster the long-term viability of the industry. This life
cycle analysis (LCA) was performed in compliance with ISO
14040:2006 and 14044:2006 standards, with the exception that
a single impact assessment method, global warming potential, was
adopted. ISO standards call for a wider range of impact assessment
categories. The Innovation Center for US Dairy is addressing addi-
tional impact categories in other projects; thus this work repre-
sents one aspect of the dairy industry’s sustainability evaluation.
A number of life cycle assessment studies have focused on dairy
in the past decade (Guinard, Verones, & Loerincik, 2009). There are
a variety of methodological approaches used in these studies. Some
report on an energy-corrected milk, or fat and protein-corrected
basis, and each uses a slightly different milk-to-beef allocation
procedure; many have focused on production only, but a few have
included additional post-farm components in the analysis. Much of
this research has been done in Australia (Lundie, Feitz, Jones,
Dennien, & Morian, 2003), Scandinavia (Berlin, 2005;Eide, 2002),
and Western European countries (Hospido, Moreira, & Feijoo, 2003).
Previous LCAs for dairy have focused primarily on agricultural
production (e.g., Basset-Mens, Ledgard, & Boyes, 2009;de Boer,
2003;Capper, Cady, & Bauman, 2009;Cederberg & Flysjö, 2004;
*Corresponding author. Tel.: þ1 479 575 7374.
E-mail address: email@example.com (G. Thoma).
Contents lists available at SciVerse ScienceDirect
International Dairy Journal
journal homepage: www.elsevier.com/locate/idairyj
0958-6946/$ esee front matter Ó2012 Elsevier Ltd. All rights reserved.
International Dairy Journal 31 (2013) S3eS14
Cederberg & Mattsson, 2000;Flysjö, Henriksson, Cederberg,
Ledgard, & Englund, 2011a;Haas, Wetterich, & Kopke, 2001;
Thomassen, Dolman, van Calker, & de Boer, 2009). A large body of
research exists on packaging in general, with some emphasis on
milk packaging in particular (Keoleian & Spitzley, 1999). Little
research on transportation of milk has been published. Life cycle-
based analysis of dairy processing is less common (Berlin,
Sonesson, & Tillman, 2008;COWI, 2000;Tomasula & Nutter,
2011;Xu & Flapper, 2009). Research on the life-cycle of milk from
retail to consumer to end-of-life has been minimal (Cederberg,
Sonnesson, Henriksson, Sund, & Davis, 2009;Eide, 2002;Gerber,
Vellinga, Opio, Henderson, & Steinfeld, 2010;Guinard et al.,
2009). Table 1 summarizes several full life cycle study results.
The majority of the studies report similar greenhouse gas (GHG)
emissions at the farm gate, ranging from approximately 0.75e1.5 kg
e per kg milk and 75e90% allocation of burdens to milk
compared to the co-product beef. A recent comparative study of
allocation methods supports this range, noting that system
expansion results in somewhat lower (as low as 63%) allocation to
milk, while mass allocation is as high as 98% (Flysjö, Cederberg,
Henriksson, & Ledgard, 2011b). One of the lowest reported carbon
footprints is the study by Eide (2002) that reports 65% allocation to
milk and 0.45 kg CO
e per kg milk. The Food and Agriculture
Organization of the United Nations (FAO) reports the US average
farm-gate GHG emission as 1 kg CO
fat and protein
corrected-milk (FPCM) (Gerber et al., 2010). These studies point to
enteric fermentation and manure management as primary sources
of GHG emissions in dairy production. Some studies point to on-
farm management as a critical area for investigating improve-
ment opportunities (Henriksson, Flysjö, Cederberg, & Swensson,
2011;Thoma et al., 2012b). Other studies investigate simpliﬁed
approaches to evaluating the potential impact/importance of
different parameters in the contribution to carbon footprint
(Asselin-Balençon et al., 2012;Flysjö et al., 2011a).
Retail businesses are beginning to engage their supplychains to
encourage adoption of best practices and development of innova-
tive solutions that reduce environmental impacts and maintain
value. Understanding both the sources of and the factors inﬂu-
encing these environmental impacts is an important ﬁrst step to
identify the most effective reduction opportunities. More detailed
analysis of on-farm GHG emissions as well as emissions associated
with milk transportation, processing, packaging, distribution are
presented elsewhere (Nutter, Ulrich, Kim, & Thoma, 2012;Thoma,
Jolliet, & Wang, 2012a;Ulrich, Thoma, Nutter, & Wilson, 2012).
The goal and scope of this work were:
Goal: Determine GHG emissions associated with consumption
of 1 kg of milk by US consumers. To account for differences in
production at the farm and processing, we used the National
Research Council (2001) approach for FPCM.
Scope: Cradle-to-grave. The system boundaries included
the energy use and GHG emissions associated with every step
of the life cycle from production of fertilizer through to either
landﬁll or municipal waste incineration of the milk packaging.
We explicitly excluded infrastructure in this analysis. Inci-
dental activities such as employee commutes, accounting, legal
services, executive air travel, and the cost of heating the
farmer’s residence, were not included. The primary time frame
for the study was 2007e2008.
2.1. Functional unit
The functional unit of this study is 1 kg of milk consumed by US
consumers. National scale reference ﬂows for US ﬂuid milk prod-
ucts are presented in Table 2 (USDA, 2007). We created a national
“average”milk as the sales volume-weighted average of the four
milk-fat content varieties for post-processor calculations. Product
loss in the supply chain, including wasted or spoiled milk by
consumers and out-of-date milk at retail, is equivalent to 29.6% loss
of all milk produced prior to it being consumed (USDA, 2010a). This
loss is reported as 12% at retail and 20% at consumption and is not
differentiated by milk-fat content. These losses in the supply chain
affect the reference ﬂows of upstream processes; speciﬁcally, the
required ﬂow into the consumption phase is approximately 1.25 kg
per kg consumed, and the ﬂow into the retail channel is approxi-
mately 1.14 kg per kg delivered to retail. This results in a reference
ﬂow of 1.42 kg milk from the farm to milk processing. Because
much of the energy in dairy feed is converted to milk solids (e.g., fat
and protein), and not all farms produce milk with standard fat and
protein composition, we have normalized on-farm production to
a standard milk (4% fat, 3.3% protein) using the National Research
Council approach (NRC, 2001) for FPCM; this is also referred to as
Energy Corrected Milk (ECM) because the calculation is based on
the ratio of the energy content, as determined by fat and protein
Summary of previously published life cycle analyses for ﬂuid milk production and consumption (beyond farm gate).
Study Functional unit (FU) kg CO
% to milk
Cederberg et al. (2009) kg ECM at retail 1.08 85% 1, 25, 298 Sweden, 1990 versus 2005
Eide (2002) kg milk at end-of-life w0.54
65% eNorway, study of 3 dairies
Gerber et al. (2010) kg FPCM at retail 2.4 w90% 1, 25, 298 International average
Gerber et al. (2010) kg FPCM at retail 1.3 w90% 1, 25, 298 US average
Guinard et al. (2009) kg milk at end-of-life 1.2 Economic 1, 25, 298 Literature review of 60
studies, primarily European
Total US sales of ﬂuid milk products in 2007.
Water (%) Density
3.26 8.6 88.13 1.0333 7398
Reduced fat milk (2%)
1.94 8.8 89.21 1.0346 8742
Low fat milk (1%)
0.96 9.1 89.92 1.0360 5257
Fat-free milk (skim)
0.11 9.1 90.84 1.0364 3971
Includes sales of ﬂavored and organic milk.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S4
concentrations, of produced milk to standard milk. The following
relationship was used to convert farm production to a fat- and
0:0929 ð4%Þþ0:05882 ð3:3%Þþ0:192
In Equation (1),Fis the percentage milk fat in the produced milk
and Pis the percentage protein in the produced milk. While it is
important to account for differences in fat and protein concentrations
at the farmlevel, for post-farm stages of the supplychain a volumetric
basis is more appropriate for calculations because the milk processing
and handling is not different for milk with different fat content. We
haveused the reported sales forwhole, 2%,1%, and skim milkfrom this
table to determine reference ﬂows for each of these main ﬂuid milk
products leaving the processor gate. For purposes of this study we
have aggregated ﬂavored milk with the appropriate typebased on its
fat content; in addition, because the density of the different varieties is
nearlythesame(Watson & Tittsler, 1961), we have not explicitly
accounted for any potential density variation effects.
Attributional LCA is intended to quantify the material and energy
inputs and outputs associated with a speciﬁc system. In practice,
industrial processes usually result in more than one valuable
product emilk, cream, and beef in this study. Here it is necessary to
apply allocation to the LCA model. Allocation is the partitioning of
the input and output ﬂows of a system among the multiple system
products. ISO 14040/14044 guidelines recommend a hierarchy of
procedures for addressing this issue. System expansion has not been
used for this project, primarily due to the lack of LCA for potential
substitute products that could be used as a basis for this approach.
Physical relationships have been adopted where feasible and
economic value or mass allocation has been used as our third option,
as described fully by Thoma et al. (2012a).InTable 3, we present
a summary of the allocation ratios and approaches adopted for this
study. We have made an effort to be consistent with allocation
methodologies at each stage of the value chain, i.e., using economic
allocation across all feed byproducts. When possible, we have
adopted causal relationships to deﬁne the allocation ratio.
2.2.1. Feed co-products allocation
Allocation of burdens between co-products arising from feed
crop processing (e.g., between distiller’s grains and ethanol or grain
meals and oils) is necessary. In such situations, we have adopted
economic allocation as the standard method. For the economic
approach, where available, we have used a ﬁve-year average price
to deﬁne the allocation ratio.
2.2.2. Milkebeef allocation
An important allocation for this study is the allocation of GHG
emissions between the co-products of beef and milk from virtually
all dairy facilities. For this study, we have chosen to allocate impacts
between the co-products milk and meat according to a biological
relation following Thoma et al. (2012a).
2.2.3. Creamemilk allocation
To derive allocation factors, we have used national milk
consumption rates for different milk fat content products coupled
with a milk fat solids mass balance (Thoma et al., 2012a). Informa-
tion from the USDA regarding the standard composition of various
milk products was used to determine the composition of each type
of milk on a fat-free basis (USDA, 2010b). Water is primarily a carrier
for milk fat and proteins. Since the principal difference between the
co-products is the fat content (Table 2), our approach to allocation
for these co-products was to assign the entire raw milk burden to
milk solids. Then using mass conservation principles, our approach
was to assign the appropriate milk protein and milk fat burdens to
each of the different milk fat products based on the reported milk fat
content of each co-product. The data do not provide sufﬁcient
resolution at the processor level to allocate milk processing impacts
among the different fat content milk products; in fact, most of the
energy requirements are volume- or mass-based. Pasteurization
energy is insensitive to milk fat content.
Summary of allocation ratios and types used in this study; the base case methodology has been shaded in the table.
Mass allocation Other
Soy oil/soy meal 31:69 19.5:80.5
Soy oil/meal/hulls 56.7:41.2:2.1 19.4:74:6.6
Cottonseed/lint 19:81 38.5:61.5
Milk/beef 94.5:5.5 95:5 (Protein
88.6:11.4 (causal by feed nutrient content)
Distiller’s Grains (dry)/ethanol 30:70 52:48
Distiller’s Grains (wet)/ethanol 24:76 51:49
Region 1 47:53 Causal relationship based
on crop nitrogen requirements
determined from reported yield
Region 2 88:12
Region 3 94:6
Region 4 92:8
Region 5 No data
Fluid milk/cream ee 80:20 (mass balance of milk solids)
retail (electricity &
1.62:98.38 (shelf space occupied)
Home (electricity) 1.62:98.38 (space allocation)
HDPE (recycled) ee System expansion using ecoinvent unit process
We have adopted economic allocation unless a particular stage allowed an allocation approach higher on the International Organization for Standardization (ISO)
This is not an allocation of burdens of co-products, but an allocation of fertilizer and fuel inputs, which are reported in aggregate, between the two crops. The inputs are
used as technosphere ﬂows into separate unit processes for each crop. The large differences between regions are primarily determined by the relative production of each crop.
More silage is grown in Region 1 compared with corn grain than the other regions, and therefore the allocation of shared inputs is not nearly equal.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S5
2.2.4. Retail and consumer refrigeration
GHG burdens associated with the retail stage are primarily
associated with electricity for compressors and loss of refrigerants
due to leakage. The allocation approach for these burdens, between
milk and other refrigerated goods, is based on space-occupied and
sales velocity metrics. Shelf space occupied, as a fraction of either
refrigerated or total store shelf space, is used to allocate annual
whole-store GHG emissions to the total sales of the item.
2.3. Data Sources
Data were collected from many sources including the USDA’s
National Agricultural Statistical Service (NASS) and Economic
Research Service, peer-reviewed literature related to LCA of milk,
and an extensive nationwide survey of dairy farm operations. The
survey was distributed to over 5000 producers, of which there were
536 voluntary responses (Popp et al., 2012;Thoma et al., 2012b).
SimaPro 7.1 was used as the primarymodeling software and the
ecoinvent database was used to provide information on the
upstream burdens associated with production of materials
including primary fuels and refrigerants (Frischknecht & Rebitzer,
2005). Background, or upstream processes included from the
ecoinvent database, which is primarily of European origin, is
believed to have a relatively small inﬂuence on the calculated
footprint because the unit processes in the ecoinvent database are
typically representative of modern technologically advanced
systems. In addition, a test was performed using a modiﬁed version
of the ecoinvent database in which all of the background electricity,
to the extent feasible has been replaced with the US primaryenergy
production mix. No signiﬁcant difference in results was observed.
Technosphere ﬂows used in the model, including materials and
energy purchased from a supplier, were characterized with
a combination of the inherent variability found during the statis-
tical data aggregation and the pedigree matrix of data quality used
by the ecoinvent database (Weidema, 1998). Thus inventory ﬂows
can be assigned a probability density function (PDF) that describes
the likelihood of a particular inventory ﬂow occurrence. The
SimaPro software platform enables Monte Carlo Analysis for
calculation of propagation of inventory uncertainty to impact
uncertainty by choosing inventory ﬂows from the PDF and aggre-
gating over multiple runs. Data from the surveys and other US-
speciﬁc information were incorporated into the model to the
extent available. We have adopted the IPCC (2006) carbon dioxide
equivalency factors of 25 for methane and 298 for nitrous oxide.
2.3.1. Dairy rations
In the assessment of GHG emissions associated with production
of the principal grains used in animal rations (Adom et al., 2012a;
Adom, Workman, Thoma, & Shonnard, 2012b), two main sources of
agricultural data were used: crop production data in terms of
annual yield, and agricultural chemical use statistics including
annual fertilizer and pesticide totals, both reported at the state level
from the USDA National Agricultural Statistics Service (USDA,
Few data have been collected at aggregate levels for cattle
forage. GHG emissions estimates from cattle forage production
were created from crop production budgets produced by state
agriculture extension specialists who provided estimates for the
inputs needed to produce alfalfa and grass hay, silage, and pasture.
After compiling inputs for speciﬁc crop production, these were
entered as inputs to a new unit process in the LCA software plat-
form. Detailed tables of regional rations by animal class are given as
supplementary materials by Thoma et al. (2012b).
2.3.2. On-farm emissions
On-farm methane measurements are not feasible as a general
method for estimation of enteric methane production. As discussed
by Thoma et al. (2012b), a comparison of models led to selection of
an enteric methane emissions model based on dry-matter intake
(Ellis et al., 2007). The American Society of Agriculture Engineers’
Standard on manure characteristics (ASAE, 2005) was used to
estimate the quantity of manure generated. IPCC (2006) emission
factors for methane were applied to calculate the total manure
management methane emissions. Daily nitrogen excretion, based
on reported crude protein content of rations, ranged from
approximately 0.2 kg day
for open heifers up to 0.43 kg day
multiparous lactating cows. These estimates were combined with
Tier-2 emission factors for speciﬁc manure management technol-
ogies (Table 10.21, IPCC, 2006), including an accounting for direct
deposition on pasture, to estimate the total on-farm N
associated with manure management.
2.3.3. Farm to processor transportation
Raw milk is delivered by unrefrigerated insulated tank trucks
from one or more farms to a processing plant (Ulrich et al., 2012).
Rail transport is used only for processed dairy products that are not
highly perishable and do not have to be delivered on a strict
schedule such as ice cream, yogurt and canned milk. The truck
makes a round trip, picking up milk at each farm along its route,
then delivering the load to the plant. Tank capacities may reach
(9000 gallons) with 22.7 m
(6000 gallons) being the most
common. This study considered only deliveries made by full
trucks delivering an average of 22 m
For this study a value of 2.4 km L
was used (5.7 miles per
gallon), which results in GHG emissions of 1.33 kg CO
(2.13 kg CO
). Because the deliveries included were
restricted to full loads with the same capacity, the emissions-per-
delivered was only a function of the round-trip distance. The
average round-trip length was 829 km.
Several large dairy co-operatives have provided an extensive
database that includes individual truck delivery from farms to ﬂuid
milk processing plants. The combined database has over 300,000
records from 2007 to 2008 and provides average transport
2.3.4. Milk processing, packaging, and distribution
Starting in February 2008, an extensive survey was sent to eight
milk processing companies. Surveys were returned for 50 indi-
vidual processing plants for their production during calendar year
2007 (Nutter et al., 2012). These 50 plants were responsible for
approximately 25% of the entire ﬂuid milk volume processed in
2007. Information requested in each survey included plant energy
consumption, truck ﬂeet fuel consumption, refrigerant purchases
for both the plant and truck ﬂeet, on-site milk packaging produc-
tion, packaged milk type and sizes, and annual production values
for total plant ﬂuid, ﬂuid milk, and packaged milk.
After distribution from the processor to the retail gate, ﬂuid milk
is displayed for consumer purchase. During this phase, there are
three distinct emissions streams: refrigerant leakage, refrigeration
electricity, and overhead electricity. For the purposes of this LCA,
milk sales were aggregated through three channels: supermarkets,
mass merchandisers (‘big box’stores) and convenience stores. One
of the major challenges associated with evaluation of the retail stage
is the allocation of whole-store energy consumption tomilk. Data on
the sales volume, space occupancy, and energy demands of milk
were analyzed to allocate the burden of this supply chain stage for
ﬂuid milk (EPA, 2008b,2009;ICF Consulting, 2005;USEIA, 2003).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S6
According to data from the Interaction Research Institute’s database
(Mateen, Innovation Center for US Dairy, Chicago, IL, USA: personal
communication, 2009), 65% of ﬂuid milk sold in the US is sold
through the supermarket channel, 21% through mass merchandise
channel and 14%is distributed through drug and convenience stores.
Detailed information for the supermarket channel is provided
below; additional information for mass merchandizing and conve-
nience stores is reported by Thoma et al. (2010).
Supermarket channel. These stores sell a broad mix of food and
a limited mix of general merchandise. Reference information for
supermarkets used to allocate retail burdens is provided in Table 4.
They have a large land footprint and most use direct expansion
refrigerant systems (EPA, 2006). Common refrigerants are R-22, R-
404A, and R-507A; for mixtures of these refrigerants, the compo-
sition of the mixture was used to determine the appropriate global
warming potential (http://www.refrigerants.com/refrigerants.
htm). It was assumed that R22 and R404A were used at 54% and
46%, respectively (EPA, 2008b). These systems have a compressor
that is housed separately from the refrigeration units, and the
refrigerant is pumped in through a pipe network. This piping
system is the source of most leaks, due to catastrophic events (e.g.,
a broken pipe). Supermarkets typically have 1814 kg of refrigerant
in a system that leaks 18% annually (EPA, 2009). The USEPA
GreenChill Program reports information regarding the sources and
causes of refrigerant loss in supermarket systems (EPA, 2009).
Burdens were allocated on an occupied-space basis, using
consumer-facing linear shelf length as the space metric. Consumer-
facing shelf space information was obtained from a proprietary
database available through the project sponsor (Table 5). Milk was
allocated a share of refrigerated space and a share of total grocery
space to account for the refrigeration and overhead burdens,
respectively. Overhead electricity demands allocated to milk
include ventilation, lighting, cooling, space heating, water heating,
and plug loads. Table 6 presents the fractional consumption of
electricity and natural gas in grocery retail outlets (USEIA, 2003);
this information coupled with data published by the American
Society of Heating, Refrigerating and Air-Conditioning Engineers
were used to generate estimates of the overhead burden (ASHRAE,
Mass merchandisers. Mass merchandisers sell 21% of the milk in
the US (Mateen, personal communication 2009). This channel
typically sells a large mix of general merchandise as well as grocery
items. It is assumed that the grocery section within a mass
merchandiser is typical of an average supermarket with a smaller
land footprint 3900 m
for a mass merchandise grocery compared
to 4343 m
for a supermarket (Table 7). Burdens associated with the
grocery section of mass merchandising were allocated in a manner
consistent with the supermarket channel.
Convenience stores. Convenience stores sell 14% of the milk in the
US (Mateen, personal communication 2009). These stores sell a mix
of grocery and general merchandise items and have the smallest
land footprint of 434 m
. This study included all stores within the
channel, and did not distinguish between fuel-selling and non-fuel-
selling locations. Convenience stores differ from typical grocery
stores in that the refrigeration systems are fully-enclosed units,
indicating that the compressors for the refrigerants are housed
within the unit itself, eliminating the need for external piping and
virtually eliminating the risk of system leakage. Current regulations
require that refrigerants be captured to be recycled or destroyed at
the end of useful life of the refrigeration system. We have assumed
that any small leakage from this recovery process is below the cut-
off criterion for the study.
2.3.6. Consumer transportation, storage and waste
For the consumer contribution, we allocated a fraction of in-
home refrigeration (1.62% of refrigeration use) to milk storage.
We allocated fuel for consumer travel based on an estimate of the
sales percentage that milk represents in typical grocery purchases
Proportion of refrigerated space dedicated to milk.
Parameter Length (m) Percent
attributed to milk
Milk refrigerated shelf length 21.3 100
Refrigerated shelf length, total 1311 1.62
All store shelf length, total 6934 0.31
Electricity and natural gas allocation at retail.
End use all channels Electricity usage (%) Natural gas usage (%)
Cooking 2 13
Allocated by shelf space 45 13
Plug Loads 17
Space heating 2 74
Water heating 1 13
Allocated by sales (overhead) 55 87
Sources are EPA (2008b) and USEIA (2003).
Average annual electrical energy consumption for retail channel outlets.
Sales channel 25th
Grocery and convenience 347 565 586
Convenience store 467 743 847
Convenience store w/gas station 406 667 850
Grocery store/food market 347 566 586
Units are kW h m
; data source is ASHRAE (2007).
Reference data for supermarket retail outlets.
Parameter Amount Reference
Refrigerant load 1814 kg ICF Consulting (2005)
Annual leak rate 18% ICF Consulting (2005)
21.3 m Mateen Personal Communication,
Innovation Center for US Dairy,
Chicago, IL (2009)
1311 m Mateen (2009)
6934 m Mateen, (2009)
kW h per square meter 556.5 kW h m
Total area of store 4343 m
(% of total)
43% USEIA (2003)
(% of total)
55% USEIA (2003)
Natural gas per
87% USEIA (2003)
Total milk sales, average
746551 US$ Mateen (2009)
Average price, liter
0.99 US$ USDA (2007)
R-22 faction in stores 54% ICF Consulting (2005)
46% ICF Consulting (2005)
Additionalinformationfor other sales channelsis presentedby Thomaet al. (2010).
Personal communication (Innovation Center for US Dairy, Chicago, IL, USA).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S7
(0.307%), and an assumed transportation distance of 10.9 km
round-trip to the grocery store; 175 trips and 229.2 kg purchased
per household (Table 8). An assumption in this analysis is that
convenience stores, supermarkets, and mass merchandise outlets
all have the same transportation distances and associated milk
purchases. It is likely that customers of convenience stores will
typically purchase smaller quantities of milk and customers of
supermarkets on each trip. Thus it is possible that there is an
overestimate of the transportation burden for this sales channel;
however, it mayalso be the case that trips to the convenience store
are not dedicated to purchase of groceries, and that some fraction of
the entire trip should actually be allocated to other consumer
activities. For this reason we have simply taken the average values
as described above.
We modeled waste scenarios in SimaPro with unit processes
from ecoinvent to model consumer disposal of milk packaging
material. Industry data provided for conventional white milk shows
that 89% of milk is delivered in high density polyethylene (HDPE)
containers, 11% in paperboard, and 1% in polyethylene tere-
phthalate (PET) (Klein, personal communication 2009). Franklin
Associates (2008) reports that HDPE is recycled at a 29% rate,
while the paperboard gable top cartons are not currently being
recycled to any measurable degree. This report also indicates that
an estimated 14% of post-consumer waste is incinerated with
energy recovery. We have modeled the incineration of these
materials, however have not accounted for energy recovery, as it
will fall below the one percent cutoff.
2.3.7. Product loss
The Economic Research Service (ERS) of the USDA publishes
estimates of food loss for most commodity foods, including dairy
(USDA, 2010a). These estimates are the best available, and report
12% loss of ﬂuid milk from retail to consumer, and an additional 20%
loss due to cooking loss, spoilage and waste at the consumer. We
have accounted for disposal as a volumetric ﬂow equal to the
volume of waste milk loss to municipal sewage treatment. For the
milk loss at retail, our assumption is that it is further processed into
dry milk solids that are used as a feed supplement for cattle or other
animals. We have assumed that it displaces dried whey as an
animal feed. Because milk is approximately 13% solids, the waste
ﬂow from retail into the return channel is taken as 13% of the out-
of-date milk weight.
3. Results and discussion
3.1. Supply chain analysis
3.1.1. Cradle-to-farm gate
Thoma et al. (2012b) present a detailed analysis of the cradle-to-
farm gate GHG emissions. The GHG emissions were reported as
1.23 kg CO
FPCM. Due to the scaling required by the product
loss in the system, 1.42 kg FPCM is required at the farm gate to
provide one kg consumed. Thoma et al. (2012b) present a summary
of the farm-gate results by production region: Region 1: Northeast;
Region 2: Southeast; Region 3: Upper Midwest; Region 4: South-
west plus High Plains; Region 5: West Coast (Popp et al., 2012).
3.1.2. Farm to processor transportation
Emissions from this phase of the value chain are dominated by
tailpipe emissions from the trucks. Loss of refrigerants is negligible
since the trucks are not refrigerated according to Ulrich et al.
(2012). The average round-trip length was 829 km giving average
emission levels of 0.049 kg CO
milk delivered to the
processor gate (Ulrich et al., 2012). Due to milk loss in the supply
chain, the transportation burden is 0.0696 kg CO
3.1.3. Processing, packaging and distribution
GHG emissions associated with processing, packaging, and
distribution are provided in Table 9. All major emission sources,
from raw milk entering the refrigerated storage silo, through
delivery of packaged ﬂuid milk to the retailer, are accounted. The
gate-to-gate cumulative GHG emission is 0.203 (0.017) kg
of packaged ﬂuid milk. This is reported as the mean with
95% conﬁdence interval and represents the inherent variability in
this supply chain stage. The contribution of the processing supply
stage is 0.288 kg CO
3.1.4. Retail channels
The retail stage of the supply chain contributes 0.099 kg
milk refrigerated, or 0.141 kg CO
assuming that all milk (including out of date returns) at retail was
refrigerated. As shown in Table 10, the burden from electricity,
refrigerants, and natural gas contribute 64.4%, 35.5%, and 0.15%,
3.1.5. Consumer transportation and storage
As described previously, in-home allocation fractions are based
on the assumption that the generic American food pantry is roughly
equivalent to grocery store stock, i.e., there is a nearly steady state
ﬂow of food through retail outlets and the American consumer’s
home. This will slightly overestimate the burden as there are items
that must be refrigerated once opened that are not refrigerated at
the retail outlet. We used a consumer transportation distance of
10.9 km round-trip to the grocery store for each shopping as shown
in Table 8 (FHA, 2009).
Energy consumption for in-home refrigeration and consumer transport.
Energy consumption area Amount
Annual refrigeration energy (USEIA, 2005) 1345 kW h y
Per capita milk consumption (USDA, 2010a) 76.4 kg y
3 persons per household, thus
229.2 kg y
Burden allocated to milk (1.62%) 0.097 kW h kg
Travel for shopping from home
10.9 km trip
; 175 trips per year
Attributed to dairy 0.307% retail shelf space allocation
Summary of greenhouse gas (GHG) emissions from milk processing.
Unit process Gate-to-gate GHG emissions
Purchased electricity 0.054 (0.0090)
Onsite fuel combustion 0.022 (0.0044)
Refrigerant loss 0.001 (0.0014)
Total 0.077 (0.0109)
Raw material 0.034 (0.0034)
Container formation 0.020 (0.0012)
Total 0.054 (0.0044)
Mobile fuel combustion 0.058 (0.0091)
Refrigerant loss 0.014 (0.0037)
Total 0.072 (0.0102)
Overall 0.203 (0.0174)
Numbers in parentheses indicate 95% conﬁdence interval of mean,
but do not account for propagation of input uncertainty.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S8
Fig. 1 presents the summary of the retail and consumer use
phases. The bar in the natural gas column for consumers represents
the gasoline consumption associated with travel to the store for
groceries; natural gas consumption at retail is too small to appear.
The use phase also includes the impact of disposal of wasted milk to
a municipal wastewater treatment facility;the ﬂow was taken to be
the estimated volume of spoiled milk disposed. Fig. 1 accounts for
product loss in the supply chain, whereas Table 10 only accounts for
gate-to-gate retail contributions.
3.1.6. Post-consumer waste management
End-of-life analysis of the packaging was based on the distri-
bution of primary packaging materials used for milk delivery in the
US and is summarized in Fig. 2. Of total HDPE packaging, 29% was
recycled and an avoided product credit was accounted. No recycling
credit for PET or paperboard was claimed; all non-recycled waste
packaging was considered to be disposed in a landﬁll or incinerated
(86% of all non-recycled waste was assumed to be disposed in
a landﬁll and the remaining 14% incinerated). We used standard
ecoinvent unit processes for waste management, including incin-
eration of post-consumer waste. Incineration unit processes
contribute less than 0.4% of GWP when summed across the entire
supply chain. Spoiled or wasted milk at the consumption phase was
assumed to be disposed into a municipal wastewater treatment
facility modeled with an ecoinvent unit process.
3.1.7. Supply chain loss/waste
In this project, we have explicitly accounted for estimated
product loss through the supply chain. Based on USDA food loss
reports, we accounted for a 12% loss (return) at retail and an
additional 20% loss from cooking, spoilage and waste at the
consumer stage. Fig. 3 shows the effect of different waste rates on
the footprint of consumed milk. The best-case scenario (3% retail
loss and 5% consumer loss) results in a 23% decrease in GHG
emissions compared with the base case. This appears to present
a signiﬁcant reduction opportunity; however, interpreting the
consequences of changes in consumer behavior is difﬁcult. For
example, if consumer waste is reduced because children stop
leaving half-full cereal bowls, there will be essentially no change in
the overall system emissions, with the exception of a small
reduction in municipal wastewater treatment emissions, because
there will be no change in the quantity of milk moving through the
Fig. 1. Greenhouse gas emissions from retail and consumer use phases. The turquoise
bar under natural gas is transport by consumers from retail to their home.
Fig. 2. Packaging disposal greenhouse gas emissions for national mix of primary
packaging. Reforming refers to electricity necessary for the recycling process of high
density polyethylene containers.
Fig. 3. Product loss is modeled at the retail and consumption stages. This ﬁgure
presents a sensitivity analysis of the carbon footprint as it is inﬂuenced by the degree
GHG burdens at retail stage of milk value chain.
milk Total MT CO
Refrigerants 0.0423 472,571
Electricity 0.0529 591,252
Natural Gas 0.0001 1515
Total 0.0953 1,065,338
Refrigerants 0.0273 96,360
Electricity 0.0342 120,560
Natural Gas 0.0001 309
Total 0.0616 217,229
Electricity 0.1311 320,538
Natural Gas 0.0002 587
Total 0.1313 321,126
Refrigerants 568,931 (35.48%)
Electricity 1,032,351 (64.37%)
Natural Gas 2411 (0.15%)
Total 1,603,693 (100%)
Values are per kg milk sold (not per kg milk consumed eretail losses are
accounted, but not consumer losses).
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S9
system. However, if parents were to put less milk on the cereal,
their milk purchase would last longer, and their consumption of
milk would remain constant, but they would purchase less to meet
that consumption demand. Thus, while the effect of consumer
waste makes a large difference in the footprint of the consumed
milk, the effects of change in this behavior on the cumulative GHG
emissions are difﬁcult to predict.
3.2. National scale impact of ﬂuid milk consumption
Fig. 4 shows the ﬂow of GHG emissions associated with the
production of ﬂuid milk in the United States. The contribution of
each stage in the supply chain is denoted both numerically and by
the width of each arrow. Each region’s contribution was deter-
mined from the USDA reported milk production (corrected to kg
FPCM) and regional GHG emission intensity (kg CO
create a national scale contribution due to production of ﬂuid milk.
The arrows leaving the top of the chart represent allocation of
cumulative up-stream burden to co-products that is removed from
the ﬂuid milk supply chain at that point. The calculations also
account for the different quantities of milk (whole, skim, 1%, and
2%) consumed in the United States and for product loss across the
supply chain. In 2007, approximately 17.4 Tg of ﬂuid milk of all
types were consumed (USDA, 2007). This resulted in an estimated
35.4 Tg CO
e in GHG emissions. The breakdown of these emissions
by supply chain stage and source is presented in Fig. 5. Enteric
methane contributes approximately 8.8 Tg CO
e and manure
management 8 Tg CO
e (6.2% of all ruminant enteric methane re-
ported as 140.8 Tg in 2005, and 13.5% of GHG emissions from
manure management, reported as the 62.1 Tg in 2005) (EPA,
2008a). These values are for ﬂuid milk only, and, of course,
exclude the allocation to co-products beef and excess cream.
The cumulative GHG emission is 2.05 kg CO
e per kg milk
consumed (17.6 pounds CO
e per gallon of milk consumed). Fig. 6
presents the breakdown of GHG emissions across the supply
chain. There is, of course, natural variability in production and
Fig. 4. Greenhouse gas ﬂow through the milk supply chain. All values (including beef and cream allocation) are Tg CO
e for all ﬂuid milk consumed in 2007. This diagram includes
an accounting for both the regional production of milk destined for consumption as a beverage and the national mix of whole, skim, 1%, and 2% milk for 2007.
Fig. 5. Greenhouse gas emissions by supply chain stage. Values are reported in Tg, and values smaller than 0.04 Tg are not reported to maintain readability of the chart.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S10
supply chain activities as well as uncertainty in reported values
for many parameters necessary for computation of the footprint.
To account for this, a Monte Carlo uncertainty analysis was per-
formed, as described in Section 2.3, with the result that the 90%
conﬁdence band varies from 1.77 to 2.4 kg CO
consumed. Of total burden associated with consumption of ﬂuid
milk, 72% is accrued by the dairy farm gate. This highlights the
signiﬁcant opportunity for the industry in on-farm improvements,
speciﬁcally in terms of manure management and controlling
enteric methane emissions. These emissions sources as well as
the incoming burden of the feed are signiﬁcantly inﬂuenced by
the on-farm feed conversion efﬁciency. Improving conversion
efﬁciency reduces GHG emissions from all three sources. Manure
management offers additional potential reductions through
adoption of management technologies that reduce methane
emissions, such as digesters or wider adoption of daily spread and
other low emission approaches. Even though the majority of
emissions occur during production, it should be noted that some
“extra”emissions are induced by waste in the downstream supply
chain due to product loss or waste.
3.3. Dairy sector contribution to the US greenhouse gas inventory
In an effort to place the entire dairy sector into context of overall
emissions of GHG in the United States, we have combined estimates
from several sources (EPA, 2011;Thoma et al., 2012b;USCB, 2011)
to create an estimate of the relative contribution of the dairy sector
to the overall US greenhouse gas inventory in 2008. To maintain
comparability from year-to-year reports of GHG emissions, the EPA
reports the GHG equivalents based on an earlier version of the IPCC
global warming potentials (IPCC, 1995). This analysis was based on
the global warming potentials published (for the IPCC) by Forster
et al. (2007).Table 11 presents a comparison of the global warm-
ing potentials from the two IPCC reports.
Table 12 presents the GHG inventory for the United States for
the years 2007 through 2009 based upon the two IPCC reports.
There is an approximately 1.6% increase in the estimate of GHG
emissions based on the revised global warming potentials. Note
that the values reported in Table 12 do not include sequestration-
associated forestry because the current study does not include
sequestration associated with the crop production for the dairy
Fig. 6. Supply chain contribution to carbon footprint of ‘generic’milk. Generic milk refers to regional-production-weighted (raw milk input) and purchase-volume-weighted (milk
fat content) average milk consumed in the US during 2007. Note that fuels, fertilizer, and milling are included in the feed stage. In addition, there is a 12% product loss accounted in
retail, and an additional 20% wasted product in the consumption phase.
Comparison of second and fourth IPCC global warming potential equivalency factors.
Gas SAR (IPCC, 1995) AR4 (Forster
et al., 2007)
O 310 298
HFC-125 2800 3500
HFC-134a 1300 1430
HFC-143a 3800 4470
HFC-152a 140 124
SF6 23,900 22,800
Comparison of US greenhouse gas emission inventory for the second (SAR; IPCC,
1995) and fourth (AR4; Forster et al., 2007) Intergovernmental Panel on Climate
Change (IPCC) reports; sequestration of carbon dioxide is not included.
Gas SAR (Tg CO
e) AR4 (Tg CO
2007 2008 2007 2008
6120 5921 6120 5921
665 677 791 806
O 325 311 313 299
HFCs 130 129 127 131
17 16 16 15
Total 7263 7061 7366 7172
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14 S11
ration, and therefore is a more directly comparable baseline for
estimation of the dairy sector contribution.
3.3.1. Estimation of overall dairy sector GHG emissions
The USDA reported 82.67 Tg of milk produced in 2008 with an
average milk fat content of 3.68%. As reported by Thoma et al.
(2012b) and Ulrich et al. (2012) the GHG emissions up to the
processor receiving silos is approximately 1.28 kg of CO
FPCM. This assumes roughly equivalent transportation distances for
both ﬂuid milk and non-ﬂuid milk uses, which is probably a slight
overestimate for milk destined for cheese production facilities,
resulting in 105.8 Tg CO
e emissions. Based on information from
the US Census Bureau, shown in Table 13, it is possible to estimate
the GHG emissions from the manufacturing stages for non-ﬂuid
milk dairy products. Combining these estimates with the retail
and consumption information reported elsewhere in this paper,
and using the assumption that retail and consumption for non-ﬂuid
milk dairy products is in the same proportion to manufacturing as
for ﬂuid milk (approximately 70% as shown in Fig. 4), the total dairy
sector GHG emissions is estimated to be 133.6 Tg, or 1.9% of the US
total for 2008 from Table 12 (7172 Tg, AR4). The breakdown by
supply chain stage is presented as Fig. 7.
This work establishes a sound and defensible baseline for GHG
emissions associated with production and consumption of ﬂuid
milk in the United States during 2007e2008. In the future, as the
industry moves to meet its 2020 goals of 25% reduction in GHG
emissions, progress can be assessed against this baseline
emission level. An important caveat is that this LCA exclusively
focuses on carbon emissions, and does not account for other
environmental impacts, such as water or air quality. Decisions
made solely on the basis of GHG emissions do not account for
potential trade-offs with other environmental impacts, and
caution should be exercised in making decisions based on a single
metric. Future comparison against the baseline developed this
study will be facilitated by the availability of the life cycle inventory
model in public US databases. The data are currently being
prepared for submission in the near future.
The results of this study show that, on average, 2.05 kg CO
emitted per kg milk consumed, but a more accurate interpretation
is that based upon knowledge uncertainty and characteristic vari-
ability, we can be 90% conﬁdent that the GHG footprint of milk lies
between 1.77 and 2.4 kg CO
milk consumed. These error
bounds are a combination of both knowledge uncertainty and
Other LCA studies for dairy production have been performed
over the past decade and report similar GHG emissions at the farm
gate (Basset-Mens et al., 2009;Capper et al., 2009;Cederberg &
Flysjö, 2004;Cederberg & Mattsson, 2000;Cederberg et al., 2009;
Eide, 2002;Gerber et al., 2010;Guinard et al., 2009;Haas et al.,
2001;Thomassen et al., 2009). A recent UN Food and Agricultural
Organization report states that the US average farm-gate GHG
emission is 1.0 kg CO
FPCM (Gerber et al., 2010), which is
approximately 20% lower than that found in this study: 1.23 kg
FPCM (90% CI: 1.1e1.45 kg CO
The LCA results give insight into the innovationopportunities of
the dairy supply chain; however, these opportunities should be
carefully evaluated and explored. Innovations and changes in supply
chain activities may yield unintended results, or may simply shift
burdens in the system without causing any real reduction in envi-
ronmental impact. A major conclusion of this study is that there is
opportunity to reduce impacts throughout the entire supply chain,
across feed and milk production, processing, distribution and
consumption. The on-farm and processing GHG emissions showed
signiﬁcant variability (Nutter et al., 2012;Thoma et al., 2012b).
Identiﬁcation and recognition of this variability suggests that
opportunities exist for improvement of those lower performers.
There is signiﬁcant value in understanding the sources and causes
of variability. Uncertainty in the system indicates opportunities for
improving modeling techniques and further exploration of the
methodology used for those calculations.
This work has pointed to manure management, feedproduction,
and enteric methane as three areas for innovation research (Thoma
et al., 2012b). Nutrient management strategies on the dairy farm
that link inorganic fertilizer use with application of manure for crop
production should be integral to any GHG reduction approach.
Anaerobic lagoons on larger farms and deep bedding on smaller
farms are manure management systems for which the GHG emis-
sions are signiﬁcantly greater than other systems, such as dry lot
and solid storage. On the surface, this seems to indicate that a shift
in practices could result in emission reductions; however, both the
economic cost and potential environmental burden shifting that
could result from changing to a different system must be consid-
ered. Methane digesters have great potential as a way to capture
and potentially utilize methane that is otherwise lost to the
atmosphere, and should be considered a high priority for these
larger systems; however, the possibility of additional land
requirements for managing nutrients retained in the digester solids
must be evaluated. Because this study only considers GHG emis-
sions, there is need for further study to understand the full range of
environmental impacts before making decisions.
Energy consumption by dairy manufacturing industry sector.
Sector description Electricity
31151N Fluid milk and butter 3,905,802 3.23 $236,516 0.198
311513 Cheese 3,765,102 3.11 $284,802 2.06
311514 Dry, condensed, and
1,524,350 1.26 $187,245 1.36
31152 Ice cream and frozen
1,317,111 1.09 $27,397 1.71
NAICS, North American Industry Classiﬁcation System.
Based on 2008 national average cost of natural gas of US$9.65 MCF
Fig. 7. Estimated contribution to the US greenhouse gas emissions inventory in 2008.
Sequestration of CO
has not been accounted for in this analysis.
G. Thoma et al. / International Dairy Journal 31 (2013) S3eS14S12
The analysis of ﬂuid milk processing plant GHG emissions
suggests some opportunities to reduce individual emissions.
Therefore, a focus on truck ﬂeet fuel usage and plant electricity
consumption is prudent since these two components are the
greatest GHG contributors.
Implementation of standard energyefﬁciency practices should be
considered for the refrigeration system, compressed air system,
motors,and lighting. Similarly, plant fuel reductions could be realized
through improved steam system efﬁciency and operating practices.
Emission savings for packaging could come from improved bottle
designs resulting in reduced material use and upgrades to modern,
energy-efﬁcient formation equipment. As an example, changing the
bottle cap manufacturing process from injection-molding to ther-
moforming may lower environmental burdens, as has been recom-
mended for theyogurt cup manufacturing process (Keoleian, Phipps,
Dritz, & Brachfeld, 2004). Finally, careful study of plant speciﬁcopti-
mization of the transport distances (i.e., truck miles) and the future
selection of transport refrigeration systems using low GWP refriger-
ants may reduce emissions for the ﬂuid milk industry.
This work was funded by the Innovation Center for US Dairy. The
quality and thoroughness of the ISO review panel was invaluable to
the project team. The review panel consisted of Olivier Jolliet from
the University of Michigan, Robert Anex from the University of
Wisconsin, and Pascal Lesage from Interuniversity Research Center
for the Life cycle of Products, Processes and Services (CIRAIG), École
Polytechnique de Montréal.
The Innovation Center played an instrumental role in collection
of the on-farm data, as well as the information gathered from the
processing facilities and dairy cooperatives. Without the strong
industry commitment to collect high quality data across the supply
chain, this study would not have been possible. The Innovation
Center also assisted with testing of the survey instrument for the
on-farm data collection and provided valuable editing to the survey
questions, enabling collection of relevant data. Finally, the Inno-
vation Center has worked with the publisher in the establishment
of this special issue.
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