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Food-miles and the Relative Climate Impacts of Food
Choices in the US
Weber, Christopher L.*, Matthews, H. Scott
Dept. of Civil and Environmental Engineering and Dept. of Engineering and Public Policy,
Carnegie Mellon University, Pittsburgh, PA 15213
clweber@andrew.cmu.edu
RECEIVED DATE:
ABSTRACT: Despite significant recent public concern and media attention to the environmental
impacts of food, few studies in the US have systematically compared the life-cycle greenhouse gas
(GHG) emissions associated with food production against long-distance distribution, aka “food-miles.”
We find that although food is transported long distances in general (1640 km delivery and 6760 km life-
cycle supply chain on average) the GHG emissions associated with food are dominated by the
production phase, contributing 83% of the average US household’s 8.1 tonne CO2e/yr footprint for food
consumption. Transportation as a whole represents only 11% of life-cycle GHG emissions, and final
delivery from producer to retail contributes only 4%. Different food groups exhibit a large range in
GHG-intensity; on average, red meat is around 150% more GHG-intensive than chicken or fish. Thus,
we suggest that dietary shift can be a more effective means of lowering an average household’s food-
related climate footprint than “buying local.” Shifting less than one day’s worth of calories from red
meat and dairy products to chicken, fish, eggs, or a vegetable-based diet achieves more GHG reduction
than buying all locally-sourced food.
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KEYWORDS: food, Food-miles, freight transport, Climate Change, input-output analysis, life cycle
analysis
Introduction
With growing public concern over climate change, information and opportunities for consumers to
lower their “carbon footprint,” a measure of the total consumer responsibility for greenhouse gas
emissions, have become increasingly available. The growing field of sustainable consumption [1-3] has
offered information to consumers on the climate and environmental impacts of their consumptive
choices. In general, much of this research has concluded that food, home energy, and transportation
together form a large share of most consumers’ personal impacts[2].
Of these three, food represents a unique opportunity for consumers to lower their personal impacts due
to its high impact, high degree of personal choice, and a lack of long-term “lock-in” effects which limit
consumers’ day-to-day choices[1].
Within the field of consumer food choice, several recent trends associated with environmental
sustainability have occurred. The continually-increasing penetration of both organic and locally grown
food in the US and EU shows that consumers are taking more notice in both how their food is produced
and where it comes from. The issue of “food-miles,” roughly a measure of how far food travels between
its production and the final consumer, has been a consistent fixture in the debate on food sustainability
since an initial report from the UK coined the term in 1995[4-8]. The focus on increased food-miles due
to increased international trade in food has led many environmental advocates, retailers, and others to
urge a “localization” of the global food supply network[9], though many have questioned the legitimacy
of this because of different production practices in different regions or the increased storage needed to
“eat locally” through all seasons[6-8]. Other advocates, pointing to research on the environmental
effects of livestock production[10], have urged consumers to shift dietary habits toward vegetable-based
diets[11].
Food has long held a prominent place in the Life Cycle assessment (LCA) literature due to its relative
importance for many environmental problems[11-13]. Because of the raw number of foods consumers
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eat, most analyses have been limited to detailed case studies of either a single food item[8, 9] or a
limited set of items[7, 13], though usually to a higher level of detail than is possible for large groups of
products. A few studies exist which look at overall diet [11, 12] but even these have usually been
limited by availability of life cycle inventory data for all products. Further, many of the analyses have
used life-cycle energy use as the relevant measure of sustainability, and thus they have not included the
substantial non-CO2 greenhouse gas (GHG) emissions associated with agriculture[8-10]. Finally, despite
the attention food-miles and transport have gotten in the literature, very few studies have analyzed
transportation upstream of the farm (eg, transport of farm equipment and supplies to the farm), which
may be important for life-cycle GHG emissions.
This analysis adds to the existing literature by considering the total life-cycle GHG emissions associated
with the production, transportation, and distribution of food consumed by American households. We
include all upstream impacts using input-output life-cycle assessment (IO-LCA), analyze all food and
non-alcoholic beverages, and include all relevant emissions of greenhouse gases in the supply chains of
food products. Several uncertainties, discussed below, complicate attempts to make definitive claims of
superiority, and results from such a holistic assessment will necessarily be averaged and context-
specific. Nonetheless, by using such a holistic assessment of climate impacts from both transportation
and production of food, we hope to inform the ongoing debate on the relative climate impacts of “food-
miles” and dietary choices. The next section describes the methods utilized in the analysis, followed by
a summary of the results obtained (full results are available in the supporting information) and a
discussion of the results.
Methods and Data
The method utilized is input-output life-cycle assessment (IO-LCA)[14, 15]. IO-LCA has
several advantages for such an analysis, such as being able to handle large bundles of goods as well as
reducing cut-off error, one of the major drawbacks of process-based LCA[16]. IO-LCA has its
drawbacks as well--aggregation in economic sectors is a significant problem--but it is ideal for analysis
of large groups of products from a scoping perspective.
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A detailed model development is presented in the supporting information and is summarized
here. As originally formalized by Leontief in his groundbreaking work in the 1930’s[15], the total
output of an economy, x, can be expressed as the sum of intermediate consumption, Ax, and final
consumption, y:
x=Ax +y
(1)
where A is the economy’s direct requirements matrix. When solved for total output, x, this equation
yields:
x=(I−A)−1y
(2)
As shown by previously[17], the direct requirements matrix can be derived in a number of different
ways. In general, the industry-by-commodity matrix, denoted here AIxC, is seen as the most useful form
of the direct requirements matrix, A, or the Leontief inverse, (I-A)-1= LIxC, since it allows the input of a
final demand of commodities and a supply chain of industrial output, which can easily be converted to
emissions using a coupled emissions vector, F = x-1f, where f is the total sectoral emissions of a
pollutant:
f=FLIxC yC
(3)
If yc, the commodity final demand, is valued in purchaser, e.g. retail, prices, the retail/wholesale
markups and final transportation costs can be distributed using a commodity-by-commodity purchaser-
producer price transformation matrix, T [3]:
f=FLIxCTyc
(4)
This discussion has so far assumed that the relevant emissions/impact data for the calculation is in terms
of emissions per industrial output, as is standard in IO-LCA[18]. However, to model the transportation
of goods, it is clear that a commodity-by-commodity model (ACxC, LCxC) would be more appropriate,
with impacts measured in terms of ton-km moved per commodity purchased rather than per industrial
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output. We denote this matrix of modal ton-km moved per commodity output as Ftkm = total ton-km, by
mode, moved by each commodity, divided by total commodity output. Data on domestic ton-km moved
by commodities was taken from the 1997 US Commodity Flow Survey (CFS)[19], which was mapped
to the input-output commodity groups from the 1997 benchmark input-output model of the US[20], the
most recent such model available for the US. Ton-km moved by international water and air transport,
which are not included in the US CFS, were included in the calculation using US import statistics,
which give data on mass of commodity, US port of entry, and exporting country[21]. The model
assumes that all users of a commodity (both final users like households and intermediate users like
industries) require the same amount of ton-km per dollar purchase of the commodity. On average, the
total ton-km, by mode, required to deliver a final demand yc can be derived as:
ftkm =FtkmLCxCTyc
(5)
Several further steps are necessary to complete and balance the economic portion of the model. See the
Supporting Information for details.
Assuming a standard energy intensity of transport per mode, the ton-km results can be converted to
energy terms, and carbon intensities of fuels from the US EPA[22] can further be used to convert to
units of tonne CO2e/$ commodity output. Energy intensities per ton-km by transport mode were taken
predominantly from the US Transportation Energy Data Book[23], though data was supplemented from
the GREET model [24] and literature [25, 26] for air freight and international water freight. The
assumed energy and carbon intensities of each type of transport are given in Table 1. Note that the
carbon intensity of gas pipelines includes US government estimates of methane leakage through
transport, explaining its high relative GHG-intensity.
MJ/ton-km
tonne CO2e/ton-km
x106Source
Inland Water 0.3 21 [23]
Rail 0.3 18 [23]
Truck 2.7 180 [23]
Air* 10.0 680* [25]
Oil Pipeline 0.2 16 [23, 24]
Gas Pipeline 1.7 180 [23, 24]
5
Int. Air* 10.0 680* [25]
Int, Water Container 0.2 14 [26]
Int, Water Bulk 0.2 11 [26]
Int, Water Tanker 0.1 7 [26]
Table 1: Energy and Greenhouse Gas Emissions per ton-km for different modes of transport. *CO2
emissions were used as an indicator for the radiative forcing effects of aviation, which are actually
higher than just CO2 emissions[27]
In order to compare the GHG emissions associated with freight transport with those associated with
production of food, the commodity-based model must be extended to an industry-based model typical of
IO-LCA. Thus, the commodity-based final demand from above, yc, must be converted to industry output
using the normalized make matrix, W, and multiplied by the industry-based production-related GHG
vector, F = CO2e/$M[28]:
f=FWLCxCTyc
(7)
where emissions from sectors that provide freight transportation have been set to zero to avoid double-
counting with the ton-km based GHG emissions derived in equation(6). However, this also removes all
passenger transportation purchased in these sectors, which were added back in extraneously (see
Supporting Information). The GHG emissions vector for production-related emissions was taken from
the EIO-LCA model, and its public data sources have been described previously [18].
Data on food consumption by households was taken from two main sources: the benchmark US input-
output accounts for total economy-wide household expenditure on food[20] and food availability
statistics from the US Department of Agriculture for household caloric consumption of food[29]. The
commodity groupings were not perfectly interchangeable between the two data sets since the
expenditure data are collected on the basis of retail food items (including restaurants and
processed/frozen food) while the availability data are collected on the basis of food inputs to retail items
(i.e., total meat, total grains, etc.). Thus for some comparisons below, it was assumed that restaurants
and processed foods contained the same caloric ratios of primary food groups as the primary food
groups themselves(see supporting information). Economy-wide and per-capita data were normalized to
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the common unit of household using data from the US Census on total population and number of
households in the US in 1997, approximately 101 million households and 267 million residents[30].
Results
Total freight ton-km from production to retail to meet food demand in the US in 1997 were
approximately 1.2x1012 ton-km, or when normalized to the 101 million households in the US in 1997,
around 12,000 ton-km/household/yr(all tons are metric tons, mt or tonne). It should be noted that this
figure does not include consumer transport to and from retail stores, which is both outside the scope of
this study and complicated by multipurpose trips[8, 31, 32]. Figure 1(a) shows a breakdown of this total
by commodity groups modeled after the USDA food groups[29]. A 50-commodity breakdown is
available in the supporting information but is aggregated here for illustrative purposes. Of the 12,000
ton-km/yr per household, 3,000 ton-km were due to the “direct” tier of the food supply chain, i.e.,
delivery from the farm or production facility to the retail store. In general, this is the distance that
advocates of the food-miles concept have identified as relevant for decision making. Thus, the total
supply chain of food contains around four times the “food-miles” of just final delivery. To put these
figures into perspective, when combined with the fact that the average household consumes around 5 kg
of food per day[29], average final delivery of food is 1640 km(1020 mi), and the total supply chain
requires movement of 6760 km(4200 mi). Food groups vary in these average distances from a low of
beverages (330 km delivery, 1,200 km total) to a high of red meat (1,800 km delivery, 20,400 km total).
By food group, the largest contributor to freight requirements is cereals/carbohydrates (14% of total),
closely followed by red meat (13%). Fruits/vegetables represent another 10% of the total, with
nonalcoholic beverages, fats/sweets/condiments, dairy products, non-red meat proteins (including
chicken, fish, eggs, and nuts), and other miscellaneous processed food products (mostly frozen foods)
all representing around 6-8% each. Final delivery (direct ton-km) as a proportion of total transportation
requirements varied from a low of 9% for red meat to a high of around 50% for fruits/vegetables,
reflecting the more extensive supply chains of meat production (i.e., moving feed to animals) compared
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to human consumption of basic foods such as fruits/vegetables and grains. By transport mode, the
majority of transportation in the total food supply chain is done by four modes, international water
(29%), truck (28%), rail (29%), and inland water (10%). Oil and gas pipelines each represent around 3%
of the total, and air and international air transport combine for less than 1% of total ton-km. This differs
from the final delivery portion of the supply chain, which is dominated by trucking (62%), with some
international water (19%) and rail transport (16%).
When measuring in terms of GHG emissions rather than ton-km, the situation changes substantially due
to the significant differences in energy intensity between transport modes. GHG emissions associated
with transport, again converted to a per-household basis, totaled 0.91 tonne CO2e/yr, with 0.36 tonne
CO2e/yr associated with final delivery, ie “food-miles”. As seen in Figure 1(b), trucking is now
responsible for the vast majority (71%) of transport-related GHG emissions due to its large share of ton-
km and relatively high GHG intensity. The remainder of emissions are associated with gas pipelines
(7%), rail (6%), air transport of passengers (5%), international water (4%), inland water (3%), and
international air freight (2%). The prominence of gas pipelines is mostly due to gas-fired power plants
and nitrogenous fertilizer production, while air passenger transportation (moving people within the
supply chains of making goods) occurs in small quantities throughout all supply chains but especially in
retailing and restaurants. Fruits/vegetables now represent as large of a household share as carbohydrates,
23% of total CO2e, due to their higher percentage of trucking as a mode. For a similar reason, since
trucking does the vast majority of final delivery, the importance of final delivery goes up from an
average of 24% of total ton-km to 39% of total GHG emissions from transport. This result lends some
credence to the focus on food-miles, although it would also say that upstream transportation
requirements are still more important than final delivery of food.
Regardless, the focus on food-miles and transport must be analyzed in terms of the overall climate
impact of food. Results in Figure 1(c) show the breakdown of total life-cycle GHG emissions associated
with household food, in terms of final delivery, supply-chain (non-direct) freight, production, and
wholesaling/retailing. Total GHG emissions are 8.1 tonne CO2e/household-yr, meaning delivery
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accounts for only 4% of total GHG emissions, and transportation as a whole for 11%. Wholesaling and
retailing of food account for another 5%, with production of food accounting for the vast majority
(83%) of total emissions.
Within food production, which totaled 6.8 tonne CO2e/household-yr, 3.0 tonne CO2e(44%) were due to
CO2 emissions, with 1.6 tonne(23%) due to methane, 2.1 tonne(32%) due to nitrous oxide, and 0.1
tonne(1%) due to HFCs and other industrial gases. Thus, a majority of food’s climate impact is due to
non-CO2 greenhouse gases. Nitrous oxide (N2O) emissions, mainly due to nitrogen fertilizer application,
other soil management techniques, and manure management, are prevalent in all food groups but
especially in animal-based groups due to the inefficient transformation of plant energy into animal-
based energy. Methane (CH4) emissions are mainly due to enteric fermentation in ruminant animals
(cattle, sheep, goats) and manure management, and are thus concentrated in the red meat and dairy
categories.
Different life-cycle stages have different importance between the different food groups. Delivery “food-
miles” account for a low of 1% of red meat’s GHG emissions to a high of 11% for fruits/vegetables, due
to the higher overall emissions intensity of red meat and the lower intensity of fruits/vegetables. Total
supply chain freight transportation similarly ranged from 6% of red meat and dairy’s impacts to 18% of
impacts of both fruits/vegetables and non-alcoholic beverages.
The results have so far focused on the total impacts of the average household in the US, but comparing
between the different types of food is more relevant for consumers wishing to lower the climate impact
of their food consumption. However, comparing between food groups is a non-trivial matter. Different
food groups have different prices, provide people with different nutrients, and of course are more or less
pleasant to eat depending on consumers’ tastes. Three possible normalizations for the impacts of
different food types are used here for comparison with the total impact numbers: expenditures on food,
which is related to consumer demand for food, mass of food, and energy content, which are a rough
measure of food’s sustenance. None are a perfect measure; expenditure is only roughly related to the
amount of energy/sustenance that food provides, and calories measure only one dimension of sustenance
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—energy—without accounting for vitamin, mineral, and other nutritional content. Nevertheless, they
provide three different ways of comparing food types and their life-cycle GHG emissions.
Figure 1(d) shows the total GHG emissions of food groups normalized by expenditure ($1997), and
Figure 2 shows a comparison of total impacts with impacts normalized by expenditure, calories, and
mass (all shown comparative to the absolute figure for red meat). Both figures show a clear trend for red
meat; no matter how it is measured, on average red meat is more GHG-intensive than all other forms of
food. Dairy products are an interesting second, as normalization by expenditure produces a similar
GHG-intensity to red meat (2.2 kgCO2/$ for dairy, 2.4 kgCO2/$ for red meat) but normalization by
calories (since dairy products are in general caloric compared to their price) produces a number around
half as intensive as red meat (5.3 gCO2/kCal compared to 10.8 gCO2/kCal). Normalization by mass
makes dairy look even better, due to the high water content (and thus mass) in the form most consumed,
milk. Interestingly, on a per-expenditure basis, the impacts of all the other food groups (including the
averaged restaurants group, which is low due to higher prices than eating at home) are remarkably
similar in impact, though for different reasons. In both measures, fruits and vegetables compare
similarly to non-red meat protein sources (chicken/fish/eggs/nuts) because although they have lower
production impacts, they have higher impacts due to delivery and transportation. Carbohydrates and
oils/sweets, in contrast, appear similar to other groups normalized by expenditure but appear much
better normalized by calories due to naturally high energy contents per mass.
Given these differences in GHG intensities, the relative importance of “localizing” food supply vs.
choosing different combinations of foods can be examined. To explore this issue, we assume that the
absolute maximum localization, “total localization”, of the average diet would be an elimination of all
delivery miles for all foods, approximately 0.36 tonneCO2/yr from Figure 1(c). While this assumption is
unrealistic for many reasons, it does show the upper-bound potential GHG reduction of localization. We
compare this potential reduction to equivalent reductions that could be made by shifts in food choice.
Table 2 shows the breakeven percentages of expenditure or calories, in shifting from red
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meat/dairy/both, to other foods in the columns which would reduce household GHG emissions as much
as a total localization of all consumed food.
It is clear that even with the unrealistic assumption of zero food-miles, only relatively small shifts in the
average household diet could achieve similar GHG reductions to localization. For instance, only 21-
24% reduction in red meat consumption, shifted to chicken, fish, or an average vegetarian diet lacking
dairy, would achieve the same reduction as total localization. Large reductions are more difficult in
shifting away from only dairy products (at least on a calorie basis) but making some shifts in both red
meat and dairy, on the order of 13-15% of expenditure or 11-19% of calories, would achieve the same
GHG reduction as total localization.
$Expenditure Chicken Grains Fruit/Veg Non-dairy Veg Diet
Red Meat 24% 21% 21% 21%
Dairy 42% 37% 37% 36%
Meat+Dairy 15% 14% 14% 13%
kCal Chicken Grains Fruit/Veg Non-dairy Veg Diet
Red Meat 22% 17% 23% 17%
Dairy 93% 33% 107% 38%
Meat+Dairy 18% 11% 19% 12%
Table 2: Shifts in expenditure (top) or calories (bottom) from row category to column category which
result in a GHG reduction of 0.36 tonneCO2e/household-yr, the equivalent of a totally “localized” diet.
“Non-dairy Veg Diet” represents the average American diet less all meat and dairy.
Discussion
Uncertainties in Results
This analysis contains several difficult to quantify uncertainties. There are well-known uncertainties
with input-output analysis in general and these have been documented previously[33, 34]. In addition to
these standard uncertainties, the most important of which are aggregation of unlike goods together and a
time lag of data, there are several specific uncertainties to do with the data and methods used here. With
respect to the calculation of freight transport, it is clear that the average household analyzed here is not
representative of the actual placement of any single home in the United States—the average distances to
market are much smaller for some households and much larger for others. Similarly, there are also
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deviations from the average energy intensities per ton-km used here; for example, refrigerated trucking
and ocean shipping of fresh foods are more energy-intensive than the average intensity of trucking or
ocean shipping. However, neither of these uncertainties are likely to change the overall results of the
paper substantially; even a household twice as far from their source of food would have only 8% of
food-related GHG emissions associated with delivery and 15% with transport as a whole.
One potential change since 1997 which could affect the average results is the increase in imports to the
US[34], which would increase the average distance to market for some foods and increase the supply
chain length for all commodities. To analyze the potential impact of this change, a simplified model
based on previous work[34] was built assuming 2004 import data and transport distances instead of
1997 data. The resulting difference on a per-household basis was substantial in terms of ton-km,
increasing total ton-km/household-yr from around 12,000 to around 15,000, with a corresponding
increase in direct food-miles from around 3000 ton-km/household-yr to 3700 ton-km/household-yr.
Thus, globalization from 1997 to 2004 increased the average distance moved by food by around 25%,
from 1640 km(1020 mi) directly and 6760 km(4200 mi) in total to 2050 km(1250 mi) directly and 8240
km(5120 mi) in total. While this is a remarkable shift in terms of distance, because ocean shipping,
which is greater than 99% of total international ocean and air shipping, is far less energy intensive than
overland trucking, the total increase in the GHG emissions associated with transport is only 5%, from
0.91 tonne CO2/household-yr (0.35 direct) to 0.96 tonne CO2/household-yr (0.36 direct). Thus, even
with the large shift in distance traveled due to globalization, the climate impacts of freight supply chains
remain dominated by overland truck transport and significantly smaller than the production impacts of
food.
Of course, many other uncertainties are important in the calculation of the production impacts of food.
The first major uncertainty is ignoring land use impacts, which is estimated to contribute up to 35% of
total GHG impact of livestock rearing[10]. While deforestation is clearly linked to global food markets,
tracing its impacts directly to consumer demand for food is a difficult task, especially given the recent
confluence of biofuel and food markets; nevertheless, it should be noted that the actual climate impacts
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of food production are much larger than just emissions of CO2, CH4, and N2O. Additionally, while
working at the aggregate and average level used in this analysis has many advantages, it does miss
substantial variation in local scale impacts (N2O emissions from soils, differing manure management
and fertilizer application practices between farms, etc; see [35] for further discussion) and in specific
food types within aggregate groups (such as differences between ruminant and non-ruminant red meat,
grass-fed vs. grain-fed meat, organic vs. conventional produce, etc). Further, several authors have noted
the importance of seasonal variations and increased storage necessary for localization of produce, which
are all only treated in an averaged sense here[7, 8]. Thus, all numbers presented should be regarded as
averaged and approximate, though it should be noted that most of these major uncertainties (land use,
increased storage) would make the benefits of localization look even more dubious compared to dietary
shift.
Relevance of Results
The production and distribution of food has long been known to be a major source of GHG and other
environmental emissions, and for many reasons, it is seen by many environmental advocates as one of
the major ways concerned consumers can reduce their “carbon footprints”. Proponents of localization,
animal welfare, organic food, and many other interest groups have made claims on the best way for
concerned consumers to reduce the impacts of their food consumption. The results of this analysis show
that for the average American household, ”buying local” could achieve, at maximum, around a 4-5%
reduction in GHG emissions due to large sources of both CO2 and non-CO2 emissions in the production
of food. Shifting less than 1 day per week’s (i.e., 1/7 of total calories) consumption of red meat and/or
dairy to other protein sources or a vegetable-based diet could have the same climate impact as buying all
household food from local providers.
We estimate the average household’s climate impacts related to food to be around 8.1 tonne CO2e/yr,
with delivery “food-miles” accounting for around 0.4 tonne CO2e/yr and total freight accounting for 0.9
tonne CO2e/yr. To put these figures into perspective, driving a 25 mi/gal (9.4 L/100km) automobile
12,000 miles/yr (19,000 km/yr) produces around 4.4 tonne CO2/yr. Expressed in this manner, a totally
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“localized” diet reduces GHG emissions per household equivalent to 1000 miles/yr (1600 km/yr) driven,
while shifting just one day per week’s calories from red meat and dairy to chicken/fish/eggs or a
vegetable-based diet reduces GHG emissions equivalent to 760 miles/yr (1230 km/yr) or 1160 miles/yr
(1860 km/yr), respectively. Shifting totally away from red meat and dairy toward chicken/fish/eggs or a
vegetable-based diet reduces GHG emissions equivalent to 5340 mi/yr (8590 km/yr) or 8100 mi/yr
(13000 km/yr), respectively. Which of these options is easier or more effective for each climate-
concerned household depends on a variety of factors, though given the difficulty in sourcing all food
locally, shifting diet for less than one day per week may be more plausible.
It should again be noted that the entire analysis performed here was based on the “average” US
household’s food expenditures. Of course, different real households will have very different dietary
habits and climate profiles. Those consuming more in high-impact categories could have even more
potential reduction in GHG emissions than calculated here. Of course, this is conversely true for
households which already exhibit low-GHG eating habits. For these households, freight emissions may
be a much higher percentage of the total impacts of food, and especially will be important for fresh
produce purchased out of season.
Finally, it should be noted that this analysis only examined climate impacts, which are only one aspect
related to food choice, and are only one dimension of the environmental impacts of food production.
Food choice is based on a variety of factors, including taste, safety, health/nutrition concerns (both
between different food types and among food types, i.e. organic vs. conventional), affordability,
availability, and environmental concerns. Similar to food choice in general, consumers that have taken
part in the localization movement have done so for many reasons other than energy and climate;
supporting local agricultural communities and food freshness are often listed as reasons to “buy local”
as well. Though this analysis shows that some food types are much less GHG-intensive than others, any
attempt to change consumer behavior based on only one dimension of food choice is unlikely to be
effective.
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ACKNOWLEDGMENT: The authors would like to thank comments by two anonymous reviewers
which improved the work considerably. This work was funded by an EPA Science to Achieve Results
Fellowship to the main author, and National Science Foundation (NSF) MUSES grant #06-28232. The
opinions expressed herein are those of the authors and not of the NSF.
SUPPORTING INFORMATION: Supporting information, including a detailed discussion of model
development and methods, detailed commodity-level results, and additional figures and tables are
available online.
15
Figure 1: Total ton-km of freight by mode per year per household (a), transport-related GHG emissions
by mode(b), and total GHG emissions by supply chain tier (c) associated with household food
consumption in the US, and comparative climate impacts of different food groups (d). The clear boxes
(Direct in panes a-b) represent final delivery portion of transport chain. Food groups are aggregates of
50 commodities (see supporting information).
16
0 500 1000 1500 2000 2500
Beverages
Cereals/Carbs
Chicken/Fish/Eggs
Dairy Products
Fruit/Vegetable
Oils/Sweets/Cond
Other Misc
Red Meat
Ton-km/household-yr
Air
Rail
Water
Truck
Gas Pipe
Oil Pipe
Intl Air
Intl Water
Direct
0 0.05 0.1 0.15 0.2
Beverages
Cereals/Carbs
Chicken/Fish/Eggs
Dairy Products
Fruit/Vegetable
Oils/Sweets/Cond
Other Misc
Red Meat
Transport mt CO2e per household-yr
Air
Rail
Water
Truck
Gas Pipe
Oil Pipe
Intl Air
Intl Water
Passenger
Direct
0 0.5 1 1.5 2 2.5
Beverages
Cereals/Carbs
Chicken/Fish/Eggs
Dairy Products
Fruit/Vegetable
Oils/Sweets/Cond
Other Misc
Red Meat
Climate Impact, mt CO2e/household-yr
Delivery
OtherFreight,Dom
OtherFreight,Int
Production,CO2
Production,CH4
Production,N2O
Production,HFC
Wholesale/Retail
0 1 2 3
Beverages
Cereals/Carbs
Chicken/Fish/Eggs
Dairy Products
Fruit/Vegetable
Oils/Sweets/Cond
Other Misc
Red Meat
Restaurants
kg CO2e/$ spent
a)
d)
c)
b))
Figure 2: Comparison of normalization factors for total GHG of food. From left to right: no
normalization (tonne CO2e/hh-yr), by expenditure (g CO2e/$1997), by energy content (g CO2/kCal) and
by mass (kg CO2e/kg). All values are shown relative to the value of red meat (2500 kgCO2e/yr, 2.4
kgCO2e/$, 10.8 gCO2e/kCal, 22.1 kg CO2e/kg)
17
0
0.25
0.5
0.75
1
CO2/hh CO2/$ CO2/kCal CO2/kg
Relative Intensity
Beverages
Cereals/Carbs
Chicken/Fish/Eggs
Dairy Products
Fruit/Vegetable
Oils/Sweets/Cond
Red Meat
References
18
