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Food-Miles and The Relative Climate Impacts of Food Choices in the United States

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Abstract

Despite significant recent public concern and media attention to the environmental impacts of food, few studies in the United States 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 U.S. household's 8.1 t 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 per week'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.
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=(IA)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]
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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.
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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).
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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
... We compared our results with simplified food-miles approaches that focus only on first-order supply chain connections [16][17][18][19]38 ('direct-only' approach) or on international trade to one destination 39 ('international only' approach), or that use the same amount of tkm per US dollar attribute for commodities, irrespective of their origin/destination and mass 40 ('distance-mass-ignorant' approach). This comparison demonstrates that our introduction of the interregional and interindustry travel mass, distances and transportation modes leads to substantial differences, and thus considerably improves the accuracy of food-miles calculations. ...
Article
Full-text available
Food trade plays a key role in achieving global food security. With a growing consumer demand for diverse food products, transportation has emerged as a key link in food supply chains. We estimate the carbon footprint of food-miles by using a global multi-region accounting framework. We calculate food-miles based on the countries and sectors of origin and the destination countries, and distinguish the relevant international and domestic transport distances and commodity masses. When the entire upstream food supply chain is considered, global food-miles correspond to about 3.0 GtCO2e (3.5–7.5 times higher than previously estimated), indicating that transport accounts for about 19% of total food-system emissions (stemming from transport, production and land-use change). Global freight transport associated with vegetable and fruit consumption contributes 36% of food-miles emissions—almost twice the amount of greenhouse gases released during their production. To mitigate the environmental impact of food, a shift towards plant-based foods must be coupled with more locally produced items, mainly in affluent countries.
... The process to calculate the environmental impact on each stage of transformation and distribution is known as the 'life cycle analyses' of that food product. Because of globalization it is now evident that the distribution of food impacts more to the environment rather than its production (Weber & Matthews, 2008). ...
Article
The aim of this paper is to measure the annual carbon footprint, when vegetables and grains travel from farms to consumers. The focus is on the food basket of Cuttack City, Odisha, India. Primarily, this is an exploratory research which includes research instruments namely interviews and survey through questionnaire with the transportation authority across the distribution channel. Additional data collected thorough secondary sources, existing literature. The major findings of this research are the amount of greenhouse gas emission, for the vegetable, rice, wheat, milk and pulses etc., which comprise the food basket, with comparative analysis of grains versus vegetables & milk products, when travels through the distribution network and reaches the consumers of Cuttack City. The study scope is limited to Cuttack City only and the product scope is limited to the vegetarian food and the milk products. This research will provide a better understanding to measure the environmental impact caused by the transportation of food items. Based on this research, distribution channels redesigning can done to make them environmental friendly and sustainable, to achieve food security.
... Due to their short shelf life, fruit and vegetables are in most cases transported by plane with the associated environmental impact. On average, the operational energy of a long-haul cargo plane, expressed in MJ/metric ton-km, is around four times more than a truck and 30 times more than a train (Weber and Matthew, 2008) ...
Chapter
Blue economy refers to the economic activities geared towards advanced sustainable management and conservation of maritime resources and coastal resources and sustainable development in order to foster economic growth. The challenges of meeting the food demand of the world's rising populations require sustainable food supply chains anchored on coastal communities and sustainable food production. Moreover, marine resources are vital to ensuring food security, accounting for two-thirds of the world's fishery production, 80% of the world's aquaculture production, and per capita supply of fish is 65% higher than the world average. As the world population grows, the volume of food needed in the future will depend on these intrinsic factors and human choices. The chapter explores the current status of sea resources and proposed some ways forward based on existing opportunities and challenges using secondary data to accelerate the sustainable use of the sea resources and analyzes some of the human actions that may affect the sustainable future of the food supply chain, food waste.
... When successful, land-based RAS facilities optimize biosecuritythat is, fish are unlikely to escape and pathogens and predators unlikely to enterthus addressing a limitation of oceanbased net pen aquaculture (RAS-N, 2022). Further, siting aquaculture on land allows communities previously unable to access local seafood a fresh option with fewer "food-miles" attached (Weber & Matthews, 2008). Thus, land-based RAS is novel not only because of its technological affordances (e.g. ...
... Although this system has increased food access in many countries, it has also represented a rise in obesity rates, high levels of non-communicable diseases (Paarlberg 2011;Popkin 2004), micronutrient deficiencies (Allen et al. 2014), as well as climate change, biodiversity loss, water, and air pollution (Easterling et al. 2007;FAO 2006;WWF 2018). The Sustainable Diets framework (Gussow and Clancy 1986;Johnston, Fanzo, and Cogill 2014;Lang 2014) and the Great Food Transformation initiative (Willett et al. 2019) emerged in Research on sustainable food consumption has focused on the environmental dimension (Béné et al. 2019), encompassing the impact and reduction of animal-based food consumption (de Boer, Schösler, and Aiking 2014;Godfray et al. 2018;Vanhonacker et al. 2013;Verain, Dagevos, and Antonides 2015), the consumption of organic foods (Honkanen, Verplanken, and Olsen 2006;Vermeir and Verbeke 2006), environmentally friendly food products (Mäkiniemi and Vainio 2014;Thøgersen 2010), and locally produced foods (Born and Purcell 2006;Weber and Matthews 2008). Other authors have explored the relationship between ethical values and sustainable food consumption, highlighting animal welfare and organic food as the main concerns linked to sustainable foods (Dowd and Burke 2013;Honkanen, Verplanken, and Olsen 2006). ...
Article
The study of sustainable food consumption is key in the transformation of current unsustainable food systems. We explore the tensions that emerge between individual motives and sustainability spheres when making food consumption choices in a university community in Bogotá, Colombia. This complex phenomenon is addressed through a qualitative methodology based on observations, visual diaries, and a game-based protocol, which allow us to delve into food consumption motives while trying to avoid social desirability bias. Our results show a plate that is high in cereals, roots, tubers, plantains, and animal-based protein, and low in vegetables, fruits, and plant-based proteins. Behind this plate, most tensions emerge between family traditions and other sustainability spheres. These tensions are related to socio-affective risks, animal suffering, time constraints, effort, and monetary costs. Integrating sociocultural elements such as family traditions in public policy becomes fundamental when promoting sustainable diets in similar contexts. This study contributes to the discussion about the sociocultural changes required in the transition toward sustainable food systems.
... 10 These examples of widespread corporate blue food fraud and corruption are disturbing and concerning especially when considering the negative effects on both large-and small-scale producers who follow the rules. There are also reputational implications which are of growing importance to consumers who are increasingly concerned about sustainability, foodmiles, 13 and the environmental and social consequences of their dietary choices. 14 The growing demand of blue foods is predicted to grow substantially, potentially by as much as double the current rate by 2050. 1 This demand is a double edge sword, with potentially good and bad consequences for consumers and the environment depending on the path taken to meet the rising demand. ...
... According to [22], the greenhouse gas (GHG) emissions from the food sector mainly come from production (83% in US), transportation (11%), and final distribution (4%). Renewable energy systems appear a natural fit for the philosophy behind vertical farming. ...
Article
Full-text available
Vertical farming (VF) is a newer crop production practice that is attracting attention from all around the world. VF is defined as growing indoor crops on multiple layers, either on the same floor or on multiple stories. Most VF operations are located in urban environments, substantially reducing the distance between producer and consumer. Some people claim that VF is the beginning of a new era in controlled environment agriculture, with the potential to substantially increase resource-use efficiencies. However, since most vertical farms exclusively use electric lighting to grow crops, the energy input for VF is typically very high. Additional challenges include finding and converting growing space, constructing growing systems, maintaining equipment, selecting suitable plant species, maintaining a disease- and pest-free environment, attracting and training workers, optimizing the control of environmental parameters, managing data-driven decision making, and marketing. The objective of the paper is to highlight several of the challenges and issues associated with planning and operating a successful vertical farm. Industry-specific information and knowledge will help investors and growers make informed decisions about financing and operating a vertical farm.
Article
Reducing greenhouse gas (GHGs) emission in the food system, which has contributed one third of the total GHGs emissions from human activities, is crucial to achieve the Paris Agreement Announcement to limit global warming to 1.5 °C or 2 °C above pre-industrial levels. The intensity of GHGs emissions from different food varies significantly, so dietary structures will certainly lead to different global warming impacts. Adjusting people's diet structure, reducing red meat and other high carbon emission foods in the production process, and increasing low carbon healthy foods such as vegetables and seafood, can not only meet people's health needs, but also reduce GHG emissions in the food production process and promote the development of cleaner production. This study estimated GHGs emissions from human food consumption at a global scale and analyzed the driving factors. To explore the reducing path of GHGs emission from individual diets, two scenarios (a plant-rich diet and a diet with mussels replacing some of the traditional carbon intensive meat) of different diet structure were evaluated. Results suggest that the cumulative GHGs emissions caused by food consumption in 2020–2060 are 374 Gt CO2 equivalent. Furthermore, the plant-rich diet can reduce total GHGs emissions by 41%, and the mussels-replacing diet can have a reduction of 4.5%, 13.6%, and 22.4%, by replacing the traditional meat by 10%, 30% and 50%, respectively. Therefore, diet structure optimization has huge potential in GHGs emission mitigation, and such affordable and effective reduction measure can provide new insights for policy makers to control emissions from the food system.
Article
Full-text available
Humanity faces an unprecedented existential threat from climate instability and global temperature rise caused by human activities, most notably the emission of greenhouse gases from combustion of fossil fuels. The threat to human health from climate instability has been called the greatest of the 21st century.4 New York State does not escape this threat. The Medical Society of the State of New York acknowledges that immediate action is needed to prevent catastrophic health effects related to climate instability.4 Physicians must warn society and advocate for protecting the health of our patients and communities. The pandemic of SARS CoV2 has revealed many weaknesses in our ability to meet large scale disasters that must be rapidly addressed if New York is to meet the challenges posed by climate change. The Medical Society of the State of New York (MSSNY) presents this white paper to guide stakeholders including physicians, MSSNY members, healthcare organizations, community members, policy makers and legislators on actions needed to protect the health of New Yorkers. This paper focuses on direct (e.g., injuries/deaths) and indirect (e.g., reduced nutrients in crops) health effects driven by fossil fuel combustion and climate instability. We address: 1) the evidence for global warming and climate instability; 2) the observed and projected environmental changes in New York State; 3) the observed and projected health and safety consequences of these changes; and 4) recommendations to mitigate, adapt and protect New Yorkers from climate change. The path ahead will stress the health sector in unprecedented ways, yet solutions bring profound opportunities to provide immediate benefits—if New York State converted to 100% renewables, reductions in air pollution would save 4000 lives and $33 billion annually in health care costs.7 Therefore, we also highlight the specific and immediate health benefits from reducing greenhouse gas emissions. MSSNY aligns with climate science experts who have sounded the alarm—the threats to New York are profound and time is limited. Climate instability is already hurting New Yorkers and will continue to do so for decades to come even with aggressive reductions in emissions. We therefore make specific calls to action by key stakeholders to protect all New Yorkers, especially the most vulnerable. The silver lining is that—if everyone acts—we will see immediate health benefits. The challenges ahead cannot be met by the medical community alone. Every sector of society must come together to create a unified and sustained response to the looming threats. MSSNY therefore recommends that governmental and non-governmental leaders join with the medical and scientific communities to combat global warming and to create a healthier, safer environment for all New Yorkers.
Book
Full-text available
Environmental life cycle assessment is often thought of as cradle to grave and therefore as the most complete accounting of the environmental costs and benefits of a product or service. However, as anyone who has done an environmental life cycle assessment knows, existing tools have many problems: data is difficult to assemble and life cycle studies take months of effort. A truly comprehensive analysis is prohibitive, so analysts are often forced to simply ignore many facets of life cycle impacts. But the focus on one aspect of a product or service can result in misleading indications if that aspect is benign while other aspects pollute or are otherwise unsustainable. This book summarizes the EIO-LCA method, explains its use in relation to other life cycle assessment models, and provides sample applications and extensions of the model into novel areas. A final chapter explains the free, easy-to-use software tool available on a companion website. (www.eiolca.net) The software tool provides a wealth of data, summarizing the current U.S. economy in 500 sectors with information on energy and materials use, pollution and greenhouse gas discharges, and other attributes like associated occupational deaths and injuries. The joint project of twelve faculty members and over 20 students working together over the past ten years at the Green Design Institute of Carnegie Mellon University, the EIO-LCA has been applied to a wide range of products and services. It will prove useful for research, industry, and in economics, engineering, or interdisciplinary classes in green design. © 2006 by Resources for the Future. All rights reserved. All rights reserved.
Article
Full-text available
The advent of the Internet and e-commerce has brought a new way of marketing and selling many products, including books. The systemwide effects of this retailing shift on costs and the environment are still unclear. Although reductions in inventories and returns provide significant environmental savings, some major concerns of the new e-commerce business models are the energy and packaging materials used by the logistics networks for product fulfillment and delivery. This study analyzes the different logistics networks and assesses the environmental and cost effects of different delivery systems. The definition of analysis system boundaries determines the overall assessment of economic and environmental effects of e-commerce for book retailing. With a return (remainder) rate of 35 percent for best-selling books, e-commerce logistics costs less and has fewer environmental effects, especially if private automobile travel for shopping is included. Excluding the need to return books, costs and environmental effects are comparable for the two delivery methods.
Article
Full-text available
A comparative study of the energy consumption of animal- and plant-based diets, and, more broadly, the range of energetic planetary footprints spanned by reasonable dietary choices was carried out. It was demonstrated that greenhouse gas (GHG) emissions resulting from various diets vary by as much as the emission difference between an average sedan and a sport utility vehicle under typical driving conditions. Most notably, it was shown that a person consuming the mean American diet is responsible for the annual emissions of a ton and a half CO 2-equivalent beyond those incurred by a plant eater consuming the same number of calories.
Article
Argonne National Laboratory has developed a vehicle-cycle module for the Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model. The fuel-cycle GREET model has been cited extensively and contains data on fuel cycles and vehicle operations. The vehicle-cycle model evaluates the energy and emission effects associated with vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling. With the addition of the vehicle-cycle module, the GREET model now provides a comprehensive, lifecycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies (e.g., hybrid electric vehicles and fuel cell vehicles). This report details the development and application of the GREET 2.7 model. The current model includes six vehicles--a conventional material and a lightweight material version of a mid-size passenger car with the following powertrain systems: internal combustion engine, internal combustion engine with hybrid configuration, and fuel cell with hybrid configuration. The model calculates the energy use and emissions that are required for vehicle component production; battery production; fluid production and use; and vehicle assembly, disposal, and recycling. This report also presents vehicle-cycle modeling results. In order to put these results in a broad perspective, the fuel-cycle model (GREET 1.7) was used in conjunction with the vehicle-cycle model (GREET 2.7) to estimate total energy-cycle results.
Article
Life-cycle assessment models attempt to quantify the environmental implications of alternative products and processes, tracing pollution discharges and resources use through the chain of producers and consumers. Present life-cycle assessments must draw boundaries that limit consideration to a few producers in the chain from raw materials to a finished product. We show that this limitation considers only a fraction of the environmental discharges associated with a product or process, thereby making current assessments unreliable. We propose an approach that uses economic input-output analysis and pollution discharge data and apply the model to automobiles, refrigerators, and computer purchases, and to a comparison of paper and plastic cups.
Article
Energy use associated with sales and distribution via business-to-consumer (B2C) e-commerce versus conventional retail is analyzed for the Japanese book sector. Results indicate that e-commerce uses considerably more energy per book than conventional retail in dense urban areas, because of additional packaging. In suburban and rural areas, the energy consumption of the two systems is nearly equal because the relative efficiency of courier services compared to personal automobile transport balances out the impact of additional packaging. The main reason e-commerce does not save energy, even in rural areas, is because of the multipurpose use of automobiles; e-commerce does consume less energy in the case of single-purpose shopping trips by automobile. Overall consumption at the national level is nearly the same: 5.6 mega-joules (MJ) per book for e-commerce and 5.2 MJ per book for traditional retail. Although this difference is smaller than the uncertainty in the result, the structure of energy use for the two systems is quite distinct, which suggests reprioritization of energy-efficiency strategies. Important factors influencing the energy efficiency of B2C e-commerce include packaging, loading factors of courier trucks, number of trips per delivery, and residential energy consumption.
Article
Conventional process-analysis-type techniques for compiling life-cycle inventories suffer from a truncation error, which is caused by the omission of resource requirements or pollutant releases of higher-order upstream stages of the production process. The magnitude of this truncation error varies with the type of product or process considered, but can be on the order of 50%. One way to avoid such significant errors is to incorporate input-output analysis into the assessment framework, resulting in a hybrid life-cycle inventory method. Using Monte-Carlo simulations, it can be shown that uncertainties of input-output– based life-cycle assessments are often lower than truncation errors in even extensive, third-order process analyses.