Greenhouse gas emissions and energy use associated with production of individual self-selected US diets

Abstract

Human food systems are a key contributor to climate change and other environmental concerns. While the environmental impacts of diets have been evaluated at the aggregate level, few studies, and none for the US, have focused on individual self-selected diets. Such work is essential for estimating a distribution of impacts, which, in turn, is key to recommending policies for driving consumer demand towards lower environmental impacts. To estimate the impact of US dietary choices on greenhouse gas emissions (GHGE) and energy demand, we built a food impacts database from an exhaustive review of food life cycle assessment (LCA) studies and linked it to over 6000 as-consumed foods and dishes from 1 day dietary recall data on adults (N = 16 800) in the nationally representative 2005–2010 National Health and Nutrition Examination Survey. Food production impacts of US self-selected diets averaged 4.7 kg CO2 eq. person⁻¹ day⁻¹ (95% CI: 4.6–4.8) and 25.2 MJ non-renewable energy demand person⁻¹ day⁻¹ (95% CI: 24.6–25.8). As has been observed previously, meats and dairy contribute the most to GHGE and energy demand of US diets; however, beverages also emerge in this study as a notable contributor. Although linking impacts to diets required the use of many substitutions for foods with no available LCA studies, such proxy substitutions accounted for only 3% of diet-level GHGE. Variability across LCA studies introduced a ±19% range on the mean diet GHGE, but much of this variability is expected to be due to differences in food production locations and practices that can not currently be traced to individual dietary choices. When ranked by GHGE, diets from the top quintile accounted for 7.9 times the GHGE as those from the bottom quintile of diets. Our analyses highlight the importance of utilizing individual dietary behaviors rather than just population means when considering diet shift scenarios.

Full text

Environ. Res. Lett. 13 (2018) 044004 https://doi.org/10.1088/1748-9326/aab0ac
LETTER
Greenhouse gas emissions and energy use associated
with production of individual self-selected US diets
Martin C Heller1,3, Amelia Willits-Smith2, Robert Meyer1, Gregory A Keoleian1and Donald Rose2
1Center for Sustainable Systems, School for Environment and Sustainability, University of Michigan, 440 Church Street, Ann Arbor, MI
48109-1041, United States of America
2Department of Global Community Health and Behavioral Sciences, Tulane University, 1440 Canal Street, Suite 2210, New Orleans, LA
70112, United States of America
3Author to whom any correspondence should be addressed.
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8 December 2017
REVISED
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ACCEPTED FOR PUBLICATION
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PUBLISHED
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E-mail: mcheller@umich.edu
Keywords: dataFIELD, cumulative energy demand, NHANES, diet shifts
Supplementary material for this article is available online
Abstract
Human food systems are a key contributor to climate change and other environmental concerns.
While the environmental impacts of diets have been evaluated at the aggregate level, few studies, and
none for the US, have focused on individual self-selected diets. Such work is essential for estimating a
distribution of impacts, which, in turn, is key to recommending policies for driving consumer
demand towards lower environmental impacts. To estimate the impact of US dietary choices on
greenhouse gas emissions (GHGE) and energy demand, we built a food impacts database from an
exhaustive review of food life cycle assessment (LCA) studies and linked it to over 6000 as-consumed
foods and dishes from 1 day dietary recall data on adults (N= 16 800) in the nationally representative
2005–2010 National Health and Nutrition Examination Survey. Food production impacts of US
self-selected diets averaged 4.7 kg CO2eq. person−1 day−1 (95% CI: 4.6–4.8) and 25.2 MJ
non-renewable energy demand person−1 day−1 (95% CI: 24.6–25.8). As has been observed
previously, meats and dairy contribute the most to GHGE and energy demand of US diets; however,
beverages also emerge in this study as a notable contributor. Although linking impacts to diets
required the use of many substitutions for foods with no available LCA studies, such proxy
substitutions accounted for only 3% of diet-level GHGE. Variability across LCA studies introduced a
±19% range on the mean diet GHGE, but much of this variability is expected to be due to differences
in food production locations and practices that can not currently be traced to individual dietary
choices. When ranked by GHGE, diets from the top quintile accounted for 7.9 times the GHGE as
those from the bottom quintile of diets. Our analyses highlight the importance of utilizing individual
dietary behaviors rather than just population means when considering diet shift scenarios.
Introduction
Agriculture is a key contributor to many environmen-
tal problems, including climate change, biodiversity
loss and land and freshwater degradation [1]. Repeated
projection studies have shown that closing global yield
gaps through sustainable intensification measures will
not be sufficient to simultaneously prevent further
agricultural expansion and achieve the deep emission
cuts needed to meet the COP-21 Paris Agreement on
combating climate change. Demand-side reductions
will also be necessary [2–5]. Thus, diet composition
has been identified as an important leverage point in
reducing the environmental impact of food systems
and in freeing up production capacity to feed future
population growth.
Considerable efforts have been made in recent
years to evaluate the environmental impact of dietary
choices [6–10]. The bulk of this effort has evalu-
ated aggregated (i.e. average) or stereotyped diets in
European countries, with a focus on climate change
impacts. Only a handful of studies have evaluated
the environmental impact of diets in the US [11–15].
Very few studies, and none in the US, have evaluated
© 2018 The Author(s). Published by IOP Publishing Ltd

Environ. Res. Lett. 13 (2018) 044004
the impacts of individual self-selected diets [16–19].
Even the few studies that have attempted this assess
only a percentage of foods consumed. Individual-level
data are needed for more nuanced modeling of dietary
change policies since they allow for understanding the
range of impacts within a population and for link-
ing of individual-level demographics (e.g. age, gender,
race-ethnicity, education, nutrition knowledge, envi-
ronmental attitudes, etc.) to the dietary behaviors of
these groups and their environmental impacts. Under-
standing the relation of specific diets to hea lth outcomes
also benefits from having the full range of diets chosen
by individuals in a population.
A major challenge in this field of research is the
establishment of environmental impacts for the diver-
sity of foods in self-selected diets. For example, while
a typical study based on aggregated national food pat-
terns might include impacts on a few hundred foods
[11], databases that support individual diet surveys
contain thousands of items, many of which are complex
recipes (e.g. lasagna) or have not been studied in the
life cycle assessment (LCA) literature (e.g. blackberry).
Our aim is to evaluate the greenhouse gas emis-
sions (GHGE) and non-renewable cumulative energy
demand (CED) associated with a representative sam-
ple of self-selected, individual diets in the US. To do
this and address the challenge described above, we have
developed dataFIELD (database of Food Impacts on the
Environment for Linking to Diets) based on an exhaus-
tive review of the literature. We have also developed an
approach for linking it to dietary recall data from the
US National Health and Nutrition Examination Survey
(NHANES)4.
Methods
Individual, 1 day dietary recall data (18+years of age,
N= 16 800) from NHANES for 2005–2010 serves as
the basis for individual food choices studied here.
NHANES sampling is selected to represent the US
civilian non-institutional population, and the dietary
recall data contain reference to 6492 specific foods and
dishes [20]. Many of these food items are prepared
foods (e.g. pepperoni pizza) and require recipes to
assign to the commodity foods typically represented
in LCA studies. In order to enable diet-level analysis
of pesticide and other residues in food commodi-
ties, the US EPA developed the Food Commodities
Intake Database (FCID) which links specific food items
in NHANES through standardized recipes to foods
in agricultural commodity form [21]. We utilize this
database to connect as-consumed foods in NHANES t o
332 commodity forms, which were in turn connectedto
environmental impacts based on our literature review.
4NHANES dataset with GHGE per person for day one diets
and accompanying documentation will be available at: https://sph.
tulane.edu/gchb/diet-environmental-impacts.
Literature review of food LCA data
We conducted a systematic search in Web of Sci-
ence and Google Scholar databases. Search terms
included combinations of ‘LCA’and ‘life cycle’with
‘food’. Further refined searches targeted individual
underrepresented foods. In addition, collected cita-
tions were cross-referencedwith the extensive review by
Clune et al [22] and relevant additional citations were
included. The literature review was limited to reports
available in the public domain. Articles and reports
written in English and published in 2005–2016 that
applied LCA methods to one or more food products
and provided primary (i.e. not cited from elsewhere)
mid-point impact assessment of GHGE and/or CED
were reviewed and inventoried. Peer reviewed jour-
nalarticlesaswellasthoroughlydocumentedreports
from governmental and non-governmental organiza-
tions (including theses) were considered. Additional
details about our methodology as well as the full listing
of references included in our database, dataFIELD5,
is provided in supporting information available at
stacks.iop.org/ERL/13/044004/mmedia.Fordatabase
consistency, mid-point indicator values were adjusted
to a functional unit of ‘kg of food,’with meat and
fish/seafood adjusted to ‘kg of edible boneless weight’.
See supporting information for details on conversion
factors used.
Linking to dietary database
To link environmental impacts to the 332 commodity
foods in FCID, we followed a four-step process (see
table 1). First, we used data from original research on
specific foods inventoried in the literature review, as
describedabove.Themean,standarddeviation,min-
imum and maximum values for CED and GHGE at
farm gate and at processor gate were calculated for
each specific food, and then matched to the FCID.
Studies of heated greenhouse vegetable production or
those of beef from dairy herds were not included in
our averages because information on market share of
these production methods is unavailable or unreliable.
Second, if we did not have an original research report
on an FCID food, we turned to reports with previously-
compiled food LCA data to supply environmental
impacts [23–29]. These resources contained data not
captured in the literature review, perhaps due to
non-English language reports or proprietary sources.
Overall, for stage 1 and 2 of the linkage process, CED
matches were made for 35% of the food commodi-
ties, and GHGE matches for 47%. Third, remaining
FCID foods were populated with values from simi-
lar foods as proxies. Specifically, we took an average
of either CED or GHGE values from existing entries
within a specific food grouping (e.g. berries, brassi-
cas, brassica greens, citrus, fresh herbs, grains, other
5The complete dataFIELD database is available at: http://css.
umich.edu/page/datafield.
2

Environ. Res. Lett. 13 (2018) 044004
Table 1. Process for assigning environmental impact data to FCID commodities.
Stage Approach for assigning environmental
impact data to each specific
Example % of FCID foods
assigned in stage
FCID food commodity GHGE CED
1 Mean of values from literature review An average of 96 studies on beef for GHGE, 19
studies for CED
2 Aggregated value from a report with
previously compiled impact data
Kale, from [23]
47% 35%
3 Proxy assignment from stage 1 or 2
foods in the same group OR from
stage 1 or 2 foods of similar form
Average of broccoli, cauliflower and cabbage for
Brussels sprouts OR bananas for plantains;
escarole for radicchio
39% 50%
4 Mass conversion factor applied to
base fruit/vegetable
Strawberry values converted for strawberry juice
(mass conversion with processing energy added)
15% 15%
greens, nuts, roots, dried spices, other tree fruit, trop-
ical fruit) to proxy for a specific food item in that
same grouping that was lacking data. Failing this
approach, other proxies of foods with similar form
were then assigned. These assignments were based on
similarities of specific crops in their botany and, most
importantly, production methods, as determined by
the expertise of our research team. Values that were
assigned from other foods in the database in this third
stage accounted for 50% of CED values and 39%
of GHGE values. Fourth, the FCID dataset includes
minimally processed forms of fruits and vegetables
(e.g. strawberry juice, dried apples). Where direct LCA
matches were not available for these forms, we applied
a mass conversion factor, gathered from nutritional
databases [30,31], to the base fruit or vegetable in
order to approximate the agricultural production bur-
dens of these processed forms. This stage accounted
for the remaining 15% of CED and 15% of GHGE
values for FCID foods. For juices, vinegar and maple
syrup, additional sources were used to develop valid
estimates. These additions are detailed in supporting
information.
Because of the inconsistency in full life cycle bound-
ary conditions across the literature review entries,
cradle-to-farm gate impact factors were chosen for
the vast majority of foods. This choice is further
supported by the fact that these commodity foods,
in many cases, become ingredients in processed, as-
consumed foods, and inclusion of life cycle stages
downstream from the farm gate would not neces-
sarily reflect impacts of the actual foods consumed.
The exceptions to this farm gate boundary condi-
tion are foods within the FCID listing that require
processing: flours, refined sugars, vegetable oils, etc
supporting information contains additional details on
these boundary condition choices, as well as an envi-
ronmentally extended input-output based estimate of
the cumulative food processing impacts excluded in
this analysis.
Impact factor variability estimates
Variability is expected in the LCA data gathered for
a given food type, both due to differences in agro-
climatic conditions and production practices, as well
as LCA methodological approaches such as allocation
choice. To characterize this variability and estimate
its influence on the impacts of diet, we calculated a
95% confidence interval around the average impact
for each food, based on the observations for that food
(or related foods) that we found in the literature.
If there were too few observations for a given food
to calculate a confidence interval, we used the con-
fidence interval for a related food or group of
foods. We used the lower and upper bounds of this
confidence interval in subsequent calculations of diet-
level variability. Supporting information also contains
detailsonthismethod.
Linking to NHANES and diet-level calculations
The FCID database contains a recipe file that links
foods as reported by NHANES respondents to ingre-
dients in the form of commodities. For example, the
recipe for 100 grams of ‘lasagna with meat,’which
is one of 17 lasagna dishes reported by respondents,
contains gram quantities of commodities, includ-
ing wheat flour, milk, beef, tomato, etc. We linked
impacts from dataFIELD to these FCID commodities
and adjusted for recipe quantities and amounts eaten in
order to assign impacts for each food consumed during
the 24 hour recall day as reported by each respondent.
See supporting information for a complete listing of
FCID foods and impacts. In some cases, when there
was sufficient LCA literature to describe the impact
of processed foods (specifically: cheese, yogurt, tofu,
beer, carbonated drinks, and liquor) we linked directly
from dataFIELD to NHANES, without using the FCID
recipes. For alcoholic beverages, we created our own
recipe file for linking from FCID to NHANES. The
impacts of edible losses were calculated for the amount
of each commodity consumed using loss factors from
the USDA’s Loss-Adjusted Food Availability (LAFA)
data series [32]. Commodity items were assigned retail
(edible food lost at outlets such as supermarkets and
restaurants) and consumer (cooking losses and uneaten
food) loss factors from the matching LAFA commodity.
If there was not a direct match, the food was assigned
loss factors for something similar (e.g. apple juice fac-
tors for apricot juice) or for an average of similar
items (e.g. an average of loss factors for blueberries,
3

Environ. Res. Lett. 13 (2018) 044004
Table 2. Characterization of literature review and linkage to the FCID, by food group.
Food groups % of lit. review
entries
#ofFCID
foodsa
% of FCID foods in group
requiring proxyb
% of group level impact from
proxiesb
GHGE CED GHGE CED
Vegetables 16.8 96 64 72 7.8 18.0
Meats 16.1 10 30 80 0.1 5.6
Beverages 13.4 34 65 68 22.7 10.2
Fruits 12.7 66 55 71 6.2 17.6
Dairy 11.4 3 0000
Fish and seafood 9.1 6 01709.9
Cereals and grains 6.4 27 52 56 8.0 10.2
Nuts and seeds 4.0 21 48 76 5.2 44.9
Eggs 2.1 1 0000
Oils and fats 2.1 13 31 31 0.6 0.4
Legumes 1.8 24 54 67 26.7 59.1
Sweeteners 1.0 9 33 33 42.0 50.1
Other 3.0 22 73 82 4.1 7.3
Total diet —332 55 66 2.6 8.2
aFull listing of FCID foods and their impact factors is provided in supporting information. The six processed foods (beer, carbonated drinks,
liquor, cheese, yogurt, tofu) not specified in FCID and directly linked to NHANES (i.e. without use of FCID recipe files) in our analysis are
included here.
bIncludes both proxy levels 3 and 4 (see table 1).
Table 3. GHGE and CED of self-selected US diets (age 18+,n= 16 800) using average LCA impact factors.
Consumed Food loss contributions Consumed +all losses
MeanaSEaRetail lossesbConsumer lossesbMeanaSEa
GHGE (kg CO2eq. per capita) per day 3.58 0.04 0.25 0.89 4.72 0.05
per 1000 kcal 1.67 0.01 0.12 0.42 2.21 0.02
CED (MJ per capita) per day 18.87 0.20 1.41 4.89 25.17 0.30
per 1000 kcal 8.92 0.07 0.68 2.35 11.95 0.11
aMean values are calculated using the average impact factor for each food in dataFIELD. SE=standard error of the mean, which takes into
account variability in diets from one individual to the next, but not variability in the assessments of environmental impacts for a given food.
(See figure 1and accompanying discussion for low and high distributions that do take into account variability in these assessments for each
food.) Calculations account for the complex survey design and sampling weights of NHANES.
bFood losses based on USDA’s Loss Adjusted Food Availability dataset (see Methods).
raspberries, and strawberries for huckleberries). After
assigning impacts to each food consumed, we summed
impacts over the entire day for each individual.
All analyses accounted for the NHANES sampling
weights and survey design parameters.
Results
Literature review characterization
Our comprehensive literature review resulted in 1645
entries (combinations of food types and production
scenarios) from 321 unique sources (listed in support-
ing information). System boundaries varied across the
LCA studies inventoried: while nearly all entries con-
sidered some form of agricultural production, 51%
accounted for processing beyond farm gate, 19% fol-
lowed products through to retail/regional distribution
hubs, and 6% included some form of use (con-
sumption) phase. Supporting information contains
additional information on the distribution of entries by
publication type and geographic origin of production.
Food database linkage characterization
Environmental impacts were assigned for the 332
unique food commodity forms in the Food Commodi-
ties Intake Database (FCID), and for seven additional
foods linked directly to NHANES. Table 2shows the
distribution by literature review entries broken down
by broad FCID food groups, with meat, fruit, vegeta-
bles and dairy accounting for more than half of the
entries. Table 2also shows the number of FCID foods
in each of these groups, as well as the percentage of
foods in these groups requiring proxy values. While
55% of foods required proxy in calculating GHGE
for the total diet, these foods accounted for only 3%
of total impact. This is because the foods requiring
proxies tend to be low impact and less frequently
consumed foods. For example, the meats group con-
tributed 57% of dietary GHGE (see table 4), but only
0.1% of this group’s impact came from proxies. There
were a number of proxies used in the legumes group,
accounting for 27% of the GHGE from this group.
However, legumes contributed only 0.3% of total
dietary GHGE (table 4), so proxied legumes account
for only 0.09% of total dietary GHGE.
US diet impact characterization
The NHANES dietary intake survey is representative
of the US population. Thus, linking dataFIELD to the
individual, self-selected diets from NHANES offers a
way to estimate the distribution of diet-related impacts
across the population on a given day. Table 3summa-
rizes these results at the distribution mean for the total
4

Environ. Res. Lett. 13 (2018) 044004
0
0.05
0.1
0.15
0.2
0.25
0 5 10 15 20 25 30
probability density
GHGE (kg CO2eq per person per day)
mean food impact factors
lower bound food impact factors
upper bound food impact factors
Quintile description for distribution based on mean food
impact factors (blue curve)
Quintile Quintile range
(kg CO2eq)
Contribution of quintile
to total US diet GHGE
1 <1.94 6%
21.94 – 2.95 10%
32.95 – 4.32 15%
44.32 – 6.91 23%
5 >6.91 46%
Descriptive statistics
mean median
(kg CO2eq per person per day)
4.7 3.6
3.8 2.8
5.6 4.3
Figure 1. Distribution of diet-relat ed GHGE per person per day amo ng US adults, National Health an d Nutrition Examination Survey
2005–2010. Data are based on one 24 hour dietary recall per person and include estimated retail- and consumer-level food losses.
Distribution in blue is based on using the mean impact (GHGE/kg food) for each food in our database. Distributions in red and green
are based on impact factors (GHGE/kgfood) at the lower and upper bounds, respectively, of a 95% confidence interval around these
mean estimates of impact for each food.
Table 4. Contributions by food groups to impacts of 1 day diets for all diets and for those ranked at the lower and higher quintile by GHGE.
% contribution to total GHGEaSum of GHGE per daya
(metric tons CO2eq. per day)
all diets 1st quintile 5th quintile all diets 1st quintile 5th quintile
Meats 56.6 27.1 70.0 5 95 514 16 458 3 35 141
Dairy 18.3 28.1 11.4 1 92 844 17 066 54 794
Beverages 5.9 11.5 3.7 61 777 6985 17 571
Fish and seafood 5.8 3.4 7.5 60 579 2094 35 826
Eggs 2.8 4.9 1.6 29 815 3009 7469
Vegetables 2.6 5.8 1.5 27 056 3525 7163
Cereals and grains 2.1 5.8 1.1 22 321 3500 5122
Fruits 1.6 4.0 0.9 16 535 2422 4178
Sweeteners 1.4 3.1 0.8 15 064 1903 3864
Other 1.2 2.1 0.7 12 645 1249 3427
Oils and fats 1.0 2.4 0.5 10 306 1464 2564
Nuts and seeds 0.4 0.9 0.2 4154 536 1012
Legumes 0.3 1.0 0.1 3535 617 688
Total of all foods — — 10 52 146 60829 478819
Mean caloric intake per capita (kcal per day) 2153 1323 2984
aEnvironmental impacts (including retail and consumer losses) for specific foods were summed within each broad food group for each
individual (based on NHANES 2005–2010 24 hour diet recall, adults aged 18 and over; N= 16 800), an d then aggregated acro ss all individuals
in the relevant category (total population, 1st quintile, or 5th quintile).
population on both a per day basis as well as normal-
ized to 1000 kilocalories (kcal) dietary intake. Tables
3also demonstrates the contribution of food losses to
the environmental impact of diet.
Figure 1provides the distribution of diet-related
GHGE per person per day across the self-selected
diets from NHANES. An analogous figure for
CED is included in supporting information. While
the distribution in figure 1shows a sharp rise
to a peak in the distribution curve at around
2.2 kg CO2eq person−1 day−1, there is also a long tail
on the distribution (truncated in figure 1:truncated
tail represents 1.3% of totalimpacts). The 20% of diets
with the highest carbon footprint account for 45.5% of
the total diet-related emissions. Also shown in figure
1is a range of distributions representing the influ-
ence of variability in emissions due to food production
methods and LCA modeling.
5

Environ. Res. Lett. 13 (2018) 044004
The distribution of GHGE by food group for all 1
day diets as shown in table 4is quite typical of Western
dietary patterns, with the dominant impacts coming
from meats and dairy. An analogous table for CED is
included in supporting information. For the total pop-
ulation, 80.6% of the meats group GHGE comes from
beef, 9.5% from poultry, 8.5% from pork, with other
meats making up the remaining 1.5%. Of interest is
the relatively high (5.9% of GHGE, 16.0% of CED)
contribution from beverages (tap and bottled water,
carbonated drinks, coffee, tea, juices, beer, wine and
spirits). Beverages have not always been identified as
a separate food group in past diet impact assessments,
but were the third most impactful group in our analysis.
Across the total population, fruit and vegetable juices
make up 33% of the GHGE in the beverages group,
followed by coffee, beer, carbonated drinks, cocktails,
and wine at 20%, 19%, 9.6%, 8.9% and 7.0%, respec-
tively. Bottled water, tap water and tea contribute less
than 2% each.
Table 4also shows how the contribution by food
group differs between lower-impact (1st quintile) and
higher-impact (5th quintile) diets. For the higher-
impact diets, meats account for 70% of total diet
GHGE, whereas they only account for 27% with
lower-impact diets. In part, this has to do with the
makeup of the meats group in each of the two quin-
tiles. Whereas poultry is the largest contributor in the
first quintile (55% of meats group GHGE), beef con-
tributes 91% of meat GHGE in the fifth quintile diets.
Although for some food groups the percent contri-
butions in table 4decline from the 1st to the 5th
quintiles (e.g. dairy % contribution to GHGE drops
from 28% to 11%), absolute impacts for both GHGE
and CED increase for all food groups between the
first and fifth quintile. This is largely because diets in
the 5th quintile have greater amounts of these foods
than in the 1st quintile, as suggested by the overall
caloric intakes. In terms of overall impact, increases in
beef intake account for 72% of the absolute increase
in GHGE between the diets of the first and fifth
quintile.
GHGE associated with the fifth quintile are 7.9
times those of the first quintile. Total caloric intake is
an important factor in ranking impacts per day, as the
consumption of more food calories typically translates
into greater environmental impact. The fifth quintile
consumes, on average, 2.25 times the kilocalories of
the first quintile. However, even when impacts are
normalized by caloric intake, GHGE of the fifth quin-
tile are five times those of the first quintile. Although
this distribution of dietary data from NHANES is
representative of diets in the US on any given day,
there are a couple of caveats. First, self-reported diets
typically understate actual intakes. Second, a distribu-
tion of one-day diets is more disperse than a ‘usual
intake’distribution. The low caloric intake reported
for the first quintile is, in part, a result of both
of these issues. Although we do not have a way to
calculate underreporting in this sample, we do know
that 26% of respondents in the first quintile reported
that their consumption on the recall day was much less
than usual.
Discussion
The dataFIELD database described and applied here
is an important step in capturing the breadth of food
LCA studies in a form that can be linked to existing
individual dietary data. It represents one of the more
comprehensive compilations available of GHGE and
CED data on food production. Further, organizing the
database for straightforward linkage with NHANES
data creates opportunities for a wide array of future
research inquiries, including direct and indirect policy
intervention simulations. The sections below provide
further discussion on the database development and
interpretation of the diet-level results.
Literature review and dataFIELD development
The literature review that underlies development of
dataFIELD found that LCA studies that can be used
to link to dietary choices have increased significantly
in recent years, but data gaps still exist for many food
types. This is consistent with other recent reviews (e.g.
[22]). A scan of the foods requiring proxy assign-
ments in table S4 (supporting information) offers a
sense of current data gaps and a target for LCA prac-
titioners interested in filling such gaps. In addition,
many foods important to evaluation of healthful diets
with low impact—nuts, legumes, meat substitutes—
are poorly represented in the literature and deserve
additional attention. Geographical representation is
biased toward Europe. As has been customary in the
diet-LCA literature, our main estimates for diet-level
impacts are based on average LCA values applied to
each food consumed. However, unlike other studies,
we have addressed variability due to production prac-
tice, geography, or LCA method by calculating upper
and lower bounds of impacts for each food and car-
rying these estimates through to diet-level impacts. As
the NHANES dietary recall data does not specify pro-
duction methods or geographical origin, we cannot be
more precise in assigning impacts from LCA studies
to foods eaten by NHANES respondents. However,
geographical specificity becomes increasingly impor-
tant with other impact categories such as water use,
eutrophication, or land use. Although currently avail-
able data in these categories are limited, we plan to
expand our database to water and land use impacts,
specific to the US food market, in a future iteration.
It has become common practice in diet impact stud-
ies to assign proxy foods as approximations in the case
of missing data, but to our knowledge, this paper is
the first attempt to quantify the contributions from
those proxy assignments. Table 2indicates that proxy
foods contribute 3% to the average diet GHGE, and 8%
6

Environ. Res. Lett. 13 (2018) 044004
Figure 2. Cumulative emission intensity of US 1 day diets using average impact factors. Diets are ranked in order of impact from low
to high. Areas under the curve are proportional to the total impact, with percentage contributions by each quintile shown above the
curve. The green box represents the cumulative emissions of those originally in the 5th quintile if their diets were to shift to diets with
average emission intensities.
to CED. Proxy assignments are made based on foods
with similar production characteristics. However, even
if we assume that all of our proxy estimates are in
error by a factor of 2 (i.e. all proxy impact factors are
doubled), the mean diet-level impacts would still only
increase by 2.6% for GHGE and 8.2% for CED.
Diet-level impacts: interpretation and comparison
with previous results
This study demonstrates the disproportionate impacts
that can be caused by some types of self-selected diets.
Figure 2displays the cumulative emissions of these
diets when ranked in order of GHGE per person per
day. GHGE associated with the fifth quintile of diets
are nearly eight times that of the first quintile and three
times that of the third (middle) quintile. If the top quin-
tile of diets (representing 44.6 million Americans on a
given day6) shifted such that their associated GHGE
were aligned with the mean impact, this would rep-
resent a one-day reduction in GHGE of 0.27 million
metric tons CO2eq. (mmt), equivalent to eliminating
661 million average passenger vehicle miles7on a given
day.
This shift—which could be done by changing
foods, reducing calories, or some combination of
these two—would be represented graphically in fig-
ure 2by removing the section of the curve above
the average emission diet line for the fifth quintile.
Current economy-wide US net emissions (based on
2015 data [34]) are 1023 mmt above the target levels
6The total population number utilized is the represented pop-
ulation for the 2005–2010 NHANES data, denoting the civilian,
non-institutionalized, age 18+population at the midpoint of the
time period: 222 909 266.
7Based on a US weighted average combined fuel economy of cars
and light trucks of 9.35kilometers (km) liter−1 (22.0 miles per gallon)
[33].
in year 2025, as submitted to the U.N. Framework
Convention on Climate Change (UNFCCC) [35]. The
hypothetical diet shift described above, if implemented
every day of the year and met by equivalent shifts
in domestic production, would account for 9.6% of
remaining reductions necessary to meet the target. (see
supporting information for the emission reduction
calculations.) Even if high emission diets (arbitrar-
ily defined here as >25 kg CO2eq. person−1 day−1 ;
the truncated tail extending above the representation
in figure 2) are excluded from the estimate based on
a presumption that they are either atypical or that
such individuals are unlikely to shift diets, moving
the remainder of the high quintile (GHGE >6.9 but
<25 kg CO2eq. person−1 day−1) to the mean GHGE
still accomplishes 9% of the reductions necessary for
the US to meet the UNFCCC target. See supporting
information for a parallel discussion on the cumulative
impacts of food losses. Our estimates of reductions are
likely to be somewhat exaggerated because a distribu-
tion of 1 day diets is known to be more dispersed than
a distribution of usual diets [36]. This is one of the
limitations of using NHANES. Since it is based on the
24 hour diet recall tool, it also tends to underestimate
total energy intake, although this is true of all self-
reported diet instruments [37]. In fact, 24 hour recalls
provide more details about foods consumed and tend
to be less biased than food frequency questionnaires
[38]. Moreover, NHANES provides the only ongoing
nationally representative source for information about
individuals’diets. Our analysis highlights the impor-
tance of looking at individual behaviors rather than just
population means, since there is clearly a wide range of
impacts being caused by self-selected diets.
Table 5offers a comparison of the results from
this study with other reported estimates of the impacts
of the US diet, as well as self-selected diets in
7

Environ. Res. Lett. 13 (2018) 044004
Table 5. Comparison of studies estimating impacts of the US diet or self selected diets in other countries.
Country Diet data sourceaImpact factor data
source
GHGE kg CO2ecapita
−1 day−1 CED MJ capita−1
day−1
consumed consumed+losses consumed+losses
This study US NHANES national
survey (SS)
Exhaustive lit.
review
3.6 4.7 25.2
Heller and
Keoleian 2015 [11]
US USDA (FB) limited lit. review 3.6 5.0
Tom et al 2016
[12]
US USDA (FB) [11], lit. review 5.1 34.5
Hallstrom et al
2017 [15]
US USDA (FB) Lit. review 3.8
Vieux et al 2012
[17]
France INCA2 national
survey (SS)
Lit. review 4.2
Meier and
Christen 2013[19]
Germany German National
Nutrition Surveys
(SS)
Hybrid EIO LCA 5.6 37.0
Rugani et al 2013
[43]
UK National Diet and
Nutrition Survey
(SS) +FB to
estimate waste
Lit. and other
(cradle to point of
sale)
8.8b
Van Dooren et al
2014 [53]
Netherlands Dutch National
Food
Consumption
Survey (SS)
Agri-footprint data
[23]
4.1
Hendrie et al 2016
[54]
Australia Australian Health
Survey (SS)
EIO LCA 18.7b(male)
13.7b(female)
B¨
alter et al 2017
[44]
Sweden LifeGene study
(SS)
Lit. identified
sources
4.7
a(SS) = self-selected diet; (FB) = food balance.
bRepresents broader boundary conditions than other studies; includes impacts throughto the point of purchase.
other countries. When excluding studies that include
broader boundary conditions, there is strong agree-
ment across results, with a coefficient of variation of
3% for GHGE with US diets only, and 7% across all
diets. Self-reported diet surveys carry a well-known
under-reporting bias [39,40] whereas food balance
based estimates (production +imports—exports—
non-food uses ±changes in stock) are often considered
to be overestimates [41,42]. A more refined food
type characterization and a more exhaustive literature
review were utilized in this study in comparison to that
of Heller and Keoleian [11]. While beverages have not
always been delineated as such in previous studies of
diet impacts [11,12,15,19,43–44], we find them
to be important contributors. This finding is further
strengthened by the fact that packaging and use phases
are not included within the boundary conditions of
our estimates. Packaging often represents a hotspot in
the life cycle impacts of beverages [45–49], and use
phase activities (heating water, brewing coffee) can be
important for hot beverages [50,51].
The boundary conditions for the current study
are cradle to farm gate for most food commodities,
and include processing for the collection of FCID
foods that are minimally processed ingredients (flours,
oils, juices, etc). As such, our reported values should
be considered underestimates of actual impacts asso-
ciated with food consumption in the US as they
include the production impacts of processed food
ingredients, but not the impacts of processing
itself. Using the US Environmentally Extended Input
Output model developed by US EPA [52]andan
approach detailed in supporting information, we
estimate that food processing not captured in our
bottom-up estimates amounts to 15% of the total
cradle to processor gate (including agricultural pro-
duction sectors) GHGE. Packaging materials represent
an additional 6%. Inclusion of these missing food
processing and packaging contributions would raise
our estimates by ∼27%, although it is important to
note that these input-output based approximations are
made for the food and agricultural sectors in aggre-
gate, and will not apply evenly across different food
types or for specific diets (i.e. they apply only at
the mean).
Impact factor variability
In figure 1, we demonstrate the influence that food
impact factor variability, as represented by our lit-
erature review, has on the diet-level impacts of the
US population. Based on this estimated variability,
the GHGE for the mean of the population ranges
from 3.8–5.6 kg CO2eq. person−1 day−1,or±19%
of the value based on average impact factors. Vari-
ability of food production systems across geographies
and production methods is expected. In most cases,
the granularity of available LCA data is not suf-
ficient to reasonably and consistently differentiate
between these food production variables. On top of
this, methodological choices within LCAs, such as
how impacts are allocated between co-products, intro-
duce an additional level of variability between studies
8

Environ. Res. Lett. 13 (2018) 044004
that cannot be effectively disaggregated from pro-
duction variability. It is important to keep in mind,
however, that even if such environmental impact data
were complete, the corresponding information in diet
databases is not available. NHANES represents the best
information on diet—both in the aggregate and in
its diversity across the population—available for the
US Yet, it does not (currently) contain information
on the methods of production for food sources (e.g.
was a tomato grown in a heated greenhouse? Was it
organically grown?) or geographic origin of production
(California? Michigan? Chile?). To further refine these
estimates would require more information on foods in
the NHANES survey, as well as betterLCA data on food
production variability.
Conclusion
This paper describes the development of dataFIELD,
a food production environmental impact database
based on an exhaustive review of the LCA litera-
ture, and provides a framework for linking this data
with individual self-selected diets of the US popu-
lation. The study demonstrates the distribution of
diet-related GHG and energy demand intensity for
self-selected diets in the US, showing that the fifth of
the diets with the highest carbon footprint account for
46% of the total diet-related GHGE burden. Behavior
change campaigns focused on these diet types could
be an efficient and effective means of reducing US
GHGE. Campaigns targeting dietary shifts, therefore,
offer a significant opportunity for state, city, business
and other organizational policy or leadership aimed
at climate change action. Getting people to change
dietary behavior is notoriously challenging [55], and
enhanced efforts are needed to better identify effec-
tive strategies for influencing diet shifts that lead to
reduced environmental impacts.
Data gaps are often a major challenge in LCA.
This study demonstrates for the first time, however,
that foods for which no LCA data currently exist do
not represent a significant contribution to the car-
bon intensity of US diets. Further, we quantify the
influence of variability in LCA data on impacts at the
diet level to be ±19% of the mean. While current
diet recall data do not capture the information nec-
essary to do so, future work could connect food choice
and diet variation of individuals with a more precise
characterization of the supply chains producing their
food to better understand the implications of sourcing.
Given the ranges in production impacts across prac-
tices and geographies, this may also be an important
aspect in reducing food system impacts.
Future work will investigate correlations between
environmental impacts and health implications
of individual US diets, as well as elucidate associations
between population demographics and diet-related
environmental impacts. Combined, these works
provide a solid foundation for policy considerations
that acknowledge diet shifts as an instrumental com-
ponent of GHGE reduction goals.
Acknowledgments
The authors would like to acknowledge the gracious
assistance of Yi Yang of CSRA, Inc. with the USEEIO
calculations, and Brittany Kovacs and Tara Narayanan
with the LCA literature review.
This work is funded by the Wellcome Trust grant num-
ber 106854/Z/15/Z.
ORCID iDs
Martin C Heller https://orcid.org/0000-0001-9204-
6222
Gregory A Keoleian https://orcid.org/0000-0002-
7096-1304
Donald Rose https://orcid.org/0000-0002-2110-
8059
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