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sustainability
Article
Standardized Recipes and Their Influence on the
Environmental Impact Assessment of Mixed Dishes:
A Case Study on Pizza
Katerina S. Stylianou 1, * , Emily McDonald 1, Victor L. Fulgoni III 2and Olivier Jolliet 1
1
Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor,
MI 48109, USA; emillaur@umich.edu (E.M.); ojolliet@umich.edu (O.J.)
2Nutrition Impact, LLC, 9725 D Drive North, Battle Creek, MI 49014, USA; vic3rd@aol.com
*Correspondence: kstylian@umich.edu; Tel.: +1-734-730-8009
Received: 20 October 2020; Accepted: 9 November 2020; Published: 13 November 2020
Abstract:
Food and diet life cycle assessment (LCA) studies offer insights on the environmental
performance and improvement potential of food systems and dietary patterns. However, the influence
of ingredient resolution in food-LCAs is often overlooked. To address this, four distinct decomposition
methods were used to determine ingredients for mixed dishes and characterize their environmental
impacts, using the carbon footprint of the U.S. daily pizza intake as a case study. Pizza-specific
and daily pizza intake carbon footprints varied substantially between decomposition methods.
The carbon footprint for vegetarian pizza was 0.18–0.45 kg CO
2
eq/serving, for meat pizza was
0.56–0.73 kg CO
2
eq/serving, and for currently consumed pizzas in the U.S. (26.3 g/person/day;
75 pizzas types) was 0.072–0.098 kg CO
2
eq/person/day. These ranges could be explained by differences
in pizza coverage, ingredient resolution, availability of ingredient environmental information,
and ingredient adjustability for losses between decomposition methods. From the approaches
considered, the USDA National Nutrient Database for Standard Reference, which reports standardized
food recipes in relative weights, appears to offer the most appropriate and useful food decompositions
for food-LCAs. The influence and limitations of sources of reference flows should be better evaluated
and acknowledged in food and diet LCAs.
Keywords: life cycle assessment; food decomposition; mixed dishes; pizza; carbon footprint
1. Introduction
Food production and consumption have a significant contribution to environmental impacts that
compromise the quality of air, soil, and water [
1
]. As environmental changes due to anthropogenic
causes become more severe, there is an increased need to better characterize the contribution of food
systems in order to identify solutions that reduce this contribution [
2
]. For more than 30 years, life cycle
assessment (LCA) has been used to study food systems and evaluate the environmental performance
of foods and diets [
3
]. Despite significant progress in food LCA [
4
], there is limited availability of
necessary environmental data, such as life cycle inventories (LCIs), to evaluate meals or mixed dishes.
Mixed dishes are defined as a mixture of ingredients with varying proportions (multi-ingredient)
and are currently providing a large portion of calories in modern diets [
5
]. Due to the lack of mixed
dishes LCIs, the environmental impacts of mixed dishes are understudied and possibly under- or
over-estimated in the limited number of studies available in the literature, since they rely on LCIs of
main agricultural commodities and simplified assumptions [6,7].
Studies that characterize the environmental impacts of mixed dishes typically focus on a
limited number of foods and have to employ different approaches to determine their ingredient
Sustainability 2020,12, 9466; doi:10.3390/su12229466 www.mdpi.com/journal/sustainability
Sustainability 2020,12, 9466 2 of 16
composition [8–11].
Methods of decomposing mixed dishes into individual ingredients include
food-specific recipes obtained from labels [
12
] and manufacturers [
13
], meal-kits [
14
], modelled/scenario
recipes [
9
,
15
], and typical recipes [
16
]. While these methods are useful when studying a single food
or small number of foods, investigating the environmental impacts of diverse mixed dishes on a
larger-scale requires consistent information that is time-consuming to collect and often might not
be easily accessible. Furthermore, the use of different food decomposition approaches can lead to
incomparable estimates of environmental impacts. There is therefore a need for harmonizing the
environmental evaluations of mixed dishes, starting with how reference flows are determined.
Standardized food recipe databases are developed by national agencies, such as the U.S.
Department of Agriculture (USDA), to assess the nutritional quality of diets. Recently, two such
databases have been utilized in evaluating the environmental impacts of the U.S. diet by decomposing
dietary patterns to ingredient-commodities. More specifically, Heller et al. (2018) used the Food
Commodity Intake Database (FCID) to estimate the environmental impacts of dietary patterns reported
in the National Health and Nutrition Examination Survey (NHANES) [
17
]. Conrad et al. (2018) used
the same database to investigate diet-level nutritional and environmental trade-offs associated with
food losses in the U.S. [
18
]. Tichenor Blackstone et al. (2018) used the Food Intakes Converted to Retail
Commodities Database (FICRCD) to quantify the environmental impacts associated with different
healthy dietary patterns recommended in Dietary Guidelines for Americans (DGA) [
19
]. While these
databases offer a consistent source of standardized food recipes that could be used in determining
reference flows for both single-ingredient and mixed dishes, they have never been used to evaluate
food-specific environmental impacts associated with mixed dishes.
As the influence of food recipes (e.g., ingredient composition) as a methodological limitation in
food LCA is often overlooked, the aim of this paper was to investigate and compare the potential
of available standardized food recipe databases as a source intermediary flows for mixed dishes.
More specifically, this analysis aims to assess and compare the respective environmental impact
estimated obtained using four such databases as decomposition methods for mixed dishes and
demonstrate them using a case study on the carbon footprint associated with the average pizza
consumption in the U.S. diet, also differentiating between meat and vegetarian pizzas.
2. Materials and Methods
The cradle-to-gate carbon footprint of the type-specific (meat vs. vegetarian) and daily consumption
of pizzas in the U.S. diet were estimated and compared using four different decomposition methods to
evaluate their applicability potential in LCA. The following sections describe the methodology, data,
and the assumptions used in this analysis.
2.1. Pizza in the U.S. Diet
The average consumption of pizza in the U.S. diet was determined using the NHANES 2011–2016
database [
5
]. NHANES is a nationally representative, cross-sectional survey administered every two
years to U.S. citizens that records daily food intakes. The population average of the daily pizza
intake was determined by the day 1 reported intake in three survey cycles (2011–2012, 2013–2014,
and 2015–2016) for participants older than 25 years old, excluding pregnant and lactating women
(N =13,332). Pregnant and lactating women were excluded from this analysis due to the special diet
that they typically follow that is likely not to be representative of the average population, especially
in the case for pizza that often contains processed meat—a food item that is often avoided during
pregnancy and lactation [
20
,
21
]. Furthermore, this analysis focused on the diets of adults >25 years
old in alignment with dietary risk factors by the Global Burden of Disease reports [
22
], which is
critical for the evaluation of healthy and sustainable foods [
23
–
26
]. All the consumed pizza types
in the database were identified by food descriptions, which included the word “pizza.” From the
77 foods identified, two items described as “pizza toppings” were excluded from the analysis as they
represented individual ingredients (see Supplementary Materials Table S1).
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2.2. Life Cycle Assessment Framework
For the pizza-specific analysis of the meat and vegetarian pizzas, a functional unit (FU) of pizza
serving size (140 g) was retained. The pizza serving size was determined based on the reference amounts
customarily consumed (RACC) servings defined by the U.S. Food and Drug Administration [
27
].
For the average consumption analysis, the FU was defined as the average daily pizza intake in the U.S.
The system boundary for the life cycle assessments were cradle-to-farm gate or cradle-to-processing
facility gate.
2.3. Environmental Assessment
2.3.1. Food Decomposition
Four publicly available databases were identified that report standardized food recipes for
the foods in the NHANES database. Serving as a decomposition (or deconstruction) approach,
each database was used to determine the intermediary flows for individual pizzas by identifying pizza
ingredients and their quantities in g per serving size of pizza.
The four decomposition methods varied in ingredient resolution and loss/waste coverage. First,
the Food Patterns Equivalents Database 2015–2016 (FPED) deconstructs foods into 37 consumption-level
food patterns that are measured in serving equivalents such as cups, tablespoons, ounces, drinks,
and grams [
28
]. Second, the Food Intakes Converted to Retail Commodities Databases 2003–2008
(FICRCD) separates foods into 65 retail-level commodities [
29
]. Third, the Food Commodity Intake
Database 2005–2010 (FCID) breaks down foods into ~500 consumption-level food commodities [
30
].
Finally, the USDA National Nutrient Database for Standard Reference, Release 28 (SR) is a food
composition database that contains ~3200 consumption-level food items [
31
]. For the purpose of this
analysis, all database components are referred to as ingredients.
While FICRCD can directly determine the ingredient composition of pizzas in mass amounts,
additional steps were required for the use of the FPED, SR, and FCID databases. In particular, food
patterns in FPED that were originally reported in serving equivalents were converted into mass
amounts in g. Fruits, vegetables, and dairy were converted from cup equivalents to g using the
average weight of one cup of food within the respective pattern. In addition, grain and protein food
groups were converted from ounce equivalents to amounts in g based on the database’s definitions.
Added sugar teaspoons were converted into g based on a weight equivalent of 4.2. A summary of
all the weights of serving equivalents used in this analysis are available in supporting information
(Supplementary Materials, Table S2). For the SR, previous versions of the database were employed to
further decompose processed and prepared food items reported in the latest version of the database.
For example, the newest pizza compositions in the SR include multi-ingredient food items such as
“fast food, pizza chain, 14” pizza, cheese topping, regular crust” along with pizza toppings that are
typically single-ingredient items. In some occasions where previous versions of SR did not allow
for deconstruction of multi-ingredient food items, information from similar items in SR or foods
in NHANES was used based on their description (Table S3). Finally, information from FPED was
used to determine dairy ingredients in FCID. More specifically, FCID reports the total dairy in foods
using “Milk, water”, “Milk, nonfat solids”, and “Milk, fat”, which is impractical in determining
dairy ingredients. Therefore, the total dairy in the food according to the FCID was reallocated into
dairy-specific ingredients (e.g., milk, yogurt, cheese) using the dairy repartition in FPED. Specifically
for this analysis, the total amount of dairy in pizzas according to the FCID decomposition was assumed
to be cheese (unspecified type).
All consumption-level ingredient amounts determined from the FPED, FCID, and SR
decompositions were converted into retail-level amounts for consistency and comparability with the
estimates from the FICRCD database. To do that, consumption-to-retail conversion factors were used
as reported in the FICRCD database. These factors account for ingredient-specific mass loss or gain
during preparation, cooking, and processing, as well as non-edible parts. For FCID and SR, these
Sustainability 2020,12, 9466 4 of 16
conversion factors were matched directly with ingredients. For FPED, conversion factors were first
classified into food groups and then aggregated to estimate food group averages (Supplementary
Materials, Table S2). These classifications were generic and were not adapted for this case study.
Detailed descriptions of the four methods are summarized in Table 1. The underlying
decompositions of individual pizzas used in this analysis for SR (Tables S3 and S4), FPED (Table S5),
FCID (Table S6), and FICRCD (Table S7) are available in supplementary materials.
Table 1. Description of decomposition methods.
Standard Reference
(SR)
Food Patterns
Equivalents Database
(FPED)
Food Commodity
Intake Database
(FCID)
Food Intakes
Converted to Retail
Commodities
Database (FICRCD)
Detailed Ingredients Food Groups Commodities Partly Aggregated
Commodities
Description
Core composition
databases in
WWEIA/NHANES. It
reports the relative
weight of ingredients
for each consumed
food.
Reports food pattern in
serving equivalents per
100 g of consumed
food.
Developed to assess
dietary exposure to
pesticides, the
database reports g
commodities per 100
g of consumed food.
Reports retail-level g
per 100 g of
consumed food,
accounting for
masses lost/gained
during preparation,
cooking, and
non-edible parts.
Resolution
~3200 single- and
multi-ingredient food
items
37 food groups ~500 commodities
65 commodities,
some of them
represent food
groups
Database
preparation
Multi-ingredient items
1
further decomposed
using previous database
versions or similar items
Serving equivalents
(e.g., standardized
portion units)
converted into g using
average weights per
serving equivalent (see
Table S2).
Milk commodities
aggregated as single
component and
assigned to a dairy
product based on
expert judgement.
Useful attributes
- Recommended
decomposition method
- Consistent with
nutritional
decomposition
- Useful to check
multi-ingredients
components from SR
and dairy components
of FCID
- Complementary
component
information on
cooking processes by
food
- Retail-to-intake
conversion factors
that are relevant for
LCA
1Processed and prepared food items comprised of multiple ingredients.
2.3.2. Life Cycle Inventory
All ingredients identified by the four decomposition methods were linked with environmental
life cycle inventory (LCI) datasets. LCIs quantify the inputs and outputs of a given product system
throughout its life cycle [
32
]. These datasets were used to quantify food production related life cycle
greenhouse gases emissions (e.g., CO2, CH4, etc.).
Ingredients were matched with available LCIs based on similarity. To maximize the coverage
of LCIs in our analysis three databases were employed. Listed in the order of priority, LCIs were
obtained from ecoinvent v3.2 [
33
], the World Food LCA Database v3.1 [
34
], and the ESU World Food
LCA database [
35
]. Since LCIs are typically region-specific, representing the region of production,
U.S.-specific LCIs were prioritized, followed by Canada, and “rest of the world” (RoW) or global (GLO)
LCIs. Averages or proxy LCIs were used when direct match between an ingredient and a LCI was not
possible, e.g., for “ingredients” that represented food groups such as fruits or for ingredients that a LCI
was not available. Proxies were selected based on production system similarities.
Overall, to test the ability of each method for high throughput decomposition food-specific
knowledge was not considered in matching ingredients with LCIs, meaning that matching was not
adapted to be specific to well-known pizza ingredients. For example, when cheese (unspecified type)
Sustainability 2020,12, 9466 5 of 16
was identified as an ingredient it was matched with the average of available cheese-LCIs and not
adapted to match with a mozzarella-LCI, which is specific to pizzas.
2.3.3. Environmental Life Cycle Impact Assessment
Carbon footprints were estimated using Impact World+v1.4 [
36
] at the midpoint level, representing
the shorter-term global warming potential over the first 100 years after emission (GWP100).
3. Results
3.1. Pizza-Specific Analysis
3.1.1. Pizza-Specific Decomposition
The decomposition of vegetarian (‘Pizza with cheese and extra vegetables, medium crust’) and
meat (‘Pizza with extra meat, medium crust’) pizzas are summarized in Table 2(for detailed ingredients
see Table S8). The vegetarian pizza chosen was representative of “extra vegetable” pizzas that are
topped with double the amount of vegetables compared to all other pizzas, while the meat pizza
chosen was representative of the “extra meat” pizzas that typically contain three times the meat of all
other pizzas. Each decomposition method appeared to identify similar ingredient categories for the
pizzas (except for meat) but the number of ingredients and the quantity of certain ingredients differed
substantially between methods. The SR and FCID methods generated similar retail-level quantities
for both pizza types that ranged between 143–148 g per serving and ingredient composition had a
100% coverage of the pizzas. While the FCID identified the highest number of ingredients (vegetarian:
44; meat: 48), the SR decomposition allowed for the most direct matching between ingredients and
LCIs as well as loss conversion factors (vegetarian and meat: 17). The FPED and FICRCD methods
generated higher total retail-level amounts at 200–2019 g per pizza serving, primarily driven from
higher quantities of dairy and vegetables. For FPED, the higher dairy estimate was the result of the
consumption-level decomposition whereas the higher vegetable estimate was obtained after adjusting
for losses. At consumption-level, the FPED decomposition covered 95% and 99% of the consumed
vegetarian and meat pizzas, respectively. The FPED (vegetarian: 7; meat: 9) and FICRCD (vegetarian:
7; meat: 8) decompositions generated the lowest ingredient resolution and primarily required the use
of average and proxy LCIs and conversion factors (for FPED only).
The main ingredients for the vegetarian pizza were vegetables, grains, and dairy. According
to the SR and FCID, vegetables made up ~50% of the consumed vegetarian pizza at retail level.
The corresponding estimate from FPED and FICRCD was ~65%. This difference can be explained by
the different way that tomato ingredients are captured in each approach. More specifically, the SR and
FCID identify canned tomatoes and tomato puree as ingredients whereas in FPED and FICRCD tomato
ingredients are ultimately reported as fresh tomatoes after adjusting for losses. Furthermore, the SR
and FCID decompositions reported about 20 g of dairy per serving of vegetarian pizza at retail level,
while FPED (42 g
dairy
/serving) and FICRCD (34 g
dairy
/serving) reported substantially higher amounts.
Even though all methods identified cheese as the only dairy ingredient, only the SR approach reported
which type of cheese was used (e.g., mozzarella). The retail-level grain ingredients varied substantially
between methods and ranged from 17 to 34 g per serving of vegetarian pizza consumed. FPED
produced the lowest estimate due to an average loss factor of 0.52 refined grains (e.g., flour, pasta, and
rice). Using this average loss factor might underestimate grain estimates in pizzas but it highlight the
limitations of low ingredient resolution decompositions methods. The four decomposition approaches
also differed in the types and amounts of oils and fats, with FPED reporting more than two times
higher estimates.
The main ingredients for the meat pizza were vegetables, meat, grains, and dairy. The decomposition
differences between methods that were observed in the vegetarian pizza were also observed for
the meat pizza, with the exception of grains and meat. For example, the SR and FCID methods
Sustainability 2020,12, 9466 6 of 16
reported 35–40 g vegetables and 21–24 g of dairy per serving of meat pizza at retail, with the FPED
(95 g
vegetables
/serving and 42 g
dairy
/serving) and FICRCD (107 g
vegetables
/serving and 44 g
dairy
/serving)
reporting estimated that were two and almost three times higher, respectively. The total amount of
grain ingredients determined by the four decomposition methods were similar to those from the
vegetarian pizza decomposition, except for the FCID approach reported a higher estimate at 38 g
of grains per serving of meat pizza. For this food, an important decomposition difference between
methods was observed for meats. All methods reported a total of 28–36 g of meat per serving of meat
pizza at retail level. Both the FCID and the FICRCD approaches attribute this meat amount solely to red
meat (beef and pork) whereas the SR and FPED allocate this amount between red meat, poultry, and
cured meat. For the latter decompositions, the poultry and cured meat estimates are similar between
methods. However, the SR reported a red meat estimate that was two times higher than the FPED.
Table 2.
Decomposition of one serving size (140 g) of vegetarian and meat pizza at consumption and
retail by ingredient groups. Detailed decompositions are available in supplementary material Table S8.
Pizza Type Vegetarian Meat
Decomposition
Method FCID SR FPED FICRCD FCID SR FPED FICRCD
# of Ingredients 44 17 7 7 48 17 9 8
Consumption
Cured meat 0.0 0.0 0.0 0.0 7.2 10.0
Dairy 19.9 20.7 41.8 24.0 21.0 41.8
Grains 31.0 27.4 32.9 38.4 27.8 33.4
Oils & fats 4.6 3.7 9.9 5.7 3.7 14.2
Other 12.1 18.7 0.0 13.8 19.0 0.0
Poultry 0.0 0.0 0.0 0.0 7.2 7.1
Red meat 0.0 0.0 0.0 24.2 14.4 6.7
Sugars 0.6 0.3 0.9 0.7 0.3 1.1
Vegetables 71.9 69.2 47.7 33.1 39.4 24.7
Total 140.0 140.0 133.4 140.0 140.0 139.1
Retail
Cured meat 0.0 0.0 0.0 0.0 0.0 10.0 12.5 0.0
Dairy 19.9 20.7 41.8 33.7 24.0 21.0 41.8 44.1
Grains 31.0 27.4 17.3 34.2 38.4 27.8 17.5 34.2
Oils & fats 4.6 3.7 9.9 3.5 5.7 3.7 14.2 3.5
Other 12.1 18.7 0.0 0.0 13.8 19.0 0.0 0.0
Poultry 0.0 0.0 0.0 0.0 0.0 8.9 9.1 0.0
Red meat 0.0 0.0 0.0 0.0 28.0 16.6 8.6 27.7
Sugars 0.6 0.3 0.9 2.1 0.7 0.3 1.1 2.1
Vegetables 79.4 72.0 133.1 130.9 35.4 39.7 95.4 107.0
Total 147.5 142.7 203.1 204.4 146.1 146.9 200.3 218.5
Note: FCID =Food Commodity Intake Database; SR =Standard Reference; FPED =Food Patterns Equivalents
Database; FICRCD =Food Intakes Converted to Retail Commodities Database.
3.1.2. Pizza-Specific Carbon Footprint
The carbon footprints of both the vegetarian and the meat pizzas varied considerably between
decomposition methods (Figure 1). The carbon footprint of vegetarian pizza serving was estimated
at 0.18 kg CO
2
eq for SR, 0.23 kg CO
2
eq for FCID, 0.35 kg CO
2
eq for FICRCD, and 0.45 kg CO
2
eq for
FPED. The impact was predominantly driven by dairy (54–61%) followed by vegetables (22–27%) in all
methods. The lowest carbon footprint of dairy was generated using the SR (0.10 kg CO
2
eq/serving)
due to a lower total dairy amount determined combined with lower greenhouse gas emissions for the
ingredients identified. More specifically, the majority of the dairy identified in SR was mozzarella,
which has a carbon footprint estimate (3.8 kg CO
2
eq/kg) that was about two times lower than the carbon
footprint of “average cheese” that was used in the other three methods (6.3 kg CO
2
eq/kg). The modest
contribution to carbon footprint from the vegetables varied in absolute terms between decomposition
methods. However, it should be mentioned that the footprint of the large vegetable quantities reported
in the FPED and FICRCD were partly balanced out by the use of average LCIs and the identification of
Sustainability 2020,12, 9466 7 of 16
relatively high-footprint vegetable components by the SR and FCID decompositions (e.g., peppers,
onions, and mushrooms). Solid fats (oils and fats) had a noticeable contribution according to the FPED
decomposition. Interestingly, only the SR and FCID decompositions reported water as an ingredient
but it had negligible contributions to carbon footprints. Decomposition differences for the rest of the
components (grains, sugars, other) had little influence on the carbon footprint of vegetarian pizza.
Figure 1.
Retail-level carbon footprint for one serving (140 g) of vegetarian and meat pizzas consumed.
FCID =Food Commodity Intake Database; SR =Standard Reference; FPED =Food Patterns Equivalents
Database; FICRCD =Food Intakes Converted to Retail Commodities Database.
For the two pizzas analyzed, all methods produced higher carbon footprint estimates for
the meat compared to the vegetarian pizza, with results varying by decomposition method:
0.56 kg CO
2
eq/serving for FCID, 0.67 kg CO
2
eq/serving for SR, 0.71 kg CO
2
eq/serving for FPED,
and 0.73 kg CO
2
eq/serving for FICRCD. Furthermore, each decomposition recognized dissimilar
components as the major impact contributors chiefly due differences in the type and amounts of
ingredients identified. For the highest (FICRCD) and the lowest (FCID) carbon footprint estimated,
red meat was the main contributor at 55% and 61%, respectively, followed by dairy (27–31%). For SR,
cured meat (35%) and red meat (39%) were the dominant contributors to carbon footprint, followed
by dairy (15%). In contrast, the highest contributor for the FPED decomposition was dairy at 35%,
which was similar to the corresponding carbon footprint estimate from FICRCD in absolute terms.
Furthermore, red meat, cured meat, and oils and fats contributed almost equally to the impact at around
15%. It should be mentioned that the FPED decomposition reported the lowest meat contribution
to impact (total of 37% that corresponded to 0.27 kg CO
2
eq/serving of meat pizza), even though it
identified a similar meat-ingredient decomposition with SR. The discrepancy observed at the impact
level between these methods was mainly due to differences in ingredient resolution and consequently
the ability to match ingredients with available LCIs. Due to a higher resolution, the SR decompositions
Sustainability 2020,12, 9466 8 of 16
enabled a more direct matching between these ingredients and LCIs, which reported higher greenhouse
gas emissions than the average LCIs used in the low resolution FPED. Finally, the vegetable contribution
to the carbon footprint was higher in FPED and FICRCD that was exclusively associated with tomato
ingredients. As mentioned in the decomposition section, the single vegetable component in meat pizza
according to both methods was tomato, which due to the low resolution of these methods, represented a
food group and reported the ultimately reported the ingredient as fresh tomato after adjusting for losses.
Thus, ingredient amount was higher than the corresponding ingredients reported in SR and FCID
(primarily tomato puree) and was matched with the average of fresh tomato LCIs (0.52 kg CO
2
eq/kg),
which was almost 50 times higher than the carbon footprint of tomato puree (0.011 kg CO
2
eq/kg).
When combined, these decomposition differences generated considerable discrepancies in the vegetable
contributions to the carbon footprints of meat pizza consumed between methods.
3.2. Daily Pizza Intake in the U.S.
The average daily consumption of pizza in the U.S. diet of adults was estimated at 26.3 g/pers/d
based on the reported consumption of 75 distinct pizzas. Large discrepancies were observed in
the total number of ingredients, ingredient composition, and carbon footprint generated by each
decomposition method. Figure 2illustrates the repartition of consumed (intake) and retail-level daily
pizza intake according to the four decomposition methods investigated in this analysis. Figure 3
presents the retail-level carbon footprint a of daily pizza consumption in the U.S. Estimates ranged
from 71.5 g CO
2
eq/pers/d for FCID up to 98.0 g CO
2
eq/pers/d for FPED, which corresponded to
0.38–0.52 kg CO
2
eq/serving pizza (140 g/serving). Overall, these findings show the influence of
decomposition method on the environmental impacts of foods in LCA.
Figure 2.
Decomposition of daily pizza intake in the U.S in consumed (intake) and retail amounts.
Missing intake at retail level is not adjusted for losses. The underlying data and calculations for these
estimates are available in Tables S9–S12 in the supplementary materials. FCID =Food Commodity
Intake Database; SR =Standard Reference; FPED =Food Patterns Equivalents Database; FICRCD =Food
Intakes Converted to Retail Commodities Database.
Sustainability 2020,12, 9466 9 of 16
Figure 3.
Carbon footprint of daily pizza consumption (left axis) and pizza serving (140 g; blue
diamond; right axis) at the retail level. The underlying data and calculations for these estimates are
available in Tables S9–S12 in the supplementary materials. FCID =Food Commodity Intake Database;
SR =Standard Reference; FPED =Food Patterns Equivalents Database; FICRCD =Food Intakes
Converted to Retail Commodities Database.
Only the SR approach was able to provide a complete coverage of all consumed pizzas using
57 ingredients, which corresponded to 27 g/pers/d of daily pizza intake at retail level. According to
this decomposition methodology at retail level, 30% of the daily pizza was vegetables, 24% grains,
while dairy and milk (other) accounted for 16% each. However, the main contributors to carbon
footprint at retail (77.6 g CO
2
eq/pers/d) were cured meat (35%), red meat (25%), and dairy (25%).
Using this decomposition approach, the average carbon footprint of a pizza serving was estimated at
0.40 kg CO2eq.
The FPED approach identified 16 ingredients that covered all 75 pizzas, underestimating pizza
consumption at 24.4 g/pers/d and estimating a retail-level pizza intake at 34.7 g/pers/d. At retail,
about half of the daily pizza intake was made out of vegetables, followed by dairy (21%) and fats
(11%). The quantities for these components were about 2–3 times higher than the corresponding
estimates from the SR. However, FPED reported half the grain of the SR (Figure 2), which resulted
from the use of aggregated estimates of weights of cup equivalents and loss conversion factors due
to the low ingredient resolution of the approach. Using such aggregated estimates might over- or
under-estimate consumed (e.g., dairy and vegetables) and retail-level quantities (e.g., vegetables and
grains). This decomposition approach resulted in the highest carbon footprint for the daily pizza
intake of 98 g CO
2
eq/pers/d at retail (0.52 kg CO
2
eq/serving), which was about 30% higher and had
a substantially different repartition than the SR method (Figure 3). More specifically, the leading
contributors of carbon footprint according to FPED were dairy (47%), oils and fats (18%), cured meat
(13%), and vegetables (11%). The ingredient-specific carbon footprint estimates from FPED were
two (dairy) to seven (oils and fats) times higher than the SR, except for cured meat (50% lower),
red meat (about four times lower), and grains (30% lower). Unlike the SR, the FPED does not contain
Sustainability 2020,12, 9466 10 of 16
water as a decomposition component, which explains the quantity difference observed in components
categorized as ‘other.’ Even though water is not anticipated to have substantial contribution from
an environmental perspective, when evaluating decomposition methods on a mass basis, the lack of
water as a component that is typically used in larger amounts might hide overestimated quantities of
other components.
The FCID approach covered only 51 pizzas (68%) that were decomposed into 57 ingredients and
corresponded to a daily pizza intake of 24.9 g/pers/d at retail (23.8 g consumed/pers/d). Since this
approach is not regularly updated, 30% of the pizza types reported to be consumed were missing
from the analysis, corresponding to 1.9 g/pers/d of the daily pizza intake. This approach generated
a retail-level carbon footprint slightly lower than the SR at 71.5 g CO
2
eq/pers/d, corresponding to
0.38 kg CO
2
eq/serving. Both the FCID and SR produced similar retail-level decompositions (Figure 2)
and ingredient contributions to the carbon footprint (Figure 3), with the exception that FCID did
not distinguish between red and cured meat. In particular, the FCID approach only identified the
meat protein (e.g., pork and beef), while the SR meat components were more descriptive in regards
to processing and preparation, which allowed for better matching with available LCIs. For example,
part of the cured meats identified in the SR were described as a mix of beef and pork that generate
15% lower carbon footprint per kg compared to beef meat identified in FCID. Interestingly, the FCID
approach offers a less than ideal decomposition of dairy products as it was intended to capture pesticide
residue that is linked to fat content in ingredients. Hence, dairy is reported as ‘Milk, fat’, ‘Milk,
nonfat solids’, and ‘Milk, water’. While in this case study it was assumed that the sum of these dairy
ingredients corresponded to cheese (unspecified type for generalizability), such an assumption would
not be possible for the evaluation of multi-ingredient foods such as pasta, pastries, and desserts that
contain different types of dairy ingredients such as cheese, milk, and yogurt.
Only 70% of the pizzas types consumed were evaluated using the FICRCD approach, corresponding
to 24.3 g pizza/pers/d. Since this approach is not updated regularly, it does not contain information on
new foods introduced in NHANES. This approach generated an overall 17-ingredient composition
of the daily pizza intake in the U.S that at retail amounted to 33.2 g/pers/d and was similar to FPED.
More specifically, according to FICRCD, daily pizza intake was mainly comprised of vegetables (50%),
grains (20%), and dairy (20%). The FICRCD approach produced a carbon footprint at 89.0 g CO
2
eq/pers/d
(0.47 kg CO
2
eq/serving), 20% higher than the SR approach. This impact was driven by dairy (41%) and
red meat (38%). The dairy and vegetable ingredients generated carbon footprint estimates that were
two to three times higher than the corresponding estimated from the SR, reflecting the underlying
retail quantity differences between approaches. The same trend was observed for red meat. However,
the difference was due to the underlying quantities of meat types in each approach; sausages that are a
mixture of beef and pork and have a lower footprint than beef make up 74% of the meat in SR, whereas
in FICRCD 63% of the meat is beef.
4. Discussion
In this paper, four public databases were evaluated as sources of standardized recipes that can
be used in the environmental impact assessment of mixed dishes. The case study on pizza in the
U.S. in this analysis illustrated that the carbon footprint of pizzas is highly driven by composition
and in particular the amount of meat present. The pizza type-specific estimates presented in this
analysis are indicative of “extra meat” and “extra vegetable” pizzas impacts, with the latter generating
a substantially lower carbon footprint. While this finding is in agreement with previous estimates
for pizzas [
37
] and other mixed dishes [
9
,
10
,
14
,
38
,
39
], it should be noted that ingredient composition
might vary greatly within pizza types that may considerably influence the impact of individual foods.
This analysis also showed that the choice of the decomposition method has a noteworthy influence on
the carbon footprint of individual foods and daily food intake. It is expected that this influence could
be also found in the overall environmental impact assessment of foods, and consequently diets, since
carbon footprint of foods is correlated with most environmental indicators considered in LCA [26].
Sustainability 2020,12, 9466 11 of 16
To provide better guidance on the use of the four databases investigated in this analysis as
decomposition approaches, their performance was summarized based on three criteria that can
potentially influence the environmental impacts of foods (Table 3): Ingredient quantity accuracy and
resolution, ingredient matching with LCIs, and database update frequency. Ingredient resolution
is of particular importance due to the large variation of the carbon footprint between food
commodities [1,4,17],
especially meat. Low- (FPED) and moderate-resolution (FICRCD) decomposition
methods often require the use of aggregated LCIs and loss adjustment factors that fail to capture
impact variability and might over- or underestimate environmental impacts as components represent
food groups. Aggregated estimates were generic and not specific to pizzas to enable the evaluation
the ability of the four methods for high-throughput food decompositions. For example, average
cheese estimates were used when the type of cheese was not specified. In addition, FPED originally
reports component quantities in serving equivalents that need to be converted into mass, an attribute
that can be challenging when the weight of serving equivalents varies considerably within a food
group. Consequently, FPED consistently reported the highest dairy quantity, which was based on
the average weight of a cup of cheese (54 g). In a sensitivity study, pizza-specific estimates were
used for the average weight of a cup of mozzarella (45 g) and mozzarella-LCI, the cheese typically
used in pizzas and a the carbon footprint of daily pizza intake with FPED was reduced by ~20% to
75.1 g CO
2
eq/pers/d, a result compatible with the SR. The FPED approach also seemed to favor oils and
fats in pizzas, a component with sizable environmental footprint. Overall, it was determined that the
approach has poor ingredient resolution and offers the lowest ability to estimate accurately ingredient
quantity and to match ingredients with LCIs, but it has a good update frequency.
Table 3.
Evaluation summary of the potential of four database as decomposition methods for mixed
dishes in life cycle assessment (LCA).
Standard Reference
(SR)
Food Patterns
Equivalents Database
(FPED)
Food Commodity
Intake Database
(FCID)
Food Intakes Converted
to Retail Commodities
Database
(FICRCD)
Ingredient quantity
accuracy and
resolution
Good
- Exact amounts of
ingredients in g
- High resolution
- Multi-ingredient
items need
decomposition
Poor
- Conversion of serving
equivalents into g
- Low resolution
- Possible
overestimation of
grains and fats
- Water content missing
Fair
- Ingredients in g
- Moderate resolution
- Problematic dairy
ingredients
- Part-specific
ingredients
(lipophilicity
differences)
- Possible
overestimation of oils
Fair
- Retail-level composition
- Ingredients in g
- Low resolution
- Possible overestimation
of dairy, sugars, and
vegetables
- Water content missing
Ingredient
matching with
LCIs
Good
Detailed ingredient
description allows for
best possible match
with LCIs
Poor
Requires aggregation
of LCIs for all
ingredients
Fair
Satisfactory ingredient
distinction (not
detailed for dairy and
meat)
Fair
Requires aggregation of
LCIs for some
ingredients
Update frequency
Good
Updated every two
years with each new
cycle of NHANES
(Latest update: 2018)
Good
Updated every two
years with each new
cycle of NHANES
(Latest update: 2018)
Poor
Not updated
frequently. Not
applicable for new
foods in NHANES
(Latest updated: 2010)
Poor
Not updated frequently.
Not applicable for new
foods in NHANES
(Latest updated: 2008)
The FICRCD and FCID approaches offer a fair ingredient resolution and matching with LCIs.
In addition to a low ingredient resolution, the FICRCD method only provides component amounts
at retail that might be appropriate only for certain LCAs [
19
,
40
]. Compared to the SR, the approach
seemed to overestimate dairy, sugars, and vegetables and it does not consider water as an ingredient.
The FCID, which has been used before as a food decomposition method [
17
,
18
,
41
,
42
], offers a satisfactory
ingredient resolution and matching with LCIs, except for dairy. An important limitation of this approach
is that it is unable to distinguish between dairy ingredients (e.g., milk, cheese, and yogurt) in the
Sustainability 2020,12, 9466 12 of 16
food [
17
]. As many food contain multiple dairy ingredients and the environmental footprints of dairy
products vary considerably [
43
], using FCID to decompose foods into ingredients is problematic for
many food and diet evaluations. Another limitation of the FCID and FICRCD databases is that they
have not been updated for nearly 10 years [
41
]. Consequently, the two approaches cannot be used in
the evaluation of foods introduced in the newer cycles of NHANES, as evident from the 20+pizzas
missing from our analysis using these approaches. Furthermore, these decomposition methods fail to
capture food composition changes over time as the food sector evolves.
Overall, the SR method seems to offer the most useful and appropriate food decomposition in the
U.S. for LCA. It quantified the consumed amounts of components accurately and showed the highest
resolution that enables the differentiation of components with varying loss rates and environmental
impacts. The SR ingredient resolution is currently higher than the commodity resolution covered by
the available LCIs, therefore it requires the use of proxies. However, proxies are typically needed for
ingredients consumed at lower amounts and that have relatively low environmental footprints [
17
].
The SR method also contains multi-ingredient components that need decomposition. As shown in
this analysis, this limitation can be addressed using either foodcode proxies or previous versions of
the database. In addition to good ingredient resolution and matching with LCIs, the SR is frequently
updated along with the NHANES cycles (typically every two years). SR decompositions can be
complemented with information from the other approaches such as the retail-to-intake loss conversion
factors from the FICRCD and the component cooking and processing methods from the FCID.
This analysis provides new insights on how food decomposition methods may influence the
environmental impact assessment of foods. While the underlying decomposition databases investigated
in this analysis have been developed and primarily used to evaluate the nutritional quality [
44
] and
dietary exposure to metal [
45
], they enable a high throughput evaluation of the environmental impacts
associated with the thousands of foods in the NHANES database. However, several limitations
should be acknowledged that most food LCAs also suffer from. First, our analysis suffered from
data gaps related to ingredient coverage and representativeness. Three LCI databases (ecoinvent
v3.2, WFLDB v3.1, and ESU World food LCA database) were used to improve coverage. To improve
representativeness, ingredients were matched with the most appropriate LCIs available, often utilizing
proxy assignments and averages. When LCIs for the same ingredient were available from multiple
production systems or regions, processed representing conventional production were selected and
regions were prioritized favoring U.S., Canada, and major import countries when information was
available. However, it is well understood that resource use and emissions data of foods mainly
cover raw and semi-processed ingredients [
7
,
46
]. These estimates can vary substantially between
commodities [
46
,
47
], within and between countries [
1
,
48
], and between production systems [
1
,
49
].
Furthermore, using multiple sources of LCIs might introduce inconsistencies between data related to
underlying assumptions, life cycle stage coverage, system boundaries, and allocation methods [
46
],
whereas the three databases applied are all ecoinvent-based approach, mostly using similar background
data for main energy and materials inputs. Consequently, the availability and choice of the most
appropriate LCI for each ingredient is critical in the environmental impact assessment of foods.
However, most standardized recipe databases in the U.S. lack such information, primarily because
their scope is focused on nutrition evaluation.
The carbon footprint estimates in our analysis are limited in covering impacts associated with
“cradle to farm gate” or “cradle to processor gate” processes, accounting for retail to consumption
losses. Therefore, the post farm/processor gate impact of pizza, such as manufacturing, packaging,
distribution, retail storage (refrigeration and freezing), preparation, cooking, and waste have not
been considered for this comparison-focused analysis. Previous studies evaluating the performance
of a small number of mixed dishes collected information for these stages and showed that the
contribution and importance of these stages to food specific impacts differs substantially between
dishes and environmental indicators [
13
–
15
,
50
–
52
]. However, such an approach would be challenging
to implement on a large-scale. Recently, Kim et al. [
53
] developed a methodology to characterize the
Sustainability 2020,12, 9466 13 of 16
environmental impacts associated with the “farm to grave” stages by food group that covers the U.S food
system. In particular, they coupled up-to-retail gate information from an environmentally extended
input–output model (EIO-LCA) with an LCA model for the retail and consumer phases. The study
found that these stages are important contributors to the carbon footprint of vegetables, grains, and
seafood (40–50%), fruits and juices (~30%), and dairy (~20%). Other studies have estimated that that
these “farm to grave” processes can increase the carbon footprint of food systems by 15–18% [
1
,
17
,
54
].
5. Conclusions
This study investigates the use of standardized recipes as decomposition methods that determine
ingredient composition of foods and enable the characterization of food-specific environmental
impacts, a methodological limitation that is often overlooked. Using a case study on pizzas in the
U.S. diet, a popular food group in modern diets with a complex composition, this analysis showed
that four distinct decomposition methods produced considerably different carbon footprint estimates.
Consequently, while the environmental impacts of individual foods are driven by meat composition,
decomposition methods can also substantially influence the performance and comparison of foods
and diets. Differences observed between methods stemmed from ingredient resolution, ingredient
quantity units, and the ability to adjust for losses and to match ingredients with available LCIs.
Therefore, consumption-centered results established with different decomposition methods might not
be comparable and could lead to misleading conclusions and recommendations. While all approaches
generated several challenges, our analysis suggests that the SR approach offers the most appropriate
and useful decomposition for foods in the U.S. In addition, it is recommended that LCA practitioners
start considering and evaluating, when possible, the influence and limitations of decomposition
methods in food and diet LCAs.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2071-1050/12/22/9466/s1,
Table S1: Average daily pizza intake in the U.S. from 2011–2016 and coverage by decomposition method. Table S2:
Average weight of unit of measure by FPED component in grams per serving equivalent. Table S3: Decomposition
of 100 g of multi-ingredient SR components at consumption level. Estimates were obtained from matching
components with food items. Table S4: SR decomposition per 100 g of individual pizzas at consumption level.
Table S5: FPED decomposition per 100 g of individual pizzas at consumption level. Table S6: FCID decomposition
per 100 g of individual pizzas at consumption level. Table S7: FICRCD decomposition per 100 g of individual
pizzas at retail level. Table S8: One serving (140 g) decomposition and carbon footprint of vegetarian and meat
pizza by decomposition method. Table S9: Aggregated SR decomposition of daily pizza intake. Table S10:
Aggregated FPED decomposition of daily pizza intake. Table S11: Aggregated FCID decomposition of daily pizza
intake. Table S12: Aggregated FICRCD decomposition of daily pizza intake.
Author Contributions:
Conceptualization: K.S.S., V.L.F.III, and O.J.; methodology: K.S.S. and O.J.; formal analysis:
K.S.S.; data curation: K.S.S., V.L.F.III, and E.M.; writing—original draft preparation: K.S.S.; writing—review and
editing: K.S.S., V.L.F.III, E.M., and O.J.; visualization: K.S.S. All authors have read and agreed to the published
version of the manuscript.
Funding:
This work has been funded by an unrestricted grant from the Dairy Research Institute (DRI), part of
Dairy Management Inc. (DMI) and the Dow Sustainability Fellows Program at the University of Michigan.
Conflicts of Interest:
The funders had no role in the design of the study; in the collection, analyses, or interpretation
of data; in the writing of the manuscript, and in the decision to publish the results. K.S.S., E.M., and O.J. have
no conflicts of interest to declare. V.L.F.III consult for and/or have received research grants from various food,
beverage, and dietary supplement companies.
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