EPA 530-R-23-014
August 2023
https://www.epa.gov/sustainable-maiiagement-food
Excess Food Opportunities Map Version
3.0-Technical Methodology

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Excess Food Opportunities Map
Version 3.0-Technical Methodology
Office of Resource Conservation and Recovery
Office of Land and Emergency Management
Washington, DC
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Abstract
This report presents the methodology behind the development of the EPA Excess Food
Opportunities Map (map) Version 3.0, which supports diversion of excess food from landfills. The
information presented by the map can be used to inform food recovery and waste management at
the local level, and identify potential sources of organic feedstocks, infrastructure gaps, and
alternatives to landfilling or incinerating excess food.
This report describes the identification of select industrial, commercial, and institutional sources
in the United States that potentially generate excess food at the establishment level, and
identification of potential recipients of these materials. Version 3.0 includes an update of all
generator and recipient datasets using mostly 2021 or more recent data. Refrigerated warehousing
and storage, and farmers markets data layers were added, as well as some subsectors to existing
sectors.
Based on the North American Industry Classification System (NAICS), 90 categories of industries,
three school types, and farmers markets representing approximately 950,000 establishments in the
US were identified as potential sources of excess food. These establishments were grouped into
the following sectors: food manufacturers and processors, food wholesale and retail, educational
institutions, the hospitality industry, correctional facilities, healthcare facilities, restaurants and
food services, and farmers markets. Several publicly and commercially available datasets
containing common business statistics for the selected industries were then compiled as a precursor
to generating establishment-level excess food estimates. Methodologies developed by various
states, industry groups, and non-profit organizations were reviewed to identify approaches to
estimating excess food generation rates by industry. Combining select methodologies with
establishment-level data resulted in a dataset that supports the map and includes approximately
950,000 potential excess food generators. The map also identifies about 6,500 potential excess
food recipients, including composting facilities, anaerobic digestion facilities, and food banks, and
infrastructure to support excess food management, including over 200 communities with
residential source separated organics programs and roughly 600 refrigerated warehousing and
storage facilities. Finally, Version 3.0 includes environmental justice (EJ) data layers, as well as
data layers for food insecurity and food assistance.
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Executive Summary
This report describes the methodologies used to create estimates for the EPA Excess Food
Opportunities Map (map) Version 3.0. This interactive map supports nationwide diversion of food
from landfills and incinerators through the display of around 950,000 potential industrial,
commercial, and institutional excess food generator locations, estimates of their excess food
generation rates, and the display of about 6,500 potential recipient locations. This map can be used
to:
Inform waste management decisions at the local level.
Identify potential sources of food for rescue and recovery.
Connect potential feedstocks to compost, anaerobic digestion, or other excess food
recyclers.
Identify potential infrastructure gaps for managing excess food.
Identify where food recovery infrastructure, better food access, and changes to food
policies may be needed, taking into account environmental justice (EJ) concerns and
data on food insecurity and food assistance.
For the purposes of this report, "excess food" refers to food—whether processed, semi-processed,
or raw—that was not used for its intended purpose and is managed in a variety of ways, such as
donation to feed people, creation of animal feed, composting, anaerobic digestion, or sending to
landfills or combustion facilities. Examples of "excess food" include unsold food from retail
stores; plate waste, uneaten prepared food, or kitchen trimmings from restaurants, cafeterias, and
households; or by-products from food and beverage processing facilities. EPA often refers to this
as "wasted food." Because EPA intends to maximize recovery and beneficial use of all discarded
organics, inedible parts (e.g., pits, rinds, bones) were included in the excess food estimates, to the
extent that they were included in the set of referenced studies. Further, this report does not include
data or estimates for on-farm food loss, including unharvested crops, nor excess food or other
organic materials from the residential sector.
Based on the North American Industry Classification System (NAICS), 90 categories of industries,
three school types, and farmers markets representing around 950,000 establishments in the US
were identified as potential sources of excess food. These establishments were grouped into the
following sectors: food manufacturers and processors (45), food wholesale and retail (24),
educational institutions (3), the hospitality industry (5), correctional facilities (1), healthcare
facilities (9), restaurants and food services (6), and farmers markets (1). Figure 1 shows that the
restaurants and food services and food wholesale and retail sectors make up the majority of
potential sources of excess food in terms of number of establishments. Commercially and publicly
available data were compiled to create a dataset of all identified establishments. The dataset
includes each establishment's name, location, a calculated estimated excess food generation rate,
and additional information such as phone numbers and websites, where available. The dataset also
includes potential recipients of excess food, including establishment name, location, phone
in

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number, website, and feedstock accepted, where available, for composting facilities, anaerobic
digestion facilities, and food banks. Data were also obtained and mapped for supporting
infrastructure, including refrigerated warehousing and storage, and communities with source
separated organics programs.
Figure 1. Non-Residential Excess Food Generating Sectors
Educational
Institutions, 13.4%
Farmer's Markets,
0.1%
Hospitality Industry,
7.0%
Correctional Facilities,
0.6%
Healthcare Facilities,
6.0%
Manufacturers/Processors, 4.6%
Food Banks, 0.10%
Sector-specific methodologies for estimating excess food generation rates were adopted from
existing studies conducted by state environmental agencies, published articles, and other sources,
such as the Food Waste Reduction Alliance (FWRA). All adopted studies used methodologies
based on commonly tracked business statistics to estimate excess food generation rates for several
or all the targeted sectors. These business statistics include number of employees, annual revenue,
number of students (for educational institutions), capacity (for correctional facilities), and number
of beds (for healthcare facilities).
Using establishment-specific statistics collected in the dataset, the methodologies were used to
estimate the amount of excess food from each establishment in each of the targeted sectors. A
range of excess food estimates was calculated for each establishment, and the high and low
estimates are displayed in the map and dataset.
The map and methodologies are not intended to provide accurate nation-wide estimates of excess
food generation, nor do they reflect establishment-specific recovery or recycling efforts. Rather,
they are intended to show estimated generation amounts, potential sources and possible recipients
of excess food. This information may be used to help the public and private sectors divert excess
food from landfills and incinerators and toward more preferred uses as reflected in EPA's Food
Recovery Hierarchy (e.g., human consumption, animal feed, industrial uses, anaerobic digestion,
composting).
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Acknowledgements
Development of Version 3.0 of the map was led by Claudia Fabiano (Office of Land and
Emergency Management), Jonathan Schroeder (Oak Ridge Institute for Science and Education
(ORISE) 2020-2022 fellow), and Juliana Beecher (ORISE fellow). The map application
development was led by Cheryl Henley (EPA Region 9) with support from Lisa Fulton, Jenny
Herring, and Jarod Martin of Innovate! Inc.
Notice
This report has been internally peer reviewed by the US Environmental Protection Agency Office
of Land and Emergency Management. Mention of trade names or commercial products does not
constitute endorsement or recommendation by EPA for use.

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Contents
Contents	vi
List of Figures	viii
List of Tables	ix
List of Abbreviations, Acronyms, and Initialisms	x
1.	Introduction	1
1.1.	Background	1
1.2.	Objectives and Approach	2
2.	Sector-Specific Data Sources and Excess Food Estimation Methodologies for
Generators	3
2.1.	Overview	3
2.2.	Food Manufacturers and Processors	4
2.2.1	Changes in Version 3.0	6
2.3.	Food Wholesale and Retail	7
2.3.1.	Overview	7
2.3.2.	Food Wholesale	8
2.3.3.	Food Retail (Supermarkets, Grocery Stores, Supercenters)	8
2.3.4.	Changes in Version 3.0	10
2.4.	Educational Institutions	10
2.4.1.	Overview	10
2.4.2.	Postsecondary Schools	11
2.4.3.	Elementary and Secondary Schools	13
2.4.4.	Changes in Version 3.0	15
2.5.	Hospitality Industry	15
2.5.1. Changes in Version 3.0	16
2.6.	Correctional Facilities	16
2.6.1. Changes in Version 3.0	18
2.7.	Healthcare Facilities	18
2.7.1. Hospitals	18
2.7.2	Nursing Homes	20
2.7.3	Changes in Version 3.0	20
2.8.	Restaurants and Food Services	20
2.8.1. Changes in Version 3.0	22
2.9.	Food Banks	22
2.9.1. 22
2.10.	Farmers Markets	23
2.10.1 Changes in Version 3.0	23
2.11 Data Analysis	23
3.	Macro Analysis of Sector-Specific Excess Food Generation Rates	23
3.1.	Food Manufacturers and Processors	25
3.2.	Food Wholesale and Retail	26
3.3.	Educational Institutions	27
3.4.	Hospitality Industry	28
3.5.	Correctional Facilities	29
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3.6.	Healthcare Facilities	29
3.7.	Restaurants and Food Services	31
3.8.	Food Banks	32
3.9.	Farmers Markets	32
4.	Data Sources for Recipients	32
4.1.	Overview	32
4.2.	Food Banks	32
4.3.	Composting Facilities	32
4.4.	Anaerobic Digestion Facilities	32
5.	Infrastructure to Support Excess Food Management	33
5.1.	Communities with Residential Source Separated Organics Programs	33
5.2.	Refrigerated Warehousing and Storage	33
6.	Environmental Justice (EJ), Food Assistance and Food Insecurity Data Layers	34
6.1.	EPA EJScreen	34
6.2.	USDA Food Environment Atlas	34
7.	Limitations and Opportunities for Improvement	34
8.	References	36
Appendix A: Glossary	41
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List of Figures
Figure 1. Non-Residential Excess Food Generating Sectors	iv
Figure 2: Food Recovery Hierarchy	2
Figure 3. Non-Residential Excess Food Generating Sectors	24
Figure 4. Proportion of Food Manufacturers and Processors by Industry Type	25
Figure 5. Proportion of Food Wholesale and Retail Establishments by Industry Type	27
Figure 6. Proportion of Educational Institutions by School Type	28
Figure 7. Proportion of Hospitality Industry Establishments by Type	29
Figure 8. Proportion of Healthcare Facilities by Industry Type	30
Figure 9. Proportion of Restaurant and Food Services Establishments by Industry Type	31
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List of Tables
Table 1. NAICS Codes for Food Manufacturers and Processors	4
Table 2. Generation Factors for Manufacturers and Processors	6
Table 3. NAICS Codes for Food Wholesalers and Retailers	7
Table 4. Generation Factors for Food Wholesale Facilities	8
Table 5. Generation Factors for Food Retail (Supermarkets, Grocery Stores, and Supercenters)	9
Table 6. Educational Institutions - School Types	10
Table 7. Generation Factors for Postsecondary Schools	11
Table 8. Generation Factors for Public and Private Elementary and Secondary Schools	14
Table 9. NAICS Codes for the Hospitality Industry	15
Table 10. Generation Factors for the Hospitality Industry	16
Table 11. Generation Factors for Correctional Facilities	17
Table 12. NAICS Codes for Hospitals	19
Table 13. Generation Factors for Hospitals	19
Table 14. NAICS Codes for Nursing Homes	20
Table 15: NAICS Codes for Restaurants and Food Services	20
Table 16. Generation Factors for Restaurants and Food Services	21
Table 17. Establishments Included in the Dataset by Sector	24
Table 18. Number of Food Manufacturers and Processors Included in the Dataset	26
Table 19. Number of Food Wholesale and Retail Establishments Included in the Dataset	27
Table 20. Number of Educational Institutions Included in the Dataset	28
Table 21: Number of Hospitality Establishments Included in the Dataset	29
Table 22: Number of Healthcare Facilities Included in the Dataset	30
Table 23: Number of Restaurants and Food Services Establishments Included in the Dataset	31
Table 24. NAICS Codes for Refrigerated Warehousing and Storage	33
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List of Abbreviations, Acronyms, and Initialisms
BJS
Bureau of Justice Statistics
BSR
Business for Social Responsibility
CCG
Cascadia Consulting Group
DHS
Department of Homeland Security
DnB
Dun & Bradstreet, Inc.
EPA
Environmental Protection Agency
ERS
Economic Research Service
FWRA
Food Waste Reduction Alliance
ICI
Industrial, Commercial, and Institutional
lbs
Pounds
MSW
Municipal Solid Waste
NAICS
North American Industry Classification System
NCDENR
North Carolina Department of Environment and Natural Resources
NCES
National Center for Education Statistics
NRDC
Natural Resources Defense Council
NSLP
National School Lunch Program
SCDOC
South Carolina Department of Commerce
ton
Short Ton (wet)
UNEP
United Nations Environment Program
US
United States
US EPA
United States Environmental Protection Agency
USD A
United States Department of Agriculture
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
1. Introduction
1.1. Background
In 2015, in alignment with Target 12.3 of the United Nations Sustainable Development Goals,
the United States Department of Agriculture (USD A) and United States Environmental Protection
Agency (EPA) announced the first ever domestic goal to reduce food loss and waste by half by the
year 2030. The EPA Excess Food Opportunities Map (map) is a tool intended to support
achievement of this goal.
The United Nations Environment Program (UNEP) estimates that approximately one third of food
produced for human consumption is excess (UNEP (n.d.)). The USDA estimated that in 2010,
approximately 66.5 million tons of food (i.e., 31% of the 430 billion pounds produced) was lost at
the retail and consumer level in the US (USDA (2014)). When food is wasted, it also wastes the
resources - such as the land, water, energy, and labor - that go into growing, storing, processing,
distributing, and preparing that food. Each year, food loss and waste from farm to kitchen embodies
an area of agricultural land the size of California and New York combined, enough energy to
power 50 million US homes for a year, and emissions (excluding landfill emissions) equal to the
annual C02 emissions of 42 coal-fired power plants (EPA (2021)).
EPA estimates that excess food generated from the food retail, food service, and residential sectors
represents approximately 21.6% (i.e., 63.1 million tons) of all Municipal Solid Waste (MSW)
generated in 2018 (US EPA (2020)). In 2019, approximately 74% of food included in the
municipal solid waste stream was either landfilled or incinerated, while the remainder was
managed in other ways including donation, animal feed, composting, and anaerobic digestion (US
EPA (2023a)). When food ends up in landfills, it releases methane, a powerful greenhouse gas.
Landfills are the third largest anthropogenic source of methane emissions in the United States
(122.6 MMT C02 Eq.), accounting for 16.9 percent of total methane emissions in 2021 (US EPA
(2023b)). Therefore, diverting excess food from landfills where it might degrade before gas
collection is implemented could significantly reduce the production of greenhouse gas emissions.
The definition of excess food varies across studies and among organizations, resulting in different
estimates of excess food. For example, the USDA considers only the edible fraction in its
accounting of food losses as its focus is on improving human nutrition (USDA (2014)). For the
purposes of this report, "excess food" refers to food—whether processed, semi-processed, or
raw—that was not used for its intended purpose and is managed in a variety of ways, such as
donation to feed people, creation of animal feed, composting, anaerobic digestion, or sending to
landfills or incinerators. EPA often refers to this as "wasted food." EPA's goal is to maximize
recovery and beneficial use of all discarded organics, so some organic materials are included in
this definition that are not intended for human consumption, such as inedible parts (e.g., pits, rinds,
bones) discarded in kitchens or during processing, and yard waste collected by municipal services
(i.e., communities with residential source separated organics that collect yard waste and excess
food). The map does not include on farm food loss such as unharvested or excess food, nor excess
food and other organic material from the residential sector.
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
To prioritize efforts to divert excess food, EPA created the Food Recovery Hierarchy (Figure 2)
(US EPA (2015a)). Source reduction is the most preferred option as it not only mitigates the
environmental impacts associated with management of excess food, but also minimizes the
impacts associated with food production, processing, and delivery to the end-user. Any other
management option chosen in a particular situation is dependent on the characteristics and the
source of the excess food, as well as the available infrastructure in the area. For example, some
food preparation residuals and/or post-consumer food discards may not be suitable for human
consumption, so the next most preferred use is for animal feed. Feeding people and
landfill/incineration are the most and least preferred options, respectively, for managing excess
food. EPA also refers to incineration as "controlled combustion."
Currently, nine states and ten localities have some form of an organic waste ban, waste recycling
law, or donation requirement that pertains to food, and there is growing interest in the practice
(Zero Food Waste Coalition (2023)). In 2021 alone, 25
states introduced food waste-related legislation (Harvard
Food Law and Policy Clinic (2022)). Private sector
businesses have made strides in setting goals, measuring
and reducing excess food, and communities are
increasingly focused on education and awareness efforts
aimed at helping their residents waste less food at home.
At the national level, EPA has developed tools and
resources for measuring, tracking, and reducing excess
food, as well as assessed the cost and environmental
impact of excess food management (US EPA (2016)). The
Agency also regularly publishes estimates of wasted food
in the US (US EPA (2023 a)). EPA recognizes the need for
tools to support a broader understanding of potential
excess food generation, and to foster collaboration and
partnership among stakeholders interested in promoting
and achieving sustainable management of food. EPA
continues to support public and private sector efforts, facilitate peer learning, provide data and
conduct research to help decision makers, and provide funding to support waste reduction efforts.
Notably, in 2022, EPA established funding opportunities through the Solid Waste Infrastructure
for Recycling Grant Program and Recycling Education and Outreach Grant Program for a total of
$350 million (US EPA (2022a)).
1.2. Objectives and Approach
The primary objective of this report is to present the methodology used to develop and update the
Excess Food Opportunities Map and dataset, including establishment-specific estimates of excess
food generation. This national-scale, interactive map is intended to help inform food recovery and
waste management decisions at the local level, and identify potential sources of organic feedstocks,
infrastructure gaps, and alternatives to landfill or combustion. EPA used the following approach
to develop Version 3.0:
Figure 2: Food Recovery Hierarchy
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
•	Using the North American Industry Classification System (NAICS), 90 categories of
industries, three school types, and farmers markets representing around 950,000
establishments in the US were identified as potential sources of excess food. These
establishments were grouped into the following sectors: food manufacturers and processors
(45), food wholesale and retail (24), educational institutions (3), the hospitality industry
(5), correctional facilities (1), healthcare facilities (9), restaurants and food services (6),
and farmers markets.
•	A literature review informed development of methodologies used to estimate excess food
generation factors for each sector (further details are provided in Section 2).
•	Publicly and commercially available data sources were mined for supplementary data to
estimate establishment-level excess food generation rates using the identified
methodologies. The resulting dataset was used to support the online map.
•	Information about potential recipients of excess food was also collected and mapped, and
includes food banks, composting facilities, and anaerobic digestion facilities.
•	Information about infrastructure that supports excess food management was also collected
and mapped, including refrigerated warehousing and storage facilities, and communities
with residential source separated organics programs.
•	Environmental justice data layers from EPA's EJScreen and data layers on food insecurity
and food assistance from USDA's Food Environment Atlas were brought in to add
additional capabilities to the map.
The resulting map provides establishment-level information such as name, geographic location,
and physical address, and where possible, estimates of excess food generation. The map also
includes similar establishment-level information about potential recipients of excess food that also
comes primarily from publicly and commercially available datasets, as well as state websites (more
details provided in Section 4).
2. Sector-Specific Data Sources and Excess Food Estimation
Methodologies for Generators
2.1. Overview
This chapter describes the methods and data sources used to estimate the excess food generation
rates for individual establishments in the 90 identified industrial, commercial, and institutional
(ICI) industries, three school types, and farmers markets. For the purposes of this report, "excess
food" refers broadly to food that was not used for its intended purpose and is managed in a variety
of ways. EPA often refers to this as "wasted food." The map does not include on farm food losses
such as unharvested crops nor excess food or other organic material from the residential sector.
These 90 industrial, commercial, and institutional (ICI) industries, three school types, and farmers
markets were grouped into the following sectors: food manufacturers and processors (45), food
wholesale and retail (24), educational institutions (3), the hospitality industry (5), correctional
facilities (1), healthcare facilities (9), restaurants and food services (6), refrigerated warehousing
and storage (1), and farmers markets (1). The list of industries and establishments included can be
found in each sector's respective section.
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
Establishment-level data for most industries came from DnB and included contact information,
location details (geo-coordinates and physical addresses), establishment type (headquarters,
branch, or single location), revenue ($USD), and number of employees. Similar establishment-
level data for educational institutions was obtained from the National Center for Education
Statistics (NCES (2021a), (2021b), (2021c)), and data for healthcare facilities and correctional
facilities was obtained from the US Department of Homeland Security (DHS (2020)). Farmers
market data was obtained from USD A (USD A (2022)).
In general, sector-specific methodologies for estimating excess food generation rates were adopted
from existing studies conducted by state environmental agencies, published articles, and other
sources, such as the Food Waste Reduction Alliance (FWRA). All adopted studies used
methodologies based on commonly tracked business statistics to estimate excess food generation
rates for several or all the targeted sectors. These business statistics include number of employees,
annual revenue, number of students (for educational institutions), capacity (for correctional
facilities) and number of beds (for healthcare facilities).
Using establishment-specific statistics collected in the dataset, the methodologies were used to
estimate the amount of excess food from each establishment in each of the targeted sectors. Where
more than one methodology was available for a sector, a range of excess food estimates was
calculated for each establishment, and the high and low estimates are displayed in the map and
dataset. If only one methodology was available for a sector, then one estimate is displayed in the
map and dataset. The excess food estimates include edible as well as inedible food to the extent
accounted for by the studies. EPA did not attempt to estimate the portions of excess food estimates
that are potentially recoverable for human consumption. If data were not available to generate an
excess food estimate, the establishment was still mapped, but no estimate is provided. Data were
available to calculate estimates for 90.7% of establishments in Version 3.0 of the map.
2.2. Food Manufacturers and Processors
Forty-five industries are included as food manufacturers and processors (Table 1).
Table 1. NAICS Codes for Food Manufacturers and Processors
No.
NAICS Code
NAICS Code Description
1
311211
Flour Milling
2
311212
Rice Milling
3
311213
Malt Manufacturing
4
311221
Wet Corn Milling
5
311224
Soybean and Other Oilseed Processing
6
311225
Fats and Oils Refining and Blending
7
311230
Breakfast Cereal Manufacturing
8
311313
Beet Sugar Manufacturing
9
311314
Cane Sugar Manufacturing
10
311340
Non-chocolate Confectionery Manufacturing
11
311351
Chocolate and Confectionery Manufacturing from Cacao Beans
12
311352
Confectionery Manufacturing from Purchased Chocolate
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
13
311411
Frozen Fruit, Juice, and Vegetable Manufacturing
14
311412
Frozen Specialty Food Manufacturing
15
311421
Fruit and Vegetable Canning
16
311422
Specialty Canning
17
311423
Dried and Dehydrated Food Manufacturing
18
311511
Fluid Milk Manufacturing
19
311512
Creamery Butter Manufacturing
20
311513
Cheese Manufacturing
21
311514
Dry, Condensed, and Evaporated Dairy Product Manufacturing
22
311520
Ice Cream and Frozen Dessert Manufacturing
23
311611
Animal (except Poultry) Slaughtering
24
311612
Meat Processed from Carcasses
25
311613
Rendering and Meat Byproduct Processing
26
311615
Poultry Processing
27
311710
Seafood Product Preparation and Packaging
28
311811
Retail Bakeries
29
311812
Commercial Bakeries
30
311813
Frozen Cakes, Pies, and Other Pastries Manufacturing
31
311821
Cookie and Cracker Manufacturing
32
311824
Dry Pasta, Dough, and Flour Mixes Manufacturing from Purchased
Flour
33
311830
Tortilla Manufacturing
34
311911
Roasted Nuts and Peanut Butter Manufacturing
35
311919
Other Snack Food Manufacturing
36
311920
Coffee and Tea Manufacturing
37
311930
Flavoring Syrup and Concentrate Manufacturing
38
311941
Mayonnaise, Dressing, and Other Prepared Sauce Manufacturing
39
311942
Spice and Extract Manufacturing
40
311991
Perishable Prepared Food Manufacturing
41
311999
All Other Miscellaneous Food Manufacturing
42
312111
Soft Drink Manufacturing
43
312120
Breweries
44
312130
Wineries
45
312140
Distilleries
The literature search identified a total of 55 studies examining excess food generation at the food
manufacturing and processing level. Many of these studies, however, are not directly useful to
methods development as some lack quantitative information on generation rates, while others
apply generation rates from earlier studies. EPA chose three studies that involved original research
(e.g., surveying food manufacturers/directly measuring excess food generated from a sample of
food manufacturers) (Table 2). These three studies were used to estimate excess food generated,
resulting in a range of values for each facility.
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
Table 2. Generation Factors for Manufacturers and Processors
SOURCE
YEAR
GENERATION
FACTOR
UNIT
FWRA
2016
0.17
lbs/revenue/year
BSR
2014
0.053
lbs/revenue/year
BSR
2013
0.062
lbs/revenue/year
These three studies establish generation factors based on pounds of excess food generated per
dollar of sales revenue per year. The 2013 and 2014 studies were developed by BSR for the FWRA,
while the 2016 study was published with FWRA as the author. These three studies are heavily
cited in other research (see NRDC (2017); Garcia-Garcia (2016); ReFED (2016)). The studies
estimated generation rates by surveying food manufacturers and processors around the nation.
Depending on the year of the survey, the surveyed manufacturers and processors represent
anywhere between 6.2 percent to 17 percent of the national food manufacturing/processing
industry, based on sales. The facilities included in the studies vary each year; because the samples
change, the studies are independent, so all three studies were used. The three generation rates from
the studies range from 0.053 to 0.17 pounds per dollar of annual industry sales revenue. It should
be noted that these studies do not contain specific generation factors for each type of manufacturer
or processor, and that excess food generation can vary depending on the type of industry (for
example, cane sugar manufacturing and meat processors likely produce different amounts of
excess food). Therefore, due to the absence of NAICS code-specific excess food generation
factors, these generation factors were applied to all facilities across all 45 NAICS codes. The three
generation factors were used in conjunction with establishment-level annual revenue data obtained
from DnB to estimate the annual amount of excess food generated by food manufacturing and
processing facilities. This is reflected in the following equation:
/tons\
Food Manufacturers and Processors Excess Food 	 =
Vyear/
lb	tons
Facility's Annual Revenue ($)x X-	—	— x
J	v 7 Annual Revenue ($) 2,0001b
Where X = 0.17, 0.053, or 0.062
2.2.1 Changes in Version 3.0
NAICS code 112930 was removed.
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
2.3. Food Wholesale and Retail
2.3.1. Overview
Twenty-four industries were included, at least partially1, under food wholesale and retail (Table
3). Establishments with NAICS codes starting with 424 were classified as food wholesale, and
those with NAICS codes starting with 423, 445, 452, 453, and 493 were classified as food retail
(i.e., supermarkets, grocery stores, and supercenters). Establishment4evel data for this sector was
obtained from DnB.
Table 3. NAICS Codes for Food Wholesalers and Retailers
No.
NAICS Code
NAICS Code Description
1
4239901
Other Miscellaneous Durable Goods Merchant Wholesalers
2
424410
General Line Grocery Merchant Wholesalers
3
424420
Packaged Frozen Food Merchant Wholesalers
4
424430
Dairy Product (except Dried or Canned) Merchant Wholesalers
5
424440
Poultry and Poultry Product Merchant Wholesalers
6
424450
Confectionery Merchant Wholesalers
7
424460
Fish and Seafood Merchant Wholesalers
8
424470
Meat and Meat Product Merchant Wholesalers
9
424480
Fresh Fruit and Vegetable Merchant Wholesalers
10
424490
Other Grocery and Related Products Merchant Wholesalers
11
445110
Supermarkets and Other Grocery (except Convenience) Stores
12
445120
Convenience Stores
13
445210
Meat Markets
14
445220
Fish and Seafood Markets
15
445230
Fruit and Vegetable Markets
16
445291
Baked Goods Stores
17
445292
Confectionery and Nut Stores
18
445299
All Other Specialty Food Stores
19
4522101
Department Stores
20
452311
Warehouse Clubs and Supercenters
21
4523191
All Other General Merchandise Stores
22
4539981
All Other Miscellaneous Store Retailers
23
4931101
General Warehousing and Storage
24
4931901
Other Warehousing and Storage
Notes:
1. Target, Walmart, BJ's, Costco, and Sam's Club were specifically searched for within DnB (since they are
not all classified as 452311) and added; they fall under the following NAICS codes: 423990, 452210,
452319,453998,493110,493190. However, only those specific stores were included for those six NAICS
codes, the remainder of the establishments classified under those six NAICS codes in DnB were not
included as they are not expected to be food retailers.	
1 Target, Walmart, BJ's, Costco, and Sam's Club were specifically searched for within DnB (since they are not all
classified as 452311) and added; they fall under the following NAICS codes: 423990, 452210, 452319, 453998,
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2.3.2. Food Wholesale
For purposes of this map, food wholesalers are those with NAICS codes 424410 through 424490.
The literature search identified 22 studies examining excess food generation among food
wholesalers. Many of these studies, however, are not directly useful for methods development.
Some lack quantitative information on generation rates, while others apply generation rates from
earlier studies. Two studies conducted by CCG defined the wholesale sector broadly, grouping
food wholesalers with other non-durable wholesalers such as apparel and chemicals. Given that
these other non-durables differ greatly from food in their waste generation patterns, EPA excluded
the two CCG studies. EPA chose one study that focused on food wholesale and involved original
research (e.g., direct analysis of facilities' excess food) (Table 4). This study was used to estimate
excess food generated, resulting in one value for each establishment.
Table 4. Generation Factors for Food Wholesale Facilities
GENERATION


GENERATION

FACTOR #
SOURCE
YEAR
FACTOR
UNIT
1
BSR
2014
0.005
Tons/thousand $
revenue
BSR (2014) collected industry generation data through a series of surveys, and the generation
factor is shown below:
/tons\
Food Wholesalers Excess Food 	 =
Vvear/
Establishment's Annual Revenue $x 0.005-
yeary
tons
thousand $ revenue
2.3.3. Food Retail (Supermarkets, Grocery Stores, Supercenters)
For purposes of this map, food retailers are those establishments classified under NAICS codes
445110, 445120, 445210, 445220, 445230, 445291, 445292, 445299, and 452311 as well as
Target, Walmart, BJ's, Costco, and Sam's Club stores.2 EPA chose seven studies that involved
original research (e.g., direct analysis of facilities' excess food) (Table 5). These seven studies
were used to estimate excess food generated, resulting in a range of values for each establishment.
493110, 493190. However, only those specific stores were included for those six NAICS codes, the remainder of the
establishments classified under those six NAICS codes in DnB were not included as they are not expected to be food
retailers.
2 Target, Walmart, BJ's, Costco, and Sam's Club were specifically searched for within DnB (since they are not all
classified as 452311) and added; they fall under the following NAICS codes: 423990, 452210, 452319, 453998,
493110, 493190. However, only those specific stores were included for those six NAICS codes, the remainder of the
establishments classified under those six NAICS codes in DnB were not included as they are not expected to be food
retailers.
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Table 5. Generation Factors for Food Retail (Supermarkets, Grocery Stores, and Supercenters)
GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
ESTABLISHMENT
TYPE
1
CCG
2006
2.31
Tons/employee/
year
Supermarket/Grocery
Store
2
Kessler
Consulting
2012
2.32
Tons/employee/
year
Supermarket/Grocery
Store
3
CCG
2015
2.02
Tons/employee/
year
Supermarket/Grocery
Store
4
Draper/Lennon
2001
1.5
Tons/employee/
year
Supermarket/Grocery
Store
5
CCG
2006
0.27
Tons/employee/
year
Supercenter
6
ReFED
2016
0.5
Tons/employee/
year
Supercenter
7
BSR
2014
0.005
tons/thousand $
revenue
Supermarket/Grocery
Store
In the relevant literature, several studies provide separate generation rates for
supermarkets/grocery stores and supercenters. Supercenters are defined as large retail
establishments that sell a complete line of grocery merchandise in addition to non-grocery goods.
Supercenters include big-box stores, such as Wal-Mart and Target and warehouse clubs such as
BJs, Sam's Club, and Costco. Supermarkets/grocery stores and supercenters exhibit different
characteristics regarding the sale of food. Most notably, supercenters often sell food items in bulk
and at a lower unit price relative to supermarkets.
CCG (2006) and CCG (2015) conducted audits of food retail sector waste. Draper/Lennon
(2001), Kessler Consulting (2012), BSR (2014), and ReFED (2016) collected data through a
series of surveys and interviews with store managers and other experts.
The five studies containing generation factors 1-6 estimated generation factors between 0.27 and
2.32 tons per employee per year. The low generation factor was reported by CCG (2006), which
sampled waste at big-box retail stores. Another low generation factor, 0.5 tons per employee per
year, was reported by ReFED (2016), who interviewed supercenters to estimate excess food per
employee. Generation rates for supercenters are likely lower than those for supermarkets/grocery
stores because they take into account all employees, not just the grocery department employees.
The higher supermarket/grocery store estimates were provided by CCG (2006) and Kessler
Consulting (2012), who conducted waste audits at supermarkets.
The 7th study quantifies excess food generated on a revenue basis. BSR (2014) collected industry
generation data through a series of surveys and estimated 10 pounds of excess food per thousand
dollars of company revenue (or 0.005 tons per thousand dollars of revenue).
Generation factors 1-4 and 7 were applied to establishments classified as supermarkets and grocery
stores (i.e., those with NAICS codes starting with 445). Generation factors 5 and 6 were applied
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to establishments classified as supercenters (i.e., NAICS code 452311, and Target, Walmart,
Costco, Sam's Club, and BJ's establishments). These generation factors were used to calculate a
range of excess food estimates for supermarkets, grocery stores, and supercenters.
Generation factors 1-6 were used in conjunction with employee data obtained from DnB and use
the following equation:
v tons
y\r~
Food Retailers Excess Food = Number of employees
Where X = 0.27 to 2.32
Generation factor 7 was used in conjunction with revenue data obtained from DnB and uses the
following equation:
/tons\
Food Retailers Excess Food 	 =
Vyear/
Establishment's Annual Revenue $x 0.005-
year/
tons
thousand $ revenue
2.3.4. Changes in Version 3.0
EPA removed two studies that had previously been used. Those studies provided generation factors
that were based on tons per establishment, which EPA deemed were not appropriate to apply
nationally because they cannot capture establishment-specific variability.
Establishments under NAICS code 445120 (convenience stores) were added. Also, Target,
Walmart, BJ's, Costco, and Sam's Club were specifically searched for within DnB (since they are
not all classified as NAICS code 452311) and added; they fall under the following NAICS codes:
423990, 452210, 452319, 453998, 493110, 493190. However, only those specific stores were
included for those six NAICS codes, the remainder of the establishments classified under those six
NAICS codes in DnB were not included as they are not expected to be food retailers.
2.4. Educational Institutions
2.4.1. Overview
The educational institutions sector consists of three types of schools: postsecondary (i.e., colleges,
universities, and professional schools), public elementary and secondary schools, and private
elementary and secondary schools (Table 6). Data were obtained from the National Center for
Education Statistics (NCES); NAICS codes are not used in NCES databases.
Table 6. Educational Institutions - School Types
No.
School Type
1
Postsecondary Schools
2
Public Elementary and Secondary Schools
3
Private Elementary and Secondary Schools
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2.4.2. Postsecondary Schools
Data for postsecondary schools were collected from the Integrated Postsecondary Education Data
System of the NCES for the 2020 school year (NCES (2021a)). These data include the name,
school type, address, geo-coordinates, phone number, website, and total enrollment of each
institution.
The literature search identified a total of 44 studies addressing excess food generation in
postsecondary school settings. Many of these studies, however, are not directly useful to methods
development. Some studies lack quantitative information on generation factors, while others apply
generation factors from earlier studies. Therefore, EPA chose ten studies that either involved
original research (e.g., directly weighing plate waste at a college dining hall) or which present
estimates widely cited in the literature (Table 7). These ten studies were used to estimate excess
food generated, resulting in a range of values for each institution.
Table 7. Generation Factors for Postsecondary Schools
GENERA
TION
FACTOR
#
SOURCE
YEAR
UNITS
GENERATION FACTOR

PRE-
CONSUMER1
POST-
CONSUMER
TOTAL
1
Ebner et al.
2014
lbs/meal
0.07
0.15
0.22
2
Sarjahani et al.2
2009
lbs/meal
0.19
0.23
0.42
3
Vannet Group
2008
lbs/meal
0.16
0.31
0.47
4
Graunke and Wilke
2008
lbs/meal
0.16
0.19
0.35
5
Draper/Lennon
2001
lbs/meal
N/A
N/A
0.35
6
Thiagarajah and
Getty
2013
lbs/meal
0.16
0.25
0.40
7
Whitehair et al.3
2013
lbs/meal
0.09
0.14
0.23
8
Kim and
Morawski2
2012
lbs/meal
0.13
0.21
0.34
9
Caton et al.
2010
lbs/meal
0.31
0.49
0.79
10
CCG
2015
lbs/stud
ent/year
N/A
N/A
22.0
Notes:
1.	Pre-consumer values are estimated for generation factors 6-9 using the average proportion of pre-
consumer excess food from studies 1-5. On average, studies 1-5 showed post-consumer excess food
to be 61.4 percent of all waste.
2.	Saijahani et al. (2009) and Kim and Morawski (2012) estimate excess food generation with and
without trays. EPA uses the average of the two estimates.
3.	Whitehair et al. (2013) studies the effect of a messaging campaign to reduce excess food. EPA uses
the baseline data as the basis for this generation factor.
Generation factors 1-5 use direct estimates of excess food generation per meal, including pre-
consumer food (i.e., excess food in the kitchen or from preparation) as well as post-consumer food
(i.e., plate waste). The highest generation factor is from Vannet Group (2008), yielding an estimate
of 0.47 pounds per meal. EPA includes this study because it weighed excess food at all stages of
the dining process, including the kitchen prep area, food serving stations, and consumer stations.
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Ebner et al. (2014), Saijahani et al. (2009), and Graunke and Wilke (2008) conducted original
research on excess food generated from college/university dining halls. EPA also included one
study that did not directly measure excess food generation, Draper/Lennon (2001), because it is
widely cited in the literature.3
The literature search also identified four additional high-quality studies that analyze only post-
consumer excess food (i.e., plate waste). Studies 6-9 have a larger range between the lowest
estimate from Whitehair et al. (2013) of only 0.14 pounds per meal, and the highest estimate from
Caton et al. (2010) of 0.49 pounds per meal. Because these studies only consider post-consumer
excess food, EPA scaled the post-consumer excess food generation factors upward using the
average proportion of the excess food generated from post-consumer excess food in studies 1-5 to
estimate a total excess food generation factor. On average, studies 1-5 showed post-consumer
excess food to be 61.4 percent of all excess food. Applying this figure to the post-consumer values
in studies 6-9 yields an estimate of total excess food generation per meal. For instance, dividing
the Whitehair et al. (2013) estimate of 0.14 pounds per meal by 0.614 provides a total excess food
estimate (pre- and post-consumer) of 0.23 pounds per meal. The pre-consumer values in Table 7
are simply the total excess food generation factor minus the post-consumer factor.
Generation factor 10 frames generation in terms of pounds per student per year and is estimated
from one source, CCG (2015). While CCG (2015) does not differentiate between the K-12 and
college/university sectors, EPA included the generation factor derived from "education sector"
because the study is recent, and the estimates are derived through direct waste sampling. EPA also
used the same generation factor for elementary and secondary schools.
The NCES database did not provide the number of meals served at each institution, so to use the
generation factors that are based on pounds per meal (1-9), EPA searched for studies that contained
data on how many meals, on average, each student consumes per year at postsecondary institutions.
•	Meals per Residential Student per Year - Students living on campus consume more
food on campus than non-residential students. Draper/Lennon (2001) applied two
separate "meals per enrolled student per year" estimates for residential and non-
residential institutions. Specifically, they assumed a total of 405 meals per residential
student per year. Two additional studies provide data on the number of meals served per
enrolled student per year at residential institutions.4 The analysis calculates the average
meals per enrolled student at residential institutions as the average of the three estimates,
equal to 285 meals per enrolled student per year.
•	Meals per Non-Residential Student per Year - Lacking additional data on meals
served per enrolled student at non-residential institutions, EPA retained the
Draper/Lennon (2001) value of 108 meals per enrolled student at non-residential
institutions.
3	See NRDC (2017), Hodge et al. (2016), Moriarty (2013), Wellesley College (2013), and US EPA (2011).
4	Ebner et al. (2014) reported two an average of 226 meals per student per year, estimates: 180 and 270 meals per enrolled student
per year according to two different methods. EPA used the average (225) as representative of Ebner et al (2014). Whitehair et al.
(2013) reported 19,046 meals served at a dining hall serving 540 students over a six-week period. Assuming an academic calendar
of 270 days following Draper/Lennon (2001), EPA estimated an average of 226 meals per student per year.
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• Weighted Average Meals per Student - EPA estimated a national average of 169 meals
served per enrolled student as the average meals served per enrolled student between
residential and non-residential institutions, weighted by the percent of students attending
residential institutions and non-residential institutions.5
Generation factors 1 through 9 use the following equation:
/tons\
Postsecondary Schools Excess Food 	 =
Vyear/
meals
ofiirlpnt lbs tons
Number of students x 	student x x	 x
year	meal 2,000 lb
Where X = 0.22 to 0.79
Generation factor 10 is based on pounds per student per year, and uses the following equation:
/tons\
Postsecondary Schools Excess Food 	 =
Vyear/
_lbs_
Qtiirlpnt tons
Number of students x 	x
year 2,000 lb
2.4.3. Elementary and Secondary Schools
Data for elementary and secondary schools were collected from NCES for the 2020-2021 school
year. Public school data were obtained from the NCES Table Generator for the 2020-2021 school
year (NCES (2021b)) and included institution name, address, phone number, website, geo-
coordinates, school level (elementary, middle, high school, and others), and the total student
enrollment for each institution. Private school data were obtained from the NCES Table Generator
for the 2017-2018 school year (NCES (2021c)) and included institution name, address, phone
number, geo-coordinates, and the total number of students enrolled for each institution.
The literature search identified a total of 32 studies addressing excess food generation in the K-12
school setting. Many of these studies, however, are not directly useful to methods development.
Some lack quantitative information on generation factors, while others apply generation factors
from earlier studies. Therefore, EPA chose five studies that either involved original research (e.g.,
waste audits at an elementary school) or that present estimates widely cited in the literature and
applied them to both public and private elementary and secondary schools (Table 8).
5 EPA estimated that 34 percent of all enrolled students attend residential institutions. EPA calculated the percent of enrolled
students attending residential institutions as sum of enrolled students at "primarily residential" and "highly residential" institutions
divided by the total number of enrolled students. See the Classification Summary Tables, Carnegie Classification of Institutions of
Higher Education, Center for Postsecondary Research, Indiana University School of Education, available at:
http://carnegieclassifications.iu.edu/downloads.php.
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Table 8. Generation Factors for Public and Private Elementary and Secondary Schools
GENERATION


GENERATION

FACTOR #
SOURCE
YEAR
FACTOR
UNITS
1
Wilkie et al.
2015
25.9
lbs/student/year
2
RecyclingWorks
Massachusetts
2013
18.0
lbs/student/year
3
CCG
2015
22.0
lbs/student/year
4
Byker et al.
2014
0.52
lbs/meal
5
Draper/Lennon
2001
0.35
lbs/meal
Generation factors 1, 2, and 3 use pounds per student per year. Wilkie et al. (2015) estimate an
average generation factor of 25.9 pounds per student per year based on sampling at three different
Florida schools.6 RecyclingWorks Massachusetts (2013) estimated an average generation factor
of 18.0 pounds per student per year, based on waste audits conducted at seven public elementary,
middle, and high schools and serves as the low estimate. CCG (2015) estimates a generation factor
of 22.0 pounds per student per year.7
Generation factors 4 and 5 use pounds (per student) per meal. Byker et al. (2014) estimated an
average generation factor of 0.52 pounds per meal at public pre-kindergarten and kindergarten
classes. EPA also included one study that did not directly measure excess food generation at typical
K-12 schools, Draper/Lennon (2001), because it is widely cited in the literature.8 Draper/Lennon
(2001) estimated an average of 0.35 pounds of excess food per meal.
The Wilkie et al. (2015) and Byker et al. (2014) studies differentiate between excess food and milk
waste. The recommended methods incorporate both excess food and milk waste, implicitly
assuming that students dispose of milk in the same trash receptacles as food.
Generation factors 1, 2, and 3 are based on pounds per student per year, and use the following
equation:
/tons\
Elementary and Secondary Schools Excess Food 	 =
Vyear/
v lbs
Qtiirlpnt tons
Number of students x 	x 	—
year 2,000 lb
Where X= 18.0, 22.0 or 25.9
0 The three schools include one public elementary school, one public high school, and one private middle/high school.
7	CCG (2015) reported a generation rate of 3.67 tons of total waste per year per 100 students in Table 39. This is converted to
excess food using the estimated percentage of total waste that is food of 30.0 percent, from Table 40. As noted earlier, the CCG
study pools all educational institutions, including colleges/universities and K-12 schools. EPA applied the same generation factor
in both sectors.
8	Draper/Lennon (2001) estimated excess food generation at colleges, universities, and independent preparatory schools. Cited in
South Carolina Department of Commerce (2015), Mercer (2013), BSR (2012), and US EPA (2011).
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The NCES database did not provide the number of meals served at each institution, so in order to
use generation factors 4 and 5 that are based on pounds per meal, EPA used data released from the
National School Lunch Program (NSLP), which reports the total number of students enrolled in
the program and the number of meals served per year.9 The result is an average of 164 meals per
student per year. Generation factors 4 and 5 use the following equation:
/tons\
Elementary and Secondary Schools Excess Food 	
Vyear/
1/C/1 meals

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Table 10. Generation Factors for the Hospitality Industry
SOURCE
YEAR
GENERATION
FACTOR
UNIT
CCG
2006
1,983
lbs/employee/year
Okazaki et. al.
2008
375
lbs/employee/year
CCG
2015
1,197
lbs/employee/year
Tetra Tech
2015
997
lbs/employee/year
Most of the relevant studies reported pounds of excess food generated per hotel employee per year.
A hotel excess food study from Hawaii (Okazaki et. al. (2008)) estimated excess food generated
per hotel food service employee, unlike the other studies that consider excess food generated per
general hotel employee. To apply data from Okazaki et al. (2008), the analysis divides the total
amount of excess food generated in Hawaii hotels (as estimated by Okazaki et al. (2008)) by the
total number of hotel employees under NAICS 7211 in Hawaii, to make the generation factor
consistent with the other studies. These four generation factors range from 375 to 1,983 pounds
per employee per year. The studies were published between 2006 and 2015 using data from three
states (California, Hawaii, and New Jersey) and Vancouver, Canada.
These generation factors were used in conjunction with employee data obtained from DnB using
the following equation:
/tons\
Hospitality Industry Excess Food 	J =
x lb
, „ ,	employee tons
Number or employees x	x ————
F y	year 2,0001b
Where X = 375, 997, 1197, or 1983
2.5.1. Changes in Version 3.0
Establishments under NAICS codes 721191 and 721199 were added.
2.6. Correctional Facilities
To estimate the amount of excess food generated by correctional facilities, facility-level data for
NAICS code 922140 was collected from DHS.
The literature search identified 27 studies on excess food generation in correctional facilities. EPA
chose six studies that provide excess food generation factors based on empirical data collected
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from various prisons (Table ll).11 These six studies were used to estimate excess food generated,
resulting in a range of values for each facility.
Table 11. Generation Factors for Correctional Facilities
GENERATION
FACTOR #
STUDY
YEAR
GENERATION
FACTOR
UNITS
1
Marion, J.
2000
1.00
lbs/inmate/
day
2
Draper/Lennon
2001
1.00
lbs/inmate/
day
3
Kessler
Consulting
2004
1.20
lbs/inmate/
day
4
Mendrey, K.
2013
1.25
lbs/inmate/
day
5
Goldstein, N.
2015
1.40
lbs/inmate/
day
6
CalRecycle
2018
0.85
lbs/inmate/
day
Two of these studies (Marion (2000) and Draper/Lennon (2001)) rely on data collected by the New
York State Department of Correctional Services (NYS DOCS) Food Discard Recovery Program
between 1990 and 1997. Using data collected by the NYS DOCS program, Marion (2000) found
that approximately one pound per day of food scraps was recoverable per inmate.12 Draper/Lennon
(2001) used Marion's findings, but also collected data from a prison food waste composting
program in Connecticut; they also found that, on average, one prisoner generates one pound of
excess food per day. Additionally, nine other sources published between 2002 and 2016 rely on
the Marion (2000) one pound per inmate per day estimate in calculating excess food generated in
correctional facilities in various states including New Jersey and South Carolina (Mercer (2013);
SCDOC (2015)).
These six excess food generation factors range from 0.85 to 1.4 pounds per inmate per day, from
studies that conducted original research and collected data from correctional facilities. Capacity
numbers were found for facilities through DHS, and that number was used as a proxy for number
of inmates. In instances where the study provided a range in the amount of excess food generated
per inmate per day, EPA used the midpoint of the range. These studies were published between
2000 and 2018 using data from six states.13 While the Marion (2000) and Draper/Lennon (2001)
11	Several studies report the role that excess food plays in the overall prison solid waste stream. In general, these studies find that
excess food makes up about 30 percent of all waste generated (Marion (2000); Kessler Consulting (2004); RecyclingWorks
Massachusetts (2013); Hodge et al (2016); CalRecycle (2018)).
12	Marion's language is ambiguous as to whether the one pound/inmate/day estimate is the total excess food generated or the
amount of excess food recovered. The analysis assumes that the recoverable portion of excess food is equivalent to excess food
generation in correctional facilities.
13	California, Connecticut, Florida, New York, Pennsylvania, and Washington.
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studies are older, they are frequently cited in other studies (see BSR (2012); RecyclingWorks
Massachusetts (2013); Labuzetta et al. (2016)); therefore, EPA retained them in this analysis.
DnB does not provide data on the number of inmates at each correctional facility, but it does
provide the number of employees at each facility. In order to use generation factors that are based
on pounds per inmate, EPA estimated the average number of inmates per employee. The Bureau
of Justice Statistics (BJS (2016), BJS (2005a), BJS (2005b)) publishes information on the number
of inmates and employees for county and city jails and for state and federal prisons:
•	County and city jails: 3.1 inmates/employee14
•	State and federal prisons: 3.4 inmates/employee15
Using these data, the following equation was used to generate estimates of excess food for
correctional facilities:
Where X = 0.85 to 1.4
2.6.1. Changes in Version 3.0
EPA used prison capacity as a proxy for # of inmates rather than an employee-to-inmate ratio.
Two studies with higher and lower generation factors were also used.
2.7.Healthcare Facilities
2.7.1. Hospitals
As listed in Table 12, establishments belonging to three NAICS codes were grouped as hospitals.
Establishment-level data for this sector was obtained from the Department of Homeland Security
(DHS (2020)).
14	In 2016, 704,500 inmates were confined in city and comity jails (BJS (2016), Table 7) and there were 226,300 total employees
(BJS (2016), Table 8). 704,500 inmates/226,300 total employees =3.1 inmates per employee in city and county jails.
15	The total number of prisoners under the jurisdiction of Federal and State adult correctional authorities was 1,525,924 at year end
2005 (BJS (2005b), page 1). The total number of employees in correctional facilities under Federal and State authority at year end
2005 was 445,055 (BJS (2005a), Table 4). 1,525,924 prisoners/445,055 total employees = 3.4 prisoners per employee in federal or
state prisons.
Correctional Facilities Excess Food
year.
Capacity (# of inmates) x
Y lb
^mEiiX365daySx tons
day	year 2,000 lb
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Table 12. NAICS Codes for Hospitals
No.
NAICS Code
NAICS Code Description
1
622110
General Medical and Surgical Hospitals
2
622210
Psychiatric and Substance Abuse Hospitals
3
622310
Specialty (except Psychiatric and Substance Abuse) Hospitals
The literature search identified a total of 46 studies addressing excess food generation in hospital
settings. Many of these studies, however, are not directly useful to methods development. Some
lack quantitative information on generation factors, while others apply generation factors from
earlier studies. EPA chose four studies that either involved original research (e.g., sorting/analysis
of hospital waste) or which present foundation estimates widely cited in the literature. These four
studies were used to estimate excess food generated, resulting in a range of values for each facility
(Table 13)
Table 13. Generation Factors for Hospitals
SOURCE
YEAR
GENERATION
FACTOR
UNITS
Draper/Lennon
2001
1,248.3
lbs/bed/year
NCDENR
2012
468.2
lbs/bed/year
Walsh
1993
663.4
lbs/bed/year
CCG
2015
232.6
lbs/bed/year
The highest generation factor is from Draper/Lennon (2001) which is widely cited in other studies
estimating excess food (see RecyclingWorks Massachusetts (2013); NRDC (2017); BSR (2012);
among others). While widely applied, the generation factors in Draper/Lennon (2001) are built on
original research developed in the 1990s, hence EPA supplemented this data point with other
studies. Both the NCDENR (2012) study and the CCG (2015) study are more recent and use
original waste sampling. The Walsh (1993) study is older but provides an additional data point for
corroboration of the generation per bed figures.16
These four generation factors were used in conjunction with hospital bed data obtained from DHS
to estimate a range of generation rates for the three NAICS codes identified as hospitals. This is
reflected in the following equation:
10 The analysis of hospitals in the NCDENR report draws on a study of Orange County, North Carolina. The only hospital in the
county is the University of North Carolina Medical Center, which has 803 beds (see
https://www.uncmedicalcenter.org/uncmc/about/). EPA's analysis uses that figure to calculate pounds of excess food per bed.
Both the CCG (2015) and Walsh (1993) studies report total solid waste generation per hospital bed. CCG (2015) provides a
detailed composition analysis indicating that 20.4 percent of the hospital solid waste is food, allowing calculation of excess food
per bed. EPA's analysis applies the same composition assumption (20.4 percent) to the Walsh (1993) solid waste per bed figure
to estimate excess food per bed.
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/tons\
Hospitals Excess Food 	
Vyear/
y lb
A hpH tons
# of Beds x —eea x
year 2,000 lb
Where X = 232.6, 468.2, 663.4, or 1248.3
2.7.2 Nursing Homes
As listed in Table 14, establishments belonging to six NAICS codes were grouped as nursing
homes. Establishment-level data for this sector was obtained from both the Department of
Homeland Security (DHS (2020)) and DnB.
Table 14. NAICS Codes for Nursing Homes

NAICS Code
NAICS Code Description
1
623110
Nursing Care Facilities
2
623210
Residential, Intellectual and Developmental Disability Facilities
3
623220
Residential Mental Health and Substance Abuse Facilities
4
623311
Continuing Care Retirement Communities
5
623312
Assisted Living Facilities for the Elderly
6
623990
Other Residential Care Facilities
Nursing homes were mapped without excess food estimates.
2.7.3 Changes in Version 3.0
Nursing homes encompassing establishments from six NAICS codes were added as a sub-category
of healthcare facilities.
2.8. Restaurants and Food Services
Six industries were classified as restaurants and food services (Table 15). Establishment-level data
for this sector was obtained from DnB.
Table 15: NAICS Codes for Restaurants and Food Services
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No.
NAICS Code
NAICS Code Description
1
722320
Caterers
2
722330
Mobile Food Services
3
722511
Full-Service Restaurants
4
722513
Limited-Service Restaurants
5
722514
Cafeterias, Grill Buffets, and Buffets
6
722515
Snack and Nonalcoholic Beverage Bars
Industries were classified as full-service or limited-service according to their six-digit NAICS
codes. Full-service establishments include Caterers (NAICS code 722320), Full-Service
Restaurants (NAICS codes 722511) and Cafeterias, Grill Buffets, and Buffets (NAICS code
722514). Limited-service establishments include Mobile Food Services (NAICS code 722330),
Limited-service Restaurants (NAICS codes 722513), and Snack and Nonalcoholic Beverage Bars
(NAICS code 722515).
The literature search identified a total of 49 studies that address excess food generation
in restaurant and food service settings. Many of these studies, however, do not provide directly
useful generation data. Some lack quantitative information on generation factors, while others
apply generation factors derived from earlier studies. EPA chose five studies that either involved
original research (e.g., sorting/analysis of facility waste) or which present generation factors that
are widely cited in the broader literature (Table 16). These five studies were used to estimate
excess food generated, resulting in a range of values for each establishment.
Table 16. Generation Factors for Restaurants and Food Services
GENERA
TION
FACTOR
#
SOURCE
YEAR
GENERATION
FACTOR
UNITS
ESTABLISHMENT
TYPE
1
CCG
2006
3,392 for full-
service
lbs/employee/year
Full-service and
limited service
estimated separately
2
2,494 for limited
service
3
Draper/
Lennon
2002
3,000
lbs/employee/year
Unspecified
4
CCG
2015
2,760
lbs/employee/year
Full-service and
limited service
estimated together
5
BSR
2014
0.033
lbs/revenue/year
Unspecified
The three studies used to establish generation factors 1-4 established factors based on pounds per
employee per year. The Draper/Lennon (2002) study, developed for the Massachusetts Department
of Environmental Protection and updated by EPA Region 1 in 2011, was widely cited (see
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RecyclingWorks Massachusetts (2013); Mercer (2013); SCDOC (2015); among others). While
widely applied, the generation factors in Draper/Lennon (2002) are built on original research
developed in the 1990s. Both the CCG (2006) and CCG(2015) studies are more recent and use
waste sampling techniques to estimate of excess food generation.
BSR (2014) collected industry generation data through a series of surveys and estimated 33
pounds of excess food generated per thousand dollars of company revenue.
Generation factors 1, 3, 4, and 5 were used to estimate excess food generation for the
establishments in the three NAICS codes classified as full-service establishments. Generation
factors 2, 3, 4, and 5 were used to estimate excess food generation rates for the establishments in
the three NAICS codes classified as limited-service establishments.
Generation factors 1-4 use the following equation:
/tons\
Restaurants and Food Services Sector Excess Food 	 :
Vvear/
X-
tyear/
lb
, „ , 'employee tons
Number or employees x	x
year 2,000 lb
Where X = 2494 to 3,392
Generation factor 5 uses the following equation:
/tons\
Restaurants and Food Services Sector Excess Food 	
Vvear/
Establishment's Annual Revenue $x 0.033 ¦
year/
lb	tons
Annual Revenue ($) 2,000 lb
2.8.1. Changes in Version 3.0
No changes were made.
2.9. Food Banks
Food banks (NAICS code 624210) are considered potential generators as well as potential
recipients of excess food. This is because some of the food they receive as donations may be
expired, degrading, or otherwise deemed unfit for human consumption. For Version 3.0 of the
map, food bank data were acquired from DnB. These data include 989 food banks, but no excess
food generation estimates.
2.9.1. Changes in Version 3.0
A new data source for food banks was used for Version 3.0 of the map. For previous versions of
the map, food bank facilities data and excess food estimates came from Feeding America.
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2.10. Farmers Markets
Farmers markets are considered potential generators of excess food. Data for farmers markets were
retrieved from the USDA Local Foods Directory (USDA (2022)), which keeps a comprehensive
list of markets in the 50 states, Washington D.C., and Puerto Rico. There were roughly 1,000
markets included in the dataset, with geographic location and website. Generation factors were not
available to calculate estimates of excess food from farmers markets.
2.10.1 Changes in Version 3.0
Farmers Markets is a new sector included in Version 3.0 of the map.
2.11 Data Analysis
Around 950,000 establishments that potentially generate excess food were included in the dataset
and map from ICI sectors based on 90 NAICS codes, three school types, and farmers markets. The
dataset provides establishment-level information including name and geographic location, and
source data included common business statistics such as revenue, number of employees, or number
of students which was used to estimate excess food generation using sector-specific equations, as
detailed in sections 2.2 to 2.10. Excess food generation rates were estimated for 90.7% of
establishments. Establishments for which generation rates could not be estimated were still
mapped. There were several equations available to calculate excess food estimates for most
sectors, resulting in a range of values for each establishment; a high and low excess food estimate
was included for each establishment in those sectors.
The data were reviewed and filtered in the following ways:
•	Duplicates were defined as establishments with identical names and physical addresses. If
an establishment had multiple observations, it was assigned the minimum for number of
employees and revenue among all its observations.
•	For data missing geographic coordinates, addresses, or other location-based information,
ArcGIS was used to fill in gaps.
3. Macro Analysis of Sector-Specific Excess Food Generation Rates
The dataset provides establishment-level estimates of excess food in each identified sector. Data
for the 952,573 establishments were obtained primarily from DnB, as well as the NCES
databases, DHS, and USDA. Excess food generation rates were estimated for 90.7% of all
establishments. Estimation was not possible if generation factor data were missing, in which case
no excess food estimate was reflected in the dataset, though the establishment was still mapped.
Estimates were not possible for food banks and farmers markets. For other sectors, estimates
were not possible for 100% of establishments.
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Figure 3. Non-Residential Excess Food Generating Sectors
Farmer's Markets,
0.1%
Manufacturers/Processors, 4.6%
Educational
Institutions, 13.4%
Hospitality Industry,
7.0%
Correctional
Facilities, 0.6%
Healthcare Facilities,
6.0%
Food Banks, 0.10%
Table 17. Establishments Included in the Dataset by Sector
F .	Establishments with	% Establishments
c . Establishments in the	r , ,	... , ,
Sector n . .	Excess Food	with Excess Food
a ase	Estimate	Estimate
Food Manufacturers &
Processors
43,738
39,473
90.2%
Food Wholesale &
Retail
197,455
194,609
98.6%
Educational
Institutions
127,547
123,719
97.0%
Hospitality Industry
67,116
47,143
70.2%
Correctional Facilities
6,118
4,713
77.0%
Healthcare Facilities
57,521
7,033
12.2%
Restaurants and Food
Services
451,092
447,233
99.1%
Food Banks
989
0
0%
Farmers Markets
997
0
0%
Total
952,573
863,923
90.7%
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3.1. Food Manufacturers and Processors
The food manufacturers and processors sector, as described in Section 2.2, includes 45 NAICS
codes. Data were obtained for 43,738 establishments, and excess food estimates were generated
for 90.2% of the establishments. Figure 4 shows the proportion of food manufacturers and
processors by industry type17. Table 18 shows more granular information about data availability
across the sector.
Figure 4. Proportion of Food Manufacturers and Processors by Industry Type
All Other
Miscellaneous
Food
Manufacturing
5%
Animal (except
poultry)
slaughtering
2%
Distilleries
3%
Commercial
bakeries
5%
Soft drink
manufacturing
Wineries
17 NAIC S codes with at least 1000 associated establishments in the dataset are highlighted in the chart and table below. The segment
"All Other NAICS Codes" includes the 37 codes with fewer than 1000 associated establishments in the dataset.
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Table 18. Number of Food Manufacturers and Processors Included in the Dataset
Industry
Establishments in the
Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
All Other



Miscellaneous Food
1,988
1,766
88.8%
Manufacturing



Animal (except poultry)
slaughtering
1,056
906
85.8%
Breweries
1,105
1,039
96.5%
Commercial bakeries
2,074
1,814
87.5%
Distilleries
1,253
1,175
93.8%
Retail bakeries
18,238
17,406
95.4%
Soft drink
manufacturing
1,518
1,029
67.8%
Wineries
5,347
5,077
95.0%
All other NAICS codes
11,159
9,261
83.0%
Total
43,738
39,473
90.2%
3.2. Food Wholesale and Retail
The food wholesale and retail sector, as described in Section 2.3, encompasses 24 NAICS codes.
Data were obtained for 197,455 establishments associated with these codes, and excess food
estimates were generated for 98.6% of establishments.
Figure 5 shows the proportion of food wholesalers and retailers by industry type; 82% of which
are food retailers (supermarkets, grocery stores, and supercenters) and 18% are food wholesalers.
Table 19 shows more granular information about data availability across the sector.
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Figure 5. Proportion of Food Wholesale and Retail Establishments by Industry Type



Wholesale
18%

W

Table 19. Number of Food Wholesale and Retail Establishments Included in the Dataset
Industry
Establishments
in the Dataset
Establishments with
Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Food Wholesalers
35,226
32,446
92.1%
Food Retailers (Supermarkets,
Grocery Stores, and
Supercenters)
162,229
162,163
99.96%
Total
197,455
194,609
98.6%
3.3. Educational Institutions
The educational institutions sector, as described in Section 2.4, encompasses three school types.
These are postsecondary schools, public elementary and secondary schools, and private elementary
and secondary schools. Figure 6 shows the proportion of educational institutions by type, and
Table 20 shows more granular information about data availability across the sector.
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Figure 6. Proportion of Educational Institutions by School Type
Private
elementary anc
secondary
schools
17%

Postsecondary
schools
5%
Public
elementary and
secondary
schools
78%
Table 20. Number of Educational Institutions Included in the Dataset
School Type
Institutions in the
Dataset
Institutions with
Excess Food
Estimate
% Institutions with
Excess Food
Estimate
Postsecondary Schools
6,435
6,170
95.9%
Public Elementary and
Secondary Schools
98,882
22,109
99.5%
Private Elementary and
Secondary Schools
22,230
95,440
96.5%
Total
127,547
123,719
97.0%
3.4. Hospitality Industry
The hospitality industry, as described in Section 2.5, encompasses five NAICS codes. Data were
obtained for 67,116 establishments associated with these codes, and excess food estimates were
generated for 70.2% of the sample.
Figure 7 shows the proportion of hospitality establishments by industry type, and Table 21
shows more granular information about data availability across the sector.
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Figure 7. Proportion of Hospitality Industry Establishments by Type
All Other Traveler
Accomodation
4%
Casino Hotels
2%
i
\
Dcd-and-
Breakfast Inns
17%

Casinos (except
Casino Hotels)
1%
Hotels and
Motels
76%
Table 21: Number of Hospitality Establishments Included in the Dataset
Industry
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Hotels and Motels
51,038
35,918
70.4%
Casino Hotels
1,037
752
72.5%
Casinos (except Casino
Hotels)
407
291
71.5%
Bed-and-Breakfast Inns
11,623
8,087
69.6%
All Other Traveler
3,011
2,095
69.6%
Accommodation
Total
67,116
47,143
70.2%
3.5.	Correctional Facilities
The correctional facilities sector, as described in Section 2.6, encompasses one NAICS code. Data
were obtained for 6,118 facilities associated with this code, and excess food estimates were
generated for 77.0% of the sample.
3.6.	Healthcare Facilities
The healthcare facilities sector, as described in Section 2.7, encompasses three NAICS codes for
hospitals and six NAICS codes for nursing homes. Data were obtained for 7,275 hospitals and
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50,246 nursing homes, and excess food estimates were generated for 12.2% of the sample.
Estimates were not generated for any nursing homes or residential care facilities, only for hospitals.
Figure 8 shows the proportion of healthcare facilities by industry type, and Table 22 shows
more granular information about data availability across the sector.
Figure 8. Proportion of Healthcare Facilities by Industry Type
Table 22: Number of Healthcare Facilities Included in the Dataset

Facilities in
the Dataset
Facilities with
% Facilities
Industry
Excess Food
with Excess

Estimate
Food Estimate
General Medical and Surgical Hospitals	5,794	5,608	96.8%
Psychiatric and Substance Abuse
Hospitals
646
616
95.4%
Specialty (except Psychiatric and
Substance Abuse) Hospitals
835
809
96.9%
Assisted Living Facilities for the
Elderly
23,358
0
0%
Nursing Care Facilities
15,694
0
0%
Other Residential Care Facilities
8,201
0
0%
Residential, Intellectual, and
658
0
0%
Developmental Disability Facilities
Residential Mental Health and
2,247
0
0%
Substance Abuse Facilities
Continuing Care Retirement
Communities
88
0
0%
Total
57,521
7,033
12.2%

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3.7. Restaurants and Food Services
The restaurants and food services sector, as described in Section 2.8, encompasses six NAICS
codes. Data were obtained for 451,092 establishments associated with these NAICS codes, and
excess food estimates were generated for 99.1% of the sample.
Figure 9 shows the proportion of restaurants and food services establishments by industry type
and Table 23 shows more granular information about data availability across the sector.
Figure 9. Proportion of Restaurant and Food Services Establishments by Industry Type
Caterers, 5.34%
Cafeterias, Grill
Buffets, and
Buffet, 0.30%
Snack and
Nonalcoholic
Beverage Bars,
0.18%
Mobile Food
Services, 0.46%
Table 23: Number of Restaurants and Food Services Establishments Included in the Dataset
Industry
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Caterers
24,103
23,868
99.0%
Mobile Food Services
2,074
2,073
99.99%
Full-Service Restaurants
257,857
255,562
99.1%
Limited-Service
Restaurants
164,887
163,591
99.2%
Cafeterias, Grill Buffets,
1,358
1,344
99.0%
and Buffets
Snack and Nonalcoholic
813
795
97.8%
Beverage Bars
Total
451,092
447,233
99.1%
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3.8.	Food Banks
Food banks, as described in Section 2.9, encompass 989 establishments, for which no excess food
estimates were generated.
3.9.	Farmers Markets
Farmers markets, as described in Section 2.10, encompass 997 markets, for which no food waste
estimates were generated.
4. Data Sources for Recipients
4.1.	Overview
The map displays facility-specific information for four categories of potential recipients of excess
food, the data sources for which are described below.
4.2.	Food Banks
Food banks (NAICS code 624210) are considered potential recipients (because they receive
donated food that would otherwise have gone to landfill, composting, etc.) as well as generators
of excess food (because some of the food they receive as donations may be deemed unfit for human
consumption and cannot be given to humans). For previous versions of the map, food bank data
were provided by Feeding America, a nationwide network of food banks, food pantries, and meal
programs. Version 2.1 included 316 food banks for which Feeding America provided data on how
much food is received and how much excess food is generated each year. For Version 3.0 of the
map, food bank data were acquired from DnB. These data include 989 food banks, but no excess
food generation estimates.
4.3.	Composting Facilities
Data for composting facilities were primarily compiled through EPA review of state government
websites, usually state departments of natural resources or environmental protection, and
communication with state government employees. Additional data were gleaned through web
searches, public data from towns, cities, composting non-profits and associations, and
communications with EPA Regional offices. Version 2.1 of the map contained 3,013 composting
facilities in the dataset for 49 states and one territory. Version 3.0 of the map contains 3,887
composting facilities in 50 states and two territories. Associated websites and types of feedstock
accepted are listed in the dataset and in the map, where information was available. This dataset
includes composting facilities that accept all types of feedstocks, not just food waste, and may
contain a few community composting sites, though these fall largely outside the scope of the map.
Facilities that are known to accept food waste are identified in the map and dataset; not all accepted
feedstocks, facility locations, or contact information was verified by EPA. Specific sources for
composting facility information are included in the dataset.
4.4.	Anaerobic Digestion Facilities
EPA updated the anaerobic digestion (AD) facilities dataset in Version 3.0 of the map, resulting
in a dataset containing 1,635 facilities. The updated Version 3.0 dataset was compiled from (1) a
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list of facilities on farms maintained by AgSTAR (US EPA (2022b)); (2) a list of stand-alone food
waste digesters, on-farm digesters that co-digest food waste, and digesters that co-digest food
waste at water resource recovery facilities (WRRFs) who responded to EPA's AD Data Collection
Survey in 2021 (US EPA (2023c)); and (3) the list of AD facilities at WRRFs maintained by the
Water Environment Federation (WEF (2019)). Additional facilities were identified through web
searches, public data from non-profits and trade associations, and state government websites.
Where available, data are included on types of feedstock (e.g., types of food waste, animal
manures) accepted by the facility. This dataset includes anaerobic digestion facilities that accept
all types of feedstock, not just food waste. Facilities that are known to accept food waste are
identified in the map and dataset.
5. Infrastructure to Support Excess Food Management
5.1. Communities with Residential Source Separated Organics Programs
In Version 3.0 of the map, 275 communities with residential curbside food waste collection were
identified from a 2021 survey by BioCycle (Goldstein (2021), following the 2017 survey by Piatt
and Streeter, published in BioCycle) supplemented with data from state government websites and
public data from select composting non-profits and associations. Specific sources for data are
identified in the dataset. All 275 communities were mapped. Some communities are counties or
solid waste districts that have programs that serve multiple cities or areas, while some communities
are single towns or cities with their own programs. Not all programs serve all households within
the specified community, and EPA was often unable to identify the number of households within
a community with access to a curbside program. As such, the total number of households with
access in this dataset is not an accurate representation of national access to curbside food waste
collection. This dataset includes communities with municipally supported residential source
separated organics programs that collect food scraps and does not include those communities that
only collect yard waste.
5.2. Refrigerated Warehousing and Storage
Refrigerated warehousing and storage facilities were mapped for the first time in Version 3.0. One
industry was classified in this sector (Table 24). Establishment-level data for this sector was
obtained from DnB.
Table 24. NAICS Codes for Refrigerated Warehousing and Storage
No.
NAICS Code
NAICS Code Description
1
493120
Refrigerated Warehousing and Storage
There are roughly 585 establishments listed with geographic location and website where available.
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6.	Environmental Justice (EJ), Food Assistance and Food Insecurity Data Layers
Environmental justice (EJ), food assistance and food insecurity data layers are new to Version 3.0
of the map. EJ is increasingly being incorporated in many aspects of environmental work. Within
the food system, EJ relates to topics such as food insecurity and access, including proximity to
establishments that sell food and the types of food that people can access and purchase.
Socioeconomic status can also affect ability to afford food, and participation rates in programs
such as the Supplemental Nutrition Assistance Program (SNAP), the Special Supplemental
Nutrition Program for Women, Infants, and Children (WIC), and the National School Lunch
Program (NSLP) reflect areas where income is lower, and food assistance is provided. Users can
overlay food access data with excess food or recipients. This type of visualization can drive policy,
infrastructure and investment decisions, and spur action to improve outcomes in a community.
6.1.	EPA EJScreen
EJScreen is EPA's environmental justice mapping and screening tool, featuring environmental and
demographic indicators including pollution types and sources, health disparities, climate change
data, and more (US EPA (2015b)). Version 3.0 of the Excess Food Opportunities Map brings in
several EJScreen layers, including Environmental Justice Indicators, and Critical Service Gaps,
which features a sublayer on Food Access. The Food Access sublayer comes from the USDA Food
Access Research Atlas (ERS (n.d.a)). Information on other data sources and EJScreen
development is available on the EJScreen webpage (US EPA (2015b)).
6.2.	USDA Food Environment Atlas
The new version of the map features Food Assistance and State Food Insecurity layers from the
USDA's Food Environment Atlas (ERS (n.d.b)). The Food Assistance layer includes these
sublayers: SNAP, WIC, FDPIR, National School Lunch Program, School Breakfast Program,
Summer Food Service Program, and Child & Adult Care. More information, including data
sources and definitions, is available on the Food Environment Atlas webpages (ERS (n.d.b)).
7.	Limitations and Opportunities for Improvement
This section summarizes limitations associated with the methodology as well as recommendations
for future improvements.
Map and methodology limitations and opportunities for improvement include the following:
1.	Generation factors. Generation factors in the methodologies adopted for this study are
based on limited measured data. Although the methodologies adopted for the map provide
a simple approach to estimate excess food generation from an ICI establishment, on-site
measurement is always preferred. Farmers markets, food banks, and some establishments
from other sectors did not have generation factors. Additionally, generation factors rely on
certain metrics - for instance, number of beds in nursing homes - which not all data sources
provide.
2.	Recoverable fraction of excess food. The recoverable fraction of excess food could be
used to feed people, which represents the most preferred use of excess food. A reliable
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estimate of the recoverable fraction of excess food is critical data needed to pursue its best
use. If methodologies become available to estimate the recoverable fraction of excess food
available by sector, EPA could include these estimates in a future version of the map.
3.	On-farm loss. This methodology and map do not address on-farm loss, including
unharvested crops or unmarketable crops. ReFED estimates that farming generates 15.5M
tons of surplus produce, of which most is left behind, and only 1.6% is donated for hunger
relief (ReFED (2023)).
4.	Food banks and other food rescue organizations. The data for food banks are limited.
While some data for food banks were obtained from DnB, there are thousands more
independent organizations, such as food pantries and soup kitchens that accept donations
and distribute food to people in need.
5.	Farmers markets. The USD A data on farmers markets improved between the mapping
stage and the publication of Version 3.0 of the map, so the map's dataset for farmers
markets should be expanded in future map updates.
6.	Communities with source separated organics programs. The data for this layer are
limited, and the total number of communities in the dataset is likely an undercount.
7.	Community composting sites. The dataset of composting facilities does not generally
include community composting sites, on-farm composting sites, or other small-scale, local
composting operations that process excess food and other organic material from the
surrounding community. This is a growing sector in composting and should be included in
future map updates.
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Excess Food Opportunities Map Version 3.0 - Technical Methodology
8. References
BJS (2005a). Census of State and Federal Correctional Facilities, 2005. NCJ 222182, Bureau of
Justice Statistics, October 2008. https://www.bis.gov/content/pub/pdf/csfcf05.pdf
(Accessed February 2019)
BJS (2005b). Prisoners in 2005. NCJ 215092, Bureau of Justice Statistics, November 2006.
https://www.bis.gov/content/pub/pdf/p05.pdf (Accessed February 2019)
BJS (2016). Jail Inmates in 2016. NCJ 251210, Bureau of Justice Statistics, February 2018.
https://www.bis.gov/content/pub/pdf/iil6.pdf (Accessed February 2019)
BSR (2012). Food Waste: Tier 1 Assessment. A report prepared by BSR for Food Waste
Reduction Alliance, 2012. http://www.kbcsandbox3.com/fw/wp-
content/uploads/2013/06/FWRA BSR Tierl FINAL.pdf (Accessed February 2019)
BSR (2014). Analysis of US Food Waste Among Food Manufacturers, Retailers, and
Wholesalers. A report prepared by BSR for Food Waste Reduction Alliance, 2014.
http://www.foodwastealliance.org/wp-
content/uploads/2014/ll/FWRA BSR Tier3 FINAL.pdf (Accessed February 2019)
Byker, C., Farris, A.R., Marcenelle, M., Davis, G.C., Serrano, E.L. (2014). Food waste in a
school nutrition program after implementation of new lunch program guidelines. Journal
of the Society for Nutrition Education and Behavior, September-October 2014, 46(5),
406-411.
CalRecycle (2018). Food Waste at Correctional Facilities.
https ://www. calrecvcle. ca. gov/ State Agencv/AgencvType/C orrectional#foodwaste
(Accessed February 2019)
Carvalho, A. (2014). Food Waste Composting at San Diego Hotels. BioCycle, January 2014, 28.
Caton, P.A., Carr, M.A., Kim, S.S., Beautyman, M.J. (2010). Energy recovery from waste food
by combustion or gasification with the potential for regenerative dehydration: A case
study. Energy Conversion and Management, Jane 2010, 51 (6), 1157-1169.
CCG (2006). Waste Disposal and Diversion Findings for Selected Industry Groups. No. 341-06-
006. A report prepared by Cascadia Consulting Group for the California Integrated Waste
Management Board, June 2006.
https://www2.calrecvcle.ca.gov/Publications/Download/787 (Accessed February 2019)
CCG (2015). 2014 Generator-Based Characterization of Commercial Sector Disposal and
Diversion in California. A report prepared by Cascadia Consulting Group for Department
of Resources Recycling and Recovery (CalRecycle), September 2015.
https://www2.calrecvcle.ca.gov/Publications/Details/1543 (Accessed February 2019)
Coker, C. (2009). Source Separated Organics Collection. BioCycle, January 2009, 23.
DHS (2020). Homeland Infrastructure Foundation-Level Data, Department of Homeland
Security, https://hifld-geoplatform.opendata.arcgis.com/ (Accessed June 2023)
Draper/Lennon (2001). Identifying, Quantifying, and Mapping Food Residuals from Connecticut
Businesses and Institutions. A report prepared by Draper/Lennon Inc. and Atlantic
Geoscience Corp. for Connecticut Department of Environmental Protection, September
36

-------
Excess Food Opportunities Map Version 3.0 - Technical Methodology
2001. http://www.ct.gov/deep/lib/deep/compost/ssomfile/ssomreport.pdf (Accessed
February 2019)
Draper/Lennon (2002). Identification, Characterization, and Mapping of Food Waste and Food
Waste Generators in Massachusetts. A report prepared by Draper/Lennon Inc. for the
Massachusetts Department of Environmental Protection, September, 2002.
http://www.mass.gov/eea/docs/dep/recvcle/priorities/foodwast.pdf (Accessed February
2019)
Dun & Bradstreet, Inc. (DnB) (2022). Commercially available datasets related to potential
generators of excess food from the correctional, hospitality, healthcare, food banks, food
manufacturers and processors, and food wholesalers and distributors industries.
Ebner J., Win S.S., Hegde S., Vadney S., Williamson A., Trabold T. (2014). Estimating the
biogas potential from colleges and universities. Proceedings from ASME 2014 8th
International Conference on Energy Sustainability collocated with the ASME 2014 12th
International Conference on Fuel Cell Science, Engineering and Technology, Jane 2014,
V002T04A005.
ERS (n.d.a), US Department of Agriculture (USDA). Food Access Research
Atlas, https://www.ers.usda.gov/data-products/food-access-research-atlas/ (Accessed
June 2023)
ERS (n.d.b), US Department of Agriculture (USDA). Food Environment
Atlas, https://www.ers.usda.gov/data-products/food-environment-atlas/ (Accessed June
2023)
FWRA (2016). Analysis of US Food Waste Among Food Manufacturers, Retailers, and
Restaurants. http://www.foodwastealliance.org/wp-content/uploads/2013/Q5/FWRA-
Food-Waste-Survev-2016-Report Final.pdf (Accessed February 2019)
Garcia-Garcia, G., Woolley, E., Rahimifard, S., Colwill, J., White, R., Needham, L. (2016). A
Methodology for Sustainable Management of Food Waste. Waste and Biomass
Valorization, September 2017, 8(6), 2209-2227.
Goldstein, N. (2015). Food Scraps to Orchard Amendment at Philadelphia Prison. BioCycle,
September 2015, 26.
Goldstein, N. (2021). Residential Food Scraps Collection Access in the US. BioCycle, October-
November 2021.
Graunke, R., Wilkie, A. (2008). Research and Solutions: AASHE Student Award-Winning
Paper: Converting Food Waste to Biogas. Sustainability, December 2008, 391-394.
Harvard Food Law and Policy Clinic. (2022). New Policies to Spur Food Waste Reduction:
Policy Finder Tool from ReFED and FLPC Provides Insights, https://chlpi.org/news-and-
events/news-andcommentary/commentary/new-policies-to-spur-food-waste-reduction-
policy-finder-tool-fromrefed-and-flpc-provides-insights/
Hodge, K.L., Levis, J.W., DeCarolis, J.F., Barlaz, M.A. (2016). Systematic Evaluation of
Industrial, Commercial, and Institutional Food Waste Management Strategies in the
United States. Environmental Science & Technology, August 2016, 50 (16), 8444-8452.
37

-------
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Kessler Consulting (2004). Waste Reduction and Recycling Guide for Florida Correctional
Facilities. A report prepared by Kessler Consulting, Inc. for the Florida Department of
Environmental Protection and the Solid Waste Authority of Palm Beach County, 2004.
https://www.calrecvcle.ca.gov/docs/cr/stateagencv/casestudies/florida.pdf (Accessed
February 2019)
Kessler Consulting (2012). Mecklenburg County NC Food Waste Diversion Study Final Report.
A report prepared by Kessler Consulting, Inc. for Mecklenburg County Solid Waste,
March 2012. http://www.waste.ccacoalition.0rg/f1le/l780/download?token=aRA8psh0
(Accessed February 2019)
Kim, K, Morawski, S. (2012). Quantifying the Impact of Going Trayless in a University Dining
Hall. Journal of Hanger & Environmental Nutrition, December 2012, 482-486.
Labuzetta, A., Hall, M., Trabold, T. (2016). Initial Roadmap for Food Scrap Recovery and
Utilization in New York State.
Marion, J. (2000). Composting 12,000 Tons of Food Residuals a Year. BioCycle, May 2000, 35.
Mendrey, K. (2013). Correctional Facility Composting in Washington State. BioCycle, August
2013, 32.
Mercer, A.G. (2013). Assessment of Food Waste Generation in Mercer County, New Jersey.
http://envirostewards.rutgers.edu/alumniassociation/PDFs/An%20Assessment%20of%20
Food%20Waste%20Generated%20in%20Mercer%20Countv %200122%2013 .pdf
(Accessed February 2019)
Moriarty, K. (2013). Feasibility Study of Anaerobic Digestion of Food Waste in St. Bernard,
Louisiana, https://www.nrel.gov/docs/fyl3osti/57082.pdf (Accessed February 2019).
NCDENR (2012). North Carolina 2012 Food Waste Generation Study. August 2012.
https://files.nc. gov/ncdeq/North%20Carolina%202012%20Food%20Waste%20Generatio
n%20Studv.pdf (Accessed February 2019).
NCES (2021a). Integrated Postsecondary Education Data System
https://nces.ed.gov/ipeds/datacenter/SelectVariables.aspx?stepId=l (Accessed June 2021)
NCES (2021b). Public Elementary/Secondary School Universe Survey
https://nces.ed.gov/ccd/elsi/tableGenerator.aspx (Accessed June 2021)
NCES (2021c). Private Schools Universe Survey https ://nces. ed. gov/ccd/el si/tabl eGenerator. aspx
(Accessed June 2021)
NRDC (2017). Estimating Quantities and Types of Food Waste at the City Level.
https://www.nrdc.org/sites/default/files/food-waste-citv-level-report.pdf (Accessed
February 2019)
Okazaki, W.K., Turn, S.Q., Flachsbart, P.G. (2008). Characterization of food waste generators: a
Hawaii case study. Waste Management, December 2008, 2483-2494.
Piatt, B. and Streeter, V. (2017). Residential Food Waste Collection Access in the US BioCycle,
December 2017, 20.
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ReFED (2016). A Roadmap to Reduce US Food Waste by 20 Percent. Rethink Food Waste
Through Economics and Data.
https://www.refed.com/downloads/ReFED Report 2016.pdf (Accessed February 2019)
ReFED (2023). Food Waste Monitor, https://insights-engine.refed.org/food-waste-
monitor?break bv=destination&indicator=tons-
surplus§or=farm&view=detail&vear=2021 (Accessed June 2023)
RecyclingWorks Massachusetts (2013). Food Waste Estimation Guide, Recycling Works
Massachusetts, https://recvclingworksma.com/food-waste-estimation-guide (Accessed
February 2019)
Sarjahani A., Serrano E.L., Johnson R. (2009). Food and non-edible, compostable waste in a
University dining facility. Journal of Hanger & Environmental Nutrition, March 2009,
95-102.
SCDOC (2015). South Carolina Food Waste Generation Report. Prepared by South Carolina
Department of Commerce, April 2015.
http://www.recvclinginsc.com/sites/default/files/all/scfoodwastegeneration summary up
dated l.pdf(Accessed June2017)
Tetra Tech (2015). 2014 ICI Waste Characterization Program. A report prepared by Tetra Tech
EBA Inc. for Metro Vancouver, June 2015.
http://www.metrovancouver.org/services/solid-
waste/SolidWastePublications/FinalReport-2014ICIWasteCharacterizationProgram3-Jun-
15.pdf (Accessed February 2019)
Thiagarajah K., Getty V.M. (2013). Impact on Plate Waste of Switching from a Tray to a
Trayless Delivery System in a University Dining Hall and Employee Response to the
Switch. Journal of the Academy of Nutrition and Dietetics, January 2013, 113(1), 141-5.
UNEP (n.d.). Food and Food Waste, http://www.fao.org/climate-change/our-work/areas-of-
work/food-loss-and-waste/en/ (Accessed February 2019)
USDA (2014). The Estimated Amount, Value, and Calories of Postharvest Food Losses at the
Retail and Consumer Levels in the United States.
https://www.ers.usda.gov/webdocs/publications/43833/43680 eibl21.pdf?v=41817
(Accessed February 2019)
USDA (2022). Local Food Directories, https://www.usdalocalfoodportal.eom/#directories
(Accessed June 2023)
US EPA (2011). Summary Analysis of Massachusetts Commercial/Institutional Food Waste
Generation Data, https://www.mass.gov/files/documents/2016/08/uo/foodsum.pdf
(Accessed February 2019)
US EPA (2015a). Food Recovery Hierarchy, http://www.epa.gov/sustainable-management-
food/food-recoverv-hierarchy (Accessed June 2023)
US EPA (2015b). EJScreen. https://www.epa.gov/eiscreen (Accessed June 2023)
US EPA (2016). Tools for Preventing and Diverting Wasted Food.
https://www.epa.gov/sustainable-management-food/tools-preventing-and-diverting-
wasted-food (Accessed June 2023)
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US EPA (2020). Advancing Sustainable Materials Management: 2018 Fact Sheet. Washington,
DC: US Environmental Protection Agency, https://www.epa.gov/sites/default/files/2021-
01/documents/2018_ff_fact_sheet_dec_2020_fnl_508.pdf(Accessed June 2023)
US EPA (2021). From Farm to Kitchen: The Environmental Impacts of US Food Waste.
https://www.epa.gov/svstem/files/documents/2021-ll/from-farm-to-kitchen-the-
environmental-impacts-of-US-food-waste 508-tagged.pdf
US EPA. (2022a). The Bipartisan Infrastructure Law: Transforming US Recycling and Waste
Management, https://www.epa.gov/infrastructure/bipartisan-infrastructure-law-
transforming-us-recycling-andwaste-management
US EPA (2022b). List of known anaerobic digestion facilities on farms provided by US EPA's
AgSTAR program.
US EPA (2023a). 2019 Wasted Food Report, https://www.epa.gov/svstem/files/documents/2023-
03/2019%20Wasted%20Food%20Report 508 opt ec.pdf (Accessed June 2023)
US EPA (2023b). Inventory of US Greenhouse Gas Emissions and Sinks: 1990-2021.
Washington, DC: US Environmental Protection Agency.
https://www.epa.gov/svstem/files/documents/2023-04/lJS-GHG-Inventorv-2023-
Chapter-7-Waste.pdf (Accessed June 2023)
US EPA (2023c). Anaerobic Digestion Facilities Processing Food Waste in the United States
(2019). https://www.epa.gov/anaerobic-digestion/anaerobic-digestion-data-collection-
proiect
Vannet Group, LLC (2008). Composting Feasibility Study for the Randolph-Macon College
Dining Facility: A Guide for Environmentally Sound and Cost-Effective Solutions to
Food Waste Recycling.
Walsh, P. Pferdehirt, W., O'Leary, P. (1993). Collection of Recyclables from Multifamily
Housing & Businesses. Waste Age, April 1993, 97-106.
WEF (2019). List of water resource recovery facilities with operating anaerobic digestion.
http://www.resourcerecoverydata.org/biogasdata.php (Accessed March 2020)
Wellesley College (2013). Food is Not Trash: Redefining Wellesley's Waste Culture by
Composting, http://www.mass.gov/eea/docs/dep/recycle/reduce/06-thru-l/food-is-
nottrash.pdf (Accessed February 2019)
Whitehair, K.J., Shanklin, C.W., and Brannon, L.A. (2013). Written Messages Improve Edible
Food Waste Behaviors in a University Dinning Facility. Journal of the Academy of
Nutrition and Dietetics, 113(1), 63-69.
Wilkie, A., Graunke, R., Cornejo, C. (2015). Food Waste Auditing at Three Florida Schools.
Sustainability, January 2-15, 7(2), 1370-1387.
Zero Food Waste Coalition. (2023). ACHIEVING ZERO FOOD WASTE MAY 2023 A State
Policy Toolkit.
https://cdn.sanitv.io/files/34avzoil/production/a517a31a81c38d76e897dd539bde3207affa
164d.pdf
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Appendix A: Glossary
The definitions below are specifically tailored to the scope and aims of this paper.
AgSTAR: An EPA effort that promotes the use of biogas recovery systems to reduce methane
emissions from livestock waste. AgSTAR assists those who enable, purchase, or implement
anaerobic digesters by identifying project benefits, risks, options, and opportunities. AgSTAR also
provides the Livestock Anaerobic Digester Database that offers basic information about anaerobic
digesters on livestock farms in the United States.
ANAEROBIC DIGESTION: The biochemical decomposition of organic matter into methane
gas and carbon dioxide by microorganisms in the absence of oxygen.
ANTHROPOGENIC METHANE EMISSIONS: Methane (CH4), a potent greenhouse gas,
emitted due to human activities.
COMPOST: An organic (derived from living matter) material that can be added to soil to help
plants grow and enhance soil health by building organic matter in the soil, providing essential plant
nutrients, retaining moisture, suppressing plant diseases and pests, and encouraging a proliferation
and diversity of beneficial microbes.
COMPOSTING: Breaking down material via bacteria in oxygen-rich environments.
Composting refers to the production of organic material (via aerobic processes) that can be used
as a soil amendment. (Food Loss and Waste Protocol, 2016)
EXCESS FOOD: For purposes of this project, the phrase "excess food" generally refers to food—
whether processed, semi-processed, or raw—that was not used for its intended purpose and is
managed in a variety of ways, such as donation to feed people, creation of animal feed, composting,
anaerobic digestion, or sending to landfills or incinerators. Examples include unsold food from
retail stores; plate waste, uneaten prepared food, or kitchen trimmings from restaurants, cafeterias,
and households; or by-products from food and beverage processing facilities. EPA also often refers
to this as "wasted food."
Because EPA's goal is to maximize recovery and beneficial use of all discarded organics, some
organic materials were included in this project that are not intended for human consumption, such
as inedible parts (e.g., pits, rinds, bones) and yard waste collected by municipal services (i.e.,
communities with residential source separated organics that collect yard waste and excess food).
Furthermore, the residential and agricultural sectors, which can also generate excess food, were
excluded from the map.
"Wasted food," "food waste," "surplus food," or "excess food" are terms commonly used to
describe food that was not used for its intended purpose.
EXCESS FOOD GENERATION FACTORS: The values used to estimate excess food
generation rates. Sector-specific surveys and/or literature-reported values were used to extract
theses values which are consistent across a sector for each establishment. Examples of excess food
generation factors are amount of excess food per employee per year, or amount of excess food per
student per year.
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FEEDSTOCK: Organic materials that are combined in specific ratios for organics recycling (e.g.,
composting or anaerobic digestion), such as excess food, yard trimmings, manures, or biosolids.
FOOD LOSS: Unused product from the agricultural sector, such as unharvested crops.
FOOD RECOVERY: The action of collecting excess food to feed people.
INEDIBLE PARTS: As defined by the FLW Protocol, these are components associated with a
food that, in a particular food supply chain, are not intended to be consumed by humans. Examples
of inedible parts associated with food could include bones, rinds, and pits/stones.
MUNICIPAL SOLID WASTE (MSW): Garbage or refuse generated by households,
commercial establishments, or institutional facilities.
ORGANICS: Carbon-based materials such as excess food or yard trimmings that can be recycled,
e.g., via composting or anaerobic digestion. Organics can be used as feedstock in composting
(creating a rich soil amendment) and anaerobic digestion (generating biogas and producing a
fertilizer substitute/soil amendment). Materials included in the definition of organics or organic
waste vary by state and local jurisdiction (some may include manures, biosolids, wood, paper, and
cardboard).
ORGANIC WASTE: Organic (carbon-based) materials in the waste stream, such as excess food
and yard trimmings.
PLATE WASTE: Post-consumer leftover food, or food that has been served and not eaten. Also
known as "front of house" excess food.
RECOVERABLE EXCESS FOOD: Food suitable for human consumption at or near the time
of disposal, and suitable for donation or sale to secondary markets.
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