EPA530-R-20-001
April 2020
https://www.epa.gov/sustainable-maiiagement-food
Excess Food Opportunities Map Version
2.1-Technical Methodology
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Excess Food Opportunities Map
Version 2.1-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 2.1, which supports diversion of excess food from landfills. The
information presented by the Map can be used to inform waste management and food recovery at
the local level, and identify potential sources of organic feedstocks, infrastructure gaps, and
disposal alternatives to landfill.
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. Based on the North American Industry Classification
System (NAICS), 76 categories of industries and three school types representing nearly 1.2 million
establishments in the US were identified as potential sources of excess food. These 76 industries
and three school types were grouped into the following sectors: food manufacturers and processors
(46), food wholesale and retail (17), educational institutions (3), the hospitality industry (3),
correctional facilities (1), healthcare facilities (3), and restaurants and food services (6). 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 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 nearly 1.2 million potential excess food generators. The map also identifies approximately
5,000 potential excess food recipients, including composting facilities, anaerobic digestion
facilities, and food banks and over 200 communities with residential source separated organics
programs.
The Version 2.0 update in 2019 included 1) an update of all generator sectors using 2018 data, 2)
the addition of the restaurant and food services sector (e.g., restaurants, caterers, etc.), and 3) an
update of the composting facilities. The Version 2.1 update in 2020 includes 1) an update of the
anaerobic digestion facilities, 2) an update of the communities with residential source separated
organics programs and 3) minor updates to the composting facilities.
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Executive Summary
This report describes the methodologies used to create estimates for the EPA Excess Food
Opportunities Map (Map) Version 2.1. This interactive map supports nationwide diversion of food
from landfills through the display of nearly 1.2 million potential industrial, commercial, and
institutional excess food generator locations, estimates of their excess food generation rates, and
the display of approximately 5,000 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
processors;
Identify potential infrastructure gaps for managing excess food.
For the purposes of this report, "excess food" refers to food—whether processed, semi-processed,
or raw—that is intended for human consumption but was removed from the supply chain 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. 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 on-farm losses, including unharvested
crops.
Based on the North American Industry Classification System (NAICS), 76 categories of industries
and three school types representing nearly 1.2 million establishments in the US were identified as
potential sources of excess food. These 76 industries and three school types were grouped into the
following sectors: food manufacturers and processors (46), food wholesale and retail (17),
educational institutions (3), the hospitality industry (3), correctional facilities (1), healthcare
facilities (3), and restaurants and food services (6). 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 number and
website, where available, for composting facilities, anaerobic digestion facilities, and food banks.
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Figure 1. Non-Residential Excess Food Generating Sectors
Restaurants and Food J
Services
55.67%
Food Banks
Food Manufacturers &
0.03%
Processors
Healthcare Facilities
0.65%
Food Wholesale and
Retail
20.28%
Educational Institutions
10.90%
Hospitality Industry
6.88%
Correctional Facilities
0.45%
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 of the targeted sectors. These business statistics include number of employees, annual
revenue, number of students (for educational institutions), number of inmates (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. More
than one methodology was available for every sector, so 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 landfill and toward more preferred uses as reflected in EPA's Food Recovery Hierarchy
(i.e., human consumption, animal feed, industrial use, anaerobic digestion, composting).
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Acknowledgements
This project was a collaboration of a team of EPA staff across many offices and regions originally
led by Charlotte Ely, Cheryl Henley, Amanda Hong (Region 9), and Steve Rock (Office of
Research and Development), with support from Jay Bassett (Region 4), Chris Beling (Region 1),
Allison Costa (Office of Air and Radiation), Claudia Fabiano (Office of Land and Emergency
Management), Melissa Pennington (Region 3), Andrea Schnitzer (Office of Land and Emergency
Management), Carol Staniec (Region 5), Stacy Takeda (Region 9), Virginia Till (Region 8), and
Jason Turgeon (Region 1). The current team is led by Claudia Fabiano (Office of Land and
Emergency Management) and Cheryl Henley (Region 9) with support from Joe Krahe (Office of
Land and Emergency Management). Nick Elger (Office of Air and Radiation), Emily Heller
(Office of Chief Financial Officer), and Melissa Pennington (Region 3) have also provided data
and support.
Funding for the project was originally provided by a Regional Sustainable Environmental Science
(RESES) grant from the Sustainable and Healthy Communities (SHC) program in the office of
Research and Development. The contract team was led by Pradeep Jain and Shrawan Singh of
Innovative Waste Consulting Services, LLC (IWCS). The data and map application development
was led by Rebecca Chapman with additional support from Jenny Herring of Innovate! Inc.
Information was provided by many organizations including BioCycle, Feeding America and the
Food Waste Reduction Alliance. Several states also reviewed data and shared their methods
during the development of Version 1.0 of the Map, with particular support from Connecticut,
Massachusetts, and Vermont.
Notice
This report has been internally peer reviewed by the U.S. 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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Table of Contents
Table of 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 3
1.3. Report Organization 3
2. Sector-Specific Data Sources and Excess Food Estimation Methodologies for
Generators 5
2.1. Overview 5
2.2. Food Manufacturers and Processors 6
2.2.1 Changes in Version 2.0 8
2.2.2 Changes in Version 2.1 8
2.3. Food Wholesale and Retail 9
2.3.1. Overview 9
2.3.2. Food Wholesale 9
2.3.3. Food Retail (Supermarkets, Grocery Stores, and Supercenters) 10
2.3.4. Changes in Version 2.0 12
2.3.5. Changes in Version 2.1 12
2.4. Educational Institutions 13
2.4.1. Overview 13
2.4.2. Postsecondary Schools 13
2.4.3. Elementary and Secondary Schools 16
2.4.4. Changes in Version 2.0 18
2.4.5. Changes in Version 2.1 18
2.5. Hospitality Industry 18
2.5.1. Changes inversion 2.0 19
2.5.2. Changes in Version 2.1 19
2.6. Correctional Facilities 20
2.6.1. Changes in Version 2.0 21
2.6.2. Changes in Version 2.1 22
2.7. Healthcare Facilities 22
2.7.1. Changes in Version 2.0 23
2.7.2. Changes in Version 2.1 23
2.8. Restaurants and Food Services 23
2.8.1. Changes in Version 2.0 25
2.8.2. Changes in Version 2.1 25
2.9. Food Banks 26
2.9.1. Changes in Version 2.0 26
2.9.2. Changes in Version 2.1 26
2.10. Data Analysis 26
3. Macro Analysis of Sector-Specific Excess Food Generation Rates 27
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3.1. Food Manufacturers and Processors 27
3.2. Food Wholesale and Retail 28
3.3. Educational Institutions 29
3.4. Hospitality Industry 30
3.5. Correctional Facilitie s 31
3.6. Healthcare Facilities 31
3.7. Restaurants and Food Services 32
3.8. Food Banks 33
4. Data Sources for Recipients 34
4.1. Overview 34
4.2. Food Banks 34
4.3. Composting Facilities 34
4.4. Anaerobic Digestion Facilities 34
5. Data Sources for Communities with Residential Source Separated Organics Programs 35
6. Limitations and Opportunities for Improvement 35
7. References 37
APPENDICES 43
Appendix A: Excess Food Characteristics 43
Appendix B: Glossary 47
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List of Figures
Figure 1. Non-Residential Excess Food Generating Sectors iv
Figure 2. US EPA Estimation of U.S. Excess Food Disposition in 2017 1
Figure 3. Food Recovery Hierarchy 2
Figure 4. Proportion of Food Manufacturers and Processors by Industry Type 28
Figure 5. Proportion of Food Wholesale and Retail Establishments by Industry Type 29
Figure 6. Proportion of Educational Institutions by School Type 30
Figure 7. Proportion of Hospitality Industry Establishments by Type 31
Figure 8. Proportion of Healthcare Facilities by Industry Type 32
Figure 9. Proportion of Restaurant and Food Services Establishments by Industry Type 33
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List of Tables
Table 1. NAICS Codes for Food Manufacturers and Processors 6
Table 2. Generation Factors for Manufacturers and Processors 7
Table 3. NAICS Codes for Food Wholesalers and Retailers 9
Table 4. Generation Factors for Food Wholesale Facilities 9
Table 5. Generation Factors for Food Retail (Supermarkets, Grocery Stores, and Supercenters) 10
Table 6. Educational Institutions—School Types 13
Table 7. Generation Factors for Postsecondary Schools 13
Table 8. Generation Factors for Public and Private Elementary and Secondary Schools 16
Table 9. NAICS Codes for the Hospitality Industry 18
Table 10. Generation Factors for the Hospitality Industry 19
Table 11. Generation Factors for Correctional Facilities 20
Table 12. NAICS Codes for Healthcare Facilities 22
Table 13. Generation Factors for Healthcare Facilities 22
Table 14. NAICS Codes for the Restaurants and Food Services Sector 24
Table 15. Generation Factors for Restaurants and Food Services 24
Table 16. Establishments Included in the Dataset by Sector 27
Table 17. Number of Food Wholesale and Retail Establishments Included in the Dataset 29
Table 18. Number of Educational Institutions Included in the Dataset 30
Table 19. Number of Hospitality Establishments Included in the Dataset 31
Table 20. Number of Healthcare Facilities Included in the Dataset 32
Table 21. Number of Restaurant and Food Services Establishments Included in the Dataset 33
Table 22. Dominant Excess Food Characteristics and Associated Industry Examples 43
Table 23. Characteristics of Excess Food Associated with Industries in the Excess Food Opportunities Map
44
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List of Abbreviations, Acronyms, and Initialisms
ABC
American Biogas Council
BJS
Bureau of Justice Statistics
BSR
Business for Social Responsibility
CCG
Cascadia Consulting Group
CTDEEP
Connecticut Department of Energy & Environmental Protection
DHS
Department of Homeland Security
EPA
Environmental Protection Agency
FWRA
Food Waste Reduction Alliance
ICI
Industrial, Commercial, and Institutional
lbs
Pounds
MassDEP
Massachusetts Department of Environmental Protection
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
UNEP
United Nations Environment Program
US
United States
US EPA
United States Environmental Protection Agency
USD A
United States Department of Agriculture
VTANR
Vermont Agency of Natural Resources
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
1. Introduction
1.1. Background
On September 16, 2015, in alignment with Target 12.3 of the United Nations Sustainable
Development Goals, the United States Department of Agriculture (USDA) 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). Production of this excess food requires
significant water, land, and additional resources.
As reflected in Figure 2, the EPA estimated that excess food generated from the commercial,
institutional, and residential sectors represents approximately 15% (i.e., 40.67 million tons) of all
Municipal Solid Waste (MSW) generated in 2017 (US EPA (2019b)). Approximately 93.7% of
food included in the municipal solid waste stream was either landfilled or combusted, and just
6.3% composted (US EPA (2019b)). Landfills are the third largest anthropogenic source of
methane emissions in the United States (107.7 MMT C02 Eq.), accounting for 16.4 percent of
total methane emissions in 2017 (US EPA (2019c)). Therefore, diverting excess food from landfills
where it might degrade before gas collection is implemented could significantly reduce the
production of greenhouse gas emissions.
Figure 2. US EPA Estimation of U.S. Excess Food Disposition in 2017
6.3%
Composted ¦
2.57 million tons
93.7% \
Landfilled &
combusted with
energy recovery
(38.1 million tons;
85%
MSW except
excess food
227.12 million tons
15%
Excess Food
40.67 million
tons
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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 is intended for human consumption but was removed from the supply chain 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. 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 unharvested crops or on-farm processing excess food, or excess
food or other organic material disposed of by the residential sector.
To prioritize efforts to divert excess food, EPA created the Food Recovery Hierarchy (Figure 3)
(US EPA (2015)). 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 recipients 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 the
recoverable fraction of excess food.
Several states have already passed legislation requiring diversion of excess food and other organics
from landfills, supporting the domestic goal of reducing excess food by 50% by 2030. These
include Massachusetts (310 CMR 19.000), California
(AB 1826), Connecticut (CGS Sec. 22a-226e), and
Vermont (Vermont Act 148), all of which set limits on the
quantity of food certain generators can send to landfill.
Furthermore, several of these states (e.g., Connecticut,
Massachusetts, and Vermont) have developed interactive
tools for mapping state-specific excess food sources,
sometimes including potential excess food recipients,
such as composting facilities, in their tools (CTDEEP
(n.d.), MassDEP (2017), VTANR (2019)). Beyond these
regulatory efforts, there are also several voluntary
regional-scale excess food generation and disposal efforts
(USDA (2014); BSR (2014)).
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 (2014); US
EPA (2016a)). The Agency also estimates a nation-wide excess food generation rate from
residential, institutional and commercial sources on an annual basis (US EPA (2019b)). The EPA
Figure 3. Food Recovery Hierarchy
Food Recovery Hierarchy
& \ Environmental Protection
\ Wwy
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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.
1.2. Objectives and Approach
The primary objective of this report is to present the methodology used to develop and update the
Dataset and Map, including establishment-specific estimates of excess food generation. This
national-scale, interactive map is intended to help inform waste management and food recovery
decisions at the local level, and identify potential sources of organic feedstocks, infrastructure
gaps, and disposal alternatives to landfill. The approach taken is as follows:
• Using the North American Industry Classification System (NAICS), 76 categories of
industries and three school types representing nearly 1.2 million establishments in the US
were identified as potential sources of excess food. These 76 industries and three school
types were grouped into the following sectors: food manufacturers and processors (46),
food wholesale and retail (17), educational institutions (3), the hospitality industry (3),
correctional facilities (1), healthcare facilities (3), and restaurants and food services (6). A
full list of industry NAICS codes and descriptions is provided in Appendix A. Agricultural
sources of excess food were not included in this study.
• An extensive 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 communities with source separated organics programs was also
collected and mapped.
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 from publicly and commercially available datasets, as well as state websites.
1.3. Report Organization
This report is organized as follows:
Chapter 1: Introduction
Chapter 2: Sector-specific data sources and excess food estimation methodologies for
generators
Chapter 3: Macro analysis of sector-specific excess food generation rates
Chapter 4: Data sources for recipients
Chapter 5: Data sources for communities with residential source separated organics
programs
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Chapter 6: Limitations and opportunities for improvement
Chapter 7: References
APPENDICES
Appendix A: Excess Food Characteristics
Appendix B: Glossary
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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 76 identified ICI industries and three school types. For
the purposes of this report, "excess food" refers broadly to post-harvest food that is produced for
human consumption but removed from the supply chain to be recovered, recycled, or disposed
(refer to Appendix B for full definition). The definition does not include unharvested crops or on-
farm processing excess and excess food or other organic material disposed of by the residential
sector.
These 76 industrial, commercial and institutional (ICI) industries and three school types were
grouped into the following sectors: food manufacturers and processors (46), food wholesale and
retail (17), educational institutions (3), the hospitality industry (3), correctional facilities (1),
healthcare facilities (3), and restaurants and food services (6). The full list of industries, and
associated excess food characteristics, is provided in Appendix A.
Establishment-level data for most industries came from Hoover's, Inc. 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 2018a, 2018b, 2018c), and data for healthcare facilities was obtained
from the U.S. Department of Homeland Security (DHS (2017)).
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), number of inmates (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. More
than one methodology was available for every sector, so 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 excess food estimate includes 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 generation rates 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 was provided. Data were
available to calculate estimates for 97.8% of establishments in Version 2.0 of the Map, and no
changes were made to the generator sectors in Version 2.1 of the Map.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
2.2. Food Manufacturers and Processors
Forty-six 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
112930
Fur-Bearing Animal and Rabbit Production
2
311211
Flour Milling
3
311212
Rice Milling
4
311213
Malt Manufacturing
5
311221
Wet Corn Milling
6
311224
Soybean and Other Oilseed Processing
7
311225
Fats and Oils Refining and Blending
8
311230
Breakfast Cereal Manufacturing
9
311313
Beet Sugar Manufacturing
10
311314
Cane Sugar Manufacturing
11
311340
Non-chocolate Confectionery Manufacturing
12
311351
Chocolate and Confectionery Manufacturing from Cacao Beans
13
311352
Confectionery Manufacturing from Purchased Chocolate
14
311411
Frozen Fruit, Juice, and Vegetable Manufacturing
15
311412
Frozen Specialty Food Manufacturing
16
311421
Fruit and Vegetable Canning
17
311422
Specialty Canning
18
311423
Dried and Dehydrated Food Manufacturing
19
311511
Fluid Milk Manufacturing
20
311512
Creamery Butter Manufacturing
21
311513
Cheese Manufacturing
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
22
311514
Dry, Condensed, and Evaporated Dairy Product Manufacturing
23
311520
Ice Cream and Frozen Dessert Manufacturing
24
311611
Animal (except Poultry) Slaughtering
25
311612
Meat Processed from Carcasses
26
311613
Rendering and Meat Byproduct Processing
27
311615
Poultry Processing
28
311710
Seafood Product Preparation and Packaging
29
311811
Retail Bakeries
30
311812
Commercial Bakeries
31
311813
Frozen Cakes, Pies, and Other Pastries Manufacturing
32
311821
Cookie and Cracker Manufacturing
33
311824
Dry Pasta, Dough, and Flour Mixes Manufacturing from Purchased
Flour
34
311830
Tortilla Manufacturing
35
311911
Roasted Nuts and Peanut Butter Manufacturing
36
311919
Other Snack Food Manufacturing
37
311920
Coffee and Tea Manufacturing
38
311930
Flavoring Syrup and Concentrate Manufacturing
39
311941
Mayonnaise, Dressing, and Other Prepared Sauce Manufacturing
40
311942
Spice and Extract Manufacturing
41
311991
Perishable Prepared Food Manufacturing
42
311999
All Other Miscellaneous Food Manufacturing
43
312111
Soft Drink Manufacturing
44
312120
Breweries
45
312130
Wineries
46
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.
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
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
These three studies establish generation factors based on pounds of excess food generated per
dollar of annual 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 studies (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 46 NAICS codes. The three
generation factors were used in conjunction with annual revenue data obtained from Hoover's, Inc.
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 Annual Revenue ($) 2,0001b
Where X = 0.17, 0.053, or 0.062.
2.2.1 Changes in Version 2.0
In Version 1.0, EPA identified 54 industries as food manufacturers and processors. Ten of those
were classified as "animal, milk, and egg producers" and were not mapped in Version 1.0. EPA
took a closer look at the industries who manufacture and process food and beverages and added
and deleted some industries in order to better ensure that the list of industries included in the Map
for this sector are appropriate. Therefore, in Version 2.0 of the Map, EPA included five additional
industries: flour milling (NAICS code 311211), rice milling (NAICS code 311212), malt
manufacturing (NAICS code 311213), soft drink manufacturing (NAICS code 312111), and
distilleries (NAICS code 312140), and removed three industries: dog and cat food manufacturing
(NAICS code 311119) other animal food manufacturing (NAICS code 311119), and ethyl alcohol
manufacturing (NAICS code 325193).
EPA also conducted a literature review to find the best available methodologies to calculate excess
food for this sector, and three studies were chosen. In Version 1.0, EPA relied on one methodology
(BSR (2014)), which is still used in Version 2.0, in addition to two other methodologies.
2.2.2 Changes in Version 2.1
No changes were made in Version 2.1.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
2.3. Food Wholesale and Retail
2.3.1. Overview
Seventeen industries were classified as 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 445 and 452 were classified as food retail (i.e., supermarkets, grocery stores, and
supercenters). Establishment-level data for this sector was obtained from Hoover's, Inc.
Table 3. NAICS Codes for Food Wholesalers and Retailers
No.
NAICS Code
NAICS Code Description
1
424410
General Line Grocery Merchant Wholesalers
2
424420
Packaged Frozen Food Merchant Wholesalers
3
424430
Dairy Product (except Dried or Canned) Merchant Wholesalers
4
424440
Poultry and Poultry Product Merchant Wholesalers
5
424450
Confectionery Merchant Wholesalers
6
424460
Fish and Seafood Merchant Wholesalers
7
424470
Meat and Meat Product Merchant Wholesalers
8
424480
Fresh Fruit and Vegetable Merchant Wholesalers
9
424490
Other Grocery and Related Products Merchant Wholesalers
10
445110
Supermarkets and Other Grocery (except Convenience) Stores
11
445210
Meat Markets
12
445220
Fish and Seafood Markets
13
445230
Fruit and Vegetable Markets
14
445291
Baked Goods Stores
15
445292
Confectionery and Nut Stores
16
445299
All Other Specialty Food Stores
17
452311
Warehouse Clubs and Supercenters
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 three studies that focused on food wholesale and involved
original research (e.g., direct analysis of facilities' excess food) (Table 4). These three studies were
used to estimate excess food generated, resulting in a range of values for each establishment.
Table 4. Generation Factors for Food Wholesale Facilities
GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
1
Okazaki et. al
2008
94.4
Tons/establishment/
year
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GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
2
US EPA
2011
147
Tons/establishment/
year
3
BSR
2014
0.01
lbs/revenue/year
Okazaki et al (2008) and US EPA (2011) established generation factors of 94.4 and 147 tons of
excess food per year per establishment, respectively. BSR (2014) collected industry generation
data through a series of surveys and estimated 10 pounds of excess food per thousand dollars of
company revenue. This is reflected in the following equation:
/tons\
Food Wholesalers Excess Food =
Vyear/
lb tons
Establishment's Annual Revenue $x 0.01 — x —
Annual Revenue ($) 2,000 lb
2.3.3. Food Retail (Supermarkets, Grocery Stores, and Supercenters)
For purposes of this Map, food retailers are those with NAICS codes 445110 through 445299 and
452311. The literature search identified 54 studies examining excess food generation among food
retailers. 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. EPA chose eight studies that involved original research (e.g., direct analysis of facilities'
excess food) (Table 5). These eight studies were used to estimate excess food generated, resulting
in a range of values for each establishment.
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
T ons/employee/year
Supercenter
6
ReFED
2016
0.5
T ons/employee/year
Supercenter
7
Okazaki et. al
2008
114.6
Tons/establishment/
year
Supermarket/Grocery
Store
8
NCDENR
2012
117
Tons/establishment/
year
Supermarket/Grocery
Store
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GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
ESTABLISHMENT
TYPE
9
BSR
2014
0.01
lbs/revenue/year
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 warehouse clubs such as BJs 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), CCG (2015), and the North Carolina Department of Environment and Natural
Resources (NCDENR (2012)) (now known as North Carolina Department of Environmental
Quality) conducted audits of food retail sector waste.1 Draper/Lennon (2001), Kessler
Consulting (2012), Okazaki et al. (2008), 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.
Studies 7 and 8 estimated the quantity of excess food generated per establishment per year in
supermarkets/grocery stores and result in generation factors of 114.6 and 117 tons per
establishment per year.
The 9th 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 0.01 pounds per dollar revenue).
Generation factors 1, 2, 3, 4, 7, 8, and 9 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 to establishments classified as supercenters (i.e., NAICS code 452311). These
generation factors were used to calculate a range of excess food estimates for supermarkets,
grocery stores, and supercenters.
Generation factors 1, 2, 3, 4, 5, and 6 were used in conjunction with employee data obtained from
Hoovers, Inc. and use the following equation:
1 North Carolina's state-specific estimate was provided by a North Carolina hauler who collected segregated food waste from a
major grocery chain.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
v tons
Food Retailers Excess Food (—) = Number of employees x employee
Vyear/ year
Where X = 0.27 to 2.32
Generation factors 7 and 8 result in generation rates of 114.6 and 117 tons of excess food per year
per establishment (no equation is needed).
Generation factor 9 was used in conjunction with revenue data obtained from Hoovers, Inc. and
uses the following equation:
/tons\
Food Retailers Excess Food =
Vyear/
lb tons
Establishment's Annual Revenue $x 0.01 — —- x
Annual Revenue ($) 2,000 lb
2.3.4. Changes in Version 2.0
In Version 2.0 of the Map, the name of this sector was changed from "Wholesalers and
Distributors" to "Wholesale and Retail" to better reflect the industries included. EPA took a closer
look at the industries included in this sector and added and deleted some industries to ensure that
the list of industries included in the Map for this sector are appropriate. Therefore, in Version 2.0
of the Map, EPA removed six industries: Grain and Field Bean Merchant Wholesalers (NAICS
code 424510), Livestock Merchant Wholesalers (NAICS code 424520), Beer and Ale Merchant
Wholesalers (NAICS code 424810), Wine and Distilled Alcoholic Beverage Merchant
Wholesalers (NAICS code 424820), Farm Supplies Merchant Wholesalers (NAICS code 424910),
and Flower, Nursery Stock, and Florists' Supplies Merchant Wholesalers (NAICS code 424930).
EPA added one industry, Warehouse Clubs and Supercenters (NAICS code 452311).
EPA also conducted a literature review to find the best available methodologies to calculate excess
food for this sector. For food wholesale and retail establishments (except supermarkets, grocery
stores, and supercenters), three studies were chosen for Version 2.0. In Version 1.0, EPA relied on
one methodology (BSR (2014)), which is still used in Version 2.0, in addition to two other
methodologies. For supermarkets, grocery stores, and supercenters, nine studies were chosen for
Version 2.0. In Version 1.0, EPA relied on one methodology (Draper/Lennon (2001)), which is
still used in Version 2.0, in addition to eight other methodologies. EPA is also no longer estimating
the recoverable fraction of excess food for supermarkets and grocery stores, as the methodology
that EPA relied on for Version 1.0 is outdated. EPA would include recoverable fraction estimates
in future updates to the Map if appropriate methodologies are available.
2.3.5. Changes in Version 2.1
No changes were made in Version 2.1.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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
2.4.2. Postsecondary Schools
Data for postsecondary schools were collected from the Integrated Postsecondary Education Data
System of the NCES for the 2016 school year (NCES (2018a)). This data includes 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 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
P RE-
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
2012
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
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10
CCG
2015
lbs/stud
ent/year
N/A
N/A
22.0
Notes:
1.
2.
3.
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.
Sarjahani et al. (2009) and Kim and Morawski (2012) estimate excess food generation with
and without trays. EPA uses the average of the two estimates.
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.
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.2
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 in order to
use the generation factors (1 through 9) that are based on pounds per meal, EPA searched for
2 See NRDC (2017), Hodge et al. (2016), Moriarty (2013), Wellesley College (2013), and US EPA (2011).
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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.3 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.
• 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.4
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/
3 Ebner et al. (2014) reported two estimates: 180 and 270 meals per enrolled student per year according to two different methods.
EPA used the average (225) as representative of Ebner etal(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.
4 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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Excess Food Opportunities Map Version 2.1 - Technical Methodology
_lbs_
ofiirlpnt tOIlS
Number of students x —student x
year 2,000 lb
2.4.3. Elementary and Secondary Schools
Data for elementary and secondary schools were collected from the NCES for the 2015-2016
school year. Public school data were obtained from the NCES Public Elementary/Secondary
School Universe Survey for the 2015-2016 school year (NCES (2018b)) 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 Private School Universe Survey for the 2015-2016 school year (NCES
(2018c)) and included institution name, address, phone number, geo-coordinates, and the total
number of students enrolled for each institution. Excess food estimates were based on five different
studies that establish generation factors of excess food based on pounds per meal or pounds per
student per year, resulting in a range of values for each institution (Table 8).
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).
Table 8. Generation Factors for Public and Private Elementary and Secondary Schools
GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
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.5 RecyclingWorks Massachusetts (2013) estimates 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. CCG (2015) estimates a generation factor of 22.0 pounds per student
per year.6
5 The three schools include one public elementary school, one public high school, and one private middle/high school.
0 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
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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.7 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 J =
v lbs
cfiirlpnt tons
Number of students x student ,
year 2,000 lb
Where X= 18.0, 22.0 or 25.9
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.8 The result is an average of 163 meals per
student per year. Generation factors 4 and 5 use the following equation:
/tons\
Elementary and Secondary Schools Excess Food J =
meals
Qtnrlpnt lbs tons
Number of students x student x x_
year meal 2,000 lb
Where X = 0.35 or 0.52
CalRecycle study pools all educational institutions, including colleges/universities and K-12 schools. EPA applied the same
generation factor in both sectors.
7 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).
8 Data from the NSLP forFY2017 includes 30.0 million students, or approximately 60 percent of the total public school enrollment,
accessed at: https://catalog.data.gov/dataset/national-school-lunch-assistance-program-participation-and-meals-served-data.
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2.4.4. Changes in Version 2.0
For Version 2.0 of the Map, school type is included in the Dataset and Map instead of NAICS
codes, because NCES data does not include NAICS codes. The NCES data also included phone
numbers and websites for many of the institutions, which were included in the Dataset and the
Map.
EPA also conducted a literature review to find the best available methodologies to calculate excess
food for this sector. For postsecondary schools, ten studies were chosen for Version 2.0. In Version
1.0, EPA relied on two methodologies, one of which (Draper/Lennon (2001)) is still used in
Version 2.0, in addition to nine other methodologies. In Version 1.0, EPA estimated plate waste
for postsecondary schools, which is not explicitly done in Version 2.0; however, the studies in
Table 7 do provide some generation factors that distinguish pre-consumer and post-consumer
generation factors that could be used to estimate each type of excess food generation.
For elementary and secondary schools, five studies were chosen for Version 2.0. In Version 1.0,
EPA relied on three methodologies, one of which (Draper/Lennon (2001)) is still used in Version
2.0, in addition to four other methodologies.
2.4.5. Changes in Version 2.1
No changes were made in Version 2.1.
2,5, Hospitality Industry
As listed in Table 9, establishments belonging to three NAICS codes were classified as the
hospitality industry.
Table 9. NAICS Codes for the Hospitality Industry
No.
NAICS Code
NAICS Code Description
1
713210
Casinos (except Casino Hotels)
2
721110
Hotels and Motels
3
721120
Casino Hotels
The literature search identified 25 studies on excess food generation in the hospitality industry.
EPA chose four studies that provide excess food generation factors based on empirical data
collected directly from sampled hotels (Table 10).9 These four studies were used to estimate excess
food generated, resulting in a range of values for each establishment.
9 Several studies report excess food generated per meal, or per guest or guest room. EPA excluded such studies from EPA's
calculations due to the lack of data on annual number of hotel guests or occupied guest rooms per year in each establishment
(Recycling Works Massachusetts (2013); Carvalho (2014); Coker (2009)).
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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.
In addition, 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 Hoover's,
Inc. using the following equation:
/tons\
Hospitality Industry Excess Food =
Vyear/
x lb
„ , employee tons
Number or employees x x —
F J year 2,0001b
Where X = 375, 997, 1197, or 1983.
2.5.1. Changes in Version 2.0
EPA conducted a literature review to find the best available methodologies to calculate excess
food for this sector, and four studies were chosen. In Version 1.0, EPA relied on two
methodologies, one of which is still used in Version 2.0 (CCG (2006)), in addition to three other
methodologies.
2.5.2. Changes in Version 2.1
No changes were made in Version 2.1.
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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 Hoover's, Inc.
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
from various prisons (Table 11).10 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.11 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)).
10 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); Recycling Works
Massachusetts (2013); Hodge et al (2016); CalRecycle (2018)).
11 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.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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. 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.12 While the Marion (2000) and Draper/Lennon (2001) studies
are older, they are frequently cited in other studies (see BSR (2012); Recycling Works
Massachusetts (2013); Labuzetta et al (2016)); therefore, EPA retained them in this analysis.
Hoovers, Inc. 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/employee13
• State and federal prisons: 3.4 inmates/employee14
Using this data, the following equation was used to generate estimates of excess food for
correctional facilities:
/tons\
Correctional Facilities Excess Food =
Vyear/
v lb
inmates Y ;nmntp days tons
Number of employees x X x x365 x —
employee day year 2,000 lb
Where X = 3.1 or 3.4 and Y = 0.85 to 1.4
2.6.1. Changes in Version 2.0
EPA conducted a literature review to find the best available methodologies to calculate excess
food for this sector. Six studies were chosen for Version 2.0. In Version 1.0, EPA relied on one
methodology (Draper/Lennon (2001)), which is still used in Version 2.0, in addition to five other
methodologies. In addition, EPA relied solely on BJS statistics to estimate an average number of
12 California, Connecticut, Florida, New York, Pennsylvania, and Washington.
13 In 2016, 704,500 inmates were confined in city and county 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.
14 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.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
inmates per employee, which resulted in slightly different inmate to employee ratios than those
estimated in Version 1.0.
2.6.2. Changes in Version 2.1
No changes were made in Version 2.1.
2.7. Healthcare Facilities
As listed in Table 12, establishments belonging to three NAICS codes were grouped as healthcare
facilities. Establishment-level data for this sector was obtained from the Department of Homeland
Security (DHS (2017)).
Table 12. NAICS Codes for Healthcare Facilities
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 Healthcare Facilities
GENERATION
SOURCE
YEAR
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 Recycling Works 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
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
original waste sampling. The Walsh (1993) study is older, but provides an additional data point
for corroboration of the generation per bed figures.15
These four generation factors were used in conjunction with hospital bed data obtained from DHS
to estimate a range of generation rates for healthcare facilities belonging to the three NAICS codes
identified as healthcare facilities. This is reflected in the following equation:
/tons\
Healthcare Facilities 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.
2.7.1. Changes in Version 2.0
For Version 2.0 of the Map, EPA used the Department of Homeland Security's Homeland
Infrastructure Foundation-Level Data (DHS (2017)) instead of Hoover's, Inc., because the data
was more comprehensive and included the number of beds per facility. The data also included
phone numbers and websites for many of the facilities, which were included in the Dataset and the
Map. Version 2.0 includes facilities in three NAICS codes, whereas Version 1.0 only included
General Medical and Surgical Hospitals (NAICS code 622110). Finally, in Version 1.0, EPA relied
on two methodologies, one of which is still used in Version 2.0 (Draper/Lennon (2001)), in
addition to three other methodologies.
2.7.2. Changes in Version 2.1
No changes were made in Version 2.1.
2.8. Restaurants and Food Services
Six industries were classified as restaurants and food services (Table 14). Establishment-level data
for this sector was obtained from Hoover's, Inc.
15 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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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Table 14. NAICS Codes for the Restaurants and Food Services Sector
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 15). These five studies were used to estimate
excess food generated, resulting in a range of values for each establishment.
Table 15. 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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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Recycling Works 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 :
Vyear/
lb
X-
, „ , '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
Vyear/
Establishment's Annual Revenue $x 0.033 ¦
year/
lb tons
Annual Revenue ($) 2,000 lb
2.8.1. Changes in Version 2.0
For Version 1.0 of the Map, EPA was not able to obtain establishment-level data for this sector
due to resource constraints, and therefore it was not included in the Dataset or Map. While Version
1.0 of this report discussed available methodologies to calculate excess food generation, EPA
sought out the newest and most appropriate methodologies to calculate excess food generation
rates for Version 2.0.
2.8.2. Changes in Version 2.1
No changes were made in Version 2.1.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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. In 2015, food bank data
was provided by Feeding America, a nationwide network of food banks, food pantries, and meal
programs. Feeding America is the nation's leading domestic hunger-relief organization and serves
virtually every community in all 50 states, Washington D.C., and Puerto Rico. Specifically,
Feeding America provided data on generation of excess food as reported by individual food banks
in its network, where available.
2.9.1. Changes in Version 2.0
No changes were made to this sector in Version 2.0. EPA is seeking to expand the number of food
banks and food rescue organizations included in the Dataset and Map for the next update.
2.9.2. Changes in Version 2.1
No changes were made in Version 2.1.
2.10. Data Analysis
Nearly 1.2 million establishments that potentially generate excess food were included in the
Dataset and Map from ICI sectors based on 76 NAICS codes and three school types. The Dataset
provides establishment-level information including name and geographic location, and includes
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 2.9. Excess food generation rates were estimated for 97.8% 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 each sector, resulting in a range
of values for each establishment; a high and low excess food estimate was included for each
establishment.
The data was reviewed and filtered in the following ways:
• Establishments identified as "Headquarters" were excluded from the Dataset because these
establishments typically serve an administrative function and do not generate excess food.
• 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.
• Observations that were identified as having unrealistically high or low quantities for
revenue and/or employees were assumed to be input errors and removed from the Dataset
based on statistical cutoffs. This step helped to avoid extreme overestimates or
underestimates. For example, correctional facilities that were listed as only having one
employee were removed.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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 1,166,790 establishments was obtained primarily from Hoover's, Inc., as well as the
NCES databases and DHS. Excess food generation rates were estimated for 97.8% of all
establishments, an increase from 86% in Version 1.0. 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.
Table 16. Establishments Included in the Dataset by Sector
Sector
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
%
Establishments
with Excess
Food Estimate
Food Manufacturers &
Processors
59,914
53,265
88.9%
Food Wholesale & Retail
236,666
236,599
100.0%
Educational Institutions
127,203
124,365
97.8%
Hospitality Industry
80,312
80,232
99.9%
Correctional Facilities
5,269
5,268
100.0%
Healthcare Facilities
7569
6919
91.4%
Restaurants and Food Services
649,541
633,849
97.6%
Food Banks
316
154
48.7%
Total
1,166,790
1,140,651
97.8%
3.1. Food Manufacturers and Processors
The food manufacturers and processors sector, as described in Section 2.2, includes 46 NAICS
codes. Data were obtained for 59,914 establishments, and excess food estimates were generated
for 88.9% of the establishments. Figure 4 shows the proportion of food manufacturers and
processors by industry type.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 4. Proportion of Food Manufacturers and Processors by Industry Type
Other Animal
Production
1%
Grain and Oilseed
Milling
3%
Beverage
Manufacturing
19%
Other Food
Manufacturing
12%
Sugar and Confectionery
Product Manufacturing
4%
Fruit and Vegetable
Preserving and Specialty
Food Manufacturing
6%
Dairy Product
Manufacturing
7%
Animal Slaughtering
and Processing
7%
Seafood Product
Preparation and
Packaging
1%
Bakeries and Tortilla
Manufacturing
40%
3.2. Food Wholesale and Retail
The food wholesale and retail sector, as described in Section 2.3, encompasses 17 NAICS codes.
Data were obtained for 236,666 establishments associated with these codes, and excess food
estimates were generated for 100.0% of establishments.
Figure 5 shows the proportion of food wholesalers and retailers by industry type; 75% of which
are food retailers (supermarkets, grocery stores, and supercenters) and 25% are food wholesalers.
Table 17 shows more granular data about data availability across this sector.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 5. Proportion of Food Wholesale and Retail Establishments by Industry Type
25% 41% 33% 1 °A
¦ Grocery and Related Product Merchant Wholesalers
¦ Grocery Stores
¦ Specialty Food Stores
¦ General Merchandise Stores, including Warehouse Clubs and Supercenters
Table 17. 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
58, 386
58,386
100.0%
Food Retailers (Supermarkets,
Grocery Stores, and
Supercenters)
178,279
178,213
100.0%
Total
236,666
236,599
100.0%
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 18 shows more granular information about data availability across the sector.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 6. Proportion of Educational Institutions by School Type
1 6% 17%
77% 1
¦ Postsecondary
¦ Public Elementary & Secondary
¦ Private Elementary & Secondary
Table 18. Number of Educational Institutions Included in the Dataset
School Type
Institutions in the
Dataset
Institutions with % Institutions with
Excess Food Excess Food
Estimate Estimate
Postsecondary Schools
7,516
6,815 90.7%
Public Elementary and
Secondary Schools
22,061
22,061 100.0%
Private Elementary and
Secondary Schools
97,626
95,488 97.8%
Total
127,203
124,364 97.8%
3.4. Hospitality Industry
The hospitality industry, as described in Section 2.5, encompasses three NAICS codes. Data were
obtained for 80,312 establishments associated with these codes, and excess food estimates were
generated for 99.9% of the sample.
Figure 7 shows the proportion of hospitality establishments by industry type, for which hotels
and motels represent the vast majority at 97.6% of the total. Table 19 shows more granular
information about data availability across the sector.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 7. Proportion of Hospitality Industry Establishments by Type
0.8% 97.6% 1.6°/
¦ Casinos (except Casino Hotels)
¦ Hotels (except Casino Hotels) and Motels
Casino Hotels
Table 19. Number of Hospitality Establishments Included in the Dataset
T , . Establishments in
Industry (|l(. Da(aset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Hotel sandMotels 78,398
78,319
99.9%
Casino Hotels 1,303
1,302
99.9%
Casinos (except Casino
Hotels) 611
611
100.0%
Total 80,312
80,232
99.9%
3.5. Correctional Facilities
The correctional facilities sector, as described in Section 2.6, encompasses one NAICS code. Data
were obtained for 5,269 facilities associated with this code, and excess food estimates were
generated for 100.0% of the sample.
3.6. Healthcare Facilities
The healthcare facilities sector, as described in Section 2.7, encompasses three NAICS codes. Data
were obtained for 7,569 establishments associated with these NAICS codes, and excess food
estimates were generated for 91.4% of the sample.
Figure 8 shows the proportion of healthcare facilities by industry type for which general medical
and surgical hospitals represent the majority at 80% of the total. Table 20 shows more granular
information about data availability across the sector.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 8. Proportion of Healthcare Facilities by Industry Type
80% 9% 11%
¦ General Medical and Surgical Hospitals
¦ Psychiatric and Substance Abuse Hospitals
¦ Specialty (except Psychiatric and Substance Abuse) Hospitals
Table 20. Number of Healthcare Facilities Included in the Dataset
Industry
Facilities in the
Dataset
Facilities with
Excess Food
Estimate
% Facilities with
Excess Food
Estimate
General Medical and
6,071
5,598
92.2%
Surgical Hospitals
Psychiatric and Substance
Abuse Hospitals
653
549
84.1%
Specialty (except
Psychiatric and Substance
845
772
91.4%
Abuse) Hospitals
Total
7,569
6,919
91.4%
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 649,541 establishments associated with these NAICS codes, and
excess food estimates were generated for 97.6% of the sample.
Figure 9 shows the proportion of restaurants and food services establishments by industry type,
for which full-service restaurants represent the majority at 53.8% of the total. Table 21 shows
more granular information about data availability across the sector.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Figure 9. Proportion of Restaurant and Food Services Establishments by Industry Type
Cafeterias, Grill
Buffets, and Buffets
0.5%
Snack and Nonalcoholic Beverage Bars
0.2%
Mobile Food Services
Limited-Service
Restaurants
40.3%
Caterers
4.4%
Full-Service
Restaurants
53.8%
Table 21. Number of Restaurant and Food Services Establishments Included in the Dataset
Industry
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Caterers
28,786
27,944
97.1%
Mobile Food Services
5,238
5,216
99.6%
Full-Service Restaurants
349,412
339,608
97.2%
Limited-Service
Restaurants
262,036
257,146
98.1%
Cafeterias, Grill Buffets,
and Buffets
3028
2933
96.9%
Snack and Nonalcoholic
1041
1002
96.3%
Beverage Bars
Total
649,541
633,849
97.6%
3.8. Food Banks
Food banks, as described in Section 2.9, encompass one NAICS code. Data were obtained for 316
establishments associated with this code, and excess food generation data exist for 49% of the
sample.
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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. Recipients make use of excess food in
different ways, depending on the state of the resource (i.e., pre-consumer, post-consumer), as well
as its macro-nutrients (i.e., lipid, carbohydrate, protein) and other biological characteristics.
Appendix A summarizes common excess food characteristics by NAICS industry.
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). Food bank data were provided by Feeding America,
a nationwide network of food banks, food pantries, and meal programs. Feeding America is the
nation's leading domestic hunger-relief organization and serves virtually every community in all
50 states, Washington D.C., and Puerto Rico. The data provided in 2015 includes 316 food banks
for which Feeding America provided data on how much food is received and how much excess
food is generated each year. No changes were made in Version 2.0 or 2.1 of the Map to the food
bank sector.
4.3. Composting Facilities
Data for composting facilities was compiled through EPA review of state government websites,
usually state departments of natural resources or environmental protection, and communication
with state government employees. Version 1.0 of the Map contained 2,499 composting facilities
in 39 states. Version 2.0 of the Map contained 3021 composting facilities in 49 states and one
territory, and facilities were point mapped (they were only mapped by zip code or county in
Version 1.0). Minor corrections were made to the Dataset in Version 2.1 for South Dakota and
Illinois, resulting in eight facilities being removed, leaving 3013 composting facilities in the
Dataset for 49 states and one territory. Associated websites and type of feedstock accepted are
listed in the Dataset and in the Map, where information was available.
4.4. Anaerobic Digestion Facilities
Data for anaerobic digestion facilities for Version 1.0 of the Map was compiled using Agency and
non-Agency sources (US EPA (2016b); ABC (2017)), resulting in a Dataset of 1,381 facilities.
The main data sources include facilities that had been listed in the EPA Waste to Biogas Mapping
Tool, supplemented by a list of facilities maintained by the EPA AgSTAR program, as well as
other facilities tracked by or known to EPA through other collaborative program work. No changes
were made in Version 2.0 of the Map to anaerobic digestion facilities. EPA updated the anaerobic
digestion facilities Dataset in Version 2.1 of the Map, resulting in a Dataset containing 1607
facilities. The updated Version 2.1 Dataset was compiled from (1) a list of facilities on farms
maintained by AgSTAR (US EPA (2019a)); (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 2018 (US
EPA (2018)); and (3) the list of facilities at WRRFs maintained by the Water Environment
Federation (WEF (2019)). In Version 2.1, anaerobic digestion facilities were point mapped (they
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
were only mapped by zip code or county previously). Where available, feedstock data was added
to the Dataset to indicate what kind of feedstocks (e.g., types of food waste, types of animal
manure) are accepted by the facility.
5. Data Sources for Communities with Residential Source Separated Organics
Programs
Data for communities with residential source separated organics programs that collect excess food
for Version 1.0 of the Map were identified from two sources (a 2011 survey published by BioCycle
(Yepsen (2012)) and Layzer (2014)). Of the 156 communities identified, data was available to map
131 communities. No changes were made in Version 2.0 of the Map. In Version 2.1 of the Map,
221 communities with residential curbside food waste collection were identified from a 2017
survey published by BioCycle (Piatt and Streeter (2017)) supplemented with data provided by
states in 2020. All 221 communities were mapped. Some communities are counties that have
programs that serve multiple cities or areas, while some communities are single towns or cities
with their own programs. In Version 2.1, data were not available for the participation rate or
amount collected for each program, so those fields were removed from the Dataset. However,
where available, data were included for the number of households with access, the processing
facility or hauler name, and material preference (i.e., types of feedstock accepted, such as types of
food and yard waste). This Dataset includes communities with residential source separated
organics programs that collect excess food, and does not include those communities that only
collect yard waste.
6. 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.
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
estimate of the recoverable fraction of excess food is critical data needed to pursue its best
use. The recoverable fraction of excess food was not estimated for any of the sectors in
Version 2.0 of the Map. 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. Some reports estimate that as much as 10
million pounds of excess food per year are produced on farms (ReFED (2016)).
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
4. Food banks and other food rescue organizations. The data for food rescue organizations
is limited. While data for food banks were provided by Feeding America and covers their
regional and partner distribution organizations, there are thousands of other organizations,
such as food pantries and soup kitchens, that accept donations and distribute food to people
in need. EPA is working to compile information on food rescue organizations to be
included in a future version of the Map.
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
7. References
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
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Wellesley College (2013). Food is Not Trash: Redefining Wellesley's Waste Culture by
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
APPENDICES
Appendix A: Excess Food Characteristics
Recipients of excess food make use of the food in different ways, depending on the state of the
resource (i.e., pre-consumer, post-consumer), as well as its macro-nutrients. In general, excess
food composition depends on the characteristics of its primary products. Table 22 lists excess food
characteristic categories and commonly associated industries.
Table 22. Dominant Excess Food Characteristics and Associated Industry Examples
No
Excess Food
Characteristics
Examples of Type of Industries
1
Lipids
Fats and oils refining and blending, fast food
2
Simple Carbohydrates
Bakeries, breweries, confectionaries and soda producers
3
Complex Carbohydrates
Fruits and vegetables processing, supermarkets and
grocery stores
4
Proteins
Meat, poultry, and dairy processing
5
Mixed Materials
Food services
6
Glycerin
Biofuel manufacturing
The types of excess food components generated by each industry based on NAICS code are listed
in Table 23. For the food manufacturing and processing and food wholesale and retail sectors,
excess food characteristics were based on the type of industry. Jacob (1993) reported that
supermarkets and grocery stores generate more than 90% of their waste, primarily complex
carbohydrates, from the produce department. Draper/Lennon (2001) reported that excess food
generated by sectors such as educational institutions, healthcare facilities, correctional facilities,
and the hospitality industry consists primarily of complex carbohydrates, mostly from fruit and
vegetable residuals, with the balance divided between meat and bakery products, with dairy
contributing just a small fraction. Excess food generated by the food services sector is generally
comprised of mixed components. Table 23 summarizes characteristics of excess food from the 76
industries plus school types selected for the Map. Note that along with proteins, simple and
complex carbohydrates, and lipids, some excess food characteristics are reflected as a mix of these
characteristics ("mixed"), or are denoted as "other" for certain sectors where these characterization
categories are not a good fit (e.g., spice and extract manufacturing).
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Table 23. Characteristics of Excess Food Associated with Industries in the Excess Food
Opportunities Map
NAICS Code
NAICS Code Description
Excess Food Characteristics
Food Manufacturers and Processors
112930
Fur-Bearing Animal and Rabbit Production
Proteins
311211
Flour Milling
Complex Carbohydrates
311212
Rice Milling
Complex Carbohydrates
311213
Malt Manufacturing
Complex Carbohydrates
311221
Wet Corn Milling
Complex Carbohydrates
311224
Soybean and Other Oilseed Processing
Lipids
311225
Fats and Oils Refining and Blending
Lipids
311230
Breakfast Cereal Manufacturing
Simple and Complex
Carbohydrates
311313
Beet Sugar Manufacturing
Complex Carbohydrates
311314
Cane Sugar Manufacturing
Complex Carbohydrates
311340
Nonchocolate Confectionery Manufacturing
Simple Carbohydrates
311351
Chocolate and Confectionery Manufacturing
from Cacao Beans
Simple Carbohydrates
311352
Confectionery Manufacturing from Purchased
Chocolate
Simple Carbohydrates
311411
Frozen Fruit, Juice, and Vegetable
Manufacturing
Simple Carbohydrates
311412
Frozen Specialty Food Manufacturing
Simple and Complex
Carbohydrates
311421
Fruit and Vegetable Canning
Complex Carbohydrates
311422
Specialty Canning
Complex Carbohydrates
311423
Dried and Dehydrated Food Manufacturing
Proteins
311511
Fluid Milk Manufacturing
Proteins
311512
Creamery Butter Manufacturing
Proteins
311513
Cheese Manufacturing
Proteins
311514
Dry, Condensed, and Evaporated Dairy Product
Manufacturing
Proteins
311520
Ice Cream and Frozen Dessert Manufacturing
Proteins
311611
Animal (except Poultry) Slaughtering
Proteins
311612
Meat Processed from Carcasses
Proteins
311613
Rendering and Meat Byproduct Processing
Proteins
311615
Poultry Processing
Proteins
311710
Seafood Product Preparation and Packaging
Proteins
311811
Retail Bakeries
Simple Carbohydrates
311812
Commercial Bakeries
Simple Carbohydrates
311813
Frozen Cakes, Pies, and Other Pastries
Manufacturing
Simple Carbohydrates
311821
Cookie and Cracker Manufacturing
Simple Carbohydrates
311824
Dry Pasta, Dough, and Flour Mixes
Simple and Complex
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Manufacturing from Purchased Flour
Carbohydrates
311830
Tortilla Manufacturing
Simple and Complex
Carbohydrates
311911
Roasted Nuts and Peanut Butter Manufacturing
Simple Carbohydrates
311919
Other Snack Food Manufacturing
Simple Carbohydrates
311920
Coffee and Tea Manufacturing
Complex Carbohydrates
311930
Flavoring Syrup and Concentrate
Manufacturing
Simple Carbohydrates
311941
Mayonnaise, Dressing, and Other Prepared
Sauce Manufacturing
Complex Carbohydrates
311942
Spice and Extract Manufacturing
Others
311991
Perishable Prepared Food Manufacturing
Simple Carbohydrates
311999
All Other Miscellaneous Food Manufacturing
Others
312111
Soft Drink Manufacturing
Simple Carbohydrates
312120
Breweries
Simple Carbohydrates
312130
Wineries
Simple Carbohydrates
312140
Distilleries
Simple Carbohydrates
Food Wholesale and Retail
424410
General Line Grocery Merchant Wholesalers
Mixed
424420
Packaged Frozen Food Merchant Wholesalers
Mixed
424430
Dairy Product (except Dried or Canned)
Merchant Wholesalers
Proteins
424440
Poultry and Poultry Product Merchant
Wholesalers
Proteins
424450
Confectionery Merchant Wholesalers
Simple Carbohydrates
424460
Fish and Seafood Merchant Wholesalers
Proteins
424470
Meat and Meat Product Merchant Wholesalers
Proteins
424480
Fresh Fruit and Vegetable Merchant
Wholesalers
Complex Carbohydrates
424490
Other Grocery and Related Products Merchant
Wholesalers
Mixed
445110
Supermarkets and Other Grocery (except
Convenience) Stores
Complex Carbohydrates
445210
Meat Markets
Proteins
445220
Fish and Seafood Markets
Proteins
445230
Fruit and Vegetable Markets
Complex Carbohydrates
445291
Baked Goods Stores
Simple Carbohydrates
445292
Confectionery and Nut Stores
Simple Carbohydrates
445299
All Other Specialty Food Stores
Simple Carbohydrates
452311
Warehouse Clubs and Supercenters
Complex Carbohydrates
Educational Institutions
n/a
Public Elementary and Secondary Schools
Complex Carbohydrates,
Proteins
n/a
Private Elementary and Secondary Schools
Complex Carbohydrates,
Proteins
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
n/a
Postsecondary Schools
Complex Carbohydrates,
Proteins
Hospitality Industry
713210
Casinos (except Casino Hotels)
Complex Carbohydrates
721110
Hotels and Motels
Complex Carbohydrates,
Proteins
721120
Casino Hotels
Complex Carbohydrates,
Proteins
Correctional Facilities
922140
Correctional Institutions
Complex Carbohydrates,
Proteins
Healthcare Facilities
622110
General Medical and Surgical Hospitals
Complex Carbohydrates,
Proteins
622210
Psychiatric and Substance Abuse Hospitals
Complex Carbohydrates,
Proteins
622310
Specialty (except Psychiatric and Substance
Abuse) Hospitals
Complex Carbohydrates,
Proteins
Restaurants and Food Services
722320
Caterers
Complex Carbohydrates,
Proteins
722330
Mobile Food Services
Complex Carbohydrates,
Proteins
722511
Full-Service Restaurants
Complex Carbohydrates,
Proteins
722513
Limited-Service Restaurants
Complex Carbohydrates,
Proteins
722514
Cafeterias, Grill Buffets, and Buffets
Complex Carbohydrates,
Proteins
722515
Snack and Nonalcoholic Beverage Bars
Complex Carbohydrates,
Proteins
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Excess Food Opportunities Map Version 2.1 - Technical Methodology
Appendix B: 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 air.
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 by enriching the soil, retaining moisture, suppressing plant diseases and pests,
reducing the need for chemical fertilizers and encouraging the production of beneficial bacteria
and fungi.
COMPOSTING: A process of combining organic wastes such as excess food, yard trimmings,
and manures, in the right ratios into piles, rows, or vessels and adding bulking agents such as wood
chips to create a soil amendment.
EXCESS FOOD: For purposes of this project, the phrase "excess food" generally refers to food—
whether processed, semi-processed, or raw—that is intended for human consumption but was
removed from the supply chain 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 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'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 is not eaten as originally intended. The terms "surplus food" or "excess food"
are often used to describe wholesome, nutritious food when discussing food recovery for donation
to feed people while the term "food waste" is commonly used to describe food unfit for human
consumption that cannot be donated and is managed in other ways, such as creation of animal feed,
composting, anaerobic digestion, or sending to landfills or combustion facilities.
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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.
FOOD LOSS: As defined by the USD A, the edible amount of food, postharvest, that is available
for human consumption but is not consumed for any reason. It includes cooking loss and natural
shrinkage (for example, moisture loss); loss from mold, pests, or inadequate climate control; and
food waste.
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.
ORGANIC RESIDUALS: Materials such as biosolids, compost, excess food, and yard
trimmings.
ORGANIC WASTE: Any discarded material that can decompose.
ORGANICS: Materials such as excess food, yard waste, food, plant-based materials, animal feed,
animal waste, wood, paper, and cardboard.
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.
VARIABLES: The parameter used for excess food estimation, which varies for each
establishment across the sector. For example, number of students or number of employees.
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