EPA530-R-19-001
March 2019
https://www.epa.gov/sustainable-irianagement-food
Excess Food Opportunities Map
Version 2.0-Technical Methodology
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Excess Food Opportunities Map
Version 2.0-Technical Methodology
Office of Resource Conversation and Recovery
Office of Land and Emergency Management
Washington, DC
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Abstract
This report presents the methodology behind development of the US EPA Excess Food
Opportunities Map (Map) Version 2.0, which supports diversion of excess food from landfills. The
information presented by the Map can be used to inform waste management 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), 89 categories of industries representing approximately 1.3 million
establishments in the US were identified as potential sources of excess food. These 89 industries
were grouped into the following categories: food manufacturers and processors (54), food
wholesalers and distributors (22), educational institutions (2), the hospitality industry (3),
correctional facilities (1), healthcare facilities (1), and the food services sector (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. Data for the restaurant and food services sector (e.g., restaurants, caterers, etc.) was not
included in Version 1.0 of the Map, but has been added to this version of the Map. All existing
generator sectors have also been updated with 2018 data. 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 more than 500,000 potential excess food
generators. The map also identifies more than 4,000 potential excess food recipients, described as
composting facilities, anaerobic digestion facilities, and food banks. Composting facilities have
been updated with 2018 data for Version 2.0 of the map.
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Executive Summaiy
This report describes the effort to create estimates for the US EPA Excess Food Opportunities Map
(Map) Version 2.0. 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 more
than 4,000 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 reuse;
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 broadly to post-harvest food that is produced
for human consumption but not consumed by humans. Note that EPA's "Advancing Sustainable
Materials Management: 2014 Fact Sheet" report characterizes food in the municipal waste stream
as post-consumer rather than post-harvest (US EPA (2016b)). 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. This report does not include discarded vegetable oil in the excess food estimate, if
exclusively provided by the studies used to develop the methodologies, since a large percentage
of used cooking oil is purchased as feedstock for biofuel or animal feed production and therefore
is not waste. Further, this report does not include on-farm losses, including unharvested crops or
processing by-products.
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 E-l shows that restaurants and food
services establishments (e.g., restaurants, caterers, etc.) and food wholesale and retail (e.g.,
supermarkets and grocery stores) make up the majority of potential sources of excess food.
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 and location for composting facilities, anaerobic digestion facilities, and food
banks, and websites, where available.
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Figure E-l. Non-Residential Excess Food Generating Sectors
Healthcare Facilities, 1% Correctional Facilities, <1% poocj Banks <1%
Hospitality Industry, 6%
Food Manufacturers
& Processors, 10%
_Food Services,
50%
Educational
Institutions, 10%_
Food Wholesalers
& Distributors,
22%
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 the Food Recovery Hierarchy
(i.e., human consumption, animal feed, industrial use, anaerobic digestion, composting).
Limitations of the Map and technical methodology include the following:
Methodologies are based on very limited measured data, some of which is nearly 20 years
old. More recent measured data and a representative sample size are always preferred.
Animal, milk, and egg producer establishments are currently missing from the Map and
Dataset.
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On-farm loss is not captured in the current Map and Dataset.
The following are recommendations for future Map and technical methodology improvement:
Additional studies are needed to update generation rates for all sectors.
Include food services establishments, and animal, milk, and egg producer data in the
Dataset to provide a more complete and useful Map.
Expand map content to include other potential recipients and sources of excess food,
potentially including on-farm loss, including unharvested crops, processing materials, or
unmarketable crops.
Research additional sources of publicly and commercially available data to increase the
percentage of establishments for which excess food generation rates can be estimated.
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Acknowledgements
This project was a collaboration of a team of EPA staff across many Offices and Regions. Team
leaders of this project were Charlotte Ely and Amanda Hong (Office of Water, Region 9), and
Steve Rock (Office of Research and Development). Key members of the team included Jay
Bassett (Region 4), Chris Beling (Region 1), Allison Costa (Office of Air), Melissa Pennington
(Region 3), Carol Staniec (Region 5), Virginia Till (Region 8), Jason Turgeon (Region 1), Cheryl
Henley and Stacy Takeda (Region 9) and Andrea Schnitzer and Claudia Fabiano (Office of Land
and Emergency Management). The Version 2.0 update team was led by Claudia Fabiano (Office
of Land and Emergency Management) with support from Joe Krahe (Office of Land and
Emergency Management) and Emily Heller (Office of Chief Financial Officer).
Funding for the project was originally provided by a Regional Sustainable Environmental Science
(RESES) grant from the Sustainable and Healthy Communities (SHC) program. The contract
team was led by Pradeep Jain and Shrawan Singh of Innovative Waste Consulting Services, LLC
(IWCS). The map application development was led by Rebecca Chapman of Innovate! Inc.
Information was provided by many organizations including Biocycle, Feeding America and the
Food Waste Reduction Alliance. Several states also 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 vii
List of Figures ix
List of Tables x
List of Abbreviations, Acronyms, and Initialisms xi
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. Overview Error! Bookmark not defined.
2.2.2. Food Manufacturers and Processors (except animal, milk, and egg producers) Error!
Bookmark not defined.
2.2.3. Animal, Milk, and Egg Producers Error! Bookmark not defined.
2.3. Food Wholesalers and Distributors 9
2.3.1. Overview 9
2.3.2. Food Wholesalers and Distributors (except supermarkets and grocery stores) 9
2.3.3. Supermarkets and Grocery Stores 10
2.4. Educational Institutions 13
2.4.1. Overview 13
2.4.2. Colleges and Universities Error! Bookmark not defined.
2.4.3. Elementary and Secondary Schools 16
2.5. Hospitality Industry 18
2.6. Correctional Facilities 20
2.7. Healthcare Facilities 21
2.8. Food Services Sector 23
2.9. Food Banks 25
2.10. Data Analysis 25
3. Macro Analysis of Sector-Specific Excess Food Generation Rates 26
3.1. Food Manufacturers and Processors 26
3.2. Food Wholesalers and Distributors 27
3.3. Educational Institutions 28
3.4. Hospitality Industry 29
3.5. Correctional Facilities 30
3.6. Healthcare Facilities 30
3.7. Food Services Sector 31
3.8. Food Banks 32
4. Data Sources for Recipients 33
4.1. Overview 33
4.2. Food Banks 33
4.3. Composting Facilities 33
vii
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4.4. Anaerobic Digestion Facilities 33
4.5. Animal, Milk, and Egg Producers Error! Bookmark not defined.
5. Data Sources for Communities with Residential Source Separated Organics Programs 34
6. Limitations and Future Research Needs 34
References 35
APPENDICES 40
A Appendix A- A Review of Excess Food Estimation MethodsError! Bookmark not defined.
A.l
A.2
A.3
A.4
A.5
A.6
A.7
A.8
Food Manufacturers and Processers Error! Bookmark not defined.
A. 1.1 Food Manufacturers and Processors except Bakeries Error! Bookmark not defined.
A. 1.2 Bakeries Error! Bookmark not defined.
Food Wholesalers, Distributors, and Supermarkets and Grocery StoresError! Bookmark
not defined.
A.2.1 Food Wholesalers and Distributors except Supermarkets and Grocery Stores ...Error!
Bookmark not defined.
A.2.2 Supermarkets and Grocery Stores
A.3.1
A.3.2
Hos
A.4.1
A.4.2
A.4.3
A.4.4
Others Error! Bookmark not defined.
A.8.1 Food Pantries
A.8.2 Senior Meal Facilities
... Error
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not
defined.
... Error
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not
defined.
... Error
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not
defined.
... Error
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defined.
... Error
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41
45
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List of Figures
Figure E-l. Non-Residential Excess Food Generating Sectors iv
Figure 1-1. US EPA Estimation of U.S. Excess Food Disposition in 2014 1
Figure 1-2. Food Recovery Hierarchy for Sustainable Management of Food (US EPA 2015) 2
Figure 2-1. Relationship between Number of Beds and U.S. Hospital RevenueError! Bookmark not
defined.
Figure 3-1. Proportion of Food Manufacturers and Processors by Industry Type 27
Figure 3-2. Proportion of Food Wholesalers and Distributors by Type 28
Figure 3-3. Proportion of Educational Institution Establishments by Type 29
Figure 3-4. Proportion of Hospitality Industry Establishments by Type 30
Figure A-l. Average Excess Food Generation Factor by School Type (VT Study) 48
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List of Tables
Table 2-1. Excess Food Estimation Methodologies for Identified Sectors. Error! Bookmark not defined.
Table 2-2. NAICS Codes for Food Manufacturers and Processors 6
Table 2-3. NAICS Codes for Food Wholesalers and Distributors 9
Table 2-4. NAICS Codes for Educational Institutions 13
Table 2-5. Parameters Used to Estimate Excess Food Generation Rates for Educational Institutions Error!
Bookmark not defined.
Table 2-6. NAICS Codes for the Hospitality Industry 19
Table 2-7. Parameters Used to Estimate Excess Food Generation Rates for Hospitality Industry Error!
Bookmark not defined.
Table 2-8. Parameters Used to Estimate Excess Food Generation Rates for Correctional Facilities ..Error!
Bookmark not defined.
Table 2-9. Parameters Used to Estimate Excess Food Generation Rates for Healthcare Facilities Error!
Bookmark not defined.
Table 2-10. NAICS Codes for the Food Services Sector 23
Table 3-1. Establishments Included in the Dataset by Sector 26
Table 3-2. Number of Food Wholesalers and Distributors Included in the Dataset 28
Table 3-3. Number of Educational Institutions Included in the Dataset 29
Table 3-4. Number of Hospitality Establishments Included in the Dataset 30
Table A-l. Studies that used Methodologies to Estimate Excess Food Generation Rates 37
Table A-2. Excess Food Generation Factors for Educational Institutions 49
Table B-l. Dominant Excess Food Characteristics and Associated Industry Examples 41
Table B-2. Characteristics of Excess Food Associated with Industries in the Excess Food Opportunities
Map 42
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List of Abbreviations, Acronyms, and Initialisms
AHD
American Hospital Directory
BJS
Bureau of Justice Statistics
BOP
Bureau of Prisons
CT
Connecticut
CTDEP
Connecticut Department of Environmental Protection
DOC
Department of Corrections
EPA
Environmental Protection Agency
FWRA
Food Waste Reduction Alliance
ICI
Industrial, Commercial, and Institutional
lb
Pounds
MA
Massachusetts
MSW
Municipal Solid Waste
NAICS
North American Industry Classification System
NCES
National Center for Education Statistics
RWMA
Recycling Works Massachusetts
SC
South Carolina
SCDOC
South Carolina Department of Commerce
SIC
Standard Industrial Classification
ton
Short Ton
UNEP
United Nations Environment Program
US
United States
US EPA
United States Environmental Protection Agency
USCB
United States Census Bureau
USD A
United States Department of Agriculture
VI
Vermont
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Excess Food Opportunities Map - 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 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 Nati ons 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 1-1, the EPA estimated that post-consumer excess food represents
approximately 15% (i.e., 38.4 million tons) of all Municipal Solid Waste (MSW) generated in
2014 (US EPA, 2016b). Approximately 95% of food included in the municipal solid waste stream
was either landfilled or combusted, and just 5% composted (US EPA, 2016b). Landfills are the
third largest anthropogenic source of methane emissions in the United States, and accounted for
17.6% of total methane emissions in 2015 (US EPA2017a). 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 1-1. US EPA Estimation of U.S. Excess Food Disposition in 2014
5%
Composted
1.9 million tons
85%
MSW except
excess food
220 million tons
95%
Landfilled &
combusted with
energy recovery
(36.5 million tons^
15%
Excess Food 38.4
million tons
generated
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Excess Food Opportunities Map - Technical Methodology
The definition of excess food varies across studies and among organizations, resulting in different
estimates of excess food. For example, while the USDA considers only the edible fraction in its
accounting of food losses as its focus is on improving human nutrition (USDA 2014), the US
Department of Energy's estimates include used vegetable oil because this is a valuable energy
resource. For the purposes of this report, "excess food" refers to post-harvest food that is intended
for human consumption but removed from the supply chain to be recovered, recycled, or disposed.
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. This definition does not include unharvested crops or on-farm
processing excess; used cooking oil (recycled as animal feed or biofuel); and excess food or other
organic material disposed of by the residential sector. Note that for EPA's "Advancing Sustainable
Materials Management: 2014 Fact Sheet" report characterizes food in the municipal waste stream
as post-consumer rather than post-harvest (US EPA 2016b).
To prioritize efforts to divert excess food, EPA created the Food Recovery Hierarchy (Figure 1-2)
(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. 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 Figure 1"^- F°°d Recovery Hierarchy
diversion of excess food and other organics from landfills, for Sustainable Management of Food
fTTS FPA 201S1
supporting the domestic goal of reducing excess food by 1 '
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. Beyond these regulatory efforts, there are also
a number of voluntary regional-scale excess food
generation and disposal efforts (USDA, 2014; US EPA,
2016; FWRA, 2014).
At the national level, US 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 2016b). The US
Food Recovery Hierarchy
United States
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Excess Food Opportunities Map - Technical Methodology
EPA recognizes the need for tools to support a broader understanding of potential excess food
generation, and to foster collaboration and partnership among stakeholders interested in promoting
and achieving sustainable management of food.
1.2. Objectives and Approach
The primary objective of this report is to present the methodology used to develop and update the
Map, including establishment-specific estimates of excess food generation. This national-scale,
interactive map is intended to help inform waste management 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 - Technical Methodology
Chapter 6: Limitations and future research needs
References
Appendix A: Excess Food Characteristics
Appendix B: Glossary
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Excess Food Opportunities Map - 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; used cooking oil (recycled as animal feed or biofuel); and excess food or
other organic material disposed of by the residential sector.
These 76 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.
Table 2-1 summarizes the methods used in this study to estimate annual establishment-specific
excess food generation rates, as well as data sources. 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 2017a, 2017b, 2017c), 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 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 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.
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Excess Food Opportunities Map - Technical Methodology
2.2. Food Manufacturers and Processors
Forty-six industries are included in Version 2.0 of the Map as food manufacturers and processors
(Table 2-2).
Table 2-1. NAICS Codes for Food Manufacturers and Processors
No.
NAICS Code
NAICS Code Description
11
112930
Fur-Bearing Animal and Rabbit Production
311211
Flour Milling
311212
Rice Milling
311213
Malt Manufacturing
14
311221
Wet Corn Milling
15
311224
Soybean and Other Oilseed Processing
16
311225
Fats and Oils Refining and Blending
17
311230
Breakfast Cereal Manufacturing
18
311313
Beet Sugar Manufacturing
19
311314
Cane Sugar Manufacturing
20
311340
Non-chocolate Confectionery Manufacturing
21
311351
Chocolate and Confectionery Manufacturing from Cacao Beans
22
311352
Confectionery Manufacturing from Purchased Chocolate
23
311411
Frozen Fruit, Juice, and Vegetable Manufacturing
24
311412
Frozen Specialty Food Manufacturing
25
311421
Fruit and Vegetable Canning
26
311422
Specialty Canning
27
311423
Dried and Dehydrated Food Manufacturing
28
311511
Fluid Milk Manufacturing
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Excess Food Opportunities Map - Technical Methodology
29
311512
Creamery Butter Manufacturing
30
311513
Cheese Manufacturing
31
311514
Dry, Condensed, and Evaporated Dairy Product Manufacturing
32
311520
Ice Cream and Frozen Dessert Manufacturing
33
311611
Animal (except Poultry) Slaughtering
34
311612
Meat Processed from Carcasses
35
311613
Rendering and Meat Byproduct Processing
36
311615
Poultry Processing
37
311710
Seafood Product Preparation and Packaging
38
311811
Retail Bakeries
39
311812
Commercial Bakeries
40
311813
Frozen Cakes, Pies, and Other Pastries Manufacturing
41
311821
Cookie and Cracker Manufacturing
42
311824
Dry Pasta, Dough, and Flour Mixes Manufacturing from Purchased
Flour
43
311830
Tortilla Manufacturing
44
311911
Roasted Nuts and Peanut Butter Manufacturing
45
311919
Other Snack Food Manufacturing
46
311920
Coffee and Tea Manufacturing
47
311930
Flavoring Syrup and Concentrate Manufacturing
48
311941
Mayonnaise, Dressing, and Other Prepared Sauce Manufacturing
49
311942
Spice and Extract Manufacturing
50
311991
Perishable Prepared Food Manufacturing
51
311999
All Other Miscellaneous Food Manufacturing
312111
Soft Drink Manufacturing
52
312120
Breweries
53
312130
Wineries
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 X-
X). These three studies were used to estimate excess food generated, resulting in a range of values for each
facility.
Table X-X. Generation Factors for Manufacturers and Processors
SOURCE
YEAR
GENERATION
FACTOR
UNIT
Food Waste
Reduction
Alliance
2016
0.17
lbs/revenue/year
BSR
2014
0.053
lbs/revenue/year
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Excess Food Opportunities Map - Technical Methodology
SOURCE
YEAR
GENERATION
FACTOR
UNIT
BSR
2013
0.062
lbs/revenue/year
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 Food Waste Reduction Alliance (FWRA), while the 2016 study was published with FWRA as
the author. These three studies are heavily cited in other food waste analyses (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
\year
lb tons
Facility's Annual Revenue ($)x X- - x
Annual Revenue ($) 2,000 lb
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.
=
8
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Excess Food Opportunities Map - Technical Methodology
2.3. Food Wholesale and Retail
2.3.1. Overview
Seventeen industries were classified as food wholesale and retail (Table 2-3). 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 2-2. NAICS Codes for Food Wholesalers and Distributors
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
16
445110
Supermarkets and Other Grocery (except Convenience) Stores
17
445210
Meat Markets
18
445220
Fish and Seafood Markets
19
445230
Fruit and Vegetable Markets
20
445291
Baked Goods Stores
21
445292
Confectionery and Nut Stores
22
445299
All Other Specialty Food Stores
452311
Warehouse Clubs and Supercenters
2.3.2. Food Wholesale
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 California EPA 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 California studies. EPA chose three
studies that focused on food wholesale and involved original research (e.g., direct analysis of facilities"
excess food) (Table X-X). These three studies were used to estimate excess food generated, resulting in a
range of values for each establishment.
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Excess Food Opportunities Map - Technical Methodology
Table X-X. Generation Factors for Food Wholesale Facilities
GENERATION FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
1
Okazaki, et. al
2008
94.4
Tons/establishment/
year
2
EPA Region 1
2011
147
Tons/establishment/
year
3
Food Waste Reduction
Alliance (BSR)
2014
0.01
lbs/revenue/year
Okazaki and EPA Region 1 established generation factors of 94.4 and 147 tons of excess food per
year per establishment, respectively. The Food Waste Reduction Alliance/BSR (2014) collected industry
generation data through a series of surveys and estimated 10 pounds of excess food per thousand dollars of
company revenue. Generation factor 3 uses 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)
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 X-X). These
eight studies were used to estimate excess food generated, resulting in a range of values for each
establishment.
Table X-X Generation Factors for Supermarkets, Grocery Stores, and Supercenters
GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
ESTABLISHMENT
TYPE
1
California EPA
2006
2.31
Tons/employee/year
Supermarket/Grocery
Store
2
Mecklenburg
County
2012
2.32
Tons/employee/year
Supermarket/Grocery
Store
3
California EPA
2015
2.02
Tons/employee/year
Supermarket/Grocery
Store
4
Draper/Lennon
2012
1.5
Tons/employee/year
Supermarket/Grocery
Store
5
California EPA
2006
0.27
Tons/employee/year
Supercenter
10
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Excess Food Opportunities Map - Technical Methodology
GENERATION
FACTOR #
SOURCE
YEAR
GENERATION
FACTOR
UNIT
ESTABLISHMENT
TYPE
6
ReFED
2016
0.5
Tons/employee/year
Supercenter
7
Okazaki, et. Al
2008
114.6
Tons/establishment/
year
Supermarket/Grocery
Store
8
North Carolina
Department of
Environment and
Natural Resources
2012
117
Tons/establishment/
year
Supermarket/Grocery
Store
9
Food Waste
Reduction Alliance
(BSR)
2014
0.01
Ibs/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.
California EPA (2006), California EPA (2015), and the North Carolina Department of Environment and
Natural Resources (now known as North Carolina Department of Environmental Quality) conducted
audits of food retail sector waste.1 Draper/Lennon (for the State of Connecticut), Mecklenburg County,
Okazaki et al., the Food Waste Reduction Alliance, and ReFED 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 California EPA (2006), which
sampled waste at big-box retail stores. Another low generation factor, 0.5 tons per employee per year, was
reported by ReFED, 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 California EPA (2006) and Mecklenburg County, 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. The Food Waste Reduction
Alliance/BSR (2014) collected industry generation data through a series of surveys and estimated 10
pounds of excess food per thousand dollars of company 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 (NAICS code 452311). These generation
1 North Carolina's state-specific estimate was provided by a North Carolina hauler who collected segregated food waste from a
major grocery chain.
11
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Excess Food Opportunities Map - Technical Methodology
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 uses the following equation:
/tons\
Food Retailers Excess Food =
Vyear/
^ tons
employee
Number of employees x
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 2012), 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
12
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Excess Food Opportunities Map - Technical Methodology
on for Version 1.0 is outdated. EPA would include recoverable fraction estimates in future updates
to the Map if appropriate methodologies are available.
2.4. 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 2-4). Data were obtained from the National Center for
Education Statistics (NCES) and NAICS codes were not used in NCES databases.
Table 2-3. Educational InstitutionsSchool 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 National Center for Education Statistics (NCES) for the 2016 school year. 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 X-X). These ten
studies were used to estimate excess food generated, resulting in a range of values for each institution.
Table X-X Generation Factors for Postsecondary Schools
GENERATION
FACTOR #
SOURCE
YEA
R
UNITS
GENERATION FACTOR ESTIMATE
PRE-
CONSUMER1
POST-
CONSUMER
TOTAL
1
Ebner et al.
2014
lbs/meal
0.07
0.15
0.22
2
Sarjahani et al.2
2009
lbs/meal
0.19
0.23
0.42
3
VA DEQ (Vannet
Group)
2008
lbs/meal
0.16
0.31
0.47
13
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Excess Food Opportunities Map - Technical Methodology
GENERATION
FACTOR ft
UNITS
GENERATION FACTOR ESTIMATE
YEA
PRE-
POST-
SOURCE
R
CONSUMER1
CONSUMER
TOTAL
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
Thiagaraiah and
Getty
2012
lbs/meal
0.16
0.25
0.40
7
Whitehair et al.3
2012
lbs/meal
0.09
0.14
0.23
8
Kim and Morawski2
2012
lbs/meal
0.13
0.21
0.34
9
Caton et al.
2010
lbs/meal
0.31
0.49
0.79
10
CalRecycle
2015
Ibs/studen
t/year
N/A
N/A
22.0
Notes:
1. Pre-consumer values are estimated for generation factors 6-9 using the average proportion of pre-
consumer waste from studies 1-5. On average, studies 1-5 showed post-consumer waste to be 61.4
percent of all waste.
2. Sarjahani et al. (2009) and Kim and Morawski (2012) estimate food waste generation with and
without trays. We use the average of the two estimates.
3. Whitehair et al. studies the effect of a messaging campaign to reduce food waste. We use 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., kitchen or preparation waste) as well as post-consumer food (i.e., plate waste). The highest
generation factor is from VA DEQ (2008), yielding an estimate of 0.47 pounds per meal. We include 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.
We also include 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. (2012) with an estimate of only 0.14 pounds per meal, and Caton et al. (2010) with an estimate 0.49
pounds per meal. Because these studies only consider post-consumer excess food, we 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 estimate of 0.14 lbs/meal by 0.614 provides a total excess food estimate
(pre- and post-consumer) of 0.23 lbs/meal. The pre-consumer values in Table X-X 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 (CalRecycle, 2015). While CalRecycle does not differentiate between the K-12 and
college/university sectors, we included the generation factor derived from "education sector" because the
study is recent and the estimates are derived through direct waste sampling. We also used the same
generation factor for elementary and secondary schools.
2 SeeNRDC (2017), Hodge et al. (2016), Battelle (2016), Moriarty (2013), Wellesley College (2013), and EPA (2011).
14
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Excess Food Opportunities Map - Technical Methodology
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
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) apply two separate "meals per
enrolled student per year" estimates for residential and non-residential institutions. Specifically,
they assume 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, we retain the Draper/Lennon (2001) value of 108
meals per enrolled student at non-residential institutions.
Weighted Average Meals per Student - We estimate 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^
Vyeary
/tons\
Postsecondary Schools Excess Food :
Vvear/
meals
student lbs tons
Number of students x 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:
3 Ebner et al. (2014) reports two estimates: 180 and 270 meals per enrolled student per year according to two different methods.
We use the average (225) as representative of Ebner et al (2014). Whitehair et al. (2012) reports 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),
we estimate an average of226 meals per student per year.
4 We estimate that 34 percent of all enrolled students attend residential institutions. We calculate 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.
15
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Excess Food Opportunities Map - Technical Methodology
/tons\
Postsecondary Schools Excess Food
Vyear/
_lbs_
-------
Excess Food Opportunities Map - Technical Methodology
GENERATION
GENERATION
FACTOR #
SOURCE
YEAR
FACTOR
UNITS
1
Wilkie,
Graunke, and
Cornejo
2015
25.9
Ibs/student/ye
ar
2
RecyclingWorks
MA
2013
18.0
Ibs/student/ye
ar
3
CalRecycle
2015
22.0
Ibs/student/ye
ar
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, Graunke, and Cornejo (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 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. CalRecycle (2015) estimates a generation factor of 22.0 pounds per student per year.6
Generation factors 4 and 5 use pounds (per student) per meal. Byker, et al. (2014) estimate an average
generation factor of 0.52 pounds per meal at public pre-kindergarten and kindergarten classes. We 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) estimate an
average of 0.35 pounds of excess food per meal.
The Wilkie and Byker 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
-------
Excess Food Opportunities Map - Technical Methodology
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
ctnHpnt lbs tons
Number of students x x X-
year meal 2,000 lb
Where X = 0.35 or 0.52
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 X-X 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.
2.5. Hospitality industry
As listed in Table 2-6, establishments belonging to three NAICS codes were classified as the
hospitality industry.
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.
18
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Excess Food Opportunities Map - Technical Methodology
Table 2-4. 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 X-X).9 These four studies were used to estimate excess food generated, resulting in
a range of values for each establishment.
Table X-X. Generation Factors for the Hospitality Industry
SOURCE
YEAR
GENERATION
FACTOR
UNIT
California
EPA/Cascadia
Consulting Group
2006
1,983
lbs/employee/year
Okazaki et. al.
2008
375
lbs/employee/year
Ca IRecycle/Cascadi
a Consulting Group
2015
1,197
lbs/employee/year
Tetra Tech/Metro
Vancouver
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 food waste generated per
hotel food service employee, unlike the other studies that consider food waste generated per general hotel
employee. To apply data from Okazaki, et al., the analysis divides the total amount of food waste generated
in Hawaii hotels (as estimated by Okazaki) by the total number of hotel employees under NAICS 7211 in
Hawaii, to make the generation factor consistent with the other studies. These generation factors range from
375 to 1,983 pounds per employee per year. These 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
\year
=
9 Several studies report food waste generated per meal, or per guest or guest room. We exclude such studies from our calculations
due to the difficulty in estimating the annual number of hotel guests or occupied guest rooms per year in the U.S. (Recycling
Works Massachusetts, 2013; Carvalho, 2014; Coker, 2009).
19
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Excess Food Opportunities Map - Technical Methodology
X
employee tons
Number or employees x x
F y year 2,0001b
Where X = 375, 997, 1197, or 1983.
2.5.1 Changes in Version 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.
2.6. Correctional Facilities
To estimate the amount of excess food generated by correctional facilities, facility-level data for
NAICS code 922140 were 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 X-X).10 These six studies were used to estimate excess food generated, resulting in a range
of values for each facility.
Table X-X Generation Factors for Correctional Facilities
GENERATION
FACTOR #
STUDY
YEAR
GENERATION
FACTOR
UNITS
1
Marion, J.
2000
1.00
Ibs/inmate/day
2
Draper/Lennon Inc.
2001
1.00
Ibs/inmate/day
3
Kessler Consulting Inc.
2004
1.20
Ibs/inmate/day
4
Mendrey, K.
2013
1.25
Ibs/inmate/day
5
Goldstein, N.
2015
1.40
Ibs/inmate/day
6
CalRecycle
2018
0.85
Ibs/inmate/day
Two of these studies (Marion, 2000; and Draper/Lennon Inc., 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 Inc. (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 one pound/inmate/day estimate in calculating
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; FL-DEP/Kessler Consulting Inc., 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 - Technical Methodology
excess food generated in correctional facilities in various states including Massachusetts, New Jersey, and
South Carolina (Michaels, 2003; Mercer, 2013; South Carolina Department of Commerce, 2015).
These six excess food generation factors range from 0.85 to 1.4 pounds per inmate per day, from studies
that conducted original research and collected data from correctional facilities. In instances where the
study provided a range in the amount of waste 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 food waste
analyses (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) 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/employee
State and federal prisons: 3.4 inmates/employee
Using this data, the following equation was used to generate estimates of excess food for
correctional facilities:
/tons\
Correctional Facilities Excess Food I ) =
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
1.6.1 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 1.0, in addition to five other methodologies. In
addition, EPA relied solely on BJS statistics to estimate an average number of inmates per employee, which
resulted in slightly different estimates than those calculated in Version 1.0.
2.7. Healthcare Facilities
As listed in Table 2-X, 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).
12 California, Connecticut, Florida, New York, Pennsylvania, and Washington.
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Excess Food Opportunities Map - Technical Methodology
Table 2-X. 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 X-X).
Table X-X. Generation Factors for Healthcare Facilities
SOURCE
YEAR
GENERATION
FACTOR
UNITS
Draper/Lennon
2001
1,248.3
lbs/bed/year
NCDENR
2012
468.2
lbs/bed/year
Walsh
1993
663.4
lbs/bed/year
CalRecycle
2015
232.6
Ibs/bed/ye
ar
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 are built on original research
developed in the 1990s, hence we supplement this data point with other studies. Both the North Carolina
Department of Environment and Natural Resources (NCDENR) study and the CalRecycle study are more
recent and use original waste sampling. The Walsh study is older, but provides an additional data point for
corroboration of the generation per bed figures.13
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:
13 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 CalRecycle and Walsh studies report total solid waste generation per hospital bed. CalRecycle 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 solid waste per bed figure to estimate
excess food per bed.
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Excess Food Opportunities Map - Technical Methodology
/tons\
Healthcare Facilities Excess Food :
Vyear/
# of Beds*
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 were 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.
2.8. Restaurants and Food Services
Six industries were classified as restaurants and food services (Table 2-10). Establishment-level
data for this sector was obtained from Hoover's, Inc.
Table 2-5. NAICS Codes for the Restaurants and Food Services Sector
No.
NAICS Code
NAICS Code Description
2
722320
Caterers
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
23
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Excess Food Opportunities Map - Technical Methodology
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
X-X). These five studies were used to estimate excess food generated, resulting in a range of values for
each facility.
Table X-X. Generation Factors for Restaurants and Food Services
GENERATION
ESTABLISHMENT
FACTOR #
SOURCE
YEAR
GENERATION FACTOR
UNITS
TYPE
1
California
EPA
3,392 for full-service
Full-service and
limited service
estimated
separately
2
2006
2,494 for limited-
service
Lbs/employee/year
3
Draper/Len
non
2002
3,000
L bs /employee /year
Unspecified
4
CalRecycle
2015
2,760
L bs /employee /year
Full-service and
limited-service
estimated together
5
BSR
2014
33
Lbs/thousand $ in
revenue/year
Unspecified
The three studies used to establish generation factors 1-4 established factors based on pounds per employee
per year. The 2002 Draper/Lennon study, developed for the Massachusetts Department of Environmental
Protection and updated by the U.S. EPA Region 1 in 2011, was widely cited (see Recycling Works
Massachusetts, 2013; Mercer, 2013; South Carolina Department of Commerce, 2013, among others). While
widely applied, the generation factors in Draper/Lennon are built on original research developed in the
1990s. Both the California EPA (2006) and CalRecycle (2015) studies are more recent and use waste
sampling techniques to estimate of excess food generation.
The Food Waste Reduction Alliance/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
\year
x lb
employee tons
Number or employees x x
F y year 2,0001b
=
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Excess Food Opportunities Map - Technical Methodology
Where X = 2494 to 3,392
Generation factor 5 uses the following equation:
/tons\
Restaurants and Food Services Sector Excess Food
Establishment's Annual Revenue $x 0.033
Vyear/
lb tons
Annual Revenue ($) 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 mapped. 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.
2.9. 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
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. 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 rescue organizations included in the Map for the next update.
2.10. Data Analysis
Approximately 1.3 million establishments that potentially generate excess food were identified
from ICI sectors based on 89 NAICS codes. Because data could not be obtained for the food
services and farm sectors, the Dataset contains just over 500,000 establishments. 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 roughly 86% of establishments. For
some sectors, there were several equations available to generate an excess food estimate, resulting
in minimum and maximum values. Establishments for which generation rates could not be
estimated were still mapped.
The data itself was reviewed and filtered in the following ways:
Duplicates were defined as establishments with identical name and physical address. They
were identified and then filtered such that the establishment with the lowest waste
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Excess Food Opportunities Map - Technical Methodology
generation estimate was kept in the dataset. In cases where duplicates had either the same
or no waste generation estimate, just one of the establishments was kept in the dataset.
Establishments identified as "Headquarters" were excluded from the dataset because these
establishments typically serve an administrative function and do not generate excess food.
Educational institutions with the word "online" in their name were removed because they
are not assumed to have a physical campus on which excess food would be generated.
In the educational institutions dataset, certain establishments included the words
"juvenile", "detention", or "correctional" in their names. These properties were moved to
the correctional facilities dataset and are identifiable in the Map because their UniquelD
starts with the letters "EDU" instead of "COR".
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 the Department of Homeland Security. 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 3-1. 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 3-1 shows the proportion of food manufacturers and
processors by industry type.
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Excess Food Opportunities Map - Technical Methodology
Figure 3-1. Proportion of Food Manufacturers and Processors by Industry Type
Ice cream and
frozen dessert
\
Breweries, 3%
r
Commercial &
Animal (except
poultry)
slaughtering, 4%
Wineries, 7%
\_All other
manufacturers &
processors, 42%
retail bakeries,
42%
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 3-2 shows the proportion of food wholesalers and distributors by industry type,
approximately one-third of which are supermarket and grocery (except convenience) stores.
Table 3-2 shows more granular data about data availability across this sector.
27
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Excess Food Opportunities Map - Technical Methodology
Figure 3-2. Proportion of Food Wholesale and Retail Establishments by Industry Type
All other
wholesalers and
distributors, 34%
Farm Supplies
Merchant
Wholesalers
(Animal feeds
(except pet
food)), 5%
All Other
Specialty Food_
Stores, 7%
Supermarkets
and Other
Grocery (except
Convenience)
Stores, 37%
Other Grocery
and Related
Products
Merchant
-Wholesalers, 9%
Baked Goods Stores, 9%
Table 3-2. 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 3-3 shows the proportion of educational institutions by type, and
Table 3-3 shows more granular information about data availability across the sector.
28
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Excess Food Opportunities Map - Technical Methodology
INSERT FIGURE 3-3 here
Table 3-3. Number of Educational Institutions Included in the Dataset
School Type
Institutions in the
Dataset
Institutions with
Excess Food
Estimate
% Institutions with
Excess Food
Estimate
Postsecondary Schools
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%
Figure 3-3. Proportion of Educational Institutions by School Type
Colleges,
Universities, &
Professional
Schools, 6%
Private Schools,
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 3-4 shows the proportion of hospitality establishments by industry type for which hotels
and motels represent the vast majority at 98% of the total. Table 3-4 shows more granular
information about data availability across the sector.
29
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Excess Food Opportunities Map - Technical Methodology
Figure 3-4. Proportion of Hospitality Industry Establishments by Type
Casino Hotels Casinos (except
Casino Hotels)
^ <1%
Hotels and Motels
98%
Table 3-4. Number of Hospitality Establishments Included in the Dataset
Industry
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Hotels and Motels
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.
30
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Excess Food Opportunities Map - Technical Methodology
Figure 3-5 shows the proportion of healthcare facilities by industry type for which general
medical and surgical hospitals represent the vast majority at XX% of the total. Table 3-5 shows
more granular information about data availability across the sector.
INSERT FIGURE 3-5
80% 9% 11%
¦ General Medical and Surgical Hospitals
¦ Psychiatric and Substance Abuse Hospitals
¦ Specialty (Except Psychiatric and Substance Abuse) Hospitals
Table 3-5. 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
Surgical Hospitals
6,071
5,598
92.2%
Psychiatric and Substance
Abuse Hospitals
653
549
84.1%
Specialty (except
Psychiatric and Substance
Abuse) Hospitals
845
772
91.4%
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.
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Excess Food Opportunities Map - Technical Methodology
Figure 3-6 shows the proportion of restaurants and food services establishments by industry type,
for which full-service restaurants represent the majority at XX% of the total. Table 3-6 shows
more granular information about data availability across the sector.
INSERT FIGURE 3-6
Table 3-6. Number of Restaurants and Food Services Establishments Included in the
Dataset
Industry
Establishments in
the Dataset
Establishments
with Excess Food
Estimate
% Establishments
with Excess Food
Estimate
Caterers
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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Excess Food Opportunities Map - Technical Methodology
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 of the Map to the food bank
sector.
4.3. Composting Facilities
Data on 3021 composting facilities was compiled in 2018 through EPA review of state government
websites, usually state departments of natural resources or environmental protection, and
communication with state government employees. Composting data were compiled for 49 states
and one territory, and associated websites and type of feedstock accepted are listed in the Dataset
and in the Map, where information was available.. The number of composting facilities in the
Dataset increased from 2,499 in 39 states in Version 1.0 to 3021 facilities in 49 states and one
territory in Version 2.0. In addition, the composting facilities are point mapped in Version 2.0, and
were only mapped by zip code or county in Version 1.0 of the Map.
4.4. Anaerobic Digestion Facilities
EPA compiled the list of 1,381 anaerobic digestion facilities using Agency and non-Agency
sources (US EPA, 2016c; ABC, 2017). The main data sources include facilities that had been listed
in the EPA Waste to Biogas Mapping Tool. These data were 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.
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Excess Food Opportunities Map - Technical Methodology
5. Data Sources for Communities with Residential Source Separated Organics
Programs
Communities with residential source separated organics programs that collect excess food were
identified in a 2011 survey published by BioCycle (Yepsen, 2012). Of the 156 communities, data
was available to map 130. An additional community was identified in a publication by Layzer
(2014), resulting in 131 mapped communities. No changes were made in Version 2.0 of the Map.
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. Due to a lack of data, 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).
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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References
ABC (2017). Operating Biogas Systems in the U.S. American Biogas Council.
http://www.americanbiogascouncil.org/biogas maps.asp (Accessed June 2017).
AgSTAR (n.d.). Biogas Recovery in the Agriculture Sector, http ://www.epa.gov/agstar
(Accessed June 2017)
AHD (2015). American Hospital Directory, state statistics.
https://www.ahd.com/state statistics.html (Accessed June 2017)
BJS (2008). Number of employees in correctional facilities under state or federal authority, by
occupational category. Bureau of Justice Statistics, December 30, 2005
http://www.bis.gov/index.cfm?tv=pbdetail&iid=1683 (Accessed June 2017)
BJS (2014). Prisoners in 2013. NCJ 247828, Bureau of Justice Statistics, September 2014.
http://www.bi s. gov/content/pub/pdf/p 13 .pdf (Accessed June 2017)
BOP (2017). Federal Bureau of Prisons statistics, http://www.bop.gov/about/statistics (Accessed
June 2017)
California Assembly Bill No. 1826, Chapter 727 (2014). available at
http://leginfo.legislature.ca.gov/faces/billNavClient.xhtml7bill id=201320140AB1826&s
earch keywords (Accessed June 2017)
CCG (2006). Waste Disposal and Diversion Findings for Selected Industry Groups. No. 341-06-
006. A report prepared by Cascadia Consulting Group for the California Integrated
Waste Management Board, June 2006.
http://www.calrecvcle.ca.gov/publications/Documents/Disposal/34106006.pdf. (Accessed
June 2017)
CCG (2015). 2014 Generator-Based Characterization of Commercial Sector Disposal and
Diversion in California. A report prepared by Cascadia Consulting Group for
Department of Resources Recycling and Recovery (CalRecycle), September 2015.
http://www.calrecvcle.ca.gov/publications/Documents/1543/20151543.pdf (Accessed
December 2017)
Clark, S. and Law, D. (2000). Multipurpose Program at Kentucky College. BioCycle, September
2000, 69.
CTDEEP (n.d.) Connecticut Department of Environmental Protection, Connecticut Food
Residual Map. http://www.depdata.ct.gov/maps/recvcling/foodresidualmap.htm
(Accessed June 2017)
CTDEP (2001). Identifying, Quantifying, and Mapping Food Residuals from Connecticut
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Excess Food Opportunities Map - Technical Methodology
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Excess Food Opportunities Map - Technical Methodology
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APPENDICES
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Excess Food Opportunities Map - Technical Methodology
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 B-l lists excess
food characteristic categories and commonly associated industries.
Table A-l. 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 A-2. For the food manufacturers and processors 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. CTDEP (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 A-2 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 - Technical Methodology
Table B-2. 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
NAICS Code
NAICS Code Description
Excess Food Characteristics
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
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311821
Cookie and Cracker Manufacturing
Simple Carbohydrates
311824
Dry Pasta, Dough, and Flour Mixes
Manufacturing from Purchased Flour
Simple and Complex
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 and Private Elementary and Secondary
Schools
Complex Carbohydrates,
Proteins
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Excess Food Opportunities Map - 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
Complex Carbohydrates,
Abuse) Hospitals
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 - 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 post-
harvest food that is intended for human consumption but not eaten as originally intended and which
then needs to be recovered, recycled, or disposed of safely.
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 collected yard waste. The following materials were
not included in this report's definition of excess food: unharvested crops, on-farm processing
scraps, and used cooking oil (recycled for animal feed or biofuel).
"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 is recycled or sent for disposal. Food waste may be sent to feed animals, for
composting, or to an anaerobic digester.
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 meal, amount of excess food per employee per
year, amount of excess food per student per year.
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Excess Food Opportunities Map - Technical Methodology
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
excess food.
FOOD RECOVERY: The action of collecting excess food to feed people.
INEDIBLE
MUNICIPAL SOLID WASTE (MSW): Garbage or refuse generated by households,
commercial establishments or institutional facilities.
ORGANIC RESIDUALS: Materials such as biosolids, composts, 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: Postconsumer 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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