Foodservice Methodology

Scope Boundary

Manufacturing Scope Boundary

This diagram communicates the scope boundary as aligned with the Food Loss and Waste Accounting and Reporting Standard[17]. Note that ReFED’s analysis also includes food sent to donations, although donations are not considered a destination within the Standard.

NOTES
  • “Food Donation” has been added as a Destination

  • “Biomaterial Processing is referred to as “Industrial Uses” in our model

  • “Co/anaerobic digestion” is referred to as “Anaerobic digestion” in our model

  • “Controlled Combustion” is referred to as “Incineration” in our model

  • “Refuse/discards” is referred to as “Dumping” in our model

Calculations

Surplus Food Calculations

Master Surplus Equation:
Tons Pre-Consumer Surplus
+ Tons Onsite Plate Waste
+ Tons Catering Overproduction
+ Tons Catering Plate Waste
-———————————————–
= Tons Foodservice Surplus

In ReFED’s data model, the following calculations are repeated for every state, year, and foodservice segment before any aggregation is done.

Table 13. Calculations Performed to Quantify U.S. Foodservice Surplus Food

DATA ITEM

DATA SOURCE OR CALCULATION

EXAMPLE

SUPPLIER PURCHASES AND CUSTOMER SALES

% National Purchases from Suppliers

Technomic Ignite Platform[43]

$__foodservice_example_national_supplier_purchases__ U.S. __foodservice_example_sub_sector_category__ location purchases from suppliers and distributors

% National US Dollars Sold

Technomic Ignite Platform[43]

$__foodservice_example_national_dollars_sold__ U.S. __foodservice_example_sub_sector_category__ location sales

State % Share of Supplier Purchases

= # State Locations for Top 500 Restaurants[43] / # US Locations for Top 500 Restaurants[43]

__foodservice_example_percent_of_locations__% of U.S. __foodservice_example_sub_sector_category__ locations in __foodservice_example_state__ for Top 500 Restaurants

% In-scope Igredients

ReFED Calculation
See Appendix O for more information
ReFED estimates the following breakdown for menu ingredients:
__foodservice_example_in_scope_ingredients__%

US Dollars State Supplier Purchases

= National Purchases from Suppliers * State % Share of Supplier Purchases * % In-scope Ingredients

$__foodservice_example_national_supplier_purchases__ U.S. __foodservice_example_sub_sector_category__ location purchases * __foodservice_example_percent_of_locations__% __foodservice_example_state__ market share * __foodservice_example_in_scope_ingredients__% in-scope
= $__TODO_foodservice_example_state_supplier_purchases__ estimated __foodservice_example_sub_sector_category__ location purchases in __foodservice_example_state__

US Dollars Sold

= National Purchases from Suppliers * State % Share of Supplier Purchases * % In-scope Ingredients

$__foodservice_example_national_dollars_sold__ U.S. __foodservice_example_sub_sector_category__ location sales * __foodservice_example_percent_of_locations__% __foodservice_example_state__ market share * __foodservice_example_in_scope_ingredients__% in-scope
= $__foodservice_example_us_dollars_sold__ estimated __foodservice_example_sub_sector_category__ location sales in __foodservice_example_state__

Wholesale Price per Lb

ReFED Calculation
See Appendix O for more information

ReFED estimates that the average wholesale price of food for __foodservice_example_sub_sector_category__ locations in __foodservice_example_year__ was $__TODO_foodservice_example_wholesale_price__ per lb.

State Tons Supplier Purchases

= US Dollars State Supplier Purchases / Wholesale Price per Lb / 2,000 Lbs per ton

= $__TODO_foodservice_example_state_supplier_purchases__ state supplier purchases / $__TODO_foodservice_example_wholesale_price__ per lb / 2,000 lbs per ton
= __foodservice_example_tons_supply__ tons of food purchased from suppliers for __foodservice_example_sub_sector_category__ locations in __foodservice_example_state__

Pre-Consumer Surplus Rate

LeanPath[30]

__foodservice_example_leanpath_surplus_rate__% of food spend not utilized by kitchens

Tons Sold

= State Tons Supplier Purchases * ( 100% - Pre-Consumer Surplus Rate )

= __foodservice_example_tons_supply__ tons of food purchased from suppliers * (100% - __foodservice_example_leanpath_surplus_rate__% )
= __foodservice_example_tons_sold__ tons sold to customers at __foodservice_example_sub_sector_category__ locations in __foodservice_example_state__

PRE-CONSUMER FOOD SURPLUS

Tons Pre-Consumer Surplus

= State Tons Supplier Purchases * Pre-Consumer Surplus Rate

= __foodservice_example_tons_supply__ tons food purchased from suppliers * __foodservice_example_leanpath_surplus_rate__% surplus rate
= __foodservice_example_tons_preconsumer_surplus__ tons pre-consumer surplus at __foodservice_example_sub_sector_category__ locations in __foodservice_example_state__

% of Pre-Consumer Surplus that is Overproduction

LeanPath[30]

__foodservice_example_percent_preconsumer_overproduction__% of pre-consumer surplus for the __foodservice_example_sub_sector__ sector is due to overproduction.

Tons Overproduction

= Tons Pre-Consumer Surplus * % of Pre-Consumer Surplus that is Overproduction

= __foodservice_example_tons_preconsumer_surplus__ tons pre-consumer surplus * __foodservice_example_percent_preconsumer_overproduction__% overproduction
= __TODO_foodservice_example_tons_preconsumer_overproduction__ tons Overproduction

Retail Price per Lb

= US Dollars Sold / Tons Sold / 2,000 lbs per ton

= $__foodservice_example_us_dollars_sold__ sold / __foodservice_example_tons_sold__ tons sold / 2,000 lbs per ton
= $__TODO_foodservice_example_retail_price__ retail value per lb sold

US Dollars Overproduction

= Tons Overproduction * Retail Price per Lb

Note: Overproduction is valued at retail rather than wholesale price, because it is ready to sell to a customer.
= __TODO_foodservice_example_tons_preconsumer_overproduction__ tons overproduction * $__TODO_foodservice_example_retail_price__ retail value per lb sold * 2,000 lbs per ton
= $__TODO_foodservice_example_dollars_preconsumer_overproduction__ overproduction

Tons Pre-Consumer Surplus (excluding Overproduction)

= Tons Pre-Consumer Surplus - Tons Overproduction

= __foodservice_example_tons_preconsumer_surplus__ tons Pre-Consumer Surplus - __TODO_foodservice_example_tons_preconsumer_overproduction__ tons Overproduction
= __foodservice_example_tons_preconsumer_surplus_excluding_overproduction__ tons Pre-Consumer Surplus (excluding Overproduction) at __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Pre-Consumer Surplus by Food Type (excluding Overproduction)

= Sum for all Food Categories[Tons Pre-Consumer Surplus (excluding overproduction) * % Food Type * Wholesale Price per Lb] * 2,000 lbs per ton

Note: Values Vary by Food Type

= Sum for all Food Types[ (X tons Pre-Consumer Surplus * Y% Food Type * $Z Wholesale Price per lb) * 2,000 lbs per ton ]

= $__foodservice_example_dollars_preconsumer_surplus__

CATERING OVERPRODUCTION

Breakdown of Sales by Customer Distribution Channel

Technomic Ignite Platform[43]

For __foodservice_example_sub_sector_category__ locations in __foodservice_example_year__:

Take-out: __foodservice_example_percent_takeout__%
Onsite Dining: __foodservice_example_percent_onsite__%
Catering: __foodservice_example_percent_catering__%
——————-
Total: 100%

% Catering Overproduction

ReFED Expert Interviews
See Appendix Q for more information

Experts estimate that __foodservice_example_percent_catering_overproduction_rate__% of food is typically left unserved at breakfast or lunch catering events.

Tons Catering Sold

= Tons Sold * % Catering

= __foodservice_example_tons_sold__ tons sold * __foodservice_example_percent_catering__% of sales is catering
= __foodservice_example_tons_catering_sold__

Tons Catering Overproduction

= Tons Catering Sold * % Catering Overproduction

Note: All Catering Overproduction was listed as “Prepared Foods” in the Food Waste Monitor.
= __foodservice_example_tons_sold__ tons sold * __foodservice_example_percent_catering__% of sales is catering * __foodservice_example_percent_catering_overproduction_rate__% food left unserved at events
= __foodservice_example_tons_catering_overproduction__ tons catering overproduction from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Catering Overproduction

= US Dollars Sold * % Catering * % Catering Overproduction

= $__foodservice_example_us_dollars_sold__ sold * __foodservice_example_percent_catering__% of sales is catering * __foodservice_example_percent_catering_overproduction_rate__% food left unserved at events
= $271,718,460 catering overproduction from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

PLATE WASTE

Average Menu Retail $ per Lb

= US Dollars Sold / Tons Sold / 2,000 lbs per ton

= $__foodservice_example_national_dollars_sold__ / __foodservice_example_tons_supply__ tons / 2,000 lbs per ton
= $__TODO_foodservice_example_average_menu_price_per_lb__/lb

Plate Waste Rate

In the plate waste study relevant to __foodservice_example_sub_sector__ locations, __foodservice_example_platewaste_rate__% of food served became plate waste

Tons Onsite Food Served

= Tons Sold * % Onsite Dining

= __foodservice_example_tons_sold__ * __foodservice_example_percent_onsite__%
= __foodservice_example_tons_onsite_food_served__ tons served at __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Onsite Food Served

= Tons Onsite Food Served * Average Menu Retail $ per Lb * 2,000 lbs per ton

= __foodservice_example_tons_onsite_food_served__ tons served * $__TODO_foodservice_example_average_menu_price_per_lb__/lb * 2,000 lbs per ton
= $__foodservice_example_dollars_onsite_food_served__ served at __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Tons Catering Served

= Tons Catering Sold - Tons Catering Overproduction

= __foodservice_example_tons_onsite_food_served__ - __foodservice_example_tons_catering_overproduction__ tons catering overproduction
= __foodservice_example_tons_catering_served__ tons catering served from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Catering Served

= Tons Catering Served * Average Menu Retail $ per Lb * 2,000 lbs per ton

= __foodservice_example_tons_catering_served__ tons catering served * $__TODO_foodservice_example_average_menu_price_per_lb__/lb * 2,000 lbs per ton
= $__foodservice_example_dollars_catering_served__ catering served from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Tons Onsite Plate Waste

= Tons Onsite Food Served * Plate Waste Rate

= __foodservice_example_tons_onsite_food_served__ * __foodservice_example_platewaste_rate__% food served becomes plate waste
= __foodservice_example_tons_onsite_platewaste__ tons onsite plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Tons Catering Plate Waste

= Tons Catering Served * Plate Waste Rate

= __foodservice_example_tons_catering_served__ tons catering served * __foodservice_example_platewaste_rate__% food served becomes plate waste
= __foodservice_example_tons_catering_platewaste__ tons catering plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Catering Plate Waste

= US Dollars Sold * % Onsite Dining * Plate Waste Rate

= $__foodservice_example_us_dollars_sold__ sold * __foodservice_example_percent_onsite__% of sales is onsite dining * __foodservice_example_platewaste_rate__% food served becomes plate waste
= $__foodservice_example_dollars_onsite_platewaste__ onsite plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Catering Plate Waste

= US Dollars Catering Served * Plate Waste Rate

= $__foodservice_example_dollars_catering_served__ catering served * __foodservice_example_platewaste_rate__% food served becomes plate waste
= $__foodservice_example_dollars_catering_platewaste__ catering plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Total Tons Plate Waste

= Tons Onsite Plate Waste + Tons Catering Plate Waste

Note: All Plate Waste was listed as “Prepared Foods” in the Food Waste Monitor.
= __foodservice_example_tons_onsite_platewaste__ tons onsite plate waste + __foodservice_example_tons_catering_platewaste__ tons catering plate waste
= __TODO_foodservice_example_tons_total_platewaste__ tons total plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Total US Dollars Plate Waste

= US Dollars Onsite Plate Waste + US Dollars Catering Plate Waste

= $__foodservice_example_dollars_onsite_platewaste__ onsite plate waste + $__foodservice_example_dollars_catering_platewaste__ catering plate waste
= $__TODO_foodservice_example_dollars_total_platewaste__ total plate waste from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

TOTAL FOOD SURPLUS

Tons Food Surplus

= Tons Overproduction + Tons Pre-Consumer Surplus (excluding Overproduction) + Tons Plate Waste (including Onsite Dining and Catering) + Tons Catering Overproduction

= __TODO_foodservice_example_tons_preconsumer_overproduction__ tons Overproduction + __foodservice_example_tons_preconsumer_surplus_excluding_overproduction__ tons not sold + __TODO_foodservice_example_tons_total_platewaste__ tons total plate waste + __foodservice_example_tons_catering_overproduction__ tons catering overproduction
= __foodservice_example_tons_surplus__ tons food surplus from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

US Dollars Food Surplus

= US Dollars Overproduction + US Dollars Pre-Consumer Surplus (excluding Overproduction) + US Dollars Plate Waste (including Onsite Dining and Catering) + US Dollars Catering Overproduction

= $__TODO_foodservice_example_dollars_preconsumer_overproduction__ overproduction + $__foodservice_example_dollars_preconsumer_surplus__ pre-consumer surplus (excluding overproduction) + $__TODO_foodservice_example_dollars_total_platewaste__ total plate waste + $__foodservice_example_us_dollars_catering_overproduction__ catering overproduction
= $__foodservice_example_dollars_surplus__ food surplus from __foodservice_example_sub_sector__ locations in __foodservice_example_state__

Cause Calculations

Table 14. Calculations Performed to Quantify the Causes of U.S. Foodservice Surplus Food

DATA ITEM

DATA SOURCE OR CALCULATION

EXAMPLE

PRE-CONSUMER SURPLUS CAUSES

Tons Overproduction

See calculation above for Tons Overproduction

= __TODO_foodservice_example_tons_total_overproduction__ tons Overproduction

US Dollars Overproduction

See calculation above for US Dollars Overproduction

= $__TODO_foodservice_example_dollars_total_overproduction__ Overproduction

% Surplus due to Cause (excluding Overproduction)

Leanpath[30]
See Appendix R for causes by segment as well as proxies segments used when data was not available for a particular segment.
Pre-consumer food surplus causes (not including Overproduction) for the __foodservice_example_leanpath_segment__ segment in __foodservice_example_year__ (used as a proxy for most restaurants). :

Breads & Bakery:
Cooking issues: __foodservice_example_cause_breads_cookingissues__%
Date Label Concerns: __foodservice_example_cause_breads_datelabels__%
Equipment issues: __foodservice_example_cause_breads_eqptissues__%
Food Safety: __foodservice_example_cause_breads_safety__%
Handling errors: __foodservice_example_cause_breads_handling__%
Other: __foodservice_example_cause_breads_other__%
Spoiled: __foodservice_example_cause_breads_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_breads_trim__%
————————————————-
Total: 100%

Dairy & Eggs:
Cooking issues: __foodservice_example_cause_dairy_cookingissues__%
Date Label Concerns: __foodservice_example_cause_dairy_datelabels__%
Equipment issues: __foodservice_example_cause_dairy_eqptissues__%
Food Safety: __foodservice_example_cause_dairy_safety__%
Handling errors: __foodservice_example_cause_dairy_handling__%
Other: __foodservice_example_cause_dairy_other__%
Spoiled: __foodservice_example_cause_dairy_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_dairy_trim__%
————————————————-
Total: 100%

Dry Goods:
Cooking issues: __foodservice_example_cause_drygoods_cookingissues__%
Date Label Concerns: __foodservice_example_cause_drygoods_datelabels__%
Equipment issues: __foodservice_example_cause_drygoods_eqptissues__%
Food Safety: __foodservice_example_cause_drygoods_safety__%
Handling errors: __foodservice_example_cause_drygoods_handling__%
Other: __foodservice_example_cause_drygoods_other__%
Spoiled: __foodservice_example_cause_drygoods_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_drygoods_trim__%
————————————————-
Total: 100%

Fresh Meat & Seafood:
Cooking issues: __foodservice_example_cause_meat_cookingissues__%
Date Label Concerns: __foodservice_example_cause_meat_datelabels__%
Equipment issues: __foodservice_example_cause_meat_eqptissues__%
Food Safety: __foodservice_example_cause_meat_safety__%
Handling errors: __foodservice_example_cause_meat_handling__%
Other: __foodservice_example_cause_meat_other__%
Spoiled: __foodservice_example_cause_meat_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_meat_trim__%
————————————————-
Total: 100%

Frozen:
Cooking issues: __foodservice_example_cause_frozen_cookingissues__%
Date Label Concerns: __foodservice_example_cause_frozen_datelabels__%
Equipment issues: __foodservice_example_cause_frozen_eqptissues__%
Food Safety: __foodservice_example_cause_frozen_safety__%
Handling errors: __foodservice_example_cause_frozen_handling__%
Other: __foodservice_example_cause_frozen_other__%
Spoiled: __foodservice_example_cause_frozen_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_frozen_trim__%
————————————————-
Total: 100%

Prepared Foods:
Cooking issues: __foodservice_example_cause_prepared_cookingissues__%
Date Label Concerns: __foodservice_example_cause_prepared_datelabels__%
Equipment issues: __foodservice_example_cause_prepared_eqptissues__%
Food Safety: __foodservice_example_cause_prepared_safety__%
Handling errors: __foodservice_example_cause_prepared_handling__%
Other: __foodservice_example_cause_prepared_other__%
Spoiled: __foodservice_example_cause_prepared_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_prepared_trim__%
————————————————-
Total: 100%

Produce:
Cooking issues: __foodservice_example_cause_produce_cookingissues__%
Date Label Concerns: __foodservice_example_cause_produce_datelabels__%
Equipment issues: __foodservice_example_cause_produce_eqptissues__%
Food Safety: __foodservice_example_cause_produce_safety__%
Handling errors: __foodservice_example_cause_produce_handling__%
Other: __foodservice_example_cause_produce_other__%
Spoiled: __foodservice_example_cause_produce_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_produce_trim__%
————————————————-
Total: 100%

Ready-to-Drink Beverages:
Cooking issues: __foodservice_example_cause_bev_cookingissues__%
Date Label Concerns: __foodservice_example_cause_bev_datelabels__%
Equipment issues: __foodservice_example_cause_bev_eqptissues__%
Food Safety: __foodservice_example_cause_bev_safety__%
Handling errors: __foodservice_example_cause_bev_handling__%
Other: __foodservice_example_cause_bev_other__%
Spoiled: __foodservice_example_cause_bev_spoiled__%
Trimmings & Byproducts: __foodservice_example_cause_bev_trim__%
————————————————-
Total: 100%

Tons Pre-Consumer Surplus due to Cause (excluding Overproduction)

= Tons Pre-Consumer Surplus by Food Type * % Pre-Consumer Surplus due to Cause

Tons due to Cooking Issues:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_cookingissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_cookingissues__%
= __foodservice_example_tons_cookingissues__ tons

Tons due to Date Label Concerns:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_datelabels__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_datelabels__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_datelabels__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_datelabels__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_datelabels__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_datelabels__%
= __foodservice_example_tons_datelabels__ tons

Tons due to Equipment Issues:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_eqptissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_eqptissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_eqptissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_eqptissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_eqptissues__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_eqptissues__%
= __foodservice_example_tons_eqptissues__ tons

Tons due to Handling Errors:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_handling__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_handling__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_handling__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_handling__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_handling__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_handling__%
= __foodservice_example_tons_handling__ tons

Tons due to Other:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_other__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_other__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_other__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_other__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_other__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_other__%
= __foodservice_example_tons_other__ tons

Tons due to Spoiled:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_spoiled__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_spoiled__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_spoiled__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_spoiled__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_spoiled__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_spoiled__%
= __foodservice_example_tons_spoiled__ tons

Tons due to Trimmings & Byproducts:
= __foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ tons surplus Breads & Bakery * __foodservice_example_cause_breads_trim__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_dairy__ tons surplus Dairy & Eggs * __foodservice_example_cause_dairy_trim__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_drygoods__ tons surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_meat__ tons surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_trim__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_frozen__ tons surplus Frozen * __foodservice_example_cause_frozen_trim__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_produce__ tons surplus Produce * __foodservice_example_cause_produce_trim__% + __foodservice_example_tons_preconsumersurplus_excl_overproduction_bev__ tons surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_trim__%
= __foodservice_example_tons_trim__ tons

US Dollars Pre-Consumer Surplus due to Cause (excluding Overproduction)

= US Dollars Pre-Consumer Surplus by Food Type * % Pre-Consumer Surplus due to Cause

US Dollars due to Cooking Issues:
= $__foodservice_example_tons_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_cookingissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_cookingissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_cookingissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_cookingissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_cookingissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_cookingissues__%
= $__foodservice_example_dollars_cookingissues__

US Dollars due to Date Label Concerns:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_datelabels__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_datelabels__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_datelabels__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_datelabels__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_datelabels__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_datelabels__%
= $__foodservice_example_dollars_datelabels__

US Dollars due to Equipment Issues:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_eqptissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_eqptissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_eqptissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_eqptissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_eqptissues__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_eqptissues__%
= $__foodservice_example_dollars_eqptissues__

US Dollars due to Handling Errors:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_handling__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_handling__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_handling__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_handling__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_handling__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_handling__%
= $__foodservice_example_dollars_handling__

US Dollars due to Other:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_other__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_other__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_other__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_other__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_other__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_other__%
= $__foodservice_example_dollars_other__

US Dollars due to Spoiled:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_spoiled__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_spoiled__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_spoiled__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_spoiled__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_spoiled__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_spoiled__%
= $__foodservice_example_dollars_spoiled__

US Dollars due to Trimmings & Byproducts:
= $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_breads__ surplus Breads & Bakery * __foodservice_example_cause_breads_trim__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_dairy__ surplus Dairy & Eggs * __foodservice_example_cause_dairy_trim__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_drygoods__ surplus Dry Goods * __foodservice_example_cause_breads_drygoods__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_meat__ surplus Fresh Meat & Seafood * __foodservice_example_cause_meat_trim__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_frozen__ surplus Frozen * __foodservice_example_cause_frozen_trim__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_produce__ surplus Produce * __foodservice_example_cause_produce_trim__% + $__foodservice_example_dollars_preconsumersurplus_excl_overproduction_bev__ surplus Ready-to-drink Beverages * __foodservice_example_cause_bev_trim__%
= $__foodservice_example_dollars_trim__

PLATE WASTE AND CATERING OVERPRODUCTION

Tons Plate Waste

See calculation above for Tons Plate Waste

= __TODO_foodservice_example_tons_total_platewaste__ tons plate waste

US Dollars Plate Waste

See calculation above for US Dollars Plate Waste

= $__TODO_foodservice_example_dollars_total_platewaste__ plate waste

Tons Catering Overproduction

See calculation above for Tons Catering Overproduction

= __foodservice_example_tons_catering_oveproduction__ tons catering overproduction

US Dollars Catering Overproduction

See calculation above for US Dollars Catering Overproduction

= $__foodservice_example_dollars_catering_oveproduction__ catering overproduction

Destination Calculations

Table 15. Calculations Performed to Quantify the Destinations of U.S. Foodservice Surplus Food

DATA ITEM

DATA SOURCE OR CALCULATION

EXAMPLE

Destination Breakdown of PreConsumer Food Surplus

Food Waste Reduction Alliance (FWRA) Survey[14]
Note: ReFED used Leanpath[30] data rather than FWRA survey data to quantify the breakdown of preconsumer surplus for states that have organic waste recycling laws (California, Connecticut, Massachusetts, Oregon, Vermont, Washington). See Appendix S (LINK!) for more information.
Donated: __foodservice_example_preconsumer_pct_donated__%
Animal feed: __foodservice_example_preconsumer_pct_animalfeed__%
Anaerobic Digestion: __foodservice_example_preconsumer_pct_ad__%
Compost: __foodservice_example_preconsumer_pct_composted__%
Land Application: __foodservice_example_preconsumer_pct_landapp__%
Sewer: __foodservice_example_preconsumer_pct_sewer__%
Dumping: __foodservice_example_preconsumer_pct_dumping__%
Trash: __foodservice_example_preconsumer_pct_trash__%
————————————————
Total: 100%

Note: ReFED excluded industrial uses (biomaterials/processing) data from the FWRA surveys, because most of this is spent cooking oil rather than preconsumer surplus.

% of Trash that is Landfilled vs Incinerated in __foodservice_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)

% of Trash that is Landfilled = __TODO_foodservice_example_pct_trash_landfilled__%
% of Trash that is Incinerated = __TODO_foodservice_example_pct_trash_incinerated__%
Breaking “Trash” into Landfill vs Incineration:

% Landfilled = % Trash * % of Trash that is Landfilled

% Incinerated = % Trash * % of Trash that is Incinerated
% Landfilled:
= __foodservice_example_preconsumer_pct_trash__% * __TODO_foodservice_example_pct_trash_landfilled__%
= __TODO_foodservice_example_pct_preconsumer_landfilled__%
% Incinerated:
= __foodservice_example_preconsumer_pct_trash__% * __TODO_foodservice_example_pct_trash_incinerated__%
= __TODO_foodservice_example_pct_preconsumer_incinerated__%

Destination Breakdown of Plate Waste

ReFED assumed that plate waste was sent to “Trash” in all states, except states that have organic waste recycling laws. For those states, Leanpath[30] plate waste destinations data was used instead. See Appendix T for more information.

Assumed 100% Trash for plate waste in __foodservice_example_state__

% of Trash that is Landfilled vs Incinerated in __foodservice_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)

% of Trash that is Landfilled = __TODO_foodservice_example_pct_trash_landfilled__%
% of Trash that is Incinerated = __TODO_foodservice_example_pct_trash_incinerated__%
Breaking “Trash” into Landfill vs Incineration:

% Landfilled = % Trash * % of Trash that is Landfilled

% Incinerated = % Trash * % of Trash that is Incinerated
% Landfilled = 100% * __TODO_foodservice_example_pct_trash_landfilled__% = __TODO_foodservice_example_pct_platewaste_landfilled__%
% Incinerated = 0% * __TODO_foodservice_example_pct_trash_incinerated__% = __TODO_foodservice_example_pct_platewaste_incinerated__%

Destination Breakdown of Catering Overproduction

ReFED assumed that catering overproduction was sent to “Trash” in all states, except states that have organic waste recycling laws. For those states, Leanpath[30] plate waste destinations data was used instead. See Appendix U for more information.

Donated: __foodservice_example_catering_pct_donated__%
Animal feed: __foodservice_example_catering_pct_animalfeed__%
Anaerobic Digestion: __foodservice_example_catering_pct_ad__%
Compost: __foodservice_example_catering_pct_composted__%
Industrial uses: __foodservice_example_catering_pct_industrial__%
Land Application: __foodservice_example_catering_pct_landapp__%
Sewer: __foodservice_example_catering_pct_sewer__%
Dumping: __foodservice_example_catering_pct_dumping__%
Trash: __foodservice_example_catering_pct_trash__%
————————————————
Total: 100%

% of Trash that is Landfilled vs Incinerated in __foodservice_example_state__ (Biocycle/Columbia University Survey[8]) (See Appendix Z)

% of Trash that is Landfilled = __TODO_foodservice_example_pct_trash_landfilled__%
% of Trash that is Incinerated = __TODO_foodservice_example_pct_trash_incinerated__%
Breaking “Trash” into Landfill vs Incineration:

% Landfilled = % Trash * % of Trash that is Landfilled

% Incinerated = % Trash * % of Trash that is Incinerated
% Landfilled = __foodservice_example_catering_pct_trash__% * __TODO_foodservice_example_pct_trash_landfilled__% = __TODO_foodservice_example_pct_catering_landfilled__%
% Incinerated = __foodservice_example_catering_pct_trash__% * __TODO_foodservice_example_pct_trash_incinerated__% = __TODO_foodservice_example_pct_catering_incinerated__%

Tons Donated

= Tons Pre-Consumer Surplus (incl or exlc overproduction??) * % Donations for Pre-Consumer Surplus + Total Tons Plate Waste * % Donations for Plate Waste + Tons Catering Overproduction * % Donations for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_donated__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_donated__%
= __foodservice_example_tons_donated__ tons

Tons Animal Feed

= Tons Pre-Consumer Surplus * % Animal Feed for Pre-Consumer Surplus + Total Tons Plate Waste * % Animal Feed for Plate Waste + Tons Catering Overproduction * % Animal Feed for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_animalfeed__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_animalfeed__%
= __foodservice_example_tons_animalfeed__ tons

Tons Industrial uses

= Tons Pre-Consumer Surplus * % Industrial uses for Pre-Consumer Surplus + Total Tons Plate Waste * % Industrial uses for Plate Waste + Tons Catering Overproduction * % Industrial uses for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_industrial__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_industrial__%
= __foodservice_example_tons_industrial__ tons

Tons Anaerobic Digestion

= Tons Pre-Consumer Surplus * % Anaerobic Digestion for Pre-Consumer Surplus + Total Tons Plate Waste * % Anaerobic Digestion for Plate Waste + Tons Catering Overproduction * % Anaerobic Digestion for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_ad__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_ad__%
= __foodservice_example_tons_ad__ tons

Tons Composted

= Tons Pre-Consumer Surplus * % Composted for Pre-Consumer Surplus + Total Tons Plate Waste * % Composted for Plate Waste + Tons Catering Overproduction * % Composted for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_composted__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_composted__%
= __foodservice_example_tons_composted__ tons

Tons Land Application

= Tons Pre-Consumer Surplus * % Land Application for Pre-Consumer Surplus + Total Tons Plate Waste * % Land Application for Plate Waste + Tons Catering Overproduction * % Land Application for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_landapp__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_landapp__%
= __foodservice_example_tons_landapp__ tons

Tons Sewer

= Tons Pre-Consumer Surplus * % Sewer for Pre-Consumer Surplus + Total Tons Plate Waste * % Sewer for Plate Waste + Tons Catering Overproduction * % Sewer for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_sewer__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_sewer__%
= __foodservice_example_tons_sewer__ tons

Tons Dumping

= Tons Pre-Consumer Surplus * % Dumping for Pre-Consumer Surplus + Total Tons Plate Waste * % Dumping for Plate Waste + Tons Catering Overproduction * % Dumping for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __foodservice_example_preconsumer_pct_dumping__% + __TODO_foodservice_example_tons_total_platewaste__ tons * 0% + __foodservice_example_tons_catering_oveproduction__ tons * __foodservice_example_catering_pct_dumping__%
= __foodservice_example_tons_dumping__ tons

Tons Landfilled

= Tons Pre-Consumer Surplus * % Landfilled for Pre-Consumer Surplus + Total Tons Plate Waste * % Landfilled for Plate Waste + Tons Catering Overproduction * % Landfilled for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __TODO_foodservice_example_pct_preconsumer_landfilled__% + __TODO_foodservice_example_tons_total_platewaste__ tons * __TODO_foodservice_example_pct_platewaste_landfilled__% + __foodservice_example_tons_catering_oveproduction__ tons * __TODO_foodservice_example_pct_catering_landfilled__%
= __foodservice_example_tons_landfilled__ tons

Tons Incineration

= Tons Pre-Consumer Surplus * % Incineration for Pre-Consumer Surplus + Total Tons Plate Waste * % Incineration for Plate Waste + Tons Catering Overproduction * % Incineration for Catering Overproduction

= __foodservice_example_tons_preconsumersurplus__ tons * __TODO_foodservice_example_pct_preconsumer_incinerated__% + __TODO_foodservice_example_tons_total_platewaste__ tons * __TODO_foodservice_example_pct_platewaste_incinerated__% + __foodservice_example_tons_catering_oveproduction__ tons * __TODO_foodservice_example_pct_catering_incinerated__%
= __foodservice_example_tons_incinerated__ tons

US Dollars Donated

= US Dollars Pre-Consumer Surplus * % Donations for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Donations for Plate Waste + US Dollars Catering Overproduction * % Donations for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_donated__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_donated__%
= $__foodservice_example_dollars_donated__

US Dollars Animal Feed

= US Dollars Pre-Consumer Surplus * % Animal Feed for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Animal Feed for Plate Waste + US Dollars Catering Overproduction * % Animal Feed for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_animalfeed__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_animalfeed__%
= $__foodservice_example_dollars_animalfeed__

US Dollars Industrial uses

= US Dollars Pre-Consumer Surplus * % Industrial uses for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Industrial uses for Plate Waste + US Dollars Catering Overproduction * % Industrial uses for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_industrial__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_industrial__%
= $__foodservice_example_dollars_industrial__

US Dollars Anaerobic Digestion

= US Dollars Pre-Consumer Surplus * % Anaerobic Digestion for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Anaerobic Digestion for Plate Waste + US Dollars Catering Overproduction * % Anaerobic Digestion for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_ad__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_ad__%
= $__foodservice_example_dollars_ad__

US Dollars Composted

= US Dollars Pre-Consumer Surplus * % Composted for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Composted for Plate Waste + US Dollars Catering Overproduction * % Composted for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_composted__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_composted__%
= $__foodservice_example_dollars_composted__

US Dollars Land Application

= US Dollars Pre-Consumer Surplus * % Land Application for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Land Application for Plate Waste + US Dollars Catering Overproduction * % Land Application for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_landapp__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_landapp__%
= $__foodservice_example_dollars_landapp__

US Dollars Sewer

= US Dollars Pre-Consumer Surplus * % Sewer for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Sewer for Plate Waste + US Dollars Catering Overproduction * % Sewer for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_sewer__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_sewer__%
= $__foodservice_example_dollars_sewer__

US Dollars Dumping

= US Dollars Pre-Consumer Surplus * % Dumping for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Dumping for Plate Waste + US Dollars Catering Overproduction * % Dumping for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __foodservice_example_preconsumer_pct_dumping__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __foodservice_example_catering_pct_dumping__%
= $__foodservice_example_dollars_dumping__

US Dollars Landfilled

= US Dollars Pre-Consumer Surplus * % Landfilled for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Landfilled for Plate Waste + US Dollars Catering Overproduction * % Landfilled for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __TODO_foodservice_example_pct_preconsumer_landfilled__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 100% + $__foodservice_example_dollars_catering_oveproduction__ * __TODO_foodservice_example_pct_catering_landfilled__%
= $__foodservice_example_dollars_landfilled__

US Dollars Incineration

= US Dollars Pre-Consumer Surplus * % Incineration for Pre-Consumer Surplus + Total US Dollars Plate Waste * % Incineration for Plate Waste + US Dollars Catering Overproduction * % Incineration for Catering Overproduction

= $__foodservice_example_dollars_preconsumersurplus__ * __TODO_foodservice_example_pct_preconsumer_incinerated__% + $__TODO_foodservice_example_dollars_total_platewaste__ * 0% + $__foodservice_example_dollars_catering_oveproduction__ * __TODO_foodservice_example_pct_catering_incinerated__%
= $__foodservice_example_dollars_incinerated__

Data Sources and Limitations

National Foodservice Purchases and Sales

Raw data and documentation:
This is confidential data from Technomic and cannot be shared.
Technomic is the leading sales and market share data company for the U.S. foodservice sector. ReFED obtained foodservice supplier purchases and customer sales data from the Technomic Ignite Platform[43]. This data is provided annually and is broken down by segment (e.g., limited service restaurants, full service restaurants, lodging, business & industry, etc.) and cuisine (e.g., burger, asian/noodle, varied menu), but is only available at the national, not state, level.

State Restaurant Locations and Employee Counts for Non-Restaurant Segments

Raw data and documentation:

For limited service restaurants, full service restaurants, and bars & taverns, ReFED allocated national sales down to the state level using the Technomic state-level locations data for the Top 500 restaurants[43]. A limitation of this approach is that sales is not always proportional to the number of locations.

Because Technomic did not have comprehensive location data for non-restaurant foodservice segments (e.g., Healthcare, Lodging, Business & Industry, Universities, etc.), ReFED used industry employee counts from the Bureau of Labor Statistics (BLS) to allocate national Technomic sales to each state for these categories[45]. ReFED mapped each BLS NAICS industry code to the equivalent Technomic segment. Similar to the locations data, a limitation of this approach is that sales is not always proportional to the number of employees.

Wholesale Price per Lb

Raw data and documentation:
This contains confidential data from Technomic and cannot be shared.
ReFED calculated average wholesale price per lb estimates for each foodservice segment by subtracting retailer price margins[4] from Nielsen retail prices[38] for hundreds of food categories. The average food category mix for each foodservice segment was estimated by combining menu data from the Technomic Ignite Platform[43] (e.g., Cheeseburger, Fries, etc.) with food type ingredient breakdown data from USDA Food Data Central[51] (e.g., A cheeseburger is 38% ground beef, 27% bread, 9% cheese, 9% tomato, 7% sauce, 7% pickles, 4% lettuce). Each foodservice segment was assigned a proxy menu based on the top restaurant by sales in each segment. For non-restaurant segments, a restaurant proxy menu was used. See Appendix O for wholesale price estimates and proxy menus used for each foodservice segment.

Pre-Consumer Surplus Rates

Leanpath is a technology company that helps foodservice companies track, weigh and analyze the amount of food that is wasted in commercial kitchens. Leanpath customers indicate the reason the food was not used, where it will be sent (e.g., composting, landfill, etc.), and the food type of the disposed food when using Leanpath’s software system. Based on the data in their system across multiple clients, Leanpath estimates that on average 4.2% of food purchases are not utilized in commercial foodservice kitchens[30].

The limitations of using the Leanpath data to estimate foodservice pre-consumer surplus rates for all foodservice segments over time are the following: (1) The 4.2% estimate was a one-time estimate and does not reflect changes in performance over time. (2) Leanpath’s current client base does not include restaurants, so if restaurants have significantly different pre-consumer surplus rates, this is not reflected. (3) The 4.2% estimate is not food type specific, so food type variations are not reflected.

Food Type Breakdown

Raw data and documentation:
This is confidential data from Technomic and cannot be shared.
ReFED used menu data from Technomic[43] in combination with food ingredient breakdown data from USDA Food Data Central[51] to estimate the food ingredient breakdown of multiple menus. The Technomic menu data listed all of the items on a menu for the Top 500 restaurants (e.g., Cheeseburger, Fries, etc.). ReFED mapped each menu item to the closest matching food item in the USDA Food Data Central database, which provides the ingredient weight breakdown of each food (e.g., A cheeseburger is 38% ground beef, 27% bread, 9% cheese, 9% tomato, 7% sauce, 7% pickles, 4% lettuce). Each foodservice segment was assigned a proxy menu based on the top restaurant by sales in each segment (e.g., McDonald’s menu was used as a proxy for Limited Service Burger Restaurants). For non-restaurant segments, a restaurant proxy menu was used. For example, since Applebee’s was the proxy menu for the Varied Menu segment, it was used as the proxy for Business & Industry cafeterias since that setting has a varied menu as well. See Appendix O for a list of the proxy menus used for each foodservice segment as well as the estimated food type breakdown of their menus. This data was used to estimate the food type breakdown of Pre-Consumer Surplus by foodservice segment.

Distribution Channels (Dine in vs Takeout vs Catering)

Raw data and documentation:
This is confidential data from Technomic and cannot be shared.
ReFED used proprietary data from Technomic[43] to estimate the amount of food that is eaten onsite or at catering events as opposed to takeout. In ReFED’s data model, takeout is considered out of scope for the Foodservice sector and is accounted for in the Residential sector modeling instead. The distribution channel data provided by Technomic is broken out separately for different types of Limited Service Restaurants (quick service, fast casual) and Full Service Restaurants (casual dining, midscale, fine dining). ReFED assumed that 100% of food was eaten onsite for other types of foodservice (Education, Healthcare, Business & Industry, Military, Corrections, Lodging, Recreation, and Transportation).

Plate Waste Rates

ReFED used multiple quantitative studies conducted by nonprofits, academics, and government organizations to estimate plate waste rates[40],[32],[15],[12],[42]. ReFED identified the latest, most suitable study available to use as a proxy for plate waste rates in each foodservice segment. See Appendix P for a list of plate waste rates and proxy assignments. Because some foodservice types are under researched and because these were all one-time studies based on a few locations, a more robust, continually updated dataset is needed to better understand plate waste rates across multiple foodservice segments over time.

Catering Overproduction Rates

Based on expert interviews with catering organizations, ReFED estimates that 28% of food is never served to clients at buffet style catering events, 38% for breakfast and lunch events, and 13% for plated events. See Appendix Q for a list of which rates were used to estimate catering overproduction for each foodservice type.

Pre-Consumer Surplus Causes

Raw data and documentation:
This is confidential data from Leanpath and cannot be shared.
Leanpath is a technology company that helps foodservice companies track, weigh and analyze the amount of food that is wasted in commercial kitchens. Leanpath customers indicate the reason the food was not used, where it will be sent (e.g., composting, landfill, etc.), and the food type when using Leanpath’s waste tracking system. Leanpath pulled aggregated data[30] from their system to estimate the percent breakdown of pre-consumer surplus causes by food type for the following segments: Business & Industry, Hospitality, Healthcare, and Education. See Appendix R for pre-consumer surplus cause data for each of these foodservice segments as well as which segments were used as proxies for others (e.g., Hospitality data was used as a proxy for restaurants).

Pre-Consumer Surplus Destinations

For most states, ReFED used data from the 2016 Food Waste Reduction Alliance (FWRA) survey[14] of restaurants in which 28 restaurant companies responded (11.8% of U.S. market share based on sales) to estimate the destination breakdown of pre-consumer surplus. Data on industrial uses (or biomaterials/processing) was excluded because most of this is spent cooking oil rather than pre-consumer surplus. Since this data indicated that 94% of pre-consumer surplus is landfilled, which is not the case in states that have organics recycling laws, ReFED instead used data from Leanpath[30] to estimate the pre-consumer surplus destinations for these states (California, Connecticut, Massachusetts, Oregon, Vermont, and Washington). ReFED did not use the Leanpath data for other states to avoid selection bias as Leanpath clients may be more likely to compost food scraps than the average foodservice business.

Because landfill versus incineration facility infrastructure varies significantly from state to state, the landfill and incineration numbers were combined into a “% Trash” number. ReFED then estimated the portion of trash that is landfilled versus incinerated in each state using data from BioCycle’s 2010 “State of Garbage in America” survey[41], which was conducted in partnership with the Earth Engineering Center of Columbia University[8]. Because these surveys were discontinued in 2010 and no other state-level data sources exist, ReFED reused these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfill facilities in each state.

Plate Waste Destinations

Raw data and documentation: - https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Foodservice_CateringPlateWasteDestinations.xlsx - https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Foodservice_OnsitePlateWasteDestinations.xlsx

ReFED assumed that plate waste was sent to “Trash” in all states, except states that have organics recycling laws. For these states (California, Connecticut, Massachusetts, Oregon, Vermont, and Washington), Leanpath plate waste destinations data was used instead[30]. ReFED did not use the Leanpath data for other states to avoid selection bias as Leanpath clients may be more likely to compost food scraps than the average foodservice business.

ReFED then estimated the portion of trash that is landfilled versus incinerated in each state using data from BioCycle’s 2010 “State of Garbage in America” survey[41], which was conducted in partnership with the Earth Engineering Center of Columbia University[8]. Because these surveys were discontinued in 2010 and no other state-level data sources exist, ReFED reused these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfill facilities in each state.

Catering Overproduction Destinations

ReFED assumed that catering overproduction was sent to “Trash” in all states, except states that have organic waste recycling laws. For states with organics recycling laws (California, Connecticut, Massachusetts, Oregon, Vermont, and Washington), Leanpath catering overproduction destinations data was used instead[30]. ReFED did not use the Leanpath data for other states to avoid selection bias as Leanpath clients may be more likely to compost food scraps than the average foodservice business.

ReFED then estimated the portion of trash that is landfilled versus incinerated in each state using data from BioCycle’s 2010 “State of Garbage in America” survey[41], which was conducted in partnership with the Earth Engineering Center of Columbia University[8]. Because these surveys were discontinued in 2010 and no other state-level data sources exist, ReFED reused these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfill facilities in each state.

Data Quality Evaluation

This quality assessment is meant to evaluate the quality of how each data source was used by ReFED to estimate food loss and waste. It is not meant to rate the quality of the study itself. A high quality study used by ReFED beyond the study’s intended purposes could result in a low data quality score. See Appendix AA for more information about the ReFED Data Quality Rubric.

Table 16. Data Quality Evaluation for Food Waste Monitor Foodservice Sector

DATA

SOURCE

DATA QUALITY SCORE

CREDIBILITY

UPDATE FREQUENCY

COVERAGE

FOOD TYPE

GEOGRAPHY

SCORE

WEIGHT

FOODSERVICE SURPLUS DATA

National Purchases from Suppliers

Technomic Ignite Platform

4

5

5

1

3

Medium 18/5 = 3.6

10%

National US Dollars Sold

Technomic Ignite Platform

4

5

5

1

3

Medium 18/5 = 3.6

8%

State Locations for Top 500 Restaurants

Technomic Ignite Platform

4

5

5

1

5

High 20/5 = 4.0

8%

State Employee Counts for Non-Restaurant Segments

U.S. Bureau of Labor Statistics Employee Levels

5

5

5

1

5

High 21/5 = 4.2

8%

Food Type Breakdown

Technomic Ignite Platform Menu Data

2

1

1

5

3

Low 12/5 = 2.4

8%

Wholesale Price per Lb

ReFED Calculations

2

5

1

5

3

Medium 16/5 = 3.2

8%

Pre-Consumer Surplus Rate

Leanpath

4

1

1

1

3

Low 10/5 = 2.0

3%

Distribution Channels (Dine in vs Takeout vs Catering)

Technomic Ignite Platform

4

1

5

3

3

Medium 16/5 = 3.2

10%

Plate Waste Rates

Plate Waste Studies

__foodservice_surplus_platewaste_rates_credibility__

__foodservice_surplus_platewaste_rates_update_frequency__

__foodservice_surplus_platewaste_rates_coverage__

__foodservice_surplus_platewaste_rates_food_type__

__foodservice_surplus_platewaste_rates_geography__

__foodservice_surplus_platewaste_rates_score_text__

__foodservice_surplus_platewaste_rates_weight_text__

% Catering Overproduction

Expert Interviews

1

1

1

2

1

Very Low 6/5 = 1.2

2%

3.6 * 10% + 3.6 * 8% + 4.0 * 8% + 4.2 * 8% + 2.4 * 8% + 3.2 * 8% + 2.0 * 3% + 3.2 * 10% + 2.2 * 35% + 1.2 * 2% = 2.93

Low

FOODSERVICE CAUSES DATA

% Pre-Consumer Surplus due to Cause

Leanpath

4

5

1

5

3

Medium 18/5 = 3.6

6%

Distribution Channels (Dine in vs Takeout vs Catering)

Technomic Ignite Platform

4

5

5

5

3

High 22/5 = 4.4

20%

Plate Waste Rates

Plate Waste Studies

__foodservice_causes_platewaste_rates_credibility__

__foodservice_causes_platewaste_rates_update_frequency__

__foodservice_causes_platewaste_rates_coverage__

__foodservice_causes_platewaste_rates_food_type__

__foodservice_causes_platewaste_rates_geography__

__foodservice_causes_platewaste_rates_score_text__

__foodservice_causes_platewaste_rates_weight_text__

% Catering Overproduction

Expert Interviews

1

1

1

2

1

Very Low 6/5 = 1.2

4%

3.6 * 6% + 4.4 * 20% + 2.0 * 70% + 1.2 * 4% = 2.54

Low

FOODSERVICE DESTINATIONS DATA

% Destination Breakdown for Pre-Consumer Surplus

FWRA Surveys, Leanpath

3

1

1

1

2

Very Low 8/5 = 1.6

15%

% Destination Breakdown for Plate Waste

Leanpath, ReFED Assumptions

__foodservice_destinations_platewaste_destinations_credibility__

__foodservice_destinations_platewaste_destinations_update_frequency__

__foodservice_destinations_platewaste_destinations_coverage__

__foodservice_destinations_platewaste_destinations_food_type__

__foodservice_destinations_platewaste_destinations_geography__

__foodservice_destinations_platewaste_destinations_score_text__

__foodservice_destinations_platewaste_destinations_weight_text__

% Destination Breakdown for Catering Overproduction

Leanpath, ReFED Assumptions

4

5

1

5

2

Medium 17/5 = 3.4

10%

% of Trash Landfilled vs Incinerated

Biocycle/Columbia University Survey

5

1

5

1

5

Medium 17/5 = 3.4

10%

1.6 * 15% + 3.4 * 65% + 3.4 * 10% + 3.4 * 10% = 3.13

Medium