Residential Methodology

Scope Boundary

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 Purchased from Grocery Stores + Tons Obtained Elsewhere )
x Surplus Rate
——————————————————————————————————
= Tons Residential Surplus

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

**Table 17. Calculations Performed to Quantify U.S. Residential Surplus Food**
DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
US Dollars Purchased from Grocery Stores	Nielsen Point-of-Sale (POS) Data[38]	$__residential_example_us_dollars_sold__ worth of __residential_example_refed_food_category__ purchased from grocery stores in __residential_example_state__ in __residential_example_year__
Tons Purchased fron Grocery Stores	Nielsen Point-of-Sale (POS) Data[38]	__residential_example_tons_sold__ tons __residential_example_refed_food_category__ purchased from grocery stores in __residential_example_state__ in __residential_example_year__
Retail Price per Lb	= US Dollars Purchased from Grocery Stores / Tons Purchased from Grocery Stores / 2,000 lbs per ton See U.S. Grocery Retail Dollar-to-Weight Conversion Factors Report[39] for more information on the price per lb data.	= $__residential_example_us_dollars_sold__ worth of __residential_example_refed_food_category__ purchased / __residential_example_tons_sold__ tons __residential_example_refed_food_category__ purchased / 2,000 lbs per ton = $__residential_example_dollarsperlb__ per lb
% of Food Obtained from Grocery Stores	USDA NHANES Survey[50]	__residential_example_percent_obtained_from_grocery_stores__% of __residential_example_refed_food_category__ obtained from grocery stores (as opposed to restaurants, farmers markets, food banks, gas stations, home gardens, etc.)
Tons Obtained Elsewhere	= Tons Purchased from Grocery Stores * (100% - % of Food Obtained from Grocery Stores) / % of Food Obtained from Grocery Stores	= __residential_example_tons_sold__ tons purchased from grocery * (100% - __residential_example_percent_obtained_from_grocery_stores__%) / __residential_example_percent_obtained_from_grocery_stores__ = __residential_example_tons_obtained_elsewhere__ tons __residential_example_refed_food_category__ obtained elsewhere
Surplus Rate	USDA Consumer-Level Food Loss Estimates[34], [52]	= __residential_example_residential_surplus_rate__% of __residential_example_refed_food_category__ brought home are wasted
Tons Surplus	= ( Tons Purchased from Grocery Stores + Tons Obtained Elsewhere) * Surplus Rate	= (__residential_example_tons_sold__ tons purchased from grocery + __residential_example_tons_obtained_elsewhere__ tons obtained elsewhere ) * __residential_example_residential_surplus_rate__% = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus
US Dollars Surplus	= Tons Surplus * Retail Price per Lb * 2,000 lbs per ton	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * $__residential_example_dollarsperlb__ per lb = $__residential_example_us_dollars_surplus__ surplus

Cause Calculations

Master Cause Equation:

Tons Surplus due to Cause = Tons Surplus * % Loss due to Cause

**Table 18. Calculations Performed to Quantify the Causes of U.S. Residential Surplus Food**
DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
% Surplus due to Cause	NRDC Home Kitchen Diaries[35]	Example data breakdown of home food waste causes for produce (See Appendix X for other food types): Considered inedible: __residential_example_percent_surplus_due_to_cause_considered_inedible__% Cooking issues: __residential_example_percent_surplus_due_to_cause_cooking_issues__% Date label concerns: __residential_example_percent_surplus_due_to_cause_date_label_concerns__% Didn’t taste good: __residential_example_percent_surplus_due_to_cause_didnt_taste_good__% Didn’t want leftovers: __residential_example_percent_surplus_due_to_cause_didnt_want_leftovers__% Inedible parts: __residential_example_percent_surplus_due_to_cause_inedible_parts__% Left out too long: __residential_example_percent_surplus_due_to_cause_left_out_too_long__% Other: __residential_example_percent_surplus_due_to_cause_other__% Spoiled: __residential_example_percent_surplus_due_to_cause_spoiled__% Too little save: __residential_example_percent_surplus_due_to_cause_too_little_to_save__% ———————————————— Total: 100%
Tons Surplus due to Cause	= Tons Surplus * % Surplus due to Cause	Tons due to Considered inedible: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_considered_inedible__% = __residential_example_tons_surplus_due_to_cause_considered_inedible__ tons Tons due to Cooking issues: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_cooking_issues__% = __residential_example_tons_surplus_due_to_cause_cooking_issues__ tons Tons due to Date label concerns: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_date_label_concerns__% = __residential_example_tons_surplus_due_to_cause_date_label_concerns__ tons Tons due to Didn’t taste good: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_didnt_taste_good__% = __residential_example_tons_surplus_due_to_cause_didnt_taste_good__ tons Tons due to Didn’t want leftovers: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_didnt_want_leftovers__% = __residential_example_tons_surplus_due_to_cause_didnt_want_leftovers__ tons Tons due to Inedible parts: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_inedible_parts__% = __residential_example_tons_surplus_due_to_cause_inedible_parts__ tons Tons due to Left out too long: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_left_out_too_long__% = __residential_example_tons_surplus_due_to_cause_left_out_too_long__ tons Tons due to Other: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_other_rate__% = __residential_example_tons_surplus_due_to_cause_other__ tons Tons due to Spoiled: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_spoiled__% = __residential_example_tons_surplus_due_to_cause_spoiled__ tons Tons due to Too little to save: = __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_too_little_to_save__% = __residential_example_tons_surplus_due_to_cause_too_little_to_save__ tons
US Dollars Surplus due to Cause	= US Dollars Surplus * % Surplus due to Cause	US Dollars due to Considered inedible: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_considered_inedible__% = $__residential_example_us_dollars_surplus_due_to_cause_considered_inedible__ US Dollars due to Cooking issues: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_cooking_issues__% = $__residential_example_us_dollars_surplus_due_to_cause_cooking_issues__ US Dollars due to Date label concerns: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_date_label_concerns__% = $__residential_example_us_dollars_surplus_due_to_cause_date_label_concerns__ US Dollars due to Didn’t taste good: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_didnt_taste_good__% = $__residential_example_us_dollars_surplus_due_to_cause_didnt_taste_good__ US Dollars due to Didn’t want leftovers: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_didnt_want_leftovers__% = $__residential_example_us_dollars_surplus_due_to_cause_didnt_want_leftovers__ US Dollars due to Inedible parts: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_inedible_parts__% = $__residential_example_us_dollars_surplus_due_to_cause_inedible_parts__ US Dollars due to Left out too long: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_left_out_too_long__% = $__residential_example_us_dollars_surplus_due_to_cause_left_out_too_long__ US Dollars due to Other: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_other_rate__% = $__residential_example_us_dollars_surplus_due_to_cause_other__ US Dollars due to Spoiled: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_spoiled__% = $__residential_example_us_dollars_surplus_due_to_cause_spoiled__ US Dollars due to Too little to save: = $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_surplus_due_to_cause_too_little_to_save__% = $__residential_example_us_dollars_surplus_due_to_cause_too_little_to_save__

Destination Calculations

Master Destination Equation:

Tons Surplus sent to Destination = Tons Surplus * % Sent to Destination

**Table 19. Calculations Performed to Quantify the Destinations of U.S. Residential Surplus Food**
DATA ITEM	DATA SOURCE OR CALCULATION	EXAMPLE
Destination Breakdown of Residential Surplus	NRDC Home Kitchen Diaries[__citation_KitchenDiaries__]	According to the NRDC Home Kitchen Diaries, this was the destination breakdown of residential surplus for __residential_example_refed_food_department__ (See Appendix Y for other food types): Animal feed: __residential_example_percent_animal_feed__% Compost: __residential_example_percent_composted__% Sewer: __residential_example_percent_sewer__% Trash: __residential_example_percent_trash__% ———————————————— Total: 100%
	% of Trash that is Landfilled vs Incinerated in __residential_example_state__ (BioCycle/Columbia University Survey)[8] (See Appendix Z)	% of Trash that is Landfilled = __residential_example_percent_of_trash_landfilled__% % of Trash that is Incinerated = __residential_example_percent_of_trash_incinerated__%
	Breaking “Trash” into Landfill vs Incineration: % Landfilled = % Trash * % of Trash that is Landfilled % Incinerated = % Trash * % of Trash that is Incinerated	% Landfilled: = __residential_example_percent_trash__% * __residential_example_percent_of_trash_landfilled__% = __residential_example_landfilled_rate__ % Incinerated: = __residential_example_percent_trash__% * __residential_example_percent_of_trash_incinerated__% = __residential_example_incinerated_rate__
Tons Animal Feed	= Tons Surplus * % Animal Feed	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_animal_feed__% animal feed = __residential_example_tons_animal_feed__ tons __residential_example_refed_food_category__ sent to animal feed
Tons Composted	= Tons Surplus * % Composted	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_composted__% composted = __residential_example_tons_composted__ tons __residential_example_refed_food_category__ composted
Tons Sewer	= Tons Surplus * % Sewer	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_sewer__% disposed down the drain = __residential_example_tons_sewer__ tons __residential_example_refed_food_category__ disposed via sewer
Tons Landfilled	= Tons Surplus * % Landfilled	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_of_trash_landfilled__% landfilled = __residential_example_tons_landfilled__ tons __residential_example_refed_food_category__ landfilled
Tons Incinerated	= Tons Surplus * % Incinerated	= __residential_example_tons_surplus__ tons __residential_example_refed_food_category__ surplus * __residential_example_percent_of_trash_incinerated__% incinerated = __residential_example_tons_incinerated__ tons __residential_example_refed_food_category__ incinerated
US Dollars Animal Feed	= US Dollars Surplus * % Animal Feed	= $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_animal_feed__% animal feed = $__residential_example_us_dollars_animal_feed__ __residential_example_refed_food_category__ sent to animal feed
US Dollars Composted	= US Dollars Surplus * % Composted	= $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_composted__% composted = $__residential_example_us_dollars_composted__ __residential_example_refed_food_category__ composted
US Dollars Sewer	= US Dollars Surplus * % Sewer	= $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_sewer__% disposed down the drain = $__residential_example_us_dollars_sewer__ __residential_example_refed_food_category__ disposed via sewer
US Dollars Landfilled	= US Dollars Surplus * % Landfilled	= $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_of_trash_landfilled__% landfilled = $__residential_example_us_dollars_landfilled__ __residential_example_refed_food_category__ landfilled
US Dollars Incinerated	= US Dollars Surplus * % Incinerated	= $__residential_example_us_dollars_surplus__ __residential_example_refed_food_category__ surplus * __residential_example_percent_of_trash_incinerated__% incinerated = $__residential_example_us_dollars_incinerated__ __residential_example_refed_food_category__ incinerated

Data Sources and Limitations

Retail Value and Tons Purchased at Grocery Stores

Raw data and documentation:

This is confidential data from Nielsen and cannot be shared.

Nielsen data represents over 85% coverage of grocery retail sales in the U.S. Each year top U.S. grocery retailers report item level point-of-sale sales data to Nielsen[38], including information about each item such as the grocery chain where it was sold, the brand name of the product, the food classification (department, category, subcategory), the weight of food and packaging, and many other attributes. ReFED used this data to quantify the retail value and weight of food sold by grocery retailers in the U.S. by year, state, and food type. For more information about the weight data, see the U.S. Grocery Retail Dollar-to-Weight Conversion Factors report[39].

Nielsen provided point-of-sale data for the years 2016-2019. In order to estimate values for the missing years 2010-2015 each subcategory was extrapolated using category-level average year-over-year linear growth rates for both sales value and sales weight. Due to the high granularity of the categories, there were some cases where the growth rates were either extremely high or extremely low. To avoid unrealistic growth estimations over time within these outlier categories, department-level growth rates were used instead if a category had a growth rate ±15%. These outlier categories represent 0.5% of total sales.

The accuracy of these estimates is limited to the accuracy of the Nielsen sales and weight data. The weight data for UPC items comes directly from up-to-date product packaging images. For non-UPC items sold in eaches, Nielsen estimates weight using a weight conversion factor (e.g., the average weight of a lemon). For other non-UPC items, Nielsen is reliant on the retailer transaction data to provide the item sale weight units (e.g., lbs of apples sold).

A limitation of using this dataset to quantify residential grocery store purchases is that a small portion of grocery store sales is actually to commercial or non-residential customers (e.g., local restaurants, local food banks, etc.). Future iterations of this work should quantify the percentage of grocery store sales that is attributed to these non-residential customers by food type so that grocery sales can be discounted to only include residential sales. In the meantime, the resulting residential surplus estimates may be slightly overestimated.

Food Obtained from Grocery Stores vs Elsewhere

Raw data and documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Residential_GroceryRates.xlsx

Every two years the National Health And Nutrition Examination Survey (NHANES)[50] is conducted as a partnership between the U.S. Department of Health and Human Services (DHHS) and the U.S. Department of Agriculture (USDA) to provide information on the health and nutritional status of people in the United States. In one portion of the study, participants are asked questions about their food intake over a two day period (e.g., food type and weight consumed, whether the food was obtained from a grocery store or restaurant, etc.). ReFED used this data to quantify the portion of each food type obtained from grocery stores versus other sources (e.g., restaurants, food pantries, convenience stores). See Appendix V as well as the raw data and documentation link above for details. The calculations were performed for each state, although the survey results are only available at the national level. Because food preferences and consumption patterns vary geographically, state-level data is needed in the future for better estimates.

Residential Food Surplus Rates

Raw data and documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Residential_FoodSurplusRates.xlsx

ReFED used the USDA Consumer-Level Food Loss Estimates[52], which are the basis of the USDA ERS Loss-Adjusted Food Availability per Capita Dataset[5]. The loss factors are based on 2004 data from Nielsen on how much food was sold at grocery stores as well as 2004 data from USDA NHANES[50] on how much food was eaten by consumers and where the food was sourced (e.g., grocery stores, restaurants, convenience stores, etc.). ReFED originally attempted to reproduce the USDA methodology using up-to-date Nielsen and NHANES data, but ended up reverting back to the original loss factors after running into the same issues that the USDA researchers faced when they originally developed the report. For several food items, the NHANES data estimates that consumers eat more than double the amount of a particular food item than was purchased in grocery stores according to the Nielsen data. The USDA research team addressed this issue by relying on expert panel estimates rather than the calculated estimates in these cases. ReFED plans to use the USDA loss factors (based on 2004 data) until more up-to-date consumption data is identified or developed. See Appendix W for details.

Residential Food Surplus Causes

Raw data and documentation: - https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Residential_CauseBreakdown_2010-2014.xlsx - https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Residential_CauseBreakdown_2015-2019.xlsx

As a part of a three-city study (New York, Nashville, Denver), Natural Resources Defense Council (NRDC) conducted an in-home study[35] where participants documented the weight and type of foods wasted over a two week period. Participants also documented the reason why they wasted the food and what they did with it (e.g., disposed of down the drain, trash, fed to animals, composted). ReFED used this data to quantify the causes of residential food waste by year, state, and food type.

There are a few limitations to using this data source for this purpose: (1) Although the study results were similar across the cities covered, rural areas were not covered. If variations in disposal habits vary in rural areas versus cities, these variations are not captured in the data. (2) Another limitation is that the two week timespan may not have been long enough to capture refrigerator cleanouts, which may have resulted in an underestimation of causes such as date label expiration and unwanted leftovers if study participants postponed their refrigerator cleanouts until the study was over. (3) Finally, because it was a one-time study, the data does not provide insight into consumer changes in disposal habits over time. Although this causal data is invaluable for understanding the major drivers of food waste in homes, more research is needed to address these data gaps.

Residential Food Surplus Destinations

Raw data and documentation:

https://refed-roadmap.s3-us-west-2.amazonaws.com/public_documentation/Documentation_Residential_FoodSurplusDestinations.xlsx

ReFED also used the NRDC Home Kitchen Diaries[35] to quantify the destination breakdown of residential food surplus. The same strengths and weaknesses of the causal data listed above apply to the destinations component of the study as well. Additionally, it’s possible that the residential composting numbers may be higher than the U.S. average due to selection bias of the people that chose to participate in the study.

ReFED further broke down the NRDC “Trash” numbers into the portion that is landfilled versus incinerated in each state according to 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 is reusing these estimates year over year to estimate the percentage of “trash” that is sent to incineration versus landfillnfacilities in each state.

Data Quality Evaluation

This rubric is designed to evaluate the quality of how each data source was utilized by ReFED to estimate food loss and waste. It is not meant to rate the quality of the study itself. See Appendix AA for more information about the ReFED Data Quality Rubric.

Table 20. Data Quality Evaluation for Food Waste Monitor Residential Sector

DATA	SOURCE	DATA QUALITY SCORE
DATA	SOURCE	CREDIBILITY	UPDATE FREQUENCY	COVERAGE	FOOD TYPE	GEOGRAPHY	SCORE	WEIGHT
RESIDENTIAL SURPLUS DATA
Retail Value Purchased at Grocery Stores	Nielsen Point-of-sale (POS) Data	4	5	5	5	5	High 24/5 = 4.8	17%
Tons Purchased at Grocery Stores	Nielsen Point-of-sale (POS) Data	4	5	5	5	5	High 24/5 = 4.8	17%
% of Food Obtained from Grocery Stores	USDA NHANES Survey	5	5	5	5	3	High 23/5 = 4.6	33%
Surplus Rate	USDA Consumer-Level Food Loss Estimates	5	1	5	3	3	Medium 17/5 = 3.4	33%
*4.8 17% + 4.8 * 17% + 4.6 * 33% + 3.4 * 33% = 4.27**							High
RESIDENTIAL CAUSES DATA
% Surplus Due to Cause	NRDC Home Kitchen Diaries	5	1	1	4	2	Low 13/5 = 2.6	100%
*2.6 100% = 2.6**							Low
RESIDENTIAL DESTINATIONS DATA
% Destination Breakdown	NRDC Home Kitchen Diaries	5	1	1	4	2	Low 13/5 = 2.6	95%
% of Trash Landfilled vs Incinerated	Biocycle/Columbia University Survey	5	1	5	1	5	Medium 17/5 = 3.4	5%
*2.6 95% + 3.4 * 5% = 2.64**							Low