In 2018, food waste in the United States was a significant issue with substantial environmental and economic consequences. Here are some key statistics:

Overall Waste Volume and Percentage:

Approximately 103 million tons (206 billion pounds) of food waste were generated in the US in 2018, according to the EPA.

This amounted to between 30-40% of the entire US food supply going uneaten.

On a per-person basis, it was roughly one pound of food wasted per person per day.

Economic Impact:

The annual food waste in America had an approximate value of $161 billion to $218 billion.

The average American family of four reportedly threw out $1,500 in wasted food per year (based on 2010 price data, which would be higher in 2018).

The restaurant industry alone incurred an estimated $162 billion in costs related to wasted food.

Environmental Impact:

Food waste was the number one material in American landfills, accounting for 24.1% of all municipal solid waste (MSW).

When food rots in landfills, it produces methane, a potent greenhouse gas that is 28 times more powerful than CO2 at trapping heat. Food waste was responsible for an estimated 58% of landfill methane emissions to the atmosphere.

The production of wasted food in the US was equivalent to the greenhouse gas emissions of 37 million cars.

Wasted food also means wasted resources like land, water, and energy. Annually, food loss and waste took up an area of agricultural land the size of California and New York combined, and wasted enough energy to power 50 million US homes for a year.

Approximately 21% of agricultural water resources and 19% of US croplands were wasted for food that was ultimately thrown away.

Sources of Food Waste:

Food waste occurs across the entire supply chain, with significant contributions from:

Households: An estimated 43% of food waste came from homes.

Grocery stores, restaurants, and food service companies: Accounted for about 40% of food waste.

Farms: Responsible for around 16% of food loss.

Manufacturers: Contributed about 2% of food waste.

Breakdown by Material (within MSW):

Food waste comprised the fourth largest material category in total MSW generation, estimated at 63.1 million tons or 21.6% in 2018.

These statistics highlight the significant scale of food waste in the US in 2018 and its wide-ranging negative impacts on the economy and the environment

Dataset's Link: https://catalog.data.gov/dataset/u-s-food-waste-flows-between-sectors-2018-v1-3-2

Food waste flows between waste-generating sectors and waste management routes are captured by these Flow-By-Sector (FBS) databases. Typically, the sectors use codes from the 2012 North American Industry Classification System (NAICS). Method 1 (m1 dataset file), the first dataset, assigns sectors to food waste creation and disposal statistics from the USEPA Wasted Food Report. The National Commercial Non-Hazardous Waste (CNHW) FBS dataset's discarded food data is attributed to sectors using the second approach, method 2 (m2 dataset file).

Column's Information

The CSV file "Food_Waste_national_2018_m2_v1.3.2_9b1bb41.csv" contains the following columns with their likely meanings:

Flowable: The type of material being tracked, in this case, "Food Waste".

Class: A classification for the "Flowable" material, here "Other".

SectorProducedBy: A numerical code indicating the sector that produced the food waste.

SectorConsumedBy: A numerical code indicating the sector that consumed or received the food waste.

SectorSourceName: The source of the sector classification, which is "NAICS_2012_Code" (North American Industry Classification System 2012 Code).

Context: This column appears to be empty in the provided data.

Location: This column seems to contain a location code, e.g., "=""00000""".

LocationSystem: The system used for location identification, which is "FIPS" (Federal Information Processing Standards).

FlowAmount: The quantity of food waste.

Unit: The unit of measurement for "FlowAmount", which is "kg" (kilograms).

FlowType: The type of flow, which is "WASTE_FLOW".

Year: The year the data pertains to, in this case, "2018".

MeasureofSpread: This column appears to be empty in the provided data.

Spread: A value related to the spread of the data, here "0.0".

DistributionType: This column appears to be empty in the provided data.

Min: Minimum value, here "0.0".

Max: Maximum value, here "0.0".

DataReliability: Data reliability value, here "0.0".

TemporalCorrelation: Temporal correlation value, here "0.0".

GeographicalCorrelation: Geographical correlation value, here "0.0".

TechnologicalCorrelation: Technological correlation value, here "0.0".

DataCollection: Data collection method or source, here "CalRecycle_WasteCharacterization".

**MetaSources...

These Flow-By-Sector (FBS) datasets capture food waste flows between waste-generating sectors and waste management pathways. The sectors are generally North American Industry Classification System (NAICS) 2012 codes. The first dataset, method 1 (m1), attributes food waste generation and disposition data from the USEPA Wasted Food Report to sectors. The second method, method 2 (m2), attributes wasted food data from the National Commercial Non-Hazardous Waste (CNHW) FBS dataset to sectors. These food waste datasets were generated with FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2). M1 is generated with https://github.com/USEPA/flowsa/blob/v1.3.2/flowsa/methods/flowbysectormethods/Food_Waste_national_2018_m1.yaml and m2 is generated with https://github.com/USEPA/flowsa/blob/v1.3.2/flowsa/methods/flowbysectormethods/Food_Waste_national_2018_m2.yaml. The metadata text files included as a supporting document records the FLOWSA tool version and input dataset bibliographic details. The CNHW data were generated in FLOWSA v1.3.0, with the method file https://github.com/USEPA/flowsa/blob/v1.3.0/flowsa/methods/flowbysectormethods/CNHW_national_2018.yaml.

The volume of food waste generated in the United States has been growing since 2016. In 2019, ***** million tons of food waste were generated in the country. This is an increase of almost **** million tons compared to the food waste generated in 2016.

Introduction

Food Waste Statistics: When we talk about food, most of us think about meals, nutrition, and the joy of eating. But rarely do we stop to consider how much of this food never even reaches a plate. Food waste is a problem that affects every corner of the world, from homes and restaurants to farms and supermarkets.

According to the latest food waste statistics available online, the world wastes around 1.05 billion tonnes of food every year, which is nearly one-fifth of all the food produced for human consumption.

Now, imagine the scale of this loss. Every day, 1 billion meals are thrown away, water that could have nourished crops is wasted, and greenhouse gas emissions increase because discarded food ends up rotting in landfills. This isn’t just an environmental issue anymore; it’s an economic drain, a social problem, and an ethical challenge.

Recently, reports say, countries spend billions of dollars producing food that never gets eaten, while millions of people around the world remain hungry. In this article, I’ll walk you through the latest food waste statistics, breaking down region by region, exploring which sectors contribute the most, and highlighting the economic, environmental, and social impacts.

We’ll also look at strategies that are working to reduce waste and what the future might hold if we take action. By the end of this, you’ll have a clear picture of just how serious the food waste problem is and why it matters to all of us. Let’s get into it.

This statistic represents the value of food that was waste every year in households in the United States in 2018, with a breakdown by leading state. In this year, households in Texas wasted about 1,100.82 U.S. dollars worth of food. About a quarter of food in American households is wasted each year.

A centralized repository of information built with data from more than 50 public and proprietary datasets and providing granular estimates of how much food goes uneaten in the U.S., why it’s happening, and where it goes.

In 2019, the largest amount of food waste in the United States was generated within the industrial sector, which encompasses food manufacturing and processing. It generated approximately ***** million tons of food waste. The entire rest of the country generated an estimated ***** million tons, spread all other sectors.

This Excel-based life cycle inventory (LCI) model develops LCI data for management of wasted food via anaerobic digestion (AD), windrow and aerated static pile (ASP) composting, landfilling and incineration. The inventory model is run for the following scenario options: >AD biogas fate: flare, combined heat and power (CHP) and renewable natural gas (RNG) >Landfill gas fate: flare, electric engine, and RNG >Compost method: windrow and ASP >Incineration technology: Grate furnace - mass burn >Digestate management: compost + land application, land application of whole digestate and digestate landfilling >Land application modeling is limited to avoided fertilizer credits and carbon sequestration benefit. Estimating emissions associated with land application is beyond the scope of this model. Implicitly, emissions associated with compost and digestate are assumed to be equivalent to those from avoided synthetic fertilizer, leading to a net zero change in impact when changing nutrient sources. The output is stored in the 'LCI' tab which can be exported into a csv or other text-based file. Definitions for the field names in the LCI sheet is included in the 'LCI Key' tab.

This statistic represents the weight of food waste in the United States in 2017, with a breakdown by source. As of that time, the residential sector generated approximately **** million wet tons of food waste.

The raw data for this paper have been received by individual states in PDF or Excel files. (For each state, there might be several PDF or Excel files for each year.) In the data we uploaded on GitHub, we transferred these raw data (the various PDFs and Excel files) into a single CSV file and have created a standardized waste outcome---specifically, state-generated, municipal solid waste (MSW) disposal. In the README file, we include more details regarding all the other supporting data and code we have used.

In 2019, approximately **** percent of food waste generated in retail, food services, and residential sectors was managed by landfill in the United States. This was the largest share among the different wasted food management solutions for waste from these sectors. Only *** percent of manufacturing and processing waste went to landfills. Overall, food waste from retail, food services, and residential sectors amounted to ** million tons, while food waste by manufacturing and processing amounted to ** million tons.

The U.S. Environmental Protection Agency (EPA) has collected and reported data on the generation and disposal of waste in the United States for more than 30 years. We use this information to measure the success of waste reduction and recycling programs across the country. Our trash, or municipal solid waste (MSW), is made up of the things we commonly use and then throw away. These materials include items such as packaging, food scraps, grass clippings, sofas, computers, tires, and refrigerators. MSW does not include industrial, hazardous, or construction waste. The data on Materials Discarded in the Municipal Waste Stream, 1960 to 2009, provides estimated data in thousands of tons discarded after recycling and compost recovery for the years 1960, 1970, 1980, 1990, 2000, 2005, 2007, 2008, and 2009. In this data set, discards include combustion with energy recovery. This data table does not include construction & demolition debris, industrial process wastes, or certain other wastes. The "Other" category includes electrolytes in batteries and fluff pulp, feces, and urine in disposable diapers. Details may not add to totals due to rounding.

These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – https://doi.org/10.1002/jaa2.59). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed), July, August and Septemper of 2022 (Jul 22 wave, 1817 initiated, 1067 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay close attention to the amounts of different foods their household throws away, feeds to animals or composts because the food is past date, spoiled or no longer wanted for other reasons. They are told to exclude items they would normally not eat, such as bones, pits, and shells. Approximately 7 days later they received the follow-up survey, which elicited the amount of waste in up to 24 categories of food and included other questions (see supplemental materials for core survey questions in Li et al. 2023). Waste amounts in each category are reported by selecting from one of several ranges of possible amounts. The gram weight for categories with volumetric ranges (e.g., listed in cups) were derived by assigning an appropriate mass to the midpoint of the selected range consistent with the food category. For the categories with highly variable weight per volume (e.g., a cup of raw asparagus weighs about 7 times more than a cup of raw chopped arugula), we use the profile of items most consumed in the United States to determine the appropriate gram weight. For display purposes, the 24 categories are consolidated into 8 more general categories. Total weekly household food waste is calculated by summing up reported gram amounts across all categories. We divide this total by the number of household members to generate the per person weekly food waste amount.

This dataset provides estimated tons generated and recycled by U.S. zip code and material. It relies on materials management reports and surveys from various states and regions, State Measurement Program (SMP) data, Ball Corporation’s Fifty States of Recycling report, EPA’s Excess Food Opportunities Map, and the U.S. Census Bureau’s American County Survey dataset. Quantities generated and recycled by zip code were estimated by dividing state reported generation and recycled quantities by the population for each state and for each material to arrive at state-specific per capita rates and then those per capita rates were applied to the population of each zip code in each corresponding state. Estimated recycling potential for each material is the difference between estimated tons generated and estimated tons recycled. Those zip codes with the greatest difference in generated and recycled tons have higher estimated recycling potential. The data was then integrated with a U.S. Census Bureau Tiger Database zip code shapefile to create the resulting data layer. The zip code shapefile was simplified to remove vertices. This dataset includes 16 recyclable material types: aluminum, cardboard, electronics, food waste, glass, HDPE bottles #2, PET bottles #1, PET other #1, PP (polypropylene) containers #5, rigid plastics #3 to #7, steel cans, tires, paper, textiles, yard trimmings, and wood. Note that there are certain materials for which data are not available for every state. In these cases, the layer will only display zip codes where data is available. This dataset is a snapshot of U.S. recycling quantities, infrastructure, and materials markets as of 2019-2021. The map was created by Industrial Economics, Inc. (IEc), a consultancy supporting EPA to develop the Recycling Infrastructure and Market Opportunities Map. The map is managed by EPA’s Office of Land and Emergency Management. This project was supported in part by an appointment to the Research Participation Program at the Office of Land and Emergency Management, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA.

The U.S. wastes 31 to 40% of its post-harvest food supply, with a substantial portion of this waste occurring at the consumer level. Globally, interventions to address wasted food have proliferated, but efforts are in their infancy in the U.S. To inform these efforts and provide baseline data to track change, we performed a survey of U.S. consumer awareness, attitudes and behaviors related to wasted food. The survey was administered online to members of a nationally representative panel (N=1010), and post-survey weights were applied. The survey found widespread (self-reported) awareness of wasted food as an issue, efforts to reduce it, and knowledge about how to do so, plus moderately frequent performance of waste-reducing behaviors. Three-quarters of respondents said they discard less food than the average American. The leading motivations for waste reduction were saving money and setting an example for children, with environmental concerns ranked last. The most common reasons given for discarding food were concern about foodborne illness and a desire to eat only the freshest food. In some cases there were modest differences based on age, parental status, and income, but no differences were found by race, education, rural/urban residence or other demographic factors. Respondents recommended ways retailers and restaurants could help reduce waste. This is the first nationally representative consumer survey focused on wasted food in the U.S. It provides insight into U.S. consumers’ perceptions related to wasted food, and comparisons to existing literature. The findings suggest approaches including recognizing that many consumers perceive themselves as being already-knowledgeable and engaged, framing messages to focus on budgets, and modifying existing messages about food freshness and aesthetics. This research also suggests opportunities to shift retail and restaurant practice, and identifies critical research gaps.

This dataset provides estimated tons generated and recycled by U.S. zip code and material. It relies on materials management reports and surveys from various states and regions, State Measurement Program (SMP) data, Ball Corporation’s Fifty States of Recycling report, EPA’s Excess Food Opportunities Map, and the U.S. Census Bureau’s American County Survey dataset. Quantities generated and recycled by zip code were estimated by dividing state reported generation and recycled quantities by the population for each state and for each material to arrive at state-specific per capita rates and then those per capita rates were applied to the population of each zip code in each corresponding state. Estimated recycling potential for each material is the difference between estimated tons generated and estimated tons recycled. Those zip codes with the greatest difference in generated and recycled tons have higher estimated recycling potential. The data was then integrated with a U.S. Census Bureau Tiger Database zip code shapefile to create the resulting data layer. The zip code shapefile was simplified to remove vertices. This dataset includes 16 recyclable material types: aluminum, cardboard, electronics, food waste, glass, HDPE bottles #2, PET bottles #1, PET other #1, PP (polypropylene) containers #5, rigid plastics #3 to #7, steel cans, tires, paper, textiles, yard trimmings, and wood. Note that there are certain materials for which data are not available for every state. In these cases, the layer will only display zip codes where data is available. This dataset is a snapshot of U.S. recycling quantities, infrastructure, and materials markets as of 2019-2021. The map was created by Industrial Economics, Inc. (IEc), a consultancy supporting EPA to develop the Recycling Infrastructure and Market Opportunities Map. The map is managed by EPA’s Office of Land and Emergency Management. This project was supported in part by an appointment to the Research Participation Program at the Office of Land and Emergency Management, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA.

Dataset for Biodegradation Journal Manuscript "Effects of Landfill Food Waste Diversion- a Focus on Microbial Populations and Methane Generation" Excel file. This dataset is associated with the following publication: Chickering, G., M. Krause, and A. Schwarber. Effects of Landfill Food Waste Diversion: a Focus on Microbial Populations and Methane Generation. BIODEGRADATION. Springer, New York, NY, USA, 1-12, (2023).

In 2020, U.S. survey respondents were asked "What best describes your attitude towards food waste?" The results show that in the United States, approximately more than half of respondents believe that they do not waste food. Almost *********** would like to waste less food but struggles to do so.

Scaled WARM is a direct impacts model of GHG emissions, Employment, Wages, and Taxes attributed to material-specific waste management pathways. The waste management pathways are based on North American Industry Classification System (NAICS) 2012 codes. This dataset is generated by fusing material-specific factors from the USEPA Waste Reduction Model (WARM) with waste generation data from USEPA Facts and Figures, Wasted Food Report, and CDDPath. Scaled WARM is generated with FLOWSA v1.3.2 (https://github.com/USEPA/flowsa/tree/v1.3.2) and the method file https://github.com/USEPA/HIO/blob/v0.1.0/flowsa/flowbysectormethods/Mixed_WARM_national_2018.yaml.

This data set is the result of a systematic review of studies on food waste disposed in the United States, an issue which major consequences for social, nutritional, economic, and environmental issues. It was created to determine how much food is discarded in the U.S., and to determine if specific factors drive increased disposal. By applying meta-analytic tools on it this dataset, it was found that the aggregate proportion of food waste in U.S. municipal solid waste from 1995 to 2013 was 0.147 (95% CI 0.137–0.157) of total disposed waste, which is lower than that estimated by U.S. Environmental Protection Agency for the same period (0.176). Further, that the proportion of food waste increased significantly with time, and there were no significant differences in food waste between rural and urban samples, or between commercial/institutional and residential samples. These results are published in the study titled Quantification of Food Waste Disposal in the United States: A Meta-Analysis (Thyberg et al., 2015).