The food waste model – developed specifically to fit the USDA ERS (2009) food availability data – uses a mass balance method to account for all material flows and adjusts the waste at the consumer level so that only the avoidable waste due to uneaten food is considered in the final analysis.
The total avoidable food waste at the distribution, retail and consumer levels amounts to over 55 MMT/year, representing nearly 29% of annual production by weight. Over 60% of this waste occurs at the consumer level. The production, processing, packaging, distribution, retail and disposal of this wasted food results in GHG emissions of at least 113 MMT CO2e/year, which is equivalent to 2% of US national emissions. Beef is the single largest contributor to this, producing 16% of all wasted emissions, because of its high emissions intensity. All animal products together contribute 57% of the wasted emissions, even though they make up only 30% of the waste by weight. Over two-thirds of the emissions occur in the production and processing of food commodities.
There is a considerable economic cost to this waste. US businesses and consumers lose as much as $198 billion per year because of wasted food. Consumer waste alone amounts to $124 billion, or nearly 63% of the total value, which works out to about $1600 per year for a family of four. The annual cost to businesses and organizations at the retail level is nearly $65 billion. There is a promising opportunity here to show both consumers and businesses that they have much to gain by reducing waste. Waste reduction can save money as well as reduce emissions.
The total GHG emissions and economic value of food waste reported in this study represent conservative lower bounds, since the analysis ignores all energy used at the consumer level as well as the cost of waste disposal. These emissions are also subject to an uncertainty of up to +/-20% due to cooking assumptions. The economic value of the waste reported here is subject to an uncertainty of up to +/-15%.
The modeling and analysis presented here can be extended in the future in several areas using the analytical framework established in this study. By modeling cooking processes in more detail, the uncertainty bands can be tightened significantly. By including the consumer-level energy use attributable to food waste – due to shopping trips, refrigeration and cooking, using real-world data – the climate change and economic impacts can be made more realistic. Consideration of the water footprint, land use and other resource uses attributable to the wasted food would add further value to the analysis and results. Finally, the methodology developed in this study can be used to monitor the environmental and economic impacts of food waste on an ongoing basis, not only within the US but also for other regions of the world.