Garnett (2008) has pointed out that food waste contributes to GHG emissions in two ways: A relatively minor impact from decomposition of the wasted food after disposal in landfills, and a potentially far more significant impact from the embedded emissions associated with its production, processing, transport and retailing. This second impact requires a life-cycle view of the wasted food.
It should also be noted that the climate change impact of food waste – as quantified by life-cycle GHG emissions – is a more complete measure of environmental impact than embedded energy or barrels of oil: It includes not only the emissions from the burning of fossil fuels but also significant other GHG emissions that are not energy-related such as methane (in agriculture and waste disposal) and nitrous oxide (in agriculture).
Besides environmental impacts, food waste also imposes an economic cost on consumers and retailers. If quantified correctly, this could provide a unique incentive to simultaneously mitigate emissions and save money through waste reduction.
The motivation for the present study is to quantify in a comprehensive manner, for the first time, the annual climate change and economic impacts of the food wasted in the US using the most recent national data available (as of this writing). This is particularly important given the position of the US as the world’s largest economy and a major consumer of resources. In conjunction, a secondary goal is to develop and demonstrate a robust food waste model and methodology – based on the principles of life cycle assessment (LCA) – that can be used to monitor the future impacts of food waste not only in the US but also in other parts of the world.
The approach adopted in this study is both bottom-up and life-cycle based: It analyzes 134 distinct food commodities accounting for most of the food consumed in the US, and then groups them into 16 food categories. Each of the 134 commodities is modeled using one or more representative production systems, based on detailed North American production data in most cases. Foods such as beef, chicken, pork and cheese are placed in their own separate categories because of their unique production characteristics and significant climate change impacts. Such an approach can provide a degree of precision and rigor that may not be possible with top-down methods.