Reducing food loss and waste (FLW) is prioritized in UN sustainable development goals (SDG) target 12.3 to contribute to “ensure sustainable consumption and production patterns”. It is expected to significantly improve global food security and mitigate greenhouse gas (GHG) emissions. Identifying “hotspots” from different perspectives of sustainability helps to prioritize the food items for which interventions can lead to the largest reduction of FLW-related impacts. Existing studies in this field have limitations, such as having incomplete geographical and food commodity coverage, using outdated data, and focusing on the mass of FLW instead of its nutrient values. To provide renewed and more informative insights, we conducted a global hotspot analysis concerning FLW with its associated GHG emissions and protein losses using the most recent data (the new FAO Food Balance Sheets updated in 2020). The findings of this research are that there were 1.9 Gt of FLW, 2.5 Gt of associated GHG emissions, and 0.1 Gt of associated protein losses globally in 2017. The results of the FLW amounts, GHG emissions, and protein losses per chain link are given on the scale of the entire world and continental regions. Next to this, food items with relatively high FLW, GHG emissions, and protein losses are highlighted to provide the implications to policymakers for better decision making. For example, fruits and vegetables contribute the most to global FLW volumes, but the product with the highest FLW-associated GHG emissions is bovine meat. For bovine meat, FLW-associated GHG emissions are highest at the consumer stage of North America and Oceania. Oil crops are the major source of protein losses in the global food chain. Another important finding with policy implications is that priorities for FLW reduction vary, dependent on prioritized sustainability criteria (e.g., GHG emissions versus protein losses).

Bio-based supply chains are by nature complex to optimize. The new logistic concept of integrated biomass logistical center (IBLC) provides us the opportunity to make full use of the idle capacity for a food/feed plant to produce biobased products so that the entire chain efficiency can be improved. Although research has been conducted to analyze the IBLC concept, is yet to be an optimization model that can optimally arrange the activities in the supply chain where an IBLC stands in the middle. To fill the knowledge gap in the literature, this paper makes the first step to develop a MILP model that enables biobased supply chain optimization with the IBLC concept, which supports logistic and processing decisions in the chain. The model is applied in a case study for a feed and fodder plant in Spain where managerial insights have been derived for transferring the plant to a profitable IBLC.

Food losses and waste (FLW) reduction and mitigating climate impact in food chains are priorities in achieving sustainable development goals. However, many FLW-reducing interventions induce additional greenhouse gas (GHG) emissions, for example, from energy, fuel, or packaging. The net effect of such interventions (expressed in GHG emissions per unit of food available for consumption) is not obvious, as is illustrated in a number of case studies. We recommend that in the decision to take on FLW-reducing interventions, the trade-offs on sustainability impacts (such as GHG emissions) are taken into consideration. Since FLW induce demand and extra operations in all stages along a supply chain, adequate representation of cumulative GHG emissions along the production and supply chain, including ‘hidden parts’ of the chain, is required, which is challenging in full LCA studies. As a workaround, the case studies in this paper are based on a generic tool, the Agro-Chain greenhouse gas Emission (ACE) calculator that includes metrics and data for common food product categories and supply chain typologies. The calculator represents the structure of a generic (fresh food) supply chain and offers data sets for, amongst others, crop GHG emission factors and FLW in different stages of the production and distribution chain. Through scenario calculations with different chain parameters (describing pre and post-intervention scenarios), the net effects of an intervention on GHG emissions and FLW per unit of food sold to the consumer can be compared with little effort. In the case studies, interventions at the production stage as well as in post-harvest operations, are analyzed. Results show that post-harvest activities (especially FLW) contribute substantially to the carbon footprint of supplied food products. The FLW-reducing interventions are considered to induce additional GHG emissions. In most case studies, FLW-reducing interventions lower total GHG associated with a unit of food supplied to a client or consumer. However, in one case study, the extra emissions due to the intervention were higher than the prevented emission from lowering food losses. Consequently, in the latter case, the intervention is not an effective GHG emission reduction intervention.