(319c) Food-Energy-Water Nexus: Modeling Energy and GHG Emissions of Water Embodied in U.S. Domestic Food Transfers

Vora, N., University of Pittsburgh
Shah, A., University of Maryland, Baltimore County
Khanna, V., University of Pittsburgh
Global population growth, climate change, and increasing urbanization are pressurizing already constrained essential food, energy, and water resources. Food, energy and water (FEW) systems are deeply interwoven, interconnected, and necessitate an integrated management approach. Central to understanding the complexity of the FEW nexus is quantitatively mapping and understanding their complex interdependencies. Existing studies have focused mostly on a single or two dimensions of the FEW nexus. There is a critical need for a holistic systems-level approach for studying FEW systems in a joint manner as opposed to studying them in isolation to avoid unintended consequences and encourage optimal outcomes. We integrate concepts of network theory with life cycle information to develop an understanding of the domestic FEW nexus for the United States.

Using publicly available disparate datasets, we develop a weighted and directed network model of interstate food transfers for the United States. The interstate food transfer model is translated into networks of embedded irrigation water, energy, and greenhouse gas (GHG) emission flows utilizing information on water footprints, irrigation datasets, and life cycle inventory databases. Specifically, we focus on four food commodity groups: livestock, cereal grains, meat, and milled grains. We utilize network theory tools and metrics to understand the structure, robustness, and environmental sustainability of these networks. Preliminary results indicate that over 600 million tons of food commodities were transferred across the U.S. along with 230 billion m3 of virtual irrigation water. Additionally, 450 billion MegaJoules (MJs) of primary energy and 30 billion kg of CO2-equivalent emissions were embedded in virtual irrigation water transferred across the U.S. It is observed that livestock and meat contributed only 13% by mass but accounted for 60% of embodied water, energy, and GHG transfers compared to grain based commodities. From a network perspective, the unweighted food transfer network is well-connected with majority of states participating in high volume of trade. However, the weighted network structure reveals that the majority of the food flow and embodied resources and emissions are controlled by a few states. Furthermore, meat and milled grain networks are denser compared to livestock and cereal grains networks. A robustness analysis highlights that despite the presence of vulnerable key states, the network is fairly robust to both random and targeted disruptions. These results reveal that while the domestic food transfer network is robust against extreme disruptions on specific nodes, the dense food transfer patterns of resource intensive products have important implications for sustainability of the food network.