Life cycle assessment of the US beef processing through integrated hybrid approach

Hybrid life cycle assessment (LCA) incorporating process-based and economic input-output (EIO)-based inventory data has been applied in various industries (e.g., wind energy, biofuel). Few hybrid LCA studies have been found in the food industry. This work analyzes the life cycle environmental impacts of the U.S. beef processing industry using process-based and integrated hybrid LCA. The process-based inventory includes all resource inputs and waste outputs associated with beef processing plant. The EIO-based inventory includes key activities missing in the process-based inventory, such as technical and management service, wood and paper, and industrial equipment. Ten environmental impact categories from TRACI v2.1 and one aggregated environmental single score are considered. The results show that environmental impact from EIO-based inventory contributes a meaningful fraction of the impact for ozone depletion (67%), respiratory effects (42%), fossil fuel depletion (38%), and smog (28%) (as opposed to process-based inventory). These results emphasize the relative potential for the U.S. beef processing industry of greening the supply chain (e.g., technical and management services, industrial equipment) to reduce environmental impacts. Furthermore, we perform uncertainty and global sensitivity analysis for key parameters of all environmental categories. The uncertainty analysis showed that the environmental impacts contributed by EIO system can range from 7% to 15% under Monte Carlo simulations (10,000 runs) when representing the impacts using an aggregated environmental single score. The global sensitivity analysis using Sobol method for all environmental categories shows that the electricity, natural gas, and wastewater treatment from process system and beef price from EIO system are the four most sensitive parameters to all ten TRACI environmental categories and the environmental single score. (c) 2020 Elsevier Ltd. All rights reserved.