Agent-based life cycle assessment for switchgrass-based bioenergy systems

• Developed an agent-based life cycle assessment framework.
• Evaluated life cycle CO2 emissions of switchgrass bioenergy pathways.
• Identified farmers’ socioeconomic factors impacting the life cycle environmental performance.

Switchgrass is a biomass crop with no established market. Its adoption will involve a wide range of socio-economic factors, making it a particularly difficult system to analyze for environmental impact estimates. Life cycle assessment (LCA) provides a methodology to quantify the environmental impacts of a product or process throughout its entire supply chain. However, traditional LCA approaches fail to account for the local variability in non-homogeneous systems. Because of the time component and other realm dynamics it is essential to visualize as the switchgrass adoption process as a Complex Adaptive System (CAS). Agent-based modeling (ABM) can be used to supplement life cycle information to account for these dynamics variances. Here, we present an Agent-Based Life Cycle Analysis (AB-LCA) model of farmers’ potential adoption of switchgrass as a biomass. The chosen modeling approach aims to understand the main factors influencing landowner decision-making and how these adoption patterns can affect the LCA of switchgrass ethanol. To help address these challenges, we developed an Agent-Based Model aimed at: (1) understanding the main factors influencing landowner decision-making and how these adoption patterns can affect the LCA of switchgrass ethanol and (2) improving the LCA modeling methodology by overcoming the issues involved with analyzing emerging technologies with dynamic and evolving supply chains. Particularly, we built an agent-based model using LCA data of switchgrass-based ethanol production that simultaneously captures socioeconomic factors, such as age, level of risk aversion, education level, and level of profit, of farmers that lead to switchgrass adoption and the changes in environmental impact that result from this particular behavior. The results show that the most influential factors affecting farmers’ decisions are their current economic situation and crop prices. Age and their level of knowledge of the new crop have some impact but with limited extent.