Framework for metamodel-driven integration of life cycle assessment and agent-based modeling

Abstract

The energy transition brings about complex environmental and societal changes that require robust science-based policy guidance. Conventional Life Cycle Assessment (LCA) methods to evaluate the implications of these changes often fail to capture the dynamic interactions among energy systems, human behavior, and environmental impacts, leading to potentially misleading or oversimplified conclusions. This study introduces a new framework for integrating societal change into LCA using Agent-Based Modeling (ABM). Our approach leverages LCA metamodels, integrated with the ABM via defined linking parameters, to enable real-time feedback between agent decisions and environmental outcomes, while minimizing computational demands. We illustrate the applications of this framework in a photovoltaic case study, examining how end-of-life choices affect abiotic depletion and climate change. The ABM-LCA integration employs metamodels with high predictive performance, achieving R² values of 0.95 for abiotic depletion and 0.82 for climate change. Linking parameters are based on the number of PV modules entering different end-of-life pathways. Societal change, as modeled in the ABM, is driven by the assumption that increased awareness of environmental impacts promotes circular behaviors. The photovoltaic case study provides an illustrative exploration of how consumer choices may affect environmental impacts, such as abiotic depletion and climate change, and how these impacts, in turn, may shape future consumer behavior. This work highlights the value of using a metamodel-driven, integrated ABM-LCA modeling framework for decision-making in complex systems, uncovering unexpected system behaviors and offering insights into sustainable transitions.