Search for a command to run...
Life Cycle Assessment (LCA) is increasingly applied beyond products to assess complex socio-technical systems and support environmental policymaking. However, conventional LCA remains limited by its static representation of processes and by its inability to account for heterogeneous, adaptive human behavior. Agent-Based Modeling (ABM) offers a complementary approach by simulating interactions and decision-making among individual agents, enabling the exploration of emergent system dynamics. This paper presents a systematic review of 33 studies coupling ABM and LCA, updating and extending previous work from 2019. The coupling is described and analyzed step by step in order to understand its operability and identify methodological issues. A focus is made on the operational implementation of decision models in ABM in relation to the economic sector under study. The depth and purpose of coupling strategies can vary widely, ranging from simple if-then behavioral rules and simplified impact calculations to multi-parameter functions within fully integrated models. Most studies focus on the economic and spatial parameters that underpin agents’ decision-making rules and examine how agents respond to technical modifications to their environment. Typically, only a limited number of impact categories are assessed, usually climate change alone. The review identifies critical gaps in the operationalization of consequential modeling, theory behind decision-making and data transparency. The paper concludes with a proposal for archetypes of ABM–LCA integration to help modelers in future studies. • 33 studies coupling ABM and LCA published from 2019 were systematically reviewed • The review clarifies how ABM and LCA are operationally connected • Agents’ decision variables are sector-dependent and predominantly economic • Behavioral and institutional effects remain underexplored in ABM–LCA studies • Gaps about consequential modeling, dynamicity, and functional units are identified