Behavioral metabolism: Integrating decision-making into material flow analysis

Understanding how human decisions shape material stocks and flows remains a persistent blind spot in socio-economic metabolism (SEM) research. While material flow analysis (MFA) and stock-flow models are widely used to quantify resource use, emissions, and infrastructure dynamics, behavioral processes are typically represented through aggregated or exogenous assumptions, such as fixed renovation rates or predefined technology adoption trajectories. This limits the ability of SEM approaches to explain why metabolic patterns diverge across places and over time, even under similar structural conditions. This contribution introduces Behavioral Metabolism as a conceptual subset of social metabolism that places human behavior at the core of metabolic change. It conceptualizes material and energy flows as emergent outcomes of heterogeneous, bounded, and socially embedded decision-making by households, firms, and institutions. Rather than treating behavior as a boundary condition, the concept positions everyday decisions and interactions as micro-foundations through which material stocks accumulate, transform, and decay. Building on sociometabolic research and recent efforts to integrate social dynamics into MFA, the paper synthesises existing approaches and identifies persistent gaps between conceptual treatments of behavior and its operational integration into material accounting. The contribution advances the field by articulating a concise set of principles that clarify how behavioral processes can be endogenized within SEM models while preserving the accounting logic of MFA. To illustrate the concept, we present a compact demonstration focused on the residential building stock of the Lausanne agglomeration (2010–2050), using a standard MFA structure coupled with behaviorally generated adoption trajectories under business-as-usual conditions. The demonstration highlights how making behavioral assumptions explicit and endogenous can generate distinct temporal dynamics and path dependencies in metabolic outcomes. Overall, Behavioral Metabolism strengthens the integration of agency into SEM and provides a foundation for more transparent and cross-scale analyses of urban metabolic change.