The “nature” and “nurture” of product lifetimes in dynamic stock modeling

Product life extension is often portrayed as one of the pillars of the circular economy since longer lifetimes slow down material turnover rates and thus decrease resource use and associated emissions. Strategies for product longevity can involve addressing the product “nature” (inherent product durability) or “nurture” (external factors). Yet, in most dynamic material flow analysis (dMFA) studies, lifetime is an intrinsic property of the cohort assigned “at birth,” so “nurture” strategies such as repair or reuse cannot be explicitly considered. Here, we introduce a Python‐based tool for comprehensive modeling of lifetime changes in dMFA, including three dimensions of lifetime: age, period (time), and cohort. The tool employs the hazard function, which directly links the outflow to the preceding year's stock, allowing for differentiating the influence of product nature and nurture on lifetimes. The tool supports dynamic stock and flow calculations and is compatible with ODYM, a commonly used dMFA framework. We apply the tool to a case study on dishwashers in Norway to illustrate nature‐ and nurture‐focused lifetime extension strategies. The framework enables linking product lifespans with events such as economic crises and pandemics. It can serve to model life extension scenarios in dMFA that (i) extend the lifetime not only of the new products entering use but also of the products already in use, thus achieving faster effects; and (ii) expand the group of potential stakeholders beyond producers and designers to, for example, consumers, repairers, and resellers. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.