Non-linearity in the Life Cycle Assessment of Scalable and Emerging Technologies

Given a fixed product system model, with the current computational framework of Life Cycle Assessment (LCA) the potential environmental impacts associated to demanding one thousand units of a product will be one thousand times larger than what results from demanding one unit only – a linear relationship. However, due to economies of scale, industrial synergies, efficiency gains, and system design, activities at different scales will perform differently in terms of life cycle impact – in a nonlinear way. This study addresses the issue of using the linear framework of LCA to study scalable and emerging technologies, by looking at different examples where technology scale up reflects nonlinearly on the impact of a product. First, computer simulation applied to an entire database is used to quantitatively estimate the effect of assuming activities in a product system are subject to improvements in efficiency. This provides a theoretical but indicative idea of how much uncertainty can be introduced by nonlinear relationships between input values and results at the database level. Then the nonlinear relations between the environmental burden per tkm of transport on one end, and the cargo mass and range autonomy on the other end is highlighted using a parametrized LCA model for heavy goods vehicles combined with learning scenarios that reflect different load factors and improvement in battery technology. Finally, a last example explores the case of activities related to the mining of the cryptocurrency Bitcoin, an emerging technology, and how the impact of scaling the Bitcoin mining production is affected nonlinearly by factors such as increase in mining efficiency and geographical distribution of miners. The paper concludes by discussing the relations between nonlinearity and uncertainty and by providing recommendations for accounting for nonlinearity in prospective LCA studies.