Life cycle inventory analysis modelling in LCSA: from basic aspects to advanced techniques

Thomas Schaubroeck and Enrico Benetto

Chapter 14 in Handbook on Life Cycle Sustainability Assessment, 2024, pp 180-202 from Edward Elgar Publishing

Abstract: At the core of Life Cycle Sustainability Assessment (LCSA) is a life cycle perspective, implying the coverage of all related human/industrial processes that constitute the studied product system and its overall sustainability impacts for a given functional unit (the function fulfilled by the considered product(s)). In this chapter, we focus on the modelling of a product system and thus the quantification of the life cycle inventory (LCI) results, comprising all elementary flows, pressures or conditions, of all the processes, scaled to the functional unit, which combined induce the sustainability impacts. First, basic aspects of LCI modelling are explained in a general and open manner, starting from modelling a process to propagating the product system derived from a research goal (attributional versus consequential). Second, the common computational framework based on matrix inversion, which has been the foundation of many LCI databases and software tools, is discussed, and some of its key limitations are highlighted. Third, other approaches brought forward in the literature to tackle these limitations and enhance the LCI modelling in various ways are then introduced. Although they are mainly considered for life cycle assessment (LCA), these approaches are relevant for the LCI of any life cycle method, thus also of LCSA. We elaborate concisely on eight prominent advanced approaches, ranging from non-linear modelling to digital twin techniques. Key case studies are pinpointed, primarily those that perform an advanced approach in combination with LCSA. A specific hurdle for LCSA is also pinpointed: the more limited, non-quantitative data and challenging modelling of many social flows and processes. At the end of this chapter, we pave a path forward where we foresee more thorough development and combinations of advanced LCI techniques with systematic application of these to full LCI databases and a translation into user-friendly tools.

Keywords: Business and Management; Development Studies; Economics and Finance; Environment; Geography; Politics and Public Policy Sociology and Social Policy (search for similar items in EconPapers)
Date: 2024
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