Stochastic Multi-Attribute Analysis (SMAA) for Comparative Life Cycle Assessment (LCA)
Comparative Life Cycle Assessment (LCA) quantifies the life cycle environmental impacts of equivalent products, technologies or processes throughout the initial stages of raw material extraction, processing, manufacturing, distribution, use and final disposal. Data generated from a comparative LCA can help steer industry practices, inform public policy interventions and accelerate research prioritization for reduction of environmental impacts. However, while these comparative studies generate valuable data, the results can be difficult to interpret via existing methods because of the existence of tradeoffs, data uncertainty, and multiple decision makers and stakeholders. In fact, comparative LCAs are multi criteria decision analytic (MCDA) problems that benefit from incorporation of tools from the operations research field into the interpretation stages.
Existing LCA interpretation methods derived from economic utility theory impose a set of normative assumptions that are incompatible with multi stakeholder environmental problems. These practices that are currently codified in the ISO standards can systematically hide the impacts that require the most attention. In addition, current approaches to weighing trade-offs rely on point estimates that ignore uncertainty in human values and provide an overly narrow view of complex environmental problems. These limitations limit the applicability of LCA in industry as a mechanism to guide sensible environmental decision making in the context of technology comparison. Therefore, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty.
This study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Assessment (SMAA-LCA) that uses internal normalization by means of outranking and exploration of feasible weight spaces. To demonstrate application of SMAA-LCA as compared to traditional methods of interpretation, this study performs a case study of a comparative LCA of laundry detergents. The novel method is more effective at identifying relevant tradeoffs in a comparison due to its relative assessment and it generates more robust results because it allows for inclusion of uncertainty in parameters and weights. This approach represents a major advancement in LCA interpretation practice because it directly studies relevant differences as opposed to performances with respect to an external baseline.
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