In light of current environmental challenges, industrial companies are increasingly required to reduce their individual environmental impact. As these companies face economic constraints, the reduction of the specific impacts needs to be achieved in the most cost-efficient manner. This is leading to trade-offs between the potential environmental improvements driven by particular measures and the costs of these measures. Due to the inherent complexity of product systems many different measures to alter the products properties exist, leading to a high number of possible combination alternatives in the foreground system and consequently to many different product system set-ups and LCA results. Modular LCAs are an approach to calculate these results by performing separated LCAs for all individual life cycle modules, which afterwards are reconnected again to form the LCA results for all possible module combinations. However, when the LCA result of one of these modules is influenced by interactions with other modules, the consideration of these influences leads to a fast rise in the data demand for a modular LCA. Modelling such an optimization problem via graph theory can be a possible way to address interdependencies between modules while still being able to provide the necessary data demand through a systematic graph design.
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