Prior work has demonstrated the integration of detailed life-cycle assessment into a traditional design modeling process. While a full life-cycle assessment provides insight into a product’s potential impact on the environment, it is often too time consuming for analysis during conceptual product design, where ideas are numerous and information is scarce. The work presented in this paper explores an approximate method for preliminary life-cycle assessments without detailed modeling requirements. Learning algorithms trained on the known characteristics of existing products allow the environmental impacts of new products to be approximated quickly during conceptual design. Artificial neural networks train on product attributes and environmental impact data from pre-existing life-cycle assessment studies. The product design team queries the trained artificial model with new high-level product attribute data to quickly obtain an approximate impact assessment for a new product concept. Tests based on simplified inventory data have shown it is possible to predict impacts on life-cycle energy consumption, and that there is a basis for the method to be used in also predicting solid material, greenhouse effect, ozone layer depletion, acidification, eutrophication, winter smog, and summer smog.

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