Machine learning to support prospective life cycle assessment of emerging chemical technologies
C. F. Blanco (Universiteit Leiden, TNO)
N. Pauliks (Universiteit Leiden)
F. Donati (Universiteit Leiden)
N. Engberg (TU Delft - Design for Sustainability)
J.M. Weber (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Increasing calls for safer and more sustainable approaches to innovation in the chemical sector necessitate adapted methods for the environmental assessment of emerging chemical technologies. While these technologies are still in the research and development phase, gaining an early understanding of their potential implications is crucial for their eventual introduction into markets worldwide. Life Cycle Assessment (LCA) is a core tool which has been recently adapted for such purpose. Prospective LCA approaches aim to develop plausible future-oriented models which account for the evolution of factors both intrinsic and extrinsic to the technologies assessed. Such future-oriented models introduce many indeterminacies, which could, to some extent, be addressed by Machine Learning techniques. Recent demonstrations of such techniques in the context of prospective LCA, as well as promising avenues for further research, are critically discussed.
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