Advances in application of machine learning to life cycle assessment: a literature review

Abstract

Purpose Life Cycle Assessment (LCA) is the process of systematically assessing impacts when there is an interaction between the environment and human activity. Machine learning (ML) with LCA methods can help contribute greatly to reducing impacts. The sheer number of input parameters and their uncertainties that contribute to the full life cycle make a broader application of ML complex and difficult to achieve. Hence a systems engineering approach should be taken to apply ML in isolation to aspects of the LCA. This study addresses the challenge of leveraging ML methods to deliver LCA solutions. The overarching hypothesis is that: LCA underpinned by ML methods and informed by dynamic data paves the way to more accurate LCA while supporting life cycle decision making. Methods In this study, previous research on ML for LCA were considered, and a literature review was undertaken. Results The results showed that ML can be a useful tool in certain aspects of the LCA. ML methods were shown to be applied efficiently in optimization scenarios in LCA. Finally, ML methods were integrated as part of existing inventory databases to streamline the LCA across many use cases. Conclusions The conclusions of this article summarise the characteristics of existing literature and provide suggestions for future work in limitations and gaps which were found in the literature.

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…Through the optimized use of resources and enhanced production efficiency, ML is contributing to the reduction of the environmental impact of fuel production. It aids in the development of cleaner, more sustainable fuels by analyzing and predicting the environmental impact of various fuel types, thereby guiding the industry toward more ecofriendly alternatives. , This commitment to sustainability is crucial in addressing the urgent global environmental challenges faced today. In conclusion, the incorporation of ML within the realm of fuel development is bringing forth substantial advancements in efficiency, sustainability, and innovation.…”

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

“…This is where ML integration with LCAs proves beneficial. ML applications in surrogate LCA generation, sensitivity analysis, and characterization factor estimation showcase how combining ML techniques with LCAs can radically improve environmental decision-making [139,140]. For the successful implementation of algal bioplastics, advancements in characterization techniques are necessary.…”

Optimizing Sustainability: A Deep Learning Approach on Data Augmentation of Indonesia Palm Oil Products Emission

“…Using natural language processing (NLP) and random forest algorithms to train models to provide quick predictions for LCA practitioners and testers in implementing LCA, Similar things have been analyzed to predict the impact of LCA on electricity consumption. Comparing feed-forward (NN) neural networks and repetitive neural (RNN) networks, although limited to one data set (Ghoroghi et al, 2022), (Koyamparambath et al, 2022), (Portolani et al, 2022).…”