Development of generalized guidelines for Life Cycle Optimization (LCO) studies through systematic review of the LCO literature

Main Presenter: Ian Turner 

Co-Authors: Nathan Pelletier

Session: Poster Session 2

Life cycle optimization (LCO) refers to the integration of life cycle assessment (LCA) with mathematical optimization techniques (Azapagic and Clift 1998). LCO allows researchers to find optimal solutions to complex, multi-objective problems, subject to various constraints. LCO may be a valuable tool for investigation of sustainability improvement strategies, capable of suggesting environmental impact reductions greater than those suggested by other methods, such as joint data envelopment analysis and LCA (Kaab et al. 2019). Performance of an LCO study, however, requires many methodological choices to be made related to definition of the optimization problem, algorithms and the LCA framework (i.e., attributional or consequential) used, etc. To aid LCA practitioners, therefore, this work aims to develop a set of generalized guidelines for performance of an LCO study.
To do so, a systematic review of the LCO literature was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach (Page et al. 2021). The Web of Science database was searched using the keywords (“life cycle assessment” OR “life cycle analysis”) AND (optimi*?) AND (algorithm), and primary research articles published within the last 10 years (i.e., 2012-2021) were screened for review. To be selected, articles had to apply the LCA framework in calculation of at least one objective function for optimization. This literature was reviewed to determine the LCA framework and optimization algorithm used, the objective functions and constraints included, and whether or not uncertainty was accounted for in the optimization. All systems studied were also classified according to the International Standard Industrial Classification of All Economic Activities (United Nations 2008) to determine which sectors more commonly applied LCO methods.
Many industrial sectors were represented by the reviewed literature, with the manufacturing sector accounting for the greatest share. Interestingly, it is rare for studies to include multiple midpoint LCIA results as objective functions, instead opting for the use of single aggregated measures representing all life cycle impacts. The majority of studies use the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a fast, efficient algorithm for performing multi-objective optimization, and uncertainty is rarely accounted for. Based on the results of this review, a generalized decision tree has been developed to help guide LCA practitioners in performance of LCO studies, regardless of the industrial sector being investigated. This research will help aid practitioners in making the methodological choices necessary for performance of an LCO study, thereby helping increase uptake of these methodologies future work.

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