Automation of Life Cycle Assessment—A Critical Review of Developments in the Field of Life Cycle Inventory Analysis

Bianca Köck (), Anton Friedl, Sebastián Serna Loaiza, Walter Wukovits and Bettina Mihalyi-Schneider
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Bianca Köck: Institute of Chemical Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
Anton Friedl: Institute of Chemical Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
Sebastián Serna Loaiza: Institute of Chemical Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
Walter Wukovits: Institute of Chemical Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria
Bettina Mihalyi-Schneider: Institute of Chemical Engineering, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria

Sustainability, 2023, vol. 15, issue 6, 1-40

Abstract: The collection of reliable data is an important and time-consuming part of the life cycle inventory (LCI) phase. Automation of individual steps can help to obtain a higher volume of or more realistic data. The aim of this paper is to survey the current state of automation potential in the scientific literature published between 2008 and 2021, with a focus on LCI in the area of process engineering. The results show that automation was most frequently found in the context of process simulation (via interfaces between software), for LCI database usage (e.g., via using ontologies for linking data) and molecular structure models (via machine learning processes such as artificial neural networks), which were also the categories where the highest level of maturity of the models was reached. No further usage could be observed in the areas of automation techniques for exploiting plant data, scientific literature, process calculation, stoichiometry and proxy data. The open science practice of sharing programming codes, software or other newly created resources was only followed in 20% of cases, uncertainty evaluation was only included in 10 out of 30 papers and only 30% of the developed methods were used in further publication, always including at least one of the first authors. For these reasons, we recommend encouraging exchange in the LCA community and in interdisciplinary settings to foster long-term sustainable development of new automation methodologies supporting data generation.

Keywords: life cycle assessment; life cycle inventory automation; machine learning; molecular modeling; knowledge engineering; digital twins; process simulation; artificial neural networks; open data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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