Systematic Literature Review on Dynamic Life Cycle Inventory: Towards Industry 4.0 Applications

Simone Cornago, Yee Shee Tan, Carlo Brondi, Seeram Ramakrishna and Jonathan Sze Choong Low
Additional contact information
Simone Cornago: Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, Singapore 138634, Singapore
Yee Shee Tan: Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, Singapore 138634, Singapore
Carlo Brondi: STIIMA-CNR-Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, Via Alfonso Corti 12, 20133 Milan, Italy
Seeram Ramakrishna: Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore
Jonathan Sze Choong Low: Singapore Institute of Manufacturing Technology, 2 Fusionopolis Way, Singapore 138634, Singapore

Sustainability, 2022, vol. 14, issue 11, 1-22

Abstract: Life cycle assessment (LCA) is a well-established methodology to quantify the environmental impacts of products, processes, and services. An advanced branch of this methodology, dynamic LCA, is increasingly used to reflect the variation in such potential impacts over time. The most common form of dynamic LCA focuses on the dynamism of the life cycle inventory (LCI) phase, which can be enabled by digital models or sensors for a continuous data collection. We adopt a systematic literature review with the aim to support practitioners looking to apply dynamic LCI, particularly in Industry 4.0 applications. We select 67 publications related to dynamic LCI studies to analyze their goal and scope phase and how the dynamic element is integrated in the studies. We describe and discuss methods and applications for dynamic LCI, particularly those involving continuous data collection. Electricity consumption and/or electricity technology mixes are the most used dynamic components in the LCI, with 39 publications in total. This interest can be explained by variability over time and the relevance of electricity consumption as a driver of environmental impacts. Finally, we highlight eight research gaps that, when successfully addressed, could benefit the diffusion and development of sound dynamic LCI studies.

Keywords: dynamic life cycle assessment; temporal differentiation; temporal; dynamic modeling; real-time (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/11/6464/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/11/6464/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:11:p:6464-:d:823826

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().