A Modular Tool to Support Data Management for LCA in Industry: Methodology, Application and Potentialities

Davide Rovelli, Carlo Brondi, Michele Andreotti, Elisabetta Abbate, Maurizio Zanforlin and Andrea Ballarino
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Davide Rovelli: Engineering, ICT and Technologies for Energy and Transportation Department, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via Alfonso Corti 12, 20133 Milan, Italy
Carlo Brondi: Engineering, ICT and Technologies for Energy and Transportation Department, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via Alfonso Corti 12, 20133 Milan, Italy
Michele Andreotti: Engineering, ICT and Technologies for Energy and Transportation Department, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via Alfonso Corti 12, 20133 Milan, Italy
Elisabetta Abbate: Engineering, ICT and Technologies for Energy and Transportation Department, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via Alfonso Corti 12, 20133 Milan, Italy
Maurizio Zanforlin: ORI Martin Ltd., Via Cosimo Canovetti 13, 25128 Brescia, Italy
Andrea Ballarino: Engineering, ICT and Technologies for Energy and Transportation Department, Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council of Italy (CNR), Via Alfonso Corti 12, 20133 Milan, Italy

Sustainability, 2022, vol. 14, issue 7, 1-31

Abstract: Life Cycle Assessment (LCA) computes potential environmental impacts of a product or process. However, LCAs in the industrial sector are generally delivered through static yearly analyses which cannot capture any temporal dynamics of inventory data. Moreover, LCA must deal with differences across background models, Life Cycle Impact Assessment (LCIA) methods and specific rules of environmental labels, together with their developments over time and the difficulty of the non-expert organization staff to effectively interpret LCA results. A case study which discusses how to manage these barriers and their relevance is currently lacking. Here, we fill this gap by proposing a general methodology to develop a modular tool which integrates spreadsheets, LCA software, coding and visualization modules that can be independently modified while leaving the architecture unchanged. We test the tool within the ORI Martin secondary steelmaking plant, finding that it can manage (i) a high amount of primary foreground data to build a dynamic LCA; (ii) different background models, LCIA methods and environmental labels rules; (iii) interactive visualizations. Then, we outline the relevance of these capabilities since (i) temporal dynamics of foreground inventory data affect monthly LCA results, which may vary by ±14% around the yearly value; (ii) background datasets, LCIA methods and environmental label rules may alter LCA results by 20%; (iii) more than 10 5 LCA values can be clearly visualized through dynamically updated dashboards. Our work paves the way towards near-real-time LCA monitoring of single product batches, while contextualizing the company sustainability targets within global environmental trends.

Keywords: LCA; modular LCA; data management; dynamic LCA; steel production; environmental labels; background datasets; visualization; environmental monitoring; automation (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 (5)

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