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

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 105 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.