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The International Journal of Life Cycle Assessment - Call for papers for a Special Issue: How Digital Technologies Could Empower Life Cycle Assessment Studies

The difficulties in collecting reliable and accurate data in developing Life Cycle Assessment (LCA) sharply limit the strategic potential of LCA-related insights (Sassanelli et al., 2019; Arzoumanidis et al., 2021). In light of these considerations, systematic data collection capable of ensuring reliability and veracity is perceived as a significant challenge for LCA operators and researchers, particularly regarding information related to new products, processes, or services (Hospido et al., 2010). One of the most critical issues connected to traditional LCA analyses is the lack of reliable and up-to-date inventory data to carry out accurate LCA studies (Notarnicola et al., 2015). In particular, the Life Cycle Inventory (LCI) phase is strongly affected by the lack of data availability and a related deep uncertainty (Notarnicola et al., 2012; Arzoumanidis et al., 2021). Accordingly, analysts are called to use estimates for evaluating and assessing input and output data, or otherwise, they are oriented to exclude certain stages of production and transformation processes from LCA studies. This methodological limitation affects the development of LCA, characterised by strong results variability and uncertainty (Solomone et al., 2010). 

Accordingly, several LCI databases have been developed over time to exceed this limitation. However, a lack of total data transparency and incompleteness, which – in turn – often leads to ambiguous data interpretation processes, characterised the majority of them (Guo and Murphy, 2012). In addition, a methodological comparability problem related to the use of different databases has also emerged among the various limitations of the traditional LCAs. This has led to substantial differences in the developed analyses that prevent managers from integrating LCA insights into their decision-making processes (Cooper et al., 2013; Zhang et al., 2020).

In the 4.0 era, using digital technologies (such as blockchain, the Internet of Things (IoT), Big Data analysis and visualisation, etc.) to support LCA analysis represents a turning point to ensure more accurate and reliable results. As a consequence, managers could more easily re-orient the strategic decision-making processes towards the twin transition: circular and digital (Bhinge et al., 2015; Nascimiento et al., 2019; Bai et al., 2020; Niero et al., 2021;  Bocken et al., 2023).

Some scholars have started to investigate the possibility of overcoming these limitations by using digital technologies in data collection and systematisation to conduct LCA analysis, which could provide higher accuracy and reliability (e.g., Song et al., 2018; Mieras et al., 2019; Kamble et al., 2020; Kumar et al., 2021). The literature on the subject has highlighted how the use, even jointly, of digital technologies can support companies not only in streamlining production and transformation processes but also in the production and collection of input data useful for conducting LCA analysis, improving corporate sustainability levels (Zhang et al., 2020). In particular, the combined application of these technologies can entail high standards of security and data verifiability, ensuring traceability within and between the supply chains and the reliability of the information produced by data analysis. It follows that the use of digital technologies affects the levels of effectiveness and reliability of the insights obtained from LCA analyses from the goal and scope definition phase to the interpretation phase (Wang et al., 2019).

However, despite the relevance of this topic, the scientific literature is still at an embryonic stage. Indeed, only a few recent articles have explored the role of digital technologies in supporting LCA studies (e.g., Wang et al., 2019; Ruggieri et al., 2021; Sica et al., 2022), mainly proposing conceptual papers and theoretical frameworks. A lack of operational research and vertical analysis on each specific LCA phase has emerged from the literature, as well as contributions that investigate the impacts that the use of energy and electronic devices used to carry out the LCA analysis could have on the environment (Sica et al., 2023).

In light of these considerations, this Special Issue (SI) aims to stimulate debate among scholars, researchers, practitioners, and policymakers on how digital technologies can contribute to developing Life Cycle Thinking (LCT) studies. 

More specifically, the SI aims to provide high-quality original theoretical and empirical contributions related but not limited to, the following topics:
-    Blockchain for data transparency 
-    Smart sensors for data collection 
-    Big Data visualisation techniques for LCA results interpretation 
-    Internet of Things for LCA studies 
-    Practical barriers to adopting digital technologies
-    Distributed Ledger Technology to support LCA 
-    DT for simplified LCA studies 
-    The role of DT in defining FU and system boundaries 
-    The role of DT for the LCI
-    The role of DT in the impact assessment phase
-    Life Cycle Thinking Data-driven Decision Making 
-    Digital Technology for life cycle assessment-based disclosure 
-    Digital technologies as enablers for all LCT-related methods, such as Life Cycle Assessment (LCA), Organisational LCA (OLCA), Life Cycle Costing (LCC), Social LCA (SLCA) and Life Cycle Sustainability Assessment (LCSA).

The guest editors also promote multidisciplinary approaches and multi-method research designs to increase the opportunity of unreveling the complexity of this fervent research topic. 

Theoretical papers as well as empirical papers and case studies are welcome: the aim of the special issue is to provide a comprehensive overview of the research problem and to provide cases and examples that can represent the starting point for future researches and discussions.

Submission Opening: November 15, 2023
Final Submission Deadline: September 30, 2024

Guest Editors

Prof. Daniela Sica
Department of Human Science for the Promotion of Quality of Life, San Raffaele University Rome, Italy.

Dr. Benedetta Esposito
Department of Management & Innovation Systems, University of Salerno, Italy. 

Prof. Stefania Supino
Department of Human Science for the Promotion of Quality of Life, San Raffaele University Rome, Italy.

Prof. Ornella Malandrino
Department of Management & Innovation Systems, University of Salerno, Italy.



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