334
T. Riedelsheimer et al.
Big Data
28 papers focus on Big Data and LC(S)A, specifically LCA and LCC. Ten papers
are not relevant for further analysis, because of only being a review paper (Song
et al. 2018) or describing LCSA-case studies of Big Data applications. These are
not considered further. Most of the research activities in the relevant papers on Big
Data is closely linked to research in the field of AI, which can be explained by the
fact that analysis of Big Data demands intelligent data analytic methods. In addition,
there is an overlap with the research on DT. The identified research mainly presents
Big Data in the context of building and construction projects, smart city or energy
generation.
Blockchain
As a specific form of distributed ledger technology, blockchain is proposed by
research as one possibility to increase data security. It can theoretically be applied for
data collection and secure data storage, especially for sensitive social data, as well as
for managing product-specific supply chain data (Abeyratne and Monfared 2016).
However, the systematic review reveals only one paper by Smetana et al. specifically
mentioning the application for sustainability assessment, namely the LCA (Smetana
et al. 2018), which also integrates neural networks and CPS and can therefore be
seen as a part of the AI and CPS research.
Cyber-physical Systems (CPS)
In total, only eight papers address CPS and sustainability assessment. One paper
specifically considers LCSA with regard to the sustainability of CPS (Gürdür and
Gradin 2017), but does not present CPS as a technological solution for LCSA execution. After filtering, only two papers are considered relevant for further analysis.
Smetana et al. present a multidisciplinary research on CPS, AI and blockchain
(Smetana et al. 2018). The authors discuss the theoretical applicability of blockchain
and neural networks for LCA and material flow analysis. They propose an application
in food production to monitor material flows. Vanderroost et al. propose a similar
concept for food packaging based on data collection with CPS for LCA (Vanderroost
2017), but without AI-consideration.
Digital Twins (DT)
In general, “a Digital Twin is a digital representation of an active unique product
[…] or unique product service system […] that comprises its selected characteristics,
properties, conditions and behaviors by means of models, information and data within
a single or even across multiple lifecycle phases” (Stark and Damerau 2019). The
review showed no research so far for DTs and LCSA. However, three papers propose
approaches for combining DT and LCA. Barni et al. present a DT as an enabler for
LCA with a focus on the manufacturing phase (Barni et al. 2018)—a topic of high
relevance for this research. Also, Rückert et al. and Wellsandt et al. mention the DT
for online LCA without detailing the concept (Rückert et al. 2018; Wellsandt 2017).
Nevertheless, all research present the DT as a concept addressing all phases of a
T. Riedelsheimer et al.
Big Data
28 papers focus on Big Data and LC(S)A, specifically LCA and LCC. Ten papers
are not relevant for further analysis, because of only being a review paper (Song
et al. 2018) or describing LCSA-case studies of Big Data applications. These are
not considered further. Most of the research activities in the relevant papers on Big
Data is closely linked to research in the field of AI, which can be explained by the
fact that analysis of Big Data demands intelligent data analytic methods. In addition,
there is an overlap with the research on DT. The identified research mainly presents
Big Data in the context of building and construction projects, smart city or energy
generation.
Blockchain
As a specific form of distributed ledger technology, blockchain is proposed by
research as one possibility to increase data security. It can theoretically be applied for
data collection and secure data storage, especially for sensitive social data, as well as
for managing product-specific supply chain data (Abeyratne and Monfared 2016).
However, the systematic review reveals only one paper by Smetana et al. specifically
mentioning the application for sustainability assessment, namely the LCA (Smetana
et al. 2018), which also integrates neural networks and CPS and can therefore be
seen as a part of the AI and CPS research.
Cyber-physical Systems (CPS)
In total, only eight papers address CPS and sustainability assessment. One paper
specifically considers LCSA with regard to the sustainability of CPS (Gürdür and
Gradin 2017), but does not present CPS as a technological solution for LCSA execution. After filtering, only two papers are considered relevant for further analysis.
Smetana et al. present a multidisciplinary research on CPS, AI and blockchain
(Smetana et al. 2018). The authors discuss the theoretical applicability of blockchain
and neural networks for LCA and material flow analysis. They propose an application
in food production to monitor material flows. Vanderroost et al. propose a similar
concept for food packaging based on data collection with CPS for LCA (Vanderroost
2017), but without AI-consideration.
Digital Twins (DT)
In general, “a Digital Twin is a digital representation of an active unique product
[…] or unique product service system […] that comprises its selected characteristics,
properties, conditions and behaviors by means of models, information and data within
a single or even across multiple lifecycle phases” (Stark and Damerau 2019). The
review showed no research so far for DTs and LCSA. However, three papers propose
approaches for combining DT and LCA. Barni et al. present a DT as an enabler for
LCA with a focus on the manufacturing phase (Barni et al. 2018)—a topic of high
relevance for this research. Also, Rückert et al. and Wellsandt et al. mention the DT
for online LCA without detailing the concept (Rückert et al. 2018; Wellsandt 2017).
Nevertheless, all research present the DT as a concept addressing all phases of a