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The Circular Economy approach aims to close the loop of materials and to reduce waste. However, relevant product data for the optimization and management of circular approaches are often missing. Stakeholders typically lack key information: Recyclers do not know which materials/compounds to expect, producers do not know enough about the recyclability of their products, and customers do not have enough information about the environmental impact of their purchases. As a solution, this paper proposes a Digital Lifecycle Passport (DLCP) that can be written and read by various stakeholders along the full product lifecycle. Based on Plattform Industrie 4.0's Asset Administration Shell, the DLCP is readable for both humans and machines. A cloud-based app (SaaS) enables all stakeholders to create and manage DLCPs. As a use case, it is demonstrated how the DLCP can improve the sorting process of electronic waste.

The Digital Product Passport (DPP) is a concept to collect and share product-related information along a product's lifecycle. The aim is to provide all stakeholders during the product lifecycle with the information they need such that a successful Circular Economy can be implemented. At the moment, several varieties of DPPs are being developed, most of them sector-specific. As of today, however, there is no common standard as to what a DPP should look like. This paper collects requirements for the DPP from different perspectives that must be fulfilled to make it broadly applicable.

A review of LCA process datasets is an important element of quality assurance for databases and for other systems to provide LCA datasets. Somewhat surprisingly, a broadly accepted and applicable set of criteria for a review of LCA process datasets was lacking so far. Different LCA databases and frameworks are proposing and using different criteria for reviewing datasets. To close this gap, a set of criteria for reviewing LCA dataset has been developed within the Life Cycle Initiative. Previous contributions to LCA dataset review have been analysed for a start, from ISO and various LCA databases. To avoid somewhat arbitrary review criteria, four basic rules are proposed which are to be fulfilled by any dataset. Further, concepts for assessing representativeness and relevance are introduced into the criteria set from established practices in statistics and materiality. To better structure the criteria and to ease their application, they are grouped into clusters. A first version of the developed review criteria was presented in two workshops with database providers and users on different levels of experience, and draft versions of the criteria were shared within the initiative. The current version of the criteria reflects feedback received from various stakeholders and has been applied and tested in a review for newly developed datasets in Brazil, Malaysia and Thailand. Overall, 14 criteria are proposed, which are organised in clusters. The clusters are goal, model, value, relevance and procedure. For several criteria, a more science-based definition and evaluation is proposed in comparison to ‘traditional’ LCA. While most of the criteria depend on the goal and scope of dataset development, a core set of criteria are seen as essential and independent from specific LCA modelling. For all the criteria, value scales are developed, typically using an ordinal scale, following the pedigree approach. Review criteria for LCI datasets are now defined based on a stringent approach. They aim to be globally acceptable, considering also database interoperability and database management aspects, as well as feedback received from various stakeholders, and thus close an important gap in LCA dataset quality assurance. The criteria take many elements of already existing criteria but are the first to fully reflect the implications of the ISO data quality definition, and add new concepts for representativeness and relevance with the idea to better reflect scientific practice outside of the LCA domain. A first application in a review showed to be feasible, with a level of effort similar to applying other review criteria. Aspects not addressed yet are the review procedure and the mutual recognition of dataset reviews, and their application for a very high number of datasets.

Access, affordability and sustainability of raw material supply chains are crucial to the sustainable development of the European Union (EU) for both society and economy. The study investigates whether and how the social life cycle assessment (S-LCA) methodology can support responsible sourcing of raw materials in Europe. The potential of social indicators already available in an S-LCA database is tested for the development of new metrics to monitor social risks in raw material industries at EU policy level. The Product Social Impact Life Cycle Assessment (PSILCA) database was identified as a data and indicators source to assess social risks in raw material industries in EU-28 and extra-EU countries. Six raw material country sectors in the scope of the European policy on raw materials were identified and aggregated among those available in PSILCA. The selection of indicators for the assessment was based on the RACER (Relevance, Acceptance, Credibility, Ease, Robustness) analysis, leading to the proposal of 9 social impact categories. An S-LCA of the selected raw material industries was, thus, performed for the EU-28 region, followed by a contribution analysis to detect direct and indirect impacts and investigate related supply chains. Finally, the social performance of raw material sectors in EU-28 was compared with that of six extra-EU countries. Considering the overall social risks in raw material industries, “Corruption”, “Fair salary”, “Health and safety” and “Freedom of association and collective bargaining” emerged as the most significant categories both in EU and extra-EU. EU-28 shows an above-average performance where the only exception is represented by the mining and quarrying sector. An investigation of the most contributing processes to social impact categories for EU-28 led to the identification of important risks originating in the supply chain and in extra-EU areas. Therefore, the S-LCA methodology confirmed the potential of a life cycle perspective to detect burdens shifting and trade-offs. However, only a limited view on the sectoral social performance could be obtained from the research due to a lack of social data. The S-LCA methodology and indicators appear appropriate to perform an initial social sustainability screening, thus enabling the identification of hotspots in raw material supply chains and the prioritization of areas of action in EU policies. Further methodological developments in the S-LCA field are necessary to make the approach proposed in the paper fully adequate to support EU policies on raw materials.

The decentralized wastewater treatment systems including collection, treatment, and recycling of treated wastewater are an efficient integrated water management solution to overcome the shortage of freshwater and to reduce pollutant emissions. In this study, life cycle assessment was performed in order to evaluate a decentralized alternative allowing the recycling of treated greywater (TGW) in a multi-family dwelling. The adopted treatment system is the moving bed biofilm reactor (MBBR). The biological treatment with MBBRs was found the main contributor to the majority of the investigated impact categories. The integration of the tertiary treatment led to an increase in the environmental load ranging between 6 and 30.4% depending on the impact category in comparison with the scenario integrating only the biological treatment. However, the final environmental profile of both scenarios presented a minor difference. Moreover, the recycling of the TGW allowed a reduction ranging between 3.9 and 17% on five impact categories compared to the no recycling scenario. It is concluded that the decentralized approach for greywater treatment and recycling is beneficial for the environment as the distribution system and the tertiary treatment didn’t increase drastically the environmental load of the plant allowing water saving. Moreover, it is recommended to use renewable energy to reduce the environmental impact associated with the energy requirement for the biological treatment which affects the global warming potential and the acidification impact categories and to substitute the polyvinylchloride pipes with polyethylene pipes to reduce emissions related to the human toxicity impact category.

Life cycle inventory (LCI) databases are commonly used in life cycle assessment (LCA) studies. They enable modern, larger case studies, make data collection more efficient, and help to establish comparability across different case studies. A database typically tries to provide one coherent and consistent modeling space, thereby allowing users to take different datasets in the appropriate database, which implies that the goal and scope of datasets in the database match the goal and scope of case studies done with the database.

The basic model of a life cycle inventory (LCI), with unit processes as smallest modeling entities, emerged already in the very early phases of life cycle assessment (LCA) method development. It is a rather simple, linear model, with a distinction between elementary flows, product flows, and waste flows. Since the early applications, this simple model proved to be very useful and allowed for various expansions. For certain issues related to LCI modeling, solutions and approaches have evolved as extensions of the basic model. Such issues and related modeling challenges include: the multifunctionality problem; the modeling of loops in product systems; the modeling of the use phase; the modeling of transport services; the consideration of time and long-term emissions in LCI; the definition of the boundary between the technosphere and biosphere; and how to address accidents, incidents, and risks. This chapter presents and explains the basic LCA model and its extensions, where some are commonly used in practice today, and some others not. Furthermore, conventions regarding the modeling of transport services, use phase and products, end of life, are presented.

This chapter explores data quality in life cycle inventory (LCI) datasets and calculation results, introduces the history, explains the relevance of data quality for life cycle assessment (LCA), and the difficulty to deal with the application-dependency of data quality. Recent data quality systems, introduced by the United States Environmental Protection Agency (US EPA) and in the course of the European “Product Environmental Footprint” (PEF) project, are elaborated in more detail. The application-dependency of data quality has led to a more refined view on data quality in a recent United Nations GLAD (Global Life Cycle Access to Data) project. GLAD distinguishes between data quality when a dataset is created and when it is used. In addition, data quality is broadened by including modeling details that are typically set differently in different application contexts. Outcomes of the GLAD project are therefore introduced in this chapter as well and it is expected that these might lead to a more comprehensive, better management of data quality for differing application contexts, as well as for creating inventory datasets.