Process planning in manufacturing today focuses on optimizing the conflicting targets of cost, quality, and time. Due to increasing social awareness and subsequent governmental regulation, environmental impact becomes a fourth major aspect. Eventually, sustainability in manufacturing ensures future competitiveness. In this paper, a framework for the planning of sustainable manufacturing is proposed. It is based on the abstraction and generalization of manufacturing resources and part descriptions, which are matched and ranked using a multi-criteria decision analysis method. Manufacturing resources provide values for cost, quality, time and environmental impacts, which multiply with their usage within a manufacturing task for a specific part. The framework is validated with a detailed modeling of a laser machine as a resource revealing benefits and optimization potential of the underlying data model. Finally, the framework is applied to a use case of a flange part with two different manufacturing strategies, i.e., laser metal-wire deposition and conventional milling. The most influential parameters regarding the environmental impacts are the raw material input, the manufacturing energy consumption and the machine production itself. In general, the framework enabled the identification of non-predetermined manufacturing possibilities and the comprehensive comparison of production resources.

Life Cycle Assessment (LCA) is a powerful and sophisticated tool to gain deep understanding of the environmental hotspots and optimization potentials of products. Yet, its cost-intensive manual data engineering and analysis workflows restrain its wider application in eco-design, green procurement, supply chain management, sustainable investment or other relevant business processes. Especially for large product portfolios and increasing reporting requirements, traditional LCA workflows and tools often fail to provide the necessary scalability. The Sustainability Data Science Life Cycle (S-DSLC) is a concept for workflow automation for multi-purpose LCA of large product portfolios. The concept integrates the frameworks of LCA, the cross-industry standard process for data mining (CRISP-DM), and the Data Science Life Cycle (DSLC). Key aspects of the concept are deep business-, stakeholder and user-understanding, deployment of LCA results in interactive browser tools (i.e. LCA-dashboards and Guided Analytics) tailored to the needs of individual roles and business processes, as well as the automation of data preparation, model generation and Life Cycle Impact Assessment based on modern data analytic tools. The demonstration of the concept shows substantial scalability improvements for dealing with large product portfolios and broad application of LCA results in various business processes.

With an increasing share of renewable energy technologies in our energy systems, the integration of not only direct emission (from the use phase), but also the total life cycle emissions (including emissions during resource extraction, production, etc.) becomes more important in order to draw meaningful conclusions from Energy Systems Analysis (ESA). While the benefit of integrating Life Cycle Assessment (LCA) into ESA is acknowledged, methodologically sound integration lacks resonance in practice, partly because the dimension of the implications is not yet fully understood. This study proposes an easy-to-implement procedure for the integration of LCA results in ESA based on existing theoretical approaches. The need for a methodologically sound integration, including the avoidance of double counting of emissions, is demonstrated on the use case of Passivated Emitter and Rear Cell photovoltaic technology. The difference in Global Warming Potential of 19% between direct and LCA based emissions shows the significance for the integration of the total emissions into energy systems analysis and the potential double counting of 75% of the life cycle emissions for the use case supports the need for avoidance of double counting.

This paper represents the first stage of extending the scope of LCA to space and is intended as a discussion starter. Based on the assumption, that the future and outlast of humanity lies within the exploration and colonisation of space, the LCA methodology as of today, is discussed with regards to its capabilities to cover the impact of human activities in space. Based on this assessment, ideas whether and how LCA can be extended are outlined. Initially, an understanding of additional environmental impacts which occur in space compared to Earth is built up by the means of literature research. The state of the art of space regulations and availability of LCAs in space and for astronautics is clarified as well. Further literature research was conducted on the LCA subtopic of regionalization. Based on this and assumptions regarding future space travel, the suitability of LCA as an assessment method is validated. Afterwards, different potential development phases of LCA towards its applicability in space are defined. For activities in space, the regarded environmental impacts have to be expanded (e.g. space debris, extra-terrestrial life toxicity, etc.). Space regulations, if in place, cover only impacts of space activities on Earth so far. LCAs for space activities are not widespread yet. One reason for this is that the state of the art LCA methodology has not been expanded and existing regionalisation approaches are not easily transferable to space. Critical issues are faced in all phases of an LCA and include widening of boundaries, definition of space regions, finding suitable reference units and ethical problems. As a result, four LCA development phases are suggested: Earth-bound, solar system-bound, transition phase and intergalactic. Each phase involves different activities and goals, which result in different system boundaries and impact categories and widen the scope of LCA subsequently. It is a long way for humanity to populate space and so, it is for enabling LCA to assess these activities. The methodology of LCA is flexible and capable to make this adaptation. This paper can be seen as a starting point of a discussion opening up many questions. Some of these questions can only be answered in the future with more certainty about the development of space colonialization.

Growing environmental awareness in society increasingly influences individual everyday decisions, such as which product to buy or how to sustainably use it. Yet, available information to support these decisions is often limited, or difficult to understand particularly regarding sustainability. Effective ways of communicating environmental impacts of individual decisions are required to close this gap. While Life Cycle Assessment (LCA) is an established tool to evaluate environmental impacts of products and services and support environmental decision-making, the results are typically standardized and based on statistical or averaged data. However, for individuals, this information might be irrelevant, as it neglects personal situation, behavior, information need, or individual level of expertise. In tackling those central issues of personalization in LCA, this article focuses on two main questions: How can individual aspects be addressed in LCA and at which stages of the methodology can LCA be personalized? For this purpose, the ISO 14040/44 standards are analyzed regarding individuality, and current approaches in literature are presented. In an explorative approach, this research identifies two general approaches of personalizing LCA. A personalized Life Cycle Inventory (LCI) enables evaluating the environmental impacts of personal(ized) products and conditions. A broader personalization approach based on the flexibility of the methodological framework of LCA aims at providing understandable and relevant results for individual stakeholders. This article provides an overview, outlines key aspects of this vision, and points out further research needs to bring the concept into application.