Branche: Renewable Energies

Conveying the complexity and multifaceted nature of the energy transition to a broad audience is a challenge. This article demonstrates how interactive serious games on a multitouch table can help make connections tangible and comprehensible. The games and the table were used in various conversational contexts. These are presented here in three case vignettes based on participant observation of the different applications, as well as situated and shared reflection. The vignettes demonstrate how interaction can trigger epistemic processes, enable shifts in perspective, and foster collective thinking, all of which are necessary for shaping the future of society as a whole.

This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics.

Electric trucks (e-trucks) play an important role in reducing CO₂ emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.

The growing demand for battery cells offers significant potential for the use of digital solutions in their manufacture, which in turn creates opportunities for added value through adaptive and flexible production systems. A key enabler is interoperable data exchange based on formalized data descriptions. Existing ontologies and information models remain too abstract for direct implementation. This paper presents a requirements analysis of data standards in battery cell manufacturing. A procedure for developing domain-specific standards based on OPC UA (Open Platform Communications Unified Architecture) is derived from the results.

This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems.

Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.

Machine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application.

Change is necessary for companies to maintain their competitive edge—both digital and ecological change. But while external support is at hand, the drive for change must come from companies themselves. In this issue of Industry 4.0 Science, experts of the Academic Society for Work and Industrial Organization discuss how the real-world application of innovative technologies lead to resource-efficient manufacturing.