A Typology for Circular Economy Data
Journal of Circular Economy
Abstract:
The circular economy (CE) is a key pillar of sustainability policies, notably the European Green Deal, requiring extensive data across the value chain. However, the lack of a clear definition of CE data creates ambiguity in its understanding and application. This study addresses this gap by investigating the fundamental research questions: What are the dimensions that define CE data? and How is CE data currently being utilized according to these dimensions? To answer these questions, this research proposes a novel typology for CE data, examining its various dimensions and subdimensions across different levels, from product-specific to macroeconomic scales. Through a literature review and an analysis of 26 CE performance measurement frameworks, 334 distinct CE data points were identified and collected, serving as the foundation for defining eight CE data dimensions within the proposed typology. This approach has provided a clear definition of what constitutes CE data, contributing positively to business applications, particularly in performance measurement. Additionally, it streamlines data collection and analysis, offering a structured approach to prioritizing and interpreting CE data for effective implementation.
Status: Veröffentlicht
Integration of Product Circularity Indicators into the Digital Product Passport based on the Asset Administration Shell
International Conference on Industry of the Future and Smart Manufacturing
Abstract:
The Circular Economy (CE) is an economic model that shifts from a traditional linear approach to a circular one. It promotes sustainable product development by addressing environmental impacts throughout the entire product lifecycle – from eco-design and
production to end-of-life. In order to achieve this goal, it becomes an absolute necessity to measure circularity at different levels,
specifically at the product level. Measurement of the product circularity would allow describing the current situation, evaluating
possible future achievements, supporting decision-making tasks, and hence directing policy interventions towards required circularity
steps. Despite its potential, measuring product circularity faces significant challenges and is a very complex task, including
dealing with all parts of the production process and of the product lifecycle, and it often touches intangible measures and business practices that can hinder the achievement of truly sustainable outcomes. This highlights the need for transparent, accessible, and interoperable information through a Digital Product Passport (DPP), enabling relevant data to be extracted and applied across various stakeholders. This paper first introduces a generic approach for modeling a template DPP, grounded in the Industry 4.0 digitalization framework known as the Asset Administration Shell (AAS), designed to support CE objectives. Subsequently, the approach is extended to incorporate Circularity Indicators (CIs) at the product level as integral elements of the DPP. The proposed DPP model is then applied to an industrial use case to demonstrate its practical applicability and benefits.
Status: Akzeptiert
Representing Executable Circular Economy R-Strategies using Behavior Trees Embedded in Digital Product Passports
International Conference on Industry of the Future and Smart Manufacturing
Abstract:
The increasing emphasis on sustainability and resource efficiency in industrial systems has driven the adoption of Circular Economy (CE) principles, including R-strategies such as repair, reuse, remanufacturing, and recycling. These strategies are critical for extending product lifecycles and reducing material waste. However, existing approaches to representing R-strategies are predominantly static or descriptive, lacking the ability to support traceability in automation systems. This raises the central research question: How can R-strategies be formalized in a standardized and machine-interpretable format that integrates seamlessly with emerging digital infrastructures such as the Digital Product Passport (DPP)? This study proposes a methodology in which R-strategies are modeled as Behavior Trees (BTs) (modular, hierarchical control structures capable of representing dynamic process logic) and encoded in XML format for integration within the Asset Administration Shell (AAS). Embedding executable BTs into the DPP enables adaptive decision-making and consistent knowledge transfer across stakeholders. The approach is validated using a real-world use case in the SmartFactory-KL, where a “Repair” strategy is applied to a 3D-printed toy truck semitrailer. When printed with poor tolerances, the semitrailer is repaired using a soldering method instead of being discarded, and the Product Carbon Footprint (PCF) is recalculated to reflect the energy used in the repair step. By representing the repair instruction as a Behavior Tree and embedding it within the Digital Product Passport, the method enables standardized knowledge sharing across the value chain, thereby supporting the development of intelligent and sustainable manufacturing systems.
Status: Akzeptiert
