Expanded Polystyrene (EPS) is a material widely used in packaging, food transport, insulation, and other industrial applications, yet it remains difficult to manage at end-of-life, especially in peripheral and insular regions. Its low density makes storage and transport inefficient, while contamination and mixed post-consumer conditions reduce reuse and recycling potential. In territories with limited local recycling or remanufacturing capacity, EPS is often directed to disposal or off-island treatment, increasing logistical and environmental burdens. This paper presents the design and early deployment of a digitally enabled circular EPS value chain in an outermost European island region. The proposed approach combines circular process redesign, a low-cost AI vision system based on RGB sensing and controlled illumination, and a FIWARE-based local data space supporting traceability, secure data exchange, and operational monitoring. The paper describes the physical and digital process flows, the system architecture, and the deployment of the vision system in a real waste-sorting environment under practical cost and infrastructure constraints. Early results indicate the feasibility of integrating AI-assisted material identification, interoperable data management, and circular coordination in a regional industrial setting, while also exposing challenges related to connectivity, staged validation, and operational integration. This study shows how affordable digital technologies can support circular EPS management in geographically constrained regions.

In modern business environments, the need for valid carbon footprint data management in Small and Medium-sized Enterprises (SMEs) is imperative. However, the high heterogeneity of information sources, combined with the absence of a common semantic framework for interpreting unstructured data, renders the carbon footprint calculation process prone to errors. This paper proposes a modular ontological approach for semantic validation of heterogeneous data intended for accurate carbon footprint estimation. The choice of a modular ontology allows for decoupling of standardized carbon accounting principles from industry-specific parameters, ensuring flexibility and ease of adaptation across different industrial sectors. The proposed approach establishes a structured semantic foundation for integrating state-of-the-art carbon estimation methodologies and serves as a unified source of reference for SMEs across different sectors. Furthermore, the proposed ontological model guarantees that automated data retrieval remains semantically consistent and grounded in established standards. The functionality of the proposed approach is demonstrated through an example from the wood and furniture sector.

Existing buildings account for a major share of energy consumption and greenhouse gas emissions in Europe, making their operational optimization essential for advancing the digital circular economy. Our paper proposes and evaluates a supervisory, data-driven control framework to improve real-time building operations in a mixed-use European campus building. The objective is to improve energy performance, indoor environmental quality, and system efficiency without replacing existing infrastructure through a scalable retrofit approach. The proposed methodology integrates IoT-based sensing, Niagara 4 interoperability, and the Honeywell Optimizer Suite to enable high-resolution monitoring and model predictive control (MPC)-based supervisory optimization. The system computes optimal setpoints for HVAC operation based on thermal dynamics, occupancy, and indoor air quality constraints, while existing controllers execute local control loops. A real-world case study conducted during the heating season demonstrates measurable reductions in energy use alongside maintained comfort conditions. The results highlight the effectiveness of digital retrofitting strategies in improving real-time control performance, reducing operational inefficiencies, and extending the functional lifespan of building systems. The study provides practical evidence that integrating digital intelligence into existing buildings is a viable pathway to support circular, data-driven energy management practices.

The efficient renovation of existing buildings is a complex process in which systematic attention to environmental, economic, and social aspects is crucial. In practice, however, there is often a lack of a standardized methodology that would provide a systematic comparison of individual building elements and project solutions, which can lead to inconsistent decision-making and subjective results. The aim of this work is to propose a database system of elements for the efficient renovation of buildings, which defines a set of key parameters enabling a comprehensive assessment of individual approaches. The proposed database contains quantitative and qualitative indicators from three main areas of sustainability assessment: environmental, economic, and social. This approach creates a unified framework for data collection, processing, and analysis, while also allowing for the comparison of solutions between different types of renovation elements. The database is designed as an expandable methodological tool that allows for the progressive addition of new building elements and also serves as a decision-making support for investors, designers, and other entities involved in the building renovation process. By integrating environmental, economic, and social criteria, it contributes to a more transparent and comprehensive assessment of the sustainability of building renovations and creates a basis for the future development of digital decision-making support tools.