The building sector is responsible for a significant share of global greenhouse gas emissions, raw material usage, and waste generation, driving the need for new circular design strategies. Among these, Design for Disassembly (DfD) promotes the reuse, repair, and recycling of building components. However, existing quantitative DfD assessment methodologies generally require extensive preliminary studies, which limit their practical use. This article presents a new quantitative DfD assessment methodology developed within the EU-funded INFINITE project, which aims to provide designers with a simple yet robust tool to evaluate the detachability potential of building integrated systems without requiring prior environmental studies. This methodology has been designed to evaluate specific DfD scores for maintenance, reuse, and recycling, using the mass and lifespan of products or systems as weighting factors. The tool was tested and validated on several systems developed during the INFINITE project. In the specific case of the Building Integrated Solar Thermal (BIST) system, it successfully identified key design improvements—such as enhanced accessibility for maintenance operations and optimized component connections. Industrial partners reported high usability and recognized the tool as a valuable decision-support instrument during early development phases. Nevertheless, the assessment methodology also revealed some limitations related to the assessment of the specific components and end-of-life scenarios, and to the absence of a holistic evaluation of trade-offs between mass-based score and environmental impacts.

Identifying buildings best suited for demand-side flexibility within a building stock is crucial for the effective operational deployment of flexibility by site managers. Existing methodologies mainly focus on energy benchmarking and rarely address flexibility explicitly. This study introduces a data-driven approach that uses historical smart meter electricity consumption data to identify buildings within a portfolio that are highly predisposed and likely to provide flexibility. Instead of relying on verified DR performance, the methodology assesses proxy indicators to evaluate suitability for programs such as load shifting and peak shaving. The approach first applies a two-step clustering process to extract typical load profiles representative of the portfolio’s consumption patterns. These profiles are then qualitatively and quantitatively analysed to assess their predisposition to flexibility in terms of load shape. Following this identification, buildings are characterized and grouped according to their operational consumption patterns using an additional clustering step. Clusters with high flexibility potential are then retained, and the most energy-intensive buildings among them are selected. Applied to a dataset of 97 university campus buildings, the methodology narrowed the sample down to 13 buildings. Under an incentive-based flexibility objective targeting peak demand periods, 12 buildings were identified as suitable for morning flexibility and one for evening flexibility.