Our current take-make-dispose economic model faces a vital challenge as it extracts resources from the natural environment at faster rates than that the natural environment can replenish. A circular economy where businesses lower their negative impact on the natural environment by transitioning towards recycling business models (RBMs), one of the four principles of circularity, is suggested as a promising solution. For a RBM to become viable, collaboration among several stakeholders and across several industries is required. In addition, the RBM should be scalable to make a positive impact. Hence, developing RBMs is complex as organizations need to consider multiple principles imposed by the recycling, collaborative, and scalability dimensions of these business models (BMs). In addition, these principles often remain general and not actionable to the practitioners. Therefore, in this study, we researched the practical guidelines for viable RBMs that are also collaborative and scalable. The empirical setting is the reuse of textile fibers to develop biocomposite products. We studied three cases using a research-through-design approach. We contribute to the literature on RBMs by showing the six minimum practical guidelines for recyclability, collaboration, and scalability. We draw implications for within sector collaborations and advance the thought that lease constructs challenge the scalability of RBM.
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Current symptom detection methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not standardised and not consistent with HVAC process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to a very limited application of energy performance diagnosis systems in practice. This paper proposes detection methods to overcome these issues, based on the 4S3F (four types of symptom and three types of faults) framework. A set of generic symptoms divided into three categories (balance, energy performance and operational state symptoms) is discussed and related performance indicators are developed, using efficiencies, seasonal performance factors, capacities, and control and design-based operational indicators. The symptom detection method was applied successfully to the HVAC system of the building of The Hague University of Applied Sciences. Detection results on an annual, monthly and daily basis are discussed and compared. Link to the formail publication via its DOI https://doi.org/10.1016/j.autcon.2020.103344