The viability of novel network-level circular business models (CBMs) is debated heavily. Many companies are hesitant to implement CBMs in their daily practice, because of the various roles, stakes and opinions and the resulting uncertainties. Testing novel CBMs prior to implementation is needed. Some scholars have used digital simulation models to test elements of business models, but this this has not yet been done systematically for CBMs. To address this knowledge gap, this paper presents a systematic iterative method to explore and improve CBMs prior to actual implementation by means of agent-based modelling and simulation. An agent-based model (ABM) was co-created with case study participants in three Industrial Symbiosis networks. The ABM was used to simulate and explore the viability effects of two CBMs in different scenarios. The simulation results show which CBM in combination with which scenario led to the highest network survival rate and highest value captured. In addition, we were able to explore the influence of design options and establish a design that is correlated to the highest CBM viability. Based on these findings, concrete proposals were made to further improve the CBM design, from company level to network level. This study thus contributes to the development of systematic CBM experimentation methods. The novel approach provided in this work shows that agent-based modelling and simulation is a powerful method to study and improve circular business models prior to implementation.
BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
Indigenous rights’ relationship to ecological justice in Amazonia has not been explicitly explored in the literature. As social scientists rarely talk about violence against non-humans, this case study of conservation in Amazonia will explore this new area of concern. Ethical inquiries in conservation also engage with the manifold ways through which human and nonhuman lives are entangled and emplaced within wider ecological relationships, converging in the notion of environmental justice, which often fails to account for overt violence or exploitation of non-humans. Reflecting on this omission, this chapter discusses the applicability of engaged social science and conservation to habitat destruction in Amazonia, and broader contexts involving violence against non-humans. The questions addressed in this chapter are: is the idea of ecological justice sufficiently supported in conservation debate, and more practical Amazonian contexts? Can advocacy of inherent rights be applied to the case of non-humans? Can indigenous communities still be considered 'traditional' considering population growth and increased consumptive practices? Concluding that the existing forms of justice are inadequate in dealing with the massive scale of non-human abuse, this chapter provides directions for conservation that engage with deep ecology and ecological justice in the Amazonian context. doi: 10.1007/978-3-030-29153-2 LinkedIn: https://www.linkedin.com/in/helenkopnina/
MULTIFILE
The COVID19 pandemic highlighted the vulnerability in supply chain networks in the healthcare sector and the tremendous waste problem of disposable healthcare products, such as isolation gowns. Single-use disposable isolation gowns cause great ecological impact. Reusable gowns can potentially reduce climate impacts and improve the resilience of healthcare systems by ensuring a steady supply in times of high demand. However, scaling reusable, circular isolation gowns in healthcare organizations is not straightforward. It is impeded by economic barriers – such as servicing costs for each use – and logistic and hygiene barriers, as processes for transport, storage and safety need to be (re)designed. Healthcare professionals (e.g. purchasing managers) lack complete information about social, economic and ecological costs, the true cost of products, to make informed circular purchasing decisions. Additionally, the residual value of materials recovered from circular products is overlooked and should be factored into purchasing decisions. To facilitate the transition to circular procurement in healthcare, purchasing managers need more fine-grained, dynamic information on true costs. Our RAAK Publiek proposal (MODLI) addresses a problem that purchasing managers face – making purchasing decisions that factor in social, economic and ecological costs and future benefits from recovered materials. Building on an existing consortium that developed a reusable and recyclable isolation gown, we design and develop an open-source decision-support tool to inform circular procurement in healthcare organizations and simulate various purchasing options of non-circular and circular products, including products from circular cascades. Circular procurement is considered a key driver in the transition to a circular economy as it contributes to closing energy and material loops and minimizes negative impacts and waste throughout entire product lifecycles. MODLI aims to support circular procurement policies in healthcare organizations by providing dynamic information for circular procurement decision making.
