In the European Union, construction is responsible for 36% of CO₂ emissions and 40% energy consumption. The reuse of construction materials has been receiving increasing attention, including regulations established by the European Union, and cities establishing goals to reuse construction materials. This is the case for Amsterdam, which established the goal of reusing 50% of construction materials in new construction by 2030. Part of the challenge of reuse of construction materials in urban areas is to optimize the waste-to-resource loops: finding the optimal scale and location for circular construction hubs—facilities that collect, store, and redistribute construction waste as secondary construction materials. In this paper, we use the supply and demand of timber construction materials in Amsterdam as a case study to find the optimal scale and location for construction hubs. We used the spatial simulated annealing algorithm as an optimization method for balancing the trade-off between small and large-scale hubs, using cost-effectiveness to compare potential locations and identify the optimal solution. We found that the optimal number of hubs for our study area is 29, with an average service radius of 3 km. This study has implications for policymakers, urban planners, and companies seeking to implement circular economy principles.

Rapid urbanization and a growing world population has exerted unsustainable pressures on the environment, exacerbating climate change through unrestrained material usage and greenhouse gas (GHG) emissions. Since the turn of the century, transitioning to a circular economy (CE) has been seen by policy makers as a potential solution for resource scarcity and climate mitigation. Cities, which possess a high density of human activities, material stock, and waste production, are major contributors to emissions. This is especially true due to the concentration of construction activities in cities – the industry is responsible for 38% of CO2 emissions and 40% energy consumption globally. On the other hand, cities can also facilitate the implementation of circular strategies, thanks to increasing availability of data on space, people, and materials in cities. While the importance of cities for the circular transition is recognized in literature, earlier studies and policy documents on “circular cities” focus on urban governance strategies. Scholars have therefore called for a deeper understanding of the spatial aspects of CE since the late 2010s, engendering the recent integration of spatial disciplines, such as urban planning, regional economics, and geography, into the study of CE. Moreover, the increasing availability of spatial data, especially on the location of material stocks and flows, provides an unprecedented opportunity to develop a data-driven understanding of where, and how far, materials should travel in a CE. This research therefore asks the question, “what determines the locations and scales of closing material loops in a circular economy?” The question was answered in 5 chapters (chs. 3-7), using both quantitative and qualitative spatial analysis methods, as well as present- and future-oriented perspectives. The research scope moves from general to specific, with earlier chapters (chs. 3-6) analysing 10 material types for the whole country of the Netherlands, and later chapters (chs. 6-7) focusing on construction materials in the city of Amsterdam and its surrounding region. Two novel data sources were used throughout the research. Waste statistics from the Dutch National Waste Registry provided current locations of waste reuse; and a prediction dataset from the Dutch Environmental Assessment Agency provided locations for future supply for construction waste and future demand for construction materials. In chapter 3, a theoretical foundation for understanding locations and scales for closing material loops was constructed by identifying the drivers, barriers, and limitations of circular urban manufacturing - processes that produce goods using local secondary resources. By conducting a literature review and interviewing experts, it was found that there were several caveats to closing material loops at a local scale. Factors that determine the locations of circular urban manufacturers were identified from three perspectives: space, people, and flow. In chapter 4, the factors affecting locations of waste reuse in the Netherlands were identified using spatial correlation. The previously identified space, people, and flow factors were translated into quantitative spatial factors that could affect the location of waste reuse. Correlations were found for flow and space-related factors, but not for people-related factors, which suggests that actors within the waste-to-resource supply chain tend to attract each other and cluster together to form agglomerations, and that locations of waste reuse are not related to attributes of the local population, such as local income, skills, or education. In chapter 5, the location and scale of waste reuse clusters in the Netherlands were then identified using spatial statistical methods. This answered the main research question from a spatial econometric perspective, identifying industrial clusters for closing material loops. It was found that all the studied materials except for glass and textiles formed statistically significant spatial clusters. To determine the scale of spatial clustering, the grid cell sizes for data aggregation were varied, to find the cell size that had the strongest spatial clustering. The best fit cell size is ~7 km for materials associated with construction and agricultural industries, and ~20–25 km for plastic and metals. In chapter 6, to answer the question from a spatial planning perspective, spatial parameters were identified for circular construction hubs - facilities that close material loops by collecting, storing, and redistributing demolition waste as secondary construction materials. Using the Netherlands as a case study, spatial parameters were extracted from two sources: Dutch governmental policy documents, and interviews with companies operating circular hubs. Four types of circular construction hubs were identified: urban mining hubs, industry hubs, local material banks, and craft centers. The spatial requirements for the four hub types were translated into a list of spatial parameters and analysis methods required to identify future locations - site selection, spatial clustering, and facility location. Finally, in chapter 7, spatial optimization was used to identify the optimal scale and location for circular timber hubs in Amsterdam and its surrounding region, answering the main research question from the perspectives of industrial ecology and logistics. The optimal scale was defined as a scale that is most cost effective, minimizing costs and maximizing emissions reductions through timber reuse. The optimal number of hubs for the study area was 29, with an average service radius of 3 km. The cost effectiveness was affected mostly by transportation and storage costs, while emissions savings had minimal effect. As an overall conclusion, five tensions were identified for determining locations and scales for closing material loops, because of the diverse and sometimes misaligned spatial perspectives. The first three tensions are conceptual, addressing contrasting perspectives for defining closing material loops - as urban manufacturing or urban mining; for their locations - as clusters or hubs; and for the factors that affect locations and scales - as spaces, people, or materials. The final two tensions are methodological, addressing contrasting approaches to time - looking at the present or the future; and to methods - quantitative or qualitative.

Adopting design approaches that allow products to last multiple use-cycles supports European Commission objectives to reduce greenhouse gas emissions and reduce primary material impacts. Remanufacturing is an example of an appropriate circular strategy and it can be applied in a variety of industries that are intensive materials users. However, most companies have not yet adopted design strategies facilitating remanufacturing at scale. In this paper, we explored how design management can facilitate the implementation of Design for Remanufacturing, based on a literature review and in-depth interviews. Seven companies active in business-to-business markets were interviewed about the design-related opportunities and barriers they see for remanufacturing. We found that access to technical knowledge is not a barrier, whereas integrating this knowledge into the existing design process is. We conclude that design management can contribute to the uptake of Design for Remanufacturing for the following reasons: by making the value of Design for Remanufacturing to the company at large explicit, by building bridges between internal and external stakeholders, and by embedding Design for Remanufacturing into existing processes by means of Key Performance Indicators (KPIs) and roadmaps.