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Sustainable urban resource management depends essentially on a sound understanding of a city's resource flows. One established method for analyzing the urban metabolism (UM) is the Eurostat material flow analysis (MFA). However, for a comprehensive assessment of the UM, this method has its limitations. It does not account for all relevant resource flows, such as locally sourced resources, and it does not differentiate between flows that are associated with the city's resource consumption and resources that only pass through the city. This research sought to gain insights into the UM of Amsterdam by performing an MFA employing the Eurostat method. Modifications to that method were made to enhance its performance for comprehensive UM analyses. A case study of Amsterdam for the year 2012 was conducted and the results of the Eurostat and the modified Eurostat method were compared. The results show that Amsterdam's metabolism is dominated by water flows and by port-related throughput of fossil fuels. The modified Eurostat method provides a deeper understanding of the UM than the urban Eurostat MFA attributed to three major benefits of the proposed modifications. First, the MFA presents a more complete image of the flows in the UM. Second, the modified resource classification presents findings in more detail. Third, explicating throughput flows yields a much-improved insight into the nature of a city's imports, exports, and stock. Overall, these advancements provide a deeper understanding of the UM and make the MFA method more useful for sustainable urban resource management. Keywords:Amsterdam circular economy industrial ecology resource management sustainable city urban planning Supporting information is linked to this article on the JIE website

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Despite the recognized potential of nature-based solutions (NBSs) to support climate adaptation, there are still wide barriers for a wider uptake of such NBS in urban areas. While tailored NBS tools could facilitate and accelerate this process, a comprehensive mapping of their availability and capacity to respond to cities’ challenges is missing. This research aims to provide an overview of tools that intend to facilitate the uptake of NBS for urban climate adaptation supporting cities in overcoming their challenges. To do so, this paper (i) presents the results of interviews and workshops with municipal officers and decision-makers from different European cities that identified the challenges they experience with NBS uptake; (ii) selects and reviews NBS tools and (iii) analyzes them on their capacity to address these implementation challenges. Our research revealed four key challenges that municipal officers experience: resources availability; level of expertise, know-how or competence; the institutional setting, and collaborative governance and planning. The results from the tools’ review show that existing tools can support overcoming a lack of expertise (31), but, to a smaller extent, can also be of use when experiencing the institutional setting (13), availability of resources (11), and collaborative governance and planning (10) as a challenge. This work provides researchers and tool developers with insights into potential market saturation as well as scarcity of certain types of tools that would match cities’ challenges, highlighting needs and opportunities for new tool development.

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To design effective strategies for sustainable urban resource management, it is essential to understand which urban characteristics underlie consumption patterns. We used multiple linear regression analyses to examine sixteen factors on their explanatory power for spatial variation in residential electricity, gas and water consumption in Amsterdam. Four models per resource were used, based on distinct spatial units aggregating high-resolution data: neighbourhoods, districts, 100 m squares and 500 m squares. We found twelve explanatory variables for spatial variability in consumption in total and nine or ten per resource. The number and relative importance of explanatory variables varies with the spatial units used. Overall, neighbourhood models explain variance in consumption data best (adjusted R 2 = 0.88, 0.86, 0.74). Income level and building type stand out for having high relative importance (top 4) in all four models for two of the three resources; migration history shows an important correlation with water consumption, which was not described hitherto. We conclude that explanatory variables for resource consumption are sensitive to size and shape of spatial units used. We recommend to use future high resolution studies for different resources of interest to determine which spatial and temporal resolutions of analysis can support urban planners and designers in formulating context-specific interventions.