More than half a century ago, Kenneth Boulding described Earth as “a single spaceship, without unlimited reservoirs of anything, either for extraction or for pollution”. The circular economy has since emerged as a paradigm addressing both resource scarcity and waste management. In Europe, since 2015, policy documents have increasingly supported this transition, emphasizing the need for monitoring to track progress, evaluate priorities, and assess decision-making impacts.

A recurring challenge in circular economy monitoring is the availability of adequate data. While monitoring extends beyond waste, waste-related data remains crucial as it reflects the potential for closing material loops. Large amounts of waste data are collected under European Regulation (EC) 2150/2002, requiring member states to report statistical data on waste generation and processing to the European Commission.

This research investigates why European Waste Statistics (EWS) fail to fully address the data availability challenge necessary to advance the circular economy. The case study focuses on the Amsterdam Metropolitan Area, using data from the Dutch National Waste Registry (Landelijk Meldpunt Afvalstoffen, LMA). Three research topics are explored: assessing the significance of policy decision impacts, understanding the semantics of waste and circular economy, and evaluating the adequacy of waste statistics for monitoring purposes.

A theoretical framework for impact significance assessment is developed, positioning significance assessment as part of a decision-making process that prioritizes alternatives based on both the context and magnitude of effects. This framework informs the design of a circular economy monitor. Monitoring requirements are further refined through a formal ontology development method, which includes interviews with prospective monitor users within the municipality of Amsterdam. By comparing user expectations with available data, tools, and socio-economic metabolism theory, misalignments are identified. Although waste statistics capture core concepts of resource flows, they often lack semantic granularity and coverage to interpret waste-related impacts, values, and circularity potentials.

An in-depth examination of the Dutch National Waste Registry highlights limitations in current data collection and gaps in circular economy theory. Four data queries illustrate that, despite these limitations, innovative computational methods can extract valuable insights into the existing waste system and its circularity potential.

The study identifies seven barriers limiting the effectiveness of EWS in circular economy monitoring, accompanied by concrete recommendations for revising the European Waste Statistics Regulation. These include financial, infrastructure, and expertise support to overcome linear-economy path dependencies, a revision of the waste definition to reduce semantic ambiguities, and the development and alignment of taxonomies based on open standards and community involvement. Recognizing that numerical data is socially produced, these measures aim to enhance the relevance, interpretability, and usability of waste statistics for circular economy policy and practice

With circular economy being high on governmental agendas, there is an increasing request from governing bodies for circularity measurements. Yet, currently existing macro-level monitoring frameworks are widely criticized for not being able to inform the decision-making. The criticism includes, among others, a lack of consensus on terminologies and definitions among scholars, politicians, and practitioners, a lack of supporting data and tools and, consequently, a lack of transparency and trustworthiness. To address those needs, a bottom-up approach to build a shared terminology is suggested as a starting point for monitoring development. The government, data providers, and tool developers are involved in the process of formal ontology development and alignment. The experiment builds upon a use case of the Amsterdam Circular Economy Monitor (2020). First, four ontology development approaches are used to create a theory-centered, a user-centered, a tool-centered, and a data-centered ontology. The ontologies are later compared, merged, and aligned to arrive at one single ontology which forms the basis of the circular economy monitor. The notes taken during the process have revealed that next to a material flow model, typical of socioeconomic metabolism analysis, policy makers are concerned with actors (i.e., institutions, companies, or groups of people) who participate in the analyzed processes and services. Furthermore, a number of terms used by the decision-makers lack clear definitions and references to be directly associated with the available data. Finally, a structured terminology alignment process between monitor users, developers, and data providers helps in exposing terminology conflicts and ambiguities.

The announcement of a new Circular Economy Action Plan as part of the European Green Deal policy has created an urgent need for the reliable information on resource flows to monitor and support the transition. An updated Monitoring Framework is set to rely as much as possible on European Statistics, however at this point there are no changes introduced in supranational statistics regulations. This raises a question whether regulations that have been created before the paradigm shift are still able to supply us with statistics necessary to inform policy makers about current successful practices, remaining barriers, positive and negative impacts of the transition and overall progress towards the set goals. This paper focuses on the Waste Statistics Regulation, specifically the relationship between the types of waste and economic activities which are considered to be the waste producers. Dutch National Waste Registry is used as a case study to compare the guidelines on pan-European waste data collection to the actual waste reports. The task of this publication is to explore to which extent the guidelines available in the Waste Statistics Regulation correspond to the operational reality. To do so it presents a computational method to link waste producers to their economic activities using a national Trade Registry. An extensive discussion of the results provides insights and recommendations for the future guidelines of waste statistics to support circular economy transition.

As appointed in the EU Circular Economy Action Plan, cities and regions in EU member countries start accompanying their circular economy strategies by monitoring frameworks, often called Circular Economy Monitors (CEM). Having the task to assess the performance towards the achievement of set targets and to steer decision-making, CEMs need to rely on a multitude of statistics and datasets. Waste statistics play an important role in circular economy monitoring as they provide insights into the remaining linear part of the economy. The collection of waste statistics is mandated by the European Commission which provides general guidelines on data collection and processing. The Netherlands has one of the most detailed waste registries among the EU countries. The country's largest metropolitan region, Amsterdam, is currently building a CEM which tracks progress over time towards the set goals, highlights which areas need improvement and estimates target feasibility. This paper uses the Amsterdam CEM as a case-study to explore how the existing system of waste registration in the Netherlands is able to support decision-making. The data is explored with the help of four queries that relate to the CEM's goals and require data mapping to be answered. The data mapping and analysis process has revealed several limitations present in the waste data collection and a number of gaps present in current circular economy research and data analysis. At the same time, the available data already supports significant insights into the status quo of the current waste system and provides opportunities for circular economy monitoring.

Urban mining aims at reusing building materials enclosed in our cities. Therefore, it requires accurate information on the availability of these materials for each separate building. While recent publications have demonstrated that such information can be obtained using machine learning and data fusion techniques applied to hyperspectral imagery, challenges still persist. One of these is the so-called 'salt-And-pepper noise', i.e.The oversensitivity to the presence of several materials within one pixel (e.g. chimneys, roof windows). For the specific case of identifying roof materials, this research demonstrates the potential of 3D city models to identify and filter out such unreliable pixels beforehand. As, from a geometrical point of view, most available 3D city models are too generalized for this purpose (e.g. in CityGML Level of Detail 2), semantic enrichment using a point cloud is proposed to compensate missing details. So-called deviations are mapped onto a 3D building model by comparing it with a point cloud. Seeded region growing approach based on distance and orientation features is used for the comparison. Further, the results of a validation carried out for parts of Rotterdam and resulting in KHAT values as high as 0.7 are discussed.

Fundamental changes in the societal use of biophysical resources are required for a sustainable transformation. Current (urban) metabolism research traces flows of energy and materials and products to capture resource use along value chains from resource extraction to production and consumption and the discharge of wastes and emissions. However, spatial relation, local carrying capacity and qualitative characteristics of the urban landscape are only featured in very few studies, even if they are becoming crucial elements towards future sustainable development. Simultaneously, spatial studies tend to neglect the dimension of processes of flows and the generated stocks that influence the construction and performance of space. Big data and GIS technologies have the potential to leverage the integration between the two fields of knowledge. Therefore, the article explores the development of an innovative method - Activity-based Spatial Material Flow Analysis -that integrates qualitative and quantitative flow specifications in material content and geographical space, starting from the analysis of waste flows relative to the Amsterdam Metropolitan Area (NL). Lastly, the article reflects on the results of the application of the AS-MFA method, namely a series of flow maps. Each flow map is a significant data-based network representation of a part of the urban metabolism within the AMA in a specific period of time.

Improving waste and resource management entails working on interrelations between different material flows, territories and groups of actors. This calls for new decision support tools for translating the complex information on flows into accessible knowledge usable by stakeholders in the spatial planning process. This article describes an open source tool based on the geodesign approach, which links the co-creation of design proposals together with stakeholders, impact simulations informed by geographic contexts, systems thinking, and digital technology—the Geodesign Decision Support Environment. Though already used for strategic spatial planning, the potential of geodesign for waste management and recycling is yet to be explored. This article draws on empirical evidence from the pioneering application of the tool to promote spatially explicit circular economy strategies in the Amsterdam Metropolitan Area.

The demand for reproducible research is on the rise in disciplines concerned with data analysis and computational methods. Therefore, we reviewed current recommendations for reproducible research and translated them into criteria for assessing the reproducibility of articles in the field of geographic information science (GIScience). Using this criteria, we assessed a sample of GIScience studies from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, and we collected feedback about the assessment from the study authors. Results from the author feedback indicate that although authors support the concept of performing reproducible research, the incentives for doing this in practice are too small. Therefore, we propose concrete actions for individual researchers and the GIScience conference series to improve transparency and reproducibility. For example, to support researchers in producing reproducible work, the GIScience conference series could offer awards and paper badges, provide author guidelines for computational research, and publish articles in Open Access formats.

The concept of Circular Economy has gained momentum during the last decade. Yet unsustainable circular systems can also create unintended social, economic and environmental damage. Sustainability is highly dependent on a system’s geographical context, such as location of resources, cultural acceptance, economic, environmental and transport geography. While in some cases an impact of the proposed change may be considered equally significant under all circumstances (e.g. increase of carbon emissions as a main contributor to the global climate change), many impacts may change both their direction and the extent of significance dependent on their context (e.g. land consumption may be positively evaluated if applied to abandoned territories or negatively if a forest needs to be sacrificed). The geographical context, (i.e. its sensitivity, vulnerability or potential) is commonly assessed by Spatial Decision Support Systems. However, currently those systems typically do not perform an actual impact assessment as impact characteristics stay constant regardless of location. Likewise, relevant Impact Assessment methods, although gradually becoming more spatial, assume their context as invariable. As a consequence, impact significance so far is also a spatially unvarying concept. However, current technological developments allow to rapidly record, analyse and visualise spatial data. This article introduces the concept of spatially varying impact significance assessment, by reviewing its current definitions in literature, and analysing to what extent the concept is applied in existing assessment methods. It concludes with a formulation of spatially varying impact significance assessment for innovation in the field of impact assessment.

Transitioning towards circular economy requires changes in the current system which yield a number of impacts on such fundamental values as human health, natural environment, exhaustible resources, social well-being and prosperity. Moreover, this process involves multiple actors and requires careful considerations of ample spatial information. While plenty of systems have already been developed to support the decision-making process, up to date no standardized framework exists for spatial modelling of impacts. The poster is based on the ongoing research and aims to suggest a standardized, yet flexible approach for impact modelling using the core concepts of GIS. The framework is expected to ensure consistency and comparability between outputs of automated simulations and elicitation of impacts defined by stakeholders and prepare impact modelling guidelines that aim to overcome the characteristics of a single project and be reused in other GDSEs.

REPAiR applies a geodesign approach to the field of waste and resource management. On that account, it aims to reveal both local and space-specific challenges of waste and resource management as well as integrated and place-based eco- innovative solutions for these challenges. In order to put this into practice, REPAiR combines Steinitz (2012) and Campagna (2014) concept of geodesign with the methodology of LifeCycle Assessments (LCA) and Living Labs (LL) and applies these to six case studies within the EU. As a reference to the peri-urban settlement structures of the six case study regions, the Living Labs, where the combined geodesign/LCA-approach will be used to discuss and co-design solutions and strategies to the specific challenges in waste and resource management, the Living Labs are called PULLs - for Peri-Urban Living Labs. As deliverable D 5.1 explains in more details, PULLs consist of a larger number of meetings, structured differently in terms of type and participants. A major component of each PULL is a series of workshops, in which regional public and private stakeholders from the field of waste and resource management are asked to participate in a co-designing process for solutions and strategies. Providing these stakeholders with a common platform of information and solution design options is the core task for a computerised interactive communication tool called Geodesign Decision Support Environment - or GDSE for short. Developing, testing and applying this GDSE is one of the pivotal elements of the REPAiR project. The GDSE will be available as an open source product once the project is finished.

In large sport’s buildings, a big part of energy can be saved by providing natural instead of mechanical ventilation. However, additional challenges arise while controlling airflow and temperatures in different zones. These measures
highly depend on the shape, construction and ventilation openings, which are mostly decided in the early design stages. Computational optimization can support these early stages of design, but needs to be performed in efficient ways. In this respect, the project proposes rapid assessment of temperature and airflow patterns using customized Grasshopper components, which would be able to evaluate a given model using CONTAM and EnergyPlus software as
simulation engine. The proposed method integrates these simulations within an environment, which is familiar to architects and is largely used for parameterization of design in its early stages. A case study (Jiangmen Sports Center, Jiangmen, China) is used to test the developed process for a large indoor sports hall.

Parametric modelling allows quick generation of a large number of design alternatives. Ultimately, it can be combined with optimization algorithms for obtaining optimal performance-driven design. However, setup of design space for optimization is a very complex task requiring designer’s a priori knowledge and experience. Therefore, this paper focuses on the process that happens before the optimization. It proposes to use multivariate analysis algorithms for exploring and understanding the relations between various design parameters, after sampling the design space. Additionally, portrayal of geometry is
introduced as an extension of conventional visualization methods, which accounts for evaluation of ill-defined design criteria by using designer’s expertise. The proposed method is computationally efficient and integrated into an environment familiar to architects. It relies on multivariate analysis algorithms together with database querying capabilities and an interactive dashboard developed for geometry portrayal.

The design of sport buildings has great impact on top-sport as well as on recreational sport-activities. It implies challenging tasks in meeting the performance-requirements. This includes the control of factors like daylight/lighting, air flow, thermal conditions, just to name a few. Such factors impact the performance of athletes and are hard to control in large sport halls; their control is even harder when the public/audience is located within the halls and require different climate conditions. While mechanical installations are often needed during competitions in order to guarantee constant conditions, relaying on mechanical installations during the daily and recreational use of the venues challenges their medium/long term sustainability. Computational form finding approaches can favour the achievement of high-performing and sustainable sport buildings. In this light, the paper tackles the use of Multi-objective and Multidisciplinary design optimization. The paper presents the concept of Multi-objective Multidisciplinary design optimization techniques to support trade-off decisions between multiple conflicting design objectives and interdisciplinary design methodology, during the conceptual design of sport buildings. The proposed method is based on parametric modelling, performance simulation tools and algorithms for computational optimization, for which the paper tackles three specific aspects. First of all, due to the complexity of large sport buildings, the formulation of the optimization and the screening of the related design variables is crucial in order to obtain a meaningful design space, which helps reducing unnecessary computational burden. Secondly, assessing performance based on measurements and analyses is crucial and can be supported by performance simulations tools; however effectively integrating performance simulations tools in the early phase of the design requires new tools. In this light, a customized computational process for the rapid assessment of temperature and airflow patterns is presented. Thirdly, the process requires the combination of design optimization and design exploration, while searching for well-performing solutions. The importance of design exploration is emphasized also for sub-optimal solutions. In order to facilitate the design exploration, the combination of optimization algorithms, multi-variate analysis algorithms and options for exploring design solutions via an interactive dashboard connected to a database are presented. To exemplify the method, specific case studies are developed as collaboration between Delft university of Technology and South China university of Technology.

Building performance simulations are usually timeconsuming. They may account for the major portion of time spent in Computational Design Optimization (CDO), for instance, annual hourly daylight and energy simulations. In this case, the optimization may become less efficient or even infeasible within a limited time frame of real-world projects, due to the computationally expensive simulations. To handle the problem, this research aims to investigate the potentials of surrogate models (i.e. Response Surface
Methodology - RSM) to be used in the building envelope design exploration and optimization that consider visual and energy performance. Specifically, the work investigates how, and to what extent, 1) problem scales may affect the application of RSM, and 2) different ways of using RSM may affect the quality of Pareto Front approximations. Thus, a series of multi-objective optimization tests are carried out; preliminary discussion is made based on the current results.