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.

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.

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.

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.

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.