Maud Lanau

Understanding the drivers and patterns of material accumulation in the built environment is key to informing urban sustainability. Urban form is often mentioned as a likely driver of resource use and environmental impacts in cities, yet the extent of its influence on material stocks (MS) and embodied carbon (EC) remains insufficiently explored. The aim of this study is to examine how urban form influences MS and its associated environmental impacts at the neighbourhood scale. To achieve this, the study develops a workflow for systematically generating and comparing alternative urban configurations representing different urban forms. First, an urban morphology simulation model was developed to parametrically generate samples of urban configurations using morphological indicators, such as building height, ground space index, building depth, network density, and street width, as input. MS and EC are then estimated using a bottom-up and building-element-level approach, enabling the exploration of how urban form influences material demand and environmental impacts. Results show that urban form plays a critical role in shaping both the magnitude and distribution of MS and EC. Building height emerged as the dominant driver of structural material demand, while compact and higher-rise configurations generally exhibited lower per capita EC and reduced transport infrastructure -related impacts. These findings highlight the role of urban form in shaping both the magnitude and distribution of urban material demand and emissions. The proposed workflow provides a novel tool for exploring resource-efficient and low-carbon urban design strategies, supporting evidence-based planning for sustainable urban development.

We present a first iteration of stylized facts for the circular economy (CE) as a way to synthesize the knowledge compiled into interesting, relevant, and empirically grounded general statements. This compilation is the result of a collaborative review, synthesis, and stylization effort on CE knowledge. It makes the existing CE literature digestible to a variety of audiences while also highlighting strengths and weaknesses of research and implementation to-date. Each stylized fact is self-contained, using the established technical language of the field. It is accompanied by a statement of evidence, which links to the relevant literature compiled by the authors of the individual facts and multiple internal reviewers from the author team. The stylized facts presented here explore five critical dimensions of the CE: 1. Stock-flow-service nexus and stock growth; 2. Material cycles and R strategies; 3. Global supply chains and environmental impacts; 4. Economic and social challenges and opportunities, including CE business models; and 5. Policy and regulation. We group all facts into these five categories. Neither the list of topics nor the list of facts is exhaustive.The facts and arguments presented here can serve as the synthesized empirical foundation that can be translated into different forms of science communication, such as teaching material, video lectures, animations, infographics, policy briefings, and more. Such materials could be used across scientific communities, practitioners, and the wider public. By presenting a concise, system-wide perspective, the stylized facts aim to improve understanding of what the CE is, what it can deliver, and where its fundamental limits lie, thereby supporting more informed decision-making and dialogue across research, policy and practice communities.

Material stocks (MS) represent the amount and composition of construction resources embodied in the built environment, and are critical to understanding long-term resource use, waste generation, and circular economy strategies. The reliability of building MS modelling depends largely on the appropriateness of archetype classification, with building structure being a key descriptor as it reflects the core materials used. Yet, structural information is often missing from building inventories, particularly in Europe. In contrast, building features such as shape (e.g., GIS polygons), age, and height can be derived from increasingly widely available data. This study develops and tests a model that predicts building structures based on building features—particularly morphological features, using Gothenburg as a case study. A dataset of 331 multi-family residential buildings was compiled, combining building features with manually identified structural types. Multinomial logistic regression was applied to examine the relationship between building features and building structures. The results show that morphological indicators and building age significantly influence structural prediction, with building height and age exerting the greatest impact. The model demonstrated strong predictive performance, with an accuracy of 83%. Based on the predicted building structures, we estimated the MS of multi-family buildings in Gothenburg, amounting to a total of 17.2 megatons, with an uncertainty from structure classification amounting to 5.8 megatons. This study supports the enrichment of building inventory with structural information, thus supporting a more robust accounting of MS.

Recycling construction materials can significantly reduce the environmental impact of the construction and demolition sector. However, implementing circularity strategies requires a spatially refined understanding of material stock dynamics. In this study, we introduce a stock-driven and rule-based simulation that forecasts – over space and time – material stocks, flows, and associated embodied carbon resulting from construction, renovation, and demolition activities. It enables the exploration of how urban parameters, such as the recycled content of new constructions, demolition rates, renovation rates, and different types of new construction, influence material flows and embodied carbon. Using Gothenburg’s residential building stock, we demonstrate the simulation's capabilities by tracking eight building materials along with their embodied carbon over time, across the parameters, and for two scenarios: a baseline, and a policy on energy efficiency. The simulation can be expanded to include more materials and built environment assets, facilitating informed decision-making and strategies for a sustainable construction sector.

Decarbonizing construction through a circular economy requires an in‐depth understanding of the materials stocked within, and flows into, between, and beyond, the global built environment. Archetype‐based bottom‐up material stock analysis (MSA) is increasingly used to estimate the quantity, location, and embodied carbon of different construction materials within existing buildings. European MSA studies typically employ archetypes based on building use (e.g., residential/non‐residential) and/or age (e.g., historic/modern), potentially missing significant variation in material composition across different construction types. Such work also generally focuses on residential buildings and derives aggregated and/or synthetic material intensities (MIs), with non‐residential buildings in the United Kingdom rarely being considered through real‐world design data. This paper investigates the suitability of different archetyping approaches in the bottom‐up estimation of material stocks and embodied carbon in UK buildings. Concrete is revealed to consistently contribute the majority of material mass irrespective of use or construction type, with steel consistently representing a majority of superstructural embodied carbon. Despite the relative agreement between overall structural MIs for use‐ and construction‐based archetypes, varying material and sub‐/superstructural composition introduces increased heterogeneity in individual‐material and sub‐building MIs when considering building construction type. Mapping of use‐ and construction‐based MIs to the building stock level is shown to be inhibited by the infrequent specification of construction type within the utilized inventory. This gives rise to a novel, parallel use and/or construction archetyping approach, reiterating the need for the inclusion of building construction types within both MSA archetyping approaches and national building inventories.

Building stock modeling is a vital tool for assessing material inventories in buildings, playing a critical role in promoting a circular economy, facilitating waste management, and supporting socio‐economic analyses. However, a major challenge in building stock modeling lies in achieving accurate component‐level assessments, as current approaches primarily rely on archetype‐based statistical data, which often lack precision. Addressing this challenge requires scalable methods for estimating the dimensions of interior components across large building stocks. In this study, we introduce the UKResi dataset, a novel dataset containing 2000 residential houses in the United Kingdom, designed to predict interior wall systems and room‐level spatial configurations using exterior building features. Benchmark experiments demonstrate that the proposed approach achieves high predictive performance, with an score of 0.829 for interior wall length and up to 0.880 for bedroom counts, 0.792 for lounge counts, and 0.943 for the kitchen counts. Contributions of this work also include the introduction of a multi‐modal approach into the field of building stock modeling, integrating exterior features and facade imagery. Furthermore, we analyze the driving factors influencing wall length and room predictions using permutation importance and SHapley Additive exPlanations values, providing insights into feature contributions, especially facade opening information being a critical driving factor of modeling interior features. The UKResi dataset serves as a foundation for future component‐level building stock modeling, offering a scalable and data‐driven solution to assess building interiors. This advancement holds significant potential for improving material inventory assessments, enabling more accurate resource recovery, and supporting sustainable urban planning.