Summary: Material usage and the related embodied environmental impact have grown in significance in the built environment. Therefore, cities and governments need to develop strategies to reduce both the consumption of resources during usage phase as well as the embodied impact of the current building stock. This article proposes a new component‐based building inventory database as a basis to develop such strategies using building stock modeling. The developed database clusters the building stock according to building typology (single‐family houses, multifamily houses, and office buildings), age, and the main construction systems of the different building components. Based on the component makeup, it lists the necessary material input and waste output for different refurbishment options for each building component. The advantages of the proposed database structure are shown based on two applications for the developed database for Switzerland. The component‐based database allows optimization of refurbishment strategies not only from an energetic perspective, but also with respect to materials, both on the input (sourcing of materials) and the output (waste streams) level. The database structure makes it possible to continuously extend the data set by adding new refurbishment options or add data such as component‐specific lifetimes, costs, or labor intensities of the refurbishment options. In combination with an aligned economic model, this would give an even more holistic view, impact, and feasibility of different refurbishment scenarios both in environmental and economic terms.

City Digital Twins have emerged as pivotal tools for representing and modelling urban systems. While existing literature emphasizes technological and framework development, limited attention has been given to the practical usability of digital twins in urban planning and decision-making processes. This paper addresses this gap by presenting a participatory approach for a city digital twin (CDTE) development, specifically tailored for supporting stakeholder communication and decision-making in the urban energy transition domain. The study, conducted in three phases, utilizes participatory methods, involving local stakeholders in the development process. The focus is on the Swedish city of Gothenburg, which is actively pursuing climate-neutral goals by 2030. The research integrates quantitative energy modelling and the creation of a web-based interface for the CDTE. The scenarios, grounded in local needs and challenges, explore the impacts of urban development models, climate warming and renovation measures on the building stock. The CDTE, developed and tested through workshops with diverse stakeholders, proves to be effective for the visualization and the discussion of various decarbonization scenarios. Key findings from users’ assessments underscore the significance of clarity and readability in scenario content and user interface for fostering interactions among different administrative spheres. This research contributes to the broader discourse on leveraging City Digital Twins for informed decision-making in urban contexts, providing insights into the practical application of digital twins in addressing the complex challenges of urban energy transition.

Buildings are responsible for around 30 to 40% of the energy demand and greenhouse gas (GHG) emissions in European countries. Building stock energy models (BSEMs) are an established method to assess the energy demand and environmental impact of building stocks. Spatial analysis of building stock energy demand has so far been limited to cases where detailed, building specific data is available. This paper introduces two approaches of using synthetic building stock energy modelling (SBSEM) to model spatially distributed synthetic building stocks based on aggregate data. The two approaches build on different types of data that are implemented and validated for two separate case studies in Ireland and Austria. The results demonstrate the feasibility of both approaches to accurately reproduce the spatial distribution of the building stocks of the two cases. Furthermore, the results demonstrate that by using a SBSEM approach, a spatial analysis for building stock energy demand can be carried out for cases where no building level data is available and how these results may be used in energy planning.