New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.

The aim of this paper is to present a proposal for a national building standard in Sweden. We define requirements for the proposed standard, e.g., it should support development of 3D city models, connect to building information models (BIM) and national registers and be based on a national classification system for the urban environment. Based on these requirements we develop an Application Domain Extension (ADE) of the building model in the proposed CityGML 3.0 standard denoted CityGML Sve-Test. CityGML 3.0 includes several new features of interest, e.g., the space concept, enhanced possibilities to convert data, and to link to other standards. In our study we create test data according to CityGML Sve-Test and evaluate it against the requirements. It is shown that BIM models (in Industry Foundation Classes, IFC, format) can be converted to CityGML Sve-Test and that a classification system facilitates this conversion. The CityGML Sve-Test dataset can be used to increase the automation level in building permissions checking and a related study shows that CityGML 3.0 has capabilities to link to legal information and be a base for 3D cadastral index maps. Based on our experience, we suggest that the national building standard should conform to international standards and, if possible, include a classification system. The exchange format (GML, JSON etc.) might change, but to be based on a standardized data model ensures harmonized structures and concepts.

The European building stock was renewed at a rapid pace during the period 1950–1975. In many European countries, the building stock from this time needs to be renovated, and there are opportunities to introduce energy efficiency measures in the renovation process. Information availability and increasingly available analysis tools make it possible to assess the impact of policy and regulation. This article describes methods developed for analyzing investments in renovation and energy performance based on building ownership and inhabitant socio-economic information developed for Swedish authorities, to be used for the Swedish national renovations strategy in 2019. This was done by analyzing measured energy usage and renovation investments made during the last 30 years, coupled with building specific official information of buildings and resident area characteristics, for multi-family dwellings in Gothenburg (N = 6319). The statistical analyses show that more costly renovations lead to decreasing energy usage for heating, but buildings that have been renovated during the last decades have a higher energy usage when accounting for current heating system, ownership, and resident socio-economic background. It is appropriate to include an affordability aspect in larger renovation projects since economically disadvantaged groups are over-represented in buildings with poorer energy performance.

The historic building stock is not homogeneous, which implies a need for differentiated energy renovation strategies in order to balance energy efficiency requirements and building conservation goals. This paper presents a new method for developing a base for differentiated energy renovation strategies for heritage-classified multifamily building stocks. Our suggested method combines different building databases using an extract, transform and load (ETL) technology. The method for this study was tested on the available information for heritage-designated and -classified multifamily buildings in the municipality of Stockholm, Sweden, and in the county of Halland, Sweden. The two cases reflect the heterogeneity of the Swedish Building stock. An important achievement is that the results visualise the relationship, not detectable before, between energy use, energy performance, year of construction and heritage classification within each of the selected building stocks. A specific result is that the energy-saving potential in the older building stock is insignificant in relation to the entire stock. The results contribute to an improved understanding of relationships both within and between the two historic building stocks, which is useful for developing differentiated energy renovation strategies.

The presence of hazardous materials hinders the circular economy in construction and demolition waste management. However, traditional environmental investigations are costly and time-consuming, and thus lead to limited adoption. To deal with these challenges, the study investigated the possibility of employing registered records as input data to achieve in situ hazardous building materials management at a large scale. Through characterizing the eligible building groups in question, the risk of unexpected cost and delay due to acute abatement could be mitigated. Merging the national building registers and the environmental inventory from renovated and demolished buildings in the City of Gothenburg, a training dataset was created for data validation and statistical operations. Four types of inventories were evaluated to identify the building groups with adequate data size and data quality. The observations’ representativeness was described by plotting the distribution of building features between the Gothenburg dataset and the training dataset. Evaluating the missing data and the positive detection rates affirmed that reports and protocols could locate hazardous materials in the building stock. The asbestos and polychlorinated biphenyl (PCB)-containing materials with high positive detection rates were highlighted and discussed. Moreover, the potential inventory types and building groups for future machine learning prediction were delineated through the cross-validation matrix. The novel study contributes to the method development for assessing the risk of residual hazardous materials in buildings.