This study investigates how energy retrofitting measures contribute to increasing the market value of multi-family residential buildings within the European real estate market. It examines how energy efficiency improvements, driven by EU decarbonization strategies, enhance the actual and perceived value of these properties. The research employs a dual-methodology approach, integrating the Cost Approach to estimate the financial impact of retrofitting with the Contingent Valuation Method (CVM) to evaluate consumer willingness-to-pay (WTP) for energy-efficient properties. Two real case studies are considered to evaluate the methodology and how the monetary value of buildings is affected by their energy efficiency. The results revealed that buildings subjected to deep energy retrofitting are more attractive to potential buyers, who are willing to pay a premium of 13.5% over properties in pre-retrofit conditions. This underscores the tangible market value increment attributed to energy efficiency enhancements. This study bridges the gap between the quantifiable costs of energy retrofitting and the market valuation, offering a dual perspective by integrating both actual cost analysis and perceived market value. Moreover, this work highlights the correlation between energy retrofit investments and increased market value in the European real estate sector.

Building Information Modelling (BIM) revolutionizes the construction industry by digitally simulating real-world entities through a defined and shared semantic structure. However, graphical information included in BIM models often contains more detailed data compared to the corresponding semantic or computable data. This inconsistency creates an asymmetry, where valuable details present in the graphical renderings are absent from the semantic description of the model. Such an issue limits the accuracy and comprehensiveness of BIM models, constraining their full utilization for efficient decision-making and collaboration in the construction process. To tackle this challenge, this paper presents a novel approach that utilizes Machine Learning (ML) to mediate the disparity between graphical and semantic information. The proposed methodology operates by automatically extracting relevant details from graphical information and transforming them into semantically meaningful and computable data. A comprehensive empirical evaluation shows that the presented approach effectively bridges the gap between graphical and computable information with an accuracy of over 80% on average, unlocking the potential for a more accurate representation of information within BIM models and enhancing decision-making and collaboration/utility in construction processes.

Urban energy systems are expected to undergo a rapid transition towards smart, sustainable, and resilient infrastructures. Within this transformation, the interaction between smart buildings and energy grids plays a critical role in shaping future urban energy solutions. Smart building–grid interaction strategies facilitate the bidirectional energy flow between buildings and urban energy systems and support the integration of renewable energy sources (RESs) into cities’ energy systems through advanced control systems, sensing technologies, and digital infrastructures. However, the adoption of these solutions remains complex due to fragmented key performance indicators (KPIs) and the diversity of enabling technologies, and it requires accurate performance-driven design and operation. Despite recent advancements, the management and evaluation of the interaction of smart buildings and urban energy systems remain challenging due to overlapping and fragmented KPIs as well as the complexity of enabling technologies. Therefore, this study aims to review the recently published research works and provide a holistic taxonomy of KPIs and enabling technologies for such interplay between smart buildings and urban energy systems to achieve the goal of sustainable energy transition in cities. The study identifies and categorizes several existing KPIs across sustainability dimensions, including technical, environmental, economic, and social, covering the KPIs to measure the performance of smart building–urban energy systems from a sustainability-aware lens, offering an integrative framework for assessing urban energy resilience and efficiency. Additionally, the study contributes to classifying the enabling technologies for smart building and urban energy system interaction and discusses the interdependencies among such technology clusters. The findings contribute to ongoing urban energy transitions by promoting systemic approaches to planning, performance evaluation, and decision-making for sustainable and equitable urban energy futures. This contributes to the sustainability of the building and energy sectors at the urban scale by promoting and helping multi-dimensional performance assessment and informed decision-making.

Achieving cost-effective energy performance while meeting sustainability goals is a challenge in retrofitting decisions within the construction industry. To enhance the decision-making process, this study introduces an IFC-based approach that integrates cost estimation and energy analysis directly within BIM. This approach supports more structured and data-informed retrofit planning by structuring cost and energy data within a semi-automated IFC-based workflow. The methodology follows a structured approach that includes three phases. The first focuses on developing a BIM model that captures the physical and semantic attributes of an existing building. This is followed by parametric energy simulations to evaluate retrofit scenarios, with cost data integrated and energy analysis reports linked to IFC elements. The final phase involves a post-retrofit cost assessment to identify the optimal scenario based on total cost, with potential for extension to other performance indicators. The framework was applied in a residential case study to evaluate the model’s functionality. The results show that IFC-based integration improves transparency, interoperability, and reliability in cost–energy assessments. By structuring data as linked IFC entities, the approach enhances BIM’s role as a decision-support tool for sustainable and economically efficient retrofitting.

Effective cost estimation for tendering plays a critical role in the building construction process, enabling efficient investment management and ensuring successful execution of the construction phase. Traditional cost estimation procedure involves manual information processing to extract and match technical data from textual description construction resources. This activity requires practitioner deep experience and manual effort, often resulting in errors and, in the worst scenario, judicial disputes. In response to the increasing demand for structured information and automated processes, this study addresses the need for Public Administrations to achieve better control over the data contained in public tendering documents provided to practitioners. To fulfill this objective, a framework is proposed to automatically retrieve information from these documents, serving as a support tool to map items within the documents, highlight missing data, and critical semantic ambiguity. The designed framework aims to develop a tool for automatically identifying similarities between work items and their corresponding elementary resource items in Price List tendering documents. By leveraging the information retrieval NLP technique of cosine similarity through TF-IDF, a methodology was developed to support and facilitate practitioners' activities. Finally, the framework was tested on four case studies extracted from Lombardy Regional Italian price list documents showing that the resulting support tool is able to automate the analysis process and efficiently reveal inconsistency. The model successfully extracted and correctly matched the elementary resource to the corresponding work query in 75% of the cases where the elementary resource was present in the list. Additionally, the model proved to be a valuable tool in helping practitioners identify missing resources