Digital Twins from Building to Urban Areas: An Open Opportunity to Energy, Environmental, Economic and Social Benefits

Abstract

Digital Twin (DT) technology is increasingly recognized as a key enabler for optimizing design, operation, and management across the built environment. While several reviews have addressed DTs in either building- or city-scale contexts, a comprehensive integration of these two perspectives remains limited. This paper provides an updated overview of DT developments from Building Digital Twins (BDTs) to Urban Digital Twins (UDTs), aiming to identify convergences, divergences, and future directions. The analysis is conducted through a review of recent literature and selected case studies, considering technical, environmental, economic, and social dimensions. Findings reveal that although BDTs and UDTs share common conceptual and technological foundations, scaling from single assets to complex urban systems introduces new challenges in terms of interoperability, governance, and data management. Furthermore, while environmental and economic benefits are relatively well-documented, social implications, such as citizen engagement, inclusivity, and behavioral modeling, remain underexplored. This review highlights the novelty of adopting a cross-scale perspective, emphasizing the importance of integrating technical and social aspects to fully exploit the potential of DTs for sustainable and resilient transitions. The study concludes by outlining research gaps and recommending strategies for developing more integrated, socially aware DT frameworks in both building and urban contexts.

1. Introduction

1.1. From Smart Buildings to Smart Cities Concept

Buildings are the largest energy-consuming sector worldwide, yet accounting for their use remains problematic. Complexities arise from inconsistent sector definitions across institutions and divergences in sectoral boundaries and flows (final and total energy, direct and indirect emissions), creating discontinuities across databases [1]. Despite these challenges, trends are clear: over 30% of global energy consumption, and 26% of global energy-related emissions, are due to the building life cycle, including material fabrication, construction, operation, and end-of-life, making their contribution to the energy transition essential [2]. Furthermore, with increasing wealth, more consumers are purchasing air conditioning systems and other energy-intensive devices for their homes, improving their well-being but at the same time shaping energy use and modifying demand patterns and peaks [3].

In recent years, the building industry has begun to address this issue by reducing energy demand and improving optimization through enhanced thermal performance of the building envelopes, both opaque and transparent, taking into account the occupants’ comfort [4] while also facilitating the integration of renewable energy applications such as photovoltaics and improving energy storage, including electrical, thermal, and even hydrogen-based systems [5,6,7]. Passive solutions have also been progressively implemented in building design, making the most of sun path, wind directions, and orographic configuration to improve energy performances. In response, rapid developments in digital technologies have contributed to accelerating urban transformation, where Information and Communication Technologies (ICT) and the Internet of Things (IoT) play a central role in collecting and analyzing vast amounts of data, giving rise to the concept of smart buildings [8,9].

Beyond the building scale, these systems generate valuable real-time data on energy use, mobility patterns, and resource demand. When aggregated across multiple buildings, this information forms the backbone of smart cities (SC) platforms, enabling coordinated demand–response, integration of renewable energy, and efficient urban resource management [10]. According to [11], a smart city is characterized by six smart components, divided into 33 relevant characteristics, as summarized in Figure 1.

The six pillars of smart cities, economy, people, governance, mobility, living, and environment, highlight the multidimensional nature of urban transformation. Each domain generates vast and heterogeneous datasets, ranging from energy consumption and mobility flows to governance processes and environmental monitoring. While smart city platforms enable the collection of such information, the real challenge lies in integrating and simulating these domains in a coherent framework. In this context, Digital Twins (DTs) emerge as a key factor allowing for real-time monitoring, scenario analysis, and cross-sector optimization. Thus, Digital Twin acts as the unifying layer that translates fragmented data from smart buildings and city infrastructures into actionable insights, supporting resilience, sustainability, and evidence-based governance.

1.2. What Is Digital Twining?

The concept of “twinning” originated in the aerospace industry during the NASA Apollo project in the 1960s, where physical spacecraft communicated with their Earth-bound replicas [12]. The term “Digital Twin” was formally coined by Dr. Michael Grieves in 2002, defining it as the “digital equivalent to a physical product” in the context of product lifecycle management [13]. Grieves later refined this concept in 2014, identifying three primary components: the physical asset in real space, its virtual replica in virtual space, and the intricate data and information connections that link them [13].

In the literature, the Digital Twin framework is often used improperly, with limited distinction from related concepts such as the digital model and the digital shadow. Therefore, it is essential to clearly differentiate the Digital Twin from these analogous paradigms to avoid conceptual ambiguities, as illustrated in Figure 2.

A digital model has the objective to reproduce the physical phenomena of a physical system. It is a useful tool for analysis, testing, and performance assessment of the physical system’s components. However, there is a complete absence of an automated mechanism in data exchange between the physical system and the digital model [14].

The digital shadow represents a step forward compared to the digital model. The main difference is related to the automated and unidirectional data flow from the physical system and the numerical model. So, the digital model is constantly updated by real-time data from the physical world, simulating the physical phenomena [14].

A Digital Twin is a dynamic virtual model that continuously collects and transmits real-time information from its physical counterpart [15], relying on a bidirectional flow of data between the physical and virtual domains. The virtual twin can be developed using various modeling approaches, including physics-based principles, data-driven methods based on artificial intelligence (AI) and machine learning (ML), or a hybrid combination of both [16]. Based on the numerical outcomes, the actuators modify the physical system’s behavior, reaching predetermined objectives.

1.3. Building and Urban Digital Twins

Building Digital Twins (BDTs) and Urban Digital Twins (UDTs) were initially developed in the 1990s for transportation and human behavior predictions (e.g., TRANSIMS framework [17]). They later evolved into agent-based representations and cellular-like models using synthetic microsimulation methods [18]. Thanks to more inductive approaches, the introduction of data-driven models, and the integration of 2D Geographic Information Systems (GIS) with 3D computer-aided design (CAD) building models, it is now possible to achieve a high level of detail, although computational challenges for real-time processing still exist [19]. Most of the DTs’ applications are aimed at “mirroring the reality” of a mechanical–electrical system, a fabricated material device, or an organic entity. This concept is no more valid when DTs are applied to urban areas where non-material features like social behaviors, human choices, and other qualitative parameters that contribute to the development of the cities, increase the analysis complexity and the computational costs [19,20]. In the literature, this change is not mentioned as a limitation for DT implementation, but instead as the possibility to improve its application as a thinking polarizer by posing sharp questions for urban planning [19].

Both BDTs and UDTs can work on the same aspects: design and planning, operation and maintenance, and energy management. BDTs are focused on individual building performances, enabling scenario testing for energy efficiency, space utilization, and occupant comfort evaluation for a better air quality; real-time control of HVAC systems; lightning and security assessment; predictive maintenance by identifying faults before they occur; and optimization of the energy consumption, with the integration of renewables and smart grid coordination [21]. On the other side, UDTs represent key technologies that involve the same aspects, but are also capable of a combined assessment of several buildings residing in the same area while considering the environmental and social interactions from outside of the buildings. In addition, they can model and predict the behavior of urban infrastructure for operations and maintenance [22], underground planning [23], air quality indexing [24], urban heat island impact evaluation [25], energy optimization, urban traffic reduction, and emergency management [26].

An emerging application field of BDTs and UDTs lies in the improved management and preventive conservation of heritage buildings and historic urban centers [27,28,29,30]. This is particularly relevant in the European context, where urbanization often revolves around the historic core of the city, whose centrality is simultaneously physical, symbolic, and strategic, making it an area characterized by conflicting needs but also significant transformative potential [31]. According to estimates by the CRESME (Economic and Social Research Center of Building Market) [32], Italy’s building heritage comprises approximately 15 million structures, of which 79.3% (around 11.9 million) are used for residential purposes. Nearly 60% of these buildings were constructed before 1970, indicating that the national building stock faces multiple structural and system-related issues. These challenges contribute to elevated energy consumption, increased operational costs, higher environmental impacts, and various safety deficiencies [33].

Reference [25] highlights the potential to create value for cities through the digitalization of urban scenarios and a loop of actions linking the physical and virtual worlds (Figure 3), enabling interactions among target sectors and the creation of data-driven scenarios. Furthermore, UDTs offer an opportunity for an environmentally oriented approach without disrupting the legacy and traditions of local communities [34], enabling scenarios with improved features such as better energy management and efficiency, reduced carbon footprint, and enhanced welfare.

BDT and UDT technologies serve not as replacements for smart buildings (SBs) and smart cities (SCs), respectively, but as facilitators and tools for their technological refinement [36,37], sharing fundamental goals such as resource optimization, integrated infrastructure management, and quality-of-life improvements [38]. Both paradigms rely heavily on overlapping core technologies, including the Internet of Things (IoT), big data, and artificial intelligence (AI) [36,38,39]. The distinctive value added by the BDTs and UDTs is their capacity for intelligent simulation [39], fundamentally transforming the SB and SC frameworks from an operational monitoring system into a predictive and prefigurative governance discipline [36,39]. Unlike their “smart” counterparts, which are often reactive, DTs are defined by a closed-loop, two-way communication system that uses adaptive models to forecast failures and prescribe real-time actions to the physical world. This predictive capability enables several intersection areas. It facilitates asset management for infrastructure by employing AI to analyze real-time data and anticipate defects, thus prolonging operational life through predictive maintenance [39].

However, UDTs enhance predictive resilience in public safety and health, providing multi-scale simulation platforms to model thousands of crisis scenarios, significantly improving the efficacy of emergency management decision support [40]. UDTs also support participatory smart governance by effectively visualizing the effects of policy interventions and future urban scenarios, which aids in informed choice-making and building societal consensus [38].

Several cities worldwide joined Urban Digital Twin initiatives, as reported in Table 1, assessing the environmental, economic, and social benefits.

1.4. Scope of the Work

In recent years, scientific literature has produced several important reviews on the role of Digital Twins in the built environment. For instance, Mousavi et al. [15] provided a comprehensive review of DT applications in the built environment, but their work mainly focused on technical capabilities and challenges without explicitly addressing the multi-scale transition from buildings to cities. Similarly, Yang et al. [13] analyzed DT architectures and trends in constructions, with particular attention to lifecycle management, yet the discussion of urban-level implications remained limited. Zhang et al. [52] concentrated specifically on Building Digital Twins, outlining frameworks and enabling technologies, but without extending the analysis to the broader socio-environmental impacts at the city scale.

On the urban side, Martella et al. [14] and Mazzetto [48] reviewed Urban Digital Twin platforms, exploring technical integration and governance challenges. Their emphasis, however, was primarily on urban infrastructure management and smart city applications, with less consideration for the environmental, economic, and especially social dimensions of DTs implementation. Batty and Bettencourt [19,20] also discussed conceptual challenges for UDTs, stressing their role in planning and complexity management, yet their perspective remained largely theoretical and did not systematically connect urban digitalization with building-scale experiences.

In contrast, this paper aims to bridge these two scales by providing an updated and integrated overview of the Digital Twin paradigm from buildings to urban areas. Compared to the cited reviews, the novelty of this work lies in three main aspects. First, it explicitly links the architectural and functional layers of BDTs and UDTs, highlighting their shared foundations while underlining the specific challenges of scaling from single assets to complex urban systems. Second, it broadens the scope beyond technical and operational benefits by systematically examining the environmental, economic, and social impacts of DTs adoption, supported by a wide set of international case studies. Third, and most importantly, it introduces a forward-looking perspective on the integration of social behavior modeling within UDTs, an emerging and scarcely addressed dimension in the current literature, which has the potential to enhance citizen participation, resilience, and welfare in digitalized urban governance.

Accordingly, this work not only summarizes the state of the art but also identifies gaps in existing research and suggests future directions for the development of cross-scale, socially aware Digital Twins capable of supporting sustainable and inclusive transitions in both the building and urban domains.

1.5. Review Methodology

To ensure robustness and transparency, the literature analyzed in this review was selected through a structured search process, leading to the identification of more than 80 relevant sources. The majority of the works were published in the last three years (Table 2), indicating that the aim of this paper was strongly oriented towards the most recent advancements in the field.

The consulted databases included Scopus, Web of Science, and Google Scholar, using combinations of keywords including “Digital Twin”, “Building Digital Twin”, “Urban Digital Twin”, “energy efficiency”, “urban planning”, and “social impacts”.

Digital Twin is the main subject. The majority of the papers focus on defining, implementing, or reviewing digital twin technology in various contexts. It serves as the primary tool or framework for research.

Smart building and smart city are the primary application contexts. The papers frequently discuss how Digital Twins are used to manage and optimize these environments. They represent the “what” and “where” of the digital twin’s application.

Energy efficiency and sustainability are the main goals. These keywords are often linked to the applications, highlighting the primary purpose of using Digital Twins in smart buildings and cities. The technology is a means to achieve these key objectives.

AI (artificial intelligence), IoT (Internet of Things), and sensors are the enabling technologies. These keywords represent the “how.” They are the foundational components that make the digital twin concept a reality. Sensors collect real-time data from the physical world, the IoT provides the network to transmit this data, and AI processes it to feed the digital twin model and generate actionable insights.

Inclusion criteria required that contributions were peer-reviewed and explicitly addressed digital twin applications at building or urban scale, with relevance to technical, environmental, economic, or social aspects. In addition, a limited number of institutional reports and project deliverables were considered in cases where these provided significant case studies (e.g., European and international urban twin initiatives).

1.6. Organization of the Work

The structure of this paper was designed to provide a clear and progressive discussion of the Digital Twin paradigm, moving from the building scale to the urban context. After the introduction, which frames the topic and highlights the main research gaps, the content is organized as follows:

Section 2—Architecture of Building and Urban Digital Twins: This section examines the four-layer paradigm, physical, connectivity, virtual, and application layers, detailing how sensors, communication technologies, data models, and user interfaces interact to establish a comprehensive digital ecosystem.

Section 3—Impacts of Digital Twins: This section systematically discusses the benefits of DT implementation across three dimensions: energy and environmental performance, with a focus on efficiency improvements and carbon reduction; economic implications, particularly in relation to operation and maintenance optimization and cost reduction; social aspects, including citizen engagement, inclusivity, and behavioral modeling.

Section 4—Conclusions and Future Directions: This section summarizes the key findings, highlights persisting challenges, and outlines future research avenues, such as the development of interoperable frameworks, the integration of social dimensions, and the adoption of advanced computational tools (e.g., artificial intelligence and quantum-based approaches) for next-generation digital twins.

2. Digital Twin on Buildings and Urban Areas

2.1. Building-Level and Urban-Level Digital Twins Architecture

The architecture of BDTs and UDTs is similar and typically conceptualized in several interconnected layers to effectively manage the inherent complexity of data flow, processing, and application functionalities, while facilitating the comprehensive interaction between the physical and virtual realms [53]. While specific terminologies may vary across different academic publications, a general consensus emerges around the presence of the following layers:

Physical layer;

Connectivity layer;

Virtual layer;

Application layer.

This structured approach provides a clear and logical pathway for raw data originating from physical sensors to inform complex virtual models, and subsequently, for the insights derived from these models to influence and control physical systems.

2.1.1. Physical Layer

The physical layer comprises the actual physical building or the group of buildings in an urban district, including structural elements and every mechanical, electrical, and plumbing system within their confines [16]. It represents the real-world asset that the Digital Twin seeks to replicate, serving as the primary source of real-time operational data, essential for the digital replica to accurately simulate, analyze, and predict its future behaviors and outcomes [16].

The physical layer of Building and Urban Digital Twins heavily relies on a robust network of sensors for data acquisition and actuators for physical control. In Table 3, a summary of sensors is reported, organized by application domain.

Sensors are fundamental for collecting real-time data from the physical environment of a building. This continuous data feed is crucial for the dynamic nature of Digital Twins, ensuring that the virtual model accurately reflects the current state and performance of the physical system. Various types of sensors are deployed for specific applications within buildings:

Motion/Occupancy—Traffic/Mobility Sensors: At the building level, occupancy/presence sensors are essential for understanding real-time building usage patterns, consequently optimizing space utilization and energy consumption [73]. Passive infrared (PIR) sensors are specifically highlighted for their role in intelligent and dynamic control of lighting devices based on pedestrian movement, leading to significant energy savings [70]. Beyond simple presence detection, computer vision systems utilizing dedicated cameras can be employed for more accurate people counting [71]. At the district level, traffic and mobility sensors are deployed to monitor vehicle flow. Additionally, PIR sensors are used for tracking pedestrian movement, and occupancy detection can leverage camera brightness or dedicated presence sensors [71], providing the basis for traffic density visualization and flow optimization.

Environmental Sensors: At the building level, these sensors are widely used to monitor indoor environmental quality (IEQ) parameters, including temperature, humidity, and CO2 levels [74]. Such data are critical for optimizing heating, ventilation, and air conditioning (HVAC) systems and ensuring occupant comfort and well-being [75]. At the district level, in addition to the cited parameters for the urban level, sensors measure PM2.5, PM10, total volatile organic compounds (tVOCs), and noise levels [74], aimed at assessing IEQ within the buildings and for broader urban air quality management.

Energy Monitoring Sensors: Devices such as smart plugs and general energy consumption monitors are used to track instantaneous and average power consumption (in Watts), operational status (on–off), and time of use for various building and urban areas equipment, utilities, lighting, and HVAC systems [71]. These granular data are vital for identifying specific energy inefficiencies and enabling targeted optimization strategies [70].

Lighting Sensors: Optical sensors and luminosity sensors are deployed to measure ambient light levels, which inform the adjustment of artificial brightness, assessment of lighting quality, and control of intelligent lighting systems, resulting in substantial energy savings [71].

Actuator Position Sensors: Sensors monitoring the positions of components like damper and radiator valves provide crucial feedback on the real-time performance and operational status of HVAC systems, enabling precise control and diagnostics [76].

Infrastructure Monitoring: At the urban level, sensors are designed to monitor the vibration of bridges and other critical infrastructure elements, providing data essential for structural health assessment and predictive maintenance [77]. These diverse sensors communicate through broader IoT platforms, ensuring a continuous and robust flow of data and information to the Digital Twin’s virtual counterpart.

2.1.2. Connectivity Layer (Data Flow/Communication Link)

The continuous and bidirectional flow of data and information between the physical and virtual twins is guaranteed by the following connectivity layer. Its robustness is fundamental for maintaining the real-time synchronization that defines a Digital Twin. The Internet of Things (IoT) infrastructure is unequivocally identified as a key enabler within this layer, providing constant connectivity through its interconnected network of physical devices, sensors, actuators, and advanced communication technologies [78] (Table 4).

Standard communication protocols are essential for facilitating low-latency, reliable, and secure data transmission between physical devices and cloud-based Digital Twin platforms [71].

2.1.3. Virtual Layer

This is the cognitive core of the Digital Twin, representing the digital counterpart of the physical building. It is meticulously designed to emulate the behavior of its physical twin with a high degree of detail and accuracy [16], integrating various data types coming from the other layers. There is no single way to characterize this layer, but some key constituent components are the following:

Building Information Modeling (BIM): BIM often serves as the foundational static geometric and semantic model, providing a structured and detailed basis for the building’s geometry, material properties, and integrated systems [74]. While BIM traditionally focuses on static representations, its integration with real-time data from IoT sensors transforms it into a dynamic component of the Digital Twin, enabling continuous monitoring, simulation, and analysis [80]. The evolution from static BIM to dynamic DT is a significant research area [81].

Data Models: The virtual twin employs various modeling approaches to represent and predict the physical system’s behavior. These include physics-based models, data-driven models (developed from collected data using AI/ML), and hybrid models that combine both approaches [16]. Data-driven models are particularly advantageous for complex systems where developing accurate physics-based models may be infeasible [16].

Data Management Systems: To handle the massive volume and diverse nature of data generated by a building DT, polyglot storage solutions are recommended. This includes a combination of relational databases, non-relational databases, time-series databases, data lakes, and data warehouses [82].

Analytics and Machine Learning (ML)/Artificial Intelligence (AI): Advanced algorithms for data processing, interpretation, prediction, and optimization are central to the virtual twin’s core [74], enabling critical functionalities such as energy consumption forecasting, automated fault detection and prediction [73], and optimization of urban processes [83].

2.1.4. Application Layer

This layer provides the interface through which users interact with a Digital Twin. It is comprehensive of real-time data visualization, interactive dashboards, and advanced applications tailored for various stakeholders [76]. Services delivered by the DT platform include comprehensive performance monitoring, continuous commissioning, advanced operational planning, and the crucial ability to perform “what-if” analyses to evaluate potential outcomes of changes. User-friendly interfaces, often incorporating 3D models, augmented reality (AR), and virtual reality (VR), significantly enhance user understanding and facilitate informed decision-making [84].

The true, transformative value of a Digital Twin is not derived from the sum of its individual layers, but rather from the seamless, continuous, and bidirectional interaction and data exchange between all these layers. For example, sophisticated analytical models in the virtual layer are rendered ineffective without reliable, real-time data streaming from the physical layer via the connectivity layer. Conversely, the raw data from the physical layer only become actionable intelligence when processed and analyzed in the virtual layer. Furthermore, the insights generated in the virtual layer only create tangible value when they are translated into actionable commands and feedback loops via the application layer, which then influences and optimizes the physical system. This highlights that robust interoperability and efficient communication protocols across these layers are not merely technical prerequisites but are fundamental enablers for DT to deliver its promised benefits, such as significant energy savings, enhanced predictive maintenance, and optimized operational efficiency. The concept of “closed-loop control” explicitly mentioned in the literature is a direct and powerful manifestation of this critical interdependence, where the virtual informs the physical, and the physical provides feedback to the virtual, in a continuous cycle.

3. The Impacts of Building and Urban-Level Digital Twins

The following chapter investigates the impacts of BDTs and UDTs on the built environment by following a clear progression. It begins by examining energy efficiency and CO₂ reduction (Section 3.1), as this is the most tangible and quantifiable benefit, often serving as the primary driver for DT adoption. The improvements in energy management and operational efficiency then naturally translate into the economic impacts (Section 3.2), which quantify the cost savings and financial viability resulting from predictive maintenance and optimized resource use. Finally, it concludes with the social benefits (Section 3.3), the most complex dimension, which explores how DTs ultimately enhance human comfort, public health, governance, and citizen well-being, completing the analysis from the technical to the human scale.

3.1. Energy Efficiency and CO₂ Reduction

The application of Digital Twins in the building sector has led to significant, quantifiable improvements in energy efficiency and a corresponding reduction in carbon emissions. The primary mechanism for achieving these benefits is DT’s capacity for continuous monitoring, predictive simulation, and dynamic optimization of energy-intensive systems, such as heating, ventilation, and air conditioning (HVAC) and lighting [85].

In [86], the researchers integrated a multi-objective genetic algorithm (GA) within a DT model to balance thermal comfort and energy consumption in real time. The DT served as a virtual testbed, allowing for the simulation of various control strategies before their physical application. This model-based predictive control approach led to a 25% reduction in HVAC energy consumption (measured in kWh, over a fixed period) while maintaining a high level of occupant comfort satisfaction at 78% (based on predictive mean vote thermal comfort index).

Salzano et al. in [87] implemented Digital Twin technology coupled to BIM Autodesk’s Tandem software and IoT sensors in a school building, monitoring HVAC system performance, identifying inefficiencies, and optimally planning maintenance interventions before failures occurred. The collected historical and real-time data were analyzed through machine learning methods. Applying regressions, neural networks, and anomaly detection algorithms, a 15% reduction in energy consumption (kWh reduction over a period of time) and a 20% improvement in system reliability were achieved (comparing the continuously operating time of the system with the historical data), resulting in a significant decrease in unplanned maintenance interventions.

Similarly, the authors in [88] explored a residential complex of 50 multi-family buildings in Cyprus, during the COVID-19 pandemic, for its design and dynamic assessment of energy performance. To forecast the building’s energy performance at an early design stage, a proper method, using a BDT, was developed, and predictions were based on historical data of similar buildings. The tests demonstrated the role of DTs in forecasting the non-renewable primary energy demand required by buildings, indicating that the deviation from effective data was under 3.5%. Although no measured results were reported in [88], the authors indicated that a significant improvement in energy efficiency was expected.

Beyond HVAC systems, DTs are also highly effective in optimizing lighting systems. A Digital Twin-based assessment framework, which integrated real-time building data and a probabilistic model of occupant behavior, was applied to a case study of university classrooms [89]. The DT revealed that lighting was left on in the absence of occupants for an average of 10.7 h per day. Simulating and implementing an “off strategy” using passive infrared sensors or a manager, it was possible to reduce power consumption by over 60% (measured in kWh). Furthermore, DT’s analysis showed that adjusting luminance levels, through lux measurement, to an appropriate range could save an additional 46% of the energy consumed, as LED lighting in most classrooms was over-designed.

In [90], a DT was employed to address the challenge of improving energy efficiency while preserving the unique character of historic buildings. Preliminary results, based on creating a digital twin for an office room, successfully demonstrated that data can be reliably collected, transmitted, and stored in the cloud, confirming the framework’s ability to generate a DT that reflects the building’s real-time status.

The DT’s capacity for predictive maintenance is another critical driver of energy savings and operational efficiency [91]. In [92], a comprehensive framework based on Digital Twin technology to enhance occupant comfort and enable predictive maintenance in smart buildings is shown. The proposed system is built around a Digital Twin that mirrors the physical building environment. It collects and processes data from various sources, including IoT sensors measuring temperature, humidity, air quality, and occupancy, and elaborates data through a predictive maintenance algorithm to forecast equipment failures and optimize maintenance schedules. The framework was tested in a real-world building environment, where the DT was deployed to monitor HVAC systems and indoor environmental quality. Beyond the results related to energy reduction, a potential increase in the system’s lifetime of at least 10% was reported.

This enables facilities managers to schedule repairs before malfunctions occur, thereby prolonging the operating life of vital equipment, minimizing operational disruptions, and preventing costly emergency repairs [93].

Collectively, these advancements in energy management, optimization, and predictive maintenance have resulted in substantial, verifiable benefits. A systematic review found that the integration of Digital Twins into building management systems can lead to overall energy savings of up to 30%, reduce operational costs, and improve predictive maintenance strategies [91].

At the urban scale, Digital Twins are a crucial tool for managing city-wide energy systems, from smart grids to transportation networks, to drive down emissions and enhance resilience. They provide a unified platform for integrated urban energy management. DTs facilitate the integration of multiple buildings and diverse energy systems, from centralized power plants to distributed renewable energy sources, and optimize energy distribution in smart grids [91].

A case study in Kassø, Denmark, developed a DT to integrate waste heat from a Power-to-X plant into a district heating system. The detailed simulations led to reductions in natural gas consumption and operational costs, underscoring the DT’s critical role in advancing energy efficiency and decarbonization in urban heating systems [94].

The role of Digital Twins in urban-level decarbonization is particularly significant. The authors in [95] investigated the role and impact of DT technology in promoting the sustainable development of the energy industry in China from a regional and spatial perspective. The study used data from 281 prefecture-level cities across a twelve-year period, from 2013 to 2024, to analyze how DT facilitates urban sustainability. The results showed that DTs significantly promote the sustainable development of the energy industry and enhance energy efficiency.

DTs also provide significant benefits in urban mobility and transportation. By leveraging the technology to simulate and analyze transportation networks, cities can test new policies and predict the impact of infrastructure changes on public roads before implementation. This connected infrastructure, which provides real-time insights into population movement, enables smoother and safer traffic flow, thereby reducing congestion and associated emissions [96].

3.2. Economic Impacts

Building energy efficiency heavily relies on the operation and maintenance (O&M) phase, which involves monitoring and optimizing building systems. As O&M can last for decades and account for up to 60–80% of a building’s total lifecycle cost [97,98,99], its effective management is crucial for reducing energy consumption, operational costs, and environmental impact.

DTs are aimed at enabling predictive maintenance, real-time data analysis, and improved decision-making, which collectively reduce operational expenditures and improve long-term financial performance [100].

In [100], the analysis of studies and industry reports found that AI-powered systems, which are integral to DTs, led to a mean 24% decrease in equipment downtime and an 18% reduction in maintenance costs.

Similarly, in [101], an AI-based IoT application for energy management in commercial buildings showed a 29.7% reduction in electricity consumption from HVAC optimization and a 23.4% reduction in lighting energy consumption, bringing up to 30% of cost savings.

The ability of DTs to simulate different scenarios also enables better resource management and cost control. In the manufacturing sector, which has processes similar to the construction industry, Digital Twins have demonstrated the potential to reduce energy consumption by up to 30% and decrease material waste by 20% through real-time simulation and optimization [102].

The economic value of urban-level Digital Twins is realized through enhanced efficiency in public services, optimized infrastructure management, and the creation of new economic opportunities. By providing a “precise decision-support system” for urban governance, DTs facilitate more efficient resource allocation and cost control across entire districts and cities [96].

A key area of economic benefit is the management of urban infrastructure. A systematic review on AI-powered Digital Twins for smart cities found that they led to an 8.5% increase in energy production, a 26.2% reduction in energy costs, and a 35% reduction in unplanned downtime [95].

Digital Twins also contribute to economic gains through improved urban mobility and public services. A report by Hexagon’s Digital Twin Industry Report found that organizations that have adopted DTs reported a 19% average cost savings. DTs allow for the simulation and analysis of transportation networks to optimize traffic flow and public transit routes, which reduces congestion and operational costs [103]. Singapore, for instance, implemented a Digital Twin of its public transportation system to monitor traffic and simulate evacuation routes, enabling the seamless rerouting of buses and trains during emergencies, thereby enhancing operational efficiency and safety. A case study in Hofbieber, Germany, utilized a DT to model its emissions footprint and simulate mitigation strategies, providing cost-effective insights without the need for large-scale physical interventions. Furthermore, a case study in Dhaka, Bangladesh, demonstrated a 12.3% gain in carbon offset ROI by using AI-augmented urban Digital Twins to optimize energy use and emissions [104].

3.3. Social Benefits

Historically, BDTs and UDTs have remained predominantly focused on the physical aspects of urban environments, or what is conceptually referred to as “space” [105]. This focus on physical infrastructure, such as buildings, transportation networks, and utilities, has often overlooked the interwoven social dimensions that shape the concept of “place” [105]. A building and a city are more than their physical assets; they are defined by the experiences and perceptions of their inhabitants, which can influence their physiological and psychological states, including stress and perceived safety [106]. The limitation of traditional DTs is that they struggle to represent the full lived building and urban experience, thereby limiting their capacity to comprehensively simulate and address the complex interplay between physical and social systems [105].

In [107], a BDT for a heritage building was proposed and validated, aimed at optimizing its thermal performance and structural preservation. The integration of DT with AI models enabled the energy-consuming equipment to reach a proper balance, targeting human thermal comfort. The AI model was trained on historical data, including occupants’ behavior and activity schedules, in the buildings.

A practical demonstration in a hospital setting, documented in [108], showed a BDT effectiveness in containing the viral charge of the COVID-19 virus. Specifically, the DT successfully monitored social distancing, managed queue and occupancy supervision, and tracked people within the room in a 1100-worker hospital canteen, thereby ensuring a safe return to the workplace during the COVID-19 pandemic.

In the scientific literature, some evidence-based articles highlight the role of social aspects in improving urban planning, empowering citizen participation, strengthening community resilience, and enhancing well-being and safety at the building level. However, direct assessment of social implications remains a major challenge, as most of the reviewed studies merely report presumed social benefits for specific groups of people or city residents.

In [109], the authors discussed the development of the Cambridge Digital Twin (CDT) pilot, conceived as a policy simulator to bridge policy silos across transport, housing, energy, and environment. The DT’s primary role is to test scenarios (e.g., teleworking prevalence) and quantify interdependencies, enhancing cross-boundary collaboration in policymaking. Socially, the DT must adhere to the “public good” and “trust” principles, promoting transparency and citizen engagement. Crucially, the paper warns that DTs risk creating “splintering urbanism” and are inadequate for tackling deep-rooted social issues like inequality. Social impact is measured not quantitatively but through the DT’s ability to facilitate better-informed decisions, achieve process alignment, and constructively deliver policy insights.

In [38,110,111], the studies converge in identifying the DT as a fundamental a rapidly evolving tool for planning and managing the urban environment. Beyond the energetic and economic impacts evaluated in the papers, the most significant one is related to improving governance and quality of life for citizens. In fact, in [110], the social impact was assessed through qualitative measurements, as outcomes of research interviews, underlying DT applicability in accelerating the services development and improving transparency and governance. Then, in [111], a direct social impact was not assessed but was viewed as a potential outcome resulting from the enabling of more efficient services (e.g., analyzing building energy efficiency potential), which indirectly benefits the community. Moreover, in [38], the DT impact was conceptualized as an alignment with the Sustainable Development Goals (SDGs) and the policies of the European Green Deal. The benefit was linked to DT’s ability to foster efficiency in “urban metabolism” (energy, water flows) and achieve genuinely “people-oriented planning,” with the ultimate aim of enhancing the population’s well-being.

In [112], an open and publicly available digital twin model of the Docklands area in Dublin is presented. The model was built on six layers of information, from the foundational terrain and buildings (using highly accurate BIM models) to infrastructure, mobility, and the real-time digital layer/smart city (collecting data from IoT sensors and citizens), aimed at policy evaluation and disaster simulation. Its primary social use is enabling a virtual feedback loop: citizens can explore proposed changes (like new buildings or green spaces) online and submit feedback directly to planners, promoting transparency and community involvement. Furthermore, the system allows citizens to tag and report real-life problems (like potholes), immediately routing location-specific data to the relevant authorities. Social impact is measured qualitatively by the DT’s effectiveness in generating citizen-driven data, leading to better-informed decisions, and facilitating unforeseen design options based on community needs.

Similarly, in [113], an urban digital twin was developed for the city of Herrebnerg, a town of 30,000 inhabitants in the Stuttgart metropolitan region (Germany). The city’s historical core faces challenges from high traffic volume and resulting in environmental pollution (emissions and noise). The integration of multiple layers of data and simulation inside the UDTs enabled the simulation of nine different traffic-planning scenarios (e.g., for reducing congestion) and verified the effects of interventions on traffic and emissions against the entire urban system. The integration of qualitative social data, collected via the mobile application “Reallabor Tracker” (“Real Lab Tracker”), allowed users to trace routes, rate public spaces, and record subjective impressions (barriers, fear, and noise). The case study strongly reflected the concept “people-at-the-center”, aimed at enhancing the quality of life for all inhabitants rather than focusing purely on economic efficiency. The social impact was measured through a mixed-method approach (empirical and computational): the primary social measurement was qualitatively assessed via a survey, where users were asked to indicate whether the adopted methods and technology significantly supported participatory and collaborative urban planning processes. However, the overall impact was linked to the DT’s capacity to serve as a practical “collaboration and communication tool” for decision support, which was intended to lead to smarter, more sustainable, and more democratic urban planning.

4. Conclusions and Future Directions

This work presented the application of DT from the building to the urban scale, highlighting this technology as an enabler for the energy transition and a more efficient urban future through predictive planning. In this context, the building and its information are transferred through data to the urban area, creating a dynamic system working on the interactions between the units and the whole. At the same time, the previous sections also revealed some limitations and challenges that need to be addressed in future research.

One major challenge for the future is technical: The existence of DT depends on the interaction between the virtual and real representations of the target entity, achieved through structured observation and data collection within the four-layer paradigm (physical, connectivity, virtual, and application). This dependency drives the field in two seemingly opposing directions: On the one hand, there is the possibility of achieving higher-quality, fine-grained data by increasing the level of disaggregation of reality; on the other hand, there is the need for a broader, cross-field approach that accounts for impacts and implications from non-target domains. Since DT relies on computing power, these two approaches differ in terms of performance and computational costs (Figure 4). Achieving the best results, therefore, requires balancing them. However, the development of tools that enable a cross-field approach while maintaining high-resolution data could be a game-changer.

The key concepts driving this transformation are interoperability, data quality and integration, and computational tools:

−. Software interoperability: Limited interoperability hinders the ability to apply the most suitable tools to the most relevant objects. Leveraging faster tools with domain-specific advantages requires overcoming constraints tied to proprietary code and software architecture.

−. Data quality and integration standards: Standardized workflows reduce the need for secondary corrections or modifications during processing, which can otherwise result in data loss or inconsistencies.

−. New computational tools: Machine learning algorithms for unsupervised training can enhance data quality and broaden correlation possibilities beyond energy-related features in buildings or econometric features in predictive urban planning. Deep learning algorithms can accelerate DT performance by computationally simplifying complex physical processes such as heat conduction, convection, fire propagation, or green area growth. More recently, although still at an early stage [114], quantum machine learning algorithms have also been explored in the context of DT, giving rise to the concept of “Quantum Digital Twins.” These have shown promising results in tasks such as protein interaction simulations or drug development, improving processing speed and reducing uncertainty. These tools represent both challenges and opportunities for expanding the scope and impact of Digital Twins in the building and urban planning sectors.

The lack of a clear definition of DT in the building sector is a well-documented problem in the literature [52]. Although DT technology itself is not new, its application in the building sector is relatively recent. However, the absence of standardized frameworks reported in the literature represents a significant limitation for a more consistent implementation of this technology in the sector. In Section 3, the impact of DT on the sector was categorized into three main areas (energy and environment, economic, and social) as commonly presented in the literature. In addition to these three dimensions, the literature also proposes energy and transport system efficiencies as potential targets for DT. These, however, are mostly independent of each other and are usually assessed with separate DT models.

This setup highlights the absence of a clear cross-field approach and represents the second major challenge for the future: a required change in mindset (Figure 5), taking into account the following factors:

−. The scope of DT for buildings, as well as for cities, should not be limited to the real-time optimization of energy systems, but should also include behavioral reproduction for predictive purposes. This has implications for energy demand forecasting, risk analysis, retrofitting, and urban planning, including the design of passive solutions. Moreover, it should be extended to wider contexts (e.g., urban areas) to maximize both scope and impact.

−. Targets can be defined a priori, but all other features must be considered in the analysis, since the energy sector is influenced and constrained by multiple factors, many of which are non-economic in nature. The Digital Twin framework is a promising tool for cross-disciplinary model integration, enabling the simulation of complex systems and the generation of equally complex insights. In the interim, while computational tools continue to evolve, it may be advisable to establish the most significant parameters for the selected targets through correlation analyses and weighting methods in order to reduce computational costs.

−. A new parameter must be added to the list of features. While the social dimension is typically considered only as an impact, the human factor should instead be regarded as a key driver for the evolution of buildings and cities, as recently reported in the literature [19]. Identifying and predicting human factors is one of the most difficult, yet also most critical, objectives of DT, particularly on the urban scale. These factors encompass economic, social, environmental, and opportunity-related dimensions, all of which can influence energy systems (e.g., consumption patterns of HVAC systems in buildings or urban areas). Accounting for them opens opportunities for predicting urban development, energy demand, and long-term consumption trends, enriched with geographical references. Such capabilities would provide valuable tools to support efficient planning aimed at energy optimization, welfare improvement, and sustainable development.

Author Contributions

R.I. contributed to conceptualization, visualization, and writing—original draft of the paper, and review and editing. P.D. contributed to supervision, conceptualization, and writing—original draft of the paper, and review and editing. F.C. was responsible for supervision, funding acquisition, conceptualization, resources, writing the original draft, and review and editing. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Main aspects of a smart city (authors’ elaboration based on [11]).

Figure 2 Differences between numerical model, digital shadow, and Digital Twin (authors’ elaboration based on [14]).

Figure 3 Actions for a correct physical and virtual integration for Urban Digital Twin (authors’ elaboration based on [35]).

Figure 4 The two opposing tendencies of DT in the building and urban planning sectors: on the one hand, the pursuit of higher definition and detail; on the other, a cross-field approach, balancing each other in terms of computational cost and efficiency. The figure on the right highlights the opposing tendencies in Digital Twins for the building and urban sectors: in BDTs, low cross-field integration makes computational demand primarily dependent on model resolution; in UDTs, by contrast, the extensive cross-field approach drives high computational cost even with low-resolution models.

Figure 5 The second major challenge for the future of DT in the building sector: shifting from independent feature analysis for isolated targets to an intercorrelated feature model, where all features influence each other, and the human factor (light blue) is also taken into account.

Table 1

Overview of Urban Digital Twin (UDT) initiatives across major cities worldwide.

Country (City)	Approach	Outcomes	Reference
Finland (Helsinki)	Development of a City Digital Twin integrating 3D models and urban data for planning and citizen services	Improved urban planning, participatory governance, and service delivery; early-stage but growing integration	[41]
Ireland (Dublin)	Pilot city-scale Digital Twin focused on integrating multiple datasets for mobility and planning	Better traffic management, infrastructure monitoring, and informed urban policy	[41]
Netherland (Rotterdam)	Multistakeholder urban Digital Twin for infrastructure and policy visualization	Enhanced decision-making, policy testing, improved urban resilience; challenges in organizational adoption	[41,42]
Germany (Hamburg)	Urban Digital Twin embedded in transformative research for participatory planning	Supports sustainability, inclusion of marginalized groups, and real-world experiments in planning	[43]
Italy (Turin)	Urban twin integrating real-time data from IoT, sensors, and land use monitoring	Traffic optimization, energy efficiency, infrastructure monitoring, predictive simulations	[44]
Croatia (Zagreb)	Development of GIS-based 3D urban models evolving into UDTs	Urban planning support, infrastructure monitoring, and simulation of future scenarios	[45,46]
Estonia (Tallin)	Case study on overcoming technical challenges in UDT development, using cloud and open source solutions	Improved data integration, cost reduction, and flexibility in urban management	[47]
Singapore & Dubai	Advanced integration of UDTs in smart city frameworks	Enhanced sustainability, resilience, and predictive modeling for city services	[48]
USA (New York City)	Multiple UDT initiatives: Columbia University’s Hybrid Twins for Urban Transportation, Brooklyn Navy Yard energy-focused DT, and Geopipe’s city-scale 3D modeling for gaming	Demonstrated traffic optimization and energy efficiency potential, but projects remain fragmented, small-scale, and with limited citizen participation	[49]
Australia (Sydney)	Sydney’s UDT integrating real-time & historical data (weather, traffic, crime, emissions). It uses predictive modeling (e.g., crash risk forecasting) and spatial ranking of suburbs.	Enhanced sustainability planning, predictive analytics for urban management, improved decision-making on spatial resource allocation.	[50]
Japan (Project PLATEAU)	Develops precise 3D city models across Japan, for their digital transformation, allowing visualization, simulation, and interactivity for physical and cyber space.	Supports national-scale urban planning, disaster management preparedness, and open-data urban governance	[51]

Table 2

Analysis of the number of sources by publication year.

Year of Publication	Number of Sources	Percentage of the Total
2020–2025	74	79%
2015–2019	16	17%
before 2015	4	4%

Table 3

Types of sensors used for smart buildings and smart cities, recognized in the literature (authors’ elaboration based on [54,55]).

Application	Type of Sensors	Reference
Motion sensors	Accelerometers	[56,57,58,59,60]
	Gyroscopes
	Barometers
	Magnetometers
	Passive Infrared
Occupancy sensors	Wi-Fi	[61,62,63,64,65]
	Image-based
	Radio-based
	Power meters
	Carbon Dioxide (CO2)
	Passive Infrared
	Threshold and mechanical
Environmental sensors	Light	[66,67,68,69]
	Air velocity
	Photometric
	Carbon Dioxide (CO2)
	Particulate matter
	Temperature and humidity
	Volatile organic compounds
	Electromagnetic fields
Energy monitoring sensors	Smart plugs	[70,71]
	Smart meters
Lighting sensors	Optical sensors	[71]
	Luminosity sensors
Infrastructure Monitoring	Light Detection and Ranging (LiDAR)	[72]
	Persistent Scatterer Interferometry (PSI)
	Micro-Electro Mechanical Systems (MEMS)
	Three-axis accelerometers

Table 4

Common communication technologies for smart buildings and smart cities (authors’ elaboration based on [79]).

Technology	Coverage Rate	Suitable for
Fiber optic	Up to 100 km	Smart city
DSL	Up to 5 km	Smart city
Coaxial cable	Up to 28 km	Smart city
PLC	Up to 3 km	Smart building
Ethernet	Up to 100 m	Smart city
Bluetooth	Up to 100 m	Smart building/Smart city
ZigBee	Up to 1600 m	Smart city
Wi-Fi	Up to 100 m	Smart building/Smart city
WiMAX	Up to 50 km	Smart city
Cellular	Up to 50 km	Smart city

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