Evaluating the Economic Impact of Digital Twinning in the AEC Industry: A Systematic Review

Abstract

This study conducts a comprehensive systematic review of the economic impact of Digital Twin (DT) technology within the Architecture, Engineering, and Construction (AEC) industry, following the PRISMA methodology. While DT adoption has been accelerated by advancements in Building Information Modelling (BIM), the Internet of Things (IoT), and data analytics, significant challenges persist—most notably, high initial investment costs and integration complexities. Synthesising the literature from 2016 onwards, this review identifies sector-specific barriers, regulatory burdens, and a lack of standardisation as key factors constituting DT implementation costs. Despite these hurdles, DTs demonstrate strong potential for enhancing construction productivity, optimising lifecycle asset management, and enabling predictive maintenance, ultimately reducing operational expenditures and improving long-term financial performance. Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems.

1. Introduction

1.1. The Evolution of the Concept

There is growing recognition of Digital Twins (DTs) as a disruptive and evolving technology in the Architecture, Engineering, and Construction, (AEC) industry, enabling virtual representations, real-time data capturing, system integration, process simulations, and smart proactive decision-making. According to IBM [1],

“A digital twin is a virtual representation of an object or system designed to reflect a physical object accurately. It spans the object’s lifecycle, is updated from real-time data and uses simulation, machine learning and reasoning to help make decisions”.

Stochastic archetype models and timeless DT models have been proposed as alternatives to traditional bottom-up building stock modelling approaches—such as archetype-based and building-by-building methods—to mitigate model uncertainties and enhance the reliability of outputs in energy and indoor environment modelling [2]. While both utilise digital models to emulate the actual settings, assets, or environments, DTs differentiate from traditional simulations by incorporating real-time data and enabling a bidirectional flow of information between the physical asset and its corresponding digital representation.

Sanderse and Weippl [3] suggest that models have been utilised to represent the real world in engineering for a relatively long time, with NASA’s physical “twins” of the Apollo spacecrafts dating back to 1967. However, creating virtual “body-doubles” using computers has become possible only in the last quarter of the 20th century. The term “Digital Twin” was first coined by Michael Grieves in a presentation on product lifecycle management (PLM) at the University of Michigan in 2002 [4,5,6,7], where all essential constituents of a DT model, the real object, the virtual object, and data capture and exchange between the physical assets and the digital model were laid out; the concept was initially referred to as a “conceptual ideal for product lifecycle management”, “Mirrored Spaces Model”, and “Information Mirroring Model” before finally being rebranded as a “Digital Twin” [4].

1.2. Digital Twins in the AEC Industry

Digital Twins are rapidly emerging as a transformative force in the AEC industry, offering the potential to enhance operational and cost efficiency, improve asset lifecycle management, and support data-driven decision-making. Established in 2017, the UK’s Centre for Digital Built Britain (DBB) played a substantial role in advancing the DT uptake within the AEC industry. The DBB’s initiatives—the National DT Programme and the Gemini Principles, published in 2018—paved the way for the systemic integration of digital technologies into the built environment. The tenth NBS annual BIM survey [8], carried out before COVID, with over 1000 participants indicated that 9% of the companies surveyed were already using DTs, sensors, and machine-to-machine communication, with another 11%, 14%, and 14% planning to use it in one-year’s, three-years’, and five-years’ time, respectively. With the best estimates based on NBS findings, nowadays at least 480 companies in the UK must be using some form of DTs, sensors, or machine-to-machine communication. However, studies have established cost factors as significant barriers to the adoption and implementation of the DT [9,10,11,12,13,14]. Beyond the costs associated with DTs, the far-reaching benefits explain the growth in DT adoption and implementation globally. Nonetheless, research shows that, in addition to integration problems, the limited awareness of the technology, shortage of experts, challenges with scalability, and high initial costs are among the main issues that have yet left the full potential of the DTs underexplored [9]. The initial cost line is just one among many other streams of cost associated with operation, maintenance, refurbishment, staff, training, and technology, to name but a few.

1.3. The Research Questions, Aim, and Rationale

Surged advancements in BIM, the IoT, and Big Data and an increasing demand for the integration of real-time data throughout buildings’ lifecycles resulted in research on DTs in the AEC industry gaining momentum since 2016. A bibliometric study by Hosamo et al. [15] reviewed publications on DT technology in AEC-FM (Architecture, Engineering, Construction, and Facilities Management) between 2016 and 2022, demonstrating an increase in research interest during this period.

Similarly to other novel and emerging technologies, Digital Twins face a wide range of challenges. While many of these challenges are well-documented, one area that remains relatively uncharted is the economic impact of Digital Twin(ning) within the AEC industry. This study, therefore, aims to systematically evaluate the economic implications of DT technologies’ adoption as reported in the existing body of literature. The overarching goal is to address the following research questions:

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What gaps exist in the current research regarding the economic impacts of Digital Twin(ning) in the AEC sector?

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Where do the critical unknowns lie that could unlock greater value for different stakeholder groups through the use of Digital Twins?

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In what ways should the economic knowledgebase be advanced to offer robust, evidence-based guidance for decision-making around the implementation of Digital Twins?

In this context, the significance of this study lies not only in what it reveals about the current knowledge but also in how it can empower AEC professionals, clients, and policymakers to evaluate and adopt DTs more strategically, ensuring that their use is grounded in demonstrable and contextually relevant economic value while also warranting its continued, sustainable, amenable, democratised, open, and affordable applicability.

1.4. The Study Outline

Given the wide range of economic themes explored in this study, a structured approach was required to examine the various cost categories and financial implications associated with DT implementation in the AEC industry. To enhance clarity, and support the organisation of the analysis, a thematic framework has been developed. Figure 1 presents the analytical flow adopted in this study, illustrating how key topics ranging from initial investment and operational costs to funding models, cost constraints, and lifecycle benefits are conceptually interconnected. This framework underpins the structure of the subsequent discussion and synthesis developed in this study.

1.5. Review Significance

DTs have emerged as a highly intriguing concept in research over the last decade, gaining significant attention for their innovative potential in construction and the built environment, as shown in Figure 2.

During this period, there have been review papers published on the application of DTs in construction projects and the built environment, specifically focusing on concepts such as project monitoring, sustainability, facility management, safety, and performance optimisation. However, despite this growing body of literature, no study has systematically reviewed the economic impact of Digital Twinning in the AEC industry. Addressing this gap, the present review aims to provide a focused synthesis of existing studies, exploring cost-related implications and identifying opportunities for cost efficiency through the adoption of DT technology in the AEC industry. Through this study, the authors aim to map the state of the art of the research on DTs and cost, thereby illustrating future research directions to better understand and quantify the cost implications of DT applications in the AEC industry.

2. Review Methodology

This study focuses on the economics of DTs in the AEC industry. A structured review methodology was employed to review the existing literature, ensuring a comprehensive understanding of the topic and identifying research gaps. The methodology includes analysing publication trends, defining a comprehensive keyword search strategy, and applying a systematic paper selection process to ensure relevance and quality.

2.1. Keyword Search

To ensure a comprehensive review, a systematic search strategy was employed using a combination of keywords, including “digital twin”, “construction”, “AEC”, “Architecture, Engineering and Construction”, “building”, “cost”, “economic”, “investment”, “capital”, and “expense”. The papers where these keywords appeared in the title, author keywords, or abstracts were selected. The final keyword search string was as follows:

TITLE-ABS-KEY (“digital twin” AND “construction” OR “AEC” OR “Architecture, Engineering and Construction” OR “building” AND “cost” OR “economic” OR “investment” OR “capital” OR “expense”).

The search was conducted in Scopus, which is one of the major academic databases. This platform was selected for their extensive coverage of high-impact journals and conference proceedings in the fields of construction and digital technologies.

2.2. Review Period

This review focused on publications from 2016 to date, which corresponds to the period when DT technology began to gain prominence in construction research as the mainstream applications of Digital Twins became available [16]. This timeframe captures the evolution of DT applications, from conceptual developments to practical implementations in construction projects. By selecting this period, this review ensures the inclusion of the most relevant and impactful studies that discuss both theoretical advancements and practical applications of DTs, with an emphasis on their cost implications. However, there were no results for the keyword combination for 2016. From 2017 onwards there has been a continuous increase in publications, showing that it is still a topic that interests researchers around the world (Figure 2).

2.3. Research Paper Selection Process

This study adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to guide the data collection process for the systematic review (Figure 3). Figure 3 was developed with reference to the structure illustrated in Tuhaise et al. [17], with modifications to align with the scope of this study. PRISMA is a well-established approach that enhances the transparency and reliability of systematic reviews by clearly outlining the rationale and step-by-step procedures for identifying, selecting, excluding, and including relevant literature [18]. From the keyword search spanning from 2016 to 2025, authors were able to retrieve 425 journal articles and 58 journal reviews from Scopus. We limited our selection to 483 journal articles and reviews, as this represents a substantial body of literature while ensuring the inclusion of high-quality, peer-reviewed sources, which are generally more reliable and credible than other types of publications.

3. Initial Investment and Implementation Costs

According to buildingSMART [19], underinvestment is a critical impediment for BIM-centred digitalisation; e.g., this is the case for DTs, because only 1% of the revenue is invested back into information technology. Cost and time have been reported as substantial barriers to the broader adoption of DTs for existing buildings [8]. The digitisation of existing assets for building their DT may be costly due to the complexity of their structural and spatial layouts, which requires the manual processing of point cloud scans. Novel methods have been proposed to address this issue [20].

3.1. High Upfront Investment and Cost Components

The upfront investment required for implementing DT technologies in the construction and built environment sector remains a major concern. The high upfront investment in the DT technology has been investigated by numerous studies and has been found to be a significant barrier to DT uptake [9,12,21,22,23,24,25]. Key cost components include hardware acquisition, software licencing, and advanced sensor systems [26,27]. For instance, high-performance computing infrastructure is essential to render detailed 3D environments and support real-time operations [26,28]. Moreover, data governance frameworks and cloud platforms are critical for integration, demanding an additional investment [28]. Parida and Moharana [29] suggest that the implementation of DTs requires substantial upfront investments in advanced technologies, including sophisticated imaging systems, software, and computational resources. For instance, Seth et al. [30] highlight that the high costs associated with developing accurate digital replicas can be prohibitive for many healthcare institutions, particularly those operating under resource constraints. This could also be the same for any sector considering the current economic situations. This sentiment is echoed by others who note that the costs of setting up and operating DTs are often perceived as too high, leading to reluctance in adoption, due to a lack of transparency in these expenses [31,32]. The results of studies by Abanda et al. [9] and Akanmu et al. [22] reveal that DT implementation requires data processing systems with scalability capabilities, resulting in excessive costs. Khoo et al. [33], however, assert that DTs are widely recognised as versatile and scalable solutions that enable cost-effective resource tracking and modelling, through two-way real-time communication, facilitating scenario simulations and solution development. They, conversely, carry on to underscore that the upfront investment and production costs (as highlighted by Jemal et al. [34] and Rasheed et al. [7] amongst others) or high initial capital costs [35] can potentially counteract the benefits of implementation of DTs. The financial problem associated with constant cloud-based information processing requires the careful consideration of the cloud providers and trade-offs between the cost and performance [36]. Kayhan et al. [37], who reviewed key Industry 4.0 strategies—including blockchains, the IoT, Big Data, DTs, and Machine Learning—for building resilience against disruptions in the healthcare supply chain, concluded that significant upfront costs, together with the need for specialised technical expertise, and potential integration issues with existing systems remain the main challenges facing the implementation of such technologies. Kim and Kim [38] argue that it is currently costly to apply DTs to noise barrier tunnels in operation. However, they claim that their proposed methodology would not incur a significant increase in cost. Another significant contributor to the upfront cost is the deployment of sensors and IoT devices for real-time monitoring. These include environmental and structural sensors, cameras, RFID tags, and communication modules, all of which must be installed, calibrated, and maintained [39,40]. In addition, the acquisition of specialised software tools, such as Unity3D, C# scripting, and database technologies, adds to the capital expenditure [28]. Though the initial costs are often high, long-term gains justify the investment in the technology [41].

3.2. Cost-Effective Strategies and Emerging Alternatives

To address, alleviate, or pre-empt high upfront investment costs, alternative approaches that aim to reduce costs through technological innovations or simplified DT concepts have been speculated. Low-cost dust sensors have been used by Khan et al. [42] to devise a BIM-based DT for automated dust control on construction sites. Using built-in Apple LiDAR combined with UAV photogrammetry, Harshit et al. [43] argue that low-cost data can be utilised to build high-quality DT models, enough to accommodate up to LOD3, for cost-effective design, operation, and maintenance strategies. To mitigate the high complexity and costs associated with DTs, researchers have proposed alternative solutions, such as vision-based digital shadowing (DS) [44]. Unlike DTs, which integrate digital and physical environments through a real-time, bidirectional connection, DS functions as an evolving digital representation that mirrors a physical entity with a time lag due to its non-real-time nature, resulting in lower cost implications. While as-built building surveys are widely used for documenting building information, developing DTs, and assessing and planning maintenance needs [45], it has been suggested that, compared to conventional manual as-built surveys, some 3D scene reconstruction techniques, such as UAV-based photogrammetry, can provide more efficient and cost-effective solutions for “digital as-built documents”—Digital Twins—with detailed visual information and measurements [46]. The integration of BIM and the IoT have been proposed to enhance the building performance and facility management where high initial setup costs have been pointed out as one of the main challenges in return for reductions in maintenance and operational costs [47]. Jiang et al. [48] describe a cost-effective method to build a DT for the clearance check and redesign methods of low-level underpasses in highway widening projects at preliminary design stages. The authors then proposed a sustainable approach to urban road planning, based on a DT, multi-criteria decision-making, and GIS (DT-MCDM-GIS) framework, where mapping the cost using Cost Distance, Cost Back Link, and Cost Path functions in GIS has been factored in to fully cater for the economic pillar of sustainability TBLs for road construction works [49].

3.3. Integration with Existing Buildings and Infrastructure Assets

Challenges associated with retrofitting or integrating new technologies such as DTs into legacy infrastructure raise issues around the interoperability across fragmented platforms. Legacy system integration, custom APIs, and protocol translation layers add to complexities around interoperability and stakeholder alignment issues. The need for protocol translation layers that will enable the effective and efficient communication of DT data comes with great costs. Studies by Aldabbas [11] and Boje et al. [14] established that the implementation of DTs on legacy systems, often through retrofitting, involves great integration costs. Integration complexities further amplify costs when existing legacy systems must be interfaced with DT platforms [28]. As highlighted by Yang and Ng [50], the absence of unified platforms for stakeholders exacerbates implementation difficulties. The lack of interoperability standards and the requirement for custom application programming interfaces (APIs) can lead to project delays and budget overruns. In complex infrastructure projects, especially those involving multiple contractors and phases, the alignment of DT systems across stakeholders presents another cost dimension. OviedoHernandez et al. [51] examined the potential of emerging trends and innovations in the operations and maintenance of photovoltaic plants, focusing on both BIM and DTs. However, their discussion of cost implications remains primarily centred around BIM. Hofmeister et al. [52] provide an example of how dynamic knowledge graphs can help to realise connected DTs, by combining previously isolated tools and data for the dynamic control of district heating networks, where minimising the associated total generation cost is one of their stated targets.

3.4. Human Resource and Organisational Readiness Costs

The upfront and continuing costs of the training and retraining of the staff and reengineering workflows to support the DT deployment can add up to significant sums. Personnel training and ongoing skills development, as well as organisational restructuring and digital strategy, have been picked up as contributors to readiness costs. Beyond the initial training expenses is the often-overlooked excessive costs of workforce retraining to operate upgraded digital infrastructure or meet regulatory requirements [10,24]. Additionally, challenges arise in sourcing and training skilled personnel capable of managing and operating DT systems [27,50]. These human resource expenses extend beyond initial training and include ongoing skill development, which is essential to keep pace with the evolving technological landscape. Organisations are also often required to restructure their workflows and re-engineer business processes to embed DT capabilities effectively, which further elevates implementation costs. Keskin et al. [53] suggest that a robust technical architecture along with a digital strategy—comprising means of lowering upfront technology costs—are, therefore, imperative.

3.5. Long-Term Economic Viability

The debate around DTs’ potential for long-term cost savings in a constant trade-off with maintenance, upkeep, and updates costs, and the unpredictability of ongoing costs needs a further in-depth evaluation. While there are some saving potentials in O&M [54], and benefits for sustainable infrastructure [55,56,57], uncertainties in economic feasibility [31] are still major concerns. Hosamo et al. [58] claim that using DTs for detecting building faults, preventing future failures, and cutting overall O&M costs remains uncertain. Hosamo et al. [54] suggested a DT approach to integrate BIM with real-time sensor data, occupant feedback, and a probabilistic model of occupant comfort to detect and predict HVAC issues that may impact comfort, leading to a potential reduction in energy consumption and a latent increase in its service life by at least 10%—resulting in significant cost savings for both O&M.

Patel et al. [57] outlined the broader implications of DT technology in infrastructure management. DTs enable the real-time monitoring and management of assets; this aligns with findings from the other literature that underscores the growing trend of employing digital models in engineering [56]. The increased efficiency and accuracy afforded by ASCE 75-22 (American Society of Civil Engineers’ Standard Guideline for Recording and Exchanging Utility Infrastructure Data) can lead to more informed decision-making when it comes to maintenance and resource allocation strategies, ultimately enhancing the sustainability of transportation infrastructure [57]. In addition, the economic feasibility of DTs is further complicated by the need for ongoing updates and maintenance. Methuselah [59] discussed how DTs can lead to cost savings and operational efficiencies, but the initial setup and continuous operational costs can deter organisations from investing in this technology. The necessity for IT expertise and the unpredictability of operational expenses are additional barriers identified by Sommer et al. [31], which contribute to the hesitance in adopting DT solutions, especially in small- and medium-sized enterprises (SMEs). Megahed and Hassan [55] delved into the realm of DTs and their evolution in the context of Building Information Modelling (BIM), cutting-edge technologies, platforms, and applications throughout the project’s lifecycle phases. Their research underscores the potential of DTs as a comprehensive approach to planning, managing, predicting, and optimising Architecture, Engineering, and Construction (AEC) projects. Greif et al. [60] suggest digital transformation as a solution to rising building activity costs. However, their focus was largely on transportation and inventory costs, with only a limited reference to operational costs. Additionally, both the extent of the transformation and the complexity of DTs are highlighted as factors that could significantly impact overall costs [60].

4. Operational and Maintenance Costs of Digital Twins in Building Management

DTs, as dynamic virtual representations of physical assets, offer substantial advantages in operational efficiency and lifecycle cost optimisation. However, their integration into building operations comes with significant operational and maintenance (O&M) costs. This review synthesises the current literature on the economic implications and recurring expenditures associated with DT implementation in the Architecture, Engineering, and Construction (AEC) sector.

4.1. Blockchain and Data Management Costs

The incorporation of blockchains into DT systems offers enhanced data integrity and security but introduces considerable cost implications. Hunhevicz et al. [61] emphasised that storing data within smart contracts on-chain can drastically increase transaction costs, especially as the volume of submitted data grows. Similarly, Nour El-Din et al. [62] and Figueiredo et al. [63] identified the high costs of blockchain data storage and transaction processes as barriers to adoption. Conversely, Figueiredo et al. [64] concluded that blockchains secure performance-based contracts and sustainability metrics; however, the economic feasibility of their full-scale deployment remains limited.

4.2. Production and Accessibility

Cost-effective DT production methods, such as 3D-printed models based on photogrammetry and laser scanning, have been explored to reduce development expenses [65]. Despite this, achieving a seamless interoperability between digital models and XR (AR/VR/MR) applications remains technically and financially challenging [66]. Low-cost, universally accessible DTs are still largely experimental and not widely scalable in practice. The energy consumption associated with sensor networks and data transmission is another significant factor, especially for large-scale urban implementations [40]. DT systems in buildings and infrastructure constantly process data from multiple sources, often relying on cloud servers that contribute to energy use. Solutions to mitigate these costs, such as edge computing and energy-efficient devices, are under exploration but are not yet universally adopted.

4.3. Maintenance, Monitoring, and Data Updating Cost

Ongoing system maintenance is an essential yet often underestimated component of DT costs. Quirk et al. [67] note that regular updates and accurate data input are necessary to keep the digital model synchronised with its physical counterpart. These updates require continuous human and technological resources. Once implemented, DT systems incur continuous operational and maintenance (O&M) costs. The O&M phase is typically the most financially burdensome throughout the asset lifecycle due to fragmented data management and insufficient standardisation. Several authors identify the recurring costs associated with maintaining the real-time data flow, remote monitoring infrastructure, and sensor recalibration [39,40]. Moreover, DTs necessitate continuous software and firmware updates, requiring a coordination between software developers, IT staff, and end-users. As the system complexity grows, so do the costs associated with error handling, version control, and user retraining. The need for dedicated IT support staff and cybersecurity professionals also grows in parallel with the increased system sophistication [27,68]. Raitviir and Lill [69] show that real-time monitoring through DTs can optimise maintenance in complex systems like water distribution networks, supporting predictive maintenance strategies that reduce emergency repair costs and extend the asset lifespan. However, the O&M phase is often the most financially demanding aspect of DTs. Siccardi and Villa [70] and Sresakoolchai and Kaewunruen [71] point out that fragmented data systems and insufficient standardisation contribute to inefficiencies and rising operational costs. Maintaining real-time data flows, recalibrating sensors, and managing remote monitoring systems require ongoing financial inputs [39,40,69].

4.4. Software, Hardware, and Cybersecurity Costs

The system complexity also necessitates frequent software updates, version control, and the retraining of staff, all of which contribute to cumulative costs [27,68]. Additionally, the need for specialised IT and cybersecurity personnel increases with the system sophistication. Cloud-based data processing and storage further add to operational expenses due to the high energy consumption and regular server maintenance [23,72]. To counter rising cloud-related expenses, researchers are investigating alternatives like edge computing and low-power devices, though adoption remains limited [40]. Data security in regulated industries such as healthcare and transportation also adds substantial recurring costs due to compliance and auditing requirements [73,74]. Furthermore, ensuring data security and privacy in high-volume DT systems remains costly and technologically challenging [27,68]. In highly regulated sectors, such as healthcare or transport, compliance with data protection legislation adds to recurring costs. Periodic updates, AI/ML model training, and the management of large heterogeneous datasets also require sustained resource allocation [28,75,76]. Despite this, predictive maintenance and automated decision-making can reduce long-term O&M expenses if properly optimised [50].

4.5. AI Integration and Predictive Maintenance

Integrating AI into DT systems enhances predictive capabilities but adds another layer of maintenance complexity. AI models require frequent training on accurate and comprehensive datasets, demanding intensive computation and thereby increasing operational costs [77]. Almatared et al. [78] also highlight the need for capital-intensive storage systems to manage the ever-growing data volumes, often necessitating periodic upgrades. AI-driven DT systems are particularly dependent on high-quality data inputs, which means that regular calibrations are crucial to maintaining system accuracy [79]. Predictive maintenance when effectively deployed can mitigate some of these ongoing expenses by anticipating issues before they become costly failures [80].

5. The Cost–Benefit Analysis and the Return on Investment of Digital Twins in the Built Environment

The implementation of DTs in the built environment has garnered increasing attention due to their potential for enhancing operational efficiency, sustainability, and return on investments (ROIs). While significant research has focused on the post-occupancy phase, specifically in operations and maintenance, studies examining DT use during the construction phase remain limited [81,82]. This review explores the cost–benefit landscape of DTs across various phases of building lifecycles, drawing from both the construction and broader infrastructure sectors.

5.1. Cost Efficiency and Lifecycle Optimisation

DTs offer substantial long-term savings by enabling predictive maintenance, real-time data analysis, and improved decision-making. Lifecycle benefits include reduced construction costs, enhanced material recycling, and improved environmental sustainability [82]. Furthermore, a DT has measurable benefits in terms of productivity, lifecycle cost reductions, and enhanced safety outcomes. The ROI can be realised through predictive maintenance, rework reductions, and optimised resource use [45,71]. For example, enhanced data visibility and real-time analytics facilitate better decision-making, which results in reduced waste and higher quality outcomes. DTs also allow for resource simulation and scenario modelling, making them effective tools for managing complex infrastructures with greater cost control [83]. One compelling example is a DT developed for a regional energy system, which achieved a substantial reduction in the one-time investment (approximately $446,000) and reduced heat and cooling losses, although it slightly increased annual operational costs [84]. This highlights a recurring theme in DT research—higher upfront costs often lead to longer-term operational efficiency [85].

5.2. Cost Implications Across Sectors

In sectors such as manufacturing and oil and gas, DTs have shown a direct impact on the ROI by reducing downtime, enhancing risk prediction, and enabling scenario-based planning [86,87]. These capabilities extend into urban planning and smart cities, where DTs allow for integrated infrastructure management and promote sustainability, especially in the face of climate change [88].

The construction industry has been slower to adopt integrated DT platforms, due to high implementation costs and technological fragmentation. Many technologies—such as the IoT, laser scanning, RFID, and smart sensors—are still used in isolation, rather than within unified DT ecosystems [89].

5.3. Demonstrated Cost Benefits

Multiple studies provide evidence of DTs improving the cost-effectiveness in specific applications. Engel et al. [90] reported a 98.2% improvement in tracking performance using DTs for energy management, while incurring only a 4.2% increase in costs. DTs have also been used in academic buildings to develop cost-efficient accessibility simulations using robotic assistance [91].

DTs in nuclear facilities offer another valuable case, where their use has been proposed to improve long-term monitoring and decommissioning practices, justifying higher upfront costs through long-term savings [92]. These examples support the idea that cost considerations should be evaluated across the entire asset lifecycle, not just at the point of implementation.

Several studies highlight that the integration of DTs leads to energy savings [28,39], an increased asset lifespan [93], and fewer disruptions during facility management activities [50]. Case studies from tunnel infrastructure and rail systems illustrate how DTs shorten inspection cycles and reduce the need for physical site visits [28,94]. AI-based fault detection and condition monitoring tools can also alert facility managers before system failures occur, enhancing both safety and cost efficiency.

5.4. Technology Integration and Cost Reduction

Advanced DT systems integrating BIM, the IoT, and artificial intelligence (AI) further drive ROIs by automating monitoring and improving fault detection. For example, Hu et al. [83] introduced an intelligent BIM-enabled DT framework for structural health monitoring that interpolates sensor data, thus reducing costs and enhancing system performance. Likewise, Aldabbas [11] noted that integrating DT technologies in modular construction enhanced production management through tracked on-site progress. On the other hand, Barkokebas et al. [25] assessed the impact of DTs on off-site construction based on lean thinking and asserted that the average waiting time per off-site module was greatly reduced according to the number of DT interventions. Furthermore, DT systems contribute to workforce productivity by offering immersive visualisation, remote collaboration tools, and automated reporting functionalities. The ROI is positively correlated with digital maturity and effective integration strategies. Projects that integrate BIM, GIS, and IoT platforms into their DT ecosystems tend to achieve greater benefits in terms of time savings and performance optimisation. Moreover, studies indicate that a higher digital maturity correlates with an improved ROI, as organisations with integrated digital ecosystems benefit more from data visibility, immersive collaboration, and automation [45,71].

5.5. Challenges and Strategic Considerations

Despite its benefits, DT implementation comes with notable challenges. High initial investments, ongoing data management costs, and technical complexity often deter adoption. However, these costs can be offset by benefits such as reduced rework, fewer site visits, and better resource use, particularly in tunnel and rail infrastructure [28,94]. Researchers like Adade and de Vries [95] argue that DTs enhance urban management efficiency and resource use, while others highlight cost savings through reduced material waste and increased safety [96,97,98]. Granting that the sustainable management of building data comes with growing operational costs, Boje et al. [99] examined the integration of BIM and DTs in facilitating a Lifecycle Sustainability Assessment (LCSA) and concluded that the integration improved the LCSA of the case study building. To further reduce implementation costs, scholars suggest leveraging hybrid systems that integrate AI for data prioritisation and BIM model conversion.

5.6. Economic Value and ROI

Despite high upfront and ongoing costs, several authors highlight the long-term financial benefits of DT adoption. Rafsanjani and Nabizadeh [100] reported improved product quality, operational efficiency, and cost reductions in organisations using DTs. Banyai and Kovacs [87] argue that comprehensive cost–benefit analyses often reveal favourable ROIs over the system lifecycle, justifying the initial investment.

6. Industry-Specific Cost Drivers

6.1. Sector-Specific Adoption Challenges

The economic implications of DTs in the AEC industry vary significantly across sectors due to differences in operational processes, regulatory environments, and technology adoption levels. These sector-specific dynamics act as key cost drivers influencing both implementation and long-term viability. Aldabbas [11], for instance, identifies sector-specific implementation barriers in modular construction projects due to different workflows, technologies, or compliance requirements that affect cost planning. On large infrastructure projects, dynamic data processing needs often demand scalable DT models, which require an additional investment to adapt effectively [101].

Additionally, organisational and cultural shifts are needed to adopt DTs effectively. The construction industry, as Liu et al. [102] argue, must embrace data-driven decision-making, a shift that is organisationally and financially demanding. Unlike the manufacturing sector, which often possesses pre-established digital infrastructures, construction lags in digital maturity [9], making DT adoption more expensive and time-consuming.

Despite these industry-specific challenges, a DT brings many benefits to the AEC industry as well. In fact, Bado et al. [56] estimate that integrating DTs could result in cost savings between 15 and 25% by 2025, demonstrating potential long-term economic benefits. For example, in modular integrated construction, Jiang et al. [103] proposed a blockchain-enabled cyber–physical DT model for monitoring the project progress and controlling the cost and quality. Their follow-up study [104] presents a smart modular integrated construction (SMiCO) system that employs UWB and RFID devices to relay real-time nD data, such as the cost, identity, and location, back to the DT model. Similarly, Grübel et al. [105] utilised Dense Indoor Sensor Networks (DISNs) in conjunction with AR-based Fused Twins (FTs), arguing that these tools enable new human–building interactions and have the potential to reduce the energy consumption, maintenance cost, and bandwidth. However, realising these savings often requires overcoming steep entry costs, which is particularly challenging for smaller firms [106].

6.2. Regulations and Compliance Costs

Cybersecurity and data governance are additional ongoing concerns. Sayed et al. [107] emphasise the critical importance of safeguarding sensitive information within Digital Twins, which may require advanced security protocols and increase compliance-related expenditures. Saretta et al. [108] similarly point out that the adherence to data protection regulations is a non-trivial cost component of DT deployment. Regulatory and legal requirements vary across sectors and influence cost structures accordingly. Alhadi et al. [73] and AlBalkhy et al. [10] highlighted how sector-specific compliance needs, particularly regarding data protection, differ between residential and non-residential sectors.

6.3. Standardisation and Interoperability Expenses

The establishment of a robust framework for connectivity and data integration is essential, which may require an investment in specialised training and compliance with standards [108]. This helps to build a strong system that allows different technologies, tools, and data sources in DTs to connect and work together smoothly. The integration complexity with existing digital systems also represents a major cost driver. DTs require a seamless interoperability with platforms like BIM. However, Liu and Lin [109] emphasise that the real-time data integration through frameworks such as Industry Foundation Classes (IFCs) is still evolving, adding to technical and financial burdens through system customisation, troubleshooting, and expert support. Saretta et al. [108] further underline the need for the investment in training and infrastructure to support these integrations, which can strain budgets and delay implementation.

Another significant economic barrier is the lack of unified standards for DT implementation. Fragmentation in technological protocols and data formats results in duplicated efforts and increased integration costs. Researchers such as Borkowski [79], Calvetti et al. [110], and Chi et al. [111] identify this standardisation gap as a key inhibitor of DT scalability. To address these challenges, Abanda et al. [9] and Akanmu et al. [22] advocate for the creation and promotion of industry-wide regulatory frameworks and common standards, which could streamline processes, reduce redundancy, and lower implementation costs across the AEC sector.

6.4. Scalability and Expansion Costs

The need to expand DT systems across large-scale projects or diverse environments introduces further financial and technical complexity. For infrastructure projects, the demand for scalable DT models capable of handling dynamic data environments is high [101]. This expansion often requires an investment in high-performance computing, cloud storage, and customised data integration solutions to support increasing volumes of data from various sources.

Ongoing operational costs must also be accounted for. These include system maintenance, personnel training, and software updates that are necessary to maintain the effectiveness of DT systems over time [108]. As the DT usage grows and becomes more embedded into daily operations, these recurring costs can accumulate significantly, especially in projects that span several years or rely on frequent updates.

7. Funding, Partnerships, and Financial Models

Within the spectrum of economic models, the construction industry is increasingly leveraging various funding opportunities, financial models, and beneficial partnership for the implementation of DTs. To advance the financial viability of the DT technology, Chen et al. [112] proposed more collaborative processes among government, academia, industry, and software creators. Such collaborative frameworks can enable the sharing of knowledge and resources, ultimately leading to optimised budget utilisation and enhanced project outcomes [113].

7.1. Funding

There is a notable trend of governmental bodies recognising the potential of DTs, often providing funding opportunities to encourage innovation in construction technology [114]. Venture capital funding by firms such as construction technology companies and government grants can serve as crucial financial instruments to catalyse the technological adoption required for DTs. As noted by Campoy-Nieves et al. [115], the collaborative administration of DTs helps share associated financial burdens.

7.2. Partnerships

Strategic partnerships play a pivotal role in facilitating the successful adoption of DTs within construction projects. Collaborations among technology providers, construction firms, and academic institutions are essential to create a comprehensive ecosystem conducive for digital transformation. Public–private partnerships (PPPs) are emerging as a favoured approach wherein public agencies and private enterprises collaborate to share the costs and risks associated with infrastructure projects, including DT initiatives. Some public–private partnerships aimed at energy-efficient processes lead to financial models that lessen the high costs of the DT technology [72]. The implementation of DTs comes with high costs and risks that are often best shared through partnership schemes. Risk-sharing agreements that distribute the risks associated with DT implementation can encourage investments by mitigating concerns about potential failures or setbacks. Organisations like the Construction Industry Institute (CII) have recognised the value of such models and are working to promote their adoption in construction practices. Academic partnerships can lead to the development of standardised practices for DT implementation, which is currently fragmented within the industry and constitutes a barrier to widespread adoption [112,114]. As emphasised by Bellavista and Di Modica [116], the transversal nature of the DT application requires the participation of diverse sectors of industry. Drawing on lessons learned and best practices from other industries, particularly in aerospace and manufacturing, can serve as models for these collaborative initiatives in construction.

7.3. Financial Models

To adequately fund DT projects, stakeholders in the construction sector must embrace innovative financing models, such as performance-based contracts and outcome-driven funding. These financing methods tie payments to the achievement of specific performance metrics facilitated by DTs, which aligns the interests of all stakeholders towards efficiency and sustainability. Financial modelling to support the lifecycle of DT projects is also crucial. Lifecycle costing models, which account for both the initial investment and ongoing operational costs, are increasingly being considered in the construction industry. This holistic financial approach allows firms to assess the total cost of ownership and the potential ROI over the lifespan of a DT, rather than solely focusing on upfront costs [113]. The concept of value engineering, where projects are analysed to optimise functionality while minimising costs, can also be applied to justify investments in DTs by clearly outlining the benefits [114].

8. Barriers to Adoption and Cost Constraints

Despite the notable benefits of DT and cyber–physical systems, the adoption of DTs in the construction industry is still in its infancy [10,22]. The widespread uptake of the technology has been hindered by several barriers, particularly concerning cost constraints and the complexity of the implementation. Davletshina et al. [36] examined the application of Geometric Digital Twins (GDTs) and noted that the development costs outweighed the projected benefits. The authors then advanced an enhanced GDT (EGDT) that utilised point clouds to generate a meshed and semantically rich model and established that the optimised model heralded affordable and scalable DT road models. The cost of the software and hardware, resistance to change, integration of data and technologies, ownership of data, return on investment (ROI), budget uncertainties, and impacts on the cashflow profile were identified as key barriers to DT applications in both construction and manufacturing sectors [9].

8.1. Resistance to Technological Change

The resistance to technological change, scepticism towards AI-driven suggestions, and concerns over job redundancies have also emerged as non-financial constraints [27]. Practitioners may fear that automation and real-time monitoring systems will replace traditional expertise, thereby limiting the workforce buy-in. To overcome these socio-cultural barriers, change management strategies and stakeholder engagement are chiefly required [45]. In the absence of proven cost–benefit cases, especially in the Global South or among SMEs, stakeholders may find it difficult to justify investment. As concluded by Nie et al. [117], the absence of a clear understanding of the specific benefits that DTs can bring to different sectors contributes to hesitation among potential users. Additionally, there are psychological barriers related to the perceived risks associated with DT technology. Concerns about data privacy and security are prevalent, particularly in industries that handle sensitive information [118]. Organisations may fear that the extensive data collection required for DTs could lead to breaches of confidentiality or the misuse of information, further complicating the decision to adopt this technology. This apprehension is particularly pronounced in sectors such as healthcare, where patient data protection is paramount [118]. To overcome most barriers to DT uptake, Alnaser et al. [12] noted that a top management endorsement and properly lined up structures, strategies, and processes to support the evolutionary nature of the DT are key.

8.2. High Initial Capital Expenditure (CapEx)

Several barriers hinder the widespread adoption of DTs in construction. The high initial capital expenditure (CapEx), lack of skilled personnel, and integration difficulties with legacy systems are consistently cited obstacles [27,45]. Costs associated with developing the necessary infrastructure, including sensors, data management systems, and the integration with existing technologies, can be prohibitive for many organisations, especially small and medium enterprises (SMEs) [119]. These SMEs struggle to justify investments without clear, short-term returns [120], especially as access to venture capital or public funding constrains their ability to pursue cutting-edge technologies. The financial burden of these upfront costs often deters potential adopters, leading to a reluctance to invest in DT technologies, despite their long-term benefits [117]. On the other hand, while the knowledge of the high initial costs of deploying DTs is acknowledged by several studies, Abanda et al. [9] highlighted cost uncertainty as a tangible hinderance to the implementation of the DT technology in the construction industry. However, while the high cost of implementation, including the cost of high-specification computers, software, and periphery equipment (e.g., goggles, GPUs, etc.), remains a significant challenge and can impose a barrier to adopt a spectrum of new technologies from BIM to the metaverse (including DT) in the AEC industry [121], it is expected that projected savings in time and costs, an improved quality, reduced rework, and streamlined O&M and repair procedures would be significant enough to justify the increase in the upfront investment and capital expenditure.

8.3. Integration/Interoperability Challenges with Incumbent/Legacy Systems

The complexity of integrating DTs into existing systems is a critical barrier to the DT uptake. Stakeholders often express concerns regarding the interoperability of DTs with legacy systems, which can hinder their effective deployment [122]. Many legacy systems do not support data synchronisation, making it necessary to develop custom middleware solutions, which adds time and costs to the implementation [28]. The integration of BIM with DTs in legacy facilities sometimes requires the reality capture of the existing facility as mesh models or point clouds. Accordingly, dense point clouds are often associated with the challenge of verifying the completeness of the data on-site [123]. Many organisations face challenges in aligning their operational processes with the requirements of DT technology. Recurring operational expenses, such as software licencing, cloud subscriptions, and data storage, continue to burden adopters [28,124].

The complexity of integrating DTs into existing systems is exacerbated by the lack of standardised frameworks and protocols for DT implementation, which can lead to confusion and inefficiencies during the integration process [125]. In the civil engineering field, Arisekola and Madson [23] argued that the lack of a unified framework for developing DTs and a greater focus on technical implementations inhibit the wider adoption of the DT. Further complicating adoption is the lack of standardised platforms and reliable APIs for seamless integration with incumbent systems [28]. The study by Abanda et al. [9] examined the application of DTs in the construction and manufacturing industries and established that while the manufacturing sector focuses on mass producing goods through standardised processes, the construction industry produces unique infrastructure and buildings, tailored to specific needs. Thus, due to the standardised nature of the manufacturing sector products, a DT is more easily implemented in the manufacturing sector than in the construction industry [9]. Furthermore, according to Akanmu et al. [22], Calvetti et al. [110], Çetin et al. [97], and Chi et al. [111], there is a specific lack of standards for data communication latency between physical buildings and their DTs, especially in the real-time monitoring of construction processes [22]. Such data communication latency standards should prescribe the required network speed, server response time, and distance between DTs and their associated physical buildings [22]. To minimise the concern of the lack of standards, Bellavista et al. [126] advocated developing clear-cut norms and regulations that incorporate applicable software and scalable architectural patterns for sector-specific DT deployment. As digitalization is a global phenomenon, Asif et al. [24] advocate for international cooperations to develop consistent and beneficial policies and regulations that would create an enabling environment for the wider adoption of digital technologies to address global digital challenges. The authors support the collaboration of government and tech providers in creating laws and standards that will regulate the collection and use of personal data and guide the development of emerging technologies [24]. Aldabbas [11] highlighted the key challenges of DT technologies, including integrating data from different technologies, sensor theft, poor lifecycle maintenance, high costs, and time-consuming real-time data transfer. Though considered as the digitised edition of the built environment for collecting and gauging data, the optimal functionality of the DT is heavily reliant on its attributes and proximity to the “real smart home” [127]. Biagini et al. [128] concluded that despite advancements in methodologies and data integration, several challenges persist in the realm of 3D reconstructions from street view images. Addressing the variability in the lighting and perspective can introduce inaccuracies in the reconstructed models. Conversely, Rematas et al. [129] explored the challenges posed by these factors and proposed strategies to mitigate exposure discrepancies during the capture of street view data, significantly improving the quality of reconstructed surfaces. According to Okonta et al. [130], despite the promising developments surrounding IFCs, certain challenges continue to hinder its implementation. The complexity of the IFC schema—highlighted by Koo et al. [131]—creates opportunities for data loss and misclassification during the exchange process, potentially jeopardising the project integrity [131]. This complexity necessitates ongoing training and capacity building among professionals to navigate and utilise IFC standards effectively. [131].

Parsinejad et al. [132] found that implementing DT technology in the context of Iranian architectural heritage presents several challenges; these challenges are related to the complexity of the geometric data and its interpretation for BIM applications as these can complicate the construction of accurate digital replicas. Furthermore, Diara and Rinaudo [133] note that current methodologies must accommodate the intricate geometrical characteristics of cultural heritage sites, which traditional BIM often overlooks.

8.4. High Operational Expenditure and Maintenance Costs

One of the barriers to high-resolution modelling is the requirement of an extensive expert user setup time [134]. Moreover, the ongoing costs related to maintenance, updates, and training can further strain budgets, making it difficult for organisations to justify the investment [117] Costs of continuous monitoring and the maintenance of various sensor networks in building processes are incremental and require proper financial planning and implementation [13,135]. Focusing on the circularity of construction processes, Banihashemi et al. [96] found that beyond the numerous overwhelming benefits of digital tools in construction processes, the absence of DTs for demolished facilities and the high cost of retrofitting digitalisation make the application of the technology less straightforward. In the same token, Asif et al. [24] considered retrofitting as chiefly challenged by the critical issues of cost and compatibility.

8.5. Skill Gaps and Workforce Training Costs

The knowledge of the DT technology is key to its acceptance and adoption. As noted by Almatared et al. [78], barriers to the integration of the DT technology include a lack of knowledge about the technology, difficulties in the integration of the ancillary systems, low user acceptance, high initial and education training costs, and a lack of trust in data security. Training requirements, including those for BIM and GIS integration, add further delays to deployment timelines [120,136]. Dossick et al. [137] argued that an essential barrier to an effective IoT adoption in construction is the knowledge and skills gap within the workforce. Ibrahim et al. [138] also concurred with this in their study and argued that there is often a lack of understanding regarding IoT technologies, which can impede their adoption. This knowledge deficiency highlights the necessity for comprehensive training programmes to equip construction personnel with the skills needed to operate and maintain IoT systems efficiently. Without adequate education and training, team members may struggle to interpret data from IoT devices, reducing the effectiveness of these technologies in boosting productivity and operational reliability. Furthermore, as technologies rapidly evolve, continuous professional development becomes essential. While the study by Lee [139] discusses IoT service platforms, it emphasises the need for professionals to stay updated on emerging applications and best practices to harness the full potential of the technology effectively. Addressing this challenge is critical, as the construction industry traditionally sees a slow uptake of new technologies, which can lead to a competitive disadvantage in an increasingly digital landscape [140]. To minimise the cost challenge of staff upskilling, Bellavista and Di Modica [116] adopted the development of guidelines to incorporate DTs through “composting existing containerized software”. Accordingly, this scheme reduced the DT investment and marked a significant advancement in the application of the technology [116].

8.6. Urban Space Digital Twin Challenges

Andritsou et al. [77] explored the potential of creating a DT of an urban space to support energy-efficient applications and predict future scenarios, including dynamic weather simulations and visualisations. The study noted that the deployment of DTs at a macroscale faced the biggest challenge of training AI models that require vast, precise, and sufficient data that cover a wide range of potential scenarios of the real world. Argyroudis et al. [74] highlighted that critical infrastructure can be more climate-resilient if the potentials of emerging and disruptive digital technologies are optimally explored. But the lack of an accord, a multidisciplinary roadmap, an integrated approach, and government support substantially hinder the uptake of these smart technologies in larger scales. According to Artopoulos et al. [141], the integration of 2D and 3D representations and their associated media and data analysis reports for buildings in an urban space, through a 3D GIS and immersive visualisation, present great opportunities for accessing information in data-rich 3D representations of the built environment, which is a form of an urban DT. The research by Shi et al. [142] emphasised the need for robust generative models that can synthesise both the geometry and texture information, ensuring that the visual fidelity of the reconstructed structures meets practical demands in urban development [142]. Çetin et al. [97] assessed digitization for a circular economy and inferred a lack of requisite instruments to organise and translate the large volumes of estate data stored in the digital systems for the purpose of circular strategies. Furthermore, regarding the use of digital technologies to drive the circular economy, Chi et al. [111] highlighted the concern of the quality associated with large data collection and the potential huge capital investment.

9. Lifecycle Financial Impacts of Digital Twinning in the AEC Industry

9.1. Construction and Operational Cost Optimisation

DTs have been widely cited as powerful tools for improving construction efficiency, with direct implications for cost reduction. By integrating real-time data, simulations, and predictive modelling, DTs enable the early detection of construction issues, more accurate resource allocation, and better coordination, all of which contribute to minimising delays and avoiding costly rework [143,144,145,146]. Jahangir et al. [145] further emphasise the DT’s capability to simulate the material performance under various conditions [147,148,149,150,151], supporting data-driven material selection and procurement strategies. Moreover, DTs have been applied in demolition planning, enabling optimised sequencing and resource management, ultimately leading to waste and cost reductions. In the modular construction context, Kosse et al. [152] proposed a DT-based “Serial Construction” framework aimed at reducing inefficiencies in prefabricated building delivery. Similarly, Kuru [153] introduced the “MetaOmniCity,” a smart city DT platform designed to streamline real estate operations. By allowing agents to conduct realistic virtual tours, it reduces the costs and time associated with property viewings. Sensor-based DTs also facilitate cost-effective construction and operational workflows. Iqbal et al. [154] propose a low-cost DT system using wireless sensors and BIM integration for the real-time monitoring of the concrete compressive strength. This approach significantly reduces labour costs and speeds up project timelines. Likewise, Khan et al. [155] report that RFID and smart sensor integration on construction sites enhances the real-time monitoring of machinery and components, improving the schedule adherence and cost predictability.

The operational phase of buildings presents numerous opportunities for cost optimisation through DT adoption. Hadjidemetriou et al. [156] demonstrate that DTs in smart buildings enable efficient energy management by integrating and automating the use of renewable energy sources, leading to notable reductions in electricity consumption. In addition, DTs contribute to operational cost savings through real-time monitoring, predictive maintenance, and optimised resource utilisation [157,158,159,160]. These functions help identify system inefficiencies, reduce downtime, and extend the asset lifespan, all of which result in measurable financial benefits.

9.2. Lifecycle Costing and Long-Term Financial Planning

DTs have proven valuable in supporting lifecycle costing (LCC) by enabling long-term planning and sustainability assessments. Kaewunruen et al. [161] illustrate how DTs can be used to conduct “sustainability and vulnerability audits” in subway infrastructure, incorporating BIM-based LCC to evaluate long-term economic impacts. These findings align with claims that DTs can reduce lifecycle costs, energy use, and carbon emissions in built assets [162,163,164]. Long-term planning benefits are further reinforced by Liu and Lin [109], who note that DTs can simulate structural performance under varying conditions, supporting strategic maintenance planning. Nour El-Din et al. [165], Pang et al. [166], and Ammar et al. [167] echo these findings, demonstrating that feedback-driven DTs can proactively guide maintenance and rehabilitation efforts, ultimately reducing the expenditure in infrastructure management.

However, as Azhar et al. [168] caution, the potential cost benefits may be compromised if BIM and DT platforms are not clearly understood or properly differentiated, particularly where terminology confusion arises between tools such as Revit, Navisworks, BIM, and DTs.

9.3. Barriers to Cost Realisation

Despite the strong evidence for DT-related cost savings, barriers to widespread adoption remain. Chief among these is the high initial investment, which includes sensor deployment, integration platforms, and workforce training. Jahangir et al. [145] and Broo and Schooling [169] point out that such costs are particularly troublesome for small- and medium-sized enterprises (SMEs), which may lack the resources or capacity to adopt complex DT systems.

These challenges highlight the importance of policy support, standardisation, and more accessible technologies to ensure that DT benefits are not limited to large-scale or well-funded organisations. Without coordinated industry-wide strategies to lower entry barriers, many AEC firms may continue to view DT implementation as financially impractical despite its long-term ROI.

10. Cost Savings of Utilising the Digital Twin Technology

Ghorbani and Messner [170] advocate for a categorical approach to defining DTs in the AEC industry, highlighting their role in improving the efficiency and effectiveness of the O&M of building assets, leading to time and cost savings, ultimately with some positive implications for sustainability [171]. Accordingly, AlBalkhy et al. [10] identified the benefits of DTs to include building resilience, cost savings, predictability, health, and safety, but the implementation of the technology is still on small scales; thus, the full potential of the DT technology in the built environment is yet to be unlocked. Also, Bass et al. [101] explored the potentials of the DT in creating building energy models of 178,337 premises of a municipality utility. The DT urban scale energy modelling scenarios revealed huge per-building electrical savings and significantly reduced annual energy costs. Furthermore, Arisekola and Madson [23] emphasised that a DT enables proactive maintenance decision-making through real-time data monitoring and analysis. Reducing operating costs has been identified as a key driver for adopting DTs in water infrastructure. Looking specifically at the 16 eight-floor buildings with 216 apartment units of Rinascimento III in Rome, Agostinelli et al. [172] assessed the potential of DTs for cost-effective building data management. The research firmly established that DTs are largely transversal and applicable to building at both micro- (apartment) and macroscales (district) for the intelligent optimisation of building energy management [172,173,174]. DTs enable preventive maintenance; minimise downtime (including repair, service, and testing times); lower operating, maintenance, and repair costs; enhance water supply efficiency; and reduce overall water supply costs [175]. Moreover, Bartie et al. [176] investigated the use of a High-Detail DT on the process simulation of the silicon PV lifecycle and underscored the predictive benefits of the technology. Focusing on the applications of AI in a sustainable building’s lifecycle, Adewale et al. [177] noted that the integration of the transformative potentials of AI technologies with DTs presented smart strategies of energy efficiency optimisation, environmental impact minimization, and waste management throughout the building’s lifecycle. To significantly reduce project costs and improve the efficiency and overall quality, Borovkov et al. [178] proposed the integration of DTs and neural networks. The authors maintained that DTs provide real-time data collection, proactive decision-making, and an enhanced coordination of both design and field operations, while the neural network learns complex links among datasets and forecast the system performance [178]. According to Mohseni et al. [179], DTs function as dynamic virtual models of physical systems, mirroring their real-time performance and enabling continuous monitoring and control. They state that employing DTs, for instance, in HVAC systems transforms traditional control strategies by allowing for an integration with Functional Mock-up Interface (FMI) standards, which facilitate the real-time co-simulation and validation of control algorithms. This integration enhances system performance and supports proactive maintenance strategies by providing insights into the system’s health over its lifecycle, thereby leading to future overall cost savings.

11. Conclusions

This systematic review has provided a comprehensive evaluation of the economic implications associated with the adoption of Digital Twin (DT) technology within the AEC industry. While DTs present transformative potential in enhancing operational efficiency, predictive maintenance, and lifecycle cost optimisation, their widespread implementation remains challenged by a substantial initial investment, integration complexities, and human capital readiness.

Figure 4 below provides a framework of the key findings in relation to the economic impacts of DTs in the AEC industry.

The findings underscore that those high upfront costs, including investments in sensors, data infrastructure, computing resources, and skilled personnel, pose a significant barrier, especially for SMEs. Additional economic hurdles stem from interoperability issues with legacy systems, a lack of standardised frameworks, and recurring operational and maintenance expenses. Despite these challenges, this review also reveals considerable long-term financial benefits of DTs, particularly in improving asset management, reducing rework, and enabling more informed, data-driven decision-making.

Cost-saving applications of DTs are evidenced across several case studies in modular construction, infrastructure management, and energy efficiency. Innovations such as vision-based digital shadowing, integrations with UAV photogrammetry, and low-cost sensor networks have emerged as practical alternatives to full-scale DT implementations, offering financially viable entry points for smaller firms or resource-constrained sectors. Furthermore, DTs demonstrate a strong return on investment in sectors with a higher digital maturity or structured financial backing, such as manufacturing and smart infrastructure.

However, economic viability is highly contextual. Sector-specific regulatory requirements, compliance costs, and digital maturity levels shape the feasibility and scope of DT deployments. The construction industry’s fragmented nature and project uniqueness exacerbate cost and integration challenges when compared to sectors with standardised production systems. To realise the full economic potential of DTs, this study highlights the critical need for standardised frameworks to streamline DT deployment and reduce redundancy; public–private partnerships and innovative financing models that share the risk and lower capital entry thresholds; policy support and incentives, particularly targeting SMEs and pilot initiatives; and capacity-building initiatives to address knowledge gaps and the workforce digital readiness.

Overall, while the cost implications of DTs remain complex and multifaceted, the potential long-term economic gains justify their exploration and strategic adoption. As digital transformation continues to redefine the built environment, stakeholders must adopt holistic cost–benefit frameworks that weigh initial expenditures against lifecycle efficiencies and sustainable value creation. Bridging the current economic gaps through coordinated industry efforts, scalable technology models, and inclusive policy instruments will be pivotal in mainstreaming DTs across the AEC industry.

Case studies reveal cost efficiencies achieved through DTs in modular construction, energy optimisation, and infrastructure management. However, limited financial resources and digital skills continue to constrain the uptake across the sector, with various extents of impact. This paper calls for the development of unified standards, innovative public–private funding mechanisms, and strategic collaborations to unlock and utilise DTs’ full economic value. It also recommends that future research explore theoretical frameworks addressing governance, data infrastructure, and digital equity—particularly through conceptualising DT-related data as public assets or collective goods in the context of smart cities and networked infrastructure systems.

12. Study Limitations and Future Research Directions

While mapping the current body of knowledge on the economic impact of Digital Twinning (DTing) in the AEC industry, this paper also identified significant research gaps, which both contributed to limitations in the findings of this study and paved the way for recommendations for future studies. Future studies are needed to establish a more rigorous knowledge and evidence base, particularly through case studies that compare projected/anticipated vs. actual cost implications of DTs, whether with favourable or rather unexpected outcomes. Such data, gathered synchronously or diachronically via cross-sectional or longitudinal approaches, will enable the creation of quantitative benchmarks for cost savings and the return on investment (ROI). These benchmarks will serve as robust, objective tools to support researchers, AEC professionals, clients, local governments, legislators, and policymakers in fulfilling their respective roles in regard to the enablement, adoption, governance, and advancement of DT technologies.

Further research could explore tertiary dimensions that contribute to the economic landscape of DTs, focusing on more context-specific and specialised areas. These might include variables such as the business size; national or regional economic conditions; sector-specific practices; and stakeholder’s priorities, needs, wants, or requirements—including those of professionals, clients, regulators, the general public, and special interest groups. Investigations could also focus on the specific project phase, such as conceptual design, detailed design, construction, handover, operation, refurbishment, or decommissioning, as each phase presents distinct economic considerations.

In addition, future studies should consider developing or applying theoretical frameworks to address governance, data infrastructure, and digital equity. A particular emphasis may be placed on conceptualising DT-related data as public assets or collective goods, especially within the evolving context of smart cities and interconnected infrastructure networks.

Understanding the differences in the ROI for DT implementations with special reference to business sizes and project scales is highly relevant to industry stakeholders and would offer practical value. Our study reveals that there is a gap in this, which presents potential for future research.

Differences in the underlying technological infrastructure between developed and developing countries significantly influence the nature, outcomes, and cost of the implementation of technology-related fields, including Digital Twinning. Contextual factors—e.g., local and central governing structures, national infrastructure, technological readiness, the general public, and stakeholder groups’ support and acceptance (of emerging technologies), incumbent structures, and institutional support, to name but a few—play a critical role and may shape both the feasibility and cost-effectiveness of the DT deployment. Detailed comparative studies between developing and developed countries—from an economic perspective—help shed light on this less investigated research area. The development of economic benchmarks (directly incurred and indirect costs, lifecycle costing, RoI, etc.) and scenario-based comparisons is a critical next step in developing a more comprehensive evidence-based understanding of the economic impact of Digital Twinning in the AEC industry.

Even though the risk is not limited only to financial risks, and risks of a particular H&S, behavioural, organisational, or cultural nature are equally if not more important, our study indicated that there is very little to no evidence of research on the financial risk of Digital Twinning in the AEC industry.