1. Introduction
Industries worldwide are progressively embracing digitalization to enhance efficiency [1], reduce costs [2], and improve decision-making processes [3]. Among the most promising technologies in this digital transformation [4,5] are Building Information Modeling (BIM) and digital twin technologies [6]. BIM offers a digital representation of a building’s physical and functional characteristics, facilitating the improved planning [7,8], design [7,9], construction [10], and management [11,12,13,14] of infrastructure. Digital twins [15,16] extend these capabilities by providing dynamic, data-driven models that reflect real-time changes, enabling predictive insights and enhanced operational performance.
Building Information Modeling (BIM) is a widely adopted digital technology that serves as a comprehensive tool for representing the physical and functional characteristics of a facility through 3D models and integrated data. BIM facilitates collaboration among stakeholders across the design, construction, and operational phases by providing a single source of truth that enhances coordination and minimizes errors. This process-driven approach supports lifecycle management by enabling data consistency and effective communication throughout a project [17].
In contrast, digital twins extend the capabilities of BIM by integrating real-time data from sensors and IoT devices, creating dynamic, evolving digital replicas of physical assets. Unlike the static models provided by BIM, digital twins allow continuous monitoring, predictive analytics, and adaptive management [18]. These technologies incorporate live data feeds, supporting advanced simulations that can predict system behavior, optimize operations, and inform decision-making processes. This real-time capability is particularly valuable for industrial infrastructures, where continuous operations and process optimization are essential for maintaining productivity and sustainability.
The unique value of digital twins lies in their ability to bridge the gap between the digital and physical realms, providing actionable insights through data-driven simulations and machine learning algorithms. For industrial facilities, this means that issues can be identified and mitigated proactively, predictive maintenance schedules can be established, and performance can be optimized to extend the lifecycle of assets. This capability not only improves operational efficiency but also aligns with broader sustainability goals by reducing energy consumption, material waste, and emissions [16].
The integration of these technologies in industrial infrastructure, especially within the paradigm of “Factories of the Future” [19], presents an opportunity to significantly enhance sustainability. These advanced factories utilize cutting-edge digital technologies to optimize production processes, reduce waste, and minimize environmental impact [20,21]. Despite this potential, there is a notable gap in research and practice on how to systematically integrate BIM and digital twin technologies to meet sustainability objectives.
While digital twins offer transformative potential, their implementation in industrial settings presents unique challenges. Integrating these systems with legacy infrastructure can be complex, requiring significant investment in interoperability solutions [18]. Additionally, the continuous real-time data processing necessary for accurate digital twin operations poses substantial demands on data handling and storage capabilities [22]. Another significant challenge is maintaining cybersecurity due to the constant flow of sensitive operational data [23]. Addressing these challenges is critical for successful deployment in industrial environments.
The purpose of this study is to develop a comprehensive set of principles for integrating BIM and digital twin technologies to promote sustainability in industrial infrastructure. This research is significant because it addresses a critical gap: the lack of structured guidelines or principles on leveraging these digital tools to minimize environmental impact, optimize resource usage, and enhance the resilience of industrial facilities. By providing a framework for sustainable integration, this paper aims to support the global shift towards more sustainable industrial operations.
The existing literature primarily focuses on the operational efficiencies and cost benefits of BIM and digital twins in industrial settings [24,25,26,27]. While some studies have explored the sustainability potential of these technologies [2,25,26,28,29,30,31,32,33,34,35], they often lack a holistic approach that integrates environmental, economic, and social dimensions of sustainability [2,13,15,36,37,38,39,40,41,42,43,44]. Controversies and divergent hypotheses exist regarding the effectiveness of digital twins in achieving long-term sustainability goals [31], particularly in the face of evolving technological standards and the complexity of data interoperability [45,46,47]. A thorough review of key publications reveals a need for more comprehensive frameworks that address these challenges.
BIM and digital twin technologies can be leveraged at each stage of an industrial facility’s lifecycle, from design to decommissioning. During the design phase, BIM aids in creating detailed, data-rich models that facilitate optimal planning and resource allocation. In the construction phase, these models provide precise information for real-time adjustments, reducing waste and enhancing efficiency. During the operational phase, digital twins enable continuous performance monitoring and predictive maintenance, ensuring sustained resource optimization and minimal environmental impact. Finally, in the decommissioning phase, data from both technologies support safe and sustainable dismantling processes, contributing to circular economy practices by maximizing material recovery and recycling.
The main aim of this paper is to formulate principles for the sustainable integration of BIM and digital twin technologies in industrial facilities. The conclusions drawn from this study highlight the potential for these technologies to drive innovation in sustainable industrial development and provide a strategic foundation for future research and practical application in the field.
2. Materials and Methods
This study employs a comprehensive literature review as its primary research method, aimed at developing a set of principles for the sustainable integration of Building Information Modeling (BIM) and digital twin technologies in industrial facilities. The literature review method is chosen for its effectiveness in synthesizing existing knowledge, identifying research gaps, and providing a solid foundation for conceptual framework development. The review focuses on academic journals, conference proceedings, and relevant industry reports published in the last decade to ensure the currency and relevance of the findings.
The data collection process involved systematic searches in major academic databases, including Scopus, Web of Science, and Google Scholar. Keywords such as “sustainable industry”, “Building Information Modeling”, “digital twin”, “industrial facilities”, “Factory of the Future”, and “facility management” were used to identify relevant studies. A total of 150 articles were initially retrieved, covering various aspects of BIM and digital twin technologies, sustainability practices in industrial settings, and lifecycle management.
To refine the selection, inclusion and exclusion criteria were applied:
Inclusion criteria:
◦. Articles published in peer-reviewed journals or high-impact conference proceedings.
◦. Studies focusing on the application of BIM and digital twin technologies in industrial or large-scale infrastructure.
◦. Research addressing sustainability challenges and solutions in industrial settings.
Exclusion criteria:
◦. Studies not available in English.
◦. Articles focusing solely on residential or small-scale commercial projects.
◦. Publications older than 2010, unless deemed seminal or foundational to the field.
Following this filtering process, 85 articles were selected for detailed analysis. These articles were categorized based on their focus areas: BIM and digital twin technologies, sustainability in industrial facilities, and integration frameworks for digital technologies and sustainability.
The development of the principles for sustainable integration of BIM and digital twin technologies was guided by insights gained from the literature review. The framework was constructed using a three-step process:
The literature was reviewed to identify recurring themes and concepts related to the integration of digital technologies in sustainable industrial development. Key themes included lifecycle management, data interoperability, and stakeholder collaboration.
The identified themes were synthesized to formulate preliminary principles. These principles were iteratively refined through further literature review and consultation with industry experts and academic peers to ensure comprehensiveness and relevance.
The draft principles were then compared against real-world case studies and examples found in the literature. This comparison helped validate the principles and refine them to ensure they address practical challenges and opportunities in the field.
To illustrate the practical application of the proposed principles, two case studies were selected based on their relevance to the study’s objectives. The case studies were chosen from the refined pool of the literature using the following criteria:
Relevance to sustainability goals: The case studies must demonstrate the application of BIM and digital twin technologies in achieving sustainability objectives within industrial settings.
Diversity in application: To provide a comprehensive view, one case study focuses on the implementation of BIM in a sustainable industrial facility, while the other examines the use of digital twin technology in lifecycle management.
Availability of detailed data: The case studies needed to provide sufficient detail to enable a thorough analysis of the challenges, solutions, and outcomes associated with the integration of digital technologies.
To illustrate the practical application of the proposed principles, two case studies were selected based on their relevance [46,48,49,50,51,52]. Recent studies have provided valuable insights into the integration and application of digital technologies such as BIM and digital twins across various fields. Shkundalov and Vilutienė (2021) [48] conducted a bibliometric analysis revealing the dominant research themes and current trends in integrating BIM, Geographic Information Systems (GISs), and web environments, emphasizing areas requiring further exploration. Mahmood and Hatem (2023) [49] surveyed the role of BIM in enhancing economic sustainability in artistic and cultural projects in Iraq, finding that, despite BIM’s potential, implementation is hindered by limited government involvement and insufficient demand from project owners. Dervishaj and Gudmundsson (2024) compared digital tools used for Life Cycle Assessment (LCA) and circular design in the built environment, highlighting their strengths and limitations in informing sustainable building practices. Mzyece, Ndekugri, and Ankrah (2019) [51] proposed an interoperability framework between BIM and the construction (design and management) regulations (CDMs), demonstrating that this integration could improve health and safety compliance and overall project outcomes. Lastly, Bhandal et al. (2022) [52] conducted a bibliometric review of digital twin applications in operations and supply chain management, identifying research gaps and showing how digital twins can enhance efficiency and resilience within supply chains.
3. Results
The principles outlined in this study synthesize established knowledge while contextualizing it within the framework of industrial infrastructure, providing a targeted approach to sustainability. This paper seeks to present a structured set of guiding principles that bridge current understanding and specific applications relevant to industrial-scale operations.
3.1. Analysis of Literature Review Findings
3.1.1. Topic Identification and Categorization
Our research involved a bibliometric analysis of the current academic landscape using the Scopus database. We quantified the number of publications that include both “digital twin” and “BIM” as keywords (Table 1). Subsequently, we determined the subset of these publications that also incorporate “sustainability” as a keyword and calculated the corresponding percentage.
The search methodology is based on the results from Scopus database according to the number of papers in the search results for title, abstract, and keywords and inputs (‘bim’ AND ‘digital twin’) and (‘bim’ AND ‘digital twin’ and ‘sustainability’).
This analysis (Figure 1) revealed that, while there is a growing body of literature on the integration of BIM and digital twin technologies, only a relatively small proportion explicitly addresses their application toward sustainability in industrial facilities. Specifically, the percentage of publications encompassing all three keywords is modest compared to the total number addressing BIM and digital twins alone. This finding underscores a significant gap in the literature, highlighting the novelty and importance of our research in contributing to this emerging interdisciplinary field. At this time, there is an obvious rise in research interest in sustainability issues among authors that dedicate to the topics of BIM and digital twin.
The literature review identified 12 key topics related to the integration of BIM and digital twin technologies in industrial settings. To refine these topics into a focused framework for sustainable integration, we applied a systematic evaluation based on four key criteria:
Frequency of mentions: Topics that were frequently discussed across the reviewed studies were considered prominent in current academic discourse. High frequency indicates a significant focus on the topic within the research community.
Relevance to research objectives: Topics were assessed for their direct relevance to the objectives of this study, specifically regarding sustainability goals in industrial infrastructure. The more directly a topic aligned with these objectives, the higher its relevance score.
Impact on sustainability: This criterion measured the potential of each topic to contribute to the sustainability of industrial facilities. Topics with high impact were those that could significantly enhance environmental performance, resource efficiency, or resilience.
Novelty and research gaps: Topics that represented new or under-explored areas of research were prioritized to highlight emerging trends and address gaps in the literature. Novel topics provide opportunities for innovation and further exploration.
3.1.2. Criteria for Selecting Key Topics
To select the top four topics for developing the principles, we evaluated each topic against the four criteria using a scoring system. Each topic was scored on a scale from 1 to 5 for each criterion, with 5 representing the highest score (Table 2). The cumulative score determined the importance of each topic.
Table 2 presents the evaluation of the 12 identified topics.
3.1.3. Selection of Top Four Topics
Based on the total scores, the top four topics selected for further development into principles are presented here.
1.. Environmental Impact Reduction (total score: 18).
◦. Highly mentioned in the literature and directly relevant to sustainability objectives.
◦. Significant impact on reducing the environmental footprint of industrial operations.
2.. Resource Efficiency Optimization (total score: 17).
◦. Widely discussed in the literature, with strong relevance to optimizing material and resource use.
◦. High potential impact on sustainability through improved resource management.
3.. Resilience and Risk Management (total score: 16).
◦. Addresses the ability of industrial facilities to adapt to disruptions and manage risks effectively.
◦. Represents a growing focus in the literature on sustainability in the face of increasing uncertainty.
4.. Data Interoperability and Integration (total score: 16).
◦. Essential for the effective implementation of digital technologies across different platforms.
◦. Represents an emerging trend and a critical research gap in current studies.
The analysis of the literature review findings highlights the prominence of these four topics in current research and their critical role in promoting sustainability in industrial settings. The selected topics—environmental impact reduction, resource efficiency optimization, resilience and risk management, and data interoperability—will form the basis for developing the principles proposed in this study. This approach ensures that the principles are grounded in the current research while also addressing key gaps and emerging trends. We highlighted major papers related to the selected topics (Table 3).
3.2. Formulation of Principles
The analysis of the literature review revealed four key topics that align with the objectives of this research, each playing a critical role in the sustainable integration of BIM and digital twin technologies in industrial settings as summarized on Figure 2. The following sections provide a detailed justification for selecting each of these topics as guiding principles for sustainability in industrial infrastructure.
The four principles proposed in this paper are informed by a synthesis of evidence in the literature and aligned with practical requirements observed in project management. While the summary of the literature serves as a reference, the principles were developed with consideration of current engineering challenges and project management needs.
3.2.1. Principle 1: Minimizing Environmental Impact
Minimizing environmental impact is a foundational principle for sustainability and was the most frequently discussed topic in the literature. The integration of BIM and digital twin technologies offers significant potential to reduce the environmental footprint of industrial facilities by optimizing design, construction, and operational phases. Studies emphasize the role of BIM in reducing energy consumption and emissions through better planning and modeling [12,37,71,72,103]. Furthermore, lifecycle assessments can be more accurately conducted with digital twins, allowing industries to monitor their environmental impact in real time and make necessary adjustments to reduce emissions and waste [12,43,53].
Environmental sustainability is a core aspect of sustainable development, and digital tools like BIM and digital twins offer precise, data-driven approaches to minimizing negative environmental impacts. This principle directly aligns with global sustainability goals, such as those outlined by the United Nations in the Sustainable Development Goals (SDGs), particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 13 (Climate Action).
BIM contributes to energy reduction through integrated tools such as energy simulation software that allow for the evaluation of various design configurations to optimize HVAC systems, building envelope properties, and daylighting strategies. For example, energy modeling using BIM can simulate and predict annual energy consumption patterns, enabling proactive design adjustments that result in significant reductions in energy use [104].
Integrating Building Information Modeling (BIM) and digital twin technologies can significantly contribute to reducing the environmental impact of industrial facilities. The World Green Building Council highlights that utilizing advanced modeling technologies, such as BIM, can enhance sustainable design practices and operational efficiency by supporting data-driven decision making throughout a building’s lifecycle (World Green Building Council, 2019) [105]. Arup’s insights emphasize the role of digital twins in optimizing energy use and material management, enabling stakeholders to monitor and adjust facility operations to minimize their carbon footprint.
3.2.2. Principle 2: Optimizing Resource Efficiency
Resource efficiency optimization emerged as a critical topic due to its direct relevance to reducing material waste, enhancing operational efficiency, and lowering costs throughout the lifecycle of industrial facilities. BIM and digital twin technologies can significantly enhance resource management by enabling the real-time monitoring of material flows and optimizing the supply chain. Researchers highlight how digital twins improve the accuracy of resource usage forecasts, reducing the need for excess materials and minimizing waste during construction and operational phases [80,82,106].
Resource efficiency is essential for both economic and environmental sustainability. By optimizing resource use through digital technologies, industries can lower costs while reducing the consumption of natural resources, aligning with the principles of a circular economy. This principle supports both sustainability and profitability, making it a key component of industrial strategy in the era of digital transformation.
BIM and digital twin technologies enable enhanced resource management by offering data-driven insights throughout the lifecycle of industrial facilities. These tools can track material use and identify inefficiencies, leading to better decision making and reduced resource wastage. For instance, digital twins, integrated with real-time monitoring systems, can model different process scenarios to identify the most resource-efficient strategies. This predictive capability allows facilities to preemptively adjust operations, ensuring optimal use of raw materials and energy. The resulting optimization not only minimizes waste but also supports circular economy initiatives by improving material recovery and reuse rates. This comprehensive approach to resource management aligns with sustainable practices and enhances the overall cost-effectiveness of industrial processes.
The implementation of BIM and digital twin technologies facilitates precise resource management across the lifecycle of industrial projects. According to the International Energy Agency (IEA), digitalization plays a crucial role in improving process efficiency and reducing resource waste in industrial settings (IEA, 2019) [107]. Arup’s industry reports highlight that digital twins, with real-time monitoring and predictive maintenance capabilities, support optimal resource use and extend the life of facility equipment by enabling proactive adjustments.
3.2.3. Principle 3: Ensuring Long-Term Resilience and Risk Management
Resilience and risk management were highlighted as key factors in ensuring the long-term sustainability of industrial infrastructure. In an increasingly unpredictable world, industrial facilities must be able to withstand and adapt to various disruptions, whether caused by natural disasters, market shifts, or technological failures. Digital twins, in particular, provide a robust platform for predictive maintenance and risk assessment, allowing facility managers to anticipate potential failures and optimize operations in real time. Predictive analytics powered by digital twins can increase the resilience of industrial facilities by reducing downtime and enhancing operational efficiency [58].
Resilience is crucial for the long-term viability of industrial operations. Ensuring that industrial facilities can adapt to unforeseen circumstances, such as climate-related risks or operational disruptions, is essential for sustainability. This principle emphasizes the proactive use of digital technologies to not only respond to risks but also to anticipate and mitigate them before they escalate.
Digital twin technology significantly contributes to long-term resilience by enabling predictive maintenance and advanced risk management strategies. By leveraging real-time data from various sensors and integrating machine learning algorithms, digital twins can forecast potential equipment failures and operational disruptions. This capability ensures that industrial facilities can preemptively address issues, minimizing downtime and avoiding costly repairs. Moreover, the use of digital twins facilitates adaptive management by simulating responses to various stress scenarios, such as fluctuations in production demand or environmental conditions. Such proactive measures enhance the facility’s ability to withstand and adapt to unforeseen challenges, fostering continuous operational stability and extending the lifecycle of critical infrastructure components.
Building resilience is essential for industrial facilities to maintain operational continuity amidst challenges. The National Institute of Standards and Technology (NIST) discusses the role of digital twin technology in simulating various operational scenarios, which aids in disaster preparedness and risk management (NIST, 2020). This simulation capability allows facilities to identify vulnerabilities, implement preventive measures, and adapt quickly to disruptions, enhancing overall resilience.
3.2.4. Principle 4: Enhancing Data Interoperability and Integration
Data interoperability is fundamental to the successful integration of BIM and digital twin technologies in industrial settings. As industries increasingly adopt digital tools, the ability to seamlessly share and integrate data across various platforms becomes critical for operational efficiency and decision making. The challenges of data interoperability, particularly in managing the vast amounts of data generated by BIM and digital twin systems are widely discussed [22,32,39,45,96,97,100,108,109,110,111]. Ensuring that data flow smoothly between different systems, stakeholders, and phases of a project is crucial for optimizing resource use, minimizing errors, and improving overall sustainability.
Data interoperability is an enabler of innovation and efficiency. Without seamless data exchange, the potential benefits of BIM and digital twins—such as enhanced decision-making, real-time monitoring, and predictive analytics—are significantly diminished. This principle addresses a key technical challenge in digital integration and is essential for the scalability and sustainability of digital technologies in industrial infrastructure.
For the successful integration of BIM and Digital Twin technologies, seamless data interoperability is crucial. The National Institute of Building Sciences (NIBS) underscores the importance of standardized data protocols to facilitate the exchange of information across different systems (NIBS, 2021) [112]. Implementing open standards such as Industry Foundation Classes (IFCs) supports effective data sharing, fostering collaboration and more efficient decision-making.
The selection of these four principles—minimizing environmental impact, optimizing resource efficiency, ensuring long-term resilience and risk management, and enhancing data interoperability—reflects the most critical findings from the literature review and aligns with the sustainability goals of this study. These principles form the foundation of a strategic framework for integrating BIM and digital twin technologies in industrial facilities to achieve greater sustainability. The next sections of this paper will explore these principles in further detail and present case studies to illustrate their practical application in real-world scenarios.
Innovative integration of BIM and digital twins can be enhanced through the use of IoT sensors and AI-driven predictive algorithms. IoT-enabled sensors facilitate real-time data collection, providing digital twins with continuous updates that enable adaptive responses to operational changes. AI algorithms, when incorporated, improve predictive analytics capabilities, allowing for more accurate forecasting and decision making in resilience planning.
While BIM and digital twin technologies offer substantial benefits, data interoperability between the two can be complex. Challenges include differing data formats, integration of legacy systems, and ensuring data consistency across platforms. Potential solutions involve adopting open data standards such as Industry Foundation Classes (IFCs) and utilizing middleware solutions to bridge software gaps. Best practices include continuous stakeholder collaboration to align data protocols and the use of cloud-based platforms that facilitate seamless data sharing and real-time updates.
3.3. Case Studies on Sustainability in Industrial Facilities Related to BIM and Digital Twin
Effective collaboration among stakeholders—engineers, architects, project managers, and sustainability consultants—is crucial for achieving shared sustainability goals. For example, integrated project delivery (IPD) approaches and collaborative platforms such as common data environment (CDE) systems facilitate transparent communication and alignment. Tools like BIM 360 and cloud-based collaboration software enable all parties to access up-to-date models and project data, ensuring consistency and coordination throughout the project lifecycle. Case studies from successful industrial projects have shown that these tools can reduce project delays and improve overall project outcomes [26,113].
In a comparative case study conducted on an industrial facility [16], a holistic framework combining BIM and digital twin technology was implemented to optimize energy consumption and simulate building-related operations. The study focused on integrating BIM data into a modular hybrid simulation environment to enhance energy modeling. The case study demonstrated the effectiveness of digital twins in predicting and managing energy consumption, leading to significant improvements in resource efficiency and sustainability. This study highlights the value of integrating BIM models into digital twins for energy optimization and the overall sustainability of industrial facilities.
Another study on digital twin applications for energy efficiency in buildings [73] examined the use of real-time data and simulations to optimize energy use in industrial facilities. In this case study, the facility achieved a 15% reduction in energy consumption by employing digital twins to monitor and adjust energy flows based on real-time data from sensors. The study demonstrated the potential for significant cost savings and sustainability improvements through the integration of BIM and digital twin technologies. A study assessed the adoption readiness of BIM and digital twin technologies in the construction industry, focusing on sustainable construction practices. This case examined key success factors such as cost optimization, resource management, and sustainability. The study found that integrating BIM and digital twins allowed for better decision making during the construction and operational phases, leading to improved sustainability outcomes. This research emphasizes the need for readiness in adopting these technologies for long-term sustainability goals.
The key takeaways based on the analyzed case studies can be summarized as follows:
Energy optimization: case studies show that BIM and digital twins can significantly enhance energy efficiency through real-time monitoring and predictive simulations.
Maintenance efficiency: predictive maintenance, facilitated by digital twins, leads to reduced downtime and extended asset lifecycles.
Resource and cost management: BIM and digital twins improve resource efficiency and cost management, particularly in managing building materials and energy consumption.
Sustainability impact: These technologies help industries achieve sustainability goals by reducing energy use, improving resource allocation, and minimizing environmental impacts.
4. Discussion
The case studies and analysis of the literature highlight several key implications for industry practices and policy, particularly concerning the integration of Building Information Modeling (BIM) and digital twin technologies to promote sustainability in industrial facilities. These implications are critical for guiding the adoption of these technologies and for developing policies that align with sustainability objectives.
4.1. Industry Implications
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Early Adoption of Digital Tools for Sustainability
The case studies demonstrate that integrating BIM and digital twin technologies early in the design and construction phases results in significant environmental and cost benefits. For industries seeking to reduce their environmental impact, early adoption is critical. Companies that incorporate these technologies at the outset can optimize energy use, minimize material waste, and enhance resource efficiency throughout the facility’s lifecycle. Industry players must prioritize investments in digital technology infrastructure and training to fully leverage these benefits.
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2.. Enhanced Predictive Maintenance and Risk Management
Digital twin technology provides powerful predictive maintenance tools that help industrial facilities mitigate operational risks and improve resilience. By continuously monitoring equipment performance and simulating potential failures, industries can reduce downtime, cut maintenance costs, and ensure long-term operational stability. This proactive approach to risk management is especially valuable in sectors like manufacturing and energy, where equipment failures can lead to substantial financial losses.
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3.. Data Interoperability for Collaborative Stakeholder Engagement
The integration of BIM and digital twins hinges on seamless data interoperability across platforms, as emphasized in the case studies. Ensuring smooth data exchange between design, construction, and operational phases is essential for optimizing workflows and decision making. Industrial stakeholders, including engineers, facility managers, and policymakers, must collaborate to establish standardized data protocols and invest in technologies that facilitate real-time data sharing and analysis.
4.2. Policy Implications
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Development of Regulatory Frameworks
As BIM and digital twin technologies become more widespread, there is a growing need for clear regulatory frameworks to support their integration into industrial operations. Governments and industry bodies must develop policies that set standards for data interoperability, cybersecurity, and sustainability metrics. Such policies will help ensure that digital technologies are adopted responsibly and contribute meaningfully to global sustainability goals.
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2.. Incentives for Sustainable Technology Adoption
To encourage the widespread adoption of BIM and digital twin technologies, policymakers should consider providing incentives such as tax breaks, grants, or subsidies for companies investing in sustainable digital tools. These incentives could accelerate the transition toward greener industrial practices, particularly in energy-intensive sectors like manufacturing and construction.
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3.. Alignment with Global Sustainability Goals
The principles discussed in this paper align with global sustainability initiatives, such as the United Nations Sustainable Development Goals (SDGs). Specifically, these technologies contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 13 (Climate Action). Governments and international organizations should work together to integrate BIM and digital twins into their sustainability frameworks and encourage cross-industry collaboration to scale these technologies globally.
4.3. Opportunities for Future Research
As digital twin technologies evolve, there are increasing opportunities for research into its integration with other emerging technologies such as artificial intelligence (AI), machine learning, and blockchain. Future research could explore how these technologies can further enhance the predictive and operational capabilities of digital twins in industrial settings. While case studies demonstrate the short-term benefits of BIM and digital twins, there is a need for longitudinal studies that track the sustainability impacts of these technologies over time. Such studies could provide deeper insights into the lifecycle benefits of digital integration and help identify areas for further improvement in resource efficiency, energy management, and risk mitigation. The widespread adoption of BIM and digital twin technologies will likely have significant socioeconomic impacts, particularly concerning workforce development. Future research should investigate how these technologies influence job creation, skills requirements, and worker training. Understanding these impacts is essential for developing education and training programs that prepare workers for the digital transformation of industries.
The integration of BIM and digital twin technologies presents numerous opportunities for advancing sustainability in industrial facilities. By focusing on minimizing environmental impact, optimizing resource efficiency, enhancing resilience, and improving data interoperability, industries can achieve significant operational and sustainability benefits. Policymakers and industry leaders must collaborate to develop the necessary regulatory frameworks and incentives to accelerate the adoption of these technologies, ensuring that they contribute to global sustainability goals. Future research will be essential for exploring the full potential of these technologies and addressing any challenges that arise as industries undergo digital transformation.
5. Conclusions
The integration of Building Information Modeling (BIM) and digital twin technologies within industrial facilities offers significant potential for advancing sustainability objectives. This paper has systematically identified and evaluated 12 key topics from the literature, selecting four that form the foundation of the guiding principles for sustainable digital integration. These principles, which focus on minimizing environmental impact, optimizing resource efficiency, enhancing long-term resilience and risk management, and improving data interoperability, were derived based on their frequency of mention in the literature, relevance to the research objectives, and potential impact on industrial sustainability.
The formulation of these principles offers a strategic framework for leveraging digital technologies to reduce environmental footprints, optimize resource consumption, and ensure the long-term operational resilience of industrial facilities. The principles reflect the intersection of operational efficiency and sustainability, guided by data-driven processes made possible through the integration of BIM and digital twins. Key contributions of the paper can be summarized as follows:
Identification and selection of key topics: This study undertook a rigorous analysis of the literature, identifying 12 topics critical to the integration of BIM and digital twins in industrial settings. Among these, the four most impactful and relevant topics were selected based on scientific criteria, including frequency of mention, relevance to sustainability, potential for reducing environmental impact, and addressing research gaps.
Formulation of sustainability principles: The paper presents a clear set of principles derived from both the literature and the practical implications of BIM and digital twin technologies. These principles are designed to guide the integration of digital tools in ways that optimize resource use, enhance resilience, ensure seamless data exchange, and contribute to overall environmental sustainability.
Contribution to sustainable industrial practices: By providing a structured framework for digital integration, this paper contributes to the broader discourse on sustainable industrial practices. The principles outlined offer a comprehensive approach for industries looking to incorporate advanced digital technologies while meeting sustainability goals.
Several areas warrant further exploration to deepen the understanding and application of BIM and digital twin technologies in industrial facilities. Future research should explore the potential of integrating BIM and digital twins with artificial intelligence, machine learning, and blockchain to further enhance operational efficiencies and predictive capabilities. Investigating the long-term sustainability impacts of these technologies will provide deeper insights into their effectiveness over time, particularly concerning lifecycle management and resource optimization. Further studies are needed to assess the socioeconomic implications of widespread digital adoption, particularly how these technologies influence job creation, skills development, and workforce transformation in industrial settings.
In conclusion, BIM and digital twin technologies represent transformative tools for achieving industrial sustainability. The principles formulated in this study provide a robust foundation for industries and policymakers alike, ensuring that digital transformation efforts align with global sustainability objectives and contribute to a more sustainable and resilient industrial future.
Conceptualization, N.B. and V.B.; Data curation, A.C. and V.B.; Formal analysis, V.B. and N.B.; Funding acquisition, V.B., Investigation, N.B., V.P. and A.C.; Methodology, N.B. and V.B.; Project administration, V.B.; Supervision, A.C.; Validation, V.P.; Writing—original draft, N.B.; Writing—review and editing, N.B., V.B. and A.C. All authors have read and agreed to the published version of the manuscript.
The authors declare no conflicts of interest.
Footnotes
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Enlarge this image.Figure 1. Share of papers mentioning sustainability among papers related to BIM and digital twin.
Enlarge this image.Figure 2. Principles for sustainable integration of BIM and digital twin technologies in industrial settings.
Papers mentioning sustainability among papers related to BIM and digital twins.
| Year | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|
| N. of papers mentioning BIM and digital twin | 1 | 6 | 30 | 70 | 127 | 203 | 269 | 225 |
| N. of papers mentioning BIM, digital twin and Sustainability | 0 | 2 | 4 | 3 | 15 | 11 | 27 | 40 |
| Share of papers mentioning sustainability among papers related to BIM and digital twin | 4% | 12% | 5% | 10% | 18% |
Key topics related to the integration of BIM and digital twin technologies in industrial settings in terms of sustainability.
| Topic | Frequency of Mention | Relevance to Research Objectives | Impact on Sustainability | Novelty and Research Gaps | Total Score |
|---|---|---|---|---|---|
| Environmental Impact Reduction | 5 | 5 | 5 | 3 | 18 |
| Resource Efficiency Optimization | 4 | 5 | 5 | 3 | 17 |
| Lifecycle Management and Sustainability | 4 | 4 | 4 | 3 | 15 |
| Resilience and Risk Management | 4 | 4 | 4 | 4 | 16 |
| Data Interoperability and Integration | 3 | 4 | 4 | 5 | 16 |
| Stakeholder Collaboration and Engagement | 3 | 4 | 3 | 4 | 14 |
| Cost Efficiency and Financial Sustainability | 4 | 3 | 3 | 2 | 12 |
| Energy Management and Optimization | 3 | 3 | 4 | 2 | 12 |
| Digital Innovation and Technological Advancement | 2 | 3 | 3 | 5 | 13 |
| Predictive Maintenance and Asset Management | 3 | 4 | 3 | 3 | 13 |
| Regulatory Compliance and Standards | 2 | 3 | 2 | 2 | 9 |
| Digital Skills and Workforce Development | 2 | 2 | 2 | 4 | 10 |
Distribution of highlighted research among the selected topics.
| № | Topic | Sources |
|---|---|---|
| 1 | Environmental Impact Reduction | [ |
| 2 | Resource Efficiency Optimization | [ |
| 3 | Resilience and Risk Management | [ |
| 4 | Data Interoperability and Integration | [ |
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; Bolshakov, Nikolai 1 ; Celani, Alberto 2
; Puglisi, Valentina 2 1 Civil Engineering Institute, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia;
2 ABC Department, Politecnico di Milano, 20133 Milano, Italy;