Sustainable business, as a concept, has been growing in prominence over the years, with businesses grounding their purpose and actions in environmental, financial, and social concerns [1]. Corporate enterprises are considering an approach to create long-term value by operating effectively in ecological, social, and economic environments [1]. Enterprises are designing strategies to minimize their negative environmental footprint by adopting a “triple bottom line” that helps to measure the environmental and social impact of a business’s operations, in addition to the financial impact [3]. Corporations find owning sustainable buildings in the asset portfolio contributes to achieving strategic corporate goals [2]. Unlike in the past when businesses committed to sustainability objectives for corporate citizenship, enterprises today are embracing sustainability as an integral component of their corporate strategy [1]. Studies have shown sustainable companies to be the most profitable [3], and research by McKinsey found companies with the highest environmental, social and governance (ESG) ratings consistently outperforming the market in the medium and long term [4]. Moreover, Deutsche Bank found companies with high ESG ratings had lower cost of debt and equity [4]. McKinsey’s research (2014) had shown that efficiency at using resources is a credible indicator of a business’s superior financial performance [4]. With integration of sustainability in the corporate goals, companies are reducing waste and energy consumption, while enhancing the brand image, building customer loyalty and attracting investments and talent [1].

Implementation and evaluation of sustainability metrics in businesses requires the inclusion of technology for enabling sustainable development. Technology facilitates businesses to model diverse scenarios and gather actionable insights to improve business performance with a connected view across economic, environmental and social metrics [5]. Modelling assumes a great significance for sustainable businesses as it provides a predictive view of outcomes by simulating different scenarios to identify the knock-on effect on profitability, customer sentiment [5] and environmental footprint. This enables simulation of alternative outcomes and provides actionable insights into a business case that help in decision making on strategic investments. Organizations need to model the triple bottom line to better understand the interdependencies between systems and accordingly, inform decisions [5]. Lack of a connected view can potentially restrict a business’s ability to simulate emissions at a granular level throughout the supply chain, assess capital allocation in different investment options and disclose environmental data across continually evolving frameworks with consistency across the audit trail. Such modelling techniques exist in different sectors in the form of building information modelling system, predictive maintenance models and digital twins [5].

Creating a simulated version of a physical product holds potential to digitize and analyze the impact of a system on the environment. Detailed simulations can provide new levels of operational excellence through enterprise-wide insights that drive improved business and sustainability operations [6]. Multiple simulations of a system with slight variations also help organizations to assess multiple digital copies in parallel [6], thereby assisting in predictive assessment of the outcome of engineering projects. Enterprises can leverage such digital simulations which mimic the behavior of physical systems to understand the climate footprint of these systems. “Digital Twins” offer a digital representation of a physical system and its environment and help to simulate real situations and assess their outcomes, for enabling predictive analytics and informed decision making. This essay investigates the significance of digital twins in reducing negative impact of engineering systems without impacting business performance on the environment and explores the diverse use cases of digital twins in several industries. In the pursuit of assessing the role of digital twins at the intersection of sustainability and business, two research questions (RQs) emerge:

RQ1: Do digital twins help in reducing carbon footprint of businesses in diverse sectors?

RQ2: Are digital twins financially viable for businesses to become sustainable?

Methodology

The essay adopts a qualitative assessment method to study the impact of digital twins of environmental footprint of businesses in different regions around the world. It explores the sustainability and financial outcomes of digital twins for businesses by studying several use cases of digital twins and examining case studies worldwide. By deriving insights from successful implementation of digital twins in diverse industries and assessing their environmental impact, the essay aims to draw inferences on the value proposition of digital twins in enabling sustainable businesses, which can eventually facilitate growth of a sustainable society. The essay investigates the intricate relationship between digital twins and sustainability, by evaluating the potential of digital twins in driving positive environmental, economic and business outcomes, which can further lead to attainment of sustainable development goals. The essay also recommends business practices and metrics for adoption of digital twins by businesses in various sectors. These recommendations expand on the responsible use of digital twins to ensure sustainability in business and sustainable development.

Digital twins in industrial sector

Concept of digital twins

Digital Twins are digital copies of systems, processes and environment of large and small physical structures [7] (or “physical twins”) and have intricate connection to the physical twins. Collating data from a digital twin can help capture the health of an entire environment of a large system, in addition to the assets within it [7]. The concept of digital twins emerged primarily in 2003 with Micheal Grieves proposing virtual digital representations equivalent to physical products as a concept to abstractly represent a real device and to act as a basis for testing under simulated conditions [8]. Such representations, known as “information mirror model” and “mirrored space model” [8], established the possibility of interfacing the physical and virtual space. Thereafter, simulations have grown to become the standard tool to realize complex systems and processes [9] and visualize the interrelationships between the numerous constituent components residing in the system and the surrounding environment. Furthermore, decreasing costs of storage and bandwidth, and increase in computing power [10] can facilitate growth of digital twins in several sectors. Combined with optimization, digital twin simulations have matured from troubleshooting tools to standard virtual validation and generative design tools [9].

Digital twins basically perform the following three functions [8]:

1. Digitizes the physical system using a digital expression to build a virtual product with identical content and nature;
2. Introduces virtual space and environmental parameters to establish association between virtual space and real space for data exchange;
3. Visually simulates the concept of integrating parameters between the physical and digital copy.

The correspondence between the numerous entities of the physical and virtual model helps the digital twin to mimic the behavior and performance of the physical machine in a real-time environment. Availability of a complete digital footprint allows businesses to visualize each and every aspect of product lifecycle, from design and development to deployment [10]. The digital representation allows for endless modifications and experimentations without depending on creation of a physical product in each iteration, thereby leading to increased speed to market, reduced defects, improved operations and new business models for increasing revenue [10]. Added benefits include capabilities to detect defects sooner in virtual models, predict outcomes faster and iterate design more efficiently [10]. Moreover, digital twins are effective in identifying ways to make products efficient, reduce their emissions, minimize waste and provide a single source of real-time information on power usage, carbon dioxide levels, temperature and other parameters to predict shortcomings and minimize environmental footprint [7] in a cost-efficient way, thereby integrating sustainability with business performance.

Figure 1 depicts a process diagram to illustrate the functionality of digital twins. A matrix of sensors collect real-time information about the physical asset to capture performance parameters and measure the performance outcomes. The data collected from sensors is also compared against performance benchmarks to predict risks of failure. Data related to history of maintenance, operation, design and failure modes are captured and fed to simulation models, which create the digital twin of the physical asset. Post data collection and cleanup phase, data analysis enables performance monitoring on a continuous basis and diagnoses any potential issue with the physical asset, as part of its predictive maintenance process.

Figure 1. Process diagram of a digital twin system

Applications of digital twins in diverse industries

Digital twins are increasingly being used in diverse industries for a variety of applications. Industrial plants leverage digital twins to promptly identify malfunctions and locate faults among numerous components in the control systems and machines [11] with high accuracy of fault localization and interpretability of fault localization paths [11]. Businesses can build performance evaluation networks in which each node of a physical machine constantly interacts with the corresponding digital twin to acquire real-time performance data and conduct a degradation analysis [11] without disturbing the operation of the physical machine. Manufacturing businesses can predict when equipment is wearing down and accordingly repair machines, extend their life and redesign them for efficient performance [12]. Digital twins can also help visualize information about working condition and complex states of a machine with contextual awareness, which eventually helps to calculate the Remaining Useful Life (RUL) of a physical machine and its individual components [11]. From supply chain perspective, digital twins can help improve packaging performance, routing efficiency and fleet management [12]. Additionally, operational and performance data can be shared among production, engineering, sales and finance teams, leading to greater collaboration and predictive analytics of the system under analysis.

In automotive industry, digital twins can collect vehicle’s performance information for designers and system engineers, while simulating functions of various systems to assess their suitability and interoperability [13]. Digital twin can simulate the quality and performance of a new component virtually [13], enabling engineers to fix any faults before manufacturing multiple copies of the component, thereby minimizing avoidable wastage. Tracking key components helps to mitigate risks of recalling thousands of finished products in the event of a problem [13]. In real estate sector, digital twins can assist engineers to monitor water usage, waste generation, energy leakage, electricity consumption and heating issues by mapping the building data generated by the digital twin [7], thereby informing engineers and builders to perform localized maintenance operations for extending life of the building. Digital twins can also empower town authorities with actionable insights, such as in Japan, “Tokyo Virtual Living Lab” integrates traffic and street data to simulate carbon emissions in Tokyo’s road network. Island communities have leveraged digital twin to optimize the use of local energy by utilizing a combination of grid improvements, while universities have integrated digital twins of buildings and energy services to create green campuses [14].

Life sciences industry mirrors the application of digital twins in simulating various organs of a human body. Dassault, for example, creates digital twin of a human heart by converting a 2-D scan of a human’s heart into an accurate full-dimensional model [12]. The realistic model of the human organ accounts for blood flow, electricity and mechanics, which eventually assists in drug development and treatment [12]. Effectiveness of novel drug formulas and medical devices on a patient’s disease treatment can be studied without adversely affecting a human patient in real life [13]. Digital twin models are also utilized to develop digital replicas of patients, medical devices and healthcare facilities, which assist in monitoring, analyzing and predicting issues in interfacing devices with patients [15]. Using digital twin of a drug, pharmaceutical businesses can run clinical trials at an accelerated pace with fewer patients required for the trials [12], which can help in rapid vaccine development and reduced wastage of chemicals for manufacturing different drugs for trials. Moreover, the impact of the drug on a wider population can be studied by simulating diverse patient characteristics to replicate their response to the drug in specific situations [12]. A Swedish university, for example, has developed a digital twin of a mice to study the efficacy of a drug on rheumatoid arthritis, without depending on real animals and humans for trials [15].

Similarly, in the mining industry, digital twins can simulate the work environment to enable miners in accurately estimating the drilling, crushing and extraction work with minimal wastage of resources, minimal emissions and minimal safety risks [12]. For example, Rio Tinto has developed a digital twin for its iron ore operations in Gudai-Darri, which assists remote operations center and field personnels in making informed decisions in seconds, rather than hours, leading to greater efficiency in using natural resources [12]. Aerospace industry utilized digital twins for aircraft maintenance, weight monitoring and defect detection, among other applications. For example, Boeing uses digital twins to improve the safety and quality of the parts and systems used to manufacture airplanes by 40 percent [12]. Predictive maintenance of airplanes helps to reduce flight emissions and fuel usage, helping airline businesses to save costs and reduce climate footprint. Digital twins also leverage Internet of Things (IoT) to assess the design of smart cities, improve resource management and reduce the environmental impact of each citizen [13].

The scope of digital twins’ application in transportation and mobility space is quite large. The utilization of digital twins enables the comprehensive simulation of the entire motorway system, encompassing traffic dynamics and traffic control strategies [16]. A digital twin can integrate real-time traffic data into its simulations, facilitating its incorporation into the decision-making processes of traffic management on motorways [16]. This integration positions digital twins as a valuable real-time feedback mechanism for TM, supplying not only current traffic conditions but also predictions derived from simulations during safety-critical decision-making processes. Digital twins emerge as invaluable tools in the pursuit of efficiency maximization and resource optimization. Organizations grappling with the evolving landscape of renewable energy and the escalating demands of Environment, Society, and Governance (ESG) regulations find substantial benefits in the application of digital twin technologies [17]. This approach enhances the anticipation of traffic behavior and the foreseeable impact of diverse control strategies on the spatio-temporal evolution of traffic [16]. The detailed microscopic run-time simulation analysis offered by DT-GM allows for a clearer understanding of these dynamics well in advance, enabling informed decisions before the deployment of control strategies in the actual system.

Environmental impact of digital twins

Digital twins enable efficient use of resources, reduce waste and identify areas of energy mismanagement, thereby helping businesses to make their operations sustainable.

Reduced emissions

In refineries, plant downtime is highly damaging to the environment and an unplanned shutdown lasting a few hours can lead to release of a year’s worth of toxins into the atmosphere [18]. For example, a forced shutdown of a refinery in California in 2017 led to the release of 31,000 lbs of sulphur dioxide within a day, which was more than the amount released by the refinery in two years before the incident [18]. Monitoring heavy machinery in real-time can reduce fuel consumption of machines by 40 percent, which eventually leads to lower greenhouse emissions [19]. Regular maintenance of the same equipment for extended periods creates less municipal solid waste over time [19]. Digital twins simulate the behavior of physical machines to identify potential failures and performance issues, which can reduce downtime, optimize energy usage and minimize breakdowns [20]. Virtual replicas of physical oil and gas fields can compile real-time data on operations and detect excessive emissions to eventually curb them [21]. AI operating on data collected using IoT sensors can enhance operational efficiency leading to less waste, electricity consumption [21] and carbon emissions related to equipment downtime [22]. Moreover, digital twins can simulate performance of industrial systems under diverse operating conditions which can result in design optimization and lower energy consumption [22]. Lower energy use benefits a business financially, while reduced emissions contribute to sustainable development.

Energy savings

Digital twins help to simulate energy scenarios to identify potential leakage points for heating systems and air conditioning systems, detect inefficiencies and develop optimization strategies [20]. Virtual simulations of a building can assist the builders to understand the interdependency among heating and ventilation systems, energy storages, alternative energy sources and potential impact on electricity networks [14]. For example, SABIC, a global chemical manufacturer, launched a digital twin-enabled sustainability program in 2009 to identify energy losses and the equipment level to optimize the overall utility system and reduce water and greenhouse gas consumption by 25 percent, while reducing material loss by 50 percent [6]. Using IoT sensors, digital twins can help real-estate businesses visualize the generation and consumption of energy in different building designs and control strategies [12]. Furthermore, digital twins can yield optimal results in terms of machine maintenance, production planning, risk mitigation and plant efficiency, which help in improved energy management [12]. Digital twins can also simulate the what-if analysis of the future impact of changes to the industrial systems, building models and climatic and demographic factors [14].

Figure 2 illustrates the functionality of digital twins in reducing energy consumption in a residential building. Lighting system and consumption information is collected by the IoT sensors, along with collection of illuminance parameters, power ratings of in-use appliances and the equivalent carbon emissions. Data from these IoT sensors is fed to the digital twin model. Information about the building’s operation schedule of lighting systems is provided as an input to the digital twin to understand how lighting facility is provided throughout a day. Information about the occupant’s lighting usage pattern is also provided to the digital twin to analyze the lighting consumption trends spread of the time of the entire day. The digital twin model simulates the energy consumption trends and analyzes consumption behavior of the occupant, basis which behavioral improvement suggestions are offered. Finally, by comparing the energy consumption for lighting systems in the previous scenario and the improved scenario, digital twins help to calculate the energy savings enabled by the improved lighting usage plan.

Figure 2. Process diagram illustrating application of digital twin in energy savings in a residential building.

Studies show that 75 percent of industrial organizations using digital twin technology witnessed improved energy efficiency [20], while digital twins showed potential of reducing energy usage in manufacturing processes by up to 15 percent [20]. Moreover, businesses can simulate various scenarios to study the impact of process modifications, system upgrades and equipment changes on the energy consumption and then physically implement the energy efficient one [20], thereby optimizing energy usage. Visualization of energy systems help business managers and operations team to understand the complex systems and the interactions among them [14]. Additionally, digital twins assist harmonious integration of different energy systems, such as energy storage, renewable energy sources and grid infrastructure, through modeling and simulation, which help businesses to ensure efficient energy utilization and decreased reliance on traditional energy sources [20]. Grid monitoring software can detect potential issues in smart grids and prevent them, thereby averting power outages, grid failure and energy loss. Moreover, digital twins also help to forecast energy costs based on changes to the energy network, helping businesses in proactive energy management [23].

Waste reduction

By identifying the process inefficiencies, digital twins minimize waste of energy and resources [20]. In the chemical industry, digital twins provide insights into solvent management and reduce solvent waste. For example, Hanwha applied digital twin to optimize its solvent recovery tower, leading to 29 percent reduction of solvent waste, which saved the business USD0.5 million per year for the hydrocarbon resin manufacture process [6]. In real estate sector, digital twins offer real-time monitoring of resource allocation and waste tracking, which improve the productivity of construction process for the businesses [12]. Consumer electronics and consumer goods businesses also stand to benefit from digital twins, as Procter and Gamble factory reduced inventory by 30 percent using a digital twin model of its warehouse operations [24]. Schneider Electric reduced material waste by 17 percent and minimized carbon dioxide emissions by 25 percent [24]. Minimization of inventory and tracking of waste helps businesses to save operational and waste management costs, which eventually impact the profitability of the businesses. Moreover, digital twins can identify ways to reuse waste heat from industrial processes [14], which leads to lower environmental footprint and increased energy optimization.

Figure 3 illustrates how digital twins enable waste reduction. The Physical Systems layer encompasses various physical resources within the industry, including products, personnel, equipment, materials, processes, environments, and facilities. Next, the Communication layer serves the purpose of facilitating data transfer between the digital twin and the physical entities within the plant. Inside the communication layer, control devices and execution tools monitor the physical components, enabling data collection and device control. This layer leverages a range of devices like sensors, cameras, actuators, and other composite equipment. Moreover, this system is responsible for establishing a reliable connection between observable plant elements and their digital counterparts, ensuring synchronization between the two. The Control and Execution Tool facilitates bidirectional communication between the physical system and the cyber system. This communication occurs via sensors, transducers, actuators, switches, etc., enabling data transmission from the physical system to the cyber system’s output and vice versa. The Simulation Tool enables the creation of virtual models of processes, allowing managers to analyze hypothetical scenarios without actual implementation, thus mitigating potential risks to operators. When operating online, the tool receives data from sensors on the physical asset and adjusts parameters accordingly in response to changes in asset conditions. The Anomaly Detection and Prediction Tool anticipates system faults, identifies anomalies, determines causal factors, and forecasts the system’s remaining operational lifespan within acceptable plant parameters. The Cloud Server Platform collects real-time field data and manages concurrent access requests for data retrieval and storage. Finally, the user layer consists of human operators who receive operational instructions for maintenance and management of the physical machines and receive warning messages in case of potential issue with the physical assets. The predictive maintenance of physical systems and their timely maintenance aids in extending the residual life of the machines, which helps in waste reduction.

Figure 3. Process diagram illustrating the use of digital twins for waste reduction.

Enablement of circular economy

Digital twins enable circular economy by reducing waste and enabling sustainable operations without any additional cost of materials [6]. Circular economy approach requires collaborative decision-making among diverse stakeholders [6] for effective operationalization of circular solutions. Digital twins provide a common source of truth to all stakeholders, enabling them to work collaboratively on circular economy approach. Moreover, the Ellen MacArthur Foundation’s research has shown that products developed for circular economy offer almost USD1 trillion in new business opportunity and 100,000 new jobs [25] for companies meeting sustainability objectives. Circular economy approaches to business hold potential to save USD500 million in materials and prevent 100 million tonnes of waste globally [25]. In construction sector, for example, digital twin-based simulations and sensor networks contribute to recording of material usage, material flows and material changes from design to construction, and maintenance to demolition [26]. Post the demolition phase, digital twins assist in segregating recyclable and reusable components, analyze the quality of salvaged components, and perform resource recovery [26]. In electronics and semiconductor industry, suppliers and manufacturers use immutable digital records comprising key product information to collect information from end-of-life processors to get maximum value from the electronic waste [27]. Reintegration of waste into the product value chain using digital twins enables circular economy for businesses, saving them costs of waste disposal and optimizing use of resources.

Digital simulations can act like predictive models to simulate the potential impact of products and services on the environment. For example, digital twins of windmills and turbines help to monitor changes in birds’ movements, reefs, airflow and aquatic life [28]. Conservationists can leverage IoT enabled digital twins to anticipate the impact of environmental changes on wildlife populations and the larger ecosystem before the commencement of physical construction of projects [28]. Digital twins can also simulate complex, real-world circumstances inside a controlled digital space to predict impact on biodiversity and climate due to the business operations.

Benefit to businesses and society

The application of digital twins in various industries lead to positive dividends in terms of profits and sustainability. Real estate businesses assess the impact of architectural designs in meeting the sustainability and wellbeing targets [7]. For example, Oscar Properties – a Norwegian residential property company – leveraged digital twins to test the alignment of building design with their corporate sustainability values [7]. Similarly, Ericsson had trained its employees in Tallinn smart factory using digital twins, which enabled the trainer sitting 8,000 kms away to equip factory workers with manufacturing-related skills [7]. The digital twin solution saved time, cost and fuel emissions related to long distance travel by the trainers. Smart city planners are leveraging large collection of digital twins to assess the sustainability of large dwelling regions. Digital twins of individual assets inside a single building are integrated to arrive at a virtual twin of the building, which is then integrated with the digital twins of other buildings in a community to create community-wide twin [14]. The community-wide twins represent the portfolio of buildings, energy grids, water systems, waste management systems and transport networks within the restricted area, such as a university campus, an airport or a district. An integrated collection of community-wide twins results in a city-wide twin that can help assess the sustainability aspects of city-wide infrastructure and networks [14]. For example, Nanyang Technological University in Singapore used digital twins to reduce waste footprint by 35 percent [14]. By developing a campus information model, the university identified immediate energy savings of 10 percent and 8.2 kilo tonnes of carbon emission reduction, besides simulating 31 percent energy savings in long term through technology implementation in buildings [14].

Adoption of circular economy principles to business operations enable corporations to close resource loops and maximize resource efficiency through the reduction, reuse and recycling of waste [29]. Digitalizing systems and infrastructures reduce the cost and environmental impact of inspections, while preventing unnecessary maintenance and downtime of systems [7], leading to lower disruption of services and supply chain for the society. Circular economy approaches can also influence civic behavior, as has been observed with Brazilian coffee in-capsules business that raised awareness about waste reduction and recycling [30]. Quality management and reverse logistics improve as digital twins help to close or narrow supply chain loops [30]. The availability of a virtual simulation of a complex system ensures that diverse impacts of the system of society and on the environment are assessed comprehensively for the engineers and managers to effectively collaborate on mitigating the negative consequences preemptively. Moreover, by drawing on data from energy networks, buildings, transport systems and environmental infrastructure, a cross-sector digital representation of a community or a city can be built that can help simulate the impact of different approaches to energy management, emissions reduction, energy storage, transportation and mobility [14].

Financial impact of digital twins on business

The financial implications of digital twins are of critical importance for assessing its benefits and Returns on Investment (ROI). Digital twins have high upfront investment costs since a twin of each system requires physical and virtual components functioning together [30]. Sourcing data and lack of data standardization [30] necessitates organizations to invest in ensuring data quality for digital twins to read and extract insights. Moreover, the magnitude of complexity of the to-be simulated system also influences the cost of building the digital twin. Collectively, the costs of development and deployment of digital twins is high for businesses. However, the financial upside of implementing digital twins has been measured qualitatively and quantitatively. Data interoperability in digital twin systems assists in reducing the cost of overcoming legacy data silos, while offering 600 percent increase in revenue growth [31]. Furthermore, digital twins provide visibility into the performance and life of an industrial asset in a multi-location and multi-department landscape. Utilizing digital twins from initial capital planning to asset operations can significantly enhance infrastructure decision-making processes. By offering advanced scenario planning and options analysis, digital twins pave the way for smarter decision-making throughout the infrastructure lifecycle. Research indicates that governments stand to gain an impressive return on investment, with approximately USD9 for every USD1 invested in digital twins for infrastructure [32]. A prime example is the UK’s National Underground Asset Register (NUAR), which boasts a remarkable return on investment ratio of 30:1 [32]. The implementation of digital twins has the potential to slash design costs by 50 percent, halve construction permitting times, and reduce overall maintenance expenses by a fifth [32]. This translates to expedited infrastructure delivery, cost savings, and enhanced quality, making digital twins indispensable tools for modern governance.

Discussion

Application of digital twins in diverse industries holds considerable financial, environmental and social impact. Digital twins reduce the volume of waste generated in factories and emissions generated during manufacturing process, while driving energy savings and enabling circular economy. Case studies from diverse sectors and different geographies demonstrate qualitatively and quantitatively the role of digital twins in reducing carbon emissions and environmental footprint. Therefore, RQ1 stands validated that digital twins help in decreasing carbon footprint of businesses in diverse sectors.

Despite the high upfront cost of development and deployment of digital twins, the economic benefits generated through predictive maintenance of industrial machinery, design optimization, quality assurance and collaborative decision making offer a positive return on investment. Moreover, the environmental benefits that get transferred to the society help in building sustainable societies. Reduced waste generation leads to reduced cost of waste management, while energy savings leads to reduced costs of energy production. Hence, it can be concluded that investments in digital twins generate dividends in the long term in the form of cost savings and social development. Therefore, RQ2 stands validated that digital twins are financially viable for businesses to become sustainable.

In the light of financial viability and sustainability benefits of digital twins, it can be concluded that digital twins assist in sustainable development. A framework for the development and implementation of digital twins can be utilized to generate sustainability-related benefits from digital twins. Industries require a structured framework that guides the adoption process and ensures alignment with organizational goals and priorities.

Strategic Alignment and Goal Setting

The framework begins with strategic alignment and goal setting, wherein organizations define clear sustainability objectives, energy efficiency targets, and emissions reduction goals. By aligning digital twin initiatives with these strategic objectives, organizations can prioritize efforts and allocate resources effectively to maximize impact. Stakeholder engagement and education play a crucial role in building awareness and fostering commitment to sustainability goals across all levels of the organization.

Technology Assessment and Selection

A critical aspect of digital twin adoption is the selection of appropriate technology solutions that support sustainability objectives. Organizations must evaluate available digital twin platforms and technologies, considering factors such as scalability, interoperability, data integration capabilities, and security features. By choosing the right technology solution, organizations can lay a solid foundation for implementing sustainability-focused digital twin applications.

Data Integration and Management

Effective data integration and management are essential for leveraging digital twins for sustainability. Organizations must identify and integrate relevant data sources, including IoT sensors, energy meters, environmental monitoring systems, and operational data repositories. Robust data management protocols ensure the quality, reliability, and security of sustainability-related data flowing into the digital twin environment, enabling accurate modeling and simulation.

Modeling and Simulation

Digital twin models play a central role in simulating energy flows, emissions generation, and resource utilization across industrial processes. Organizations must develop accurate and predictive models that capture the complex interactions between physical assets, environmental factors, and operational parameters. Advanced modeling techniques, such as physics-based models, machine learning algorithms, and optimization algorithms, enhance the fidelity and performance of sustainability simulations.

Visualization and Decision Support

Intuitive visualization interfaces and decision support tools empower stakeholders to gain real-time insights into sustainability metrics, energy consumption patterns, and emissions profiles. By enabling scenario analysis and decision support capabilities, organizations can identify optimization opportunities and evaluate the potential impact of sustainability initiatives. Visualization tools facilitate communication and collaboration among stakeholders, driving informed decision-making and action.

Optimization and Control

Optimization algorithms embedded within the digital twin environment enable organizations to optimize energy usage, minimize emissions, and maximize resource efficiency. Real-time data analytics and control strategies dynamically adjust operational parameters to respond to changing environmental conditions while maintaining sustainability objectives. By continuously optimizing processes, organizations can achieve sustainable outcomes while enhancing operational performance.

Continuous Monitoring and Performance Evaluation

Continuous monitoring mechanisms track sustainability performance metrics, energy efficiency indicators, and emissions levels in real time. Regular performance evaluations and audits assess the effectiveness of digital twin interventions and identify areas for further improvement. By leveraging performance data, organizations can refine strategies, implement corrective actions, and drive continuous improvement in sustainability outcomes.

Collaboration and Knowledge Sharing

Collaboration among internal teams and external partners facilitates knowledge sharing, best practice exchange, and innovation in sustainability initiatives. By fostering a collaborative ecosystem, organizations can leverage collective expertise and resources to address complex sustainability challenges. Engagement with industry networks, research collaborations, and community forums facilitates learning and drives progress in digital twin applications for sustainability.

Regulatory Compliance and Reporting

Ensuring compliance with environmental regulations, energy efficiency standards, and emissions reporting requirements is paramount for sustainable operations. Organizations must develop robust reporting mechanisms to document sustainability achievements, energy savings, and emissions reductions for internal and regulatory purposes. Transparent reporting builds trust with stakeholders and demonstrates a commitment to environmental stewardship.

Risk Management and Resilience Planning

Assessing risks related to sustainability, energy supply chain disruptions, and climate change impacts enables organizations to develop resilience plans and mitigate potential threats. Integrating resilience planning and risk management strategies into digital twin models enhances adaptive capacity and reduces vulnerability to environmental uncertainties. By proactively addressing risks, organizations can safeguard operations and sustain long-term performance.

Long-Term Strategy and Scalability

Developing a long-term roadmap for digital twin adoption aligns with sustainability goals and supports scalable implementations over time. Phased approaches, investment priorities, and scalability considerations ensure that digital twin initiatives evolve in tandem with changing business needs and technological advancements. Continuous evaluation and refinement of the strategy enable organizations to stay agile and responsive to emerging sustainability opportunities.

A structured framework encompassing strategic alignment, stakeholder engagement, technology assessment, data integration, modeling and simulation, visualization, optimization, continuous monitoring, collaboration, regulatory compliance, risk management, long-term strategy development, and scalability considerations, organizations can effectively leverage digital twins to drive sustainable outcomes and secure a resilient future.

Challenges with digital twins

Adoption of digital twins in diverse industries is subject to the development and implementation challenges. Data availability issues, lack of standardization and security challenges pose barriers to the implementation of digital twins and to transformation of legacy systems.

Data availability and quality issues

Ownership of data poses a major impediment to the utility of digital twins in various industries. Data is sometimes owned by the private entities, or else by the federal agencies [33]. Unavailability of real-time data or availability of data from a limited number of sensors impact the efficacy of the digital twins [33]. Inconsistencies in data sources can lead to ineffective simulations that can impact the quality of predictive analytics and system maintenance [34]. Challenges in connectivity, security and privacy of sensitive data inhibit the adoption of digital twins in several industries [12], especially healthcare. Varied data formats, particularly in healthcare sector, obstruct the integration of digital twins with existing legacy systems [34]. Phasing out of legacy systems and replacement with modern equipment have high upfront cost initially for new infrastructure, specialized training programs and state-of-the-art software [34]. Unavailability of real-time, open and quality data and fragmentation in data management also limit the benefits that businesses can generate from digital twins, and it can lead to further delay in adoption of digital twins.

Lack of data standardization

Absence of a particular standard for data formats across diverse entities within a sector, and across various sectors, inhibit the application of digital twins. Issues with format of data being fed to the digital twin prevent its integration into the data platform and can lead to lock-in through proprietary standards [33]. Application of open standards can potentially help to mitigate challenges related to lack of a standardized modeling approach. However, the current state of interoperability issues leads to challenges in data storage, processing and analysis. Moreover, alignment with healthcare standards and certifications, and navigating the regulatory hurdles in diverse sectors limits the full-scale adoption of digital twins.

Infrastructural challenges

Increased demand for power and storage and significant investments in technology platforms [12] restrict small-scale entities with limited capital from implementing digital twins for efficiencies. High expenditure in operations and maintenance [12] hinder the deployment of digital twin solutions. Technical complexity of advanced computational techniques and unavailability of qualified professionals to work with digital twins can also prevent businesses to leverage digital twins for sustainable business operations [34]. Need for constant internet connectivity can prevent the application of digital twins in developing and underdeveloped countries.

Additionally, stakeholder-oriented barriers can impact cooperation at all levels, while system instability and sudden failure can deter investments in digital twins. High upfront costs, technology challenges and inadequate expertise among industry professionals contain the scalability potential and impact the value proposition of digital twins for businesses in all sectors. Such challenges can check the capability of businesses to use technology for sustainable operations and delay the onset of digital twins as enablers of sustainable development.

Ethical and legal considerations for digital twins

Effective application of digital twin in diverse sectors, especially sensitive ones like healthcare, is subject to ethical and legal challenges. Unethical collection and treatment of data can lead to loss of trust in the digital twin ecosystem. For example, the accurate representation of patient population characteristics in the data used for machine learning training is essential to prevent misleading or discriminatory predictive analyses by digital twins [36]. The process of generating valuable information involves four key phases: data collection, data management, data analysis, and information utilization, each requiring distinct information and communication technologies (ICTs) and information systems to achieve their objectives [37]. Ethical considerations permeate each phase, encompassing concerns such as surveillance, data accessibility, and algorithmic design integrity, particularly relevant in the context of digital twins for personalized health care services [37]. Despite addressing data-related apprehensions, inadequately designed algorithms can distort health conditions portrayal or propagate coercive healthism. Furthermore, potential exploitation of data collection processes by service providers, accumulating irrelevant data for commercial purposes, underscores the need for regulatory clarity and ownership delineation regarding digital twin data [37]. Presently, no dedicated legal framework governs digital twins; however, extant regulations, notably the General Data Protection Regulation (GDPR), apply to data processing activities [35]. The lack of specificity in data rights allocation between individuals and digital twin creators raises pertinent questions regarding data ownership and control rights, particularly concerning potential data commercialization and exchange modalities, necessitating regulatory attention and clarity [35].

Conclusion

Digital twins hold immense potential in enabling sustainable operations for businesses and sustainable development in the society. Using digital twins, businesses can drive energy savings, reduce waste, track and cut down on emissions, build a circular economy and analyze environmental data on a continuous basis. By creating virtual copy of the complex systems and simulating the interactions between different components of a large macrosystem, engineers, designers and other stakeholders can collaborate using a single source of truth. Application of digital twins builds process efficiency and drives business sustainability. However, the high upfront cost of development, complexity of implementation and lack of technical infrastructure impede businesses from utilizing digital twins to transform their business processes. Ethical and legal challenges need to be addressed by the businesses before developing a digital twin for their products and systems, irrespective of the industry they belong to. By enabling an underlying infrastructure and by mitigating the associated challenges, businesses can promote sustainable development in the society they operate in.

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