Digital twin (DT) technologies are increasingly promoted for smart-city governance, yet many deployments remain fragmented and fail to support integrated lifecycle operations across planning, construction, and long-term management. This study proposes a digital-twin–driven urban lifecycle paradigm and examines its city-scale implementation through a structured case study of the Singapore–Nanjing Eco Hi-Tech Island. The study synthesizes a five-layer technical architecture encompassing sensing, data platforms, analytical modeling, service integration, and application scenarios, and organizes eight digital twin application domains spanning governance, community, education, transportation, industrial parks, ecology, security, and tourism. To demonstrate the scale and operational trajectory of the implementation, the paper reports phased quantitative deployment targets and lifecycle indicators (2020–2030), including environmental monitoring coverage, digital infrastructure penetration, public service integration, and governance digitalization. By linking scenario-based demand–supply data matching with lifecycle-oriented indicators, the case illustrates how digital twins can function as an enabling infrastructure for coordinated urban governance beyond isolated pilot projects. The findings offer transferable design and implementation insights for cities seeking to operationalize digital twins at the city scale.
big datadigital twinIoTlifecycle managementSingapore-Nanjing Eco Islandsmart cityurban planningThe author(s) declared that financial support was received for this work and/or its publication. This research was funded by the National Key Research and Development Program of China, grant numbers 2024YFF1306200 and 2024YFF1306203.section-at-acceptanceSmart Technologies and CitiesIntroduction
Urban centres are undergoing profound societal, economic, and spatial transformations alongside rapid technological change (Maeng and Nedović-Budić, 2008; Bibri and Krogstie, 2017a). Innovation in information and communication technologies improves the efficiency and quality of communication, thereby reshaping urban production structures and spatial organization. In this context, large-scale data technologies—ranging from sensors and demodulators to governmental statistics—have been increasingly used to identify urban scenarios, observe trends, and forecast urban events, supporting more effective, equitable, and intelligent urban governance (Caragliu et al., 2011; Angelidou, 2015). These developments have accelerated the emergence of “smart cities.” Smart-city applications are becoming pervasive in everyday life, and crowd-sensing has been recognized as a powerful means to address diverse urban challenges (Alvear et al., 2018).
Despite substantial investments, many smart-city initiatives remain fragmented and produce limited operational impact. First, implementations are often project-based and domain-specific (e.g., transport, safety, energy), resulting in siloed platforms, incompatible data models, and weak cross-department interoperability (Alvear et al., 2018). Second, current solutions frequently emphasize visualization dashboards or static 3D models, while lacking robust mechanisms for continuous data synchronization, model updating, and closed-loop decision execution (Ding et al., 2019). Third, lifecycle discontinuities persist: data generated during planning and construction are rarely structured and transferred for long-term operation and maintenance, which constrains evidence-based governance (van Meerten and Smit, 2024). Fourth, governance challenges—such as unclear data ownership, privacy protection, and accountability for algorithm-assisted decisions—reduce public trust and limit sustained participation (Haraguchi et al., 2024). As a result, many systems are difficult to scale beyond pilots and to operate as durable urban infrastructure.
However, many smart-city efforts still struggle to translate heterogeneous data streams into coherent, actionable, city-scale operational capacity across planning, construction, operation, and management. The latest approach to the construction of intelligent urban centers is the concept of the “digital twin,” grounded in two-dimensional spatial cartography and the interplay between corporeal and virtual municipalities (Jones et al., 2020). It centers on the comprehensive life cycle of urban schematics, erection, operation, and management, with the intention of enhancing resource allocation efficiency, elevating the scientific caliber of urban governance, and refining urban amenities (Harpham and Boateng, 1997). Realizing a digital twin requires capabilities to represent the real world within a virtual environment (El Saddik, 2018), together with mechanisms that can feed decisions generated in the virtual environment back to real-world urban systems (Wu et al., 2021). The expansion of big data and the maturation of IoT technologies are pivotal to the viability of digital twins. Big data enables cities to derive actionable insights from large, heterogeneous datasets, while IoT networks connect distributed sensing devices (e.g., sensors, RFID, and Bluetooth) to real-world urban services (Hashem et al., 2016).
In parallel, national programs such as “Digital China” and “new infrastructure” have accelerated the deployment of urban digital foundations, including ubiquitous sensing networks, data centres, high-throughput connectivity, and cloud–edge computing backbones (Liu, 2021; Tang and Zhao, 2023). Coupled with China’s extensive practice of new-town development, these initiatives make it feasible to embed a city-scale digital twin early in the process of spatial expansion, enabling rapid roll-out of perception devices and a coherent data architecture for governance and public services. In such settings, digital twins can move beyond project-based pilots and evolve into an operating infrastructure that supports lifecycle integration across planning, construction, operation, and service delivery (Camposano et al., 2021). At the same time, this fast-track model requires continuous refinement: sustained bottom-up participation, transparent data governance, and robust privacy safeguards are essential complements to top-down system design, helping broaden inclusivity and build long-term public trust (Delacroix and Lawrence, 2019).
More broadly, cities have entered a data-intensive stage characterized by rapidly growing high-resolution spatio-temporal datasets. With unprecedented spatial granularity, understanding of urban systems is shifting from holistic, low-frequency observation to high-frequency analysis based on heterogeneous evidence (Batty, 2012, 2018). Spatio-temporal data are expanding in volume and variety, increasingly transcending conventional administrative statistical units and enabling description and prediction of individuals, events, and entities at macro-, meso-, and micro-scales (Succi and Coveney, 2019). Big data refers to large-scale datasets that are structurally complex, rapidly generated, and diverse (Rajasekar et al., 2015), and it continues to reshape lifestyles, work patterns, and cognition. In digital communication, time-division multiplexing is a fundamental principle (Cimini, 1985). Analogously, urban space is evolving from static spatial partitioning toward demand-driven temporal sharing, and ultimately toward high-frequency dynamic synchronization between the supply and demand of spatial resources—an important tendency in information-technology-driven urban development.
A closely related concept is the Cyber-Physical System (CPS), often discussed as an enabling paradigm for digital twins (Alam and El Saddik, 2017). Through measurement and observation, physical products and services can be replicated in cyberspace based on digital data collected from the real world, enabling anticipation of future states and events (Wellman, 2001; Zhuang et al., 2018). CPS aims to optimize control of real-world systems by continuous monitoring, modelling, and the use of relevant databases (Xiong et al., 2015). In Gelernter’s “Mirror Worlds,” this idea is described as a cyberspace that faithfully reflects physical reality (Gelernter, 1991). A well-known early example is NASA’s use of digital twins in aerospace engineering (Glaessgen and Stargel, 2012). Beyond problem-solving in the real world, mirror-world interpretations of digital twins have also been extended to virtual environments for entertainment and communication, such as games and the metaverse (Ning et al., 2023). Digital twins are further discussed from an IoT perspective—using robots and sensors to collect and store diverse real-world data (Qi et al., 2021)—and from an AI and big-data processing perspective, where decision-making in cyberspace is supported by analytics and knowledge generation (Lu et al., 2020).
In general terms, a digital twin constructs a virtual environment that abstracts physical space, processes data within that environment, and reflects insights back to the real world (Jones et al., 2020; Wang and Luo, 2021). Its functional foundation includes: (i) information acquisition; (ii) creation of a virtual environment consistent with the physical space; (iii) information dissemination and interaction within the virtual environment; (iv) information processing and manipulation; and (v) information application that uses processed outputs to support real-world actions (Zheng et al., 2019). The information application dimension of the virtual environment can support multiple downstream functions (Hehenberger and Bradley, 2016).
These capabilities are particularly relevant for urban management through spatial response. At the present stage, a typical digital-twin–enabled spatial response process can be described as follows. First, the characteristics of spatial entities and socio-economic elements are translated into real-time data through IoT, Building Information Modeling (BIM), Geographic Information Systems (GIS), and related technologies, forming a dynamic and structured City Information Model (CIM) within the virtual environment (López et al., 2018). Second, algorithms and software analyses in the virtual city support diagnosis of current urban issues and discovery of operational patterns from large-scale data (Yin et al., 2015). Third, simulations and evaluations are conducted to explore alternative futures under different decision options, informing spatial response strategies that can guide urban management (Wu et al., 2022). Finally, after managerial deliberation, selected strategies are implemented through concrete measures in planning, construction, and operation/maintenance to optimize the allocation of urban elements and improve urban operational efficiency (Silva et al., 2018).
Despite the growing body of research on digital twins for smart cities, existing studies predominantly focus on conceptual definitions, technical components, or sector-specific applications, while relatively limited attention has been paid to how digital twins can be operationalized as an integrated, city-scale infrastructure across the full urban lifecycle. In particular, there remains a lack of systematic frameworks that link digital twin architectures with concrete application scenarios and coordinated governance processes spanning planning, construction, operation, and service delivery.
To address this gap, the objective of this study is to develop and articulate a transferable, lifecycle-oriented framework for implementing city-scale digital twins as an operable urban governance infrastructure. Drawing on a structured case study of the Singapore–Nanjing Eco Hi-Tech Island, the paper examines how digital twin technologies can be embedded into real-world urban development and management processes beyond isolated pilot projects.
The main contributions of this paper are threefold. First, it proposes a digital-twin–driven urban lifecycle paradigm that integrates planning, construction, operation, and governance. Second, it synthesizes a five-component city-scale digital twin architecture encompassing sensing, data platforms, analytical modeling, service integration, and application scenarios. Third, it develops a scenario-based demand–supply data matching framework to support cross-domain coordination and adaptive urban management.
The remainder of the paper is organized as follows. Section 2 establishes the conceptual foundation of city-scale digital twins and their maturity tiers. Section 3 presents the Singapore–Nanjing Eco Hi-Tech Island case study, including the technical architecture, application scenarios, and implementation mechanisms. Section 4 discusses implementation considerations and governance implications, and Section 5 concludes with key findings and directions for future research.
Digital twins and space response
This section establishes the conceptual foundation of digital twins from a socio-spatial perspective. It explains how physical–digital coupling supports urban space response, and it introduces the key concepts used in the remainder of the paper. The section is organized into three parts: (1) explaining physical–digital coupling and the closed-loop mechanism, (2) defining four maturity tiers of city-scale digital twins, and (3) illustrating lifecycle applications and summarizing key capabilities (perception, simulation, and control).
Reconstructing the relationship between physical and digital spaces in digital twins
Within the aforementioned management process of digital twin–enabled interaction between physical and virtual spaces, the municipal information model at the executive tier and the digital response strategies operating across the physical–digital interface synergistically coalesce to constitute the Digital Twin of the city (Figure 1). The genesis of the Digital Twin concept originates from industrial and manufacturing domains, signifying the process of recording, simulating, and forecasting an object’s complete lifecycle trajectory within both the tangible realm and the digital virtual sphere. This process entails the digitization of tangible world constituents—individuals, entities, occurrences—and the establishment of a bidirectional correlation between the physical and virtual realms, thereby reconstructing the relationship between spatial entities and their digital counterparts (Rosen et al., 2015; Tao et al., 2018). Digital twins are grounded in copious streams of real-time, continuous data procured across various strata of the physical domain (Baduge et al., 2022; Qi et al., 2021). Its fundamental value resides in its capacity to comprehensively forge a data synchronization connecting the physical and digital dimensions, thus cataloging, dissecting, and prognosticating transformations spanning the entire lifecycle of an operational entity.
The interaction process between physical and digital spaces in the digital twin city.
Diagram comparing physical urban space to virtual urban space with two levels: decision-making managerial and executive operating. Physical space involves response strategies and urban activities, while virtual space features digital strategies and city information models, linked by planning, digitization, and analysis.
Applying the concept of the digital twin to encompass the entire life cycle, encompassing city planning, construction, management, and operation, signifies the establishment of a comprehensive linkage through the ubiquitous fusion of HCPS (information-physical systems) rooted in the trinity of “people, entities, and objects” within a real-time online framework (Liu et al., 2023). This approach engenders the manifestation of the complete process, all constituents, and all participants in the guise of “digital twins,” culminating in a synergy between actuality and instantaneous interaction. Within the virtual realm, a harmonizing and corresponding “twin city” is reconstituted, reframing the city as a coupled system of physical and digital spaces—from planning and construction to management—facilitating the real-time visualization of the city’s entire state and enabling collaborative, intelligent decision-making in urban administration (Lv and Xie, 2022). The urban infrastructure, encompassing water resources, energy, transportation, and healthcare systems, demands digitization (Al Nuaimi et al., 2015). This endeavor will foster the optimal distribution of diverse resource components, encompassing water resources, energy, transportation, ecology, and more, in addition to responsive, intelligent optimization of urban functioning. In this sense, digital twins initiate a paradigm of urban evolution in which physical space and digital space coexist and continuously reshape one another (Bibri, 2018).
The digital twin city embodies a dual conception: it embodies the mapped manifestation of the physical city within virtual space, while concurrently functioning as a multifaceted and unified technological ecosystem buttressing the establishment of the new twin city (Pan and Zhang, 2021a). It lends support to and propels urban planning, construction, and services, thereby ensuring the secure and methodical operation of the city.
In summary, city-scale digital twins rely on a bidirectional, closed-loop linkage: real-world sensing and data acquisition continuously update the virtual city, while analyses and decisions generated in the virtual city are fed back—through managerial or automated channels—into real-world actions and interventions.
Maturity tiers of city-scale digital twins
Tailoring to the distinct requisites across various phases within the twin city life cycle, the establishment of digital twins can be stratified into four tiers of digitization, progressively ascending in the degree of sophistication: Status Twins, Operational Twins, Simulation Twins, and Autonomous Twins (Ding et al., 2019; Lu et al., 2020; Lv and Xie, 2022; Madni et al., 2019) (Figure 2). These levels do not simply correspond to stages of urban planning, but rather reflect the deepening reconstruction of the relationship between physical space and digital space.
Four levels of physical–digital space integration in the digital twin city.
Infographic illustrating four types of digital twins for urban management: Status Twins for accurate mapping, Operational Twins for virtual-reality interaction, Simulation Twins for algorithmic simulation, and Autonomous Twins for intelligent intervention, each with brief descriptive text.
Status Twins denote the consolidation of all pertinent constituents of urban functioning into data-driven intelligence via CIM, effectuating a precise, comprehensive, and synchronized portrayal of the city’s prevailing state (Singh et al., 2022). This level primarily focuses on mirroring physical space into digital form, providing an accurate mapping of spatial entities.
Operational Twins encompass the analysis and discernment of extant urban issues based on historical databases, simultaneous scrutiny through cloud computing and machine learning technologies, alongside the distillation and elucidation of the operational tenets governing the urban milieu (Peng et al., 2023). At this stage, the digital space begins to interpret and reframe the dynamics of physical space, offering insights into how the two domains interact.
Simulation Twins, founded upon well-established and data-derived urban operation principles (physical or procedural models), enriched by virtual reality and analogous technologies, simulate development scenarios within diverse contextual frameworks and operational paradigms, serving as benchmarks for decision-making (Shahzad et al., 2022). Here, the digital space actively generates alternative futures for the physical city, thus reshaping spatial relations through predictive experimentation.
Autonomous Twins, accomplished through the application of Artificial Intelligence and automated control methodologies, delegate the full spectrum of decision-making and procedural execution to computers—an enduring aspiration in the trajectory of digital twin advancement (Dwivedi et al., 2021). At this level, the boundary between physical and digital spaces becomes most fluid, as interventions in the physical city can be triggered and optimized directly through digital intelligence.
Together, the four tiers correspond to different construction and governance objectives, and more fundamentally represent progressively deeper modes of coupling between physical and digital dimensions across the city lifecycle.
Precision cartography epitomizes the vision underlying the formation of the status quo twin. This entails the comprehensive digital modeling of the city’s infrastructure—comprising thoroughfares, bridges, manhole covers, streetlights, edifices, and more—accomplished through sensor deployment across all strata, encompassing aerial, surface, subterranean, and aquatic domains (Deren et al., 2021). Concomitantly, the city’s operational status can be dynamically observed and vigilantly tracked, culminating in the precise depiction and alignment of the virtual city onto the physical realm within the informational domain (Qi et al., 2021).
The vision encapsulating real-virtual interplay characterizes the construction of operational twins. This refers to the existence of discernible traces of urban infrastructural development and an array of components, alongside online accessibility of information for urban inhabitants and visitors alike (Pan et al., 2013). The future iteration of the digital twin city resides as a learning twin (Aheleroff et al., 2021). Within this impending digital twin city, the entirety of urban nuances can be surveyed within the physical urban milieu, while a spectrum of information can be explored within the virtual urban expanse. Urban planning, construction endeavors, and a plethora of human activities extend beyond the physical domain, expanding substantially within the virtual realm. The convergence of virtual reality and its harmonious fusion with the tangible world will delineate a new blueprint for the city’s forthcoming progression (Lamnabhi-Lagarrigue et al., 2017).
Algorithmic simulation characterizes the construction vision of the simulation twin. This entails the creation of a corresponding virtual replica for the physical city, simulating the actions of individuals, entities, and objects within the urban landscape within a software-based environment (Deren et al., 2021). Furthermore, by harnessing cloud computing and edge computing, we can adroitly steer citywide traffic signal control, the orchestration of electrical and thermal energy, the management of major projects’ cycles, optimal positioning of infrastructure, and more (Gupta et al., 2017).
Intelligent intervention underpins the construction vision of the autonomous twin. This encompasses the early detection of plausible adverse influences, clashes, latent hazards, and the like within the cityscape, realized through the blueprints and schematics of the digital twin city, analog simulations, and analogous techniques. This proactive stance involves offering astute early warnings and pragmatic, feasible countermeasure suggestions (Cheng et al., 2023). This futuristic vantage on the city’s initial development trajectory and operational state serves as a guidepost for optimizing the physical city’s planning and administration. It accentuates enhanced service provisioning to the populace, thus infusing urban existence with intellectual discernment (Bibri and Krogstie, 2017b).
Moving from a Simulation Twin to an Autonomous Twin requires more than improved simulation fidelity; it depends on several technical and governance “gateways.” First, cities need reliable sensing–actuation interfaces so that model outputs can be translated into safe and timely interventions (e.g., signal control, asset dispatch, and operational scheduling). Second, decision automation requires model validation, uncertainty handling, and continuous monitoring for drift, so that automated actions remain robust under changing conditions. Third, autonomy must be constrained by safety rules, fail-safe mechanisms, and human-in-the-loop override—particularly in high-stakes domains. Fourth, secure and auditable pipelines (e.g., access control and logging) are needed to ensure traceability and accountability for automated or semi-automated decisions. Together, these gateways define the practical conditions under which simulation-based “what-if” capability can evolve into trusted, closed-loop autonomous operation.
Lifecycle applications and key capabilities
During the phase of planning and conceptualization, whether it pertains to incremental expansion or the formulation of comprehensive urban strategies, the urban data perception and visualization platform embodied by CIM exhibits the capacity to actively detect and archive vast volumes of real-time cityscape data (Zhu et al., 2023). This accumulation facilitates the establishment of a foundational urban database, enabling swift determination of the informational landscape within the focal precinct (Guerrero-Prado et al., 2021). This expedites the extraction of pertinent location-based information. Building upon this foundation, the robust computational prowess available can be harnessed to execute intricate algorithms and models, thereby conducting analyses and simulations imperative for preliminary planning requisites. These encompass assessments such as the accessibility appraisal of road networks, evaluations of subway line connectivity, appraisals of drainage system capacities, examinations of traffic flow dynamics, and more (Bibri, 2019). Additionally, this computational prowess proves indispensable in orchestrating automated design aspects, encompassing site selection for structures, the layout planning of road networks, as well as the formulation of subterranean passageways (Jiang et al., 2021). This orchestrated process effectuates immediate refinements during the planning and design phase, forestalling the necessity for post-validation reconfiguration, thus averting unnecessary deviations and redundant redesign efforts, ultimately culminating in the elimination of iterative validation-stage redesigns and circumventing unwarranted diversions or the inefficiencies associated with repetitious construction endeavors (Kaewunruen and Lian, 2019).
Amid the construction phase, the digital twin city demonstrates its prowess in effecting real-time data acquisition and transmission by virtue of infrastructural digitalization and modernization endeavors. This process culminates in the assembly of exhaustive project lifecycle data, concurrently facilitating the real-time supervision and feedback mechanism governing construction advancement (Zhang et al., 2017). This engenders the attainment of superlative quality and efficiency in the oversight of construction projects, thereby yielding a repository of citywide data archives rooted in CIM and BIM. These archives furnish the elemental information substratum and cognitive pathways requisite for immediate responses within subsequent managerial and operational echelons (Carnahan et al., 2010). Simultaneously, by means of data scrutiny, synthesis, and harmonization, astute responsiveness to urban requisites is achieved, harmoniously harmonized with data-propelled novel construction techniques, such as modular and assembly-based approaches, engendering demand-responsive, batch-oriented, and adaptable construction methodologies (Davis et al., 2012). This transformative trajectory forges a departure from conventional paradigms characterized by isolated site and edifice creation, ushering in a reduction in energy consumption and fiscal outlays within the urban construction continuum.
In the realm of operational services, building upon the copious real-time data garnered from the preliminary digital planning and construction stages of the digital twin city, the capacity arises to furnish open data interfaces for internet enterprises. Swiftly infiltrating the public service domain through a streamlined operational paradigm, a swift integration of online public service portals rooted in internet infrastructure is accomplished (Gilrein et al., 2021). This amalgamation, coupled with data analytics and the convergence of virtual and augmented reality technologies, serves to materialize urban service scenarios, catering to diverse service recipients and encompassing a spectrum of service content (Aronson and Cowhey, 2017). This endeavor propels innovation in both online and offline integration, specialization, and precision-driven service modalities. In parallel, a relentless expansion of offline public service resources ensues, catalyzing the accelerated pivot of service models towards amalgamated virtual-reality frameworks, immersive scenarios, bespoke provisions, and proactive responsiveness (Anumbe et al., 2022). Moreover, the amalgamation of top-down intelligent infrastructure operations, urban data governance, and bottom-up sharing economy dynamics, facilitated through data-oriented services, equipment leasing ventures, and advertising endeavors, serves to amplify the operational scope and financial viability of twin cities (Soe, 2017). Concomitantly, the substantial reservoir of economic and resident activity data amassed through the city’s digital twin serves as a reservoir of prospective business insights, thereby rendering the city’s investment promotion endeavors more precise and informed (Pan and Zhang, 2021b).
Concerning urban governance, the establishment of a three-dimensional, all-encompassing Internet of Things (IoT) and CIM platform facilitates a holistic digital characterization and surveillance of urban constituents. Strengthening the three pivotal faculties of perception, connectivity, and computation forms the bedrock for pervasive awareness and digital transformation of urban governance mechanisms (Lanzolla et al., 2021). On one facet, the comprehensive elevation of urban infrastructure towards intelligence enables component-level, full lifecycle, visualized management and upkeep of urban edifices and infrastructures (Du et al., 2023). Conversely, propelled by the substantial urban data procured through real-time tracking of urban dynamics, analyses, and simulations underpinned by robust big data technology appraise a gamut of facets ranging from public wellbeing, environmental stewardship, public safety, urban amenities, trade and industry undertakings, to disaster early warning. This real-time assessment swiftly and precisely detects urban quandaries, culminating in intelligent evaluation and swift crisis management. Furthermore, extended and strategic urban policies can be subjected to rigorous testing and validation through digital twin simulations before their official implementation. This compels the digital transformation of urban infrastructure, whilst concurrently catalyzing the metamorphosis of urban management decision-making processes towards digital simulations (Zhang, 2020). Moreover, with the dense deployment of urban sensor networks and the widespread adoption of mobile devices, the digital twin urban governance paradigm will likewise shift towards decentralization, simplification, and widespread engagement (Belli et al., 2020). This transformative framework empowers administrators to navigate governmental affairs with heightened celerity and responsiveness.
The key capabilities involve perception, simulation, and control. The city is a complex system composed of heterogeneous components, including physical constructs, organisms, and human beings. For analytical clarity, this study highlights three representative subsystems—the ecological environment, the built environment, and the social/behavioral environment. These subsystems do not exhaust all urban dimensions (such as economic, cultural, or institutional systems), but they capture the main domains where digital twins can be effectively applied and are sufficiently representative for discussing perception–simulation–control pathways. Under the dynamic changes of these subsystems, digital twin technology provides new solutions for the unified management of ecological and social infrastructures (Table 1). Through perception, simulation, and control, the management of urban systems can achieve dynamic and real-time feedback, including the following aspects:
Management pathways of digital twin in representative subsystems of the city.
Sensing
Simulation/Prediction
Intervention/control
Ecology
●
○
◌
Human behavior
●
○
○
Built environment
●
●
●
The dark green shaded cells in Table indicate high-intensity values, while light green cells represent baseline levels.
Sensing: comprehensively perceive the ecological environment, human behavior, and built environment, and deliver all kinds of information to the management platform in real time through sensors and data collection technologies.
Simulation/Prediction: based on real-time data, accurate simulation and prediction of the built environment is carried out, which is a more efficient process compared to the ecological environment and human behavior. In addition, the digital twin can integrate multiple data sources to improve the accuracy and reliability of the simulation.
Intervention/Control: digital twins provide strong support for intervention and control of urban systems. Although it is difficult to intervene in the ecological environment and human behavior, digital twin technology can monitor the changes of these systems in real time, make timely adjustments and optimizations, and achieve finer management.
Singapore-Nanjing eco-Hi-Tech Island digital twin city application scenario planning
This section presents the Singapore–Nanjing Eco Hi-Tech Island (SNEHI) as a structured case study to illustrate city-scale implementation of the proposed digital-twin approach. We first introduce the technical architecture of the SNEHI digital twin, then describe the eight application scenarios supported by the shared infrastructure. We further examine the data supply–demand relationships across scenarios and conclude with key implementation considerations and lessons that are transferable to other new-town developments.
Research design and case-study protocol
We adopt a structured case study approach to examine how a city-scale digital twin can be implemented as an operable infrastructure across the urban lifecycle. The case analysis follows a consistent protocol: we use (1) the five-component architecture to organize technical evidence, (2) the four maturity tiers (Status, Operational, Simulation, Autonomous) to interpret capability progression, and (3) bidirectional data–decision loops and demand–supply data matching to analyze how scenario-level functions translate into governance actions. This protocol is intended to be reusable for analyzing other city-scale digital twin deployments.
Site and vision
The Singapore–Nanjing Eco Hi-Tech Island (SNEHI) is a joint initiative of the Jiangsu Provincial Government and Singapore’s Ministry of Trade and Industry. Covering 15.21 km2, the project represents a new type of cross-border collaboration that integrates industrial upgrading, ecological management, and urban development (Figure 3). The master plan envisions a gross floor area of 7.41 million m2, accommodating 110,000 residents and offering 100,000 jobs (SNECO, n.d.).
Location of Eco-Tech Island (Source: www.sneco.com).
Map highlighting Hexi CBD in Nanjing, showing transport connections including metro lines 4, 10, 13, and 17, Jiangxinzhou Bridge, Yangtze River tunnels, and proximity to Lukou International Airport and major railway stations.
Unlike traditional eco-city projects that often emphasize spatial form and land-use efficiency, SNEHI explicitly integrates digital twin infrastructures into its vision, highlighting technology as the backbone of ecological and industrial innovation (Jones et al., 2020; Silva et al., 2018; Batty, 2018). This design choice reflects the broader evolution of smart city thinking, where urban intelligence is not limited to ICT-enabled governance but is embedded into the ontology of urban development itself (Bibri, 2018).
Positioned along the Yangtze River Economic Belt, the project demonstrates how digital twin logics—real-time sensing, integrated data, and lifecycle governance—can be scaled up from industrial pilots to city-level paradigms (Deren et al., 2021; Lv and Xie, 2022). The aspiration of building a “low-carbon smart island” is thus not only an urban branding strategy but also a prototype of a systemic digital twin city model that links planning, construction, operation, and services into a coherent framework.
To provide quantitative reference for the phased implementation of the digital twin in the SNEHI, Table 2 summarizes the indicator system adopted in the project’s development and planning framework. Rather than reporting ex-post performance outcomes, the table presents measurable deployment-oriented targets across key domains, including ecological monitoring, information infrastructure, public services, digital economy, and urban governance, for the periods 2020, 2025, and 2030. These indicators reflect the intended scale and phased development trajectory of the digital twin implementation at the city level.
Quantitative deployment targets and lifecycle indicators for the SNEHI digital twin (2020–2030).
Primary indicators
Secondary indicator index
Unit
2020
2025
2030
Green & Ecological Sustainability
Coverage rate of real-time monitoring for key environmental elements
%
–
80%
100%
Proportion of ecological data integration in key areas
%
–
70%
100%
Compliance rate of new projects with green building standards
%
–
70%
100%
Comprehensive energy consumption intensity per unit building area
kWh/㎡·year
Baseline
−15%
−30%
Share of renewable energy usage
%
10%
20%
50%
Intelligent detection rate of environmental anomaly events
%
10%
70%
90%
Carbon emission intensity (per unit output)
t CO₂/100 million CNY GDP
Baseline
−25%
−50%
Ecological system resilience recovery time
Hours
–
≤168
≤72
Digital support rate for ecological decision-making
Large-scale public buildings, schools, and government agencies are using BIM technology for development, construction, and operation.
Percentage
80%
100% (New Projects)
100% (All Items)
Unified coding for social service infrastructure such as streetlights and trash cans
Percentage
50%
70%
100%
Coverage of environmental monitoring service system in key areas
Percentage
60%
70%
0.9
Non-classified video surveillance resources invested by the government are shared among departments.
Percentage
80%
90%
1
Coverage of key units in network security awareness
Percentage
80%
100% (New Projects)
100% (All Items)
Social video surveillance resource aggregation rate
Percentage
80%
95%
100%
The contribution index of AI in urban refined governance and emergency management
Percentage
50%
70%
–
The statistical rates of key energy consumption data for public buildings, schools, and government agencies (data on electricity consumption, tap water, sewage discharge, gas, etc.) must be uploaded to the smart operation center in real time.
Percentage
60%
100% (New Projects)
100% (All Items)
The main energy consumption data of residential communities (data on electricity, water, gas, sewage discharge, etc. are uploaded to the smart operation center daily) are statistically analyzed.
Percentage
70%
100% (New Projects)
100% (All Items)
Fire safety information (fire lanes, fire hydrants) for public buildings, schools, and government offices is available online in real time.
Percentage
100% (Newly built area)
–
–
Information security protection
Intelligent regulatory coverage of critical information infrastructure
Percentage
90%
100%
–
Network security management evaluation score
Score
80
95
100
Technical architecture of the eco Hi-Tech Island digital twin
To support the development objectives of a green and intelligent eco-island, the SNEHI integrates digital twin technologies as an enabling infrastructure. These technologies support real-time monitoring, data integration, and scenario-based analysis of environmental, infrastructural, and operational parameters. By embedding such capabilities across planning, construction, and governance processes, the digital twin provides a structured mechanism through which sustainability-oriented goals can be operationalized and tracked throughout the urban lifecycle.
Figure 4 shows the overall framework of the SNEHI digital twin. This framework is organized as a city-level technical architecture that supports data-driven planning, construction, operation, and service management. It is built on five main components.
Schematic diagram of the overall framework of digital twin city construction in SINGAPORE NANJING ECO HI-TECH ISLAND.
Diagram illustrating a "Smart Island" framework with layers for planning, construction, operation, and management on the left, and analysis, simulation, and monitoring on the right. Central components include intelligent sensory monitoring systems, data transfer, a real-time mapping twin city platform, and application scenarios such as government, community, education, security, transportation, ecology, and tourism. Key elements involve high-performance computing, city information models, machine learning, and monitored entities like people, vehicles, urban components, economic activities, and natural conditions.Multi-source sensing and data acquisition
A network of IoT devices, including environmental sensors, traffic detectors, surveillance cameras, mobile communication records, and smart meters, provides real-time and multi-dimensional data. Edge computing units preprocess the data locally to reduce latency and improve reliability (Qi et al., 2021; Baduge et al., 2022). This pervasive sensing layer echoes the theoretical emphasis on “ubiquitous perception” as the foundation for twin cities, ensuring that ecological, infrastructural, and social systems are continuously represented in data form (Deren et al., 2021).
Data middle platform
All collected data are integrated into a data middle platform, where they are standardized, structured, and linked. Spatial–temporal indexing and knowledge graphs are used to improve data interoperability and accessibility (Pan and Zhang, 2021a). Rather than functioning as a passive warehouse, the middle platform becomes an active “urban data fabric”, bridging fragmented departmental silos and enabling cross-domain collaboration (Silva et al., 2018).
Analytical and modeling layer
Based on the data platform, multiple analytical models are applied. Machine learning and deep learning methods are used for traffic and environmental prediction, reinforcement learning for adaptive traffic control and resource allocation, and spatiotemporal data mining for mobility and service demand analysis (Lu et al., 2020; Peng et al., 2023; Shahzad et al., 2022). These modeling approaches embody the shift from descriptive to prescriptive governance, where simulations are not only explanatory but also generative of new governance strategies (Lv and Xie, 2022).
Service integration
The results of analysis and modeling are provided through APIs and service platforms. These outputs are used for tasks such as adaptive traffic management, environmental monitoring, public service dashboards, and government decision support (Ding et al., 2019; Madni et al., 2019). Here the architecture demonstrates how digital twins create a continuous feedback loop: data are sensed, processed, and returned to urban actors as actionable services, institutionalizing the sense–analyze–respond cycle.
Because service outputs are delivered via APIs and shared platforms, the architecture emphasizes privacy and security controls across the data lifecycle. At ingestion, it follows data minimization and supports data classification by sensitivity and purpose. At the data platform and service layers, personal identifiers (where present) are handled through de-identification/anonymization where applicable, combined with role-based access control and audit logging to ensure traceability. For transmission and storage, the platform is designed to apply encryption in transit and at rest, together with controlled interfaces for data sharing. At the application layer, purpose limitation and least-privilege access are emphasized to reduce privacy and accountability risks. Because the accessible SNEHI materials do not provide verifiable project-specific protocol parameters (e.g., exact encryption standards), we describe these safeguards as architecture-level mechanisms and clarify the documentation scope.
Application scenarios
The architecture directly supports the eight application domains of the Eco Hi-Tech Island: governance, community, education, transportation, industrial park, ecology, security, and tourism (Silva et al., 2018; Deren et al., 2021). By rooting these scenarios in the same technical backbone, the island avoids the pitfalls of isolated smart city pilots and instead establishes a holistic digital twin ecosystem.
This technical architecture exemplifies how the digital twin evolves from a representational mirror into an operational infrastructure (Tao et al., 2018; Lv and Xie, 2022). In the SNEHI case, the architecture itself becomes the hidden but essential “city operating system”, enabling innovation across diverse domains.
The construction of the SNEHI digital twin system is grounded in multi-source sensing and intelligent networks, aiming to establish an overarching framework akin to the “neural system” of the Eco Hi-Tech Island. By deploying various IoT devices—such as video surveillance, environmental sensors, and positioning equipment—a comprehensive intelligent sensing network is created, enabling real-time collection and feedback of data on human activities, environmental factors, and infrastructure operational status. The application of embedded sensing devices facilitates the intelligent upgrading of infrastructure, allowing urban operations to be systematically recorded and dynamically monitored.
At the data layer, high-speed communication and edge computing enable the real-time aggregation and unified management of heterogeneous multi-source data, forming a data middle platform. This platform not only governs temporal, spatial, and multimodal data but also leverages artificial intelligence algorithms for in-depth modeling and analysis. This includes predictive learning, relational network modeling, and scenario simulations driven by generative AI, thereby forming the “urban brain” of the Eco Hi-Tech Island. This urban brain provides intelligent support for governance decisions, resource allocation, and public service delivery.
The core of the digital twin is built on three-dimensional spatial modeling and semantic mapping, establishing a correspondence between physical space and virtual models. Combined with AI-driven simulation and prediction, it achieves full-lifecycle modeling and dynamic representation of infrastructure systems, energy networks, and environmental evolution processes. The integration of generative models and physical information fusion methods enhances the precision and interpretability of simulations under complex scenarios.
Overall, this digital twin system integrates the entire chain of planning, construction, operation, and governance, promoting a systematic upgrade of urban infrastructure and management services. It establishes a sustainable urban operational model centered on data-driven and intelligent innovation, providing methodological support for enhancing governance efficiency and exploring future urban forms on the Eco Hi-Tech Island.
Scenarios and demand–supply data matching
The eight scenarios of the SNEHI (Civic Services, Community, Education, Transportation, Park, Ecology, Security, and Tourism; Figure 5) are not independent modules but interlinked arenas where the digital twin serves as the common enabling infrastructure. This reflects the academic view that digital twins generate value not only through technical fidelity but also through their ability to integrate heterogeneous urban subsystems into a shared decision-making framework (Jones et al., 2020; Lv and Xie, 2022; Bibri, 2018).
Application scenarios of the SNEHI digital twin: civic services, community, education, transportation, park, ecology, security, and tourism.
Diagram showing a smart city data transfer network connecting diverse domains including ecology, transportation, security, tourism, flood control, environment, finance, education, commercial, community, city big data center, and city operation management center for data sharing and exchange.Twin civic services
Twin Civic Services highlight how digital feedback loops reshape the provision of public services. Citizen workflows, online platforms, and intelligent office systems are not merely administrative tools; they constitute a civic-facing mechanism where service allocation dynamically adjusts to real-time demand (Harpham and Boateng, 1997; Soe, 2017). To address challenges in traditional governance, such as information silos, departmental barriers, and process inefficiencies, the SNEHI establishes a “twin civic services” system. This system leverages digital twin technology to standardize and unify data collection across departments, creating a digital mirror of governance operations that enables real-time cross-departmental data sharing and dynamic modeling. By integrating natural language processing, multimodal large-scale models, and graph neural networks, the system automatically parses policy texts, identifies responsibility relationships, and optimizes workflows. Additionally, reinforcement learning and causal inference are employed to enhance governance processes and predict policy outcomes, while generative AI supports scenario simulations and emergency planning. This approach significantly improves the precision and intelligence of governance operations, providing efficient and sustainable technical support for public service delivery. This illustrates that digital twins redefine service delivery as an iterative, data-driven process rather than a static routine, thereby fostering a more responsive and participatory model of urban governance (Figure 6).
Schematic representation of the twin civic services scenario.
Aerial digital illustration of a smart city highlighting key components such as City Big Data Platform, Twin Office System, Operations Management Center, File Transfer Robot, and Intelligent Citizen Service, with connecting blue lines to show integration.
To address bottom-up participation, the Civic Services scenario is designed to incorporate citizen inputs as demand-side signals. Based on the accessible SNEHI case materials, citizen interaction is primarily mediated through digital service channels (e.g., online portals/apps) that allow residents to submit service requests, complaints, and feedback. These inputs are ingested into the governance workflow (e.g., ticketing and routing), assigned to responsible units, coordinated across departments when needed, and tracked through status updates until closure. The resolution outcomes and follow-up feedback are then returned to citizens, forming a feedback-to-decision loop that can inform service optimization and resource reallocation over time. Where participation features are not explicitly documented (e.g., deliberative co-design), we treat them as a future extension and state the corresponding data and governance requirements.
Twin community
Twin Community builds on IoT-enabled homes and neighborhood platforms. While such technologies are often described as convenience tools, the SNEHI case illustrates how they also act as experimental arenas for redefining collective life, linking resident satisfaction, environmental monitoring, and property management into one data ecosystem (Qi et al., 2021; Belli et al., 2020).
Twin education
Twin Education represents a convergence of AI-based learning analytics and educational governance. Here, the digital twin is not only a pedagogical assistant but also a governance instrument that allows continuous adaptation of curricula and resource distribution (Peng et al., 2023).
Twin transportation
Twin Transportation reshapes urban mobility by embedding MaaS and autonomous driving into the city’s circulation system (Figure 7). This demonstrates the capacity of digital twins to orchestrate systemic transitions—from private car dependence toward integrated, low-carbon mobility regimes (Wu et al., 2022; Du et al., 2023).
Schematic representation of the twin transportation scenario.
Aerial view illustration of an urban island with labeled routes and smart mobility infrastructure, highlighting level one and two smart hubs, driverless car-sharing lanes, bus loop, vehicle appointment stops, and bicycle smart parking spots.Twin park
Twin Park extends the twin logic into industrial ecosystems. The data-driven management of enterprise energy and environmental performance reflects the digital twin’s role in aligning industrial production with sustainability imperatives (Jiang et al., 2021; Davis et al., 2012).
Twin ecology
Twin Ecology capitalizes on the island’s natural assets. Real-time environmental sensing and adaptive regulation reveal how digital twins can institutionalize “ecological intelligence” as a core function of urban governance (Deren et al., 2021; Cheng et al., 2023).
Twin security
Twin Security applies predictive analytics to health and safety. Rather than treating safety as a reactive function, the case demonstrates a shift to anticipatory governance through risk modeling and automated interventions (Cheng et al., 2023).
Twin tourism
Twin Tourism leverages IoT and cloud platforms to balance flows and enhance visitor experiences (Figure 8). In this way, digital twins integrate cultural and economic functions, linking tourism management with real-time service optimization (Ning et al., 2023; Klar et al., 2023).
Schematic representation of the twin tourism scenario.
Aerial illustration of an island urban area featuring labeled icons for island-wide Wi-Fi coverage, scenic spot management facilities, visitor service facilities, monitoring sensors, electronic scenic spot guides, and personalized tour itineraries across green spaces and residential zones.
Across all eight domains, the SNEHI case confirms that the digital twin is not a sectoral tool but a systemic enabler that transforms how urban functions co-evolve. This integrative orientation distinguishes it from fragmented smart city pilots.
The application scenarios of SNEHI operate through a dynamic matching of demand-side and supply-side data (Table 3). For example, in transportation, travel demand data (population distribution, trajectories, traffic flows) are continuously compared against supply-side data (road status, vehicle operations). This ensures that congestion management and service allocation become adaptive processes rather than pre-set plans (Wu et al., 2022; Zhang, 2020). These scenarios share a common enabling infrastructure (sensing–data–modeling–service integration), which is why the framework supports lifecycle continuity rather than isolated, project-based applications. These indicators represent a proposed evaluation framework intended to guide future empirical assessment once longitudinal operational data become available.
Demand–supply data matching across eight digital-twin scenarios in SNEHI.
Application scenario
Demand side
Demand-side data
Connection side
Supply side
Supply side data
Data sources and integration
Optimization mechanisms
Candidate operational evaluation indicators
Twinning Government
Efficient government decision-making and citizens’ needs
Citizens’ workflow, decision-making information, and service quality feedback
Full chain connection from the smart government platform, online office platform, to smart customer service
Government departments, intelligent office systems
Various types of government service data, office process efficiency data
Government service data is collected in real time through the smart government platform and online office platform, including citizens’ office flow, service quality feedback, etc. AI and big data technology can help analyze the peak demand for government services, decision-making feedback time, adjust the allocation of government resources and workflow in real time, and improve administrative efficiency.
Through the citizens’ office feedback, the system can predict future office peaks, deploy resources in advance, optimize service windows, and dynamically adjust the system response.
Average reduction in processing time for government services (%)
One-stop completion rate of high-frequency administrative services (%)
Decision–implementation–feedback loop response time (hours/days)
Frequency of data-informed policy adjustments (times/year)
Accuracy of government service demand forecasting (%)
Twin Community
Comfortable home life and efficient property management
Resident satisfaction, community activity participation rate, and property service evaluation
Dynamic services through the community IoT system, smart home appliances, monitoring system, etc.
Property companies, community service centers
Property management, environmental monitoring, and community activity data
Community Internet of Things system, smart home appliances, smart access control, and monitoring system provide real-time activity data of residents and evaluation of property services. Various types of environmental monitoring equipment provide real-time data, such as air quality and noise.
Based on data analysis, property companies can adjust community management resources in real time, dynamically optimize security, facility maintenance, and public services, and enhance tenants’ satisfaction.
Median response time of community public services (minutes)
Energy consumption per capita for community facilities (kWh/person)
Real-time fluctuation index of resident satisfaction
Accuracy of community safety incident prediction (%)
Peak crowding index of public spaces (real-time density / designed capacity)
Twin Schools
Deep integration of information technology and subject teaching
Student learning, teaching feedback, and online assessment data
Connection of teaching resources through intelligent education facilities, big data analysis, and education management systems
Schools, educational institutions
Use of teaching resources, student performance, and course participation rate
Students’ learning behaviors are collected in real time through intelligent educational facilities, including online learning hours, homework submission time, online assessment data, etc. Through the education management system and big data analysis platform, schools can dynamically analyze the teaching needs of different classes.
Real-time data feedback can help schools dynamically adjust the difficulty of courses and optimize the allocation of teaching resources, such as pushing personalized tutoring programs promptly to meet the learning needs of different students.
Student learning engagement index (active learning time / total course duration)
Coverage rate of personalized learning interventions (%)
Average response cycle for learning pathway adjustment (days)
Improvement rate of teaching resource utilization efficiency (%)
Prediction error rate of learning performance outcomes (%)
Twin Transportation
Passenger Travel and Freight Demand
Real population spatial distribution, travel trajectory, traffic flow data
Dynamic scheduling and information sharing of transportation modes based on ITS and MaaS services
Road status, transportation operation data, and real-time scheduling information
Traffic flow and travel trajectory are collected in real time through traffic cameras, mobile applications, vehicle sensors, etc. MaaS (Mobility as a Service) platform integrates various types of vehicles and real-time road information through big data analysis to predict traffic congestion and optimize scheduling.
Based on the traffic flow data and real-time scheduling information, the traffic management department can dynamically adjust the signal light hours, reasonably allocate road network resources, and reduce traffic congestion.
Reduction rate of average peak-hour commuting time (%)
Road network operational efficiency index (actual speed / free-flow speed)
Multimodal coordination efficiency in MaaS (transfer time/cost)
Success rate of adaptive traffic signal control (%)
Traffic congestion prediction lead time (minutes)
Twin Park
Enterprise service and green low-consumption park
Enterprise service demand, energy consumption data, and environmental monitoring data
Realize the connection between enterprise and park services through the park service platform and energy management system.
Park management company, service facilities
Enterprise utilization, energy consumption monitoring data, and facility service capacity
Energy consumption data and environmental monitoring data of enterprises are acquired in real time through the energy management system and IoT sensors, and integrated into the park service platform. An environmental monitoring system provides environmental regulation data through real-time monitoring of pollutant emissions in the park.
Based on the dynamic data of enterprise energy consumption and environmental monitoring, the park management can adjust the energy supply strategy in real time, optimize the use efficiency of service facilities, and ensure the green development of the park.
Comprehensive energy intensity per unit of economic output (kWh per 10,000 CNY)
Response time of enterprise-oriented park services (hours)
Share of renewable energy integration within the park (%)
Real-time carbon emission intensity of the park (kg CO₂/m2)
Load-balancing index of park facilities (peak–valley coefficient)
Twin Ecology
Urban Water Management and Environmental Quality Monitoring
Water resource usage, environmental quality data, pollutant emissions
Dynamic connection based on intelligent irrigation, wastewater treatment, and environmental monitoring systems
Environmental protection departments, water companies
Water supply, wastewater treatment capacity, and environmental monitoring data
Water resource management and environmental monitoring data are acquired in real time by the intelligent irrigation system, wastewater treatment system, and various environmental sensors. The sensors record data such as water level, flow rate, pollutant concentration, etc., and realize spatial and temporal mapping through a GIS system.
Through real-time water usage and environmental monitoring feedback, environmental protection departments can dynamically adjust water supply strategies, reduce pollution emissions, and respond to environmental emergencies promptly.
Water supply–demand matching index (supply / demand)
Prediction lead time for pollutant emissions (hours/days)
Ecosystem disturbance response time (event to recovery)
Stability rate of environmental quality compliance (% of time meeting standards)
Spatial exposure index of environmental risks (person-hours)
Twinning Security
Spatial and temporal distribution of public health and safety risks
Facilities and cases, spatial and temporal distribution of security risks, public security events
A dynamic connection between public health services and security services through wearable devices, personal health data, and algorithms, combined with public health services
Security management, emergency response agencies
Security facility status, incident response data, and community safety levels
Spatio-temporal distribution data of public safety is collected in real time through monitoring devices, smart wearable devices, and various types of sensors. The data analysis platform compares the spatial and temporal distribution of events with historical data to generate a security risk prediction model.
The security system dynamically adjusts the monitoring of key areas based on real-time data and combines it with the emergency response mechanism to achieve a more refined public safety management.
Early warning success rate of public safety incidents (%)
Average emergency response arrival time (minutes)
Accuracy of dynamic identification of high-risk areas (%)
Elasticity index of security resource allocation (deployment speed)
Reduction rate of community safety risk exposure (%)
Twinning Tourism
Intelligent scenic area management and high-quality tourism experience
Tourist flow, scenic area service demand, and consumption data
Connecting tourism services and information through a scenic area information management platform, an intelligent ticketing system, and a data analysis system
Tourism management department, scenic spot operation company
Tourist feedback, scenic spot service capacity, and event management data
Through the management system of the scenic spot, tourists’ flow, service demand, and consumption data are uploaded in real time, and the intelligent ticketing system and information management platform integrate tourists’ behavioral data to predict future tourists’ flow.
The tourism management department dynamically adjusts the scenic area service supply based on the real-time data feedback of visitor flow and scenic area service capacity to optimize the visitor experience and avoid overcrowding.
Real-time utilization rate of scenic area carrying capacity (%)
Spatial distribution evenness of tourist dwell time
Perceived–actual crowding deviation rate (%)
Dynamic matching efficiency of tourism service resources
Real-time tourist experience satisfaction index
However, the value of this mechanism extends far beyond the transportation sector. In governance, citizen demands—captured through online platforms, service feedback, and workflow records—are constantly reconciled with government capacity, producing adaptive adjustments in staffing and resource allocation (Harpham and Boateng, 1997; Soe, 2017). In education, student learning behaviors are dynamically matched with teaching resources and curricula, enabling personalized interventions (Peng et al., 2023). In ecology, water use patterns and pollutant emissions are balanced with treatment capacities and environmental thresholds, creating an anticipatory regulatory model (Deren et al., 2021; Cheng et al., 2023).
From a theoretical perspective, the supply–demand alignment mechanism operationalizes the core cycle of digital twins: sensing → integration → analysis → simulation → feedback → adjustment (Jones et al., 2020; Qi et al., 2021; Pan and Zhang, 2021a). This cycle enables the city to function as a self-regulating system, where mismatches between citizen needs and institutional capacity are continuously identified and resolved. By embedding this cycle across multiple domains, SNEHI illustrates how digital twins transform fragmented sectoral management into an integrated governance logic.
Compared with earlier smart city projects that often emphasized one-directional data flows or static dashboards, the SNEHI case demonstrates a “bidirectional and iterative” approach. Demand does not simply trigger service provision; it also reshapes institutional configurations in real time. Conversely, supply-side capacities are no longer treated as fixed constraints but as adjustable resources that can be optimized through algorithmic scheduling and predictive analytics (Lv and Xie, 2022; Bibri and Krogstie, 2017b).
Thus, the data supply–demand mechanism represents not only a technical architecture but also an institutional innovation. It exemplifies how digital twins can shift urban governance from reactive and fragmented service delivery toward a holistic and anticipatory system. This positions the SNEHI model as a concrete realization of lifecycle-oriented digital twin governance, with lessons applicable to other urban contexts seeking sustainable and adaptive pathways (Bibri, 2018; Klar et al., 2023; Mariani et al., 2018).
While accessibility and disability-inclusive services are not explicitly documented in the available SNEHI case materials, the proposed digital twin framework is designed to accommodate such requirements. In particular, accessibility needs can be represented as demand-side signals (e.g., barrier-free routing requests, service complaints, accessibility incidents) and linked to supply-side capacities (e.g., step-free infrastructure availability, elevator/ramps status, accessible fleet and facilities) through the same sensing–data–modeling–service–scenario pipeline. This provides a structured pathway to integrate accessibility considerations into scenario design, service orchestration, and maintenance prioritization when relevant data and stakeholder inputs are available. Similarly, the Generative AI components mentioned in the scenario descriptions should be interpreted as potential enablers rather than uniformly deployed functions. Based on the available SNEHI case materials, Generative AI–enabled capabilities are not documented as fully operational, city-wide components; therefore, we revised the wording across scenarios to distinguish between currently implemented functions, pilot/under-development features, and planned or conceptual extensions, so as not to imply full deployment where this is not yet the case.
Elements for implementation
The implementation of the SNEHI digital twin does not merely represent a sequence of technical deployments or pilot projects; it illustrates a layered mechanism through which digital infrastructures, governance routines, and institutional arrangements are mutually reshaped. While local planning documents emphasize “foundational reinforcement” and “exemplary demonstration,” from a research perspective, these pilots can be understood as iterative experiments in embedding data-driven processes into the urban lifecycle (Lv and Xie, 2022; Bibri, 2018).
By integrating the eight application scenarios into a comprehensive “panoramic map of the island”, the project demonstrates more than visualization. It constitutes a methodological attempt to reconstruct the interlinkages among people, infrastructures, and institutions within a real–virtual continuum (Appendix Table S1). The three-dimensional digitization of physical and social components is not only descriptive but also provides the empirical ground for predictive analytics, multi-level coordination, and anticipatory governance (Pan and Zhang, 2021a; Deren et al., 2021).
The critical innovation here lies in treating the supply–demand feedback loops of different sectors (energy, ecology, transport, governance) as an integrated operating logic. Rather than addressing each domain in isolation, the implementation process consolidates heterogeneous datasets into a shared modeling infrastructure. This creates conditions for cross-sectoral optimization—linking ecological thresholds with economic activity, or aligning public service allocation with demographic change (Cheng et al., 2023; Klar et al., 2023).
Moreover, the project points toward a paradigmatic shift from “digital support for planning” to “digital co-production of governance”. Real-time monitoring of energy and water use, pollution control, and environmental quality not only improves efficiency but redefines how urban risks are identified, distributed, and managed. Similarly, emergency response and event management, once reactive, are reframed as anticipatory systems supported by simulation and automated feedback (Bibri and Krogstie, 2017b; Du et al., 2023).
Finally, the SNEHI case reveals that the institutional innovation of collaborative urban services—linking government, academia, industry, and citizens—forms a core element of digital twin implementation. Rather than a linear upgrade of service platforms, the digital twin fosters a new urban ecosystem in which public value is co-created across domains. This highlights its potential contribution not just to local service delivery but to the broader theory of smart sustainable cities, where governance is understood as a continuous negotiation between digital infrastructures and socio-spatial practices (Harpham and Boateng, 1997; Bibri, 2018; Jin et al., 2014).
Based on the SNEHI synthesis, we summarize a reusable framework for city-scale digital twins: (1) define the physical–digital coupling and closed-loop mechanism (continuous synchronization and feedback execution), (2) implement the five-component architecture with explicit interfaces across layers, and (3) operationalize applications through scenario templates that specify demand-side signals, supply-side capacities, analytics/model functions, and decision outputs (summarized in Table 2). This summary is intended as a transferable checklist for cities seeking to move from isolated pilots to lifecycle-oriented operations.
Evaluation scope and KPI framework
Outcome-level performance metrics (e.g., percentage reduction in congestion or energy savings) are not available in the accessible SNEHI project materials and therefore cannot be reported in this study. To avoid overstating impact, we treat SNEHI as implementation and design evidence for how a city-scale digital twin can be operationalized across the urban lifecycle. Nevertheless, to facilitate future empirical evaluation, we provide a KPI framework that specifies measurable indicators, data requirements, and suggested evaluation designs (e.g., baseline-before/after comparisons or matched-area comparisons) once operational performance datasets become accessible. We additionally provide a set of candidate outcome KPIs with measurement definitions and data requirements in Appendix Table S2 to enable future empirical evaluation when operational datasets become accessible.
To bridge the gap between high-level digital twin visions and practical implementation, this study introduces a scenario-based evaluation framework with quantitatively defined indicators to support future assessment across eight digital twin application domains. Rather than reporting realized performance outcomes, the framework is designed to align measurable indicators with annual work plans and development milestones, providing a structured basis for tracking the progression and maturity of digital twin initiatives over time.
By emphasizing core digital twin capabilities—such as real-time sensing, predictive analytics, adaptive optimization, and closed-loop feedback—the framework clarifies how digital twin concepts can be operationalized beyond top-level design and technical architecture, and embedded within the concrete processes of planning, construction, management, and operation. The use of standardized and quantifiable indicators enhances transparency, operational feasibility, and cross-scenario comparability, offering a practical reference for evidence-informed governance and continuous system refinement as longitudinal operational data become available.
Discussion
This section discusses cross-cutting requirements that shape whether a city-scale digital twin can move from a technical system to an operable governance infrastructure. We first summarize requirements for construction and application, then clarify how feedback from the virtual twin can be translated into real-world actions, and finally reflect on broader implications and limitations of current smart-city practices.
Construction and application of digital twins
Digital assets are evolving into a city’s most invaluable and pioneering resource (Angelidou, 2014, 2015). The essence of the digital twin is to optimize the linkage between supply and demand through the seamless integration of data and computation, thereby achieving heightened precision in alignment and maximizing the efficiency of finite stock resources (Lamnabhi-Lagarrigue et al., 2017). To embody the digital twin concept, requisite functionality must be accessible alongside the requisite technological means for realization. This necessitates the inclusion of “perception and comprehension” as the genesis and input phases within the virtual domain, while “alteration in spatial context” serves as the output emerging from the virtual realm. Furthermore, by means of “abstraction,” “cognition,” “realization,” and “elaboration” of the virtual space, the digital twin’s adaptability can be harnessed. This cyclical structure within the virtual realm is built upon the SECI model (Lee and Kelkar, 2013), enabling versatile employment of the digital twin—transforming tacit knowledge into formal comprehension. This model harmoniously aligns with the TRIZ model (Ilevbare et al., 2013), fostering the structured transition of tacit knowledge into formal wisdom.
In the Nanjing case, this theoretical foundation materializes through the rapid integration of CIM, IoT, and AI algorithms into the very process of new-town development. Unlike retrofitting digital layers into existing urban fabrics, the digital twin was embedded from the outset of spatial expansion, creating a synchronized evolution of physical and virtual infrastructures. The construction of the SNEHI exemplifies the rapid deployment of digital twin systems under China’s Digital China and new infrastructure strategies, particularly in large-scale new-town projects. By embedding digital twins from the planning stage, SNEHI achieves synchronized physical-digital growth, enabling rapid formation of digitized governance and public service systems with high operational efficiency and adaptability. This approach, distinct in its integration from the ground up, forms a locally unique model with global demonstration potential, offering lessons for new urban developments worldwide. However, compared to the incremental digital transformation of existing cities, this model risks over-reliance on top-down policy and singular technological pathways, potentially limiting its openness and replicability (Van Bossuyt et al., 2025). This reflects not only the operationalization of digital twin concepts but also an institutional innovation that departs from most existing studies focused on partial applications or sectoral pilots.
Feedback between digital twins and real space
We posit that the feedback loop to the tangible realm is accomplished via human and societal actions or by fostering shifts in behavioral patterns. Hence, the output of the digital twin must assume a configuration intelligible to humans. Information tangential to conduct need not invariably be exhibited. Instead, conveying requisite and substantial data is optimal. As such, employing visualization methods characterized by apt spatial representations is pivotal. With the advancement of artificially intelligent automatons, methodologies have emerged proposing the conveyance of feedback to the physical domain through artificial intelligence and robots, obviating the need for human intermediaries (Luck and Aylett, 2000). In such instances, it is not imperative to relay information in a manner decipherable by humans; for instance, a cleaning automaton can fulfill its role by discerning the presence of obstacles in its vicinity, bypassing the necessity for an exhaustive room model (Forlizzi and DiSalvo, 2006).
In practice, the Nanjing digital twin demonstrates that feedback loops extend beyond technical automation. Citizen workflows, service allocation, and governance dashboards create a mode of “responsive governance” where urban decisions are continuously adjusted according to real-time conditions (Esperança et al., 2025). While SNEHI’s digital twin system has achieved rapid deployment through government-led initiatives and IT enterprise support, ensuring system-wide implementation, its long-term success hinges on integrating bottom-up participation mechanisms to complement top-down designs. Current reliance on government investment and technological inputs from large corporations must be balanced with enhanced public participation, robust data privacy protections, and cross-departmental collaboration. Future development should prioritize engaging diverse stakeholders to foster collaborative governance, enhancing transparency and sustainability while expanding the digital twin’s role in social governance (Haraguchi et al., 2024). However, this raises unresolved tensions: how to safeguard privacy within massive real-time monitoring (van Meerten and Smit, 2024); how to ensure that vulnerable groups are not marginalized in algorithmic decision-making (Sanchez et al., 2025); and how to balance top-down efficiency with bottom-up participation (Haraguchi et al., 2024). These concerns remain underexplored in current Chinese cases, but they are central in global debates and demand further scrutiny.
Reflections on the current smart city construction
For an extended duration, the concept and implementation of smart cities have primarily revolved around the establishment and crafting of fundamental networks such as the Internet of Things (IoT), cloud computing, and mobile Internet (Jin et al., 2014). Yet, this perspective inaccurately encapsulates the essence of smart cities. Presently, numerous urban development models focus not on constructing authentic smart cities, but rather on transposing the limitations of conventional planning – characterized by “tall structures, expansive thoroughfares, and vast plazas” – into the realm of information technology with attributes like “high-speed connectivity, extensive data, and sizable platforms”. Regrettably, the nucleus of attention centers solely on technology, overlooking the very essence of the city itself. Be it integrators, internet corporations, or traditional real estate entities, their capacity to grapple with the multifaceted and intricate urban systems remains insufficient. This inadequacy necessitates an assembly of heightened research and development capabilities, coupled with a holistic perspective on urban scenarios, and the formation of industrial clusters, to birth a novel blueprint for smart city construction (Mariani et al., 2018). Considering the intricate challenges that manifest in urban operations and the prevailing impasse in the evolution of smart cities, there arises a need to contemplate a systematic smart city framework, one capable of accommodating the multifarious functions of urban life. Moreover, the exploration extends towards the domain of advancing high-practice product research and development and fostering industrial clusters within the established framework, thereby actualizing authentic wisdom across diverse scenarios (Klar et al., 2023). Leveraging digital twin technology harmonized with the Internet, IoT, and operators, we shall curate a comprehensive data-centric model encompassing the entire trajectory of smart city conception, construction, operation, and management, facilitating a proactive strategic foresight.
The Nanjing Eco Hi-Tech Island illustrates one such framework: a “new-town model” of digital twin implementation. Here, the state-led expansion of urban space coincided with the construction of a parallel digital infrastructure, effectively allowing the city to ‘grow digitally and physically’ in unison. This mode has advantages—rapid rollout of digital governance and seamless integration of infrastructures—but also limitations, as it privileges greenfield sites while offering fewer lessons for retrofitting existing dense urban fabrics. Moreover, its reliance on government-led investment and large technology providers raises questions about long-term adaptability, citizen empowerment, and inclusiveness. Beyond governance, digital twins in SNEHI catalyze the digital economy, generating vast data assets that function as both governance tools and valuable resources for broader economic and social interactions. By integrating AI, big data analytics, and multimodal interactions, the digital twin enables predictive governance and intelligent services, aligning with the digital era’s urban development trends and demonstrating the potential for deep integration of technology, space, and society. However, this introduces governance risks: data centralization may exacerbate power imbalances, and algorithm-driven predictions carry concerns of technical bias and social exclusion. Sustainable digital twin cities thus depend on balancing efficiency with fairness, innovation with regulation, and embedding ethical constraints and social oversight in institutional designs (Garske et al., 2024; Rehan and Rehan, 2025).
Future-oriented reflection should therefore address three aspects. First, how can such top-down architectures be complemented by bottom-up participation, ensuring public trust, privacy safeguards, and inclusivity (Haraguchi et al., 2024; Bäumer et al., 2024)? Second, how can international collaborations, such as the Sino-Singapore partnership in Nanjing, foster mutual learning and establish shared standards for data governance and interoperability (Huzzat et al., 2025; Quek et al., 2023)? Third, how can the massive data assets generated in such projects be mobilized not only for governance but also as a foundation for digital economies, innovation ecosystems, and long-term sustainability (Garske et al., 2024; Rehan and Rehan, 2025)? These open questions point to the dual character of the Nanjing case—as both a pioneering demonstration of digital twin practice and as a reminder of the governance, ethical, and social complexities that remain unresolved.
Limitations and future work
This study has two main limitations due to data constraints: (1) outcome-level performance KPIs for the proposed framework are not reported, and (2) accessibility and disability-inclusive services are not explicitly covered in the available SNEHI documentation and therefore are not evaluated in this study. Future work should incorporate accessibility-related datasets and stakeholder co-design (e.g., disability advocacy groups and service providers) to define and evaluate inclusive-service KPIs and assess how well the framework supports people with disabilities, using metrics such as barrier-free network coverage, step-free route availability and reliability, accessible transit uptime, and accessibility-related response time and satisfaction. While citizen feedback channels can be incorporated as demand-side signals, the available SNEHI materials do not provide sufficient evidence to quantify participation intensity or to assess co-design outcomes; future work should evaluate participation quality and inclusiveness using interaction logs and survey-based measures.
Conclusion
To conclude, this study demonstrates the latent capacity of digital twin technology in reshaping the governance and development of smart cities. Using the SNEHI as a case study, it shows how digital twins can move beyond fragmented, sectoral applications to form a holistic framework that integrates planning, construction, operation, and service delivery. The findings reveal that digital twins can establish a foundational urban repository, enabling real-time data integration, advanced modeling, and predictive simulation. More importantly, the Nanjing case illustrates an innovative “integrated lifecycle model” in which digital infrastructures and physical space co-evolve from the outset of new-town development. This distinguishes it from prior smart city approaches and highlights its potential as a replicable framework for systemic governance innovation. While this study emphasizes representative subsystems—ecology, built environment, and human behavior—the proposed framework is extendable to economic, institutional, and cultural dimensions. In doing so, it not only grounds theoretical debates in a concrete urban experiment but also contributes a new paradigm of digital twin urbanism to the broader literature.
This study is not without limitations. First, the case analysis is primarily centered on a newly developed district, which may not fully capture the complexities of retrofitting digital twin frameworks into dense, established urban fabrics. Second, while the research highlights institutional and technical innovations, issues such as data privacy, inclusiveness, and the role of public participation remain underexplored. Finally, the generalizability of the Nanjing model requires further comparative research across different governance contexts and international collaborations. Future work should therefore deepen the empirical assessment of digital twin impacts, particularly in existing urban cores, broaden the evaluation to include ethical and societal dimensions, and explore how data assets generated through digital twin platforms can underpin long-term digital economy development.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsc.2026.1733281/full#supplementary-material
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Edited by: Leye Wang, Peking University, China
Reviewed by: Riccardo De Benedictis, National Research Council (CNR), Italy