2.1. Urban-technological symbiosis

Throughout human history, urban developments have always been paired with technological innovation. In the 19th century, the Industrial Revolution introduced new urban and infrastructure solutions for homes, transport, and manufacturing, signifying the start of unprecedented urban development in the decades to come (Townsend, Reference Townsend2013; Mora and Deakin, Reference Mora and Deakin2019). Today, Komninos et al. (Reference Komninos, Kakderi, Panori and Tsarchopoulos2019) distinguishes three smart city phases that have transformed the way we live. According to Komninos et al. (Reference Komninos, Kakderi, Panori and Tsarchopoulos2019), the first phase involves digital spaces such as webpages, forums, and chatrooms and their inclusion of simple city representations and visualizations to improve communication between experts and non-experts. The advances of cloud computing in the second phase have enabled community participation and people-driven innovation through co-creation and crowdsourcing to spur a burgeoning user-centric digital urban ecosystem, that grant greater convenience and accessibility. In the latest phase, the pervasive diffusion of cost-effective physical user interfaces like mobile phones and sensors as part of the Internet of Things has enabled the availability of continuous real-time big data. When coupled with artificial intelligence developments, urban problems are resolved just in time with real-time insights and responses in new modes of human–machine interactions (Komninos et al., Reference Komninos, Kakderi, Panori and Tsarchopoulos2019). In this era characterized by growing complexity and volatilities, real-time responses in city management operations and governance, enabled by cyber–physical intelligence, become critical for a more inclusive, diverse, and sustainable urban environment, unachievable by human capabilities (Townsend, Reference Townsend2013; Anthopoulos, Reference Anthopoulos2017; Komninos et al., Reference Komninos, Kakderi, Panori and Tsarchopoulos2019; Kunzmann, Reference Kunzmann2020).

Considering these trends, it is unsurprising that city administrators have already acknowledged the significance of technology in enhancing urban life. They are willing to fund and support a myriad of new and ongoing smart city projects, often small start-ups and experimental projects developed by research organizations, technology and non-technology firms, and individuals (Hollands, Reference Hollands2015; Rabari and Storper, Reference Rabari and Storper2015; van Winden and van den Buuse, Reference van Winden and van den Buuse2017; Rech et al., Reference Rech, Pistauer and Steger2018; Komninos et al., Reference Komninos, Kakderi, Panori and Tsarchopoulos2019; Mora and Deakin, Reference Mora and Deakin2019). This is a stark contrast to the initial market space dominated by big software firms such as IBM, CISCO, Microsoft, and Oracle (Townsend, Reference Townsend2013; Kitchin, Reference Kitchin2014; Grossi and Pianezzi, Reference Grossi and Pianezzi2017). Moreover, cities deploying information and communication technology (ICT) are demarcated as smart cities (Söderström et al., Reference Söderström, Paasche and Klauser2014; Grossi and Pianezzi, Reference Grossi and Pianezzi2017). Implied in its meaning, ordinary cities are perceived as better when adopting ICT solutions in their operating models to become smarter (Townsend, Reference Townsend2013). Thus, there is a growing popularity of the smart city agenda amongst city administrations.

Nevertheless, cities should exercise caution and prudence in their optimistic fervor for smart cities. The influence of big software firms and their corporate ambitions remain entwined with current smart city developments (Townsend, Reference Townsend2013; Grossi and Pianezzi, Reference Grossi and Pianezzi2017). The corporatization of city governance and their services, as well as the radical dependence on technology and data have been normalized implicitly without acknowledgment of their pitfalls. In guiding our arguments, we present some common criticisms of smart cities. First, corporatization could perpetuate a technological lock-in. This could cement a corporate dependency on a monopoly that cannot be easily undone or diverted (Kitchin, Reference Kitchin2014; Marvin et al., Reference Marvin, Luque-Ayala and McFarlane2015). Second, the embedding of pervasive digital solutions in urban environments may leave cities vulnerable to viruses, glitches, crashes, and hacks, despite the potential problems solved (Townsend, Reference Townsend2013; Kitchin, Reference Kitchin2014). For example, the subway network is dependent on the continuous delivery of software, in which software crashes could inhibit subway functionality (Kitchin, Reference Kitchin2014). Third, the development of automated digital surveillance networks has raised concerns on the rights to privacy, confidentiality, and freedom of expression (Townsend, Reference Townsend2013; Kitchin, Reference Kitchin2014; Ho, Reference Ho2017). In particular, citizens are increasingly wary and anxious of large-scale smart city projects and their impact on their privacy (Rabari and Storper, Reference Rabari and Storper2015; Shimizu et al., Reference Shimizu, Osaki, Hashimoto and Karasawa2021). Last, another criticism is that many smart cities have a common tendency to inaccurately associate the quantification of urban problems with the notion that they are solvable or optimizable through computational procedures (Kitchin, Reference Kitchin2014; Marvin et al., Reference Marvin, Luque-Ayala and McFarlane2015; Rabari and Storper, Reference Rabari and Storper2015). In reality, cities are complex living systems (Batty, Reference Batty2013; Yamu, Reference Yamu2014) with uncontrollable, unpredictable, “wicked” problems in which introducing one solution may lead to more problems even with coordinated efforts (Rittel and Webber, Reference Rittel and Webber1973). In this regard, adoption of a technocratic governance diminishes their scope, which ignores wider social and environmental perspectives that shape urban life and their individual identities (Kitchin, Reference Kitchin2014).

These criticisms are exemplified through the distinct smart city implementations across geographies. Smart city projects in Asia often feature infrastructure-oriented projects implemented by developmental states like Singapore and South Korea (Ho, Reference Ho2017). But city administrations and researchers are questioning the effectiveness of these solutions for enhancing quality of life relative to their capital cost. Ho (Reference Ho2017) examines the energy management capabilities of smart homes in Singapore, which fail to implement lasting behavioral changes to reduce residential energy consumption. In contrast, smart city projects in Europe and North America often emphasize on their decentralized and participatory projects (Ho, Reference Ho2017). One criticism of such cities is the tendency for corporations to reframe urban sustainability challenges to prioritize corporate interests by managing the symptoms over addressing their root causes, while plausibly marginalizing their citizens (Kitchin, Reference Kitchin2014, Reference Kitchin2015). Evans et al. (Reference Evans, Karvonen, Luque-Ayala, Martin, McCormick, Raven and Palgan2019) broaches on the penchant of current clean energy approaches for urban populations toward smart meters and smart grids managed by a digital system. Instead, there is little discussion on developing new forms of community energy provisions or reducing resource consumption to empower citizens. Accordingly, when city governance is perceived as long-term market opportunities for vendors, smart cities foreground economic developments over their promises for social and environmental sustainability (Townsend, Reference Townsend2013; Kitchin, Reference Kitchin2014, Reference Kitchin2015; Marvin et al., Reference Marvin, Luque-Ayala and McFarlane2015; Ho, Reference Ho2017; Evans et al., Reference Evans, Karvonen, Luque-Ayala, Martin, McCormick, Raven and Palgan2019).

Embedding technologies into cities and their inhabitants’ lifestyles also holds the unique challenge of managing the different developmental phases of cities. Smart cities are often marketed with one-size-fits-all narratives that uses popular canonical examples such as Songdo or Masdar City (Kitchin, Reference Kitchin2015). In actuality, there are various permutations of smart cities and their technologies that cannot be clearly defined or classified (Townsend, Reference Townsend2013; Komninos et al., Reference Komninos, Kakderi, Panori and Tsarchopoulos2019). Cities are unique living systems that develop at their own paces, with rarely, shared milestones, trajectories, or technologies. For example, as an existing city, Singapore often retrofits and upgrades its infrastructure with new technologies as part of its Smart Nation initiatives (Smart Nation Singapore, 2021). On the other hand, greenfield smart cities like Songdo City in South Korea embed the latest technologies directly into their city design and construction. Furthermore, cities develop at a slower pace than technology advances. By the time a city has completed their latest smart city project, the implemented technologies may be outdated, with a newer, improved version available on the market. In an era of rapid technological advances, an ever-growing digital ecosystem, and varying city development phases, interoperability and compatibility become vital requirements in ensuring that smart cities are not just able to speak to each other, but to communicate internally, today and in future.

Presenting these contrasting perspectives on smart cities, it becomes clear that the technological influence on urban governance is complex. Even when faced with challenges like interoperability and compatibility, smart cities have demonstrated the power of technology to redefine and reconfigure the relations within and between people, their communities, governments, and the urban environment (Townsend, Reference Townsend2013; Anthopoulos, Reference Anthopoulos2017; Evans et al., Reference Evans, Karvonen, Luque-Ayala, Martin, McCormick, Raven and Palgan2019). As cities continue to evolve in the face of rapid urbanization, climate change, resource scarcity, globalization, and their intensifying inter-city competition (Angelidou, Reference Angelidou2015), neither striving for technocratic dominance nor disposing all technologies are optimal solutions (Kitchin, Reference Kitchin2016). It is more convincing that human–cyber–physical interactions will hold value and evolve, leading to a stable symbiotic relationship between technology and cities. Hence, in order to understand the urban development opportunities of tomorrow, this paper suggests to add a technological dimension to the trifecta of economic, environmental, and social sustainability goals that guides city planning. Moreover, we will scrutinize the complexity of interoperability and compatibility within the current digital ecosystem to foster their potential to enhance urban governance.

2.2. The emerging digital ecosystem and city digital twins

Empowered by the decreasing entry costs, increasing government support and economic opportunities, the proliferation of various smart city solutions across scales has led to an ever-growing digital ecosystem (Angelidou, Reference Angelidou2015; van Winden and van den Buuse, Reference van Winden and van den Buuse2017; Rech et al., Reference Rech, Pistauer and Steger2018; Komninos et al., Reference Komninos, Kakderi, Panori and Tsarchopoulos2019). These digital solutions often capitalize on knowledge management processes such as collecting public information to address specific issues (Angelidou, Reference Angelidou2015). Regardless, many are not able to interoperate with other solutions, which has resulted in often unsuccessful developments (Kitchin, Reference Kitchin2014; Hollands, Reference Hollands2015; Marvin et al., Reference Marvin, Luque-Ayala and McFarlane2015; Van et al., Reference Van den Abeele, Hoebeke, Moerman and Demeester2015). This is concerning when predominant investors are city administrations, who are left with less financial resources and a challenging task of managing fragmented and ineffective approaches to smart cities. Moreover, as they are often represented by small-scale pilot projects, it is questionable if such micro-solutions are sufficient to address challenges at the city scale, especially considering the scales of urban information flows and complexities. Thus, micro-solutions regularly fail to embrace a digital ecosystem framework necessary to produce greater value to urban communities (Kitchin, Reference Kitchin2014; Angelidou, Reference Angelidou2015; Hollands, Reference Hollands2015; Van et al., Reference Van den Abeele, Hoebeke, Moerman and Demeester2015).

We argue that the next years inevitably need to focus on building a flourishing urban modeling ecosystem that considers organizational practicalities, resources, interoperability, and compatibility. Given the growing anxiety and wariness of large-scale digital solutions in urban landscapes (Rabari and Storper, Reference Rabari and Storper2015; Shimizu et al., Reference Shimizu, Osaki, Hashimoto and Karasawa2021), it becomes increasingly difficult to convince citizens to support and fund bigger smart city projects. In contrast, successful examples of smart cities adopt a system of systems approach, streamline urban knowledge management processes, and balance the economic, sociocultural, spatial, and institutional perspectives of cities (Yigitcanlar et al., Reference Yigitcanlar, Han, Kamruzzaman, Ioppolo and Sabatini-Marques2019). Considering these two perspectives, smart cities will likely converge toward individual micro-solutions to address urban challenges while protecting the interests of citizens.

In enabling interoperability and compatibility across micro-solutions, the current knowledge management approaches of CDTs should be of interest. Given the complexity of virtually replicating physical systems, CDTs generally involve the integration of multiple micro-solutions as components to form their physical, network, and computing layers (Semeraro et al., Reference Semeraro, Lezoche, Panetto and Dassisti2021; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). This architecture is necessary to incorporate a large amount of data and models to replicate and address urban complexities. To the extent of our knowledge, CDT have two approaches, namely, system and semantic integration.

Traditional system integration approaches connect various individual digital systems, services, and tools into a single DT application through a common interface. The DT often provides the same functionalities as the underlying applications, except that it is now consolidated and convenient for users to access in a single platform. At present, such an approach is one reason for the weak interoperability and compatibility between digital solutions.

Semantic integration approaches could address the conundrum from a different direction. These approaches adopt Semantic Web technologies to provide a common framework that allows data and their relationships to be represented using ontologies, providing context to the data, and facilitating it being shared and reused across applications and systems (W3C, 2009). These technologies have already been proposed as suitable candidates to overcome many of the current challenges facing smart city developments, including data interoperability issues, poor machine readability, and the scalability of solutions to large and complex systems such as cities (W3C, 2015; Ronzhin et al., Reference Ronzhin, Folmer, Maria, Brattinga, Beek, Lemmens and van’t Veer2019). By following the principles of Linked Data (Berners-Lee, Reference Berners-Lee2006; Bizer et al., Reference Bizer, Heath, Berners-Lee and Sheth2011), the Semantic Web enables the discovery, integration, querying, and transfer of information between different domains and systems via the World Wide Web (Noy et al., Reference Noy, Gao, Jain, Narayanan, Patterson and Taylor2019; Akroyd et al., Reference Akroyd, Mosbach, Bhave and Kraft2021).

Answering the first research question, the literature highlights the trajectory of city planning and governance processes toward an ever-growing digital ecosystem of individual micro-solutions for supporting their workflows and decisions. Amidst the growing diversity of approaches and systems, interoperability and compatibility are vital enablers to ensure that such costly solutions can benefit their citizens and support city administrations. This endeavor would require a common platform to access and share data across city administration departments, thus, demanding they change their workflows to achieve their desired outcomes. In this regard, the approaches to CDTs offer new opportunities for city administrations to integrate various digital systems, formats, applications, and even other DTs into a broader ecosystem, and create a common platform (Bolton et al., Reference Bolton, Butler, Dabson, Enzer, Evans, Fenemore, Harradence, Keaney, Kemp, Luck, Pawsey, Saville, Schooling, Sharp, Smith, Tennison, Whyte, Wilson and Makri2018).

2.3. State-of-the-art city digital twins

This section introduces the CDTs through a state-of-the-art on their definitions and discussion. In this paper, CDTs are considered an ecosystem of DTs, which connects the different information flows, but may not have all DT features. Today, a growing diversity of DTs are being developed for various use cases across geographies. Although they tend to have no fixed technology or systems, DTs commonly adopt building models (from building information modeling [BIM], geographic information system [GIS] or other sources) and Internet of Things data into their approaches. Additionally, they are characterized by their capabilities, rather than technologies (Boschert et al., Reference Boschert, Heinrich and Rosen2018; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Fundamentally, a DT provides a digital representation of the state and behavior of a real-world system, environment, and/or processes, and can bilaterally interact in real-time with their physical counterpart throughout its life cycle (Batty, Reference Batty2018; Bolton et al., Reference Bolton, Butler, Dabson, Enzer, Evans, Fenemore, Harradence, Keaney, Kemp, Luck, Pawsey, Saville, Schooling, Sharp, Smith, Tennison, Whyte, Wilson and Makri2018; Kritzinger et al., Reference Kritzinger, Karner, Traar, Henjes and Sihn2018; Shahat et al., Reference Shahat, Hyun and Yeom2021; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021).

Given their amalgamation with various technologies, DTs are often confounded with similar technologies in their definition, capabilities, and challenges, and should be clarified to progress their developments (Kritzinger et al., Reference Kritzinger, Karner, Traar, Henjes and Sihn2018; Shahat et al., Reference Shahat, Hyun and Yeom2021; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Firstly, DTs are not 3D or BIM models. Traditional 3D models merely represent a physical object or entity at a specific time into the digital realm, but DTs are able to update their description over time (Shahat et al., Reference Shahat, Hyun and Yeom2021; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). BIM models incorporate their physical information into the digital representation to enable comprehensive intelligent urban management (Semeraro et al., Reference Semeraro, Lezoche, Panetto and Dassisti2021; Shahat et al., Reference Shahat, Hyun and Yeom2021). The difference between DTs and BIM belies their cyber–physical interactions. BIM models require manual data insertions, which is achieved autonomously for DTs in real-time (Semeraro et al., Reference Semeraro, Lezoche, Panetto and Dassisti2021; Shahat et al., Reference Shahat, Hyun and Yeom2021). Secondly, DTs are not simulation models. Simulation models predict future states of a physical system based on a fixed set of assumptions that is abstracted from reality (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Although DTs may incorporate simulation models, they are also able to update the parameters to the actual physical states in real time (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Lastly, DTs are not surrogate, Machine Learning, or Artificial Intelligence models. Such models are computationally cost-effective approximate models that adopt a set of training data to mimic the behavior of the original simulation model (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Instead, DTs virtually represents the real-time states of a physical system, rather than being a mimicry of a model of reality (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021).

In the context of smart cities, CDTs are expected to replicate the complexity of cities in real-time. This augments the monitoring and steering of city processes and services across domains such as transport, energy, and health, while supporting decision-making processes to analyze, plan, and govern the built environment (Batty, Reference Batty2018; Semeraro et al., Reference Semeraro, Lezoche, Panetto and Dassisti2021; Shahat et al., Reference Shahat, Hyun and Yeom2021; VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Moreover, CDTs are anticipated to enable an unprecedented scope for stakeholder engagement in public participation and collaboration between the experts as well as non-human actors and machines (Dembski et al., Reference Dembski, Wössner, Letzgus, Ruddat and Yamu2020; Shahat et al., Reference Shahat, Hyun and Yeom2021). Furthermore, CDTs are potentially place-based solutions. At the city level, a one-size-fit-all computational procedure and algorithm is insufficient to replicate and address their unique combination of contexts, complexities, and problems. As DTs are able to update their assumptions based on real-time feedback loops, they could generate place-based insights and solutions, tailored to the nuances of localities like their unique characteristics, lifestyles, history, and policies.

Nevertheless, due to their ongoing experimental status, current adoption of CDTs in smart cities remain limited. A recent review by Shahat et al. (Reference Shahat, Hyun and Yeom2021) highlights three key research challenges that contributes to the incomplete status of current CDTs. First, attempts to include non-physical systems such as social behaviors and economic systems often encounter significant challenges (Batty, Reference Batty2018; Shahat et al., Reference Shahat, Hyun and Yeom2021). Second, despite demonstrating the capacity to update the virtual representation, current CDTs have yet to achieve mutual cyber–physical integration (Shahat et al., Reference Shahat, Hyun and Yeom2021). Notably, there is a common misconception that the virtual-to-physical connection of DTs must reflect a physical state change. However, this connection is often intended to generate feedback serving an outcome, which need not be a physical state change (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). For example, when the outcome is to optimize inspection campaigns of large infrastructures, identification of the highest risk areas for targeted inspections are sufficient to meet the DT’s notion of virtual-to-physical connection, without any change to the physical infrastructure (VanDerHorn and Mahadevan, Reference van der Horn and Mahadevan2021). Last, the knowledge management potential of CDTs is not fully realized. Knowledge management processes create, share, and utilize knowledge to drive collaboration and innovation, bolstering organizational learning, efficiency, and performances (Angelidou, Reference Angelidou2015; Israilidis et al., Reference Israilidis, Odusanya and Mazhar2021). Underpinning these processes are the availability of the complex, extensive, and heterogenous city data. Although we acknowledge that data is not neutral nor apolitical, it remains important to describe the information flows of a city, which is impossible to accomplish through human capabilities alone (Kitchin, Reference Kitchin2014; Evans et al., Reference Evans, Karvonen, Luque-Ayala, Martin, McCormick, Raven and Palgan2019). Currently, the size and complexity of city data, alongside the lack of standardization, have inhibited the level of data quality and accuracy across various sources (Shahat et al., Reference Shahat, Hyun and Yeom2021). Hence, interoperability and compatibility between these different data sources and their technologies becomes a key bottleneck for present and future city administrations toward making headway on CDT and smart city applications.

The remainder of this paper will introduce and evaluate five CDT research and development projects at various development stages, through a critical reflection on their approaches and relevance for practitioners. The paper will also highlight how current knowledge management approaches, undertaken by CDTs, have the capacity to address interoperability and compatibility concerns. As smart cities move toward real-time cyber–physical bilateral interactions, it is vital to acknowledge the significant knowledge gap in the growing complexity of urban systems. As urban systems and their interactions continue to evolve, their growing complexity hinders the capacity to represent the behavior and operation of urban systems in digital formats, if it is even possible (Batty, Reference Batty2007; West, Reference West2017). Recognizing the broader urban dynamics, this reflection will be structured based on the four meta-practice (MP) identified in planning processes by von Richthofen et al. (Reference von Richthofen, Herthogs, Kraft and Cairns2022). Representational MP is the act of representing entire or parts of urban systems, often in a visual manner. Evaluative MP is the act of assessing properties of an urban environment to determine if they satisfy particular requirements or accomplish goals via single or multi-criteria monitoring and modeling. Projective MP is the act of creating specifications of new urban systems or their parts, based on an envisioned or desired (future) urban system or its properties at many scales. Synthetical MP is the act of managing, gathering, using, creating, and synthesizing the inherent data and knowledge flows to plan urban systems. Thus, this reflection provides a foundation to pave the way toward adopting the CDTs in practice and ultimately, delivering smart city promises and addressing urban problems.

3.1. The system integration approach

This section presents two DT applications based on the traditional system integration process.

3.1.2. Cambridge city-level digital twin

The Cambridge city-level DT project, hosted at the Cambridge Centre for Smart Infrastructure and Construction (CSIC) and developed in collaboration with local authorities, explores how data describing the built environment can help improve city planning, management and the delivery of public services. The Cambridge CDT is a tool that facilitates city-level integration by coordinating and connecting various other digital tools and datasets. The project has completed two phases to date, one focusing on the broader city region and one zooming into the specifics of a local strategic development site. Simulating future journeys to work using existing and open data sources on land use, transport and commuting patterns such as the UK Census, Business Register and Employment Survey, and Labour Force Survey through the modeling framework in Figure 1, the initial digital model prototype generated two digital transformation scenarios regarding remote working and electric vehicles, to demonstrate the DT’s potential policy use to address interdependence across the policy domains of land-use planning, transport, energy, and air quality, and support scenario development and analysis (Nochta et al., Reference Nochta, Wan, Schooling and Parlikad2021b).

Figure 1. Modeling framework for the Cambridge CDT prototype for journeys to work. Reprinted with permission from Nochta et al. (Reference Nochta, Wan, Schooling and Parlikad2021b). The original figure was published under the terms of Creative Commons Attribution License 4.0.

The second phase aims to understand how combining conventional data with emerging sensor-based “big data sets” could improve the model’s quality and the analytical outputs’ policy relevance. Using a large transport monitoring dataset via Automatic Number Plate Recognition, the CDT model has been extended to explore travel patterns to, from and around the Cambridge Biomedical Campus—one of the designated strategic development sites in the city region, with the potential to provide thousands of new jobs and homes over the course of a decade. The extended model features a new algorithm for inferring the purpose of a trip and the potential socio-economic characteristics of car users according to anonymized vehicle trajectories, to understand traffic patterns and their policy implications (Wan et al., Reference Wan, Tang, Wang and Schooling2021).

3.2. The semantic integration approach

This section presents the World Avatar—a DT that employs a semantic integration approach in conjunction with a dynamic knowledge graph (Akroyd et al., Reference Akroyd, Mosbach, Bhave and Kraft2021). In the initial phase of development, the research focused on using the knowledge graph to overcome interoperability and compatibility issues in the decarbonization of the chemical industry in Singapore. The project has since broadened its scope to include a range of applications such as air quality assessment, consumer energy, and city planning. Although we acknowledge the World Avatar DT as a largely experimental CDT, practical applications for it could be developed in the long term.

3.2.2. The World Avatar digital twin applications

The World Avatar project aims to connect data and computational agents in real-time to generate a living digital “avatar” of the real world, that remains up to date and any analysis outputs are self-consistent (Akroyd et al., Reference Akroyd, Mosbach, Bhave and Kraft2021). The name “World Avatar” seeks to convey the possibility of representing every aspect of the real world and extend the idea of DTs to consider the possibility of representing abstract concepts and processes. Effectively, the World Avatar is an all-encompassing DT comprising every conceivable domain. Three applications of the World Avatar knowledge graph are presented, demonstrating its cross-domain capability at the national level.

3.2.2.1. Cross-Domain Air Quality Assessment

The first application relates to the assessment of the impact of shipping on air quality in Singapore. Singapore has both a high population density and hosts one of the world’s busiest ports. It is therefore natural to ask how hard-to-abate sectors like shipping influence factors such as air quality in different regions of Singapore (Farazi et al., Reference Farazi, Salamanca, Mosbach, Akroyd, Eibeck, Aditya, Chadzynski, Pan, Zhou, Zhang, Lim and Kraft2020). In what follows, we elaborate on the interaction between the computational agents that contribute to the air quality calculation.

The interactions between the agents are illustrated in Figure 2. Input agents update the knowledge graph with real-time information about the weather, location, and speed of ships so that it remains current in time. An emissions agent uses information about each ship to estimate its emissions. An atmospheric dispersion agent is able to use the information about the built environment, weather, and the emissions from each ship to simulate the dispersion of the emissions. Virtual sensor agents monitor the resulting air quality estimates at different locations.

Figure 2. Real-time cross-domain estimation of the impact of emissions from shipping to air quality. Adapted with permission from Farazi et al. (Reference Farazi, Salamanca, Mosbach, Akroyd, Eibeck, Aditya, Chadzynski, Pan, Zhou, Zhang, Lim and Kraft2020). Copyright © 2020 American Chemical Society.

The calculations performed by agents can take any form, including physics-based models with a theoretical structure, gray box models that combine some theoretical structure with data-driven components, and pure data-driven models. The agents are able to wrap around existing software, including both open-source and commercial software. Furthermore, agents modify the dynamic knowledge graph based on changing context and data availability (Eibeck et al., Reference Eibeck, Chadzynski, Lim, Aditya, Ong, Devanand, Karmakar, Mosbach, Lau, Karimi, Foo and Kraft2020). Through the integration of models and data from different domains, agents acting on the dynamic knowledge graph are able to perform tasks within and across domains to simulate the behavior of systems and the consequences of current activities. As a proof-of-concept, it highlights how the data and model integration capabilities of the dynamic knowledge graph are general and can be applied to any domain of interest inclusive of urban systems.

3.2.2.2. Consumer Energy Usage Data in Smart City Development

The second application concerns the consumer energy market in Singapore. The recent liberalization of Singapore’s electricity market in 2018 has given consumers more choice and flexibility in selecting suitable electricity retailers and pricing plans to meet their needs. In light of this, the Consumer Energy Usage Data in Smart City Development (CEUS) project aims to develop a knowledge-enabled, data-driven common platform to provide real-time information about consumer energy usage to enable individual consumers and local government to make more informed decisions and promote more active participation in the energy market. This would empower consumers, foster innovation for a consumer-oriented energy grid, and promote the provision of more decarbonized, resilient and affordable electricity. An overview of the CEUS project is illustrated in Figure 3.

Figure 3. Overview of the CEUS project.

The CEUS project has three key components. First, a Singapore-specific Common Information Model grammar was developed to represent consumer energy usage information in a standardized format. Common Information Model is a well-established open standard for information modeling in the power systems domain that provides standard unambiguous definitions and representations of various energy related concepts (Britton and deVos, Reference Britton and deVos2005; Becker and Saxton, Reference Becker and Saxton2008). A Common Information Model grammar has to be developed to cater for the idiosyncrasies of Singapore’s consumer energy market arising from its recent liberalization. Second, the standards will be developed to adopt a consumer semantic format to enhance its interoperability and compatibility with other data formats and systems. Establishing the grammar in the form of standardized consumer semantics extends the expressivity of its formal definitions, enabling it to encode the necessary contextual information to support complex tasks such as automation and reasoning. The resulting Common Information Model will be integrated with an autonomous agent framework based on a dynamic knowledge graph to enable seamless and effective consumer energy usage data exchange with third party services such as electricity retailers and regulatory bodies. Third, the CEUS project reviewed existing smart city policies to identify and suggest implementation solutions that add value for policy makers. CEUS acts as a testbed for greater interoperability and compatibility between diverse technological systems to overcome existing data silos and share data with more stakeholders while respecting consumer privacy. In particular, CEUS tests how consumer energy data can be made interoperable and allow seamless integration between key planning software such as GIS and BIM. By laying a foundation for the integration of real-time energy consumption data into planning technologies, the information provided by the CEUS platform paves the way toward a consumer DT (Balijepalli et al., Reference Balijepalli, Sielker and Karmakar2021).

3.2.2.3. Cities Knowledge Graph

The third application is the Cities Knowledge Graph (CKG), which explores the idea of using a dynamic knowledge graph as a knowledge management system to support city planning processes. City planners, guided by policy standards and targets, produce planning documents and proposals that synthesize information and goals from various cross-domain and multidisciplinary stakeholder dialogue. Implicitly, city planning processes rely on information flows such as gathering site information, requesting data from specialists, retrieving past proposals and decisions, building data repositories, and communicating with stakeholders. However, ongoing digitalization efforts for smart cities have yet to achieve the systematic and automatic discovery, preparation, interpretation, and delivery of relevant city planning data to support policy development.

To chart the emerging territory of semantic city planning systems, the CKG project has surveyed and categorized ongoing research efforts related to Semantic Web technologies and city planning around four meta-practices in the planning process (von Richthofen et al., Reference von Richthofen, Herthogs, Kraft and Cairns2022). It was identified that although current systems present data, they provide little capabilities to synthesize knowledge. The CKG aims to provide a Semantic City Planning System that supports the urban development process (city planning and urban governance) in three ways (von Richthofen et al., Reference von Richthofen, Herthogs, Kraft and Cairns2022). First, the CKG automates aspects of data gathering and processing in order to generate useful information and knowledge about cities. Second, tools, concepts and targets from different city planning departments and distinct knowledge domains are integrated to formulate more comprehensive complex definite planning questions. Third, the CKG will conduct advanced scenario planning, supporting planners to analyze different variants of their proposals.

Currently, the CKG researchers have established a Semantic 3D City Database in the form of an urban knowledge graph. To support representational MP, it can load and link 3D city data through a OntoCityGML ontology (Chadzynski et al., Reference Chadzynski, Krdzavac, Farazi, Lim, Li, Grisiute, Herthogs, von Richthofen, Cairns and Kraft2021), automatically load, update, and evaluate city data (Chadzynski et al., Reference Chadzynski, Li, Grisiute, Farazi, Lindberg, Mosbach, Herthogs and Kraft2022b), as well as create visualizations and interfaces dynamically (Chadzynski et al., Reference Chadzynski, Li, Grišiute, Chua, Yan, Tai, Lloyd, Agarwal, Akroyd, Herthogs and Kraft2022a). An ontology of Singapore’s land use planning regulations was added to the database (Silvennoinen et al., Reference Silvennoinen, Chadzynski, Farazi, Shi, Grisiute, Richthofen, Cairns, Kraft and Herthogs2022), enabling the search for plots with particular land use or program combinations, while filtering results by other domain criteria (e.g., distance to parks). This ontology was extended with a classification of mixed-use archetypes occurring in Singapore, using Google Maps data (Shi et al., Reference Shi, Silvennoinen, Chadzynski, von Richthofen, Kraft, Cairns and Herthogs2022). The design of the knowledge graph aims to provide management functions that enable users to perform queries and infer knowledge about the system so that they can review development states at various scales, formulate relevant policy themes, and enable dialogue within policy networks. The CKG has been applied to create cross-domain multi-scale urban planning indicators that can assess the on-site solar energy harvesting potential of different land plots in Singapore (Grisiute et al., Reference Grisiute, Shi, Chadzynski, Silvennoinen, Richthofen and Herthogs2022). Another application is to determine the urban energy performance of different land use allocations in Singapore (Shi et al., Reference Shi, Herthogs, Li, Chadzynski, Lim, von Richthofen, Cairns and Kraft2021). Existing urban-scale simulation tools are also being integrated, for example, the agent-based mobility simulation framework MATSim (Grisiute et al., Reference Grisiute, Silvennoinen, Li, Chadzynski, von Richthofen and Herthogs2022). One of the future steps is to automatically gather, conceptually represent, and structure governance instruments such as policy standards, principles, and targets in order to automatically infer new knowledge and semantic relationships between these instruments and the concepts they build on, evaluate specific targets and their coverage or level of representation, and use these insights to inform planning synthesis processes.

4.1. Opportunities for interoperability and compatibility

Each of the five CDT projects and their different goals corroborate with the literature findings on the CDT’s potential to aid urban planning and governance processes. The Herrenberg DT and Cambridge CDT showcase the potential and relevance of adopting participatory planning processes as another source of knowledge to integrate urban governance insights into data and modeling results. Current DT discourses are largely confined to a techno-centric perspective with emphasis on demonstrating technical functionality (Nochta et al., Reference Nochta, Wan, Schooling and Parlikad2021b). These examples illustrate a tendency for city administrations across distinct localities to follow and be influenced by technological developments, with a focus on interoperability and data sharing. However, cities are complex living systems with various intangible influences (policy, human behavior, and commercial interests) that are difficult, if not impossible, to capture through data and digital technologies (Rittel and Webber, Reference Rittel and Webber1973; Bettencourt et al., Reference Bettencourt, Lobo, Helbing, Kühnert and West2007; West, Reference West2017). These solutions should extend beyond technical requirements, and be more sensitive to governance processes and their requirements such as privacy concerns. Consequently, city administrations should be involved in co-creating these digital solutions for their needs. Without their involvement, it remains unclear how the benefits proposed by current technology-driven approaches will be delivered and sustained. Thus, there is a need for interdisciplinary insights and deliberative participatory processes to become interoperable and compatible with technical data, involving prospective users ranging from the professionals who are responsible for planning and managing cities to the inhabitants of the cities, in addition to researchers and technology suppliers (Nochta et al., Reference Nochta, Wahby and Schooling2021a).

To accomplish this, the Herrenberg DT facilitated stakeholder engagement, even in the data collection stage, by involving citizens as part of a citizen science project to collect environment data from sensors and conducting public consultation through the developed mobile application “Reallabor Tracker” to gain data on the pedestrian and cyclists routes and their perceptions (Dembski et al., Reference Dembski, Wössner, Letzgus, Ruddat and Yamu2020). Moreover, the incorporation of virtual reality to enhance real-life perception can significantly support communication and decision making between stakeholders inclusive of politicians, administrative staff, experts from diverse disciplines, and citizens (Dembski et al., Reference Dembski, Wössner, Letzgus, Ruddat and Yamu2020). Using spatial and visual representation as a translation aid, the resulting reduction in complexity allows simulation results to be presented in an easier to understand manner than conventional methods and avoids the need for specialist language. Consequently, citizen participation in urban planning and design processes becomes easier and more attractive, while including groups previously excluded by conventional methods, such as children, teenagers, residents with a low level of education, migration background, or language barriers. For the Cambridge CDT, further engagement with stakeholders and residents in the area were conducted to include their perspectives and requirements when framing the problem (Nochta et al., Reference Nochta, Wan, Schooling and Parlikad2021b). Following a socio-technical framework, such insights enable the participation of interested/affected parties, improving the transparency in decision-making processes while establishing responsibilities and accountability for a more meaningful policy implementation that coordinate and mediate diverse interests and goals. Thus, when interoperability and compatibility of different types of knowledge are realized, CDTs have the potential to tackle urban complexity by visualizing complex processes and dependencies in urban systems, simulating possible outcomes and impacts, while also considering its citizens’ idiosyncratic needs and requirements through enabling participatory and collaborative planning.

On the semantic front, knowledge graphs are an opportunity to address the prevailing interoperability and compatibility issues in the ever-growing digital ecosystem. The three World Avatar applications highlight the knowledge graph’s potential to overcome the existing professional silos in practice, for instance only targeting traffic congestion without coordinating or considering other policy domains. The World Avatar examples demonstrate the capabilities of dynamic knowledge graphs as a knowledge representation and management tool that can integrate data sources and computational models from different domains. The ontological approach provides a standardized and structured semantic framework that can be wrapped around new and existing digital tools, applications, and systems. Furthermore, by overcoming the significant institutional and technical difficulties in data sharing and management, the CDTs enable the optimization of entire urban systems across policy domains. Ultimately, through augmenting data management and technological adoption across various sectors and actors, these projects and their diverse applications highlight that CDTs are expected to enhance rather than replace human agency and democracy in urban governance models to become more data-driven and evidence-based in addressing existing urban challenges and delivering smart city ideals.

4.2. System versus semantic digital twin

The reflection on the knowledge management approaches of CDTs is structured through the lens of the four planning MP identified in Section 2.4. Applying this framework, we aimed to center the discussion on the CDT’s potential to meet the needs and requirements of city administrations and planners. In comparing their capacity to enable interoperability and compatibility in the synthetical MP, the semantic CDT is more suitable with its dynamic ontological approach. System-integrated CDTs, as shown by the Herrenberg and Cambridge CDT, are still reliant on static manual data inputs and configurations to build the application and its components for a specific use case. Despite their success, these CDTs are also yet another digital tool that is often incompatible and not interoperable with other DTs in terms of their systems, formats, and protocols. In this sense, CDTs are both an ecosystem of micro-solutions, as well as a DT agent of a larger ecosystem of CDTs. Consequently, the growing complexity of the digital ecosystem and the associated large data streams, along with existing organizational silos and data management approaches will continue to challenge system integration adopters when they attempt to balance the connection of more recent tools and systems without disrupting existing established systems. In contrast, semantic CDTs such as the World Avatar DT could immediately integrate more data for new use cases without complex data transformation, as long as there is an existing ontology covering their domain. If no ontologies exist, an ontology needs to be developed or derived from other ontologies, which introduces a barrier to entry. Thus, answering the second research question, the semantic approach implemented in CDTs is a new opportunity to address the interoperability and compatibility conundrum. Future opportunities does not stop at connecting the data flows of individual DTs across domains to a CDT, but also enable feedback from the CDT toward the individual DTs.

However, as ongoing research and development projects, CDTs’ capacity to support the entire planning process remains inconclusive. The Herrenberg DT supports representational MP as a communication tool to present planning proposals to the public for feedback. Nevertheless, the proposals are often based on the practitioners’ assumptions with few impacts on the evaluative or projective MP beyond encouraging participatory processes. The Cambridge CDT moves beyond representational MP to evaluative MP by conducting scenario analysis in the first phase and modeling existing traffic patterns in the second, in order to make more informed policy decisions. But they have yet to support projective MP and generate automated predictions or planning proposals to address these issues identified. In contrast, the semantic approach has enabled the World Avatar CDT to support representational, evaluative, and projective MP through its dynamic agent framework.

Although the semantic CDT are arguably more developed to handle more planning-related tasks and MP, it remains unclear which approach is better as all CDTs have yet to be adopted in practice or evaluated. It is impractical to make a comparison and evaluation between the two approaches based solely on feature lists. Given that planning processes revolve around making more informed decisions and proposals (Stojanovski et al., Reference Stojanovski, Partanen, Samuels, Sanders and Peters2020), an appropriate evaluation criterion should instead be the data processing and analytical capabilities alongside their planning outcomes. Moreover, it can be argued that CDTs are not yet designed to support the technical deliverables of urban planning and governance processes. Design professionals are required to identify issues through various information sources, evaluate existing proposals, suggest new strategies, and communicate their ideas through visual mediums (Stojanovski et al., Reference Stojanovski, Partanen, Samuels, Sanders and Peters2020). In this aspect, existing CDT applications have yet to generate new strategies, suggestions, or insights for design professionals to perform their tasks. They are limited to specific use cases in primarily urban mobility, which does not comprehensively cover the entire urban planning or governance systems. This may overlook certain intangible aspects that cannot be translated into digital format. Thus, as a result of their current experimental nature, there remains a lack of evidence with practical planning outcomes that can evaluate and validate the CDTs’ potential to improve urban planning or governance processes.