Digital Twins for Climate-Responsive Urban Development: Integrating Zero-Energy Buildings into Smart City Strategies

Abstract

As climate change intensifies the frequency and severity of extreme weather events, the urgency for resilient and sustainable urban development becomes increasingly critical. This study investigates the role of digital twins in advancing climate-responsive urban strategies, with a focus on their integration into zero-energy buildings (ZEBs) and smart city frameworks. A systematic literature review was conducted following PRISMA guidelines, covering 1000 articles initially retrieved from Scopus and Web of Science between 2014 and 2024. After applying inclusion and exclusion criteria, 70 full-text articles were analyzed. Bibliometric analysis using VOSviewer revealed five key application areas of digital twins: energy efficiency optimization, renewable energy integration, design and retrofitting, real-time monitoring and control, and predictive maintenance. The findings suggest that digital twins can contribute to up to 30–40% improvement in building energy efficiency through enhanced performance monitoring and predictive modeling. This review synthesizes trends, identifies research gaps, and contextualizes the findings within the Middle Eastern urban landscape, where climate action and smart infrastructure development are strategic priorities. While offering strategic guidance for urban planners and policymakers, the study also acknowledges limitations, including the regional focus, lack of primary field data, and potential publication bias. Overall, this work contributes to advancing digital twin applications in climate-resilient, zero-energy urban development.

1. Introduction

There has been a significant impact of climate change on many cities during the last few years. Heatwaves, extreme precipitation events, droughts, and sea-level rises are some extreme weather events that are more prevalent in cities than other regions, because of climate change. Moreover, technological innovation has affected human society in a variety of ways. Urban planning and design practices could be greatly enhanced by the digital transformation in this scenario. The combined prevention of and reduction in carbon emissions would help to prevent climate change.

In working towards carbon neutrality and resilience, cities across the world play a vital role. The past few years have seen an unprecedented amount of city-scale data and information become available. As part of urban climate action being planned and designed by decision-makers and urban practitioners, a variety of data can be processed and managed from a variety of databases. For example, the carbon emissions of the building sector can be assessed and simulated, the effects of extreme precipitation or urban heat islands can be simulated, and the consequences on the built environment can also be evaluated. This scenario illustrates how, over the last few years, the digital twin concept has attracted more attention in urban planning and urban design than other digital enablement technologies in the Industry 4.0 ambit. Its purpose is to create a digital representation of real-world data and information flow, which can be transferred from the physical sector to the digital sector [1].

Today, digital twin tools serve as a central resource for urban planners, designers, and decision-makers in the urban context and at the building level, especially in terms of energy efficiency, energy consumption, sustainability, strategy, retrofit, preventive maintenance, building system analysis, and energy management, among other aspects. A digital twin, as defined by [2], is a digital representation of a real-life subject, object, machine, process, or human that is dynamic and self-evolving. By exchanging real-time data and retaining historic data, it represents the exact state of the physical twin at any given moment in time [2]. A digital twin not only mimics a physical twin, but any changes in the twin are also mimicked by its physical counterpart. The three main components required in this respect are a physical twin (a real-world entity), a digital twin (a representation of the physical twin that mirrors it in real time), and a linking mechanism to allow data to flow automatically between the twins in both directions [2,3,4].

In response to climate change, this study clarifies the relationship between using digital twin tools at both urban and building scales to help to construct zero-energy buildings in the energy management era. The digital twin, one of the most recent developments in smart city paradigms, is examined in this chapter. The goal is to understand the role of these tools in supporting urban scenario planning and building design, in order to manage energy consumption in zero-energy buildings within local climate action in Middle Eastern cities. Since digital twins and urban digital twins have recently been widely adopted, research surrounding them has increased, but there is still little knowledge available within this relatively new field, especially when it comes to the specific challenges that they pose when developing and implementing them. In order to fill this gap, this study examines the key challenges of urban digital twins and digital twin technologies in the context of zero-energy buildings in response to climate change. No review study from an environmental perspective has thus far systematically summarized the existing work on the technical, computational, legal, and regulatory challenges of digital twins with respect to climate change. A review study such as this examines new insights into the rapidly developing field of digital twins and urban digital twins, a trend that will undoubtedly accelerate over the next few years and become a major driver of zero-energy building modeling. In order to resolve these challenges, scholars, practitioners, and policymakers need to understand the critical challenges of digital twins and urban digital twins.

Although this study draws on a global set of peer-reviewed literature, particular attention is given to the Middle Eastern context due to its distinctive environmental and urban challenges. The region faces extreme heat, water scarcity, and high per capita energy consumption, while simultaneously investing in large-scale smart city projects. Despite these pressures, the application of digital twin technologies in climate-responsive architecture remains underexplored in this context. Therefore, this study interprets global findings through a regional lens to provide actionable insights for Middle Eastern cities—while also contributing to the broader discourse on sustainable urban development in arid and rapidly urbanizing regions worldwide.

As this study demonstrates, zero-energy buildings (ZEBs) are not only passive consumers of digital innovation but active agents within the smart city paradigm. Their integration supports energy autonomy, grid flexibility, and low-carbon development, making them foundational to sustainable urban transitions. Accordingly, this paper investigates how digital twins can optimize ZEB design and performance as part of broader smart-city strategies.

As a result, the research has been organized as follows. A theoretical framework is presented in the Section 2, which examines climate change integration strategies for urban environments, digital transformation, and urban digital twins as a method and tool for integrating mitigation and adaptations, in order to support scenario planning. The Section 3 describes the research methodology and materials used. The Section 4 presents the results of the systematic review approach. The Section 5 is devoted to a discussion of the main parameters and the development of specific and target indicators. This chapter concludes with a summary of the findings.

2. Literature Review

The rapid evolution of technologies such as the internet, big data, artificial intelligence (AI) [5], blockchain, the Internet of Things (IoT) [6], and fifth-generation wireless systems (5G) has transformed urban innovation landscapes. These advancements have catalyzed new societal formats, including the sharing economy, autonomous vehicles, digital currencies, and smart marketing strategies. Concurrently, emerging concepts such as ecological sustainability and public health are gaining traction in urban agendas, prompting city leaders to actively reimagine urban development models [7].

Among these digital transformations, the concept of the digital twin has become increasingly central. Initially developed by Professor Michael Grieves at the University of Michigan in 2003 in the context of product lifecycle management, a digital twin comprises three essential components: a physical entity, a virtual model, and a dynamic data link between the two [8]. Although the concept emerged earlier, the term “digital twin” was formalized in a 2010 NASA technology roadmap [9].

While digital twins were first adopted in the aerospace sector, their application has since expanded across various industries. Today, manufacturing remains one of the primary domains for digital twin deployment, including in product design, structural engineering, agriculture, and recycling. In the construction sector, however, widespread adoption did not occur until after 2018. Most studies to date have focused on the construction and operation/maintenance phases, with limited applications in the design phase, which remains underdeveloped [10].

In urban planning, the application of digital twins—often referred to as urban digital twins (UDTs)—emerged around 2018. However, digital modeling in cities has a much longer history. The first computational urban models date back to the 1950s with the emergence of commercial computing. A notable example is the Chicago Area Transportation Study (CATS) conducted in 1955, which marked the beginning of systemic urban modeling practices [11]. Over the following decades, various models were developed to represent the social and spatial dimensions of urban environments.

Although urban infrastructure models and transportation models have traditionally evolved through distinct disciplinary pathways, UDTs offer a unifying framework that integrates physical, social, and economic dimensions. By coupling real-time urban data streams with dynamic modeling capabilities, UDTs allow for interactive, autonomous decision-making processes that extend beyond conventional static models [10,12].

The role of digital twins in climate change adaptation has been formally acknowledged in recent policy frameworks, particularly within the European Union’s Climate Adaptation Strategy [13] and the IPCC’s AR6 Working Group II Report [5]. These frameworks emphasize the need for digital technologies that support decision-making under uncertainty. Digital twins offer such support by simulating past, present, and projected climate impacts across built and natural environments. When integrated with open-source environmental data—such as groundwater levels, soil moisture, and stream flow—digital twins can underpin real-time, physics-based environmental models that inform disaster risk reduction and water security strategies.

Moreover, digital twins can serve as virtual laboratories for testing the effectiveness of various climate adaptation strategies. By enabling the simulation of different scenarios—such as flood risks or heatwave responses—digital twins allow policymakers and urban designers to evaluate interventions in near-real-world conditions [14]. These capabilities position digital twins as a critical instrument in advancing climate-resilient urban planning.

To further analyze trends and research hotspots, bibliometric analysis was conducted using VOSviewer 1.6.17. This enabled the identification of co-occurring terms, author keywords, and thematic clusters across the selected studies. The goal was to highlight the evolution of research, the core application domains of digital twins, and existing technical and regulatory gaps—rather than to test or quantify the direct impact of digital twin tools on specific performance metrics. As a result of the initial literature search conducted on 20 December 2023, the search initially returned 1000 documents. After duplicate removal and eligibility screening based on title, abstract, and full-text availability, 70 articles were retained for full analysis. The selection criteria focused on studies addressing the application of digital twins to building energy performance, urban sustainability, climate resilience, and smart infrastructure. Term co-occurrence analysis was performed in VOSviewer on a total of 504 documents. In order to obtain the most accurate results, several steps were taken in the co-occurrence analysis. According to Figure 1, the final output is a network of nodes and links, where the node size is proportional to the frequency of terms and the link width is proportional to the strength of the relationships between terms. A decade range of publication of between 2014 and 2024 was chosen for the analysis documents.

The VOSviewer software visualizes bibliometric networks based on distance to indicate relatedness. With the help of VOSviewer, keyword co-occurrence networks were developed based on author keywords. A distance-based map generated by the VOSviewer shows the strength of the relationship between two items, with a smaller distance indicating a stronger relationship. Items are sized according to their frequency in relevant publications. Clusters developed by VOSviewer are represented by different colors. Figure 2 shows the annual publication trend of digital twins, zero-energy buildings, and climate change, demonstrating that the trend varies greatly annually. The early attempts at using digital twins with regard to climate change were in 2014, which delivered just nine publications in the area of digital twins, zero-energy buildings, and climate change.

Figure 1 and Figure 3 illustrate several clusters as follows. The largest cluster (2022, colored orange) contains the following keywords: sustainability, digital twin, sustainable development, smart cities, artificial intelligence, climate change, machine learning, and Industrial Revolution 4.0. The second-largest cluster (2024, colored yellow) contains the following keywords: digital twin, smart cities, artificial intelligence, energy management, buildings, and simulations. The third-largest cluster (2023, colored bright orange) contains the following keywords: artificial intelligence, climate change, smart city, digital twin, virtual realty, deep learning, and nanotechnology.

3. Materials and Methods

This study follows a systematic literature review approach to map and synthesize current knowledge on the use of digital twins in supporting climate-responsive strategies—particularly the integration of zero-energy buildings (ZEBs) within smart city frameworks.

The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two leading academic databases—Scopus and Web of Science—were queried using combinations of keywords including “urban digital twin,” “zero energy buildings,” “climate change,” and “smart cities.” The search was restricted to peer-reviewed journal articles published between January 2014 and January 2024, written in English, and relevant to the fields of architecture, engineering, and construction (AEC).

The study period (2014–2024) was selected to focus on the most recent decade of research, corresponding with the surge in digital twin applications in urban planning, building performance, and climate action following global policy milestones such as the Paris Agreement (2015).

A systematic literature review was conducted to address the following questions:

• In response to climate change, how can the digital twin play a role in constructing zero-energy buildings?
• How can we achieve the maximum response to climate change through the use of digital twins to build zero-energy buildings?
• In order to achieve smart cities and zero-energy communities, what are the parameters that should be included in the design framework for using digital twins?

The research criteria were based on the following string:

“Urban Digital Twin” OR “2Digital Twin” AND “Zero Energy Buildings” AND “Climate Change” included in <Title> OR <Abstract> OR <Keywords> OR “Urban Data Platform” in <Title>.

By limiting the search to the last ten years, only papers published between 1 January 2014 and 1 January 2024 were included. Keeping the review up to date through this deliberate use of recent publications ensures that it reflects current knowledge and adheres to current research methods. Various keywords were used in the literature search, including urban digital twin, digital twins, zero-energy buildings, climate change, smart cities, and zero-energy communities.

The selection criteria included all of the following:

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Research field: architecture, engineering, and construction (AEC)

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Language: English

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Publication date: 2014 to 2024

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Type of work: scientific articles (scholarly journals)

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Availability: full text

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Subject: the role of using digital twins in net-zero-energy buildings in the housing sector as a response to climate change

In contrast, the exclusion criteria were as follows:

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Not belonging to the research field (AEC)

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Not being in English

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Publication before 2012

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Not being a scientific article (scholarly journal)

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Full text being unavailable

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Not being related to the subject of interest

With the identification criteria applied on the platform selected, 257 records were identified through searching ScienceDirect. Following the PRISMA, the screening and eligibility steps consisted of removing duplicates and articles that were not available in English. This resulted in 70 full-text articles included in the analysis, as shown in Figure 4.

To strengthen the computational framing of the study, a conceptual digital twin model has been incorporated to demonstrate how data acquisition, predictive modeling, and optimization routines interact in a real-time decision–support loop. This model, represented both as pseudocode and a visual system workflow (Figure 5), outlines the logical architecture of a climate-responsive digital twin framework. Although not platform-dependent, the model highlights how sensor data, BIM integration, and AI-driven forecasting can converge to support ZEB optimization and smart city planning. This abstraction is intended to guide future work toward formal simulation and implementation.

Figure 5 shows a schematic flow diagram illustrating the integration of real-time data acquisition, predictive analytics, and simulation in a digital twin environment. The system ingests data from IoT sensors, Building Information Models (BIM), and environmental sources to assess energy performance and inform adaptive control strategies. Outputs include actionable recommendations for reducing energy consumption, optimizing renewable integration, and supporting ZEB targets in smart urban contexts.

4. Result and Discussion

The extracted data showed that digital twins have a significant role to play at all levels, from the building level to the city and urban level, all of which are interconnected. The perspective of a building can be used to solve a variety of problems, including energy consumption, energy control, and improving building performance. Additionally, digital twins support smart cities through connectivity, monitoring, and control.

According to

Table 1. Topics discussed in the selected articles (grouped by theme).

Thematic Cluster	Topic (Ref.)
Digital Twin Applications	Urban digital twins [10,15,16,17,18,19,20,21,22,23,24,25,26]
	Digital twin cities [27]
	DT and building performance simulation [3,28,29,30,31,32]
	DT for disaster management [33]
	DT in the operation and maintenance phase [34]
	DT and metaphor [35]
	DT and the circular economy [36]
	DT and nature-based solutions [37]
	Occupant behaviors and DT management [38]
	Resilience assessment and DT [39]
	DT in industries [40,41,42,43]
	DT and built environments [44];
AI and Smart Technologies	AI in green buildings [45,46]
	AI and climate change [47,48]
	DT and intelligent green buildings [49]
	DT and blockchain [50]
Urban Sustainability Strategies	Smart sustainable cities and DT [51,52,53,54,55,56,57,58,59,60]
	Building climate-neutral and resilient cities [1]
	DT and climate change adaptation [61]
	Digitalization and decarbonization [62,63]
	DT and sustainability transitions [64]
	DT and energy systems on an urban scale [65];
Integration Frameworks	Definitions of digital twins [2,66,67,68,69]
	DT integration with IoT, BIM, GIS, big data, and AI [12,60,70,71,72,73,74,75,76]
	Energy (DT) across the lifecycle and carbon emissions [77,78,79]

, displaying the carefully selected papers, 25 topics were discussed across 70 full-text articles, covering five indicators of digital twins in response to climate change (as shown in

Table 2. Digital twins: five indicators in response to climate change vs. topics discussed in the selected articles (70 full-text articles).

N.	Topics Discussed in Selected Articles	Digital Twins: Five Indicators in Response to Climate Change					Ref. No. From Table 1
		Energy Efficiency Optimization	Renewable Energy Integration	Design and Retrofitting	Real-Time Monitoring and Control	Predictive Maintenance
1	Urban digital twins (UDT)		√	√	√		[10,15,16,17,18,19,20,21,22,23,24,25,26]
2	Smart sustainable cities and DT	√	√	√	√	√	[51,52,53,54,55,56,57,58,59,60]
3	DT and building performance simulation (thermal comfort, energy efficiency, and energy consumption)	√		√	√		[3,28,29,30,31,32]
4	AI in green buildings	√	√	√	√		[45]
5	Definitions of digital twins (DT)	√		√	√		[2,66,67,68,69]
6	Digital twin cities (DTC)		√	√		√	[27]
7	DT for disaster management		√	√	√	√	[33]
8	DT in the operation and maintenance phase		√	√	√	√	[34]
9	DT and climate change adaptation		√	√	√	√	[61]
10	Building climate-neutral and resilient cities with UDT	√	√	√			[1]
11	DT in industries	√		√	√	√	[40,41,42,43]
12	Digital twins and built environments	√	√	√	√	√	[44]
13	Resilience assessment and DT			√	√	√	[39]
14	DT and the circular economy	√		√	√	√	[36]
15	AI and climate change	√	√	√	√	√	[47,48]
16	DT and energy systems on an urban scale	√	√	√			[65]
17	DT and metaphor			√	√		[35]
18	DT and intelligent green buildings	√	√	√	√	√	[49]
19	DT and sustainability transitions		√		√	√	[64]
20	DT integration with IoT, BIM, GIS, big data, and AI	√		√	√	√	[12,60,70,71,72,73,74,75,76]
21	DT and blockchain			√	√
22	Energy (DT) across the lifecycle and carbon emissions	√	√	√	√	√
23	Digitalization and decarbonization	√	√	√	√		[62,63]
24	Occupant behaviors and DT management	√		√	√		[38]
25	DT and nature-based solutions			√		√	[37]

), namely, energy efficiency optimization, renewable energy integration, design and retrofitting, real-time monitoring and control, and predictive maintenance.

The five climate-response indicators used to classify the digital twin applications—(1) energy efficiency optimization, (2) renewable energy integration, (3) design and retrofitting, (4) real-time monitoring and control, and (5) predictive maintenance—are adapted from frameworks established by the Intergovernmental Panel on Climate Change [5] and the International Energy Agency [80].

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Energy efficiency optimization refers to the use of digital models to reduce energy waste by simulating and enhancing building system performance.

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Renewable energy integration involves strategies that enable digital twins to facilitate the placement, sizing, and operation of solar, wind, or geothermal energy systems.

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Design and retrofitting focus on how digital twins can inform energy-efficient decisions during the design or upgrade of buildings.

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Real-time monitoring and control capture the ability of digital twins to use live data streams to manage and optimize energy usage dynamically.

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Predictive maintenance involves anticipating failures and inefficiencies through machine learning and data analytics, improving operational performance.

These indicators provide a structured framework to evaluate digital twin roles in climate mitigation and adaptation.

While this study does not conduct new experimental validation, several of the reviewed sources—such as [3,28]—report that digital twin technologies have led to energy efficiency gains in the range of 30–40% in monitored building systems. These findings, derived from prior empirical research, suggest strong potential for digital twins to serve as enabling technologies in the transition toward zero-energy buildings.

In addition, the review highlights that digital twins support broader smart city goals through system-wide data integration, enhanced urban decision-making, and energy-resilient infrastructure planning. Table 1 and Table 2 synthesize the thematic focus areas and climate-related indicators drawn from the literature. Rather than proving causality, this study organizes existing findings to map the current landscape, guide future inquiry, and contextualize the application of digital twins in high-risk urban environments, with a focus on the Middle East.

Using the extracted data from this systematic review attempted to answer the following research questions:

Question 1: In response to climate change, how can the digital twin play a role in constructing zero-energy buildings?

As buildings transition to net-zero energy, digital twins can enable efficient energy management, optimize renewable energy utilization, and support sustainable design and operation. In order to achieve net-zero-energy buildings, digital twins play a key role:

• Energy efficiency optimization: Building systems can be modeled and simulated with digital twins, enabling energy savings and the optimization of energy consumption. The digital twin can monitor and analyze real-time data to identify inefficiencies and enhance energy efficiency by integrating real-time data from sensors and IoT devices [81].
• Renewable energy integration: Renewable energy sources can be integrated into building systems with the help of digital twins. Digital twins can optimize the placement, sizing, and operation of renewable energy technologies, such as solar panels, wind turbines, and geothermal systems, to maximize energy generation and minimize fossil fuel use.
• Design and retrofitting: In order to achieve net-zero-energy goals, digital twins can assist in the design and retrofitting of buildings. A digital twin can help architects and engineers to make informed decisions about building orientation, insulation, HVAC systems, and other energy-efficient features, by simulating different design scenarios and evaluating their energy performance. In this way, energy consumption is optimized throughout the lifecycle of the building.
• Real-time monitoring and control: The real-time monitoring and control of building systems is enabled by digital twins. With the integration of data from sensors, meters, and energy-management systems, digital twins can reveal energy usage patterns, detect anomalies, and enable proactive energy management. As a result, energy efficiency can be maintained, and areas for improvement can be identified [82].
• Predictive maintenance: To enhance energy efficiency, digital twins can support predictive maintenance strategies. Digital twins can predict equipment failures, optimize maintenance schedules and prevent energy waste due to malfunctioning systems, by analyzing real-time data and using machine learning algorithms. The energy efficiency of buildings is improved as a result.

To summarize the answer to this question, digital twins can be used to achieve net-zero-energy buildings. As part of the transition to energy-efficient and sustainable buildings, digital twins improve energy efficiency, integrate renewable energy sources, support design and retrofitting, enable real-time monitoring and control, and facilitate predictive maintenance.

Question 2: How can we achieve the maximum response to climate change through the use of digital twins to build zero-energy buildings?

The key strategy for achieving net-zero-energy buildings through the use of digital twins is decarbonization and energy efficiency. By optimizing energy efficiency, digital twins can help to decarbonize buildings. Using virtual twins and real-time data, digital twins can identify energy-saving opportunities, monitor energy consumption patterns, and suggest adaptations to improve efficiency. Building design, construction, and operation stakeholders can maximize the response to climate change by utilizing the capabilities of digital twins in these areas. Data-driven decision-making is made possible by digital twins, which enable a holistic understanding of building performance. As a result, greenhouse gas emissions are reduced, energy resources are efficiently used and net-zero-energy buildings are created. In summary, digital twins have the potential to drive the transition towards sustainable and energy-efficient buildings, contributing to the achievement of climate change and net-zero-energy buildings [83].

Question 3: In order to achieve smart cities and zero-energy communities, what are the parameters that should be included in the design framework for using digital twins?

It is important to consider several parameters when designing a framework for net-zero-energy buildings.

Table 3. The correlation between digital twins, smart cities, and zero-energy buildings as a response to climate change.

The Correlation Between Digital Twins, Smart Cities, and Zero-Energy Buildings as a Response to Climate Change
Design and retrofitting for zero-energy buildings	Aspect 1: Digital twins and building performance			Climate change adaptation and mitigation strategies
	A-Real-time monitoring and control phase		Stakeholders and the private sector
		a.1. Energy efficiency optimization (efficient lighting systems, etc.)
		a.2. Passive heating and cooling strategies
		a.3. Energy-efficient equipment and utilities
		a.4. Water management
	B-Operation and maintenance phase
		b.1. Building envelope
		b.2. Waste management
		b.3. Lifecycle assessment
		b.4. Optimization based on occupancy level
Smart cities	Aspect 2: Urban digital twins
	A-Climate change adaptation		Government organization
	B-Disaster management
	C-Digital twins and nature-based solutions
Sustainable energy production	Aspect 2: Digital twins and renewable energy production
	A-Renewable energy integration		Investors
	B-Predictive maintenance

shows the correlation between digital twins, smart cities, and zero-energy buildings as a response to climate change, and includes three aspects with respect to the building level, the urban level, and sustainable energy production.

Table 3 illustrates the integration of digital twins at three different levels in relation to climate change adaptation and mitigation strategies. At the building level, the digital twin engages in the new design or retrofitting of existing buildings under the aspect of “digital twin and building performance.” This level uses the digital twin concept as a tool with which to support the design and the building with real-time monitoring and control, from conception to occupancy. In addition, all automation, devices, equipment, and utilities can be monitored and controlled in real time to achieve energy efficiency optimization. It is also imperative to note that the operation and maintenance of a building includes materiality, waste management, and lifecycle considerations [81,82,83,84].

On the urban level, digital twins can be utilized to support and develop several protocols by predicting various climate scenarios in the future. In this way, several extreme events can be addressed using action plans. The nature-based solutions can provide different urban solutions for extreme climatic phenomena, such as heatwaves or urban heat islands.

In addition to the digital twin, renewable energy production is a third aspect. A digital twin can enable sustainable renewable energy production to be managed and transferred at both building and urban levels.

Climate change adaptation and mitigation strategies can be affected by the aforementioned aspects. Furthermore, this will reflect the positive impact that our cities will have on the transformation into smart cities, which is one of the country’s 2030 visions. The implementation of each country’s 2030 vision cannot be accomplished without stakeholders, the private sector, investors, and governmental organizations as a whole.

A holistic approach should be taken to consider these parameters, adapted to the specific needs and context of each project. It is possible to achieve net-zero-energy buildings by incorporating these parameters into the design framework, thus significantly reducing energy consumption and the impact on the environment.

ZEBs as Pillars of Smart City Development

Zero-energy buildings (ZEBs) play a critical role in realizing the operational goals of smart cities. Their integration facilitates demand-side energy flexibility, enhances building–grid interaction, and supports distributed renewable energy generation. Digital twins amplify this potential by enabling real-time performance modeling, predictive energy analytics, and lifecycle energy management. As urban areas pursue resilience and climate neutrality, ZEBs function not in isolation but as interconnected nodes within the broader smart-city ecosystem—supporting adaptive infrastructure, environmental monitoring, and user-responsive design. Commentary can be added on how digital twins and ZEBs align with Middle Eastern national visions (e.g., Bahrain, UAE, Saudi Arabia), especially in relation to energy diversification and smart infrastructure.

5. Future Work

Future research should build upon this study’s conceptual framework by developing formal computational models and simulation environments to test and validate digital twin strategies for climate-responsive urban development. While this paper provides a high-level synthesis supported by a conceptual pseudocode workflow, upcoming studies should aim to translate these frameworks into algorithmic structures, supported by real-time data streams and validated against real-world performance.

Specifically, future work could involve the following:

• Designing and implementing machine learning algorithms that predict building energy behavior in response to dynamic climatic and occupancy conditions.
• Integrating real-time sensor networks with cloud-based digital twin platforms, enabling adaptive control strategies and energy optimization in zero-energy buildings.
• Developing modular simulation models tailored to different building types and urban morphologies, particularly in hot–arid regions like the Middle East.
• Assessing the computational performance and scalability of these models across neighborhood or city-wide digital twin platforms.
• Exploring the use of generative design algorithms in tandem with digital twins to simulate retrofitting or energy upgrade scenarios for existing building stock.

By formalizing the computational underpinnings of digital twin applications, future research can bridge the gap between concept and implementation, ensuring both technical robustness and policy relevance in the transition to climate-resilient smart cities.

6. Conclusions

This study presents a comprehensive synthesis of the emerging role of digital twin technologies in climate-responsive urban development, with a specific focus on their integration into zero-energy buildings (ZEBs) within smart city frameworks. By conducting a systematic literature review spanning a decade (2014–2024), the research identifies how digital twins contribute to energy efficiency, real-time performance monitoring, and predictive modeling—crucial elements in both mitigating and adapting to the impacts of climate change.

The findings underscore the transformative potential of digital twins in supporting sustainable urban planning and advancing climate action. Particularly in the Middle Eastern context, where extreme weather events and rapid urbanization pose significant challenges, digital twins offer a robust platform for embedding renewable energy systems, optimizing building energy use, and enhancing urban resilience. The study also highlights critical parameters—technical, legal, and regulatory—that must be addressed to ensure the effective deployment of digital twins in ZEB design and broader smart city strategies.

Nevertheless, the analysis reveals several barriers hindering widespread implementation, including issues of system interoperability, data privacy and governance, and the demand for high-performance computational infrastructure. Overcoming these challenges will require coordinated, cross-sectoral efforts involving policymakers, urban planners, engineers, and architects, alongside the development of inclusive regulatory frameworks that support standardization and data sharing.

In conclusion, digital twins represent a pivotal tool in achieving climate-resilient, zero-energy urban environments. Central to this vision is the strategic integration of ZEBs into smart city frameworks, where digital twins enable real-time energy modeling, efficient resource use, and decentralized energy autonomy. Their successful deployment will require cross-disciplinary collaboration, policy innovation, and empirical testing to align digital capabilities with urban sustainability goals.