[Consolidated Reply] Query 8: Leveraging Digital Twins for Climate Action & Resilience in Asia-Pacific

Digital twins are revolutionizing climate issues and actionable tasks by creating high-fidelity, dynamic virtual replicas for real-time environments. The digital twins technology integrates real-time data, advanced simulations, adopts AI/ML and provides predictive analysis. Based on the latest developments in climate technology digital twin-based solutions are increasingly being developed to address climate change, and used to intersect with real-time data environments and to create futuristic learning models. Climate Technologies deployment by help of Digital Twins are used across global Earth systems for urban infrastructures. In climate technologies, the digital twins serve as the bridge between complex Earth sciences ecosystems and actionable decision-making. Digital twins are designed in such a way to represent the operational overwrought system for climate. These technologies adopted for a particular region, re-transforms a climate model about a known place for environmental prediction. These usually prepare and respond to climate data and informed prior before disaster.

This idea fundamentally changing our capacity to safeguard communities and economies in an era of climate volatility. The integration of AI, high-performance computing, and IoT creates a dynamic feedback loop where digital twins infrastructure learns, adapts, and optimizes itself against evolving climate risk. For companies and organizations deploying climate technologies, usually performing on an integrated digital twin, which has capabilities to build & adaptive capacity, well before facing escalating vulnerability to climate shocks which traditional planning tools cannot anticipate.

See the visual below, it captures the urgency and innovation behind climate resilience efforts powered by digital twins. This image captures a futuristic control room where planners and policymakers interact with holographic maps of Asia-Pacific. Real-time overlays show flood risks, heatwave zones, and sea level rise projections. Augmented reality (AR) dashboards integrate IoT, AI, and satellite data, supporting predictive models for urban flood management, coastal protection, and infrastructure planning.

A dashboard based system could be like a dynamic, data-rich control room overlooking a stylized map of Asia-Pacific. On the screens showing the following tools: (1) Digital twins of cities pulse with real-time data for flood simulations, heatwave alerts, coastal erosion etc.; (2) IoT feeds and satellite overlays stream into holographic dashboards; (3) Planners and policymakers interact with the models using AR interfaces, adjusting infrastructure plans and emergency protocols.

Digital twins are being used to simulate environmental systems, such as urban-heat islands, coastal flooding, and Fogs/Haze emissions management. Also, to test mitigation strategies and support net-zero goals. These models allow researchers to interact with real-time data and explore the impact of different climate policies or technologies. The digital twin tools help visualize long-term climate scenarios and support evidence-based decision-making.

From a digital delivery and BIM implementation perspective, I’d like to share a few practical observations relevant to climate resilience use cases in the Asia-Pacific context.

Digital Twin Applications Supporting Climate Decision-Making

In infrastructure and urban projects, Digital Twin approaches are increasingly being used in the following ways:

1) Urban Flood & Stormwater Management: Integration of BIM + GIS + hydraulic simulation models • Scenario testing for extreme rainfall events • Visualizing flood depth impact on transport corridors and utilities • Supporting early-stage planning decisions (e.g., drainage redesign, retention basins).

2) Transport & Critical Infrastructure: Linking asset models with climate exposure data (heat stress, inundation risk) • Lifecycle analysis of materials under extreme temperature scenarios • Simulation of disruption impacts on mobility networks.

3) Energy & Utilities: Monitoring asset performance under climate stress (transformer overheating, grid vulnerability) • Digital replicas for predictive maintenance in high-risk coastal zones.

In many cases across Asia-Pacific, the most effective twins are not city-wide “mega platforms,” but targeted infrastructure twins focused on specific risks.

Deployment & Scaling Challenges in the Region

From implementation experience, three recurring constraints shape outcomes:

1) Data Maturity. Many organizations lack structured asset data. Without standardized data environments, scaling is difficult. A Digital Twin built on fragmented data becomes a visualization layer — not a decision system.

2) Institutional Ownership. Climate resilience spans water, transport, energy, and urban authorities. Without clear governance ownership, Digital Twins remain pilot initiatives.

3) Human Capacity. The challenge is rarely technology. It’s interdisciplinary expertise — combining climate modelling, infrastructure engineering, and digital systems integration.

What Actually Enables Scaling

Based on practical delivery insights: (1) Start with a single high-impact climate risk use case, not a city-wide ambition; (2) Define measurable resilience KPIs (flood downtime reduction, energy reliability improvement); (3) Integrate Digital Twin outputs directly into planning and budgeting cycles; and (4) Build internal capability — not just external consultants.

Digital Twins for climate resilience must be framed as a governance and risk management tool, not a technology showcase.

Closing Thought

In the Asia-Pacific context, where climate exposure is accelerating, the value of Digital Twins lies less in visual sophistication and more in their ability to: (1) Support scenario-based decision making; (2) Improve inter-agency coordination; and (3) Link long-term climate risk with asset lifecycle planning.

The shift needed is from “building a twin” to “embedding digital resilience into decision systems.”

I would be very interested in learning how others in the region are addressing institutional alignment and long-term funding models for these platforms.

Digital Twins as Decision-Support Systems for Climate Resilience

A consistent theme across contributions is that Digital Twins deliver maximum value when embedded in operational and governance systems rather than functioning as standalone dashboards.

Across the Asia-Pacific region, DTs are increasingly used to: (1) Support scenario-based climate risk modelling; (2) Integrate BIM, GIS, IoT, satellite, and hydraulic datasets; (3) Simulate extreme weather events (heatwaves, typhoons, storm surges, flooding); (4) Link climate projections with asset lifecycle management; and (5) Inform capital investment and retrofit decisions.

Members emphasized that the most effective Digital Twins are often targeted, sector-specific systems, rather than city-wide mega platforms.

Sectoral Applications Across the Region

(a) Urban Flood & Stormwater Management: In Singapore, the Virtual Singapore platform evolved into a functional urban Digital Twin integrating LiDAR-based 3D city models, rainfall radar data, drainage networks, and hydrodynamic simulation tools. Its predictive flood mapping capabilities directly informed pump operations, drainage upgrades, and zoning decisions. Similarly, municipal-scale Digital Twin systems in the Republic of Korea have integrated satellite precipitation forecasts, IoT river-level sensors, and AI-based runoff modelling. By simulating multiple rainfall-return scenarios, authorities improved early-warning systems and pre-emptive infrastructure operations. Key Insight: Digital Twins are most impactful when connected to real-time operational systems (e.g., sluice gates, pumps) rather than remaining analytical planning tools.

(b) Coastal Protection & Sea-Level Rise: In Australia, coastal Digital Twins have simulated sea-level rise, storm surges, and erosion impacts on transport corridors and port infrastructure. High-resolution bathymetry, satellite altimetry, and AI-enhanced modelling support long-term relocation and adaptation strategies. In Pacific Island contexts, simplified cloud-based DT frameworks have integrated drone imagery and satellite data to guide ecosystem-based adaptation measures such as mangrove restoration. Key Insight: Even modular, lower-cost Digital Twin architectures can meaningfully support climate adaptation if designed around clear risk scenarios.

(c) Energy & Infrastructure Resilience: In Japan, smart-grid Digital Twins combine weather forecasts, demand prediction algorithms, and grid asset monitoring to manage heatwave-induced peak loads and reduce blackout risks. In India, pilot Digital Twin initiatives in metro rail, ropeways, townships, and cold storage projects have extended beyond 3D/5D BIM into 6D–9D BIM applications: 6D/7D for energy modelling for heatwave resilience and cooling system optimization; 8D for disaster and extreme-environment scenario simulations; 9D for lean construction, waste reduction, and embodied carbon minimization. Key Insight: Climate-aligned BIM must extend into operations and maintenance phases to unlock resilience benefits.

What Enables Effective Deployment?

Across case experiences, three enabling dimensions emerged:

1) Data Maturity and Integration: Structured asset data environments are foundational. Fragmented or siloed datasets reduce DTs to visualization layers rather than decision systems. Integration of BIM + GIS + climate models + IoT is essential. Standardized data governance frameworks and cybersecurity protocols are critical before scaling.

2) Institutional Ownership and Governance Alignment: Digital Twins for climate resilience span multiple sectors — water, transport, energy, urban planning. Without clear institutional ownership, platforms remain pilot projects; budget allocation becomes uncertain; and inter-agency coordination weakens. Successful cases embedded DTs into national smart city strategies or disaster management authorities, ensuring sustained funding beyond innovation grants. Members stressed that Digital Twins must be framed as governance and risk-management tools, not technology showcases.

3) Human Capacity and Interdisciplinary Expertise: A recurring constraint across the Asia-Pacific region is the shortage of professionals who can bridge climate science, infrastructure engineering, data analytics, and digital systems integration. Over-reliance on external consultants can limit knowledge transfer. Effective approaches include university–government collaborations, municipal capacity-building programs, and phased sector-based deployment before multi-sector expansion.

4) Financing and Sustainability Models: Financing experiences reveal that initial DT systems are often supported by public innovation funds or climate adaptation programs; public–private partnerships reduce upfront capital costs; cloud-based subscription models enable smaller municipalities to access advanced modelling; and multilateral development banks increasingly recognize DTs as climate-risk assessment tools. However, long-term operational financing remains challenging because benefits are often measured in avoided losses rather than direct revenue generation.

Scaling Challenges:

Members identified persistent constraints: (1) 80% of BIM use still stops at 5D (costing), limiting lifecycle resilience integration; (2) Absence of globally standardized frameworks integrating BIM and Climate Change Technologies beyond project delivery (e.g., ISO 19650 focuses primarily on information management); (3) Fragmented data ownership across ministries and agencies; (4) Limited alignment between climate modelling timelines and infrastructure budgeting cycles; and (5) Scaling requires moving from isolated pilots to institutionalized digital resilience systems.

Strategic Lessons for Asia-Pacific:

Across contributions, several regional lessons stand out: (1) Start with a high-impact, clearly defined climate risk use case; (2) Define measurable resilience KPIs (e.g., reduced flood downtime, improved energy reliability); (3) Embed Digital Twin outputs into planning and capital budgeting processes; (4) Invest in institutional alignment before technological sophistication; (5) Prioritize modular, cloud-based architectures in resource-constrained contexts; (6) Treat Digital Twins as long-term governance infrastructure.

Conclusion:

The Asia-Pacific region is highly climate-exposed and infrastructure-intensive. Digital Twin technologies offer significant potential to: (1) Enhance scenario-based climate decision-making; (2) Strengthen inter-agency coordination; (3) Integrate climate risk into asset lifecycle planning; and (4) Reduce disaster losses and improve system reliability.

Yet technology alone is insufficient. The long-term success of Digital Twins for climate action will depend on: (1) Institutional embedding; (2) Sustainable financing; (3) Data governance frameworks; and (4) Human capacity development.

Digital Twin (DT) technologies are increasingly emerging as decision-support systems rather than mere visualization platforms in the Asia-Pacific region. From my professional engagement in climate resilience planning, infrastructure modelling, and interdisciplinary policy consultations, I have observed that successful Digital Twin deployments share three characteristics: (i) integration of multi-source real-time data, (ii) institutional ownership beyond pilot funding cycles, and (iii) clear linkage to operational decision-making.

Below, I provide situation- and example-based insights drawn from regional experiences.

Digital Twin Applications Supporting Climate Resilience Decision-Making

(a) Urban Flood Management – Singapore and Republic of Korea

In Singapore, the Virtual Singapore platform evolved into a functional urban Digital Twin integrating LiDAR-based 3D city models, drainage sensor networks, rainfall radar data, and hydrodynamic simulation tools. During high-intensity rainfall events, the DT system enabled predictive flood mapping at sub-district resolution. Rather than serving only as a planning tool, it directly informed drainage capacity upgrades, pump station operations, and land-use zoning adjustments in flood-prone zones.

Similarly, in the Republic of Korea, municipal-scale Digital Twin flood forecasting systems have been deployed in coastal cities vulnerable to typhoons. By integrating satellite precipitation forecasts, IoT river-level sensors, and AI-based runoff modelling, authorities were able to simulate multiple rainfall-return scenarios (e.g., 50-year and 100-year events). The outputs supported early-warning protocols and pre-emptive gate operations in estuarine barrages. Importantly, this reduced response time and minimized economic losses during extreme events.

Key Lesson: Digital Twins became effective when directly connected to operational control systems (e.g., sluice gates, pumping infrastructure), rather than functioning only as scenario-planning dashboards.

(b) Coastal Protection and Sea-Level Rise – Australia and Pacific Island States

In Australia, Digital Twin applications for coastal cities such as Sydney and Melbourne have been used to simulate sea-level rise scenarios combined with storm surge modelling. High-resolution bathymetric data, satellite altimetry, and AI-enhanced coastal erosion models enabled infrastructure planners to evaluate long-term relocation strategies for transport corridors and port infrastructure.

In selected Pacific Island contexts, simplified Digital Twin frameworks were developed for coral reef monitoring and shoreline change prediction. While resource constraints limited high-end modelling, cloud-based platforms allowed integration of drone imagery and satellite data to guide ecosystem-based adaptation strategies (e.g., mangrove restoration placement).

Key Lesson: Even resource-limited Digital Twins can meaningfully inform climate adaptation if built on modular and scalable digital architectures.

(c) Energy and Infrastructure Resilience – Japan and India

Japan has advanced the integration of Digital Twins within smart-grid systems to manage heatwave-induced peak loads. By combining weather forecasts, demand prediction algorithms, and grid asset monitoring, utilities simulated stress scenarios and optimized distributed energy storage dispatch. This reduced blackout risks during extreme heat events.

In India, pilot Digital Twin initiatives for metro rail systems and urban transport infrastructure have incorporated climate stress testing (flood exposure, heat stress on tracks). Scenario modelling informed drainage retrofits and material resilience upgrades. Although still evolving, these systems demonstrated how infrastructure Digital Twins can bridge climate modelling with asset management planning.

Key Lesson: Sector-specific Digital Twins are most impactful when aligned with asset lifecycle management and capital investment planning.

Policy Frameworks, Financing Approaches, and Institutional Capacity

From experience in policy advisory and cross-sector engagement, three enabling dimensions have shaped successful deployment:

(a) Policy and Governance Integration

Digital Twin scaling requires institutional embedding within urban planning or disaster management authorities. In Singapore and Korea, DT systems were integrated into national smart city strategies, ensuring sustained budget allocation beyond pilot phases. A common barrier in developing Asia-Pacific contexts has been fragmented data governance. Ministries responsible for water, transport, and environment often maintain siloed datasets, limiting interoperability. Establishing national data-sharing frameworks and cybersecurity protocols proved essential before technical deployment could scale.

(b) Financing and Public-Private Collaboration

Blended financing models were instrumental. Initial DT platforms were often supported through public innovation grants or climate adaptation funds, while private technology providers contributed through public–private partnerships (PPP). In some cases, cloud-based subscription models reduced upfront infrastructure costs, enabling smaller municipalities to access advanced modelling capabilities without heavy capital investment. Multilateral development banks increasingly recognize Digital Twins as climate-risk assessment tools eligible under adaptation finance frameworks. However, long-term operational funding remains a challenge when benefits are diffuse and indirect (e.g., avoided disaster losses rather than immediate revenue generation).

(c) Human Expertise and Institutional Capacity

A major constraint across the Asia-Pacific region is not hardware or software, but technical capacity. Digital Twin systems require interdisciplinary expertise — climate science, data engineering, AI modelling, and urban planning. Where local expertise was limited, over-reliance on external consultants reduced knowledge transfer and sustainability. Successful cases invested in: (1) Capacity-building programs within municipal agencies; (2) University–government research collaborations; and (3) Open-data training initiatives for public officials. Resource-limited environments benefited from phased deployment — starting with single-sector pilots (e.g., flood management) before expanding to multi-sector Digital Twins.

Concluding Perspective

The Asia-Pacific experience demonstrates that Digital Twins are most transformative when positioned as decision-support ecosystems rather than visualization tools. Their effectiveness depends less on technological sophistication and more on governance integration, institutional ownership, and human capacity development.

For inclusive regional scaling, priority actions should include: (1) Standardized data governance frameworks; (2) Regional knowledge-sharing platforms to reduce duplication; (3) Capacity-building partnerships between universities, governments, and industry; and (4) Modular, cloud-based Digital Twin architectures suitable for resource-constrained contexts.

Digital Twin technologies hold substantial promise for strengthening climate resilience in Asia-Pacific cities. However, their long-term impact will depend on aligning technological innovation with institutional reform and sustainable financing mechanisms.

With over a decade of experience in the BIM field and prior knowledge in Climate Change Technologies, I have observed that while we are already using BIM (Digital Twins), we are not leveraging its full potential — particularly in addressing climate change. However, in several projects, we have begun applying BIM within the climate change context.

With Metros, Ropeways, Mega Townships & Cold Storage Projects

We have progressed projects beyond simple 3D/5D models into 6D–9D BIM which try to address climate-related challenges:

1) Energy & Thermal Resilience (6D/7D): In cold storage and metro projects, we utilize energy modelling to simulate the impact of heatwaves on cooling systems. By developing Digital Twins enriched with historical heatwave data, we simulate energy consumption patterns and predict potential system failures before the design stage.

2) Safety & Disaster Planning (8D): For ropeways and large townships, we apply 8D BIM to simulate extreme environmental scenarios. These simulations support site selection and feasibility assessment, and guide design retrofits and execution strategies aligned with climate risks.

3) Waste & Carbon Reduction (9D): Through Lean 9D BIM and modular construction approaches, we have reduced material waste and the carbon footprint of large warehouses and township developments.

Scaling-Up Challenges

Most projects stop at 5D (costing). Approximately 80% of the industry still treats BIM as a static construction tool. For meaningful climate resilience, BIM must be implemented through the operations phase.

1) The Global Standards Gap. There is no ISO-style standard that integrates BIM with Climate Change Technologies. Currently, practices vary widely, limiting scalability. Although ISO 19650 exists, it primarily addresses information management up to project delivery and does not sufficiently cover post-execution BIM implementation for operational climate performance.

2) The Talent Shortage. There is a significant shortage of professionals who understand both construction engineering and climate science, and who can use complex climate data into practical engineering solutions.

From experience working with climate resilience systems for local governments in the Philippines, many current Digital Twin implementations are primarily used for visualization and simulation in planning environments. These systems typically integrate geospatial datasets, hazard models, and infrastructure layers to help planners evaluate flood risks, infrastructure exposure, and climate scenarios. However, these platforms often rely on specialized analytical tools used mainly by technical teams, which can limit their direct use by frontline decision-makers during emergencies.

In practice, a gap was observed between analytical digital twin environments and operational decision-making on the ground. Resource constraints, fragmented institutional data, and limited technical capacity make it difficult for local governments to use complex modelling platforms in real-time response situations. Work focuses on bridging this gap by translating environmental signals, infrastructure data, and incident information into operational decision-support tools that can be used by local responders and government agencies. This approach prioritizes accessibility, interoperability, and practical deployment in resource-constrained environments.

Dear members,

My team has worked substantially with establishing digital twins for cyber-physical systems and we are just starting to use them for Sustainable Water-based Cooling in Megacities. This will start in Singapore but we expect to establish collaboration with others in the Asia-Pacific area.

Sustainable Water-based cooling in Megacities — Singapore and Denmark pioneer

APCTT Input 1

Dear members,

Many thanks for your interesting and informative responses. Here are some points I would like to bring to your notice.

Digital Twins (DT) in climate resilience in the Asia-Pacific region are no longer just isolated models but rather integrated decision support systems. However, there are some gaps in the region, especially in linking global climate scenarios, multi-hazard risks, and social vulnerability to operationalise decisions.

Recent breakthroughs in the development of ‘system of systems’ and Earth-scale Digital Twins provide insights into using high-resolution climate models with city-level and infrastructure-level Digital Twins. This allows cities to test climate adaptation options (e.g., drainage systems, land use planning, retrofits) under consistent long-term climate scenarios, rather than responding to single events. Therefore, in the Asia-Pacific region, sector-specific Digital Twins (e.g., flood, heat) can utilise standardised regional climate projections to assess the full spectrum of risks.

Currently, there is greater understanding that Digital Twins can tackle various climate-related hazards and cross-sector systems, including water, energy, transportation, and coastal zones. This is because the risk of climate-related disasters tends to cascade, especially in megacities and small island states.

Another shift in the development of Digital Twins in the Asia-Pacific region is the need to incorporate ‘with and for humans’ Approach. DTs integrating social vulnerability and participatory decision support should be able to compare options such as grey infrastructure (e.g., levees) and nature-based solutions (e.g., mangroves, green corridors, cooling centres) not only in terms of risk mitigation but also in terms of equity.

Additionally, I feel that the scaling up of Digital Twins is more an institutional and financial issue than a technological one. Evidence suggests that DTs need to be seen as part of a broader “digital climate risk infrastructure,” moving beyond pilot projects to full-fledged governance, validation, and public accountability systems. Governments should seek to advance the uptake of Digital Twins by ensuring that DT outputs are used to inform planning tools such as environmental impact assessments, building codes, zoning laws, and public investment plans, with clear definitions on data ownership, management, and updates.

Finally, Digital Twins can be positioned as a new form of digital public infrastructure providing standardised risk metrics, scenario analyses, and monitoring data justify resilient design choices and quantifying avoidable losses for blended finance. This would require structured “DT-readiness” pathways to build geospatial and climate-risk literacy, integration skills, and cross-disciplinary expertise within local and national institutions.

Thank you

References

1. Zhu, M., & Jin, J. (2025). Data-driven urban digital twins and critical infrastructure under climate change: A review of frameworks and applications. Urban Planning, 10(3).

2. Ali, E., & Mansour, A. (2025). Digital twin for climate resilience: Transforming smart cities for a sustainable future. ISPRS Archives XLVIII-G-2025, 139–146.

3. Bauer, P. et al. (2024). Digital twins of the Earth with and for humans. Communications Earth & Environment, 5, 343.

4. Tan, B. et al. (2024). Assessing governance implications of city digital twin technology. Urban Analytics Lab / TFSC Digital Twin Maturity Study.

5. Satria, A. et al. (2025). Integrating local knowledge and governance for effective climate resilience. 3(2), 45–60.

6. United Nations Development Programme (2025). Closing the finance gap: New routes to climate resilience in Asia-Pacific.

7. International Monetary Fund (2024). Unlocking Climate Finance in Asia Pacific: Transitioning to a Resilient Future (Departmental Paper).

APCTT Input 2

Just wanted to share one more famous case from Singapore:

Grid Digital Twin for Energy Resilience

The Energy Market Authority (EMA) and SP Group have implemented a Grid Digital Twin to manage the increasing volatility of the energy sector driven by climate change and the transition to renewables.

This DT consists of two layers: an Asset Twin for monitoring the health of physical infrastructure (transformers, cables) and a Network Twin for simulating power flow. As extreme heatwaves drive up cooling demand, the twin predicts grid stress points, allowing for proactive maintenance and preventing localized blackouts.

To meet climate targets, Singapore is rapidly scaling solar energy and EV charging. The Digital Twin acts as a Virtual Power Plant (VPP), simulating the impact of intermittent renewable sources on the grid. This ensures that even during adverse weather conditions, the energy system remains stable and resilient.

Many thanks to all who contributed to this query!

This Consolidated Reply is a systematic compilation of all responses received and additional desk research. If you have further information to share on this topic, please send it at [email protected].