Interview: practitioners on Digital twins for infrastructure & industry — what they wish they knew earlier

Organizations deploying digital twins across Asia-Pacific infrastructure projects are reporting 15-25% reductions in operational carbon emissions and 20-35% decreases in unplanned downtime—yet a 2024 McKinsey analysis reveals that nearly 60% of digital twin implementations fail to achieve their projected ROI within the first three years. This stark disparity between success stories and widespread underperformance underscores a critical knowledge gap: practitioners who have navigated both triumph and failure possess hard-won insights that no vendor whitepaper can provide.

In conversations with infrastructure engineers, sustainability directors, and digital transformation leads across Singapore, Japan, Australia, and South Korea, a consistent pattern emerges. The difference between digital twin deployments that deliver measurable sustainability impact and those that become expensive data visualization tools lies not in technology selection, but in how teams define success metrics from day one, structure their MRV (Measurement, Reporting, and Verification) frameworks, and maintain organizational alignment throughout multi-year implementation cycles.

Why It Matters

The global digital twin market reached USD 16.8 billion in 2024 and is projected to exceed USD 110 billion by 2030, with Asia-Pacific representing the fastest-growing regional segment at a compound annual growth rate of 37.5%. This explosive growth is driven by intensifying regulatory pressure—including Singapore's mandatory building energy benchmarking, Japan's Green Growth Strategy, and Australia's Climate Active certification requirements—combined with the recognition that infrastructure accounts for approximately 79% of global greenhouse gas emissions when considering both embodied and operational carbon.

For practitioners in the Asia-Pacific region, digital twins represent a convergence of necessity and opportunity. The 2024 IPCC Sixth Assessment Report emphasizes that infrastructure decisions made between 2025 and 2035 will lock in emissions pathways for 50-100 years. Digital twins offer the capability to model, simulate, and optimize these long-lived assets before construction begins and throughout their operational life. However, the technology's potential remains unrealized without rigorous attention to the KPIs that genuinely drive sustainability outcomes.

"We spent eighteen months building what we thought was a world-class digital twin for a port facility in Vietnam," recalls a senior project manager at a multinational engineering consultancy. "Beautiful 3D visualization, real-time sensor integration, the whole package. But when our sustainability team asked for lifecycle carbon data, we realized we'd optimized for operational visibility without ever defining what 'good' looked like for emissions reduction. That was an expensive lesson in the importance of upfront KPI definition."

Key Concepts

Digital Twins represent virtual replicas of physical assets, systems, or processes that are continuously updated through real-time data streams from IoT sensors, building management systems, and external data sources. Unlike static BIM (Building Information Modeling) models, digital twins maintain bidirectional data flows that enable both monitoring and predictive simulation. In infrastructure contexts, digital twins typically encompass structural systems, energy flows, material inventories, and operational parameters.

MRV (Measurement, Reporting, and Verification) constitutes the methodological framework through which sustainability claims are quantified, documented, and independently validated. For digital twin implementations, MRV protocols determine how sensor data translates into emissions calculations, what uncertainty bounds apply to model predictions, and how results are audited against recognized standards such as ISO 14064 or the GHG Protocol.

Benchmark KPIs are standardized performance metrics that enable comparison across facilities, portfolios, and industry sectors. In digital twin contexts, benchmark KPIs might include energy use intensity (EUI measured in kWh/m²/year), carbon intensity (kgCO₂e/m² or kgCO₂e/unit output), predictive maintenance accuracy (percentage of failures predicted within specified timeframes), and model fidelity (deviation between predicted and actual measurements).

OPEX (Operational Expenditure) refers to ongoing costs required to maintain and operate assets, distinct from capital expenditure. Digital twins directly impact OPEX through energy optimization, predictive maintenance reducing repair costs, and automated compliance reporting. Leading implementations demonstrate OPEX reductions of 10-20% within 24 months of deployment.

LCA (Life Cycle Assessment) provides a systematic methodology for evaluating environmental impacts across an asset's entire lifespan—from raw material extraction through manufacturing, construction, operation, and end-of-life disposition. Digital twins enhance LCA accuracy by providing granular operational data and enabling scenario modeling for maintenance interventions, material replacements, and eventual decommissioning strategies.

Verification in the context of digital twins encompasses both technical validation (confirming that models accurately represent physical systems) and sustainability verification (third-party attestation that emissions calculations and sustainability claims meet established standards). Robust verification protocols are essential for digital twins used in carbon credit generation, regulatory compliance, or ESG reporting.

What's Working and What Isn't

What's Working

Phased Implementation with Clear Milestone KPIs: Practitioners consistently emphasize that successful digital twin deployments begin with narrowly scoped pilots targeting specific, measurable outcomes. A water utility in Melbourne deployed their initial digital twin across a single pumping station, targeting a 15% reduction in energy consumption within six months. By achieving 18% reduction and documenting the methodology, they secured executive buy-in for network-wide expansion. "Start with a problem everyone agrees needs solving, define success numerically, and deliver fast," advises their digital infrastructure lead.

Integration of Operational and Embodied Carbon Tracking: Organizations achieving the highest sustainability impact treat digital twins as unified platforms for both operational and embodied carbon management. Singapore's Housing & Development Board (HDB) has integrated material passport data with operational monitoring across several public housing developments, enabling whole-life carbon optimization that accounts for maintenance material selections alongside energy efficiency. This integrated approach has demonstrated 12-18% improvements in lifecycle carbon performance compared to operational-only optimization.

Cross-Functional Governance Structures: Digital twin implementations that establish joint ownership between IT, operations, sustainability, and finance teams consistently outperform those housed within single departments. A Japanese manufacturing conglomerate created a dedicated "Digital Twin Center of Excellence" reporting directly to the Chief Sustainability Officer, with embedded representatives from each business unit. This structure ensures that sustainability KPIs receive equal weighting with operational efficiency metrics in all platform development decisions.

What Isn't Working

Technology-First Implementation Without Outcome Definition: The most common failure mode involves organizations selecting digital twin platforms based on feature comparisons rather than alignment with specific sustainability objectives. "We evaluated six vendors based on visualization capabilities and integration APIs," admits an infrastructure director at an Australian property developer. "Only after implementation did we realize none of them supported the carbon accounting methodology required by our NABERS commitments. We spent another eight months on custom development."

Underestimating Data Quality Requirements: Digital twin accuracy depends entirely on input data quality, yet many implementations assume existing sensor networks and maintenance records will provide adequate foundation. Practitioners report that 40-60% of initial project timelines are consumed by data cleansing, sensor calibration, and gap-filling exercises. Organizations that budget for comprehensive data quality assessment before platform selection consistently achieve faster time-to-value.

Insufficient Attention to Model Maintenance and Drift: Digital twins require continuous calibration as physical assets age, operational patterns shift, and external conditions change. Organizations treating digital twins as one-time implementations rather than ongoing programs frequently encounter model drift—divergence between predicted and actual performance—that undermines confidence in sustainability projections. Successful implementations budget 15-25% of initial development costs annually for model maintenance and validation.

Key Players

Established Leaders

Siemens offers the Xcelerator portfolio, including building and infrastructure digital twins deployed across major Asia-Pacific transportation and smart city projects. Their Singapore-based ASEAN headquarters serves as a showcase for integrated building twin technology.

Bentley Systems specializes in infrastructure digital twins with their iTwin platform, supporting major rail, road, and water infrastructure projects across Australia, Japan, and Southeast Asia with emphasis on asset lifecycle management.

AVEVA provides industrial digital twin solutions extensively adopted in Asia-Pacific oil and gas, power generation, and manufacturing sectors, with particular strength in process optimization and emissions monitoring.

Autodesk delivers Tandem platform capabilities for building digital twins, with growing presence in sustainable construction projects across the region and integration with their widely-adopted design software ecosystem.

Dassault Systèmes offers the 3DEXPERIENCE platform supporting complex infrastructure and manufacturing digital twins, with significant deployments in Japanese automotive and aerospace sectors emphasizing sustainability metrics.

Emerging Startups

Willow (Sydney, Australia) has developed a digital twin platform specifically designed for building portfolios with strong sustainability analytics, including automated NABERS and Green Star compliance reporting capabilities.

Cityzenith focuses on urban-scale digital twins incorporating emissions modeling and climate scenario analysis, with pilot deployments in smart city initiatives across Southeast Asia.

Akselos (Singapore/Switzerland) provides physics-based digital twins for complex industrial assets, enabling predictive maintenance that extends asset lifespans and reduces material consumption.

Twinview (Japan) specializes in manufacturing digital twins with integrated lifecycle assessment capabilities, serving Japanese industrial clients seeking Scope 1, 2, and 3 emissions visibility.

Greo (Singapore) delivers AI-powered building digital twins with particular focus on tropical climate optimization and region-specific energy efficiency benchmarks.

Key Investors & Funders

Temasek Holdings has made significant investments in smart infrastructure and digital twin enabling technologies through their sustainability-focused portfolio allocation.

SoftBank Vision Fund has backed multiple digital twin and IoT platform companies with infrastructure applications across the Asia-Pacific region.

Climate Tech VC Funds including Breakthrough Energy Ventures have increasingly targeted digital twin startups demonstrating measurable emissions reduction outcomes.

Asian Development Bank (ADB) provides financing for digital twin integration in sustainable infrastructure projects across developing Asia-Pacific economies.

Japan Green Investment Corp for Carbon Neutrality (JICN) supports digital twin deployments in Japanese industrial decarbonization initiatives under the Green Innovation Fund.

KPI Benchmark Table

KPI Category	Metric	Good Performance	Leading Performance	Measurement Frequency
Energy Efficiency	EUI Reduction	10-15% within 12 months	>20% within 12 months	Monthly
Carbon Intensity	Operational CO₂e/m²	15-20% reduction vs baseline	>25% reduction vs baseline	Quarterly
Predictive Maintenance	Failure Prediction Accuracy	70-80%	>85%	Continuous
Model Fidelity	Predicted vs Actual Deviation	<10% variance	<5% variance	Monthly
Data Completeness	Sensor Coverage	80-90% of critical points	>95% of critical points	Initial + Annual
Verification	Third-Party Audit Frequency	Annual	Semi-annual	Per schedule
OPEX Impact	Maintenance Cost Reduction	10-15%	>20%	Annual
LCA Integration	Embodied Carbon Visibility	Material categories tracked	Full material passport	Project milestones

Examples

1. Changi Airport Group, Singapore — Terminal Operations Optimization

Changi Airport deployed a comprehensive digital twin across Terminal 4, integrating over 6,500 IoT sensors monitoring HVAC, lighting, passenger flows, and baggage handling systems. Within 18 months of full operation, the implementation achieved a 22% reduction in energy consumption per passenger movement and a 17% decrease in peak cooling loads through predictive climate control. The digital twin's MRV framework, aligned with Singapore's Building and Construction Authority (BCA) Green Mark certification, enabled automatic generation of quarterly sustainability reports. Key success factors included early engagement with BCA auditors to validate the digital twin's measurement methodology and investment in a dedicated data science team for ongoing model calibration. Total lifecycle carbon savings are projected at 45,000 tCO₂e over the terminal's 30-year operational horizon.

2. Tokyo Metro Corporation, Japan — Rail Infrastructure Predictive Maintenance

Tokyo Metro implemented digital twins across 143 stations and 195 kilometers of track, initially targeting structural health monitoring and subsequently expanding to energy optimization. The twin integrates real-time vibration sensors, thermal imaging, and ridership data to predict component failures 4-6 weeks in advance with 82% accuracy. This predictive capability has reduced emergency maintenance interventions by 34% and extended average component lifespans by 18%, directly reducing material consumption and associated embodied carbon. Energy optimization algorithms analyzing passenger load patterns have achieved 11% reduction in traction power consumption during off-peak periods. The implementation required development of custom verification protocols acceptable to Japanese transport regulators, a process that consumed 14 months but established a replicable framework for subsequent rail operators.

3. Sydney Water Corporation, Australia — Water Network Optimization

Sydney Water's digital twin deployment spans 23,000 kilometers of water mains and 27,000 kilometers of wastewater pipes, representing one of the largest infrastructure digital twins in the Southern Hemisphere. The platform integrates hydraulic modeling, energy consumption monitoring, and leak detection analytics. Within two years of deployment, the system identified 847 previously undetected leaks, reducing non-revenue water losses by 8% and associated pumping energy by 12%. Real-time optimization of pump scheduling based on demand forecasting and time-of-use electricity pricing has reduced energy costs by AUD 4.2 million annually while cutting operational emissions by approximately 15,000 tCO₂e per year. The implementation's success relied on extensive stakeholder engagement with local councils and state regulators to establish data sharing protocols and verification standards compliant with Australian infrastructure reporting requirements.

Action Checklist

• Define 3-5 specific, quantifiable sustainability KPIs before evaluating any digital twin platform, ensuring alignment with regional regulatory requirements and certification schemes
• Conduct comprehensive data quality assessment across all intended data sources, budgeting 20-30% of project timeline for data preparation and sensor calibration
• Establish cross-functional governance structure with explicit sustainability representation and KPI ownership assigned to specific individuals
• Develop MRV framework specifying measurement methodologies, uncertainty bounds, and verification protocols aligned with ISO 14064 or regional equivalents
• Begin with narrowly scoped pilot targeting single asset or system, with 6-month timeline to demonstrate measurable sustainability outcomes
• Budget 15-25% of initial implementation costs annually for ongoing model maintenance, calibration, and validation activities
• Engage third-party verification providers early in implementation to validate methodology before scaling
• Document and publish internal benchmark KPIs with clear definitions of "good" and "leading" performance thresholds
• Establish automated reporting workflows connecting digital twin outputs to sustainability disclosure requirements (CDP, TCFD, regional regulations)
• Create knowledge sharing mechanisms to capture practitioner learnings and accelerate capability building across the organization

FAQ

Q: What is a realistic timeline for achieving measurable sustainability impact from a digital twin implementation? A: Based on practitioner experiences across Asia-Pacific, organizations should expect 6-9 months from project initiation to validated sustainability metrics from initial pilot deployments, with 18-24 months typically required for enterprise-scale implementations. However, these timelines assume adequate data quality at project start; organizations requiring significant sensor deployment or data cleansing should add 3-6 months. Quick wins in energy optimization often appear within 3-4 months, while lifecycle carbon improvements require longer measurement periods to validate.

Q: How should organizations balance investment in digital twin technology versus direct emissions reduction interventions? A: Practitioners recommend a "measure before you manage" philosophy—digital twins provide the visibility necessary to prioritize high-impact interventions. However, the balance depends on organizational maturity. Organizations with limited emissions visibility should prioritize digital twin investment (typically 5-8% of sustainability capital budget). Organizations with established measurement systems may allocate more to direct interventions while using digital twins for optimization and verification. The key principle is ensuring digital twin investments generate actionable insights rather than serving as substitutes for emissions reduction.

Q: What are the most common reasons digital twin implementations fail to deliver sustainability outcomes? A: Three failure modes dominate: (1) Technology-first selection without upfront outcome definition, resulting in platforms misaligned with sustainability requirements; (2) Underinvestment in data quality, leading to models too inaccurate for meaningful decision support; (3) Treating implementation as a one-time project rather than ongoing program, causing model drift that erodes confidence in sustainability projections. Organizations can mitigate these risks through rigorous KPI definition, comprehensive data assessment, and realistic ongoing maintenance budgeting.

Q: How do verification requirements for digital twin-based sustainability claims differ across Asia-Pacific jurisdictions? A: Verification requirements vary significantly. Singapore's BCA Green Mark and Building Energy Benchmarking programs accept digital twin-derived data with specified accuracy thresholds and periodic sensor calibration requirements. Australia's NABERS ratings require additional validation steps but are increasingly accommodating digital twin methodologies. Japan's regulatory environment remains more conservative, typically requiring parallel conventional measurement for initial certification periods. Organizations operating across multiple jurisdictions should engage local verification bodies early to understand specific requirements and develop compliant methodologies.

Q: What skills and organizational capabilities are essential for successful digital twin implementation? A: Successful implementations require hybrid teams combining (1) domain expertise in the specific infrastructure type, (2) data engineering capabilities for integration and quality management, (3) sustainability and carbon accounting expertise for MRV framework development, and (4) change management skills for organizational adoption. Many organizations underestimate the change management component—digital twins fundamentally alter decision-making workflows, requiring sustained effort to embed new practices. Building internal capability is preferable to complete reliance on vendors, as ongoing model maintenance and adaptation require deep organizational knowledge.

Sources

• McKinsey & Company. "Digital twins: The art of the possible in product development and beyond." 2024 Industry Report.
• International Energy Agency. "Digitalisation and Energy." IEA Technology Report, 2024.
• IPCC. "Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report." Intergovernmental Panel on Climate Change, 2024.
• Building and Construction Authority Singapore. "Green Mark Certification Standard GM:2021." BCA, 2024.
• MarketsandMarkets. "Digital Twin Market - Global Forecast to 2030." Market Research Report, 2024.
• GHG Protocol. "Corporate Standard and Scope 3 Guidance." World Resources Institute and World Business Council for Sustainable Development, 2024.
• Asian Development Bank. "Digital Technologies for Climate Action in Asia and the Pacific." ADB Sustainable Development Working Paper Series, 2024.