Sustainability Atlas
Climate Tech & Data·12 min read··...

Explainer: Digital twins for infrastructure & industry — a practical primer for teams that need to ship

A practical primer: key concepts, the decision checklist, and the core economics. Focus on implementation trade-offs, stakeholder incentives, and the hidden bottlenecks.

The global digital twin market surged past $17 billion in 2024 and is projected to reach $24–28 billion by 2025, growing at a compound annual growth rate of 37–40% according to Fortune Business Insights and Precedence Research. For infrastructure and industrial applications specifically, this technology is delivering measurable sustainability outcomes: AI-enhanced digital twins have demonstrated 25–30% reductions in building energy consumption and up to 20% decreases in transportation-related carbon emissions (World Economic Forum, March 2024). As organizations face mounting pressure to decarbonize operations while maintaining asset performance, digital twins have emerged as a critical bridge between physical infrastructure and data-driven sustainability management.

Why It Matters

Infrastructure represents one of the most carbon-intensive sectors globally, with buildings alone accounting for approximately 40% of global energy consumption and 33% of greenhouse gas emissions. The International Energy Agency estimates that 70–80% of the infrastructure needed for 2050 remains unbuilt, creating both an unprecedented decarbonization challenge and an opportunity to embed sustainability from the design phase forward.

Digital twins address this challenge by creating dynamic, data-rich virtual replicas of physical assets—from individual HVAC systems to entire city grids—that enable real-time monitoring, predictive maintenance, and scenario modeling. Unlike static building information models (BIM), digital twins continuously ingest sensor data, apply machine learning algorithms, and provide actionable insights for operational optimization.

The sustainability case is compelling: research published in 2024 demonstrates that digital twins integrated with IoT sensors can achieve up to 50% reduction in building carbon footprints through real-time monitoring and predictive optimization (UCL Discovery, 2024). For utilities and grid operators, the technology enables 15% reductions in energy transmission losses while increasing renewable energy integration by 20%.

Beyond environmental benefits, the economic rationale is equally strong. Organizations deploying digital twins report 25% reductions in unplanned downtime and 20% decreases in maintenance costs. For oil and gas operators, this translates to approximately €36 million in annual savings per rig through avoided unplanned stoppages (Hexagon, 2025).

Key Concepts

Understanding digital twins requires distinguishing between several related but distinct concepts:

Digital Twin Types:

  • Product twins model individual assets (a wind turbine, a pump, a building system) and currently represent 46.5% of market deployments
  • Process twins simulate operational workflows and material flows across systems
  • System twins integrate multiple assets and processes for cross-functional optimization, essential for infrastructure applications

Enabling Technology Stack:

  • IoT sensors and smart meters provide continuous data streams on equipment performance, environmental conditions, and energy consumption
  • Cloud and edge computing platforms (Azure Digital Twins, AWS IoT TwinMaker) enable scalable data processing and real-time analytics
  • Machine learning algorithms including Long Short-Term Memory (LSTM) networks for energy forecasting and Artificial Neural Networks for pattern recognition
  • 3D visualization engines such as NVIDIA Omniverse for immersive simulation and scenario testing

Integration with Sustainability Frameworks: Digital twins increasingly serve as the technical backbone for carbon accounting and MRV (Measurement, Reporting, and Verification) systems. By capturing Scope 1, 2, and 3 emissions data at the asset level, they enable automated ESG reporting and compliance with emerging disclosure requirements under frameworks like the EU Corporate Sustainability Reporting Directive (CSRD) and SEC climate disclosure rules.

Sector-Specific KPIs

SectorPrimary KPITypical RangeBest-in-Class
Commercial BuildingsEnergy Use Intensity (kWh/m²/year)150–250<100
ManufacturingOverall Equipment Effectiveness (%)60–75%>85%
Power GenerationUnplanned Downtime (hours/year)200–400<50
Water UtilitiesNon-Revenue Water (%)25–35%<15%
TransportationAsset Availability (%)85–92%>97%
Smart CitiesCarbon Intensity (tCO2e/capita)5–12<3

What's Working

Predictive Maintenance at Scale

The most mature use case for infrastructure digital twins is predictive maintenance, where the technology has moved decisively from pilot to production. General Electric's Predix platform monitors over 1 million assets globally, using physics-based models combined with machine learning to predict equipment failures 30–60 days in advance. This capability is particularly valuable for critical infrastructure where unplanned outages carry significant safety and financial consequences.

Building Energy Optimization

IKEA's deployment represents a benchmark for facilities management: the company monitors 42 million square feet across its global portfolio using 7,000 data points tracking 6,000 HVAC components. Real-time energy consumption and emissions monitoring enables continuous optimization, with documented energy savings exceeding 25% in retrofitted facilities.

Urban-Scale Carbon Reduction

The Malaysian Bertam City project, documented in 2024 research, demonstrated a 39.5% reduction in energy consumption and 22.3% decrease in carbon emissions through digital twin-enabled building design optimization and solar PV integration. This urban-scale application illustrates the technology's potential for new development in tropical climates.

Grid Modernization

Siemens Gamesa deployed digital twins for wind farm operations in Denmark (April 2025), enabling real-time turbine performance optimization and predictive maintenance scheduling. The technology has proven essential for maximizing capacity factors in variable renewable generation, addressing a key barrier to grid-scale clean energy deployment.

What's Not Working

Data Integration Challenges

Despite technological maturity, many organizations struggle with the fundamental challenge of data interoperability. Legacy infrastructure often lacks sensor instrumentation, and where sensors exist, proprietary protocols create integration barriers. The World Economic Forum estimates that 60% of digital twin implementations fail to achieve projected ROI due to data quality and integration issues.

Skills Gap and Organizational Resistance

Successful digital twin deployment requires interdisciplinary teams spanning operational technology (OT), information technology (IT), data science, and domain expertise. Many infrastructure organizations lack this talent combination, and traditional organizational structures create silos between teams that must collaborate for digital twin success.

Upfront Cost Barriers for SMEs

While enterprise solutions have matured, small and medium-sized facility operators face significant barriers to adoption. Full-featured platforms from major vendors require substantial licensing fees and implementation services, often exceeding $500,000 for initial deployment. Cloud-based solutions are addressing this gap, but SME adoption lags considerably behind large enterprises.

Cybersecurity Vulnerabilities

Connecting critical infrastructure to digital systems creates new attack surfaces. The 2024 proliferation of IoT-enabled digital twins has outpaced security best practices in many organizations, with researchers identifying vulnerabilities in building automation systems and industrial control networks that could enable both data theft and physical system manipulation.

Key Players

Established Leaders

Siemens (Germany) operates the Xcelerator platform and MindSphere industrial IoT suite, with deep integration across building automation, manufacturing, and smart infrastructure. Their 2024 partnership with AWS extended cloud capabilities for building digital twins.

Microsoft (USA) Azure Digital Twins has emerged as the dominant cloud platform, with particular strength in enterprise integration and developer tooling. The April 2024 collaboration with Siemens to converge Digital Twin Definition Language with W3C Thing Description Standard addresses critical interoperability challenges.

Bentley Systems (USA) focuses specifically on infrastructure engineering through its iTwin platform, with strong adoption in transportation, water, and construction sectors. Their open-source approach to digital twin standards has attracted significant developer ecosystem support.

NVIDIA (USA) Omniverse provides the visualization and simulation layer increasingly essential for complex infrastructure twins, with the Earth-2 project demonstrating planetary-scale climate modeling capabilities.

General Electric Vernova (USA) leads in energy sector applications through GridBeats EnergyAPM, with the deepest installed base in power generation and grid infrastructure.

Emerging Startups

Cognite (Norway) was named a digital twin industry leader for 2025, specializing in industrial data management and contextualization—addressing the critical data integration challenges that limit many implementations.

Neara (Australia) focuses specifically on utility infrastructure modeling, enabling electric utilities to simulate grid performance under various climate scenarios.

Gradyent (Netherlands) raised €28 million in Series B funding for energy grid optimization, with particular focus on district heating networks and thermal infrastructure.

Akselos (Switzerland) provides structural digital twins for marine and offshore infrastructure, with a 2025 partnership with ABS (American Bureau of Shipping) extending capabilities for maritime assets.

Key Investors

Khosla Ventures led the $35 million seed round for Viven in 2025, signaling continued venture interest in AI-enhanced digital twin applications. High-Tech Gründerfonds and Earlybird VC have been active in European digital twin startups, particularly in Germany.

Government funding has accelerated significantly: the US CHIPS Program allocated $285 million for semiconductor-focused digital twin research, while the EU Digital Twin Centre received £37.6 million with participation from Thales and other aerospace firms.

Examples

1. Siemensstadt Square (Berlin, Germany)

Siemens invested €750 million in the June 2024 launch of Siemensstadt Square, a mixed-use urban development designed from the ground up as a digital twin-enabled sustainable district. The project integrates building performance monitoring, renewable energy management, and transportation optimization across 70 hectares, serving as both a corporate campus and a living laboratory for urban digital twin applications. Early performance data indicates 30% energy reduction compared to conventional development.

2. UK High-Speed Rail Network

Network Rail and HS2 have deployed infrastructure digital twins across the UK's rail network to model carbon impact before construction begins. Engineers can now simulate alternative designs and materials, optimizing for both operational performance and lifecycle emissions. The technology has enabled identification of approximately 15% carbon reduction opportunities in planned rail infrastructure through design optimization and material substitution.

3. ITER Fusion Reactor (France)

The International Thermonuclear Experimental Reactor, a collaboration of 35 countries, uses comprehensive digital twins to manage one of the world's most complex infrastructure projects. The digital twin enables coordination across global supply chains, predicts maintenance requirements for components operating in extreme conditions, and optimizes the path toward 500 MW fusion power output—demonstrating the technology's capability for managing unprecedented engineering complexity.

Action Checklist

  • Conduct asset inventory and sensor audit: Map existing instrumentation across target infrastructure and identify gaps requiring new sensor deployment. Prioritize high-energy or high-maintenance assets for initial implementation.

  • Establish data governance framework: Define ownership, quality standards, and access controls for the operational data that will feed digital twins. Address legacy system integration and proprietary protocol challenges before platform selection.

  • Select platform based on integration requirements: Evaluate vendors against specific interoperability needs—Azure Digital Twins for Microsoft ecosystem alignment, iTwin for infrastructure engineering workflows, or industrial platforms for manufacturing applications.

  • Start with single-asset pilot: Deploy initial digital twin for one high-value asset with clear baseline metrics (energy consumption, maintenance frequency, downtime). Target 90-day proof of concept before scaling.

  • Build cross-functional implementation team: Assign dedicated resources from facilities/operations, IT, sustainability, and finance. Establish regular coordination cadence and clear decision-making authority.

  • Define sustainability KPIs and reporting requirements: Align digital twin outputs with ESG disclosure frameworks (GHG Protocol, CSRD) and internal carbon accounting systems. Ensure MRV capabilities meet emerging regulatory standards.

  • Develop cybersecurity and resilience plan: Implement network segmentation, access controls, and monitoring for connected infrastructure. Plan for graceful degradation if digital systems fail.

FAQ

Q: What distinguishes a digital twin from Building Information Modeling (BIM)?

A: While BIM provides a static 3D model of building design and construction data, digital twins are dynamic systems that continuously ingest real-time operational data from sensors, apply analytics, and enable ongoing optimization. BIM typically ends at construction handover; digital twins extend through the entire asset lifecycle. Many organizations use BIM as the geometric foundation for digital twins, adding IoT integration and analytics layers to create living operational models.

Q: What is the typical ROI timeline for infrastructure digital twins?

A: Most organizations report positive ROI within 12–24 months, driven primarily by energy cost savings and reduced unplanned maintenance. A 2024 analysis by Hexagon found that manufacturing digital twins deliver 25% productivity improvements and 20% cost reductions on average. However, ROI varies significantly by implementation scope and asset type—building HVAC optimization typically shows faster returns than complex industrial process twins.

Q: How do digital twins support Scope 3 emissions tracking?

A: Digital twins can extend beyond owned assets to model supply chain flows, enabling Scope 3 visibility that is otherwise extremely difficult to achieve. By integrating supplier data and logistics information, organizations can trace embedded carbon through material inputs and product distribution. This capability is increasingly essential as disclosure requirements expand to cover full value chain emissions.

Q: What are the minimum data requirements for a useful digital twin?

A: At minimum, useful digital twins require real-time sensor data at 15-minute intervals for key operational parameters (energy consumption, temperature, equipment status), plus historical baseline data covering at least 12 months of operation. More sophisticated applications benefit from sub-minute data granularity and integration with weather, occupancy, and production scheduling systems.

Q: Are there open standards for digital twin interoperability?

A: The Digital Twin Consortium has published reference architectures, and the April 2024 Siemens-Microsoft collaboration to converge Digital Twin Definition Language with W3C Thing Description Standard represents significant progress toward interoperability. However, the market remains fragmented, and organizations should evaluate vendor lock-in risks carefully when selecting platforms.

Sources

Topics

digitaltwinsLCAunit economicsgeospatialMRVScope 3

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