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1881351

全球建築數位孿生市場：依組件、類型、應用和產業劃分－市場規模、產業趨勢、機會分析和預測（2025-2033 年）

Global Digital Twin for Buildings Market: By Component, Type, Application, Industry - Market Size, Industry Dynamics, Opportunity Analysis and Forecast for 2025-2033
出版日期: | 出版商: Astute Analytica | 英文 234 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

全球建築數位孿生市場持續快速成長，反映出人們對這項技術在建築和設施管理領域變革潛力的認識不斷提高。 2024 年，該市場規模為 20.7 億美元，預計到 2033 年將呈指數級增長，達到驚人的 262.3 億美元。這意味著 2025 年至 2033 年的複合年增長率 (CAGR) 為 32.6%，凸顯了數位孿生解決方案在全球範圍內的加速普及和日益增長的重要性。

推動這一顯著成長的主要因素是對提高建築環境營運效率、預測性維護和永續性的迫切需求。建築物業主和管理者正日益尋求優化資源利用、降低能耗和最大限度減少停機時間的方法，同時確保居住者的舒適和安全。數位孿生技術透過創建實體建築的詳細即時模型，並實現持續的監測和分析，為實現這些目標提供了一個先進的平台。

市場趨勢

建築數位孿生市場由西門子、歐特克、IBM 和微軟等領先的科技和工程公司引領。這些公司透過提供包含軟體解決方案、感測器和無人機等硬體組件以及各種服務的綜合產品組合來展開激烈競爭。它們的戰略重點是將數位孿生技術與其他新興技術（尤其是人工智慧 (AI)）相集成，以提高數位模型的智慧和回應能力。

2025 年 11 月，在巴塞隆納舉行的智慧城市博覽會上，一項重要進展誕生： "數位孿生應用商店" 正式上線。這個創新平台匯集了來自不斷成長的供應商和機構網路的經過驗證的產品，創造了一個集中式的數位孿生工具、服務和資料集市場。此外，NVIDIA 創始人兼首席執行官黃仁勳於 2025 年 10 月發布了“NVIDIA Omniverse DSX”，這是一個專為設計和運營千兆瓦級 AI 工廠而設計的全面開放藍圖。

核心成長驅動因子

推動建築數位孿生市場發展的關鍵因素之一是迫切需要透過改造現有建築來提高永續性和效率。日益增強的環保意識和監管壓力正促使政府和企業優先考慮老舊基礎設施的現代化改造，並確保其符合現代能源和性能標準。改造面臨獨特的挑戰，因為許多老舊建築最初的設計並未考慮能源效率或智慧技術整合。數位孿生技術能夠對建築系統進行詳細的監控、分析和優化，從而提供強大的解決方案，而無需徹底的重建。

新的機會與趨勢

生成式人工智慧 (AI) 與數位孿生技術的整合為建築管理系統提供了巨大的機遇，使其功能遠超基本的監控。這種融合使得創建能夠預測未來、自我最佳化的建築成為可能，這些建築可以動態地適應不斷變化的環境和需求。透過整合生成式人工智慧演算法，數位孿生可以模擬數千種運行場景，提供前所未有的洞察力和遠見。這些先進的模型使建築業主和管理者能夠預測未來的需求和挑戰，並做出積極主動的決策，從而提高效率、舒適度和永續性。

優化障礙

數位孿生技術相關的高昂初始投資和實施成本是可能阻礙整體市場成長的重大挑戰。開發和部署數位孿生解決方案需要大量資金，因為它需要物聯網感測器、資料儲存基礎設施和高效能運算能力等先進硬體。這種前期投資對許多組織來說可能是一個障礙，尤其是缺乏投資此類先進技術所需財力的中小企業 (SME)。將數位孿生技術整合到現有樓宇管理系統中的複雜性也增加了財務負擔，通常需要專業知識和大量的客製化工作。

目錄

第一章：研究架構

  • 研究目標
  • 產品概述
  • 市場區隔

第二章：研究方法

  • 質性研究
    • 一手和二手資料來源
  • 量化研究
    • 一手和二手資料來源
  • 依地區劃分的原始調查受訪者
  • 研究假設
  • 市場規模估算
  • 資料三角驗證

第三章：摘要整理：全球樓宇數位孿生市場

第四章：全球建築數位孿生市場概論

  • 產業價值鏈分析
  • 建築數位孿生市場主要趨勢
  • PESTLE 分析
  • 波特五力分析
    • 供應商議價能力
    • 買方議價能力
    • 替代品威脅
    • 新進入者威脅
    • 競爭強度
  • 市場動態與趨勢
    • 成長因素
    • 阻礙因素與推動因素
    • 挑戰
    • 主要趨勢
  • 新冠疫情對市場成長趨勢的影響評估
  • 市場成長與展望
    • 市場收入估計與預測（2019-2032）
  • 競爭格局概覽
    • 市場集中度
    • 公司市佔率分析（價值，2023 年）
    • 競爭格局圖

第五章 全球建築數位孿生市場分析：依組件劃分

  • 主要發現
  • 市場規模及預測，2019-2032 年
    • 軟體
    • 服務

第六章 全球建築數位孿生市場分析：依類型劃分

  • 主要發現
  • 市場規模及預測，2019-2032 年
    • 描述性孿生
    • 資訊性孿生
    • 預測性孿生數位孿生
    • 綜合型數位孿生
    • 自主型數位孿生

第七章 全球建築數位孿生市場依應用領域分析

  • 主要見解
  • 市場規模及預測（2019-2032）
    • 自動化進度監控
    • 效能與規劃模型
    • 資源管理與物流
    • 安全監控
    • 品質評估
    • 設備利用率最佳化
    • 勞動力監控和追蹤

第八章 全球建築數位孿生市場依產業垂直領域分析

  • 主要發現
  • 市場規模及預測（2019-2032）
    • 製造業
    • 航空航太與國防
    • 石油與天然氣
    • 公用事業
    • 醫療保健與生命科學
    • 汽車
    • 建築
    • IT與電信
    • 零售
    • 消費品與包裝
    • 交通運輸
    • 智慧城市
    • 其他

第九章 全球建築數位孿生市場分析：依地區劃分

  • 主要見解
  • 市場規模及預測（2019-2032）
    • 北美
    • 歐洲
    • 亞太地區
    • 南美
    • 中東和非洲

第十章：北美建築數位孿生市場分析

第十一章：歐洲建築數位孿生市場分析

第十二章：亞太地區建築數位孿生市場分析

第十三章：南美洲建築數位孿生市場分析

第十四章：中東與非洲建築數位孿生市場分析

第15章 企業簡介

  • ABB
  • Accenture PLC
  • Ansys, Inc.
  • Priori Technologies, Inc.
  • Bentley Systems, Incorporated
  • Cisco Systems, Inc.
  • Dassault Systems, Inc.
  • DHL International GmbH.
  • DXC Technology Company
  • GE Digital(Predix)
  • IBM Corporation
  • Microsoft Azure
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • Other Major Players
簡介目錄
Product Code: AA0923603

The global digital twin for buildings market is undergoing rapid expansion, reflecting a growing recognition of the technology's transformative potential across the construction and facilities management sectors. Valued at US$ 2.07 billion in 2024, the market is projected to soar to an impressive US$ 26.23 billion by 2033. This represents a compound annual growth rate (CAGR) of 32.6% during the forecast period from 2025 to 2033, underscoring the accelerating adoption and increasing importance of digital twin solutions worldwide.

The primary forces driving this remarkable growth are the pressing demands for enhanced operational efficiency, predictive maintenance, and sustainability within the built environment. Building owners and managers are increasingly seeking ways to optimize resource use, reduce energy consumption, and minimize downtime, all while maintaining occupant comfort and safety. Digital twins provide a sophisticated platform to achieve these goals by creating highly detailed, real-time digital replicas of physical buildings that enable continuous monitoring and analysis.

Noteworthy Market Developments

Key market players in the digital twin for buildings sector are dominated by large technology and engineering firms such as Siemens, Autodesk, IBM, and Microsoft. These companies compete fiercely by offering comprehensive portfolios that encompass not only software solutions but also hardware components like sensors and drones, alongside a wide array of services. Their strategic focus lies in integrating digital twin technology with other emerging technologies, notably artificial intelligence (AI), to enhance the intelligence and responsiveness of digital models.

In November 2025, a significant development unfolded with the launch of The Digital Twin Appstore at the Smart City Expo World Congress in Barcelona. This innovative platform consolidates verified offerings from an expanding network of vendors and organizations, creating a centralized marketplace for digital twin tools, services, and datasets. Another notable advancement occurred in October 2025 when Jensen Huang, the founder and CEO of NVIDIA, introduced NVIDIA Omniverse DSX. This platform represents a comprehensive, open blueprint specifically engineered for designing and operating gigawatt-scale AI factories.

Core Growth Drivers

A significant demand driver in the digital twin for buildings market is the urgent need to retrofit existing buildings to enhance sustainability and improve efficiency. As awareness of environmental issues intensifies and regulatory pressures mount, governments and corporations are prioritizing the modernization of aging infrastructure to meet contemporary energy and performance standards. Retrofitting older buildings presents a unique challenge, as many were not originally designed with energy efficiency or smart technology integration in mind. Digital twin technology offers a powerful solution by enabling detailed monitoring, analysis, and optimization of building systems without requiring complete reconstruction.

Emerging Opportunity Trends

A tremendous opportunity is emerging in the integration of generative artificial intelligence (AI) with digital twin technology, transforming the capabilities of building management systems far beyond basic monitoring. This fusion enables the creation of predictive, self-optimizing buildings that can adapt dynamically to changing conditions and demands. By incorporating generative AI algorithms, digital twins can simulate thousands of operational scenarios, providing a level of insight and foresight that was previously unattainable. These advanced models allow building owners and managers to anticipate future needs and challenges, enabling proactive decision-making that enhances efficiency, comfort, and sustainability.

Barriers to Optimization

The high initial investment and implementation costs associated with digital twin technology present a significant challenge that could hamper the overall growth of the market. Developing and deploying digital twin solutions requires substantial financial resources, particularly due to the need for advanced hardware, such as IoT sensors, data storage infrastructure, and high-performance computing capabilities. These upfront expenditures can be prohibitive for many organizations, especially small and medium-sized enterprises that may lack the capital to invest in such sophisticated technology. The complexity of integrating digital twins into existing building management systems also adds to the financial burden, often requiring specialized expertise and extensive customization.

Detailed Market Segmentation

By component, the software component dominates the digital twins for buildings market, holding the largest share at 77.30%, and is also projected to experience the fastest growth with a remarkable CAGR of 32.80% during the forecast period. This commanding position is driven by the development of sophisticated software platforms that seamlessly integrate cutting-edge technologies such as the Internet of Things (IoT), artificial intelligence (AI), and advanced analytics. These platforms enable the creation of dynamic and manageable digital replicas of physical buildings, allowing for real-time monitoring, simulation, and optimization of building performance.

By type, the informative twin is expected to maintain its dominant position within the global digital twins for buildings market, projected to generate the highest market revenue share of 27.51%. This type of digital twin plays a crucial role by creating a comprehensive digital representation of a building's physical assets, which is continuously updated with real-time data to facilitate ongoing monitoring and analysis. The value of an informative twin lies in its dynamic nature, as it goes beyond static modeling by reflecting the current state of the building throughout its lifecycle.

By application, the resource management and logistics segment accounts for 21.87% of the market. This significant share underscores the increasing recognition of digital twins as powerful tools for enhancing operational efficiency and providing predictive oversight across building management processes. The clear return on investment in these areas is a major factor driving adoption, as organizations seek to optimize resource use, reduce downtime, and extend the lifespan of critical infrastructure components.

By industry, the construction industry is poised to become a major consumer of digital twin technology within the buildings market, reflecting a growing trend toward digital transformation in how projects are designed, planned, and executed. One of the most notable impacts of this shift is the integration of artificial intelligence (AI) with Building Information Modeling (BIM) workflows, which is expected to streamline project delivery times throughout 2025 and beyond. By enhancing BIM with AI capabilities, construction teams can automate complex analyses, detect potential issues early, and optimize design parameters more efficiently than ever before.

Segment Breakdown

By Component

  • Software
  • Cloud
  • On-premise
  • Services
  • Professional
  • Managed

By Type

  • Descriptive twin
  • Informative twin
  • Predictive twin
  • Comprehensive twin
  • Autonomous twin

By Application

  • Automated Progress Monitoring
  • As-executed Vs. As-planned Models
  • Resource Management and Logistics
  • Safety Monitoring
  • Quality Assessment
  • Optimization Of Equipment Usage
  • Monitoring And Tracking Of Workers

By Industry

  • Manufacturing
  • Aerospace & Defense
  • Oil & Gas
  • Utilities
  • Healthcare & Life Sciences
  • Automotive
  • Construction
  • IT & Telecom
  • Retail
  • Consumer Goods & Packaging
  • Transportation
  • Smart Cities
  • Other

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America holds a commanding position in the digital twin for buildings market, controlling a dominant 46.82% share, which is largely fueled by robust investments from both public and private sectors. This region's leadership is underpinned by a growing recognition of the value digital twins bring to building design, operation, and maintenance, driving widespread adoption across various projects and initiatives. In 2024, the U.S. General Services Administration (GSA) took a significant step by mandating the use of digital twin technology for 10 of its new federal building projects.
  • Meanwhile, in Canada, a major smart city initiative in Toronto is pushing the boundaries of digital twin deployment by planning to install 50,000 Internet of Things (IoT) sensors across 25 downtown commercial buildings by 2025. This ambitious project aims to create a highly connected urban environment where digital twins can provide real-time data and analytics to optimize building performance, energy consumption, and occupant comfort.

Leading Market Participants

  • ABB
  • Accenture PLC
  • Ansys, Inc.
  • Priori Technologies, Inc.
  • Bentley Systems, Incorporated
  • Cisco Systems, Inc.
  • Dassault Systemes, Inc.
  • DHL International GmbH.
  • DXC Technology Company
  • GE Digital (Predix)
  • IBM Corporation
  • Microsoft Azure
  • Oracle Corporation
  • PTC Inc.
  • Robert Bosch GmbH
  • SAP SE
  • Siemens AG
  • Other Major Players

Table of Content

Chapter 1. Research Framework

  • 1.1. Research Objective
  • 1.2. Product Overview
  • 1.3. Market Segmentation

Chapter 2. Research Methodology

  • 2.1. Qualitative Research
    • 2.1.1. Primary & Secondary Sources
  • 2.2. Quantitative Research
    • 2.2.1. Primary & Secondary Sources
  • 2.3. Breakdown of Primary Research Respondents, By Region
  • 2.4. Assumption for the Study
  • 2.5. Market Size Estimation
  • 2.6. Data Triangulation

Chapter 3. Executive Summary: Global Digital Twins for Buildings Market

Chapter 4. Global Digital Twins for Buildings Market Overview

  • 4.1. Industry Value Chain Analysis
  • 4.2. Key Trends in Digital Twins for Buildings Market
  • 4.3. PESTLE Analysis
  • 4.4. Porter's Five Forces Analysis
    • 4.4.1. Bargaining Power of Suppliers
    • 4.4.2. Bargaining Power of Buyers
    • 4.4.3. Threat of Substitutes
    • 4.4.4. Threat of New Entrants
    • 4.4.5. Degree of Competition
  • 4.5. Market Dynamics and Trends
    • 4.5.1. Growth Drivers
    • 4.5.2. Restraints
    • 4.5.3. Challenges
    • 4.5.4. Key Trends
  • 4.6. Covid-19 Impact Assessment on Market Growth Trend
  • 4.7. Market Growth and Outlook
    • 4.7.1. Market Revenue Estimates and Forecast (US$ Bn), 2019 - 2032
  • 4.8. Competition Dashboard
    • 4.8.1. Market Concentration Rate
    • 4.8.2. Company Market Share Analysis (Value %), 2023
    • 4.8.3. Competitor Mapping

Chapter 5. Global Digital Twins for Buildings Market Analysis, By Component

  • 5.1. Key Insights
  • 5.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 5.2.1. Software
      • 5.2.1.1. On-premise
      • 5.2.1.2. Cloud
    • 5.2.2. Services
      • 5.2.2.1. Professional
      • 5.2.2.2. Managed

Chapter 6. Global Digital Twins for Buildings Market Analysis, By Type

  • 6.1. Key Insights
  • 6.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 6.2.1 Descriptive twin
    • 6.2.2 Informative twin
    • 6.2.3 Predictive twin
    • 6.2.4 Comprehensive twin
    • 6.2.5 Autonomous twin

Chapter 7. Global Digital Twins for Buildings Market Analysis, By Application

  • 7.1. Key Insights
  • 7.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 7.2.1. Automated Progress Monitoring
    • 7.2.2. As-executed Vs. As-planned Models
    • 7.2.3. Resource Management and Logistics
    • 7.2.4. Safety Monitoring
    • 7.2.5. Quality Assessment
    • 7.2.6. Optimization Of Equipment Usage
    • 7.2.7. Monitoring And Tracking Of Workers

Chapter 8. Global Digital Twins for Buildings Market Analysis, By Industry

  • 8.1. Key Insights
  • 8.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 8.2.1. Manufacturing
    • 8.2.2. Aerospace & Defense
    • 8.2.3. Oil & Gas
    • 8.2.4. Utilities
    • 8.2.5. Healthcare & Life Sciences
    • 8.2.6. Automotive
    • 8.2.7. Construction
    • 8.2.8. IT & Telecom
    • 8.2.9. Retail
    • 8.2.10. Consumer Goods & Packaging
    • 8.2.11. Transportation
    • 8.2.12. Smart Cities
    • 8.2.13. Others

Chapter 9. Global Digital Twins for Buildings Market Analysis, By Region

  • 9.1. Key Insights
  • 9.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 9.2.1. North America
      • 9.2.1.1. The U.S.
      • 9.2.1.2. Canada
      • 9.2.1.3. Mexico
    • 9.2.2. Europe
      • 9.2.2.1. The U.K.
      • 9.2.2.2. Germany
      • 9.2.2.3. France
      • 9.2.2.4. Spain
      • 9.2.2.5. Russia
      • 9.2.2.6. Rest of Europe
    • 9.2.3. Asia Pacific
      • 9.2.3.1. China
      • 9.2.3.2. Japan
      • 9.2.3.3. India
      • 9.2.3.4. Australia & New Zealand
      • 9.2.3.5. Korea
      • 9.2.3.6. ASEAN
      • 9.2.3.7. Rest of Asia Pacific
    • 9.2.4. South America
      • 9.2.4.1. Argentina
      • 9.2.4.2. Brazil
      • 9.2.4.3. Rest of South America
    • 9.2.5. Middle East & Africa
      • 9.2.5.1. UAE
      • 9.2.5.2. Saudi Arabia
      • 9.2.5.3. South Africa
      • 9.2.5.4. Rest of Middle East & Africa

Chapter 10. North America Digital Twins for Buildings Market Analysis

  • 10.1. Key Insights
  • 10.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 10.2.1. By Component
    • 10.2.2. By Type
    • 10.2.3. By Application
    • 10.2.4. By Industry
    • 10.2.5. By Country

Chapter 11. Europe Digital Twins for Buildings Market Analysis

  • 11.1. Key Insights
  • 11.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 11.2.1. By Component
    • 11.2.2. By Type
    • 11.2.3. By Application
    • 11.2.4. By Industry
    • 11.2.5. By Country

Chapter 12. Asia Pacific Digital Twins for Buildings Market Analysis

  • 12.1. Key Insights
  • 12.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 12.2.1. By Component
    • 12.2.2. By Type
    • 12.2.3. By Application
    • 12.2.4. By Industry
    • 12.2.5. By Country/Region

Chapter 13. South America Digital Twins for Buildings Market Analysis

  • 13.1. Key Insights
  • 13.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 13.2.1. By Component
    • 13.2.2. By Type
    • 13.2.3. By Application
    • 13.2.4. By Industry
    • 13.2.5. By Country

Chapter 14. Middle East & Africa Digital Twins for Buildings Market Analysis

  • 14.1. Key Insights
  • 14.2. Market Size and Forecast, 2019 - 2032 (US$ Bn)
    • 14.2.1. By Component
    • 14.2.2. By Type
    • 14.2.3. By Application
    • 14.2.4. By Industry
    • 14.2.5. By Country

Chapter 15. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 15.1. ABB
  • 15.2. Accenture PLC
  • 15.3. Ansys, Inc.
  • 15.4. Priori Technologies, Inc.
  • 15.5. Bentley Systems, Incorporated
  • 15.6. Cisco Systems, Inc.
  • 15.7. Dassault Systems, Inc.
  • 15.8. DHL International GmbH.
  • 15.9. DXC Technology Company
  • 15.10. GE Digital (Predix)
  • 15.11. IBM Corporation
  • 15.12. Microsoft Azure
  • 15.13. Oracle Corporation
  • 15.14. PTC Inc.
  • 15.15. Robert Bosch GmbH
  • 15.16. SAP SE
  • 15.17. Siemens AG
  • 15.18. Other Major Players