Digital Twin Cities: Framework and Global Practices

The report defines the digital twin city as a virtual recreation of a physical city used for simulation, monitoring, and control. It introduces a '4+5' framework consisting of four internal core elements (infrastructure, data resources, platform capacity, and application scenarios) and five external supporting elements (strategy and mechanisms, stakeholders, funding and business models, standards and evaluation, and cybersecurity). The document details global case studies from China, Malaysia, USA, France, and Australia, highlighting applications in traffic management, carbon reduction, and infrastructure planning. It identifies key challenges such as data privacy, lack of interdisciplinary talent, and over-reliance on government funding, while providing recommendations for governments and industry to foster a human-centric, socio-technical approach to urban transformation.

• The digital twin city is characterized by four primary technical features: accurate mapping of the physical city, analytical insights derived from the digital version, virtual-real interaction, and intelligent intervention in the physical city.
• The report proposes a '4+5' framework for digital twin city construction. The four internal core elements are infrastructure, data resources, platform capacity, and application scenarios. The five external supporting elements are strategy and mechanisms, stakeholders, funding and business models, standards and evaluation, and cybersecurity.
• Digital twin technology is projected to have significant economic impact, with an estimated $280 billion in savings for urban planning, construction, and operations by 2030. The global digital twin market is expected to reach $48.2 billion by 2026, growing at a compound annual growth rate (CAGR) of 58%.
• Current funding for digital twin city projects is heavily skewed toward the public sector, with original research indicating that 66.7% of projects are funded by government investment, while only 33.3% result from enterprise investment or joint ventures.
• Global application scenarios for digital twin cities vary by region: over 40% of global practices focus on environmental and low-carbon development, whereas in China, 54% relate to smart transportation and 31% to smart communities.
• Case studies demonstrate diverse urban benefits: Kunming, China, reduced average traffic delays by more than 10%; Mawan Smart Port in Shenzhen increased comprehensive operational efficiency by 30%; and Virtual Rennes in France reduced metro station construction time by 70%.
• The report identifies critical challenges including the risk of 'flashy but useless' models that prioritize visualization over simulation, the lack of standardized data governance, and the risk of cyberattacks due to the centralized processing of massive amounts of data.
• To ensure sustainability, the report recommends that governments shift toward human-centric, demand-driven applications and that industry players lower technical thresholds through open-source software and 'low code' or 'zero code' platforms to encourage SME participation.

APA

World Economic Forum (2022). Digital Twin Cities: Framework and Global Practices. https://www3.weforum.org/docs/WEF_Global_Digital_Twin_Cities_Framework_and_Practice_2022.pdf

Chicago

World Economic Forum. Digital Twin Cities: Framework and Global Practices. 2022. https://www3.weforum.org/docs/WEF_Global_Digital_Twin_Cities_Framework_and_Practice_2022.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Digital Twin Cities: Framework and Global Practices |date=April 2022 |url=https://www3.weforum.org/docs/WEF_Global_Digital_Twin_Cities_Framework_and_Practice_2022.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2022digital, author = {{World Economic Forum}}, title = {{Digital Twin Cities: Framework and Global Practices}}, institution = {World Economic Forum}, year = {2022}, month = apr, url = {https://www3.weforum.org/docs/WEF_Global_Digital_Twin_Cities_Framework_and_Practice_2022.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }