Potential and limitations of digital twins to achieve the

  • Wright, L. & Davidson, S. How to tell the difference between a model and a digital twin. Adv. Model. Simul. Eng. Sci. 7, 13 (2020).

    Article 

    Google Scholar 

  • Grieves, M. & Vickers, J. in Transdisciplinary Perspectives on Complex Systems (eds Kahlen, J. et al.) 85–113 (Springer, 2017).

  • Boschert, S. & Rosen, R. in Mechatronic Futures (eds Hehenberger, P. & Bradley, D.) 59–74 (Springer, 2016).

  • Tao, F. & Qi, Q. Make more digital twins. Nature 573, 490–491 (2019).

    CAS 
    Article 

    Google Scholar 

  • Niederer, S. A., Sacks, M. S., Girolami, M. & Willcox, K. Scaling digital twins from the artisanal to the industrial. Nat. Comput. Sci. 1, 313–320 (2021).

    Article 

    Google Scholar 

  • Bauer, P. et al. The digital revolution of Earth-system science. Nat. Comput. Sci. 1, 104–113 (2021).

    Article 

    Google Scholar 

  • Rosen, R., Von Wichert, G., Lo, G. & Bettenhausen, K. D. About the importance of autonomy and digital twins for the future of manufacturing. IFAC PapersOnLine 48, 567–572 (2015).

    Article 

    Google Scholar 

  • Tao, F., Zhang, H., Liu, A. & Nee, A. Y. Digital twin in industry: state-of-the-art. IEEE Trans. Industr. Inform. 15, 2405–2415 (2018).

    Article 

    Google Scholar 

  • Cannoodt, R., Saelens, W., Deconinck, L. & Saeys, Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat. Commun. 12, 3942 (2021).

    CAS 
    Article 

    Google Scholar 

  • Bruynseels, K., Santoni de Sio, F. & van den Hoven, J. Digital twins in health care: ethical implications of an emerging engineering paradigm. Front. Genet. 9, 31 (2018).

    Article 

    Google Scholar 

  • Laubenbacher, R., Sluka, J. P. & Glazier, J. A. Using digital twins in viral infection. Science 371, 1105–1106 (2021).

    CAS 
    Article 

    Google Scholar 

  • Bauer, P., Stevens, B. & Hazeleger, W. A digital twin of Earth for the green transition. Nat. Clim. Change 11, 80–83 (2021).

    Article 

    Google Scholar 

  • Voosen, P. Europe is building a ‘digital twin’ of Earth to revolutionize climate forecasts. Science https://doi.org/10.1126/science.abf0687 (2020).

  • Deren, L., Wenbo, Y. & Zhenfeng, S. Smart city based on digital twins. Comput. Urban Sci. 1, 4 (2021).

    Article 

    Google Scholar 

  • Francisco, A., Mohammadi, N. & Taylor, J. E. Smart city digital twin-enabled energy management: toward real-time urban building energy benchmarking. J. Manag. Eng. 36, 04019045 (2020).

    Article 

    Google Scholar 

  • Jiang, Y., Yin, S., Li, K., Luo, H. & Kaynak, O. Industrial applications of digital twins. Phil. Trans. R. Soc. Lond. A 379, 20200360 (2021).

    Google Scholar 

  • Marmolejo-Saucedo, J. A., Hurtado-Hernandez, M. & Suarez-Valdes, R. Digital twins in supply chain management: a brief literature review. In Proc. ICO 2019: Intelligent Computing and Optimization Vol. 1072 (eds Vasant, P. et al.) 653–661 (Springer, 2020).

  • El-Zahab, S. & Zayed, T. Leak detection in water distribution networks: an introductory overview. Smart Water 4, 5 (2019).

    Article 

    Google Scholar 

  • Clemen, T. et al. Multi-agent systems and digital twins for smarter cities. In Proc. 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation 45–55 (ACM, 2021).

  • Havard, V., Jeanne, B., Lacomblez, M. & Baudry, D. Digital twin and virtual reality: a co-simulation environment for design and assessment of industrial workstations. Prod. Manuf. Res. 7, 472–489 (2019).

    Google Scholar 

  • Onile, A. E., Machlev, R., Petlenkov, E., Levron, Y. & Belikov, J. Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review. Energy Rep. 7, 997–1015 (2021).

    Article 

    Google Scholar 

  • Dembski, F., Wössner, U., Letzgus, M., Ruddat, M. & Yamu, C. Urban digital twins for smart cities and citizens: the case study of Herrenberg, Germany. Sustainability 12, 2307 (2020).

    Article 

    Google Scholar 

  • Lian, B. et al. Application of digital twins for remote operation of membrane capacitive deionization (mCDI) systems. Desalination 525, 115482 (2022).

    CAS 
    Article 

    Google Scholar 

  • Designing Disruption: the critical role of Virtual Twins in accelerating Sustainability (Dassault Systèmes and Accenture, 2021).

  • Deng, S. et al. Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet Things J. 7, 7457–7469 (2020).

    Article 

    Google Scholar 

  • Engström, R. E. et al. Succeeding at home and abroad: accounting for the international spillovers of cities’ SDG actions. npj Urban Sustain. 1, 18 (2021).

    Article 

    Google Scholar 

  • Amirebrahimi, S., Rajabifard, A., Mendis, P. & Ngo, T. A BIM-GIS integration method in support of the assessment and 3D visualisation of flood damage to a building. J. Spat. Sci. 61, 317–350 (2016).

    Article 

    Google Scholar 

  • Rajabifard, A. et al. in Sustainable Development Goals Connectivity Dilemma (ed. Rajabifard, A.) 243–255 (CRC, 2019).

  • Sabri, S. & Rajabifard, A. in Sustainable Development Goals Connectivity Dilemma (ed. Rajabifard, A.) 199–211 (CRC, 2019).

  • Assarkhaniki, Z., Sabri, S. & Rajabifard, A. Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement. Big Earth Data 5, 497–526 (2021).

    Article 

    Google Scholar 

  • Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11, 233 (2020).

    CAS 
    Article 

    Google Scholar 

  • Karvonen, A. et al. The ‘New Urban Science’: towards the interdisciplinary and transdisciplinary pursuit of sustainable transformations. Urban Transform. 3, 9 (2021).

    Article 

    Google Scholar 

  • Bettencourt, L. M. A. Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems (MIT Press, 2021).

  • Acuto, M. & Parnell, S. Leave no city behind. Science 352, 873 (2016).

    CAS 
    Article 

    Google Scholar 

  • Kapteyn, M. G., Pretorius, J. V. & Willcox, K. E. A probabilistic graphical model foundation for enabling predictive digital twins at scale. Nat. Comput. Sci. 1, 337–347 (2021).

    Article 

    Google Scholar 

  • Ragnedda, M. & Gladkova, A. (eds) Digital Inequalities in the Global South (Springer, 2020).

  • Pick, J. B. & Azari, R. Global digital divide: influence of socioeconomic, governmental, and accessibility factors on information technology. Inf. Technol. Dev. 14, 91–115 (2008).

    Article 

    Google Scholar 

  • Chinn, M. D. & Fairlie, R. W. The determinants of the global digital divide: a cross-country analysis of computer and internet penetration. Oxf. Econ. Pap. 59, 16–44 (2007).

    Article 

    Google Scholar 

  • Rodriguez, F. & Wilson, E. J. Are Poor Countries Losing the Information Revolution? (World Bank, 2000).

  • Niu, J., Tang, W., Xu, F., Zhou, X. & Song, Y. Global research on artificial intelligence from 1990–2014: spatially-explicit bibliometric analysis. ISPRS Int. J. Geoinf. 5, 66 (2016).

    Article 

    Google Scholar 

  • Schrotter, G. & Hürzeler, C. The digital twin of the city of Zurich for urban planning. J. Photogramm. Remote. Sens. Geoinf. Sci. 88, 99–112 (2020).

    Google Scholar 

  • United Nations Statistics Division in The Sustainable Development Goals Report 2019 (United Nations, 2019); https://unstats.un.org/sdgs/report/2019/goal-11/

  • Derudder, B. & Van Meeteren, M. Engaging with ‘urban science’. Urban Geogr. 40, 555–564 (2019).

    Article 

    Google Scholar 

  • Bai, X. et al. Networking urban science, policy and practice for sustainability. Curr. Opin. Environ. Sustain. 39, 114–122 (2019).

    Article 

    Google Scholar 

  • Hillier, B. in Digital Urban Modeling and Simulation Vol. 242 (eds Arisona, S. M. et al.) 24–48 (Springer, 2012).

  • Smajgl, A., Brown, D. G., Valbuena, D. & Huigen, M. G. Empirical characterisation of agent behaviours in socio-ecological systems. Environ. Model. Softw. 26, 837–844 (2011).

    Article 

    Google Scholar 

  • Karlsson, J. M., Bring, A., Peterson, G. D., Gordon, L. J. & Destouni, G. Opportunities and limitations to detect climate-related regime shifts in inland Arctic ecosystems through eco-hydrological monitoring. Environ. Res. Lett. 6, 014015 (2011).

    Article 

    Google Scholar 

  • Laikre, L. et al. Compromising genetic diversity in the wild: unmonitored large-scale release of plants and animals. Trends Ecol. Evol. 25, 520–529 (2010).

    Article 

    Google Scholar 

  • Edmonds, B. & Meyer, R. (eds) Simulating Social Complexity: A Handbook (Springer, 2013).

  • Slater, T. Shaking Up the City: Ignorance, Inequality, and the Urban Question (Univ. California Press, 2021).

  • Brusaporci, S. in 3D Printing: Breakthroughs in Research and Practice (ed. Information Resources Management Association) 333–360 (IGI Global, 2017).

  • Zou, J. & Schiebinger, L. AI can be sexist and racist—it’s time to make it fair. Nature 559, 324–326 (2018).

    CAS 
    Article 

    Google Scholar 

  • Fuso Nerini, F. et al. Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nat. Energy 3, 10–15 (2018).

    Article 

    Google Scholar 

  • Zhao, Z. et al. Synergies and tradeoffs among Sustainable Development Goals across boundaries in a metacoupled world. Sci. Total Environ. 751, 141749 (2021).

    CAS 
    Article 

    Google Scholar 

  • Tzachor, A., Devare, M., King, B., Avin, S. & Ó hÉigeartaigh, S. Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities. Nat. Mach. Intell. 4, 104–109 (2022).

    Article 

    Google Scholar 

  • Chen, Y. & Landry, D. Capturing the rains: comparing Chinese and World Bank hydropower projects in Cameroon and pathways for south–south and north nouth technology transfer. Energy Policy 115, 561–571 (2018).

    Article 

    Google Scholar 

  • Stilgoe, J., Owen, R., & Macnaghten, P. in The Ethics of Nanotechnology, Geoengineering and Clean Energy (eds Maynard, A. & Stilgoe, J.) 347–359 (Routledge, 2020).

  • Stahl, B. C. & Wright, D. Ethics and privacy in AI and big data: implementing responsible research and innovation. IEEE Secur. Priv. 16, 26–33 (2018).

    Article 

    Google Scholar 

  • Jirotka, M., Grimpe, B., Stahl, B., Eden, G. & Hartswood, M. Responsible research and innovation in the digital age. Commun. ACM 60, 62–68 (2017).

    Article 

    Google Scholar 

  • Kaissis, G. et al. End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3, 473–484 (2021).

    Article 

    Google Scholar 

  • Transforming our World: The 2030 Agenda for Sustainable Development (United Nations, 2015).

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