Dynamic computer models of cities known as ‘digital twins’ could help drive sustainable development across the world’s urban areas, an international team of authors argues in the journal Nature Sustainability.
Digital twins are more than just static models. They incorporate near-real-time data from sensors and other sources to produce “virtual replicas,” the authors explain—“in silico equivalents of real-world objects.”
The concept of digital twins first arose in manufacturing, and they are primarily used in product and process engineering. But the models have also been employed in fields ranging from personalized medicine to climate forecasting, at scales from the molecular to the planetary.
Many researchers have posited that digital twins will be a powerful tool for sustainability efforts. But nobody has taken a rigorous look at the benefits and pitfalls of urban digital twins. The new study takes on that task, paying particular attention to the potential for the modeling approach to help achieve the UN Sustainable Development Goals.
Digital twins have a variety of potential benefits in this realm, the researchers say. They can help cities allocate resources more efficiently—design more effective water grids, predict traffic congestion to guide transportation planning, simulate consumer behavior to recommend energy-saving measures, and so on.
In addition, “In silico models provide a virtual space where new clean technologies, which promise resource efficiency but may cause unintended harm, can be tested at a speed and scale that may otherwise be inhibited by the precautionary principle,” the researchers write. For example, they could help cities figure out how to incorporate renewable sources of energy into the grid without compromising reliability.
Digital twins could also help scientists and policymakers to collaborate across disciplines, agencies, levels of government, and geographic distances. And they could aid cities in monitoring and reporting progress on the Sustainable Development Goals or other sustainability aims.
Some of the authors of the paper have been involved in the development of a digital twin for Fishermans Bend, an urban renewal project in Melbourne, Australia. The model includes more than 1,400 layers of both historical and real-time data from public and private sources. More than 20 government agencies and municipalities are using the model to analyze how proposed buildings will affect sunlight falling on open space and vegetation, forecast tram traffic patterns, and address other planning questions.
Digital twin models are also being used in cities including Zurich, Singapore, and Shanghai to monitor noise and pollution and facilitate urban planning that takes into account population growth and climate change.
But there are pitfalls to the digital twin approach, too. Because they require so much data, advanced computing power, and technological know-how, digital twins have the potential to exacerbate digital divides, especially between high-income and lower-income countries.
What’s more, even the most complex model may fall short in representing the multifarious nature of a real-life city. The data necessary to underpin a successful digital twin may be unavailable, inaccessible, or incompatible with other sources. And the social-science aspects of digital twins are especially poorly understood.
Finally, models can be optimized for the wrong targets. There are inherent contradictions between different Sustainable Development Goals, and programmers have to take care about how outcomes and parameters are prioritized, the researchers say. For whom and by whom are these decisions made—and who’s left out of the process?
To avoid these pitfalls of digital twins—and reap the potential benefits, the researchers recommend that governments and international institutions get involved in bridging digital divides; leaving digital twin technology to the marketplace virtually guarantees that low-resource countries will be left behind.
They also call on those creating and implementing digital twins of cities to pay attention to social and ethical responsibility. “A central question that derives from these issues is: to what extent are those who may be affected by the decisions based on simulation models included in their design and deployment?” they write.
“Interestingly in such instances, digital twins themselves can raise awareness among planners and policymakers of socioeconomic inequalities, thereby becoming instruments of inclusion,” the researchers add.
Source: Sarah DeWeerdt, Anthropocene