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The concept of 'digital twins' has gained traction in urban planning, referring to virtual replicas of physical cities that simulate real-time data from sensors, traffic flows, and energy grids. Proponents argue that these models enable predictive governance, allowing officials to test infrastructure changes before implementation, thereby reducing costs and environmental impact. However, critics caution that digital twins rely heavily on data accuracy and algorithmic assumptions; incomplete datasets or biased models could lead to flawed decisions, exacerbating social inequalities. Moreover, the high computational demands and privacy concerns pose significant barriers to equitable adoption, particularly in developing regions. Thus, while digital twins offer transformative potential, their efficacy hinges on transparent data governance and inclusive design frameworks.