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Urban Intelligence for a Circular Future: Advanced Digital Technologies for Waste Prevention, Reuse, and Wise Construction

  • Wendy Maita Nieves 1,*,   
  • Félix Puime Guillén 2,*

Received: 31 Dec 2025 | Revised: 10 Jun 2026 | Accepted: 03 Jul 2026 | Published: 16 Sep 2026

Abstract

Urban areas generate growing volumes of waste and material flows that increasingly challenge the sustainability of current linear management models in cities. This paper examines how advanced digital technologies can enable a systemic transition toward circular urban systems, with a particular focus on waste prevention, reuse, and high value recycling. Building on insights from recent developments in smart energy systems, digitalized building management, and resource efficient construction practices, the study argues that urban intelligence—understood as the integration of IoT sensors, real time data analytics, artificial intelligence, and digital platforms—plays a pivotal role in optimizing material cycles across the city. The analysis identifies key mechanisms through which digital innovation enhances circularity: early detection of inefficiencies that lead to waste generation, improved tracing and valorization of materials, automation supported sorting and recovery processes, and data driven coordination among urban stakeholders. While digital tools significantly improve operational performance and environmental outcomes, the findings also highlight persistent challenges related to data governance, interoperability, institutional capacity, and social equity. The paper concludes that urban intelligence is not merely supportive but foundational for scaling circular economy strategies in cities, and calls for integrated policies, investment in digital infrastructures, and future research aimed at quantifying long term circular benefits at the urban scale.

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Maita Nieves, W.; Guillén, F. P. Urban Intelligence for a Circular Future: Advanced Digital Technologies for Waste Prevention, Reuse, and Wise Construction. Progress in Circular Economy 2026, 1 (1), 3.
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