AI for Sustainable Smart Cities

Harness agentic and generative AI to build resilient urban infrastructure AI for Sustainable Smart Cities: Generative and Agentic Innovations in Urban Systems and Climate Resilience offers a practice-oriented treatment of how autonomous AI agents and generative models can optimize urban infrastructure design, operation, and governance. Structured across 20 chapters, the book connects data science with civil, environmental, and systems engineering to address sustainability at the city scale. AI for Sustainable Smart Cities delivers technical frameworks for integrating intelligent agents into energy systems, transportation networks, construction workflows, emergency response, and environmental monitoring. Case studies demonstrate AI-driven predictive maintenance, adaptive city management, and resource optimization. The book also addresses ethical, secure, and transparent deployment of AI within public and critical infrastructure systems. Key topics include: Agentic AI architectures enabling autonomous, context-aware decision-making for real-time urban operations and infrastructure management across city systemsGenerative AI techniques for simulation, design optimization, and scenario planning in renewable energy and circular economy applicationsCross-sectoral frameworks linking mobility, energy, and construction operations to scalable sustainability strategies for growing urban populationsMethods for shifting from reactive to proactive infrastructure management using data-driven forecasting and intelligent resource allocationGuidance on responsible AI adoption covering transparency, security, and governance protocols for public-sector and critical infrastructure deploymentDesigned for civil and environmental engineers, AI engineers, urban planners, and infrastructure specialists, this book provides the technical depth needed to implement intelligent, adaptive urban systems. Graduate students and government professionals engaged in smart city planning and digital transformation will also find direct applicability to their work.