Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance

Emerging smarter eco-cities, inherently intertwined with environmental governance, function as experimental sites for testing novel technological solutions and implementing environmental reforms aimed at addressing complex challenges. However, despite significant progress in understanding the distinct roles of emerging datadriven governance systems-namely City Brain, Smart Urban Metabolism (SUM), and platform urbanism-enabled by Artificial Intelligence of Things (AIoT), a critical gap persists in systematically exploring the untapped potential stemming from their synergistic and collaborative integration in the context of environmental governance. To fill this gap, this study aims to explore the linchpin potential of AIoT in seamlessly integrating these data-driven governance systems to advance environmental governance in smarter eco-cities. Specifically, it introduces a pioneering framework that effectively leverages the synergies among these AIoT-powered governance systems to enhance environmental sustainability practices in smarter eco-cities. In developing the framework, this study employs configurative and aggregative synthesis approaches through an extensive literature review and in-depth case study analysis of publications spanning from 2018 to 2023. The study identifies key factors driving the co-evolution of AI and IoT into AIoT and specifies technical components constituting the architecture of AIoT in smarter eco-cities. A comparative analysis reveals commonalities and differences among City Brain, SUM, and platform urbanism within the frameworks of AIoT and environmental governance. These data-driven systems collectively contribute to environmental governance in smarter eco-cities by leveraging realtime data analytics, predictive modeling, and stakeholder engagement. The proposed framework underscores the importance of data-driven decision-making, optimization of resource management, reduction of environmental impact, collaboration among stakeholders, engagement of citizens, and formulation of evidence-based policies. The findings unveils that the synergistic and collaborative integration of City Brain, SUM, and platform urbanism through AIoT presents promising opportunities and prospects for advancing environmental governance in smarter eco-cities. The framework not only charts a strategic trajectory for stimulating research endeavors but also holds significant potential for practical application and informed policymaking in the realm of environmental urban governance. However, ongoing critical discussions and refinements remain imperative to address the identified challenges, ensuring the framework's robustness, ethical soundness, and applicability across diverse urban contexts.