1The full potential of AI can only be realized when supported by robust data ecosystems and effective digital infrastructures. The second section of this report explores critical components for successfully deploying AI in environmental contexts: data access, quality, infrastructure, governance, and ethics. All these elements are essential for harnessing AI’s capabilities in sectors such as energy, water and waste management, where precision, efficiency, and scalability are crucial.

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2In the first article, Hamed Alemohammad, highlights that one of the key aspects for integrating AI successfully into an environmental context depends fundamentally on the strength of the underlying data ecosystem. He highlights four essential dimensions: data access, governance, quality, and ethics. While AI has shown significant promise in improving environmental monitoring, prediction, and management, challenges like data fragmentation, quality inconsistencies, and proprietary control of data remain major barriers to its widespread adoption. For AI to offer sustainable solutions, Alemohammed argues it is vital to eliminate data silos and ensure interoperability across systems. He also advocates for the creation of open data platforms, the development of adaptive governance frameworks, and the high-quality data to ensure AI is ethically and transparently managed.

3David Olawade emphasizes the importance of data infrastructure in enabling AI applications across environmental sectors. He underscores that AI’s success in managing energy, water and waste systems is deeply reliant on data quality and the readiness of digital infrastructure across energy, water and waste management systems. Olawade explains that in cities like Barcelona and Amsterdam, where AI is integrated into smart water and waste management systems, the robustness of data infrastructure plays a crucial role in optimizing resource use and operational efficiency. He further explores the challenges these cities face in maintaining this infrastructure, such as ensuring data accuracy, managing latency in data processing, and securing data privacy in shared frameworks. Olawade argues that as cities move toward smart, data-driven systems, the importance of data infrastructure becomes even greater, as it enables real-time monitoring and more efficient decision-making processes.

4Together, these two perspectives underscore that the successful deployment of AI for managing energy, water, and waste depends not just on the technology itself but on the solid foundations of the data on which it is built. By ensuring data is accessible, high-quality, and ethically managed, AI can fulfill its potential to contribute meaningfully to environmental sustainability, offering solutions that are both effective and equitable.