1This section explores the evolving role of governance in leveraging AI for climate change mitigation and adaptation through two crucial focus areas: governing AI deployment to enhance planetary stability and governing data to ensure it is standardized, comprehensive, and interoperable. Together, these topics shed light on how the responsible development and use of AI can contribute to a more sustainable future.

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2According to Francesca Larosa, AI technologies offer significant potential to reduce emissions, improve resource efficiency, and help achieve climate targets. However, the rapid advancement of AI also brings socio-environmental risks, including increased energy consumption, resource exploitation, and potential inequalities. Larosa outlines the Earth Alignment Principle, a governance framework designed to guide AI development in ways that contribute to planetary stability. This principle focuses on three core criteria: prioritizing AI systems that support sustainable production and consumption, ensuring equitable access to AI technologies, and fostering social cohesion. Larosa advocates for proactive, mission-oriented governance that steers AI development towards the common good by creating forward-looking, coordinated policies. By pursuing AI with these principles Larosa envisions a balanced, planet-positive future, where technology contributes to climate change mitigation and resilience without exacerbating environmental damage.

3Masaru Yarime emphasizes the critical role that data governance plays in enabling AI to reach its full climate potential. While AI offers powerful tools for climate change mitigation and adaptation, its effectiveness relies heavily on the availability and quality of data. The article discusses the challenges of data fragmentation, lack of interoperability, and insufficient governance, which hinder AI’s potential in addressing climate change. He calls for a comprehensive approach to data governance that ensures data is accurate, transparent, and accessible while addressing ethical issues like data privacy and bias, and tackling institutional challenges. He stresses the need for trust-based models, such as climate data trusts, to build mutual confidence and facilitate cross-sector cooperation. By implementing robust governance structures, Yarime argues that organizations can ensure AI systems are reliable and fair, empowering global cooperation to tackle climate change.

4Together, these articles underscore the importance of governance ‒ both at the technical and institutional levels ‒ in enabling AI to contribute effectively to climate action. As AI systems become more deeply integrated into the climate response, ensuring their alignment with global sustainability goals and ethical principles will be essential. By fostering responsible AI development, data transparency, and equitable access, these governance frameworks offer pathways toward a more sustainable, AI-powered future.