This project catalyzes the use of predictive analytical tools to support governments in transforming food systems. It allows decision makers to compare future policy intervention options in the food system, illuminating the full impacts of a policy decision on natural, social, human and produced capital. Modeling techniques and tools are becoming more efficient through machine learning and the use of Earth Observations.
Activity Type Policy/Regulatory GuidanceResearch/Reports/Assessments
Data centres are integral to the transformation underpinning modern energy systems and economies. As the backbone of digitalization, they enable critical services ranging from cloud computing to Artificial Intelligence and Internet of Things ecosystems. Their rapid expansion, however, poses significant sustainability and infrastructure challenges that necessitate action to align with climate goals and ensure energy systems resilience. The document examines the evolving role of data centres in the power sector, emphasizing their growing environmental footprint and impact on grid infrastructure, offering policy recommendations on five priority areas:
1. Establishing robust key performance indicators on sustainability across the full lifecycle of data centres (including carbon, water, and material circularity metrics, sensitive to regional context)
2. Enhancing data transparency and availability to better understand and manage environmental impacts;
3. Enabling demand-side flexibility through grid integration, advanced automation, and regulatory clarity;
4. Promoting circular economy principles by extending hardware lifespan, improving reuse and recycling;
5. Fostering public-private partnerships to drive investment, innovation, and workforce development aligned with sustainable outcomes.
The document urges redefining the performance criteria for data centres, moving beyond uptime and throughput to embedding sustainability at the core of design, operation, and governance, to prevent compromising climate targets and broader resource management efforts.
SCP-HAT is deploying AI to enable the tracing of environmental pressures and impacts along the supply chain of goods and services consumed within a given country. It identifies hotspot areas of unsustainable production and consumption to support setting priorities in national policies and transition plans.
Activity Type AI Tools/SolutionsThe Global Initiative on Resilience to Natural Hazards through AI Solutions seeks to advance the use of AI and other emerging technologies in disaster management while ensuring it is safe, responsible, and effective. A core focus of the initiative is laying the foundation for international AI standards and best practices to guide the design, deployment, and governance of AI solutions for hazard detection, monitoring, response, and recovery. By convening expert working groups, workshops, collaborative challenges, and connecting the dots between standards and existing proof-of-concept projects connecting the dots between standards and existing proof-of-concept projects, initiative provides a platform for governments, industry, and civil society to contribute to interoperable, ethical, and inclusive AI frameworks. Through these standards-driven efforts, the initiative aims to strengthen resilience to natural hazards and support the UN’s broader goals of disaster management, sustainable development, and technological innovation.
Activity Type AI Tools/SolutionsTrainings/WorkshopsResearch/Reports/AssessmentsPolicy/Regulatory GuidanceAwareness/AdvocacyNetworks/Mentorship/Exchange