AI & Environment Resource HubDiaz et al. · 2024-12-22
The project explores innovative ways to address Barcelona’s sustainability challenges, such as carbon emissions, air pollution, and energy inefficiency. By leveraging AI, IoT sensors, and Nature-Based Solutions (NBS), the project aims to optimize municipal budgets for interventions that transform underutilized urban spaces into productive assets.
Diaz et al.. 2024-12-22. Green Liminals: Feasibility Study for AI-Driven Budget Optimization for Urban Sustainable Solutions. https://blog.iaac.net/green-liminals-feasibility-study-for-ai-driven-budget-optimization-for-urban-sustainable-solutions/ (AI & Environment Resource Hub; record paper-065; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Biodiversity and ecosystems; Built environment and infrastructure
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Biodiversity and ecosystems; Built environment and infrastructure
In the Environmental Impacts of Data Centers 101 course, you will learn to analyze the environmental impacts of data centers using a life cycle assessment (LCA) perspective that goes beyond what you see in the news. This will include energy, water, land use, carbon emissions, global supply chains, e-waste concerns, ecological impacts, and environmental justice case studies. Whether you work in tech, sustainability, policy, or are just curious about how AI systems operate behind the scenes, this course gives you the clarity and frameworks to understand these impacts from end-to-end.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Biodiversity and ecosystems; Built environment and infrastructure
This course will help you understand AI's climate implications and identify practical next steps within your organization. The course begins with demystifying the connection between AI, Large Language Models (LLMs), data centers, and energy and water demand. Then you will learn about AI's environmental footprint, the related environmental and community impacts, you will evaluate real-world applications of AI across climate adaptation, energy transition, and nature conservation, and understand the business and policy landscape shaping corporate decisions.
Shared topics: Cross-cutting sustainability; Governance and society; Energy and electricity; Climate and greenhouse gases; Built environment and infrastructure
Open access book assembling cutting-edge research exploring AI infrastructures and sustainability across media and communication, covering AI-driven media, energy consumption, climate change, and policies/ethics.