AI & Environment Resource HubCoalition for Sustainable AI · Publication date unknown
Materials, Waste & Circular Economy
Coalition for Sustainable AI. Date unknown. Coalition for Sustainable AI. https://www.sustainableaicoalition.org/ (AI & Environment Resource Hub; record org-061; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Materials and critical minerals; E-waste and circularity
The United States throws away $6.5 billion worth of reusable material every year, and the recycling rate has remained flat for over a decade. Now, tech companies are using artificial intelligence and robotics to make the process safer, more efficient, and more common. But can they really raise the recycling rate in a country with more than 9,000 recycling programs?
Shared topics: Cross-cutting sustainability; Materials and critical minerals; E-waste and circularity
In this episode, Stephanie McLarty speaks with Rich Savoie, CEO of Adiona Tech, about the importance of sustainable logistics and the role of AI in optimizing transport emissions. They discuss a circular pilot project in Australia focused on recycling champagne corks and the challenges faced in the logistics of recycling. Rich shares insights on the significance of commercial transport emissions, the lessons learned from the pilot, and how AI can help improve supply chain efficiency. The conversation emphasizes the need for businesses to engage CFOs in sustainability initiatives and the urgency of taking action towards greener logistics.
Shared topics: Cross-cutting sustainability; Materials and critical minerals; E-waste and circularity
This research explores how artificial intelligence (AI) can be leveraged to optimize packaging design, reduce operational costs, and enhance sustainability in e-commerce. As packaging waste and shipping inefficiencies grow alongside global online retail demand, traditional methods for determining box size, material use, and logistics planning have become economically and environmentally inadequate.
Shared topics: Materials and critical minerals; E-waste and circularity
Matbench Discovery is a benchmarking framework for machine learning models used in materials discovery, highlighting the importance of task-specific metrics and demonstrating that universal interatomic potentials can effectively pre-screen stable inorganic materials in high-throughput workflows.