Analytical assessment of green AI architectures for advancing sustainable resource use in circular economies

Abstract

This We present a concise analytical write-up focusing on green AI architectures and sustainable resource use in circular economies. Two chenges in the transition to a circular economy are insufficient resource efficiency and sustainability assessment of decisions affecting resource use. AI-enabled solutions can contribute positively but often with energy and emissions costs that undermine their ecological value. The analytical lens examines green AI architectures that avoid, minimize or counterbalance adverse energy, data, and life-cycle sustainability impacts.

The idea behind the circular economy is to use as few resources as possible and keep and recover as many materials as possible in socio-technical systems.  It works by using closed-loop systems, product designs that make it easy to take things apart, and service models that make products last longer.  The end goal is to separate economic growth from the use and depletion of natural resources.  Encouraging the effective use of resources makes it easier to put circular economy ideas into action and speeds up the shift to fully circular production systems.  The main goal of resource circularity is to close the material loop by efficiently getting valuable resources back through processes like extraction, collection, recovery, recycling, reproduction, and remanufacturing.  Even though AI doesn't directly help, its impact on these processes is both big and life-changing.