  By integrating Artificial Intelligence (AI) with the Internet of Things
(IoT), Artificial Intelligence of Things (AIoT) has revolutionized many fields.
However, AIoT is facing the challenges of energy consumption and carbon
emissions due to the continuous advancement of mobile technology. Fortunately,
Generative AI (GAI) holds immense potential to reduce carbon emissions of AIoT
due to its excellent reasoning and generation capabilities. In this article, we
explore the potential of GAI for carbon emissions reduction and propose a novel
GAI-enabled solution for low-carbon AIoT. Specifically, we first study the main
impacts that cause carbon emissions in AIoT, and then introduce GAI techniques
and their relations to carbon emissions. We then explore the application
prospects of GAI in low-carbon AIoT, focusing on how GAI can reduce carbon
emissions of network components. Subsequently, we propose a Large Language
Model (LLM)-enabled carbon emission optimization framework, in which we design
pluggable LLM and Retrieval Augmented Generation (RAG) modules to generate more
accurate and reliable optimization problems. Furthermore, we utilize Generative
Diffusion Models (GDMs) to identify optimal strategies for carbon emission
reduction. Numerical results demonstrate the effectiveness of the proposed
framework. Finally, we insightfully provide open research directions for
low-carbon AIoT.
