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. Simulation results demonstrate the effectiveness of the proposed framework. Finally, we insightfully provide open research directions for low-carbon AIoT.

翻译：通过将人工智能（AI）与物联网（IoT）深度融合，人工智能物联网（AIoT）已在诸多领域引发变革。然而，随着移动技术的持续演进，AIoT正面临能源消耗与碳排放的双重挑战。幸运的是，生成式人工智能（GAI）凭借其卓越的推理与生成能力，在降低AIoT碳排放方面展现出巨大潜力。本文探讨了GAI在碳减排中的潜能，并提出了一种新型基于GAI的低碳AIoT解决方案。具体而言，我们首先分析了导致AIoT碳排放的主要影响因素，继而介绍了GAI技术及其与碳排放的内在关联。随后，我们聚焦于GAI如何降低网络组件的碳排放，探索了其在低碳AIoT中的应用前景。在此基础上，我们提出了一种基于大语言模型（LLM）的碳排放优化框架，其中设计了可插拔的LLM与检索增强生成（RAG）模块，以生成更精准可靠的优化问题。此外，我们利用生成扩散模型（GDM）识别最优碳减排策略。仿真结果验证了所提框架的有效性。最后，我们深刻展望了低碳AIoT领域的开放研究方向。