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.

翻译：通过将人工智能与物联网融合，人工智能物联网已在众多领域引发革命性变革。然而，随着移动技术的持续进步，人工智能物联网正面临能耗与碳排放的严峻挑战。值得庆幸的是，生成式人工智能凭借其卓越的推理与生成能力，在降低人工智能物联网碳排放方面展现出巨大潜力。本文深入探讨了生成式人工智能在减少碳排放方面的潜力，并提出了一种创新的生成式人工智能驱动的低碳人工智能物联网解决方案。具体而言，我们首先分析了导致人工智能物联网碳排放的主要影响因素，继而系统介绍了生成式人工智能技术及其与碳排放的关联。随后，我们聚焦于生成式人工智能如何降低网络组件的碳排放，探索了其在低碳人工智能物联网中的应用前景。在此基础上，我们提出了一个基于大语言模型的碳排放优化框架，其中设计了可插拔的大语言模块与检索增强生成模块，以生成更精准可靠的优化问题。进一步地，我们利用生成式扩散模型来识别最优的碳减排策略。数值实验结果验证了所提框架的有效性。最后，我们前瞻性地提出了低碳人工智能物联网的开放研究方向。