## 1 Introduction  
The environmental impact of technological development, particularly in the realm of software, has become an increasingly relevant and concerning topic in recent years. Technological advancements and the widespread use of software applications and devices have resulted in a range of often underestimated environmental consequences. While digital technologies have undoubtedly enhanced our lives in many ways, they have also led to significant greenhouse gas emissions, increased energy consumption, and waste production. The rapidly growing sector of the software industry relies on a vast infrastructure, including data centres, servers, network devices, and user devices, all of which require substantial energy to operate [1, 2]. In 2018, it was estimated that data centres alone consumed 1% of the global energy supply [3]. The increasing use of Cloud services, the explosion of data generated, and the growing computational power required to run complex algorithms have contributed to a significant rise in energy consumption and carbon emissions [4]. Consequently, improving the energy efficiency of data centres has become a crucial focus for reducing the environmental impact of the software industry [5]. The availability of Cloud instances, providing access to significant hardware resources, has facilitated the widespread use of Artificial Intelligence (AI). In recent years, the use of AI has experienced unprecedented growth, revolutionising various industries and profoundly impacting our daily lives. The substantial computational resources needed to train deep neural networks and run complex algorithms have led to a significant increase in CO\({}_{2}\)eq emissions [6]. Recent research has highlighted the environmental impact of AI; for instance,training GPT-2 was found to produce emissions five times greater than those of a car throughout its entire lifecycle [7]. The infrastructure supporting these algorithms, including the energy supply and efficiency of data centres, plays a significant role in the overall calculation of emissions. Furthermore, fully harnessing clean energy is also an ethical issue. Whenever non-renewable energy sources are prioritised, the opportunity to utilise cleaner energy is lost due to inefficient storage systems [8]. Therefore, in this context, the efficient utilisation of the involved technologies becomes relevant. Addressing and mitigating the environmental impact of AI requires a comprehensive approach that considers both the computational resources involved and the efficiency of the infrastructure supporting these technologies. The purpose of this paper is to confirm the effectiveness of two carbon-aware AI training strategies: Flexible Start and Pause & Resume. As their names suggest, these strategies temporally shift workloads to optimally schedule the training of AI algorithms, aligning high computational loads with the most favourable moments on the grid. Carbon emissions on the grid vary considerably throughout the day and from region to region, due to the intermittent availability of renewable energy sources. These two strategies, which have already been benchmarked in the literature, wait for the optimal time to start or resume the training workload based on a carbon intensity threshold. The two strategies, which have been previously benchmarked in the literature, aim to initiate or resume training workloads during periods of low carbon intensity. Flexible Start waits for the best moment to start the training workload based on a carbon intensity threshold, while Pause & Resume pauses the training when carbon intensity is high and resumes it when it falls below the threshold. In a previous study [9], Flexible Start and Pause & Resume were tested on a benchmark of 11 AI workloads in the domains of Natural Language Processing (NLP) and Computer Vision across 16 different geographical regions.