## 7 Conclusions  
In this paper, we have replicated experiments from a state-of-the-art study on carbon-aware AI training strategies [9]. These strategies, known as Flexible Start and Pause & Resume, leverage time resources to reduce the carbon footprint of training workloads. Their names are self-explanatory, as they schedule energy-intensive computations during optimal times, based on the Marginal Operating Emissions Rate (MOER). While the previous study demonstrated the effectiveness of these strategies in specific scenarios and highlighted their straightforward implementation for industry adoption, further research was necessary to evaluate their effectiveness across different scenarios. This research aims to determine the extent to which emission reductions are consistent in a different context. Particularly, while previous research focused on NLP and Computer Vision, our study uses the Fintech domain as a benchmark, defining a testbed of four algorithms for the fraud detection task: Isolation Forest, Support Vector Machine, autoencoder, and HF-SCA (a highly customised U-Net). The workloads in our study differ in their shorter duration and the geographical regions considered, specifically aligning with the presence of AWS data centres in Europe. It is crucial to note that the regions we studied are generally greener than those considered in previous research. Our results are generally consistent with existing literature, though we observed some notable differences in the effectiveness of the strategies. For Flexible Start, we confirm that shorter workloads yield higher carbon savings. However, the percentages observed in our experiments are about half of those reported in state-of-the-art studies, which may be due to the different variability ranges of MOER in the regions considered. For Pause & Resume, the greener the region, the more effective the strategy becomes. In fact, the results for HF-SCA are comparable to those for a 6.1B transformer, despite the latter's workload being more than ten times longer. We can summarise the main outcomes of our research as follows:  
* We reimplemented the code for Flexible Start and Pause & Resume, which is available upon request. * We confirmed the effectiveness of two existing carbon-aware AI training strategies, providing additional useful insights. * We contributed to the field of Green AI, hopefully encouraging further research efforts in this specific domain. The results of our research are intended to benefit the following categories of stakeholders:
* Industry: our findings provide more evidence on the effectiveness of the proposed strategies, enabling industry players to decide if, when, and how to apply these strategies to make their AI usage greener and more aligned with the ecological transition, particularly with regard to ESG requirements. * AI Community: the results can be used to develop AI libraries and frameworks that natively support carbon-aware scheduling of training workloads. * Researchers in Green AI and Green Software: researchers can use our results to assess and compare the effectiveness of the proposed strategies in new scenarios and to develop and benchmark new strategies against those analysed in this work. Further research is needed to enhance our understanding of carbon-aware training strategies. Specifically, similar analyses should be applied to strategies involving the spatial relocation of workloads, as relying solely on a specific region limits the potential carbon savings. Another interesting aspect is the potential of these strategies to mitigate the carbon emissions of the inference phase; in this case, the same analysis would reveal the extent to which the strategies could make AI models deployed in production greener. Additionally, the implications of delaying user responses due to unfavourable MOER at specific times should be considered, particularly concerning the time sensitivity of different domains.