## 4 Materials and Methods  
### Research Methodology  
In Figure 2, we report the adopted research methodology. It is made of four steps, enumerated from 1 to 4. Step 1 consists of what we have already done in the previous sections, i.e., an overall review of the scientific background on the topic of Green AI and a careful study of the state-of-the-art carbon-aware strategies, namely, Flexible Start and Pause & Resume. Once these strategies have been identified and analysed, the idea is to re-implement them and conduct benchmarks to confirm their validity in different conditions by performing an in vitro experimentation. To do this, it is necessary to choose different workloads \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **BERT** & **BERT** & **\begin{tabular}{c} **BERT** \\ **Time-** \\ **tune** \\ \end{tabular} & \begin{tabular}{c} **BERT** \\ **LM** \\ \end{tabular} & \begin{tabular}{c} **\begin{tabular}{c} **6.1B** \\ **Trans-** \\ **former** \\ \end{tabular} & \begin{tabular}{c} **Dense** \\ **121** \\ \end{tabular} & \begin{tabular}{c} **Dense** \\ **169** \\ \end{tabular} & \begin{tabular}{c} **Dense** \\ **201** \\ \end{tabular} & \begin{tabular}{c} **ViT** \\ **Tiny** \\ \end{tabular} & \begin{tabular}{c} **ViT** \\ **Small** \\ \end{tabular} & \begin{tabular}{c} **ViT** \\ **Base** \\ \end{tabular} & \begin{tabular}{c} **ViT** \\ **Large** \\ \end{tabular} & \begin{tabular}{c} **ViT** \\ **Huge** \\ \end{tabular} \\ \hline \hline Hours & 6 & 36 & 192 & 0.3 & 0.3 & 0.4 & 19 & 19 & 21 & 90 & 216 \\ \hline kWh & 3.1 & 37.3 & 13,812.4 & 0.02 & 0.03 & 0.04 & 1.3 & 2.2 & 4.7 & 93.3 & 237.6 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} 24 h \\ increase \\ \end{tabular} } & Flexible & 14.5\% & 3.4\% & 0.5\% & 26.8\% & 26.4\% & 25.9\% & 5.6\% & 5.3\% & 4.2\% & 1.3\% & 0.5\% \\  & Pause \& 19.0\% & 8.5\% & 2.5\% & 27.7\% & 27.3\% & 27.1\% & 12.5\% & 12.3\% & 11.7\% & 4.7\% & 2.4\% \\ \hline \multirow{2}{*}{
\begin{tabular}{c} 100\% \\ increase \\ \end{tabular} } & Flexible & 7.0\% & 4.1\% & 2.6\% & 1.8\% & 2.5\% & 2.7\% & 5.0\% & 4.8\% & 3.9\% & 3.3\% & 3.0\% \\  & Pause \& 9.5\% & 11.0\% & 11.4\% & 2.0\% & 2.8\% & 3.1\% & 11.0\% & 11.0\% & 10.8\% & 11.4\% & 11.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The reductions in percent averaged over the year and across the 16 regions for the Flexible Start and Pause & Resume allowing for a 24 h increase and a 100% increase in job duration. Hours and energy consumption refer to training workload without strategies. Figure 2: Research methodology adopted in this paper. (Step 2). Specifically, in this work, we selected algorithms from the field of AD, particularly fraud detection, because of the availability of a large dataset of real banking transactions provided by a well-known Italian bank. Therefore, we tracked the energy consumption of training those AD algorithms, and then we projected such energy needs on a dataset of historical Marginal Emissions Operating Emissions Rate (MOER) provided by the research partner WattTime (Step 3). The idea is to conduct a retrospective analysis to understand what the carbon emissions could be if training were executed in one way rather than another. These training jobs were executed on a remote machine, and their energy consumption was monitored during the execution. Once the energy consumption data is obtained and mapped with the marginal emissions, benchmark calculations were performed to provide a comparative analysis of the different strategies (Step 4). Specifically, all strategies were executed under the same conditions for the considered year, 2021, as well as regions and days for each month. The idea is to calculate the percentage reductions in carbon emissions for each strategy by averaging the results over the entire year of 2021. For each month, we selected six equal starting times, corresponding to six days. This choice is made to accurately describe the average emissions for each month. This approach aligns with the methodology used by [PERSON] et al.