## 5 Discussion  
In Section 4, we compared our model predictions against reported electricity generation and CO\({}_{2}\) emissions data reported from national inventories. For sounding and generation model validation, we included a baseline approach using training set averages, which is a standard ML baseline. For annual validation, we compared with EIA- and EMBER-derived annual country-/fuel-specific averages. The reader may be left wondering why we did not present comparisons against other approaches, such as the Emissions Database for Global Atmospheric Research (EDGAR), the open-source data inventory for Anthropogenic CO\({}_{2}\) (ODIAC), or CO\({}_{2}\) concentrations derived from OCO-2/3. In the case of EDGAR, it provides estimates at the annual and country level, so we can neither compare individual power plants nor individual timestamps, and, since our ML method is applicable to only specific power plants with the NDT and/or FGD technologies producing water vapor plume proxy signals, we cannot simply sum to obtain the country totals. However, by augmenting our ML method with the EIA- and EMBER-based country-/fuel-specific averages to fill the gaps for plants that lack the water vapor plume signal, our previous studies did indeed match very well with EDGAR [50,51]. As for ODIAC, it is also not directly comparable because it mixes in other fossil fuel activities outside of electricity and is a gridded product, so it does not report individual assets. We considered comparing it to CARMA, which underlies ODIAC and focuses on individual power plants, but it has not been updated since 2012 and only provides static estimates, thus failing to provide as recent and granular a comparison as the EIA- and EMBER-based averages. Finally, we explored matching our instantaneous sounding-level model predictions against some of the studies that use OCO-2/3 highlighted in Table 2. However, they either did not report the timestamps needed for matching ([PERSON] et al. [15]) or the lack of overlapping timestamps between imagery sources produced ten or fewer observations to compare against ([PERSON] et al. [7] and [PERSON] et al. [12]). The low availability of processed space-based CO\({}_{2}\) concentrations remains the primary challenge in comparing against this data source. For example, [PERSON] et al. [15], which presented the largest number of observations out of these three papers, had an average of only 1.4 observations per plant (range 1-6) over four years, while our methods for the same set of plants, have a mean of 836 observations per plant (range 473-1510) over the same period. In the future, we would be eager to coordinate directly with science teams to perform a comprehensive comparison, and, as direct CO\({}_{2}\) remote sensing approaches continue to expand over the next few years, we look forward to incorporating these data into our modeling efforts. Our results in Section 4 reveal where our models perform well, as well as a few regions and plant types with larger errors and, in some cases, a negative bias in estimates. We identified the following sources of error that may have contributed:  
1. FGD usage. Because our models assume continuous wet FGD usage, if a plant is mislabeled and has dry FGD instead of wet or does not run its FGD continuously, our models will tend to underpredict emissions. We manually filtered out the most obvious examples of plants that showed no FGD signal but had other signs of activity from our FGD models. However, there could be plants we missed and therefore underestimated their emissions. 2. Changing plant characteristics. For simplicity, our current filtering approach is not time-dependent and assumes fixed plant characteristics; however, plants may change over time and add or remove FGD or NDT units. A plant may meet FGD or NDT criteria one year and not another, yet we treat all years the same. To address this issue for the time being, we filtered out plants that did not meet the filtering criteria for one or more years during the period of analysis (2015-2022 inclusive).