## 4 Results  
We used two mechanisms to validate our models. First, through cross-validation on our training set, performance was measured on the held-out fold and aggregated across the four folds (details in Section 3.8). Second, additional generation and emissions data were gathered from plants with NDT and/or FGD in Turkiye (not reported to ENTSO-E or EU ETS with the rest of Europe) and India (Appendices A.4 and A.5), plus plants with NDT in the US, Europe (reported to ENTSO-E or EU ETS), and Australia that did not meet our strict filters for training but did qualify with a looser filter due to a lower capacity or a more heterogeneous set of cooling types (Section 3.4). We refer to the first mechanism on training plants as \"cross-validation\" and the second on non-training plants as \"external validation\". Table 2 below summarizes the fraction of the total power sector emissions and the number of observations, plants, and countries to which we applied our ML methods, as compared to prior work we discussed in Section 2. We validated our methodology on nearly twice as many plants, with two-to-four orders of magnitude more observations, and a broader selection of countries than any prior study. Furthermore, our inference predictions encompass 1042 power plants across 41 countries, including several plants in each of the five countries that produce the most carbon emissions from power generation (in order): China, US, India, Russia, and Japan [50]. Figure 6 displays the coverage of our emission estimates for 2022 on a map. Using these two mechanisms, cross-validation and external validation, the performance was evaluated for our sounding, generation, and emissions models. The results are detailed in this section. We also calculated 90% confidence intervals by block bootstrapping the test set (blocking on plants, i.e., resampling plants rather than images to ensure plants were not split up). Our validation sets consist of combustion power plants with NDT and/or FGD technologies. Few plants burn exclusively one type of fuel; most use a variety of types. Plants that use NDT may have any variety of fuel mix, even 0% coal, but plants with FGD all burn some amount of coal because the purpose of FGD is to remove the \(\text{SO}_{2}\) emitted when coal is burned. The vast majority of plants in our validation sets burn some amount of coal. Of the 207 plants in our emissions validation dataset, 16 (8%) did not burn any coal since 2016. These 16 plants all burn mostly gas, with oil or waste heat filling the gaps (no biomass). There are two plants that burn biomass in this emissions validation dataset; however, both still burn mostly coal. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Approach** & **\% Power Emissions** & **\# Observations** & **\# Plants** & **\# Countries** & **Years** \\ \hline Climate TRACE ML & & & & & \\ Cross-val & 7\% & 157,831 & 139 & 12 & 2015–2022 \\ External val (generation) & 2\% & 107,440 & 101 & 16 & 2018–2022 \\ External val (\(\text{CO}_{2}\)) & 7\% & 162,588 & 207 & 17 & 2019–2022 \\ All inference & 32\% & 1,198,167 & 1042 & 41 & 2015–2022 \\ \hline [PERSON] [32] & \textless{1\%} & 2131 & 146 & 18 & 2019–2021 \\ [PERSON] et al. [31] & \textless{1\%} & 1639 & 146 & 11 & 2020 \\ [PERSON] et al. [15] inference & 4\% & 106 & 78 & 15 & 2018–2021 \\ [PERSON] et al. [15] validation & \textless{1\%} & 50 & 22 & 1 & 2018–2021 \\ [PERSON] et al. [7] & \textless{1\%} & 28 & 21 & 4 & 2014–2020 \\ [PERSON] et al. [12] & \textless{1\%} & 20 & 14 & 6 & 2014–2018 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coverage summary of Climate TRACE’s ML approach, as compared with prior work discussed in Section 2; all studies that included ten or more plants are listed.