## 6 Conclusions  
Applying ML models to multi-spectral satellite imagery enables the identification of power plant generation activity and emissions with greater spatial coverage and resolution, more frequent revisits, and lower data latency than currently reported data. Additionally, combining imagery from multiple satellites provides more observations, and thereby more activity estimates, over current GHG-concentration remote sensing observations. The approach developed here can be applied to countries where the generation or emission data are not publicly available or cannot possibly be obtained due to technical limitations. Our ML and satellite monitoring approach creates the ability to provide publicly available power plant emission estimates on a frequent and plant-wise basis. Currently, our estimated power plant CO\({}_{2}\) emissions are available on the Climate TRACE website (climatetrace.org, accessed on 28 March 2024). As of fall 2023, the website contains country-level annual CO\({}_{2}\) emissions estimates during the period 2015-2022 and power plant, source-level, annual CO\({}_{2}\) emissions estimates during the period 2019-2022. The source-level estimates include power plants for the US, Europe (ENTSO-E for generation and EU ETS for emissions), Australia, Turkishye, and India, as discussed in Section 4.3. Additionally, the models were run on FGD and NDT plants in other regions of the world meeting the criteria described in Section 3.4. In total, we used our ML method to estimate the emissions of 1042 power plants, representing 3% of power plants globally but responsible for roughly 32% of the global combustion power plant CO\({}_{2}\) emissions averaged over 2015-2022, representing a major step forward in the world's ability to monitor power plant emissions. Our models' predictions were found to be more accurate than the alternative baseline calculations using country- and fuel-specific capacity factor averages. We identified a negative bias in Australia and Turkiye and hypothesized some sources for this error (e.g., plants running at higher average capacity factors than the bulk of the training data, as can be seen in Appendix D), that we will work to mitigate in the future. We continue to refine and improve the accuracy and coverage of our predictions in an effort to provide plant-level emissions estimates for more power plants. This includes the following:  
* Improving our regression models by better understanding the relationship between plume size, generation, and weather conditions;
* Creating mechanisms to estimate model bias;
* Including new and additional satellite measurements, e.g., thermal and SWIR, that can identify activity related to emissions;
* Sourcing additional reported data from regions outside the current training set to both validate and mitigate model bias;
* Investigating new proxy signals at plants that do not use NDT or FGD as well as signals widely applicable to other fuel sources;
* Increasing the precision of the carbon intensity modeling of individual power plants. The use of satellite imagery that is available at low latency allows for estimates to be derived at a higher recency than other GHG inventory methods and can track and identify the emissions down to the source level. This approach is a promising step forward in providing more up-to-date estimates and can complement current approaches to estimate emissions. This work can provide useful information to governments, corporations, and citizens that seek to reduce their GHG emissions to meet The Paris Agreement and sustainability goals. As this project is an ongoing effort, the Climate TRACE website (climatetrace.org, accessed on 28 March 2024) will continue to be updated with the best-known available methods to provide global coverage for power plant emissions estimation, and contributions from the community are welcome and encouraged. Conceptualization, H.D.C., M.A., J.F., H.K., A.R.K., J.O., I.S.-R., A.F., J.J., J.L. and C.M. ; methodology, H.D.C., M.A., J.F., H.K., A.R.K., J.O. and A.F.