sub-sector accounts for an average of \(\sim\)44% or \(\sim\)15 G\(\mathrm{\text{G}}\)CO\({}_{2}\), representing \(\sim\)31% of total global GHG emissions from 2015 to 2020 [2]. To curb power plant emissions requires monitoring their activity.  
The monitoring of power plant emissions varies globally and involves self-reporting. This includes the continuous emissions monitoring systems (CEMS) that are employed in countries with strict emissions laws, such as Japan, the US, the European Union (EU), and South Korea [5; 6]. CEMS equipment is installed at individual power plants and measures emissions, providing reliable measurements to ensure that the plant is complying with country emission regulations. However, CEMSs are costly, require specialized teams to maintain and calibrate, come in different forms that impact the measurement quality, and have limited deployment globally with some countries only deploying them at the largest emitting power plants [5; 6; 7]. A more common approach to monitoring emissions is bottom-up self-reporting. This approach quantifies power plant emissions using fuel consumption, fuel quality, and emission factors [8; 9]. To use the bottom-up approach accurately and effectively requires quality data and, if not known, can have high uncertainties in fuel properties, which translates into high uncertainty in estimating power plant emissions [7; 8; 10]. Lastly, self-reporting varies by region in terms of latency and granularity. This creates challenges for policymakers in designing strategies to mitigate and reduce a country's emissions and, consequently, in meeting sustainability goals [7; 11].  
Within the last decade, efforts have been made to use GHG monitoring satellites and aerial surveys to infer and improve power plant emission monitoring [7; 10; 11]. This includes studies that have derived CO\({}_{2}\) emissions from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Global Airborne Observatory (GAO) aerial surveys, the Ozone Monitoring Instrument (OMI) on Aura, Orbiting Carbon Observatory-2 and -3 (OCO-2 and -3), and the Precursore Iperspettrale della Missione Applicativa (PRISMA) satellites [6; 7; 10; 11]. These missions were not originally designed for monitoring CO\({}_{2}\) point sources and yet have provided robust emission estimates in a consistent way--not derived from the varying approaches described above. However, these studies have limited deployment, and some GHG monitoring satellites work best when observing some of the highest emitting power plants based on their sensor's CO\({}_{2}\) sensitivity and detection threshold [7; 11]. Alternatively, to provide higher spatial and temporal resolutions for power plants, multi-spectral imaging satellites that observe Earth's land and ocean provide an opportunity to measure power plant emissions. Numerous multi-spectral satellites are in orbit, and one can combine the imagery from many of these satellites to produce robust and continuous global coverage, identifying more detailed power plant activity related to emissions.  
Climate TRACE (Tracking Real-time Atmospheric Carbon Emissions) is a coalition of organizations working towards improved emissions monitoring (climatetrace.org, accessed on 28 March 2024). Climate TRACE members WattTime (vatttime.org, accessed on 28 March 2024) and TransitionZero (transitionzero.org, accessed on 28 March 2024) developed the methods in this article that use multi-spectral satellite imagery combined with in situ (reported) generation data to train machine learning (ML) models to infer a power plant's generation via its activity. These models were trained using satellite imagery on plants in countries with reported hourly or sub-hourly generation data. Subsequently, they were applied globally to estimate power plant activity, which was combined with regional-, plant-type-, and fuel-specific carbon intensities to estimate the plant-level CO\({}_{2}\) emissions.