###### Abstract
Thus, given the considerable scale of CO\({}_{2}\)/NO\({}_{\rm x}\) emissions from power plants, reducing the carbon footprint is crucial for mitigating the impacts of climate change and transitioning towards a more sustainable energy future for our society [20]. To achieve the goal of reducing CO\({}_{2}\) and NO\({}_{\rm x}\) emissions, one primary step is to accurately and effectively monitor CO\({}_{2}\) and NO\({}_{\rm x}\) through the accurate and reliable monitoring of carbon emissions, especially regarding the processes of fossil fuel power plants, as this is essential for ensuring compliance with environmental regulations, reporting emissions to relevant authorities, and informing emission reduction strategies [14]. There are two major ways to monitor CO\({}_{2}\) emissions from fossil fuel plants: continuous emission monitoring (CEM) and predictive emission monitoring (PEM) [21]. Continuous Emission Monitoring is one of the most widely used methods for monitoring carbon emissions in fossil fuel power plants. CEM is designed to continuously measure the concentration of pollutants in the flue gas emitted from power plant stacks. Such a system comprises all the essential equipment needed to measure the concentration of gas or particulate matter, as well as the emission rate, by utilizing pollutant analyzer readings and a conversion formula, chart, or software to generate outcomes in the relevant emission standard units [22]. CEM provides real-time, accurate measurements of CO\({}_{2}\) emissions, enabling power plant operators to demonstrate compliance with environmental regulations and report emissions to the relevant authorities. As mentioned before, the complex nature of CEM data, which include large volumes of high-frequency measurements, can pose challenges for data management, analysis, and interpretation. Predictive Emission Monitoring is an alternative approach to monitoring carbon emissions in fossil fuel power plants. Unlike CEM, which directly measures pollutant concentrations, PEM uses process parameters and emission factors to estimate emissions. These systems rely on mathematical models that correlate process variables, such as fuel consumption, boiler temperature, and flue gas flow rate, with emission levels. The input data of the control system, historical data, and process parameters are fed in to connect with the computation algorithm to obtain the prediction of CO\({}_{2}\) and NO\({}_{\rm x}\) emissions. Thus, the accuracy of PEM largely depends on how well the built-in mathematical methods consider and model the above process variables [23; 24]. PEM offers several advantages over CEM, including lower installation and maintenance costs and greater flexibility in monitoring multiple emission sources [23]. The installation, operation, and maintenance of CEM require expensive hardware, regular calibration, and skilled personnel to ensure accurate and consistent measurements. It is estimated that the capital cost of PEM can be 50% lower than CEM [25,26]. In addition, its operational cost is estimated to be 10-20% lower than CEM [27]. However, the accuracy of PEM is highly dependent on the quality of the underlying models and the availability of reliable process data. Inaccurate or incomplete data can lead to significant discrepancies between estimated and actual emissions, potentially compromising compliance with environmental regulations. In addition, the complexity of power plant process operations and the variability of fuel quality and combustion conditions make it difficult to analyze and interpret the vast amounts of data generated by CEM [23]. Furthermore, PEM may not provide the same level of temporal resolution as CEM, which can limit the ability to detect short-term emission spikes or anomalies [26]. From what we have reviewed above, it can be shown that PEM is more robust and cost-effective compared to CEM. However, traditional PEM relies largely on process parameters for accurate and effective model development.