As a significant component of China′s energy structure, thermal power generation enterprises have long been the main sourceof carbon emissions in the country. With the global push for a low-carbon economy, those enterprisesare shifting from " dual control of energy consumption" to " dual control of carbon emissions." Under this backdrop, accurately measuring the carbon emissions of coal-firedpower plants has become crucial. In carbon measurement for coal-fired power plants, flue gas flow impacts the accuracy of the online monitoring method. In contrast, coal consumption, carbon content in coal, and carbon content in fly ash jointly determine the reliability ofthe calculation method.Currently, most coal-fired plants only perform real-time monitoring of flow and coal consumption. However, direct, high-frequency, short-cycle monitoring of carbon content in coal and fly ash in harsh plant environments requires significant humanand material resources and flow monitoring equipment is easily affected by the flue gas environment. Soft measurement technology, with itsefficiency and low cost, provides an alternative method for monitoring key parameters in traditional carbon emission measurements.Firstly,this study reviews the establishment of a soft measurement model, including data preprocessing, auxiliary variable selection, model establishment, and model correction. Data preprocessing ensures data quality and improves modeling efficiency. Auxiliary variable selection enhances modeling efficiency by filtering out useful variables. The soft measurement model, based on mechanism and data-driven modeling,is key to predicting target variables. Model correction optimizes the model with actual data, improving prediction accuracy.Secondly, thestudy analyzes issues in monitoring flue gas flow, coal consumption, coal carbon content, and fly ash carbon content. It discusses the research progress and application of soft measurement technology for these parameters. Mechanism modeling, based on energy balanceand mass conservation principles, has high interpretability and stability but is complex and less accurate. Data - driven modeling,using machine learning and data from distributed control systems (DCS) offers higher accuracy, but lacks transparency and generalizationability.Finally, this study summarizes and prospects the development and application of soft measurement technology in the field of carbonemission measurement. It provides suggestions for integrating the time-series structure of various plant parameters, the computational limitations of the plant itself, and the development of methods combining mechanism analysis and data-driven approaches. It summarizes theapplication scenarios of predictive CO emission systems abroad and anticipates the application of such systems combined with soft measurement technology in domestic and international coal-fired power plants.