Estimating carbon emissions from thermal power plants based on thermal characteristics

Carbon emissions from thermal power plants (TPPs) are a significant source of anthropogenic carbon and a priority for carbon reduction efforts. The accurate estimating of carbon emissions from TPPs is essential research in the field of carbon reduction. In this study, we take TPPs located in the United States as examples to discuss the relationship between the thermal characteristics of the power plant and its annual carbon emissions. Our data consists of land surface temperature (LST) product generated from Landsat 8 thermal data, LST product from ERA5-Land reanalysis dataset, and carbon emission data reported by the U.S. Energy Information Administration (EIA). Based on pertinent research of urban heat islands, we propose the concept of thermal characteristic index (TCI), which can provide a quantitative description of power plant thermal characteristics. Furthermore, we convert TCI to annual thermal characteristic index (A-TCI) by annual end-point interpolation and Simpson integration of the TCI series to match the annual carbon emission data and then establish a TCI-based annual carbon emission estimation model (TACEE-Model). Using the TACEE-Model, we predict the annual carbon emission values and evaluate the model’s performance. Experimental results indicate that the TACEE-Model exhibits great predictive capability, with the mean absolute percentage error (MAPE) of 0.0325, and the average prediction percentage error of 3.24%. The application of annual end-point interpolation and Simpson integration can significantly enhance the model’s prediction accuracy, resulting in a 51.42% reduction in MAPE. Overall, this research presents a new method for verifying annual carbon emissions from TPPs, which can contribute to the supervisory oversight of carbon emission reduction from TPPs.