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It is of great significance to analyze the calorific value and carbon dioxide emission level of coal with different degrees of coalification for energy conservation and emission reduction in coal-fired power plants, and elemental carbon and fixed carbon are closely related to the carbon dioxide emission level and calorific value. Due to the complexity of matrix effects and differences in regression tasks, modeling only all coal samples will limit the quantitative performance of laser-induced breakdown spectroscopy (LIBS). In this work, we combine a dual-cycle variable selection mechanism with competitive adaptive reweighted sampling to optimize partial least squares regression (PLSR). According to the difference between coal samples in coal quality characteristics and prediction tasks, the matrix correction of elemental carbon and fixed carbon is realized by qualitative classification and by using generalized spectra (GS), respectively. The coefficient of determination (R2) of the training set and test set of elemental carbon was improved from 0.7178 and 0.7095 to 0.9534 and 0.9515, respectively. The root-mean-square error of cross-validation (RMSECV) and the root-mean-square error of prediction (RMSEP) decreased from 1.1723 wt% and 1.2488 wt% to 0.4504 wt% and 0.4871 wt%, respectively. The R2 of the training set and test set of fixed carbon was also improved from 0.8252 and 0.8236 to 0.9814 and 0.9749. The RMSECV and RMSEP decreased from 1.2274 wt% and 1.3548 wt% to 0.4421 wt% and 0.4711 wt%, respectively. According to Chinese national standards and IPCC guidelines, the maximum error in CO2 emission factors for all coal samples during the accounting period is 1.7581%. Taking into account the level of CO2 emissions and the net calorific value, the selection of coal with different degrees of coalification can guide the combustion of mixed coal in power plants to achieve high efficiency and clean power generation. This work demonstrates the great potential of in situ LIBS for online applications in coal-fired power plants. [ABSTRACT FROM AUTHOR]