Explainable Modeling of the Management Mechanism Underlying Carbon Accounting-Deviation Formation in Coal-Fired Power Plants

This study develops a management-driven and interpretable framework for identifying the formation of scenario-derived accounting increments in coal-fired power plants. Based on real monthly operational and accounting data from a coal-fired power plant in Guangdong Province in 2024, key management activities, including on-site metering, sampling standardization, coal testing timeliness, and net calorific value testing compliance, were represented as structured management-state variables. A baseline accounting scenario and a management-corrected accounting scenario were constructed, and their difference was defined as a scenario-derived accounting increment. Bootstrap resampling, Monte Carlo simulation, ensemble learning, and CatBoost-based SHAP analysis were integrated to characterize how management states affect parameter admissibility, trigger correction rules, and alter accounting results. Under the random split setting, the Best Stacking model achieved an RMSE of 70.76, an MAE of 39.99, and an R2 of 0.999987, and was therefore retained as the primary predictive benchmark. Source-month grouped validation provided an additional internal robustness check, with CatBoost achieving an R2 of 0.990007 under the stricter grouped validation setting. CatBoost-based SHAP analysis showed that net calorific value testing compliance was the dominant management-state contributor, with a contribution share of 73.1%, while coal consumption and purchased electricity ranked second and third, indicating that activity-data magnitude amplified the scenario-derived accounting increments once correction rules were triggered. The proposed framework supports whole-process data-quality control and risk-oriented verification governance in carbon accounting.