An Interpretable Machine Learning-Based Framework for CO2 Emission Prediction and Optimization: A Case Study of a University Campus

Carbon peaking and carbon neutrality targets have become central to global climate governance. Building accurate CO2 emission prediction models to forecast trends and inform mitigation strategies is crucial for addressing climate change. This work proposes an interpretable, integrated prediction optimization framework grounded in fine-grained categories and emission factors, coupling seasonal, demographic, and temporal effects. A Random Forest (RF) model, interpreted via SHapley Additive exPlanations (SHAP) and correlation analysis, enables attribution of key drivers and prioritization of control strategies. Using comprehensive data from a university campus located in Shandong Province, we conduct detailed carbon accounting and derive actionable emission reduction plans under two distinct scenarios—high-comfort soft control and low-comfort hard control. Results demonstrate strong applicability to campus “large-scale community” settings, enabling differentiated control across building types and seasons. The framework achieves accurate emission predictions with R2 of 0.92, identifies energy consumption as the dominant emission source, and realizes 20–30% reduction potential in key building categories during different seasons while maintaining operational viability. This study provides substantial methodological support for regional CO2 reduction strategies, sustainable development pathways, and the achievement of carbon-neutrality goals.