An Interpretable Machine Learning-Based Framework for CO 2 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 CO 2 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 R 2 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 CO 2 reduction strategies, sustainable development pathways, and the achievement of carbon-neutrality goals.