## 4 Results and Discussion
### Feature Importance and SHAP Analysis
This has direct implications for infrastructure planning, suggesting that sitting decisions for slag  
Figure 9: SHAP dependence plots illustrating the marginal effect of key input variables on the predicted CO\({}_{2}\) emission cost. processing facilities should consider the carbon intensity of regional grids. Interestingly, Processing Energy Intensity (x\({}_{2}\))--while the leading factor in cost prediction--has a reduced but still notable impact on CO\({}_{2}\) emissions. As illustrated in Figure 6 (top-right), its SHAP influence becomes pronounced only after 1.5 kWh/ton, indicating a nonlinear threshold beyond which additional energy input significantly amplifies emissions. In essence, not all increases in energy use are equally carbon-intensive; their impact is modulated by both grid context and energy efficiency of the process [77]. Transport Distance (x\({}_{1}\))--while a major cost driver--has relatively muted importance in CO\({}_{2}\) emissions (Figure 9, top-left), with SHAP values rarely exceeding 1.0 across the 5-250 km range. This suggests that diesel-based freight emissions are less significant compared to electricity-based emissions, especially in processing-intensive configurations. Gradation Adjustment Level (x\({}_{4}\)), although present in the model, demonstrates minimal SHAP contribution in both cost and emission predictions. From an engineering perspective, these results underscore a critical insight: emission mitigation cannot be achieved solely through energy minimization--it must be pursued through a combination of input control (e.g., moisture management), strategic facility sitting (grid-aware), and process efficiency (post-threshold load optimization). This explains why emissions-driven optimization paths often diverge from cost-optimal configurations, justifying the need for dual-objective frameworks like the one employed in this study. Moreover, the SHAP interaction behavior between x\({}_{2}\) and x\({}_{5}\)--as seen in regions of the SHAP space with both high energy use and high grid factors--reinforces that emissions are a system-level outcome, not a single-variable function. This supports integrated policy measures, such as emissions-weighted tariffs or carbon-adjusted location scoring, especially in public procurement of green infrastructure materials. To formalize these interaction patterns, we computed SHAP interaction values using TreeExplainer's interaction matrix. The interaction strength between features was quantified using the mean absolute SHAP interaction values across all samples. The highest interaction strength was observed between Moisture Content (x\({}_{3}\)) and Energy Intensity (x\({}_{2}\)), with a mean value of 0.14, suggesting that the emission impact of energy use is highly modulated by moisture levels. Additional notable interactions included Transport Distance (x\({}_{1}\)) \(\times\) Grid CO\({}_{2}\) Factor (x\({}_{5}\)) (0.10), and Moisture Content \(\times\) Gradation Level (x\({}_{4}\)) (0.09), both of which indicate operational synergies between logistics and material conditioning processes. These quantitative insights confirm the non-additive nature of the GBR model and highlight key coupled variables that may require coordinated policy or engineering interventions. In conclusion, the SHAP analysis for CO\({}_{2}\) emission prediction provides deep interpretability to the GBR model, validates its alignment with known physical and empirical behaviors, and offers actionable levers for low-carbon material logistics. By exposing how emissions are driven by the interaction of process, material, and geography, this layer of analysis enhances both scientific credibility and practical usability of the modeling framework.