## 4 Results and Discussion
### Feature Importance and SHAP Analysis  
To enhance the interpretability of the gradient boosted regression (GBR) model and quantify the relative contribution of each input variable to the predicted outputs, a comprehensive SHAP (SHapley Additive exPlanations) analysis was performed. SHAP offers a unified framework grounded in cooperative game theory, capable of decomposing complex model predictions into additive feature attributions. This capability is particularly valuable in engineering applications where model transparency is crucial for regulatory compliance, public sector decision-making, and model validation across heterogeneous operational scenarios. The global SHAP summary plot for cost prediction identified Transport Distance (X\({}_{1}\)) and Energy Intensity (X\({}_{2}\)) as the most influential variables, followed by Grid Emission Factor (X\({}_{5}\)) and Gradation Level (X\({}_{4}\)). These rankings are consistent with physical expectations and highlight the key operational drivers of economic cost in slag-based infrastructure projects. To further elucidate feature behavior, Figure 4 presents SHAP dependence plots for four selected variables, offering deeper insight into interaction effects and nonlinear dependencies. In the top-left panel of Figure 6, SHAP values for transport distance exhibit a near-linear relationship with cost impact, particularly beyond 100 km. Interestingly, the variance of SHAP values increases slightly at long distances (>200 km), reflecting interactions with gradation level and moisture content that affect overall transport mass and handling requirements. This observation is consistent with findings by [PERSON] et al. [70], who reported that transport of building materials contributes significantly to the environmental impacts of construction, accounting for over 45% of life-cycle costs. The top-right panel highlights the sensitivity of predicted cost to Energy Intensity (x\({}_{2}\)), which includes mechanical treatment stages such as crushing, sieving, and moisture conditioning. The SHAP curve shows a pronounced positive slope between 1.2 and 1.8 kWh/ton, after which the impact saturates. This nonlinear response suggests threshold behavior in equipment efficiency, possibly influenced by system load or stage multiplicity. The bottom-left panel addresses Moisture Content (x\({}_{3}\)) and its effect on predicted CO\({}_{2}\) emissions. SHAP values rise sharply for moisture values above 8%, aligning with thermodynamic principles: drying energy requirements scale exponentially with initial water mass, as described by [PERSON] and latent heat relationships in kiln-drying systems. In maritime or humid climates where ambient slag exposure is high, this becomes a critical design consideration. This observation is in line with that studied by [PERSON] et al. [71], who demonstrated that even modest increases in initial water content can elevate CO\({}_{2}\) emissions by over 20% due to additional heat input for pre-drying. The bottom-right panel examines the influence of Grid CO\({}_{2}\) Emission Factor (x\({}_{5}\)). SHAP values grow nearly linearly as the grid factor increases from 0.3 to 1.0 kg CO\({}_{2}\)/kWh, reinforcing the dominant role of energy source mix in determining environmental burden. This result supports prior analyses by [PERSON] et al. [72], who emphasized that electrification without grid decarbonization can actually worsen life-cycle emissions in materials processing sectors. An important insight from these plots is the interaction between x\({}_{2}\) and x\({}_{5}\), particularly visible when high energy demand overlaps with carbon-intensive grids. In such cases, the marginal emission impact per kWh can exceed 1.0 kg CO\({}_{2}\)/kWh, suggesting that regional grid decarbonization is not merely a background variable but an actionable design lever. Similarly, co-dependence between transport distance and gradation level implies that logistics planning should be carried out jointly with material quality requirements, rather than sequentially or in isolation.