## 4 Results and Discussion
### Feature Importance and SHAP Analysis
Together, these SHAP results offer a powerful diagnostic toolkit for stakeholders to not only understand model behavior but also identify leverage points for intervention. The ability to trace how each feature influences cost and emission predictions across different ranges and interaction conditions significantly strengthens the operational credibility of the GBR-based surrogate model and supports evidence-based policy formulation. In Figure 7, which summarizes the model behavior for cost prediction, the dominant variables are clearly Transport Distance (X\({}_{1}\)) and Processing Energy Intensity (X\({}_{2}\)), with mean SHAP values of 1.25 and 1.08, respectively. This aligns with traditional life-cycle cost assessments (e.g., [PERSON] et al. [73], [PERSON] [74]), which have consistently identified fuel-related haulage expenses and electricity-based processing charges as the principal contributors to the economic footprint of heavy construction materials. The cost impact of the Grid Emission Factor (X\({}_{5}\)), while notable, is relatively moderate (0.79), likely because regional energy price differences are less extreme than emission intensity variations. Gra  
Figure 6: SHAP dependence plots illustrating the marginal effect of key input variables on the predicted cost. dation Adjustment (X4) and Moisture Content (X3) show limited influence on cost, except in edge cases involving high processing demand, suggesting that they are secondary in economic terms. To understand the internal mechanics of the GBR model in predicting equivalent CO2 emissions, SHAP analysis was extended specifically to the emission target variable. Figure 8 summarizes the global feature importance rankings based on mean absolute SHAP values, while Figure 9 presents dependence plots that reveal how each input variable influences the emission output across its operational range. Together, these results offer a granular understanding of which process characteristics and regional factors most significantly drive the environmental footprint of slag-based base layer construction. Figure 8: Feature importance for CO2 emission prediction. Figure 7: Feature importance for cost prediction. The SHAP summary plot (Figure 8) reveals that Moisture Content at the source (x\({}_{3}\)) is the most dominant factor affecting CO\({}_{2}\) emissions, followed closely by the Grid CO2 Emission Factor (x\({}_{5}\)) and Processing Energy Intensity (x\({}_{2}\)). It is important to note that SHAP values quantify the marginal contribution of each input feature to the model's prediction under the structure of the trained GBR model. While SHAP provides insight into feature influence, it does not imply causality in a mechanistic or physical sense. This finding strongly supports the thermodynamic rationale that higher moisture levels necessitate additional thermal energy for pre-drying, particularly under field compaction protocols. As shown in Figure 6 (bottom-left), SHAP values for x\({}_{3}\) increase sharply and almost linearly beyond 8% moisture, confirming the exponential relationship between water mass and required drying energy described by Fourier heat conduction and latent heat principles. [PERSON] et al. [75] demonstrated that a 3% increase in moisture content is associated with a 25-30% increase in predicted CO\({}_{2}\) emissions under the learned model. The second-most important feature, Grid CO\({}_{2}\) Emission Factor (x\({}_{5}\)), displays a near-linear correlation with model output (see Figure 9, bottom-right). This indicates that even when total energy consumption remains constant, the carbon footprint can vary significantly depending on the regional electricity mix. As highlighted in [PERSON] et al. [76], electrification strategies that are not coupled with aggressive grid decarbonization can paradoxically worsen life-cycle emissions. In this context, the model's behavior emphasizes the criticality of where processing is conducted, not just how much energy is used.