An Integrated Financial–Sustainability Framework for Predicting Green Infrastructure Project Success

Ahmad A. Tareemi ()
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Ahmad A. Tareemi: Department of Civil and Environmental Engineering, Umm Al-Qura University, P.O. Box 715, Mecca 24382, Saudi Arabia

Sustainability, 2025, vol. 17, issue 21, 1-31

Abstract: To overcome the inadequacy of traditional financial metrics in appraising green infrastructure, this study develops and validates an integrated framework combining financial and sustainability indicators to more accurately predict project performance. Employing a mixed-methods design, this study synthesized metrics from expert interviews (N = 24) and literature, then collected data from 42 completed projects in Gulf Cooperation Council countries. The framework’s predictive validity was tested using a novel application of a Gradient Boosting Machine (XGBoost) model, with SHAP (SHapley Additive exPlanations) analysis ensuring model interpretability. The integrated framework yielded higher out-of-sample discriminatory performance (AUC-ROC = 0.88) than a baseline using only traditional metrics (AUC-ROC = 0.71). In SHAP analyses, RBCR and LCC contributed most to the model’s predictions, whereas NPV and IRR contributed least. These results indicate stronger predictive associations for sustainability-oriented metrics in this study’s model. Because the design is cross-sectional and predictive, all findings are associational rather than causal; residual confounding is possible. The validated, interpretable model is therefore positioned as a decision support tool that complements, rather than replaces, expert appraisal.

Keywords: green infrastructure; investment appraisal; machine learning; project performance; sustainability metrics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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