Modeling Sustainable Economic Decisions Under Uncertainty: A Robust Optimization Framework via Nonlinear Scalarization

Sustainable economic decision-making increasingly requires robust methodologies capable of withstanding deep uncertainty, especially in volatile financial and resource-constrained environments. This paper introduces a unified optimization framework based on nonlinear scalarizing functionals, designed to support resilient planning under structural ambiguity. By integrating performance objectives with risk boundaries, the proposed model generalizes classical robustness paradigms—such as strict and reliable robustness—into a single tractable and economically interpretable formulation. The key innovation lies in translating scenario-based uncertainty into a directional performance index that reflects both feasibility and desirability, aligned with stakeholder-defined sustainability criteria. A case study in multi-scenario portfolio allocation demonstrates the model's ability to maintain return stability while respecting predefined risk tolerances, even in the absence of complete probabilistic information. The results highlight how scalarization enables transparent, behaviorally consistent, and computationally efficient decision-making, offering a valuable contribution to sustainable resource allocation and risk-sensitive economic policy. This work contributes to bridging the gap between abstract optimization theory and applied sustainability challenges, promoting robust and adaptive strategies for decision-makers operating under uncertainty.