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, particularly 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. A key innovation lies in translating scenario-based uncertainty into a directional performance index, aligned with stakeholder-defined sustainability criteria and encoded via a preference vector k. This scalarization approach supports behaviorally consistent and computationally efficient decision-making even in the absence of complete probabilistic information. A case study in multi-scenario portfolio allocation demonstrates the model’s capacity to maintain return stability while respecting predefined risk tolerances. Computational benchmarks confirm the framework’s scalability to larger problem instances, validating its practical applicability. Beyond financial applications, the model also holds promise for sustainable policy design, infrastructure planning, and resource allocation under deep uncertainty. This work contributes to bridging the gap between abstract optimization theory and applied sustainability challenges, offering a robust and adaptive decision-support tool for real-world implementation.