Environmental Science and Technology Parks (ESTPs) can be powerful platforms for driving sustainable industrial transformation, particularly when the focus is on decarbonization and energy resilience. Yet, many ESTP initiatives fall short because site selection often relies on fragmented criteria, inconsistent comparisons across regions, and a weak handling of uncertainty in both expert judgment and environmental constraints. This chapter introduces a hybrid soft-computing framework to prioritize locations for an industrial-energy ESTP in a micro-region of Buenos Aires Province, Argentina. Six municipalities are evaluated using a mission-driven criteria system that balances industrial capacity, logistics, energy transition potential, and environmental governance. The approach combines Fuzzy-AHP for weighting criteria under uncertainty with TOPSIS for ranking alternatives, supported by scenario-based sensitivity analysis to test ranking stability. Results reveal a robust top-ranked hub across scenarios, with mid-ranked options varying according to environmental and logistics priorities. The framework offers a transparent, replicable tool for aligning sustainability policy with practical siting decisions in emerging-economy contexts.

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