Leveraging Industry 4.0 for SDGs: A DEMATEL- ISM Decision Framework Integrating Fuzzy Hesitant and Rough Sets

Research background & objectives. Industry 4.0 (I4.0) technologies reshape production yet yield mixed sustainability outcomes. This study quantifies I4.0 → SDG effects and derives a causal, actionable roadmap. Gaps, method, and advantages. Prior work often lacks (i) explicit uncertainty handling, (ii) causal structure, and (iii) policy-oriented prioritization. We propose a hybrid FHS + RST → DEMATEL → ISM pipeline. Uncertainty is decomposed into epistemic (conflicting evidence), aleatory (cross-region/sector variability), model/structural (thresholds and layering), and scenario (adoption/policy paths); these maps respectively to FHS hesitancy, RST approximations, DEMATEL/ISM sensitivity, and parameterized scenarios. The framework integrates expert judgment with cross-industry data, yielding transparent reduction, robust weights, and interpretable hierarchy. Results & significance. Median gains include +30% energy efficiency, -20% carbon emissions, and -25% waste across representative cases; robotics raise productivity 30-45% yet reduce low-skill jobs 15-22%, implying reskilling needs. Regions flagged as infrastructure-inadequate show attenuated IoT/Big-Data effects, validating an "connectivity → enablers → scalable support"sequence. The approach advances uncertainty-aware, causally structured decision-making for smart manufacturing, sustainable operations, and digital-infrastructure planning within the conference scope.