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This dataset was compiled to test the hypothesis that a structural decoupling exists in how uncertainty is treated in high-impact biorefinery techno-economic assessment (TEA) and life cycle assessment (LCA). Specifically, we hypothesized that while probabilistic methods are routinely applied to downstream financial and environmental indicators, intrinsic feedstock variability is rarely propagated through the non-linear technical core of process models—creating a systematic blind spot that deterministic averaging cannot reveal. Data collection: Scopus was searched using TITLE-ABS-KEY ((TEA OR LCA) AND (Biorefinery OR Biomass) AND (Sensitivity OR Monte Carlo)). From 757 initial records (2020–2025), studies with Field-Weighted Citation Impact > 2.0 were retained. Phase 1 screening required primary case studies with explicit process modeling and reported feedstock composition (at minimum mean values). After exclusions, 159 studies underwent full methodological coding in Phase 2. Classification framework: Studies were categorized by uncertainty approach: Hybrid stochasticity (HYB; probabilistic methods applied to financial/LCA parameters only), Local sensitivity (OAT; one-at-a-time perturbations), Scenario-based (SCN; discrete scenarios), and Deterministic (DET; point estimates only). Feedstock variability reporting was coded as Yes (dispersion metrics provided), Partial (incomplete), or No (point estimates only). What the data show: The dataset reveals a persistent methodological asymmetry. Probabilistic methods are widely adopted for market and environmental indicators, yet upstream technical transformation stages remain predominantly mean-based. Only a minority of studies propagate feedstock variability through non-linear process cores where kinetic constraints, transport limitations, and mass–energy coupling govern system behavior. When feedstock variability is reported, it is often incomplete or collapsed into point estimates. Key findings: Of the 159 audited studies, 47% employed OAT, 31% HYB, 15% SCN, and 7% DET. Full propagation of feedstock variability occurred in only 12% of cases; 23% partially reported variability, and 65% collapsed inputs into point estimates. This layered methodological decoupling means that curvature-driven effects originating from intrinsic variability remain unresolved, even when sophisticated financial risk analysis is applied. How to interpret and use these data: Each row represents one peer-reviewed study with full methodological classification. The "Observed modeling approach" column directly maps to the categories defined above. "Feedstock variability explicitly reported?" indicates data quality. Researchers can use this dataset to: (1) benchmark their own uncertainty practices against the field, (2) identify literature gaps for meta-analyses, or (3) extract case studies illustrating specific uncertainty approaches.