A multiphase high-strength steel austempered at 260 °C for 24 h was investigated by quasi-in-situ tensile characterization and EBSD-based crystal plasticity finite element modeling. The experimental observations reveal that local plastic deformation is strongly heterogeneous: von Mises strain concentrates preferentially near bainitic-ferrite packets, phase boundaries, and retained-austenite/martensite–austenite regions, whereas blocky retained austenite contributes to strain accommodation at the early deformation stage. To quantify the underlying stress–strain partitioning, a quasi-two-dimensional representative volume element was reconstructed from EBSD data and implemented in ABAQUS through a user-defined material subroutine. The model contained the real grain morphology, phase distribution, and crystal orientation information of the 24 h austempered specimen. A rate-dependent crystal plasticity constitutive framework with BCC matrix, FCC retained austenite, and transformed martensite branches was calibrated against the macroscopic tensile curve. The simulated tensile response agrees well with the experimental curve before macroscopic instability, and the predicted local fields are consistent with the quasi-in-situ strain maps. The results show that local plastic strain first accumulates in M/A-related regions and phase-boundary-neighboring zones, while high Mises stress migrates dynamically with slip activity and stress-induced martensitic transformation. Retained-austenite transformation increases the local load-bearing capacity, modifies interphase load transfer, and delays the direct linkage of strain-localization bands. The present work clarifies the coupling among retained-austenite stability, TRIP-assisted load redistribution, and microstructural strain partitioning in multiphase high-strength steel, providing a mesoscale basis for microstructure-guided strength–ductility optimization.

Against the backdrop of China’s dual-carbon goals—peaking carbon emissions by 2030 and achieving carbon neutrality by 2060—capacity optimization in the steel industry can no longer rely solely on aggregate output reduction. Instead, governance must shift toward a multidimensional approach that combines scale control, structural adjustment, and coordinated regional allocation. This study develops a quantifiable and interpretable assessment model for capacity-adjustment pressure. Monthly provincial crude steel output is used as a high-frequency proxy for capacity utilization and production adjustment. Additive time-series decomposition is applied to extract three components from monthly output—trend, residual, and volatility—which respectively represent structural evolution, short-term deviations, and exposure to shocks. The model further incorporates multidimensional variables, including downstream steel demand, resource and transport constraints, scrap steel ratio, and policy constraints. On this basis, a two-stage hybrid assessment framework is developed. In the first stage, extreme gradient boosting (XGBoost) is used to learn nonlinear relationships and derive data-driven feature importance. In the second stage, a composite pressure index is constructed and transformed into a standardized 0–100 score through a robust rank-based mapping mechanism. Dual thresholds are then used to generate three policy recommendations: maintaining current capacity, structural optimization, and capacity reduction. The results show that production trends and volatility intensity are the primary drivers of capacity-adjustment pressure, while pronounced spatial heterogeneity requires highly localized strategies. The classification assigns 25 provinces to maintaining current capacity, 3 to structural optimization, and 3 to targeted capacity reduction. Finally, integrating SHapley Additive exPlanations (SHAP) enhances model interpretability and provides a quantitative basis for shifting from indiscriminate capacity suppression toward differentiated, region-specific capacity governance, thereby supporting the sustainable low-carbon development of the global steel industry.