To address the global environmental crisis and achieve sustainable development in the construction industry, the thermal-engineering requirements for building envelope structures in China have been increasing year by year. However, the contradiction between the long-term environmental benefits brought by high-level energy-saving standards and the short-term initial-investment increment has become increasingly prominent. Based on the marginal benefit theory and multi-objective optimisation algorithm, this study constructed a sustainable optimisation framework for the envelope parameters of building energy-saving design standards in cold regions of China. Taking residential buildings and public buildings in the typical city of Shijiazhuang as examples, the optimal marginal benefit values of the thermal parameters of the building envelope were obtained, as well as a multi-objective-optimisation optimal solution that comprehensively weighs the three-dimensional goals of energy efficiency, life-cycle cost, and thermal comfort. This sustainable optimisation framework can provide a scientific and quantified decision-making basis for relevant stakeholders, such as government departments, builders, or architects.

Latin hypercube sampling (LHS) has become the mainstream sampling method for global sensitivity analysis (GSA) in building performance simulation (BPS) owing to its efficient space-filling characteristics. However, a structural mismatch between the diversity of GSA methods and their specific sampling requirements leaves building researchers without systematic decision criteria. This study focuses on three LHS-compatible GSA methods—the variance-based RBD-FAST (random balance design Fourier amplitude sensitivity test), the rank–regression-based SRRC (standardized rank regression coefficient), and the distribution-based PAWN (moment-independent measure)—and systematically compares their performance and complementary characteristics in the BPS context. Using a typical rural residential building in North China as a case study, we conduct a multi-indicator sensitivity analysis based on 1000 LHS samples. The results demonstrate that the three methods are fully consistent in identifying dominant parameters. For linear output indicators (electricity consumption and carbon emissions), the SRRC and the RBD-FAST exhibit strong rank correlation (Spearman $\rho$ > 0.90), validating the reliability of the LHS-GSA framework. For nonlinear outputs (cost and thermal comfort), the inter-method correlations diverge significantly, with moderate consistency between the SRRC and PAWN on the cost indicator ($\rho$ = 0.62) and weak correlation between the RBD-FAST and the other two methods ($\rho$ = 0.00–0.29), revealing differential adaptability of GSA methods to model complexity. This study provides a method-selection framework for LHS-based sensitivity analysis in BPS and recommends a multi-method cross-validation strategy to ensure robustness of analytical results.