<p>Sustainable supply chain (SSC) plays a pivotal role in promoting responsible production and consumption, economic growth, and industrial innovation and infrastructure goals as outlined in the United Nations SDGs. However, how to effectively evaluate and improve the sustainable performance of supply chain has become an important challenge in achieving sustainable development. This paper aims to propose a multi-attribute group decision-making method by integrating COPRAS, DEMATEL, and TOPSIS with basic uncertain information (BUI) to address the SSC performance evaluation and improvement for automotive manufacturing enterprises under the “dual-carbon" goals in China. Firstly, an enhanced BUI-COPRAS methodology is proposed to calculate the fundamental weightings of criteria across four dimensions feasibility, reliability, criticality, and data acquisition effort to optimize the selection of SSC performance evaluation criteria. These criteria are then validated through a text analysis-based method and reconstructed into a comprehensive evaluation index system according to the triple-bottom-line (TBL) principle. Secondly, considering the potential interrelationships between criteria, an improved BUI-DEMATEL method is proposed to calculate the correlation importance of criteria, which is further integrated with the fundamental importance to determine the comprehensive weights of criteria. Subsequently, the TOPSIS method is extended to the environment with BUI for proposing an improved BUI-TOPSIS-based SSC performance evaluation model. Finally, considering the dynamic nature of the supply chain environment, a dynamic performance evaluation model based on the integrated impacts of time decay and policy is established to further analyze the trend of supply chain performance changes. A case study of SSC performance evaluation in the automotive manufacturing industry validates the feasibility and effectiveness of the proposed method and further proposes targeted strategies for performance improvement. The outputs provide theoretical support and policy implications for the sustainable development of automotive manufacturing enterprises. Additionally, sensitivity analysis substantiates the criticality of criterion weighting schemes in performance evaluation frameworks, while comparative analysis empirically validates the methodological superiority of the proposed approach.</p>