This paper investigates data integration within digital twin (DT)-driven multiscale manufacturing systems to enhance sustainable performance monitoring. DT data, encompassing products, materials, machines, and processes and their geometric, behavioural, rules, and physical characteristics, facilitates the system-level measurement of sustainability performance metrics. By identifying and categorising DT data and using standardised sustainability metrics, this study applies a generic matching approach to assess the key contributions of DT data to sustainability metrics in a manufacturing case. We examine the scalability of this approach and reveal the imbalanced contributions of DT data across manufacturing stages to sustainability dimensions. Generally, DTs are effective in facilitating environmental performance measurement but less so in capturing social and human capital metrics. This underscores the importance of integrating other management system databases to investigate the key contributors to all aspects of sustainability metrics.