The efficiency of sustainability assessments of textile products is generally prevented because of a lack of available and reliable data across complex and globalized supply chains. The purpose of this study is to evaluate how blockchain traceability data can improve the Life Cycle Assessment (LCA) of textile products and to measure the actual value of exploiting this specific traceability data. To do so, a case study consisting of two LCAs modeling the production of wool top lots in China was conducted. A first LCA was conducted with generic data and the second with the added value of specific blockchain traceability data. Based on the second LCA, different wool top lot composition scenarios were then modeled to account for the environmental impact of different farming practices. Two main results were obtained: the environmental impact of wool top lots can vary up to +118% between two batches depending on their composition, and the specific data changes drastically from the impact calculated with generic data, with +36% calculated impact for the same wool composition of batches. Therefore, it was concluded that blockchain traceability data could be a strong asset for conducting LCA at the batch level by providing differentiated data on batch composition and origin and providing readily available specific data for a more representative assessment.