Influencing Factors and Prediction Model for the Carbon Footprint of Textile Finishing Production: Case Study of 672 Textile Products

Given the significant energy consumption and environmental impact of the textile industry, it is essential to characterize the carbon footprint of its production processes. This study presents a novel analytical framework for estimating the carbon footprint at the process level in textile manufacturing. Using a dataset of 672 textile products as a case study, we systematically analyzed and calculated the carbon emissions associated with finishing-stage operations. Key influencing factors were subsequently validated through extensive correlation analysis. Furthermore, several machine learning-based predictive models were developed, including PCR, PLSR, GA-ELM, PSO-ELM, GA-SVR, and PSO-SVR. The results indicate that: (1) Steam consumption accounts for nearly all of the carbon footprint per unit product (97.24%), while electricity contributes only 2.76%; (2) For most processes, the primary influencing factors are the job allowance ratio and machine speed. The job allowance ratio has the most substantial impact on both electricity and steam consumption, as well as the overall carbon footprint; (3) The GA-SVR model demonstrates superior fitting accuracy and lower prediction errors compared to other methods. This framework establishes a standardized carbon accounting system for textile production, enabling precise identification of emission hotspots and supporting the development of targeted decarbonization strategies. By leveraging data-driven environmental impact assessment and facilitating evidence-based decision-making, this approach significantly advances sustainable textile manufacturing.