Reducing the environmental footprint of cotton T-shirt production through automation and reshoring: a scenario-based comparative life cycle assessment

Purpose
The modern textile industry faces major sustainability challenges driven by overconsumption, competitive pressure, and the relocation of production to low-wage countries. Re-shoring textile manufacturing closer to target markets requires advanced manufacturing technologies. While most textile production steps are already highly automated, garment assembly remains largely manual. The integration of a novel, fully automated sewing robot has the potential to fundamentally transform this final production step.
Methods
This study assesses the environmental impacts of alternative cotton T-shirt production chains for the European and U.S. markets using a scenario-based comparative life cycle assessment (LCA). A total of fifteen scenarios are analysed – comprising nine scenarios for the European market and six scenarios for the U.S. market – differing in the degree of automation, geographical configuration, transport modes (including sea and air freight), and overproduction rates. Fully automated scenarios incorporate a novel sewing robot, enabling nearshoring and demand-oriented production. In addition, an innovative dyeing technology is evaluated for selected European scenarios, and fast-fashion supply chains relying on air cargo are explicitly considered.

Results and discussion
The results show that integrating a fully automated sewing robot can substantially reduce the environmental footprint of cotton T-shirt production. Climate change impacts decrease from approximately 5.5-7.1 kg CO2-Eq per T-shirt in traditional semi-automated supply chains to 1.5-3.8 kg CO2-Eq in fully automated scenarios, primarily due to reduced overproduction, shorter transport distances, and higher process efficiency. In addition, the adoption of a novel dyeing technology leads to a significant reduction in freshwater ecotoxicity impacts, confirming dyeing as a key environmental hotspot. However, fast-fashion supply chains relying on air freight offset efficiency gains and lead to higher overall environmental impacts. Some uncertainty remains due to limited empirical data for emerging automation technologies.

Conclusions
Fully automated sewing robots represent a promising pathway for reducing the environmental footprint of cotton T-shirt production by enabling efficient, demand-oriented, and regionally located manufacturing. In addition to automation and nearshoring, process-level innovations – particularly in textile dyeing – play a critical role in reducing environmental hotspots, notably freshwater ecotoxicity and other chemical-related impacts. In contrast, fast-fashion supply chains relying on air freight largely offset efficiency gains and should be avoided from an environmental perspective. Future research should assess the full life cycle of garments, evaluate alternative materials, and quantify the environmental footprint of the sewing robot itself.