Sustainability issues are increasingly influencing firms’ decision making, leading to the creation of new business models for finding solutions to environmental and societal challenges. This work aims to explore what is the role played by industrial districts in firms’ orientation towards sustainability. It implements a Propensity Score Matching technique on a novel datatabase with information on 1300 Italian fashion firms. Their sustainability orientation is measured using the Quantitas Intelligent Business Analyzer (QIBA), an original Natural Language Processing-based data mining technique, which allows scraping firms’ websites and analyzing their content adopting a Term Frequency–Inverse Document Frequency weighting scheme. Findings suggest the existence of a sustainability-driven industrial district effect, i.e. a positive association between the sustainability orientation of fashion firms and their localization in industrial districts.