Data-driven ESG assessment for blockchain services: A comparative study in textiles and apparel industry

This paper introduces a data-driven ESG assessment approach using blockchain technology and stochastic multicriteria acceptability analysis (SMAA-2) to address the data opaqueness and assessment subjectivity problems. On the one hand, blockchain provides a transparent and trackable ledger to store and share ESG data among the listed companies, investors, and stakeholders alike. On the other hand, SMAA-2 provides the advantages of robustness and no subjective weight preferences to assess the ESG data. Based on real ESG data, this research quantitatively compares the ESG performances of 71 textiles and apparel listed companies in Hong Kong. Sensitivity analyses are conducted to verify the stability of the proposed approach with scaling weight preferences of environmental criteria (e.g., water consumption, energy consumption, and greenhouse gas emission). Results show that the proposed data-driven ESG assessment approach can analyze the companies' sustainability performances and benchmark the sustainability level of the company in the peer industry.