A Computational Study of Sustainability Reports: How Vagueness Serves Greenwashing - Erica Cutuli (Umanistica Digitale, 2025)
Today, the automatic processing of large volumes of natural language text is increasingly feasible. However, challenges persist in addressing linguistic ambiguity and vagueness. This study aims to analyze phenomena of vagueness and imprecision in sustainability reports, with a particular focus on greenwashing. The corpus analyzed consists of 225 Italian-language sustainability reports in PDF format, published by 45 companies between 2017 and 2021. These documents were processed using the Sketch Engine tool and a Python notebook. The analysis began with a targeted search for keywords commonly associated with greenwashing, followed by the annotation of a sample of concordances. Specifically, each extracted example was evaluated to determine whether it met the criteria for an environmental claim and whether it exhibited vagueness. When applicable, instances of vagueness were classified into five semantic categories (quantity, degree, time, category, and softening stance-taking). The dual contribution of this research lies in its provision of a deeper understanding of linguistic strategies in sustainability reports, promoting corporate transparency and accountability, while simultaneously establishing a foundation for the automated identification of greenwashing-related claims through the development of an Italian-language dataset for Artificial Intelligence training, thereby advancing digital linguistic equality.