AI Based Analysis of Environmental Disclosure Tone and Readability (gjstudies, 2021)
This research introduces a novel computational framework for analyzing corporateenvironmental disclosures through the dual lenses of linguistic tone and textual readability, employing artificial intelligence techniques that diverge from traditional sentiment analysis and financial text mining approaches. We propose a hybrid methodologycombining transformer-based language models with psycholinguistic dictionaries andgraph-based coherence metrics to quantify not just what is said about environmentalperformance, but how it is communicated. Our approach uniquely integrates threeunconventional dimensions: (1) a multi-scale tone analysis capturing micro-sentimentfluctuations alongside macro-narrative arcs, (2) a readability assessment that accountsfor domain-specific environmental terminology rather than general linguistic complexity, and (3) a coherence metric evaluating logical flow between environmental claimsand supporting data. We apply this framework to a corpus of 2,500 environmentaldisclosures from SP 500 companies between 1995 and 2004, revealing previously undocumented patterns of strategic obfuscation in high-risk industries. Results demonstratethat companies in environmentally sensitive sectors employ significantly more complex sentence structures when discussing negative environmental impacts comparedto positive achievements (p ¡ 0.01), while maintaining an artificially optimistic tonethrough selective positive framing. Furthermore, we identify a ’readability gap’ whereenvironmental performance metrics are presented with 42