Using Generative Artificial Intelligence to Evaluate the Quality of Chinese Environmental Information Disclosure in Chemical Firms

Abstract

Environmental information disclosure plays a critical role in corporate sustainability, yet existing evaluation approaches often rely on subjective judgment or limited textual features. This study proposes a structured framework for assessing the environmental information disclosure quality (EIDQ) of chemical enterprises and develops a generative artificial intelligence (GAI)-driven automated scoring system to enhance evaluation consistency. Using 190 Environmental, Social, and Governance (ESG) reports from 38 Chinese chemical firms between 2020 and 2024, we applied a multi-stage process combining indicator construction, DeepSeek-V3.2–based large language model (LLM) scoring, and cross-model validation. The results show that EIDQ exhibited a steady upward trend over the study period, reflecting a shift toward more quantitative and verifiable disclosure practices. The AI-generated scores demonstrated a high degree of alignment with human expert evaluations, and robustness tests confirmed the method’s transferability across different large language models. These findings provide methodological evidence for the feasibility of AI-assisted EIDQ assessment and offer practical implications for corporate sustainability reporting and regulatory oversight.