Environmental Specificity Information Disclosure of Heavily Polluting Companies and Stock Price Crash Risk

Using Natural Language Processing (NLP) techniques, this study empirically examines the impact of environmental specificity information disclosure on stock price crash risk, employing a sample of A-share listed companies in heavily polluting industries from 2013 to 2022. The study finds that environmental specificity information disclosure by heavily polluting companies significantly mitigates future stock price crash risk. Mechanism tests reveal that such disclosure reduces crash risk through pathway: improving corporate reputation. Heterogeneity analysis demonstrates that the inhibitory effect of environmental specificity information on crash risk is particularly prominent among firms characterized by low analyst coverage and weak government environmental regulation. This research verifies that environmental specificity information disclosure enhances information efficiency in heavily polluting enterprises, thereby providing empirical evidence for improving capital market resource allocation efficiency and optimizing environmental information disclosure systems.