详情
Tracing the Green Footprint: The Evolution of Corporate Environmental Disclosure Through Deep Learning Models
Environmental disclosure in emerging markets remains poorly understood, despite its critical role in sustainability governance. Here, we analyze 42,129 firm-year environmental disclosures from 4,571 Chinese listed firms (2008-2022) using machine learning techniques to
characterize disclosure patterns and regulatory responses. We show that increased disclosure volume primarily comprises boilerplate content rather than material information. Cross-sectional analyses reveal systematic variations across industries, with manufacturing and high-pollution sectors exhibiting more comprehensive disclosures than consumer and technology sectors. Notably, regional rankings in environmental disclosure volume do not align with local economic development levels. Through examination of staggered regulatory implementation, we demonstrate that market-based mechanisms generate more substantive disclosures compared to command-and-control approaches. These results provide empirical evidence that firms strategically manage environmental disclosures in response to institutional pressures. Our findings have important implications for regulatory design in emerging markets and advance understanding of voluntary disclosure mechanisms in sustainability governance.