Abstract
Corporate sustainability disclosure credibility has been the focal point of environmental human rights protection as the United Nations acknowledgement of the right to a healthy environment overlaps with compulsory reporting regimes in India, China, and the United States. Nevertheless, current approaches to identifying greenwashing are based on delayed litigation results or text analyses of individual documents, which results in the disclosure gap being gauged incompletely and unevenly across jurisdictions. This study suggests a two-layered measurement system that operationalises the gap as an external and internal issue of divergence. The framework is a combination of a satellite-derived divergence index, which is built by convolutional neural network detection of indicators at the facility level of environmental metrics, and a semantic inconsistency score, which is computed by adversarial large language model auditing of sustainability reports. Applied to a pilot sample of 150 listed corporations, the framework yields a four-cell taxonomy of disclosure failure, whose joint divergence measures predict litigation incidence and abnormal returns, offering regulators a reusable instrument for assurance reform.