The Application and Challenges of ESG Intelligent Analytics in Corporate Assessment

Abstract

With the deepening global adoption of sustainable development, environmental, social, and governance (ESG) assessments have become core indicators for measuring a company's overall value. Traditional ESG assessment methods suffer from limitations such as low data processing efficiency and high subjectivity. The introduction of intelligent analytics technology offers a new approach to addressing these issues. This article explores the core architecture of ESG intelligent analytics technology and analyzes the specific applications of artificial intelligence and big data technologies in corporate ESG data collection, risk assessment, and performance forecasting, including practical case studies using solutions such as the IBM Envizi ESG Suite. The study finds that technologies such as natural language processing and machine learning have significantly improved the efficiency and accuracy of ESG assessments. However, practical challenges remain, such as uneven data quality, inconsistent rating systems, and insufficient model interpretability. To address these issues, this article proposes optimization strategies such as strengthening data standardization, promoting algorithm transparency, and improving regulatory mechanisms. This research aims to provide a reference for corporate ESG management practices and the application of intelligent analytics technology, thereby contributing to the achievement of sustainable development goals.