Fairness and Efficiency Issues in ESG Rating Data Processing:A Study of ESG Ratings for Chinese Real Estate Enterprises
As sustainable development becomes a topic widely discussed and the ESG concept is introduced,the demand for high-quality ESG data is increasingly rising.Compared to established systems like credit ratings and accounting standards that measure corporate performance,ESG as an evaluation dimension is still in the developmental stage.Issues such as low fairness in data disclosure,lack of reliability,and insufficient transparency lead to inefficiencies in measuring corporate ESG performance,preventing stakeholders from receiving timely and effective information.This paper focuses on the existing ESG indicators framework and conducts an ESG rating study using publicly available data from Chinese real estate companies.It identifies several issues,including low disclosure rates,inconsistent disclosure standards,and numerous outliers.To enhance the efficiency and fairness of data processing in ESG ratings,the paper proposes corresponding solutions to the problems in current ESG data analysis and usage.Applying data science techniques such as NLP,clustering,and building function libraries effectively improves the efficiency of ESG data processing.
NETL
NSTL
万方数据
