  In the evolving field of corporate sustainability, analyzing unstructured
Environmental, Social, and Governance (ESG) reports is a complex challenge due
to their varied formats and intricate content. This study introduces an
innovative methodology utilizing the "Unstructured Core Library", specifically
tailored to address these challenges by transforming ESG reports into
structured, analyzable formats. Our approach significantly advances the
existing research by offering high-precision text cleaning, adept
identification and extraction of text from images, and standardization of
tables within these reports. Emphasizing its capability to handle diverse data
types, including text, images, and tables, the method adeptly manages the
nuances of differing page layouts and report styles across industries. This
research marks a substantial contribution to the fields of industrial ecology
and corporate sustainability assessment, paving the way for the application of
advanced NLP technologies and large language models in the analysis of
corporate governance and sustainability. Our code is available at
https://github.com/linancn/TianGong-AI-Unstructure.git.
