  Over the last decade, several regulatory bodies have started requiring the
disclosure of non-financial information from publicly listed companies, in
light of the investors' increasing attention to Environmental, Social, and
Governance (ESG) issues. Publicly released information on sustainability
practices is often disclosed in diverse, unstructured, and multi-modal
documentation. This poses a challenge in efficiently gathering and aligning the
data into a unified framework to derive insights related to Corporate Social
Responsibility (CSR). Thus, using Information Extraction (IE) methods becomes
an intuitive choice for delivering insightful and actionable data to
stakeholders. In this study, we employ Large Language Models (LLMs), In-Context
Learning, and the Retrieval-Augmented Generation (RAG) paradigm to extract
structured insights related to ESG aspects from companies' sustainability
reports. We then leverage graph-based representations to conduct statistical
analyses concerning the extracted insights. These analyses revealed that ESG
criteria cover a wide range of topics, exceeding 500, often beyond those
considered in existing categorizations, and are addressed by companies through
a variety of initiatives. Moreover, disclosure similarities emerged among
companies from the same region or sector, validating ongoing hypotheses in the
ESG literature. Lastly, by incorporating additional company attributes into our
analyses, we investigated which factors impact the most on companies' ESG
ratings, showing that ESG disclosure affects the obtained ratings more than
other financial or company data.
