Tracking ESG Disclosures of European Companies with Retrieval-Augmented Generation (Proposals Track)
Kerstin Forster (LMU Munich & Munich Center for Machine Learning); Victor Wagner (LMU Munich & Sustainability Reporting Navigator); Lucas Elias Keil (University of Cologne & Sustainability Reporting Navigator); Maximilian A. Müller (University of Cologne & Sustainability Reporting Navigator); Thorsten Sellhorn (LMU Munich & Sustainability Reporting Navigator); Stefan Feuerriegel (LMU Munich & Munich Center for Machine Learning)
Abstract
Corporations play a crucial role in mitigating climate change and accelerating progress toward environmental, social, and governance (ESG) objectives. However, structured information on the current state of corporate ESG efforts remains limited. In this paper, we propose a machine learning framework based on a retrieval-augmented generation (RAG) pipeline to track ESG indicators from N=9,200 corporate reports. Our analysis includes ESG indicators from 600 of the largest listed corporations in Europe between 2014 and 2023. We focus on two key dimensions: first, we identify gaps in corporate sustainability reporting in light of existing standards. Second, we provide comprehensive bottom-up estimates of key ESG indicators across European industries. Our findings enable policymakers and financial markets to effectively assess corporate ESG transparency and track progress toward global sustainability objectives.