This paper reviews the use of AI in the ESG field: textual analysis to measure firms’ ESG incidents or verify the credibility of companies’ concrete commitments, satellite and sensor data to analyze companies’ environmental impact or estimate physical risk exposures, machine learning to fill missing corporate data (GHG emissions etc.). Recent advances in LLMs now make it possible to provide investors with more accurate information about a company’s sustainable policy, innovation or supply chain relationships, or to detect greenwashing, We also discuss potential challenges, in terms of transparency, manipulation risks and costs associated with these new data and tools.

Challenges with traditional extra-financial data

Data provided by extra-financial rating agencies are essential but raise a number of questions about their use. Based on company reporting, supplemented by human analysis, there is a certain degree of subjectivity in the choices made by each rating agency on the relevant ESG criteria and their weightings. The different methodological choices made by the various agencies cause these ratings to be loosely correlated with one other.  In addition, ratings are reviewed infrequently, sometimes with different timings depending on the company, and ratings tend to be revised in the direction of a stronger correlation with financial performance (Berg et al., 2020). Finally, the differences in the imputation methods used by ESG analysts to deal with missing data can cause large ‘discrepancies’ among the providers, which are using different gap filling approaches. Interestingly, the discrepancies among ESG data providers are not only large, but actually increase with the quantity of publicly available information. Companies that provide greater ESG disclosure tend to have more variations in their ESG ratings (Christensen et al., 2019).

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