Summary
Developments in Artificial Intelligence (AI) and machine learning have led to the creation of a new type of ESG data that do not necessarily rely on information provided by companies. 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).
Discussion and Challenges
AI provides interesting avenues to fill ESG data. However, there are a number of challenges. AI methods can be a black box, subject to the same types of revisions in the methodologies as in traditional ESG ratings. For example, NLP techniques relying on an ontology can be incomplete and revised ex-post. Hughes et al. (2021) show that the criteria used by Truvalue Labs to assess ESG risks of companies tend to largely overweight certain key issues (the ones that generate the more ESG controversies), defined at the company level and which can fluctuate over time, while for traditional rating providers, the weightings tend to be more stable and evenly distributed.
These alternative ratings based on NLP signals become more of a public “sentiment” indicator. This also means that they are also more prone to manipulation. This is particularly true when the primary source of data comes from blogs or social media.
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