Uncovering ESG Ratings: The (Im)Balance of Aspirational and Performance Features

Abstract

ABSTRACT Environmental, Social, and Governance (ESG) scores are crucial for evaluating corporate sustainability. However, the undisclosed and complex methodologies used by rating agencies have raised concerns about their reliability and consistency. This study replicates LSEG's ESG scoring methodology using machine learning to shed light on the key drivers behind ESG ratings, with a focus on the balance between forward‐looking promises (aspirational) and past achievements (performance). Our analysis finds that approximately 60% of ESG scores are based on aspirational promises, while only approximately 40% reflect actual performance. This imbalance suggests a potential over‐reliance on future commitments, which could inflate ESG scores and mislead investors about a company's true sustainability efforts, even accounting for LSEG's transparency stimulation mechanism, where non‐disclosure of material data is penalized. The findings emphasize the need for greater transparency and a clearer distinction between aspirational and performance metrics to ensure credible ESG assessments for informed investment decisions.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 25 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.