ReviewOpen research Europe2025
Divergence and aggregation of ESG ratings: A survey.
Review in Open research Europe, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Explainable machine learning to predict the cost of capital.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This paper reviews the existing literature on Environmental, Social, and Governance (ESG) ratings divergence and aggregation methods. It highlights the challenges posed by inconsistent ESG ratings and their implications for investment decisions. Design/methodology/approach: The study conducts a comprehensive review of prior research focusing on ESG ratings, examining their correlation levels and the methodologies employed to assess corporate sustainability. It also investigates traditional aggregation techniques and modern machine learning approaches used to address these inconsistencies. Findings: The review reveals that ESG ratings exhibit a low level of correlation across different providers, raising concerns about their reliability as investment indicators. Although some studies propose advanced aggregation methods to enhance accuracy, significant gaps remain in understanding how to effectively consolidate ESG information to create a dependable sustainability indicator. Originality: This paper provides a critical analysis of the current state of ESG rating methodologies, emphasizing the need for improved aggregation strategies. It underscores the importance of future research in leveraging ESG data to develop more consistent and reliable measures of corporate sustainability.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.