COMPARING ESG RANKINGS AND 2050 TEMPERATURE SCORES VIA FUZZY GROUP DECISION-MAKING

Abstract

ESG represents a social responsibility metric that defines the level of societal acceptance, sustainable development, and corporate social responsibility and is used for investment across different industries. ESG analysis has been criticized for its lack of comparability due to differences across rating agencies, which can be attributed to heterogeneity of subjects, diverse themes, many scales, and the complexity of content. This paper proposes a comprehensive ESG assessment model that uses fuzzy logic and group decision-making to increase the credibility and precision of ESG assessments, especially with respect to climate change goals such as the Paris Agreement, which aims to limit global warming to less than 2°C by the year 2050. The model comprises four stages: (1) identifying subject preferences; (2) extracting latent preference data via the fuzzy inference system; (3) aggregating information via the TOPSIS method to derive ESG rankings; and (4) comparing the proposed method’s ESG score rankings with harvest rankings, GDM rankings and temperature score (2050) rankings to assess conformity to climate goals. This research includes 24 firms from the Kaggle and Harvest Fund (2022) and reveals significant gaps between ESG ratings and climate change targets. Some of the high ESG companies were not aligned with the temperature change mitigation goals, which indicates possible gaps in conventional assessments. The proposed method integrates various preferences, reveals the underlying patterns, and increases the accountability of ESG ratings.  Future work should focus on unexplored ESG aspects, such as rating issues and corporate misconduct, to increase the accuracy of ESG ratings.