Purpose

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

Design/methodology/approach

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

Findings

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

Practical implications

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

Social implications

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

Originality/value

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

中文翻译：

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社会绩效评级必须是特殊的吗？具有预测有效性的社会绩效评级探索

目的

本研究的目的是开发一种评估社会绩效的方法。传统上，环境、社会和治理 (ESG) 评级提供商使用主观加权算术平均值将一组社会绩效 (SP) 指标合并为一个评级。为了克服这个问题，本研究通过应用机器学习 (ML) 和人工智能 (AI) 来解决社会争议，探讨了对 ESG 的 SP 部分进行评级的新方法的先决条件。

设计/方法论/途径

本研究提出使用数据驱动的评级方法，该方法根据 SP 特征对社会争议预测的贡献得出其相对重要性。作者使用所提出的方法来解决总体 ESG 评级的权重问题，并进一步研究预测是否可行。

发现

作者发现机器学习模型能够以较高的预测性能和有效性来预测争议。研究结果表明，ESG 评级的权重问题可以通过数据驱动的方法来解决。然而，所提议的评级方法的决定性先决条件是通过一系列广泛的 SP 指标来预测社会争议。结果还表明，可以使用这种基于机器学习的人工智能方法来开发预测有效的评级。

实际影响

这项研究为 ESG 评级问题提供了实用的解决方案，对投资者、ESG 评级者和社会责任投资产生影响。

社会影响

所提出的基于机器学习的人工智能方法可以帮助实现更好的 ESG 评级，进而有助于提高 SP，从而通过可持续发展对组织和社会产生影响。

原创性/价值

据作者所知，这项研究是首批提供独特方法来解决 ESG 评级问题并通过关注 SP 指标来提高可持续性的研究之一。