This study constructs a proposed model to investigate the link between environmental, social, and governance (ESG) disclosures and ESG scores for publicly traded companies in the Borsa Istanbul Sustainability (XUSRD) index. In this context, this study considers 66 companies, examining recently structured ESG disclosures for 2022 that were published for the first time as novel data and applying a multilayer perceptron (MLP) artificial neural network algorithm. The relevant results are fourfold. (1) The MLP algorithm has explanatory power (i.e., R2) of 79% in estimating companies’ ESG scores. (2) Common, environment, social, and governance pillars have respective weights of 21.04%, 44.87%, 30.34%, and 3.74% in total ESG scores. (3) The absolute and relative significance of each ESG reporting principle for companies’ ESG scores varies. (4) According to absolute and relative significance, the most effective ESG principle is the common principle, followed by social and environmental principles, whereas governance principles have less significance. Overall, the results demonstrate that applying a linear approach to complete deficient ESG disclosures is inefficient for increasing companies’ ESG scores; instead, companies should focus on the ESG principles that have the highest relative significance. The findings of this study contribute to the literature by defining the most significant ESG principles for stimulating the ESG scores of companies in the XUSRD index.

中文翻译：

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对环境、社会和治理披露与评分之间的联系进行建模：伊斯坦布尔证券交易所可持续发展指数中上市公司的案例

本研究构建了一个拟议模型，以调查伊斯坦布尔证券交易所可持续发展 (XUSRD) 指数中上市公司的环境、社会和治理 (ESG) 披露与 ESG 评分之间的联系。在此背景下，本研究考虑了 66 家公司，检查了最近首次作为新颖数据发布的 2022 年结构化 ESG 披露，并应用了多层感知器 (MLP) 人工神经网络算法。相关结果有四重。（1）MLP算法在估算公司ESG得分方面的解释力（即R2）为79%。（2）共同支柱、环境支柱、社会支柱和治理支柱在ESG总分中的权重分别为21.04%、44.87%、30.34%和3.74%。（3）每项ESG报告原则对公司ESG评分的绝对和相对重要性各不相同。（4）从绝对重要性和相对重要性来看，最有效的ESG原则是共同原则，其次是社会和环境原则，治理原则的重要性次之。总体而言，结果表明，应用线性方法来完成有缺陷的 ESG 披露对于提高公司的 ESG 分数效率不高；相反，公司应该关注具有最高相对重要性的 ESG 原则。本研究的结果通过定义最重要的 ESG 原则来刺激 XUSRD 指数中公司的 ESG 分数，从而为文献做出贡献。