arXiv cs.AI· Sofia Baiocco, Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Camilla Mazzoli, Federica Parlapiano, Giulia Quaglieri, Davide Traini, Domenico Ursino, Luca Virgili·· 1 小时前AI 评分31
ESGPred：结合领域语言建模与图学习的模块化 ESG 预测框架
A Modular Framework for ESG Prediction through Domain-Specific Language Modeling and Graph Learning
AI 导读
研究者提出 ESGPred，一个将领域语言建模与公司关系网络上的图学习相结合的模块化 ESG 评分预测框架，可预测整体 ESG 评级及环境、社会、治理各分项。基于 2,134 家上市公司 2020 至 2023 年发布的 5,266 份可持续发展报告，并以 LSEG Data & Analytics 的评级为基准的实验显示，引入图关系学习优于纯文本基线方法。
正文 · 原文
Abstract:Environmental, Social and Governance (ESG) scores are increasingly used to evaluate corporate sustainability, supporting investment decisions, ensuring regulatory compliance and assessing risk. However, current ESG evaluations largely rely on proprietary methodologies that often produce inconsistent ratings. This motivates the development of data-driven prediction frameworks. In this paper, we present ESGPred, a modular framework for predicting ESG scores by combining domain-specific language modeling with graph-based learning over a network that models the relationships among the companies being evaluated. In addition to presenting the rationale behind ESGPred and detailing its ESG score prediction process, we show that ESGPred can effectively predict both overall ESG ratings and their individual Environmental, Social, and Governance components. We evaluated ESGPred using a dataset of 5,266 sustainability reports published by 2,134 publicly listed companies between 2020 and 2023. In this evaluation, we used ESG ratings provided by LSEG Data & Analytics as ground truth. Our experimental results demonstrated that integrating graph-based relational learning improves prediction performance over purely text-based baselines.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.16831 [cs.AI] |
| (or arXiv:2611.16831v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16831 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Luca Virgili [view email]
[v1]
Thu, 12 Nov 2026 14:02:59 UTC (10,559 KB)
来源：arXiv cs.AI · arxiv.org