The Sustainable Development Goals (SDGs) were introduced by the United Nations in order to encourage policies and activities that help guarantee human prosperity and sustainability. SDG frameworks produced in the finance industry are designed to provide scores that indicate how well a company aligns with each of the 17 SDGs. This scoring enables a consistent assessment of investments that have the potential of building an inclusive and sustainable economy. As a result of the high quality and reliability required by such frameworks, the process of creating and maintaining them is time-consuming and requires extensive domain expertise. In this work, we describe a data-driven system that seeks to automate the process of creating an SDG framework. First, we propose a novel method for collecting and filtering a dataset of texts from different web sources and a knowledge graph relevant to a set of companies. We then implement and deploy classifiers trained with this data for predicting scores of alignment with SDGs for a given company. Our results indicate that our best performing model can accurately predict SDG scores with a micro average F1 score of 0.89, demonstrating the effectiveness of the proposed solution. We further describe how the integration of the models for its use by humans can be facilitated by providing explanations in the form of data relevant to a predicted score. We find that our proposed solution enables access to a large amount of information that analysts would normally not be able to process, resulting in an accurate prediction of SDG scores at a fraction of the cost.

翻译：联合国提出的可持续发展目标（SDGs）旨在鼓励有助于保障人类繁荣与可持续性的政策及活动。金融行业构建的SDG框架旨在提供评分，以衡量企业与17项可持续发展目标中每一项的契合程度。这套评分体系能够对具有建设包容性与可持续性经济潜力的投资进行一致性评估。由于此类框架对高质量和高可靠性的要求，其创建与维护过程耗时且需要深厚的领域专业知识。本文描述了一个数据驱动的系统，旨在自动化SDG框架的创建流程。首先，我们提出了一种新颖的方法，用于从多个网络来源收集并筛选与企业相关的文本数据集及知识图谱。随后，我们基于这些数据训练并部署分类器，用于预测给定企业与SDG的契合程度评分。结果表明，我们的最优模型能够以0.89的微平均F1值准确预测SDG评分，验证了所提出方案的有效性。我们还进一步阐述了如何通过提供与预测评分相关的数据解释，促进人类分析师对模型的使用。研究发现，我们的解决方案能够实现分析师通常无法处理的海量信息访问，从而以极低的成本实现SDG评分的精准预测。