NLP for SDGs: Measuring Corporate Alignment with the Sustainable Development Goals

Abstract

This article uses advanced natural language processing (NLP) methods to identify companies that are aligned with the UN Sustainable Development Goals (SDGs) based on the text in their sustainability disclosures. Using the Corporate Social Responsibility (CSR) reports of Russell 1000 companies between 2010–2019, we apply a logistic classifier, support vector machines (SVM), and a fully-connected neural network to predict alignment with the SDGs. Specifically, we use word embeddings to augment dictionary-based input features, as well as the embeddings as features themselves, based on Word2Vec and Doc2Vec models to classify companies’ alignment with the SDGs over time. Notably, the Doc2Vec embedding inputs to the SVM classifier result in high average accuracy of above 80% for predicting alignment.