Sustainability reporting standards state that material information should be disclosed, but materiality is not easily nor consistently defined across companies and sectors. Research finds that materiality assessments by reporting companies and sustainability auditors are uncertain, discretionary, and subjective. This chapter investigates a machine learning approach to sustainability reporting materiality assessments that has predictive validity. The investigated assessment methodology provides materiality assessments of disclosed as well as non-disclosed sustainability items consistent with the impact materiality GRI (Global Reporting Initiative) reporting standard. Our machine learning model estimates the likelihood that a company fully complies with environmental responsibilities. We then explore how a state-of-the-art model interpretation method, the SHAP (SHapley Additive exPlanations) developed by Lundberg and Lee (A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-December, pp 4766–4775, 2017), can be used to estimate impact materiality.

Natalia Berg, docent vid Linnéuniversitetet visade resultat från sin forskning om hållbarhetsredovisning som bland annat väcker frågor om vad det är som rapporteras och vad det är som görs? Rubriken för presentationen, som gavs på engelska, var ”Sustainability reporting: Opportunities and Challenges”.

This study examines whether private information exchange between institutional investors and public companies in engagement dialogs on sustainability issues improves the publicly disclosed measurements of the target company’s financial and non-financial performance and transparency. It uses a unique dataset containing 326 private reports related to environmental, social, and anti-corruption recommendations to address material incidents among publicly traded MSCI World Index portfolio companies of Nordic institutional investors. The results indicate that target companies appear to have similar values with matched companies on sustainability performance and transparency ratings in the 3 years following the initiation of private reporting. Unexpected sustainability incidents are subsequently reflected in the next year’s fall in the market value of target companies relative to MSCI World Index. This paper provides empirical evidence for the legitimacy-based provision of private sustainability information used in a larger disclosure system of public companies.

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.