HIDA Lecture: Machine Learning for Monitoring Sustainability

Abstract:

Reaching global sustainability goals—whether through corporate accountability or public sector initiatives—demands more effective tools to monitor and evaluate progress. In this lecture, the speaker introduces a suite of machine learning (ML) methods, including large language models (LLMs), that enable scalable, data-driven sustainability tracking across both corporate and public domains.

The lecture highlights an open-source ML framework capable of extracting millions of environmental, social, and governance (ESG) indicators from corporate reports. This framework provides a systematic view of ESG transparency and performance among Europe’s largest companies, revealing notable disparities in reporting and limited progress in key environmental areas.

Extending beyond the private sector, the presentation explores how LLMs can enhance the monitoring of development aid by analyzing unstructured data and supporting more informed decision-making.

This lecture showcases how AI-powered monitoring systems can equip policymakers, investors, and global institutions with the tools needed for more transparent, equitable, and impactful sustainability efforts.

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