Greenwashing: Regulatory Enforcement, Prevention and Detection

Abstract

The prevalence of sustainability disclosures is rapidly increasing, with many such disclosures becoming mandatory across major jurisdictions. At the same time, greenwashing – defined as misleading sustainability claims exaggerating or misrepresenting environmental or other sustainability performance – has proliferated. This has attracted intensified global regulatory scrutiny. Despite academic interest, there is limited knowledge about how regulators identify, assess, and sanction greenwashing claims in practice.

The thesis addresses this gap, examining greenwashing through a regulatory lens. In so doing, unique greenwashing datasets are systematically constructed, including a dataset of global greenwashing regulatory enforcement cases between 2015-2024, as well as a greenwashing taxonomy consolidated from eight regulatory guidelines. These datasets provide the thesis’ conceptual and empirical foundation, clarifying how greenwashing is characterised in academic research and regulatory action.

Building on this foundation, the thesis comprises three interconnected studies investigating: (1) how regulators define, interpret, and act against greenwashing; (2) whether sustainability assurance potentially reduces greenwashing by addressing regulator-relevant subject matters; and (3) how generative large language models (LLMs) can support large-scale automated detection of greenwashing based on regulatory indicators.