Project overview
British Academy Leverhulme Grant.
This research investigates the specificity of ESG targets in CEO compensation, highlighting the concerns over
vague ESG-linked incentives. Through machine learning-based textual analysis of DEF 14A filings, we
develop a novel “Specific Targets Index” (STI) to quantify the clarity of ESG objectives in executive
compensation contracts. Based on the constructed STI, the study examines the correlation between the
vagueness of ESG targets, CEO compensation levels, and firm ESG performance, aiming to clarify whether
specific and measurable ESG incentives effectively align CEO interests with corporate sustainability goals.
The findings offer valuable insights to corporate governance and investment strategies, providing actionable
guidance for structuring executive compensation to better support sustainable business models.
This research investigates the specificity of ESG targets in CEO compensation, highlighting the concerns over
vague ESG-linked incentives. Through machine learning-based textual analysis of DEF 14A filings, we
develop a novel “Specific Targets Index” (STI) to quantify the clarity of ESG objectives in executive
compensation contracts. Based on the constructed STI, the study examines the correlation between the
vagueness of ESG targets, CEO compensation levels, and firm ESG performance, aiming to clarify whether
specific and measurable ESG incentives effectively align CEO interests with corporate sustainability goals.
The findings offer valuable insights to corporate governance and investment strategies, providing actionable
guidance for structuring executive compensation to better support sustainable business models.
