Do Governance Structures Drive Green Building Adoption? A Machine Learning Approach With Random Forests

ABSTRACT This study examines the determinants of firms' propensity to adopt green buildings in the Euro Stoxx 300 and the S&P 500 indices, during 2012–2023. Using random forest binary classifiers, we assess the relative importance of financial, sectoral, geographic, and climate governance predictors and uncover nonlinear relationships often overlooked by econometric approaches. Results show that sectoral affiliation is the most influential determinant in both markets. Governance‐related predictors are collectively highly influential, although they exhibit different patterns across institutional contexts. In Europe, the presence of a Sustainability Committee charged with climate strategy is the most influential factor, whereas in the United States, nonfinancial/environmental performance disclosure plays a prominent role. CEO‐related mechanisms show asymmetric effects. Other board characteristics, such as gender diversity, independence, size, skills, experience, turnover, meetings, and remuneration, also matter, but their impact varies by institutional context. Overall, the findings highlight that corporate governance plays a decisive yet asymmetric role in sustainable‐building adoption.