Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Business Strategy and the Environment

DOI: 10.1002/bse.70641

Authors: María del Carmen Valls Martínez, José Manuel Santos‐Jaén, María Estefanía Sánchez Pacheco, Fernando José Zambrano Farías

Institutions: University of Almería, Universidad de Murcia, University of Guayaquil, Universidad Internacional del Ecuador

Language: English

Topic: Sustainable Building Design and Assessment

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: hybrid

Estimated APC paid: $4,480