Gender Equality, Governance and National Innovation Capability for Sustainable Development: Cross‐National Machine Learning Evidence

ABSTRACT This study examines how gender equality and governance conditions relate to national innovation capability, using an exploratory machine‐learning approach on cross‐national panel data. Gender equality is widely recognized as a human right and a core Sustainable Development Goal (SDG 5), yet its association with country‐level innovation capability remains insufficiently mapped in sustainability‐oriented innovation research. To address this gap, we compile a harmonized dataset for 109 countries (2007–2019) from the Global Innovation Index, the Worldwide Governance Indicators, UNDP gender indicators, and World Bank development controls. We conceptualize innovation as national innovation capability and operationalize it using the GII score, a composite index that combines innovation inputs and outputs. Across decision‐tree and ensemble models, governance quality emerges as consistently informative for predicting innovation capability. Gender equality indicators also display substantial predictive relevance, indicating that inclusion‐related conditions are systematically associated with cross‐country variation in innovation capability. The findings contribute to sustainability science and innovation policy by clarifying how gender equality and institutional quality jointly map onto innovation capability while maintaining an exploratory, non‐causal interpretation of model outputs.