Innovation, governance, inclusion: ESG pillars powering economic growth

Abstract

Using multiple dynamic analytical approaches, the research examines how individual environmental social governance (ESG) elements affect economic growth in Germany. The research employs linear regression and Random Forest machine learning alongside frequency-domain Granger causality analysis to verify that ESG, gender inclusion and governance quality determine economic growth the most and environmental innovation is the second most significant factor. Environmental quality and innovation contribute positively to economic performance, but social development generates economic advantages only over time because sustainability investments need patience. The tests for reverse causality show that economic growth does not automatically create better environmental conditions and improved governance performance, so leaders must design policies that directly focus on ESG issues. The research adds value to previous studies by analyzing distinct ESG factors using machine learning and econometric methods for time-sensitive cause-and-effect relationships. The proposed policies work to support green innovation networks while improving institutional setup mechanisms alongside integrated social equity plans for national development frameworks.

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