How Governance Relates to Sustainability Across Economic, Social, and Environmental Domains: Evidence from Interpretable Machine Learning

Abstract

Sustainable development is multidimensional, yet governance is often examined through aggregate SDG performance, leaving limited understanding of whether different governance dimensions are equally salient across economic, social, and environmental domains. This study investigates domain-specific governance salience using cross-national data from 2015 to 2024. Governance is measured through the Worldwide Governance Indicators, while sustainability performance is modeled through the overall SDG index and three domain-specific indices covering economic, social, and environmental sustainability. Tree-based machine learning and SHAP analysis are used to compare the relative importance of governance indicators and socioeconomic control variables. The findings reveal a domain-sensitive governance pattern that is obscured by aggregate SDG performance. Governance indicators are secondary in the overall SDG model but become more salient in the economic dimension, especially through Government Effectiveness, Rule of Law, and Regulatory Quality. In the social dimension, governance retains moderate relevance alongside health and development-related conditions. By contrast, the environmental domain exhibits limited predictability, highlighting an important boundary condition in the role of governance. Further analysis of high-income countries reveals that specific governance dimensions exhibit greater predictive relevance, although the overall governance pattern remains less consistent. These findings provide support for a metagovernance-informed perspective, showing that the relevance of governance for sustainable development differs across sustainability domains.