Green economic growth: Convergence patterns and eco-productivity clusters

Abstract

Given the intersecting challenges of climate instability, resource constraints, and digital transformation, there is an urgent need for scholarly inquiry into the evolving patterns of green economic growth to inform evidence-based strategies for fostering sustainable and inclusive development across the European region. This study explores green growth trajectories in European Union (EU) countries and Ukraine, focusing on convergence patterns, eco-productivity clustering, and the influence of digitalisation and institutional quality. Using the Malmquist–Luenberger productivity index (TFPCH) and σ- and β-convergence approaches, the analysis reveals evidence of long-term β-convergence, while short-term convergence remains weak due to institutional and technological disparities. Conditional convergence results highlight the positive role of institutional quality, whereas digitalisation, proxied by AI investments, shows a limited uniform impact. Cluster analysis identifies three eco-productivity groups, with Ukraine forming a distinct cluster marked by weaker institutions and declining green productivity. The findings suggest that convergence is not automatic, requiring strong governance and regionally adaptive policies. Recommendations include strengthening institutional capacity, addressing the digital divide, supporting knowledge transfer, and investing in green-oriented human capital. The study acknowledges limitations related to timeframe, digital proxies, and data coverage, and calls for future research incorporating broader digital and social indicators and spatial econometric analysis to better understand regional spillovers.