Export Diversification, Green Finance & Energy Efficiency: An Empirical Analysis of Central Asian Transition Economies

Abstract

This study examines the determinants of carbon intensity in Central Asian economies from 2004 to 2023, employing advanced panel data estimation techniques to uncover the dynamic relationships between foreign direct investment (FDI), green finance, industrial activity, export diversification, and carbon emissions. Using fixed effects (FE), random effects (RE),s system generalized method of moments (GMM), and Kinky Least Squares (KLS) models, we analyze how economic and financial factors influence carbon intensity while accounting for country-specific heterogeneity and temporal persistence.

Our findings reveal three key insights: First, carbon intensity exhibits strong path dependence, with system GMM results showing a significant persistence effect (1.8446), indicating that current emissions patterns reinforce future trajectories. Second, while FDI initially exacerbates carbon intensity—supporting the pollution haven hypothesis—its long-term impact may reverse due to technology spillovers, as shown by system GMM estimates (-0.7718). Third, export diversification emerges as the most effective decarburization strategy, with system GMM results demonstrating an exceptionally large negative effect (-25.5062**), while green finance consistently reduces emissions across all model specifications.

The study highlights the critical role of structural economic transformation in achieving climate goals, particularly for developing regions like Central Asia. Policy implications emphasize the need for stricter environmental regulations on FDI, targeted green financial mechanisms, and policies promoting export diversification to break carbon lock-in effects. By combining multiple advanced econometric approaches, this research provides robust evidence for policymakers seeking to align economic development with low-carbon transitions in emerging economies.