Does Globalization Accelerate or Mitigate Greenhouse Gas Emissions? Evidence from Energy Transition and Technological Innovation in Central Asia

Greenhouse gas (GHG) emissions have emerged as a critical challenge to sustainable development in Central Asia, where rapid economic transformation, rising energy demand, and increasing globalization have intensified environmental pressures. This study investigates the determinants of GHG emission growth in Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan over the period 2000–2024 within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework. Specifically, the analysis examines the effects of economic growth, energy intensity, renewable energy consumption, technological innovation, urbanization, and globalization on annual GHG emission growth. To ensure robust inference in the presence of cross-sectional dependence and heteroskedasticity, the empirical analysis employs Driscoll–Kraay standard errors (DKSE), Panel-Corrected Standard Errors (PCSE), and Feasible Generalized Least Squares (FGLS). Furthermore, the Method of Moments Quantile Regression (MMQR) is applied to examine distributional heterogeneity by assessing whether the effects of the explanatory variables vary across the lower, median, and upper quantiles of the conditional distribution of GHG emission growth. The empirical findings reveal that energy intensity is the dominant driver of GHG emission growth across all estimation techniques, whereas renewable energy adoption and technological innovation significantly mitigate environmental degradation by reducing emissions growth. The MMQR results further demonstrate that the estimated effects of the explanatory variables vary across the conditional distribution of GHG emission growth. In particular, the mitigating effect of globalization becomes more pronounced toward the upper quantiles of the conditional distribution, indicating that its environmental consequences differ across the distribution of GHG emission growth rather than across predefined groups of countries. By contrast, the effects of economic growth and urbanization exhibit greater heterogeneity across the conditional distribution, while the effects of energy intensity, renewable energy, and technological innovation remain broadly consistent in direction. These findings underscore the importance of improving energy efficiency, accelerating the deployment of renewable energy technologies, strengthening innovation capacity, and promoting environmentally sustainable economic integration to achieve long-term climate objectives in Central Asia. By providing comprehensive evidence based on complementary mean-based estimators and distribution-sensitive quantile analysis, this study contributes to the growing literature on the determinants of GHG emissions in emerging economies and offers important policy implications for balancing economic development with climate change mitigation and environmental sustainability.