Nuriddin Shanyazov

Type of the article: Research Article

Abstract
This study aims to empirically examine the dynamic effects of geopolitical risk, economic policy uncertainty, and climate policy uncertainty on CO₂ emissions in G7 economies, utilizing annual data from 1990 to 2022. To account for cross-sectional dependence and parameter heterogeneity, the analysis employs a cross-sectional autoregressive distributed lag (CS-ARDL) model. Diagnostic tests confirm significant cross-sectional dependence and slope heterogeneity among the variables. All variables are integrated of order one, I (1), confirmed by unit root tests. In contrast, the cointegration test provides a strong indication of a stable long-run relationship among geopolitical risk, policy uncertainty measures, and CO₂ emissions. The outcomes show that a 1% rise in the geopolitical risk index leads to a statistically significant long-run rise of 0.042% in per capita CO₂ emissions. In addition, a 1% increase in economic policy uncertainty and climate policy uncertainty is associated with long-run increases of 0.028% and 0.015%, respectively. These results remain robust across alternative estimators. Overall, the evidence suggests that heightened geopolitical risk and policy-related uncertainties significantly exacerbate environmental degradation in G7 economies, highlighting the necessity for strategies that improve stability, reduce uncertainty, and encourage renewable energy adoption as part of a long-term environmental strategy.

Type of the article: Research Article

Abstract
Mitigating the effects of climate change has emerged as a crucial global need, with carbon dioxide emissions serving as the principal driver of greenhouse gas accumulation. This paper analyzes the factors influencing CO₂ emissions in G20 countries from 2000 to 2021, emphasizing the effects of renewable energy consumption, trade openness, economic growth, and energy intensity. The study utilizes advanced dynamic panel econometric techniques, namely, the Augmented Mean Group (AMG) Estimator and the Common Correlated Effects Mean Group (CCEMG) Estimator, which address cross-sectional dependence and parameter heterogeneity among nations. The analysis indicates that the use of renewable energy noticeably decreases CO₂ emissions, with elasticity values between –0.15 and –0.16. The effect is especially significant in lower-income G20 countries and during the post-2005 era. Economic growth indicates a strong positive correlation with CO₂ emissions, characterized by elasticity values ranging from 0.83 to 0.89, whereas energy intensity also displays positive effects with coefficients between 0.69 and 0.82. Trade openness exhibits insignificant statistical effects in both models. The heterogeneity study reveals that the emission-reduction potential of renewable energy is significantly greater in emerging nations than in advanced economies, with coefficients of –0.25 and –0.08, respectively. The results highlight the essential role of renewable energy transitions and enhancements in energy efficiency for meeting climate goals, especially when aligned with specific policies for various income levels and timeframes within the G20 context.

Type of the article: Research Article

Abstract
This study examines the long-term and short-term dynamic interactions between PM2.5 pollution and its anthropogenic determinants, namely industrial activity, urbanization, economic growth, total energy use, and renewable energy utilization, across five Central Asian countries (Kazakhstan, Uzbekistan, Kyrgyzstan, Tajikistan, and Turkmenistan) from 1992 to 2023. Preliminary tests validate pronounced cross-sectional dependence and notable slope heterogeneity, substantiating the application of the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model. The Westerlund cointegration results demonstrate a strong long-term equilibrium relationship. The long-term CS-ARDL estimations indicate that industrial activity is the primary driver of PM2.5 pollution, followed by total energy consumption. The analysis reveals evidence supporting the upward-sloping segment of the Environmental Kuznets Curve (EKC), as economic growth significantly elevates PM2.5 levels. In contrast, the squared GDP term is insignificant, suggesting the absence of a turning point in pollution reduction. Renewable energy consumption has a negligible moderating effect. The Error Correction Term is negative and statistically significant, indicating that approximately 24.5% of deviations from the long-term equilibrium are corrected each year. The findings indicate that environmental stability in Central Asia necessitates a strategic transformation of industrial and energy policy, underscoring the importance of coordinated regional initiatives to modernize grids and promote green industrial practices to decouple economic expansion from particulate pollution.

Type of the article: Research Article

Abstract
The study examines the nexus between environmental tax revenues, renewable energy adoption, transport research and development expenditure, and green growth across EU-27 countries from 2000 to 2024. The study addresses the critical gap in understanding how fiscal environmental instruments and technological innovation in transport sectors contribute to sustainable development outcomes. Using panel data analysis, the paper employs cross-sectionally augmented autoregressive distributed lag (CS-ARDL) and method of moments quantile regression (MMQR) models to analyze both short-run and long-run relationships while accounting for cross-sectional dependence and heterogeneity. Results reveal that environmental tax revenues positively influence green growth with a long-run elasticity of 0.358, indicating that a 1% increase in environmental taxes enhances adjusted net savings by 0.358%. Renewable energy adoption demonstrates a stronger positive effect with an elasticity of 0.531 in the long run, while transport R&D expenditure exhibits a coefficient of 0.289, suggesting significant contributions to sustainable outcomes. The MMQR analysis demonstrates heterogeneous effects across quantiles, with stronger impacts observed at higher green growth levels. Cross-sectional dependency tests confirm significant spatial spillover effects among EU member states. The findings provide empirical evidence supporting the effectiveness of coordinated environmental fiscal policies and targeted innovation investments in transport sectors.