Manzura Masharipova

The paper deals with the growing importance of understanding energy consumption patterns and their relationship with export performance in developing economies. The study aims to analyze the bidirectional relationship between commodity exports and energy consumption in Uzbekistan from 1992 to 2023. A vector autoregression (VAR) model is employed as the primary methodology, incorporating four main variables: commodity exports, GDP per capita, renewable energy consumption, and renewable electricity generation. Unit root tests (ADF and PP) were conducted, revealing that all variables are stationary in their first differences, which necessitated cointegration analysis. The Johansen test results confirm the existence of a single long-run cointegration vector among the variables. Granger causality tests identify a bidirectional causal relationship between GDP per capita and exports, with changes in GDP per capita having a significant impact on exports (p < 0.001) and vice versa. The results demonstrate that renewable electricity has a positive effect on exports (coefficient = 0.055, p = 0.01), while GDP per capita shows a negative effect on exports (coefficient = –0.0002, p = 0.039). Additionally, VAR analysis reveals that past values of exports significantly influence current export levels, with a lagged coefficient of 0.883 (p < 0.05). These findings have practical implications for policymakers in Uzbekistan seeking to balance economic growth objectives with sustainable energy development strategies.

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.