Xolilla Xolmuratov

This study investigates the intricate interplay between the utilization of renewable energy sources and the fluctuations in unemployment rates in Uzbekistan during the period from 2000 to 2022, utilizing the Autoregressive Distributed Lag model. The analysis leverages World Bank data to explore both short-term and long-term impacts, offering critical insights for policymakers and stakeholders. The findings reveal that an increase in renewable energy consumption has a statistically significant and negative impact on unemployment rates in the long run, with a 1% rise in renewable energy usage leading to a 1.86% decrease in unemployment. In addition, gross fixed capital formation and government expenditure significantly contribute to job creation, while domestic credit to the private sector shows a positive association with unemployment, suggesting inefficiencies in credit allocation. The results emphasize the crucial role of strategic investments in renewable energy as a pathway to address economic and environmental challenges. By promoting renewable energy initiatives, Uzbekistan can align its economic growth with sustainability goals, reduce carbon emissions, and create employment opportunities. This study provides empirical evidence supporting the expansion of renewable energy infrastructure as a means to foster economic development and social well-being. It also underscores the necessity of enhancing financial policy frameworks to ensure efficient credit distribution and maximize the employment benefits of renewable energy projects. The policy recommendations derived from this study advocate for targeted investments, financial sector reforms, and the development of specialized training programs to cultivate a skilled workforce in renewable energy sectors.

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.