The impact of renewable energy consumption on unemployment rates in Uzbekistan: An ARDL approach

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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.

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    • Table 1. Descriptive statistics
    • Table 2. Correlation analysis
    • Table 3. Unit root tests: With intercept only
    • Table 4. Bound test results
    • Table 5. Long-run coefficients ARDL (1,1,0,0,0)
    • Table 6. ECM form
    • Table 7. Diagnostic tests
    • Conceptualization
      Fozil Xolmurotov, Sukhrob Davlatov, Ergash Ibadullaev, Xolilla Xolmuratov , Alisher Sherov
    • Data curation
      Fozil Xolmurotov, Xolilla Xolmuratov
    • Investigation
      Fozil Xolmurotov, Gulsanam Arabova
    • Methodology
      Fozil Xolmurotov, Xolilla Xolmuratov
    • Software
      Fozil Xolmurotov
    • Formal Analysis
      Obidjon Khamidov
    • Validation
      Obidjon Khamidov, Xolilla Xolmuratov , Alisher Sherov, Gulsanam Arabova
    • Visualization
      Obidjon Khamidov, Ergash Ibadullaev
    • Writing – review & editing
      Obidjon Khamidov, Gulsanam Arabova
    • Funding acquisition
      Sukhrob Davlatov
    • Resources
      Sukhrob Davlatov, Ergash Ibadullaev, Alisher Sherov
    • Supervision
      Sukhrob Davlatov, Alisher Sherov
    • Writing – original draft
      Ergash Ibadullaev, Gulsanam Arabova
    • Project administration
      Xolilla Xolmuratov