The Relationship Between Central Asia's CO2 Emissions, Unemployment Rate, And Economic Development ARDL-PANEL APPROACH
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Abstract
This paper studies the effect of unemployment rate and GDP per capita on carbon dioxide emissions in Central Asia was determined using the "Panel ARDL - PMG" model. In the study, the unemployment rate and GDP per capita in Central Asia have long-term positive effect on the annual CO2 emissions, and a short-term positive effect was observed only in the countries' economic development factor. According to the results, unemployment rate in two countries in Central Asia has a negative effect on annual CO2 emissions in the short term, and in three countries it has a positive effect. Also, GDP per capita has a positive effect on annual CO2 emissions in the short term in one country in four countries. It was estimated that the secret was statistically insignificant. These results have important implications for policymakers, emphasizing the potential role of unemployment mitigation strategies in promoting environmental sustainability in Central Asia.
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References
BP. (2021). BP statistical review of world energy 2021. Retrieved from https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf
Carfora, A., Pansini, R. V., & Scandurra, G. (2019). The causal relationship between energy consumption, energy prices, and economic growth in Asian developing countries: A replication. Energy Strategy Reviews, 23, 81–85. https://doi.org/10.1016/j.esr.2018.10.004
Domen, K. (2009, June 7–12). Efficient hydrogen evolution sites of photocatalysts for water splitting. In Proceedings of the 21st North American Meeting (NAM) (San Francisco, CA, USA).
Kuziboev, B., Rajabov, A., Ibadullaev, E., Matkarimov, F., & Ataev, J. (2024). The role of renewable energy, tax revenue, and women governance in environmental degradation for developing Asian countries. Energy Nexus, 13, 100262. https://doi.org/10.1016/j.nexus.2023.100262
Kuziboev, B., Ibadullaev, E., Saidmamatov, O., Rajabov, A., Marty, P., Ruzmetov, S., & Sherov, A. (2023). The role of renewable energy and human capital in reducing environmental degradation in Europe and Central Asia: Panel quantile regression and GMM approach. Energies, 16, 7627. https://doi.org/10.3390/en16227627
Melas, D. (2007). Atmospheric diffusion and dispersion. Aristotle University of Thessaloniki.
Mrabet, A., & Jarboui, S. (2017). Do institutional factors affect the efficiency of GDP and CO2 emissions? Evidence from Gulf and Maghreb countries. International Journal of Global Energy Issues, 40, 259.
Nguyen, A. T. (2019). The relationship between economic growth, energy consumption and carbon dioxide emissions: Evidence from Central Asia. Eurasian Journal of Business and Economics, 12(24), 1–15.
Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74, 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x
Pesaran, M. H. (2004). General diagnostic tests for cross-sectional dependence in panels. Cambridge Working Papers in Economics, 435. Cambridge University, UK.
Pesaran, M. H., Shin, Y., & Smith, R. J. (1996). Testing for the existence of a long-run relationship. Faculty of Economics, University of Cambridge.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326. https://doi.org/10.1002/jae.616
Pesaran, M. H., & Smith, R. P. (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics, 94, 79–113. https://doi.org/10.1016/0304-4076(94)01644-F
Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association, 94, 621–634. https://doi.org/10.1080/01621459.1999.10474156
Sh., N., O., S., K., I., S., W., F., Y., G., R., & C., J. (2022). CO2 emissions and macroeconomic indicators: Analysis of the most polluted regions in the world. Energies, 15(8), 2928. https://doi.org/10.3390/en15082928
Volchyn, I., & Haponych, L. (2018). Carbon dioxide emissions at the Ukrainian pulverized-coal thermal power plants. Scientific Works of NUFT, 24, 131–142.
Wang, Y., Duan, F., Ma, X., & He, L. (2019). Carbon emissions efficiency in China: Key facts from regional and industrial sectors. Journal of Cleaner Production, 206, 850–869. https://doi.org/10.1016/j.jclepro.2018.09.185
Wang, S., Li, G., & Fang, C. (2018). Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renewable and Sustainable Energy Reviews, 81, 2144–2159. https://doi.org/10.1016/j.rser.2017.06.025
Xu, L., Du, H., & Zhang, X. (2021). Driving forces of carbon dioxide emissions in China’s cities: An empirical analysis based on the geodetector method. Journal of Cleaner Production, 287, 125169. https://doi.org/10.1016/j.jclepro.2020.125169