Balancing economic growth with environmental sustainability remains a serious challenge for many transition economies. This study analyzes the relationship between economic growth, service sector expansion, urbanization, and environmental degradation in Uzbekistan. Employing yearly temporal series data from 1991 to 2024, the research implements an autoregressive distributed lag (ARDL) bounds testing methodology to investigate the short-run and long-run relationships among the variables. Carbon dioxide (CO2) emissions are used as proxies for environmental degradation, while GDP per capita, service sector value added, and urban population growth represent key economic and structural factors. The empirical results show that there is a stable long-run cointegration relationship between the variables. The findings show that economic growth significantly reduces CO2 emissions in the long run, which supports the Environmental Kuznets Curve (EKC) hypothesis for Uzbekistan. Conversely, the expansion of the service sector and rapid urbanization are found to increase CO2 emissions and contribute to environmental degradation. The error correction model shows a rapid adjustment rate of approximately 66.5% to the long-run equilibrium after short-term shocks. Through these results, we attempted to reveal the importance of sustainable urban development, green economic policies, and the development of an environmentally friendly service sector in Uzbekistan.
Firuza Achilova, N. Rizaev, F. Xolmurotov et al.· Economies· 0 citations
Type of the article: Research ArticleAbstractCentral Asian countries face acute air quality challenges, with PM2.5 concentrations in major cities exceeding World Health Organization guidelines several times over, while industrial CO2 emissions continue to rise alongside economic development. Understanding the empirical linkage between these pollutants is essential for designing integrated environmental policies. The purpose of this study is to assess the impact of industrial CO2 emissions on PM2.5 air pollution in five Central Asian countries – Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan – using balanced panel data for the period 2000–2020 (N = 105) obtained from the World Bank’s World Development Indicators. Pooled OLS, fixed effects, and random effects estimators were applied, with GDP per capita, urbanization, and energy intensity as control variables. Model selection was based on the Hausman test, and robustness was verified through ten alternative specifications. The random effects model (Hausman χ2 = 0.412, p = 0.521) reveals a statistically significant positive relationship: a one million metric ton increase in industrial CO2 emissions is associated with a 0.87–0.89 µg/m3 rise in mean annual PM2.5 concentration (p < 0.01). The coefficient remains stable across all robustness checks (0.823–0.923). GDP per capita shows a significant negative effect (−1.92, p < 0.05), supporting the Environmental Kuznets Curve hypothesis, while energy intensity has a positive effect (p < 0.05). Country-specific effects reveal substantial heterogeneity, with Tajikistan exhibiting the highest baseline PM2.5 (+26.25 µg/m3 above Kazakhstan) and Turkmenistan the lowest (+7.49 µg/m3). These findings confirm the co-pollutant hypothesis and justify integrated climate-air quality policies with country-specific strategies.AcknowledgmentsThe authors would like to thank the anonymous reviewers for their constructive comments and suggestions that helped improve this manuscript.
Sobirjon S. Ruziyev, Hulkar R. Turobova, S. Khomidov et al.· Environmental Economics· 0 citations
