The primary aspiration of this paper is to learn about the effects of economic growth, energy consumption, agriculture and irrigation water consumption and agriculture productivity on environmental pollution in five countries of Central Asia. The data cover the period from 1992 to 2020 by applying panel data models, namely the Panel FMOLS, Panel DOLS and Panel ARDL-PMG approaches. The results indicate that there is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production on CO₂ emissions while agriculture value added and trade openness have a negative and statistically significant influence on CO₂ emissions in Central Asia. Country specific short-run coefficients from Panel ARDL reveal that energy consumption is the main driver for rise in the level of CO₂ emissions in the countries under the study. Indeed, country level analysis generates unique nexus correlation among agriculture, energy and environmental degradation in each country of Central Asia.

Environmental sustainability concerns are growing worldwide. Reducing carbon dioxide (CO₂) emissions is crucial to combating global warming and reaching sustainable global economic development. Many recent studies have focused on key indicators of CO₂ emissions, but less consideration has been given to associated factors such as renewable energy and human capital. This article applies the two-step system FOD-GMM (Forward-Orthogonal Deviations-Generalized Method of Moments) to estimate the role of renewable energy and human capital in reducing environmental degradation in Europe and Central Asia. The results reveal that renewable energy consumption and human capital have a significant negative impact on CO₂ emissions in the EU and Central Asian countries, government efficiency has a positive effect on CO₂ emissions, and economic development has a neutral effect, confirming the strong role of renewable energy and human capital in reducing CO₂ emissions in EU and Central Asian countries. The role of human capital and renewable energy in promoting CO₂ reduction should be fully utilized. Policymakers should develop infrastructure for renewable energy and education to support the decrease in CO₂ emissions in Europe and Central Asia.

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.

Central Asia (CA) is a young integrated region formed after the collapse of the Soviet Union, with most of its infrastructure based on fossil fuels. The traditional energy and water infrastructure is facing huge inefficiency and technical losses. This study investigates the transition of the green, blue, and energy economies in Central Asia using a small-dimensional panel dataset on five countries, Uzbekistan, Kazakhstan, Kyrgyzstan, Tajikistan, and Turkmenistan, over the period 1995–2018. The authors analyze the impact of ecological footprint, water withdrawal, and energy consumption on gross domestic product. In applying the Panel Vector Error Correction Model, evidence was found supporting the long-running association between variables. Furthermore, the adjustment coefficients suggest that only GDP growth will adjust toward equilibrium. Overall, the findings suggest a more effective role of green transition compared to blue and energy transitions.

Evaluating the current ecotourism situation in the Aral Sea region of Uzbekistan and offering strategies for its sustainable development are the main objectives of the study. The threats, opportunities, weaknesses and strengths (TOWS) analysis was applied to identify the required management strategies. The article aims to review the literature on ecotourism promotion in the Aral Sea region. Based on TOWS, the results show that state support during the pandemic should be strategically coordinated to secure the sustainability of the ecotourism industry in Uzbekistan.

Central Asian (CA) countries (Kazakhstan, Kyrgyzstan, Turkmenistan, Tajikistan, Uzbekistan) are assumed to be one of the most attractive tourist destinations since this particular geographical location holds immense potential in tourism products. Due to the COVID-19 pandemic, the flourishing tourism sector of these countries has been immensely affected. The aim of this study is to examine the impact of Central Asian tourists' risk attitudes toward traveling during the COVID-19 pandemic through consideration of sociodemographic characteristics. The research was conducted during January through April 2021 based on a sample of 966 respondents via an online questionnaire. In the survey, risks are divided into four main categories: health, psychological, financial and travel destination. Nominal regression was used to identify the way in which risk perception affected travel intentions during COVID-19 and the research findings indicate that Central Asian tourists’ risk perception has done so. Hygiene, disinfection and a reliable health system in destinations (21%) will be leading factors in future travel.