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.

Energy plays an essential role in the modern society and can serve as one of the vital parameters of socio-economic development. Despite developments in technology, over three billion persons living in rural parts of the low- and middle-income countries continue to cover their energy needs for cooking through traditional ways by burning biomass resources. This paper as a case study focuses on the Aral Sea region of Uzbekistan, possessing a well-developed agricultural production with high livestock numbers and intensive crop production. The manure of the livestock farms is not used efficiently and the energy supply of the farms depends primarily on centrally produced gas and electricity. Some areas are not yet connected to the gas grid. Agriculture causes huge environmental damages in its current form. The benefit of biogas production would therefore be fivefold: (1) local energy source, (2) mitigation of environmental impacts, (3) reducing CH4-emissions, (4) producing organic fertilizer as a side product and (5) additional earnings for farmers.

This study examines the long-run relationship between carbon dioxide (CO₂) emissions and key macroeconomic and sectoral drivers in ten ASEAN economies from 1995 to 2023. Employing Driscoll–Kraay standard errors, Prais–Winsten regression, heteroskedastic panel-corrected standard errors, Fully Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Cointegrating Regression (CCR) estimators, the analysis accounts for cross-sectional dependence, slope heterogeneity, and endogeneity. Results indicate that GDP exerts a more-than-unitary positive effect on emissions, with a negative GDP-squared term supporting the Environmental Kuznets Curve. Agriculture raises emissions through land-use change and high-emission cultivation practices, while tourism shows a negative association likely reflecting territorial accounting effects. Trade openness increases emissions, highlighting the carbon intensity of export structures, whereas foreign direct investment exerts no significant net effect. These results suggest that ASEAN economies must accelerate renewable energy adoption, promote climate-smart agriculture, embed enforceable environmental provisions in trade policy, and implement rigorous sustainability screening for FDI to achieve low-carbon growth trajectories.

This study investigates whether information and communication technology (ICT) constitutes a sustained driver of economic growth in four Central Asian economies—Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan—over the period 2000–2022. Using an extended endogenous growth framework, this study employs the following long-run growth model: economic growth is specified as a function of ICT development, gross capital formation, trade openness, human capital, government effectiveness, and inflation. A composite ICT index is constructed using Principal Component Analysis (PCA). Long-run relationships are examined using a panel cointegration approach, and long-run elasticities are estimated using FMOLS, DOLS, and CCR techniques. The results reveal that ICT development exerts a negative and statistically significant effect on economic growth in the long run, indicating limited technological absorptive capacity and insufficient institutional readiness in the region. In contrast, capital formation, trade openness, human capital, and government effectiveness positively and significantly promote growth, while inflation hampers economic performance. The findings suggest that ICT investment alone is insufficient for sustainable growth without complementary institutional strengthening, human capital development, digital skills enhancement, improved broadband quality, and governance reforms to increase the productive use of ICT.

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.

Remittances are a crucial part of economic expansion, especially in Central Asia. Nevertheless, it is not possible to ignore its environmental damage. This paper is a pioneer in investigating the association among CO₂ emissions, remittances, energy consumption and economic development in Central Asian countries (Uzbekistan, Kazakhstan, Kyrgyzstan and Tajikistan) spanning the period of 1995–2022. As a methodology, the FMOLS estimator is applied to check linear impact and long-run association as well. Panel threshold regression model and 2SLS method are applied to examine potential non-linear relations among the studied variables. Hausman–Taylor and Amacurdy estimators are employed to control the endogeneity issue among the variables of interest. The results suggest the existence of a long-run relationship among the studied variables. Precisely, applying the FMOLS method, remittances negatively impact CO₂ emissions in the long run. The relationship between CO₂ emissions and remittances is distorted when the endogeneity issue is considered with the Panel threshold regression model, 2SLS method, and Hausman–Taylor and Amacurdy estimators. This distortion validates the linear impact of remittances on CO₂ emissions in CA. The Dumitrescu–Hurlin causality test shows that all independent variables have a causal effect on the dependent variable, validating the effect of the studied variables. Consequently, decision-makers should facilitate remittances towards more environmentally friendly and sustainable solutions to prevent the detrimental effects of remittance inflows on carbon emissions in Central Asia.