The United Nations lists 17 Sustainable Development Goals for Agenda 2030, one of which is SDG-10, which focuses on eradicating inequality and addressing critical regional and global challenges. The fight against income inequality is heavily dependent on foreign direct investment all over the world. In this connection, the present study aimed to investigate the individual and interactive impact of foreign direct investment, human capital, and economic growth on income inequality by employing the interactive model. Based on the panel data set covering ten counties spanning each region of Asia from 1990 to 2020. In light of the slope homogeneity, cross-sectional dependency tests, and Westerlund co-integration test, we discover that all of the variables are cointegrated over the long run. A cross-sectional IPS (CIPS) unit root test is employed to check stationarity. Additionally, the study used the Augmented Mean Group (AMG) approach to produce accurate results in estimation. The results confirm that FDI affects inequality negatively. However, the impact of FDI is more effective in the presence of human capital. It means that human capital deepens the effect of FDI on inequality; the country will be more effective in reducing inequality by having a higher level of human capital and consider it a more powerful tool to bring equality. To reduce inequality, it is suggested that a policy mix of FDI and HC could be made.

The goal of this study was to contribute to the ongoing debate on the relationship between renewable energy (RE) and CO₂ emissions. In particular, we explored the link between RE and CO₂ emissions in a sample of major renewable energy-consuming countries for the period 2000–2015. Therefore, the major contribution of this study was to answer the question of whether a substantial shift to renewable energy consumption will lead to lower CO₂ emissions. Using the two-step generalized method of moments (GMM) estimator, our empirical results suggested that RE has a significant negative effect on CO₂ emissions. For example, a one percentage point increase in RE leads to a 0.5% decrease in CO₂ emissions.

As emerging Asian Belt and Road Initiative (BRI) economiesgrow quickly and adopt new digital technologies, managing their natural resources sustainably has become an increasingly challenging task.A comprehensive understanding of the complex, integrated effects of financial technology (Fintech), industrial growth, and clean energy adoption under a regime of accelerating digitalization is critical for evidence-based policymaking. This study investigates the long-run and short-run impacts of Fintech, industrial value-added, renewable energy consumption, trade openness, and economic growth on natural resource rents across 19 emerging Asian BRI economies from 2000 to 2023. Crucially, it incorporates an interactive term (Digitalization × Fintech) to capture their synergistic effects. To address potential cross-sectional dependence and slope heterogeneity inherent in the panel data, the Cross-sectional Autoregressive Distributed Lag (CS-ARDL) model is employed. The long-run results indicate that digitalization, Fintech, REC, trade openness, and the combined Digitalization × Fintech effect all exert a significant negative influence on natural resource extraction, indicating overall improvements in resource efficiency over time. Conversely, industrial value-added and economic growth show a significant positive influence on resource use. The error correction term confirms a rapid adjustment rate of approximately 35% annually toward the long-run equilibrium. The results of this study indicate governing agencies and fiscal policymakers should put forth standard regulations around the adoption of digital technology and Fintech practices, resource-based industrial activity, trade policy, sustainable economic growth, energy, and resource management.

This paper investigates labor migration in Central Asia from 2013 to 2023, targeted on economic and demographic factors, including GDP per capita, unemployment, inflation, remittances, and population growth. Using panel regression analysis and World Systems Theory, the study highlights key factors influencing migration trends, such as economic disparities, reliance on remittances, and shared language or borders. Findings show that there is a high out-migration from Kyrgyzstan and Tajikistan because of significant unemployment and poverty rates, whilst Kazakhstan demonstrates economic stability which leads to less migration rates. In the last years, Uzbekistan shows progress in reducing migration through economic development. The results determine the need for more targeted policies to tackle against economic issues, reduce reliance on remittances and promote sustainable development.