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Artificial Intelligence (AI) can enhance the efficient use of green energy and increase the scalability of renewable energy. The global shift towards green energy has to managed and optimized which is a major challenge. Deep learning can play a vital role in predictive maintenance and storage. The data available from renewable energy systems can be analyzed by the artificial intelligence (AI) in real time to predict and manage energy generation and demand response which drastically reduces wastage of energy and distribution of energy can be more efficient. In this context, the integration of AI with renewable energy systems represents a paradigm shift in addressing the global energy crisis. Currently, an increased importance is gained by green technologies in order to obtain a sustainable economic wealth.

Asian countries are facing environmental degradation issue which is the result of nonrenewable energy usage for growth however this issue cannot be unnoticed. This study is an effort to highlight the causes for an emerging issue of environmental degradation in selected Asian economies. The research findings demonstrate that urban population, energy consumption and economic growth increases carbon emission CO2 (i.e., environmental degradation) in Asian countries. It is suggested that strategy formulation in Asian economies should implement and indorse renewable energy sources in order to meet up the high energy demand and to reduce CO2 emissions for certain sustainable economic development in Asian countries. [ABSTRACT FROM AUTHOR]

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The present research study is an attempt to analyse the effects of various levels of education and income inequality on real GDP per capita, in the COVID-19 pandemic era, including 70 countries. We found empirical evidence that the relationship of various levels of education with growth of real income per capita remained the same in 2020 as it was in 2010, except for the slight difference in the magnitudes of the coefficients. The primary school enrolment of the two decades earlier has revealed as the increasing factor to the income per person. However, when we took a decade early or so values the relationship became converse indicating primary school enrolment as the decreasing factor to the income per capita. The empirics indicated that the secondary school enrolment remained contributing factor to the growth of income per capita, and it is the most effective level of education to increase the real GDP per capita. Moreover, there is no empirical evidence in the favour of relationship between tertiary education and the income per person. The income inequality has also provided insignificant results in our analysis. [ABSTRACT FROM AUTHOR]

This research is the earliest attempt to understand the impact of inflation and the interest rate on output growth in the context of Pakistan using the wavelet transformation approach. For this study, we used monthly data on inflation, the interest rate, and industrial production from January 1991 to May 2020. The COVID-19 pandemic has affected economies around the world, especially in view of the measures taken by governmental authorities regarding enforced lockdowns and social distancing. Traditional studies empirically explored the relationship between these important macroeconomic variables only for the short run and long run. Firstly, we employed the autoregressive distributed lag (ARDL) cointegration test and two causality tests (Granger causality and Toda-- Yamamoto) to check the cointegration properties and causal relationship among these variables, respectively. After confirming the long-run causality from the ARDL bound test, we decomposed the time series of growth, inflation, and the interest rate into different time scales using wavelet analysis which allows us to study the relationship among variables for the very short run, medium run, long run, and very long run. The continuous wavelet transform (CWT), the cross-wavelet transform (XWT), cross-wavelet coherence (WTC), and multi-scale Granger causality tests were used to investigate the co-movement and nature of the causality between inflation and growth and the interest rate and growth. The results of the wavelet and multi-scale Granger causality tests show that the causal relationship between these variables is not the same across all time horizons; rather, it is unidirectional in the short-run and medium-run but bi-directional in the long-run. Therefore, this study suggests that the central bank should try to maintain inflation and the interest rate at a low level in the short run and medium run instead of putting too much pressure on these variables in the long-run. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The main aim of this research paper is to examine non-performing assets in agricultural loans in India. The agricultural sector is a major pillar of the Indian economy, it represents the primary source of livelihood for about 58% of its population. Agricultural loans are very important in order to achieve technological development in agriculture and implicitly to reduce costs and use sustainable strategies. The data sample was collected from 80 lenders (bank managers) and 1167 borrowers of agriculture credit of Dakshina Kannada district in India. The empirical results are relevant and contribute to a better understanding of the impact of non-performing assets in agricultural loans on a sustainable economic growth in India. [ABSTRACT FROM AUTHOR]