No abstract

Countries encounter conflicting policy options in reaching fast development goals due to high resource use, rapid economic expansion, and environmental degradation. Thus, the present research examined the connection between CO2 emissions and urbanization, globalization, hydroelectricity, and economic expansion in China utilizing data spanning the period between 1985 and 2018. The novel quantile-on-quantile (QQ) and quantile regression (QR) approaches were applied to assess this interconnection. The QQ approach is characterized by its ability to incorporate quantile regression fundamentals and non-parametric estimation research. As a result, the method appears to transform the quantile of one parameter into another. The QQ outcomes revealed that in all quantiles (0.1–0.95), gross domestic product (GDP), urbanization, and globalization trigger CO2 emissions in China, while in each quantile (0.1–0.985), hydroelectricity consumption mitigates CO2 emissions. The QR outcomes also affirmed the outcomes of the QQ regression estimates. Policies are suggested based on these findings.

Several investigations show that remittances, renewable energy, and innovation promote the socioeconomic advancement of a nation. Nevertheless, the impacts of remittances and renewable energy on ecological quality are yet to be evaluated thoroughly. Therefore, the current investigation assesses the effects of remittances and renewable energy on CO2 emissions while taking into account the roles of technological innovation, globalization, and economic growth. Toward this end, this paper depends on yearly data between 1990 and 2019. The study employed bounds testing and its results disclosed long-term connections between CO2 and the regressors. Moreover, unlike prior studies that employ time-domain causality, we employed frequency domain causality, which considers causality at different frequencies. Furthermore, the ARDL long- and short-run results showed that economic growth amplified CO2 emissions, while green energy, remittances, and globalization lessened CO2 emissions. Lastly, the frequency domain causality approach revealed that globalization, renewable energy, economic growth, technological innovation, and remittances could predict CO2 emissions in the long-term. These findings’ sturdiness was established utilizing DOLS and FMOLS regression. Several policy recommendations are suggested in light of these ground-breaking discoveries.

This study aims to fill the gap in the literature by specifically investigating the impact of country risk on the credit risk of the banking sectors operating in Brazil, Russia, India, China, and South Africa (BRICS), emerging countries. More specifically, we explore whether the country-specific risks, namely financial, economic, and political risks significantly impact the BRICS banking sectors’ non-performing loans and also probe which risk has the most outstanding effect on credit risk. To do so, we perform panel data analysis using the quantile estimation approach covering the period 2004–2020. The empirical results reveal that the country risk significantly leads to increasing the banking sector’s credit risk and this effect is prominent in the banking sector of countries with a higher degree of non-performing loans (Q.25 = − 0.105, Q.50 = − 0.131, Q.75 = − 0.153, Q.95 = − 0.175). Furthermore, the results underscore that an emerging country’s political, economic, and financial instabilities are strongly associated with increasing the banking sector’s credit risk and a rise in political risk in particular has the most positive prominent impact on the banking sector of countries with a higher degree of non-performing loans (Q.25 = − 0.122, Q.50 = − 0.141, Q.75 = − 0.163, Q.95 = − 0.172). Moreover, the results suggest that, in addition to the banking sector-specific determinants, credit risk is significantly impacted by the financial market development, lending interest rate, and global risk. The results are robust and have significant policy suggestions for many policymakers, bank executives, researchers, and analysts.