Trade openness, human capital, natural resource, and carbon emission nexus: a CS-ARDL assessment for Central Asian economies.

There is a call for global efforts to preserve the ecological systems that can sustain economies and people's lives. However, carbon emission (CEM) threatens the sustainability of humanity and ecological systems. This analysis looked into the influence of energy use (ERU), human capital (HCI), trade openness (TOP), natural resource (NRR), population, and economic growth (ENG) on CEM. The paper gathered panel data from the Central Asia region from 1990 to 2020. The CS-ARDL was applied to establish the long-term interaction among the indicators. The paper's findings indicated the presence of the environmental Kuznets curve (EKC) in the Central Asia regions. Also, the empirical evidence highlighted that energy use, natural resources, and trade openness cause higher levels of CEM. However, the research verified that CEM can be improved through human capital and urban population growth. The study also found that HCI moderates the interaction between NRR and CEM. The causality assessment indicated a one-way interplay between ENG, ERU, NRR, and CEM. The study proposes that to support ecological stability in these regions, policy-makers should concentrate on developing human capital, investing in renewable energy sources, and utilizing contemporary technologies to harness natural resources in the economies of Central Asia.

Exploring the nexus link of environmental technology innovation, urbanization, financial development, and energy consumption on environmental pollution: Evidence from 27 emerging economies.

The core intent of the present study seeks to probe the connection linking environmental technology innovation (ENVTI), economic growth (ECG), financial development (FID), trade openness (TROP), urbanization (URB) and energy consumption (ENC) on environmental pollution (ENVP) by employing 27 chosen African economies panel data. These variables merit critical attention when implementing decarbonization policies and significantly safeguarding a country's well-being in pursuit of massive industrialization and economic expansion. The fully modified ordinary least squares (FMOLS), the dynamic ordinary least square (DOLS), and the pooled mean group (PMG) estimation techniques were utilized to analyze the series from 2000 through 2020. This research used the FMOLS for long-run connections interaction of the variables, while the DOLS and PMG were used for robustness checks. Further, the Pedroni, Kao, and Westerlund cointegration approaches were employed to determine cointegration in the series. Also, the cross-sectional Im, Pesaran, and Shin (CIPS) and the cross-sectional augmented Dickey-Fuller (CADF) unit root testing approaches were utilized to check the stationarity of the series. Again, the stochastic impact on regression, population, affluence, and technology (STIRPAT) model, and the environmental Kuznets curve (EKC) was used as the theoretical framework supporting this research. The findings of the long-run analysis give credence to the EKC assumption demonstrating that a significant long-term ECG will support the decrease in ENVP when nations experience increases in the level of income. Further, this study found that ENVTI and URB are conducive to reducing ENVP in the long run. The current research finding is sensitive to the respective nations' income levels. This empirical research furnishes prudent policies tailored for the respective countries' pursuit of ECG and reducing ENVP.

The spillover effect of green finance development on rural revitalization: an empirical analysis based on China's provincial panel data.

With the overall victory of poverty alleviation in China, the focus of rural work has been transformed into rural revitalization. Therefore, based on the panel data of 30 provinces and cities in China spanning 2011 to 2019, this research used the entropy-TOPSIS method to calculate the weights of each index of the two rural revitalization and green finance systems. This research also constructs the spatial Dubin model to empirically analyze the direct effects and spatial spillover effects of green finance development on the level of rural revitalization. Additionally, this research calculates the weight of each indicator of rural revitalization and green finance using entropy-weighted TOPSIS. This research reveals that the current state of green finance is not conducive to increasing local rural revitalization and does not significantly affect all provinces. Further, the number of human resources can improve the local level of rural revitalization, not the entire province. These dynamics benefit the growth of local rural revitalization in the surrounding areas if employment and technology levels are developed domestically. Moreover, this research reveals that education level and air quality have a spatial crowding effect on rural revitalization. Thus, when developing rural revitalization and development policies, it is vital to prioritize the high-quality development of finance to be closely monitored by local governments at the respective levels. Furthermore, the stakeholders must pay critical attention to the connection between supply and demand and between financial institutions and agricultural enterprises in the provinces. Again, the policymakers must also increase policy preference, deepen regional economic cooperation, and improve the supply of essential rural elements to play a more significant role in green finance and support rural revitalization.

The role of green finance and energy innovation in neutralizing environmental pollution: Empirical evidence from the MINT economies.

Pursuing ecological sustainability while mitigating the effects of environmental pollution has become a global pursuit. Moreover, the issue of how emerging economies like Mexico, Indonesia, Turkey, and Nigeria (MINT) economies can significantly reduce environmental pollution (EVP) remains elusive. This study sought to investigate the interplay between economic growth, green finance, renewable energy use, natural resource rent, energy innovation, urbanization and environmental pollution by analyzing panel data from 1990 to 2020. This research employed the novel econometrics approach CS-ARDL to examine the short and long-term relationships among the series. The research outcome disclosed that economic growth, natural resource rent and urbanization increase environmental pollution. In contrast, the empirical findings of this study revealed that environmental pollution could be neutralized through effective mechanisms such as green finance, renewable energy consumption, and the promotion of energy innovation. This research provides a fresh insight from the MINT economies and contributes to the existing literature by examining factors contributing to environmental pollution. This research also provides a benchmark for policy-makers and governments to invest in environmentally-friendly technologies to exploit the natural resources in these countries to mitigate the effect of environmental pollution.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model.

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study's selected countries.

Efficiency Measurement and Heterogeneity Analysis of Chinese Cultural and Creative Industries: Based on Three-Stage Data Envelopment Analysis Modified by Stochastic Frontier Analysis.

Industry sustainability plays a vital role in shaping the environment for cultural and creative business development. However, considering the influence of the external environment and random factors on the technical efficiency (T.E.) of cultural and creative industries with the inherent defects of the traditional data envelopment analysis (DEA) model; this manuscript analyzed the operating efficiency of 56 cultural and creative enterprises using the three-stage DEA model from 2012 to 2018. An analysis of the results shows that differences in efficiency exist between stage one and stage three DEA. Furthermore, the environmental elements and statistical noise measured by the stochastic frontier analysis (SFA) in stage two reveal positive and negative influences on the creative cultural enterprises at different stages. As a result, the overall efficiency of the listed cultural and creative industries was revealed to be low in China. Finally, this study suggested effective countermeasures and recommendations for better-operating efficiency systems for cultural and creative enterprises.