RESUMO
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.
Assuntos
Dióxido de Carbono , Dióxido de Carbono/análise , Desenvolvimento Econômico , Poluição Ambiental , Indonésia , México , Nigéria , Energia Renovável , TurquiaRESUMO
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.