In the modern era of globalization, the rapid increase in information and telecommunication technologies (ICTs) contributes in various sectors of an economy; however, the environmental consequences of ICTs cannot be ignored. Therefore, the study investigates the nexus between ICTs, economic growth, financial development, and environmental quality in emerging economies. The novel feature of the study is that the interaction term of ICT is introduced with economic growth and financial development. The empirical findings of the study are based on panel mean group (MG) and augmented mean group (AMG) estimation methods from 1990 to 2015. The following empirical results are established: first the ICTs significantly affect CO emissions. Second, the moderating effect of ICT and financial development stimulate the level of CO emissions. Third, economic growth contributes CO emission; however, the interaction between ICT and GDP mitigates the level of pollution. Policy thresholds with the R&D in ICT sector are required to mitigate the level of CO emission. Introduction of green ICTs projects in the financial sector is a better choice to improve the energy efficiency.

This study analyzes the effects of increased income and renewable energy on environmental quality, which has been ignored in the existing literature. An important contribution of this study is to analyze the role of renewable and nonrenewable energy in relation to the rising level of carbon emissions in the leading emitting countries. This research further examines the heterogeneous impacts of rising income levels and EKC investigation for CO 2 emissions. The Kao cointegration, generalized method of moments (GMM), random effects, fixed effect (FE) regression models, and panel causality techniques are employed for panel data estimations. The empirical outcomes mention that an increase in income moderates the ratio of consumption of renewable energy to CO 2 emissions. Increased income contributes more to the energy mix, which contributes to environmental pollution, through nonrenewable energy. This research reports policy-relevant critical masses beyond which an increase in income negatively affects the link between renewable energy and CO 2 emissions.

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In the current context of the COVID-19 pandemic, researchers are working with health professionals to inform governments on how to formulate health strategies. In this study, we examine the correlation between environmental and climate indicators and COVID-19 outbreak in the top 10 most affected states of the USA. In doing so, PM 2.5 , temperature, humidity, environmental quality index, and rainfall are included as crucial meteorological and environmental factors. Kendall and Spearman rank correlation coefficients, quantile regression, and log-linear negative binominal analysis are employed as an estimation strategy. The empirical estimates conclude that temperature, humidity, environmental quality index, PM 2.5 , and rainfall are significant factors related to the COVID-19 pandemic in the top 10 most affected states of the USA. The empirical findings of the current study would serve as key policy input to mitigate the rapid spread of COVID-19 across the USA.

In the real world, economic covariates follow asymmetric and time-varying patterns. Therefore, it is imperative to integrate these effects while estimating environmental and economic relationships. Although prevailing literature reveals various emissions-deriving and eliminating factors, however, there is a dearth of empirical evidence that estimates the asymmetric and time-varying effect of globalization, natural resources, and financial development from a multidimensional perspective in China. In doing so, we employ the nonlinear autoregressive distributed lag (NARDL) and cross-wavelet modeling framework to explore the long- and short-run nonlinear and time-variant association between globalization, natural resources, financial development, and carbon emissions from 1980 to 2017. The NARDL method has the benefit of discriminating the long-term and short-term asymmetric carbon emission responses due to a positive and negative shock in our primary variables of interest. Mainly, the findings of NARDL estimations confirm that positive shocks in globalization and financial developments have a significant positive impact on carbon emissions, whereas negative shock in natural resources has a significant positive impact on carbon emissions. Similarly, the outcomes of continuous wavelet transformation and wavelet transformation coherence confirm the causal linkages between covariates; however, this effect varies across different time and frequency domains. These results imply that environmental researchers should consider asymmetric transmission channels and time–frequency associations among variables to devise long-term sustainable policies.