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This paper aims to investigate coal consumption and environmental sustainability in South Africa by examining the role of financial development and globalization by using a dataset covering the period from 1980 to 2017. The study utilized the Auto-regressive Distributed Lag Model (ARDL) approach in addition to the Bayer and Hank combined co-integration, fully modified Ordinary least squares (FMOLS), and Dynamic ordinary least Squares (DOLS). The study further utilized the frequency domain causality test to capture the causal linkage between the series. The advantage of the frequency domain causality is that it can capture causal linkages between series at different periods. The Bayer and Hanck co-integration and ARDL bounds tests reveal co-integration among the series. The empirical findings based on the ARDL long-run estimation reveal that a 1% increase in coal consumption increases environmental degradation by 1.077%, while a 1% increase in financial development decreases the environmental degradation by 0.973%. Furthermore, a 1% increase in economic growth decreases environmental quality by 1.449%. The outcomes of the FMOLS and DOLS approaches also provide supportive evidence for the ARDL long-run results. Furthermore, the results of the frequency domain causality test reveal that at a significance level of 1%, coal consumption Granger causes CO2 emissions at different frequencies, while financial development Granger causes CO2 emissions in the long run and short run at a significance level of 10%. In terms of policy suggestions, South Africa should embrace policies that encourage energy consumers to shift toward renewable energy. Furthermore, financial reforms should be implemented to curb environmental degradation

This paper aims to investigate coal consumption and environmental sustainability in South Africa by examining the role of financial development and globalization by using a dataset covering the period from 1980 to 2017. The study utilized the Auto-regressive Distributed Lag Model (ARDL) approach in addition to the Bayer and Hank combined co-integration, fully modified Ordinary least squares (FMOLS), and Dynamic ordinary least Squares (DOLS). The study further utilized the frequency domain causality test to capture the causal linkage between the series. The advantage of the frequency domain causality is that it can capture causal linkages between series at different periods. The Bayer and Hanck co-integration and ARDL bounds tests reveal co-integration among the series. The empirical findings based on the ARDL long-run estimation reveal that a 1% increase in coal consumption increases environmental degradation by 1.077%, while a 1% increase in financial development decreases the environmental degradation by 0.973%. Furthermore, a 1% increase in economic growth decreases environmental quality by 1.449%. The outcomes of the FMOLS and DOLS approaches also provide supportive evidence for the ARDL long-run results. Furthermore, the results of the frequency domain causality test reveal that at a significance level of 1%, coal consumption Granger causes CO2 emissions at different frequencies, while financial development Granger causes CO2 emissions in the long run and short run at a significance level of 10%. In terms of policy suggestions, South Africa should embrace policies that encourage energy consumers to shift toward renewable energy. Furthermore, financial reforms should be implemented to curb environmental degradation

Achieving environmental sustainability has become a global initiative whilst addressing climate change and its effects. However, the role of energy production and consumption in economic development remains critical amidst environmental pollution. Thus, the need for innovation and clean energy alternatives is critical while pursuing sustainable development. This country-specific study focuses on Argentina, where economic growth trajectory is embedded with high CO2 emissions. This study assesses the long-term and causal impact of financial development and renewables on environmental pollution while accounting for the role of economic development and globalization using yearly data spanning 1980 to 2017. A battery of econometric methods is applied to underscore the interaction between the parameters of interest. The findings of Maki and ARDL tests of cointegration alongside Kripfganz & Schneider critical approximation p-values affirm long-run equilibrium interaction between variables. The outcomes of autoregressive distributed lag, fully-modified and dynamic ordinary least squares demonstrate that while economic expansion dampens environmental quality--globalization and renewables improve the environment. This finding suggests pollution-driven economic growth trajectory in Argentina with high dependence on fossil fuels. Besides, the Gradual shift causality test finds evidence of one-way causality from renewable energy consumption, economic growth, and globalization to CO2 emissions. Argentina's pathway in achieving sustainable development requires gradual and inclusive economic shift towards green growth.

The research assesses the impact of CO2 emissions and energy use on economic performance and considers trade openness, urbanization, and agriculture in Indonesia utilizing data covering the period from 1965–2019. The current research employed the Dynamic Ordinary Least Square (DOLS), Autoregressive distributed lag (ARDL), and Fully Modified Ordinary Least Squares (FMOLS) methods. Furthermore, the Gradual shift and Wavelet coherence tests are utilized to capture the direction of causality. The ARDL bounds test discloses a long run interaction among the parameters of interest. The empirical evidence depicts that emissions, agriculture, energy use, and urbanization triggers economic growth. Besides, the growth-induced energy hypothesis is confirmed. This result is resonated by the causality analysis where GDP drives energy one-way in Indonesia. This proposes that Indonesia can embark on conservative energy policies, as such actions will not hurt its growth. Furthermore, there is one-way causality from agriculture to GDP. These outcomes have far-reaching significance for GDP growth and the selected variables in Indonesia.