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Environmental degradation caused by various human activities has been a subject of attention over the globe. There is a concern on how to maintain a clean environment and at the same time achieve optimum production of food and non-food products amidst global energy demand. To this end, this study examines the impact of agricultural development, energy use, and economic growth on CO2 emissions in the emerging seven countries that comprises China, India, Brazil, Mexico, Russia, Indonesia, and Turkey for the annual time frequency from 1990 to 2016. The study uses a battery of econometrics techniques for soundness of analysis the consist of pooled mean group autoregressive distributed lag methodology, dynamic ordinary least squares, and fully modified ordinary least squares as estimation techniques alongside Dumitrescu and Hurlin causality test for the direction of causality analysis. Empirical results revealed that value-added agriculture and economic growth are drivers of CO2 emission in the E7 countries, and the rise in renewable energy causes a reduction in CO2 emissions, while in the short run, economic growth has a positive impact on emissions in the focus countries. Causality analysis shows that there is a feedback causality between economic growth and emissions, between value-added agriculture and energy usage, between emission and value-added agriculture, and between economic growth and agricultural development. Furthermore, energy use does not cause emissions directly; it causes economic growth and value-added agriculture which causes emissions. This position aligns with the advocacy of the United Nations Sustainable Development Goal (UN-SDG) Targets 7 and 13 of clean energy access and mitigation of climate changes issues.

Numerous nations' policies have integrated the imperative of restraining the expansion of CO2 emissions, recognizing the increasingly dire and unmanageable consequences it entails. Despite empirical literature identifying diverse drivers of CO2, the impact of national security, healthcare, entitlement programs, and total petroleum stocks has largely been overlooked. Thus, this study aims to bridge this gap by investigating, for the first time, the role of these economic variables in determining whether they contribute to CO2 reduction or escalation in the United States. To evaluate the interconnections among these variables, this study utilizes monthly data spanning from 1985 to 2022. Employing contemporary quantile approaches like Recursive CQ correlations, Cross-Quantilogram, and nonparametric quantile causality, the study effectively accommodates the nonlinear nature of the variables. These analytical techniques offer a comprehensive assessment of the relationships among the variables under scrutiny. The outcomes of the Cross-Quantilogram analysis reveal that health care, national security, and entitlement programs enhance ecological quality at different quantiles. Conversely, total petroleum stocks are associated with ecological deterioration. Based on these results, the study recommends a focus on raising awareness regarding sustainable procurement strategies, embracing environmentally friendly technologies, and improving energy efficiency in healthcare facilities.

AbstractA large volume of environmental science and pollution research has focused on the contributions of various forms of energy consumption to emissions. However, little attention is given to the impact of human activities such as tourism. Hence, this study investigates the impact of tourist arrivals, energy use, and economic growth on CO2 emissions in the G7 countries for the period 1995–2018. The study employed the use of dynamic panel estimations, namely dynamic ordinary least square, fully modified ordinary least squares and panel pooled mean group-autoregressive distributed lag model (PMG-ARDL) estimation techniques to establish long-run and short-run relationships between the study variable of interest, while the Dumitrescu Hurlin non-causality test was used to test for causality direction among the variables outlined. Empirical findings from the regression revealed that economic growth, tourism and energy use are strong drivers of emission levels in the G7 bloc, while the causality analysis revealed that there is unidirectional causality from CO2 to energy use, CO2 to economic growth (GDP) and GDP to tourist arrivals. These outcomes imply that tourism, energy use and economic growth have no direct effect on emissions, but rather emissions predict economic growth and energy use. Furthermore, tourist arrivals predict energy use; economic growth predicts tourism. Overall based on the study of empirical outcomes, we suggest that to achieve more significant results in reducing emissions, governments of the G7 countries should continue to emphasize green tourism as well as increase the share of renewable energy in their regional energy mix. More policy direction was outlined in the concluding section of this study.

This research offers a new perceptive of the dynamic connection between CO2 and globalization, technological innovation (TI), economic growth, and renewable energy (REN) consumption in Spain. The current research applied the wavelet tools to assess these interconnection utilizing data stretching from 1980 to 2018. The outcomes of these analyses disclose that the association among the series evolves over frequency and time. The current analysis uncovers notable wavelet coherence and strong lead and lag connections in the frequency domain, whereas the time domain indicates inconsistent correlations among the variables of interest. The outcomes of the wavelet analysis from the economic perspective affirm that in the short and medium term, globalization, economic growth, and TI contribute to environmental degradation; however, in the long term, globalization, economic growth, and TI do not contribute to the degradation of the environment. Moreover, at different frequencies, REN consumption helps mitigate environmental degradation in Spain. Furthermore, the spectral causality test unveiled that in the long term, REN utilization, globalization, TI, and economic growth can predict CO2 emissions in Spain.

Sustainable development policies for achieving net-zero emissions require understanding the factors that influence carbon emissions. Capitalizing on the limitations of the existing literature, this study applies the quantile-on-quantile approach to investigate economic globalization's impact on carbon emissions in Australia for 1970-2018. The results from the quantile-on-quantile revealed a positive feedback linkage between globalization and carbon emissions at all quantiles. The results further indicated that while there is a positive feedback linkage between economic growth and carbon emissions at most quantiles, a positive feedback interconnection exists between carbon emissions and coal consumption at all quantiles. As a robustness check, we employed the quantile regression test, and the results from quantile regression are consistent with the findings from the quantile-on-quantile approach. The consistency of the results suggests that these study findings are reliable and suitable for informing policies that seek to address carbon emissions in Australia. The policy implications for Australia are discussed.

This research investigates the drivers of consumption-based carbon emissions in Brazil by using a dataset covering the period between 1990 and 2018. These dynamics were examined by employing the ARDL bounds, DOLS, and gradual shift causality tests. The ARDL long- and short-run estimation outcomes reveal that: (a) renewable energy use stimulates the sustainability of the environment; (b) economic growth increases environmental degradation; and (c) technological innovation enhances the quality of the environment. In addition, the gradual shift causality test results disclosed that renewable energy consumption, economic growth, technological innovation and public-private partnership investment in energy can predict consumption-based carbon emissions in Brazil. Therefore, Brazilian policymakers should actively encourage the R&D of low-carbon technologies and renewable energy consumption. Domestic consumption levels, on the other hand, should be targeted, specifically those that are more energy-intensive and cause a rise in CO2 emissions due to consumption.

The environmental Kuznets curve (EKC) hypothesis is of great importance to understanding the relationship between economic activity and environmental degradation. Given the current wave of climate change and environmental crisis traced to rising environmental pollution from economic activities, it has become important to investigate the impact of economic expansion on the environment especially in the emerging-7 countries that are responsible for a large amount of global economic activity. This study investigates the N-shaped EKC for the E-7 countries using data spanning the period 1995-2018. The study employs the use of PMG-ARDL estimator and heterogeneous causality tests to establish the long run and short run and direction of causality respectively regarding the variables of interest. According to study empirical results, the long-run results fail to confirm the presence of an N-shaped EKC in the emerging 7 countries but rather confirms the existence of an inverted U-shaped EKC in the study countries. While renewable energy and non-renewable energy have a positive and significant relationship with CO2 emissions, short run results show that there is no significant relationship between economic expansion, renewable energy, non-renewable energy and CO2 emissions. Causality tests showed a bi-directional causality between GDP- and GDP-squared and a uni-directional causality from CO2 emissions to GDP-cubed, non-renewable energy and CO2 emissions, renewable energy, and CO2 emissions. The study suggests increased use of renewable energy to mitigate pollutant emissions in the E-7 countries.