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The topic of whether globalization, energy consumption and financial development can substantially reduce emissions during the globalization era remains unanswered. In this context, this research highlights empirical indications supporting this theoretical discord; assessing the effect of globalization, energy consumption and financial development on the CO2 emissions in Japan (utilizing a dataset that spans between 1990 and 2019). The study employed the Autoregressive Distributed Lag (ARDL) technique and frequency domain causality to probe these relationships. Unlike other conventional causality tests, the frequency domain causality test can capture causality at different frequencies. The findings from the ARDL analysis disclosed that globalization and renewable energy contribute to the mitigation of CO2 emissions while fossil fuel, economic growth and financial development caused an upsurge in CO2 in Japan. Furthermore, the frequency domain demonstrated that all the exogenous variables can forecast CO2 mostly in the long-term which implies that any policy initiated based on the exogenous variables will impact emissions of CO2. Based on the results obtained, Japan has to improve its financial systems and increase its use of renewable energy. Furthermore, Japan needs to restructure its policy regarding globalization owing to the fact that it contributes to the degradation of the environment. Since globalization is a major driver of economic growth, the government should concentrate on luring and licensing investors that use environmentally beneficial (net-zero) technology.

Some of the globe’s most economically advanced nations make up the G7 (Canada, Japan, France, Germany, Italy, United States and United Kingdom). Nevertheless, in tandem with such strong economic growth, the environmental conditions in these nations have deteriorated, raising serious issues among stakeholders. Therefore, we examine the effect of eco-innovation and trade openness on CO2 emissions in G7 nations. We also take into account the role of renewable energy, economic growth and nonrenewable energy use using a dataset covering the period from 1990–2019. We employed recent econometric techniques such as slope heterogeneity (SH) and cross-sectional dependence (CSD), Westerlund cointegration, fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), panel quantile regression and panel causality tests to assess these associations. The outcomes of the CSD and SH tests disclosed that using a first-generation unit root test will produce biase outcomes. Furthermore, the outcomes of the Westerlund cointegration disclosed support long-run association between CO2 and its drivers. In addition, the results of the long-run estimators (FMOLS and DOLS) unveiled that nonrenewable energy and trade openness contribute to the damage to the environment while economic expansion, renewable energy and eco-innovation enhance the quality of the environment. Furthermore, the outcomes of GDP, REC and ECO curb CO2 while NREC energy and TO surge CO2. Finally, the outcomes of the panel causality test unveiled that CO2 emissions can be predicted by all the exogenous variables.

The association between economic complexity (sophisticated economic structure) and carbon emissions has major implications for environmental sustainability. In addition, globalization can be an important tool for attaining environmental sustainability and it may also moderate the association between economic complexity and carbon emissions. Thus, this research examines the effects of economic complexity, economic growth, renewable energy, and globalization on CO2 emissions in the top 10 energy transition economies where renewable energy and globalization have greatly increased over the last 3 decades. Furthermore, this study also evaluates the joint effect of globalization and economic complexity on carbon emissions. Keeping in view the presence of slope heterogeneity and cross-sectional dependence in the data, this research utilized second-generation unit root tests (CIPS and CADF), Westerlund cointegration approach, and CS-ARDL and CCEMG long-run estimators over the period of 1990–2018. The results affirmed the presence of cointegration among the considered variable. Long-run findings revealed that globalization, renewable energy, and economic complexity decrease carbon emissions. Conversely, economic growth increases carbon emissions. Moreover, the joint impact of economic complexity and globalization stimulates environmental sustainability. Based on these findings, the government of these groups of economies should continue to expand the usage of renewable energy. They should also promote interaction with the rest of the world by adopting the policy of opening up.

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.

The level of economic income, population density and sources of energy supply is critical in assessing environmental quality. Recent empirical studies paid limited attention to the role of renewable (RE) and fossil energy (NRE) supply in carbon pollution regarding the Environmental Kuznets Hypothesis (EKC). Therefore, this study investigates the asymmetric relationships between carbon emissions and energy sources on the one hand and the environmental Kuznets hypothesis on the other hand for OECD countries, comprising countries with significant renewable energy supplies. The study includes the annual data from 1990 to 2021 and performs panel non-linear ARDL regression. The empirical results clearly show that RE and NRE have asymmetric effects on emissions in the long run but not in the short run. Both positive and negative shocks in RE reduce CO2 emissions in OECD economies, while asymmetric shocks in NRE substantially increase them. Increasing RE supply is clearly effective in reducing emissions. However, unlike most previous studies, this study shows that RE does not significantly reduce CO2 emissions in OECD countries. The error correction term (ect.) in the NARDL model is negative and significant. The magnitude of the term indicates that the system will return to long-term equilibrium about 4.2 years after any shock. Furthermore, we show that the EKC Hypothesis is supported in OECD countries. The turning point of the EKC is at $4085.77 per capita. Besides, regression with Driscoll-Kraay standard errors and Augmented Mean Group (AMG) estimator approach were used for robustness checks. The findings from the robustness check are consistent with the NARDL findings. Policies based on the promotion of a low-carbon and sustainable green environment should place greater emphasis on renewable resources even in OECD countries. Moreover, while many studies in the literature address asymmetric effects and EKC as energy consumption or utilisation, the novelty of this study is that it approaches the issue regarding energy supply with asymmetric effects for RE and NRE.

The current paper assesses the drivers of health care expenditure such as urbanization, natural resources, economic expansion, and CO2 utilizing quarterly data from 2000Q1 to 2018Q4. The research applied the novel dual adjustment approach to identify the long run association between healthcare expenditure and urbanization, economic growth, natural resource and CO2 emissions. The main novelty of the dual adjustment approach is that the approach offers another way to cointegration analysis by relaxing the implicit assumption of the singular adjustment in cointegration analysis. The outcome of the dual adjustment approach affirmed cointegration among the variables in the long run. Furthermore, we applied fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS) and canonical cointegrating regression (CCR) estimators and their results disclosed that economic growth, urbanization, and CO2 emissions increase health care expenditure while natural resource rent mitigates healthcare expenditure in China. Moreover, the spectral causality test uncovered that urbanization, economic growth, natural resource, and CO2 emissions can predict healthcare expenditure at various frequencies. Based on these findings, China’s policymakers should establish strategic environmental management policies that improve healthy and clean air to reduce healthcare costs. In addition, policymakers in China should reevaluate their urban development strategies to avoid negative externalities associated with fast urbanization.

Transport is an essential infrastructure for development. With its high share of gross domestic product (GDP), it makes a significant contribution to total CO2 emissions in Malaysia. It is therefore important to pay greater attention to reducing CO2 emissions and sustainable development in this sector. Therefore, this study aims at estimating the relationship between transport CO2 emissions and its key drivers using the Autoregressive Distributed Lag (ARDL) technique. The time period covered by the study extends from 1978 to 2018. It further investigates the response of CO2 emissions to shocks in the value of other variables by employing the generalized impulse response approach. The results suggest that urbanization is the major contributor to the increase in CO2 emissions followed by the carbon intensity of energy in the long-run. Carbon intensity of energy, GDP per transport worker and urbanization contribute significantly to increases in transport CO2 emissions in the short- and long-run. Testing the Environmental Kuznets Curve (EKC) hypothesis recommends that Malaysia continue to be on track to reach the highest level of income and welfare to give pay more attention to the environment. Therefore, the country maintains its CO2 emissions level in the future because of economic development. Therefore, these findings show that energy and environmental policymakers need to pay more attention to improving energy efficiency and the use of low-carbon technologies and electrification in the transport sector and the use of high-quality public transport, particularly in urban areas, for sustainable urban development.