Does globalization and energy usage influence carbon emissions in South Asia? An empirical revisit of the debate

Abstract

The 2030 United Nations Sustainable Development Goal (SDG) 13 agenda hinges on attaining a sustainable environment with the need to “take urgent action to combat climate change and its impacts”. Hence, this study empirically revisits the debate on the effect of nonrenewable energy and globalization on carbon emissions within the framework of the Kuznets hypothesis using an unbalanced panel data from seven South Asian countries (Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka) covering 1980–2019. The variables of interest are carbon emissions measured in metric tons per capita, energy use measured as kg of oil equivalent per capita, and globalization index. To address five main objectives, we deploy four techniques: panel-corrected standard errors (PCSE), feasible generalized least squares (FGLS), quantile regression (QR), and fully modified ordinary least squares (FMOLS). For the most part, the findings reveal that the (1) inverted U-shaped energy-Kuznets curve holds; (2) U-shaped globalization-Kuznets curve is evident; (3) inverted U-shaped turning points for nonrenewable energy are 496.03 and 640.84, while for globalization are 38.83 and 39.04, respectively; (4) globalization-emission relationship indicates a U-shaped relationship at the median and 75th quantile; and (5) inverted U-shaped energy-Kuznets holds in Pakistan but a U-shaped nexus prevails in Nepal and Sri Lanka; inverted U-shaped globalization-Kuznets holds in Bangladesh and Sri Lanka, but U-shaped nexus is evident in Bhutan, Maldives, and Nepal. Deductively, our results show that South Asia countries (at early stage of development) are faced with the hazardous substance that deteriorates human health. Moreover, the non-linear square term of the nonrenewable energy-emissions relationship is negative, which validates the inverted U-shaped EKC theory. Overall, the effect of energy and globalization on carbon emissions is opposite while the consistency at the 75th quantile result indicates that countries with intense globalization are prone to environmental degradation.

What we learn from nexus between greener growth andenergy‐relatedemissions: Sustainability perspective evidence in context of financial globalization

“…With respect to financial globalization, the results show that financial globalization exerts a negative impact on ERGHG emissions, which supports the earlier findings of Gu et al (2023), Adeleye et al (2023), and Farouq et al (2021). The economic intuition behind the negative coefficient of financial globalization at different quantiles is that financial globalization enables countries to shift their pollution emission industries to other countries where the environmental protection regulations and laws are not well established.…”

“…The positive impact of globalization on the environment varies from country to country, depending on heterogeneous conditions and government policy measures. One group of studies infers positive effect of globalization and the environment (Bilgili et al, 2020; Rehman et al, 2023; Sadiq et al, 2023), while other groups of studies argue that globalization improves environmental quality (Adeleye et al, 2023; Wang et al, 2020). Hence, the researchers are not agreed to a standard view regarding the effects of globalization on environmental performance.…”

The Dynamics of Economic Globalization and Income Inequality in Sub-Saharan African Countries: An Empirical Insight from Two Stage Least Square and Fixed Effect Approach

“…The cross-sectional dependence test helped in deciding whether to use first generation panel unit root test [89,90] or the second generation panel unit root tests [87,88,91]. However, the second-generation unit root tests is supreme in case of cross-sectional dependence to avoid spurious regression [92]. The study applied the [88] test for cross-sectional dependency (CD) which can be applied to small and large pane [92].…”

“…However, the second-generation unit root tests is supreme in case of cross-sectional dependence to avoid spurious regression [92]. The study applied the [88] test for cross-sectional dependency (CD) which can be applied to small and large pane [92]. The outcome of the cross sectional dependence test in Table 1 makes it suitable for both the first and second-generation unit root tests to be conducted in this study (appendix 3 and 4).…”

The impact of green FDI on environmental quality in less developed countries: A case study of load capacity factor based on PCSE and FGLS techniques

“…Colin and colleague [ 16 ] highlight the importance of PCSE in addressing issues like heteroskedasticity and autocorrelation. By adjusting standard errors, PCSE ensures more accurate and reliable results, especially in scenarios with numerous cross-sections and short periods [ 17 ]. FGLS, on the other hand, improves the precision and dependability of estimates compared to Ordinary Least Squares (OLS).…”