Syed Rahim

In the contemporary era, it is evident that consumption-based carbon emissions (CCO2e), adjusted for international trade, are a more accurate and robust measure of environmental pollution than production-based emissions. Therefore, many studies have focused on exploring the determinants of CCO2e; however, the literature could not yet discern environment-related R&D budget (ERRD) and political risk index (PRI) as a new driver. To fill this gap, the current paper aims to divulge the dynamic effects of ERRD and PRI on CCO2e while taking imports, exports, and GDP as control regressors. To do so, the G7 nations’ data from 1990 to 2020 is utilized, and several advanced econometric tests and techniques are deployed to tackle the advanced issues (i.e., cross-section dependence, heterogeneity, and endogeneity) in econometrics. The CS-ARDL estimates suggest that PRI and ERRD significantly lessen the environmental quality by disrupting the detrimental effects of CCO2e. Likewise, the rise in exports possesses a negative association with the ratio of CCO2e. However, CCO2e significantly tends to surge on account of an increase in GDP and imports. GDP squared exhibits the negative link with CCO2e, confirming the EKC hypothesis in G7 nations. Besides, the AMG technique and causality test validate the robustness of our findings. In addition, the policies related to CCO2e by authorities will take approximately more than a year to absorb their effects.

Investment in the new energy industry (IEI) and industrial pollution prevention (IPP) is an emerging strategy for carbon neutrality. However, little attention has been paid to exploring its direct linkage with environmental degradation. Therefore, the present study is an effort to contribute to a thin body of literature by recruiting IPP and IEI in China’s provincial-level carbon emissions (CO₂). With the use of the latest maximum data from 1998 to 2017, the long-run relationships are estimated through “augmented mean group estimator (AMG), panel fully modified ordinary least square (FMOLS), dynamic ordinary least square (DOLS), and Pairwise Dumitrescu Hurlin (DH) Panel Causality Tests.” According to the above-stated econometric techniques, IEI and IPP negatively affect CO₂ emissions, which means the reduction in environmental degradation. The study also validates the environmental Kuznets curve (EKC) hypothesis for 30 provinces of China. Additionally, IPP shows the long-run unidirectional causal relationship with CO₂ emissions, while income level has a bidirectional causal relationship with CO₂ emissions. Findings have robust policy implications for China’s provinces.

Attaining sustainable development and cleaner production is a major challenge both for developed and developing economies; income, institutional regulations, institutional quality and international trade are the key determinants of environmental externalities. The current work attempts to study the role of environmental taxes and regulations on renewable energy generation for developed economies. For that, the authors have used the annual dataset for the period 1994 to 2018. More specifically, the study investigates the impacts of environmental taxes, environment-related technologies and the environmental policy stringency index on renewable electricity generation in 29 developed countries. Given the short available data of these countries, the authors have developed panel cointegration and panel regressions models (fully modified ordinary least square (FMOLS), quantile regressions). The heterogeneous panel empirics stated that environmental regulations and income level support renewable electricity generation. The conclusions further mention that bureaucratic qualities such as decision making and trade openness tend to reduce renewable energy generation. The empirical findings allowed us to draw new narrative and implications. Overall, the conclusions argue that innovative regulations and policies can be useful for attaining specific sustainable development goals (e.g., SDG-7: cleaner and cheap energy).