Demystifying circular economy and inclusive green growth for promoting energy transition and carbon neutrality in Europe
This paper examines the role of the circular economy and inclusive green growth in promoting energy transition and carbon neutrality for several European countries during 2009–2021, using an advanced econometric strategy. To achieve this objective, we employed a three-pronged empirical strategy. The first strategy involved standard panel specifications, such as pooled ordinary least squares, Fixed Effects, Roger panel regression, white panel regression, and Driscoll–Kraay standard errors. The second strategy explored the long- and short-term dynamics of the relationships using the dynamic specifications of the Generalised Method of Moments, comprising the augmented Arellano–Bond, Ahn–Schmidt, Arellano–Bond, and Arellano–Bover/Blundell–Bond estimators. It further controls for endoegeneity using the Two stages Lease Square (2SLS and Lewbel 2SLS). The third strategy employed the Machado and Silva quantiles via moments to re-evaluate the heterogeneity drivers of carbon neutrality. Furthermore, an alternative and complementary strategy based on the statistical procedures of Hausman–Taylor and Feasible Generalized Least Squares was employed to verify robustness. The findings suggest that prior carbon emissions positively predict future emissions. Also, our results reveal that the adoption of a circular economy, including green growth and renewable energy, can significantly contribute to reducing carbon emissions. In contrast, domestic economic drivers, and eco-innovation increase carbon emissions. We propose that policymakers mandate producers to oversee the entirety of their product life cycles as a means to mitigate carbon emissions. Furthermore, endorsing training programs and educational initiatives aimed at cultivating the requisite skills for the energy transition and the adoption of circular economy practices is imperative for ensuring the realization of a successful low-carbon economy.
Crafting monetary policy beyond low carbon legacy
Are monetary policy and financial development important prerequisities for realising a low carbon economy? The answer is yes, but this work argues that its importance rests on different associated mechanisms. We test this assertion by using a panel of 24 OECD countries, spanning the period 2000–2019. The analysis is framed in four empirical strategies. The first method involves employing standard panel specifications, which control for unobserved error term components. The second examines the long- and short-term dynamics of the relationships using Generalized Method of Moment (GMM) dynamic specifications. The third employs the Machado and Silva Quantile via Moment approach to reassess the drivers of carbon neutrality heterogeneity. Additionally, alternative and supplementary approaches based on statistical procedures, such as Hausman-Taylor and Feasible Generalized Least Squares, are carried out to test the robustness of the findings. The analysis indicates that lagged carbon emissions have a significant and positive impact on subsequent carbon emissions. The findings also suggest that both monetary policy and financial development are critical in mitigating carbon emissions. Conversely, an upsurge in the share price index is linked to an increase in carbon emissions. These findings underscore the significance of integrating monetary and financial development into a comprehensive strategy that considers both current and past carbon emissions to attain sustainable environmental outcomes.
Unlocking information technology infrastructure for promoting climate resilience and environmental quality
We examine the role of Information Technological Infrastructure (ITI) in promoting climate resilience and environmental quality in OECD countries. Our empirical strategy is framed in the advanced econometrics methodology. Our analysis begins with standard specification, which controls for unobserved factors in the panel data. We then explore the variables' long and short-run relationships using the Generalised Method of Moment dynamic family specifications. We also evaluate the heterogeneity drivers of environmental quality using Quantile via Moment. The analysis is also extended using alternative and complementary statistical procedures by Hausman–Taylor and Feasible Generalised Least Squares as robustness checks. Our findings indicate that ITI and renewable energy significantly mitigates carbon emissions and can be helpful in achieving a net-zero target. In contrast, the empirical evidence reveals that economic growth and non-renewable energy usage are harmful to the environment. The finding also suggests a significant degree of heterogeneity exists in the covariates on the conditional distribution of environmental quality and its driven factors. While the findings reaffirm the significance of ITI in ensuring careful planning and monitoring of critical infrastructure, they also show that ITI can be used to balance the entire system by creating resilience. We strongly suggest that policymakers should use ITI to spur innovation and drive better solutions for energy transition and environmental improvement.
An Empirical Analysis of Fishery Kuznets Curve Hypothesis: Evidence from OECD Countries
Fish farms are occasionally built on delicate natural habitats, which can have severe environmental effects. Many industrial fishing methods also devastate aquatic habitats with the addition of overfishing practices. While environmentalists and academics have been concerned about the rapid extinction of fishery resources, the existing evidence is still inconclusive. This study examined whether catching overfishing could explain the environmental Kuznets curve (EKC) hypothesis among Organisation for Economic Cooperation and Development (OECD) countries. The study used four empirical strategies, which are framed in the panel time series analysis. (a) it assessed the prior behaviour of the variables based on second-generation panel unit roots tests using Cross-sectional ADF(CADF) and Cross-sectional IPS. (b) it also assessed the potential long-run relationship among the variables using the Westerlund Panel Cointegration test. (c) it explores the fishery Kuznets curve for OECD countries using Cross-sectional Dependency - Autoregressive Distributed Lag (CS-ARDL) and Augmented Mean Group (AMG) methodology. (d) it also examines the short direction of causality among the variables using the Dumitrescu – Hurlin Panel Granger Causality test, which is built on standardised panel statistics suitable for small sample properties, even in the presence of cross-sectional dependency. Five main findings can be deduced from our analysis. Firstly, the preliminary check confirms are only stationary only after the first difference. Secondly, the analysis also suggests that there is a long-run relationship among the variables. Thirdly, the findings showed that fish exploration has a negative effect on carbon emissions. Fourthly, the quadratic term of economic growth unidirectionally Granger causes CO2 emissions. Fifthly, we found evidence in support of the EKC hypothesis across OECD countries. Finally, our results highlighted the importance of fishery control in promoting environmental quality among OECD countries. We recommend reform in public policy that encourages sustainable fishing along with innovative research into modern fishing methods that reduces emissions. A better method of fishing can be developed with the support of cutting-edge research, which can also help reduce water pollution and the threat that overfishing poses to biodiversity.
Re-evaluating the impacts of green innovations and renewable energy on carbon neutrality: Does social inclusiveness really matters?
This study explores the impact of green innovation and renewable energy on carbon emissions, considering the mediating role of social inclusivity for a panel of 24 countries in the Organization for Economic Co-operation and Development (OECD) from 1994 to 2019. The empirical strategy is framed in a generalized method of moments dynamic panel, which is novel for assessing the short-and long-term relationships among the variables. By controlling for confounders, we assessed the mechanism by which green innovation and renewable energy contribute to carbon emissions. Furthermore, for consistency with prior empirical research, we extended the analysis using alternative statistical specification by Hausman–Taylor and the feasible generalized least squares, which controls for potential endogeneity issues and cross-panel correlation. Our analysis suggests that green innovation and economic growth are positive and statistically significant predictors of carbon emissions. However, renewable energy and social inclusiveness were both negative and significant predictors of carbon emissions. These results suggest that renewable energy and social inclusiveness can serve as remedies for promoting environmental quality and reducing carbon emissions in OECD countries. Therefore, we recommend promoting the expansion of renewable energy at a lower cost to unserved and underserved communities and promoting social inclusiveness to achieve a net zero emission target.
Extricating the impacts of emissions trading system and energy transition on carbon intensity
Emissions trading systems (ETS) are market-driven mechanisms designed to reduce greenhouse gas emissions (GHGs) by levying the cost of carbon. Although ETS has been implemented effectively in certain regions, concerns about its efficacy in Organisation for Economic Co-operation and Development (OECD) countries persist, as it may be hindered by a combination of factors, such as exorbitant costs, inadequate coverage, political reluctance, policy disruptions, and a lack of clear understanding of the underlying mechanism through which it affects carbon intensity. In this study, we analyse the effects of the ETS and energy transition on carbon intensity for a panel of 24 OECD countries during 2000-2019 using advanced dynamic econometrics. Our empirical approach involves three primary specifications. Utilizing standard panel methods, which are innovative in controlling unobserved heterogeneity. We then explored the long-and short-run relationships using the generalised method of moments (GMM) dynamic family, and applying the quantiles via moments model to re-evaluate the heterogeneity drivers of carbon neutrality. We also use an alternative and complementary statistical procedure by Hausman–Taylor and the feasible generalised least squares (FGLS) model as robustness checks. Our findings indicate that implementing an ETS and investing in renewable energy can significantly reduce carbon emissions. However, economic growth and carbon taxes increase carbon emissions. These findings emphasize the importance of adopting a comprehensive strategy towards an effective emission trading system and expansion of renewable energy in reducing carbon emissions. Moreover, prioritizing current and past emissions is necessary for a quick transition to a low-carbon economy in OECD countries.
Pooling cross-sectional and time series data for estimating causality between technological innovation, affluence and carbon dynamics: A comparative evidence from developed and developing countries
The trends in affluence and carbon emissions remain a concern in view of the pressing need to establish conditions for sustainable development. Scientific evidence reveals that if far-reaching reforms of technological innovation are not undertaken, the environment will deteriorate beyond repair, which will be detrimental to the development agenda. Our study explored whether technological innovation has failed to anticipate the rise in affluence factors that dictate the level of economic activities and associated carbon emissions. We analysed the relationships between technological innovation and affluence and carbon emissions. The empirical strategy followed the standard panel fixed effects, Arellano–Bover/Blundell–Bond dynamic panel analysis, and the Hausman–Taylor methodology. The results of our dynamic econometric tests did not reject the Stochastic Impact by Regression on Population, Affluence, and Technology framework for the successful reduction of carbon emissions through technological innovation. Our findings further suggest that improved technological innovation can not only predict and identify carbon emissions but may also be used to monitor and mitigate their impacts. We recommend strategies for promoting technological innovation to accelerate reductions in carbon emissions without compromising sustainability.