The main objective of this study is to investigate the impact of unemployment on Jordan's economy over the period 1991–2019. This study used the auto-regressive distributed lag (ARDL) model to investigate the relationship between the unemployment rate and the other variables. Also, we employ the ARDL bootstrap cointegration approach to examine the correlation and long-run relationship among the variables. The empirical finding indicated a long-run relationship between the unemployment rate, economic growth, education, female population, and urban population in Jordan. Our finding shows the negative linkage between economic growth and unemployment, and a positive relationship among the education, female population, and urban population and unemployment in Jordan.

This research explores the role of green resource productivity, renewable energy, economic globalization, and economic growth towards advancing the pursuits of decarbonization in top energy transition economies. This study achieves this objective by adopting both asymmetric and symmetric econometric methods for the period between 1990 and 2021. This study adopted the Method of Moments Quantile Regression (MMQR) to uncover the asymmetric effect of the regressors on carbon emissions. The MMQR results suggest that in all quantiles, green resource productivity, renewable energy, and economic globalization mitigate CO₂ emissions, while economic growth induces CO₂ emissions. Additionally, for the robustness analysis, we disaggregated economic globalization into trade and financial globalization and investigated their roles towards achieving decarbonization. We uncovered that financial globalization mitigates CO₂ emissions at all quantiles, but at the lower and middle quantiles, trade globalization mitigates CO₂ emissions. Moreover, for the symmetric estimation, the following estimators: Fixed effect Ordinary Least Square, Fully Modified Ordinary Least Square, and Dynamic Ordinary Least Square estimators were employed in this study. Their outcome corroborated the findings of this MMQR. For the Granger causality inference, the outcome suggests that there is a bi-directional causality between renewable energy and CO₂ emissions. Furthermore, we find a feedback causality association between financial globalization and CO₂ emissions, and a one-way causal interconnection is detected from CO₂ emissions to trade globalization. Moreover, we detected that there is a causal association flowing from economic globalization to CO₂ emissions. Finally, an unidirectional causal interaction is detected from economic growth to CO₂ emissions. Consequently, the research’s findings provide applicable policy.

Ecological footprint (EFP) measures the amount of area, that is land or sea, which is required to absorb the waste generated through human activities or to support the production of resources consumed by populations. EFP index therefore includes six dimensions that are cropland, forestland, carbon, fishing grounds, grazing land, and built-up area. Human activities have impacted the environment, leading to global warming, widespread droughts, and diseases. The present study aims to investigate the role of renewable energy (RE) and energy efficiency on the EFP index. Past researchers have widely used carbon emission (CE) to represent environmental impact, and recent studies have shown that EFP index is a better proxy of environmental degradation. Therefore, the present research differs from past studies in that it compares on how the determinants of environmental degradation affects EFP index and CE. Panel dataset of the OECD countries from 1990 to 2020 is employed. The CS-ARDL, DCCEMG, and AMG techniques, which overcome dynamics, heterogeneity, and cross-sectional dependence, are employed. The main findings depict that RE significantly reduces EFP and CE, while economic growth significantly exacerbates them. Energy efficiency reduces CE, but does not significantly affect EFP. Non-renewable energy and research & development significantly increase CE, while an insignificant positive effect is observed with EFP. This paper shows that factors that significantly influence CE may not always significantly affect the EFP index. Thus, to reduce environmental degradation it is fundamental to understand on how each dimension of EFP is influenced.