Assessing the spillover effects of various forms of energy on CO2 emissions - An empirical study based on dynamic spatial Durbin model.

Previous studies ignored the geospatial dynamics spillover effects of energy consumption on CO2 emissions while assessing such impacts in developed and developing countries. Moreover, most studies wrongfully assess spillover effects in its aggregated format rather than decomposing by its components. This is important as not all energy sources share the same characteristics. We fill these gaps in the literature by investigating the spillover effects of various forms of energy, including fossil fuels, renewable energy, and nuclear power, on CO2 emissions in 135 developed and developing countries from 2000 to 2019. We used the Dynamic Spatial Durbin Model (DSDM) to better understand the results. A series of indicative tests confirmed using the DSDM model and including spatial interaction of CO2 emissions in the analysis. Our findings show evidence of indirect spillover effects of the various energy sources on CO2 emissions. Further considering the spillover effects of the energy sources of neighbouring countries, the paper finds that the driving increase in CO2 emissions mainly came from the energy consumption of the country itself and neighbouring countries' energy consumption. Nevertheless, the results indicate that the direct effects of energy consumption often exceed its indirect effects. The results also confirm that total and fossil energy consumption harms the environment, whereas adopting renewable and nuclear energy sources reduces CO2 emissions. Lastly, we find nuclear energy is the most environmentally sustainable energy source. The study concludes that the Dynamic Spatial Durbin Model is paramount in estimating the environmental impact of energy consumption in our sample. The practical policy implications drawn from this study could be used to promote increased collaboration to hasten the energy transition process and address global warming and climate change.

Modelling for disability: How does artificial intelligence affect unemployment among people with disability? An empirical analysis of linear and nonlinear effects.

There is a growing debate among scholars regarding the impact of artificial intelligence (AI) on the employment opportunities and professional development of people with disability. Although there has been an increasing body of empirical research on the topic, it has generally yielded conflicting findings. This study contributes to the ongoing debate by examining the linear and nonlinear effects of AI on the unemployment of people with disability in 40 countries between 2007 and 2021. Using the system Generalized Methods of Moments and panel smooth transition regression, the main conclusions are as follows. First, AI reduces the unemployment of people with disability in the full sample. Second, upon disaggregating the sample based on income level (high income/non-high income) and gender (men/women), the linear model only detects an inverse correlation between AI and unemployment among people with disability in high-income countries and among men, whereas it does not influence unemployment in non-high-income countries and women. Third, the panel smooth transition regression model suggests that the effects of AI on the unemployment of people with disability and among women are only observed once artificial intelligence interest search exceeds a specific threshold level. The effects of AI in non-high-income economies and among women are not significant in the lower regime, which confirms the nonlinear association between AI and the unemployment rate of people with disability. These findings have important policy implications for facilitating the integration of people with disability into the labor market.

Do institutions contribute to environmental sustainability? A global analysis using the dynamic spatial Durbin and threshold models.

There has been a recent surge in attention to the potential involvement of institutions in enhancing environmental quality. This research contributes to the ongoing debate by analyzing the spillover and nonlinear effects of institutions on the load capacity factor (LCF) in a sample of 100 countries between 2000 and 2019. The spillover effects are analyzed using the dynamic spatial Durbin model (DSDM), while the dynamic threshold model is implemented to estimate the nonlinear impacts of institutions. The Moran's I and Geary's C tests reveal a positive spatial autocorrelation for the LCF. The DSDM indicates the existence of positive direct and indirect (spillover) effects of political stability, control of corruption, and the rule of law on the LCF. Moreover, control of corruption has the highest positive influence on the environment. When conducting the threshold analysis, the locally weighted scatterplot smoothing curve indicates a nonlinear relationship between institutions and LCF, while the threshold test suggests a single threshold and two regimes. The dynamic panel threshold model reveals that government effectiveness and the rule of law positively affect the environment under both regimes. On the contrary, the positive effects of control of corruption, regulatory quality, and political stability are only observed under the upper regime. Furthermore, control of corruption has the highest positive environmental impact, albeit it needs more time to be achieved. The research emphasizes the importance of international collaboration and the design of both short- and long-term strategies to enhance institutional quality and, consequently, safeguard the environment.

A revisit of the EKC hypothesis in top polluted African countries via combining the ARMEY curve into the Kuznets curve: A Fourier ARDL approach.

The present research reexamines the validity of the EKC hypothesis in the top three polluted nations in Africa, Algeria, Egypt, and South Africa, over the period 1970-2020. The central idea of the research is to reexamine the EKC hypothesis by incorporating the ARMEY curve linking government spending and GDP into the Kuznets curve, as suggested by Isik et al. Environ Sci Pollut Res 29(11):16472-16483, (2022) and Ongan et al. Environ Sci Pollut Res 29(31):46587-46599, (2022). To this end, the ARDL equation with a Fourier function is implemented to estimate the long-run drivers of environmental deterioration. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model results revealed that the composite model is only valid in Algeria, and the optimal government spending that maximizes CO2 emissions is 16.88% of gross domestic product. On the contrary, the results showed that the composite model is not valid for South Africa and Egypt due to the failure of the desired shapes for the three curves. The outcomes also confirm the role of energy consumption and population as key drivers of environmental degradation in the three countries.

Analyzing the symmetric and asymmetric effects of disaggregate natural resources on the ecological footprint in Saudi Arabia: insights from the dynamic ARDL approach.

The present study examines the symmetric and asymmetric effects of natural resource exploitation on the ecological footprint in Saudi Arabia during the period 1981-2018. The analysis is performed for total natural resources and different types of natural resources, including oil, natural gas, and minerals. This research employs the dynamic Autoregressive Distributed Lag (DYNARDL) simulation approach. In addition to its statistical and computational superiority, the DYNARDL allows assessing the environmental repercussions of shocks that occurred on natural resources in both the short- and long-run. The findings suggest that total, oil, and natural gas rents have a positive symmetric association with the ecological footprint in the long run, while no significant impact is identified for mineral resources. When conducting the asymmetric analysis, the findings reveal that only increases in total, oil, and natural gas rents deteriorate the ecological footprint in the long run, while no effects are confirmed for decreases in natural resource rents. The shock analysis reveals that a 10% increase in total and oil rents leads to environmental degradation by 3% in the long run, while a similar upsurge in natural gas rents induces a 4% deterioration in environmental quality. These findings may help design effective resource-use policies to achieve environmental sustainability in Saudi Arabia.

A new methodology for assessing the energy use-environmental degradation nexus.

The international community is more than ever before worrying about the unremitting global warming and climate change and the responsibility of extensive energy use for that situation. This article contributes to the existing literature by examining whether energy consumption predicts CO2 emissions during the past 50 years in the five most polluting nations in the world. To do this, we have been using the recently developed predictability test of Westerlund and Narayan (Journal of Banking and Finance, 36, 2632-2640, 2012, Journal of Financial Econometrics, 13, 342-375, 2015). We take thereby into account the problem of endogeneity and persistence in the explanatory variable. Likewise, this test has the advantage of treating the problem of heteroscedasticity. Using several predictive evaluation measures and assuming the historical average as a benchmark, we find that the basic model of the predictability test of Westerlund and Narayan (2012, 2015) surpasses the benchmark model. These findings reveal that primary energy consumption predicts CO2 emissions in the world and all countries, for different forecast horizons. Further, the in-sample evidence of predictability has been supported by the out-of-sample analysis.